Search Results (12,273)

Background/Objectives: The TEG^® 6s is a hemostasis analyzer system used to assess viscoelastic properties of whole blood. Although it provides rapid and comprehensive insights, interpreting thromboelastography tracings requires robust clinical training and is not standardized. Our aim was to develop and evaluate a prototype of MetaTEG—an AI-driven support tool for interpreting TEG^® 6s tracings, with a focus on the Global Hemostasis-Heparin Neutralization cartridge. Methods: TEG^® 6s tracings, obtained from Haemonetics Corporation’s internal case library, were used to train a custom AI agent developed with Microsoft Copilot Studio. Five different clinical cases were used for testing. The AI agent incorporated knowledge graph augmentation and prompt engineering to integrate published literature and assess coagulation states based on R-time, maximum amplitude, and other key parameters. The evaluation of the AI virtualization outcomes included semiquantitative scoring by an internal TEG expert panel who assessed the AI’s diagnostic accuracy, tracing interpretation, and therapeutic recommendations. UI/UX prototypes were designed in Figma to demonstrate potential integration into clinical workflows. Results: Using this proof-of-concept dataset and qualitative expert scoring, MetaTEG accurately described and interpreted TEG^® 6s tracings and suggested potential treatment options. Reviewers consistently rated the AI-generated outputs as both clinically useful and accurate. Conclusions: The MetaTEG prototype represents the first application of generative AI for TEG^® 6s interpretation, showcasing the potential of AI-powered, human-in-the-loop decision support in hemostasis diagnostics. Full article

To address the limitation of traditional cellular automata models in effectively integrating temporal and spatial information, this study extends the previously developed Land use Simulation and Decision-Support system (LandSDS). By incorporating a graph attention network (GAT), a transformer, and an agent-based model (ABM) into a cellular automata framework informed by remote sensing time series, GT-LandSDS is constructed. Specifically, GAT dynamically captures higher-order spatial dependencies among land parcels; the self-attention mechanism of the transformer extracts land use change characteristics from multi-period observations; and ABM captures human behavioral decisions of three types, namely traffic, resident, and government. Based on this framework, GT-LandSDS derives CA transition rules from multiple dimensions and enhances the dynamic exploration of land use change across space and time. Using Guangxi Zhuang Autonomous Region as a case study, the model was validated with remote sensing land use data from six periods (2000, 2005, 2010, 2015, 2020, and 2023), and scenario-based future predictions were generated. The results show that: (1) The overall accuracy reaches 0.926, while the Kappa coefficient is 0.820, and the figure of merit (FoM) for change simulation is 0.034, indicating a relative advantage over ANN-CA, LSTM-CA, and UESP in overall pattern simulation, although fine-scale change reproduction remains limited; (2) Three development scenarios were then assessed: continuing historical trends, theoretical high-intensity urban expansion, and karst landform conservation under a green transformation development policy. The land use pattern of the area from 2023 to 2035 was predicted. The findings reveal that accelerating urbanization leads to rapid expansion of construction land, increasing by more than 88% compared with 2023, and causes substantial cropland loss. In contrast, intervention through the green transformation development policy limits construction land growth to 26.5%, effectively curbing urban sprawl while protecting forest, grassland, and cropland resources in the karst landscape. This study offers new insights into land use change simulation in ecologically fragile regions subject to strong policy interventions. It provides a scientific basis for coordinating ecological conservation and high-quality development in karst areas. Full article

Wrist fractures present significant challenges in clinical diagnosis, often leading to treatment delays and compromised recoveries. Manual diagnosis is resource-intensive and error-prone. To address these challenges, we develop DeepWristFNet, a compact convolutional architecture designed for end-to-end wrist fracture classification using a small dataset of 193 wrist X-ray images. The DeepWristFNet architecture integrates multi-scale convolutional operations with Fire and Shuffle modules within a compact network design, followed by fully connected layers for binary classification. We applied data pre-processing techniques such as data augmentation, image enhancement, and image resizing to increase the number of images, improve image quality, and resize images to match the DeepWristFNet input size. The proposed method comprised three phases. In the first phase, we trained, validated, and tested end-to-end and achieved validation and testing accuracies of 99.04% and 87.93%, respectively. Testing was performed on a hold-out subset of image instances that was kept separate from model development. The evaluated hold-out images originated from the same dataset distribution and included the corresponding augmented variants. In the second phase, we further evaluated the learned representation by extracting deep features from the first fully connected layer of DeepWristFNet. ReliefF was then used to select informative features, which were subsequently evaluated using 10 conventional machine learning classifiers. Out of 10 classifiers, 5 classifiers, i.e., Efficient linear SVM, quadratic SVM, Narrow NN, wide NN, and medium NN, achieved 100% testing accuracy on unseen samples. In the third phase, an auxiliary Fuzzy Inference System provides an intensity-based foreground-background representation of the X-ray images. This representation provides complementary visual information for interpretation but is not intended to directly classify or localize fractures. Grad-CAM is additionally used to visualize image regions contributing to the DeepWristFNet predictions, providing a model-specific explanation of the classification decision. Additionally, we evaluated how well the proposed DeepWristFNet approach performed against cutting-edge deep transfer learning models. In the evaluated experiments, DeepWristFNet outperformed the compared pre-trained deep learning architectures on the unseen hold-out subset from the same dataset distribution (test set). This study demonstrates the potential of DeepWristFNet for wrist fracture classification under a small-data setting. However, further evaluation on larger, independently collected clinical datasets is required to establish its robustness, generalizability, and suitability for clinical decision support. Full article

The World Bank’s Lighthouse Strategy identifies Morocco as a first mover exporter of green hydrogen and its derivatives to Europe; however, the engineering feasibility of the associated transport and storage network has not been quantitatively demonstrated. This study addresses that gap through an integrated spatial and multiperiod optimization framework that couples a spatially explicit Mixed Integer Linear Programming (MILP) model with a FlexSim/FloWorks digital twin for discrete event and hydraulic simulation. The MILP simultaneously optimizes electrolysis deployment, hydrogen storage technologies, and multimodal transport across a four node Moroccan export corridor (TanTan, Mohammedia, Jorf Lasfar, and Tanger Med) for the 2030, 2040, and 2050 planning horizons under a net present value objective. The optimal configuration combines a dedicated hydrogen backbone pipeline for the high volume production corridor with shortsea cabotage for the distribution branches, achieving a full chain levelized cost of ammonia (LCOA) of 1176 USD/t, consistent with the World Bank benchmark and reducing costs by 57 USD/t compared with an all cabotage configuration. The optimal network remains robust over a wide range of capital cost and financing assumptions, while the digital twin confirms the hydraulic and operational feasibility of the integrated pipeline–shipping system without critical port congestion. These findings demonstrate that combining optimization with digital twin validation provides a robust engineering basis for planning Morocco’s green hydrogen export infrastructure and supports investment decisions aligned with future CBAM compliant hydrogen and ammonia supply chains. Full article

Mobility-as-a-Service (MaaS) has emerged as a promising approach to urban transportation by integrating multiple mobility services into a single digital platform for trip planning, booking, and payment. More recently, Artificial Intelligence (AI) has expanded the capabilities of MaaS, enabling more efficient data processing, predictive analytics, personalized services, and intelligent decision support. This narrative review examines the current state of research on AI-enabled MaaS from both technological and socioeconomic perspectives. The analysis covers five major research areas: data integration and interoperability, predictive systems and demand forecasting, AI-enabled decision support for policy and planning, fairness and ethical AI, and cybersecurity and privacy protection. The findings show that successful implementation depends not only on advances in AI algorithms but also on high-quality interoperable data, effective governance, regulatory support, public trust, and collaboration among stakeholders. The review concludes that the main challenges facing AI-enabled MaaS are no longer primarily technical but organizational, institutional, and social. Future research should focus on trustworthy and explainable AI, privacy-preserving learning, standardized evaluation methods, fairness-aware optimization, resilient cybersecurity, and long-term assessments of MaaS impacts on sustainable urban mobility. Full article

Artificial intelligence (AI) and machine learning (ML) are transforming the landscape of rheumatological and systemic inflammatory disease management, offering unprecedented capacity to integrate complex, multidimensional data for diagnostic support, disease monitoring, and therapeutic decision-making. This comprehensive narrative review, based on a non-systematic literature search of PubMed/MEDLINE and Google Scholar combined with the authors’ clinical expertise, provides a clinically oriented synthesis of current and emerging AI applications across the full spectrum of immune-mediated inflammatory diseases—including rheumatoid arthritis, systemic lupus erythematosus, vasculitis, inflammatory bowel disease, psoriatic arthritis, systemic sclerosis, inflammatory myopathies, and sarcoidosis—with particular attention to applications that have demonstrated or are approaching clinical utility. We discuss deep learning-based image analysis, natural language processing of electronic health records, multi-omic biomarker discovery, and the application of Fourier transform-based signal processing to biological time series as a novel approach to continuous disease monitoring. Fourier transform methods—already foundational in MRI reconstruction, cardiac electrophysiology, and clinical neurophysiology—are here systematically extended to rheumatological and inflammatory disease signals, including accelerometry, electromyography, heart rate variability, and longitudinal biomarker time series. The phenomenon of large language model hallucination—particularly critical in rare inflammatory diseases—is addressed alongside retrieval-augmented generation as a mitigation strategy. We further argue that AI-driven methods do not merely improve the interpretation of clinical data, but fundamentally expand what is observable—with profound epistemological implications for clinical knowledge transmitted through generations of medical tradition. Ethical considerations and future directions toward precision inflammatory disease medicine are outlined. Full article

The time available for a counter-drone system to detect an unauthorized aircraft and determine an appropriate response can be limited. Although cloud processing remains useful for storage and offline analysis, communication delays may constrain its use in time-critical decision loops. This paper proposes EdgeTwin-DRL, an edge-assisted digital twin framework that integrates multimodal sensing and multi-agent deep reinforcement learning (DRL) for counter-drone detection and response optimization. The digital twin maintains a synchronized representation of the protected airspace using radar, electro-optical/infrared (EO/IR), radio-frequency (RF), and acoustic observations. This synchronized state is used by cooperative DRL actors to adjust computational-resource allocation, detection sensitivity, and candidate countermeasures, while a model-based forward-evaluation assesses proposed responses before they are passed to the simulated response pathway. The framework is evaluated in a simulation testbed in which the RF sensing models are calibrated and independently validated using publicly available datasets, while the remaining sensing components are parameterized using published experimental measurements. Within this calibrated simulation environment, EdgeTwin-DRL achieved a false-positive rate of 1.4% and reduced mean detection-to-response latency by up to 72% relative to the Cloud-DRL baseline and by 26% relative to the MAPPO baseline without calibrated, environment-dependent sensing under the communication and computational assumptions used in the simulator. The evaluation was conducted across modeled urban, suburban, and open-field conditions. These results demonstrate the comparative performance of the proposed architecture within the simulated environment and motivate further investigation of edge-assisted digital twins for counter-drone decision support. Hardware-in-the-loop and controlled field validation are required before operational deployment. Full article

Urban services increasingly depend on interconnected sensing, communication, computing, decision-support, and response functions, yet technical performance alone does not establish service effectiveness. This structured integrative review examines WSNs, WBANs, V2X systems, 5G-enabled edge–cloud infrastructures, and prospective 6G capabilities across health, mobility, environmental monitoring, energy, water, infrastructure safety, and climate resilience. A five-database search covering January 2018–March 2025 was supplemented by citation tracing and a documented gap-directed update with a final cutoff of 1 July 2026. The analytical corpus comprised 40 peer-reviewed studies, five deployment cases, and one scope-boundary case. An outcome-mediated perception–network–edge/cloud–decision–response framework enabled categorical comparison of heterogeneous evidence without pooling non-comparable measures. Attribution was classified as T1 (comparatively evaluated downstream outcome), T2 (technical or bounded operational outcome), or T3 (conceptual linkage): three studies were T1, 34 T2, and three T3. Ten studies supported target-level SDG alignment, whereas none reached indicator-level correspondence. Evidence remained concentrated at communication, processing, decision-support, and bounded operational endpoints, with limited assessment of response availability and disruption–recovery conditions. Among the deployment cases, only SFpark supported T1 interpretation. These findings characterize the selected corpus and identify a persistent gap between technical performance and comparative, longitudinal, and distributionally assessed urban-service outcomes. Full article

Timely and accurate classification of lung diseases from chest X-ray images remains an important healthcare challenge. Machine-learning and deep-learning methods can support automated classification, but their evaluation may be affected by class imbalance, feature redundancy, dataset leakage, and computational cost. This paper evaluates an artificial immune system (AIS)-evolved decision-tree ensemble using fold-specific ResNet18 features. All within-dataset experiments use duplicate-family-aware five-fold splits. Within each fold, standardization and adaptive principal component analysis (PCA) are fitted to the training features, and the Synthetic Minority Over-sampling Technique (SMOTE) is applied only to the reduced training data. Each candidate tree is assigned an affinity based on out-of-bag macro-F1. In the primary run, mean within-dataset macro-F1 was 98.07%, 99.48%, and 98.26% for Datasets 1–3, respectively, and 96.66% for the exploratory Dataset 4. Because of extensive cross-dataset image reuse and conflicting labels, Dataset 4 does not provide independent evidence of clinical lung-cancer detection. A five-seed repeated-initialization analysis repeated the complete fold-specific feature and classification pipeline while preserving the same folds. Mean macro-F1 differences between AIS and the prespecified static comparator for each dataset, calculated as AIS minus the comparator, were $-0.18$, 0.00, $-0.35$, and $-0.13$ percentage points for Datasets 1–4, respectively. Using the same sign convention, mean differences between AIS and the fixed random tree ensemble ranged from $-0.05$ to $+0.05$ percentage points. Population diagnostics showed that evolution improved individual-tree macro-F1 but reduced pairwise disagreement, without a consistent majority-vote gain. Median latency from an already decoded image to prediction ranged from 38.86 to 61.20 ms on one CPU thread and from 3.43 to 6.36 ms on an RTX 4090. The results do not establish a practically important or consistent predictive advantage from AIS evolution. The study provides a reproducible and duplicate-controlled framework for evaluating AIS-based tree ensembles. Full article

Polymer Additive Manufacturing (AM) offers substantial opportunities for material-efficient and distributed production, yet its transition towards genuine circularity remains constrained by cumulative material degradation, fragmented recovery strategies, and the limited integration of lifecycle, environmental, economic, and industrial considerations. This critical systematic review examines circularity in polymer AM beyond conventional end-of-life recycling by integrating material behaviour, manufacturing-induced evolution, degradation mechanisms, lifecycle performance and value retention, recovery pathways, and sustainability assessment within a unified systems perspective. Following a PRISMA-based selection process, 3214 records were progressively screened to a final corpus of 175 peer-reviewed studies published between 2015 and 2025. The evidence demonstrates that polymer circularity is not an intrinsic material property, but an emergent lifecycle outcome governed by polymer chemistry, manufacturing history, cumulative degradation, functional-value retention, waste-stream quality, recovery technology, infrastructure, and environmental and economic conditions. Based on this synthesis, the review introduces Intelligent Circular Polymer Additive Manufacturing (ICPAM), a lifecycle-wide framework integrating Design for Circularity, degradation-aware manufacturing, adaptive recovery, digital intelligence, industrial implementation, and continuous feedback. ICPAM further incorporates multi-criteria decision support for selecting context-dependent circular strategies and a qualitative/semi-quantitative maturity assessment for identifying lifecycle bottlenecks and implementation priorities. The resulting framework shifts polymer AM circularity from reactive waste management towards proactive lifecycle engineering, while recognizing that emerging regenerative materials and digital technologies still require substantial industrial validation before their full circular potential can be realized. Full article

Background/Objectives: Cesarean delivery has been linked with breastfeeding difficulties. However, the mechanistic pathways underlying these challenges remain incompletely understood. This systematic review aimed to synthesize evidence regarding the physiological, clinical, psychological, and health system-related mechanisms contributing to breastfeeding challenges following cesarean delivery. Methods: A systematic search was conducted in PubMed/MEDLINE and Scopus for studies published between 2020 and 2026. Studies with observational, interventional, and qualitative designs that examined breastfeeding challenges following cesarean delivery were eligible. A theory-informed narrative synthesis was applied, categorizing challenges into physiological, clinical, psychological, and health system domains and integrating them into a pathway-based conceptual framework. Results: Twenty-six studies were included. Across heterogeneous study designs, commonly reported breastfeeding challenges included delayed lactogenesis, postoperative pain and functional limitations, difficulties with early mother–infant interaction, concerns regarding human milk sufficiency, reduced breastfeeding self-efficacy, and variability in postpartum breastfeeding support. These findings were organized into physiological, clinical, psychological, and health-system domains. Cross-study synthesis suggested potential interactions among these domains, particularly involving delayed breastfeeding initiation, breastfeeding difficulties, lactation-related concerns, maternal perceptions, and subsequent feeding decisions. However, the proposed relationships were not uniformly tested within individual studies. Intervention studies suggested that strategies including skin-to-skin contact, breastfeeding counseling, and enhanced lactation support may improve selected breastfeeding outcomes. Conclusions: The findings suggest that breastfeeding challenges following cesarean delivery may involve interconnected biological, behavioral, clinical, and health-system factors. Understanding these mechanisms is essential for developing supportive and context-sensitive interventions that optimize breastfeeding establishment while maintaining adequate infant nutrition. Full article

Although large-scale wind and solar power provide green electricity, their intermittency and reverse-peak characteristics pose severe challenges to the secure operation of power systems. Pumped storage hydropower (PSH), as the most mature and economically attractive large-scale energy storage technology, enables temporal energy shifting and serves as a core flexible resource for smoothing renewable fluctuations and peak load shaving. In a new power system dominated by renewables, the reverse distribution between resources and loads gives rise to a typical “three-zone coexistence” pattern, i.e., renewable-rich zones, load centers, and hub zones coexist. However, existing research lacks in-depth modeling of zonal functional differences and fails to reveal the coupling mechanism between inter-zonal section constraints and the temporal energy behavior of pumped storage plants (PSPs). To address these gaps, this paper proposes a zonal-differentiated optimal dispatch model for multiple PSPs considering inter-zonal section constraints. The model establishes a “zonal role–PSP behavior” matching mechanism, assigning differentiated objectives and operational constraints to PSPs located in different zones, and thereby automatically generating charging/discharging strategies that match each zone’s functional positioning. It integrates section power flow constraints with the energy balance equations of PSPs in each zone into a unified framework, quantifying how section congestion restricts the “cross-zone energy shifting” efficiency of PSPs. Furthermore, a congestion-driven adaptive rule is derived from the above coupling framework. Case studies on a three-zone test system demonstrate that the proposed model effectively reduces wind and solar curtailment, alleviates section overloading, and lowers total operating costs, while the adaptive rule provides real-time decision support for dispatchers. The proposed model is applicable to power grids at various levels exhibiting the “three-zone coexistence” characteristic, offering theoretical support and a practical tool for the joint dispatch of multiple PSPs under high-penetration renewable energy integration. Full article

Model Predictive Control (MPC) offers a structured approach for autonomous-vehicle motion planning by optimizing predicted vehicle behavior over a finite horizon. As learning-based components become increasingly integrated into autonomous systems, interpretable interfaces between decision-making and control become increasingly important. Motivated by the need to examine how predictive depth should vary with maneuver context, this paper proposes and evaluates an adaptive phase-aware horizon-selection formulation within a hybrid Maximum Entropy Deep Inverse Reinforcement Learning-Model Predictive Control (MEDIRL-MPC) framework. The formulation conditions prediction depth explicitly on the recognized maneuver phase, providing an interpretable scheduling signal rather than maintaining the horizon as a globally fixed controller parameter. The considered architecture combines a MEDIRL-informed driving cost, a sampling-based Cross-Entropy Method planner, a kinematic vehicle model, and a scenario manager that identifies the current lane-change phase and provides the corresponding reference information. Controlled experiments are conducted in the CARLA simulator using a static-obstacle lane-change scenario. Fixed-horizon baselines, phase-wise analysis, common-state counterfactual comparisons, and controlled robustness experiments are used to evaluate the effect of prediction depth. The results indicate that the prediction horizon length greatly affects closed-loop behavior and computational demand, and that the relative suitability of different horizons varies across each scenario phase. The phase-aware policy further demonstrates that predictive depth can be allocated selectively across maneuver phases while preserving successful maneuver execution, and remains successful and lane-safe under controlled variations in target speed, obstacle distance, and activation distance. These findings support maneuver phase as an interpretable context for prediction-horizon adaptation within the evaluated architecture, while limiting the conclusions to the investigated scenario and experimental setting. Full article

Background: Perioperative antithrombotic management is a complex and high-risk aspect of anesthesia practice, requiring a careful balance between bleeding and thromboembolic risks. Large language models (LLMs) are increasingly explored for clinical decision support, but their reliability in complex perioperative antithrombotic scenarios remains uncertain. Methods: In this blinded, scenario-based study, 100 elective surgical cases, including grey-zone scenarios, were developed to reflect clinically relevant perioperative antithrombotic decisions. The European Society of Anaesthesiology and Intensive Care/European Society of Regional Anesthesia and Pain Therapy (ESAIC/ESRA) 2022 guideline was incorporated into the standardized prompt as a common framework for neuraxial safety and relevant antithrombotic interruption intervals. Four LLMs (GPT-5.4 Thinking, Claude Opus 4.5, DeepSeek v3.2 Reasoning and Qwen3.5-Plus) were evaluated using a standardized prompt. Model responses were presented without model identity and independently assessed by two experienced anesthesiologists, neither of whom was an author of the present study, across five clinical domains on a 5-point Likert scale, with a separate clinical applicability assessment. Results: Using a uniform four-domain composite across all 100 scenarios, overall expert-rated performance differed across models (Friedman χ² = 28.75, p < 0.001; Kendall’s W = 0.096). Claude had the highest median primary composite score, although its difference from DeepSeek was not statistically significant. Leave-one-domain-out sensitivity analyses showed that between-model separation was particularly sensitive to the clinical-rationale domain; exclusion of this domain reduced Kendall’s W to 0.030. A secondary five-domain analysis restricted to the 51 common-proceed scenarios, all of which were standard rather than grey-zone cases, also showed an overall between-model difference (Friedman χ² = 55.13, p < 0.001; Kendall’s W = 0.360). Because each scenario was queried only once, fine-grained differences in model ordering should be considered provisional. Although all models performed well in drug discontinuation decisions, greater variability was observed in discontinuation timing, anesthesia choice and clinical reasoning. Postponement behaviour varied substantially across models, with DeepSeek and Qwen recommending postponement more often than ChatGPT and Claude. Conclusions: LLMs show promise as supportive tools in perioperative decision-making, but their performance remains variable, especially in complex situations. At present, they should be used cautiously and always under expert supervision, rather than as independent decision-makers. Full article

Urban traffic environments can elevate physiological stress, yet most existing studies assess this indirectly through infrastructural or traffic-related proxies rather than direct physiological measurement. This pilot study proposes a geographic information system (GIS)–Analytical Hierarchy Process (AHP) framework that integrates wearable heart-rate sensing with spatial analysis to identify localized physiological activation patterns at urban intersections. The proposed framework is presented as a methodological proof-of-concept and is not yet validated as a decision-support tool; application to urban health assessment or smart-city planning would require testing on a substantially larger and independently sampled spatial dataset. Data were collected from ten participants across 118 repeated commuting passes by private automobile at six intersections in Kragujevac, Serbia. An AHP-weighted urban stress index combining heart rate, the traffic-intensity proxy, time of day, and acceleration events (CR = 0.0115) was computed and mapped using inverse-distance-weighted interpolation. A linear mixed-effects model showed a significant positive association between an ordinal, time-of-day-based traffic-intensity proxy and heart rate across the 118 passes (8.90 bpm per ordinal unit, p < 0.001); because this proxy is derived from time-of-day categories, the association is best interpreted as an exploratory time-of-day–heart-rate relationship rather than a validated causal effect of traffic, and a sensitivity analysis confirmed that the same three intersections ranked highest across alternative weighting scenarios. The results indicate a consistent spatial relationship between intersections associated with higher traffic-intensity proxy values and elevated physiological activation. Although based on a limited pilot-scale dataset, the proposed framework demonstrates the feasibility of combining wearable physiological sensing with GIS–AHP spatial analysis and offers a methodological proof-of-concept for smart-city and urban-health research in medium-sized cities, pending validation on larger, independently sampled spatial datasets. Full article