Search Results (2,475)

This review examines hydrogen-assisted fatigue crack growth rate (HA-FCGR) in pipeline steels with a focus on implications for integrity assessment of hydrogen transport systems. Existing natural gas pipelines offer a cost-effective pathway for hydrogen transmission; however, hydrogen embrittlement (HE) significantly alters fatigue behavior. This paper integrates scientometric analysis with a systematic review to evaluate the influence of material microstructure, welds, loading conditions, hydrogen pressure, and environmental variables on fatigue crack growth rates (FCGR). The synthesis confirms that HA-FCGR is most pronounced in the Paris region and is strongly governed by hydrogen pressure and loading frequency, while the role of material strength is less definitive than traditionally assumed. Recent advances in machine learning demonstrate strong predictive capability for FCGR; however, their integration into risk-based inspection and pipeline integrity frameworks remains limited. To bridge the gap between laboratory-scale understanding and field implementation, the concept of a near-real-world hydrogen pipeline testbed is introduced, enabling synchronized measurement of pressure cycling, material degradation, and system-level response. The review identifies critical research needs, including weld-focused fatigue datasets, realistic pressure-cycle validation, uncertainty-aware modeling, and integration of physics-based and data-driven approaches for decision-making. These findings provide a pathway toward reliable and scalable integrity assessment for hydrogen transport in existing pipeline infrastructure. Full article

This paper proposes a Machine Learning (ML) framework designed to attribute liability between drivers and pedestrians in traffic crashes. This study applies classification algorithms and interpretability techniques to analyze judicial rulings related to pedestrian crashes in Badajoz, Spain, from 2015 to 2024. The primary objective is to identify recurring crash patterns and determine liability levels for the parties involved. Several classification algorithms were evaluated, including Support Vector Machines (SVM), Neural Network (NN), Decision Trees (DT), Boosted Trees (BT), Naïve Bayes (NB), Random Forest (RF), K-Nearest Neighbors (K-NN), and Logistic Regression (LR). Among them, the quadratic-kernel SVM achieved the highest overall performance. To address the severe class imbalance of the data, stratified k-fold cross-validation and the Synthetic Minority Oversampling Technique (SMOTE) were applied to enhance the robustness and generalization capability of the model. A multiclass classification framework was implemented, and SHAP (SHapley Additive exPlanations) was integrated to improve interpretability by quantifying the contribution of each feature to the model’s predictions. The analysis identified critical factors that play a significant role in determining liability outcomes: driver license status, crash location, lighting conditions, reaction time, and the presence of drugs or alcohol. This research aims to contribute to the legal domain. While most existing studies have focused on predicting injury severity, few have addressed liability attribution. This is a multifactorial task that requires a comprehensive analysis of judicial decisions. The results demonstrate that machine learning-driven liability attribution can support judicial decision-making and provide valuable insights for the development of proactive urban traffic safety strategies. Full article

Gaussian process-based Bayesian optimization (BO) is a sample-efficient sequential strategy for optimizing expensive black-box functions. The Gaussian process provides a probabilistic approximation of the unknown function, while an acquisition function balances exploration and exploitation to select the next evaluation point. Despite significant research efforts, no master acquisition function has been identified. This paper proposes a novel adaptive acquisition function that dynamically adjusts the exploration–exploitation trade-off based on the evolution of the optimization process, rather than using fixed or random scheduling. While implemented here within a GP-based BO framework, the core switching mechanism is surrogate-agnostic: the exploitative component requires only a surrogate point prediction, and the explorative component is entirely model-free. Unlike traditional approaches, where mechanisms like UCB/LCB lean toward exploration over iterations, or fixed strategies that switch from exploratory (EI) to exploitative (PI) behavior at predetermined points, the proposed method makes purely exploitative decisions using only the GP’s prediction. However, it discards these decisions when they have low potential for significant improvement, instead focusing on uncertainty reduction. Notably, this approach uses inverse distance weighting for uncertainty quantification rather than the GP’s predictive uncertainty, avoiding bias from the GP’s predictions. Testing on benchmark functions demonstrates that the proposed acquisition function is almost always Pareto optimal, offering the most balanced trade-off between convergence to the global optimum and exploration capability compared to state-of-the-art alternatives. Full article

Long-Range Wide-Area Networks (LoRaWANs) combine long-range and low-power communication, making them a key technology for Internet of Things (IoT) applications. This systematic mapping study provides a comprehensive analysis of research on LoRaWAN network technology, focusing on performance and robustness optimization published between 2015 and 2026. Through a rigorous screening of2746 papers, we identified and analyzed 209 papers that met strict inclusion criteria and addressed network-layer optimization mechanisms. The studies were retrieved from IEEE Xplore, ACM Digital Library, SpringerLink, and Scopus using a PICO-based search strategy, and synthesized descriptively without effect-size meta-analysis. Our analysis reveals a rapidly growing research field, with 53.1% of the 209 included studies were published in the recent period (2023–2026), predominantly simulation-based evaluation approaches (72.2%), and strong geographic concentration in Europe (38.8%) and Asia (35.4%). We identified that performance optimization is the primary focus (96.2% of papers), while robustness optimization remains significantly underfocused (27.3% of papers), representing a critical research gap. This study identifies and prioritizes five research gaps, including the need for real-world field studies, multi-objective optimization frameworks, and lightweight machine learning approaches for edge devices. This mapping study provides structured guidance for future research in LoRaWAN optimization and supports evidence-based decision-making in the field. Full article

This study develops mixed-integer programming (MIP) models for workforce allocation and delivery service design under delivery-date commitment requirements in third-party logistics (3PL) systems facing demand surge conditions. Existing research on delivery commitments has largely focused on customer behavior or simplified operational settings, with limited attention to integrated optimization frameworks that jointly consider workforce assignment, service scheduling, and service differentiation in realistic logistics environments. To address this gap, two MIP models are proposed for free and fee-based delivery services, respectively, incorporating customer delivery-date preferences, workforce heterogeneity, multi-skilled labor allocation, and capacity constraints within a unified decision-making framework. A real-world case study from a Chinese 3PL provider is used to evaluate the models. Computational results show that the fee-based service design improves delivery commitment reliability, workforce utilization, and profitability compared with the free-delivery setting, particularly under high-demand and capacity-constrained conditions. The findings highlight the operational value of service differentiation and workforce flexibility, and provide a prescriptive analytics framework to support integrated delivery planning in modern logistics systems. Full article

Improving the environmental performance of ageing residential buildings requires design strategies that consider both thermal performance and material-related embodied impacts. This study provides early-stage, wall-level comparative evidence on the decarbonization potential of alternative external wall assemblies. A representative Hungarian “Kádár” Cube house is used as a reference to evaluate three configurations under identical geometric and climatic conditions: (i) the original uninsulated masonry wall, (ii) a masonry wall retrofitted with an External Thermal Insulation Composite System (ETICS), and (iii) a modular wall system based on Structural Insulated Panels (SIPs) applied in a morphology preserving reconstruction scenario. The analysis combines steady-state thermal transmittance (U-value) calculations with a cradle-to-gate (A1–A3) embodied-carbon assessment. Results show that both ETICS and SIP solutions substantially reduce heat loss compared to the baseline wall. The SIP configuration achieves the lowest U-value and cradle-to-gate embodied carbon due to its lightweight structure and reduced material mass. As the study focuses exclusively on wall-level A1–A3 impacts and steady-state thermal indicators, the findings support early design-stage decision-making rather than full building-level decarbonization modelling. The results highlight the importance of jointly considering thermal-transmittance performance and embodied impacts when comparing retrofit and modular reconstruction options for ageing residential buildings. Full article

U.S. renewable energy consumption has expanded substantially over the past five decades, but this transition cannot be adequately characterized by aggregate growth alone. This study developed an integrated empirical, forecasting, uncertainty, reconciliation, scenario, and planning framework for U.S. renewable energy consumption using a complete monthly multisector panel from January 1973 through December 2025. The analytic dataset contained 3180 sector–month observations across 636 monthly periods and five reporting sectors: Commercial, Electric Power, Industrial, Residential, and Transportation. The framework combined data harmonization, mutually exclusive source-family construction, long-run trend analysis, source-mix diversification metrics, structural-regime diagnostics, sector–source panel analysis, rolling-origin forecast benchmarking, probabilistic interval assessment, hierarchical reconciliation, future scenario analysis, and decision-focused planning evaluation. Annual reported total renewable energy consumption increased from 2475.547 trillion Btu in 1973 to 7050.214 trillion Btu in 2025, equivalent to approximately 2.476 quadrillion Btu and 7.050 quadrillion Btu, respectively. The results show that U.S. renewable energy growth was also a source-mix transformation: the portfolio became less concentrated as wind, solar, transportation biofuels, renewable diesel, waste, and other emerging sources gained importance alongside legacy wood and hydroelectric power. Sector–source heterogeneity was substantial, with Electric Power, Industrial, and Transportation showing distinct renewable-source profiles. Forecasting performance depended strongly on model family, horizon, validation window, target group, and evaluation lens. Strong statistical baselines and feature-based tree models remained competitive or superior to several deep learning architectures, while time-series foundation models provided useful modern comparators but required calibration and horizon-specific interpretation. All five selected foundation model comparators completed successfully. ChronosBolt was the fastest and strongest completed foundation model comparator, followed in runtime by TimesFM, Moirai/Uni2TS, TimeGPT, and LagLlama; however, foundation model forecasts remained too smooth for peak-sensitive planning and did not displace the strongest feature-based tree models in point-forecast benchmarking. Probabilistic diagnostics showed that nominal coverage alone was insufficient because interval width, Winkler score, CRPS, and visual inspection revealed target-specific miscalibration, underforecast bias, and weak peak coverage. Hierarchical and decision-focused evaluation changed the model-selection narrative: bottom-up and reconciled hierarchical forecasts produced stronger planning-loss and planning-value profiles than many nominally advanced alternatives, while selected tree-based models were particularly useful for preserving source-share allocation. Scenario analysis showed that solar acceleration increased projected totals but also increased concentration and coherence divergence, whereas diversification reduced concentration but required wider uncertainty buffers. Overall, U.S. renewable energy consumption should be analyzed as a dynamic, diversified, hierarchical, and planning-sensitive system. The proposed framework provides a reproducible basis for evaluating renewable energy growth, source-mix evolution, forecast reliability, uncertainty, source allocation, scenario trade-offs, and planning value beyond single-model forecasting claims. Full article

Background: Driven by Vision 2030, Saudi Arabia is rapidly integrating Artificial Intelligence into its healthcare ecosystem. This study investigates the patterns, predictors, and sociodemographic determinants of AI reliance and dependence in healthcare decision making, focusing on how trust influences the shift toward personalized digital diagnosis. Methods: A cross-sectional study was conducted with 627 adults in Saudi Arabia using convenience sampling. Data collected via online questionnaires were analyzed using JMP student edition version 18 software to evaluate user interaction with symptom checkers, wearables, and generative AI. A multidimensional framework assessed how trust and dependence influence health-seeking behaviors. Results: The findings reveal high AI engagement, with 63.7% of respondents using AI tools weekly. Conversational AI and LLMs are the dominant interfaces (92.2%), primarily serving as “gatekeepers” for personalized diagnosis (71.6%) and treatment suggestions (76.9%) before formal consultations. While gender significantly impacts reliance (p = 0.0037), trust was identified as the only significant predictor of overall engagement (p < 0.0001). Notably, age, education, and income had no statistical impact (p > 0.05), indicating uniform adoption across groups. Conclusions: For surveyed cohorts, trust is the primary determinant of AI reliance, overriding traditional demographic factors. Fostering user trust is essential for the successful implementation of AI-driven personalized healthcare solutions. Full article

Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R² of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R² increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments. Full article

This study investigates whether shareholder value creation, defined as beta-adjusted outperformance relative to a market benchmark, can be effectively predicted in an emerging market using a sequential machine learning framework. While prior research has predominantly focused on profitability forecasting or stock return prediction, the prediction of risk-adjusted shareholder value creation remains relatively underexplored, particularly in emerging economies such as Morocco. To address this gap, the study develops a predictive framework that combines market-based indicators, macroeconomic variables, and accounting fundamentals using only information realistically available to investors at each decision date. These variables are organized into firm-level temporal sequences based on a monthly decision-date panel of non-financial firms listed on the Casablanca Stock Exchange over the period 2010–2024. To capture nonlinear relationships and temporal dependencies in financial data, the empirical analysis compares baseline models with deep learning architectures, including GRU, LSTM, and CNN1D. The results indicate that deep learning models consistently outperform naïve and linear benchmark models, suggesting that shareholder value creation exhibits a measurable degree of predictability. With an AUC of 0.700 and a PR-AUC of 0.727, CNN1D achieves the strongest performance in the final evaluation setting and ranks as the best-performing model according to the primary AUC criterion. The findings also reveal that macroeconomic variables generate the strongest standalone predictive signal, whereas market-based variables exhibit comparatively weaker predictive power when considered in isolation. By extending financial prediction toward a risk-adjusted, benchmark-based, and investor-oriented framework, and by providing new empirical evidence on the value of temporal modeling and multi-source financial information for forecasting shareholder value creation in an emerging market context, this study contributes to the growing literature at the intersection of financial forecasting and artificial intelligence. Full article

Background: Community-based mental health promotion has gained increasing relevance as a strategy to strengthen population well-being and complement formal healthcare services. However, existing initiatives remain fragmented, and their integration into health systems and public policy frameworks has not been systematically examined. This scoping review aimed to map community-based mental health promotion strategies and analyze their alignment with public health systems and policy frameworks. Methods: A scoping review was conducted following the Joanna Briggs Institute methodology and reported according to the PRISMA-ScR guidelines. Searches were conducted in April 2025 across major databases, including Scopus and PubMed, covering studies published between 1990 and 2024. The retrieved records were subsequently reviewed and analyzed by the researchers between 1 May 2025 and September 2025 Documents published after 2024 were used only as contextual or policy references and were not included in the review corpus. Eligibility criteria were defined using the Population–Concept–Context framework. Two reviewers independently screened records and extracted data. Results: A total of 3799 records were identified, of which 76 studies met the inclusion criteria. Most interventions were implemented in school (18.4%) and community (21.1%) settings and focused on strengthening psychosocial skills, social support, and resilience. Common intervention components included community participation, cultural adaptation, and facilitator training. Several strategies were linked to broader public health frameworks, such as primary health care, intersectoral action, and social determinants of health. Reported outcomes were generally positive, although evaluation methods and indicators varied widely. Conclusions: Community-based mental health promotion interventions represent a valuable complement to healthcare systems, particularly in resource-constrained settings. Strengthening their integration into public policies and health system planning may improve sustainability, equity, and population impact. This review highlights key gaps in implementation and evaluation and provides evidence to inform decision-making in community health, prevention, and mental health policy development. Full article

Background/Objectives: Although Hospital Information Systems (HIS) are essential to the provision of contemporary healthcare, clinical professionals’ use of HIS is still uneven. Robust healthcare decision-making is based on the systematic collection, storage, and analysis of health data, and it is crucial to comprehend the elements that promote or impede adoption. In a tertiary-care multi-specialty hospital in Chennai, India, this study sought to evaluate the role of the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—on the Behavioural Intention of healthcare professionals to adopt HIS. Methods: 140 medical professionals (physicians, nurses, and hospital technicians) from a 750-bed teaching hospital where HIS had been in use for at least 24 months were chosen by stratified random sampling to participate in a descriptive, cross-sectional study. The original UTAUT instrument was modified into a structured, self-administered questionnaire using a validated 5-point Likert scale. Expert review was used to demonstrate face validity, while Cronbach’s Alpha (α > 0.70) was carried out. Statistical analysis methods included Pearson correlation, multiple linear regression, one-way ANOVA with Tukey’s HSD post hoc analysis, and Structural Equation Modelling (SEM). Results: The majority of responders in the sample were female (51.5%), primarily nurses (47%), and had less than five years of work experience (36%). All four UTAUT constructs were found to be significantly correlated with Behavioural Intention by Pearson correlation, with Performance Expectancy showing the strongest association. The structural model explained a significant proportion of the variance in technology adoption. Multiple regression analysis indicated that Performance Expectancy (β = 0.480, p < 0.01) and Social Influence (β = 0.180, p < 0.05) were significant positive predictors of Behavioural Intention. Confirmatory Factor Analysis verified acceptable measurement boundaries (χ²/df = 1.42, RMSEA = 0.043, SRMR = 0.062, CFI = 0.94. An exploratory one-way ANOVA revealed that perceptions of Facilitating Conditions differed significantly by professional designation (F (2, 137) = 6.42, p = 0.002), with nurses scoring significantly lower than physicians (p = 0.002) and technicians (p = 0.011). Conclusions: Performance Expectancy is the main driver of healthcare professionals’ Behavioural Intention to adopt HIS. Compared to doctors and technical professionals, nurses reported considerably lower perceptions of Facilitating Conditions, indicating a substantial support gap. In order to close the clinical digital gap and enhance patient safety, these findings advocate for role-specific infrastructure investments and focused implementation techniques. Full article

Understanding vegetation sustainability is crucial for ensuring ecological security in dryland interior river systems. Focusing on the Manas River Basin in Xinjiang, our research extracted Landsat time-series data from 2000 to 2024 via Google Earth Engine, employing statistical approaches alongside Geodetector modeling to quantitatively evaluate the spatiotemporal dynamics of vegetation sustainability and its influencing factors. Our findings reveal that the basin’s Normalized Difference Vegetation Index (NDVI) displayed a significant upward trajectory (Sen’s slope = 0.010/yr, R² = 0.95, p < 0.01), with distinct temporal phases: the period 2000–2013 was characterized by rapid oasis expansion driven by cultivated land, while the period 2014–2024 was characterized by systematic vegetation improvement with a stabilizing land use pattern. Spatially, areas exhibiting extremely significant improvement accounted for 56.24% of the total basin area (concentrated mainly in artificial oases and the mid-mountain zone), and non-significant degradation accounted for only 1.89%. Land use type and soil texture were identified as the dominant spatial differentiation factors, followed by annual precipitation, with all pairwise factor interactions exhibiting enhancement effects. By identifying the optimal thresholds for vegetation growth (annual average temperature of 0.82–3.96 °C, elevation of 1826–2598 m, and loamy sand), this study defines the boundaries for sustainable vegetation development. These findings deliver a theoretical foundation for zonation management and habitat rehabilitation planning, supplying decision-making support for safeguarding regional ecological security and fostering sustainable development of oasis systems in arid Central Asia. Full article

Early prediction of academic outcomes is vital to enabling timely intervention, supporting at-risk students, and improving educational planning and institutional performance. However, this task becomes particularly challenging when data availability is limited, such as in small or graduate-level programs. This study explores the potential of data augmentation techniques, specifically the Synthetic Minority Oversampling Technique, to enhance the performance of machine learning models applied to such constrained educational datasets. We conduct a comparative analysis using four datasets derived from prior research, each representing a distinct educational use case: one focused on predicting academic success in graduate programs, another on student dropout in virtual learning environments, a third on dissertation performance prediction, and a fourth addressing multi-class performance prediction in undergraduate coding courses. By applying consistent machine learning methods in the original and augmented datasets, we systematically evaluate the impact of data augmentation on classification performance using accuracy, precision, recall, and the F1 score. The results demonstrate marked improvements, with accuracy increases up to 21% and precision gains exceeding 25% in some models, notably with KNN and MLP. While not all algorithms benefit equally, our findings highlight data augmentation as a practical and impactful strategy for improving early prediction capabilities in Educational Data Mining (EDM). By leveraging multiple datasets and diverse educational contexts, this contribution provides robust evidence supporting the broader goal of enhancing decision-making and personalized support in digital learning environments. Full article

Background/Objectives: Atrial fibrillation (AF) imposes a substantial long-term clinical and healthcare system burden, recurrent hospitalizations, impaired quality of life, and increasing long-term healthcare costs. Although patient-reported outcome measures (PROMs) are increasingly used in AF research and clinical practice, their broader role in healthcare delivery, implementation, and system-level decision-making remains insufficiently defined. Existing assessment strategies frequently prioritize symptom burden while underrepresenting cognitive, emotional, social, and functional dimensions of AF-related impairment. This narrative implementation review examines the current role of PROMs in AF management from a healthcare system and implementation perspective. Methods: Literature addressing AF-specific and generic PROM instruments, implementation strategies, health system integration, value-based care, and digital health approaches was reviewed and synthesized across PubMed, Scopus, and Google Scholar. Particular emphasis was placed on implementation barriers, workflow integration, evidence strength, and challenges encountered across diverse healthcare settings. Results: Current PROM frameworks incompletely capture several important dimensions of AF burden, including cognitive dysfunction, sleep disturbance, emotional distress, social participation, sexual health, and productivity loss. Beyond conventional symptom assessment, PROMs may support longitudinal patient monitoring, treatment evaluation, shared decision-making, and patient-centred care. Emerging evidence also suggests potential roles in outpatient prioritization, healthcare quality assessment, and value-based healthcare initiatives, although prospective AF-specific implementation studies remain limited. Mapping PROM applications to the 2024 ESC AF-CARE pathway demonstrates the strongest alignment with the Evaluation and Reducing symptoms domains while supporting patient engagement, comorbidity management, and individualized care planning. Implementation remains constrained by clinician workload, questionnaire fatigue, limited interoperability, heterogeneous digital infrastructure, and variability in organizational resources, with these challenges potentially being more pronounced in smaller or resource-limited healthcare systems. Conclusions: PROM integration in AF care may provide opportunities to strengthen patient-centered management and improve healthcare system responsiveness beyond conventional rhythm- and symptom-focused approaches. Successful implementation may require careful adaptation to local healthcare infrastructure, workflow feasibility, and long-term sustainability. Future developments involving digital platforms, wearable technologies, and artificial intelligence-assisted interpretation may further expand the clinical and operational relevance of PROM-guided AF care. Full article