@article {pmid40942982,
year = {2025},
author = {Liang, F},
title = {Decentralized and Network-Aware Task Offloading for Smart Transportation via Blockchain.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {17},
pages = {},
doi = {10.3390/s25175555},
pmid = {40942982},
issn = {1424-8220},
abstract = {As intelligent transportation systems (ITSs) evolve rapidly, the increasing computational demands of connected vehicles call for efficient task offloading. Centralized approaches face challenges in scalability, security, and adaptability to dynamic network conditions. To address these issues, we propose a blockchain-based decentralized task offloading framework with network-aware resource allocation and tokenized economic incentives. In our model, vehicles generate computational tasks that are dynamically mapped to available computing nodes-including vehicle-to-vehicle (V2V) resources, roadside edge servers (RSUs), and cloud data centers-based on a multi-factor score considering computational power, bandwidth, latency, and probabilistic packet loss. A blockchain transaction layer ensures auditable and secure task assignment, while a proof-of-stake (PoS) consensus and smart-contract-driven dynamic pricing jointly incentivize participation and balance workloads to minimize delay. In extensive simulations reflecting realistic ITS dynamics, our approach reduces total completion time by 12.5-24.3%, achieves a task success rate of 84.2-88.5%, improves average resource utilization to 88.9-92.7%, and sustains >480 transactions per second (TPS) with a 10 s block interval, outperforming centralized/cloud-based baselines. These results indicate that integrating blockchain incentives with network-aware offloading yields secure, scalable, and efficient management of computational resources for future ITSs.},
}

@article {pmid40942955,
year = {2025},
author = {Dembski, J and Wiszniewski, B and Kołakowska, A},
title = {Anomaly Detection and Segmentation in Measurement Signals on Edge Devices Using Artificial Neural Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {17},
pages = {},
doi = {10.3390/s25175526},
pmid = {40942955},
issn = {1424-8220},
support = {WPN/4/68/Rural IoT/2022//National Centre for Research and Development/ ; },
abstract = {In this paper, three alternative solutions to the problem of detecting and cleaning anomalies in soil signal time series, involving the use of artificial neural networks deployed on in situ data measurement end devices, are proposed and investigated. These models are designed to perform calculations on MCUs, characterized by significantly limited computing capabilities and a limited supply of electrical power. Training of neural network models is carried out based on data from multiple sensors in the supporting computing cloud instance, while detection and removal of anomalies with a trained model takes place on the constrained end devices. With such a distribution of work, it is necessary to achieve a sound compromise between prediction accuracy and the computational complexity of the detection process. In this study, neural-primed heuristic (NPH), autoencoder-based (AEB), and U-Net-based (UNB) approaches were tested, which were found to vary regarding both prediction accuracy and computational complexity. Labeled data were used to train the models, transforming the detection task into an anomaly segmentation task. The obtained results reveal that the UNB approach presents certain advantages; however, it requires a significant volume of training data and has a relatively high time complexity which, in turn, translates into increased power consumption by the end device. For this reason, the other two approaches-NPH and AEB-may be worth considering as reasonable alternatives when developing in situ data cleaning solutions for IoT measurement systems.},
}

@article {pmid40942833,
year = {2025},
author = {Zhang, L and Wu, S and Wang, Z},
title = {LoRA-INT8 Whisper: A Low-Cost Cantonese Speech Recognition Framework for Edge Devices.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {17},
pages = {},
doi = {10.3390/s25175404},
pmid = {40942833},
issn = {1424-8220},
support = {2323013-2023XDRH001//This work was supported in part by Yantai City 2023 School-Land Integration Development Project Fund under Grant 2323013-2023XDRH001/ ; 2323013-2023XDRH001;2023TSGC0823;//he Science and Technology-Based Small and Medium-Sized Enterprise Innovation Capacity Enhancement Project of Shandong Pro: 2323013-2023XDRH001;2023TSGC0823;/ ; 62201491;ZR2021QF097//This research was funded by Chinese National Nature Science Foundationand the Natural Science Foundation of Shandong Province: 62201491;ZR2021QF097/ ; },
abstract = {To address the triple bottlenecks of data scarcity, oversized models, and slow inference that hinder Cantonese automatic speech recognition (ASR) in low-resource and edge-deployment settings, this study proposes a cost-effective Cantonese ASR system based on LoRA fine-tuning and INT8 quantization. First, Whisper-tiny is parameter-efficiently fine-tuned on the Common Voice zh-HK training set using LoRA with rank = 8. Only 1.6% of the original weights are updated, reducing the character error rate (CER) from 49.5% to 11.1%, a performance close to full fine-tuning (10.3%), while cutting the training memory footprint and computational cost by approximately one order of magnitude. Next, the fine-tuned model is compressed into a 60 MB INT8 checkpoint via dynamic quantization in ONNX Runtime. On a MacBook Pro M1 Max CPU, the quantized model achieves an RTF = 0.20 (offline inference 5 × real-time) and 43% lower latency than the FP16 baseline; on an NVIDIA A10 GPU, it reaches RTF = 0.06, meeting the requirements of high-concurrency cloud services. Ablation studies confirm that the LoRA-INT8 configuration offers the best trade-off among accuracy, speed, and model size. Limitations include the absence of spontaneous-speech noise data, extreme-hardware validation, and adaptive LoRA structure optimization. Future work will incorporate large-scale self-supervised pre-training, tone-aware loss functions, AdaLoRA architecture search, and INT4/NPU quantization, and will establish an mJ/char energy-accuracy curve. The ultimate goal is to achieve CER ≤ 8%, RTF < 0.1, and mJ/char < 1 for low-power real-time Cantonese ASR in practical IoT scenarios.},
}

@article {pmid40942784,
year = {2025},
author = {Yu, M and Du, Y and Zhang, X and Ma, Z and Wang, Z},
title = {Efficient Navigable Area Computation for Underground Autonomous Vehicles via Ground Feature and Boundary Processing.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {17},
pages = {},
doi = {10.3390/s25175355},
pmid = {40942784},
issn = {1424-8220},
support = {2022YFB4703603//National Key Research and Development Program of China/ ; },
abstract = {Accurate boundary detection is critical for autonomous trackless rubber-wheeled vehicles in underground coal mines, as it prevents lateral collisions with tunnel walls. Unlike open-road environments, underground tunnels suffer from poor illumination, water mist, and dust, which degrade visual imaging. To address these challenges, this paper proposes a navigable area computation for underground autonomous vehicles via ground feature and boundary processing, consisting of three core steps. First, a real-time point cloud correction process via pre-correction and dynamic update aligns ground point clouds with the LiDAR coordinate system to ensure parallelism. Second, corrected point clouds are projected onto a 2D grid map using a grid-based method, effectively mitigating the impact of ground unevenness on boundary extraction; third, an adaptive boundary completion method is designed to resolve boundary discontinuities in junctions and shunting chambers. Additionally, the method emphasizes continuous extraction of boundaries over extended periods by integrating temporal context, ensuring the continuity of boundary detection during vehicle operation. Experiments on real underground vehicle data validate that the method achieves accurate detection and consistent tracking of dual-sided boundaries across straight tunnels, curves, intersections, and shunting chambers, meeting the requirements of underground autonomous driving. This work provides a rule-based, real-time solution feasible under limited computing power, offering critical safety redundancy when deep learning methods fail in harsh underground environments.},
}

@article {pmid40942759,
year = {2025},
author = {Honarparvar, S and Honarparvar, Y and Ashena, Z and Liang, S and Saeedi, S},
title = {GICEDCam: A Geospatial Internet of Things Framework for Complex Event Detection in Camera Streams.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {17},
pages = {},
doi = {10.3390/s25175331},
pmid = {40942759},
issn = {1424-8220},
abstract = {Complex event detection (CED) adds value to camera stream data in various applications such as workplace safety, task monitoring, security, and health. Recent CED frameworks have addressed the issues of limited spatiotemporal labels and costly training by decomposing the CED into low-level features, as well as spatial and temporal relationship extraction. However, these frameworks suffer from high resource costs, low scalability, and an increased number of false positives and false negatives. This paper proposes GICEDCAM, which distributes CED across edge, stateless, and stateful layers to improve scalability and reduce computation cost. Additionally, we introduce a Spatial Event Corrector component that leverages geospatial data analysis to minimize false negatives and false positives in spatial event detection. We evaluate GICEDCAM on 16 camera streams covering four complex events. Relative to a strong open-source baseline configured for our setting, GICEDCAM reduces end-to-end latency by 36% and total computational cost by 45%, with the advantage widening as objects per frame increase. Among corrector variants, Bayesian Network (BN) yields the lowest latency, Long Short-Term Memory (LSTM) achieves the highest accuracy, and trajectory analysis offers the best accuracy-latency trade-off for this architecture.},
}

@article {pmid40942729,
year = {2025},
author = {Gong, R and Zhang, H and Li, G and He, J},
title = {Edge Computing-Enabled Smart Agriculture: Technical Architectures, Practical Evolution, and Bottleneck Breakthroughs.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {17},
pages = {},
doi = {10.3390/s25175302},
pmid = {40942729},
issn = {1424-8220},
abstract = {As the global digital transformation of agriculture accelerates, the widespread deployment of farming equipment has triggered an exponential surge in agricultural production data. Consequently, traditional cloud computing frameworks face critical challenges: communication latency in the field, the demand for low-power devices, and stringent real-time decision constraints. These bottlenecks collectively exacerbate bandwidth constraints, diminish response efficiency, and introduce data security vulnerabilities. In this context, edge computing offers a promising solution for smart agriculture. By provisioning computing resources to the network periphery and enabling localized processing at data sources adjacent to agricultural machinery, sensors, and crops, edge computing leverages low-latency responses, bandwidth optimization, and distributed computation capabilities. This paper provides a comprehensive survey of the research landscape in agricultural edge computing. We begin by defining its core concepts and highlighting its advantages over cloud computing. Subsequently, anchored in the "terminal sensing-edge intelligence-cloud coordination" architecture, we analyze technological evolution in edge sensing devices, lightweight intelligent algorithms, and cooperative communication mechanisms. Additionally, through precision farming, intelligent agricultural machinery control, and full-chain crop traceability, we demonstrate its efficacy in enhancing real-time agricultural decision-making. Finally, we identify adaptation challenges in complex environments and outline future directions for research and development in this field.},
}

@article {pmid40942716,
year = {2025},
author = {Ali, EM and Abawajy, J and Lemma, F and Baho, SA},
title = {Analysis of Deep Reinforcement Learning Algorithms for Task Offloading and Resource Allocation in Fog Computing Environments.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {17},
pages = {},
doi = {10.3390/s25175286},
pmid = {40942716},
issn = {1424-8220},
abstract = {Fog computing is increasingly preferred over cloud computing for processing tasks from Internet of Things (IoT) devices with limited resources. However, placing tasks and allocating resources in distributed and dynamic fog environments remains a major challenge, especially when trying to meet strict Quality of Service (QoS) requirements. Deep reinforcement learning (DRL) has emerged as a promising solution to these challenges, offering adaptive, data-driven decision-making in real-time and uncertain conditions. While several surveys have explored DRL in fog computing, most focus on traditional centralized offloading approaches or emphasize reinforcement learning (RL) with limited integration of deep learning. To address this gap, this paper presents a comprehensive and focused survey on the full-scale application of DRL to the task offloading problem in fog computing environments involving multiple user devices and multiple fog nodes. We systematically analyze and classify the literature based on architecture, resource allocation methods, QoS objectives, offloading topology and control, optimization strategies, DRL techniques used, and application scenarios. We also introduce a taxonomy of DRL-based task offloading models and highlight key challenges, open issues, and future research directions. This survey serves as a valuable resource for researchers by identifying unexplored areas and suggesting new directions for advancing DRL-based solutions in fog computing. For practitioners, it provides insights into selecting suitable DRL techniques and system designs to implement scalable, efficient, and QoS-aware fog computing applications in real-world environments.},
}

@article {pmid40942708,
year = {2025},
author = {Xu, T and Zou, K and Liu, C and Yue, Y},
title = {Special Issue on Advanced Optical Technologies for Communications, Perception, and Chips.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {17},
pages = {},
doi = {10.3390/s25175278},
pmid = {40942708},
issn = {1424-8220},
abstract = {With the iterative upgrade and popular application of new information technologies such as 5G, cloud computing, big data, and artificial intelligence (AI), the global data traffic and the demand for computing power has ushered in explosive growth [...].},
}

@article {pmid40942702,
year = {2025},
author = {Tasmurzayev, N and Amangeldy, B and Imanbek, B and Baigarayeva, Z and Imankulov, T and Dikhanbayeva, G and Amangeldi, I and Sharipova, S},
title = {Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {17},
pages = {},
doi = {10.3390/s25175272},
pmid = {40942702},
issn = {1424-8220},
support = {AP26103523//Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakh-stan/ ; },
abstract = {Cardiovascular disease remains the world's leading cause of mortality, yet everyday care still relies on episodic, symptom-driven interventions that detect ischemia, arrhythmias, and remodeling only after tissue damage has begun, limiting the effectiveness of therapy. A narrative review synthesized 183 studies published between 2016 and 2025 that were located through PubMed, MDPI, Scopus, IEEE Xplore, and Web of Science. This review examines CVD diagnostics using innovative technologies such as digital cardiovascular twins, which involve the collection of data from wearable IoT devices (electrocardiography (ECG), photoplethysmography (PPG), and mechanocardiography), clinical records, laboratory biomarkers, and genetic markers, as well as their integration with artificial intelligence (AI), including machine learning and deep learning, graph and transformer networks for interpreting multi-dimensional data streams and creating prognostic models, as well as generative AI, medical large language models (LLMs), and autonomous agents for decision support, personalized alerts, and treatment scenario modeling, and with cloud and edge computing for data processing. This multi-layered architecture enables the detection of silent pathologies long before clinical manifestations, transforming continuous observations into actionable recommendations and shifting cardiology from reactive treatment to predictive and preventive care. Evidence converges on four layers: sensors streaming multimodal clinical and environmental data; hybrid analytics that integrate hemodynamic models with deep-, graph- and transformer learning while Bayesian and Kalman filters manage uncertainty; decision support delivered by domain-tuned medical LLMs and autonomous agents; and prospective simulations that trial pacing or pharmacotherapy before bedside use, closing the prediction-intervention loop. This stack flags silent pathology weeks in advance and steers proactive personalized prevention. It also lays the groundwork for software-as-a-medical-device ecosystems and new regulatory guidance for trustworthy AI-enabled cardiovascular care.},
}

@article {pmid40941861,
year = {2025},
author = {Luo, H and Dai, S and Hu, Y and Zheng, Q and Yu, X and Chen, B and Li, Y and Wang, C and Li, H},
title = {Integrating Knowledge-Based and Machine Learning for Betel Palm Mapping on Hainan Island Using Sentinel-1/2 and Google Earth Engine.},
journal = {Plants (Basel, Switzerland)},
volume = {14},
number = {17},
pages = {},
doi = {10.3390/plants14172696},
pmid = {40941861},
issn = {2223-7747},
support = {42461063//National Natural Science Foundation of China/ ; HSZK-KYQD-202427//Hainan Normal University Talent Research start-up fund project/ ; 1630012025503//the Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences/ ; 621QN297//the National Natural Science Foundation of Hainan Province/ ; KJRC2023D40//the science and technology talent innovation project of Hainan Province/ ; },
abstract = {The betel palm is a critical economic crop on Hainan Island. Accurate and timely maps of betel palms are fundamental for the industry's management and ecological environment evaluation. To date, mapping the spatial distribution of betel palms across a large regional scale remains a significant challenge. In this study, we propose an integrated framework that combines knowledge-based and machine learning approaches to produce a map of betel palms at 10 m spatial resolution based on Sentinel-1/2 data and Google Earth Engine (GEE) for 2023 on Hainan Island, which accounts for 95% of betel nut acreage in China. The forest map was initially delineated based on signature information and the Green Normalized Difference Vegetation Index (GNDVI) acquired from Sentinel-1 and Sentinel-2 data, respectively. Subsequently, patches of betel palms were extracted from the forest map using a random forest classifier and feature selection method via logistic regression (LR). The resultant 10 m betel palm map achieved user's, producer's, and overall accuracy of 86.89%, 88.81%, and 97.51%, respectively. According to the betel palm map in 2023, the total planted area was 189,805 hectares (ha), exhibiting high consistency with statistical data (R[2] = 0.74). The spatial distribution was primarily concentrated in eastern Hainan, reflecting favorable climatic and topographic conditions. The results demonstrate the significant potential of Sentinel-1/2 data for identifying betel palms in complex tropical regions characterized by diverse land cover types, fragmented cultivated land, and frequent cloud and rain interference. This study provides a reference framework for mapping tropical crops, and the findings are crucial for tropical agricultural management and optimization.},
}

@article {pmid40940454,
year = {2025},
author = {Rosenblum, J and Dong, J and Narayanasamy, S},
title = {Confidential computing for population-scale genome-wide association studies with SECRET-GWAS.},
journal = {Nature computational science},
volume = {},
number = {},
pages = {},
pmid = {40940454},
issn = {2662-8457},
support = {203045//National Science Foundation (NSF)/ ; 203045//National Science Foundation (NSF)/ ; 203045//National Science Foundation (NSF)/ ; },
abstract = {Genomic data from a single institution lacks global diversity representation, especially for rare variants and diseases. Confidential computing can enable collaborative genome-wide association studies (GWAS) without compromising privacy or accuracy. However, due to limited secure memory space and performance overheads, previous solutions fail to support widely used regression methods. Here we present SECRET-GWAS-a rapid, privacy-preserving, population-scale, collaborative GWAS tool. We discuss several system optimizations, including streaming, batching, data parallelization and reducing trusted hardware overheads to efficiently scale linear and logistic regression to over a thousand processor cores on an Intel SGX-based cloud platform. In addition, we protect SECRET-GWAS against several hardware side-channel attacks. SECRET-GWAS is an open-source tool and works with the widely used Hail genomic analysis framework. Our experiments on Azure's Confidential Computing platform demonstrate that SECRET-GWAS enables multivariate linear and logistic regression GWAS queries on population-scale datasets from ten independent sources in just 4.5 and 29 minutes, respectively.},
}

@article {pmid40939950,
year = {2025},
author = {Smith, DS and Ramadass, K and Jones, L and Morse, J and Fabbri, D and Coco, JR and Bao, S and Basford, M and Embi, PJ and Omary, RA and Gore, JC and Pulley, JM and Landman, BA},
title = {Secondary use of radiological imaging data: Vanderbilt's ImageVU approach.},
journal = {Journal of biomedical informatics},
volume = {},
number = {},
pages = {104905},
doi = {10.1016/j.jbi.2025.104905},
pmid = {40939950},
issn = {1532-0480},
abstract = {OBJECTIVE: To develop ImageVU, a scalable research imaging infrastructure that integrates clinical imaging data with metadata-driven cohort discovery, enabling secure, efficient, and regulatory-compliant access to imaging for secondary and opportunistic research use. This manuscript presents a detailed description of ImageVU's key components and lessons learned to assist other institutions in developing similar research imaging services and infrastructure.

METHODS: ImageVU was designed to support the secondary use of radiological imaging data through a dedicated research imaging store. The system comprises four interconnected components: a Research PACS, an Ad Hoc Backfill Host, Cloud Storage System, and a De-Identification System. Imaging metadata are extracted and stored in the Research Derivative (RD), an identified clinical data repository, and the Synthetic Derivative (SD), a de-identified research data repository, with access facilitated through the RD Discover web portal. Researchers interact with the system via structured metadata queries and multiple data delivery options, including web-based viewing, bulk downloads, and dataset preparation for high-performance computing environments.

RESULTS: The integration of metadata-driven search capabilities has streamlined cohort discovery and improved imaging data accessibility. As of December 2024, ImageVU has processed 12.9 million MRI and CT series from 1.36 million studies across 453,403 patients. The system has supported 75 project requests, delivering over 50 TB of imaging data to 55 investigators, leading to 66 published research papers.

CONCLUSION: ImageVU demonstrates a scalable and efficient approach for integrating clinical imaging into research workflows. By combining institutional data infrastructure with cloud-based storage and metadata-driven cohort identification, the platform enables secure and compliant access to imaging for translational research.},
}

@article {pmid40924788,
year = {2025},
author = {Sanjalawe, Y and Fraihat, S and Al-E'mari, S and Abualhaj, M and Makhadmeh, S and Alzubi, E},
title = {Smart load balancing in cloud computing: Integrating feature selection with advanced deep learning models.},
journal = {PloS one},
volume = {20},
number = {9},
pages = {e0329765},
doi = {10.1371/journal.pone.0329765},
pmid = {40924788},
issn = {1932-6203},
mesh = {*Cloud Computing ; *Deep Learning ; Algorithms ; Neural Networks, Computer ; *Workload ; Humans ; },
abstract = {The increasing dependence on cloud computing as a cornerstone of modern technological infrastructures has introduced significant challenges in resource management. Traditional load-balancing techniques often prove inadequate in addressing cloud environments' dynamic and complex nature, resulting in suboptimal resource utilization and heightened operational costs. This paper presents a novel smart load-balancing strategy incorporating advanced techniques to mitigate these limitations. Specifically, it addresses the critical need for a more adaptive and efficient approach to workload management in cloud environments, where conventional methods fall short in handling dynamic and fluctuating workloads. To bridge this gap, the paper proposes a hybrid load-balancing methodology that integrates feature selection and deep learning models for optimizing resource allocation. The proposed Smart Load Adaptive Distribution with Reinforcement and Optimization approach, SLADRO, combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms for load prediction, a hybrid bio-inspired optimization technique-Orthogonal Arrays and Particle Swarm Optimization (OOA-PSO)-for feature selection algorithms, and Deep Reinforcement Learning (DRL) for dynamic task scheduling. Extensive simulations conducted on a real-world dataset called Google Cluster Trace dataset reveal that the SLADRO model significantly outperforms traditional load-balancing approaches, yielding notable improvements in throughput, makespan, resource utilization, and energy efficiency. This integration of advanced techniques offers a scalable and adaptive solution, providing a comprehensive framework for efficient load balancing in cloud computing environments.},
}

*Cloud Computing
*Deep Learning
Algorithms
Neural Networks, Computer
*Workload
Humans

@article {pmid40924537,
year = {2025},
author = {Degatano, K and Awdeh, A and Cox Iii, RS and Dingman, W and Grant, G and Khajouei, F and Kiernan, E and Konwar, K and Mathews, KL and Palis, K and Petrillo, N and Van der Auwera, G and Wang, CR and Way, J},
title = {Warp Analysis Research Pipelines: Cloud-optimized workflows for biological data processing and reproducible analysis.},
journal = {Bioinformatics (Oxford, England)},
volume = {},
number = {},
pages = {},
doi = {10.1093/bioinformatics/btaf494},
pmid = {40924537},
issn = {1367-4811},
abstract = {SUMMARY: In the era of large data, the cloud is increasingly used as a computing environment, necessitating the development of cloud-compatible pipelines that can provide uniform analysis across disparate biological datasets. The Warp Analysis Research Pipelines (WARP) repository is a GitHub repository of open-source, cloud-optimized workflows for biological data processing that are semantically versioned, tested, and documented. A companion repository, WARP-Tools, hosts Docker containers and custom tools used in WARP workflows.

The WARP and WARP-Tools repositories and code are freely available at https://github.com/broadinstitute/WARP and https://github.com/broadinstitute/WARP-tools, respectively. The pipelines are available for download from the WARP repository, can be exported from Dockstore, and can be imported to a bioinformatics platform such as Terra.

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

@article {pmid40919327,
year = {2025},
author = {El-Warrak, LO and Miceli de Farias, C and De Azevedo Costa, VHDM},
title = {Simulation-based assessment of digital twin systems for immunisation.},
journal = {Frontiers in digital health},
volume = {7},
number = {},
pages = {1603550},
pmid = {40919327},
issn = {2673-253X},
abstract = {BACKGROUND: This paper presents the application of simulation to assess the functionality of a proposed Digital Twin (DT) architecture for immunisation services in primary healthcare centres. The solution is based on Industry 4.0 concepts and technologies, such as IoT, machine learning, and cloud computing, and adheres to the ISO 23247 standard.

METHODS: The system modelling is carried out using the Unified Modelling Language (UML) to define the workflows and processes involved, including vaccine storage temperature monitoring and population vaccination status tracking. The proposed architecture is structured into four domains: observable elements/entities, data collection and device control, digital twin platform, and user domain. To validate the system's performance and feasibility, simulations are conducted using SimPy, enabling the evaluation of its response under various operational scenarios.

RESULTS: The system facilitates the storage, monitoring, and visualisation of data related to the thermal conditions of ice-lined refrigerators (ILR) and thermal boxes. Additionally, it analyses patient vaccination coverage based on the official immunisation schedule. The key benefits include optimising vaccine storage conditions, reducing dose wastage, continuously monitoring immunisation coverage, and supporting strategic vaccination planning.

CONCLUSION: The paper discusses the future impacts of this approach on immunisation management and its scalability for diverse public health contexts. By leveraging advanced technologies and simulation, this digital twin framework aims to improve the performance and overall impact of immunization services.},
}

@article {pmid40918039,
year = {2025},
author = {Zhou, Y and Wu, Y and Su, Y and Li, J and Cai, J and You, Y and Zhou, J and Guo, D and Qu, X},
title = {Cloud-magnetic resonance imaging system: In the era of 6G and artificial intelligence.},
journal = {Magnetic resonance letters},
volume = {5},
number = {1},
pages = {200138},
pmid = {40918039},
issn = {2772-5162},
abstract = {Magnetic resonance imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, artificial intelligence (AI) algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency.},
}

@article {pmid40910315,
year = {2025},
author = {Zhang, YH and He, JY and Lin, SJ and Ai, BJ and Shi, ZH and Zhang, HY},
title = {[Development and practice of an interactive chromatography learning tool for beginners based on GeoGebra： a case study of plate theory].},
journal = {Se pu = Chinese journal of chromatography},
volume = {43},
number = {9},
pages = {1078-1085},
pmid = {40910315},
issn = {1872-2059},
abstract = {This study developed a GeoGebra platform-based interactive pedagogical tool focusing on plate theory to address challenges associated with abstract theory transmission， unidirectional knowledge delivery， and low student engagement in chromatography teaching in instrumental analysis courses. This study introduced an innovative methodology that encompasses theoretical model reconstruction， tool development， and teaching-chain integration that addresses the limitations of existing teaching tools， including the complex operation of professional software， restricted accessibility to web-based tools， and insufficient parameter-adjustment flexibility. An improved mathematical plate-theory model was established by incorporating mobile-phase flow rate， dead time， and phase ratio parameters. A three-tier progressive learning system （single-component simulation， multi-component simulation， and retention-time-equation derivation modules） was developed on a cloud-based computing platform. An integrated teaching chain that combined athematical modeling （AI-assisted "Doubao" derivation）， interactive-parameter adjustment （multiple adjustable chromatographic parameters）， and visual verification （chromatographic elution-curve simulation） was implemented. Teaching practice demonstrated that： （1） The developed tool transcends the dimensional limitations of traditional instruction， elevating the classroom task completion rate to 94% and improving the student accuracy rate for solving advanced problems to 76%. （2） The dynamic-parameter-adjustment feature significantly enhances learning engagement by enabling 85% of the students to independently use the tool in subsequent studies and experiments. （3） The AI-powered derivation and regression-analysis modules enable the interdisciplinary integration of theoretical chemistry and computational tools. The process of deriving chromatographic retention-time equations through this methodological approach proved more convincing than the current textbook practice of directly presenting conclusions. The developed innovative "theoretical-model visualizable-model-parameter adjustable-interactive-knowledge generating" model provides a new avenue for addressing teaching challenges associated with chromatography theory， and its open-source framework and modular design philosophy can offer valuable references for the digital teaching reform in analytical chemistry.},
}

@article {pmid40897813,
year = {2025},
author = {Ting, T and Li, M},
title = {Enhanced secure storage and data privacy management system for big data based on multilayer model.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {32285},
pmid = {40897813},
issn = {2045-2322},
abstract = {As big data systems expand in scale and complexity, managing and securing sensitive data-especially personnel records-has become a critical challenge in cloud environments. This paper proposes a novel Multi-Layer Secure Cloud Storage Model (MLSCSM) tailored for large-scale personnel data. The model integrates fast and secure ChaCha20 encryption, Dual Stage Data Partitioning (DSDP) to maintain statistical reliability across blocks, k-anonymization to ensure privacy, SHA-512 hashing for data integrity, and Cauchy matrix-based dispersion for fault-tolerant distributed storage. A key novelty lies in combining cryptographic and statistical methods to enable privacy-preserving partitioned storage, optimized for distributed Cloud Computing Environments (CCE). Data blocks are securely encoded, masked, and stored in discrete locations across several cloud platforms, based on factors such as latency, bandwidth, cost, and security. They are later retrieved with integrity verification. The model also includes audit logs, load balancing, and real-time resource evaluation. To validate the system, experiments were tested using the MIMIC-III dataset on a 20-node Hadoop cluster. Compared to baseline models such as RDFA, SDPMC, and P&XE, the proposed model achieved a reduction in encoding time to 250 ms (block size 75), a CPU usage of 23% for 256 MB of data, a latency as low as 14 ms, and a throughput of up to 139 ms. These results confirm that the model offers superior security, efficiency, and scalability for cloud-based big data storage applications.},
}

@article {pmid40890227,
year = {2025},
author = {Mushtaq, SU and Sheikh, S and Nain, A and Bharany, S and Ghoniem, RM and Taye, BM},
title = {CRFTS: a cluster-centric and reservation-based fault-tolerant scheduling strategy to enhance QoS in cloud computing.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {32233},
pmid = {40890227},
issn = {2045-2322},
abstract = {Cloud systems supply different kinds of on-demand services in accordance with client needs. As the landscape of cloud computing undergoes continuous development, there is a growing imperative for effective utilization of resources, task scheduling, and fault tolerance mechanisms. To decrease the user task execution time (shorten the makespan) with reduced operational expenses, to improve the distribution of load, and to boost utilization of resources, proper mapping of user tasks to the available VMs is necessary. This study introduces a unique perspective in tackling these challenges by implementing inventive scheduling strategies along with robust and proactive fault tolerance mechanisms in cloud environments. This paper presents the Clustering and Reservation Fault-tolerant Scheduling (CRFTS), which adapts the heartbeat mechanism to detect failed VMs proactively and maximizes the system reliability while making it fault-tolerant and optimizing other Quality of Service (QoS) parameters, such as makespan, average resource utilization, and reliability. The study optimizes the allocation of tasks to improve resource utilization and reduce the time required for their completion. At the same time, the proactive reservation-based fault tolerance framework is presented to ensure continuous service delivery throughout its execution without any interruption. The effectiveness of the suggested model is illustrated through simulations and empirical analyses, highlighting enhancements in several QoS parameters while comparing with HEFT, FTSA-1, DBSA, E-HEFT, LB-HEFT, BDHEFT, HO-SSA, and MOTSWAO for various cases and conditions across different tasks and VMs. The outcomes demonstrate that CRFTS average progresses about 48.7%, 51.2%, 45.4%, 11.8%, 24.5%, 24.4% in terms of makespan and 13.1%, 9.3%, 6.5%, 21%, 22.1%, 26.3% in terms of average resource utilization compared to HEFT, FTSA-1, DBSA, E-HEFT, LB-HEFT, BDHEFT, HO-SSA, and MOTSWAO, respectively.},
}

@article {pmid40890211,
year = {2025},
author = {Kishor, I and Mamodiya, U and Patil, V and Naik, N},
title = {AI-Integrated autonomous robotics for solar panel cleaning and predictive maintenance using drone and ground-based systems.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {32187},
pmid = {40890211},
issn = {2045-2322},
abstract = {Solar photovoltaic (PV) systems, especially in dusty and high-temperature regions, suffer performance degradation due to dust accumulation, surface heating, and delayed maintenance. This study proposes an AI-integrated autonomous robotic system combining real-time monitoring, predictive analytics, and intelligent cleaning for enhanced solar panel performance. We developed a hybrid system that integrates CNN-LSTM-based fault detection, Reinforcement Learning (DQN)-driven robotic cleaning, and Edge AI analytics for low-latency decision-making. Thermal and LiDAR-equipped drones detect panel faults, while ground robots clean panel surfaces based on real-time dust and temperature data. The system is built on Jetson Nano and Raspberry Pi 4B units with MQTT-based IoT communication. The system achieved an average cleaning efficiency of 91.3%, reducing dust density from 3.9 to 0.28 mg/m[3], and restoring up to 31.2% energy output on heavily soiled panels. CNN-LSTM-based fault detection delivered 92.3% accuracy, while the RL-based cleaning policy reduced energy and water consumption by 34.9%. Edge inference latency averaged 47.2 ms, outperforming cloud processing by 63%. A strong correlation, r = 0.87 between dust concentration and thermal anomalies, was confirmed. The proposed IEEE 1876-compliant framework offers a resilient and intelligent solution for real-time solar panel maintenance. By leveraging AI, robotics, and edge computing, the system enhances energy efficiency, reduces manual labor, and provides a scalable model for climate-resilient, smart solar infrastructure.},
}

@article {pmid40890146,
year = {2025},
author = {Maciá-Lillo, A and Mora, H and Jimeno-Morenilla, A and García-D'Urso, NE and Azorín-López, J},
title = {AI edge cloud service provisioning for knowledge management smart applications.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {32246},
pmid = {40890146},
issn = {2045-2322},
support = {PID2023-152804OB-I00//Agencia Estatal de Investigación/ ; PID2023-152804OB-I00//Agencia Estatal de Investigación/ ; PID2023-152804OB-I00//Agencia Estatal de Investigación/ ; PID2023-152804OB-I00//Agencia Estatal de Investigación/ ; PID2023-152804OB-I00//Agencia Estatal de Investigación/ ; },
abstract = {This paper investigates a serverless edge-cloud architecture to support knowledge management processes within smart cities, which align with the goals of Society 5.0 to create human-centered, data-driven urban environments. The proposed architecture leverages cloud computing for scalability and on-demand resource provisioning, and edge computing for cost-efficiency and data processing closer to data sources, while also supporting serverless computing for simplified application development. Together, these technologies enhance the responsiveness and efficiency of smart city applications, such as traffic management, public safety, and infrastructure governance, by minimizing latency and improving data handling at scale. Experimental analysis demonstrates the benefits of deploying KM processes on this hybrid architecture, particularly in reducing data transmission times and alleviating network congestion, while at the same time providing options for cost-efficient computations. In addition to that, the study also identifies the characteristics, opportunities and limitations of the edge and cloud environment in terms of computation and network communication times. This architecture represents a flexible framework for advancing knowledge-driven services in smart cities, supporting further development of smart city applications in KM processes.},
}

@article {pmid40877550,
year = {2025},
author = {Kim, EM and Lim, Y},
title = {Mapping interconnectivity of digital twin healthcare research themes through structural topic modeling.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {31734},
pmid = {40877550},
issn = {2045-2322},
support = {NRF-2022R1A2C1009890//National Research Foundation of Korea/ ; NRF-2022R1A2C1009890//National Research Foundation of Korea/ ; },
mesh = {Humans ; *Delivery of Health Care ; Computer Security ; Machine Learning ; Algorithms ; Cloud Computing ; },
abstract = {Digital twin (DT) technology is revolutionizing healthcare systems by leveraging real-time data integration and advanced analytics to enhance patient care, optimize clinical operations, and facilitate simulation. This study aimed to identify key research trends related to the application of DTs to healthcare using structural topic modeling (STM). Five electronic databases were searched for articles related to healthcare and DT. Using the held-out likelihood, residual, semantic coherence, and lower bound as metrics revealed that the optimal number of topics was eight. The "security solutions to improve data processes and communication in healthcare" topic was positioned at the center of the network and connected to multiple nodes. The "cloud computing and data network architecture" and "machine-learning algorithms for accurate detection and prediction" topics served as a bridge between technical and healthcare topics, suggesting their high potential for use in various fields. The widespread adoption of DTs in healthcare requires robust governance structures to protect individual rights, ensure data security and privacy, and promote transparency and fairness. Compliance with regulatory frameworks, ethical guidelines, and a commitment to accountability are also crucial.},
}

Humans
*Delivery of Health Care
Computer Security
Machine Learning
Algorithms
Cloud Computing

@article {pmid40877244,
year = {2025},
author = {Zhang, Y and Ran, H and Guenther, A and Zhang, Q and George, C and Mellouki, W and Sheng, G and Peng, P and Wang, X},
title = {Improved modelling of biogenic emissions in human-disturbed forest edges and urban areas.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {8064},
pmid = {40877244},
issn = {2041-1723},
support = {42321003//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Volatile Organic Compounds/analysis ; *Forests ; *Models, Theoretical ; Cities ; *Environmental Monitoring/methods ; *Air Pollutants/analysis ; Ecosystem ; Air Pollution/analysis ; Climate ; },
abstract = {Biogenic volatile organic compounds (BVOCs) are critical to biosphere-atmosphere interactions, profoundly influencing atmospheric chemistry, air quality and climate, yet accurately estimating their emissions across diverse ecosystems remains challenging. Here we introduce GEE-MEGAN, a cloud-native extension of the widely used MEGAN2.1 model, integrating dynamic satellite-derived land cover and vegetation within Google Earth Engine to produce near-real-time BVOC emissions at 10-30 m resolution, enabling fine-scale tracking of emissions in rapidly changing environments. GEE-MEGAN reduces BVOC emission estimates by 31% and decreases root mean square errors by up to 48.6% relative to MEGAN2.1 in human-disturbed forest edges, and reveals summertime BVOC emissions up to 25‑fold higher than previous estimates in urban areas such as London, Los Angeles, Paris, and Beijing. By capturing fine-scale landscape heterogeneity and human-driven dynamics, GEE-MEGAN significantly improves BVOC emission estimates, providing crucial insights to the complex interactions among BVOCs, climate, and air quality across both natural and human-modified environments.},
}

Humans
*Volatile Organic Compounds/analysis
*Forests
*Models, Theoretical
Cities
*Environmental Monitoring/methods
*Air Pollutants/analysis
Ecosystem
Air Pollution/analysis
Climate

@article {pmid40871988,
year = {2025},
author = {Panagou, IC and Katsoulis, S and Nannos, E and Zantalis, F and Koulouras, G},
title = {A Comprehensive Evaluation of IoT Cloud Platforms: A Feature-Driven Review with a Decision-Making Tool.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {16},
pages = {},
doi = {10.3390/s25165124},
pmid = {40871988},
issn = {1424-8220},
abstract = {The rapid proliferation of Internet of Things (IoT) devices has led to a growing ecosystem of Cloud Platforms designed to manage, process, and analyze IoT data. Selecting the optimal IoT Cloud Platform is a critical decision for businesses and developers, yet it presents a significant challenge due to the diverse range of features, pricing models, and architectural nuances. This manuscript presents a comprehensive, feature-driven review of twelve prominent IoT Cloud Platforms, including AWS IoT Core, IoT on Google Cloud Platform, and Microsoft Azure IoT Hub among others. We meticulously analyze each platform across nine key features: Security, Scalability and Performance, Interoperability, Data Analytics and AI/ML Integration, Edge Computing Support, Pricing Models and Cost-effectiveness, Developer Tools and SDK Support, Compliance and Standards, and Over-The-Air (OTA) Update Capabilities. For each feature, platforms are quantitatively scored (1-10) based on an in-depth assessment of their capabilities and offerings at the time of research. Recognizing the dynamic nature of this domain, we present our findings in a two-dimensional table to provide a clear comparative overview. Furthermore, to empower users in their decision-making process, we introduce a novel, web-based tool for evaluating IoT Cloud Platforms, called the "IoT Cloud Platforms Selector". This interactive tool allows users to assign personalized weights to each feature, dynamically calculating and displaying weighted scores for each platform, thereby facilitating a tailored selection process. This research provides a valuable resource for researchers, practitioners, and organizations seeking to navigate the complex landscape of IoT Cloud Platforms.},
}

@article {pmid40871763,
year = {2025},
author = {Rao, S and Neethirajan, S},
title = {Computational Architectures for Precision Dairy Nutrition Digital Twins: A Technical Review and Implementation Framework.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {16},
pages = {},
doi = {10.3390/s25164899},
pmid = {40871763},
issn = {1424-8220},
support = {R37424//NSERC/ ; },
mesh = {Animals ; Cattle ; *Dairying/methods ; Animal Feed ; Female ; Algorithms ; Telemetry ; },
abstract = {Sensor-enabled digital twins (DTs) are reshaping precision dairy nutrition by seamlessly integrating real-time barn telemetry with advanced biophysical simulations in the cloud. Drawing insights from 122 peer-reviewed studies spanning 2010-2025, this systematic review reveals how DT architectures for dairy cattle are conceptualized, validated, and deployed. We introduce a novel five-dimensional classification framework-spanning application domain, modeling paradigms, computational topology, validation protocols, and implementation maturity-to provide a coherent comparative lens across diverse DT implementations. Hybrid edge-cloud architectures emerge as optimal solutions, with lightweight CNN-LSTM models embedded in collar or rumen-bolus microcontrollers achieving over 90% accuracy in recognizing feeding and rumination behaviors. Simultaneously, remote cloud systems harness mechanistic fermentation simulations and multi-objective genetic algorithms to optimize feed composition, minimize greenhouse gas emissions, and balance amino acid nutrition. Field-tested prototypes indicate significant agronomic benefits, including 15-20% enhancements in feed conversion efficiency and water use reductions of up to 40%. Nevertheless, critical challenges remain: effectively fusing heterogeneous sensor data amid high barn noise, ensuring millisecond-level synchronization across unreliable rural networks, and rigorously verifying AI-generated nutritional recommendations across varying genotypes, lactation phases, and climates. Overcoming these gaps necessitates integrating explainable AI with biologically grounded digestion models, federated learning protocols for data privacy, and standardized PRISMA-based validation approaches. The distilled implementation roadmap offers actionable guidelines for sensor selection, middleware integration, and model lifecycle management, enabling proactive rather than reactive dairy management-an essential leap toward climate-smart, welfare-oriented, and economically resilient dairy farming.},
}

Animals
Cattle
*Dairying/methods
Animal Feed
Female
Algorithms
Telemetry

@article {pmid40870956,
year = {2025},
author = {Alamri, M and Humayun, M and Haseeb, K and Abbas, N and Ramzan, N},
title = {AI-Powered Adaptive Disability Prediction and Healthcare Analytics Using Smart Technologies.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {15},
number = {16},
pages = {},
doi = {10.3390/diagnostics15162104},
pmid = {40870956},
issn = {2075-4418},
abstract = {Background: By leveraging advanced wireless technologies, Healthcare Industry 5.0 promotes the continuous monitoring of real-time medical acquisition from the physical environment. These systems help identify early diseases by collecting health records from patients' bodies promptly using biosensors. The dynamic nature of medical devices not only enhances the data analysis in medical services and the prediction of chronic diseases, but also improves remote diagnostics with the latency-aware healthcare system. However, due to scalability and reliability limitations in data processing, most existing healthcare systems pose research challenges in the timely detection of personalized diseases, leading to inconsistent diagnoses, particularly when continuous monitoring is crucial. Methods: This work propose an adaptive and secure framework for disability identification using the Internet of Medical Things (IoMT), integrating edge computing and artificial intelligence. To achieve the shortest response time for medical decisions, the proposed framework explores lightweight edge computing processes that collect physiological and behavioral data using biosensors. Furthermore, it offers a trusted mechanism using decentralized strategies to protect big data analytics from malicious activities and increase authentic access to sensitive medical data. Lastly, it provides personalized healthcare interventions while monitoring healthcare applications using realistic health records, thereby enhancing the system's ability to identify diseases associated with chronic conditions. Results: The proposed framework is tested using simulations, and the results indicate the high accuracy of the healthcare system in detecting disabilities at the edges, while enhancing the prompt response of the cloud server and guaranteeing the security of medical data through lightweight encryption methods and federated learning techniques. Conclusions: The proposed framework offers a secure and efficient solution for identifying disabilities in healthcare systems by leveraging IoMT, edge computing, and AI. It addresses critical challenges in real-time disease monitoring, enhancing diagnostic accuracy and ensuring the protection of sensitive medical data.},
}

@article {pmid40870275,
year = {2025},
author = {Gao, H},
title = {Research on Computation Offloading and Resource Allocation Strategy Based on MADDPG for Integrated Space-Air-Marine Network.},
journal = {Entropy (Basel, Switzerland)},
volume = {27},
number = {8},
pages = {},
doi = {10.3390/e27080803},
pmid = {40870275},
issn = {1099-4300},
support = {62271303//National Natural Science Foundation of China/ ; },
abstract = {This paper investigates the problem of computation offloading and resource allocation in an integrated space-air-sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges. This algorithm is designed to minimize the total system costs, balancing energy consumption and latency through partial task offloading within a cloud-edge-device collaborative mobile edge computing (MEC) system. A comprehensive system model is proposed, with the problem formulated as a partially observable Markov decision process (POMDP) that integrates association control, power control, computing resource allocation, and task distribution. Each M-IoT device and UAV acts as an intelligent agent, collaboratively learning the optimal offloading strategies through a centralized training and decentralized execution framework inherent in the MADDPG. The numerical simulations validate the effectiveness of the proposed MADDPG-based approach, which demonstrates rapid convergence and significantly outperforms baseline methods, and indicate that the proposed MADDPG-based algorithm reduces the total system cost by 15-60% specifically.},
}

@article {pmid40863461,
year = {2025},
author = {Massimi, F and Tedeschi, A and Bagadi, K and Benedetto, F},
title = {Integrating Google Maps and Smooth Street View Videos for Route Planning.},
journal = {Journal of imaging},
volume = {11},
number = {8},
pages = {},
pmid = {40863461},
issn = {2313-433X},
abstract = {This research addresses the long-standing dependence on printed maps for navigation and highlights the limitations of existing digital services like Google Street View and Google Street View Player in providing comprehensive solutions for route analysis and understanding. The absence of a systematic approach to route analysis, issues related to insufficient street view images, and the lack of proper image mapping for desired roads remain unaddressed by current applications, which are predominantly client-based. In response, we propose an innovative automatic system designed to generate videos depicting road routes between two geographic locations. The system calculates and presents the route conventionally, emphasizing the path on a two-dimensional representation, and in a multimedia format. A prototype is developed based on a cloud-based client-server architecture, featuring three core modules: frames acquisition, frames analysis and elaboration, and the persistence of metadata information and computed videos. The tests, encompassing both real-world and synthetic scenarios, have produced promising results, showcasing the efficiency of our system. By providing users with a real and immersive understanding of requested routes, our approach fills a crucial gap in existing navigation solutions. This research contributes to the advancement of route planning technologies, offering a comprehensive and user-friendly system that leverages cloud computing and multimedia visualization for an enhanced navigation experience.},
}

@article {pmid40859495,
year = {2025},
author = {Tang, H and Yuan, Y and Liu, H and Hu, S},
title = {Application of a "nursing education cloud platform"-based combined and phased training model in the education of standardized-training nurses: A quasi-experimental study.},
journal = {Medicine},
volume = {104},
number = {34},
pages = {e44138},
pmid = {40859495},
issn = {1536-5964},
mesh = {Humans ; *Cloud Computing ; Clinical Competence ; Female ; Male ; Adult ; Models, Educational ; *Education, Nursing/methods ; Curriculum ; },
abstract = {The evolution of nursing education has rendered traditional standardized-training models increasingly inadequate, primarily due to their inflexible curricula, limited personalized instruction, and delayed feedback loops. While stage-based training models offer improved coherence through structured planning, they encounter difficulties in resource integration and real-time interaction. Contemporary advancements in cloud computing and Internet of Things technologies present novel opportunities for educational reform. Nursing Education Cloud Platform (NECP)-based systems have demonstrated efficacy in medical education, particularly in efficient resource management, data-driven decision-making, and the design of adaptable learning pathways. Despite the nascent implementation of cloud platforms in standardized nurse training, the sustained impact on multifaceted competencies, including professional identity and clinical reasoning, warrants further investigation. The primary objective of this investigation was to assess the effectiveness of a NECP-integrated, phased training model in enhancing standardized-training nurses' theoretical comprehension, practical competencies, professional self-perception, and clinical decision-making capabilities, while also examining its potential to refine nursing education methodologies. This quasi-experimental, non-randomized controlled trial evaluated the impact of a NECP-based training program. The study encompassed an experimental group (n = 56, receiving cloud platform-based training from September 2021 to August 2022) and a control group (n = 56, undergoing traditional training from September 2020 to August 2021). Group assignment was determined by the hospital's annual training schedule, thus employing a natural grouping based on the time period. Propensity score matching was utilized to mitigate baseline characteristic imbalances. The intervention's effects were assessed across several domains, including theoretical knowledge, operational skills, professional identity, and clinical reasoning abilities. ANCOVA was employed to account for temporal covariates. The experimental group scored significantly higher than the control group in theoretical knowledge (88.70 ± 5.07 vs 75.55 ± 9.01, P < .05), operational skills (94.27 ± 2.04 vs 90.95 ± 3.69, P < .05), professional identity (73.18 ± 10.18 vs 62.54 ± 15.48, P < .05), and clinical reasoning ability (60.95 ± 8.90 vs 51.09 ± 12.28, P < .05). The integration of the "NECP" with a phased training model demonstrates efficacy in augmenting nurses' competencies. However, the potential for selection bias, inherent in the non-randomized design, warrants careful consideration in the interpretation of these findings. Further investigation, specifically through multicenter longitudinal studies, is recommended to ascertain the generalizability of these results.},
}

Humans
*Cloud Computing
Clinical Competence
Female
Male
Adult
Models, Educational
*Education, Nursing/methods
Curriculum

@article {pmid40857554,
year = {2025},
author = {Brown, S and Kudia, O and Kleine, K and Kidd, B and Wines, R and Meckes, N},
title = {Comparing Multiple Imputation Methods to Address Missing Patient Demographics in Immunization Information Systems: Retrospective Cohort Study.},
journal = {JMIR public health and surveillance},
volume = {11},
number = {},
pages = {e73916},
doi = {10.2196/73916},
pmid = {40857554},
issn = {2369-2960},
mesh = {Retrospective Studies ; Humans ; Female ; Male ; *Information Systems/statistics & numerical data ; *Demography/statistics & numerical data ; Child, Preschool ; Cohort Studies ; Adolescent ; Child ; *Immunization/statistics & numerical data ; Adult ; Infant ; Middle Aged ; },
abstract = {BACKGROUND: Immunization Information Systems (IIS) and surveillance data are essential for public health interventions and programming; however, missing data are often a challenge, potentially introducing bias and impacting the accuracy of vaccine coverage assessments, particularly in addressing disparities.

OBJECTIVE: This study aimed to evaluate the performance of 3 multiple imputation methods, Stata's (StataCorp LLC) multiple imputation using chained equations (MICE), scikit-learn's Iterative-Imputer, and Python's miceforest package, in managing missing race and ethnicity data in large-scale surveillance datasets. We compared these methodologies in their ability to preserve demographic distribution, computational efficiency, and performed G-tests on contingency tables to obtain likelihood ratio statistics to assess the association between race and ethnicity and flu vaccination status.

METHODS: In this retrospective cohort study, we analyzed 2021-2022 flu vaccination and demographic data from the West Virginia Immunization Information System (N=2,302,036), where race (15%) and ethnicity (34%) were missing. MICE, Iterative Imputer, and miceforest were used to impute missing variables, generating 15 datasets each. Computational efficiency, demographic distribution preservation, and spatial clustering patterns were assessed using G-statistics.

RESULTS: After imputation, an additional 780,339 observations were obtained compared with complete case analysis. All imputation methods exhibited significant spatial clustering for race imputation (G-statistics: MICE=26,452.7, Iterative-Imputer=128,280.3, Miceforest=26,891.5; P<.001), while ethnicity imputation showed variable clustering patterns (G-statistics: MICE=1142.2, Iterative-Imputer=1.7, Miceforest=2185.0; P: MICE<.001, Iterative-Imputer=1.7, Miceforest<.001). MICE and miceforest best preserved the proportional distribution of demographics. Computational efficiency varied, with MICE requiring 14 hours, Iterative Imputer 2 minutes, and miceforest 10 minutes for 15 imputations. Postimputation estimates indicated a 0.87%-18% reduction in stratified flu vaccination coverage rates. Overall estimated flu vaccination rates decreased from 26% to 19% after imputations.

CONCLUSIONS: Both MICE and Miceforest offer flexible and reliable approaches for imputing missing demographic data while mitigating bias compared with Iterative-Imputer. Our results also highlight that the imputation method can profoundly affect research findings. Though MICE and Miceforest had better effect sizes and reliability, MICE was much more computationally and time-expensive, limiting its use in large, surveillance datasets. Miceforest can use cloud-based computing, which further enhances efficiency by offloading resource-intensive tasks, enabling parallel execution, and minimizing processing delays. The significant decrease in vaccination coverage estimates validates how incomplete or missing data can eclipse real disparities. Our findings support regular application of imputation methods in immunization surveillance to improve health equity evaluations and shape targeted public health interventions and programming.},
}

Retrospective Studies
Humans
Female
Male
*Information Systems/statistics & numerical data
*Demography/statistics & numerical data
Child, Preschool
Cohort Studies
Adolescent
Child
*Immunization/statistics & numerical data
Adult
Infant
Middle Aged

@article {pmid40857351,
year = {2025},
author = {Nguyen, C and Nguyen, T and Trivitt, G and Capaldo, B and Yan, C and Chen, Q and Renzette, N and Topaloglu, U and Meerzaman, D},
title = {Modular and cloud-based bioinformatics pipelines for high-confidence biomarker detection in cancer immunotherapy clinical trials.},
journal = {PloS one},
volume = {20},
number = {8},
pages = {e0330827},
doi = {10.1371/journal.pone.0330827},
pmid = {40857351},
issn = {1932-6203},
mesh = {Humans ; *Computational Biology/methods ; *Neoplasms/therapy/genetics/immunology ; *Biomarkers, Tumor/genetics ; *Immunotherapy/methods ; *Cloud Computing ; Clinical Trials as Topic ; Exome Sequencing ; Reproducibility of Results ; },
abstract = {BACKGROUND: The Cancer Immune Monitoring and Analysis Centers - Cancer Immunologic Data Center (CIMAC-CIDC) network aims to improve cancer immunotherapy by providing harmonized molecular assays and standardized bioinformatics analysis.

RESULTS: In response to evolving bioinformatics standards and the migration of the CIDC to the National Cancer Institute (NCI), we undertook the enhancement of the CIDC's extant whole exome sequencing (WES) and RNA sequencing (RNA-Seq) pipelines. Leveraging open-source tools and cloud-based technologies, we implemented modular workflows using Snakemake and Docker for efficient deployment on the Google Cloud Platform (GCP). Benchmarking analyses demonstrate improved reproducibility, precision, and recall across validated truth sets for variant calling, transcript quantification, and fusion detection.

CONCLUSION: This work establishes a scalable framework for harmonized multi-omic analyses, ensuring the continuity and reliability of bioinformatics workflows in multi-site clinical research aimed at advancing cancer biomarker discovery and personalized medicine.},
}

Humans
*Computational Biology/methods
*Neoplasms/therapy/genetics/immunology
*Biomarkers, Tumor/genetics
*Immunotherapy/methods
*Cloud Computing
Clinical Trials as Topic
Exome Sequencing
Reproducibility of Results

@article {pmid40855077,
year = {2025},
author = {Nazmul Haque, SM and Uddin, MJ},
title = {Monitoring LULC dynamics and detecting transformation hotspots in sylhet, Bangladesh (2000-2023) using Google Earth Engine.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {31263},
pmid = {40855077},
issn = {2045-2322},
abstract = {Sylhet, located in the northeastern part of Bangladesh, is characterized by a unique topography and climatic conditions that make it susceptible to flash floods. The interplay of rapid urbanization and climatic variability has exacerbated these flood risks in recent years. Effective monitoring and planning of land use/land cover (LULC) are crucial strategies for mitigating these hazards. While former studies analyzed LULC in parts of Sylhet using traditional GIS approaches, no comprehensive, district-wide assessment has been carried out using long-term satellite data and cloud computing platforms. This study addresses that gap by applying Google Earth Engine (GEE) for an extensive analysis of LULC changes, transitions, and hot/cold spots across the district. Accordingly, this work investigates the LULC changes in Sylhet district over the past twenty-three years (2000-2023). Using satellite imagery from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI), the LULC is classified in six selected years (2000, 2005, 2010, 2015, 2020, and 2023). A supervised machine learning algorithm, the Random Forest Classifier, is employed on the cloud computing platform Google Earth Engine to analyze LULC dynamics and detect changes. The Getis-Ord Gi[*] statistical model is applied to identify land transformation hot spot and cold spot areas. The results reveal a significant increase in built-up areas and a corresponding reduction in water bodies. Spatial analysis at the upazila level indicates urban expansion in every upazila, with the most substantial increase observed in Beani Bazar upazila, where urban areas expanded by approximately 1500%. Conversely, Bishwanath upazila experienced the greatest reduction in water bodies, with a decrease of about 90%. Sylhet Sadar upazila showed a 240% increase in urban areas and a 72% decrease in water bodies. According to hotspot analysis, Kanaighat upazila has the most amount of unchanging land at 7%, whereas Balaganj upazila has the largest amount of LULC transformation at 5.5%. Overall, the urban area in the Sylhet district has grown by approximately 300%, while water bodies have diminished by about 77%, reflecting trends of urbanization and river-filling. These findings underscore the necessity of ensuring adequate drainage infrastructure to decrease flash flood hazards in the Sylhet district and offer insightful information to relevant authorities, politicians, and water resource engineers.},
}

@article {pmid40854942,
year = {2025},
author = {Dhanaraj, RK and Maragatharajan, M and Sureshkumar, A and Balakannan, SP},
title = {On-device AI for climate-resilient farming with intelligent crop yield prediction using lightweight models on smart agricultural devices.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {31195},
pmid = {40854942},
issn = {2045-2322},
abstract = {In Recent time, with the utilization of Artificial Intelligence (AI), AI applications have proliferated across various domains where agricultural consumer electronics are no exception. These innovations have significantly enhanced the intelligence of agricultural processes, leading to increased efficiency and sustainability. This study introduces an intelligent crop yield prediction system that utilizes Random Forest (RF) classifier to optimize the usage of water based on environmental factors. By integrating lightweight machine learning with consumer electronics such as sensors connected inside the smart display devices, this work is aimed to amplify water management and promote sustainable farming practices. While focusing on the sustainable agriculture, the water usage efficiency in irrigation should be enhanced by predicting optimal watering schedules and it will reduce the environmental impact and support the climate resilient farming. The proposed lightweight model has been trained on real-time agricultural data with minimum memory resource in sustainability prediction and the model has achieved 90.1% accuracy in the detection of crop yield suitable for the farmland as well as outperformed the existing methods including AI-enabled IoT model with mobile sensors and deep learning architectures (89%), LoRa-based systems (87.2%), and adaptive AI with self-learning techniques (88%). The deployment of computationally efficient machine learning models like random forest algorithms will emphasis on real time decision making without depending on the cloud computing. The performance evaluation and effectiveness of the proposed method are estimated using the important parameter called prediction accuracy. The main goal of this parameter is to access how the AI model accurately predicts the irrigation needs based on the sensor data.},
}

@article {pmid40852091,
year = {2025},
author = {Ozlem, K and Gumus, C and Yilmaz, AF and Tuncay Atalay, A and Atalay, O and Ince, G},
title = {Cloud-Based Control System with Sensing and Actuating Textile-Based IoT Gloves for Telerehabilitation Applications.},
journal = {Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)},
volume = {7},
number = {8},
pages = {2400894},
pmid = {40852091},
issn = {2640-4567},
abstract = {Remote manipulation devices extend human capabilities over vast distances or in inaccessible environments, removing constraints between patients and treatment. The integration of therapeutic and assistive devices with the Internet of Things (IoT) has demonstrated high potential to develop and enhance intelligent rehabilitation systems in the e-health domain. Within such devices, soft robotic products distinguish themselves through their lightweight and adaptable characteristics, facilitating secure collaboration between humans and robots. The objective of this research is to combine a textile-based sensorized glove with an air-driven soft robotic glove, operated wirelessly using the developed control system architecture. The sensing glove equipped with capacitive sensors on each finger captures the movements of the medical staff's hand. Meanwhile, the pneumatic rehabilitation glove designed to aid patients affected by impaired hand function due to stroke, brain injury, or spinal cord injury replicates the movements of the medical personnel. The proposed artificial intelligence-based system detects finger gestures and actuates the pneumatic system, responding within an average response time of 48.4 ms. The evaluation of the system further in terms of accuracy and transmission quality metrics verifies the feasibility of the proposed system integrating textile gloves into IoT infrastructure, enabling remote motion sensing and actuation.},
}

@article {pmid40851640,
year = {2025},
author = {Saratkar, SY and Langote, M and Kumar, P and Gote, P and Weerarathna, IN and Mishra, GV},
title = {Digital twin for personalized medicine development.},
journal = {Frontiers in digital health},
volume = {7},
number = {},
pages = {1583466},
pmid = {40851640},
issn = {2673-253X},
abstract = {Digital Twin (DT) technology is revolutionizing healthcare by enabling real-time monitoring, predictive analytics, and highly personalized medical care. As a key innovation of Industry 4.0, DTs integrate advanced tools like artificial intelligence (AI), the Internet of Things (IoT), and machine learning (ML) to create dynamic, data-driven replicas of patients. These digital replicas allow simulations of disease progression, optimize diagnostics, and personalize treatment plans based on individual genetic and lifestyle profiles. This review explores the evolution, architecture, and enabling technologies of DTs, focusing on their transformative applications in personalized medicine (PM). While the integration of DTs offers immense potential to improve outcomes and efficiency in healthcare, challenges such as data privacy, system interoperability, and ethical concerns must be addressed. The paper concludes by highlighting future directions, where AI, cloud computing, and blockchain are expected to play a pivotal role in overcoming these limitations and advancing precision medicine.},
}

@article {pmid40850704,
year = {2025},
author = {Beć, KB and Grabska, J and Huck, CW},
title = {Handheld NIR spectroscopy for real-time on-site food quality and safety monitoring.},
journal = {Advances in food and nutrition research},
volume = {115},
number = {},
pages = {293-389},
doi = {10.1016/bs.afnr.2025.01.002},
pmid = {40850704},
issn = {1043-4526},
mesh = {Spectroscopy, Near-Infrared/instrumentation/methods ; *Food Safety/methods ; *Food Quality ; *Food Analysis/methods/instrumentation ; Food Contamination/analysis ; Humans ; Quality Control ; },
abstract = {This chapter reviews the applications and future directions of portable near-infrared (NIR) spectroscopy in food analytics, with a focus on quality control, safety monitoring, and fraud detection. Portable NIR spectrometers are essential for real-time, non-destructive analysis of food composition, and their use is rapidly expanding across various stages of the food production chain-from agriculture and processing to retail and consumer applications. The functional design of miniaturized NIR spectrometers is examined, linking the technological diversity of these sensors to their application potential in specific roles within the food sector, while discussing challenges related to thermal stability, energy efficiency, and spectral accuracy. Current trends in data analysis, including chemometrics and artificial intelligence, are also highlighted, as the successful application of portable spectroscopy heavily depends on this key aspect of the analytical process. This discussion is based on recent literature, with a focus on the last five years, and addresses the application of portable NIR spectroscopy in food quality assessment and composition analysis, food safety and contaminant detection, and food authentication and fraud prevention. The chapter concludes that portable NIR spectroscopy has significantly enhanced food analytics over the past decade, with ongoing trends likely to lead to even wider adoption in the near future. Future challenges related to ultra-miniaturization and emerging consumer-oriented spectrometers emphasize the need for robust pre-calibrated models and the development of global models for key applications. The integration of NIR spectrometers with cloud computing, IoT, and machine learning is expected to drive advancements in real-time monitoring, predictive modeling, and data processing, fitting the growing demand for improved safety, quality, and fraud detection from the farm to the fork.},
}

Spectroscopy, Near-Infrared/instrumentation/methods
*Food Safety/methods
*Food Quality
*Food Analysis/methods/instrumentation
Food Contamination/analysis
Humans
Quality Control

@article {pmid40839622,
year = {2025},
author = {Cui, D and Peng, Z and Li, K and Li, Q and He, J and Deng, X},
title = {An novel cloud task scheduling framework using hierarchical deep reinforcement learning for cloud computing.},
journal = {PloS one},
volume = {20},
number = {8},
pages = {e0329669},
doi = {10.1371/journal.pone.0329669},
pmid = {40839622},
issn = {1932-6203},
mesh = {*Cloud Computing ; *Deep Learning ; Algorithms ; Humans ; Reinforcement Machine Learning ; },
abstract = {With the increasing popularity of cloud computing services, their large and dynamic load characteristics have rendered task scheduling an NP-complete problem.To address the problem of large-scale task scheduling in a cloud computing environment, this paper proposes a novel cloud task scheduling framework using hierarchical deep reinforcement learning (DRL) to address the challenges of large-scale task scheduling in cloud computing. The framework defines a set of virtual machines (VMs) as a VM cluster and employs hierarchical scheduling to allocate tasks first to the cluster and then to individual VMs. The scheduler, designed using DRL, adapts to dynamic changes in the cloud environments by continuously learning and updating network parameters. Experiments demonstrate that it skillfully balances cost and performance. In low-load situations, costs are reduced by using low-cost nodes within the Service Level Agreement (SLA) range; in high-load situations, resource utilization is improved through load balancing. Compared with classical heuristic algorithms, it effectively optimizes load balancing, cost, and overdue time, achieving a 10% overall improvement. The experimental results demonstrate that this approach effectively balances cost and performance, optimizing objectives such as load balance, cost, and overdue time. One potential shortcoming of the proposed hierarchical deep reinforcement learning (DRL) framework for cloud task scheduling is its complexity and computational overhead. Implementing and maintaining a DRL-based scheduler requires significant computational resources and expertise in machine learning. There are still shortcomings in the method used in this study. First, the continuous learning and updating of network parameters might introduce latency, which could impact real-time task scheduling efficiency. Furthermore, the framework's performance heavily depends on the quality and quantity of training data, which might be challenging to obtain and maintain in a dynamic cloud environment.},
}

*Cloud Computing
*Deep Learning
Algorithms
Humans
Reinforcement Machine Learning

@article {pmid40835668,
year = {2025},
author = {Manhary, FN and Mohamed, MH and Farouk, M},
title = {A scalable machine learning strategy for resource allocation in database.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {30567},
pmid = {40835668},
issn = {2045-2322},
abstract = {Modern cloud computing systems require intelligent resource allocation strategies that balance quality-of-service (QoS), operational costs, and energy sustainability. Existing deep Q-learning (DQN) methods suffer from sample inefficiency, centralization bottlenecks, and reactive decision-making during workload spikes. Transformer-based forecasting models such as Temporal Fusion Transformer (TFT) offer improved accuracy but introduce computational overhead, limiting real-time deployment. We propose LSTM-MARL-Ape-X, a novel framework integrating bidirectional Long Short-Term Memory (BiLSTM) for workload forecasting with Multi-Agent Reinforcement Learning (MARL) in a distributed Ape-X architecture. This approach enables proactive, decentralized, and scalable resource management through three innovations: high-accuracy forecasting using BiLSTM with feature-wise attention, variance-regularized credit assignment for stable multi-agent coordination, and faster convergence via adaptive prioritized replay. Experimental validation on real-world traces demonstrates 94.6% SLA compliance, 22% reduction in energy consumption, and linear scalability to over 5,000 nodes with sub-100 ms decision latency. The framework converges 3.2× faster than uniform sampling baselines and outperforms transformer-based models in both accuracy and inference speed. Unlike decoupled prediction-action frameworks, our method provides end-to-end optimization, enabling robust and sustainable cloud orchestration at scale.},
}

@article {pmid40826811,
year = {2025},
author = {Park, SY and Takayama, C and Ryu, J and Sattah, M and Badii, Z and Kim, JW and Shafer, RW and Gorbach, PM and Lee, HY},
title = {Design and evaluation of next-generation HIV genotyping for detection of resistance mutations to 28 antiretroviral drugs across five major classes including lenacapavir.},
journal = {Clinical infectious diseases : an official publication of the Infectious Diseases Society of America},
volume = {},
number = {},
pages = {},
doi = {10.1093/cid/ciaf458},
pmid = {40826811},
issn = {1537-6591},
abstract = {BACKGROUND: The emergence and spread of HIV drug-resistant strains present a major barrier to effective lifelong Antiretroviral Therapy (ART). The anticipated rise in long-acting subcutaneous lenacapavir (LEN) use, along with the increased risk of transmitted resistance and Pre-Exposure Prophylaxis (PrEP)-associated resistance, underscores the urgent need for advanced genotyping methods to enhance clinical care and prevention strategies.

METHODS: We developed the Portable HIV Genotyping (PHG) platform which combines cost-effective next-generation sequencing with cloud computing to screen for resistance to 28 antiretroviral drugs across five major classes, including LEN. We analyzed three study cohorts and compared our drug resistance findings against standard care testing results and high-fidelity sequencing data obtained through unique molecular identifier (UMI) labeling.

RESULTS: PHG identified two major LEN-resistance mutations in one participant, confirmed by an additional independent sequencing run. Across three study cohorts, PHG consistently detected the same drug resistance mutations as standard care genotyping and high-fidelity UMI-labeling in most tested specimens. PHG's 10% limit of detection minimized false positives and enabled identification of minority variants less than 20% frequency, pointing to underdiagnosis of drug resistance in clinical care. Furthermore, PHG identified linked cross-class resistance mutations, confirmed by UMI-labeling, including linked cross-resistance in a participant who reported use of long-acting cabotegravir (CAB) and rilpivirine (RPV). We also observed multi-year persistence of linked cross-class resistance mutations.

CONCLUSIONS: PHG demonstrates significant improvements over standard care HIV genotyping, offering deeper insights into LEN-resistance, minority variants, and cross-class resistance using a low-cost high-throughput portable sequencing technology and publicly available cloud computing.},
}

@article {pmid40808017,
year = {2025},
author = {Wu, J and Bian, Z and Gao, H and Wang, Y},
title = {A Blockchain-Based Secure Data Transaction and Privacy Preservation Scheme in IoT System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40808017},
issn = {1424-8220},
support = {Grant 809 No.U24B20146//National Natural Science Foundation of China/ ; No.M21034//Beijing Natural Science/ ; GrantNo.2020YFB1005503//the National Key Research and Development Plan in China/ ; },
abstract = {With the explosive growth of Internet of Things (IoT) devices, massive amounts of heterogeneous data are continuously generated. However, IoT data transactions and sharing face multiple challenges such as limited device resources, untrustworthy network environment, highly sensitive user privacy, and serious data silos. How to achieve fine-grained access control and privacy protection for massive devices while ensuring secure and reliable data circulation has become a key issue that needs to be urgently addressed in the current IoT field. To address the above challenges, this paper proposes a blockchain-based data transaction and privacy protection framework. First, the framework builds a multi-layer security architecture that integrates blockchain and IPFS and adapts to the "end-edge-cloud" collaborative characteristics of IoT. Secondly, a data sharing mechanism that takes into account both access control and interest balance is designed. On the one hand, the mechanism uses attribute-based encryption (ABE) technology to achieve dynamic and fine-grained access control for massive heterogeneous IoT devices; on the other hand, it introduces a game theory-driven dynamic pricing model to effectively balance the interests of both data supply and demand. Finally, in response to the needs of confidential analysis of IoT data, a secure computing scheme based on CKKS fully homomorphic encryption is proposed, which supports efficient statistical analysis of encrypted sensor data without leaking privacy. Security analysis and experimental results show that this scheme is secure under standard cryptographic assumptions and can effectively resist common attacks in the IoT environment. Prototype system testing verifies the functional completeness and performance feasibility of the scheme, providing a complete and effective technical solution to address the challenges of data integrity, verifiable transactions, and fine-grained access control, while mitigating the reliance on a trusted central authority in IoT data sharing.},
}

@article {pmid40778132,
year = {2025},
author = {Chapman, OS and Sridhar, S and Chow, EY and Kenkre, R and Kirkland, J and Dutta, A and Wang, S and Zhang, W and Brown, M and Luebeck, J and Lo, YY and Rodriguez-Fos, E and Henssen, AG and Okonechnikov, K and Ghasemi, DR and Pajtler, KW and Kawauchi, D and Bafna, V and Paul, M and Yip, K and Mesirov, JP and Chavez, L},
title = {Extrachromosomal DNA associates with poor survival across a broad spectrum of childhood solid tumors.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {40778132},
support = {U01 CA253547/CA/NCI NIH HHS/United States ; U24 CA264379/CA/NCI NIH HHS/United States ; T15 LM011271/LM/NLM NIH HHS/United States ; P30 CA051008/CA/NCI NIH HHS/United States ; R21 NS120075/NS/NINDS NIH HHS/United States ; F31 CA271777/CA/NCI NIH HHS/United States ; R21 NS130137/NS/NINDS NIH HHS/United States ; P30 CA030199/CA/NCI NIH HHS/United States ; R01 NS132780/NS/NINDS NIH HHS/United States ; },
abstract = {Circular extrachromosomal DNA (ecDNA) is a common form of oncogene amplification in aggressive cancers. The frequency and diversity of ecDNA has been catalogued in adult and some childhood cancers; however, its role in most pediatric cancers is not well-understood. To address this gap, we accessed large pediatric cancer genomics data repositories and identified ecDNA from whole genome sequencing data using cloud computing. This retrospective cohort comprises 3,631 solid tumor biopsies from 2,968 patients covering all major childhood solid tumor types. Aggressive tumor types had particularly high incidences of ecDNA. Pediatric patients whose tumors harbored extrachromosomal DNA had significantly poorer five-year overall survival than children whose tumors contained only chromosomal amplifications. We catalogue known and potentially novel oncogenes recurrently amplified on ecDNA and show that ecDNA often evolves during disease progression. These results highlight patient populations that could potentially benefit from future ecDNA-directed therapies. To facilitate discovery, we developed an interactive catalogue of ecDNA in childhood cancer at https://ccdi-ecdna.org/.},
}

@article {pmid33544692,
year = {2021},
author = {Vahidy, F and Jones, SL and Tano, ME and Nicolas, JC and Khan, OA and Meeks, JR and Pan, AP and Menser, T and Sasangohar, F and Naufal, G and Sostman, D and Nasir, K and Kash, BA},
title = {Rapid Response to Drive COVID-19 Research in a Learning Health Care System: Rationale and Design of the Houston Methodist COVID-19 Surveillance and Outcomes Registry (CURATOR).},
journal = {JMIR medical informatics},
volume = {9},
number = {2},
pages = {e26773},
pmid = {33544692},
issn = {2291-9694},
abstract = {BACKGROUND: The COVID-19 pandemic has exacerbated the challenges of meaningful health care digitization. The need for rapid yet validated decision-making requires robust data infrastructure. Organizations with a focus on learning health care (LHC) systems tend to adapt better to rapidly evolving data needs. Few studies have demonstrated a successful implementation of data digitization principles in an LHC context across health care systems during the COVID-19 pandemic.

OBJECTIVE: We share our experience and provide a framework for assembling and organizing multidisciplinary resources, structuring and regulating research needs, and developing a single source of truth (SSoT) for COVID-19 research by applying fundamental principles of health care digitization, in the context of LHC systems across a complex health care organization.

METHODS: Houston Methodist (HM) comprises eight tertiary care hospitals and an expansive primary care network across Greater Houston, Texas. During the early phase of the pandemic, institutional leadership envisioned the need to streamline COVID-19 research and established the retrospective research task force (RRTF). We describe an account of the structure, functioning, and productivity of the RRTF. We further elucidate the technical and structural details of a comprehensive data repository-the HM COVID-19 Surveillance and Outcomes Registry (CURATOR). We particularly highlight how CURATOR conforms to standard health care digitization principles in the LHC context.

RESULTS: The HM COVID-19 RRTF comprises expertise in epidemiology, health systems, clinical domains, data sciences, information technology, and research regulation. The RRTF initially convened in March 2020 to prioritize and streamline COVID-19 observational research; to date, it has reviewed over 60 protocols and made recommendations to the institutional review board (IRB). The RRTF also established the charter for CURATOR, which in itself was IRB-approved in April 2020. CURATOR is a relational structured query language database that is directly populated with data from electronic health records, via largely automated extract, transform, and load procedures. The CURATOR design enables longitudinal tracking of COVID-19 cases and controls before and after COVID-19 testing. CURATOR has been set up following the SSoT principle and is harmonized across other COVID-19 data sources. CURATOR eliminates data silos by leveraging unique and disparate big data sources for COVID-19 research and provides a platform to capitalize on institutional investment in cloud computing. It currently hosts deeply phenotyped sociodemographic, clinical, and outcomes data of approximately 200,000 individuals tested for COVID-19. It supports more than 30 IRB-approved protocols across several clinical domains and has generated numerous publications from its core and associated data sources.

CONCLUSIONS: A data-driven decision-making strategy is paramount to the success of health care organizations. Investment in cross-disciplinary expertise, health care technology, and leadership commitment are key ingredients to foster an LHC system. Such systems can mitigate the effects of ongoing and future health care catastrophes by providing timely and validated decision support.},
}

@article {pmid26918190,
year = {2016},
author = {Dinov, ID},
title = {Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data.},
journal = {GigaScience},
volume = {5},
number = {},
pages = {12},
pmid = {26918190},
issn = {2047-217X},
support = {P30 AG053760/AG/NIA NIH HHS/United States ; U54 EB020406/EB/NIBIB NIH HHS/United States ; P20 NR015331/NR/NINR NIH HHS/United States ; P30 DK089503/DK/NIDDK NIH HHS/United States ; P50 NS091856/NS/NINDS NIH HHS/United States ; },
mesh = {Computational Biology/*methods ; Delivery of Health Care/*statistics & numerical data ; Humans ; *Models, Theoretical ; Neuroimaging/statistics & numerical data ; Principal Component Analysis ; Reproducibility of Results ; *Software ; },
abstract = {Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be 'team science'.},
}

Computational Biology/*methods
Delivery of Health Care/*statistics & numerical data
Humans
*Models, Theoretical
Neuroimaging/statistics & numerical data
Principal Component Analysis
Reproducibility of Results
*Software

@article {pmid40821997,
year = {2025},
author = {Isik, MS and Parente, L and Consoli, D and Sloat, L and Mesquita, VV and Ferreira, LG and Sabbatini, S and Stanimirova, R and Teles, NM and Robinson, N and Costa Junior, C and Hengl, T},
title = {Light use efficiency (LUE) based bimonthly gross primary productivity (GPP) for global grasslands at 30 m spatial resolution (2000-2022).},
journal = {PeerJ},
volume = {13},
number = {},
pages = {e19774},
doi = {10.7717/peerj.19774},
pmid = {40821997},
issn = {2167-8359},
abstract = {The article describes production of a high spatial resolution (30 m) bimonthly light use efficiency (LUE) based gross primary productivity (GPP) data set representing grasslands for the period 2000 to 2022. The data set is based on using reconstructed global complete consistent bimonthly Landsat archive (400TB of data), combined with 1 km MOD11A1 temperature data and 1° CERES Photosynthetically Active Radiation (PAR). First, the LUE model was implemented by taking the biome-specific productivity factor (maximum LUE parameter) as a global constant, producing a global bimonthly (uncalibrated) productivity data for the complete land mask. Second, the GPP 30 m bimonthly maps were derived for the global grassland annual predictions and calibrating the values based on the maximum LUE factor of 0.86 gCm[-2]d[-1]MJ[-1]. The results of validation of the produced GPP estimates based on 527 eddy covariance flux towers show an R-square between 0.48-0.71 and root mean square error (RMSE) below ~2.3 gCm[-2]d[-1] for all land cover classes. Using a total of 92 flux towers located in grasslands, the validation of the GPP product calibrated for the grassland biome revealed an R-square between 0.51-0.70 and an RMSE smaller than ~2 gCm[-2]d[-1]. The final time-series of maps (uncalibrated and grassland GPP) are available as bimonthly (daily estimates in units of gCm[-2]d[-1]) and annual (daily average accumulated by 365 days in units of gCm[-2]yr[-1]) in Cloud-Optimized GeoTIFF (~23TB in size) as open data (CC-BY license). The recommended uses of data include: trend analysis e.g., to determine where are the largest losses in GPP and which could be an indicator of potential land degradation, crop yield mapping and for modeling GHG fluxes at finer spatial resolution. Produced maps are available via SpatioTemporal Asset Catalog (http://stac.openlandmap.org) and Google Earth Engine.},
}

@article {pmid40820020,
year = {2025},
author = {Periasamy, JK and Prabhakar, S and Vanathi, A and Yu, L},
title = {Enhancing cloud security and deduplication efficiency with SALIGP and cryptographic authentication.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {30112},
pmid = {40820020},
issn = {2045-2322},
abstract = {Cloud computing enables data storage and application deployment over the internet, offering benefits such as mobility, resource pooling, and scalability. However, it also presents major challenges, particularly in managing shared resources, ensuring data security, and controlling distributed applications in the absence of centralized oversight. One key issue is data duplication, which leads to inefficient storage, increased costs, and potential privacy and security risks. To address these challenges, this study proposes a post-quantum mechanism that enhances both cloud security and deduplication efficiency. The proposed SALIGP method leverages Genetic Programming and a Geometric Approach, integrating Bloom Filters for efficient duplication detection. The Cryptographic Deduplication Authentication Scheme (CDAS) is introduced, which utilizes blockchain technology to securely store and retrieve files, while ensuring that encrypted access is limited to authorized users. This dual-layered approach effectively resolves the issue of redundant data in dynamic, distributed cloud environments. Experimental results demonstrate that the proposed method significantly reduces computation and communication times at various network nodes, particularly in key generation and group operations. Encrypting user data prior to outsourcing ensures enhanced privacy protection during the deduplication process. Overall, the proposed system leads to substantial improvements in cloud data security, reliability, and storage efficiency, offering a scalable and secure framework for modern cloud computing environments.},
}

@article {pmid40817336,
year = {2025},
author = {Wang, J and Li, K and Han, T and Sun, Y and Hong, M and Shao, Y and Sun, Z and Liu, M and Li, F and Su, Y and Jia, Q and Liu, Y and Liu, J and Jiang, J and Ochir, A and Davaasuren, D and Xu, M and Sun, Y and Huang, S and Zou, W and Sun, F},
title = {Long-term Land Cover Dataset of the Mongolian Plateau Based on Multi-source Data and Rich Sample Annotations.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1434},
pmid = {40817336},
issn = {2052-4463},
abstract = {The Mongolian Plateau (MP), with its unique geographical landscape and nomadic cultural features, is vital to regional ecological security and sustainable development in North Asia. Existing global land cover products often lack the classification specificity and temporal continuity required for MP-specific studies, particularly for grassland and bare area subtypes. To address this gap, a new land cover classification was designed for MP, which includes 14 categories: forests, shrubs, meadows, real steppes, dry steppes, desert steppes, wetlands, water, croplands, built-up land, barren land, desert, sand, and ice. Using machine learning and cloud computing, the novel dataset spanning the period of 1990-2020. Random Forest algorithm was employed to integrate training samples with multisource features for landcover classification, and a two-step Random Forest classification strategy to improve detail land cover results in transition regions. This process involved accurately annotating 64,345 sample points within a gridded framework. The resulting dataset achieved an overall accuracy of 83.6%. This land cover product and its approach has potential for application in vast arid and semi-arid areas.},
}

@article {pmid40811182,
year = {2025},
author = {Ahmad, T and Schuchart, J and Al Ars, Z and Niethammer, C and Gracia, J and Hofstee, HP},
title = {GenMPI: Cluster Scalable Variant Calling for Short/Long Reads Sequencing Data.},
journal = {IEEE transactions on computational biology and bioinformatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TCBBIO.2025.3595409},
pmid = {40811182},
issn = {2998-4165},
abstract = {Rapid technological advancements in sequencing technologies allow producing cost effective and high volume sequencing data. Processing this data for real-time clinical diagnosis is potentially time-consuming if done on a single computing node. This work presents a complete variant calling workflow, implemented using the Message Passing Interface (MPI) to leverage the benefits of high bandwidth interconnects. This solution (GenMPI) is portable and flexible, meaning it can be deployed to any private or public cluster/cloud infrastructure. Any alignment or variant calling application can be used with minimal adaptation. To achieve high performance, compressed input data can be streamed in parallel to alignment applications while uncompressed data can use internal file seek functionality to eliminate the bottleneck of streaming input data from a single node. Alignment output can be directly stored in multiple chromosome-specific SAM files or a single SAM file. After alignment, a distributed queue using MPI RMA (Remote Memory Access) atomic operations is created for sorting, indexing, marking of duplicates (if necessary) and variant calling applications. We ensure the accuracy of variants as compared to the original single node methods. We also show that for 300x coverage data, alignment scales almost linearly up to 64 nodes (8192 CPU cores). Overall, this work outperforms existing big data based workflows by a factor of two and is almost 20% faster than other MPI-based implementations for alignment without any extra memory overheads. Sorting, indexing, duplicate removal and variant calling is also scalable up to 8 nodes cluster. For pair-end short-reads (Illumina) data, we integrated the BWA-MEM aligner and three variant callers (GATK HaplotypeCaller, DeepVariant and Octopus), while for long-reads data, we integrated the Minimap2 aligner and three different variant callers (DeepVariant, DeepVariant with WhatsHap for phasing (PacBio) and Clair3 (ONT)). All codes and scripts are available at: https://github.com/abs-tudelft/gen-mpi.},
}

@article {pmid40807915,
year = {2025},
author = {Liu, S and Shan, N and Bao, X and Xu, X},
title = {Distributed Collaborative Data Processing Framework for Unmanned Platforms Based on Federated Edge Intelligence.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807915},
issn = {1424-8220},
support = {62102436; 62406337; 2021CFB279; 202250E060; 614221722050603//National Natural Science Foundation of China under; Natural Science Foundation of Hubei Province; National Defence Science and Technology Key Laboratory Foundation Project/ ; },
abstract = {Unmanned platforms such as unmanned aerial vehicles, unmanned ground vehicles, and autonomous underwater vehicles often face challenges of data, device, and model heterogeneity when performing collaborative data processing tasks. Existing research does not simultaneously address issues from these three aspects. To address this issue, this study designs an unmanned platform cluster architecture inspired by the cloud-edge-end model. This architecture integrates federated learning for privacy protection, leverages the advantages of distributed model training, and utilizes edge computing's near-source data processing capabilities. Additionally, this paper proposes a federated edge intelligence method (DSIA-FEI), which comprises two key components. Based on traditional federated learning, a data sharing mechanism is introduced, in which data is extracted from edge-side platforms and placed into a data sharing platform to form a public dataset. At the beginning of model training, random sampling is conducted from the public dataset and distributed to each unmanned platform, so as to mitigate the impact of data distribution heterogeneity and class imbalance during collaborative data processing in unmanned platforms. Moreover, an intelligent model aggregation strategy based on similarity measurement and loss gradient is developed. This strategy maps heterogeneous model parameters to a unified space via hierarchical parameter alignment, and evaluates the similarity between local and global models of edge devices in real-time, along with the loss gradient, to select the optimal model for global aggregation, reducing the influence of device and model heterogeneity on cooperative learning of unmanned platform swarms. This study carried out extensive validation on multiple datasets, and the experimental results showed that the accuracy of the DSIA-FEI proposed in this paper reaches 0.91, 0.91, 0.88, and 0.87 on the FEMNIST, FEAIR, EuroSAT, and RSSCN7 datasets, respectively, which is more than 10% higher than the baseline method. In addition, the number of communication rounds is reduced by more than 40%, which is better than the existing mainstream methods, and the effectiveness of the proposed method is verified.},
}

@article {pmid40807868,
year = {2025},
author = {Cui, M and Wang, Y},
title = {An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud Computing.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807868},
issn = {1424-8220},
abstract = {Workflow scheduling in cloud computing is attracting increasing attention. Cloud computing can assign tasks to available virtual machine resources in cloud data centers according to scheduling strategies, providing a powerful computing platform for the execution of workflow tasks. However, developing effective workflow scheduling algorithms to find optimal or near-optimal task-to-VM allocation solutions that meet users' specific QoS requirements still remains an open area of research. In this paper, we propose a hybrid QoS-aware workflow scheduling algorithm named HLWOA to address the problem of simultaneously minimizing the completion time and execution cost of workflow scheduling in cloud computing. First, the workflow scheduling problem in cloud computing is modeled as a multi-objective optimization problem. Then, based on the heterogeneous earliest finish time (HEFT) heuristic optimization algorithm, tasks are reverse topologically sorted and assigned to virtual machines with the earliest finish time to construct an initial workflow task scheduling sequence. Furthermore, an improved Whale Optimization Algorithm (WOA) based on Lévy flight is proposed. The output solution of HEFT is used as one of the initial population solutions in WOA to accelerate the convergence speed of the algorithm. Subsequently, a Lévy flight search strategy is introduced in the iterative optimization phase to avoid the algorithm falling into local optimal solutions. The proposed HLWOA is evaluated on the WorkflowSim platform using real-world scientific workflows (Cybershake and Montage) with different task scales (100 and 1000). Experimental results demonstrate that HLWOA outperforms HEFT, HEPGA, and standard WOA in both makespan and cost, with normalized fitness values consistently ranking first.},
}

@article {pmid40807829,
year = {2025},
author = {Mtowe, DP and Long, L and Kim, DM},
title = {Low-Latency Edge-Enabled Digital Twin System for Multi-Robot Collision Avoidance and Remote Control.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807829},
issn = {1424-8220},
support = {2019R1G1A1100699//National Research Foundation of Korea/ ; },
abstract = {This paper proposes a low-latency and scalable architecture for Edge-Enabled Digital Twin networked control systems (E-DTNCS) aimed at multi-robot collision avoidance and remote control in dynamic and latency-sensitive environments. Traditional approaches, which rely on centralized cloud processing or direct sensor-to-controller communication, are inherently limited by excessive network latency, bandwidth bottlenecks, and a lack of predictive decision-making, thus constraining their effectiveness in real-time multi-agent systems. To overcome these limitations, we propose a novel framework that seamlessly integrates edge computing with digital twin (DT) technology. By performing localized preprocessing at the edge, the system extracts semantically rich features from raw sensor data streams, reducing the transmission overhead of the original data. This shift from raw data to feature-based communication significantly alleviates network congestion and enhances system responsiveness. The DT layer leverages these extracted features to maintain high-fidelity synchronization with physical robots and to execute predictive models for proactive collision avoidance. To empirically validate the framework, a real-world testbed was developed, and extensive experiments were conducted with multiple mobile robots. The results revealed a substantial reduction in collision rates when DT was deployed, and further improvements were observed with E-DTNCS integration due to significantly reduced latency. These findings confirm the system's enhanced responsiveness and its effectiveness in handling real-time control tasks. The proposed framework demonstrates the potential of combining edge intelligence with DT-driven control in advancing the reliability, scalability, and real-time performance of multi-robot systems for industrial automation and mission-critical cyber-physical applications.},
}

@article {pmid40807741,
year = {2025},
author = {Stojanović, R and Đurković, J and Vukmirović, M and Babić, B and Miranović, V and Škraba, A},
title = {Medical Data over Sound-CardiaWhisper Concept.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807741},
issn = {1424-8220},
support = {P5-0018//The Slovenian Research and Innovation Agency/ ; C3330-22-953012//Ministry of Higher Education, Science and Innovation of the Republic of Slovenia/ ; 3330-22-3515//Ministry of Higher Education, Science, and Innovation of the Republic of Slovenia/ ; VI ARCA//European Union Interreg/ ; },
abstract = {Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver-typically a smartphone, tablet, or other gadget-and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications.},
}

@article {pmid40804083,
year = {2025},
author = {Cui, G and Zhang, W and Xu, W and Bao, H},
title = {Efficient workflow scheduling using an improved multi-objective memetic algorithm in cloud-edge-end collaborative framework.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {29754},
doi = {10.1038/s41598-025-08691-y},
pmid = {40804083},
issn = {2045-2322},
support = {No. 2023C01042//Zhejiang Provincial Science and Technology Program/ ; },
abstract = {With the rapid advancement of large-scale model technologies, AI agent frameworks built on foundation models have become a central focus of artificial-intelligence research. In cloud-edge-end collaborative computing frameworks, efficient workflow scheduling is essential to reducing both server energy consumption and overall makespan. This paper addresses this challenge by proposing an Improved Multi-Objective Memetic Algorithm (IMOMA) that simultaneously optimizes energy consumption and makespan. First, a multi-objective optimization model incorporating task execution constraints and priority constraints is developed, and complexity analysis confirms its NP-hard nature. Second, the IMOMA algorithm enhances population diversity through dynamic opposition-based learning, introduces local search operators tailored for bi-objective optimization, and maintains Pareto optimal solutions via an elite archive. A dynamic selection mechanism based on operator historical performance and an adaptive local search triggering strategy effectively balance global exploration and local exploitation capabilities. Experimental results on 10 standard datasets demonstrate that IMOMA achieves improvements of 93%, 7%, and 19% in hypervolume and 58%, 1%, and 23% in inverted generational distance compared to MOPSO, NSGA-II, and SPEA-II algorithms. Additionally, ablation experiments reveal the influence mechanisms of scheduling strategies, server configurations, and other constraints on optimization objectives, providing an engineering-oriented solution for real-world cloud-edge-end collaborative scenarios.},
}

@article {pmid40804077,
year = {2025},
author = {Maray, M},
title = {Intelligent deep learning for human activity recognition in individuals with disabilities using sensor based IoT and edge cloud continuum.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {29640},
pmid = {40804077},
issn = {2045-2322},
support = {KSRG-2024-269//King Salman Center for Disability Research/ ; },
abstract = {Aging is associated with a reduction in the capability to perform activities of everyday routine and a decline in physical activity, which affects physical and mental health. A human activity recognition (HAR) system can be a valuable tool for elderly individuals or patients, as it monitors their activities and detects any significant changes in behavior or events. When integrated with the Internet of Things (IoT), this system enables individuals to live independently while ensuring their well-being. The IoT-edge-cloud framework enhances this by processing data as close to the source as possible-either on edge devices or directly on the IoT devices themselves. However, the massive number of activity constellations and sensor configurations make the HAR problem challenging to solve deterministically. HAR involves collecting sensor data to classify diverse human activities and is a rapidly growing field. It presents valuable insights into the health, fitness, and overall wellness of individuals outside of hospital settings. Therefore, the machine learning (ML) model is mostly used for the growth of the HAR system to discover the models of human activity from the sensor data. In this manuscript, an Intelligent Deep Learning Technique for Human Activity Recognition of Persons with Disabilities using the Sensors Technology (IDLTHAR-PDST) technique is proposed. The purpose of the IDLTHAR-PDST technique is to efficiently recognize and interpret activities by leveraging sensor technology within a smart IoT-Edge-Cloud continuum. Initially, the IDLTHAR-PDST technique utilizes min-max normalization-based data pre-processing model to optimize sensor data consistency and enhance model performance. For feature subset selection, the enhanced honey badger algorithm (EHBA) model is used to effectively reduce dimensionality while retaining critical activity-related features. Finally, the deep belief network (DBN) model is employed for HAR. To exhibit the improved performance of the existing IDLTHAR-PDST model, a comprehensive simulation study is accomplished. The performance validation of the IDLTHAR-PDST model portrayed a superior accuracy value of 98.75% over existing techniques.},
}

@article {pmid40799928,
year = {2025},
author = {Sorin, V and Collins, JD and Bratt, AK and Kusmirek, JE and Mugu, VK and Kline, TL and Butler, CL and Wood, NG and Cook, CJ and Korfiatis, P},
title = {Evaluating prompt and data perturbation sensitivity in large language models for radiology reports classification.},
journal = {JAMIA open},
volume = {8},
number = {4},
pages = {ooaf073},
pmid = {40799928},
issn = {2574-2531},
abstract = {OBJECTIVES: Large language models (LLMs) offer potential in natural language processing tasks in healthcare. Due to the need for high accuracy, understanding their limitations is essential. The purpose of this study was to evaluate the performance of LLMs in classifying radiology reports for the presence of pulmonary embolism (PE) under various conditions, including different prompt designs and data perturbations.

MATERIALS AND METHODS: In this retrospective, institutional review board approved study, we evaluated 3 Google's LLMs including Gemini-1.5-Pro, Gemini-1.5-Flash-001, and Gemini-1.5-Flash-002, in classifying 11 999 pulmonary CT angiography radiology reports for PE. Ground truth labels were determined by concordance between a computer vision-based PE detection (CVPED) algorithm and multiple LLM runs under various configurations. Discrepancies between algorithms' classifications were aggregated and manually reviewed. We evaluated the effects of prompt design, data perturbations, and repeated analyses across geographic cloud regions. Performance metrics were calculated.

RESULTS: Of 11 999 reports, 1296 (10.8%) were PE-positive. Accuracy across LLMs ranged between 0.953 and 0.996. The highest recall rate for a prompt modified after a review of the misclassified cases (up to 0.997). Few-shot prompting improved recall (up to 0.99), while chain-of-thought generally degraded performance. Gemini-1.5-Flash-002 demonstrated the highest robustness against data perturbations. Geographic cloud region variability was minimal for Gemini-1.5+-Pro, while the Flash models showed stable performance.

DISCUSSION AND CONCLUSION: LLMs demonstrated high performance in classifying radiology reports, though results varied with prompt design and data quality. These findings underscore the need for systematic evaluation and validation of LLMs for clinical applications, particularly in high-stakes scenarios.},
}

@article {pmid40792199,
year = {2025},
author = {Hizem, M and Aoueileyine, MO and Belhaouari, SB and El Omri, A and Bouallegue, R},
title = {Sustainable E-Health: Energy-Efficient Tiny AI for Epileptic Seizure Detection via EEG.},
journal = {Biomedical engineering and computational biology},
volume = {16},
number = {},
pages = {11795972241283101},
pmid = {40792199},
issn = {1179-5972},
abstract = {Tiny Artificial Intelligence (Tiny AI) is transforming resource-constrained embedded systems, particularly in e-health applications, by introducing a shift in Tiny Machine Learning (TinyML) and its integration with the Internet of Things (IoT). Unlike conventional machine learning (ML), which demands substantial processing power, TinyML strategically delegates processing requirements to the cloud infrastructure, allowing lightweight models to run on embedded devices. This study aimed to (i) Develop a TinyML workflow that details the steps for model creation and deployment in resource-constrained environments and (ii) apply the workflow to e-health applications for the real-time detection of epileptic seizures using electroencephalography (EEG) data. The methodology employs a dataset of 4097 EEG recordings per patient, each 23.5 seconds long, from 500 patients, to develop a robust and resilient model. The model was deployed using TinyML on microcontrollers tailored to hardware with limited resources. TensorFlow Lite (TFLite) efficiently runs ML models on small devices, such wearables. Simulation outcomes demonstrated significant performance, particularly in predicting epileptic seizures, with the ExtraTrees Classifier achieving a notable 99.6% Area Under the Curve (AUC) on the validation set. Because of its superior performance, the ExtraTrees Classifier was selected as the preferred model. For the optimized TinyML model, the accuracy remained practically unchanged, whereas inference time was significantly reduced. Additionally, the converted model had a smaller size of 256 KB, approximately ten times smaller, making it suitable for microcontrollers with a capacity of no more than 1 MB. These findings highlight the potential of TinyML to significantly enhance healthcare applications by enabling real-time, energy-efficient decision-making directly on local devices. This is especially valuable in scenarios with limited computing resources or during emergencies, as it reduces latency, ensures privacy, and operates without reliance on cloud infrastructure. Moreover, by reducing the size of training datasets needed, TinyML helps lower overall costs and minimizes the risk of overfitting, making it an even more cost-effective and reliable solution for healthcare innovations.},
}

@article {pmid40790850,
year = {2025},
author = {Osório, NS and Garma, LD},
title = {Teaching Python with team-based learning: using cloud-based notebooks for interactive coding education.},
journal = {FEBS open bio},
volume = {},
number = {},
pages = {},
doi = {10.1002/2211-5463.70097},
pmid = {40790850},
issn = {2211-5463},
abstract = {Computer programming and bioinformatics are increasingly essential topics in life sciences research, facilitating the analysis of large and complex 'omics' datasets. However, they remain challenging for students without a background in mathematics or computing. To address challenges in teaching programming within biomedical education, this study integrates team-based learning (TBL) with cloud-hosted interactive Python notebooks, targeting enhanced student engagement, understanding, and collaboration in bioinformatics in two Masters level classes with 28 biomedical students in total. Four interactive notebooks covering Python basics and practical bioinformatics applications-ranging from data manipulation to multi-omics analysis-were developed. Hosted on github and integrated with Google Colaboratory, these notebooks ensured equal access and eliminated technical barriers for students with varied computing setups. During the TBL session, students were highly engaged with the notebooks, which led to a greater interest in Python and increased confidence in using bioinformatics tools. Feedback highlighted the value of TBL and interactive notebooks in enriching the learning experience, while also identifying a need for further development in bioinformatics research skills. Although more validity evidence is needed in future studies, this blended, cloud-based TBL approach effectively made bioinformatics education more accessible and engaging, suggesting its potential for enhancing computational training across life sciences.},
}

@article {pmid40786095,
year = {2024},
author = {González, LL and Arias-Serrano, I and Villalba-Meneses, F and Navas-Boada, P and Cruz-Varela, J},
title = {Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria.},
journal = {F1000Research},
volume = {13},
number = {},
pages = {981},
pmid = {40786095},
issn = {2046-1402},
mesh = {*Bacteriocins/classification/chemistry/biosynthesis ; *Deep Learning ; *Lactobacillales/metabolism ; *Neural Networks, Computer ; Amino Acid Sequence ; },
abstract = {BACKGROUND: The rise of antibiotic-resistant bacteria presents a pressing need for exploring new natural compounds with innovative mechanisms to replace existing antibiotics. Bacteriocins offer promising alternatives for developing therapeutic and preventive strategies in livestock, aquaculture, and human health. Specifically, those produced by LAB are recognized as GRAS and QPS. This study aims to develop a deep learning model specifically designed to classify bacteriocins by their LAB origin, using interpretable k-mer features and embedding vectors to enable applications in antimicrobial discover.

METHODS: We developed a deep learning neural network for binary classification of bacteriocin amino acid sequences (BacLAB vs. Non-BacLAB). Features were extracted using k-mers (k=3,5,7,15,20) and vector embeddings (EV). Ten feature combinations were tested (e.g., EV, EV+5-mers+7-mers). Sequences were filtered by length (50-2000 AA) to ensure uniformity, and class balance was maintained (24,964 BacLAB vs. 25,000 Non-BacLAB). The model was trained on Google Colab, demonstrating computational accessibility without specialized hardware.

RESULTS: The '5-mers+7-mers+EV' group achieved the best performance, with k-fold cross-validation (k=30) showing: 9.90% loss, 90.14% accuracy, 90.30% precision, 90.10% recall and F1 score. Folder 22 stood out with 8.50% loss, 91.47% accuracy, and 91.00% precision, recall, and F1 score. Five sets of 100 LAB-specific k-mers were identified, revealing conserved motifs. Despite high accuracy, sequence length variation (50-2000 AA) may bias k-mer representation, favoring longer sequences. Additionally, experimental validation is required to confirm the biological activity of predicted bacteriocins. These aspects highlight directions for future research.

CONCLUSIONS: The model developed in this study achieved consistent results with those seen in the reviewed literature. It outperformed some studies by 3-10%. Its implementation in resource-limited settings is feasible via cloud platforms like Google Colab. The identified k-mers could guide the design of synthetic antimicrobials, pending further in vitro validation.},
}

*Bacteriocins/classification/chemistry/biosynthesis
*Deep Learning
*Lactobacillales/metabolism
*Neural Networks, Computer
Amino Acid Sequence

@article {pmid40783576,
year = {2025},
author = {Gao, Z and Liu, D and Zheng, C},
title = {Vehicle-to-everything decision optimization and cloud control based on deep reinforcement learning.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {29160},
pmid = {40783576},
issn = {2045-2322},
abstract = {To address the challenges of decision optimization and road segment hazard assessment within complex traffic environments, and to enhance the safety and responsiveness of autonomous driving, a Vehicle-to-Everything (V2X) decision framework is proposed. This framework is structured into three modules: vehicle perception, decision-making, and execution. The vehicle perception module integrates sensor fusion techniques to capture real-time environmental data, employing deep neural networks to extract essential information. In the decision-making module, deep reinforcement learning algorithms are applied to optimize decision processes by maximizing expected rewards. Meanwhile, the road segment hazard classification module, utilizing both historical traffic data and real-time perception information, adopts a hazard evaluation model to classify road conditions automatically, providing real-time feedback to guide vehicle decision-making. Furthermore, an autonomous driving cloud control platform is designed, augmenting decision-making capabilities through centralized computing resources, enabling large-scale data analysis, and facilitating collaborative optimization. Experimental evaluations conducted within simulation environments and utilizing the KITTI dataset demonstrate that the proposed V2X decision optimization method substantially outperforms conventional decision algorithms. Vehicle decision accuracy increased by 9.0%, rising from 89.2 to 98.2%. Additionally, the response time of the cloud control system decreased from 178 ms to 127 ms, marking a reduction of 28.7%, which significantly enhances decision efficiency and real-time performance. The introduction of the road segment hazard classification model also results in a hazard assessment accuracy of 99.5%, maintaining over 95% accuracy even in high-density traffic and complex road conditions, thus illustrating strong adaptability. The results highlight the effectiveness of the proposed V2X decision optimization framework and cloud control platform in enhancing the decision quality and safety of autonomous driving systems.},
}

@article {pmid40783426,
year = {2025},
author = {Murala, DK and Prasada Rao, KV and Vuyyuru, VA and Assefa, BG},
title = {A service-oriented microservice framework for differential privacy-based protection in industrial IoT smart applications.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {29230},
pmid = {40783426},
issn = {2045-2322},
abstract = {The rapid advancement of key technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and edge-cloud computing has significantly accelerated the transformation toward smart industries across various domains, including finance, manufacturing, and healthcare. Edge and cloud computing offer low-cost, scalable, and on-demand computational resources, enabling service providers to deliver intelligent data analytics and real-time insights to end-users. However, despite their potential, the practical adoption of these technologies faces critical challenges, particularly concerning data privacy and security. AI models, especially in distributed environments, may inadvertently retain and leak sensitive training data, exposing users to privacy risks in the event of malicious attacks. To address these challenges, this study proposes a privacy-preserving, service-oriented microservice architecture tailored for intelligent Industrial IoT (IIoT) applications. The architecture integrates Differential Privacy (DP) mechanisms into the machine learning pipeline to safeguard sensitive information. It supports both centralised and distributed deployments, promoting flexible, scalable, and secure analytics. We developed and evaluated differentially private models, including Radial Basis Function Networks (RBFNs), across a range of privacy budgets (ɛ), using both real-world and synthetic IoT datasets. Experimental evaluations using RBFNs demonstrate that the framework maintains high predictive accuracy (up to 96.72%) with acceptable privacy guarantees for budgets [Formula: see text]. Furthermore, the microservice-based deployment achieves an average latency reduction of 28.4% compared to monolithic baselines. These results confirm the effectiveness and practicality of the proposed architecture in delivering privacy-preserving, efficient, and scalable intelligence for IIoT environments. Additionally, the microservice-based design enhanced computational efficiency and reduced latency through dynamic service orchestration. This research demonstrates the feasibility of deploying robust, privacy-conscious AI services in IIoT environments, paving the way for secure, intelligent, and scalable industrial systems.},
}

@article {pmid40781521,
year = {2025},
author = {Zhang, H and Zhang, R and Sun, J},
title = {Developing real-time IoT-based public safety alert and emergency response systems.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {29056},
pmid = {40781521},
issn = {2045-2322},
abstract = {This paper presents the design and evaluation of a real-time IoT-based emergency response and public safety alert system tailored for rapid detection, classification, and dissemination of alerts during critical incidents. The proposed architecture combines a distributed network of heterogeneous sensors (e.g., gas, flame, vibration, and biometric), edge computing nodes (Raspberry Pi, ESP32), and cloud platforms (AWS IoT, Firebase) to ensure low-latency and high-availability operations. Communication is facilitated using secure MQTT over TLS, with fallback to LoRa for rural or low-connectivity environments. A prototype was implemented and tested across four emergency scenarios fire, traffic accident, gas leak, and medical distress within a smart city simulation testbed. The system achieved such as consistent alert latency under 450 ms, detection accuracy exceeding 95%, and scalability supporting over 12,000 concurrent devices. A comprehensive comparison against seven state-of-the-art systems confirmed superior performance in latency, reliability (99.1% alert success), and uptime (99.8%). These results underscore the system's potential for deployment in urban, industrial, and infrastructure-vulnerable environments, with future work aimed at incorporating AI-driven prediction and federated learning for cloudless operation.},
}

@article {pmid40781088,
year = {2025},
author = {Wei, X and Li, R and Xiang, S and Qin, L and Luo, X and Xue, J and Fan, X},
title = {Smart fiber with overprinted patterns to function as chip-like multi-threshold logic switch circuit.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {7314},
pmid = {40781088},
issn = {2041-1723},
abstract = {There is a growing demand for precise health management, capable of differentially caring every inch of skin as an on-body network. For which, each network node executes not only multi-physiological sensing, but also in-situ logic computing to save cloud computing power for massive data analysis. Herein, we present a smart fiber with multilayers of overprinted patterns, composed of many small units with 0.3 mm long to function as a one-dimension (1D) array of chip-like multi-threshold logic-switch circuit. Via soft contact of curved surfaces between fiber and ink-droplet, an overprinting method is developed for stacking different layers of patterns with a line width of 75 μm in a staggered way, enabling batch production of circuit units along one long fiber. A smart fiber with high density of >3000 circuit units per meter can be woven with fiber-type sensors to construct a textile-type body-covering network, where each node serves as a computing terminal.},
}

@article {pmid40779102,
year = {2025},
author = {Ramos, M and Shepherd, L and Sheffield, NC and Mahmoud, A and Pagès, H and Wokaty, A and Righelli, D and Risso, D and Davis, S and Oh, S and Waldron, L and Morgan, M and Carey, V},
title = {Bioconductor's Computational Ecosystem for Genomic Data Science in Cancer.},
journal = {Methods in molecular biology (Clifton, N.J.)},
volume = {2932},
number = {},
pages = {1-46},
pmid = {40779102},
issn = {1940-6029},
mesh = {*Neoplasms/genetics ; *Genomics/methods ; Humans ; *Software ; *Computational Biology/methods ; Databases, Genetic ; *Data Science/methods ; },
abstract = {The Bioconductor project enters its third decade with over two thousand packages for genomic data science, over 100,000 annotation and experiment resources, and a global system for convenient distribution to researchers. Over 60,000 PubMed Central citations and terabytes of content shipped per month attest to the impact of the project on cancer genomic data science. This report provides an overview of cancer genomics resources in Bioconductor. After an overview of Bioconductor project principles, we address exploration of institutionally curated cancer genomics data such as TCGA. We then review genomic annotation and ontology resources relevant to cancer and then briefly survey analytical workflows addressing specific topics in cancer genomics. Concluding sections cover how new software and data resources are brought into the ecosystem and how the project is tackling needs for training of the research workforce. Bioconductor's strategies for supporting methods developers and researchers in cancer genomics are evolving along with experimental and computational technologies. All the tools described in this report are backed by regularly maintained learning resources that can be used locally or in cloud computing environments.},
}

*Neoplasms/genetics
*Genomics/methods
Humans
*Software
*Computational Biology/methods
Databases, Genetic
*Data Science/methods

@article {pmid40777969,
year = {2024},
author = {Wu, Y and Li, K and Tang, L and Li, G and Huang, D and Yang, Y and Song, S and Peng, L},
title = {A Review of emergency medical services for stroke.},
journal = {African health sciences},
volume = {24},
number = {3},
pages = {382-392},
pmid = {40777969},
issn = {1729-0503},
mesh = {Humans ; *Emergency Medical Services/organization & administration ; *Stroke/diagnosis/therapy ; },
abstract = {In the past decade, Emergency Medical Services have been associated with innovations in technology; the 911 telephone system and two-way radio have developed the notification, scheduling, and response processes. The recent twenty years have witnessed the unparalleled innovation changes of the computer framework. These new frameworks in mobile, social, cloud computing or big data concentrations essentially affect the entire society. In the last ten years, major innovation and strategic improvements have occurred, which will affect the concepts and communication methods of Emergency Medical Service in the future. Emergency Medical Service can treat various diseases in the correct way. For example, Emergency Medical Service personnel's early recognition of stroke performance is an important ideal consideration for patients with stroke patients. Pre-stroke screening tools that have been preliminarily evaluated for sensitivity and specificity are necessary to improve detection rates for the pre-court stroke by Emergency Medical Service experts. This is an excellent time for Emergency Medical Service to play a key role in achieving and transcending vision. The motivation behind this article is to provide extensive investigations and unique opportunities for Emergency Medical Service personnel groups to solve how to improve.},
}

Humans
*Emergency Medical Services/organization & administration
*Stroke/diagnosis/therapy

@article {pmid40776321,
year = {2025},
author = {Safi, A and Shaikh, M and Hoang, MT and Shetty, A and Kabil, G and Wang, AP},
title = {Decoding Sepsis: A Technical Blueprint for an Algorithm-Driven System Architecture.},
journal = {Studies in health technology and informatics},
volume = {329},
number = {},
pages = {1970-1971},
doi = {10.3233/SHTI251304},
pmid = {40776321},
issn = {1879-8365},
mesh = {*Sepsis/diagnosis ; Humans ; *Algorithms ; *Machine Learning ; Emergency Service, Hospital/organization & administration ; Cloud Computing ; },
abstract = {This paper presents a scalable, serverless machine learning operations (ML Ops) architecture for near real-time sepsis detection in Emergency Department (ED) waiting rooms. Built on Amazon Web Services (AWS) cloud environment, the system processes HL7 messages via MuleSoft, using Lambda for data handling, and SageMaker for model deployment. Data is stored in Aurora PostgreSQL and visualized in on-premise Tableau™. With 99.7% of HL7 messages successfully processed, the system shows strong performance, though occasional downtime, code set mismatches, and peak execution times reveal areas for optimization.},
}

*Sepsis/diagnosis
Humans
*Algorithms
*Machine Learning
Emergency Service, Hospital/organization & administration
Cloud Computing

@article {pmid40776130,
year = {2025},
author = {Kishimoto, K and Sugiyama, O and Iwao, T and Yakami, M and Nambu, M and Yutani, A and Fukuyama, K and Saito, K and Kuroda, T},
title = {Integrating Scalable Analytical Tools and Data Warehouses on Private Cloud.},
journal = {Studies in health technology and informatics},
volume = {329},
number = {},
pages = {1584-1585},
doi = {10.3233/SHTI251113},
pmid = {40776130},
issn = {1879-8365},
mesh = {*Cloud Computing ; *Data Warehousing/methods ; *Computer Security ; *Information Storage and Retrieval/methods ; *Electronic Health Records/organization & administration ; Systems Integration ; },
abstract = {This study addresses the need for efficient and scalable data warehouse solutions by integrating on-premises environments with private cloud-based infrastructures. Kubernetes was employed to dynamically generate secure virtual machines, offering users independent environments for data analysis. Performance testing demonstrated query speeds, with 240,000 records extracted from a 301GB dataset in 12.4 seconds. Security measures, using a VPN connection between hospital networks and Google Cloud, allowed the safe use of Google's APIs. This scalable infrastructure can accommodate diverse analytical needs.},
}

*Cloud Computing
*Data Warehousing/methods
*Computer Security
*Information Storage and Retrieval/methods
*Electronic Health Records/organization & administration
Systems Integration

@article {pmid40775842,
year = {2025},
author = {Leeming, G and Hughes, J and Joyce, D and Buchan, I},
title = {Building a Learning Health System-Focused Trusted Research Environment for Mental Health.},
journal = {Studies in health technology and informatics},
volume = {329},
number = {},
pages = {174-178},
doi = {10.3233/SHTI250824},
pmid = {40775842},
issn = {1879-8365},
mesh = {Humans ; *Learning Health System/organization & administration ; Machine Learning ; *Mental Health Services/organization & administration ; *Mental Health ; Natural Language Processing ; },
abstract = {Trusted Research Environments (TREs) are increasingly used as platforms for secure health data research, but they can also be used for implementing research findings or for action-research (researchers supporting health professionals to solve problems with advanced data analytics). Most TREs have been designed to support analysis of well-structured and coded data, however, with much clinical data recorded as unstructured notes, especially in mental health care, there needs to be a greater variety of tools and data management services available for safe research that includes natural language processing and anonymisation of data sources. The Mental Health Research for Innovation Centre (M-RIC), co-hosted by the University of Liverpool and Mersey Care NHS Foundation Trust, has implemented a novel TRE design that incorporates modern data engineering concepts to improve how researchers access a wider variety of linked data and machine learning tools, to be able to both undertake research and then deploy these tools directly into mental health care.},
}

Humans
*Learning Health System/organization & administration
Machine Learning
*Mental Health Services/organization & administration
*Mental Health
Natural Language Processing

@article {pmid40771358,
year = {2025},
author = {Adams, MCB and Griffin, C and Adams, H and Bryant, S and Hurley, RW and Topaloglu, U},
title = {Enhancing Gen3 for clinical trial time series analytics and data discovery: a data commons framework for NIH clinical trials.},
journal = {Frontiers in digital health},
volume = {7},
number = {},
pages = {1570009},
pmid = {40771358},
issn = {2673-253X},
support = {R24 DA055306/DA/NIDA NIH HHS/United States ; R24 DA058606/DA/NIDA NIH HHS/United States ; R25 DA061740/DA/NIDA NIH HHS/United States ; U24 DA057612/DA/NIDA NIH HHS/United States ; },
abstract = {This work presents a framework for enhancing Gen3, an open-source data commons platform, with temporal visualization capabilities for clinical trial research. We describe the technical implementation of cloud-native architecture and integrated visualization tools that enable standardized analytics for longitudinal clinical trial data while adhering to FAIR principles. The enhancement includes Kubernetes-based container orchestration, Kibana-based temporal analytics, and automated ETL pipelines for data harmonization. Technical validation demonstrates reliable handling of varied time-based data structures, while maintaining temporal precision and measurement context. The framework's implementation in NIH HEAL Initiative networks studying chronic pain and substance use disorders showcases its utility for real-time monitoring of longitudinal outcomes across multiple trials. This adaptation provides a model for research networks seeking to enhance their data commons capabilities while ensuring findable, accessible, interoperable, and reusable clinical trial data.},
}

@article {pmid40759726,
year = {2025},
author = {Hu, Y and Li, Y and Cui, B and Su, H and Zhu, P},
title = {Internet of things enabled deep learning monitoring system for realtime performance metrics and athlete feedback in college sports.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {28405},
pmid = {40759726},
issn = {2045-2322},
mesh = {Humans ; *Deep Learning ; *Athletic Performance ; *Athletes ; *Internet of Things ; Universities ; Neural Networks, Computer ; Wearable Electronic Devices ; Male ; *Sports ; Feedback ; Young Adult ; },
abstract = {This study presents an Internet of Things (IoT)-enabled Deep Learning Monitoring (IoT-E-DLM) model for real-time Athletic Performance (AP) tracking and feedback in collegiate sports. The proposed work integrates advanced wearable sensor technologies with a hybrid neural network combining Temporal Convolutional Networks, Bidirectional Long Short-Term Memory (TCN + BiLSTM) + Attention mechanisms. It is designed to overcome key challenges in processing heterogeneous, high-frequency sensor data and delivering low-latency, sport-specific feedback. The system deployed edge computing for real-time local processing and cloud setup for high-complexity analytics, achieving a balance between responsiveness and accuracy. Extensive research was tested with 147 student-athletes across numerous sports, including track and field, basketball, soccer, and swimming, over 12 months at Shangqiu University. The proposed model achieved a prediction accuracy of 93.45% with an average processing latency of 12.34 ms, outperforming conventional and state-of-the-art approaches. The system also demonstrated efficient resource usage (CPU: 68.34%, GPU: 72.56%), high data capture reliability (98.37%), and precise temporal synchronization. These results confirm the model's effectiveness in enabling real-time performance monitoring and feedback delivery, establishing a robust groundwork for future developments in Artificial Intelligence (AI)-driven sports analytics.},
}

Humans
*Deep Learning
*Athletic Performance
*Athletes
*Internet of Things
Universities
Neural Networks, Computer
Wearable Electronic Devices
Male
*Sports
Feedback
Young Adult

@article {pmid40752643,
year = {2025},
author = {Lima, DB and Ruwolt, M and Santos, MDM and Pu, K and Liu, F and Carvalho, PC},
title = {Q2C: A software for managing mass spectrometry facilities.},
journal = {Journal of proteomics},
volume = {321},
number = {},
pages = {105511},
doi = {10.1016/j.jprot.2025.105511},
pmid = {40752643},
issn = {1876-7737},
abstract = {We present Q2C, an open-source software designed to streamline mass spectrometer queue management and assess performance based on quality control metrics. Q2C provides a fast and user-friendly interface to visualize projects queues, manage analysis schedules and keep track of samples that were already processed. Our software includes analytical tools to ensure equipment calibration and provides comprehensive log documentation for machine maintenance, enhancing operational efficiency and reliability. Additionally, Q2C integrates with Google™ Cloud, allowing users to access and manage the software from different locations while keeping all data synchronized and seamlessly integrated across the system. For multi-user environments, Q2C implements a write-locking mechanism that checks for concurrent operations before saving data. When conflicts are detected, subsequent write requests are automatically queued to prevent data corruption, while the interface continuously refreshes to display the most current information from the cloud storage. Finally, Q2C, a demonstration video, and a user tutorial are freely available for academic use at https://github.com/diogobor/Q2C. Data are available from the ProteomeXchange consortium (identifier PXD055186). SIGNIFICANCE: Q2C addresses a critical gap in mass spectrometry facility management by unifying sample queue management with instrument performance monitoring. It ensures optimal instrument utilization, reduces turnaround times, and enhances data quality by dynamically prioritizing and routing samples based on analysis type and urgency. Unlike existing tools, Q2C integrates queue control and QC in a single platform, maximizing operational efficiency and reliability.},
}

@article {pmid40750821,
year = {2025},
author = {Lin, SY and Wang, JQ and Peng, SM and Yang, MH and Jia, S},
title = {Multihop cost awareness task migration with networking load balance technology for vehicular edge computing.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {28126},
pmid = {40750821},
issn = {2045-2322},
support = {KJRC2023029//Weifang University of Science and Technology/ ; 2023CXGC010111//Natural Science Foundation of Shandong Province/ ; },
abstract = {6G technology aims to revolutionize the mobile communication industry by revamping the role of vehicular wireless connections. Its network architecture will evolve towards multi-access edge computing (MEC) distributing cloud applications to support inter-vehicle applications such as cooperative driving. As the number of tasks offloaded to MEC servers increases, local MEC servers associated with vehicles may encounter insufficient computing resources for task offloading. This issue can be mitigated if neighboring servers can collaboratively provide computing capabilities to the local server for task migration. This paper investigates dynamic resource allocation and task migration mechanisms for cooperative vehicular edge computing (VEC) servers to expand computing capabilities of local server. Then, the multihop cost awareness task migration (MCATM) mechanism is proposed in this paper, which ensures that tasks can be migrated to the most suitable VEC server when the local server is overloaded. The MCATM mechanism begins by addressing whether the nearest VEC server can handle the computational tasks. We subsequently address the issue of duplicate selection to choose an appropriate VEC server for task migration among n-hop neighboring servers. Next, we focus on finding efficient transmission paths between the local and destination VEC servers to facilitate seamless task migration. The MCATM includes (i) the weight variable analytic hierarchy process (WVAHP) to select a suitable server among multihop cooperative VEC servers for task migration, and (ii) the pre-allocation with cost balance (PACB) path selection algorithm. The simulation results demonstrate that the MCATM enables the migration of computational tasks to appropriate neighboring VEC servers with the aim of increasing the task migration success rate while balancing network traffic and computing server capabilities.},
}

@article {pmid40747000,
year = {2025},
author = {Eisenbraun, B and Ho, A and Meyer, PA and Sliz, P},
title = {Accelerating structural dynamics through integrated research informatics.},
journal = {Structural dynamics (Melville, N.Y.)},
volume = {12},
number = {4},
pages = {041101},
pmid = {40747000},
issn = {2329-7778},
abstract = {Structural dynamics research requires robust computational methods, reliable software, accessible data, and scalable infrastructure. Managing these components is complex and directly affects reproducibility and efficiency. The SBGrid Consortium addresses these challenges through a three-pillar approach that encompasses Software, Data, and Infrastructure, designed to foster a consistent and rigorous computational environment. At the core is the SBGrid software collection (>620 curated applications), supported by the Capsules Software Execution Environment, which ensures conflict-free, version-controlled execution. The SBGrid Data Bank supports open science by enabling the publication of primary experimental data. SBCloud, a fully managed cloud computing platform, provides scalable, on-demand infrastructure optimized for structural biology workloads. Together, they reduce computational friction, enabling researchers to focus on interpreting time-resolved data, modeling structural transitions, and managing large simulation datasets for advancing structural dynamics. This integrated platform delivers a reliable and accessible foundation for computationally intensive research across diverse scientific fields sharing common computational methods.},
}

@article {pmid40746923,
year = {2025},
author = {Masjoodi, S and Anbardar, MH and Shokripour, M and Omidifar, N},
title = {Whole Slide Imaging (WSI) in Pathology: Emerging Trends and Future Applications in Clinical Diagnostics, Medical Education, and Pathology.},
journal = {Iranian journal of pathology},
volume = {20},
number = {3},
pages = {257-265},
pmid = {40746923},
issn = {1735-5303},
abstract = {BACKGROUND & OBJECTIVE: Whole Slide Imaging (WSI) has emerged as a transformative technology in the fields of clinical diagnostics, medical education, and pathology research. By digitizing entire glass slides into high-resolution images, WSI enables advanced remote collaboration, the integration of artificial intelligence (AI) into diagnostic workflows, and facilitates large-scale data sharing for multi-center research.

METHODS: This paper explores the growing applications of WSI, focusing on its impact on diagnostics through telepathology, AI-powered diagnoses and precision medicine, and educational advancements. In this report, we will highlight the profound impact of WSI and address the challenges that must be overcome to enable its broader adoption.

RESULTS & CONCLUSION: Despite its many advantages, challenges such as infrastructure limitations and regulatory issues need to be addressed for broader adoption. The future of WSI lies in its ability to integrate with cloud-based platforms and big data analytics, continuing to drive the digital transformation of pathology.},
}

@article {pmid40743515,
year = {2025},
author = {Beyer, D and Delancey, E and McLeod, L},
title = {Automating Colon Polyp Classification in Digital Pathology by Evaluation of a "Machine Learning as a Service" AI Model: Algorithm Development and Validation Study.},
journal = {JMIR formative research},
volume = {9},
number = {},
pages = {e67457},
pmid = {40743515},
issn = {2561-326X},
mesh = {Humans ; *Colonic Polyps/classification/pathology/diagnosis ; *Machine Learning ; *Algorithms ; Artificial Intelligence ; Sensitivity and Specificity ; },
abstract = {BACKGROUND: Artificial intelligence (AI) models are increasingly being developed to improve the efficiency of pathological diagnoses. Rapid technological advancements are leading to more widespread availability of AI models that can be used by domain-specific experts (ie, pathologists and medical imaging professionals). This study presents an innovative AI model for the classification of colon polyps, developed using AutoML algorithms that are readily available from cloud-based machine learning platforms. Our aim was to explore if such AutoML algorithms could generate robust machine learning models that are directly applicable to the field of digital pathology.

OBJECTIVE: The objective of this study was to evaluate the effectiveness of AutoML algorithms in generating robust machine learning models for the classification of colon polyps and to assess their potential applicability in digital pathology.

METHODS: Whole-slide images from both public and institutional databases were used to develop a training set for 3 classifications of common entities found in colon polyps: hyperplastic polyps, tubular adenomas, and normal colon. The AI model was developed using an AutoML algorithm from Google's VertexAI platform. A test subset of the data was withheld to assess model accuracy, sensitivity, and specificity.

RESULTS: The AI model displayed a high accuracy rate, identifying tubular adenoma and hyperplastic polyps with 100% success and normal colon with 97% success. Sensitivity and specificity error rates were very low.

CONCLUSIONS: This study demonstrates how accessible AutoML algorithms can readily be used in digital pathology to develop diagnostic AI models using whole-slide images. Such models could be used by pathologists to improve diagnostic efficiency.},
}

Humans
*Colonic Polyps/classification/pathology/diagnosis
*Machine Learning
*Algorithms
Artificial Intelligence
Sensitivity and Specificity

@article {pmid40740546,
year = {2025},
author = {Delogu, F and Aspinall, C and Ray, K and Heinsfeld, AS and Victory, C and Pestilli, F},
title = {Breaking barriers: broadening neuroscience education via cloud platforms and course-based undergraduate research.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1608900},
pmid = {40740546},
issn = {1662-5196},
abstract = {This study demonstrates the effectiveness of integrating cloud computing platforms with Course-based Undergraduate Research Experiences (CUREs) to broaden access to neuroscience education. Over four consecutive spring semesters (2021-2024), a total of 42 undergraduate students at Lawrence Technological University participated in computational neuroscience CUREs using brainlife.io, a cloud-computing platform. Students conducted anatomical and functional brain imaging analyses on openly available datasets, testing original hypotheses about brain structure variations. The program evolved from initial data processing to hypothesis-driven research exploring the influence of age, gender, and pathology on brain structures. By combining open science and big data within a user-friendly cloud environment, the CURE model provided hands-on, problem-based learning to students with limited prior knowledge. This approach addressed key limitations of traditional undergraduate research experiences, including scalability, early exposure, and inclusivity. Students consistently worked with MRI datasets, focusing on volumetric analysis of brain structures, and developed scientific communication skills by presenting findings at annual research days. The success of this program demonstrates its potential to democratize neuroscience education, enabling advanced research without extensive laboratory facilities or prior experience, and promoting original undergraduate research using real-world datasets.},
}

@article {pmid40735132,
year = {2025},
author = {Saghafi, S and Kiarashi, Y and Rodriguez, AD and Levey, AI and Kwon, H and Clifford, GD},
title = {Indoor Localization Using Multi-Bluetooth Beacon Deployment in a Sparse Edge Computing Environment.},
journal = {Digital twins and applications},
volume = {2},
number = {1},
pages = {},
pmid = {40735132},
issn = {2995-2182},
support = {R21 DC021029/DC/NIDCD NIH HHS/United States ; },
abstract = {Bluetooth low energy (BLE)-based indoor localization has been extensively researched due to its cost-effectiveness, low power consumption, and ubiquity. Despite these advantages, the variability of received signal strength indicator (RSSI) measurements, influenced by physical obstacles, human presence, and electronic interference, poses a significant challenge to accurate localization. In this work, we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency (RF)-dense modern building environment. Through a proof-of-concept study, we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09-2.94 m, whereas additional beacons offer minimal incremental benefit in such settings. Furthermore, our framework for BLE-based localization, implemented on an edge network of Raspberry Pies, has been released under an open-source license, enabling broader application and further research.},
}

@article {pmid40732552,
year = {2025},
author = {Kim, MG and Kil, BH and Ryu, MH and Kim, JD},
title = {IoMT Architecture for Fully Automated Point-of-Care Molecular Diagnostic Device.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {14},
pages = {},
pmid = {40732552},
issn = {1424-8220},
mesh = {Humans ; *COVID-19/diagnosis ; *Point-of-Care Systems ; SARS-CoV-2/isolation & purification ; Chlamydia trachomatis/genetics/isolation & purification ; *Molecular Diagnostic Techniques/instrumentation/methods ; *Internet of Things ; Neisseria gonorrhoeae/genetics/isolation & purification ; Point-of-Care Testing ; Cloud Computing ; },
abstract = {The Internet of Medical Things (IoMT) is revolutionizing healthcare by integrating smart diagnostic devices with cloud computing and real-time data analytics. The emergence of infectious diseases, including COVID-19, underscores the need for rapid and decentralized diagnostics to facilitate early intervention. Traditional centralized laboratory testing introduces delays, limiting timely medical responses. While point-of-care molecular diagnostic (POC-MD) systems offer an alternative, challenges remain in cost, accessibility, and network inefficiencies. This study proposes an IoMT-based architecture for fully automated POC-MD devices, leveraging WebSockets for optimized communication, enhancing microfluidic cartridge efficiency, and integrating a hardware-based emulator for real-time validation. The system incorporates DNA extraction and real-time polymerase chain reaction functionalities into modular, networked components, improving flexibility and scalability. Although the system itself has not yet undergone clinical validation, it builds upon the core cartridge and detection architecture of a previously validated cartridge-based platform for Chlamydia trachomatis and Neisseria gonorrhoeae (CT/NG). These pathogens were selected due to their global prevalence, high asymptomatic transmission rates, and clinical importance in reproductive health. In a previous clinical study involving 510 patient specimens, the system demonstrated high concordance with a commercial assay with limits of detection below 10 copies/μL, supporting the feasibility of this architecture for point-of-care molecular diagnostics. By addressing existing limitations, this system establishes a new standard for next-generation diagnostics, ensuring rapid, reliable, and accessible disease detection.},
}

Humans
*COVID-19/diagnosis
*Point-of-Care Systems
SARS-CoV-2/isolation & purification
Chlamydia trachomatis/genetics/isolation & purification
*Molecular Diagnostic Techniques/instrumentation/methods
*Internet of Things
Neisseria gonorrhoeae/genetics/isolation & purification
Point-of-Care Testing
Cloud Computing

@article {pmid40732410,
year = {2025},
author = {Dong, J and Tian, M and Yu, J and Li, G and Wang, Y and Su, Y},
title = {DFPS: An Efficient Downsampling Algorithm Designed for the Global Feature Preservation of Large-Scale Point Cloud Data.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {14},
pages = {},
pmid = {40732410},
issn = {1424-8220},
support = {No. 42106180//National Natural Science Foundation of China/ ; },
abstract = {This paper introduces an efficient 3D point cloud downsampling algorithm (DFPS) based on adaptive multi-level grid partitioning. By leveraging an adaptive hierarchical grid partitioning mechanism, the algorithm dynamically adjusts computational intensity in accordance with terrain complexity. This approach effectively balances the global feature retention of point cloud data with computational efficiency, making it highly adaptable to the growing trend of large-scale 3D point cloud datasets. DFPS is designed with a multithreaded parallel acceleration architecture, which significantly enhances processing speed. Experimental results demonstrate that, for a point cloud dataset containing millions of points, DFPS reduces processing time from approximately 161,665 s using the original FPS method to approximately 71.64 s at a 12.5% sampling rate, achieving an efficiency improvement of over 2200 times. As the sampling rate decreases, the performance advantage becomes more pronounced: at a 3.125% sampling rate, the efficiency improves by nearly 10,000 times. By employing visual observation and quantitative analysis (with the chamfer distance as the measurement index), it is evident that DFPS can effectively preserve global feature information. Notably, DFPS does not depend on GPU-based heterogeneous computing, enabling seamless deployment in resource-constrained environments such as airborne and mobile devices, which makes DFPS an effective and lightweighting tool for providing high-quality input data for subsequent algorithms, including point cloud registration and semantic segmentation.},
}

@article {pmid40731363,
year = {2025},
author = {Demieville, J and Dilkes, B and Eveland, AL and Pauli, D},
title = {High-resolution phenomics dataset collected on a field-grown, EMS-mutagenized sorghum population evaluated in hot, arid conditions.},
journal = {BMC research notes},
volume = {18},
number = {1},
pages = {332},
pmid = {40731363},
issn = {1756-0500},
support = {DE-AR0001101//United States Department of Energy Advanced Projects Research Agency-Energy/ ; DE-AR0001101//United States Department of Energy Advanced Projects Research Agency-Energy/ ; DE-SC0020401//United States Department of Energy Biological and Environmental Research Program/ ; DE-SC0020401//United States Department of Energy Biological and Environmental Research Program/ ; DE-SC0020401//United States Department of Energy Biological and Environmental Research Program/ ; DE-SC0020401//United States Department of Energy Biological and Environmental Research Program/ ; 2021-51181-35903//United States Department of Agriculture National Institute of Food and Agriculture Specialty Crops Research Initiative/ ; 2021-51181-35903//United States Department of Agriculture National Institute of Food and Agriculture Specialty Crops Research Initiative/ ; DBI 2417511//National Science Foundation Division of Biological Infrastructure/ ; DBI 2417511//National Science Foundation Division of Biological Infrastructure/ ; IOS 2102120//National Science Foundation Division of Integrative Organismal Systems/ ; },
mesh = {*Sorghum/genetics/growth & development ; *Phenomics/methods ; Phenotype ; Droughts ; Arizona ; Seasons ; Genotype ; Hot Temperature ; },
abstract = {OBJECTIVES: The University of Arizona Field Scanner (FS) is capable of generating massive amounts of data from a variety of instruments at high spatial and temporal resolution. The accompanying field infrastructure beneath the system offers capacity for controlled irrigation regimes in a hot, arid environment. Approximately 194 terabytes of raw and processed phenotypic image data were generated over two growing seasons (2020 and 2022) on a population of 434 sequence-indexed, EMS-mutagenized sorghum lines in the genetic background BTx623; the population was grown under well-watered and water-limited conditions. Collectively, these data enable links between genotype and dynamic, drought-responsive phenotypes, which can accelerate crop improvement efforts. However, analysis of these data can be challenging for researchers without background knowledge of the system and preliminary processing.

DATA DESCRIPTION: This dataset contains formatted tabular data generated from sensing system outputs suitable for a wide range of end-users and includes plant-level bounding areas, temperatures, and point cloud characteristics, as well as plot-level photosynthetic parameters and accompanying weather data. The dataset includes approximately 422 megabytes of tabular data totaling 1,903,412 unique unfiltered rows of FS data, 526,917 cleaned rows of FS data, and 285 rows of weather data from the two field seasons.},
}

*Sorghum/genetics/growth & development
*Phenomics/methods
Phenotype
Droughts
Arizona
Seasons
Genotype
Hot Temperature

@article {pmid40727626,
year = {2025},
author = {Kaneko, R and Akaishi, S and Ogawa, R and Kuwahara, H},
title = {Machine Learning-based Complementary Artificial Intelligence Model for Dermoscopic Diagnosis of Pigmented Skin Lesions in Resource-limited Settings.},
journal = {Plastic and reconstructive surgery. Global open},
volume = {13},
number = {7},
pages = {e7004},
pmid = {40727626},
issn = {2169-7574},
abstract = {BACKGROUND: Rapid advancements in big data and machine learning have expanded their application in healthcare, introducing sophisticated diagnostics to settings with limited medical resources. Notably, free artificial intelligence (AI) services that require no programming skills are now accessible to healthcare professionals, allowing those in underresourced areas to leverage AI technology. This study aimed to evaluate the potential of these accessible services for diagnosing pigmented skin tumors, underscoring the democratization of advanced medical technologies.

METHODS: In this experimental diagnostic study, we collected 400 dermoscopic images (100 per tumor type) labeled through supervised learning from pathologically confirmed cases. The images were split into training, validation, and testing datasets (8:1:1 ratio) and uploaded to Vertex AI for model training. Supervised learning was performed using the Google Cloud Platform, Vertex AI, based on pathological diagnoses. The model's performance was assessed using confusion matrices and precision-recall curves.

RESULTS: The AI model achieved an average recall rate of 86.3%, precision rate of 87.3%, accuracy of 86.3%, and F1 score of 0.87. Misclassification rates were less than 20% for each category. Accuracy was 80% for malignant melanoma and 100% for both basal cell carcinoma and seborrheic keratosis. Testing on separate cases yielded an accuracy of approximately 70%.

CONCLUSIONS: The metrics obtained in this study suggest that the model can reliably assist in the diagnostic process, even for practitioners without prior AI expertise. The study demonstrated that free AI tools can accurately classify pigmented skin lesions with minimal expertise, potentially providing high-precision diagnostic support in settings lacking dermatologists.},
}

@article {pmid40724469,
year = {2025},
author = {Zhao, M and Chen, H},
title = {Identity-Based Provable Data Possession with Designated Verifier from Lattices for Cloud Computing.},
journal = {Entropy (Basel, Switzerland)},
volume = {27},
number = {7},
pages = {},
pmid = {40724469},
issn = {1099-4300},
support = {3282024048//Fundamental Research Funds for the Central Universities/ ; },
abstract = {Provable data possession (PDP) is a technique that enables the verification of data integrity in cloud storage without the need to download the data. PDP schemes are generally categorized into public and private verification. Public verification allows third parties to assess the integrity of outsourced data, offering good openness and flexibility, but it may lead to privacy leakage and security risks. In contrast, private verification restricts the auditing capability to the data owner, providing better privacy protection but often resulting in higher verification costs and operational complexity due to limited local resources. Moreover, most existing PDP schemes are based on classical number-theoretic assumptions, making them vulnerable to quantum attacks. To address these challenges, this paper proposes an identity-based PDP with a designated verifier over lattices, utilizing a specially leveled identity-based fully homomorphic signature (IB-FHS) scheme. We provide a formal security proof of the proposed scheme under the small-integer solution (SIS) and learning with errors (LWE) within the random oracle model. Theoretical analysis confirms that the scheme achieves security guarantees while maintaining practical feasibility. Furthermore, simulation-based experiments show that for a 1 MB file and lattice dimension of n = 128, the computation times for core algorithms such as TagGen, GenProof, and CheckProof are approximately 20.76 s, 13.75 s, and 3.33 s, respectively. Compared to existing lattice-based PDP schemes, the proposed scheme introduces additional overhead due to the designated verifier mechanism; however, it achieves a well-balanced optimization among functionality, security, and efficiency.},
}

@article {pmid40724375,
year = {2025},
author = {Robertson, R and Doucet, E and Spicer, E and Deffner, S},
title = {Simon's Algorithm in the NISQ Cloud.},
journal = {Entropy (Basel, Switzerland)},
volume = {27},
number = {7},
pages = {},
pmid = {40724375},
issn = {1099-4300},
support = {62422//John Templeton Foundation/ ; },
abstract = {Simon's algorithm was one of the first to demonstrate a genuine quantum advantage in solving a problem. The algorithm, however, assumes access to fault-tolerant qubits. In our work, we use Simon's algorithm to benchmark the error rates of devices currently available in the "quantum cloud". As a main result, we objectively compare the different physical platforms made available by IBM and IonQ. Our study highlights the importance of understanding the device architectures and topologies when transpiling quantum algorithms onto hardware. For instance, we demonstrate that two-qubit operations on spatially separated qubits on superconducting chips should be avoided.},
}

@article {pmid40723733,
year = {2025},
author = {Xue, K and Jin, X and Li, Y},
title = {Exploring the Influence of Human-Computer Interaction Experience on Tourist Loyalty in the Context of Smart Tourism: A Case Study of Suzhou Museum.},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {15},
number = {7},
pages = {},
pmid = {40723733},
issn = {2076-328X},
abstract = {As digital technology evolves rapidly, smart tourism has become a significant trend in the modernization of the industry, relying on advanced tools like big data and cloud computing to improve travelers' experiences. Despite the growing use of human-computer interaction in museums, there remains a lack of in-depth academic investigation into its impact on visitors' behavioral intentions regarding museum engagement. This paper employs Cognitive Appraisal Theory, considers human-computer interaction experience as the independent variable, and introduces destination image and satisfaction as mediators to examine their impact on destination loyalty. Based on a survey of 537 participants, the research shows that human-computer interaction experience has a significant positive impact on destination image, satisfaction, and loyalty. Destination image and satisfaction play a partial and sequential mediating role in this relationship. This paper explores the influence mechanism of human-computer interaction experience on destination loyalty and proposes practical interactive solutions for museums, aiming to offer insights for smart tourism research and practice.},
}

@article {pmid40721628,
year = {2025},
author = {He, J and Ye, Q and Yang, Z and Wang, S and Wang, J},
title = {A compact public key encryption with equality test for lattice in cloud computing.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {27426},
doi = {10.1038/s41598-025-12018-2},
pmid = {40721628},
issn = {2045-2322},
support = {62202490//the National Natural Science Foundation of China/ ; 62102440//the National Natural Science Foundation of China/ ; },
abstract = {The rapid proliferation of cloud computing enables users to access computing resources and storage space over the internet, but it also presents challenges in terms of security and privacy. Ensuring the security and availability of data has become a focal point of current research when utilizing cloud computing for resource sharing, data storage, and querying. Public key encryption with equality test (PKEET) can perform an equality test on ciphertexts without decrypting them, even when those ciphertexts are encrypted under different public keys. That offers a practical approach to dividing up or searching for encrypted information directly. In order to deal with the threat raised by the rapid development of quantum computing, researchers have proposed post-quantum cryptography to guarantee the security of cloud services. However, it is challenging to implement these techniques efficiently. In this paper, a compact PKEET scheme is pro-posed. The new scheme does not encrypt the plaintext's hash value immediately but embeds it into the test trapdoor. We also demon-strated that our new construction is one-way secure under the quantum security model. With those efforts, our scheme can withstand the chosen ciphertext attacks as long as the learning with errors (LWE) assumption holds. Furthermore, we evaluated the new scheme's performance and found that it only costs approximately half the storage space compared with previous schemes. There is an almost half reduction in the computing cost throughout the encryption and decryption stages. In a nutshell, the new PKEET scheme is less costly, more compact, and applicable to cloud computing scenarios in a post-quantum environment.},
}

@article {pmid40716656,
year = {2025},
author = {Chen, R and Lin, M and Chen, J and Lin, L and Wang, J and Li, X and Wang, J and Huang, X and Qian, L and Liu, S and Long, Y and Guo, D and Qu, X and Han, H},
title = {Reproducibility assessment of magnetic resonance spectroscopy of pregenual anterior cingulate cortex across sessions and vendors via the cloud computing platform CloudBrain-MRS.},
journal = {NeuroImage},
volume = {318},
number = {},
pages = {121400},
doi = {10.1016/j.neuroimage.2025.121400},
pmid = {40716656},
issn = {1095-9572},
abstract = {Proton magnetic resonance spectroscopy ([1]H-MRS) has potential in clinical diagnosis and understanding the mechanism of illnesses. However, its application is limited by the lack of standardization in data acquisition and processing across time points and between different magnetic resonance imaging (MRI) system vendors. This study examines whether metabolite concentrations obtained from different sessions, scanner models, and vendors can be reliably reproduced and combined for diagnostic analysis-an important consideration for rare disease research. Participants underwent magnetic resonance scanning once on two separate days within one week (one session per day, each including two [1]H-MRS scans without subject movement) on each machine. Absolute metabolite concentrations were analyzed for reliability of within- and between- session using the coefficient of variation (CV), intraclass correlation coefficient (ICC) and Bland-Altman (BA) plot, and for reproducibility across the machines using the Pearson correlation coefficient. As for within- and between- session, most of the CV values for a group of all the first or second scans of a session, and from each session were below 20 %, and most of ICCs ranged from moderate (0.4≤ICC<0.59) to excellent (ICC≥0.75), which indicated high reliability. Most of the BA plots had the line of equality between 95 % confidence interval of bias (mean difference), therefore the differences over scanning time could be negligible. Majority of the Pearson correlation coefficients approached 1 with statistical significance (P < 0.001), showing high reproducibility across the three scanners. Additionally, the intra-vendor reproducibility was greater than the inter-vendor ones.},
}

@article {pmid40715607,
year = {2025},
author = {Christy, C and Nirmala, A and Teena, AMO and Amali, AI},
title = {Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {27058},
pmid = {40715607},
issn = {2045-2322},
abstract = {The development of intelligent transportation systems relies heavily on Cloud-assisted Vehicular Ad Hoc Networks (VANETs); hence, these networks must be protected. Particularly susceptible to a broad range of assaults are VANETs because of their extreme dynamism and decentralization. Connected vehicles' safety and efficiency could be compromised if these security threats materialize, leading to disastrous road accidents. Solving these issues will require an advanced Intrusion Detection System (IDS) with real-time threat recognition and neutralization capabilities. A new method for improving VANET security, a multi-stage Lightweight IntrusionDetection System Using Random Forest Algorithms (MLIDS-RFA), focuses on feature selection and ensemble models based on machine learning (ML). A multi-step approach is employed by the proposed system, with each stage dedicated to accurately detecting specific types of attacks. Regarding feature selection, MLIDS-RFA uses machine-learning approaches to enhance the detection process. The outcome is a reduction in the amount of processing overhead and a shortening of the response times. The detection abilities of ensemble models are enhanced by integrating the strengths of the Random Forest algorithm (RFA), which safeguards against intricate dangers. The practicality of the proposed technology is demonstrated by conducting thorough simulation analyses. This research demonstrates that the system can reduce false positives while maintaining high detection rates. This research ensures next-generation transport networks' secure and reliable functioning and prepares the path for VANET protection upgrades. MLIDS-RFA has improved detection accuracy (96.2%) and computing efficiency (94.8%) for dynamic VANET management. It operates well with large networks (97.8%) and adapts well to network changes (93.8%). The comprehensive methodology ensures high detection performance (95.9%) and VANET security by balancing accuracy, efficiency, and scalability.},
}

@article {pmid40715332,
year = {2025},
author = {Punitha, S and Preetha, KS},
title = {Enhancing reliability and security in cloud-based telesurgery systems leveraging swarm-evoked distributed federated learning framework to mitigate multiple attacks.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {27226},
pmid = {40715332},
issn = {2045-2322},
abstract = {Advances in robotic surgery are being driven by the convergence of technologies such as artificial intelligence (AI), 5G/6G wireless communication, the Internet of Things (IoT), and edge computing, enhancing clinical precision, speed, and real-time decision-making. However, the practical deployment of telesurgery and tele-mentoring remains constrained due to increasing cybersecurity threats, posing significant challenges to patient safety and system reliability. To address these issues, a distributed framework based on federated learning is proposed, integrating Optimized Gated Transformer Networks (OGTN) with layered chaotic encryption schemes to mitigate multiple unknown cyberattacks while preserving data privacy and integrity. The framework was implemented using TensorFlow Federated Learning Libraries (FLL) and evaluated on the UNSW-NB15 dataset. Performance was assessed using metrics including precision, accuracy, F1-score, recall, and security strength, and compared with existing approaches. In addition, structured and unstructured security assessments, including evaluations based on National Institute of Standards and Technology (NIST) recommendations, were performed to validate robustness. The proposed framework demonstrated superior performance in terms of diagnostic accuracy and cybersecurity resilience relative to conventional models. These results suggest that the framework is a viable candidate for integration into teleoperated healthcare systems, offering improved security and operational efficiency in robotic surgery applications.},
}

@article {pmid40701146,
year = {2025},
author = {Baker, J and Stricker, E and Coleman, J and Ketkar, S and Tan, T and Butler, AM and Williams, L and Hammonds-Odie, L and Murray, D and Lee, B and Worley, KC and Atkinson, EG},
title = {Implementing a training resource for large-scale genomic data analysis in the All of Us Researcher Workbench.},
journal = {American journal of human genetics},
volume = {},
number = {},
pages = {},
pmid = {40701146},
issn = {1537-6605},
support = {OT2 OD026556/OD/NIH HHS/United States ; U2C OD023196/OD/NIH HHS/United States ; OT2 OD025315/OD/NIH HHS/United States ; OT2 OD026551/OD/NIH HHS/United States ; U24 OD023121/OD/NIH HHS/United States ; OT2 OD026552/OD/NIH HHS/United States ; OT2 OD026549/OD/NIH HHS/United States ; OT2 OD025337/OD/NIH HHS/United States ; OT2 OD026555/OD/NIH HHS/United States ; OT2 OD026550/OD/NIH HHS/United States ; OT2 OD026553/OD/NIH HHS/United States ; OT2 OD023205/OD/NIH HHS/United States ; OT2 OD025276/OD/NIH HHS/United States ; OT2 OD026557/OD/NIH HHS/United States ; OT2 OD026554/OD/NIH HHS/United States ; U24 OD023163/OD/NIH HHS/United States ; OT2 OD023206/OD/NIH HHS/United States ; U24 OD023176/OD/NIH HHS/United States ; OT2 OD026548/OD/NIH HHS/United States ; OT2 OD025277/OD/NIH HHS/United States ; OT2 OD031932/OD/NIH HHS/United States ; },
abstract = {A lack of representation in genomic research and limited access to computational training create barriers for many researchers seeking to analyze large-scale genetic datasets. The All of Us Research Program provides an unprecedented opportunity to address these gaps by offering genomic data from a broad range of participants, but its impact depends on equipping researchers with the necessary skills to use it effectively. The All of Us Biomedical Researcher (BR) Scholars Program at Baylor College of Medicine aims to break down these barriers by providing early-career researchers with hands-on training in computational genomics through the All of Us Evenings with Genetics Research Program. The year-long program begins with the faculty summit, an in-person computational boot camp that introduces scholars to foundational skills for using the All of Us dataset via a cloud-based research environment. The genomics tutorials focus on genome-wide association studies (GWASs), utilizing Jupyter Notebooks and the Hail computing framework to provide an accessible and scalable approach to large-scale data analysis. Scholars engage in hands-on exercises covering data preparation, quality control, association testing, and result interpretation. By the end of the summit, participants will have successfully conducted a GWAS, visualized key findings, and gained confidence in computational resource management. This initiative expands access to genomic research by equipping early-career researchers from a variety of backgrounds with the tools and knowledge to analyze All of Us data. By lowering barriers to entry and promoting the study of representative populations, the program fosters innovation in precision medicine and advances equity in genomic research.},
}

@article {pmid40693169,
year = {2025},
author = {Dal, I and Kaya, HB},
title = {Multidisciplinary Evaluation of an AI-Based Pneumothorax Detection Model: Clinical Comparison with Physicians in Edge and Cloud Environments.},
journal = {Journal of multidisciplinary healthcare},
volume = {18},
number = {},
pages = {4099-4111},
pmid = {40693169},
issn = {1178-2390},
abstract = {BACKGROUND: Accurate and timely detection of pneumothorax on chest radiographs is critical in emergency and critical care settings. While subtle cases remain challenging for clinicians, artificial intelligence (AI) offers promise as a diagnostic aid. This retrospective diagnostic accuracy study evaluates a deep learning model developed using Google Cloud Vertex AI for pneumothorax detection on chest X-rays.

METHODS: A total of 152 anonymized frontal chest radiographs (76 pneumothorax, 76 normal), confirmed by computed tomography (CT), were collected from a single center between 2023 and 2024. The median patient age was 50 years (range: 18-95), with 67.1% male. The AI model was trained using AutoML Vision and evaluated in both cloud and edge deployment environments. Diagnostic accuracy metrics-including sensitivity, specificity, and F1 score-were compared with those of 15 physicians from four specialties (general practice, emergency medicine, thoracic surgery, radiology), stratified by experience level. Subgroup analysis focused on minimal pneumothorax cases. Confidence intervals were calculated using the Wilson method.

RESULTS: In cloud deployment, the AI model achieved an overall diagnostic accuracy of 0.95 (95% CI: 0.83, 0.99), sensitivity of 1.00 (95% CI: 0.83, 1.00), specificity of 0.89 (95% CI: 0.69, 0.97), and F1 score of 0.95 (95% CI: 0.86, 1.00). Comparable performance was observed in edge mode. The model outperformed junior clinicians and matched or exceeded senior physicians, particularly in detecting minimal pneumothoraces, where AI sensitivity reached 0.93 (95% CI: 0.79, 0.97) compared to 0.55 (95% CI: 0.38, 0.69) - 0.84 (95% CI: 0.69, 0.92) among human readers.

CONCLUSION: The Google Cloud Vertex AI model demonstrates high diagnostic performance for pneumothorax detection, including subtle cases. Its consistent accuracy across edge and cloud settings supports its integration as a second reader or triage tool in diverse clinical workflows, especially in acute care or resource-limited environments.},
}

@article {pmid40690134,
year = {2025},
author = {Onur, D and Özbakır, Ç},
title = {Pediatrics 4.0: the Transformative Impacts of the Latest Industrial Revolution on Pediatrics.},
journal = {Health care analysis : HCA : journal of health philosophy and policy},
volume = {},
number = {},
pages = {},
pmid = {40690134},
issn = {1573-3394},
abstract = {Industry 4.0 represents the latest phase of industrial evolution, characterized by the seamless integration of cyber-physical systems, the Internet of Things, big data analytics, artificial intelligence, advanced robotics, and cloud computing, enabling smart, adaptive, and interconnected processes where physical, digital, and biological realms converge. In parallel, healthcare has progressed from the traditional, physician-centered model of Healthcare 1.0 by introducing medical devices and digitized records to Healthcare 4.0, which leverages Industry 4.0 technologies to create personalized, data-driven, and patient-centric systems. In this context, we hereby introduce Pediatrics 4.0 as a new paradigm that adapts these innovations to children's unique developmental, physiological, and ethical considerations and aims to improve diagnostic precision, treatment personalization, and continuous monitoring in pediatric populations. Key applications include AI-driven diagnostic and predictive analytics, IoT-enabled remote monitoring, big data-powered epidemiological insights, robotic assistance in surgery and rehabilitation, and 3D printing for patient-specific devices and pharmaceuticals. However, realizing Pediatrics 4.0 requires addressing significant challenges-data privacy and security, algorithmic bias, interoperability and standardization, equitable access, regulatory alignment, the ethical complexities of consent, and long-term technology exposure. Future research should focus on explainable AI, pediatric-specific device design, robust data governance frameworks, dynamic ethical and legal guidelines, interdisciplinary collaboration, and workforce training to ensure these transformative technologies translate into safer, more effective, and more equitable child healthcare.},
}

@article {pmid40687746,
year = {2025},
author = {Parashar, B and Malviya, R and Sridhar, SB and Wadhwa, T and Shareef, J},
title = {IoT-enabled medical advances shaping the future of orthopaedic surgery and rehabilitation.},
journal = {Journal of clinical orthopaedics and trauma},
volume = {68},
number = {},
pages = {103113},
pmid = {40687746},
issn = {0976-5662},
abstract = {The Internet of Things (IoT) connects smart devices to enable automation and data exchange. IoT is rapidly transforming the healthcare industry. Understanding of the framework and challenges of IoT is essential for effective implementation. This review explores the advances in IoT technology in orthopaedic surgery and rehabilitation. A comprehensive literature search was conducted by the author using databases such as PubMed, Scopus, and Google Scholar. Relevant peer-reviewed articles published between 2010 and 2024 were preferred based on their focus on IoT applications in orthopaedic surgery, rehabilitation, and assistive technologies. Keywords including "Internet of Things," "orthopaedic rehabilitation," "wearable sensors," and "smart health monitoring" were used. Studies were analysed to identify current trends, clinical relevance, and future opportunities in IoT-driven orthopaedic care. The reviewed studies demonstrate that IoT technologies, such as wearable motion sensors, smart implants, real-time rehabilitation platforms, and AI-powered analytics, have significantly improved orthopaedic surgical outcomes and patient recovery. These systems enable continuous monitoring, early complication detection, and adaptive rehabilitation. However, challenges persist in data security, device interoperability, user compliance, and standardisation across platforms. IoT holds great promise in enhancing orthopaedic surgery and rehabilitation by enabling real-time monitoring and personalised care. Moving forward, clinical validation, user-friendly designs, and strong data security will be key to its successful integration in routine practice.},
}

@article {pmid40685723,
year = {2025},
author = {Gomase, VS and Ghatule, AP and Sharma, R and Sardana, S and Dhamane, SP},
title = {Cloud Computing Facilitating Data Storage, Collaboration, and Analysis in Global Healthcare Clinical Trials.},
journal = {Reviews on recent clinical trials},
volume = {},
number = {},
pages = {},
doi = {10.2174/0115748871379249250701065507},
pmid = {40685723},
issn = {1876-1038},
abstract = {INTRODUCTION: Healthcare data management, especially in the context of clinical trials, has been completely transformed by cloud computing. It makes it easier to store data, collaborate in real time, and perform advanced analytics across international research networks by providing scalable, secure, and affordable solutions. This paper explores how cloud computing is revolutionizing clinical trials, tackling issues including data integration, accessibility, and regulatory compliance.

MATERIALS AND METHODS: Key factors assessed include cloud platform-enabled analytical tools, collaborative features, and data storage capacity. To ensure the safe management of sensitive healthcare data, adherence to laws like GDPR and HIPAA was emphasized.

RESULTS: Real-time updates and integration of multicenter trial data were made possible by cloud systems, which also showed notable gains in collaborative workflows and data sharing. High scalability storage options reduced infrastructure expenses while upholding security requirements. Rapid interpretation of complicated datasets was made possible by sophisticated analytical tools driven by machine learning and artificial intelligence, which expedited decision-making. Improved patient recruitment tactics and flexible trial designs are noteworthy examples.

CONCLUSION: Cloud computing has become essential for international clinical trials because it provides unmatched efficiency in data analysis, communication, and storage. It is a pillar of contemporary healthcare research due to its capacity to guarantee data security and regulatory compliance as well as its creative analytical capabilities. Subsequent research ought to concentrate on further refining cloud solutions to tackle new issues and utilizing their complete capabilities in clinical trial administration.},
}

@article {pmid40685425,
year = {2025},
author = {Yang, X and Yao, K and Li, S and Du, X and Wang, C},
title = {A smart grid data sharing scheme supporting policy update and traceability.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {26343},
doi = {10.1038/s41598-025-10704-9},
pmid = {40685425},
issn = {2045-2322},
support = {no. 2023CYZC-09//the Industrial Support Plan Project of Gansu Provincial Education Department/ ; no. 23YFGA0081//the Key Research and Development Program of Gansu Province/ ; nos. 62362059, 62172337//the National Natural Science Foundation of China/ ; },
abstract = {To address the problems of centralized attribute authority, inefficient encryption and invalid access control strategy in the data sharing scheme based on attribute-based encryption technology, a smart grid data sharing scheme that supports policy update and traceability is proposed. The smart contract of the blockchain is used to generate the user's key, which does not require a centralized attribute authority. Combined with attribute-based encryption and symmetric encryption technology, the confidentiality of smart grid data is protected and flexible data access control is achieved. In addition, online/offline encryption and outsourced computing technologies complete most of the computing tasks in the offline stage or cloud server, which greatly reduces the computing burden of data owners and data access users. By introducing the access control policy update mechanism, the data owner can flexibly modify the key ciphertext stored in the cloud server. Finally, the analysis results show that this scheme can protect the privacy of smart grid data, verify the integrity of smart grid data, resist collusion attacks and track the identity of malicious users who leak private keys, and its efficiency is better than similar data sharing schemes.},
}

@article {pmid40685390,
year = {2025},
author = {Yin, X and Zhang, X and Pei, L and Hu, R and Ye, K and Cai, K},
title = {Optimization and benefit evaluation model of a cloud computing-based platform for power enterprises.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {26366},
pmid = {40685390},
issn = {2045-2322},
abstract = {To address the challenges associated with the digital transformation of the power industry, this research develops an optimization and benefit evaluation model for cloud computing platforms tailored to power enterprises. It responds to the current lack of systematic optimization mechanisms and evaluation methods in existing cloud computing applications. The proposed model focuses on resource scheduling optimization, task load balancing, and improvements in computational efficiency. A multidimensional optimization framework is constructed, integrating key parameters such as path planning, condition coefficient computation, and the regulation of task and average loads. The model employs an improved lightweight genetic algorithm combined with an elastic resource allocation strategy to dynamically adapt to task changes across various operational scenarios. Experimental results indicate a 46% reduction in failure recovery time, a 78% improvement in high-load throughput capacity, and an average increase of nearly 60% in resource utilization. Compared with traditional on-premise architectures and static scheduling models, the proposed approach offers notable advantages in computational response time and fault tolerance. In addition, through containerized deployment and intelligent orchestration, it achieves a 43% reduction in monthly operating costs. A multi-level benefit evaluation system-spanning power generation, grid operations, and end-user services-is established, integrating historical data, expert weighting, and dynamic optimization algorithms to enable quantitative performance assessment and decision support. In contrast to existing studies that mainly address isolated functional modules such as equipment health monitoring or collaborative design, this research presents a novel paradigm characterized by architectural integration, methodological versatility, and industrial applicability. It thus addresses the empirical gap in multi-objective optimization for industrial-scale power systems. The theoretical contribution of this research lies in the establishment of a highly scalable and integrated framework for optimization and evaluation. Its practical significance is reflected in the notable improvements in operational efficiency and cost control in real-world applications. The proposed model provides a clear trajectory and quantitative foundation for promoting an efficient and intelligent cloud computing ecosystem in the power sector.},
}

@article {pmid40681592,
year = {2025},
author = {Cao, J and Yu, Z and Zhu, B and Cao, M and Yang, J},
title = {Construction and efficiency analysis of an embedded system-based verification platform for edge computing.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {26114},
pmid = {40681592},
issn = {2045-2322},
support = {U22B2005//National Natural Science Foundation of China/ ; 62071481 and 61501471//National Natural Science Foundation of China/ ; },
abstract = {With the profound convergence and advancement of the Internet of Things, big data analytics, and artificial intelligence technologies, edge computing-a novel computing paradigm-has garnered significant attention. While edge computing simulation platforms offer convenience for simulations and tests, the disparity between them and real-world environments remains a notable concern. These platforms often struggle to precisely mimic the interactive behaviors and physical attributes of actual devices. Moreover, they face constraints in real-time responsiveness and scalability, thus limiting their ability to truly reflect practical application scenarios. To address these obstacles, our study introduces an innovative physical verification platform for edge computing, grounded in embedded devices. This platform seamlessly integrates KubeEdge and Serverless technological frameworks, facilitating dynamic resource allocation and efficient utilization. Additionally, by leveraging the robust infrastructure and cloud services provided by Alibaba Cloud, we have significantly bolstered the system's stability and scalability. To ensure a comprehensive assessment of our architecture's performance, we have established a realistic edge computing testing environment, utilizing embedded devices like Raspberry Pi. Through rigorous experimental validations involving offloading strategies, we have observed impressive outcomes. The refined offloading approach exhibits outstanding results in critical metrics, including latency, energy consumption, and load balancing. This not only underscores the soundness and reliability of our platform design but also illustrates its versatility for deployment in a broad spectrum of application contexts.},
}

@article {pmid40678452,
year = {2025},
author = {C, BS and St, B and S, S},
title = {Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance.},
journal = {MethodsX},
volume = {15},
number = {},
pages = {103461},
pmid = {40678452},
issn = {2215-0161},
abstract = {Cloud computing continues to rise, increasing the demand for more intelligent, rapid, and secure resource management. This paper presents AdaPCA-a novel method that integrates the adaptive capabilities of AdaBoost with the dimensionality-reduction efficacy of PCA. What is the objective? Enhance trust-based access control and resource allocation decisions while maintaining a minimal computational burden. High-dimensional trust data frequently hampers systems; however, AdaPCA mitigates this issue by identifying essential aspects and enhancing learning efficacy concurrently. To evaluate its performance, we conducted a series of simulations comparing it with established methods such as Decision Trees, Random Forests, and Gradient Boosting. We assessed execution time, resource use, latency, and trust accuracy. Results show that AdaPCA achieved a trust score prediction accuracy of 99.8 %, a resource utilization efficiency of 95 %, and reduced allocation time to 140 ms, outperforming the benchmark models across all evaluated parameters. AdaPCA had superior performance overall-expedited decision-making, optimized resource utilization, reduced latency, and the highest accuracy in trust evaluation among the evaluated models. AdaPCA is not merely another model; it represents a significant advancement towards more intelligent and safe cloud systems designed for the future.•Introduces AdaPCA, a novel hybrid approach that integrates AdaBoost with PCA to optimize cloud resource allocation and improve trust-based access control.•Outperforms conventional techniques such as Decision Tree, Random Forest, and Gradient Boosting by attaining superior trust accuracy, expedited execution, enhanced resource utilization, and reduced latency.•Presents an intelligent, scalable, and adaptable architecture for secure and efficient management of cloud resources, substantiated by extensive simulation experiments.},
}

@article {pmid40677066,
year = {2025},
author = {Zhao, N and Wang, B and Wang, ZH and Zhang, QL},
title = {[Spatiotemporal Evolution of Ecological Environment Quality and Ecological Management Zoning in Inner Mongolia Based on RSEI].},
journal = {Huan jing ke xue= Huanjing kexue},
volume = {46},
number = {7},
pages = {4499-4509},
doi = {10.13227/j.hjkx.202405303},
pmid = {40677066},
issn = {0250-3301},
mesh = {China ; *Ecosystem ; *Remote Sensing Technology ; *Environmental Monitoring/methods ; *Conservation of Natural Resources ; Spatio-Temporal Analysis ; Ecology ; },
abstract = {Inner Mongolia serves as a crucial ecological security barrier for northern China. Examining the spatial and temporal evolution of ecological environment quality， along with the zoning for ecological management， is crucial for enhancing the management and development of ecological environments. Based on the Google Earth Engine cloud platform， four indicators-heat， greenness， dryness， and wetness-were extracted from MODIS remote sensing image data spanning 2000 to 2023. The remote sensing ecological index （RESI） model was constructed using principal component analysis. By combining the coefficient of variation （CV）， Sen + Mann-Kendall， and Hurst indices， the spatial and temporal variations and future trends of ecological environmental quality of the Inner Mongolia were analyzed. The influencing mechanisms were explored using a geographical detector， and the quadrant method was employed for ecological management zoning based on the intensity of human activities and the quality of the ecological environment. The results indicated that： ① The ecological environment quality of Inner Mongolia from 2000 to 2023 was mainly characterized as poor to average， with a spatial trend of decreasing quality from east to west. From 2000 to 2005， Inner Mongolia experienced environmental degradation， followed by a gradual improvement in ecological environment quality. ② Inner Mongolia exhibited the largest area of non-significantly improved and non-significantly degraded regions， and the overall environmental quality was more stable. However， ecosystems in the western region were more fragile and prone to fluctuations. The area of sustained degradation versus sustained improvement in the future trend of change was larger， and the western region is expected to be the main area of improvement in the future. ③ The results of single-factor detection showed that the influences on RSEI values were， in descending order， precipitation， soil type， land use type， air temperature， vegetation type， elevation， population density， GDP， and nighttime lighting； the interactions among driving factors on RSEI changes showed a bivariate or nonlinear enhancement， which suggests that the interactions of each driving factor could improve the explanatory power of spatial variations in ecological environment quality. ④ Based on the coupling of human activity intensity and ecological environment quality， the 12 league cities of Inner Mongolia were divided into ecological development coordination zones， ecological development reserves， and ecological development risk zones. This study can provide a scientific basis for ecological environmental protection and sustainable development in Inner Mongolia.},
}

China
*Ecosystem
*Remote Sensing Technology
*Environmental Monitoring/methods
*Conservation of Natural Resources
Spatio-Temporal Analysis
Ecology

@article {pmid40677063,
year = {2025},
author = {Yang, M and Liu, EQ and Yang, Y and Gao, B and Guan, L},
title = {[Quantitative Analysis of Wetland Evolution Characteristics and Driving Factors in Ruoergai Plateau Based on Landsat Time Series Remote Sensing Images].},
journal = {Huan jing ke xue= Huanjing kexue},
volume = {46},
number = {7},
pages = {4461-4472},
doi = {10.13227/j.hjkx.202406190},
pmid = {40677063},
issn = {0250-3301},
abstract = {The Ruoergai Wetland， China's largest high-altitude marsh， plays a crucial role in the carbon cycle and climate management. However， the Ruoergai Wetland has experienced significant damage as a result of human activity and global warming. Based on the Google Earth Engine （GEE） cloud platform and time-series Landsat images， a random forest algorithm was applied to produce a detailed classification map of the Ruoergai wetlands from 1990 to 2020. Through the transfer matrix and landscape pattern index， the spatiotemporal change law and change trend of wetlands were analyzed. Then， the influencing factors of wetland distribution were quantitatively analyzed using geographic detector. The results showed that： ① The total wetland area averaged 3 910 km[2] from 1990 to 2020， dominated by marshy and wet meadows， accounting for 83.13% of the total wetland area. From 1990 to 2010， the wetland area of Ruoergai showed a decreasing trend， and from 2010 to 2020， the wetland area increased slightly. ② From 1990 to 2020， the decrease in wetland area was mainly reflected in the degradation of wet meadows into alpine grassland. There were also changes among different wetland types， which were mainly reflected in the conversion of marsh meadows and wet meadows. ③ From 1990 to 2010， the wetland landscape tended to be fragmented and complicated， and the aggregation degree decreased. From 2010 to 2020， wetland fragmentation decreased， and the wetland landscape became more concentrated. ④ Slope， temperature， and aspect were the main natural factors affecting wetland distribution. At the same time， population density has gradually become a significant social and economic factor affecting wetland distribution. The results can provide scientific support for the wetland protection planning of Ruoergai and serve for the ecological preservation and high-level development of the area.},
}

@article {pmid40665235,
year = {2025},
author = {Narasimha Raju, AS and Venkatesh, K and Rajababu, M and Kumar Gatla, R and Jakeer Hussain, S and Satya Mohan Chowdary, G and Ganga Bhavani, T and Kareemullah, M and Algburi, S and Majdi, A and Abdulhadi, AM and Ahmad Khan, W},
title = {Colorectal cancer unmasked: A synergistic AI framework for Hyper-granular image dissection, precision segmentation, and automated diagnosis.},
journal = {BMC medical imaging},
volume = {25},
number = {1},
pages = {283},
pmid = {40665235},
issn = {1471-2342},
mesh = {Humans ; *Colorectal Neoplasms/diagnostic imaging ; *Diagnosis, Computer-Assisted/methods ; Colonoscopy ; *Image Interpretation, Computer-Assisted/methods ; },
abstract = {Colorectal cancer (CRC) is the second most common cause of cancer-related mortality worldwide, underscoring the necessity for computer-aided diagnosis (CADx) systems that are interpretable, accurate, and robust. This study presents a practical CADx system that combines Vision Transformers (ViTs) and DeepLabV3 + to accurately identify and segment colorectal lesions in colonoscopy images.The system addresses class balance and real-world complexity with PCA-based dimensionality reduction, data augmentation, and strategic preprocessing using recently curated CKHK-22 dataset comprising more than 14,000 annotated images of CVC-ClinicDB, Kvasir-2, and Hyper-Kvasir. ViT, ResNet-50, DenseNet-201, and VGG-16 were used to quantify classification performance. ViT achieved best-in-class accuracy (97%), F1-score (0.95), and AUC (92%) in test data. The DeepLabV3 + achieved segmentation state-of-the-art for tasks of localisation with 0.88 Dice Coefficient and 0.71 Intersection over Union (IoU), ensuring sharp delineation of areas that are malignant. The CADx system accommodates real-time inference and served through Google Cloud for information that accommodates scalable clinical implementation. The image-level segmentation effectiveness is evidenced by comparison with visual overlay and expert-manually deliminated masks, and its precision is illustrated by computation of precision, recall, F1-score, and AUC. The hybrid strategy not only outperforms traditional CNN strategies but also overcomes important clinical needs such as detection early, balance of highly disparate classes, and clear explanation. The proposed ViT-DeepLabV3 + system establishes a basis for advanced AI support to colorectal diagnosis by utilizing self-attention strategies and learning with different scales of context. The system offers a high-capacity, reproducible computerised colorectal cancer screening and monitoring solution and can be best deployed where resources are scarce, and it can be highly desirable for clinical deployment.},
}

Humans
*Colorectal Neoplasms/diagnostic imaging
*Diagnosis, Computer-Assisted/methods
Colonoscopy
*Image Interpretation, Computer-Assisted/methods

@article {pmid40665167,
year = {2025},
author = {Islam, U and Alatawi, MN and Alqazzaz, A and Alamro, S and Shah, B and Moreira, F},
title = {A hybrid fog-edge computing architecture for real-time health monitoring in IoMT systems with optimized latency and threat resilience.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {25655},
pmid = {40665167},
issn = {2045-2322},
mesh = {Humans ; *Internet of Things ; *Cloud Computing ; *Computer Security ; Monitoring, Physiologic/methods ; },
abstract = {The advancement of the Internet of Medical Things (IoMT) has transformed healthcare delivery by enabling real-time health monitoring. However, it introduces critical challenges related to latency and, more importantly, the secure handling of sensitive patient data. Traditional cloud-based architectures often struggle with latency and data protection, making them inefficient for real-time healthcare scenarios. To address these challenges, we propose a Hybrid Fog-Edge Computing Architecture tailored for effective real-time health monitoring in IoMT systems. Fog computing enables processing of time-critical data closer to the data source, reducing response time and relieving cloud system overload. Simultaneously, edge computing nodes handle data preprocessing and transmit only valuable information-defined as abnormal or high-risk health signals such as irregular heart rate or oxygen levels-using rule-based filtering, statistical thresholds, and lightweight machine learning models like Decision Trees and One-Class SVMs. This selective transmission optimizes bandwidth without compromising response quality. The architecture integrates robust security measures, including end-to-end encryption and distributed authentication, to counter rising data breaches and unauthorized access in IoMT networks. Real-life case scenarios and simulations are used to validate the model, evaluating latency reduction, data consolidation, and scalability. Results demonstrate that the proposed architecture significantly outperforms cloud-only models, with a 70% latency reduction, 30% improvement in energy efficiency, and 60% bandwidth savings. Additionally, the time required for threat detection was halved, ensuring faster response to security incidents. This framework offers a flexible, secure, and efficient solution ideal for time-sensitive healthcare applications such as remote patient monitoring and emergency response systems.},
}

Humans
*Internet of Things
*Cloud Computing
*Computer Security
Monitoring, Physiologic/methods

@article {pmid40665124,
year = {2025},
author = {Khaldy, MAA and Nabot, A and Al-Qerem, A and Jebreen, I and Darem, AA and Alhashmi, AA and Alauthman, M and Aldweesh, A},
title = {Adaptive conflict resolution for IoT transactions: A reinforcement learning-based hybrid validation protocol.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {25589},
pmid = {40665124},
issn = {2045-2322},
abstract = {This paper introduces a novel Reinforcement Learning-Based Hybrid Validation Protocol (RL-CC) that revolutionizes conflict resolution for time-sensitive IoT transactions through adaptive edge-cloud coordination. Efficient transaction management in sensor-based systems is crucial for maintaining data integrity and ensuring timely execution within the constraints of temporal validity. Our key innovation lies in dynamically learning optimal scheduling policies that minimize transaction aborts while maximizing throughput under varying workload conditions. The protocol consists of two validation phases: an edge validation phase, where transactions undergo preliminary conflict detection and prioritization based on their temporal constraints, and a cloud validation phase, where a final conflict resolution mechanism ensures transactional correctness on a global scale. The RL-based mechanism continuously adapts decision-making by learning from system states, prioritizing transactions, and dynamically resolving conflicts using a reward function that accounts for key performance parameters, including the number of conflicting transactions, cost of aborting transactions, temporal validity constraints, and system resource utilization. Experimental results demonstrate that our RL-CC protocol achieves a 90% reduction in transaction abort rates (5% vs. 45% for 2PL), 3x higher throughput (300 TPS vs. 100 TPS), and 70% lower latency compared to traditional concurrency control methods. The proposed RL-CC protocol significantly reduces transaction abort rates, enhances concurrency management, and improves the efficiency of sensor data processing by ensuring that transactions are executed within their temporal validity window. The results suggest that the RL-based approach offers a scalable and adaptive solution for sensor-based applications requiring high-concurrency transaction processing, such as Internet of Things (IoT) networks, real-time monitoring systems, and cyber-physical infrastructures.},
}