Search Results (24,468)

Accurate reliability assessment of wind turbine gears is important for reducing maintenance costs and improving the sustainability of wind turbine gearbox operation. This study proposes a reliability modeling framework centered on shock–degradation dependence and a time-varying degradation failure threshold. First, a mapping relationship between shock signals and stress is established. Subsequently, a state-dependent shock-induced degradation increment model is introduced to describe the bidirectional mechanism by which shocks accelerate degradation and degradation states amplify shock-induced damage. Furthermore, a time-varying degradation failure threshold model is developed to characterize the dynamic attenuation of the gear load-bearing boundary during service. On this basis, a hybrid Copula function is employed to establish the joint reliability model. A numerical case study was conducted on the sun gear of a 2 MW wind turbine gearbox. The results show that, under the specified numerical operating conditions and model parameter settings, the proposed model under the fixed-threshold condition predicts a relative increase of approximately 6.1% in failure probability compared with the model assuming independent shock and degradation processes. When the time-varying degradation failure threshold is considered, the failure probability shows a relative increase of approximately 30% compared with that under the fixed-threshold condition. The proposed model can effectively characterize the failure evolution of wind turbine gears under complex loading conditions and provide theoretical support for predictive maintenance. Full article

Background: Preventing mobility-related loss of independence (LoI) is a critical public health priority. While the drivers of LoI are complex, this study examines how personal, social, and environmental factors influence these outcomes to identify actionable preventive targets. We applied the International Classification of Functioning, Disability, and Health (ICF) framework to examine these pathways across racial and ethnic groups. Methods: Using 2023 National Health and Aging Trends Study (NHATS) data (N = 7106), we conducted path analyses among non-Hispanic (NH) White, NH Black, and Hispanic older adults. We assessed associations between health conditions, body function, physical performance, social participation, and environmental factors. Results: The ICF-based model explained 36.6–38.2% of LoI variance. While physical performance was the strongest direct predictor of independence across all groups, indirect pathways varied. Social participation restrictions are associated with higher LoI score among NH White and Black adults. For Hispanic adults, limited English proficiency functioned as a key environmental variable negatively associated with body function and physical performance. Additionally, shared living arrangements among NH Black participants were negatively associated with physical performance. By gender, only NH White females exhibited associations with better physical and social outcomes. Conclusions: Pathways to LoI are associated with group-specific structural factors. To effectively prevent functional decline, healthcare policies should move beyond generic services toward culturally responsive interventions. Prioritizing targeted lifestyle behaviors—specifically social engagement for NH Black populations and linguistic accessibility for Hispanic adults—may help reduce health inequities and proactively preserve independence. Full article

Background: Artificial intelligence (AI) is increasingly being integrated into radiology and may improve diagnostic efficiency and workflows. Its successful adoption depends on workforce readiness, organizational capacity, and effective governance. This study evaluated radiology professionals’ perceptions of AI integration in Saudi Arabia; sustainability was considered only as a conceptual implementation context and was not directly measured. Methods: An online cross-sectional survey using convenience sampling was conducted among healthcare professionals involved in radiology services in Saudi Arabia, using an expert-reviewed and pilot-tested 30-item questionnaire covering knowledge, attitudes, implementation readiness, and perceived barriers. Responses from 295 healthcare professionals were analyzed using descriptive statistics, Cronbach’s alpha, Spearman correlation, and Kruskal–Wallis testing. Results: Participants demonstrated positive attitudes toward AI integration (3.57 ± 0.69) and moderate knowledge (3.35 ± 0.70), whereas implementation readiness was comparatively lower (3.04 ± 0.79). Perceived barriers showed the highest domain score (3.67 ± 0.64). Major barriers included implementation costs (3.98 ± 0.71), limited digital infrastructure (3.90 ± 0.75), insufficient staff training (3.84 ± 0.77), and lack of technical expertise (3.82 ± 0.78). Positive correlations between knowledge, attitudes, and implementation readiness were observed. Conclusions: Among participating respondents, support for AI integration was generally favorable, but lower perceived institutional readiness and organizational, infrastructural, and governance barriers may constrain implementation. Because the convenience sample lacked a sampling frame and a calculable response rate, the results should not be interpreted as national estimates. The survey also did not measure clinical, economic, resource-use, or environmental outcomes and therefore does not establish sustainability benefits. Future probability-based and implementation studies should evaluate representativeness and these outcomes directly. Full article

This study presents the development of an Artificial Intelligence (AI)-based legal assistant using the Retrieval-Augmented Generation (RAG) architecture to provide legal assistance to citizens of the Republic of Kazakhstan. The proposed solution is designed to generate accurate, evidence-based responses to user queries using the regulatory legal acts of the Republic of Kazakhstan as the primary source of information. A legal corpus comprising 101,000 legislative documents and court decisions, with approximately 77 million tokens in Kazakh and Russian, was constructed to support the retrieval component of the system. To identify the most effective semantic retrieval method, three multilingual embedding models—Multilingual-E5-Large, BGE-M3, and KazEmbed-V5—were evaluated for vector search. The experimental results showed retrieval accuracies of 87.6%, 76.8%, and 83.3%, respectively. The GPT-5.4 and Llama-4-Scout-17B-16E-Instruct large language models were used to generate legal reasoning and responses based on documents retrieved through semantic search. The quality of the generated responses was evaluated using two complementary approaches. First, legal experts assessed the factual correctness and legal validity of the answers. Second, automatic evaluation was performed using word-level F1, BLEU, ROUGE, and BERTScore-F1 metrics. Among all evaluated configurations, GPT-5.4 combined with Multilingual-E5-Large achieved the highest overall accuracy (88.5%), whereas Llama-4-Scout-17B-16E-Instruct combined with KazEmbed-V5 achieved an accuracy of 83.6%. Based on the proposed architecture and the selected semantic retrieval and language models, an AI legal assistant was developed and integrated into the “Adal Azamat” legal services platform providing users in Kazakhstan with practical access to AI-assisted legal consultation. Full article

Introduction: Chemotherapy-induced peripheral neuropathy (CIPN) is a common and dose-limiting toxicity in child, adolescent, and young adult (CAYA) oncology populations. Despite its clinical impact, objective biomarkers for early detection and monitoring remain limited. Neurofilament light chain (NfL), a marker of axonal injury, has emerged as a potential circulating biomarker of CIPN in adults and potentially for CAYAs. This pilot study evaluates the association between NfL and patient-reported CIPN severity in CAYAs. Methods: We conducted a prospective pilot study of 26 patients with acute lymphoblastic leukemia or lymphoma. CIPN was assessed using FACT-GOG/NTx scores. Linear mixed-effects models evaluated associations between NfL and neuropathy over time, adjusting for age and time from baseline. Logistic mixed models assessed the relationship between NfL and clinically significant neuropathy (FACT-GOG/NTx ≤ 40). Results: NfL was significantly associated with worsening neuropathy. A 50-unit increase in NfL corresponded to a 1.1-point decrease in FACT-GOG/NTx score (p < 0.001). Each doubling of NfL was associated with a 0.61-point decrease in FACT-GOG/NTx (p < 0.001). Higher NfL levels increased odds of neuropathy (OR 4.62, p < 0.001). Associations were strongest in leukemia patients and not observed in Hodgkin lymphoma when separately analyzed. Conclusions: This pilot study demonstrates that circulating NfL correlates with patient-reported neuropathy severity, supporting its role as a potential biomarker for CIPN in CAYAs. Differences between leukemia and lymphoma cohorts may reflect treatment-specific neurotoxicity patterns and should be validated in larger prospective studies. Limitations include small sample size, heterogeneity, and limited power for subgroup analyses. If validated, NfL could be incorporated into routine toxicity monitoring to identify patients at highest risk for progressive CIPN, enabling earlier supportive care interventions, referral to rehabilitation services, or enrollment in biomarker-guided prevention and treatment trials before irreversible nerve injury occurs. Full article

With the widespread adoption of cloud computing technology, modern cloud platforms have become increasingly complex and dynamic, posing significant challenges for efficient resource management. Accurate forecasting of cloud resource loads has therefore become essential for improving service quality, optimizing resource utilization, and reducing operational costs. To address the intrinsic characteristics of cloud load time series, including nonlinear fluctuations, multi-scale temporal dependencies, and redundant high-dimensional features, this paper proposes the MST-iTransformer model, which integrates multi-scale temporal encoding, sparse attention, and adaptive feature selection mechanisms. Specifically, a multi-scale temporal encoding module is developed to capture and fuse temporal dependencies across multiple periodic scales. Furthermore, an adaptive feature selection module is introduced to dynamically assign importance weights to resource features, enhancing informative variables while suppressing redundant ones. Meanwhile, a sparse attention mechanism is incorporated to reduce computational overhead while maintaining forecasting accuracy. The proposed model is evaluated on the Alibaba Cluster Trace dataset. Experimental results demonstrate that MST-iTransformer achieves MSE, RMSE, and MAE values of 0.5559, 0.7456, and 0.4968, respectively. Compared with the original iTransformer, the proposed model achieves simultaneous reductions in prediction errors and inference latency, validating the effectiveness of the multi-scale temporal encoding, sparse attention mechanism, and adaptive feature selection modules in improving forecasting accuracy and computational efficiency. These improvements provide reliable prediction support for resource scheduling and elastic scaling in cloud data centers. Full article

Multi-energy virtual power plants (VPPs) aggregate electricity, heating, and natural gas resources to provide flexible regulation services to the external power grid. Their operational flexibility, however, cannot be accurately characterized using equipment capacities or single-period energy balances alone, because district heating and natural gas networks introduce heat transport delays, pipeline thermal storage, pressure dynamics, and linepack effects. This paper proposes an operational flexibility boundary assessment method for electricity–heating–gas VPPs based on a dynamic energy circuit model (ECM). The frequency-domain ECM converts heating-network temperature dynamics and gas-network pressure dynamics into algebraic constraints, which are integrated with electric-network and multi-energy coupling-device constraints. The net exchange power at the point of common coupling (PCC) is used as the external flexibility interface, and the period-wise upper and lower boundaries are determined subject to network and device constraints, terminal-state recovery requirements, and an economic feasibility limit. Case studies on an electricity–heating–gas VPP demonstrate that the dynamic ECM captures the intertemporal regulation capability provided by pipeline thermal storage and gas-network linepack. Compared with the static model, the dynamic ECM exhibits consistently greater downward flexibility and comparable or lower upward flexibility in several periods, thereby correcting the underestimation of electrical absorption capability and the optimistic estimation of power-export capability caused by the static approximation. The economic feasibility constraint further excludes high-cost boundary schedules, yielding a technically feasible and economically acceptable flexibility range. Full article

Contemporary urban development imposes an increasingly pronounced need for aligned spatial transformations with the preservation of natural resources, adaptation to climate change, and the improvement of quality of life. In this context, the concept of green infrastructure has been recognized as an integrative framework that connects ecological processes with spatial and urban planning. However, the ways in which green infrastructure is strategically articulated and operationalized through planning processes vary depending on the institutional and spatial context. The aim of this paper is to identify the dominant strategic approaches to green infrastructure planning and to examine their implications for spatial and urban planning. The research is based on a qualitative comparative analysis of selected European green infrastructure strategies, while the Green Infrastructure Strategy of the City of Belgrade is considered as an analytical case study within the specific institutional and planning context of Serbia. The analysis focuses on three key components of strategic articulation—visions, objectives, and measures—in order to identify recurring patterns that shape the integration of green infrastructure into planning practice. The results indicate a high degree of convergence among strategic approaches, particularly regarding the emphasis on quality of life, ecosystem services, biodiversity conservation, and the strengthening of climate resilience. The identified patterns indicate the need for transformation of regulatory frameworks, the improvement of planning procedures and instruments, and the adoption of a spatial logic based on connectivity and multifunctionality. The analytical examination of the Belgrade Strategy shows that its structure of visions, objectives, and measures encompasses the key regulatory, procedural, and spatial prerequisites for incorporating green infrastructure into planning practice, and at the same time, its operationalization depends on institutional capacities, information systems, financial instruments, and mechanisms of intersectoral coordination. The paper concludes that green infrastructure strategies play an important role in mediating between public policies and spatial planning solutions, while their effectiveness depends on the alignment of regulatory, procedural, and spatial instruments. Full article

This study aimed to identify socioenvironmental vulnerability profiles among Mexican households and analyze their association with health expenditure. Data from 86,102 households included in the 2024 National Household Income and Expenditure Survey were analyzed. Socioenvironmental profiles were constructed using housing, basic services, sanitation, household energy, socioeconomic stratum, and overcrowding through factor analysis of mixed data and k-means. Internal validation, stability analyses, algorithm comparisons, and sensitivity analyses supported a three-profile solution representing low, intermediate, and high vulnerability. Health expenditure was examined using survey-weighted descriptive estimates, exploratory nonparametric comparisons, and a survey-adjusted two-part model. The intermediate vulnerability profile showed higher odds of reporting health expenditure than the low vulnerability profile (OR = 1.129, 95% CI: 1.048 to 1.217, p = 0.002), whereas the high vulnerability profile showed no significant difference. Among households with positive expenditure, high vulnerability was associated with lower logarithmic expenditure ($\beta =-0.341$, 95% CI: $-0.490$ to $-0.192$, p < $0.001$), whereas the intermediate profile was not significantly different in the main model. The association for high vulnerability remained significant in sensitivity analyses, while results for the intermediate profile were more sensitive to model specification and income adjustment. Despite several statistically significant associations, effect sizes in the exploratory comparisons were small and the regression models explained a limited proportion of the variability in health expenditure. The findings therefore indicate modest associations between socioenvironmental vulnerability and health expenditure, with household economic resources and other unmeasured health-related factors likely contributing to the observed differences. Full article

Cloud task scheduling is a critical component of cloud computing systems because it directly affects resource allocation, workload distribution, execution efficiency, and service cost. However, many metaheuristic algorithms suffer from population diversity loss, premature convergence, and an inadequate balance between global exploration and local exploitation when solving complex and large-scale optimization problems. To address these limitations, this study develops an Enhanced Human Urbanization Algorithm (EHUA) for numerical optimization and cloud task scheduling. Inspired by the collaborative resource-allocation behavior of modern logistics networks, three coordinated mechanisms are reformulated within the adventurer–city–citizen structure of the original Human Urbanization Algorithm: a logistics-hub-guided adaptive exploration mechanism, a supply–demand-based dynamic redistribution mechanism, and a cooperative logistics delivery exploitation mechanism. These mechanisms reduce excessive dependence on a single capital, adaptively regulate city search ranges, and strengthen citizen-level solution refinement. The performance of EHUA is evaluated on the CEC2014 and CEC2020 benchmark suites using convergence analysis, box plots, numerical statistics, Wilcoxon signed-rank tests, Friedman rankings, and ablation experiments. EHUA obtains the best mean fitness values on 20 of the 30 CEC2014 functions under both 30- and 50-dimensional settings, on 8 of the 10 CEC2020 functions at 10 dimensions, and on all 10 functions at 20 dimensions, demonstrating strong overall competitiveness and repeatability without implying universal superiority on every problem. EHUA is further applied to cloud task scheduling under workload scales ranging from 100 to 10,000 tasks. Considering comprehensive cost, monetary cost, execution time, and load cost, the proposed method consistently achieves low comprehensive scheduling costs and maintains favorable trade-offs among individual objectives as the workload increases. These results indicate that EHUA provides an effective and scalable optimization framework for complex benchmark problems and cloud task scheduling applications. Full article

Background: Mental health disorders are an increasing concern in Saudi Arabia, including in the Eastern Province, where one study found that of the 42.5% of medical students who reported a clear need for mental health services, only 16.2% had actually used them. Stigma and other psychosocial factors are thought to shape help-seeking behavior, yet comparative studies remain scarce. This study compares attitudes toward seeking professional psychological help, and perceived stigma, between students in health-related and non-health-related programmes at King Faisal University. Methods: In this cross-sectional study, 589 students at King Faisal University, Saudi Arabia, were recruited through convenience sampling. Data were collected with an online, self-administered questionnaire covering sociodemographic characteristics and three validated Arabic instruments: the Attitudes Toward Seeking Professional Psychological Help–Short Form (ATSPPH-SF), the Stigma Scale for Receiving Psychological Help (SSRPH), and the Hopkins Symptom Checklist-25 (HSCL-25). Results: Of the 589 respondents, 580 (98.5%) were included in the analysis—279 (48.1%) health-related and 301 (51.9%) non-health-related students. Most were aged 18–21 (76.9%), Saudi nationals (96.9%), single (91.9%), and of middle income (79.2%). Attitudes toward seeking professional psychological help did not differ significantly between health-related and non-health-related students (Welch’s t(566) = −1.48, p = 0.138, Cohen’s d = −0.12, a negligible effect). Perceived stigma (SSRPH) was significantly higher among health-related students (10.85 vs. 10.16; t(578) = 2.56, p = 0.011). Perceived social stigma was inversely and significantly associated with attitudes toward seeking professional psychological help (r = −0.262, p < 0.001). Together, stigma and psychological distress (HSCL-25) explained a modest 7.9% of the variance in help-seeking attitudes (R² = 0.079). Conclusions: Health-related and non-health-related students at this single university did not differ significantly in their attitudes toward seeking professional psychological help. Perceived stigma showed the stronger association with less favorable attitudes, whereas psychological distress was associated with a smaller, positive association. These findings, from a single-center convenience sample, point to a need for stigma-reduction efforts and confidential mental health services at King Faisal University. Full article

Electric-shovel top sheave bearings with sensor-embedded slots operate under harsh service loads, making them prone to fatigue crack initiation and propagation. Accurate predictions of crack growth within the bearing body are therefore essential for intelligent bearing design and reliability assessments because the bearing integrity directly affects shovel service life and safety. This paper presents a sub-modeling-based method that embeds initial cracks while preserving actual roller-ring boundary conditions and ensuring computational efficiency via adaptive mesh refinement. A global model first identifies critical crack-prone zones, after which the sub-model systematically examines the effects of the initial crack angle and sensor-embedded slot depth on the propagation behavior. The results indicate that both factors significantly increased the stress intensity factor (SIF). Among the evaluated designs, the 15 mm -deep slot produced the highest SIFs and the shortest predicted crack-propagation life, indicating that slot depth was a key design parameter under the investigated conditions. The findings provide theoretical support for the structural design and fatigue evaluation of intelligent electric-shovel top sheave bearings. Full article

Ni–Mo alloy claddings were fabricated on low-carbon steel substrates using powder metallurgy combined with hot isostatic pressing (PM-HIP). The interfacial microstructure, elemental interdiffusion, phase composition and microhardness distribution of the bimetallic composite were investigated systematically. The results show that sound metallurgical bonding without pores, cracks and element dilution is achieved under the HIP process of 1100 °C, 120 MPa and 4 h holding time. Interdiffusion of Fe, Ni and Mo atoms forms a 20–50 μm thick interfacial transition layer, and Mo exhibits a relatively low diffusion capacity due to its large atomic radius. Two intermetallic phases, Ni₄Mo and NiMo, are formed in the cladding layer because of the inhomogeneous distribution of Mo. A prominent microhardness gradient is observed throughout the composite, and the interfacial layer presents the highest hardness of 905 HV resulting from multiple strengthening mechanisms. As an effective alternative to traditional welding and cladding technologies, the PM-HIP process exhibits great potential for manufacturing complex bimetallic components with prospective service prospects in severe corrosive environments. Full article

High Bandwidth Memory couples many DRAM dies to a host through independent channels, and a memory controller presents each channel to the workload as either available or isolated. This paper takes that binary service interface as the modeling primitive and builds a closed-form framework for evaluating stack and system behavior on top of it. Each die is treated as a binary component whose reliability is composed from DRAM, through-silicon via and micro-bump contributions, with the via bundle itself modeled as a threshold subsystem; the dies are then aggregated as a threshold structure over the stack, and stacks are aggregated over the system. The result is an evaluation whose cost grows linearly rather than exponentially with the number of dies, and which yields not only reliability but the full distribution of delivered bandwidth, its moments, and the sensitivity of system availability to each component. Three design questions are answered directly: where to direct reliability investment, how many stacks to provision for a given availability target, and which bandwidth threshold minimizes cost when bandwidth and reliability requirements are imposed together. The approximations the framework makes are bounded rather than assumed. A three-state baseline quantifies the error introduced by the binary representation and shows it is governed by a single measurable quantity, and distribution-free inequalities bound the effect of correlation among component failure mechanisms, which proves negligible in the regime where HBM parts are qualified. Application to a representative stack identifies DRAM cell reliability as the dominant bottleneck and shows that a single spare via per bundle is sufficient at typical defect rates. Full article

Background: Effective opioid-sparing analgesia after median sternotomy remains an unmet need in cardiac surgery. We evaluated whether bilateral intercostal cryoanalgesia, added to standard multimodal analgesia, was associated with postoperative pain, opioid consumption, recovery and early hospital-resource use after isolated coronary artery bypass grafting (CABG). Methods: This single-centre, non-randomised comparative service evaluation included 60 patients undergoing isolated CABG through median sternotomy between 1 November 2025 and 30 May 2026 (30 cryoanalgesia; 30 standard care). The prespecified primary endpoint was cumulative movement-evoked pain burden, quantified as the area under the curve (AUC) for scores recorded on postoperative days 1–3. Secondary endpoints included rest-pain AUC, opioid consumption expressed as oral morphine equivalents (OME), time to first bowel opening, postoperative length of stay and an exploratory break-even calculation. Results: Cryoanalgesia was associated with lower movement-pain AUC (adjusted mean difference −3.56 score-days, 95% confidence interval [CI] −4.62 to −2.50; p < 0.001) and rest-pain AUC (−3.16 score-days, 95% CI −4.19 to −2.14; p < 0.001). Total observed opioid consumption was lower by 112.9 mg OME (95% CI −192.8 to −33.1; p = 0.006), length of stay by 1.32 days (95% CI −1.93 to −0.71; p < 0.001), and time to first bowel opening by 0.49 days (95% CI −0.92 to −0.06; p = 0.027). Conclusions: In this small non-randomised evaluation, bilateral intercostal cryoanalgesia was associated with lower early pain and opioid exposure and faster recovery. Selection, temporal and residual confounding preclude causal or cost-effectiveness conclusions. Prospective randomised evaluation with fixed observation periods and longer-term safety follow-up is required. Full article