@article {pmid42209538,
year = {2026},
author = {Cheng, YT and Hoang, NT and Shinoda, Y and Islam, K and Motoshita, M and Kadoya, T and Kanemoto, K},
title = {Mapping global resource driven nature loss in the mining sector from 2001 to 2022.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-73792-9},
pmid = {42209538},
issn = {2041-1723},
abstract = {Global mineral extraction is expected to surge due to the growing demand for clean energy. While mining is critical to modern society, its environmental impacts, though increasingly studied, remain undocumented for half of the world's mining areas and are rarely analysed at the commodity level. Here, we introduce a novel approach integrating remote sensing, machine learning, and cloud computing to classify approximately 70,000 mining sites by commodity. Using this newly detailed dataset, we quantify the nature loss associated with 20 extracted commodities, focusing on deforestation and habitat destruction. From 2001 to 2022, mining activities worldwide resulted in the removal of 16,268 km[2] of forest cover, with 65.64% occurring in tropical and subtropical regions. Notably, approximately half of this deforestation was attributed to the extraction of gold, coal, aluminium (bauxite), nickel-cobalt and copper, primarily in countries intersecting the Amazon, Southeast Asian, and Congo Basin rainforests. Our analysis also reveals that deforestation-to-mining area ratios and biodiversity risks vary by mining location, and conservation threats do not always scale with deforestation rates. By providing commodity-specific maps of mining-induced nature loss, our work equips companies and organisations with actionable insights to identify risks within their supply chains and implement targeted mitigation strategies.},
}

@article {pmid42211407,
year = {2026},
author = {Churches, TR and Green, R and Nguyen, PB and Shawon, MSR and Jorm, LR},
title = {E-Research Institutional Cloud Architecture (ERICA): An Orchestration Meta-Framework for Establishing Trusted Research Environments Using Public Cloud Computing.},
journal = {International journal of population data science},
volume = {6},
number = {1},
pages = {3373},
pmid = {42211407},
issn = {2399-4908},
mesh = {*Cloud Computing ; *Computer Security ; Internet ; Academia ; New South Wales ; *Research ; Confidentiality ; Australia ; Humans ; },
abstract = {INTRODUCTION: The E-Research Institutional Cloud Architecture (ERICA) is a code-driven orchestration framework that automates the configuration and management of Amazon Web Services (AWS) resources to provide trusted research environments (TREs) for sensitive data. Independent ERICA TREs are now operational in universities and government agencies. The framework was developed by the University of New South Wales, Australia, with support from the Australian Research Data Commons.

OBJECTIVES: ERICA was designed to overcome the limitations of traditional on-premise TREs by providing secure, scalable, and flexible cloud environments that protect privacy while enabling advanced, data-intensive research.

APPROACH: Using an infrastructure-as-code model, ERICA delivers consistent, reliable, and error-free setup of Project Spaces. It integrates robust security features, including encryption-at-rest and in transit, and multi-factor authentication. Hosted in AWS onshore data centres, ERICA ensures data sovereignty while supporting diverse operating systems and high-performance computing configurations. The architecture also allows rapid deployment of new AWS services, including generative AI tools, within research workspaces.

DISCUSSION: ERICA implements the Five Safes framework-covering safe projects, people, data, settings, and outputs-to ensure compliance and secure research. Its modular architecture enables multiple independent TREs, each governed by host-institution policies and capable of supporting hundreds of Project Spaces. This flexibility allows replication across any jurisdiction with AWS public cloud infrastructure. However, reliance on AWS introduces challenges, including charges in US dollars and delayed rollout of new services in smaller regions.

CONCLUSIONS: ERICA represents a step change in providing privacy-by-design cloud infrastructure for sensitive, data-intensive research. By combining strong governance with the scalability of AWS, it enables researchers to work securely with large, complex datasets while rapidly adopting cutting-edge analytical tools. ERICA TREs offer a replicable, future-proof model for supporting secure research at scale.},
}

*Cloud Computing
*Computer Security
Internet
Academia
New South Wales
*Research
Confidentiality
Australia
Humans

@article {pmid42214073,
year = {2026},
author = {Sparrock, LS and Vidrine, JI and Vinci, CE and El Naqa, IM and Sutton, SK and Salloum, RG and Dallery, J and Crane, TE and Penedo, FJ and Brockway, SJ and Jones, SR and Hoogland, CE and Reich, RR and Gonzalez-Calderon, G and Simmons, VN and Vidrine, DJ},
title = {Creation of an mHealth Infrastructure to Support the Development and Delivery of mHealth Interventions: Protocol for Demonstration Projects Addressing Smoking Cessation in Cancer Care.},
journal = {JMIR research protocols},
volume = {15},
number = {},
pages = {e92288},
doi = {10.2196/92288},
pmid = {42214073},
issn = {1929-0748},
mesh = {Humans ; *Smoking Cessation/methods ; *Telemedicine ; *Neoplasms/therapy ; Mobile Applications ; Digital Health ; },
abstract = {BACKGROUND: Cancer remains a leading cause of morbidity worldwide. To reduce this burden, scalable, effective approaches are needed to address modifiable risk factors for cancer and support behavioral self-management. With smartphone ownership now nearly ubiquitous, mobile health (mHealth) interventions offer a powerful means to extend the reach, accessibility, and sustainability of evidence-based treatments for a variety of modifiable risk factors (eg, excessive alcohol use, physical inactivity, poor diet, and smoking). Moreover, the flexibility of mHealth platforms enables efficient delivery of novel interventions, supports innovative study designs, and facilitates real-time data collection to advance public health research.

OBJECTIVE: Despite the great potential of mHealth interventions, developing high-quality mHealth tools is complex, time-consuming, and resource-intensive. To address these challenges, we are developing a coordinated, accessible, research-grade infrastructure for mHealth app development, testing, and dissemination.

METHODS: The mHealth Florida infrastructure (mFLi) will provide a comprehensive, low-code software platform that enables researchers to build apps compatible with major mobile operating systems, namely, Apple iOS and Google Android. Through a modular interface, users will select from a menu of prebuilt features to tailor functionality to specific study needs. The platform will include 3 integrated environments (development, testing, and production), allowing researchers to prototype, evaluate, and deploy mHealth interventions. This infrastructure will be developed and maintained by a multidisciplinary team, ensuring that the platform is technically robust and usable and adheres to institutional and regulatory standards. To demonstrate the platform's functionality, utility, and adaptability, a multisite study comprising three initial projects focused on smoking cessation among patients with cancer is being conducted: (1) participant screening and enrollment, (2) randomization and treatment delivery, and (3) data processing using machine learning methods with on-device and cloud-based approaches.

RESULTS: This study was funded in May 2023, and ethics approval was obtained from all involved sites' institutional review boards between February 2024 and October 2025. Recruitment began in March 2025 and enrollment is ongoing. As of January 2026, 41% (37/90) of the target sample have been enrolled and 21% (19/90) have completed their 6-month assessment. Data collection will be completed once the final participant completes their 6-month assessment (expected May 2027), with analyses commencing thereafter. Study findings are anticipated to be published in a peer-reviewed journal in 2027.

CONCLUSIONS: Collectively, these projects will illustrate how mFLi can streamline app development, facilitate rapid translation of research into practice, and reduce barriers for researchers and developers. Ultimately, mFLi is designed to accelerate innovation in mHealth research, enhance access to behavioral interventions, and improve health outcomes among diverse populations.

TRIAL REGISTRATION: ClinicalTrials.gov NCT06909357; https://clinicaltrials.gov/study/NCT06909357.

PRR1-10.2196/92288.},
}

Humans
*Smoking Cessation/methods
*Telemedicine
*Neoplasms/therapy
Mobile Applications
Digital Health

@article {pmid42215516,
year = {2026},
author = {Li, X and Li, J and Xing, Z and Liu, S},
title = {Monitoring alpine wetland of Haizishan using Otsu method and Sentinel-1 SAR in Hengduan Mountains, China.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-46543-5},
pmid = {42215516},
issn = {2045-2322},
abstract = {This study focuses on delineating water extent within the alpine wetlands of Haizishan, Qinghai-Tibet Plateau, a meteorologically complex region. Situated within a 6,000 km[2] Quaternary ice sheet landscape with over 600 water bodies, this area offers a crucial setting to examine climate change impacts on high-altitude wetland systems. Employing Sentinel-1 SAR data and the Google Earth Engine (GEE) platform, we implemented an Improved OTSU algorithm, achieving > 95% accuracy in water surface delineation despite persistent cloud cover. Notably, wetland water extent expanded from 52.9 km[2] to 54.8 km[2] between 2015 and 2022, correlating with increased precipitation. This expansion, and its precipitation-driven nature, distinguishes these non-glacial-fed wetlands hydrologically from glacial water bodies in the broader region, particularly impacting shallower wetland areas sensitive to thermal variations.},
}

@article {pmid42215677,
year = {2026},
author = {Rout, J and Mishra, M and Barik, RC and Yadav, DK and Prasad, DK and Sekh, AA},
title = {Stacked multi-fusion CNN: an adaptive attention model for privacy preserving deepfake forensics.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-55325-y},
pmid = {42215677},
issn = {2045-2322},
abstract = {The emergence of Generative Artificial Intelligence (Gen-AI) and Generative Adversarial Network (GAN)-based deepfakes poses significant security risks in sociocultural and sociopolitical domains. This necessitates the development of advanced and effective detection methods to prevent vulnerability in social networks. Classical Machine Learning (ML) algorithms have their limitations, especially in classifying the deepfakes. To address these issues, this paper suggests a privacy-preserving Stacked Multi-Fusion (SMF) Convolutional Neural Network (CNN) approach to classify deepfakes. An improved CNN model is proposed, integrating an adaptive multi-scale attention framework with enhanced residual blocks and a Squeeze-and-Excitation (SE) mechanism. The selection of these components is backed with an ablation study to report the individual contribution to the overall optimal architecture. A hybrid lossless multilayer cryptosystem based on a chaos-based approach, Deoxyribonucleic Acid (DNA)-based computing, etc., is developed to secure images in cloud storage. The efficacy of the proposed SMF model is validated using the 140K Real and Fake Faces (RFF) image dataset. The proposed approach was found to achieve stable performance with minimal variation in various tests. It achieved a test accuracy and ROC-AUC of 97.80 and 99.79, respectively. This paper provides comprehensive relevant factors for building effective synthetic media detection systems.},
}

@article {pmid42206982,
year = {2026},
author = {Spichak, S},
title = {Biocomputing: Beyond the Hype.},
journal = {Journal of medical Internet research},
volume = {28},
number = {},
pages = {e100949},
doi = {10.2196/100949},
pmid = {42206982},
issn = {1438-8871},
mesh = {Humans ; *Computational Biology ; Artificial Intelligence ; },
abstract = {Biocomputing is a nascent but rapidly developing field at the intersection of biology and computer science. In this News and Perspectives article, JMIR Correspondent Simon Spichak reports on its current and potential applications for health care research and beyond.},
}

Humans
*Computational Biology
Artificial Intelligence

@article {pmid42194316,
year = {2026},
author = {Oloko-Oba, M and Esenogho, E and Aruleba, K},
title = {From Biosignals to Bedside: A Review of Real-Time Edge Machine Learning for Wearable Health Monitoring.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {5},
pages = {},
doi = {10.3390/bioengineering13050559},
pmid = {42194316},
issn = {2306-5354},
abstract = {Wearable devices increasingly capture biosignals such as electrocardiograms, photoplethysmograms, inertial signals, and electrodermal activity during daily life, enabling earlier detection and continuous monitoring outside the clinic. Real-time edge machine learning can convert these streams into timely, privacy-preserving inference by placing computation on a wearable (device-only) or a paired phone, with intermittent cloud assist used selectively for dashboards, summarisation, and lifecycle management. Clinical adoption remains uneven because free-living data are noisy, labels are often delayed, and device ecosystems evolve over time. This narrative review organises the literature as an end-to-end deployment pathway: sensing and artefact management, streaming windowing and multimodal alignment, and model families suited to on-device inference. We compare classical feature-based pipelines with learned representations, including compact CNN/TCN and recurrent and efficient attention-based models, and discuss when self-supervised pretraining and distillation are most useful in low-label settings. We then synthesise deployment engineering levers (quantisation, pruning, and distillation) and benchmarking requirements, emphasising runtime constraints that determine feasibility: latency per update, peak RAM, energy per inference, duty cycle, and thermal behaviour. Applications are grouped across cardiovascular monitoring, blood pressure and haemodynamics, sleep and respiration, and movement and stress, with explicit attention to false-alert burden, adherence, and workflow integration. To support translation, we provide a validation ladder and a reliability toolkit covering calibration, uncertainty-aware thresholds and deferral, drift monitoring triggers, and safe update governance. The novelty of this review is a deployment-oriented synthesis that ties modelling choices to edge tiers and resource budgets and provides reusable reporting templates, including an edge-cost card and comparative tables spanning modalities, models, deployment levers, applications, and reliability requirements.},
}

@article {pmid42195503,
year = {2026},
author = {Zhang, Z},
title = {A Review of Embedded Artificial Intelligence Research (2023-2026): Technological Advancements, Representative Advances, and Future Prospects.},
journal = {Micromachines},
volume = {17},
number = {5},
pages = {},
doi = {10.3390/mi17050586},
pmid = {42195503},
issn = {2072-666X},
abstract = {Since the publication of the "Review of Embedded Artificial Intelligence Research" in 2023, driven by innovations in hardware architectures, advances in lightweight algorithms, and the maturation of edge-cloud collaboration technologies, embedded artificial intelligence (embedded AI) has progressed from "technically feasible" to "large-scale deployment". As a continuation of that review, this article systematically surveys the core advances in embedded AI from 2023 to 2026. At the hardware level, it examines engineering progress in non-von Neumann architectures such as compute-in-memory and neuromorphic chips, as well as heterogeneous integration technologies. At the algorithmic level, it covers dynamic adaptive lightweighting, specialized edge-side optimization of large models (including on-device large language model fine-tuning and edge diffusion models), and lightweight multimodal approaches. In terms of deployment paradigms, it discusses edge-side full training, federated edge learning, edge-cloud collaborative intelligence, and emerging paradigms. At the application level, it illustrates the "perception-decision-execution" pipeline in industrial IoT, wearable healthcare, autonomous driving, embodied intelligence, and smart agriculture. The article also analyzes core challenges including ultra-low-power design for extreme scenarios, cross-platform standardization, edge-side data security and privacy, and model robustness in complex environments. Based on these findings, four research directions are proposed to guide future work.},
}

@article {pmid42197891,
year = {2026},
author = {Rosa-Bilbao, J},
title = {A Low-Code Containerized Edge Architecture for IIoT Telemetry Orchestration: Mitigating Cloud API Rate Limits Through Dual-Path Routing.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {10},
pages = {},
doi = {10.3390/s26103082},
pmid = {42197891},
issn = {1424-8220},
support = {PID2021-122215NB-C33//Ministerio de Ciencia e Innovación/ ; },
abstract = {This paper investigates whether a low-code workflow engine can operate as practical Industrial Internet of Things (IIoT) middleware at the edge when cloud application programming interface (API) rate limits make direct telemetry upload unsustainable. The main contribution is a dual-path architecture in which a Hot Path persists all telemetry locally, while a Cold Path selectively forwards only anomalous or summary events to cloud services. The architecture is implemented as a lightweight containerized stack based on n8n, Eclipse Mosquitto, InfluxDB, and Grafana, and evaluated on a Raspberry Pi 4 under baseline, cloud-only saturation, and edge-filtered stress scenarios. Under the cloud-only condition, the external endpoint is throttled to approximately 60 requests/min, yielding a rejection rate of 98.0% (95% Wilson confidence interval: 97.43-98.44%). Under the dual-path condition, the same inbound load is fully retained locally while outbound cloud traffic is reduced by 98.0%, thereby avoiding throttling without sacrificing edge-side data fidelity. The measured Hot Path processing latency remains around 5 ms on average, with observed peaks below 10 ms, which is compatible with soft real-time monitoring workloads. Compared with more established low-code tools such as Node-RED, the novelty of the study is not the existence of visual orchestration itself, but the combination of containerized deployment, explicit hot/cold decoupling, and an empirical rate-limit mitigation analysis focused on low-cost edge hardware.},
}

@article {pmid42197916,
year = {2026},
author = {Chen, GH and Ma, HY and Yu, W and Wen, J and Chen, K and Wang, JJ and Chen, SD and Sun, YL},
title = {Terminal-Edge-Cloud Collaborative Computation Offloading and Resource Allocation Strategy Based on Improved Mayfly Algorithm for District Heating Systems.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {10},
pages = {},
doi = {10.3390/s26103110},
pmid = {42197916},
issn = {1424-8220},
abstract = {The rapid digitalization of district heating systems (DHSs) has driven the large-scale deployment of thermal Internet of Things (TIoT) sensors, which generate massive real-time operational data. Traditional centralized computing architectures struggle to process massive concurrent data. Furthermore, they fail to balance the stringent low-latency demands of real-time control tasks with the low-energy constraints of battery-powered terminal devices. To solve the complex problem of minimizing the weighted sum of system latency and energy consumption, we propose an Improved Mayfly Algorithm (IMA). The algorithm integrates five targeted structural enhancements: random position update masking, differential evolution (DE)-based crossover, targeted subset mutation with boundary scaling, adaptive population reset mechanism, and simulated annealing (SA)-driven local search, to efficiently navigate the high-dimensional rugged decision space and mitigate premature convergence. Extensive simulation results show that the proposed collaborative architecture achieves the lowest total system cost compared with traditional isolated computing paradigms (local-only, edge-only, and cloud-only). Notably, the proposed IMA reduces the total baseline weighted cost by 17.2% compared with the standard MA. Furthermore, under maximum practical industrial workloads (750 concurrent tasks, representing a highly complex 2250-dimensional MINLP space), the IMA maintains strong scalability and dominance, outperforming the second-best algorithm (BWO) by 15.8%. This research provides a low-latency, energy-efficient scheduling solution for TIoT-enabled DHS, and offers technical support for the intelligent and low-carbon transformation of urban energy infrastructure.},
}

@article {pmid42198259,
year = {2026},
author = {Altharawi, A and Alqahtani, SM},
title = {Integrative Computational Chemistry Approaches in Modern Drug Discovery: Advances in Docking, Pharmacophore Modeling, Molecular Dynamics, and Virtual Screening.},
journal = {Pharmaceutics},
volume = {18},
number = {5},
pages = {},
doi = {10.3390/pharmaceutics18050565},
pmid = {42198259},
issn = {1999-4923},
abstract = {Computational chemistry has played a central role in early-stage drug discovery by accelerating target selection, hit identification, and lead optimization. This review summarizes recent developments in molecular docking, pharmacophore modeling, molecular dynamics (MD), and virtual screening (VS), with a focus on their application in practical drug discovery workflows. Advances in docking protocols, including consensus scoring, physics-based rescoring, and ensemble approaches, addressed the challenges of receptor flexibility. Both ligand-based and structure-based pharmacophore models facilitated scaffold hopping and guided library prioritization. MD simulations were used to assess binding pose stability, identify cryptic binding pockets, and characterize solvent interactions. These simulations also supported free-energy calculations using endpoint and alchemical methods. Large-scale VS campaigns employed curated compound libraries, often composed of make-on-demand molecules, and relied on high-performance computing or cloud infrastructure to screen up to 10[9] compounds. Hits were validated using orthogonal biophysical assays and filtered by absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. Integrated pipelines combining pharmacophore modeling, docking, MD, and free-energy calculations improved enrichment rates and reduced the number of compounds requiring synthesis. Several case studies demonstrated the identification of nanomolar-affinity leads from ultra-large screening campaigns. The review also addressed ongoing challenges, such as inconsistent scoring of binding affinity, protonation, and tautomeric errors, dataset bias, and reproducibility issues. Strategies to mitigate these limitations included standardized library preparation, adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and the use of prospective benchmarking protocols. The review discussed emerging trends, including the use of quantum chemistry for electronic structure refinement, ensemble docking guided by cryo-electron microscopy (cryo-EM) data, and the integration of computational tools with automated synthesis and high-throughput screening in closed-loop discovery systems. These approaches have the potential to accelerate the design-make-test cycle, increase hit novelty, and improve decision-making in early drug development programs.},
}

@article {pmid42199228,
year = {2026},
author = {Ye, Z and Liao, L and Qiu, G and Xiao, Y and Huang, D and Duan, Z and Pu, S and Wen, J},
title = {Application of crop growth models in crop yield assessment.},
journal = {Frontiers in plant science},
volume = {17},
number = {},
pages = {1819890},
pmid = {42199228},
issn = {1664-462X},
abstract = {Global food security is facing formidable challenges due to rising temperatures, frequent extreme weather events, growing water scarcity, cropland reduction, fluctuations in international food trade, and rising food demand. Crop production systems are complex, multi-factor dynamic systems influenced collectively by crop cultivars, climatic conditions, soil properties, and management practices, which exhibit strong spatiotemporal variability. Crop growth models have emerged as essential tools in smart agriculture, integrating knowledge from crop physiology, ecology, meteorology, soil science, and agronomy to simulate crop growth processes dynamically. This systematic review focuses on six key aspects of crop growth modeling: (1) introduction of major crop models; (2) assessing climate change impacts on crop yields; (3) predicting yield potential and yield gaps; (4) identifying yield-limiting factors; (5) formulating adaptation strategies; and (6) challenges and future research directions. Future research should focus on the deep integration of crop growth models with remote sensing, the Internet of Things (IoT), big data, cloud computing, and artificial intelligence technologies to establish intelligent "space-air-ground" decision-making systems that support precision, unmanned, and climate-resilient agriculture.},
}

@article {pmid42200970,
year = {2026},
author = {Valsalan, P and Siddiqui, MM},
title = {AI-Driven Hybrid Detection and Classification Framework for Secure Sleep Health IoT Networks.},
journal = {Clocks & sleep},
volume = {8},
number = {2},
pages = {},
doi = {10.3390/clockssleep8020023},
pmid = {42200970},
issn = {2624-5175},
abstract = {Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO2, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber-physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022-2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization.},
}

@article {pmid42201543,
year = {2026},
author = {Huynh, T and Nguyen, M and Ha, HTT and Ngo, L},
title = {Development of a serverless, interactive application for Alzheimer's disease detection and visualization using MRI images.},
journal = {The neuroradiology journal},
volume = {},
number = {},
pages = {19714009261456608},
doi = {10.1177/19714009261456608},
pmid = {42201543},
issn = {2385-1996},
abstract = {BackgroundAlzheimer's disease requires early detection for effective intervention with disease-modifying treatments, yet significant implementation barriers persist in clinical practice, including limited computational infrastructure and the gap between research model performance and practical deployment in resource-constrained settings.ObjectivesTo develop and evaluate a computer-aided diagnosis system for classifying cognitive states (AD, MCI, and CN) from structural MRI, with automated preprocessing and cost-effective cloud deployment suitable for resource-constrained healthcare facilities.DesignComputer-aided diagnosis system integrating neural architecture search with serverless cloud infrastructure.MethodsA multi-view MRI analysis model was optimized through neural architecture search, incorporating Universal Inverted Bottleneck blocks and Kolmogorov-Arnold Networks. Automated MRI preprocessing using FSL was deployed through cloud-based serverless functions for scalable image processing. Evaluation used the ADNI dataset (1687 individuals: 368 AD, 625 MCI, and 694 CN). A web application was developed providing patient management, MRI visualization, and automated diagnostic prediction.ResultsThe model achieved 86.7% accuracy and 0.900 AUC in three-class classification with 1.7 million parameters. High specificity was observed across all classes (CN: 91.0%, MCI: 91.8%, AD: 97.3%), with 100% CN-AD specificity ensuring no AD cases were misclassified as cognitively normal. Operational costs were approximately 0.028 USD per diagnosis for typical hospital workloads.ConclusionThis system provides a cost-effective approach for early Alzheimer's diagnosis accessible to resource-constrained environments. Despite challenges in MCI classification, the combination of neural architecture search with serverless deployment demonstrates progress toward clinically deployable automated AD detection. Future work should focus on prospective clinical validation and integration of interpretability features.},
}

@article {pmid42202663,
year = {2026},
author = {Pescol, F and Buonocore, TM and Tibollo, V and Failla, G and Traversi, E and La Rovere, MT and Sacchi, L and Ricciardi, W and Bellazzi, R},
title = {Evaluating large language models for structuring cardiology reports: a real-world clinical study on patient subtyping and trial recruitment.},
journal = {International journal of medical informatics},
volume = {217},
number = {},
pages = {106509},
doi = {10.1016/j.ijmedinf.2026.106509},
pmid = {42202663},
issn = {1872-8243},
abstract = {BACKGROUND: Artificial Intelligence (AI) methods have emerged as useful tools for supporting patient recruitment in clinical trials (CTs). Despite several studies having recently proposed promising applications of Large Language Model (LLMs) for patient recruitment in CTs, their implementation in routine clinical practice remains limited.

METHODS: In this study, we present a comprehensive pipeline, developed and tested in a real-world clinical setting, to obtain highly detailed patient subtyping and eligibility assessment for specific CTs. Our solution leverages cardiological discharge letters, a rich yet underutilized source of patient data, to extract detailed structured clinical information through LLMs. Patient subtyping and eligibility assessment are performed through a rule-based approach, based on the extracted information, to maximize deterministic and interpretable outputs. We employed OpenAI's GPT-4.1 within the cloud-based service Microsoft Azure Machine Learning Studio, deployed in the hospital infrastructure. Validation was conducted on a sample of 100 discharge letters through exact-match comparison between the model's output and a ground-truth template, pre-populated by expert clinicians.

RESULTS: Our results confirm the feasibility and effectiveness of the proposed approach in real-world clinical scenarios. GPT-4.1 achieved high values of information extraction accuracy for most clinical variables (0.94 ± 0.08), resulting in a limited number of false negatives (FN) and false positives (FP) in both patient subtyping (0.12 and 0.13, respectively) and eligibility assessment. At the criterion-level, the proportion of FNs and FPs was below 3% for most criteria (13 and 11 of the 14 criteria examined, respectively).

CONCLUSION: Overall, our study presents a notable step towards the integration of AI-driven approaches into real-world clinical practice for patient recruitment in CTs, highlighting both its practicality and effectiveness in meeting the stringent demands of healthcare settings.},
}

@article {pmid42204202,
year = {2026},
author = {Al-Alshaikh, HA},
title = {A federated learning-enabled energy-aware anomaly detection algorithm for secure big data analytics in IoT-based smart healthcare systems.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-53494-4},
pmid = {42204202},
issn = {2045-2322},
support = {IMSIU-DDRSP2603//Imam Mohammad Ibn Saud Islamic University (IMSIU)/ ; },
abstract = {The rapid expansion of Internet of Things (IoT) devices in smart healthcare systems has led to the generation of large volumes of diverse medical data. This creates challenges in ensuring secure, scalable, and energy-efficient anomaly detection. Traditional centralized deep learning methods rely on continuously sending sensitive patient data to cloud servers, which increases communication overhead, consumes more energy, and raises privacy concerns. To overcome these limitations, this paper presents a Federated Learning-enabled Energy-Aware Anomaly Detection framework (FL-EAD) designed for IoT-based smart healthcare environments. The proposed approach allows distributed IoT devices to collaboratively train models while keeping patient data stored locally, sharing only model updates instead of raw data. An energy-aware client selection strategy is incorporated to determine device participation in each federated learning round based on factors such as residual energy and communication cost. This helps reduce unnecessary energy consumption. Furthermore, a hybrid deep learning model combining an autoencoder with an attention-based Long Short-Term Memory (LSTM) network is used to effectively capture both spatial and temporal patterns in healthcare data streams. The proposed framework is evaluated using a publicly available healthcare IoT dataset. Experimental results show that FL-EAD improves anomaly detection performance and overall system efficiency compared to traditional centralized methods and standard federated learning approaches. Notable improvements are observed in accuracy, F1-score, energy usage, communication overhead, and detection latency. Overall, the results suggest that the proposed framework offers a practical and privacy-preserving solution for scalable anomaly detection in next-generation IoT-enabled smart healthcare systems.},
}

@article {pmid42186915,
year = {2026},
author = {Pandey, U and Stumpf, AJ and Lin, YF},
title = {Aquifer Thermal Energy Storage: Groundwater for Efficient Data Center Cooling in the United States.},
journal = {Ground water},
volume = {},
number = {},
pages = {},
doi = {10.1111/gwat.70084},
pmid = {42186915},
issn = {1745-6584},
abstract = {Data centers are energy end users with the fastest growing need for electricity in the United States, mainly because of the rapid expansion of cloud computing and artificial intelligence (AI). A substantial portion of this electricity, between 10% and 40%, is used for cooling. As the number of data centers increases and the sector's energy demand continues to rise exponentially, there is an urgent need to explore the use of alternative energy systems that are more efficient and sustainable. This article explores aquifer thermal energy storage (ATES) as a technically feasible and currently underutilized solution for data center cooling in the United States. Previous case studies from Europe and assessments based in the United States are considered, and the potential of ATES for reducing electricity usage for data centers, which would reduce overall greenhouse gas emissions and support sustainable energy operations.},
}

@article {pmid42187905,
year = {2026},
author = {Chen, X and Wu, W and Liang, G and Tan, H and Yu, Y},
title = {A Post-Quantum Authentication and Key Agreement Protocol Based on Lattice-Based KEM for Secure Network Environments.},
journal = {Entropy (Basel, Switzerland)},
volume = {28},
number = {5},
pages = {},
doi = {10.3390/e28050490},
pmid = {42187905},
issn = {1099-4300},
support = {6022310051K//the Scientific Research Startup Fund for Shenzhen HighCaliber Personnel of SZPT/ ; },
abstract = {In emerging environments such as cloud computing and the Internet of Things (IoT), secure authentication and key negotiation play a crucial role in protecting data transmitted over public networks. However, many existing authentication protocols are still designed based on classical public-key cryptography primitives, and quantum computing may threaten their security. To address this challenge, we propose a post-quantum authentication and key agreement protocol that uses the lattice-based Kyber key encapsulation mechanism (KEM). Our proposed protocol integrates cryptographic authentication, smart card protection, and post-quantum key encapsulation mechanisms, enabling mutual authentication between users and servers and securely establishing session keys. The security of the protocol is formally analyzed in the Real-or-Random (ROR) model under the random oracle assumption and the IND-CCA security of the underlying KEM scheme. Furthermore, through informal security analysis, we have further demonstrated that the protocol possesses important security properties, including anonymity, untraceability, perfect forward confidentiality, and resistance to known attacks. In addition, the computational cost and communication overhead of the proposed scheme are evaluated and compared with several representative authentication protocols. The results show that the proposed protocol can provide strong security while maintaining low computational cost and communication overhead.},
}

@article {pmid42189997,
year = {2026},
author = {Dalle Vaglie, M and Francini, S and Chirici, G and Martellozzo, F},
title = {A large-scale framework for estimating soil carbon, nitrogen, pH, and salinity dynamics for 1985-2023.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {123},
number = {22},
pages = {e2534913123},
doi = {10.1073/pnas.2534913123},
pmid = {42189997},
issn = {1091-6490},
support = {862555//ERA NET Cofound FOSC/ ; 101084481//ERA NET Cofound FORWARDS/ ; 101036849//ERA NET Cofound SUPERB/ ; 2020E52THS//MULTIFORT PRIN 2020/ ; },
abstract = {Soil is fundamental to sustaining life on Earth, providing ecosystem services, regulating climate, and playing a central role in global food systems. In the last decades, due to human activities and climate change, soils worldwide have experienced substantial changes in their key properties, resulting in alterations to their functions. In this context, global soil mapping is crucial for identifying degradation trends and informing effective adaptation strategies. To address these challenges, this work leverages advances in machine learning and cloud computing to develop HUMERIS, a global dataset spanning 1985 to 2023 and covering four key soil properties: salinity (ECe), pH, nitrogen (N), and organic carbon (OC) across natural ice-free land surfaces globally. The goal of HUMERIS is to create a framework able to predict long-term soil dynamics across spatial scales, time, and depth. For topsoil over the reference period, the analysis suggests an increase in N (+0.4% per year) and OC (+0.5% per year), associated with a decrease of ECe (-0.2% per year) and stable values of pH. Looking at biomes and land cover classes two contrasting dynamics emerge. Colder regions show an increase in predicted OC and N compared to warmer ones, while land-use analysis reveals that areas converted from natural to cropland exhibit a relative decrease of -0.2%. These results suggest shifts in global soil properties with implications for agroecological modeling, socioeconomic analysis, and sustainable land management.},
}

@article {pmid42191276,
year = {2026},
author = {Therachiyil, L and Anand, A and Ahmad, A and Korashy, HM and Uddin, S},
title = {Advancements in computational tools in proteomics: Revolutionizing data analysis.},
journal = {International review of cell and molecular biology},
volume = {402},
number = {},
pages = {89-142},
doi = {10.1016/bs.ircmb.2025.11.003},
pmid = {42191276},
issn = {1937-6448},
mesh = {*Proteomics/methods ; Humans ; *Computational Biology/methods ; Data Analytics ; Animals ; *Data Analysis ; },
abstract = {Proteomics generates highly sophisticated and complex data. This necessitates the importance of computational tools and databases that could untangle this complexity and deliver meaningful biological insights. These tools or algorithms that offer curated repositories are critically useful for the identification, characterization and functional analysis of proteins. These repositories provide with essential data such as protein sequences, family nomenclature, domains, enzymatic properties, structures, post-translational modifications (PTMs), interactions, and functional annotations. Despite the fact that significant advancements have been done in proteomics data analysis tools, challenges prevail due to the dynamic nature of proteins, data complexity and data integration issues. To address these issues, developing a comprehensive and robust platform by integrating diverse databases and bioinformatic analytical tools, coupled with scalable algorithms is critical for data integration. This review envisions the common proteomic approaches, data acquisition, data processing, and functional analysis of proteomics data and also emphasize on the importance and advancements of proteomics technologies with the integration of computational methods that inherited proteomics to unravel biologicals processes and disease etiology. The review also notes down the challenges faced by researchers during data analysis and possible ways to prevent this in the future such as expanding cloud computing infrastructure for large-scale data processing as well as management to ensure easy accessibility and computational efficiency. Future advancements in data analysis should focus on enhancing data integration, improving analytical precision, and leveraging cloud computing, Artificial Intelligence, and machine learning for large-scale data processing, that could help in driving improvements in biomedical research and therapeutic development.},
}

*Proteomics/methods
Humans
*Computational Biology/methods
Data Analytics
Animals
*Data Analysis

@article {pmid42191607,
year = {2026},
author = {Li, YL and Zhang, LY and Li, X and Zhang, GZ and Ren, YX and Guo, JC and Bai, B},
title = {[Ecological Environment Monitoring and Driving Factors Analysis of the Greater Bay Area Based on Improved Remote Sensing Ecological Index].},
journal = {Huan jing ke xue= Huanjing kexue},
volume = {47},
number = {5},
pages = {3190-3202},
doi = {10.13227/j.hjkx.202504062},
pmid = {42191607},
issn = {0250-3301},
mesh = {*Environmental Monitoring/methods ; *Remote Sensing Technology/methods ; China ; *Ecosystem ; Bays ; Principal Component Analysis ; *Conservation of Natural Resources ; Ecology ; },
abstract = {The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the most economically vibrant regions in China. Analyzing the temporal-spatial variation patterns and driving mechanisms of its ecological environment quality is of significant importance for implementing the strategy of ecological civilization construction during the process of high-quality economic development. Based on the Google Earth Engine (GEE) cloud platform and MODIS remote sensing data, this study constructs an improved remote sensing ecological index (KRSEI) suitable for high-vegetation areas using principal component analysis (PCA), incorporating greenness (KNDVI), humidity (WET), heat (LST), and dryness (NDBSI). The Sen+Mann-Kendall method, Hurst index, coefficient of variation (CV), and parameter-optimized geographical detector model are employed to analyze the temporal-spatial changes and future trends of ecological environment quality in the GBA from 2000 to 2020 and to explore its influencing mechanisms. The results show that: ① The contribution rate of the model's first principal component (PC1) exceeded 83.91%, which better integrated the characteristics of each indicator compared to the traditional RSEI. ② The average KRSEI values in the study area from 2000 to 2020 were 0.56, 0.49, 0.57, 0.57, and 0.55, respectively, showing an overall fluctuating downward trend. The "good" ecological grade accounted for the largest area (23.31%-40.42%), while the "poor" grade accounted for 8.39%-15.65%. The combined area proportion of "excellent" and "good" regions increased by 6.62%, while that of "poor" and "very poor" regions increased by 4.75%. The regional habitat quality exhibited a spatial pattern of "high in the periphery, low in the center," with ecological degradation expected to dominate future changes. ③ The overall ecological quality of the region showed good stability, but high-intensity development zones such as economic belts and free trade zones exhibited high variability. ④ Optimal parameter geographical detector analysis indicated that elevation was the primary factor in the spatial analysis of ecological environment quality, and the interaction between elevation and land use had the strongest driving force on the spatial differentiation of KRSEI. This study provides scientific references for the sustainable development of the GBA and the improvement of ecological environment monitoring mechanisms, facilitating the coordinated development of the economy and ecological environment in the region.},
}

*Environmental Monitoring/methods
*Remote Sensing Technology/methods
China
*Ecosystem
Bays
Principal Component Analysis
*Conservation of Natural Resources
Ecology

@article {pmid42191608,
year = {2026},
author = {Feng, DM and Xia, J and Zhang, BL},
title = {[Spatial-temporal Evolution and Zoning Management of Ecological Environment Quality in Northeast China Based on MRSEI from a Multi-scale Perspective].},
journal = {Huan jing ke xue= Huanjing kexue},
volume = {47},
number = {5},
pages = {3203-3223},
doi = {10.13227/j.hjkx.202504177},
pmid = {42191608},
issn = {0250-3301},
abstract = {Northeast China is a vital ecological barrier in China, possessing abundant forest resources, mineral resources, and the largest grain production area in the country. However, the region has long been affected by human activities, resulting in a relatively fragile ecological environment. Clarifying the spatial-temporal evolution of the ecological environment quality and implementing zoning management are of great significance for promoting ecological governance and sustainable development in Northeast China. Utilizing the Google Earth Engine (GEE) cloud platform and combining the ecological characteristics of Northeast China, the modified remote sensing ecological index (MRSEI) based on five indicators of comprehensive greenness (mNDVI), humidity (WET), dryness (NDBSI), heat (LST), and air pollution (DI) was constructed. The MRSEI was employed to analyze the spatial-temporal evolution characteristics of the ecological environment quality in Northeast China from 2000 to 2024 from a multi-scale perspective (pixel, urban type, and resource type scales). The Sen+Mann-Kendall trend analysis method and Hurst index were used to investigate the change trends of MRSEI within the study period and in the future. Finally, a zoning management plan for the ecological environment in the study area was proposed based on the coupling of human activity intensity and ecological environment quality. The results show that: ① The assessment results of ecological environment quality by MRSEI and RSEI were consistent to a certain extent. However, MRSEI was more accurate than RSEI in identifying local ecological environment elements, especially in industrial and mining land, construction land, and areas with high vegetation cover, with evaluation results more closely matching the actual surface conditions. ② From 2000 to 2024, the ecological environment quality in Northeast China exhibited a trend of rapid improvement followed by slow degradation. The MRSEI increased by 16.65% from 2000 to 2014 and decreased by 1.75% from 2014 to 2024. Spatially, the study area generally showed a pattern of "high in the east and low in the west." The MRSEI values of growth-oriented and forestry-oriented resource-based cities decreased slowly from 2000 to 2024. Petroleum-oriented resource-based cities had a poor ecological base, with an average MRSEI value lower than that of other types of cities. ③ The change trends of ecological environment quality from 2000 to 2024 were mainly characterized by non-significant improvement, extremely significant improvement, and non-significant degradation. The Hurst index values ranged from 0.06 to 0.95. The area with 0 }

@article {pmid42191762,
year = {2026},
author = {Hosseinlou, F and Ghaffari, A and Mirzaei, A and Kazem, AAPH},
title = {Energy-aware priority-based task scheduling in cloud data centers using bacterial foraging optimization.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-50060-w},
pmid = {42191762},
issn = {2045-2322},
abstract = {Cloud computing is growing exponentially, and data centers consume more and more energy. As a result, developing energy-efficient task scheduling algorithms has emerged as a prominent research problem and challenge. This paper presents a new method for priority-aware task scheduling in cloud data centers using the Hyper-Heuristic Bacterial Foraging Optimization (BFO-HH) algorithm. In addition, it introduces a new approach to dynamically selecting and combining 4 low-level heuristics (Task Selection, Virtual Machine Migration, Load Balancing, Resource Consolidation) to minimize operational cost, reduce energy consumption, and improve Quality of Service (QoS). All experiments were performed using the CloudSim 3.0.3 toolkit, over heterogeneous synthetic workloads comprising between 20 and 200 cloudlets. The performance of BFO-HH is compared with four well-known metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC). The experimental results demonstrate that, under the tested CloudSim environment and for workloads ranging from 20 to 200 tasks, BFO-HH consistently outperforms all comparative algorithms across multiple metrics. For example, for 200 tasks, BFO-HH exhibits 9.9% less energy consumption, 9.3% achieves shorter makespan, reduces Service Level Agreement (SLA) violations by 28%, increases resource utilization by 7%, and reduces operational cost by 14%. against the state-of-the-art base algorithms. Albeit such improvements are statistically significant, as evidenced by standard deviations and a 95% confidence interval.},
}

@article {pmid42192190,
year = {2026},
author = {V, A and S, KSR},
title = {An intelligent cloud firewall framework for multi-cloud security using lstm anomaly detection and federated learning.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-53470-y},
pmid = {42192190},
issn = {2045-2322},
abstract = {The evolving nature of the threat landscape against cloud services is outpacing the capabilities of traditional security measures. Current firewall implementations in cloud services may provide a foundational layer of security, but they have significant limitations regarding their ability to respond to recently identify zero day vulnerabilities and to protect sensitive information from potential attacks utilizing quantum computing, as well as validating audit logs in multi-tenanted, shared cloud service landscapes. In this research, we present an innovative integrated approach to addressing each of these limitations by combining AI driven Anomaly detection techniques with post quantum cryptography authentication, a Zero Trust Architecture (ZTA), and blockchain based audit logging. Our proposed AI enhanced cloud firewall uses a Long Short Term Memory (LSTM) deep learning model to analyze and classify traffic patterns across IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) environments and dynamically creates adaptive firewall policies with sub-second response times. Experimental testing on simulated cloud traffic sets demonstrated that our proposed framework achieved a detection rate of 94.7% and a False Positive Rate (FPR) of 2.1%, representing improvements of 24.7% and 12.9%, respectively, when compared to traditional rule-based firewalls. Additionally, the blockchain anchored audit logging mechanism will create tamper proof audit logs, and the post-quantum cryptography layer will prevent attacks using the CRYSTALS-Kyber and CRYSTALS-Dilithium algorithms. These test results demonstrate that our proposed framework is a scalable, resilient, and security hardened solution for future generations of cloud computing landscapes.},
}

@article {pmid42179771,
year = {2026},
author = {Kurien, RA and Kannan, G and Thanawut, K and Suttiruengwong, S and Sriamornsak, P},
title = {Building the next frontier: Artificial intelligence in 3D-printed medicines.},
journal = {Biomaterials translational},
volume = {7},
number = {1},
pages = {55-78},
pmid = {42179771},
issn = {2096-112X},
abstract = {Artificial intelligence (AI) and 3D printing are transforming pharmaceutical manufacturing by enabling the production of personalized medications. AI supports real-time decision-making in diagnostics and robotics, although its application in pharmaceutical research remains at an early stage. 3D printing, particularly additive manufacturing, provides precise control over drug formulation, allowing the design of patient-specific dosage forms with tailored release profiles. Machine learning and deep neural networks are used to predict formulation parameters, optimize processing conditions, and support the design of innovative drug delivery geometries. Technological platforms such as cloud computing and blockchain enhance data security, transparency, and scalability. Printable materials-including thermoplastic polymers, hydrogels, and bioinks-demonstrate utility in AI-assisted manufacturing systems. The integration of AI, smart materials, and 3D printing advances intelligent drug production technologies aligned with Industry 4.0 principles. Key considerations include regulatory compliance, data reliability, ethical implications, and pathways for clinical translation. Clinical medicine is rapidly advancing through the adoption of 3D printing and AI, enabling personalized prosthetics, accurate surgical planning, and bioprinted tissues. AI-driven segmentation and optimization enhance the accuracy and efficiency of 3D-printed anatomical models for pre-operative preparations and medical training. Cardiology, oncology, and orthopedics are increasingly adopting these technologies to improve patient outcomes and clinical workflows. Future directions include broader adoption across specialties, bioprinting for regenerative health care, and AI-optimized systems for targeted drug delivery. This review addresses the current challenges and limitations of AI and 3D-printed medicines, pharmaceutical manufacturing, case studies, ethical considerations, and future perspectives.},
}

@article {pmid42178330,
year = {2026},
author = {Al Nuaim, A and Qayyum, J},
title = {A hierarchical neuromorphic multi agent framework for energy aware and secure 6G resource optimization using Neuro6G agent.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-54349-8},
pmid = {42178330},
issn = {2045-2322},
support = {Grant No. KFUxxxx//Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University/ ; },
abstract = {The convergence of Sixth-Generation (6G) wireless networks and neuromorphic computing presents significant opportunities for intelligent, energy-efficient resource management in distributed architectures. This paper introduces Neuro6G-Agent, a hierarchical neuromorphic agentic intelligence framework that integrates Energy-Aware Spiking Neural Networks (EA-SNNs) with multi-agent reinforcement learning to enable energy-conscious cognitive collaboration across cloud-edge-end 6G deployments. The framework addresses three principal challenges in distributed 6G resource management: energy sustainability, end-to-end latency under ultra-dense connectivity, and security resilience against adversarial threats. A three-tier architecture is employed, comprising cloud orchestrators, edge coordinators, and end devices, each operating dedicated neuromorphic agents with autonomous decision-making and trust-aware collaborative learning capabilities. The framework incorporates adaptive threshold EA-SNNs for event-driven processing, a distributed trust computation mechanism for secure multi-agent cooperation, and a hierarchical resource optimization algorithm responsive to dynamic workload conditions. Experimental evaluation across three public benchmark datasets-DeepMIMO (6G channel modeling), DVS128 Gesture (neuromorphic sensing), and CICIDS-2017 (network intrusion detection) demonstrates a 34.7% reduction in energy consumption, a 28.3% decrease in end-to-end latency, and a 95.6% security threat detection accuracy compared to state-of-the-art baseline methods, validated across ten independent experimental runs (p < 0.01). These results confirm the viability of neuromorphic intelligence for addressing complex optimization challenges in next-generation wireless architectures.},
}

@article {pmid42178334,
year = {2026},
author = {Ghaban, W and Alatawi, HS},
title = {Federated multi-cloud task scheduling with load balancing using multi-objective NSGA-II and reinforcement learning.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-51105-w},
pmid = {42178334},
issn = {2045-2322},
abstract = {Task scheduling in federated multi-cloud environments is challenging owing to heterogeneous service-level agreements, decentralized resource control, and dynamic workload characteristics. The existing hybrid optimization approaches lack real-time adaptability across cloud providers also assumes centralized coordination. To work with these issues, this paper proposes Multi-Objective Non-Dominated Sorting Genetic Algorithm with Q-Learning (MO-NSGAQ). This is a hybrid multi-objective scheduling framework that tightly integrates Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Q-learning within a federated broker architecture. This proposed framework simultaneously optimizes execution cost, makespan, load imbalance, and resource utilization while adapting to inter-cloud heterogeneity. Extensive simulations are done using synthetic workloads, such as Google Cloud job traces, and IoT-based workloads. These simulations demonstrate that MO-NSGAQ reduces makespan by up to 18-32%, improves resource utilization by 10-22%, and achieves better load balance compared to existing baselines. The results confirm the proposed framework effectiveness for adaptive and scalable federated cloud scheduling.},
}

@article {pmid42173932,
year = {2026},
author = {Alshari, HA and Almogahed, EM and Al-Mekhlafi, MM},
title = {Impact of adopting digital FinTech techniques on banks' cyber and systemic risks.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-53260-6},
pmid = {42173932},
issn = {2045-2322},
abstract = {The rapid advancement of financial technology (FinTech) mandates a thorough investigation into its adverse effects, focusing on cyber risks and systemic vulnerabilities. This study aims to examine the impact of digital FinTech techniques, specifically artificial intelligence, big data, cloud computing, and blockchain technology, on cyber and systemic risks in Yemeni banks. Data for the study was collected through a questionnaire distributed to 332 respondents at the managerial level. The model's constructs were validated using structural equation modeling with Partial Least Squares (PLS). The findings indicate that implementing digital FinTech techniques in banks significantly influences cyber risks. Among these techniques, blockchain technology has the most substantial impact on cyber risks, followed by big data technology. However, only blockchain technology affects systemic risks. This research is vital for professionals, individuals interested in FinTech development, and decision-makers who aim to manage the cyber and systemic risks associated with FinTech innovations.},
}

@article {pmid42175071,
year = {2026},
author = {Okada, K and Konishi, S and Wada, S and Sugimoto, K and Yutani, A and Kishimoto, K and Iwao, T and Terao, S and Nomura, K and Aoyagi, Y and Kuroda, T and Takeda, T},
title = {Development and Pilot Evaluation of the Full-Cloud Personal Health Train for Secure Federated Analysis Across Clinical Research Core Hospitals in Japan.},
journal = {Studies in health technology and informatics},
volume = {336},
number = {},
pages = {1247-1251},
doi = {10.3233/SHTI260398},
pmid = {42175071},
issn = {1879-8365},
mesh = {Japan ; Pilot Projects ; Humans ; *Electronic Health Records/organization & administration ; *Computer Security ; *Cloud Computing ; *Biomedical Research ; },
abstract = {Real-world data derived from electronic medical records are increasingly used in medical research, but data sharing among institutions still faces privacy and operational challenges. To address these challenges, we developed the Full-Cloud Personal Health Train (FC-PHT), a cloud-based federated analysis system that allows secure, end-to-end data extraction and analysis without transferring raw data. The FC-PHT architecture, implemented using Google Cloud services, consists of three major environments: Analysis Environment, Handler Environment, and Data Provider Environment, which work seamlessly to perform federated analysis. As a pilot evaluation, the FC-PHT was implemented across three Clinical Research Core Hospitals in Japan, and a preliminary analysis of heart failure biomarker related to angiotensin receptor-neprilysin inhibitor (ARNI) therapy was conducted using real-world data databases in these hospitals to evaluate the system's functionality and feasibility. In the ARNI-related biomarker analysis, BNP showed no notable change after therapy, whereas NT-proBNP tended to decrease, consistent with expected pharmacological effects. The pilot evaluation demonstrated the feasibility of performing cloud-based federated analysis across institutions using the FC-PHT without physical data transfer. These findings suggest that the FC-PHT can securely and efficiently enable multi-institutional real-world data analysis while maintaining privacy and reducing operational burdens on researchers and data management staff.},
}

Japan
Pilot Projects
Humans
*Electronic Health Records/organization & administration
*Computer Security
*Cloud Computing
*Biomedical Research

@article {pmid42175263,
year = {2026},
author = {Shi, Y and Yang, E and Xie, Z and Gong, Y},
title = {An Automated Synchronization Strategy for MAUDE Literature Dashboard.},
journal = {Studies in health technology and informatics},
volume = {336},
number = {},
pages = {1964-1968},
doi = {10.3233/SHTI260594},
pmid = {42175263},
issn = {1879-8365},
mesh = {*Patient Safety ; *User-Computer Interface ; Humans ; *Cloud Computing ; *Information Storage and Retrieval/methods ; *Equipment and Supplies ; },
abstract = {The Manufacturer and User Facility Device Experience (MAUDE) has become an important resource for investigating device-related patient safety events (PSEs). Guided by the FAIR principles, a dashboard has been developed to retrieve MAUDE literature from PubMed, thereby helping researchers stay up to date. Manual updates for the dashboard are labor-intensive and error-prone, leading to inaccuracies and potentially compromising platform reliability. In response, we proposed a low-cost, cloud-based automated synchronization strategy for scheduled MAUDE dashboard updates using Google Cloud Platform (GCP) services. Such an automated synchronization strategy fundamentally enhances the existing pipeline, bolsters the reliability and sustainability of the dashboard, and improves the visibility of MAUDE studies.},
}

*Patient Safety
*User-Computer Interface
Humans
*Cloud Computing
*Information Storage and Retrieval/methods
*Equipment and Supplies

@article {pmid42162030,
year = {2026},
author = {Karthika, RN and Valliyammai, C and UmaRani, V and Karthi, G},
title = {A blockchain-enabled scalable and secure architecture for cloud big data storage using sharding and swarming.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-51254-y},
pmid = {42162030},
issn = {2045-2322},
abstract = {Cloud computing enables on-demand access to centralized resources, including applications, networks, and storage, while big data and blockchain technology drive innovation in data management. The rapid growth of data stored in the cloud poses significant challenges, such as ensuring scalability, real-time analytics, and compliance with regulatory frameworks. Big data systems often struggle with elasticity, as many current solutions fail to provide rapid scalability under fluctuating workloads. To address these challenges, blockchain technology integrates big data storage in the cloud, creating a secure and efficient framework. Blockchain offers cryptographically secure, decentralized data management that enhances the reliability and value of big data for analysis. The novel architecture integrates sharding and swarming techniques to overcome the scalability limitations of blockchain-based big data systems and sharding partitions data into smaller fragments stored and accessed using unique partition keys, improving manageability and reducing system load. Swarming complements this approach by enabling parallel data retrieval from the fastest and nearest nodes, minimizing latency and enhancing access speed. An optimized Proof of Work (PoW) mechanism further ensures computational fairness and reduces energy consumption, addressing critical efficiency concerns. This integration of blockchain with cloud-based big data storage provides a transformative solution for secure, scalable, and high-performance data management, advancing decentralized applications and analytics capabilities.},
}

@article {pmid42163451,
year = {2026},
author = {Afzal, Y and Nawaz, N and Khan, AA and Yousaf, MJ and Khan, MZ and Arif, E and Salahuddin, M and Mohamed, MA and Ullah, S},
title = {Sustainable Control of Carbon Emissions and Energy Consumption Through a Green Data Center Approach.},
journal = {TheScientificWorldJournal},
volume = {2026},
number = {1},
pages = {e1158756},
pmid = {42163451},
issn = {1537-744X},
abstract = {Data center management, the foundation of contemporary cloud computing, has made energy saving a top priority. Among other difficulties, the placement of virtual machines (VMs) has a major impact on data center resource and energy usage. Assigning VMs to physical machines (PMs) is a challenging NP-hard problem, especially in large-scale infrastructures where it is computationally infeasible to find an ideal solution. To solve the VM placement problem, the proposed study formulates it as a restricted optimization problem with the goal of preserving performance while lowering energy consumption. The explosive growth of data centers has resulted in higher energy consumption and higher carbon dioxide (CO2) emissions, which are a primary cause of climate change. Globally, governments, energy-focused organizations, and business executives have taken notice of this expanding environmental impact. This study provides a comprehensive analysis of data center energy consumption patterns, environmental effects, and trends in energy consumption. It also suggests doable energy-saving measures, such as installing energy-efficient infrastructure and upgrading air conditioning systems. The paper also presents an improved genetic algorithm-based method that is tailored for energy-conscious VM deployment, successfully striking a balance between computing economy and convergence accuracy. The suggested solution highly increased data centers' energy efficiency by incorporating this strategy within a profile-based virtual resource management model. Additionally, policy suggestions for sustainable data center management are delineated, advancing the more general objective of ecologically conscious cloud computing. Experimental results demonstrate that the proposed method achieves up to 50% reduction in execution time, 48% fewer generations for convergence, and approximately 7% reduction in energy consumption compared to traditional first fit decreasing (FFD) methods. Additionally, the integration of task classification improves energy efficiency by up to 15% and reduces the number of active PMs. These findings highlight the effectiveness of the proposed framework in enabling scalable, energy-efficient, and environmentally sustainable cloud data center management.},
}

@article {pmid42167734,
year = {2026},
author = {Kim, YW and Kook, JH and Choi, Y and Park, W},
title = {Voice Recognition for Periodontal Probing Medical Records under Korean-English Bilingual Conditions: A Feasibility Study.},
journal = {Healthcare informatics research},
volume = {32},
number = {2},
pages = {118-124},
doi = {10.4258/hir.2026.32.2.118},
pmid = {42167734},
issn = {2093-3681},
support = {//Yonsei University College of Dentistry/ ; },
abstract = {OBJECTIVES: This study evaluated the feasibility of voice recognition-based electronic medical record (EMR) documentation for periodontal probing in dentistry, particularly emphasizing Korean-English bilingual speech patterns and real-world clinical conditions.

METHODS: Experiments were conducted in a dental chair setting during routine clinical hours. Environmental noise levels were measured, and two microphone types (stationary and pin-type) were evaluated. Periodontal probing phrases composed of three-digit numbers and positional terms were used for speech recognition. Consistent with common clinical practice in Korea, numerical values were spoken in Korean, whereas positional terms were spoken in English. Two speech-to-text application programming interfaces, Google Cloud Speech-to-Text and Naver Clova Speech Recognition, were assessed. Recognition accuracy was evaluated for both numerical components and complete bilingual phrases.

RESULTS: The mean environmental noise level was 60.65 dB and was minimally influenced by activity at adjacent dental chairs. The stationary microphone failed to capture speech effectively, whereas the pin-type microphone demonstrated stable recognition performance. For three-digit number recognition, accuracy was 88.3% with Google and 96.8% with Naver. For full-phrase recognition, complete matching was achieved in 36.7% of cases for Google and 52.5% for Naver. Partial recognition occurred more frequently for numerical components than for English positional terms.

CONCLUSIONS: Voice recognition-based EMR documentation for periodontal probing demonstrated preliminary feasibility in a dental clinical environment; however, performance was influenced by Korean-English bilingual speech patterns. These findings suggest that bilingual speech characteristics should be considered when implementing voice recognition systems in dental EMR workflows. Further optimization is required before routine clinical application.},
}

@article {pmid42167737,
year = {2026},
author = {Yu, H and Lee, K and Lee, S and Kim, Y},
title = {Transferable Migration Framework Derived from a Large-scale Tertiary Hospital EHR System.},
journal = {Healthcare informatics research},
volume = {32},
number = {2},
pages = {145-155},
doi = {10.4258/hir.2026.32.2.145},
pmid = {42167737},
issn = {2093-3681},
abstract = {OBJECTIVES: Migrating legacy on-premise electronic health record (EHR) systems in tertiary hospitals to modern cloud-native platforms presents technical and strategic challenges. We aimed to establish an optimized roadmap for transitioning legacy monolithic systems to a microservice architecture-based cloud-native EHR (MCEHR).

METHODS: We conducted a 3-month strategic assessment and case study based on the modernization requirements of a global healthcare provider. The methodology incorporated semi-structured interviews with key stakeholders, including clinical informatics officers and system architects, to identify critical pain points such as .NET 4.0 end-of-support risks and performance bottlenecks during peak clinical hours. Phased hybrid migration was adopted, analyzing over 20 TB of Oracle-based legacy data and evaluating the technical feasibility of transitioning to .NET 9 and RESTful APIs. To ensure clinical safety, a proof-of-concept (PoC) environment was developed to simulate high-concurrency clinical workloads, emphasizing system resilience and transaction integrity during intensive order-entry periods.

RESULTS: The transition to .NET 9 and MCEHR demonstrated 100% transaction integrity across 1,012 complex clinical test cases. Frontend and backend modernization showed high feasibility; however, migration of business logic embedded within legacy Oracle views represented a primary technical bottleneck, necessitating targeted decoupling. The PoC confirmed that RESTful API-based services maintained stable throughput under heavy concurrent loads, significantly reducing the risk of system-induced delays in clinical workflows.

CONCLUSIONS: Transitioning to an MCEHR architecture is complex but strategically essential. The proposed task force team roadmap outlines staged upgrades incorporating core technology modernization (.NET 9 and RESTful APIs), selective business component migration, and parallel DevOps adoption.},
}

@article {pmid42168178,
year = {2026},
author = {Wei, Z and Peng, Z and Wan, YH and Wang, Z and Han, Z and Zhao, X and Shi, E and Li, YJ and Chu, W and Zhang, J and Qian, L and Zhang, J and Sun, QF and Sun, L},
title = {Observation of Kondo cloud-coupling in a mirror-symmetric carbon nanotube array-molybdenum structure.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-73493-3},
pmid = {42168178},
issn = {2041-1723},
abstract = {Magnetic impurities in a non-magnetic metal are screened by conduction electrons, forming a spin cloud known as a Kondo cloud. Experimental detection of this Kondo screening cloud in dilute magnetic alloys remains challenging. Here we show evidence of Kondo clouds and their coupling in devices composed of mirror-symmetric carbon nanotube arrays separated by a molybdenum strip. The Kondo effect in these devices depends sensitively on the width of the molybdenum strip. For widths of 0.3-1.2 micrometers and 1.5-3.0 micrometers, the Kondo temperature forms two plateaus at about 107.8 kelvin and 15.8 kelvin, respectively. These results are explained by the formation of Kondo clouds and by intra-edge and inter-edge coupling within molybdenum. Calculations based on a two-spin model reproduce the two plateaus and their crossover. These findings may open opportunities for applications in spintronics and quantum computing.},
}

@article {pmid42154737,
year = {2026},
author = {Ban, H and Zhou, W and Peng, Y},
title = {Towards Edge Holography via Implicit Neural Representation and Compression.},
journal = {IEEE transactions on visualization and computer graphics},
volume = {32},
number = {5},
pages = {3325-3334},
doi = {10.1109/TVCG.2026.3679061},
pmid = {42154737},
issn = {1941-0506},
abstract = {Holographic displays offer the promise of realistic 3D visualization for virtual and augmented wearable solutions. Nevertheless, existing computer-generated holography (CGH) methods often struggle with either a high computational burden or limited display realism. While the emerging cloud-edge computing mechanism can enable the real-time streaming of holograms, classic image compression techniques struggle to efficiently encode and decode the substantial high-frequency information inherent in hologram data. In light of these challenges, we present a display-aware and lightweight CGH framework, leveraging implicit neural representations (INRs) and camera-calibrated wave propagation, to generate and compress high-fidelity phase-only holograms. Specifically, our approach interprets hologram generation as a continuous function approximation problem, enabling the network, with reduced parameters, to effectively learn the inherent periodicity and high-frequency components of 2D and 3D hologram data. To enable efficient deployment, we further incorporate quantization-aware training, followed by entropy coding. Experimental results evaluated on an unfiltered holographic display prototype demonstrate that the proposed INR-CGH retains image quality comparable to that of existing optimization-based methods in both 2D and 3D scenarios. In addition, our compact INR representation achieves up to 11× compression rate with minimal quality degradation and can be further reduced via quantization-aware training. The resulting model enables ≥250 fps in decoding speed, paving the way towards edge holography.},
}

@article {pmid42147039,
year = {2026},
author = {López, RSI and Oscata, RG and Condori-Coaquira, ÁR and Medina, RIG and López-Gonzales, JL and Tocto-Cano, E},
title = {AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security.},
journal = {Frontiers in artificial intelligence},
volume = {9},
number = {},
pages = {1750992},
pmid = {42147039},
issn = {2624-8212},
abstract = {Cloud computing environments face persistent structural challenges in cost control, dynamic resource allocation, and security risk management, which traditional infrastructure approaches fail to address adequately. This systematic literature review aimed to synthesize empirical evidence on the application of artificial intelligence (AI) and machine learning (ML) models for cost optimisation, resource management, and security enhancement in cloud computing environments. Following the PRISMA 2020 guidelines and the Kitchenham-Charters methodology, a structured search was conducted across IEEE Xplore, Web of Science, ScienceDirect, and the ACM Digital Library, covering the period 2020-2025. From an initial pool of 216 records, 18 primary studies were selected after applying the PICOC framework, predefined inclusion and exclusion criteria, and a dual-reviewer quality assessment process yielding substantial inter-rater agreement (Cohen's κ = 0.86). The synthesized evidence demonstrates that predictive provisioning systems and intelligent load-balancing mechanisms reduce operational costs by up to 85%, metaheuristic algorithms such as the Whale Optimization Algorithm and Particle Swarm Optimization improve energy efficiency by 30%-40% and increase resource utilization by up to 80%, and deep learning-based intrusion detection systems achieve accuracy levels exceeding 92%. These findings confirm that AI constitutes a structural mechanism for strengthening economic efficiency, operational resilience, and the sustainability of cloud infrastructures. However, heterogeneity in simulation environments, limited validation in production-scale deployments, and insufficient coverage of virtual machine migration dynamics represent critical gaps requiring standardized benchmarking frameworks and empirical validation in hybrid and multicloud architectures. A quantitative synthesis (Table 1) reveals that metaheuristic algorithms achieve 30%-40% cost and energy efficiency improvements, while ensemble deep learning approaches attain >97% security threat detection rates.},
}

@article {pmid42149770,
year = {2026},
author = {Liu, S and Zhou, Y and Zhang, J and Zhou, S and Cui, W and Lin, Q and Moscibroda, T and Zhang, H and Weng, D and Wu, Y},
title = {RCInvestigator: towards Better Investigation of Anomaly Root Causes in Cloud Computing Systems.},
journal = {IEEE transactions on visualization and computer graphics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TVCG.2026.3694461},
pmid = {42149770},
issn = {1941-0506},
abstract = {Root cause analysis (RCA) is critical for maintaining the availability and efficiency of cloud computing systems. However, identifying root causes from the large-scale, high-dimensional monitoring data generated by these complex environments is a significant challenge. Current approaches often rely on time-consuming manual analysis to ensure flexibility and reliability, while recent automated methods lack the crucial insights provided by domain experts. To bridge this gap, we propose RCInvestigator, a visual analytics system that facilitates interactive root cause investigation by establishing a tight collaboration between human experts and machine analysis. Our approach addresses three key challenges: a) modeling databases for the root cause investigation, b) inferring root causes from large-scale time series, and c) building comprehensible investigation results. We demonstrate the effectiveness and utility of RCInvestigator through two real-world case studies, which received positive feedback from domain experts.},
}

@article {pmid42140997,
year = {2026},
author = {Jefflin Deno, JS and Karthi Sree, S and Maheswari, S and Sasikumar, P},
title = {Smart mobility infrastructure: improving campus parking efficiency in real time.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {42140997},
issn = {2045-2322},
abstract = {The issue of parking management in university campuses has continued to face challenges owing to space constraints and the absence of real-time information. This problem is addressed by the proposed solution in this study, which is robust and intelligent and specifically designed for university campuses. This paper advances real-time campus parking through three innovations based on established You Only Look Once, Version 8 (YOLOv8) and Raspberry Pi tools: (1) campus-specific YOLOv8n fine-tuning (94.2% mean Average Precision (mAP), 450ms on Pi 4B); (2) adaptive Message Queuing Telemetry Transport (MQTT). Quality of Service (QoS) reducing 20% packet loss; and (3) S3-integrated forecasting producing 45% simulated efficiency gains. A user-friendly, web-based dashboard offers live parking updates to students, faculty, and visitors. This allows users to check space availability before arriving and reduces search time. The modular architecture supports decentralized deployment, with each Raspberry Pi independently managing a designated parking zone. The design is inherently scalable, enabling additional sensors and cameras to be added as needed to cover larger or more complex parking areas. To ensure privacy and reduce bandwidth use, live video and image access are restricted to management, maintaining data security and network efficiency. By combining edge computing, sensor fusion, and cloud services, the proposed solution enhances automation, improves user experience, and advances smart campus initiatives. This framework provides a scalable, adaptable model for modernizing parking infrastructure in educational institutions and beyond.},
}

@article {pmid42136599,
year = {2026},
author = {Matimba, A and Garson, KL and Trovão, NS and George, S and Li, YT and Evans, D and Batista da Rocha, J and Otieno, J and Mokaya, J and Calvert-Joshua, T and Blankenship, HM and Mulder, N},
title = {Mapping pathogen genomics training provision: a structured analysis within a global consortium network.},
journal = {Frontiers in public health},
volume = {14},
number = {},
pages = {1768827},
pmid = {42136599},
issn = {2296-2565},
mesh = {*Genomics/education ; Humans ; Surveys and Questionnaires ; Curriculum ; *Public Health/education ; Global Health ; },
abstract = {BACKGROUND: Pathogen genomics plays a central role in infectious disease surveillance and outbreak response. However, information about available training initiatives remains fragmented, limiting visibility into how programmes are structured, delivered, and assessed.

METHODS: We conducted a structured survey to characterise pathogen genomics training initiatives identified through the PHA4GE Training and Workforce Development Working Group and affiliated professional networks.

RESULTS: Eighty-one courses were analysed representing pathogen genomics training initiatives from 17 countries. Over half (52%) targeted academic or research audiences and 46% targeted public health professionals. Majority of courses were delivered as short, limited-duration standalone courses. Beginner-level courses accounted for 58% of offerings, whereas only 6% were classified as advanced. Bioinformatics or genomic data analysis was widely represented (72%), while specialised areas such as biostatistics and systems administration were less frequently included. Nearly half (48%) of courses focused on broadly applicable genomic methods rather than being restricted to a single pathogen. Among courses centred on specific organisms, viral pathogen themes were most commonly represented. Over one-third of courses (38%) did not include structured assessments, with only 7% incorporating quizzes or exams. Most courses relied on local computing resources such as laptops or desktops during delivery (93%). Use of high-performance computing (HPC) and cloud platforms was limited during training but was higher after training, with 37% and 39% of courses indicating use, respectively.

CONCLUSION: This landscape analysis identifies structural patterns, including geographic concentration of providers, predominance of introductory formats, variability in assessment practices and in the use of advanced computing infrastructure across training phases. The findings provide empirical insight into characteristics of pathogen genomics training that may inform efforts to strengthen coordinated and sustainable workforce development strategies.},
}

*Genomics/education
Humans
Surveys and Questionnaires
Curriculum
*Public Health/education
Global Health

@article {pmid42137895,
year = {2026},
author = {Guo, C and He, Z and Niu, M and Liu, K},
title = {Navigating AI deployment in precision livestock farming: current trends and future prospects.},
journal = {Animal frontiers : the review magazine of animal agriculture},
volume = {16},
number = {2},
pages = {14-25},
pmid = {42137895},
issn = {2160-6064},
}

@article {pmid42122857,
year = {2026},
author = {Yang, L and Zhao, Y and Jin, C and Wu, S and Lu, Z and Hao, M and Bi, C and Xu, K},
title = {Accurate Identification of Ilex (Aquifoliaceae) Taxa Based on Leaf Morphology Using Deep Learning.},
journal = {Plants (Basel, Switzerland)},
volume = {15},
number = {9},
pages = {},
doi = {10.3390/plants15091365},
pmid = {42122857},
issn = {2223-7747},
abstract = {Holly (Ilex L.) is a genus of woody dioecious plants with substantial ecological and economic value. However, its high species diversity and morphological similarity make accurate identification challenging. To address this, we constructed a multi-taxon Ilex leaf image dataset. We then trained six deep learning models-GoogLeNet, ResNet50, ResNet101, DenseNet121, DenseNet169, and EfficientNet-B3-using a unified PyTorch framework on cloud computing resources. Leaf images were preprocessed by background removal, resizing, cropping, and normalization. Model performance was evaluated using accuracy, F1-score, and Grad-CAM visualizations. Under an image-level data split that may overestimate generalization, all six models achieved over 99% classification accuracy on preprocessed leaf images under controlled laboratory conditions. DenseNet121 and DenseNet169 performed best, reaching 99.65% accuracy. Because images of the same leaf or same plant could appear in both training and test sets under this split, plant-level cross-validation is required to assess real-world generalizability. The reported accuracies represent an upper-bound estimate under image-level splitting. The framework offers a rapid and accurate tool for preliminary screening under controlled conditions, but its performance on raw field photographs and across different collection sites remains to be validated.},
}

@article {pmid42129248,
year = {2026},
author = {Murala, DK and Batta, KB and Madhura, K and Vuyyuru, VA and Romeo, B},
title = {A fuzzy logic and blockchain-enhanced framework for secure, explainable eHealth in Society 5.0.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-52222-2},
pmid = {42129248},
issn = {2045-2322},
abstract = {The constraints of current intelligent healthcare systems are extensively addressed in this study, which offers a thorough framework for expanding eHealth within the human-centered paradigm of Society 5.0. Reliance on centralised cloud systems, which have delay for real-time IoMT data, single points of failure, and serious data privacy/security threats, are some of the disadvantages of current approaches. Traditional healthcare AI models are often black-boxes, lacking explainability and transparency (XAI), which are necessary for patient acceptance and physician trust. To overcome these issues, the Proposed Approach uses a novel combination of specialised technologies and a multi-tiered architecture of cloud services, edge computing, and IoMT. The Health Prediction using Cloud Edge 2.0 (HPCE 2.0) algorithm employs fuzzy logic to combine static Electronic Health Records (EHRs) with dynamic real-time IoMT data to handle medical input uncertainty and imprecision. This algorithm predicts health severity accurately and individually. Proof of Authentication 2.0 (PoAh 2.0) consensus ensures data integrity and non-repudiation in the blockchain-enhanced architecture's immutable, decentralised ledger. By adding XAI (LIME/SHAP) to give local and alternative answers, the system stops being a black box and becomes an open collaborator. Edge-cloud integration improves performance by lowering delay for important real-time alerts. Security tests show that the PoAh 2.0 system works well and can be expanded by quickly building and verifying blocks. This makes sure that strict privacy rules are followed while keeping the good predictive performance of a cardiac arrest prediction case study. This platform sets a new standard for interpretable, safe, and responsive AI-driven healthcare.},
}

@article {pmid42120489,
year = {2026},
author = {Bouabidi, HE and Ghmary, ME and Hebabaze, SE and Amnai, M},
title = {Machine learning-driven adaptive parameter selection for homomorphic encryption in edge computing.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-52587-4},
pmid = {42120489},
issn = {2045-2322},
abstract = {The rapid proliferation of Internet of Things (IoT) devices and edge computing infrastructures has intensified concerns regarding data security and privacy, particularly when sensitive information is processed beyond centralized cloud environments. Homomorphic encryption (HE) offers a compelling solution by enabling computations directly on encrypted data; however, its substantial computational overhead continues to limit deployment in latency-sensitive edge applications. This paper presents a machine learning-driven framework designed to enhance the practicality of homomorphic encryption for intelligent edge environments. Our approach integrates gradient boosting optimization with the Cheon-Kim-Kim-Song (CKKS) encryption scheme to dynamically adjust cryptographic parameters in response to varying workload characteristics. We develop rigorous mathematical foundations, including formal optimization models, complexity analysis, and security proofs grounded in the Ring Learning With Errors (RLWE) assumption. We validate our framework through comprehensive experiments on the Heart Failure Prediction dataset from the UCI Machine Learning Repository, employing an expanded parameter space spanning 20 configurations across polynomial degrees from 4096 to 16,384, with security levels validated using the Albrecht-Player-Scott lattice-estimator methodology. The empirical results demonstrate substantial improvements over a conservative static baseline: latency reduction of 53.09%, throughput enhancement of 285.28%, and energy savings of 72.53%, all while maintaining computational accuracy above 0.999 and cryptographic security guarantees of at least 128 bits. Statistical analysis confirms the significance of these improvements with a p-value of [Formula: see text] and a large effect size (Cohen's [Formula: see text]). Five-fold cross-validation confirms model generalisability ([Formula: see text]). Furthermore, we provide interpretability analysis through SHAP (SHapley Additive exPlanations) values to elucidate the ML optimizer's decision-making process, addressing concerns about transparency in automated cryptographic systems. Crucially, unlike simple heuristic baselines that may sacrifice computational accuracy for speed, our ML optimizer enforces accuracy as a hard constraint, ensuring reliable results in safety-critical applications. This work offers both theoretical rigor and empirical validation, demonstrating that machine learning-enhanced homomorphic encryption can serve as an effective and secure solution for edge computing applications in healthcare, industrial IoT, and smart city domains.},
}

@article {pmid42120623,
year = {2026},
author = {Yellanki, VS and Sah, B},
title = {TaWSA-WRN: a Taylor wave search optimized WideResNet framework for intrusion detection with response-aware mitigation in cloud computing.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-52050-4},
pmid = {42120623},
issn = {2045-2322},
abstract = {Targeting cloud computing environments has become increasingly attractive to sophisticated cyberattackers due to their open, scalable, and distributed nature. Intrusion Detection Systems (IDSs) analyse traffic patterns to detect these attacks; deep learning has become a common approach for building such systems, but many suffer from overfitting, high false-positive rates, and/or unstable training behaviour. To overcome these drawbacks, this paper introduces a Taylor Wave Search Algorithm-optimised Wide Residual Network (TaWSA_WRN) framework for intrusion detection and mitigation in cloud environments, which protects against incoming attacks. The network traffic data accessed from benchmark datasets, such as NSL-KDD and CICIDS2017, are subsequently preprocessed by restoring missing values and applying Min-Max normalisation. Finally, the TaWSA_WRN model performs feature selection and intrusion classification, using the Taylor Wave Search Algorithm to enhance parameter optimisation and improve learning stability by optimising a hyperparameter. This model-agnostic interpretability approach, using SHAP, also offers valuable insights into domain-relevant traffic features. The empirical results show that the proposed approach can achieve maximum TNR, accuracy, and TPR of 96.857%, 97.190%, and 97.589%, respectively. We introduce the mitigation component as a response-guided strategy based on detections rather than a single network-level defence. The proposed framework enables greater reliability, interpretability, and robustness, thereby facilitating detection in a secure cloud computing environment. Code implementation is available at https://github.com/vedasahithi/wideResNet-for-intrusion-detection-.},
}

@article {pmid42120936,
year = {2026},
author = {Ali, FA and Mali, S and Mahakud, R and Yadav, G},
title = {AI-enabled smart surveillance system for secure monitoring and authentication.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-52387-w},
pmid = {42120936},
issn = {2045-2322},
abstract = {The proposed Smart Surveillance System presents a novel, hardware-integrated prototype demonstration aimed The proposed Smart Surveillance System presents a groundbreaking hardware-integrated prototype that decisively validates the effectiveness of a dual-branch anti-spoofing model on the low-power edge device, Raspberry Pi 3B+. This prototype goes beyond algorithmic performance research, showcasing a fully functional proof of concept. In contrast to existing surveillance solutions that typically rely on centralized cloud processing or basic recognition systems, our system employs an advanced dual-branch model that utilizes both spatial and frequency-domain features. This approach enables real-time anti-spoofing with an impressive error rate of less than 2%. What truly sets our system apart is its seamless end-to-end integration of cloud-based authentication, edge-level inference for rapid response, and an interactive live video conferencing feature. This configuration empowers immediate verification and action during potential spoofing events. With IoT-enabled devices, our system ensures effortless communication for live streaming, automated alerts, and scalable cloud data management. Coupled with edge computing, it guarantees real-time decision-making with minimal latency. Experimental results confirm its high accuracy in distinguishing genuine users from spoofing attempts, positioning our solution as a lightweight, proactive, and user-interactive surveillance option that is perfectly suited for homes, enterprises, and public infrastructures.},
}

@article {pmid42108617,
year = {2026},
author = {Fisher, JB and Anderson, MC and Miralles, DG and Mallick, K and Stoy, PC and Ryu, Y and Bastiaanssen, WGM},
title = {Evapotranspiration Everywhere, All the Time: Towards a Unified View From Earth Observation.},
journal = {Global change biology},
volume = {32},
number = {5},
pages = {e70898},
doi = {10.1111/gcb.70898},
pmid = {42108617},
issn = {1365-2486},
support = {80NSSC22K0936//Aeronautics Research Mission Directorate/ ; 80NSSC23K0309//Aeronautics Research Mission Directorate/ ; 80NSSC24K1617//Aeronautics Research Mission Directorate/ ; INTER/ANR/22/17204507/HiDRATE//FNR-ANR Inter programme/ ; 2012893//National Science Foundation/ ; 2422397//National Science Foundation/ ; },
mesh = {*Plant Transpiration ; Earth, Planet ; *Earth Sciences ; Ecosystem ; Water ; *Water Cycle ; },
abstract = {Scientists want to know everything, everywhere, and all the time. This is particularly true in Earth science, where we seek to understand processes that span from the molecular to the planetary scale in how the world works, how it affects us, and how we impact it-especially the water cycle. Evapotranspiration (ET) was the last component to be measured in closing the water cycle: for decades, closing the water budget meant adding up all the measurable components, then inferring ET as the residual. Early measurements relied on water loss from pans and weighing lysimeters, followed by sensors inserted into plants to monitor sap flow and leaf chambers capturing transpiration. Scaling up to ecosystems became possible through eddy-covariance flux towers and further across landscapes through proximal sensing with drones, aircraft, and, ultimately, with satellites. While enormous progress has been made to measure or estimate ET everywhere and all the time, no single approach has yet achieved both simultaneously. Flux towers help with all the time, but not everywhere. Satellites can do everywhere, but not all the time (except, in part, for geostationary satellites, though with insufficient spatial coverage and resolution). A new advent of smallsat constellations is moving us to everywhere and all the time in detail, though we are only in the beginning of that era. This paper discusses the evolution and revolution of Earth observation for ET, as we advanced from the first Landsat and development of ET models through the progression of increasingly higher spatiotemporal resolution across international space agencies and commercial industry with increasing ET model sophistication, cloud computing, and machine learning. We continue to march ahead towards ET everywhere, all the time, and use that knowledge to better manage water and sustain our planet.},
}

*Plant Transpiration
Earth, Planet
*Earth Sciences
Ecosystem
Water
*Water Cycle

@article {pmid42113833,
year = {2026},
author = {Zhang, S and Yang, H and Kim, H and Shin, I and Wu, RMX},
title = {Action-factorized Rainbow deep Q-network with token Transformer for computation offloading in edge computing-enabled Internet of Ships.},
journal = {PloS one},
volume = {21},
number = {5},
pages = {e0348376},
doi = {10.1371/journal.pone.0348376},
pmid = {42113833},
issn = {1932-6203},
mesh = {Algorithms ; *Ships ; *Internet ; *Deep Learning ; Wireless Technology ; Neural Networks, Computer ; },
abstract = {Edge computing (EC) in the Internet of Ships (IoS) reduces the latency and energy burdens of cloud-centric architectures, but fully realizing its benefits requires effective computation offloading strategies. Designing such strategies in dynamic maritime environments remains challenging due to the high-dimensional, combinatorial decision space, strict system constraints, and rapidly varying maritime wireless channels. This study proposes the action-factorized Rainbow deep Q-network (DQN) with token Transformer, a deep reinforcement learning (DRL) algorithm for discovering effective computation offloading strategies in EC-enabled IoS (EC-IoS). The core innovation of the algorithm lies in a novel action factorization mechanism coupled with our custom token Transformer-based state and action encoders, which effectively handle the complex decision space. Built upon Rainbow DQN and further accelerated with a parallel training architecture, the algorithm improves learning efficiency and stability. Experimental results illustrate that the computation offloading strategies learned by our algorithm significantly outperform multiple baselines on the weighted latency-energy objective. More importantly, these strategies achieve a zero rate of invalid actions, satisfy all system constraints, and ensure practical feasibility. Overall, the study demonstrates that the algorithm provides a robust method for computation offloading, effectively balancing latency and energy consumption in EC-IoS, thereby supporting maritime digitalization and automation.},
}

Algorithms
*Ships
*Internet
*Deep Learning
Wireless Technology
Neural Networks, Computer

@article {pmid42115249,
year = {2026},
author = {Niyasudeen, F and Mohan, M},
title = {Energy-aware load balancing in cloud environments using graph neural networks and grey wolf optimization.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-51660-2},
pmid = {42115249},
issn = {2045-2322},
abstract = {Cloud computing infrastructures must constantly maintain a balance between resource utilization and energy consumption to work effective under dynamic workloads. The training expense and generalizability of machine learning-based load balancing algorithms are both considerable, while existing methods often fails in adjusting in real time conditions. To work with both energy-aware and scalable load balancing, this research proposes a hybrid framework that combines Graph Neural Networks (GNNs) with the Grey Wolf Optimization (GWO) method. GNN gives accurate workload representation, representing the intricate structural relationships among virtual machines (VMs), tasks, and resources. Afterwards, the usage of GWO maximizes task-to-virtual machine mappings by reducing load imbalance, energy consumption, and task completion time. The hybrid framework utilizes sustainability parameters while constantly adapting to workload variations, unlike traditional methods. The experiments are done in CloudSim, where the proposed hybrid model reduced energy consumption by approximately 18-27%, decreases task completion time by 12-20%, and improves resource utilization balance by 15-22% when compared to existing approaches. The simulations are run in CloudSim Plus environment using real-world traces.},
}

@article {pmid42115269,
year = {2026},
author = {Bappy, AS and Seppänen, T and Hoque, MZ},
title = {SENSEYE: a resource-aware visionary framework for assisting individuals with visual disabilities.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-51257-9},
pmid = {42115269},
issn = {2045-2322},
abstract = {Despite significant recent advances, visual aid systems are still limited by the use of conventional computer vision algorithms, constrained sensor capabilities, high power consumption, and reliance on cloud-based processing, which introduce latency and privacy risks. Current assistive technologies for visually impaired individuals often suffer from a lack of secure and competent communication and an inability to deal with complex computer vision tasks. This paper introduces SENSEYE, a resource-aware visionary framework that employs edge computing with a secure and competent communication mechanism. The proposed architecture integrates IoT Edge and virtual decentralized services in a portable system that is small, cost-effective, and power-efficient. The integration of open AI models with advanced functionality in this system design helps to recognize objects, locate moving obstacles, detect sudden changes, perceive a summary from a live feed, and represent them as audio in real time. SENSEYE integrates real-time object detection, scene comprehension, and global positioning system (GPS)-based navigation into a portable, low-latency device. This system leverages optimized lightweight AI models, e.g., SSD-MobileNetV2 and VILA1.5-3b, to provide accurate environmental awareness and seamless auditory feedback through efficient speech processing. The system also enables secure remote assistance via video streaming and real-time GPS location sharing, ensuring enhanced user safety and connectivity. The evaluations confirm superior accuracy, power efficiency, and responsiveness compared to traditional sensor-based or cloud-reliant systems. Although challenges remain, particularly in constrained sensor capabilities, power efficiency trade-offs, and potential sensory overload, future work will focus on improving wearability, optimizing energy consumption, and advancing multimodal, user-centric AI integration for enhanced accessibility. This paper presents a novel resource-aware edge AI architecture with integrated real-time perception and secure IoT communication, establishing a scalable, privacy-preserving, and interpretable foundation for next-generation assistive systems for visually impaired individuals.},
}

@article {pmid42106382,
year = {2026},
author = {Dandabathula, G and Roy, S and Ghatage, OS and Salunkhe, SS and Nandani, D and Bera, AK and Srivastav, SK},
title = {A national-scale sandy beach dataset for India derived from high-resolution satellite imagery and deep learning.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-026-07408-8},
pmid = {42106382},
issn = {2052-4463},
abstract = {Sandy beaches are important coastal features with ecological, social, and economic significance, yet comprehensive national-scale datasets mapping their extent in India remain scarce. Here, we present a high-resolution, geo-referenced dataset of sandy beach extents along the Indian coastline, derived from IRS ResourceSat-2/2 A LISS-IV multispectral imagery (5.8 m resolution) collected between 2021 and 2024. The dataset was derived from cloud-free imagery acquired under low-tide conditions to ensure consistent delineation of exposed sandy beach extents. Fourteen representative coastal sites were manually annotated to train and validate a U-Net deep learning model, which was subsequently applied to the entire Indian coastline. The model achieved robust performance on independent test tiles, with an intersection-over-union of 0.84, precision of 0.90, recall of 0.94, and F1-score of 0.92. Accuracy was further validated at two independent beaches with low tidal influence, yielding an RMSE of 11.8 m[2] (<0.05% relative deviation) between U-Net-derived and manually digitized areas. The final dataset is distributed in Shapefile format to ensure compatibility with standard GIS workflows, supporting applications such as coastal monitoring, habitat assessment, and shoreline change analysis, as well as serving as training data for future machine learning models. An accompanying Google Earth Engine web application is provided for interactive visualization and exploration of the mapped sandy beaches. This dataset represents the first comprehensive, high-resolution mapping of sandy beaches across India, enabling improved coastal management, conservation planning, and research into coastal dynamics.},
}

@article {pmid42099704,
year = {2026},
author = {Boath, JC and Gilani, SAM and Tiemo, TH and Tantry, A},
title = {Innovative technologies and workplace collaborations in the energy sector based in the United Arab Emirates.},
journal = {Frontiers in artificial intelligence},
volume = {9},
number = {},
pages = {1798647},
pmid = {42099704},
issn = {2624-8212},
abstract = {INTRODUCTION: The UAE energy sector is navigating digital transformation mandates such as the UAE AI Strategy 2031 and Net Zero commitments, with technologies like AI, IoT and cloud computing creating new avenues for real-time coordination, data-driven decision-making and cross-functional collaboration. These oppor tunities are tempered by challenges of organisational readiness, cultural iner tia and technological integration. Yet, research on innovative practices in the UAE energy context remains limited. Therefore, this study investigates the role of AI, IoT and cloud computing in shaping workplace collaboration in the UAE energy sector.

METHODS: An explanatory sequential mixed-methods design was adopted which involved Phase 1 (15 October, 2024-31 January, 2025) interviews with 15 professionals in operations, IT and leadership roles from major energy companies, analysed via thematic analysis. Phase 2 (15 February, 2025-15 May, 2025) distributed a survey to a broader sample, yielding 115 valid responses, which were analysed quan titatively. The study is primarily grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), with the Technology Acceptance Model (TAM), Resource-Based View (RBV) and Actor-Network Theory (ANT) serving as supporting interpretive lenses.

RESULTS: Findings show that AI, IoT, and cloud platforms enhance collaboration, especially in remote coordination and predictive decision sup port, but adoption is hindered by resistance to change, fragmented systems and uneven digital literacy.

DISCUSSION: Practical implications include modular rollouts, digital maturity audits and AI onboarding programs. Policy recommendations include national collaboration standards, KPI integration and incentives for joint innova tion projects.},
}

@article {pmid42102471,
year = {2026},
author = {George, GM and Kumareshan, N},
title = {Quantum convolution searched binary neural networks based autism spectrum disorder detection using MRI images in cloud computing.},
journal = {Psychiatry research. Neuroimaging},
volume = {361},
number = {},
pages = {112226},
doi = {10.1016/j.pscychresns.2026.112226},
pmid = {42102471},
issn = {1872-7506},
abstract = {Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that causes discrepancies in social interaction and behavioral changes. The developments of neuroimaging techniques, like Magnetic Resonance Imaging (MRI) is employed to detect brain abnormalities. Due to the heterogeneity of disease severity and symptoms, the detection of ASD is difficult. To solve such complexity, a novel model named Fractional Painting Training Based Optimization trained Quantum Convolution Searched Binary Neural Network (FPTO_QCSBNN) is proposed for ASD detection in cloud. A cloud-based detection system offers the analysis and storage of large-scale neuroimages. Moreover, it provides faster diagnosis with scalable storage. Initially, the cloud system is simulated, and pre-processing is done using Mid-Point filter and Region of Interest (ROI) extraction. Image enhancement is done by gamma correction method, and pivotal region is extracted using functional connectivity. The optimal grid selection in pivotal region extraction is done using FPTO, and features are extracted from enhanced image. Using features and pivotal region extracted image, QCSBNN detects ASD, and it is trained by FPTO. Furthermore, developed FPTO_QCSBNN attains the accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) of 91.37%, 91.32%, and 91.89%.},
}

@article {pmid42092915,
year = {2026},
author = {Rahimzadeh, V and Walsh, B and Rehm, HL and Cho, M and McGuire, AL},
title = {Characterizing core outcomes of responsible stewardship for human genomic data in the cloud.},
journal = {BMC medical genomics},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12920-026-02382-x},
pmid = {42092915},
issn = {1755-8794},
support = {K01HG013112/HG/NHGRI NIH HHS/United States ; },
abstract = {We present findings from a scoping review of the genomic data sharing literature used to inform a core outcomes set for responsible data stewardship in the cloud. Genomic and related health data stemming from government funded research have undergone mass migration to the cloud where they can be more securely stored, accessed, and analyzed within shared computing environments. While this migration reflects a shift in privacy and security infrastructure for cloud-based repositories as oversight becomes more globalized, it also necessitates new data stewardship responsibilities to align authorized data access with ethical data use. Responsible data stewardship refers to the ethical and secure management, use, and protection of data to ensure its accuracy, privacy, integrity, and appropriate access throughout its lifecycle. Practicing responsible data stewardship is therefore one axis by which repositories can demonstrate trustworthiness in their data access and management practices. We searched the cloud governance and data sharing literature indexed in Web of Science as well as Elicit using the scoping review approach by Arksey and O'Mally. A total of 46 articles met our inclusion criteria, to which we applied a deductive, thematic content analysis to generate relevant outcome domains as well as individual core outcomes of cloud-based data. Our qualitative synthesis resulted in 35 individual core outcomes organized under 9 core domains: transparency, authentication, auditing, accountability, security, sustainability, standardization, consent and compliance, and community engagement.},
}

@article {pmid42098196,
year = {2026},
author = {Karadas, F and Usanmaz, B},
title = {Anomaly-based data reduction for energy-efficient edge computing in IoT with LoRa.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-48086-1},
pmid = {42098196},
issn = {2045-2322},
abstract = {Edge computing, a key component of Internet of Things (IoT) systems, enables data processing close to the data source. In low-power, resource-constrained IoT environments, it can reduce dependency on centralized cloud systems, lower communication load, and minimize overall energy consumption. However, transmitting all sensor data from edge devices still incurs significant communication and energy costs. The key research gap is that existing approaches rarely address this inefficiency through selective transmission; specifically, few studies have explored how filtering and sending only anomalous data can improve energy efficiency. To bridge this gap, we propose an anomaly detection-based data reduction method operating at the edge using LoRa technology. We apply two unsupervised learning algorithms, DBSCAN and Isolation Forest, to identify and transmit only anomalous sensor instances. The proposed methods are evaluated against a full-data transmission (FDT) baseline. The key contributions are demonstrated through experimental results: DBSCAN reduces data volume by 98.19% and energy consumption by 98.10%, while Isolation Forest achieves reductions of 97.32% and 97.32%, respectively. These findings confirm that anomaly-driven selective transmission significantly reduces the communication load while ensuring high energy efficiency in edge computing systems.},
}

@article {pmid42098457,
year = {2026},
author = {Patel, P and Gopal, KM and Patel, NC and Sahu, BK and Tripathy, SP and Altayeb, M and Panchyk, M and Touti, E},
title = {Fuzzy adaptive human memory optimization based optimal task scheduling in cloud computing.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-51611-x},
pmid = {42098457},
issn = {2045-2322},
abstract = {Task scheduling has become a significant research focus in cloud computing because of the growing need for efficient resource utilization. This paper explores an innovative scheduling approach harnessing Human Memory Optimization (HMO) and Fuzzy Adaptive Human Memory Optimization (FAHMO). Such techniques are inspired by human cognitive principles and employ an adaptive search strategy that maintains an effective trade-off between exploration and exploitation. By maintaining a history of successful and unsuccessful scheduling decisions, HMO enables continuous enhancement of scheduling efficiency. Integrating fuzzy logic into FAHMO improves the decision-making process by effectively managing uncertainty and ambiguity in task scheduling, thereby producing more flexible and efficient solutions. Comparative analysis demonstrates that HMO and FAHMO outperform conventional metaheuristic algorithms, including PSO-PGA, in terms of convergence speed and task completion time. The results confirm that the proposed approach significantly reduces makespan and enhances overall cloud task scheduling performance. Specifically, FAHMO achieved up to 67.46% improvement in makespan and 63.18% in convergence accuracy compared to PSO-PGA.},
}

@article {pmid41993383,
year = {2026},
author = {Jurgens, JA and Bueckle, A and Vora, J and Maurya, MR and Ahooyi, TM and Zheng, E and Stear, B and Wang, D and Ree, C and Ramachandran, S and Nekrutenko, A and Brandes, M and Thaker, S and Katz, DH and Munoz-Torres, MC and Diamant, I and Chun, HE and Simmons, JA and Tasian, SK and Jenkins, SL and Evangelista, JE and Dodia, H and Saha, S and Lindquist, MA and Gajjala, V and Nemarich, C and Zhen, J and Ross, KE and Byrd, AI and Shilin, A and Metzger, VT and Bologa, CG and Srinivasan, S and Jang, D and Kumar, P and Taub, LD and Levanto, MP and Petrosyan, V and Anandakrishnan, M and Kim, M and Clarke, DJB and Ivich, A and Crichton, DJ and Smallen, S and Bordelon, D and Chen, C and Schroeder, AJ and Mahabal, A and Cao-Berg, I and Kim, S and Masood, D and Yu, K and Gaulton, KJ and Jimenez-Morales, D and Rincon, JM and Honick, BJ and Wang, W and Wu, CH and Milosavljevic, A and Blood, PD and Boline, J and Oprea, TI and Lambert, CG and de Bono, B and Park, PJ and Silverstein, JC and Flannick, J and Yang, JJ and Grethe, JS and Subramaniam, S and Tiemeyer, M and Clark, T and Wheeler, MT and Kahn, A and Burnette, J and Ranzinger, R and Schatz, MC and Gibson, L and Burtt, NP and Carson, JP and Chen, JY and Ping, P and Davis, S and Taylor, DM and Börner, K and Dillman, A and Bursey, K and Ma'ayan, A and , and Mazumder, R and Roth, ME and Greene, CS},
title = {The Common Fund Data Ecosystem (CFDE).},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.04.10.717672},
pmid = {41993383},
issn = {2692-8205},
abstract = {The NIH Common Fund Data Ecosystem (CFDE) integrates data resources from 18 NIH Common Fund programs for discovery and integrative analysis. These programs generate valuable but heterogeneous datasets that can be difficult to discover, access, and reuse. CFDE aims to provide a collaborative, community-built infrastructure that links and enriches Common Fund programs. We describe the evolution, structure, and core technologies of CFDE, including practical approaches that support submission, integration, visualization, and public release of multimodal data. Training programs and workforce initiatives lower barriers to adoption. CFDE has devised solutions to critical issues facing cross-program initiatives, including data scale and heterogeneity, dataset integration, and long-term sustainability. We demonstrate the utility of linking Common Fund resources through integrative tools and cross-dataset queries to yield insights that would otherwise be infeasible. Collectively, CFDE shows that a standards-driven, federated approach enhances and unifies cross-disciplinary resources, fostering collaboration and data-driven discovery.},
}

@article {pmid42085835,
year = {2026},
author = {Jiang, C and Wu, W and Zhong, Q},
title = {Self-supervised multi-scale cloud workload prediction with time series data augmentation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {202},
number = {},
pages = {109034},
doi = {10.1016/j.neunet.2026.109034},
pmid = {42085835},
issn = {1879-2782},
abstract = {Accurate cloud workload forecasting is critical for proactive resource provisioning, cost control, and Service Level Agreement (SLA) compliance; however, it is hindered by the scarcity and heterogeneity of labels. We present Time Series Augmentation for Multi-Scale Prediction (TSA-MSP), a self-supervised framework that achieves near-fully supervised accuracy with limited labels. Conceptually, TSA-MSP couples cloud-informed augmentations-multi-scale time warping, frequency-domain mixing, and periodic pattern injection-with a hierarchical multi-scale contrastive objective and efficient fine-tuning using lightweight adapters to capture short- and long-range workload patterns. We conducted an empirical experimental study on real-world traces (Alibaba 2018, Google 2019, Azure 2019): self-supervised pre-training on unlabeled data followed by fine-tuning with small labeled subsets. We evaluated the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Symmetric Mean Absolute Percentage Error (sMAPE), and a peak-oriented metric (Peak Prediction Accuracy, PPA) and performed ablations (augmentations, scales), label efficiency, and cross-dataset transfer tests. Remarkably, with only 20% labels, TSA-MSP attains RMSE within ∼ 1.5-1.7% of fully supervised training (100% labels) across all three datasets; for example, on Alibaba, it achieves MAE/RMSE 0.241/0.335 versus 0.238/0.330 for a fully supervised Pathformer. Against the best self-supervised baseline at 20% labels, TSA-MSP reduces RMSE by ∼ 6.6-6.9%. In a 5%-label setting, it outperformed supervised training from scratch by ∼ 23-26% RMSE. Cross-dataset transfer further improves the RMSE by ∼ 2.4-5.2% over direct target-only pre-training. By combining domain-aware augmentations with multi-scale self-supervision and efficient adaptation, TSA-MSP delivers accurate forecasts under scarce labels, improving peak readiness while enabling faster, more cost-effective deployment for real-world cloud resource management.},
}

@article {pmid42086677,
year = {2026},
author = {Ganesh, V and Khade, N and Taiwade, H and Deshmukh, PV},
title = {A two-stage generative-AI fusion intrusion detection system for calibrated and reliable cloud security.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-51527-6},
pmid = {42086677},
issn = {2045-2322},
abstract = {The rapid expansion of cloud computing has significantly increased the scale, sophistication, and frequency of cyber threats, making reliable intrusion detection essential for secure cloud environments. However, conventional single-stage intrusion detection systems exhibit high false-positive rates, poor probability calibration, and degraded performance under severe class imbalance, thereby limiting their real-world deployment. This paper proposes a novel two-stage Generative-AI fusion intrusion detection model that integrates discriminative and generative learning to enhance predictive reliability and decision confidence. In the first stage, a binary classifier combining a Feature-Token Transformer and a Variational Autoencoder differentiates normal from malicious traffic using class-balanced focal loss and a precision-oriented routing threshold to minimize false alarms while preserving detection sensitivity. In the second stage, detected attack samples are forwarded to a conditional multi-class classifier for fine-grained attack categorization, incorporating SMOTE-based rebalancing, class-balanced focal loss, and class-specific threshold optimization to improve minority-class detection. The post-hoc temperature scaling is applied at both stages to produce well-calibrated probability estimates and support risk-aware decision-making. The performance of the proposed model measured on the UNSW-NB15 and NSL-KDD benchmark datasets indicate that proposed model performed well as compared to the single-stage Generative-AI fusion and evolutionary feature-selection baselines model and obtained the accuracy score of 94.03% and 77.07%, respectively to delivered the improved calibration and reduced false-alarm, and provide the robust and deployment-ready solution for modern cloud security.},
}

@article {pmid42072178,
year = {2026},
author = {Garay, CE and Mansilla, GN and Madrid, RE and González Colombres, A and Jerez, SJ},
title = {Design and Pilot Evaluation of an IoT-Based Blood Pressure Monitoring System for Rabbits.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {4},
pages = {},
doi = {10.3390/bioengineering13040384},
pmid = {42072178},
issn = {2306-5354},
support = {PIUNT G621//Universidad Nacional de Tucumán/ ; PIUNT G728//Universidad Nacional de Tucumán/ ; 2025//Institucional funds from the Instituto Superior de Investigaciones Biológicas (INSIBIO), CONICET/ ; 2025//CIASUR (UTN-FRT)/ ; },
abstract = {Telemedicine, driven by the Internet of Things (IoT) and wireless connectivity, is essential for managing cardiovascular diseases, where hypertension remains the primary risk factor. In preclinical research, rabbits are superior biological models compared to rodents due to their human-like lipid metabolism. However, continuous blood pressure monitoring in this species remains challenging. The gold-standard technique (direct carotid catheterization) requires terminal procedures, and indirect methods (Doppler, oscillometric) show limited agreement with direct measurements. Furthermore, commercially available implantable telemetry platforms, while enabling real-time monitoring in freely moving animals, require costly surgical implantation, specialized proprietary hardware, and post-operative recovery periods that may confound early hemodynamic data. To address these limitations, this study presents a low-cost, customizable, and minimally invasive monitoring system utilizing a pressure transducer in the central auricular artery. The device integrates an ESP32 microcontroller with IoT technology for digital signal processing and seamless wireless data transmission to the ThingSpeak cloud platform. Unlike implantable telemetry, the proposed approach avoids surgical implantation and its associated costs and recovery time, while still enabling continuous, real-time hemodynamic tracking throughout the experimental period. A pilot evaluation against the BIOPAC MP100 reference (carotid artery) demonstrated relative errors of 1.60% for mean arterial pressure, 8.58% for systolic blood pressure, and 2.43% for diastolic blood pressure. By reducing invasiveness and enhancing remote data accessibility, this system provides a promising framework for the preclinical evaluation of antihypertensive agents and cardiovascular mechanisms, bridging the gap between edge computing and remote clinical diagnostics.},
}

@article {pmid42076178,
year = {2026},
author = {Lai, L and Ding, S and Li, Z and Luo, Z and Wang, H},
title = {An Intelligent Micromachine Perception System for Elevator Fault Diagnosis.},
journal = {Micromachines},
volume = {17},
number = {4},
pages = {},
doi = {10.3390/mi17040401},
pmid = {42076178},
issn = {2072-666X},
support = {No. 2025JD-1-05//Guangdong Institute of Special Equipment Inspection and Research/ ; },
abstract = {Elevator fault diagnosis heavily relies on high-precision sensing of microscopic physical states. Although Micro-Electro-Mechanical System (MEMS) sensors can capture such subtle features, they are constrained by high-frequency data streams, environmental noise, and the semantic gap between raw sensor data and actionable maintenance decisions. This study proposes a collaborative edge-cloud intelligent diagnosis framework specifically designed for elevator systems. On the edge side, a lightweight temporal Transformer model, ELiTe-Transformer, was designed and deployed on the Jetson platform. This model enhances sensitivity to event-driven MEMS signals through an industrial positional encoding mechanism and by integrating linear attention and INT8 quantization techniques, achieving a real-time inference latency of 21.4 ms. On the cloud side, retrieval-augmented generation (RAG) technology was adopted to integrate physical features extracted at the edge with domain knowledge, generating interpretable diagnostic reports. The experimental results show that the overall accuracy of the system reaches 96.0%. The edge-cloud collaborative framework improves the accuracy of complex fault diagnosis to 92.5%, and the adoption of RAG reduces the report hallucination rate by 71.4%. This work effectively addresses the bottlenecks of MEMS perception in elevator fault diagnosis, forming a closed loop from micro-signal acquisition to high-level decision support.},
}

@article {pmid42076437,
year = {2026},
author = {Li, JE and Hwang, YT},
title = {IFA-ICP: A Low-Complexity and Image Feature-Assisted Iterative Closest Point (ICP) Scheme for Odometry Estimation in SLAM, and Its FPGA-Based Hardware Accelerator Design.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {8},
pages = {},
doi = {10.3390/s26082326},
pmid = {42076437},
issn = {1424-8220},
support = {112-2221-E-005-095//National Science and Technology Council, Taiwan/ ; },
abstract = {Odometry estimation, which calculates the trajectory of a moving object across timeframes, is a critical and time-consuming function in SLAM (Simultaneous Localization and Mapping) systems. Although LiDAR-based sensing is most popular for outdoor and long-range applications because of its ranging accuracy, the sparsity of laser point cloud poses a significant challenge to feature extraction and matching in odometry estimation. In this paper, we investigate odometry estimation from two aspects, i.e., algorithm optimization, and system design/implementation. In algorithm optimization, we present an image feature-assisted odometry estimation scheme that leverages the richness of image information captured by a companion camera to enhance the accuracy of laser point cloud matching. This also serves as a screening mechanism to reduce the matching size and lower the computing complexity for a higher estimation rate. In addition, various schemes, such as adaptive threshold in image feature point selection, principal component analysis (PCA)-based plane fitting for laser point interpolation, and Gauss-Newton optimization for calculating the transform matrix, are also employed to improve the accuracy of odometry estimation. The performance of improved odometry estimation is verified using an existing FLOAM (Fast Lidar Odometry and Mapping) framework. The KITTI dataset for autonomous vehicles with ground truth was used as the test bench. Simulation results indicate that the translation error and rotation error can be reduced by 16.6% and 1.3%, respectively. Computing complexity, measured as the software execution time, also reduced by 63%. In system implementation, a hardware/software (HW/SW) co-design strategy was adopted, where complexity profiling was first conducted to determine the task partitioning and time-consuming tasks are offloaded to a hardware accelerator. This facilitates real-time execution on a resource-constrained embedded platform consisting of a microprocessor module (Raspberry Pi) and an attached FPGA board (Pynq Z2). Efficient hardware designs for customized DSP functions (adaptive threshold and PCA) were developed in an FPGA capable of completing one data frame in 20ms. The final system implementation met the target throughput of 10 estimations per second, and can be scaled up further.},
}

@article {pmid42076557,
year = {2026},
author = {Chen, J and Mao, X and Li, X and Zhou, Q and Huang, C and Wu, C},
title = {An Optical Method for the Rapid Measurement of Corrugated Plate Depth Based on Line Laser Sensor.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {8},
pages = {},
doi = {10.3390/s26082446},
pmid = {42076557},
issn = {1424-8220},
support = {Nos. 52305535//The National Natural Science Foundation of China/ ; },
abstract = {This paper presents a non-contact depth detection method for corrugated heat exchanger plates, aiming to improve measurement efficiency and accuracy. The system integrates a line laser sensor with a precision linear guide rail, enabling continuous acquisition of high-resolution 2D surface profiles as the sensor moves along the plate. To reduce data redundancy while preserving geometric features, a multi-stage data reduction strategy is proposed. This strategy combines the angle-chord height criterion with spline-based filtering to identify key regions of curvature and eliminate unnecessary point cloud data. For depth extraction, a two-stage feature recognition algorithm is designed. First, a coarse analysis locates candidate peaks and valleys by identifying local extrema in the reduced 2D data. Then, a fine detection process is applied: local B-spline fitting is performed near each candidate point, and a binary search algorithm is used to accurately determine the spline extrema. By computing the vertical distance between precisely located peaks and valleys, the system rapidly extracts the corrugation depth parameters. This method achieves a high balance between speed and precision, offering a practical and reliable solution for automated surface morphology inspection in heat exchanger manufacturing.},
}

@article {pmid42076668,
year = {2026},
author = {Huang, X and Tian, B},
title = {Joint Service Chain Orchestration and Computation Offloading via GNN-Based QMIX in Industrial IoT.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {8},
pages = {},
doi = {10.3390/s26082559},
pmid = {42076668},
issn = {1424-8220},
support = {N/A//China Mobile Research Institute/ ; },
abstract = {In IIoT edge computing, multi-edge server collaborative scheduling faces two core issues due to random task arrivals, heterogeneous resources, and complex topology: traditional model-driven methods cannot make dynamic decisions in dynamic environments, and conventional MARL fails to characterize inter-node topological dependencies and load correlations. To address this, this paper investigates the joint optimization of task offloading, computing resource allocation, and SFC orchestration in IIoT, constructs a cloud-edge-end collaborative architecture, and models the problem as a POMDP to minimize the overall system cost under multiple constraints. A graph-guided value-decomposition MARL method is proposed, which extracts spatial topology and neighborhood-load features of edge nodes via a GNN and combines them with the QMIX framework to realize multi-agent centralized training and distributed execution. Simulations show that the algorithm converges stably under different server scales and task loads, significantly outperforms benchmark algorithms, and can suppress performance degradation in high-load scenarios, demonstrating its robustness and scalability in complex industrial environments.},
}

@article {pmid42078152,
year = {2026},
author = {Joshi, A and Adhikari, K and Taori, R and Krishnappa, D and Jain, A and Chelliah, L and Fatima, L and Krishnappa, R},
title = {From Spreadsheet To Prediction Tool: A Practical Artificial Intelligence Guide For Urologists.},
journal = {Cureus},
volume = {18},
number = {5},
pages = {e108082},
pmid = {42078152},
issn = {2168-8184},
abstract = {Artificial intelligence (AI) and machine learning (ML) are transforming urological practice; however, most clinicians lack the technical background required to develop, evaluate, or critically appraise predictive models. Existing resources are often written by data scientists for a technical audience, highlighting the need for a practical, clinician-oriented framework that enables urologists to build and deploy meaningful AI tools using data they already possess. Following an overview of the AI landscape in urology, we present a structured nine-part framework that includes clinical data appraisal; data cleaning and variable engineering; model selection (e.g., logistic regression, Random Forest, Extreme Gradient Boosting (XGBoost), and Cox regression); train-test splitting; cross-validation; performance evaluation (including area under the curve (AUC), calibration, and decision curve analysis); AI-assisted coding using platforms such as Google Colab and large language models (LLMs); web application deployment (e.g., Hugging Face, Gradio, GitHub, Render, and Google Cloud); manuscript preparation aligned with Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis-Artificial Intelligence (TRIPOD-AI) reporting standards; and ethical considerations for responsible AI deployment. Each component is illustrated with real-world examples and supported by validated prompt templates. Applying this framework to a high-risk prostate cancer cohort, the lead author, without prior programming experience, successfully developed and publicly deployed a validated multi-outcome prediction tool within 72 hours using entirely free, open-source infrastructure. AI-based clinical prediction tools are increasingly accessible to urologists with structured datasets and a systematic approach. This guide aims to democratize AI model development by enabling clinicians to extract actionable insights from existing data, build validated tools, and contribute meaningfully to the evolving landscape of AI-driven urological care, without the need to write code from scratch.},
}

@article {pmid42069783,
year = {2026},
author = {Nagavel, V and Bhuvaneswari, PTV and Ramesh, P and Tamaraiselvi, K},
title = {Secured framework for IoT based healthcare application.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-51036-6},
pmid = {42069783},
issn = {2045-2322},
abstract = {The Internet of Things (IoT) allows continuous health monitoring through the interconnection of wearable and medical devices with computing and storage infrastructures. As cyber threats grow and the sensitivity of healthcare information increases, data integrity, privacy, and access control become critical concerns in digital healthcare environments. This paper proposes a secure IoT-based healthcare framework that integrates machine learning and blockchain techniques for data protection and intelligent health risk assessment.The proposed framework uses cryptographic hashing, Merkle tree construction, digital signatures, and a threshold-based blockchain validation mechanism to enhance the integrity and secure handling of healthcare data in a permissioned simulation environment. The blockchain validation mechanism is evaluated through simulated tampering, replay, and high-load scenarios to assess the integrity verification capability of the proposed framework.Various machine learning models are trained and evaluated on medical datasets to predict disease risk, and their performance is measured using accuracy, precision, recall, and F1-score metrics. The framework is implemented in Python and deployed in a scalable cloud-based environment. Experimental results demonstrate that the proposed framework improves healthcare data integrity verification and supports reliable predictive performance for secure digital healthcare applications.},
}

@article {pmid42069805,
year = {2026},
author = {Duong, T and Le, D and Williams, V and Stewart, S and Zhao, Y and Zitu, M and El Naqa, I and Rollison, D and Thieu, T},
title = {OncoPT: long-context transformer models for in hospital tumor phenotype extraction from pathology reports.},
journal = {NPJ digital medicine},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41746-026-02630-5},
pmid = {42069805},
issn = {2398-6352},
support = {P30 CA076292-25S4/NH/NIH HHS/United States ; P30 CA076292-25S4/NH/NIH HHS/United States ; P30 CA076292-25S4/NH/NIH HHS/United States ; P30 CA076292-25S4/NH/NIH HHS/United States ; },
abstract = {Despite recent advances in medical informatics, extracting tumor information from pathology reports remains a challenge in modern cancer registry and surveillance workflows. These documents often have an unstructured format, complex medical content, and a considerably lengthy context, creating significant challenges for automated phenotypic information extraction. Although some recent language models such as BERT, GatorTron, and GPT-4 have demonstrated efficacy in medical applications, they are either constrained by sequence length limitations or cloud-based computing that violates the handling of protected health information. We introduce two oncology pathology-optimized transformer models OncoPT, based on Longformer and BigBird architectures and trained on real-world pathology reports. OncoPT efficiently processes reports up to 4,096 tokens, making it suitable for hospitals' onsite deployment with limited resources. We apply OncoPT to a common malignancy (exemplified by breast cancer) and a rare malignancy (exemplified by gastric cancer), across five key tumor phenotypes: Subsite, Histology, Grade, Stage, and Laterality. The results demonstrate that OncoPT achieves state-of-the-art weighted F-1 on a private pathology dataset and surpasses commercial chatbots (ChatGPT 4o and o1) on the public CORAL dataset (up to 30% improvement). These findings highlight the robustness of OncoPT models with the added benefit of preserving the privacy of patient health information.},
}

@article {pmid42071035,
year = {2026},
author = {Rai, HM and Razaque, A and Agarwal, N and Agarwal, S and Abdallah, HA and Kant, S},
title = {Advanced security in fog environments using encryption and adaptive user activity tracking.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-51379-0},
pmid = {42071035},
issn = {2045-2322},
abstract = {The use of fog computing is on the rise, adding new dimensions to security and, more specifically, to data protection in fog cloud environments. Storing fog-computing data increases the likelihood of data exploitation when it is uploaded to fog-computing storage. In this paper, Adaptable User Activity Tracking (ASUT) is introduced, integrating AES-256, SHA-512, and user activity tracking (UAT). The need to integrate activity monitoring into the ASUT to collect statistical information on user actions has been stated. The file uploaded to the fog computing storage is encrypted using a 256-bit AES key. Then, this key is hashed with SHA-512 and stored in the fog cloud. The AES expansion is used to decrypt the data, while the SHA-512 hash of the AES key is used to verify that the user-provided key matches the original before decryption proceeds-the hash is irreversible, and the original key is never stored in plaintext. The user must know the initial key to access the file further. When the client re-enters the fog, the algorithm compares the hashes of the two: the initial and the second entry keys. In parallel, the fog cloud broadcasts the user's actions to track any abnormal activity on the account. This mechanism helps mitigate risks of unauthorized data access and suggests ways to improve user protection. The proposed ASUT is designed using Python and PHP. Experimental results show that ASUT achieves 43.39% faster encryption, 66% faster decryption, and 19.86% higher throughput compared to the best-performing competing method, indicating improved computational efficiency and practical feasibility under the evaluated conditions.},
}

@article {pmid42060995,
year = {2026},
author = {Ullah, Z and Kim, HH and Son, M},
title = {On-device artificial intelligence agent based on language models for electrochemical water desalination.},
journal = {Water research},
volume = {301},
number = {},
pages = {125995},
doi = {10.1016/j.watres.2026.125995},
pmid = {42060995},
issn = {1879-2448},
abstract = {Electrochemical water treatment is essential for tackling global water scarcity but remains difficult to optimize due to limited expertise and computing resources at many treatment facilities. Here, we introduce an intelligent on-device platform that combines electrochemical process knowledge with large language models deployed directly on edge devices such as Raspberry Pi. This system integrates theoretical understanding with real-time optimization, eliminating the need for cloud connectivity while ensuring data privacy and accessibility. Tested against 320 published studies, it achieves a 60 % reduction in hallucination rates and maintains high predictive accuracy (R[2] > 0.80) for key variables such as effluent concentration and energy, even with incomplete sensor inputs. Notably, prediction accuracy for challenging parameters, such as the applied current (the driving force for electrochemical water desalination), improves from 0.03 to 0.63. By bringing intelligence to the data rather than sending it to the cloud, this approach makes advanced water-treatment intelligence feasible in resource-limited, data-imperfect, decentralized environments where physics-based models cannot be deployed.},
}

@article {pmid42061843,
year = {2026},
author = {Nahass, GR and Ende, JV and Hubschman, S and Beltran, B and Kolli, B and Berek, C and Edmonds, JD and Chan, RVP and Setabutr, P and Larrick, JW and Yi, D and Tran, AQ},
title = {Simulated Workflow Feasibility Evaluation of a Web-Based Periorbital Measurement Platform: Development and Usability Study.},
journal = {JMIR human factors},
volume = {13},
number = {},
pages = {e82859},
doi = {10.2196/82859},
pmid = {42061843},
issn = {2292-9495},
mesh = {Humans ; *Workflow ; Feasibility Studies ; *Artificial Intelligence ; *Image Processing, Computer-Assisted/methods ; *Internet ; Adult ; *Mobile Applications ; },
abstract = {BACKGROUND: Periorbital measurements such as margin to reflex distances, palpebral fissure height, and scleral show are critical in diagnosing and managing conditions like ptosis and disorders of the eyelid. However, deployment of automated periorbital measurement algorithms in structured research workflows remains limited by the lack of integrated capture and data management infrastructure.

OBJECTIVE: We developed and evaluated Glorbit, a lightweight, browser-based application for automated periorbital distance measurement using artificial intelligence (AI). The objective was to evaluate end-to-end workflow feasibility of the platform under simulated, operator-run conditions.

METHODS: The application integrates a DeepLabV3 segmentation model into a modular image processing pipeline with secure, site-specific Google Cloud storage, supporting local preprocessing and cloud upload through Firebase-authenticated logins. The full workflow-metadata entry, facial image capture, segmentation, and upload-was tested. After the session, the participants completed a Likert-style survey.

RESULTS: Glorbit successfully ran on all tested platforms, including laptops, tablets, and mobile phones across major browsers. A total of 15 volunteers were enrolled in this study in which the app completed predefined workflow steps in all simulated, operator-run sessions. The segmentation model produced outputs on all images, and the average session duration was 101.7 (SD 17.5) seconds. Simulated experience scores on a 5-point Likert scale were uniformly high.

CONCLUSIONS: Glorbit is a cross-platform application that supports structured periorbital image capture and automated inference within a unified workflow. In simulated, operator-run testing, the platform demonstrated successful execution of predefined workflow steps across devices. These findings support the technical feasibility of the system as a research-oriented data collection framework and may inform future evaluations in broader research settings.},
}

Humans
*Workflow
Feasibility Studies
*Artificial Intelligence
*Image Processing, Computer-Assisted/methods
*Internet
Adult
*Mobile Applications

@article {pmid42062300,
year = {2026},
author = {Fan, P and Zhang, H and Su, X and Yang, X and Liu, Y and Pan, S and Li, X and Ying, Y and Pan, Y and Ping, J},
title = {Microinterventional in-sensor computing system for real-time metabolic health assessment.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-72520-7},
pmid = {42062300},
issn = {2041-1723},
abstract = {Microneedle biosensors enable dynamic monitoring of interstitial fluid biomarkers, but remain constrained by sensing interface susceptibility to motion artifacts and the prohibitive energy consumption of wireless cloud-based processing. Here, we report a bio-inspired, self-anchoring microinterventional in-sensor computing system. By leveraging a starfish-inspired suction cup-microneedle self-anchoring mechanism, the system effectively counteracts microneedle extrusion, attenuating signal fluctuations by 38-fold and enhancing signal intensity by up to 5.49-fold compared to conventional planar devices. Crucially, the high-fidelity data acquisition reduces the computational burden, enabling the deployment of a lightweight algorithm (43 KB) on a coin-sized embedded circuit, achieving 98.68% diagnostic accuracy and a 120-h battery life via local closed-loop feedback. Validation in a porcine model confirmed the system's capability to capture continuous biochemical dynamics. This co-design of a robust biomimetic interface and lightweight deep learning paves the way for next-generation wearables capable of performing high-fidelity, on-chip metabolic risk stratification in dynamic daily settings.},
}

@article {pmid42052188,
year = {2026},
author = {Trachtenberg, A and Spelkov, A and Akabayov, B},
title = {Distribution-preserved sampling (DPS) for smarter machine learning assisted ultra-large-scale virtual screening.},
journal = {RSC advances},
volume = {16},
number = {24},
pages = {21855-21866},
pmid = {42052188},
issn = {2046-2069},
abstract = {Ultra-large-scale structure-based virtual screening (SBVS) for identifying novel bioactive compounds poses significant computational challenges. These challenges arise from the size of available chemical libraries, which can contain billions of molecules that require exhaustive docking and scoring, placing prohibitive demands on CPU/GPU resources. Small- and mid-sized laboratories often lack access to the high-performance computing clusters or cloud resources necessary to process such workloads in a timely manner. Furthermore, managing and analyzing the resulting terabytes of docking data requires robust data-handling pipelines and expertise that are not universally accessible. Here, we present a data-driven drug development pipeline that leverages a subset of molecules from a database with a common scaffold, reducing the chemical search space by tens to hundreds of orders of magnitude. In this case, the common scaffold that is the key to allowing this reduction is the 2-phenylthiazole moiety, identified through NMR fragment screening. We started with a subset of over 400 000 drug-sized 2-phenylthiazole-containing molecules selected from the zinc database and trained a random forest regression model on about 1% of this data to predict binding scores for the entire library. For this purpose, we used a distribution-preserving sampling approach based on KMeans clustering and binning, and we evaluated its statistical fidelity using KS, Wasserstein, JS, and KL divergence metrics. Our approach preserved the distribution of docking scores, demonstrating the utility of data-driven strategies for scalable virtual screening and establishing a benchmark dataset for machine learning in drug discovery.},
}

@article {pmid42052730,
year = {2026},
author = {Paul, R and Coster, D and Zhao, Y and Liu, Y and Liu, Y},
title = {On cloud microfluidic experiment platform powered by in situ maskless lithography.},
journal = {Lab on a chip},
volume = {},
number = {},
pages = {},
doi = {10.1039/d6lc00035e},
pmid = {42052730},
issn = {1473-0189},
abstract = {Microfluidics has seen rapid growth across research and development, offering significant potential for scientific, engineering, and commercial applications. Yet, current teaching methodologies often lack a hands-on, problem-based learning approach, limiting students' practical experience. Researchers in low-resource regions also face barriers to accessing comprehensive microfluidic setups. Addressing this gap, we introduce a cloud-based platform for microfluidic experiments that integrates in situ polymerization, advanced fluid flow control, high-speed imaging, and edge computing. This first-of-its-kind platform allows users to fabricate high-resolution microfluidic devices and conduct experiments remotely through a web interface, expanding access for researchers, educators, and students. It features an image-assisted in situ polymerization process that enables easy pattern generation from uploaded images or CAD files, alongside robust computational resources for data processing, analysis, and machine learning applications. We demonstrate the platform in an educational setting through a Biological Fluid Mechanics course project and in research through experiments on organoid mechanical stretching. This work marks a milestone in democratizing access to microfluidic technology and advancing experimental capabilities in research and education.},
}

@article {pmid42055149,
year = {2026},
author = {Çetinkaya, Ç and Bazzocchi, M and Dave, VS and Halkude, B and Hussain, AS and Prasad Vallabha, CK and Razavi, SM and Stephens, J and Steiner, R and Sultan, T and Sun, CC and Tantuccio, AS and Tajarobi, P and Ündey, C and Wikström, H and Xu, X},
title = {A review of the State-of-the-Art: progress in ultrasonic and acoustic techniques for quality assessment in the development and manufacturing of oral solid dosage forms - Part II: Applications and emerging directions.},
journal = {International journal of pharmaceutics},
volume = {},
number = {},
pages = {126925},
doi = {10.1016/j.ijpharm.2026.126925},
pmid = {42055149},
issn = {1873-3476},
abstract = {This review is presented in two parts: Part I addresses the theoretical foundations and principles of ultrasonic and acoustic techniques, while Part II-presented here-focuses on their practical applications and emerging directions in advanced pharmaceutical tablet manufacturing. Building on the concepts established in Part I, this article (Part II) surveys real-world use cases and forward-looking innovations, highlighting how ultrasonic techniques enable rapid, non-destructive evaluation of Critical Quality Attributes (CQAs), including dissolution performance, tensile strength, porosity, friability, coating thickness, and subsurface defects. These methods are shown to align with United States Pharmacopeia (USP) reference tests and support regulatory frameworks and initiatives, including Process Analytical Technology (PAT), Quality by Design (QbD), Continuous Manufacturing (CM), and Real-Time Release Testing (RTRT). Illustrative case studies from both industry and academia demonstrate the integration of ultrasonic tools into advanced manufacturing ecosystems. Key developments include the use of 3D printing for Oral Solid Dosage (OSD) forms, ML-driven quality analytics, cyber-physical systems for closed-loop control, and cloud-based Manufacturing Execution Systems (MES) secured by public blockchain and cloud computing infrastructure. Despite these advances, challenges persist-such as multi-scale material complexity, signal degradation from material irregularities, and the need for robust multimodal data integration-which necessitate the use of sophisticated software and ML-based predictive modeling. Looking ahead, the convergence of wave sensing, robotics, industrial IoT, and digital twin technology is poised to transform pharmaceutical manufacturing into a highly automated, data-centric enterprise. This transformation promises not only to advance RTRT and CM but also to deliver unprecedented levels of quality, traceability, efficiency, and agility across the tablet formulation, design, and production lifecycle.},
}

@article {pmid42048396,
year = {2026},
author = {Song, P and Li, J and Wu, N},
title = {How digital transformation in "diverse directions" activates enterprise total factor productivity: A mechanism study based on dynamic capability reconfiguration.},
journal = {PloS one},
volume = {21},
number = {4},
pages = {e0347212},
doi = {10.1371/journal.pone.0347212},
pmid = {42048396},
issn = {1932-6203},
mesh = {Artificial Intelligence ; *Digital Technology/economics ; Cloud Computing ; Humans ; Blockchain ; *Efficiency ; Big Data ; Models, Theoretical ; Economic Development ; },
abstract = {Digital transformation is a key force driving high-quality economic development, yet the economic consequences of different digital technology directions vary significantly. Existing research often treats it as a homogeneous whole, overlooking the "diverse direction" characteristics of technologies, making it difficult to explain the "productivity paradox." From the perspective of dynamic capability reconstruction, this paper constructs a theoretical model of "Digital 'Diverse Direction' Transformation-Dynamic Capability-Total Factor Productivity (TFP)." Using the LDA topic model to conduct semantic analysis on the annual reports of A-share listed companies, we categorize digital transformation into five directions: Artificial Intelligence (AI), Big Data, Cloud Computing, Blockchain, and Digital Technology Application. We empirically test their differentiated impacts on enterprise TFP and the underlying mechanisms. The findings are as follows: First, while digital transformation overall significantly promotes enterprise TFP, there is significant "technological heterogeneity" among different directions. AI has the strongest promoting effect, followed by Big Data and Digital Technology Application, whereas the impacts of Cloud Computing and Blockchain are not yet significant. This provides micro-evidence for explaining the "productivity paradox." Second, mechanism tests indicate that dynamic capability reconstruction is the key transmission path; digital transformation enhances TFP by strengthening organizational coordination and integration capabilities, change and reconstruction capabilities, and learning and absorption capabilities. Third, heterogeneity analysis reveals that digital transformation exerts a more pronounced effect on enhancing productivity in labor-intensive and technology-intensive industries, as well as in the manufacturing sector. These conclusions deepen the theoretical understanding of the economic consequences of digital transformation and provide empirical evidence and decision-making references for enterprises to choose adaptable and differentiated transformation paths.},
}

Artificial Intelligence
*Digital Technology/economics
Cloud Computing
Humans
Blockchain
*Efficiency
Big Data
Models, Theoretical
Economic Development

@article {pmid42050010,
year = {2026},
author = {Sravan, P and Shaik, MA},
title = {DRL-based multi-objective task scheduling for edge-cloud computing: latency, energy, and SLA optimisation.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-49824-1},
pmid = {42050010},
issn = {2045-2322},
}

@article {pmid42038155,
year = {2026},
author = {Bose, R and Mondal, S and Sutradhar, S and Das, S and Faheem, M and Roy, S and Ahmad Khan, A},
title = {Enhancing Cloud-Based Healthcare Security With Quantum-Secure HealthChain: A Quantum Computing and Blockchain Integrated Framework.},
journal = {Health science reports},
volume = {9},
number = {5},
pages = {e72367},
pmid = {42038155},
issn = {2398-8835},
abstract = {BACKGROUND AND AIMS: Rising quantum hazards and flaws in conventional encryption make cloud-based healthcare data security harder. Quantum-Secure HealthChain, a new architecture using blockchain and quantum computing, improves medical data security, patient privacy, and data fidelity.

METHODS: To prevent quantum attacks, the proposed system uses Quantum Key Distribution (QKD) for safe cryptographic key exchange and quantum-resistant encryption. Blockchain technology secures medical records, while multi-layered encryption ensures data privacy. Quantum Biometric Authentication improves access control using quantum entanglement and biometric data. Key generation, encryption, blockchain storage, authentication, and decryption are system process steps. Experimental evaluation focuses on encryption speed, resource economy, throughput, and scalability using simulated healthcare data.

RESULTS: Experimental data demonstrate system strength and efficiency. Encryption and decryption perform consistently for 1 to 100 MB data sizes with negligible overhead. Throughput can reach 105 transactions per second under normal demand; CPU (82%) and memory (210 MB) utilization are low. Scalability studies show linear expansion lets the system handle increased data volumes and user demands without sacrificing performance. Security study confirms quantum attack, data corruption, and unauthorized access resistance.

CONCLUSION: Quantum-Secure HealthChain offers a revolutionary method to cloud-based healthcare system security. Blockchain-quantum computing integration ensures strong authentication, safe key exchange, and quantum-resistant encryption. Its security, scalability, and efficiency make it a future-ready platform for safe medical data management, reducing quantum computing hazards.},
}

@article {pmid42038659,
year = {2026},
author = {Ghafi, AK and Pirkhedri, A and Akhbarifar, S and Shafiabadi, MH},
title = {Epidemic Forecasting via Hybrid Deep Learning With Unified Visibility and Temporal Graphs Under Stochastic Noise Modelling.},
journal = {Healthcare technology letters},
volume = {13},
number = {},
pages = {e70073},
pmid = {42038659},
issn = {2053-3713},
abstract = {Epidemic forecasts are unreliable when surveillance data are noisy or incomplete and when underreporting and rapidly changing population behaviour distort observed incidence, degrading the stability of conventional statistical and deep-learning models. We aim to develop an interpretable, uncertainty-aware forecasting pipeline that remains robust under data corruption and is practical for real-time use. We convert COVID-19 incidence into multilayer temporal graphs: global cumulative counts are differenced to daily incidence, normalised, and segmented into overlapping 30-day windows; for each window, we build a visibility graph from the empirical series and a matched-length visibility graph from stochastic simulations (fractional Brownian motion and Lévy-type dynamics) to represent reporting and behavioural randomness. We fuse the graphs (weighted edge averaging), extract compact descriptors (mean degree, clustering coefficient, entropy) and train a lightweight regressor to predict the 7-day-ahead average incidence. On the Johns Hopkins COVID-19 dataset, the method outperforms ARIMA, LSTM and standard GCN baselines (MAE = 0.0558; RMSE = 0.0709). Stress tests with noise and missingness and ablations show that stochastic augmentation and graph fusion materially improve robustness, while a cloud-oriented deployment reduces inference time by >60% and memory usage by 35%, enabling low-latency monitoring for timely public-health decision-making.},
}

@article {pmid42045423,
year = {2026},
author = {Ahmad, W and Silaparasetti, L and Arya, P and Pandya, S and Kumar, P and Khan, T and Alabdulatif, A},
title = {Energy-constrained workflow scheduling for DVFS-enabled heterogeneous systems in cloud computing environments.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-49785-5},
pmid = {42045423},
issn = {2045-2322},
support = {QU-APC-2026//Qassim University/ ; },
}

@article {pmid41959064,
year = {2026},
author = {Bright, M and Mi, X and Duarte, D and Carey, E and Lyu, B and Wang, Y and Nimmerjahn, A and Yu, G},
title = {AQuA2-Cloud: a web platform for fluorescence bioimaging activity analysis.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {41959064},
issn = {2692-8205},
abstract = {BACKGROUND: Advanced biological imaging analysis platforms such as Activity Quantification and Analysis (AQuA2) enable accurate spatiotemporal activity analysis across diverse cell populations within many species. These tools are increasingly important for investigating cellular signaling dynamics and behavior. However, despite advances in the accuracy and species capability of AQuA2, it remains computationally demanding for analysis of long time-series datasets and requires all users to maintain a MATLAB license, which may limit accessibility and large-scale deployment.

RESULTS: To address these limitations, we have designed and made available AQuA2-Cloud, a portable software stack and web platform developed as an improvement and further evolution of AQuA2. This container-deployable system permits multi-user cloud-based high accuracy activity quantification with intuitive workflows, export of analysis data and project files, and comparable processing times. The platform offers integrated features such as in-browser analysis control interfaces, asynchronous program state control, multiple users and user management, support for unreliable connections, file uploading and downloading via web browsers and File Transfer Protocol, and centralized organization of analysis output.

CONCLUSION: AQuA2-Cloud constitutes a cloud-native solution for laboratories or research groups seeking to centralize analysis of spatiotemporal biological imaging datasets while reducing software installation and licensing barriers for end users. The platform enables researchers with minimal technical expertise to perform advanced bioimaging analysis through standard web browsers while maintaining the analytical capabilities of AQuA2. AQuA2-Cloud source code, deployment procedures, and documentation are freely available at (https://github.com/yu-lab-vt/AQuA2-Cloud).},
}

@article {pmid42029755,
year = {2026},
author = {Rathupetsane, EM and Kganyago, M},
title = {Geospatial analysis of flooding events using Sentinel-1 and Sentinel-2 data: a tale of two South African cities.},
journal = {Environmental monitoring and assessment},
volume = {198},
number = {5},
pages = {},
pmid = {42029755},
issn = {1573-2959},
mesh = {South Africa ; *Floods/statistics & numerical data ; *Environmental Monitoring/methods ; Cities ; Humans ; Climate Change ; Spatial Analysis ; },
abstract = {The frequency and severity of floods have increased in the last five decades due to climate change and human activities, significantly impacting human lives, economies, and infrastructure. South Africa is among the most affected regions, primarily due to informal settlements, limited resources, and a weak capacity to respond to the growing flood risk, with annual impacts increasing. Earth Observation data offers crucial insights for flood monitoring and risk management, yet studies and proactive measures remain limited in the country. Therefore, this paper conducted a geospatial analysis of recent flooding incidents (i.e., 2017-2022) in two South African cities using Sentinel-1 and Sentinel-2 datasets within a cloud computing environment. Specifically, we evaluated the potential of Sentinel-1 Radar and Sentinel-2 multispectral data for flood mapping using thresholding techniques and estimated the number of people affected by incorporating statistical and building-count data. The results showed that Sentinel-2 misclassified many areas due to confusion with clouds shadows. In contrast, Sentinel-1 showed greater potential for rapidly mapping floods near the incident date and estimating the number of people exposed, making it suitable for rapid flood assessments. Consequently, flooded areas derived from Sentinel-1 imagery were more realistic, indicating that about 60,000 people were cumulatively affected by flooding in eThekwini in April 2019 and October 2017, respectively. Comparatively, relatively few people (i.e., ~ 42,068 in March 2018 and 39,903 in February 2020) were affected by the various flood incidents in Johannesburg. Overall, the study has the potential to provide pertinent information on flooded areas and to aid follow-up analysis, such as infrastructure damage assessment, thereby offering prospects for informing not only disaster management and policy formulation but also critical decisions and resource allocation.},
}

South Africa
*Floods/statistics & numerical data
*Environmental Monitoring/methods
Cities
Humans
Climate Change
Spatial Analysis

@article {pmid42031862,
year = {2026},
author = {Sharawy, AA and Sakr, RH and Eladrosy, W and Alrahmawy, MF},
title = {WS-SSA: workflow scheduling in cloud computing using salp swarm algorithm.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {42031862},
issn = {2045-2322},
}

@article {pmid42020563,
year = {2026},
author = {Xie, L and Liu, C and Ding, Z and Tang, N and Shi, Y},
title = {Edge-enabled IoT framework for real-time tobacco quality monitoring.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-47038-z},
pmid = {42020563},
issn = {2045-2322},
abstract = {Tobacco quality inspection plays a vital role in ensuring standardized processing, reducing economic losses, and improving industrial automation. However, traditional inspection methods often suffer from inefficiency, high labor costs, and limited real-time capabilities. To address these challenges, this paper proposes an Internet of Things (IoT)-based data acquisition and edge computing framework enhanced with deep learning models for tobacco quality inspection. The system integrates heterogeneous sensing devices for multi-source data collection, including moisture, color, and texture features, while leveraging edge computing nodes to conduct real-time preprocessing, feature extraction, and anomaly detection. Specifically, a convolutional neural network (CNN) is employed to extract spatial texture and color features, while a long short-term memory (LSTM) network captures temporal dependencies in moisture and process variations. A lightweight data transmission protocol and optimized scheduling algorithm are designed to balance computational efficiency and energy consumption. Experimental results demonstrate that the proposed hybrid edge model achieves an accuracy of 96.3% in tobacco quality classification, while reducing average processing latency by 38% compared with cloud-only architectures. In addition, complexity profiling shows that the deployed model requires 12.8M parameters, 3.42 GFLOPs, 486 MB peak runtime memory, and 25.0 ms end-to-end latency at [Formula: see text] input resolution on a Jetson Xavier NX platform. The study provides a practical solution for real-time and scalable tobacco quality monitoring, offering theoretical insights and engineering value for smart agriculture and industrial IoT applications.},
}

@article {pmid42008450,
year = {2026},
author = {Wanna, Y and Wiratchawa, K and Intharah, T},
title = {TinyAct: A framework for real-time action recognition in the cloud through distillation learning.},
journal = {PloS one},
volume = {21},
number = {4},
pages = {e0347245},
doi = {10.1371/journal.pone.0347245},
pmid = {42008450},
issn = {1932-6203},
mesh = {Humans ; Deep Learning ; *Cloud Computing ; *Machine Learning ; Video Recording ; Algorithms ; *Pattern Recognition, Automated/methods ; },
abstract = {Human action recognition has become increasingly important for applications in security surveillance, healthcare monitoring, and smart environments. However, existing deep learning models typically require substantial computational resources, making deployment on resource-constrained edge devices challenging. To address this limitation, we propose TinyAct, a lightweight framework for real-time human action recognition that combines edge computing with cloud-based processing through knowledge distillation. TinyAct employs a 3D video autoencoder to extract compact spatiotemporal features from video sequences, coupled with classical machine learning classifiers for action prediction. The framework utilizes an AIoT (Artificial Intelligence of Things) architecture where feature extraction occurs on edge devices while classification is performed in the cloud, enabling real-time processing with reduced bandwidth requirements. To enhance performance, we implement knowledge distillation using the ILA-ViT-B/16 transformer as a teacher model to transfer temporal knowledge to our compact student architecture. Our experiments on the Kinetics-400 dataset demonstrate that TinyAct achieves competitive performance while maintaining computational efficiency. Using 16-frame video clips with 1024-dimensional latent features, Random Forest achieved the highest baseline accuracy of 57.00%, followed by SVM (55.00%) and XGBoost (54.00%). The autoencoder-based feature extraction significantly reduces computational overhead compared to end-to-end deep learning approaches while preserving essential spatiotemporal information for accurate action recognition. The knowledge distillation experiments reveal that training configuration critically affects performance, with non-pretrained student models achieving better results (15.11% with SVM) than pretrained ones under teacher supervision. This suggests that joint optimization of the encoder and classifier is essential for effective knowledge transfer in resource-constrained settings. TinyAct's modular architecture enables flexible deployment across diverse hardware configurations, supporting both lightweight edge inference and cloud-based training pipelines. The framework demonstrates that effective human action recognition can be achieved without computationally intensive deep networks, making it suitable for smart surveillance systems, IoT applications, and embedded devices where computational resources are limited.},
}

Humans
Deep Learning
*Cloud Computing
*Machine Learning
Video Recording
Algorithms
*Pattern Recognition, Automated/methods

@article {pmid42009763,
year = {2026},
author = {Manavalan, T and Gunasekaran, P and Elumalai, S and Verma, LP and Kumar, G},
title = {Hybrid Monarch Butterfly Optimization-DenseNet framework for energy-aware task scheduling in cloud environments.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-47949-x},
pmid = {42009763},
issn = {2045-2322},
}

@article {pmid42011237,
year = {2026},
author = {Kapetanidou, IA and Kotsiopoulos, T and Thanasoulis, G and Bizopoulos, P and Liatifis, A and Skoularikis, M and Nizamis, A and Sarigiannidis, P and Votis, K},
title = {A telemetry dataset on resource utilisation and power consumption in the edge-cloud continuum.},
journal = {Data in brief},
volume = {66},
number = {},
pages = {112734},
doi = {10.1016/j.dib.2026.112734},
pmid = {42011237},
issn = {2352-3409},
abstract = {This paper presents a telemetry dataset capturing resource utilization and power consumption metrics across the ENACT edge-cloud continuum. The dataset contains empirical telemetry collected in real-time for both infrastructure nodes and application workloads. More specifically, a distributed weather forecasting scenario has been emulated, comprising five pods: two different weather data sources, two forecasting services (one per node/computing layer) and one long-term storage pool. A cloud-based machine and an edge device belonging to the same Kubernetes cluster have been considered for the deployment of the application pods, corresponding to heterogeneous computing tiers. Data acquisition was performed using ENACT's Telemetry Data Collector and Monitoring Engine which measures telemetry and energy metrics at node and pod levels in real-time. The resulting dataset provides time-series records including CPU, memory and disk utilization, network throughput, and energy consumption for the cloud node, the edge node and the five application pods. Telemetry data was collected during two distinct phases: for a period with application workloads running normally and for a baseline period when applications were removed from the cluster. This allows for assessing the impact of the applications activity in terms of resource usage and energy consumption. This dataset offers valuable insights for the research community in distributed systems, the edge-cloud continuum and cognitive computing, wherein datasets on real-world data, especially reflecting both infrastructure-level and application-level telemetry, are currently very limited. It is particularly useful for developers and research scientists that require such data for tasks such as training and fine-tuning time-series forecasting models, benchmarking anomaly detection models and validating scheduling algorithms and energy-aware strategies, to name a few.},
}

@article {pmid41999094,
year = {2026},
author = {Ermel, UH and Schwartz, J and Zhao, Z and Ji, D and Peck, A and Yu, Y and Paraan, M and Carragher, B and Frangakis, AS and Harrington, KIS},
title = {copick: An open dataset interface and toolkit for collaborative annotation and analysis of cryo-electron tomography data.},
journal = {Protein science : a publication of the Protein Society},
volume = {35},
number = {5},
pages = {e70578},
doi = {10.1002/pro.70578},
pmid = {41999094},
issn = {1469-896X},
mesh = {*Electron Microscope Tomography/methods ; *Software ; Humans ; Cryoelectron Microscopy ; User-Computer Interface ; Image Processing, Computer-Assisted ; },
abstract = {Cryo-electron tomography (cryoET) enables visualization of macromolecular complexes within intact cellular environments. Continued improvements in instrumentation, sample preparation, and data-processing pipelines have increased both the scale and the complexity of cryoET datasets, making manual analysis challenging. To support scalable, collaborative annotation, we developed copick, an open-source dataset application programming interface (API) and accompanying tool suite for cryoET analysis. Copick provides standardized access to tomograms, segmentations, point annotations, meshes, and feature maps across local storage, high-performance computing systems, cloud platforms, and public repositories. Plugins for napari and ChimeraX enable human-in-the-loop workflows for particle picking, segmentation, inspection of machine-learning outputs, and project-level collaboration. A multi-resolution Open Microscopy Environment (OME)-Zarr architecture supports responsive visualization and cross-platform access. Copick additionally provides a Model Context Protocol interface enabling automated generation of annotation-curation pipelines using natural-language instructions. Together, these tools support reproducible, scalable, and collaborative cryoET analysis.},
}

*Electron Microscope Tomography/methods
*Software
Humans
Cryoelectron Microscopy
User-Computer Interface
Image Processing, Computer-Assisted

@article {pmid41998135,
year = {2026},
author = {Vaidya, S and Jethava, G and Jethava, S},
title = {Enhancing smart factory performance via hybrid scheduling and intelligent resource management.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-49107-9},
pmid = {41998135},
issn = {2045-2322},
abstract = {The rise of connected devices since the industrial revolution has led to vast data generation and new digital challenges. A huge data from smart assets demanded scalable, private, and low-latency solutions. We propose a fog computing approach that brings analytics closer to devices. Our system enhances a standard machine-to-machine architecture using container-based orchestration for autonomy and peer-to-peer cyber-physical system communication. The focus is on smart factories and industrial Internet of Things (IIoT) applications. Recent progress on lightweight deep learning algorithms and fog computing permits multiple model inference tasks to run simultaneously on these resource-limited edge devices, so that we can collaboratively make one thing instead of getting good model quality in each single task. However, the high running latencies overall in multi-model inferences are a drawback for real-time applications. The proposed method introduces a hybrid partial swarm optimization-genetic algorithm scheduler that merges particle swarm optimization and genetic algorithm techniques to fine-tune task initiation times and minimize latency. By leveraging the strengths of both algorithms, it dynamically updates scheduling decisions for enhanced efficiency. This AI-driven model integrates IoT and digital twins to support adaptive, real-time optimization in smart manufacturing environments. Its innovation lies in balancing complex trade-offs across multiple objectives, delivering significant gains in agility and performance within the Industry 4.0 paradigm.},
}

@article {pmid41786875,
year = {2026},
author = {Dhiman, G and Singh, KD and Singh, PD and Alghamdi, NS and Albakri, GS},
title = {A novel approach to reliable and flexible distributed computing with virtualization in smart healthcare applications.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {41786875},
issn = {2045-2322},
abstract = {Currently, fast and efficient computing is driving the new disruptive technologies required by modern healthcare systems. A simulation model is presented that studies how virtualization affects the performance of task offloading in smart health-care environments. Virtualization technology is suitable for task-offloading processes. A simulation-based framework can be used to examine how virtualization overhead influences task offloading efficiency in smart healthcare environments. Therefore, the proposed model uses virtualization to manage resources more effectively and make the system more reliable. Important factors such as how long a task takes to finish, how much energy it uses, how much data it can handle, and how well it can grow when more tasks are added must be considered for smart healthcare. The results were compared with those of a system that did not use virtualization; thus, we can clearly observe how virtualization changes the overall performance. The findings show that better resource use, lower energy consumption, and improved fault recovery are possible, which makes the proposed system suitable for real healthcare applications. In the future, this work can be extended by adding predictive analytics, improving machine-learning–based scheduling, using multiple cloud platforms, and making virtualization even more energy-efficient.},
}

@article {pmid41991544,
year = {2026},
author = {Al-Jumaili, AHA and Seno, ME and Awad, WK and Muniyandi, RC and Hasan, MK},
title = {A fusion deep Q-learning and particle swarm optimization algorithm for adaptive resource allocation in cloud computing circumstances.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-33498-2},
pmid = {41991544},
issn = {2045-2322},
abstract = {Effective resource allocation in cloud computing continues a critical challenge due to dynamic loads, stringent service-level expectations, and the need to balance execution time, energy, and cost. This study suggests a hybrid framework that integrates Deep Q-Learning (DQL) with Particle Swarm Optimization (PSO) to aid adaptive, multi-objective scheduling. DQL learns allocation strategies through interaction with the cloud environment, while PSO performs global search to refine action selection and accelerate convergence. Using Cloud Sim with real and synthetic workloads (Google Cluster, Planet Lab traces), the proposed method achieved a 35% reduction in average task execution time (from 245 s to 159 s) and a ~ 40% relative growth in resource utilization (from 60.1% to 84.6%), reduced SLA violations from 28 to 8, and lowered energy consumption to 6.3 kWh, outperforming standalone and hybrid models across 30 independent runs. Statistical tests (two-tailed t-test, α = 0.05) confirm significance. These results demonstrate that coupling reinforcement learning among swarm intelligence yields adaptive, high-quality decisions on behalf of real-time cloud resource scheduling.},
}

@article {pmid41991750,
year = {2026},
author = {Bndyan, Q and Salih, R and Ali, KAA and Mousa, KM and Ahmed, DH},
title = {A neural network-based framework for enterprise financial error correction using AI and big data.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-48510-6},
pmid = {41991750},
issn = {2045-2322},
}

@article {pmid41992036,
year = {2026},
author = {Shao, K and Wang, Y and Wang, B and Sang, Y},
title = {Edge station deployment by fewest covered user first for cost improvement.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-48089-y},
pmid = {41992036},
issn = {2045-2322},
support = {26AXQXT070//Henan Province School Enterprise Collaborative Innovation Project/ ; ZZZX202436//Zhengzhou Basic Research and Applied Basic Research Project/ ; YJS2025GZZ64//Henan Province Joint Graduate Training Base Project/ ; 2023IT043//China University Research Innovation Fund/ ; 24B520021//Key Research Project of Higher Education Institutions in Henan Province/ ; YuGongXin Data [2024] No. 266//University Intelligent Teaching Big Data Industry Integration Innovation Center/ ; 252102221017//Project of Science and Technology in Henan Province/ ; },
abstract = {In this paper, we address the edge station deployment problem (ESDP), which aims to determine optimal sites for deploying edge stations so as to maximize user coverage and minimize deployment cost. We first formulate the ESDP as a binary linear programming model and prove its NP-hardness by reducing the set covering problem to a specialized instance of ESDP. To solve the ESDP in polynomial time, we propose a novel heuristic algorithm that prioritizes covering users who are within range of the fewest candidate sites. Our algorithm iteratively selects the site that can cover the most users from among the candidate sites capable of covering those least-covered users. To evaluate the performance of our algorithm, we conduct simulation experiments based on a real-world dataset. Experimental results demonstrate that our algorithm achieves 100% user coverage with lower deployment cost compared to several classical and state-of-the-art algorithms.},
}

@article {pmid41982557,
year = {2026},
author = {Choi, CMS and Rangnekar, A and Jiang, J and Veeraraghavan, H},
title = {Topological data analysis visualization for interpretable assessment of AI contouring quality.},
journal = {Proceedings of SPIE--the International Society for Optical Engineering},
volume = {13929},
number = {},
pages = {},
pmid = {41982557},
issn = {0277-786X},
abstract = {Advances in artificial intelligence have increased the availability of auto-segmentation tools. However, conventional accuracy metrics cannot capture regional segmentation differences between AI models or with respect to reference segmentations, necessary to interpret contouring variations. To address this, we developed a novel distance metric based on topological data analysis (TDA) to evaluate 3D point cloud representations of segmentations applied to six organs-at-risk (OARs) and lung gross tumor volume (GTV). A total of 34 CTs and 54 CBCTs were analyzed to compare a bespoke AI segmentation method with reference clinical contours. TDA involved: (1) converting segmentations into 3D point clouds, (2) clustering them into regions via K-means with fixed seeds and cluster numbers determined by the Elbow method, (3) constructing directed graphs for AI and reference clusters using centroids as nodes, and (4) computing distances using unbalanced optimal mass transport. Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were also calculated. TDA successfully identified local regions of high deviation in both OARs and GTVs of varying shapes. It correlated positively with HD95 and negatively with DSC based on Pearson's correlation coefficient. Computation was efficient, averaging 1.72 s, and TDA effectively highlighted regions of greatest mismatch, providing quantitative visualization of poor concordance. In conclusion, we developed a new TDA metric for comparing auto-segmentation of GTV and OARs. Importantly, it allows visualization of mismatching regions thus potentially allowing faster contour editing and evaluation.},
}

@article {pmid41985122,
year = {2026},
author = {Jeong, S and Ko, HW and Kang, JH and Ko, J and Sim, I and Xu, Z and Jeong, H and Kim, SI and Choi, Y and Jo, S and Shim, JW and Seo, PH and Chae, MS and Kim, Y and Rehman, A and Yeon, H and Park, BI and Suh, YW and Lee, H and Kim, J and Lee, K and Bae, SH and Park, MC and Mun, S},
title = {Event-Driven Neuromorphic Gaze Decoding via e-Skin Electrooculography.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.5c19720},
pmid = {41985122},
issn = {1936-086X},
abstract = {Wearable eye-tracking technologies remain constrained by bulky optics, high power consumption, and reliance on external computation. We present a hardware-software codesigned electrooculography (EOG) interface that integrates ultrathin conformal e-skin sensors with resistive random-access memory (RRAM) crossbar, used to implement synaptic vector-matrix multiplication within a neuromorphic processing pipeline for real-time gaze decoding. Conformal e-skin sensors provide stable and continuous acquisition of both vertical and horizontal oculomotor signals, which are transformed into attention-guided spike features for classification by a lightweight spiking neural network (SNN). Implemented on an RRAM array with quantized weights after noise-aware training, the system achieves robust inference while substantially lowering latency and energy demand. The proposed framework is designed for local edge-level computation, thereby preserving user privacy and eliminating the need for cloud-based inference. By eliminating head-mounted optics, this glassless architecture enables unobtrusive and energy-efficient wearable interfaces. These results establish flexible bioelectronics with neuromorphic processors to advance immersive computing, assistive interfaces, and mobile health monitoring.},
}

@article {pmid41979928,
year = {2026},
author = {Padliya, T},
title = {From Episodic Screening to Continuous Insight: AI Architectures for Colorectal Care.},
journal = {IEEE pulse},
volume = {17},
number = {1},
pages = {15-20},
doi = {10.1109/MPULS.2026.3659236},
pmid = {41979928},
issn = {2154-2317},
mesh = {Humans ; *Colorectal Neoplasms/diagnosis/prevention & control ; *Artificial Intelligence ; *Early Detection of Cancer/methods ; *Mass Screening/methods ; },
abstract = {Integrating wearables and clinical data to reshape colorectal cancer (CRC) prevention.Colorectal cancer (CRC) pathways are still dominated by episodic screening through colonoscopy and stool-based tests, despite growing access to consumer sensors and mobile computing. This article presents an end-to-end AI architecture for multimodal colorectal risk stratification that combines traditional screening data with passively collected activity patterns, heart rate variability, stool frequency logs, and nutrition context. We describe a layered design consisting of data ingestion, feature engineering, temporal modeling, risk scoring, and clinician-facing decision support. Implementation patterns are illustrated using cloud-native and edge components suitable for deployment in both high-resource and resource-constrained health systems. The article discusses issues such as bias, data sparsity, longitudinal drift, and integration with existing screening guidelines. As a result, it provides a reference model that future clinical and engineering teams can adapt when building continuous, AI-assisted colorectal screening and monitoring tools.},
}

Humans
*Colorectal Neoplasms/diagnosis/prevention & control
*Artificial Intelligence
*Early Detection of Cancer/methods
*Mass Screening/methods

@article {pmid41969925,
year = {2026},
author = {Maga, AM and Fillion-Robin, JC},
title = {MorphoCloud: Democratizing Access to High-Performance Computing for Morphological Data Analysis.},
journal = {F1000Research},
volume = {15},
number = {},
pages = {53},
pmid = {41969925},
issn = {2046-1402},
mesh = {*Software ; *Imaging, Three-Dimensional ; Humans ; *Data Analysis ; },
abstract = {BACKGROUND: The digitization of biological specimens has revolutionized morphology, generating massive 3D datasets such as microCT scans. While open-source platforms like 3D Slicer and SlicerMorph have democratized access to advanced visualization and analysis software, a significant "compute gap" persists. Processing high-resolution 3D data requires high-end GPUs and substantial RAM, resources that are frequently unavailable at Primarily Undergraduate Institutions (PUIs) and other educational settings. This "digital divide" prevents many researchers and students from utilizing the very data and software that have been made open to them.

METHODS: We present MorphoCloud, a platform designed to bridge this hardware barrier by providing on-demand, research-grade computing environments via a web browser. MorphoCloud utilizes an "IssuesOps" architecture, where users manage their remote workstations entirely through GitHub Issues using natural-language commands (e.g., /create, /unshelve). The technology stack leverages GitHub Issues and Actions for front-end and orchestration respectively, JetStream2 for backend compute, and Apache Guacamole to deliver a high-performance, GPU-accelerated desktop experience to any modern browser.

RESULTS: The platform enables a streamlined lifecycle for remote instances, which come pre-configured with the SlicerMorph ecosystem, R/RStudio, and AI-assisted segmentation tools like NNInteractive and MEMOs. Users have access to a persistent storage volume that is decoupled from the instance. For educational purposes, MorphoCloud supports "Workshop" instances that allow for bulk provisioning and stay online continuously for short-term events. This identical environment ensures that instructors can conduct complex 3D workflows without the typical troubleshooting delays caused by heterogeneous student hardware.

CONCLUSION: MorphoCloud demonstrates that true scientific accessibility requires not just open data and software, but also open infrastructure. By abstracting the complexities of cloud administration into a simple, command-driven interface, MorphoCloud empowers researchers at under-resourced institutions to engage in high-performance morphological analysis and AI-assisted segmentation.},
}

*Software
*Imaging, Three-Dimensional
Humans
*Data Analysis

@article {pmid41971520,
year = {2026},
author = {Ayad, N},
title = {EvalTest: a web-based tool for the assessment of diagnostic test performance.},
journal = {Biochemia medica},
volume = {36},
number = {2},
pages = {020101},
pmid = {41971520},
issn = {1846-7482},
mesh = {Humans ; *Software ; *Internet ; *Diagnostic Tests, Routine ; ROC Curve ; Reproducibility of Results ; },
abstract = {INTRODUCTION: Accurate diagnostic tests are essential in clinical and laboratory medicine. Evaluation of diagnostic test performance requires advanced statistical expertise and the use of specialized or proprietary software. The aim of this work is to develop an open-source R Shiny application for facilitating diagnostic test evaluation by integrating statistical rigor with user-friendly interactivity.

MATERIALS AND METHODS: The EvalTest application was developed in the R programming language using the Shiny framework and released as an open-source package on the Comprehensive R Archive Network, with the full source code available on GitHub. In addition, the application is available on a cloud platform and accessible via web browser. Statistical formulas used to compute diagnostic performance indicators and their confidence intervals were implemented within the R environment to ensure transparency and reproducibility.

RESULTS: The developed application provides an interactive interface for importing Excel datasets, setting test variable type, selecting test and reference variables, and specifying disease prevalence. It automatically computes key diagnostic performance indicators, including sensitivity, specificity, predictive values, likelihood ratios, accuracy, the Youden index, and the area under the curve (AUC), along with their 95% confidence intervals. EvalTest also generates confusion matrices, receiver operating characteristic (ROC) curves with confidence bands, and identifies the optimal cutoff for quantitative tests. All numerical and graphical outputs can be exported in suitable formats to facilitate reporting and documentation.

CONCLUSIONS: EvalTest provides an open and reproducible solution for diagnostic test evaluation by computing and visualizing key performance measures with their confidence intervals, supporting its use in clinical and research settings.},
}

Humans
*Software
*Internet
*Diagnostic Tests, Routine
ROC Curve
Reproducibility of Results

@article {pmid41959061,
year = {2026},
author = {Emissah, HA and Tecuatl, C and Ascoli, GA},
title = {Automated Proofreading of Digitally Reconstructed Neural Morphology Enhances Accuracy, Scalability, and Standardization.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.03.27.714818},
pmid = {41959061},
issn = {2692-8205},
abstract = {BACKGROUND: The rapid expansion of large-scale neuroscience datasets has increased the need for automated, accurate, and standardized quality control (QC). Manual proofreading of 3-dimensional neural morphology (SWC files) remains labor-intensive, error-prone, and non-scalable. We developed and evaluated a fully automated, machine-learning driven QC pipeline to standardize neural reconstructions, detect and correct structural anomalies, and rectify dendritic labeling in pyramidal neurons.

METHODS: We developed an end-to-end, cloud-deployed pipeline for automated QC, correction, and standardization of SWC-formatted neural morphologies. The framework integrates deterministic structural normalization, topology repair, geometric correction, quantitative morphometric analysis, and graph-based dendritic relabeling within a containerized React/Flask architecture deployed on Amazon Web Services. Rule-based algorithms systematically detect, classify, and correct structural irregularities including overlapping nodes, spurious side branches, non-positive radii, disconnected components, and anomalously long parent-child connections. A graph convolutional network, trained on Sholl-derived features from 20,500 pyramidal neurons, performs dendritic relabeling. Model training employed an 80/10/10 train-validation-test split with adaptive learning-rate scheduling and distributed execution across ten runs to evaluate stability and reproducibility. The pipeline generates images of the final product and computes quantitative morphometrics using L-Measure.

RESULTS: All neuronal reconstructions were processed without manual intervention. Automated normalization and topology repair restored structurally coherent and biologically accurate morphologies suitable for quantitative analysis and visualization without data loss. Dendritic relabeling achieved a mean accuracy of 99.51%, consistent between validation and test sets, with class-weighted precision of 0.978, recall of 0.977, and F1-score of 0.977. Enforcing a single apical dendritic tree per neuron improved anatomical consistency without reducing classification performance. Distributed training completed all runs in approximately 25 hours, demonstrating scalability and reproducibility for large datasets.

CONCLUSIONS: We present a fully automated and cloud-scalable open-source pipeline for standardizing neural reconstructions and performing biologically consistent dendritic classification with near-perfect accuracy. The automated correction and relabeling procedures do not alter or compromise the size or unaffected morphological detail of the original SWC files, ensuring geometric fidelity and compatibility with downstream analysis tools. This open-access framework provides a robust foundation for high-throughput neural morphology curation and large-scale neuroanatomical analysis.},
}

@article {pmid41961529,
year = {2026},
author = {Ashraf, AR and Rádli, R and Vörösházi, Z and Fittler, A},
title = {Code-Based Versus AutoML Methods for Pill Recognition in Clinical Settings: Comparative Performance Study.},
journal = {JMIR medical informatics},
volume = {14},
number = {},
pages = {e79160},
doi = {10.2196/79160},
pmid = {41961529},
issn = {2291-9694},
mesh = {Humans ; *Machine Learning ; Medication Errors/prevention & control ; Artificial Intelligence ; },
abstract = {BACKGROUND: Visual identification and verification of medications during dispensing and administration are prone to human error, particularly in high-pressure and high-volume clinical settings. Misidentification can lead to medication errors, posing risks to patient safety and placing a burden on health care systems. Recent advances in computer vision and object detection offer promising solutions for automated solid oral dosage form (pill) recognition. However, comprehensive studies comparing code-based and no-code (automated machine learning [AutoML]) approaches for pill recognition are lacking.

OBJECTIVE: This study aimed to evaluate and compare the performance, cost, usability, and deployment feasibility of pill recognition models developed with Ultralytics YOLO11 and 3 cloud-based AutoML platforms (Amazon Rekognition Custom Labels, Google Vertex artificial intelligence [AI] AutoML Vision, and Microsoft Azure Custom Vision) using multiple datasets, including real-world clinical images.

METHODS: Five training subsets of increasing size (1230, 3450, 7380, 14,400, and 26,880 images) from 30 commonly dispensed medications were used to train models on YOLO11 and 3 AutoML platforms. Models were evaluated on 6 datasets from different environments: clinical images from 3 hospitals, a verification dataset, a laboratory dataset, and an exhaustive testing set. Performance metrics, including accuracy, precision, recall, and mean average precision, were calculated. We evaluated the impact of training data size on performance and benchmarked training time, platform costs, and limitations.

RESULTS: No single platform dominated across all test environments. On the verification dataset (optimal conditions), accuracy ranged from 80.83% (YOLO11) to 91.60% (Google Vertex AI) when trained with the full training dataset. YOLO11 showed consistent performance improvement with increasing training data (accuracy: 63.06%-80.83%) and achieved near-perfect precision and mean average precision scores (0.95-1.00). Google Vertex AI reached above 90% accuracy on 3 training subsets but showed unpredictable declines. Amazon Rekognition maintained near-perfect precision (0.92-1.00) but had the highest false negative rates (up to 0.74), missing many pills. Custom Vision demonstrated steady performance improvements (77.08%-85.62% accuracy) but lagged behind other AutoML platforms, probably due to its older YOLOv2-based architecture. On clinical datasets, accuracy fluctuated (20.62%-90%) depending on the dataset and platform. Training costs and time varied: YOLO11 (open-source), Microsoft Azure (US $9.50-US $28.60, allowed user-predefined training duration), Google Vertex AI (US $69.30 with consistent 2.5-3-hour training times), and Amazon Rekognition (US $5.43-US $43.89 with size-dependent training time scaling, reaching nearly 40 hours on the full 26,880-image dataset).

CONCLUSIONS: Each platform offers distinct advantages and trade-offs: YOLO11 provides the highest flexibility and lowest platform costs but requires technical expertise, while AutoML platforms can offer high performance at a higher cost but with limited user control, introducing unpredictability. The performance variations demonstrate that successful clinical deployment requires careful platform selection based on specific performance requirements, budget constraints, and available technical resources, followed by rigorous validation using real-world, representative data to ensure patient safety in clinical workflows.},
}

Humans
*Machine Learning
Medication Errors/prevention & control
Artificial Intelligence

@article {pmid41952847,
year = {2026},
author = {Ha, T and Nketia, KA and Neudorf, S and Shirtliffe, SJ},
title = {A field boundary dataset for the canadian prairies derived from sentinel-2 imagery using the segment anything model.},
journal = {Data in brief},
volume = {66},
number = {},
pages = {112691},
pmid = {41952847},
issn = {2352-3409},
abstract = {This article presents a Prairie-wide spatial vector dataset of agricultural field boundaries across Alberta, Saskatchewan, and Manitoba, Canada. The dataset was generated from Sentinel-2 Level-2A surface reflectance imagery (10 m spatial resolution) using an automated segmentation workflow based on the Segment Anything Model version 2 (SAM2). Sentinel-2 imagery was accessed in Google Earth Engine (GEE), filtered using cloud/quality masks, and aggregated into seasonal RGB composites representing key crop phenological periods (early-, mid-, and late-season) for large-scale segmentation input. The composite images were exported and processed with a SAM2 segmentation pipeline (tiled inference and mosaic-based post-processing) to delineate candidate field units without manually labeled training samples. Segmentation outputs were then post-processed using rule-based filtering and topology repair (removal of small artifacts/sliver polygons, hole filling, boundary cleaning, and geometry validity correction). Final vector outputs are distributed in ESRI Shapefile and GeoParquet formats with geometry attributes for downstream spatial analysis. The workflow code and processing scripts are provided to support reproducibility and adaptation to other regions. This dataset provides a consistent field-scale boundary reference layer for agricultural monitoring, crop and yield modeling, soil and environmental analysis, cropland mapping, land management, and machine-learning applications across the Canadian Prairies. •Prairie-wide field boundary datasets generated using automated segmentation of seasonal Sentinel-2 composites with the Segment Anything Model version 2, followed by vectorization and topological post-processing.•Vector outputs provided in both Shapefile and GeoParquet formats, enabling efficient use in traditional GIS, cloud-native, and big-data geospatial workflows.•A consistent, large-scale field boundary reference dataset supporting agricultural analysis, spatial modeling, cropland mapping, and machine learning applications across the Canadian Prairies.},
}