@article {pmid42931853,
year = {2026},
author = {Fahim, NH},
title = {Smart livestock management using sensing technologies: applications, digital integration, and future perspectives.},
journal = {Tropical animal health and production},
volume = {58},
number = {8},
pages = {},
pmid = {42931853},
issn = {1573-7438},
mesh = {Animals ; *Livestock/physiology ; *Biosensing Techniques/veterinary/methods/instrumentation ; *Animal Husbandry/methods/instrumentation ; Artificial Intelligence ; Animal Welfare ; Digital Technology ; },
abstract = {Smart livestock management is increasingly enabled by advances in sensing and biosensing technologies that support continuous, data-driven monitoring of animal health, welfare, reproduction, productivity, and environmental conditions. Recent digital innovations have further accelerated this transition, creating opportunities for more predictive and sustainable livestock management. This review synthesizes current knowledge on conventional sensors, emerging biosensing technologies, and their integration within smart livestock systems. It critically examines technology readiness, wearable, implantable, and environmental sensing platforms. It evaluates their applications in disease surveillance, animal welfare assessment, reproductive management, milk production optimization, and growth monitoring. The review further explores how the convergence of sensors and biosensors with the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), cloud and edge computing, and digital decision-support systems (DSS) enables continuous data integration, predictive analytics, and evidence-based management. Current barriers to large-scale implementation, including technical reliability, interoperability, data management, infrastructure requirements, economic feasibility, and regulatory and ethical considerations, are also discussed. The available evidence indicates that integrating sensing, biosensing, and digital technologies provides a more comprehensive understanding of animal physiological, behavioral, and environmental status than individual technologies alone, supporting earlier interventions and improved production efficiency. Continued advances in integrated monitoring platforms, intelligent analytics, and next-generation biosensors are expected to accelerate the transition toward predictive, resilient, and sustainable livestock production.},
}

Animals
*Livestock/physiology
*Biosensing Techniques/veterinary/methods/instrumentation
*Animal Husbandry/methods/instrumentation
Artificial Intelligence
Animal Welfare
Digital Technology

@article {pmid42938302,
year = {2026},
author = {Ahluwalia, P and Lord, D and Arbery, M and Pampatwar, N and Mohan, A and Vashisht, A and Mondal, AK and Kolhe, R},
title = {Decentralizing Clinical Tumor Exome Analysis: On-Premises GPU-Based Secondary Analysis in a Clinical Molecular Laboratory.},
journal = {Genes},
volume = {17},
number = {10},
pages = {},
pmid = {42938302},
issn = {2073-4425},
mesh = {Humans ; *Exome/genetics ; *Colorectal Neoplasms/genetics ; *Exome Sequencing/methods ; Genomics/methods ; Class I Phosphatidylinositol 3-Kinases/genetics ; Proto-Oncogene Proteins B-raf/genetics ; High-Throughput Nucleotide Sequencing/methods ; },
abstract = {Background/Objectives: Clinical tumor sequencing has become central to precision oncology, yet secondary genomic analysis remains heavily dependent on cloud or centralized computational infrastructure, creating barriers related to cost, turnaround time, data governance, and accessibility. We demonstrate that secondary analysis of clinical tumor exomes can be performed entirely within a clinical molecular laboratory using workstation-class GPU hardware, eliminating dependence on external computational infrastructure. Methods: Sequence data generated off site were analyzed on premises with a variant-calling workflow run on benchtop GPU workstations. Accuracy was assessed against the Genome in a Bottle germline and Sequencing Quality Control Phase 2 (SEQC2) HCC1395 somatic reference materials and recall of the variants listed on the clinical molecular reports for 172 institutional colorectal tumor exomes. Results: We analyzed all 172 exomes from reads to called variants on device, at a median of 25.1 min of on-device compute across the 170 exomes with complete timing. On the germline reference material, an F-measure of 0.9963 was obtained genome-wide, and 0.9945 restricted to the exome target. Against those reports, we recovered all 153 reported variants across the four driver genes (KRAS, NRAS, PIK3CA, and BRAF). Coding-variant recall on this development cohort was 95.74% (1168/1220) requiring exact reference and alternate allele agreement, and 98.44% (1201/1220) when 33 indels matching on coding or protein position alone were also counted. Call sets were identical across triplicate runs of five exomes on both devices. Conclusions: Secondary analysis of solid tumor exomes was completed on workstation-class hardware in a colorectal cancer cohort, showing that clinical molecular laboratories can independently perform this step without reliance on external computing resources. The architecture, in which each site analyzes its own reads, can serve as a node in a federated genomic system.},
}

Humans
*Exome/genetics
*Colorectal Neoplasms/genetics
*Exome Sequencing/methods
Genomics/methods
Class I Phosphatidylinositol 3-Kinases/genetics
Proto-Oncogene Proteins B-raf/genetics
High-Throughput Nucleotide Sequencing/methods

@article {pmid42944446,
year = {2026},
author = {Farrell, C and Pu, Y and Regan, S and Abraham, L and Naughton, O},
title = {Optimising Irish Fen Habitat Mapping: An Evaluation of Temporal Windowing and Spectral Features Using Google Earth Engine and Random Forest.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {20},
pages = {},
pmid = {42944446},
issn = {1424-8220},
support = {CW_2023_17_WSCH_3//South East Technological University/ ; },
abstract = {High spectral similarity and overlapping habitat boundaries present a substantial challenge for mapping and monitoring Ireland's ecologically vital fen ecosystems. This study establishes a reproducible remote sensing workflow by combining Google Earth Engine (GEE) with Sentinel-2 imagery for Irish fen classification. This study uses a Random Forest machine learning model to evaluate five temporal windows against three spectral feature sets across three distinct sites. The results indicate that a growing season window (June-September) incorporating both bands and spectral indices captures peak phenological information more effectively than annual composites, producing high within-site, pixel-level overall accuracy across all study sites (Scragh Bog: 95.24%, Liskeenan Fen: 94.13%, Ballymore Fen: 94.43%). Although raw spectral bands achieved marginally higher localised accuracies at Liskeenan (+0.24%) and Ballymore (+1.65%), the combined feature set was maintained as the standard workflow to ensure cross-site stability, biophysical parameterisation, and resilience against localised soil background noise. To accommodate narrow, fragmented geometries, training data was extracted via pixel-level stratified sampling; while this introduces spatial autocorrelation, classification outputs showed coherent within-site spatial patterns, although accuracy estimates remain subject to spatial autocorrelation. Crucially, the Google Earth Engine-based Random Forest model maintained stable within-site classification performance when reducing the training-testing ratio from a standard 80:20 split down to a highly constrained 50:50 split. Ultimately, this workflow provides a cloud-based framework for monitoring vulnerable wetland ecosystems under real-world geometric and ground-truth data limitations.},
}

@article {pmid42927376,
year = {2026},
author = {Bachir, MA and Nawathey, N and Bachir, AS and Reddy, AJ and Webster, J and Gill, S and Patel, R},
title = {Cloud-Based AutoML Classification of Malignant and Non-malignant Colorectal Findings on Abdominal Computed Tomography: A Proof-of-Concept Study.},
journal = {Cureus},
volume = {18},
number = {9},
pages = {e116802},
pmid = {42927376},
issn = {2168-8184},
abstract = {Colorectal cancer (CRC) is a major cause of cancer-related morbidity and mortality worldwide, and computed tomography (CT) plays an important role in its clinical evaluation and staging. However, CT interpretation may be influenced by lesion conspicuity, acquisition characteristics, and reader experience. Artificial intelligence (AI)-based image analysis may provide a reproducible adjunct to conventional radiologic assessment. This study evaluated a cloud-based automated machine-learning image-classification model for distinguishing malignant from non-malignant abdominal CT images. Images were obtained from the publicly available ColonCancerCT-2025 dataset, which contains CT imaging and clinical information from 40 patients, including 12 malignant and 28 non-malignant cases. For the present analysis, 3,827 CT images were included. The images were divided into training, validation, and test sets in an approximately 80:10:10 ratio, comprising 3,062 training images, 383 validation images, and 382 test images. Patient-level separation among these partitions could not be confirmed from the retained study workflow. A single-label binary image-classification model was developed using Google Cloud AutoML (Google LLC, Mountain View, CA, USA). Model performance was evaluated using average precision, precision, recall, precision-recall analysis, confidence-threshold behavior, and a normalized confusion matrix. A confidence threshold of 0.50 was used for the primary performance assessment. At this threshold, the classifier achieved an overall average precision of 0.998, with precision and recall of approximately 99%. Class-specific average precision was 0.998 for malignant images and 0.999 for non-malignant images. The normalized confusion matrix demonstrated correct classification of 97% of malignant images and 100% of non-malignant images. Representative correctly classified images received prediction probabilities of 0.947 for the malignant class and 0.960 for the non-malignant class. These findings demonstrate the feasibility of cloud-based automated image classification for colorectal CT analysis; however, the limited number of patients, lack of confirmed patient-level separation between dataset partitions, absence of independent external testing, and image-level rather than examination-level evaluation substantially limit conclusions regarding clinical generalizability. Larger patient-level, multi-institutional studies are required before such a model can be considered for clinical decision support.},
}

@article {pmid42919994,
year = {2026},
author = {Sowa, Y and Ito, H and Inafuku, N and Kodama, T and Hori, T and Muranishi, Y and Higai, S and Kurokawa, Y and Shimizu, Y and Nakamura, Y},
title = {Automated Aesthetic Evaluation After Breast Reconstruction Using Routine Postoperative Photographs: A No-Code AutoML Approach.},
journal = {Aesthetic plastic surgery},
volume = {},
number = {},
pages = {},
pmid = {42919994},
issn = {1432-5241},
abstract = {BACKGROUND: Aesthetic assessment after breast reconstruction remains subjective and is often affected by inter-rater variability. A practical and reproducible evaluation method is needed for routine clinical use. This study aimed to develop an automated algorithm for classifying postoperative breast aesthetics into three categories: excellent, fair, and bad using Google Cloud AutoML Vision, without requiring advanced programming expertise.

METHODS: A total of 519 frontal two-dimensional photographs obtained 1 year after breast reconstruction were retrospectively collected. Images were annotated into three aesthetic categories based primarily on the Harris method, with additional semi-quantitative elements from the Sawai aesthetic scale. The original dataset was divided into training (n = 336), validation (n = 84), and test (n = 99) sets. Data augmentation was applied only to the training set, increasing it to 1,344 images. In total, 1,527 images were uploaded to AutoML Vision to build a single-label image-classification model. Model performance was compared with that of plastic surgeons, nurses, and laypersons using the same test set.

RESULTS: The algorithm achieved a precision of 62.1%, recall of 59.6%, and overall accuracy of 60.6% on the test set. The weighted Cohen's kappa coefficient was 0.47. Mean classification accuracy was 78.9% for plastic surgeons, 60.2% for nurses, and 59.3% for laypersons. The algorithm showed numerically higher agreement with the reference labels than nurses and laypersons, although its overall performance remained moderate.

CONCLUSIONS: An automated aesthetic assessment model for breast reconstruction was successfully developed using AutoML Vision. Although performance remained inferior to that of plastic surgeons, the model demonstrated moderate agreement and may serve as a practical adjunctive tool for standardized postoperative aesthetic evaluation.

LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .},
}

@article {pmid42909785,
year = {2026},
author = {Shah, S},
title = {Spatiotemporal monitoring of water quality degradation and trophic state in a severely polluted tropical urban lake in Southern India.},
journal = {Environmental science and pollution research international},
volume = {},
number = {},
pages = {},
pmid = {42909785},
issn = {1614-7499},
abstract = {Bellandur Lake in Bangalore, India represents one of the most severely polluted urban water bodies in South Asia, necessitating systematic monitoring for environmental management. This study employed Sentinel-2 Multispectral Instrument imagery to assess spatiotemporal variations in water quality parameters from January 2023 to December 2025. Six water quality indicators were derived using established remote sensing algorithms: chlorophyll-a (estimated using the Normalised Difference Chlorophyll Index as the primary method), turbidity, total suspended solids, coloured dissolved organic matter, Secchi depth, and floating algae index. A total of 100 Sentinel-2 images were processed using Google Earth Engine, yielding 93 valid observations across 28 months after excluding monsoon periods affected by cloud cover and confining analysis to complete years 2023-2025. Results indicated severely degraded conditions with mean NDCI-based chlorophyll-a of 71.31 µg/L (median 70.24 µg/L), consistently classifying the lake as hypereutrophic. Mann-Kendall trend analysis revealed statistically significant increasing trends in chlorophyll-a (τ = 0.144, p = 0.041) and floating algae index (τ = 0.419, p < 0.001), with concurrent significant decreasing trends in turbidity (p < 0.001) and total suspended solids (p = 0.001). Pixel-wise FDR-corrected trend analysis identified 33.7% of the lake area exhibiting significant increasing chlorophyll-a trends. The lake consistently exceeded Central Pollution Control Board standards, with 100% of observations surpassing permissible total suspended solids limits and 98.9% of observations classified as hypereutrophic. These findings are interpreted as indicative of relative spatial and temporal patterns given the absence of site-specific in situ calibration; however, they provide valuable baseline information for management interventions and demonstrate the utility of satellite remote sensing for monitoring polluted urban water bodies.},
}

@article {pmid42904475,
year = {2026},
author = {Apte, AP and LoCastro, E and Iyer, A and Elguindi, S and Jiang, J and Thor, M and Oh, JH and Veeraraghavan, H and Shukla-Dave, A and Deasy, JO},
title = {Artificial intelligence apps for medical image analysis using pyCERR and cancer genomics cloud.},
journal = {Medical physics},
volume = {53},
number = {11},
pages = {e70701},
doi = {10.1002/mp.70701},
pmid = {42904475},
issn = {2473-4209},
support = {P30 CA008748//MSK Cancer Center/ ; //Simons Foundation/ ; MATH-23-001//Breast Cancer Research Foundation/ ; U54CA274291//NIH ROBIN cooperative group/ ; //Seven Bridges Cancer Research Data Commons Cloud/ ; /CA/NCI NIH HHS/United States ; HHSN261201400008C/NH/NIH HHS/United States ; 17X146/NH/NIH HHS/United States ; HHSN261201500003I/NH/NIH HHS/United States ; 75N91019D00024/NH/NIH HHS/United States ; },
mesh = {*Genomics ; *Cloud Computing ; *Neoplasms/diagnostic imaging/genetics/radiotherapy ; *Artificial Intelligence ; *Image Processing, Computer-Assisted/methods ; Humans ; Radiomics ; *Software ; *Diagnostic Imaging ; },
abstract = {PURPOSE: To provide a software resource that enables medical researchers and practitioners to develop and apply reproducible, out-of-the-box, AI-based analysis workflows for radiological and radiotherapy data.

The framework comprises two main components: the Python-native computational environment for radiological research (pyCERR) for radiological image processing, and the cancer genomics cloud (CGC), a National Cancer Institute-funded resource that supports cloud-hosted collaborative projects, with tools for managing collaborators and compute-hardware from providers such as Amazon Web Services and Google Cloud Platform. pyCERR is open-source (GNU GPL) and distributed at https://github.com/cerr/pycerr; it provides user-friendly data structures, simplified access to metadata across radiological imaging modalities, and tools for data transformation, AI-based image segmentation, radiomics, dynamic contrast-enhanced MRI analysis, radiotherapy dose-volume histogram (DVH) feature extraction, and normal-tissue complication and tumor-control models. Radiomics features comply with the Image biomarker standardization initiative (IBSI) standards and are validated against published reference values. Unit tests covering data input/output, radiomics, DVH features, and visualization run on every code update. Workflows are containerized for reproducible, identical execution on the CGC and on local hardware. The CGC enables AI inference from radiological images to be combined with other data types such as genomics and proteomics. Our public CGC project (https://cgc.sbgenomics.com/u/sevenbridges/pycerr-radiological-radiotherapy-imageanalysis), accessible with free registration, currently includes apps for organ-at-risk (OAR) and lung-nodule segmentation based on published models trained in-house and clinically validated. These apps can be run interactively from a web browser or programmatically via a REST API with client support for multiple programming languages. Notebooks demonstrating the CGC API and pyCERR capabilities are provided at https://github.com/cerr/pycerr-notebooks.

POTENTIAL APPLICATIONS: The framework facilitates end-to-end radiological image analysis and reproducible research, including data access from public and private repositories, training and inference with AI models, and utilities for data management, visualization, and simplified access to image metadata.},
}

*Genomics
*Cloud Computing
*Neoplasms/diagnostic imaging/genetics/radiotherapy
*Artificial Intelligence
*Image Processing, Computer-Assisted/methods
Humans
Radiomics
*Software
*Diagnostic Imaging

@article {pmid42898595,
year = {2026},
author = {Yoo, JS and Lee, CH and Kim, JG and Kim, J and Kim, G and Kim, T and Rhee, JY},
title = {Broiler detection system for multi-tier broiler house using a cloud platform.},
journal = {Journal of animal science and technology},
volume = {68},
number = {5},
pages = {1631-1644},
pmid = {42898595},
issn = {2055-0391},
abstract = {Broilers are bred at a higher density than large livestock, and their relatively small size makes monitoring challenging, requiring a reliable, cost-effective automatic monitoring system for precision farming. Particularly in the case of managing large-scale broiler breeding systems, such as multi-tier broiler houses, it is important to reduce investment in computing facilities and to improve productivity through automatic monitoring technology. However, financial ability of farmers to equip and manage the high-specification computing equipment required by these technologies is limited, necessitating development of affordable monitoring systems. We there propose a lightweight broiler-detection model that augments YOLOv5 with two efficiency modules: Bi-directional Feature Pyramid Network (BiFPN), which fuses multi-scale features with learnable weights to sharpen small-object and GhostNet, which replaces many convolutions with inexpensive "ghost" operations to cut parameters. Performance was benchmarked against the original YOLOv5 on a high-spec local GPU machine and CPU based cloud platform. The proposed model attained 90.5% mAP@0.5, marginally below the baseline's 92.8%, yet inference time dropped by 30% on CPU (50.9 ms) and 50% on GPU (1.8 ms). By implementing lightweight models, farmers can utilize monitoring systems from anywhere via mobile devices, leveraging cloud-based technology. This eliminates the necessity for expensive hardware, offering an affordable and practical solution for improving farming management.},
}

@article {pmid42903062,
year = {2026},
author = {Li, Q},
title = {A multi-granularity narrative cloud selection framework for architectural meaning-making integrating the PIA model and three-way decision theory.},
journal = {Frontiers in psychology},
volume = {17},
number = {},
pages = {1869490},
pmid = {42903062},
issn = {1664-1078},
abstract = {Architectural experience depends not only on perceptual fluency but also on whether users can successfully construct higher-order semantic meaning from complex built form. In the Pleasure-Interest Model of Aesthetic Liking (PIA model), the controlled-processing stage differentiates interest, confusion, and boredom. In conventional architectural cognition research, however, confusion is often treated as a negative indicator of communicative failure. This paper challenges that assumption and argues that confusion is especially important in architectural meaning-making because it identifies a population that still possesses interpretive motivation but has not yet achieved semantic resolution due to a mismatch between narrative granularity and cognitive accessibility. To address this problem, the paper proposes a conceptual and heuristic framework that integrates the PIA model, three-way decision theory, and granular computing. First, the three states in the controlled-processing stage of the PIA model are reinterpreted through a three-way decision structure, in which confusion is reconstructed as a strategic boundary region rather than as a purely negative endpoint. Second, a multi-granularity optimization mechanism and a utility-based selection function are introduced to identify narrative descriptions that balance novelty and comprehensibility for target perceivers. Third, a memorial-hall case and a computer-simulated illustrative statement-scoring exercise are used to illustrate the feasibility of the proposed procedure. Rather than claiming full empirical validation, the paper positions its contribution as a design-oriented framework for improving architectural communication, user-centered narrative intervention, and the cultural-social dimension of human-centered sustainability in the built environment.},
}

@article {pmid42896124,
year = {2026},
author = {Li, P and Gong, H and Liu, H and Han, S and Lu, Y and Yan, Y},
title = {Integrating Large Language Models and Metaverse-Related Technologies in Urology Education: Viewpoint.},
journal = {JMIR medical education},
volume = {12},
number = {},
pages = {e93235},
pmid = {42896124},
issn = {2369-3762},
mesh = {*Large Language Models ; *Urology/education ; Humans ; },
abstract = {Urology education must prepare learners to integrate anatomical visualization, diagnostic reasoning, technical performance, communication, and ethical judgment, often under restricted working hours and limited access to high-fidelity cases. This viewpoint argues that large language models (LLMs) and metaverse-related technologies should be integrated as complementary, supervised educational tools rather than treated as autonomous replacements for educators or clinical experience. LLMs can support cognitive apprenticeship, documentation practice, assessment, and communication simulations, whereas extended reality, cloud computing, and virtual digital humans can support spatial, procedural, and remote learning. Our aim is to articulate 3 key messages: first, the educational value of these technologies depends on alignment with defined urological competencies; second, implementation should proceed in stages from governed data infrastructure to intelligent interaction and immersive multimodal training; and third, educational effectiveness must be established through prospective evaluations of transfer to clinical performance, not through technical novelty alone. We emphasize the need for rigorous randomized controlled trials, identify requirements for haptic and biomechanical fidelity, and discuss hallucination, bias, privacy, accountability, and deskilling. The responsible future of intelligent urology education will therefore require collaboration among learners, educators, clinicians, engineers, ethicists, institutions, and professional bodies.},
}

*Large Language Models
*Urology/education
Humans

@article {pmid42884548,
year = {2026},
author = {Jia, S and Zhang, J},
title = {Budget-Constrained Edge Orchestration of DAG-Based Sensor-Data Workflows with Multicast Result Dissemination.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {19},
pages = {},
pmid = {42884548},
issn = {1424-8220},
support = {C619300A129//People's Government of Yunnan Province/ ; },
abstract = {Distributed sensing applications increasingly transform observations through multi-stage pipelines executed across heterogeneous edge-cloud resources. A sensing request is operationally complete only after its actionable output reaches all subscribed monitoring or control endpoints. However, workflow scheduling and result dissemination are commonly optimized separately, even though the server executing the sink task determines the source of dissemination. This paper studies their coupling under a per-request budget. We model each sensor-data workflow as a directed acyclic graph of service-dependent processing stages and intermediate-data transfers, followed by shared delivery of the sink output. We formulate lexicographic minimization of end-to-end sensing-response delay and total cost, subject to precedence, server capacity, service availability, multicast connectivity, and budget constraints. We then propose Priority-Aware Constraint-based Large Neighborhood Search (PAC-LNS), which combines topology-aware prioritization, dynamic ready-window scheduling, bounded local refinement, dissemination-budget reservation, and shared-tree construction. We implement PAC-LNS scheduling and shared-tree construction in MATLAB and evaluate synthetic DAGs and Alibaba trace-derived computation workloads: feasibility determines admission, delay and cost are compared on common successful requests, and acceptance rate and online runtime are reported separately. In trace replay with R=5000, PAC-LNS accepts 89.04-90.12% of requests as the budget increases from B=100 to B=200, while its online scheduling and tree-construction time averages 16.644 ms per request across these settings. These results support joint processing and delivery orchestration for budget-constrained edge-assisted sensing services.},
}

@article {pmid42876506,
year = {2026},
author = {Ailabouni, F and Román-Gallego, JÁ},
title = {Q-LEASE: Lifecycle-bound zero-trust authorization for HPC-to-QPU job handoffs.},
journal = {Open research Europe},
volume = {6},
number = {},
pages = {302},
pmid = {42876506},
issn = {2732-5121},
abstract = {BACKGROUND: Zero-Trust access control for cloud APIs is well established, but hybrid High-Performance Computing (HPC)-to-Quantum Processing Unit (QPU) workflows have a lifecycle existing mechanisms were not built for: a job queues for minutes to days, is dispatched, executes, and only then releases results, while identity, policy, and hardware calibration state can change throughout. Existing token, proof-of-possession, and workload-identity mechanisms do not separate permission to queue from permission to execute, nor bind authorization to current hardware state.

METHODS: We designed and implemented Q-LEASE, a two-capability protocol separating a Queue Admission Ticket from a single-use Execution Capability bound to a circuit hash, backend identity, and calibration epoch, re-evaluated by a real Open Policy Agent (OPA) engine immediately before dispatch. The prototype uses real Ed25519/PASETO tokens, DPoP-style proof-of-possession, SPIFFE-shaped workload identity, and a phase-aware revocation state machine, deployed as six containerised services on a Kubernetes (kind) cluster with Kueue-driven queueing. We compared Q-LEASE against four representative baselines across 16 attack/failure scenarios (2400 attack trials, 500 clean trials), and verified core safety properties with TLA+/TLC and token-flow security with the Tamarin prover.

RESULTS: Q-LEASE blocked 90.0% of unauthorized-execution attempts (95% confidence interval (CI) 87.0-92.4%), versus 12.5-37.9% for the four baselines. The remaining gap came from queue/execution separation and circuit/backend/calibration-epoch binding, which no baseline implements. Both TLA+ safety properties held, and all five Tamarin lemmas were proved automatically. Q-LEASE's mean policy-decision latency was 44.3 ms, approximately 34.4 ms higher than the baselines, with no measurable increase in end-to-end workflow completion time relative to a real, contention-driven queue wait, while running against a real Kueue-admitted workload on a real cluster.

CONCLUSIONS: Separating queue admission from execution authorization, and binding it to hardware state re-checked immediately before dispatch, closes a gap that composing existing OAuth-family mechanisms does not, at a latency cost small relative to realistic QPU queue waits.},
}

@article {pmid42884223,
year = {2026},
author = {Alnaim, AK and Alwakeel, AM},
title = {Network-Slice-AwareVNF Orchestration for Mission-Critical IoT in 6G Networks: A Multi-Objective Deep Reinforcement Learning Approach.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {19},
pages = {},
doi = {10.3390/s26196023},
pmid = {42884223},
issn = {1424-8220},
support = {KFU264620//King Faisal University/ ; },
abstract = {Mission-critical Internet of Things (MC-IoT) applications, such as remote surgery, autonomous drone swarms, industrial safety monitoring, etc., have ultra-stringent requirements for reliability (99.999%), latency (sub-millisecond) and inter-slice isolation that require dedicated network slices with carefully orchestrated virtual network function (VNF) chains. While prior solutions to VNF orchestration seek to merge often conflicting requirements into a single objective with a scalar reward, or to statically configure the allocation of resources that fail to adjust to dynamic mission-critical workloads, both strategies are inadequate. In this paper, we propose a network slicing-aware NFV orchestration framework called SliceNFV, which uses multi-objective deep reinforcement learning (MO-DRL) to optimize the resource allocation of VNFs per slice, the inter-slice isolation enforcement, and the efficiency of the cross-slice resource sharing. It is formulated as the orchestration problem as a multi-objective constrained Markov decision process (MO-CMDP), and a novel Pareto-Conditioned Proximal Policy Optimization (PC-PPO) algorithm is developed to learn a portfolio of non-dominated orchestration policies, allowing the operator to choose operating points along the reliability-efficiency frontier without retraining. A temporal convolutional network (TCN) is a scalable network that uses a predictive slice-aware VNF scaling mechanism to forecast the arrival of mission-critical events and scale resources in advance to avoid SLA breaches. The results of extensive simulation-based assessment across three progressive scenarios-steady-state operation, mission-critical demand surge and infrastructure degradation-highlight that SliceNFV offers 99.998% reliability when operating under an SLA violation rate of 0.08%, whereas all other baselines exceed their corresponding SLAs; in addition, SliceNFV significantly improves Pareto hypervolume by 36.4% over traditional multi-objective approaches while increasing infrastructure utilization to 76%.},
}

@article {pmid42884268,
year = {2026},
author = {Albalawi, A and Mohaisen, L},
title = {Fog-Deployable XGBoost Framework for DDoS Detection in IoT Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {19},
pages = {},
doi = {10.3390/s26196068},
pmid = {42884268},
issn = {1424-8220},
abstract = {The rapid growth of Internet of Things (IoT) deployment has increased exposure to Distributed Denial of Service (DDoS) attacks, while the latency and bandwidth costs of cloud-centric intrusion detection constrain real-time response. This paper presents a fog-based DDoS detection framework that combines XGBoost with information-gain feature selection. The framework uses 23 of the 78 features, a 70.5% reduction, at a measured accuracy cost of 0.007 percentage points. The evaluation used a sample drawn from every file of the CICDDoS2019 release, with benign traffic retained and attack traffic sampled to an attack-to-benign ratio of approximately 10:1. Feature selection, hyperparameter tuning, and threshold calibration were confined to the training partition. Under a random stratified split, the model reached 99.98% accuracy at a 0.058% false-positive rate. Under the capture-day split designated by the dataset authors, accuracy fell to 91.61%, and the false-positive rate rose to 8.82%, equivalent to 8820 false alerts per 100,000 benign flows. Per-request inference on a two-core profile took 1.0 to 5.2 ms over three repeated runs; feature extraction and network transit were not measured. Deployment would require monitoring for distribution shift and periodic retraining.},
}

@article {pmid42884347,
year = {2026},
author = {Valdés-Quintero, J and Vásquez-Salazar, RD and Parra, JC and Olmos-Severiche, C and Camargo-Perea, AG and Tibavija-Abril, CA and Díaz-Paz, JP},
title = {Optimization of Spatial Downscaling Models for Satellite Imagery Based on Deep Learning and Generative Artificial Intelligence.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {19},
pages = {},
doi = {10.3390/s26196147},
pmid = {42884347},
issn = {1424-8220},
support = {Proyecto Menor cuantía 2026//Politécnico Colombiano Jaime Isaza Cadavid/ ; },
abstract = {Spatial downscaling of satellite imagery, the reconstruction of high-resolution outputs from coarser-resolution inputs, is a critical enabler of long-term land cover monitoring, yet the domain adaptation gap between natural-image super-resolution models and satellite sensor characteristics remains largely unaddressed. This study proposes a transfer learning framework for Landsat-to-Sentinel-2 spatial downscaling at a scale factor of ×3, systematically comparing shallow and deep fine-tuning strategies applied to two state-of-the-art architectures: SwinIR and ESRGAN. All models were trained and evaluated on a newly constructed dataset of 3500 paired patches assembled through an automated Google Earth Engine pipeline with rigorous cloud, water, and spectral variability filters spanning latitudes -30° to 30°. Performance is assessed using a hybrid evaluation framework combining pixel-wise metrics (MSE, PSNR, SSIM) with perceptual metrics (LPIPS, DISTS), and statistical significance is established through Friedman tests with Nemenyi post hoc analysis. Results demonstrate that off-the-shelf pretrained models fail to outperform bicubic interpolation on pixel-wise metrics, remaining statistically indistinguishable from it, thereby revealing a domain adaptation gap. Domain-specific fine-tuning completely reverses this degradation: ESRGAN with deep fine-tuning achieves the best performance across all five metrics simultaneously, reducing MSE by 48.7% and improving PSNR by 1.84 dB relative to the pretrained baseline, with all improvements statistically confirmed at p<0.0001. The findings establish that intermediate unfreezing of feature extraction blocks represents the optimal adaptation strategy, and that pixel-wise and perceptual metric families can diverge, making hybrid evaluation a methodological necessity rather than a redundancy.},
}

@article {pmid42884432,
year = {2026},
author = {Silvi, A and Fejzic, E and Salvo, ED and Colonnese, S},
title = {ECG Vertex-Time Activation Representation for IoT-XR Heart Monitoring.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {19},
pages = {},
doi = {10.3390/s26196230},
pmid = {42884432},
issn = {1424-8220},
support = {B53C22004050001//European Union - Next Generation EU under the Italian National Recovery and Resilience Plan (NRRP)(European Commission)/ ; B53D23002460006//National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, /investment 1.1, Call for tender No. 104 published on 2.2.2022 by the Italian Ministry of University and Research (MUR)/ ; },
mesh = {Humans ; *Electrocardiography/methods ; Signal Processing, Computer-Assisted ; *Internet of Things ; *Heart/physiology ; Algorithms ; Wearable Electronic Devices ; Monitoring, Physiologic/methods ; },
abstract = {Electrocardiogram (ECG) monitoring via wearable devices generates rich physiological data, yet conventional time-domain representations offer limited support for spatial interpretation and immersive visualization. We introduce a novel vertex-time representation of the ECG signal, introducing the Ideal Vertex Time (IVT) and Eigen Vertex Time (EVT) vectors which map temporal activation intervals and eigenbeat amplitudes onto a three-dimensional heart point cloud. A key novelty is that this approach serves a dual purpose: it enables graph-spectral ECG feature extraction for classification tasks, and it drives immersive rendering within an Internet of Things pipeline, based on MQTT, cloud processing, and Extended Reality visualization. This work is presented as a proof of concept. The mapping from ECG landmarks to anatomical regions is model-driven and has not been independently validated against patient-specific electrophysiological measurements. Herein, the framework is evaluated for classification purposes on an affective-state dataset and integrated into an Internet-of-Things-enabled e-health monitoring pipeline, whose prototype replays previously recorded ECG signals rather than acquiring them from a wearable device. Overall, this work extends conventional time-domain electrocardiographic analysis toward a spatially structured and immersive monitoring framework. No comparison with conventional ECG descriptors was performed; thus, no advantage over such features is claimed. Its clinical utility remains to be assessed within a patient-specific monitoring pipeline, computing the vertex-time features alongside conventional ECG descriptors and weighting them jointly to identify the best representation for each patient.},
}

Humans
*Electrocardiography/methods
Signal Processing, Computer-Assisted
*Internet of Things
*Heart/physiology
Algorithms
Wearable Electronic Devices
Monitoring, Physiologic/methods

@article {pmid42859768,
year = {2026},
author = {Jimenez-Maggiora, GA and Bruschi, S and Abdel-Latif, S and Aisen, PS and Rissman, RA},
title = {ATRI LIMS: a cloud-native open-source laboratory information management system for biomarker-driven Alzheimer's disease clinical trials.},
journal = {JAMIA open},
volume = {9},
number = {5},
pages = {ooag198},
pmid = {42859768},
issn = {2574-2531},
support = {U24 AG057437/AG/NIA NIH HHS/United States ; },
abstract = {OBJECTIVES: This article summarizes the architecture, design principles, and implementation strategy of the ATRI Laboratory Information Management System (LIMS), a cloud‑native, open‑source platform supporting biomarker‑driven Alzheimer's disease (AD) clinical research. The system addresses challenges associated with managing large‑scale, multimodal biomarker, and biobanking data across multisite studies.

METHODS: The ATRI LIMS employs a Model-View-Controller architecture implemented in Django, with Single‑Page Application interfaces built in ReactJS. A hybrid database model integrates a normalized core schema with study‑specific extensions. Deployment on Amazon Web Services incorporates multifactor authentication, encryption, and a web application firewall. Regulatory support is provided through audit trails and electronic signatures. Development relied on collaboration between informatics engineers and laboratory personnel, with user acceptance testing guiding workflow and interface refinement.

RESULTS: As of February 2026, the ATRI LIMS supports seven clinical trials and observational studies and manages more than 1.2 million biospecimens. The platform synchronizes biospecimen metadata and assay outputs with clinical datasets, improves operational efficiency through automated workflows and quality control, and facilitates compliant sample sharing for secondary research. AI‑assisted development accelerated feature delivery while maintaining code quality.

DISCUSSION: The ATRI LIMS offers a scalable, adaptable informatics framework for biomarker‑intensive AD research. Its modular design, cloud‑native architecture, and comprehensive data management capabilities support accurate specimen tracking and coordinated data integration. An open-source release is planned to enable adoption and adaptation by other research programs.

CONCLUSIONS: The ATRI LIMS enhances informatics infrastructure for modern AD research by enabling scalable, efficient, and compliant biomarker and biospecimen workflow management.},
}

@article {pmid42851260,
year = {2026},
author = {Mohammed Lateef, H and Abduljawad Al-Shibly, M and Hadi Ali Al-Jumaili, A and D Madeeh, O and A S Al-Hitawi, M and Arrova Dewi, D and Zakree Bin Ahmad Nazri, M and Nasir Kadhim, S},
title = {Intelligent Cloud Resource Usage Potentially to Improve Task Scheduling by the use of Artificial Intelligence.},
journal = {F1000Research},
volume = {15},
number = {},
pages = {366},
pmid = {42851260},
issn = {2046-1402},
mesh = {*Cloud Computing ; *Artificial Intelligence ; Predictive Learning Models ; Random Forest ; Prediction Algorithms ; Humans ; Workload ; Neural Networks, Computer ; },
abstract = {BACKGROUND: The high variability of workloads makes it very difficult for cloud datacenters to efficiently schedule their tasks and resource allocation. The correct forecasting of future resource utilization allows us to proactively scale and implement more sophisticated scheduling policies, which eventually results in has potential to improve resource utilization and fewer failures.

METHODS: This study uses the Google Cluster Trace v3 dataset, which contains a wealth of job and task data (start and end times, CPU and memory usage, scheduling class, and priority) to create supervised machine and deep-learning models that can predict future of CPU usage.

CONCLUSIONS: This study evaluates Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), and Neural Network (NN) models for predicting future CPU usage, which are quite successful in predicting computer resource usage (94% validation accuracy and R [2] of 0.90). These results underscore the potential of data driven approach definitely machine and deep learning-based CPU demand predictions as valuable tools for cloud schedulers, improving resource management, and reducing operational costs. Also discuss the feasibility of deploying the proposed solution on distributed platforms such as Spark and Google Cloud and outline future research directions to integrate predictive models with real-time cloud resource management.},
}

*Cloud Computing
*Artificial Intelligence
Predictive Learning Models
Random Forest
Prediction Algorithms
Humans
Workload
Neural Networks, Computer

@article {pmid42856408,
year = {2026},
author = {Bong, S and Guidi, G and Cork, M and Henneman, L and Dominici, F},
title = {Delayed Coal Plant Retirements in the Era of Data Centers Carry a Measurable Mortality Cost.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.09.29.26364336},
pmid = {42856408},
abstract = {Coal-fired power plants remain a major source of fine particulate matter (PM 2.5) exposure and mortality in the United States. Announced retirement dates mark when those burdens are expected to end, but rising electricity demand, partly from data centers supporting cloud computing and artificial intelligence, has led utilities and regulators to revisit them. Of 50 coal plants scheduled in January 2024 to retire before 2035, 20 had later or removed dates by January 2026. Projecting coal PM 2.5 and mortality among Medicare beneficiaries from the 20 plants for 2026-2035, we estimate the revised schedule would result in 1,256 additional deaths (1,136 to 1,376), raising mortality to 3.1 times the original burden. Eighteen plants operate in balancing authorities with hyperscale data-center demand, and federal orders and utility filings supporting several plants' operation cited data-center growth. Retirement schedules are revised faster than health consequences are assessed, and those delays carry a measurable mortality cost.},
}

@article {pmid42858091,
year = {2026},
author = {Li, N and Ma, H},
title = {Multi-Latency Multi-Agent Task Offloading Strategy for Smart Education Platforms Based on Edge Computing.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {236},
pages = {},
doi = {10.3791/71569},
pmid = {42858091},
issn = {1940-087X},
mesh = {Humans ; Cloud Computing ; Algorithms ; },
abstract = {In smart education platforms, large-scale real-time interactive tasks usually involve complex subtask dependencies. The transmission, computation, and queuing delays generated by different subtasks are coupled with each other, making it difficult for traditional task offloading methods to achieve efficient scheduling. Meanwhile, multi-user concurrent requests further intensify edge computing resource competition. To address these challenges, this study proposes a task offloading scheme based on mobile edge computing (MEC) and multi-agent deep deterministic policy gradient (MADDPG). First, a smart education platform architecture including cloud nodes, edge nodes, and terminal devices is constructed to achieve cloud-edge-device collaborative computing. Second, addressing the shortcomings of existing task offloading methods that primarily focus on single latency metrics, ignore task dependencies, and lack flexibility in discrete offloading decisions, a MADDPG model is designed that integrates multi-latency perception, directed acyclic graph (DAG) task constraints, and continuous offloading ratio decision-making. This model incorporates task state, network state, and edge resource state into the agent decision-making process, achieving dynamic task offloading and resource optimization under multi-objective conditions. To validate the effectiveness of the proposed method, 19,200 task instances are generated using a DAG generator, and comparative experiments are conducted against ACO, GWO, DDPG, PPO, DQN, and Greedy Offloading methods. The results demonstrate that the proposed method outperforms all comparison approaches in terms of task completion time, energy consumption, task completion rate, and network adaptability. When the number of tasks reaches 50, the task completion time is reduced to 16.2 s, and the total energy consumption decreases to 0.95 J. When the number of users increases to 60, the task completion rate remains at 96.8%. The proposed scheme effectively addresses edge computing offloading problems under multi-task, multi-user, and multi-latency coupling conditions, providing a solution for intelligent resource scheduling in complex edge environments.},
}

Humans
Cloud Computing
Algorithms

@article {pmid42844398,
year = {2026},
author = {Miltiadis, I and Stashevskaya, NA and Burko, P},
title = {Radiology research platform: automating multicenter radiological research workflows using a cloud-native platform.},
journal = {European radiology},
volume = {},
number = {},
pages = {},
pmid = {42844398},
issn = {1432-1084},
abstract = {Large-scale, multi-institutional radiological datasets, requisite for next-generation AI and epidemiological studies, remain an unmet need due to fragmented infrastructure, institutional firewalls, and the laborious manual entry of data into research-specific electronic data capture tools. We present a proof-of-concept, cloud-native platform to facilitate multicenter radiological collaboration. The platform architecture enforces a clean separation between a React-based client UI, a cloud backend in Google Firebase, and secure local networks. The image data and metadata of each institution are securely and locally hosted through a localized edge-node Orthanc DICOM server. Critical metadata are reverse-proxied to the cloud database via REST APIs, negating any need for complicated virtual private network setups. Using dynamic JSON-based structured evaluation forms and enrollment algorithms such as consensus or fixed-volume study designs, PIs define clinical workflows to be dynamically pushed to radiologists, who then evaluate pending cases within a single workspace, including an embedded zero-footprint Open Health Imaging Foundation viewer and a standardized reporting form. KEY POINTS: Question How can radiologists from multiple sites and institutions participate in collaborative research projects without requiring VPN configurations or hardware shipments? Findings A cloud-native platform based on localized edge nodes and a zero-footprint web viewer enabled automated synchronization of imaging metadata and dynamic assignment of evaluation queues, eliminating manual data curation steps and streamlining radiologist workflows. Clinical relevance This application overcomes complex IT barriers and bypasses manual data entry to speed up the generation of radiological datasets and studies. Consequently, it accelerates the development of novel protocols and guidelines for patient care worldwide.},
}

@article {pmid42850727,
year = {2026},
author = {Zhang, J and Ouyang, H and Cheng, Y and Xiong, N and Ma, X and Li, X and Zhu, G and Zhang, L and Sun, J and Wang, R},
title = {A Biomimetic Solvatochromic Chip With Optimized Chemical Diversity for On-Site Liquid Identification.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e78158},
doi = {10.1002/advs.78158},
pmid = {42850727},
issn = {2198-3844},
support = {2024YFB3814100//National Key Research and Development Program of China/ ; 62471459//National Natural Science Foundation of China/ ; U24A20228//National Natural Science Foundation of China/ ; 62301540//National Natural Science Foundation of China/ ; 2024JCJCQN-66//Natural Science Foundation of Shaanxi Province/ ; YSBR-109//Chinese Academy of Sciences Project for Young Scientists in Basic Research/ ; ActFiber-FFG/CAS//Joint Project from Austrian Research Promotion Agency and Chinese Academy of Sciences/ ; 931688/030GJHZ2025029MI//Joint Project from Austrian Research Promotion Agency and Chinese Academy of Sciences/ ; 23520711000//Shanghai International Science and Technology Cooperation Project/ ; 24QA2710600//Shanghai Rising Star Program/ ; },
abstract = {Colorimetric sensors for instant and on-the-spot identification of organic liquids have been actively developed across diverse fields such as chemical hazard inspection, manufacturing process monitoring, and beverage safety. Multi-target sensing scenarios often necessitate large-scale sensor arrays, which inevitably increase the fabrication complexity, complicate visual interpretation, and exacerbate data redundancy. Here, we present a biomimetic photonic-gustatory chip (BiPC) comprising a polyacrylate-based solvatochromic disk (PSD) array. The elastic photonic crystal PSDs generate distinct color patterns when swelled by organic solvents. To fully unlock the sensing potential, a Hansen solubility parameter (HSP) framework was developed to assess the overall swelling cross-reactivity toward target liquids, facilitating the design of a compact PSD array with HSP-guided chemical diversity and maximized inter-solvent response distinctiveness. Using intuitive pattern recognition, an HSP-optimized four-PSD array effectively differentiates solvent types and mixing ratios, homologues and isomers, as well as alcoholic beverages. Furthermore, we developed a smartphone application that integrated image capture with deep-learning-assisted cloud computing for on-site solvent identification, achieving high accuracy (97.1% across 80 liquid classes) within 30 s. The HSP framework is generalizable to the design of compact sensor arrays for various field-deployable liquid sensing scenarios.},
}

@article {pmid42810401,
year = {2026},
author = {Shankar, R},
title = {From Cloud to Pocket: Frontier Edge Foundation Models and the Future of Clinical Informatics.},
journal = {Applied clinical informatics},
volume = {17},
number = {4},
pages = {882-888},
pmid = {42810401},
issn = {1869-0327},
mesh = {*Medical Informatics/trends ; *Cloud Computing ; Natural Language Processing ; },
abstract = {ABSTRACT: BACKGROUND: Open foundation models designed for on-device and edge deployment, exemplified by the Qwen 3.5 small series (March 2, 2026) and Gemma 4 (April 2, 2026), challenge the default of routing all clinical natural language processing through commercial cloud application programming interfaces (APIs).

ABSTRACT: OBJECTIVES: This study aimed to examine the implications of these models for clinical informatics, for clinical informaticians, healthcare chief information officers, governance committees and NLP researchers.

ABSTRACT: METHODS: Narrative (non-systematic) perspective. We summarized the technical advances of both releases and situated them within the 2024 to 2026 peer-reviewed evidence on locally deployed clinical large language models. We searched PubMed, Scopus and Google Scholar (January 2024 to June 2026) and added citation tracking.

ABSTRACT: RESULTS: Both releases narrow the gap between local and frontier cloud capability for structured extraction and template-based documentation. The existing evidence comes from 3 to 70 billion parameter models on institutional servers. Clinical benchmarks at 0.8 to 4 billion parameters are absent. Hallucination, quantization, endpoint security, liability and unvalidated multimodal and multilingual claims remain open concerns. Frontier cloud models retain clear advantages for complex clinical reasoning.

ABSTRACT: CONCLUSION: For a defined subset of tasks, cloud-by-default may no longer be the most defensible choice. The field needs reporting standards, governance and liability frameworks, task-appropriate routing, and edge-aware benchmarks.},
}

*Medical Informatics/trends
*Cloud Computing
Natural Language Processing

@article {pmid42840795,
year = {2026},
author = {Odeh, M and Odeh, DM and Hourani, W and Shadfan, A and Jirjees, F},
title = {Digital transformation in community pharmacy management: Pharmacists' readiness for cloud computing systems, implementation concerns, and adoption support.},
journal = {Exploratory research in clinical and social pharmacy},
volume = {24},
number = {},
pages = {100845},
pmid = {42840795},
issn = {2667-2766},
abstract = {BACKGROUND: Cloud computing is increasingly positioned as a key enabler of digital transformation in pharmacy practice, offering potential benefits for information access, workflow flexibility, data storage, backup, and service continuity. However, pharmacists' readiness to adopt cloud-enabled pharmacy systems remains insufficiently examined, particularly in community pharmacy settings.

OBJECTIVE: This study assessed pharmacists' readiness for cloud computing adoption as part of digital transformation in community pharmacy management, focusing on perceived usefulness, digital-service orientation, service quality, cost and value, security and privacy, infrastructure readiness, backup and recovery, and demographic predictors of adoption support.

METHODS: An analytical cross-sectional study was conducted using a validated electronic questionnaire. Eligible participants were licensed pharmacists with current or previous community-pharmacy experience. Descriptive statistics were used to summarize response patterns, chi-square tests examined bivariate associations, and Cramér's V was used to quantify the strength of statistically significant associations. Multivariable logistic regression identified independent predictors of readiness and support for cloud-service adoption.

RESULTS: Pharmacists showed strong receptiveness toward cloud-enabled pharmacy services. Most respondents agreed or strongly agreed that cloud computing could be useful for the pharmacy sector (89.1%), support wider electronic pharmaceutical services (93.7%), improve service quality (89.6%), and improve service flexibility (89.1%). However, uncertainty remained regarding cost reduction, return on investment, start-up cost, internet reliability, offline continuity, and privacy/security risks. In multivariable regression analysis, female pharmacists had higher odds of supporting cloud services than male pharmacists (OR = 7.024; 95% CI: 1.60-30.72; p = 0.010), and pharmacists aged ≤35 years had higher odds than older pharmacists (OR = 5.778; 95% CI: 2.31-14.39; p < 0.001). The model demonstrated good discrimination (AUC = 0.820; 95% CI: 0.683-0.956).

CONCLUSION: Pharmacists demonstrated high perceived readiness for cloud computing adoption, but implementation depends on resolving cost, connectivity, offline-continuity, and privacy/security concerns. A data-informed and Diffusion of Innovations-informed readiness framework is proposed to guide future validation and implementation planning.},
}

@article {pmid42837877,
year = {2026},
author = {George, GM and Kumareshan, N},
title = {Fractional painting training-based optimization enabled deep learning for autism spectrum disorder using MRI images in cloud computing.},
journal = {Psychiatry research. Neuroimaging},
volume = {364},
number = {},
pages = {112346},
doi = {10.1016/j.pscychresns.2026.112346},
pmid = {42837877},
issn = {1872-7506},
abstract = {Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that impairs social interactions. Prior models focus on target individuals older than five years and depend heavily on expert analysis, potentially missing subtle or early indicators of ASD. To address these limitations, Fractional Painting Training-based Optimization-enabled Spiking Attention Neural Network (FPTO_SANN) is devised for ASD. Initially, a cloud is simulated, and brain Magnetic Resonance Imaging (MRI) images of individuals with autism are collected from a specific dataset. Image preprocessing is done by Geometric Mean filtering along with Region of Interest (RoI) extraction, and pivotal regions are identified based on functional connectivity using Fractional Painting Training based Optimization (FPTO). In parallel, feature extraction is conducted on the preprocessed MRI images to obtain Grey Level Co-occurrence Matrix (GLCM) features along with Local Combination Adaptive Ternary Pattern (LCATP) and Discrete Cosine Transform (DCT) features. Finally, ASD classification is performed using a Spiking Attention Neural Network (SANN) that is trained utilizing FPTO, which is developed by integrating Painting Training Based Optimization (PTBO) and Fractional Concept (FC). The FPTO_SANN has attained superior outcomes by considering the metrics, namely accuracy, True Negative Rate (TNR), and True Positive Rate (TPR), with values of 96.67%, 96.98%, and 96.01%.},
}

@article {pmid42840467,
year = {2026},
author = {Bachir, MA and Bachir, A and Nawathey, N and Reddy, AJ and Cheema, JK and Brahmbhatt, T and Webster, J and Patel, R},
title = {Code-Free Automated Machine Learning for Referable Glaucoma Classification From Retinal Fundus Photographs: A Proof-of-Concept Study Using Google Cloud Vertex AI.},
journal = {Cureus},
volume = {18},
number = {9},
pages = {e115841},
pmid = {42840467},
issn = {2168-8184},
abstract = {Artificial intelligence has demonstrated substantial potential for automated glaucoma screening from retinal fundus photographs; however, conventional deep-learning development often requires programming expertise, specialized knowledge of neural-network design, and substantial computational resources. This study evaluated whether a commercially available, code-free automated machine-learning (AutoML) platform could generate a high-performing classifier for distinguishing referable glaucoma (RG) from non-referable glaucoma (NRG). A publicly available balanced retinal fundus image dataset derived from the EyePACS Artificial Intelligence for Robust Glaucoma Screening dataset was used. The study included 9,540 color fundus photographs, with 4,770 images in each class. Images were assigned to training (n=7,632; 80%), validation (n=954; 10%), and held-out internal testing (n=954; 10%) partitions. A single-label image-classification model was developed using Google Cloud Vertex AI AutoML (Google LLC, California, US) through its graphical interface without investigator-written machine-learning code or manual neural-network architecture design. Model training used a maximum computational budget of eight node-hours. Performance was evaluated using average precision, precision, recall, precision-recall analysis, confidence-threshold analysis, and a row-normalized confusion matrix. The AutoML model achieved an overall average precision of 0.974. At the default confidence threshold of 0.50, overall precision and recall were both 92.5%. Class-specific average precision was 0.977 for both RG and NRG. The model correctly classified 94% of NRG images and 91% of RG images. Deployment to a cloud-based prediction endpoint successfully generated class predictions and confidence scores for representative retinal fundus photographs. A fully graphical AutoML workflow therefore produced strong internal classification performance for distinguishing RG from NRG without investigator-written machine-learning code or manual neural-network development. These findings support the feasibility of commercial AutoML platforms as accessible tools for ophthalmic image-classification research. Because the model was evaluated using a single derived dataset without external testing, further validation across independent populations, institutions, and imaging systems is necessary before clinical implementation.},
}

@article {pmid42833589,
year = {2026},
author = {Arnaoutakis, DJ and Patel, RJ and Hamouda, M and Patel, S and Pavlock, SM and Shames, M and Malas, MB},
title = {A Multi-Institution Analysis Demonstrates that Intelligent Maps Improve Intraoperative Safety and Efficiency During Fenestrated/Branched Endovascular Repairs.},
journal = {Annals of vascular surgery},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.avsg.2026.09.029},
pmid = {42833589},
issn = {1615-5947},
abstract = {INTRODUCTION: Augmented intelligence (AI) tools have become popular additions to medicine. AI can transform operating rooms with advanced visualization and aid surgeons with decision-making. The AI tool we are investigating in this study allows patient specific maps to be created with a cloud computing software based on preoperative CT scans which are superimposed with fluoroscopic images in the operating room. The image fusion arguably permits easier identification and cannulation of visceral vessels. A unique feature of this software is that the map can be manually moved and adjusted in the operating room as the aorta is manipulated. The goal of our study is to evaluate the impact of AI on the intraoperative outcomes of fenestrated/branched endovascular aortic repairs (F/BEVARs).

METHODS: This is a multi-institution review of patients who underwent a F/BEVAR at tertiary care centers from August 2015-December 2022. Patients were stratified based on the use of AI during F/BEVAR. Primary outcomes included operative time, radiation exposure, fluoroscopy time, and contrast use. Secondary outcomes included 30-day complications. Multivariate linear regression models were used to evaluate the association between AI and outcomes after adjustment for potential confounders.

RESULTS: There were 344 patients included in this analysis; 248 (72.1%) patients underwent procedures using AI, and 96 (27.9%) had procedures without AI. There were no significant differences in baseline characteristics between the two groups. There was a significant difference in aneurysm diameter at time of repair, 65.3mm in the AI group and 64.1mm in the non-AI group, p=0.047. On univariate analysis there was not a significant difference in operative time, fluoroscopy time or radiation exposure in the non-AI group compared to the AI group. After adjusting for potential confounders, we found that the AI group had a significant reduction in contrast use - 62.4cc (p<0.0001), radiation exposure - 916.4 mGy (p=0.002), and fluoroscopy time - 16.6 minutes (p=0.001). Additionally, no significant difference was found in 30-day events between the groups.

CONCLUSION: Incorporating AI technology into the operating room during F/BEVAR can result in a significant reduction in contrast use, radiation exposure and fluoroscopy time. Therefore, AI can improve safety in the operating room for both patient and providers and should be considered as an adjunct to F/BEVAR cases. Additional studies are needed to confirm our findings and further extrapolate this benefit.},
}

@article {pmid42835317,
year = {2026},
author = {Zhou, G and Wu, L},
title = {From 5G smart hospitals to 6G-enabled learning hospitals: a governance-oriented framework for healthcare technology, policy, and responsible deployment.},
journal = {Frontiers in digital health},
volume = {8},
number = {},
pages = {1923445},
pmid = {42835317},
issn = {2673-253X},
abstract = {OBJECTIVES: Discussion of 6G in healthcare often moves directly from anticipated network capabilities to proposed clinical applications, although application-level evidence, current connectivity alternatives, and 6G-specific evidence differ substantially in maturity. This narrative review develops an evidence-calibrated, governance-oriented framework for prospective 6G-enabled learning hospitals and examines when future 6G may add meaningful value beyond existing infrastructure.

METHODS: A multidisciplinary narrative synthesis covered smart hospitals, learning health systems, 5G/6G communication, IoMT, edge-cloud computing, AI/XAI, robotics, digital twins, cybersecurity, interoperability, implementation, and digital-health governance. Structured searches of PubMed, Web of Science, IEEE Xplore, and Google Scholar covered 1 January 2010-31 January 2026, supplemented by relevant standards, regulatory and policy sources, and targeted recent studies. Evidence was interpreted using an author-developed Evidence Maturity Classification (EMC) spanning demonstrated implementation, clinically grounded empirical, technical/benchmark, and prospective evidence.

RESULTS: The synthesis produced a seven-layer architecture, nine use-case analyses, a capability and evidence-maturity matrix, a twelve-domain risk-governance-metric framework with decision gates, a five-phase implementation roadmap, and an executive checklist. Across use cases, evidence for the underlying healthcare application was more mature than evidence for incremental benefit specifically attributable to 6G. Existing wired networks, Wi-Fi, private cellular systems, 5G/5G-Advanced, and edge-cloud infrastructure already support many monitoring, telemedicine, imaging, home-care, logistics, maintenance, and selected teleoperation functions. Future 6G is therefore most relevant when meaningful residual capability gaps remain, particularly combinations of mobility continuity, predictable end-to-end service, dense device coordination, multimodal synchronization, integrated sensing or positioning, and communication-computing orchestration.

CONCLUSIONS: 6G adoption should follow demonstration of clinical or operational value, a residual capability gap, and governance readiness. Hospitals should use the least complex infrastructure that meets requirements, generate evidence through risk-proportionate pilots, and scale advanced connectivity only when incremental value is demonstrated against optimized current alternatives. The framework provides a structured basis for these decisions but requires prospective validation across diverse hospital settings.},
}

@article {pmid42829544,
year = {2026},
author = {Hadri, H and Fail, A and Sadik, M},
title = {Advancing Ultrasound Beamforming With Deep Learning: A Comprehensive Review of Methods, Datasets, Benchmarks, and Computational Challenges.},
journal = {International journal of biomedical imaging},
volume = {2026},
number = {},
pages = {4617573},
pmid = {42829544},
issn = {1687-4188},
abstract = {Ultrasound imaging is a widely used diagnostic tool, and beamforming techniques are integral to its performance. Traditional methods such as delay-and-sum (DAS) and delay-multiply-and sum (DMAS) have limitations in terms of image quality and computational efficiency. Recently, deep learning approaches have shown significant promise in improving ultrasound beamforming, offering enhanced image resolution and quality, alongside potential for real-time processing. This review is aimed at providing a comprehensive overview of the current state-of-the-art in deep learning-based ultrasound beamforming. It focuses on analyzing various deep learning models, their applications, challenges, and the integration of hardware optimization strategies to enhance performance and efficiency. The review examines a range of deep learning models applied to ultrasound beamforming, including convolutional neural networks (CNNs), generative adversarial networks (GANs), transformer-based models, and hybrid approaches. It also discusses the challenges associated with dataset limitations, model interpretability, and the risk of overfitting. Furthermore, the review explores hardware acceleration using field-programmable gate arrays (FPGAs), along with cloud-edge frameworks for real-time inference. The findings highlight the potential of deep learning models to outperform traditional methods in terms of image quality and resolution. However, challenges such as the need for large and diverse datasets, the black box nature of deep learning models, and the risk of overfitting remain. Hardware optimization through FPGAs has proven effective in enabling real-time processing, but the integration of edge computing with cloud-based solutions offers promising avenues for balancing performance, latency, and energy efficiency. Deep learning-based ultrasound beamforming has great potential to advance medical imaging. Future efforts should enhance model generalizability with diverse and synthetic data, optimize hardware for real-time use, and establish standardized validation protocols to support clinical adoption.},
}

@article {pmid42828042,
year = {2026},
author = {Jia, F and Li, X and Song, H and Liu, X and Tang, Y and Wen, Q and Wu, P},
title = {Decision-centered wearable biosensors for personalized rehabilitation: integrating multimodal monitoring, artificial intelligence, and closed-loop intervention.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {14},
number = {},
pages = {1948186},
pmid = {42828042},
issn = {2296-4185},
abstract = {Personalized rehabilitation requires repeated assessment of movement, physiological tolerance, fatigue, and adherence, yet conventional clinic-based measurements provide only intermittent snapshots. The convergence of wearable biosensors with artificial intelligence, edge-cloud computing, and connected data infrastructures creates an opportunity to convert continuous multimodal signals into timely rehabilitation decisions. This critical narrative review synthesizes physiological, biochemical, electrophysiological, biomechanical, and behavioral sensing within a monitoring-interpretation-feedback-adaptation-outcome framework. We first evaluate analytical validity, real-world robustness, biocompatibility, wearability, calibration drift, and power continuity. We then examine signal-quality control, multimodal fusion, digital biomarkers, federated learning, digital twins, and predictive models according to latency, uncertainty, generalizability, privacy, and decision utility. The review follows biosensor-derived information across a graded spectrum of action, from clinician-reviewed monitoring and real-time biofeedback to assist-as-needed adaptation, functional electrical stimulation, and state-responsive neuromodulation. Applications include motor and physiological assessment, fatigue-aware dose adjustment, musculoskeletal and neurological rehabilitation, posture correction, sensor-rich prosthetics, and Internet of Things-enabled home rehabilitation. Across these settings, greater autonomy increases the evidence and safety requirements: models should expose confidence, systems should degrade safely when signal quality falls, and digital infrastructures should preserve interoperability, version traceability, privacy, and accountable clinician oversight. The central translational gap is not sensor sensitivity alone but whether an integrated biosensor system produces a reliable metric, supports an explicit and proportionate decision, and improves an outcome important to the patient. Decision-centered evaluation can help move wearable biosensing from fragmented technology demonstrations toward secure, personalized, continuous, and clinically accountable rehabilitation.},
}

@article {pmid42829367,
year = {2026},
author = {Khan, DZ and Mao, Z and Wijekoon, A and Das, A and Williams, SC and Blandford, A and Jain, A and Harris, L and Borg, A and Dorward, NL and Clarkson, MJ and Bano, S and McCulloch, P and Stoyanov, D and Marcus, HJ},
title = {First-in-human evaluation of real-time pixel-level AI-assisted anatomical segmentation in neurosurgery: pituitary surgery as an exemplar.},
journal = {Pituitary},
volume = {29},
number = {5},
pages = {},
pmid = {42829367},
issn = {1573-7403},
mesh = {Humans ; *Pituitary Gland/surgery ; *Neurosurgical Procedures/methods ; *Artificial Intelligence ; Pituitary Neoplasms/surgery ; Neurosurgery/methods ; },
abstract = {INTRODUCTION: Precise anatomical navigation is fundamental to safe endoscopic pituitary surgery, a high-stakes procedure characterised by a challenging learning curve. While traditional navigation systems often rely on workflow-disrupting probes or static preoperative imaging, advancements in computer vision AI (CVAI) now enable dynamic, real-time pixel-level anatomical segmentation directly from live surgical video. Our group has previously conducted a series of preclinical human-computer interaction studies to refine the system's design, alongside digital and high-fidelity physical simulations demonstrating the potential benefit of AI assistance in improving surgical performance, training, and safety. Building on this foundation, the current study represents a first-in-human evaluation of real-time pixel-level CVAI anatomical segmentation in the neurosurgical operating room - assessing feasibility, human factors and clinical outcomes, while iteratively improving the system.

METHOD: Guided by the DECIDE-AI and IDEAL frameworks, this single-centre evaluation comprises an initial proof-of-concept of CVAI anatomical segmentation in endoscopic transsphenoidal pituitary surgery. The AI model utilised a DINOv3-derived vision transformer architecture, deployed via a high-performance edge computing unit to achieve low-latency real-time inference without reliance on cloud infrastructure. Feasibility and functionality were assessed via structured questionnaire, prospective observation, and blinded retrospective review of the recordings of the endoscopic surgical video feed and wider operating room environment. Continuous multi-stakeholder feedback through validated human factors surveys drove iterative technical refinements between cases. Routine clinical outcomes, aligning with the standard pituitary surgery core outcome set, were collected.

RESULTS: Eight patients with pituitary adenomas were enrolled. The CVAI system was successfully deployed in six cases, demonstrating acceptable real-time pixel-level sella segmentation accuracy. Deployment failed pre-operatively in two cases owing to a single platform-level boot-configuration issue. Iterative refinement between cases was driven by our experience and surgical team feedback. This resulted in the integration of additional anatomical structure segmentations (e.g., carotid arteries), enhanced model accuracy via training dataset expansion, and hardware firmware upgrades. Multi-stakeholder surveys demonstrated satisfactory system feasibility, usability, and acceptability among the surgical team. Both prospective observation and retrospective video review confirmed the absence of adverse events, including no significant distraction to the primary surgeon, and there were no AI-related clinical complications.

CONCLUSION: This first-in-human early clinical evaluation (IDEAL Stage 1) of real-time pixel-level AI anatomical segmentation in live neurosurgery demonstrates feasibility, showcases iterative system evolution, and reports clinical and human factors outcomes. Future work will include a larger single-centre case series (IDEAL Stage 2a) with more surgical teams to further iterate the system and explore its impact on safety, training and workflow. As the underpinning AI models improve and integrate with other intra-operative navigational technologies, such tools will likely be the cornerstone of intra-operative surgical decision support systems.},
}

Humans
*Pituitary Gland/surgery
*Neurosurgical Procedures/methods
*Artificial Intelligence
Pituitary Neoplasms/surgery
Neurosurgery/methods

@article {pmid42826932,
year = {2026},
author = {Wang, J and Li, M and Zhao, P and Li, J},
title = {DNA hydrogels in bone tissue engineering: where molecular ingenuity meets skeletal renaissance.},
journal = {Journal of materials chemistry. B},
volume = {},
number = {},
pages = {},
doi = {10.1039/d6tb01765g},
pmid = {42826932},
issn = {2050-7518},
abstract = {Bone tissue engineering is an emerging medical technology that utilizes scaffold materials to promote bone tissue regeneration and repair. Current scaffolds include biodegradable porous polymers, inorganic ceramics, and growth factors, but they face limitations such as difficulty in maintaining long-term biological activity in vivo and restricted applications. DNA hydrogels, composed of natural polymer chains and small molecules, exhibit excellent biocompatibility, biodegradability, bioactivity, high mechanical strength, and low toxicity. They have been applied in tissue repair, particularly in bone regeneration, serving as cell scaffolds, guiding tissue regeneration, and enabling gene therapy. This review summarizes recent advances in DNA hydrogels for bone repair, outlines their fabrication methods and performance characteristics, discusses applications involving growth factors, bone defect repair, bone regeneration, drug delivery, and organoid development, and explores their integration with 3D/4D/5D/6D printing, big data, cloud computing, artificial intelligence, machine learning, precision manufacturing, and automation. Despite existing challenges, DNA hydrogels offer new insights into bone tissue engineering due to their programmability and superior biological properties, and hold great promise as a key tool in bone repair.},
}

@article {pmid42822310,
year = {2026},
author = {Mahdianpari, M and Mohammadimanesh, F and Merchant, MA and Granger, J},
title = {PAWI: The first high-resolution, multi-class, Pan-Arctic Wetland Inventory.},
journal = {Journal of environmental management},
volume = {418},
number = {},
pages = {130889},
doi = {10.1016/j.jenvman.2026.130889},
pmid = {42822310},
issn = {1095-8630},
abstract = {Arctic wetlands are key regulators of global methane (CH4) emissions, yet uncertainty in wetland spatial extent and class composition limits the accuracy of CH4 budget models. Current mapping products lack circumpolar coverage and consistent thematic classification standards, constraining the ability to model ecosystem responses to Arctic warming. Here, we present the first high-resolution Pan-Arctic Wetlands Inventory (PAWI), produced at 10 m resolution using multi-sensor satellite imagery (e.g., Sentinel-1, Sentinel-2, and ALOS PALSAR-2), ArcticDEM topography, and environmental and hydrological datasets, using a machine learning Random Forest classifier. Wetlands are classified into bog, fen, swamp, marsh, and water. The final product achieves an overall accuracy of 89% (Kappa = 0.86) and estimates that 20% of the Arctic landmass is wetland. The PAWI provides a consistent, ecologically relevant baseline for improving CH4 flux modeling, assessing climate vulnerability, and supporting conservation planning. By integrating advances in remote sensing, machine learning, and multi-national data harmonization, this work addresses a critical gap in Arctic wetland mapping and establishes a transferable framework for large-scale ecosystem classification in remote regions.},
}

@article {pmid42812070,
year = {2026},
author = {Zhao, T and Jiao, Y and Ni, M and Luo, Y and Xia, S and Xu, Y and He, Y},
title = {[Efficient dental panoramic radiographic diagnosis under resource constraints via edge quantization and multi-modal synergy].},
journal = {Nan fang yi ke da xue xue bao = Journal of Southern Medical University},
volume = {46},
number = {9},
pages = {2266-2275},
pmid = {42812070},
issn = {1673-4254},
mesh = {Algorithms ; *Radiography, Panoramic/methods ; Humans ; },
abstract = {OBJECTIVES: To propose a full-scenario framework for edge-side quantization deployment and multi-modal collaborative diagnosis and treatment based on the prior high-precision algorithm YOLOv11-TDSP.

METHODS: A data-aware post-training quantization (PTQ) mechanism based on Kullback-Leibler divergence was constructed. The optimal distribution range of the features was determined using the calibration dataset for near-lossless compression of the FP32 model to INT8 precision. A cross-platform heterogeneous compilation pipeline was then established, with in-depth operator fusion and instruction set optimization for Intel Hybrid Architecture, Rockchip RK3588 and Ascend 310P to achieve extreme software-hardware decoupling. An integrated detection-diagnosis collaborative inference architecture for resource-constrained edge devices was constructed. The ultra-quantized detection model was used to free critical computing resources to drive the locally deployed lightweight large model Qwen2.5-3B, which, along with an expert rule base, allowed for an edge-side closed loop from visual detection to text report generation.

RESULTS: Experiments showed that the INT8-quantized model had a weight of only 6.1 MB (57.3% compression ratio), with mAP@0.5 of 95.4% and only a 0.4% precision loss. The model demonstrated excellent generalization on both standard office terminals and resource-limited mobile diagnostic devices. In the mobile diagnostic scenario constrained by computing power and power consumption (Intel i5-12450H), the inference speed of the model reached 22.91 FPS with a 3.64 speedup. The system achieved millisecond-level lesion detection and clinically compliant report generation on devices with only 8 GB RAM in a fully offline environment.

CONCLUSIONS: This framework allows deployment of high-precision algorithms on low-end hardware and enables a paradigm shift of panoramic dental radiograph analysis from cloud dependence to edge inclusiveness while ensuring patient privacy, providing a low-cost, high-reliability engineering solution for intelligent upgrading of primary medical facilities.},
}

Algorithms
*Radiography, Panoramic/methods
Humans

@article {pmid42817332,
year = {2026},
author = {Liu, S and Zhu, M and Wang, XA and Fan, Z and Zhou, X},
title = {SM2-PRE+: A Lightweight Pairing-Free Proxy Re-Encryption Scheme for Secure IoMT Healthcare Data Sharing.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {18},
pages = {},
doi = {10.3390/s26185761},
pmid = {42817332},
issn = {1424-8220},
mesh = {*Computer Security ; Algorithms ; *Internet of Things ; *Information Dissemination ; Humans ; },
abstract = {With the rapid development of the Internet of Medical Things (IoMT), wearable sensors and intelligent medical terminals continue to generate a large amount of sensitive medical data, which needs to be uploaded to the cloud platform for storage and sharing. However, IoMT devices usually have the characteristics of limited computing resources and limited energy, and it is difficult for traditional high-complexity encryption schemes to meet the requirements of security and efficiency. In addition, the existing Proxy Re-Encryption (PRE) scheme has the risk of authorization transfer, and it is difficult to achieve fine-grained and secure sharing of medical data. In response to the above problems, this paper proposes a lightweight non-pairing proxy re-encryption enhancement scheme SM2-PRE+ based on the SM2 algorithm, which is used for the secure sharing of IoMT medical sensing data. The scheme combines the SM2 elliptic curve cipher algorithm and the SM4 symmetric encryption algorithm to achieve data protection through a double-layer key structure, and it uses the re-encryption mechanism of message binding to enhance the authorization control ability. Compared with the traditional PRE scheme, the proposed scheme avoids bilinear pairing operations, reduces the computing overhead of resource-limited equipment, and supports collusion-resistant and authorization non-transferability. Security analysis shows that the scheme meets the security requirements of IND-CCA under the Random Oracle Model (ROM). Performance analysis results show that SM2-PRE+ has low computing overhead and storage burden, which is suitable for wearable medical devices, intelligent sensing terminals, and cloud-assisted IoMT data sharing scenarios.},
}

*Computer Security
Algorithms
*Internet of Things
*Information Dissemination
Humans

@article {pmid42817388,
year = {2026},
author = {Vujičić, D and Marković, D and Obla Anandbabu, P and Kulkarni, SR and Stamenković, Z and Ranđić, S},
title = {Regional Ground-Based IoT Solar Irradiance Monitoring: A Multi-Site Study Across Mountain, Rural, and Urban Environments.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {18},
pages = {},
doi = {10.3390/s26185781},
pmid = {42817388},
issn = {1424-8220},
abstract = {In this paper, the solar irradiance is investigated in a part of central Serbia, where a low-cost IoT sensor network was deployed at three locations: a mountain slope, an open field near a village, and an obstructed position in the city center. All three locations are in the same NASA POWER grid cell. After multi-stage quality control, 26,145 valid daytime records were compared to the satellite reference. The satellite assigns identical values to all three positions but the measured mean daytime irradiances are 267.7, 351.5 and 19.6 W/m[2], respectively. The rural station is in the best agreement with the reference (R[2] = 0.567); the mountain station suffers from a persistent shading bias (MBE = -138.9 W/m[2]); the signal at the urban station is attenuated by surrounding buildings and vegetation by a factor of ~12. Also, 25 regression models (gradient boosting, recurrent, convolutional, fully connected and graph-based) were trained on the 25-year monthly NASA POWER record for the same cell. XGBoost obtained R[2] = 0.998, the hybrid TCN-GNN R[2] = 0.968 and a plain ReLU network R[2] = 0.912, and a seasonal model with calendar features was used to reconstruct the satellite reference for two months of 2026 not yet available in the archive. The deployed hardware, network and energy behavior are documented quantitatively: per-site link delivery ratios of 96.8%, 83.5% and 58.2%, the photovoltaic harvesting record of the nodes, and the absence of any energy-aware transmission scheduling. Two of the trained models were compiled for the ESP32 nodes and measured on the deployment hardware: a depth-limited gradient-boosted corrector runs in 46.3 µs using 11.1 kB of flash, and an INT8 fully connected network in 138.1 µs using 3.0 kB, while the graph hybrid cannot be converted for microcontroller execution at all. This defines an edge-cloud partition in which local inference performs bias correction and fault detection while the cloud tier retains the heavy models and periodic retraining. To the best of the authors' knowledge, this is the first multi-site ground-based IoT irradiance record for central Serbia with such different terrain types.},
}

@article {pmid42817432,
year = {2026},
author = {Negulescu, C and Borangiu, T and Răileanu, S and Anghel, VV},
title = {An Embedded Multi-Sensor IoT Platform for Agricultural Monitoring Using Meshtastic-Based LoRa Communication and Cloud Microservices.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {18},
pages = {},
doi = {10.3390/s26185839},
pmid = {42817432},
issn = {1424-8220},
abstract = {The paper presents a low-cost agricultural monitoring platform based on embedded IoT sensing, LoRa mesh communication, and an Edge-Fog-Cloud architecture designed for real-time environmental monitoring in smart agriculture applications. The proposed system integrates distributed sensor nodes built around the Heltec Mesh Node T114 platform, combining an nRF52840 microcontroller with an SX1262 LoRa transceiver to support low-power, long-range communication in agricultural environments. Environmental data acquisition is performed using integrated BME280 temperature, humidity, and pressure sensors, capacitive soil moisture sensors, and TEMT6000 light sensors. A custom firmware architecture extending the Meshtastic framework was developed to support sensor integration, telemetry generation, packet forwarding, and energy-aware scheduling within the LoRa mesh network. The platform combines embedded telemetry modules, intermediate routing mechanisms, and cloud-based microservices responsible for data aggregation and processing. Experimental results showed stable radio-link conditions in the tested node-to-router configuration, with RSSI values around -55 dBm and SNR values of approximately 6-7 dB. Channel utilization varied between approximately 4% and 12%, while transmission airtime utilization remained below 5%. These results support the feasibility of the proposed sensing, communication, and data-processing architecture under the tested configuration. The architecture is designed to accommodate additional sensing and routing nodes, while multi-hop performance, long-term energy consumption, and network scalability remain to be evaluated in larger deployments.},
}

@article {pmid42817679,
year = {2026},
author = {Farooq, A and Haroon, H and Khan, F},
title = {Transcriptomic Analysis Identifies the PVT1-miR-34a Axis as a Novel Non-Coding RNA Therapeutic Target in Diffuse Intrinsic Pontine Glioma.},
journal = {JPMA. The Journal of the Pakistan Medical Association},
volume = {76(Suppl 1)},
number = {9},
pages = {S17},
doi = {10.47391/JPMA-7ANOS-ABS-16},
pmid = {42817679},
issn = {0030-9982},
mesh = {Humans ; *MicroRNAs/genetics/metabolism ; *RNA, Long Noncoding/genetics ; *Brain Stem Neoplasms/genetics ; *Diffuse Intrinsic Pontine Glioma/genetics ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; RNA, Competitive Endogenous ; },
abstract = {OBJECTIVES: Diffuse intrinsic pontine glioma (DIPG) is a lethal paediatric brainstem tumour with a median survival under one year and no effective therapy. Although non-coding RNAs (ncRNAs) regulate tumour progression, their regulatory networks in DIPG remain poorly characterised. This study aimed to identify dysregulated lncRNA- miRNA-mRNA axes in DIPG through integrated transcriptomic analysis, computationally validate these networks, and investigate their downstream impact on cancer-related pathways to reveal novel therapeutic targets for RNAbased precision medicine.

METHOD: This study was conducted at the Precision Medicine Lab, National Centre in Big Data and Cloud Computing (NCBC), Peshawar, from 1st July to 22nd August 2025. RNA-seq datasets comprising 25 DIPG and 45 normal brain samples were obtained from GEO. Following normalization and differential expression analysis (padj < 0.05, |log2FC| > 2), dysregulated lncRNAs, miRNAs, and mRNAs were integrated into a ceRNA network using LncBase and miRTarBase. KEGG pathway enrichment, STRING protein-protein interaction analysis, Cytoscape network visualization, and Spearman correlation analysis were performed to identify and validate functionally relevant regulatory interactions.

RESULTS: PCA demonstrated clear separation between DIPG and control transcriptomes. PVT1 was significantly upregulated (log2FC = 3.44), whereas miR-34a was downregulated (log2FC = -3.66). Eleven oncogenic mRNAs, including NOTCH1, PDGFRA, CDK4, SOX2, and MYC, were identified as predicted targets and formed a highly connected STRING interaction network. KEGG enrichment highlighted "MicroRNAs in cancer," while correlation analysis supported an inverse PVT1-miR-34a relationship and positive associations with these oncogenes, consistent with a ceRNA regulatory mechanism.

CONCLUSIONS: The PVT1-miR-34a axis appears to promote oncogenic pathways in DIPG. Targetting PVT1 or restoring miR-34a may represent potential RNA-based therapeutic strategies, warranting further experimental validation through reporter and knockdown assays.},
}

Humans
*MicroRNAs/genetics/metabolism
*RNA, Long Noncoding/genetics
*Brain Stem Neoplasms/genetics
*Diffuse Intrinsic Pontine Glioma/genetics
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
RNA, Competitive Endogenous

@article {pmid42810386,
year = {2026},
author = {Khan Mamun, MMR},
title = {A tri-modal, offline, and explainable framework for 12-lead electrocardiogram interpretation: fusing deterministic rules, a local residual network, and an agentic vision-language model.},
journal = {Physiological measurement},
volume = {},
number = {},
pages = {},
doi = {10.1088/1361-6579/aeaded},
pmid = {42810386},
issn = {1361-6579},
abstract = {Automated electrocardiogram (ECG) interpretation is increasingly delivered as cloud deep-learning services, trading data sovereignty, auditability and robustness for accuracy. This study tests whether an offline, explainable framework can remove that trade-off. Approach. Three independent interpreters feed a decision-fusion layer: a deterministic engine of 26 executable clinical criteria, each finding linked to a highlighted signal segment and a literature source; a compact one-dimensional residual network trained on PTB-XL and run on-device via the Open Neural Network Exchange runtime; and an optional agentic vision-language model reading a rendered ECG image behind a security screen with human-in-the-loop escalation. Format-agnostic ingestion, lead-aware capability tiering and a three-tier signal-quality gate precede interpretation. Evaluation covers diagnostic accuracy, on-device latency, noise resilience, modality ablation and zero-shot external validation. Main results. On the PTB-XL test fold (n = 2198) the network reaches a macro-averaged area under the receiver operating characteristic curve (AUROC) of 0.932 (95% confidence interval (CI) 0.918-0.943), matching published GPU baselines, while the complete pipeline runs in 108 ± 25 ms on a CPU with no network access. Performance degrades gracefully to a signal-to-noise ratio of 5 dB (AUROC 0.951) as the quality gate rejects uninterpretable signals, and rules and network agree only weakly (Cohen's kappa 0.26), indicating complementary errors. Applied without retraining to an independent cohort (Chapman-Shaoxing, n = 1599) the model retains a macro-AUROC of 0.898. In a small pilot (25 records; 30 rule/network disagreements) the general-purpose agentic model was the weakest adjudicator (accuracy 0.267, 95% CI 0.142-0.444; exact McNemar test versus rules, p = 0.09). Significance. Edge inference, intrinsic explainability and governed multi-modal fusion give competitive accuracy with auditable findings. The agentic layer is positioned for governance rather than diagnosis, motivating ECG-specialized models. The open-source system is a research prototype, not a medical device.},
}

@article {pmid42798834,
year = {2026},
author = {Hung, LH and Nasam, N and Biju, C and Lloyd, W and Yeung, KY},
title = {Single-cell RNA sequencing data processing using cloud-based serverless computing.},
journal = {GigaByte (Hong Kong, China)},
volume = {2026},
number = {},
pages = {gigabyte191},
pmid = {42798834},
issn = {2709-4715},
support = {U24 HG012674/HG/NHGRI NIH HHS/United States ; UM1 HG012654/HG/NHGRI NIH HHS/United States ; },
abstract = {Single-cell RNA sequencing (scRNA-seq) has become a routine method for measuring cell activities. We present a novel and generalizable methodology using serverless cloud computing to accelerate computationally intensive workflows. We create an on-demand "supercomputer" using rapidly deployable cloud serverless functions as automatically provisioned computation units. We tested our methodology of optimizing an scRNA-seq workflow by leveraging serverless functions on the cloud using two publicly available peripheral blood mononuclear cell (PBMC) datasets. In addition, we demonstrate our approach using a 450 GB human scRNA-seq knockout dataset, comprising 13 samples from different developmental time points, designed to study the temporal impact of perturbations on pancreatic differentiation. We compared the execution time of the scRNA-seq serverless workflow with an optimized workflow without serverless functions running on identical hardware, and demonstrate speedups for all tested datasets, reaching 7.0-fold for the largest dataset. Our software is open source and distributed under the MIT license. Code and documentation are publicly available at https://github.com/BioDepot/scRNA-serverless.},
}

@article {pmid42376364,
year = {2026},
author = {Darmono, D and Fadlullah, YA and Ma'ruf, K and Maulana, MR and Saputra, AA and Sulaiman, RB and Bagaskara, BB and Setiawan, RJ and Sahril, S},
title = {Sentiment Analysis of Acceptance TVET Online Courses on the Skill Academy App from Google Play: Leveraging Text Mining with Comparison Machine Learning Model.},
journal = {F1000Research},
volume = {15},
number = {},
pages = {899},
doi = {10.12688/f1000research.181728.2},
pmid = {42376364},
issn = {2046-1402},
mesh = {*Data Mining/methods ; *Machine Learning ; Classification Algorithms ; Humans ; Internet ; Boosting Machine Learning Algorithms ; Random Forest ; Predictive Learning Models ; },
abstract = {BACKGROUND: Online Technical and Vocational Education and Training (TVET) has expanded rapidly in Indonesia through platforms such as Skill Academy. User reviews provide valuable insights into user acceptance, but their volume makes manual analysis impractical. Therefore, this study applies text mining to analyze user sentiment and compare eight machine learning models for sentiment classification.

METHODS: The study used 3,000 reviews collected via web scraping using the google-play-scraper library. The data was then anonymized, cleaned, translated into English, and automatically sentiment-labeled using VADER. The validity of the labeling was verified by comparing it with TextBlob and the original star-rating categories using Cohen's Kappa. Eight classification algorithms Naive Bayes, SVM, Logistic Regression, Random Forest, Decision Tree, KNN, XGBoost, and LightGBM were trained using a TF-IDF pipeline with GridSearchCV and five-fold cross-validation.

RESULTS: The labeling validation results showed moderate agreement, with a Cohen's Kappa of 0.500 for TextBlob and 0.533 for the original star rating categories. On the test data, Logistic Regression achieved the best performance with an accuracy of 85.48%, a macro-F1 score of 76.08%, a balanced accuracy of 80.86%, and a macro-AUC of 0.9486, followed by SVM and XGBoost. The sentiment distribution was dominated by positive reviews at 75.31%, followed by negative reviews at 15.57%, and neutral reviews at 9.12%. The SMOTETomek experiment improved balanced accuracy and recall for the minority class but reduced macro-precision. Word cloud and word frequency analysis revealed that the main complaints were related to the app being slow, resource-intensive, and prone to errors, while user appreciation centered on ease of learning and the quality of the material.

CONCLUSIONS: Research shows that logistic regression achieved the best classification performance. While user sentiment was largely positive, improving application stability remains essential for sustaining user acceptance, enhancing learning experiences, and supporting long-term adoption of online TVET platforms.},
}

*Data Mining/methods
*Machine Learning
Classification Algorithms
Humans
Internet
Boosting Machine Learning Algorithms
Random Forest
Predictive Learning Models

@article {pmid42781037,
year = {2026},
author = {Bachir, MA and Nawathey, N and Bachir, A and Reddy, AJ and Cheema, JK and Patel, R},
title = {Code-Free AutoML for Binary Classification of Fractured and Non-fractured Bone Radiographs From a Heterogeneous Public Dataset Using Google Cloud Vertex AI: A Proof-of-Concept Study.},
journal = {Cureus},
volume = {18},
number = {8},
pages = {e115047},
pmid = {42781037},
issn = {2168-8184},
abstract = {Fracture detection on radiographs can be challenging, particularly for subtle or nondisplaced injuries, while development of artificial intelligence (AI) models often requires programming expertise and specialized computational resources. This proof-of-concept study evaluated whether a commercially available code-free automated machine-learning platform could distinguish fractured from non-fractured bone radiographs. A publicly available heterogeneous dataset containing 420 radiographic images, including 130 labeled as fractured and 290 labeled as non-fractured, was imported into Google Cloud Vertex AI AutoML Image Classification (Google, Inc., Mountain View, CA, USA) and divided into 336 training, 42 validation, and 42 test images. The dataset included radiographs from multiple anatomical regions; however, the publicly available metadata did not provide a sufficiently detailed anatomical distribution to reliably quantify upper-extremity, lower-extremity, spinal, or other fracture categories. Importantly, these counts represent radiographic images rather than confirmed independent patients, and patient-level distribution and independence across the training, validation, and test subsets could not be verified. No additional preprocessing, augmentation, or relabeling was performed by the investigators. Model performance was assessed using average precision, platform-reported aggregate precision and recall, confidence-threshold analysis, and a row-normalized confusion matrix. The trained classifier achieved a micro-averaged area under the precision-recall curve (AuPRC), reported by Vertex AI as average precision, of 0.987. At a confidence threshold of 0.50, model-level precision and recall were each 90.5%. Because these values summarize performance across both classes, they should not be interpreted as fracture-specific sensitivity. Class-specific average precision was 0.994 for non-fractured images and 0.972 for fractured images, while fracture-class recall was 77%. The row-normalized confusion matrix demonstrated correct classification of 77% of fractured radiographs and 97% of non-fractured radiographs. However, the retained Vertex AI output provided normalized percentages rather than raw class counts; therefore, the absolute numbers of correctly and incorrectly classified fractured and non-fractured images could not be verified. Given the small 42-image test set, the absence of raw counts limits the precision, interpretability, and reproducibility of these class-specific performance estimates and should be considered an important methodological limitation. The model was also successfully deployed to an online prediction endpoint and generated high-confidence classifications for representative images. These findings demonstrate that code-free AutoML can support rapid development and deployment of a functional, internally evaluated fracture-classification model without custom programming. However, the 77% fracture-class recall and small 42-image test set indicate that the model's performance should be interpreted cautiously and not as evidence of clinical diagnostic effectiveness. The small test set, class imbalance, limited clinical metadata, uncertain patient-level independence, and absence of external validation limit generalizability. Code-free AutoML may therefore be useful for proof-of-concept medical imaging research and education, but external validation is required before clinical implementation.},
}

@article {pmid42791937,
year = {2026},
author = {Elhanashi, A and Saponara, S},
title = {Intelligent Real-Time Healthcare and Biomedical Monitoring Systems: A Narrative Review of AI, IoT, and Emerging Technologies.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {9},
pages = {},
doi = {10.3390/bioengineering13091065},
pmid = {42791937},
issn = {2306-5354},
abstract = {BACKGROUND: Chronic disease management, an ageing global population, and the aftermath of the COVID-19 pandemic have pushed real-time, continuous health monitoring from a research curiosity toward routine clinical practice. Artificial intelligence (AI), the Internet of Medical Things (IoMT), edge computing, and next-generation wireless networks are converging to enable systems that sense, interpret, and act on physiological data outside the traditional hospital setting.

METHODS: This is a narrative review, not a PRISMA-guided systematic or scoping review; it synthesises 75 sources (a mixture of primary studies, systematic/scoping reviews, and meta-analyses), individually verified against their published record. The topics span IoMT system architecture and security, wearable and implantable sensing, deep learning for electrocardiogram (ECG), fall-related and human-activity signal analysis, edge and TinyML deployment, federated learning, blockchain-based health-record security, medical imaging diagnostics, explainable AI (XAI), continuous glucose monitoring, 5G/6G-enabled telemonitoring, digital twins, consumer-grade and contactless cardiac sensing, neurological and mental health monitoring, and the materials, regulatory, and acute care infrastructure surrounding real-time deployment. Studies were organised into a five-layer architectural taxonomy spanning perception, edge, network, cloud, and application layers.

RESULTS: Reported accuracies for deep learning models on ECG arrhythmia classification range from 91% to 99.5% across the reviewed studies, but the figures come from different datasets, class definitions, and validation protocols and are therefore not directly comparable; within this heterogeneous evidence, edge-deployed models report accuracies in the 85-96% range at substantially reduced power budgets. Deep-learning-based fall detection and chest radiograph classification are each reported, in the individual studies reviewed, to outperform threshold-based or classical alternatives, though this has not been established through head-to-head comparison across the full evidence base. Federated learning and blockchain are discussed as technical mechanisms that can contribute to data privacy and record integrity; neither constitutes regulatory compliance with frameworks such as HIPAA or the GDPR on its own. Persistent obstacles identified across the reviewed literature include dataset heterogeneity, limited external clinical validation, energy-constrained edge hardware, low clinician trust in opaque models, and fragmented interoperability standards.

CONCLUSIONS: The evidence reviewed here is consistent with, but does not by itself establish, a layered, privacy-preserving, and explainable architecture that couples lightweight on-device inference with federated or blockchain-secured cloud learning as a design direction for future real-time healthcare monitoring systems. Future work should prioritise standardised benchmarking, prospective clinical validation, and regulatory-aligned data-governance frameworks.},
}

@article {pmid42798236,
year = {2026},
author = {Sonowal, G and Rughani, P and Gogia, G},
title = {Forensic data deduplication: A comprehensive survey of methods and challenges for digital investigation.},
journal = {Journal of forensic sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/1556-4029.70480},
pmid = {42798236},
issn = {1556-4029},
support = {//Ministry of Electronics and Information Technology/ ; },
abstract = {The exponential expansion of digital evidence across traditional computing systems, mobile devices, cloud infrastructures, and IoT ecosystems presents new issues for forensic investigators. Modern forensic acquisitions frequently produce multi-terabyte datasets with substantial inter-case redundancy, which significantly affects storage capacity, processing time, and investigation costs. Data deduplication is a potential approach; however, its use in forensic contexts is limited by special operational requirements, such as total data integrity, court-admissible audit trails, and resistance to anti-forensic manipulation. This paper provides a comprehensive survey and evaluation of forensic data deduplication methods. We propose a five-dimensional forensic taxonomy that classifies techniques based on granularity, forensic integrity, temporal placement, verification methodology, and scope & scale. Using multi-criteria decision analysis, we create a Deduplication Reliability Index (DRI) to compare existing systems to forensic requirements statistically. We also systematically identify six types of adversarial threats particular to forensic deduplication, such as hash collision exploitation, deduplication oracle attacks, and reference mapping manipulation. Our analysis shows that file-level and fixed-block deduplication have the highest DRI scores (4.35-4.30), indicating that they are optimally suited for forensic adoption, whereas semantic, probabilistic, and lossy techniques score less than 3.0 due to poor verification and integrity assurances. The report concludes by proposing crucial research goals, such as standardized validation frameworks, adversarial-resilient deduplication structures, and efficient zero-knowledge verification algorithms for forensics.},
}

@article {pmid42770061,
year = {2026},
author = {Bachir, MA and Nawathey, N and Bachir, A and Reddy, AJ and Gill, S and Patel, R},
title = {Classification of Benign Hematogones and B-cell Acute Lymphoblastic Leukemia in Peripheral Blood Smear Images Using Artificial Intelligence.},
journal = {Cureus},
volume = {18},
number = {8},
pages = {e114985},
pmid = {42770061},
issn = {2168-8184},
abstract = {B-cell acute lymphoblastic leukemia (ALL) is a hematologic malignancy characterized by the abnormal proliferation of immature B-lineage lymphoblasts. Automated analysis of peripheral blood smear images may improve the speed and consistency of cell classification while supporting specialist interpretation. In this study, a cloud-based artificial intelligence image classification model was developed to differentiate benign hematogones from early pre-B, pre-B, and pro-B ALL categories. The model was trained and evaluated using 3,208 microscopic images from a publicly available dataset. Images were divided into training, validation, and testing subsets containing 2,568, 320, and 320 images, respectively, and model development was performed using Google Cloud AutoML (Google LLC, California, US). Performance was evaluated using average precision, precision, recall, precision-recall analysis, confidence-threshold evaluation, and a multiclass confusion matrix. At a confidence threshold of 0.50, the model achieved an average precision of 1.00, a precision of 100%, and recall of 99.4%. Class-specific correct classification rates were 100% for benign hematogones, early pre-B ALL, and pro-B ALL, and 98% for pre-B ALL, with the remaining pre-B images classified as early pre-B ALL. These findings demonstrate strong image-level performance within the internal testing dataset. External, multi-institutional, and patient-level validation is required before the model can be considered for clinical application.},
}

@article {pmid42780197,
year = {2026},
author = {Laiton, C and Lusk, N and Browning, Y and Summers, M and Taormina, M and Wang, D and Siegle, J and Myers, H and Tan, B and Kosillo, P and Peterson, E and Toglia, D and Lakunina, A and Burckhardt, S and Young, J and Kapoor, M and Wang, T and Rohde, J and Lynch, G and Yao, S and Narayan, S and Hooper, M and Way, S and Waters, J and Tasic, B and Chandrashekar, J and Glaser, A and Feng, D and Seshamani, S and Svoboda, K},
title = {3D-MAESTRO: A scalable, modular, portable pipeline for automated processing of large-scale volumetric brain microscopy data.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.09.09.750494},
pmid = {42780197},
issn = {2692-8205},
abstract = {Light microscopy is routinely used to explore the cellular and molecular structure of tissues, but the scale and complexity of data remains a bottleneck for discovery. We introduce 3D-MAESTRO (3D-Microscopy Automation and Execution with Scalable Tools, Rendering, and Orchestration): an automated image processing workflow for large-scale microscopy data, built for scalable execution across cloud and local computing environments. 3D-MAESTRO orchestrates denoising, stitching of image tiles, atlas registration, and segmentation. Its modular architecture permits the integration and benchmarking of new packages, ensuring that performance evolves as more efficient or accurate algorithms emerge. We introduce a new 3D image template for automated registration of mouse brains cleared with aqueous reagents and an efficient method for detection of fluorescent cells. We apply 3D-MAESTRO to lightsheet images of whole mouse brains in the context of diverse anatomical and functional experiments, illustrating high-throughput and reproducible mapping of microscopic structures across the brain.},
}

@article {pmid42761787,
year = {2026},
author = {Zhang, H and Hu, Y and Yu, F and Yang, J},
title = {Longitudinal impact of cloud-based medical imaging: an analysis using ARIMA modeling and Monte Carlo simulation.},
journal = {Frontiers in public health},
volume = {14},
number = {},
pages = {1874190},
pmid = {42761787},
issn = {2296-2565},
mesh = {Monte Carlo Method ; *Cloud Computing/economics ; Humans ; *Diagnostic Imaging/economics/statistics & numerical data ; Longitudinal Studies ; },
abstract = {BACKGROUND: Cloud-based medical imaging systems represent a paradigm shift in healthcare digitization. However, existing evaluations often rely on linear models that overlook critical methodological issues, particularly time-series autocorrelation in longitudinal data and uncertainty quantification in economic assessments.

OBJECTIVE: To quantify the geographic, economic, and film-related environmental impacts of cloud medical imaging adoption using ARIMA modeling and Monte Carlo simulation.

METHODS: We analyzed 15,709 cleaned cloud access records from November 2021 to March 2025 and 464 month-by-format film utilization records covering 114 calendar months from January 2015 to June 2024 at a major tertiary hospital. Time-series autocorrelation was evaluated using ARIMA modeling. Geographic inequality was assessed using the Gini coefficient. Economic robustness was evaluated using a six-scenario deterministic sensitivity analysis and Monte Carlo simulation (10,000 iterations).

RESULTS: The system exhibited marked geographic concentration (Gini coefficient = 0.980, 95% CI: 0.977-0.982), with 85.6% of access originating from local and provincial users. ARIMA(1,1,2) modeling of monthly film savings suggested an upward trend in film savings (monthly increase: 281.5 sheets). Forecasts suggested a plateauing trend rather than indefinite growth. Monte Carlo simulation yielded a mean estimated savings of ¥15.75 million (95% CI: [¥13.16, ¥18.35] million). Film-related avoided emissions were estimated at 341 tons of CO2e.

CONCLUSION: This single-center study suggests that cloud imaging can be associated with substantial film-related cost savings and avoided emissions in a large urban tertiary hospital. Broader generalizability, net system-wide economic benefit, and full environmental impact require multicenter validation and more comprehensive life-cycle assessment.},
}

Monte Carlo Method
*Cloud Computing/economics
Humans
*Diagnostic Imaging/economics/statistics & numerical data
Longitudinal Studies

@article {pmid42768594,
year = {2026},
author = {Wang, Y and Scali, S and Kyriienko, O},
title = {Polaritonic machine learning for graph-based data analysis.},
journal = {Physical review. E},
volume = {114},
number = {2-2},
pages = {025305},
doi = {10.1103/fgy1-n4vp},
pmid = {42768594},
issn = {2470-0053},
abstract = {Photonic and polaritonic systems offer a fast and efficient platform for accelerating machine learning (ML) through physics-based computing. To gain a computational advantage, however, polaritonic systems must meet the following: (1) exploit features that specifically favor nonlinear optical processing; (2) address problems that are computationally hard and depend on these features; and (3) integrate photonic processing within broader ML pipelines. In this work, we propose a polaritonic machine learning approach for solving graph-based data problems. We demonstrate how lattices of condensates can efficiently embed relational and topological information from point cloud datasets. This information is then incorporated into a pattern recognition workflow based on convolutional neural networks (CNNs), leading to significantly improved learning performance compared to physics-agnostic methods. Our extensive benchmarking shows that photonic machine learning achieves over 90% accuracy for Betti number classification and clique detection tasks, a substantial improvement over the 35% accuracy of bare CNNs. Our study introduces a distinct way of using photonic systems as fast tools for feature engineering, while building on top of high-performing digital machine learning.},
}

@article {pmid42769732,
year = {2026},
author = {Bachir, MA and Bachir, AS and Reddy, AJ and Cheema, J and Webster, J and Brahmbhatt, T and Patel, R},
title = {Code-Free Classification of Pediatric Pneumonia on Chest Radiographs Using Google Cloud Vertex AI AutoML: A Proof-of-Concept Internal Validation Study.},
journal = {Cureus},
volume = {18},
number = {8},
pages = {e114986},
pmid = {42769732},
issn = {2168-8184},
abstract = {Pneumonia is a clinically significant respiratory infection for which timely and accurate diagnosis is essential. Chest radiography is commonly used in the evaluation of suspected pneumonia; however, radiographic interpretation may be affected by reader experience, workload, image quality, and interobserver variability. This study evaluated whether a commercially available, code-free automated machine learning platform could distinguish pediatric chest radiographs labeled as normal from those labeled as pneumonia. A publicly available pediatric chest radiograph dataset was uploaded to Google Cloud Vertex AI AutoML Image Classification. Of 5,863 source images, 5,824 were successfully imported, including 1,579 normal radiographs and 4,245 pneumonia radiographs. Vertex AI randomly divided the imported images into training, validation, and testing subsets using an 80-10-10 allocation. A single-label binary image-classification model was trained using an eight node-hour budget, with all remaining user-configurable settings left at platform defaults. The model achieved an average precision of 0.999. At a confidence threshold of 0.50, platform-reported aggregate precision and recall were both 98.6%. The row-normalized confusion matrix demonstrated correct classification of 97% of normal radiographs and 99% of pneumonia radiographs. Model training was completed in two hours and eight minutes without custom neural-network programming or dedicated local high-performance computing hardware. These findings demonstrate the technical feasibility of developing a high-performing medical image-classification model using a code-free, cloud-based AutoML platform. However, the results represent internal benchmark performance on a single curated pediatric dataset. Image-level randomization, unavailable patient identifiers, class imbalance, proprietary model development, and the absence of external validation limit clinical generalizability. Independent multi-institutional evaluation is required before clinical implementation can be considered.},
}

@article {pmid42759707,
year = {2026},
author = {Shih, CC and Lin, SH and Yen, WJ and Cheng, GC and Chen, WE and Shiao, CC},
title = {Artificial Intelligence for Detecting Fall Events, Assessing Fall Risks, and Preventing Fall Occurrences in the Elderly: A Narrative Review.},
journal = {Clinical medicine (London, England)},
volume = {},
number = {},
pages = {100642},
doi = {10.1016/j.clinme.2026.100642},
pmid = {42759707},
issn = {1473-4893},
abstract = {Falls in the elderly represent a critical public health crisis, necessitating a shift from reactive to proactive management. This review evaluates artificial intelligence (AI) across four primary modalities: wearable sensors, vision-based systems, ambient devices, and natural language processing and large language models applied to electronic health records. Machine learning and deep learning algorithms enable real-time detection, near-fall identification, and predictive risk stratification. Hybrid edge-cloud frameworks and multimodal fusion enhance accuracy and scalability. However, challenges include the "simulation gap" in training data, "black box" interpretability, and privacy concerns, "alarm fatigue" among nursing staff, and "class imbalance." Future directions emphasize hybrid systems, integrating multiple sensor streams with edge-cloud computing, to enhance ecological validity and responsiveness. Ultimately, seamless integration of interpretable AI into clinical workflows offers a transformative path toward personalized prevention, reducing fall-related morbidity and empowering elderly independence.},
}

@article {pmid42752543,
year = {2026},
author = {Xu, L and Quan, ZX},
title = {CoSAG-nf: A Scalable Nextflow Pipeline for Co-assembly, Optimization, and Interactive Visualization of High-Throughput Single-Cell Genomes.},
journal = {Bioinformatics (Oxford, England)},
volume = {},
number = {},
pages = {},
doi = {10.1093/bioinformatics/btag671},
pmid = {42752543},
issn = {1367-4811},
abstract = {MOTIVATION: Single-cell amplified genomes (SAGs) are crucial for resolving intra-population microbial heterogeneity and accurately understanding the metabolic potential of microbial dark matter populations. However, SAGs generated through multiple displacement amplification (MDA) of genomic DNA from single cells with single-copy chromosomes are highly fragmented and prone to contamination, severely hindering high-quality genome reconstruction and functional analysis, which greatly limits their scientific utility. Co-assembly of related SAGs can substantially improve genome quality, but to our knowledge no automated pipeline exists for high-throughput processing, forcing manual implementation of complex workflows that scale poorly to modern dataset sizes.

RESULTS: We present CoSAG-nf, an automated high-throughput co-assembly and optimization pipeline for SAGs, implemented following the nf-core framework standards. The pipeline performs alignment-free clustering using sourmash MinHash signatures, then employs iterative tetranucleotide frequency profiling to identify and exclude outlier SAGs from co-assembly groups. CheckM2 quality assessment guides dynamic selection of optimal SAG combinations to optimize genome completeness and minimize contamination. Fully containerized, CoSAG-nf ensures reproducibility and scalability for the high-throughput processing of large-scale SAG datasets across diverse computing environments, including HPC and cloud platforms. The pipeline generates comprehensive HTML reports with quality metrics and taxonomic annotations, providing an end-to-end solution for automated high-throughput single-cell genome reconstruction.

AVAILABILITY: CoSAG-nf is freely available under the MIT License at: https://github.com/linfengxu/CoSAG-nf. Archival code repository snapshots are published at zenodo with doi: https://doi.org/10.5281/zenodo.21525244.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
}

@article {pmid42755843,
year = {2026},
author = {Hu, B and Shi, W and Xiong, S and Li, S and Wang, K and Gong, N},
title = {Quantum-edge synergy for dynamic acoustic compensation in ultrasound safety: a narrative review of tissue heterogeneity challenges.},
journal = {Frontiers in medical technology},
volume = {8},
number = {},
pages = {1775679},
pmid = {42755843},
issn = {2673-3129},
abstract = {BACKGROUND: Conventional ultrasound safety monitoring relies on the Mechanical Index (MI) and Thermal Index (TI), which lack tissue-specificity. Consequently, real-time safety profiling remains imprecise. Dynamic, tissue-specific corrections such as tissue perfusion rate (TPR) aim to address this limitation. This narrative review examines how integrating these corrections with quantum-cloud computational frameworks may enable enhanced safety and image quality in ultrasonic applications.

OBJECTIVE: We assessed the potential of an integrated quantum-cloud framework to reduce ultrasound-related bioeffects without compromising image quality, compared to standard MI/TI monitoring in patients undergoing clinical ultrasound imaging.

METHODS: A narrative synthesis of the eligible literature was performed, guided by thematic analysis frameworks. Quantitative outcomes were descriptively summarized with medians and ranges; formal meta-analysis was not conducted because heterogeneity in TPR measurement protocols, device manufacturers, and outcome definitions violated the commutability assumption required for statistical pooling.

RESULTS: Qualitative synthesis indicated that tissue-specific, multi-parameter correction strategies were consistently associated with more conservative acoustic exposure and heightened safety awareness than static MI/TI monitoring alone, although the consistency and clinical magnitude of these benefits varied across anatomical applications and study designs. The certainty of the current evidence was judged low to moderate, limited by non-standardized TPR measurement protocols and heterogeneous outcome definitions.

CONCLUSIONS: Together, these data suggest that multi-parameter acoustic correction coupled with cloud computing holds promise for improving ultrasound safety profiling and imaging precision. However, the current low-to-moderate certainty of evidence, due to methodological heterogeneity, necessitates future validation of edge-cloud synergies to realize robust, personalized monitoring systems.},
}

@article {pmid42758670,
year = {2026},
author = {Rahman, HMH and Mahmud, H and Hasan, MM and Hussain, SG},
title = {Estimating aman and aus rice area in Bangladesh using sentinel-1 imagery and machine learning algorithms.},
journal = {PloS one},
volume = {21},
number = {9},
pages = {e0355502},
doi = {10.1371/journal.pone.0355502},
pmid = {42758670},
issn = {1932-6203},
mesh = {*Oryza/growth & development ; Bangladesh ; *Machine Learning ; Random Forest ; Algorithms ; Support Vector Machine ; Classification Algorithms ; Prediction Algorithms ; Seasons ; Crops, Agricultural/growth & development ; Predictive Learning Models ; },
abstract = {Rice (Oryza sativa L.) is Bangladesh's staple crop, cultivated across three distinct seasons: Rabi (Boro), Kharif-1 (Aus) and Kharif-2 (Aman). This study develops an integrated, cloud-independent framework for mapping Aus and Aman rice areas using freely available Sentinel-1 C-band Synthetic Aperture Radar (SAR) imagery; to accomplish this, supervised machine-learning (ML) algorithms are used in the Google Earth Engine (GEE) platform. Time-series dual-polarization (VV and VH) backscatter data were analyzed for June-November 2021 (Aman) and March-August 2022 (Aus). Four ML models such as Classification and Regression Tree (CART), Random Forest (RF), k-Nearest Neighbour (k-NN), and Support Vector Machine (SVM) were evaluated to identify the most accurate mapping approach. Among all ML models, k-NN achieved the highest accuracy of 94.5% and 99.8% for Aman and Aus, respectively. Despite the higher accuracy, RF for Aman (92.7% accuracy) and CART for Aus (99.6%) were finally selected based on their lower deviation from area estimates reported by Bangladesh Bureau of Statistics (BBS). The estimated Aman area (5.72 Mha) was closely matched with the official figure from the Bangladesh Bureau of Statistics (BBS), while the Aus estimate (0.76 Mha) showed a 29% underestimation compared to BBS data (1.06 Mha). However, the results demonstrate that Sentinel-1 SAR combined with ML classifiers provides a reliable, scalable, and weather-resilient approach for multi-season rice mapping. The proposed methodology establishes a strong foundation for timely production forecasting and climate-resilient agricultural planning, particularly in cloud-prone tropical regions.},
}

*Oryza/growth & development
Bangladesh
*Machine Learning
Random Forest
Algorithms
Support Vector Machine
Classification Algorithms
Prediction Algorithms
Seasons
Crops, Agricultural/growth & development
Predictive Learning Models

@article {pmid42746478,
year = {2016},
author = {Zhang, M and Wang, L and Jajodia, S and Singhal, A and Albanese, M},
title = {Network Diversity: A Security Metric for Evaluating the Resilience of Networks Against Zero-Day Attacks.},
journal = {IEEE transactions on information forensics and security},
volume = {11},
number = {5},
pages = {},
pmid = {42746478},
issn = {1556-6021},
support = {9999-NIST/ImNIST/Intramural NIST DOC/United States ; },
abstract = {Diversity has long been regarded as a security mechanism for improving the resilience of software and networks against various attacks. More recently, diversity has found new applications in cloud computing security, moving target defense, and improving the robustness of network routing. However, most existing efforts rely on intuitive and imprecise notions of diversity, and the few existing models of diversity are mostly designed for a single system running diverse software replicas or variants. At a higher abstraction level, as a global property of the entire network, diversity and its effect on security have received limited attention. In this paper, we take the first step toward formally modeling network diversity as a security metric by designing and evaluating a series of diversity metrics. In particular, we first devise a biodiversity-inspired metric based on the effective number of distinct resources. We then propose two complementary diversity metrics, based on the least and the average attacking efforts, respectively. We provide guidelines for instantiating the proposed metrics and present a case study on estimating software diversity. Finally, we evaluate the proposed metrics through simulation.},
}

@article {pmid42751711,
year = {2026},
author = {Nawathey, N and Bachir, MA and Gill, S and Reddy, AJ and Desai, A and Vyas, A and Patel, R},
title = {Cloud-Based Artificial Intelligence Classification of Common Intracranial Tumors on Magnetic Resonance Imaging.},
journal = {Cureus},
volume = {18},
number = {8},
pages = {e114682},
pmid = {42751711},
issn = {2168-8184},
abstract = {Brain tumors comprise a heterogeneous group of neurological conditions for which timely and accurate classification is important for diagnostic evaluation, treatment planning, and prognosis. Magnetic resonance imaging is the principal imaging modality used to evaluate intracranial tumors; however, image interpretation may be time-consuming and dependent on specialist expertise. This study aimed to develop and internally evaluate a cloud-based artificial intelligence model for classifying glioma, meningioma, pituitary tumor, and no-tumor two-dimensional brain MRI images from a single publicly available dataset using image-level validation. A total of 5,335 images were obtained from a publicly available dataset and divided into training, validation, and testing subsets using an approximately 80-10-10 distribution. The dataset contained 1,400 glioma images, 1,292 meningioma images, 1,362 pituitary tumor images, and 1,281 no-tumor images. Model development was performed using the Google Cloud AutoML image-classification platform in a single-label multiclass configuration. Performance was evaluated using overall and class-specific average precision, precision, recall, precision-recall curves, confidence-threshold analysis, and a multiclass confusion matrix. At a confidence threshold of 0.50, the model achieved an overall average precision of 0.986, precision of 99.2%, and recall of 99.1%. Class-specific average precision was 1.000 for glioma, 1.000 for no-tumor images, 0.986 for pituitary tumors, and 0.973 for meningiomas. Correct classification rates ranged from approximately 99% to 100% across the four categories. These findings demonstrate the feasibility of using an accessible cloud-based AutoML platform for brain MRI image classification. These findings demonstrate strong internal image-level classification performance; however, the inability to confirm patient-level independence, absence of external validation, and use of a single public dataset limit interpretation of the reported metrics. Independent patient-level and multi-institutional validation is required before the model's diagnostic performance or clinical applicability can be established.},
}

@article {pmid42740251,
year = {2026},
author = {Alnaim, AK and Alwakeel, AM},
title = {EdgeTwin-DRL: Real-Time Counter-UAS Detection and Response Optimization Using Edge-Assisted Digital Twins and Multi-Agent Deep Reinforcement Learning.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {17},
pages = {},
pmid = {42740251},
issn = {1424-8220},
support = {KFU264619//King Faisal University/ ; },
abstract = {The time available for a counter-drone system to detect an unauthorized aircraft and determine an appropriate response can be limited. Although cloud processing remains useful for storage and offline analysis, communication delays may constrain its use in time-critical decision loops. This paper proposes EdgeTwin-DRL, an edge-assisted digital twin framework that integrates multimodal sensing and multi-agent deep reinforcement learning (DRL) for counter-drone detection and response optimization. The digital twin maintains a synchronized representation of the protected airspace using radar, electro-optical/infrared (EO/IR), radio-frequency (RF), and acoustic observations. This synchronized state is used by cooperative DRL actors to adjust computational-resource allocation, detection sensitivity, and candidate countermeasures, while a model-based forward-evaluation assesses proposed responses before they are passed to the simulated response pathway. The framework is evaluated in a simulation testbed in which the RF sensing models are calibrated and independently validated using publicly available datasets, while the remaining sensing components are parameterized using published experimental measurements. Within this calibrated simulation environment, EdgeTwin-DRL achieved a false-positive rate of 1.4% and reduced mean detection-to-response latency by up to 72% relative to the Cloud-DRL baseline and by 26% relative to the MAPPO baseline without calibrated, environment-dependent sensing under the communication and computational assumptions used in the simulator. The evaluation was conducted across modeled urban, suburban, and open-field conditions. These results demonstrate the comparative performance of the proposed architecture within the simulated environment and motivate further investigation of edge-assisted digital twins for counter-drone decision support. Hardware-in-the-loop and controlled field validation are required before operational deployment.},
}

@article {pmid42744836,
year = {2026},
author = {Nasir, K and Badri, SK and Khattak, A and Alghazzawi, DM and Alghamdi, MY and Alkhozae, M and Almakky, A and Alhazmi, RM and Asghar, MZ},
title = {HED-ID: a federated expert system for interpretable and resource-adaptive intrusion detection on edge devices.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {42744836},
issn = {2045-2322},
abstract = {The proliferation of edge computing in industrial and IoT networks necessitates expert systems that support accurate, interpretable, and resource-efficient intrusion detection under strict privacy and computational constraints. This paper presents HED-ID, a fully integrated federated expert system designed for real-time, edge-oriented anomaly detection. The system combines: (i) a stacked bidirectional GRU (BiGRU) architecture with hybrid temporal-spatial attention for sequential and feature-level anomaly modeling, (ii) a resource-adaptive Grey Wolf Optimizer (A-GWO) that modulates exploration-exploitation behavior based on real-time CPU utilization and Bayesian epistemic uncertainty, with formal convergence proof under bounded resource shifts (Appendix A), (iii) federated SHAP, which aggregates local explanations using weighted averaging to produce consistent global attributions without sharing raw data, (iv) a multi-objective fitness function that simultaneously considers accuracy, model complexity, and predictive uncertainty, and (v) SHAP-guided iterative pruning for reducing computational overhead while preserving model fidelity. Empirical evaluation on CICIDS-2017, UNSW-NB15, ToN-IoT, and a noisy-edge variant of ToN-IoT (10-20% controlled packet loss and jitter) demonstrates 95.6% accuracy in cloud-like conditions and 93.8% on Raspberry Pi 4 (4 GB RAM) before pruning, with inference latency maintained within 18-22 ms. Post-pruning, memory usage decreases from 92 to 115 MB to 78-96 MB, with ≤ 1.1% accuracy degradation. To support deployment, Docker orchestration templates, pruning threshold configurations, and Grafana-based monitoring are provided-reducing retraining interventions from 11 to 4 per year in a simulated 50-device edge environment.},
}

@article {pmid42729775,
year = {2026},
author = {Gangji, RR and Alimohamed, MZ},
title = {A systematic review: Evaluating the implementation determinants and progress of artificial intelligence, big data, and cloud computing with East African healthcare systems.},
journal = {Digital health},
volume = {12},
number = {},
pages = {20552076261490181},
pmid = {42729775},
issn = {2055-2076},
abstract = {OBJECTIVES: East African health systems face dual challenges of infectious disease burdens and rising non-communicable diseases within resource-constrained environments. Artificial Intelligence (AI), Big Data, and Cloud Computing offer transformative potential for healthcare delivery through enhanced diagnostics, optimized resource allocation, and improved care continuity.

METHODS: We conducted a systematic review following PRISMA 2020 guidelines, searching PubMed, IEEE Xplore Web of Science, and Google Scholar for studies on AI, Big Data, and Cloud Computing applications in East-African healthcare systems (2014-2024). Risk of bias was assessed using the ROBIS-I tool, with narrative synthesis used to analyze findings. Thematic analysis was guided by the Consolidated Framework for Implementation Research (CFIR).

RESULTS: From 129 identified studies, 19 met inclusion criteria. Findings revealed promising pilot implementations demonstrating preliminary evidence of improved diagnostic accuracy and operational efficiency. However, based on the available evidence, no interventions achieved sustainable scale-up using predefined scaling criteria. Five key barriers emerged: inadequate infrastructure (reported in 74% of studies), limited workforce digital literacy (58%), incomplete governance frameworks (47%), poor system interoperability (42%), and unsustainable financing models dependent on donor funding (37%).

CONCLUSION: A significant policy-practice gap exists between comprehensive national digital health strategies and ground-level implementation. While pilot projects show measurable benefits, systematic barriers prevent scaling to routine practice. Based on the available evidence, success may require coordinated investments across infrastructure, workforce development, governance, interoperability, and sustainable financing, supported by implementation science approaches. However, the moderate risk of bias in included studies and the developing state of implementation research in the region warrant cautious interpretation of these findings.},
}

@article {pmid42740175,
year = {2026},
author = {Smelcerz, K},
title = {Characterization of Latency Sources in a MicroPython-Based ESP32 Edge-Cloud Sensor Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {17},
pages = {},
pmid = {42740175},
issn = {1424-8220},
abstract = {This paper presents the design and experimental characterization of a distributed ESP32/MicroPython edge-cloud sensing system with packet-level latency decomposition. Sensor nodes transmit periodic telemetry to an ESP32 gateway over ESP-NOW; the gateway appends reception and MQTT-publication timestamps and forwards records through a local Mosquitto bridge v2.1.2, EMQX Cloud v5, Telegraf v1.36.0, and InfluxDB Cloud Serverless (Storage Engine Version 3). A three-probe two-way gateway-referenced synchronization procedure provides corrected sender timestamps while exposing an interval-based synchronization-uncertainty diagnostic. The bridge-assisted campaign comprised three independent 30 min repetitions with one, three, and five active nodes. Across runs, mean gateway-referenced node-to-gateway latency was 23.17 ± 0.13 ms, 24.13 ± 0.12 ms, and 24.84 ± 0.47 ms, respectively; the corresponding p95 values were 28 ms, 33 ms, and 37-38 ms. Mean gateway-processing latency remained nearly unchanged at 13.31-13.46 ms. Exact full-run database-visible PDR was 100% in all one-node runs, 99.28-99.88% in the three-node runs, and 96.75-97.05% in the five-node runs. Independent GPIO/oscilloscope validation showed a reproducible positive software-to-hardware difference of 12.132 ± 1.819 ms across run means, so the local metric is interpreted as a gateway-referenced application-level delivery metric rather than unbiased physical one-way radio latency. Relative to aggregate end-to-end reporting, the instrumentation separates local, gateway, and downstream ingestion contributions rather than claiming a universally faster transport method. Quantitative performance and scaling claims are confined to the evaluated bridge-assisted configuration and controlled indoor periodic workload.},
}

@article {pmid42740181,
year = {2026},
author = {Xiao, Y and Xiao, H and Fang, C and Wu, S and Zhao, M and Wang, E and Xu, C},
title = {SEELE: Sense-Driven Edge-Cloud Foreground-Background Split Rendering for Immersive Media Services.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {17},
pages = {},
pmid = {42740181},
issn = {1424-8220},
support = {2023YFB2904400//National Key Research and Development Program of China/ ; 62301070//National Natural Science Foundation of China/ ; 62225105//National Natural Science Foundation of China/ ; 62394323//National Natural Science Foundation of China/ ; //Beijing University of Posts and Telecommunications-China Mobile Communications Group Co., Ltd. Joint Institute/ ; 2025KYQD12//Fundamental Research Funds for the Central Universities/ ; NST20250303//State Key Laboratory of Networking and Switching Technology/ ; },
abstract = {Immersive media services increasingly rely on edge-cloud rendering to deliver interactive visual content under dynamic network, computing, and mobility conditions. Rendering an entire scene as a single service couples interaction-sensitive foreground content with context-oriented background content, making it difficult to jointly control latency, quality, synchronization, and migration overhead. This paper studies sense-driven edge-cloud foreground-background split rendering for immersive media services. We formulate an online decision problem in which foreground and background rendering layers can be independently controlled under long-term system and migration cost budgets. The formulation turns structural scene separation into a coupled layer-state control problem by preserving asymmetric QoE roles and a common composition requirement. We propose SEELE, a Lyapunov-guided online control algorithm that represents accumulated budget pressure with two virtual queues and converts the long-term constrained problem into lightweight per-slot decisions. The resulting per-slot rule balances immediate QoE loss against queue-weighted system and migration costs. Under sustained resource and network stress, SEELE provides steady-state QoE statistically comparable to a pretrained PPO policy while significantly reducing synchronization violations and improving composition stability. It also improves steady-state QoE and system debt over deterministic and QoE-prioritized baselines. A prototype implementation and controlled characterization further validate split-stream deployment, runtime observability, practical control hooks, and the latency-capacity tradeoff of layered rendering.},
}

@article {pmid42740198,
year = {2026},
author = {Bolikulov, F and Zohirov, K and Mannonov, G and Khudayorov, U and Temirov, Z and Mingboev, U and Hafizov, E and Abdusalomov, A and Cho, YI},
title = {Fusion of MLP, XGBoost, and QAT-Optimized PointNet++ for Predicting Short-Term Dendrometer-Derived Stem Dynamics: An Edge-Oriented Computational Framework.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {17},
pages = {},
pmid = {42740198},
issn = {1424-8220},
abstract = {Urban-forest monitoring increasingly requires intelligent sensor-driven systems capable of characterizing short-term tree responses while operating efficiently within Internet of Things (IoT) and edge-computing environments. This study proposes a fusion-based artificial intelligence framework that integrates Quantization-Aware Training (QAT)-optimized PointNet++ models with machine-learning regression to predict a short-term dendrometer-derived stem-diameter response expressed in biomass-equivalent units. The framework combines 1024-point LiDAR tree representations, geometric measurements, and environmental sensor data through three components: QAT-optimized PointNet++ models for 34-species classification and trunk-crown part segmentation, frozen model-based prediction and geometric feature extraction, and MLP and XGBoost regression models for prediction of the short-term target. The dataset contained 2694 trees from five regions of South Korea, with the target derived from dendrometer-based stem-diameter measurements recorded over a 14-day interval between 8 September 2022 and 22 September 2022. Importantly, this short-term signal reflects both structural and reversible water-status-related stem dynamics and is therefore not interpreted as direct dry-biomass accumulation or carbon sequestration. The QAT-optimized models retained 92.52% segmentation accuracy (82.67% mIoU) and 80.46% species-classification accuracy, while the regression model reached R[2] = 0.9663 and RMSE = 0.4437 kg for the defined biomass-equivalent target. Quantization reduced the saved model size of both encoders by approximately 10.5× (21 MB → 2 MB) and accelerated CPU inference by up to 4.1×. These efficiency measurements were obtained on an ×86 desktop CPU and therefore characterize computational compression benefits rather than completed deployment or field validation on a low-power embedded device. These results demonstrate the computational feasibility of combining compressed point-cloud perception with multimodal prediction of short-term dendrometer-derived stem dynamics. Validation over seasonal and multi-year periods using independent biomass-reference measurements would be required before extending the framework to long-term biomass accumulation or carbon-sequestration assessment.},
}

@article {pmid42725323,
year = {2026},
author = {Weilguny, L and Probul, N and Ren, Y and Pons, N and Baumbach, J and Almeida, M},
title = {Fedflow: cloud orchestration for federated learning with the FeatureCloud platform.},
journal = {Bioinformatics advances},
volume = {6},
number = {1},
pages = {vbag255},
pmid = {42725323},
issn = {2635-0041},
abstract = {MOTIVATION: Federated learning (FL) enables collaborative model training on geographically distributed genomic and clinical datasets while complying with data privacy laws and regulatory constraints. FeatureCloud is an existing platform for FL that provides an accessible web-based interface and a large repository of implemented methods. However, due to its graphical interface, FeatureCloud requires manual interaction of all participants, limiting automation, iteration, and reproducibility.

RESULTS: We introduce fedflow, a Python-based command-line tool for headless orchestration of FL tasks with FeatureCloud. This tool uses distributed computing resources such as virtual machines or cloud instances to automate such workflows. This allows for scalable federated computing either in local simulations or deployed in a trusted environment. Further, we demonstrate how fedflow can be used to integrate FeatureCloud in reproducible Snakemake workflows. For this, we reanalyse a metagenomic dataset with two federated algorithms and compare the results to the centralized approach with pooled data. Overall, fedflow enables automation of multi-client FL tasks, facilitates embedding of FeatureCloud in standard bioinformatics pipelines and thereby helps increase reproducibility.

AVAILABILITY: Fedflow is open-source and available at https://github.com/W-L/fedflow.},
}

@article {pmid42717706,
year = {2026},
author = {Noh, H and Lee, MG and Kim, H and Ko, M and Noh, J and Go, T and Yu, J and Park, SK and Cho, H and Kim, KM},
title = {Dual-State Programmable Oxide Transistor for Time-Based Cryptography.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e74951},
doi = {10.1002/adma.74951},
pmid = {42717706},
issn = {1521-4095},
support = {RS-2023-00216619//National Research Foundation of Korea/ ; RS-2023-NR077077//National Research Foundation of Korea/ ; RS-2023-00216992//National Research Foundation of Korea/ ; RS-2026-25542514//National Research Foundation of Korea/ ; RS-2025-02433006//National Research Foundation of Korea/ ; },
abstract = {As conventional digital computing becomes increasingly constrained by scalability and energy-efficiency limits, computing based on intrinsic physical dynamics of devices has emerged as a promising alternative. In this context, three-terminal devices have attracted attention because their additional terminal offers greater flexibility for implementing higher-order dynamics than two-terminal devices. Here, we present a dual-state programmable oxide transistor (DUPOT) that exhibits a previously unreported form of high-dimensional dynamical behavior with both the threshold voltage (Vth) and the saturation current (Isat) independently tunable. Additionally, the programmed Vth state exhibits long-term memory (LTM) characteristics, whereas the Isat state shows short-term memory (STM) behavior, enabling more complex computing functionalities. We elucidate its operating mechanisms and demonstrate robustness, and further showcase its use in time-based cryptography, which fully exploits its rich dynamical behavior. Our array-level demonstration supports diverse forms of time-based cryptography, including time-release encryption and time-bound encryption, and can be extended to cloud cryptographic systems, marking a new milestone in the study of computing devices with higher-order dynamics.},
}

@article {pmid42723687,
year = {2026},
author = {Acharya, MR and Tripathy, S and Pattnaik, PK},
title = {Low-Latency Digital Health Framework for Rural Areas Leveraging Fog Computing and 5G.},
journal = {F1000Research},
volume = {15},
number = {},
pages = {661},
pmid = {42723687},
issn = {2046-1402},
mesh = {*Digital Health ; Humans ; *Cloud Computing ; Algorithms ; *Telemedicine ; *Computer Communication Networks ; },
abstract = {BACKGROUND: The rural locations of telemedicine and urgent care are slowed down by communication latency as a result of the poor network infrastructure and overreliance on centralized cloud computing that adds to time lag in responding. Even with the development of networking technology, reliable low-latency systems to support rural areas are yet to be developed.

METHODS: The current paper suggests a Fog-5G Latency Optimization (F5GLO) application framework, which integrates 5G connectivity and fog computing to permit local data processing. To reduce the transmission delay and latency, healthcare data is stored at local fog nodes to allow predictive mobility of the fog node activation and low latency routing algorithms to utilize resources effectively and guarantee efficiency in transfer.

RESULTS: The model is capable of cutting end to end latency by up to 87 percent in comparison to the conventional cloud-based models thereby enhancing critical healthcare applications such as remote patient monitoring and emergency medical services. It is also strong in various network traffic conditions.

CONCLUSIONS: Fog computing plus 5G networks introduce agility to the healthcare service delivery in the remote environment where quick processing of clinical data locally and transmission can improve the reliability of the given services by transmitting information to decision makers faster. This is a realistic incremental solution to the issue of healthcare provision to the populations that are not within reach of the giant facilities.},
}

*Digital Health
Humans
*Cloud Computing
Algorithms
*Telemedicine
*Computer Communication Networks

@article {pmid42708252,
year = {2026},
author = {Tribbia, C and Bejster, M and Green, T and Kasal, J and Rabbitte, MM and Cygan, H},
title = {Harnessing Cloud-Based Technology to Train School Nurses: A Type 1 Diabetes Exemplar.},
journal = {NASN school nurse (Print)},
volume = {},
number = {},
pages = {1942602X261484934},
doi = {10.1177/1942602X261484934},
pmid = {42708252},
issn = {1942-6038},
abstract = {In the United States, more than 40% of children and adolescents live with a chronic health condition, behavioral health need, or a specific learning disability that impacts their daily experience in the school setting. Among these conditions, type 1 diabetes (T1D) is one of the most prevalent chronic health conditions among youth. School nurses are essential in promoting student health and academic success by ensuring students have the support and resources needed to remain healthy and ready to learn. This exemplar describes an online back-to-school training for school nurses utilizing the school district's Google Workspace platform, providing school nurses with five evidence-based practice (EBP) resources and a recorded training on T1D management in a self-paced, online format. With the evolving landscape of technology, school districts should ascertain how to harness available technology in their district to create accessible and sustainable EBP resources and training materials for school nurses.},
}

@article {pmid42704848,
year = {2026},
author = {Leitão, AL and Enguita, FJ},
title = {From wireframes to immersive reality: a historical perspective on molecular visualization software.},
journal = {Briefings in bioinformatics},
volume = {27},
number = {5},
pages = {},
pmid = {42704848},
issn = {1477-4054},
mesh = {*Software/history ; *Computational Biology/methods/history ; History, 21st Century ; History, 20th Century ; *Computer Graphics ; Virtual Reality ; },
abstract = {The evolution of molecular visualization software represents a pivotal yet often underappreciated dimension of computational structural biology and bioinformatics. As the scale and complexity of structural datasets increase, spanning from cryo-electron microscopy reconstructions to proteome-wide AI-predicted models, visualization tools have become essential for data interpretation, hypothesis generation, and effective scientific communication. Widely used platforms such as PyMOL, UCSF Chimera, and emerging virtual reality environments reflect decades of progressive innovation driven by advancements in computational hardware, graphics rendering, and algorithm development. This review traces the historical development of molecular graphics software, from early vector-based applications like FRODO and O, through interactive tools including RasMol and VMD, to modern real-time 3D visualization frameworks. We highlight key technological milestones and design principles that continue to shape contemporary software architectures, underscoring the interdisciplinary collaborations among chemists, structural biologists, and computer scientists that have propelled the field. Given the current integration of artificial intelligence, immersive visualization, and cloud computing in structural bioinformatics workflows, revisiting this topic provides critical context for guiding the design of more intuitive, scalable, and accessible visualization platforms. By documenting and critically analyzing this trajectory, the present work aims to honor the foundational contributions to molecular graphics while positioning the field as a dynamic and integral component of future bioinformatics research and computational discovery.},
}

*Software/history
*Computational Biology/methods/history
History, 21st Century
History, 20th Century
*Computer Graphics
Virtual Reality

@article {pmid42702393,
year = {2026},
author = {Li, D and Wajid, I and Liu, H and Li, S and Wang, Q},
title = {Smart medical technologies in peritoneal dialysis: current applications and future perspectives.},
journal = {Kidney research and clinical practice},
volume = {},
number = {},
pages = {},
doi = {10.23876/j.krcp.25.417},
pmid = {42702393},
issn = {2211-9132},
abstract = {With the global rise in end-stage kidney disease cases, peritoneal dialysis (PD) has become a critical treatment modality for maintaining patients' quality of life. This narrative review aims to summarize and analyze the current applications and effectiveness of smart medical technologies in PD treatment, focusing on patient monitoring, treatment optimization, and healthcare delivery. This review examines the integration of smart medical technologies, including the Internet of Things (IoT), artificial intelligence, and cloud computing, in the management of PD. Key advancements discussed include: 1) IoT-enabled sensors for real-time monitoring of ultrafiltration volume and dialysate turbidity, enabling early detection of infection risks; 2) machine learning algorithms for personalized dialysis prescription adjustments to preserve residual kidney function; and 3) telemedicine platforms that enhance physician-patient collaboration and reduce the need for hospital visits. These innovations represent a significant step toward precision and intelligent management of PD, improving operational efficiency and selected outcomes. Future research should focus on addressing challenges related to data security, algorithm transparency, and real-world clinical adoption.},
}

@article {pmid42699504,
year = {2026},
author = {Prasad, PWC and Hutabarat, DP and Sayeed, MS and Md Mohiuddin, G},
title = {A systematic review and evaluation framework for IoB-based adaptive health coaching systems.},
journal = {Frontiers in digital health},
volume = {8},
number = {},
pages = {1816418},
pmid = {42699504},
issn = {2673-253X},
abstract = {INTRODUCTION: The convergence of the Internet of Things (IoT), artificial intelligence (AI), and behavioural analytics has contributed to the emergence of the Internet of Behaviours (IoB) as a framework for personalized and context-aware digital health interventions. This study systematically reviews and synthesizes the architectures, sensing technologies, behavioural analytics methods, machine learning approaches, intervention strategies, and evaluation practices underlying IoB-enabled adaptive health coaching systems.

METHODS: Following the PRISMA 2020 guidelines, a systematic literature review was conducted across IEEE Xplore, Scopus, Web of Science, PubMed, and ACM Digital Library. A total of 75 eligible peer-reviewed studies published between 2015 and 2025 met the predefined eligibility criteria and were included in the final evidence synthesis.

RESULTS: The synthesis identified wearable and multimodal sensing, edge cloud architectures, AI-driven behavioural modelling, and just-in-time adaptive interventions (JITAIs) as recurring technological and intervention components. Comparatively stronger empirical support was observed for continuous monitoring, behavioural and physiological pattern recognition, remote patient monitoring, elderly care, and selected chronic disease applications. In contrast, evidence for advanced adaptive coaching, digital twins, metaverse-enabled healthcare, behavioural simulation, and emerging AI or large language model (LLM) enabled systems was more heterogeneous and frequently derived from prototype, simulation-based, or early-stage evaluations. Across the evidence base, substantial variation was observed in study designs, populations, datasets, intervention types, validation settings, and outcome measures, limiting direct cross-study comparison and conclusions regarding comparative effectiveness. Key limitations included insufficient longitudinal and clinical validation, incomplete reproducibility, fragmented interoperability, heterogeneous evaluation metrics, and unresolved privacy and governance challenges.

DISCUSSION: Based on recurring architectural, technological, and methodological patterns identified across the included studies, this review proposes a synthesized reference architecture and evaluation framework for IoB enabled adaptive health coaching. These evidence-informed conceptual synthesis frameworks are intended to guide future research and implementation rather than serve as universally validated standards. Future research should prioritize longitudinal and multi-site validation, representative datasets, reproducible reporting, interoperable clinical integration, privacy-preserving analytics, and the combined evaluation of technical, behavioural, and clinically meaningful outcomes.},
}

@article {pmid42694739,
year = {2026},
author = {Duan, X and Guo, Y and Yu, Y and Li, S and Sun, Y},
title = {Visualization-driven digital technologies in oncology: current applications, technological advances, and future directions.},
journal = {Frontiers in oncology},
volume = {16},
number = {},
pages = {1824142},
pmid = {42694739},
issn = {2234-943X},
abstract = {Precise anatomical understanding and spatial cognition are fundamental to effective oncologic management. While traditional two-dimensional imaging serves as the diagnostic standard, it often lacks the intuitive depth required for complex decision-making. This review synthesizes the role of visualization-driven digital technologies in oncology-including AI-driven 3D reconstruction, augmented reality (AR), multimodal fusion, radiomics, and digital twins-as cognitive interfaces across the entire continuum of cancer care. We comprehensively review current applications, ranging from deep learning-enhanced early screening and segmentation in pre-treatment planning to AR-guided surgical navigation and adaptive radiotherapy during treatment. Furthermore, we highlight the emerging utility of radiomics in post-treatment surveillance and the humanistic value of immersive visualization in patient education for anxiety reduction. Despite these advancements, clinical integration remains impeded by challenges related to intraoperative precision stability, economic feasibility in resource-limited settings, and data privacy risks associated with biometric re-identification. Looking ahead, we propose that the future of oncologic visualization lies in the convergence of physics-informed deep learning for dynamic motion correction, cloud-based edge computing for cost democratization, and privacy-preserving collaborative learning strategies. Current advances in visualization technologies support the transition from passive anatomical viewing toward more interactive, data-driven clinical decision support, though the integration of these approaches into comprehensive predictive models requires further prospective validation.},
}

@article {pmid42697903,
year = {2026},
author = {Jumah, JAB and Mirghani, HO and Alateeq, S and Algahtani, MA and Althubyani, BY and Alsaadi, N and Abdallah, A and Alazzam, MB},
title = {Clinical risk-aware reinforcement learning for latency-constrained healthcare IoT scheduling.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {42697903},
issn = {2045-2322},
mesh = {Reinforcement Machine Learning ; Humans ; *Internet of Things ; Electrocardiography ; Arrhythmias, Cardiac/diagnosis ; Digital Health ; },
abstract = {The rapid growth of the Healthcare Internet of Things (HIoT) has enabled continuous, real-time patient monitoring through wearable and bedside devices. These systems generate time-sensitive physiological data that are essential for the early detection of critical conditions such as arrhythmias and hypoxia. However, conventional cloud-centric architectures introduce significant end-to-end latency, which can compromise timely clinical response in safety-critical scenarios. Mobile Edge Computing (MEC) mitigates this limitation by bringing computation closer to data sources; yet, existing scheduling approaches remain largely system-centric and do not adequately incorporate patient-specific clinical risk into their decision-making. To address this gap, this paper presents CRAI-LCS, a clinical risk-aware Reinforcement learning (RL) framework for latency-constrained scheduling in HIoT systems. Unlike prior simulation-driven studies, CRAI-LCS integrates real physiological data from the PhysioNet MIT-BIH Arrhythmia database to construct realistic, data-driven workloads. Specifically, electrocardiogram (ECG) signals are segmented into time-windowed tasks with clinically grounded characteristics, including input size, computational demand, and urgency-aware deadlines. The framework combines data-driven clinical risk estimation, deadline-violation prediction, and RL-based scheduling to dynamically prioritize high-risk tasks while efficiently managing system resources. Experimental results demonstrate that CRAI-LCS consistently outperforms baseline approaches in terms of latency, deadline compliance, and resource utilization under realistic workload conditions. Ablation studies further confirm the individual contributions of the clinical risk-awareness and predictive scheduling components. Overall, these findings highlight the importance of incorporating real physiological data into scheduling design, providing a more reliable and clinically relevant foundation for next-generation healthcare edge intelligence systems.},
}

Reinforcement Machine Learning
Humans
*Internet of Things
Electrocardiography
Arrhythmias, Cardiac/diagnosis
Digital Health

@article {pmid42680963,
year = {2026},
author = {Chong, J and McClatchy, S and Mitchell, J and Li, S},
title = {Practicing Data Science in Interactive Notebooks.},
journal = {Methods in molecular biology (Clifton, N.J.)},
volume = {3063},
number = {},
pages = {1-18},
pmid = {42680963},
issn = {1940-6029},
mesh = {*Software ; *Metabolomics/methods ; *Data Science/methods ; *Computational Biology/methods ; Humans ; User-Computer Interface ; },
abstract = {The Jupyter Notebook is a platform for interactive computing that displays code and results in the same browser, making it valuable for teaching, prototyping, data analysis, and collaboration. Its explicit and transparent structure greatly reproducibility while its backend server supports flexible deployment. In the past few years, Jupyter notebooks and similar tools have become increasingly popular. In this chapter, we will review key aspects of data analysis in a cloud environment and demonstrate common tasks for analyzing metabolomics data using template notebooks. This is an accompaniment to the basic bioinformatics tools and essential data science toolkit introduced in the first edition.},
}

*Software
*Metabolomics/methods
*Data Science/methods
*Computational Biology/methods
Humans
User-Computer Interface

@article {pmid42693990,
year = {2026},
author = {Kurbanova, DI and Romanenko, SG and Pavlikhin, OG and Lesogorova, EV and Eliseev, OV and Krasilnikova, EN and Zemlyanov, VA and Teplykh, EA and Khaybulaev, KZ},
title = {[A cloud-based system for automatic diagnostics of dysphonia via acoustic parameters using artificial intelligence models].},
journal = {Vestnik otorinolaringologii},
volume = {91},
number = {4},
pages = {78-80},
doi = {10.17116/otorino20269104178},
pmid = {42693990},
issn = {0042-4668},
mesh = {Humans ; *Dysphonia/diagnosis/physiopathology ; *Artificial Intelligence ; *Cloud Computing ; },
abstract = {The diagnosis of voice disorders remains a persistent challenge in otorhinolaryngology, as the analysis of complex data demands specialized clinical expertise. A promising approach involves the implementation of artificial intelligence (AI) and machine learning methods, which can automate the analysis of acoustic voice parameters, objectively differentiating between norm and pathology. The creation of user-friendly, secure and accessible cloud-based platforms utilizing environments like Google Drive and Colaboratory reduces the need for expensive infrastructure. This is a critical prerequisite for the successful integration of AI into clinical practice.},
}

Humans
*Dysphonia/diagnosis/physiopathology
*Artificial Intelligence
*Cloud Computing

@article {pmid42679057,
year = {2026},
author = {Malycha, J and White, J and Phillips, A and Dykes, L and Wiklendt, L and Lambert, P and Boylan, A and Beilby, J},
title = {A Health Informatics System in the South Australian Public Health Network: Implementation Report.},
journal = {JMIR medical informatics},
volume = {14},
number = {},
pages = {e86564},
pmid = {42679057},
issn = {2291-9694},
mesh = {Humans ; South Australia ; Electronic Health Records ; *Medical Informatics/methods ; *Public Health/methods ; Digital Health ; Public Health Infrastructure ; },
abstract = {BACKGROUND: Following 2 decades of electronic medical record (EMR) adoption, most large public health systems hold comprehensive digital clinical data but lack the complementary informatics capability to return those data to clinicians, coders, and operational teams in a usable form. In South Australia (SA), the statewide Sunrise (Altera Digital Health) EMR system has digitized documentation and ordering since 2017; however, tools for back-end data extraction and enriched clinical information displays were not prioritized, and clinical, operational, and research users have reported ongoing difficulties accessing timely data.

OBJECTIVE: The aim of this study is to describe the development, governance, and deployment of a cloud-native health informatics system (HIS) in the Central Adelaide Local Health Network (CALHN), which serves approximately 40% of SA public patients, and to report implementation outcomes in accordance with the iCHECK-DH (Guidelines and Checklist for the Reporting on Digital Health Implementations) guidelines.

METHODS: The HIS comprises 6 architectural layers deployed on Microsoft Azure with Red Hat OpenShift (IBM), extracting Sunrise EMR data in near real time under a read-only model in which all patient data remain within the SA Health network at all times. Implementation proceeded through 5 overlapping phases (2021-2026), governed across 4 domains: technical (security impact assessment, information asset classification, and independent cybersecurity review), clinical (a clinical governance committee that has met quarterly since May 2022), corporate (incorporation of HeartAI Pty Ltd in 2022, with conflicts of interest declared and managed under SA Health policy), and ethical (human research ethics committee [HREC], reference 18079 with subsequent amendments). Development was funded through approximately Aus $1.84 million (Aus $1=US $0.72 as of August 24, 2026) in competitive and institutional grants under a public-private model with CALHN and AusHealth.

UNLABELLED: Three applications have reached deliberately different stages of maturity. The CALHN Critical Care Informatics System (CCCIS) has been implemented and evaluated: it has been deployed across the 2 CALHN intensive care units (ICUs; 54 beds, approximately 5000 admissions per year) since 2022, is in daily clinical use, and automates the submission of 115 variables to the Australian and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation (ANZICS CORE) registry. In a formal evaluation by 8 senior intensivists, its mean System Usability Scale score was 76 of 100, and the overall workload was low (NASA Task Load Index: 21/100). CODEXA, an AI-assisted clinical coding application, is in operational validation: models trained on 500,000 episodes and tested on 50,000 held-out episodes achieved a pooled F1-score of 71% across the full case mix and 57.86% (precision 64.72%; recall 55.53%) on complex acute episodes, in line with published benchmarks of 58% to 61% obtained from curated research datasets. The Patient Flow application is in co-design with CALHN's Network Operations Centre, with interface prototype evaluations completed in July 2026. In May 2026, the platform received unconditional endorsement from Digital Health SA's Technical Design Review Committee, the highest technical governance approval in SA Health, concluding a 5-year governance pathway.

CONCLUSIONS: A locally developed informatics platform can be governed, deployed, and validated in a public health system; demonstrating clinical outcomes and transitioning to operational status are the next priorities.},
}

Humans
South Australia
Electronic Health Records
*Medical Informatics/methods
*Public Health/methods
Digital Health
Public Health Infrastructure

@article {pmid42680826,
year = {2026},
author = {Cecchini, D and Nigam, A and Tang, M and Reis, J and Koop, M and Gottinger, A and Nicoll, CR and Wang, Y and Jayaraj, A and Çınaroglu, SS and Törner, R and Malets, Y and Gehev, M and Padmanabha Das, KM and Churion, K and Kim, J and Thomas, N and Li, Y and Seo, HS and Dhe-Paganon, S and Secker, C and Haddadnia, M and Hasson, A and Li, M and Kumar, A and Levin-Konigsberg, R and Choi, EB and Shapiro, GI and Cox, H and Sebastian, L and Braithwaite, C and Bashyal, P and Radchenko, DS and Kumar, A and Yang, L and Aquilanti, PY and Gabb, H and Alhossary, A and O'Neill, E and Wagner, G and Aspuru-Guzik, A and Moroz, YS and Kalodimos, CG and Fackeldey, K and Schuetz, JD and Mattevi, A and Arthanari, H and Gorgulla, C},
title = {AI-enhanced adaptive virtual screening of large libraries for ligand discovery.},
journal = {Nature biotechnology},
volume = {},
number = {},
pages = {},
pmid = {42680826},
issn = {1546-1696},
support = {GM136859 and GM158220//U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)/ ; CADET Grant//American Lebanese Syrian Associated Charities (ALSAC)/ ; 390685689//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 390685689//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 101094471//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)/ ; },
abstract = {Ultralarge virtual screenings (ULVSs) evaluate billions of molecules for drug discovery but face cost, flexibility and scalability limits. We introduce AdaptiveFlow, an open-source platform that makes ULVSs more accessible, scalable and efficient and supports artificial intelligence (AI) and machine learning (ML) method development. AdaptiveFlow provides a screening-ready version of the Enamine REAL Space, to our knowledge the largest library of ready-to-dock, drug-like molecules, comprising 69 billion compounds, also available in SELFIES format. An 18-dimensional grid of molecular properties prioritizes promising chemical subspaces, with optional active learning, reducing computational costs by orders of magnitude. AdaptiveFlow integrates >1,500 docking protocols, including GPU-accelerated and ML-based methods, and achieves near-linear scaling on up to 5.6 million CPUs in the Amazon Web Services cloud. We identified nanomolar inhibitors of two disease-relevant targets, ferroptosis suppressor protein 1 (FSP1) and poly(ADP-ribose) polymerase 1. Co-crystal structures provided mechanistic insights into FSP1 inhibition. AdaptiveFlow enables drug discovery at unprecedented scale and supports the development of AI-driven methods.},
}

@article {pmid42670126,
year = {2026},
author = {Gong, XQ and Deng, JH and Zhu, YF and Zhang, YY and Xu, P and Zhang, M},
title = {[Spatial-temporal Changes and Prediction Analysis of Long-term Ecological Environment Quality in the Urban Agglomeration Around Poyang Lake Based on GEE].},
journal = {Huan jing ke xue= Huanjing kexue},
volume = {47},
number = {8},
pages = {5509-5520},
doi = {10.13227/j.hjkx.202507335},
pmid = {42670126},
issn = {0250-3301},
abstract = {Objectively assessing the spatiotemporal evolution and future trends of the ecological environmental quality （EEQ） of the urban agglomeration around Poyang Lake is crucial for achieving a balance between economic development and ecological preservation in the region. Utilizing the Google Earth Engine （GEE） cloud platform， this study filtered MODIS remote sensing images from 2000 to 2020 to extract ecological indicators. The remote sensing ecological index （RSEI） model was constructed via principal component analysis （PCA） to characterize EEQ. The spatiotemporal changes in EEQ within the urban agglomeration around Poyang Lake were analyzed using Theil-Sen slope estimation， coefficient of variation （CV）， and Mann-Kendall tests. Furthermore， the Hurst exponent was introduced to analyze future trends in EEQ. Finally， the CA-Markov model was applied to simulate and predict the EEQ for the year 2030. The results indicate that： The EEQ in the urban agglomeration around Poyang Lake exhibited a fluctuating upward trend from 2000 to 2014， followed by a slight decline thereafter. Over the 21-year period， the overall EEQ remained at a moderate to high level， showing an overall improvement trend. Areas classified as "excellent" were primarily concentrated in the northeastern and western regions， while the central and southern areas were predominantly characterized by "poor" and "fair" grades. The dominant trend of EEQ change was non-significant improvement， covering the largest areal proportion. The centers of gravity for all EEQ grades were located within Nanchang City and its surrounding areas. Over the past five years， these centers exhibited a minor shift predominantly towards the southeast. EEQ demonstrated overall stability， characterized mainly by low and relatively low fluctuations. Future changes are projected to consist primarily of persistent improvement and anti-persistent improvement. By 2030， the EEQ of the urban agglomeration around Poyang Lake is predicted to show some improvement； however， areas classified as "Poor" and "Fair" are expected to remain concentrated in the central， southwestern， and southeastern parts of the study area. This study provides a methodological foundation for rapidly and accurately evaluating regional EEQ， investigating its long-term dynamics， and forecasting its future trajectory. It is essential for reconciling regional development with the natural environment.},
}

@article {pmid42667837,
year = {2026},
author = {Ma, S and Cao, Y and Liu, Y and Liu, X and Liu, B and Zhuang, Y and Hu, L},
title = {Edge-cloud collaboration-driven predictive modeling for high-performance computing centers.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {205},
number = {Pt C},
pages = {109508},
doi = {10.1016/j.neunet.2026.109508},
pmid = {42667837},
issn = {1879-2782},
abstract = {Accurate coolant flow prediction is critical for active thermal management in high-performance computing (HPC) centers, yet it is inherently challenged by mixed-timescale dynamics and high-frequency workload surges. Existing deep learning methods often prioritize global accuracy on smoothed stationary trends, which may lead to phase delays during abrupt thermal transients. In addition, high-capacity architectures can introduce non-negligible computational overhead for latency-sensitive, resource-constrained edge controllers. To overcome these limitations, this study proposes a deployment-oriented edge-cloud collaboration (ECC) framework integrated with a transient-aware predictive architecture, named FS-Attention, designed to balance transient responsiveness, engineering deployability, and decision transparency. FS-Attention couples local feature synthesis, temporal-memory encoding, and attention-based temporal refinement to improve coolant-flow tracking under non-stationary operating conditions. Evaluations on the real-world Frontier supercomputer dataset show that the feature synthesis attention (FS-Attention) model achieves competitive full-year prediction accuracy, with a coefficient of determination (R[2]) of 0.8744 and a root mean square error (RMSE) of 0.0350. Under isolated critical thermal events (CTEs), FS-Attention obtains the lowest RMSE of 0.0757, slightly lower than the Temporal Fusion Transformer (TFT) and 5.61% lower than the standard Transformer. Platform-based profiling further shows an inference latency of 0.016 ms and a parameter size of 323.1 K, suggesting model-side compatibility with facility-side edge execution, while attention-shift analysis provides diagnostic evidence of temporally adaptive model behavior under dynamic thermal conditions.},
}

@article {pmid42655506,
year = {2026},
author = {Ahmad, N and Cao, Y and Liu, W},
title = {A Blockchain-Enabled Security Framework for Cloud-Based Sensor Systems with Deep Learning-Driven Attack Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {16},
pages = {},
pmid = {42655506},
issn = {1424-8220},
abstract = {Cloud-integrated sensor and Internet of Things (IoT) systems enable scalable data storage, processing, and intelligent monitoring, but their distributed nature exposes network-flow and host-level data to unauthorized access, tampering, and cyberattacks. This study proposes a Weighted Symmetric Hashed Blockchain (WSHB) framework that integrates mutual-information-based feature weighting, deep-learning-based attack classification, AES-256-GCM authenticated encryption, cryptographic hashing, and permissioned-ledger logging. The framework was evaluated independently using HIKARI-2021 for network-based intrusion detection and ADFA-LD for host-based intrusion detection. A transparent comparative evaluation was conducted against LSTM and CNN-RNN baselines using identical data splits, preprocessing settings, input representations, hyperparameter-search budget, and repeated initialization seeds. The proposed WSHB-DNN classifier achieved macro-F1 scores of 0.6837 on HIKARI-2021 and 0.7914 on ADFA-LD, showing the strongest overall classification performance among the evaluated models. The cryptographic and permissioned-ledger components provide confidentiality protection, record-level authentication, integrity verification, and tamper-evident logging for confirmed attack-event records. These results demonstrate the potential of WSHB as a reproducible framework for attack classification and secure event logging in cloud-integrated sensor environments.},
}

@article {pmid42644537,
year = {2026},
author = {Sakthivel, TG and Ashok, R and M, A and Kumar, APS},
title = {Harnessing Digital Technologies in the Agro-Food Sector: An IoT-Driven Precision Agriculture Framework for Achieving Sustainable Development Goals.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {234},
pages = {},
doi = {10.3791/73067},
pmid = {42644537},
issn = {1940-087X},
mesh = {*Agriculture/methods ; *Sustainable Development ; *Digital Technology/methods/instrumentation ; *Internet of Things ; Artificial Intelligence ; Agricultural Irrigation/methods ; },
abstract = {The agro-food industry is quickly digitizing to solve resource restrictions, climate unpredictability, and sustainable food production using Internet of Things (IoT), Artificial intelligence (AI), cloud computing, and data analytics. Due to rigid irrigation schedules and limited field monitoring, traditional agriculture wastes water, reduces crop yield, and harms the environment. The IoT-Driven Precision Agriculture (IoT-PA) system in this paper optimizes agricultural resource use using distributed sensor networks, real-time soil moisture and weather monitoring, and intelligent irrigation control. The proposed platform gathers environmental data, analyses field conditions, and automatically recommends irrigation to maximize crop growth and avoid water waste. Farmers may make data-driven agricultural decisions with continuous monitoring and fast notifications from the framework. The IoT-PA architecture improves irrigation efficiency, agricultural yield, and sustainable resource management compared to traditional farming. Higher agricultural production and efficient water and resource use assist Sustainable Development Goal (SDG) 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production) of the United Nations Sustainable Development Goals. The suggested smart agricultural system is scalable and practicable, promoting sustainable food production and environmental conservation.},
}

*Agriculture/methods
*Sustainable Development
*Digital Technology/methods/instrumentation
*Internet of Things
Artificial Intelligence
Agricultural Irrigation/methods

@article {pmid42655341,
year = {2026},
author = {Huang, S and Zhou, Y and Liu, Y},
title = {Multi-Objective Optimization for Data Center HVAC Systems Based on Edge-Cloud Collaborative Deep Reinforcement Learning.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {16},
pages = {},
doi = {10.3390/s26165031},
pmid = {42655341},
issn = {1424-8220},
abstract = {The sustained growth of cloud computing and AI training workloads drives data center expansion. Optimizing their control is therefore critical for reducing operational costs. Edge real-time control is indispensable for guaranteeing thermal safety, data sovereignty, and offline availability. Yet deploying Deep Reinforcement Learning (DRL) in production Heating, Ventilation, and Air Conditioning (HVAC) environments confronts cold-start risks, edge-cloud computational asymmetry, and multi-objective conflicts spanning energy efficiency, electricity cost, and thermal safety. To address these challenges, this paper proposes an edge-cloud collaborative physics-informed reinforcement learning framework for production data center HVAC control. The framework integrates a physics-informed cold-start solution using Adaptive Particle Swarm Optimization (APSO) to generate physically constrained initial policies on a gray-box digital twin without expert demonstration data, a three-time-scale edge-cloud architecture coordinating minute-level edge Soft Actor-Critic (SAC) real-time inference, weekly edge APSO online model identification, daily cloud Non-dominated Sorting Genetic Algorithm III (NSGA-III) thermal storage scheduling, and a constraint-aware safe projection layer that embeds thermal safety hard constraints directly into the neural network policy. The framework is validated through a seven-month production deployment spanning the complete summer-to-winter transition, comprising approximately 3.2 million sensor records and evaluated with rigorous statistical methods.},
}

@article {pmid42655434,
year = {2026},
author = {Xu, K and Wang, H and Li, C and Liu, H and Xing, C},
title = {A CPU-NPU Heterogeneous Edge Fault Diagnosis Framework for Industrial Sensor Data.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {16},
pages = {},
doi = {10.3390/s26165125},
pmid = {42655434},
issn = {1424-8220},
support = {62232009//National Natural Science Foundation of China/ ; },
abstract = {Industrial sensor-based fault diagnosis often requires continuous data acquisition, local data processing, and timely model inference on edge devices. Although deep learning-based diagnostic methods have achieved promising performance, many existing approaches rely on cloud-centered processing pipelines that introduce communication overhead and potential data privacy concerns. This paper presents a CPU-NPU heterogeneous edge fault diagnosis framework for industrial sensor data. The framework runs on an RK3588 local edge device and includes SQLite- and RingBuffer-based data management, sliding window generation, micro-batch construction, and model inference. The CPU is responsible for data access and buffering, preprocessing, and micro-batch preparation, while the NPU executes fault diagnosis models using the RKNN runtime environment. By performing inference locally, the framework reduces the continuous transmission of raw sensor data and supports real-time fault diagnosis under resource-constrained edge devices. Experimental results demonstrate high consistency between ONNX-based CPU inference and RKNN-based NPU inference after model conversion. Furthermore, the effects of different data input paths and micro-batch configurations are systematically evaluated. A cross-platform comparison between server-class CPU/GPU execution and embedded NPU deployment is also conducted in terms of latency, throughput, and energy efficiency. The results show that RingBuffer-based streaming input significantly reduces data access overhead, while the effectiveness of NPU acceleration depends on both model structure and micro-batch size. The cross-platform results further demonstrate the energy efficiency advantages of the RK3588 platform, making it more suitable for practical deployment in resource-constrained edge scenarios. These findings provide practical insights for deploying fault diagnosis models on heterogeneous edge devices.},
}

@article {pmid42625390,
year = {2026},
author = {Liu, J and Yang, L and Xiao, Q and Wang, Y and Wang, Z and Zhang, R and Li, Z and Zhou, J and Liu, H and Yao, L and Yang, K and Chen, Z and Wan, X and Chang, T and Li, Y and Huang, T and Zhao, W and Zhang, X and Li, Y},
title = {Bio-OS: An Integrated Cloud-native Environment for Data-Intensive Biomedical Research.},
journal = {Genomics, proteomics & bioinformatics},
volume = {},
number = {},
pages = {},
doi = {10.1093/gpbjnl/qzag085},
pmid = {42625390},
issn = {2210-3244},
abstract = {The increasing volume and complexity of biomedical data, especially driven by high-throughput biotechnologies, have highlighted the need for scalable, interoperable, and reproducible data analysis frameworks. While cloud computing has revolutionized data processing and collaboration in life sciences, challenges remain in building cloud platforms tailored to biomedical research and ensuring platform interoperability. To address these limitations, we propose Bio-OS, an open-source, cloud-native framework designed to support the rapid development of interoperable biomedical cloud platforms. Bio-OS aligns with the Global Alliance for Genomics and Health (GA4GH) cloud workstream standards-including Workflow Execution Service (WES), Task Execution Service (TES), Data Repository Service (DRS), and Tool Registry Service (TRS) - while introducing innovations such as interactive execution models, workspace-centric data management, and AI-driven enhancements. These features facilitate adherence to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, promoting reproducibility and collaboration in biomedical research. Deployments across multiple research institutions demonstrated Bio-OS's ability to build biomedical cloud platforms and validated its efficacy in supporting data-intensive applications in life sciences.},
}

@article {pmid42627101,
year = {2026},
author = {Dey, P},
title = {Digital Biobanking in the Era of Artificial Intelligence.},
journal = {Biopreservation and biobanking},
volume = {},
number = {},
pages = {19475535261474367},
doi = {10.1177/19475535261474367},
pmid = {42627101},
issn = {1947-5543},
abstract = {The terminology "Biobank" is used for the organized collection of biological materials consisting of tissue samples, blood, serum, body fluids, and DNA/RNA materials for scientific research. A biobank extracts the information from the biospecimens to generate databases, which are continuously updated. Over the years, there have been significant advances in digital pathology and artificial intelligence (AI). Digital pathology primarily relies on whole-slide imaging and cloud computing. AI, on the contrary, is able to handle vast amount of data and to recognize the patterns of the histopathology images in more meaningful ways. A digital biobank is defined as the integration of the bio-specimens with whole-slide images, molecular data, longitudinal clinical information, and metadata. Digital biobanking can do real-time monitoring of the quality of the biospecimen, rapid retrieval of data, and AI-based research work. It may take a pivotal role in precision medicine by utilizing AI-based models for patient management. This review discusses the evolution from conventional to digital biobanking and emphasizes AI-based quality control, multimodal data integration, and predictive analysis in oncology. In addition, virtual biobanks, hybrid physical-virtual models, and digital twins are also discussed. The article also discusses the key challenges related to the implementation of digital biobanking, including standardization, interoperability, and sustainability.},
}

@article {pmid42627683,
year = {2026},
author = {Warmer, S and Arzideh, K and Morys, M and Idrissi-Yaghir, A and Bednarsch, J and Heise, D and Oezcelik, A and Reschke, M and Ulmer, TF and Neumann, UP and Nensa, F and Borys, K and Hosch, R and Schmitz, SM},
title = {Zero-Shot Classification of Postoperative Complications From Real-World Discharge Letters According to the Clavien-Dindo System Using Large Language Models in Liver Surgery: Comparative Study.},
journal = {Journal of medical Internet research},
volume = {28},
number = {},
pages = {e92407},
doi = {10.2196/92407},
pmid = {42627683},
issn = {1438-8871},
mesh = {Humans ; Large Language Models ; Female ; *Postoperative Complications/classification ; Middle Aged ; Male ; Aged ; *Patient Discharge ; *Liver/surgery ; },
abstract = {BACKGROUND: The standardized extraction of postoperative complications from unstructured routine clinical documentation remains a major unresolved challenge in digital surgery and health informatics. Although the Clavien-Dindo classification is the established standard for grading postoperative complications, its application in routine clinical documentation is largely implicit and unstructured, limiting scalable quality assessment in surgical care.

OBJECTIVE: This study aimed to assess the capability of open-weight and proprietary large language models (LLMs) to classify postoperative complications according to the Clavien-Dindo system using discharge letters, benchmarked against expert annotation.

METHODS: We analyzed discharge letters from 650 surgical cases of 649 patients (median 67, IQR 58-73 y; 229/649, 35% female) who underwent hepatobiliary surgery between 2010 and 2024. The cohort included grade I-II complications in 24% (153/650), grade III-IV in 19% (121/650), and grade V (death) in 6% (42/650) of patients. A total of 4 open-weight (Qwen3-235B [Alibaba Cloud], Llama-3.3-70B [Meta AI], GPT-OSS-120B [OpenAI], Ministral-3-8B [Mistral AI]) and 2 proprietary (GPT 5.1 [OpenAI], Gemini 3 Pro [Google]) LLMs were prompted to infer complication grades directly from the discharge letters in a zero-shot setting. Model performance was evaluated against expert assessment using accuracy, F1-scores, and Cohen κ. To assess interrater reliability and establish a human benchmark, a stratified 10% (n=65) subset was independently annotated by a second clinician, and Cohen κ was calculated between annotators and between each model and the primary expert.

RESULTS: Interrater agreement between the 2 clinical annotators yielded a Cohen κ of 0.75, providing a human benchmark for model performance interpretation. On the full 650-case dataset, open-weight models achieved accuracies ranging from 0.75 to 0.78 for fine-grained prediction, with weighted F1-scores of 0.76-0.78 and macroaveraged F1-scores of 0.50-0.63. For binary classification, accuracies ranged from 0.93 to 0.94, with weighted F1-scores of 0.93-0.95 and Cohen κ of 0.76-0.79, approaching the human interrater benchmark. On a balanced 50-case subset, used as the sole basis for direct cross-model comparison, proprietary models achieved accuracies of 0.78 for fine-grained and 0.94-0.98 for binary classification. An ensemble approach yielded additional gains in classification performance.

CONCLUSIONS: LLMs demonstrated promising accuracy in classifying postoperative complications from discharge letters in a zero-shot setting, with performance approaching the upper bound of human interrater agreement. Open-weight models offer a particularly attractive trade-off between accuracy and computational efficiency, while ensemble strategies further enhance robustness. These results support the potential of LLMs to standardize complication assessment at scale and enable data-driven quality monitoring in surgical care.},
}

Humans
Large Language Models
Female
*Postoperative Complications/classification
Middle Aged
Male
Aged
*Patient Discharge
*Liver/surgery

@article {pmid42621661,
year = {2026},
author = {Montin, E and Nguyen, XT and Lattanzi, R},
title = {MR Optimum: A web-based open-source tool for standardized signal-to-noise ratio evaluation in MRI.},
journal = {Computer methods and programs in biomedicine update},
volume = {9},
number = {},
pages = {},
pmid = {42621661},
issn = {2666-9900},
abstract = {Signal-to-noise ratio (SNR) is a key performance metric in magnetic resonance imaging (MRI) to evaluate pulse sequences, receive coils, and image reconstruction algorithms. A variety of methods have been proposed to estimate SNR. However, the lack of consistent and broadly available open-source implementations has been a challenge for reliable SNR comparisons in clinical and research settings. To address this gap, this work introduces MR Optimum, a cloud-native, open-source platform for standardized SNR analysis. MR Optimum integrates established SNR estimation techniques within a flexible, modular software architecture. A web-based user interface supports data upload, task configuration, cloud computations, and real-time results visualization. MR Optimum leverages serverless computing technologies (AWS Lambda and Fargate) to perform scalable, event-driven processing of MRI rawdata and allow users to calculate SNR using established methods: multiple replicas, pseudo multiple replicas, generalized pseudo multiple replicas, and analytic methods. Results include SNR maps, noise covariance and noise coefficient matrices, coil sensitivity profiles, and g factor maps. The web interface enables interactive visualization and histogram analysis based on regions of interest. Results can be exported in MATLAB, NIfTI, and JSON formats. By providing a unified computational environment, MR Optimum ensures reproducibility, and democratizes access to state-of-the-art SNR estimation, promoting multi-center harmonization and quality assurance.},
}

@article {pmid42622184,
year = {2026},
author = {Wolski, M and Podsiadlo, P},
title = {BONE DATA LAKE: A SCALABLE PLATFORM FOR LARGE-SCALE RADIOGRAPHIC TRABECULAR BONE TEXTURE ANALYSIS.},
journal = {Osteoarthritis imaging},
volume = {6 Suppl 1},
number = {},
pages = {100447},
doi = {10.1016/j.ostima.2026.100447},
pmid = {42622184},
issn = {2772-6541},
abstract = {INTRODUCTION: Trabecular bone (TB) texture analysis of hand and knee X-ray images may serve as a biomarker for detection, prediction and monitoring of OA. However, the scale and heterogeneity of imaging data pose challenges for data storage, standardization, cataloguing, security, and efficient OA analysis.

OBJECTIVE: Our objective is to address this problem through the development of a platform, called the Bone Data Lake (BDL) that enables the secure storage of large numbers of X-ray images, TB texture regions and TB texture parameters, independent of their format, size and source.

METHODS: The BDL was developed as a cloud-based platform (Wolski et al. 2025; Figure 1) using Amazon Web Services. It comprises three components: a raw data storage, a processed data storage, and data reference system. The first component operates as a repository of data in its raw original format. The second component reformats the data into a standardized format. The last component is the central catalog, which holds metadata information, such as storage locations of original and reformatted data. The BDL's performance was evaluated using a dataset of 20,000 knee and hand X-ray images of varying formats (DICOM, PNG, JPEG, BMP, and compressed TIFF) and sizes (0.3 MB to 66.7 MB). The dataset represents convenience samples of X-ray images downloaded from the ReadMyXray website (https://readmyxray.curtin.edu.au). Images were uploaded into BDL and automatically converted to a standardized TIFF format. TB regions of interest (ROIs) were selected on the standardized images, and their catalog was automatically constructed. Fractal signature texture parameters were calculated for the ROIs and stored in Microsoft Excel files, which were automatically transformed into CSV files and cataloged.

RESULTS: The BDL efficiently transforms X-ray images and catalogues TB texture regions and parameters. For 20, 000 images, BDL achieved approximately tenfold reduction in processing time compared with the workstation (8 minutes versus 82 minutes). The data catalog successfully stored metadata including image ID, timestamps, resolutions, image sizes, and ROI orientation. Schema of data in the Excel files was correctly detected and the corresponding CSV file was generated.

CONCLUSION: The BDL is a scalable and flexible data storage platform for X-ray and TB bone texture images and parameters. This platform provides a foundation for future OA research infrastructure and supports the development of reliable, secure, and collaborative systems for the detection and prediction of OA from radiographs.},
}

@article {pmid42613942,
year = {2026},
author = {Chen, K and Zheng, Y and Gu, Y and Liu, G},
title = {Wearable Sensing Devices for Continuous Animal Health Monitoring.},
journal = {Small methods},
volume = {},
number = {},
pages = {e70982},
doi = {10.1002/smtd.70982},
pmid = {42613942},
issn = {2366-9608},
support = {22174121//National Natural Science Foundation of China/ ; 22211530067//National Natural Science Foundation of China/ ; T2250710180//National Natural Science Foundation of China/ ; //Natural Science Foundation of Guangdong Provincial Basic and Applied Basic Research Fund 2022 (Guangdong Hybribio)/ ; 2021CX02Y066//Guangdong Pearl River Talent Program/ ; //Shenzhen Bay Open Laboratory Fund 2021/ ; //CUHKSZ-Boyalife Joint Laboratory Fund/ ; UDF01002012//the University Development Fund/ ; //the 1+1+1 CUHK-CUHK(SZ)-GDSTC Joint Collaboration Fund/ ; //Guangdong Basic Research Center of Excellence for Aggregate Science Fund/ ; },
abstract = {Continuous, noninvasive animal health monitoring systems are increasingly required in livestock farming, companion animal care, and wildlife conservation. The translation of wearable sensing technologies across these sectors is complicated. Their practical performance depends not only on sensing accuracy but also on the variability of anatomical features, uncontrolled movement, environmental exposure, wireless connectivity, power supply, and animal welfare considerations. This review presents a system-level, design-oriented analysis of animal health monitoring sensing technologies. It classifies sensing modalities into physical and physiological, biochemical and molecular, environmental and spatial, and multimodal sensing, followed by examining their integration into on-animal and ingestible devices. Communication, edge and cloud computing, power management, and representative applications across livestock, companion animals, and wildlife are also discussed. The review further identifies key barriers to translation, including the need for signal stability, interoperability between systems, energy supply constraints, maintenance burden, and ethical and welfare concerns related to long-term monitoring. Future systems should prioritize species- and site-specific integration, long-term field validation, interoperable data architectures, welfare-conscious design, and clinically or managerially actionable outputs. This framework offers practical guidance for translating continuous sensing into scalable decisions for animal health and welfare.},
}

@article {pmid42618521,
year = {2026},
author = {Ullah, F and Uddin, Z and Sardaraz, M and Javed, M and Aziz, T},
title = {Enhancing security and efficiency in healthcare cloud computing with blockchain integration and LZW compression: The BICCM framework.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {},
number = {},
pages = {9287329261479267},
doi = {10.1177/09287329261479267},
pmid = {42618521},
issn = {1878-7401},
abstract = {Modern healthcare system produces large amount of sensitive clinical data, but no existing framework simultaneously addresses and satisfy rigorous security guarantees, storage efficiency and the throughput demand of clinical operations/transactions. All existing framework addresses these requirements in isolation such as cloud architectures lack cryptographic immutability and standalone blockchain systems suffer by high gas costs and block size limitations. To resolve this gap, we present the Blockchain-Integrated Cloud Compression Model (BICCM) a three-tier architecture that combines (i) Ethereum based smart contracts with Role-Based Access Control (RBAC) and Zero-Knowledge Proof (ZKP) authentication, (ii) Lempel-Ziv-Welch (LZW) lossless compression applied at a dedicated light node layer and (iii) a blockchain secured cloud object store with on-chain SHA-256 has anchoring. COVID-19 medical records from Pakistan Bureau of statistics (PBS-2023) in six size classes range from 4.93 to 39.80KB were used for evaluation.BICCM achieves 65.81 to 85.34% storage reduction, 64.3 to 84.4% gas savings (mean 76.4%), 45.4 to 83.2% execution-time improvement, and 50% transaction cost reduction Compared to the traditional Ethereum blockchain outperformed four competing algorithms (LZ-String, LZ77, Run-Length Encoding, Huffman) for all evaluated data sizes, attaining compression ratios of 2.92 to 6.82. Smart contract deployment on auxiliary nodes reduced ETH costs by 88.6% compared to the main deployment node. BICCM offers a scalable, safe and HIPAA/GDPR complaint platform for blockchain cloud healthcare data management with full implementation across all twelve assessed security criteria. Future work will follow a strategy for federated learning integration and post-quantum cryptography transition.},
}

@article {pmid42611639,
year = {2026},
author = {Nalawade, P and Kumbharkar, P and Agarkar, P and Kumbharkar, A and Jha, S},
title = {An Experimental Protocol for Explainable AI-Driven Secure Cloud Data Migration Using Synthetic Healthcare Data.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {234},
pages = {},
doi = {10.3791/71612},
pmid = {42611639},
issn = {1940-087X},
mesh = {*Cloud Computing ; *Artificial Intelligence ; *Computer Security ; *Electronic Health Records ; Humans ; },
abstract = {In healthcare systems, more and more cloud data migration is being done, but this also changes the times when data transfer is probably the biggest risk in terms of security. This paper describes a reproducible protocol for explainable artificial intelligence (XAI)-based secure cloud data migration using a synthetic healthcare dataset and a controlled cloud environment. The developed framework merges zero-trust architecture, temporal least privilege, encrypted communication, centralized monitoring, and explainable anomaly detection to have a more secure, transparent, and auditable migration. The tests use a 10 GB dataset of synthetic electronic health records, comprising approximately 20 million records across 28 relational tables. The migration process was carried out on Amazon web services (AWS) using PostgreSQL databases and private virtual networks. For anomaly detection, Isolation Forest was utilized, and Shapley additive explanations (SHAP) served for the secure event interpretation. The framework was evaluated on ten separate migration attempts using metrics such as credential exposure duration, incident detection time, anomaly-detection accuracy, migration latency, and data integrity. Under the configuration tested, credential exposure was reduced from 24 h to 1 h (a 95.8% reduction), the accuracy of anomaly detection was 97.4%, incident detection time was reduced to about 15 min, and 100% data integrity was preserved through checksum validation. However, the stronger security measures resulted in an average migration latency increase of 11%. These results showcase the promise of merging explainable AI with secure cloud migration workflows for managing healthcare data.},
}

*Cloud Computing
*Artificial Intelligence
*Computer Security
*Electronic Health Records
Humans

@article {pmid42607672,
year = {2026},
author = {Higgins, DM and Blackden, C and Brown, M and Diaz, C and Duffy, L and Friedman, C and Guo, Y and Koptyra, M and Li, Q and Lubneuski, A and Miller, D and Moulder, B and Payton, W and Reed, E and Rife, W and Sickler, A and Singh, N and Warkow, A and Wenger, E and Wong, J and Zhang, B and Zhong, C and Zhu, Y and Altefrohne, G and Beauregard, D and Costanza, J and Girard, E and Langis, LF and Lemonnier, L and Paul, A and Pelletier, C and St-Onge, K and Thibert, JP and Mattioni, M and Saha, S and Rozowsky, J and Trboljevac, M and Zecevic, M and Knight, J and Kuffel, G and Reddy, R and Ribeyre, P and Farrow, B and Cox, Y and Ericson, A and Vargas, JF and Forman, S and Labrecque, S and Torstenson, E and Ho, KY and Vasilevsky, NA and Boja, E and Cotton, V and Daee, D and Fournier, MV and Guidry Auvil, JM and Harris, AC and Li, H and Wang, L and Davis-Dusenbery, B and Haendel, MA and Lilly, J and Storm, PB and Volchenboum, S and Grossman, RL and Carroll, R and DiGiovanna, J and Taylor, D and Ferretti, V and Resnick, A and Heath, AP},
title = {The Gabriella Miller Kids First Data Resource for genomic research in pediatric cancer and congenital anomalies.},
journal = {American journal of human genetics},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.ajhg.2026.07.010},
pmid = {42607672},
issn = {1537-6605},
abstract = {Nine-year-old brain tumor patient Gabriella Miller challenged members of Congress to "stop talking and start doing" when providing federal funding for research into cures for pediatric cancer and congenital anomalies. Though she ultimately lost her life to that cancer, her advocacy efforts resulted in the 2014 Gabriella Miller Kids First Research Act, launching the Gabriella Miller Kids First Pediatric Research Program at the National Institutes of Health (NIH). The overarching goal of the Gabriella Miller Kids First Pediatric Research Program is to help researchers uncover new insights into the biology of childhood cancer and congenital anomalies. Following the signing of the Gabriella Miller Kids First Research Act 2.0 in January 2025, the program has been extended at NIH through 2028 to advance the groundwork laid in the program's first ten years. The Gabriella Miller Kids First Data Resource Center has since honored her legacy by building a comprehensive data resource for genomic research into pediatric conditions. Data from more than 30,000 participants annotated with demographic and clinical information related to their diagnoses have been released for secondary research and analysis using the center's web-based platforms. This paper analyzes the outcomes of the initiative and highlights breakthroughs made by the larger research community resulting from the availability of this data resource. We explore the future expansion of the data resource to include new modalities and tools for supporting life-saving research for children like Gabriella Miller.},
}

@article {pmid42604860,
year = {2026},
author = {Prajapati, P and Mewada, H and Shah, P},
title = {MobDedup: Mobile data deduplication for enhancing digital forensics.},
journal = {Journal of forensic sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/1556-4029.70442},
pmid = {42604860},
issn = {1556-4029},
abstract = {Smartphones provide crucial but duplicate information in forensic investigations. The major deduplication tools designed for the cloud ignore constraints on phone storage, power, data fragmentation, and dynamic handling, leading to processing delays and limiting their efficient use. It also affects evidence integrity and increases the potential risk of data leakage. This paper proposes a data deduplication technique for resource-constrained Android devices with limited computing power and memory. The use of the Blake2b-Hash Algorithm in the proposed deduplication algorithm reduces overhead and enables dynamic memory allocation. Multiple strategies were proposed and tested using a wide range of Smartphones. The proposed algorithm is validated based on file analysis time, device storage and memory utilization, and storage overhead. The paper also assesses the hash function's cryptographic robustness. The experimental analysis demonstrates that the proposed method achieved a 45.90% processing time to identify 29.52% duplicate files. A 48.55% reduction in storage space was achieved, and battery consumption was reduced to 81.15% compared to MD5 and SHA224. It achieved up to 52.71% RAM optimization, along with only 0.0033% storage overhead to maintain the metadata of deleted files for forensic purposes. This proves the effectiveness and reliability of the proposed solutions in facilitating digital forensic analysis.},
}

@article {pmid42602675,
year = {2026},
author = {Gupta, D and Bagga, D and Abraham, P},
title = {Cloud evidence lifecycle framework: a six-stage log-centric method for multi-tenant AWS forensic investigations.},
journal = {MethodsX},
volume = {17},
number = {},
pages = {104075},
pmid = {42602675},
issn = {2215-0161},
abstract = {The Cloud Evidence Lifecycle Framework (CELF) is a six-stage forensic methodology that is log-centric, and that is designed for use in a multi-tenant Amazon Web Services environment. This replaces the traditional technique for seizing physical evidence today with a focus on configuration-based immutability and cross-service correlation of the evidence. Each stage of the CELF contains tenant boundary validation and log integrity controls, including: Tenant Context Definition, Evidence Surface Identification, Log-Centric Preservation, Structured Evidence Collection, Cross-Service Examination, and Attribution & Isolation Validation. - The CELF is mapped to the native AWS services of CloudTrail, S3 Object Lock, IAM, VPC Flow Logs, AWS Config, and CloudWatch, and aligned with the specific forensic objectives of each service, thereby providing a replicable configuration-level blueprint which is aligned with the guideline provided in NIST SP 800-201. Validation within a controlled two-tenant AWS eu-north-1 environment has demonstrated that all of the API activity to date has been captured, that all of the logs are immutable with respect to the evidence preservation, and that all of the events that have been reconstructed chronologically across the services (without requiring hypervisor access) have been reconstructed.},
}

@article {pmid42596453,
year = {2026},
author = {Liu, J and Shen, K and Cui, S and Xu, M and Ou, J and Hu, K and Han, J and Yao, T and Dai, J and Tang, M},
title = {Enhanced Tomlinson-Harashima precoding and adapted likelihood-based radius-directed equalization for transmitter-side bandwidth-limited faster-than-Nyquist systems over dynamic channels.},
journal = {Optics express},
volume = {34},
number = {15},
pages = {28100-28115},
doi = {10.1364/OE.600076},
pmid = {42596453},
issn = {1094-4087},
abstract = {The proliferation of bandwidth-intensive applications, such as AI cloud computing, has necessitated high-baud-rate transmission. However, this progression is often impeded by the transmitter-side bandwidth bottleneck, particularly in cost-sensitive short-reach scenarios. Although adaptive equalizers (AEQs) can mitigate such a bandwidth bottleneck, they do so at the cost of introducing equalization-enhanced high-frequency noise (EEHN), which severely degrades system performance. Tomlinson-Harashima precoding (THP) has emerged as a promising solution to counter this effect. However, its practical deployment is hindered by two critical limitations that this paper aims to resolve. First, conventional THP suffers from residual EEHN due to its inability to mitigate precursor inter-symbol interference (ISI). Second, its associated AEQ, which depends on training sequences for convergence, demonstrates unstable tracking performance over practical dynamic channels characterized by fast rotation of state of polarization (RSOP). To overcome these challenges, we propose an enhanced THP scheme at the transmitter to suppress the EEHN and a likelihood-based selection (LBS) radius-directed equalization (RDE) scheme adapted for THP-expanded constellations at the receiver (Rx) to ensure stable operation over dynamic channels. The effectiveness of the proposed scheme is validated through simulations and experiments. For a transmitter with a 3 dB bandwidth constrained to one-third of the baud rate, the proposed enhanced THP achieves approximately 1 dB sensitivity gain over conventional THP under static channel conditions. Under dynamic channels with a 500 krad/s RSOP, the AEQ relying solely on training sequences fails to converge, whereas the AEQ additionally incorporating payload symbols for RDE updates also suffers from notable degradation owing to frequent decision errors. In contrast, the proposed scheme operates reliably up to 1000 krad/s. At the 500 krad/s and 1000 krad/s RSOP speeds, it achieves a sensitivity improvement of over 1.5 dB and 3.8 dB, respectively. The proposed scheme offers a comprehensive solution for enhancing the performance of transmitter-side bandwidth-limited faster-than-Nyquist systems over practical dynamic channels.},
}

@article {pmid42598327,
year = {2026},
author = {Barua, NK and Salla, V and Kleinke, H},
title = {A Machine Learning Web Application for Real-Time Thermoelectric Property Predictions.},
journal = {ACS omega},
volume = {11},
number = {31},
pages = {45804-45814},
pmid = {42598327},
issn = {2470-1343},
abstract = {The growing adaptation of machine-learning studies for thermoelectric research is transforming the realm of material discovery. However, many of these studies are purely investigative and offer limited practical utility. Herein, we developed a resource- and cost-effective, single-page application machine-learning framework. The application is deployed in Microsoft Azure cloud using Docker containers, with the static webpage hosted on GitHub. The web page, https://kleinkeresearchgroup.github.io/TE_Predictions, is publicly accessible through a browser-based interface and serves as guidance for researchers to predict thermoelectric properties. In addition to thermoelectric property predictions from previous studies, we include an additional machine learning model for property predictions on the power factor of thermoelectric materials. Moreover, we developed an Azure Function App, https://fn-research-kleinke-app-jl-2025.azurewebsites.net/index, as an alternative to present a rapid and affordable option for deployment.},
}

@article {pmid42590215,
year = {2026},
author = {Aşiret, E and Yaşar, B and Efe, BT and Tozan, SE and Ergani, HM and Ünlü, RE},
title = {The Gemini Eye in Microsurgery: Video-Based Capillary Refill and Chromatic Assessment for Free Flap Monitoring.},
journal = {Journal of clinical medicine},
volume = {15},
number = {15},
pages = {},
doi = {10.3390/jcm15156114},
pmid = {42590215},
issn = {2077-0383},
abstract = {Background/Objectives: Postoperative free flap monitoring relies on clinical assessment of colour, turgor, and capillary refill time (CRT). The high frequency of required assessments renders this process labour-intensive and inherently subjective, with sensitivity dependent on observer experience and fatigue. This study evaluated the feasibility of a large multimodal AI (Artificial Intelligence) model (Gemini 3 Flash, Google AI Studio) applied without any task-specific training or fine-tuning, with each video analysed in an independent session, to establish whether meaningful diagnostic agreement is achievable before any domain-specific training is introduced, with the ultimate goal of supporting the development of a machine-based secondary safety net that augments, rather than replaces, the primary clinical assessment of the responsible surgeon in postoperative free flap surveillance. Methods: One hundred and forty-three postoperative video recordings from 143 different patients who underwent fasciocutaneous free flap reconstruction for extraoral defects were analysed. The cohort was deliberately enriched for pathological cases to ensure adequate representation of each vascular compromise category. Assessment was based on intrapatient comparison between flap and adjacent native tissue, without task-specific training or prior clinical information. AI outputs were compared against consensus assessments of three senior plastic surgeons (two associate professors, one full professor) for four parameters: flap colour, turgor, CRT interpretation, and clinical diagnosis. Agreement was evaluated using Cohen's kappa (κ). Receiver operating characteristic (ROC) analysis assessed the discriminative performance of the AI confidence score. Results: Statistically significant agreement was demonstrated across all four parameters (p < 0.001 for all): flap colour (κ = 0.613, 95% CI: 0.489-0.737, 80.4%), turgor (κ = 0.676, 95% CI: 0.558-0.794, 83.9%; assessed from visual surrogates rather than direct palpation), CRT interpretation (κ = 0.487, 95% CI: 0.360-0.614, 74.1%), and clinical diagnosis (κ = 0.524, 95% CI: 0.399-0.649, 74.1%). Normal flaps were correctly identified in 81.0% of cases and venous compromise in 69.4%. ROC analysis identified a confidence score percentile cutoff of 89 as the optimal threshold for discriminating compromised from normal flaps, with scores below 89 indicating a compromised (pathological) result (AUC = 0.667, 95% CI: 0.579-0.755; sensitivity 77.0%, specificity 52.4%; overall accuracy 62.9%). Conclusions : A structured-prompted multimodal LLM (Large Language Model) demonstrated statistically significant agreement with the consensus judgement of senior plastic surgeons in postoperative free flap assessment without task-specific training, relying solely on intrapatient visual comparison. These findings constitute a proof-of-concept for AI-based video analysis as a potential adjunctive approach in free flap surveillance, warranting prospective validation with independent clinical outcome data before any claim of clinical utility, with possible broader applicability across clinical domains pending such validation. Key limitations include the absence of independent clinical outcome or confirmation of diagnostic categories, deliberately enriched sampling, a small arterial insufficiency subgroup, and the current absence of a formal data processing agreement for the cloud-based AI platform used.},
}

@article {pmid42590616,
year = {2026},
author = {Zhang, H and Chen, Y and Chen, S and Zhao, Z and Zhu, D},
title = {Lightweight Anonymous Group Authentication and Quantum-Cloud Key Distribution Based on PUF for Classical Network Environments.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {15},
pages = {},
doi = {10.3390/s26154840},
pmid = {42590616},
issn = {1424-8220},
support = {No. 20250102045JC//the Science and Technology Department of Jilin Province/ ; },
abstract = {With the rapid development of quantum computing, in response to its disruptive threats to traditional cryptosystems and the urgent demand for lightweight and highly scalable secure group communication among resource-constrained devices in large-scale Internet of Things (IoT) scenarios, this paper proposes a lightweight anonymous group authentication scheme that integrates Physical Unclonable Functions (PUFs), distributed Gossip algorithms, and quantum key distribution. By exploiting the uniqueness and unclonability derived from the inherent physical characteristics of PUF hardware, the scheme fundamentally eliminates attack vectors against quantum computers without requiring devices to pre-store any secret keys in their memory, while the QKCS pre-provisions CRPs and key seeds, which is the standard enrollment procedure in PUF-based systems. Combined with information-theoretically secure quantum keys as session keys, it forms a dual protection mechanism: anti-forgery at the physical layer and anti-quantum attack at the cryptographic layer. Innovatively, the Gossip algorithm is deeply integrated with group key agreement, converting global broadcast into local iterative interactions between nodes, which effectively alleviates broadcast storms and improves the scalability and fault tolerance of the protocol. Meanwhile, a pseudonym mechanism is introduced to achieve anonymous identity protection, and a dynamic key update strategy guarantees forward and backward security when members join or leave the group. Formal verification based on BAN logic and security analysis show that the proposed protocol can resist typical attacks such as replay attacks, man-in-the-middle attacks, and impersonation attacks. Performance evaluations demonstrate that our scheme outperforms existing comparable schemes in terms of computational cost, communication overhead, and dynamic group management efficiency, demonstrating its potential for resource-constrained IoT environments, pending further validation on real hardware platforms.},
}

@article {pmid42590676,
year = {2026},
author = {Xiang, Z and Chen, S and Hu, X and Xu, X and Yang, Y and Yang, J and Zhan, Y},
title = {Asynchronous Cross-Modal Dynamic Graph Learning for Intelligent Sensing of AI Computing Infrastructure Expansion.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {15},
pages = {},
doi = {10.3390/s26154902},
pmid = {42590676},
issn = {1424-8220},
abstract = {The rapid expansion of artificial intelligence computing infrastructure and the semiconductor industry has made the dynamic sensing of policy planning, technological innovation, capital investment, and physical construction essential for industrial monitoring and resource allocation. However, policy documents, remote-sensing imagery, patent relations, and economic indicators exhibit substantial heterogeneity, temporal misalignment, and variations in data quality, making it difficult for conventional unimodal or synchronously fused methods to accurately characterize the continuous evolution of construction activities. To address these challenges, a multimodal industrial sensing dataset for artificial intelligence computing infrastructure was constructed, and an artificial intelligence-driven asynchronous cross-modal dynamic graph prediction framework was developed. Differential temporal lags among policy announcements, capital investment, patent growth, and physical construction are learned through an asynchronous cross-modal alignment module. Interference caused by cloud contamination, missing text, patent disclosure delays, and incomplete economic observations is dynamically suppressed through a reliability-aware fusion mechanism. Time-varying propagation relationships among countries, enterprises, patents, and industrial parks are further modeled using an economically modulated dynamic heterogeneous graph. Experimental results demonstrate that, in next-window construction event prediction, the proposed model achieved an Accuracy of 91.72%, a Precision of 91.08%, a Recall of 90.64%, an F1-score of 90.86%, and a ROC-AUC of 95.67%, significantly outperforming baseline methods including XLM-R, Swin Transformer, TimesNet, HGT, and TGN. In construction intensity forecasting, the model achieved an MAE of 0.104, an RMSE of 0.153, a MAPE of 10.91%, an R2 of 0.854, and a Pearson correlation coefficient of 0.928. Ablation experiments further confirmed the effectiveness of asynchronous alignment, reliability calibration, missing-modality compensation, economic modulation, and long-term graph memory. The proposed approach provides a temporally interpretable intelligent sensing method for monitoring computing center construction, evaluating industrial park expansion, allocating energy and communication infrastructure, and planning equipment supply.},
}

@article {pmid42592932,
year = {2026},
author = {Hadfield, H},
title = {Closed-form solutions for rigid transformations between heterogeneous groups of noisy geometric primitives.},
journal = {Philosophical transactions. Series A, Mathematical, physical, and engineering sciences},
volume = {384},
number = {2326},
pages = {},
doi = {10.1098/rsta.2025.0110},
pmid = {42592932},
issn = {1471-2962},
abstract = {Computing the transformation between noisy collections of geometric primitives is a classic problem in three-dimensional computer vision and geometric computing. In this paper, we consider minimum sets of noisy objects and provide closed-form formulae to compute the Euclidean transformation that best transforms from one of these sets to another set. We enumerate these for all primitive object groups that appear in common three-dimensional computer vision problems and describe example applications. This article is part of the theme issue 'Modern applications of geometric algebra'.},
}

@article {pmid42581344,
year = {2026},
author = {Frey, J and Schindler, Z and Ali, M and Braun-Wimmer, J and Gerberding, K and Kröner, K and Larysch, E and Lusk, D and Vanja-Jehle, J and Wardius, Y and Weidenfeller, M and Kattenborn, T and Seifert, T},
title = {Introducing GalaxyR: an easy-to-use R implementation of the Galaxy API.},
journal = {BMC bioinformatics},
volume = {27},
number = {1},
pages = {},
pmid = {42581344},
issn = {1471-2105},
mesh = {*Software ; *Computational Biology/methods ; Workflow ; User-Computer Interface ; Internet ; },
abstract = {BACKGROUND: Standardisation, accessibility, and reproducibility remain persistent challenges in data‑intensive scientific research. The Galaxy platform addresses these issues by providing a web-based environment for executing, sharing, and publishing computational workflows, yet programmatic access has been largely centred on the Python ecosystem through BioBlend.

METHODS: Here, we introduce GalaxyR, a native R package that provides a comprehensive and structured interface to the Galaxy application programming interface. GalaxyR enables users to manage histories, upload and retrieve data, execute tools and workflows, monitor jobs, and inspect results directly from within the R environment. By integrating Galaxy's scalable computational infrastructure with R's widely adopted data analysis ecosystem, GalaxyR facilitates automated, reproducible, and resource‑efficient workflows without requiring local high‑performance computing resources. The package supports both HTTPS‑ and FTP‑based data transfer, robust history and dataset management, and programmatic workflow orchestration across Galaxy instances.

RESULTS: An application example demonstrates the use of GalaxyR for large-scale processing of drone-based laser scanning data, where computationally intensive tree‑level segmentation was delegated to the Galaxy infrastructure, while workflow control and preprocessing were handled in R within a few lines of code.

CONCLUSIONS: GalaxyR thus bridges a critical gap for R users, significantly expanding access to Galaxy‑based analyses and enabling scalable, reproducible research across bioinformatics, ecology, remote sensing, and related data-driven disciplines.},
}

*Software
*Computational Biology/methods
Workflow
User-Computer Interface
Internet

@article {pmid42577369,
year = {2026},
author = {Chilipa, R and Nyirenda, C},
title = {Neuro-fuzzy systems in internet of medical things: a systematic review on applications, taxonomy, challenges and open issues.},
journal = {Frontiers in digital health},
volume = {8},
number = {},
pages = {1857965},
pmid = {42577369},
issn = {2673-253X},
abstract = {The growing demand for real-time, adaptive, and explainable analytics in the Internet of Medical Things (IoMT) has increased interest in neuro-fuzzy systems for connected healthcare. By combining neural learning with fuzzy inference, these systems can support predictive modelling while offering rule-based reasoning structures that require explicit evaluation for interpretability, stability, and clinical usability. This paper presents a systematic review based on structured database searches, eligibility screening, quality appraisal, and narrative synthesis of 55 peer-reviewed journal articles published between January 2020 and July 2025 on neuro-fuzzy systems in IoMT and connected-health contexts. The included articles were retrieved from MDPI, SpringerLink, ScienceDirect/Elsevier, IEEE Xplore, Wiley Online Library, and Taylor & Francis, with Google Scholar used only for verification and citation tracing. An article-level, deployment-aware taxonomy was developed to classify the evidence by architecture, application, design, deployment environment, and reported metrics. Neural-network-based optimization was the most frequently reported architecture, accounting for 28 articles (50.9%), followed by hybrid neuro-fuzzy systems with 11 articles (20.0%), deep neuro-fuzzy systems with 9 articles (16.4%), and evolving neuro-fuzzy systems with 7 articles (12.7%). This distribution indicates that the evidence base is still shaped mainly by static or offline neuro-fuzzy designs, while evolving architectures for streaming, non-stationary, and patient-specific IoMT data remain comparatively underexplored. Predictive systems formed the largest application category, followed by detection-oriented systems. Deployment reporting remained limited, with 34 of the 55 included articles (61.8%) not specifying an execution environment, while only 6 articles (10.9%) reported latency or processing-time evidence. Overall, the evidence remains mainly retrospective, experimental, simulation-based, benchmark-driven, or prototype-level. The review identifies recurring gaps in online adaptability, interoperability, deployment-aware performance evaluation, and practical clinical interpretability. Although neuro-fuzzy systems show promise as adaptive models for connected-health applications, the current evidence does not yet establish routine clinical readiness or clinical effectiveness. Future work should strengthen evolving neuro-fuzzy modelling, prospective validation, deployment-aware evaluation, safety assessment, interpretability assessment, and integration with electronic health record and telemedicine workflows.},
}

@article {pmid42574480,
year = {2026},
author = {Smith, W and Woolland, O and Margetts, L},
title = {Integrating Automated Simulation Workflows with 3D Visualization for Virtual Experiments in the Metaverse.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {233},
pages = {},
doi = {10.3791/71833},
pmid = {42574480},
issn = {1940-087X},
mesh = {*Software ; Workflow ; *Computer Simulation ; *Imaging, Three-Dimensional/methods ; },
abstract = {In many virtual experiments, multiple software packages are used with varied simulation types, pre- and post-processing tools, and tools to visualize the experiments' results-often a combination of all. The typical method of integrating these is a manual one, with bespoke solutions created for each application area, which scales poorly and impedes sharing and reproducibility. This protocol demonstrates the deployment and use of a locally containerized workflow system. Following it, users will launch a local Galaxy instance using Docker, create and run an OpenMC neutronics simulation workflow, pass the outputs through a chain of format-conversion tools, and load the results into both ParaView and NVIDIA Omniverse for visualization. The containerized deployment promotes reproducibility and portability on any machine meeting the hardware requirements described in Section 1. Once the system is running, workflows can be re-run against new inputs without manual reconfiguration, additional simulation codes can be wrapped as new tools with modest effort, and tools can be used in multiple workflows and application areas. The approach supports the findable, accessible, interoperable, reusable (FAIR) data principles: run histories capture full provenance metadata, workflows are exportable as portable files and can be shared directly between Galaxy instances, and tools are packaged in version-controlled containers that can be published in a public repository. Scalability to high-performance computing (HPC) or cloud resources via Galaxy's Pulsar system is a natural extension of the architecture described here. The method is demonstrated through a fusion neutronics case study. OpenMC is used to simulate neutron transport in a Direct Accelerated Geometry Monte Carlo (DAGMC) computer-aided design (CAD) geometry, producing a tritium breeding ratio (TBR) result and a neutron-track dataset. The simulation workflows are then connected to the NVIDIA Omniverse as the metaverse platform for invocation and visualization.},
}

*Software
Workflow
*Computer Simulation
*Imaging, Three-Dimensional/methods