Search Results (1,481)

Facade thermal defect diagnosis is a critical prerequisite for energy-efficiency retrofitting of old residential buildings. However, conventional infrared thermography is easily affected by environmental conditions and occupant behavior, making it difficult to distinguish persistent thermal defects from transient anomalies. To address this challenge, this study proposes an integrated diagnostic framework for old residential buildings in Wuhan, China, combining unmanned aerial vehicle (UAV) infrared thermography, multi-temporal data acquisition, 3D flight-path planning, thermal anomaly recognition, facade spatial mapping, and temporal screening. Field experiments were conducted to determine key acquisition parameters, including sensor preheating time, imaging distance, and acquisition timing. Thermal anomalies were identified through image-processing techniques and mapped onto facade representations derived from 3D models. Repeated observations across different times and days were then used to evaluate anomaly recurrence and spatial stability. The results show that preheating the sensor for at least 10 min, maintaining a UAV-to-facade distance of 8–10 m, and acquiring data around 17:00 provide more reliable thermal images. Multi-temporal screening effectively reduces false positives caused by temporary disturbances, while persistent anomalies associated with window–wall joints, floor slabs, wall surfaces, and moisture-related areas can be identified more robustly. The proposed framework provides a practical workflow for facade thermal defect diagnosis and retrofit-oriented decision support. Full article

Autonomous vehicles operating at signalized intersections face fundamental challenges arising from queue dynamics, signal-phase transitions, and tightly coupled multi-vehicle interactions. Conventional motion-planning methods, which rely primarily on instantaneous perception, are inherently reactive and struggle to reason about short-term traffic evolution. This paper presents a Transformer-based predictive motion-planning framework that embeds short-term traffic state prediction directly into the structure of the planning problem. A lightweight spatial–temporal Transformer model is designed to forecast traffic occupancy, queue evolution, and interaction patterns using historical trajectories, signal-phase information, and road topology. By converting predicted traffic dynamics into explicit spatial–temporal constraints, a hierarchical motion planner jointly optimizes path geometry and speed profiles through dynamically constructed feasible corridors. The proposed framework is evaluated using a joint SUMO–CARLA simulation platform under realistic traffic conditions derived from real-world datasets, including pNEUMA and CitySim. The experimental results across straight-through, queueing, and turning scenarios show that prediction-aware planning significantly reduces high-risk driving time and intersection travel time while maintaining stable real-time computational performance. Beyond scenario-level improvements, the results indicate that transforming traffic prediction into planning constraints provides a generalizable paradigm for proactive, feasibility-aware autonomous driving at signalized intersections. From a methodological perspective, the proposed framework can be interpreted through the lens of symmetry and asymmetry in intelligent transportation systems: the conventional symmetric decoupling between prediction and planning modules is deliberately broken by embedding predicted traffic states as time-varying, directionally asymmetric constraints, while the permutation symmetry of the multi-head attention mechanism is preserved over lane-segment tokens to provide a structured inductive bias for traffic state forecasting. This symmetry-aware design highlights how controlled symmetry breaking in modeling and optimization can yield safer, more efficient, and more adaptive autonomous driving behaviors in signalized urban environments. Full article

Manual lifting is a common occupational activity associated with an increased risk of low back disorders. In this study, 22 participants from UPMC Hamot, organized into 11 pairs, were recruited. A combination of motion capture techniques and an injury assessment tool was used to investigate the relationships among body anthropometrics, three-dimensional trunk and lower-limb kinematics, and lumbar spinal loading. Potential differences in lifting mechanics were observed between male and female participants. Males exhibited greater trunk flexion and higher compressive loading, while females demonstrated greater hip and knee flexion with reduced trunk motion. These findings indicate that spinal loading during lifting is influenced by an interaction between anthropometric characteristics and movement coordination patterns, with variable behavioral trends affecting load distribution across the trunk and lower extremities. The results provide biomechanical insight that may inform the development of bio-ergonomic training techniques aimed at reducing lumbar spine loading and minimizing injury risk in occupational lifting tasks. Full article

Despite occupying essential roles in healthcare, warehouses, and manufacturing, night shift workers remain underserved by transportation systems designed around standard daytime schedules. Using the Baltimore Metropolitan Area as a case study, this study provides an in-depth examination of night shift workers’ travel behavior and commute wellbeing. Survey data on commute choices, commute durations, travel costs, and perceived perspectives were collected from workers employed in night shift-based occupations. Structural equation modeling was then used to examine the relationships between travel behavior and perceived inconvenience by constructing a latent variable of commute burden. The results show that longer commute times and reliance on public transit are significant indicators of commute burden among this population. To further investigate why commute burden is associated with public transit, we conducted spatial visualizations and K-means clustering based on origin-destination nighttime trips derived from anonymized mobile phone data. Land use maps and nighttime public transit networks were referenced to identify travel flows and mobility patterns between high-flow zones, revealing the need to expand nighttime transit services in those areas. These findings reflect limited transit accessibility affecting night shift workers and offer actionable insights for nighttime transit planning and public service improvements in similar metropolitan contexts. Full article

The increasing pace of urbanization and climate change necessitate a holistic assessment of building energy performance during the early design phase. This study proposes an Evolutionary Field Optimization (EFO)-based multi-input multi-output artificial neural network (MIMO-ANN) model to simultaneously predict the heating load, cooling load, CO₂ emissions, and lighting energy consumption of smart buildings. The model’s dataset consists of 7963 observations generated via EnergyPlus building energy simulations of standardized TOKİ residential units constructed post-earthquake in Türkiye. No operational or physically measured building energy consumption data were used in the model development process. For the validation setting, the simulation-generated dataset was split into training (60%), validation (10%), and test (30%) subsets. The EFO algorithm was employed to automatically optimize the ANN architecture by dynamically determining the optimal number of hidden layers and neurons. The optimization process demonstrated strong global search capability and fast convergence, reducing the objective function by approximately 86% within 10 iterations. Experimental results on the test subset showed exceptional predictive accuracy for simulation data, with test R² values ranging from 0.9996 to 0.9998 across all four outputs, indicating that the optimized network topology effectively avoided overfitting. While the model’s performance under real-world operational uncertainties and varying occupant behaviors remains to be fully investigated, the proposed EFO-ANN framework provides a computationally efficient and highly accurate analytical core for early-stage design. It serves as a strategic decision-support tool intended for architects and engineers designing post-disaster housing, public authorities forming national energy efficiency policies, and developers building predictive engines for digital twin-enabled smart building systems. Full article

Essential tremor (ET) often remains disabling despite pharmacologic, surgical, and stimulation-based options. This feasibility study evaluated LaceUp, a passive wearable sleeve combining soft compression with distributed embedded weight. Ten adults with ET completed a single laboratory visit with a fixed task battery under baseline, unweighted compression sleeve, wrist weight, and LaceUp conditions. Outcomes included wrist inertial measurement unit (IMU) tremor-band power (4–12 Hz), root mean square (RMS) jerk, digitized Archimedes spirals, handwriting, satisfaction/preference surveys, and follow-up Canadian Occupational Performance Measure (COPM) ratings. On the more-affected limb, LaceUp was associated with the largest median tremor-band power reduction (−32.9%) versus wrist weights (−25.3%) and the unweighted sleeve (−19.5%), while RMS jerk reductions were similar across conditions. Spiral and handwriting measures were heterogeneous and appeared most informative in participants with greater baseline severity. Six of nine participants who completed the preference ranking selected LaceUp as preferred. At follow-up, two of six reported continued use, and one exceeded the commonly cited 2-point COPM satisfaction threshold. Because this small pilot used a fixed order, effects cannot be separated from period, learning, expectancy, fatigue, behavioral adaptation, or measurement effects. Findings support feasibility and identify candidate endpoints, severity-based enrollment considerations, and operational constraints for future studies. Full article

While existing studies confirm the impact of occupant psychology and behavior on evacuation efficiency, most focus on general scenarios, leaving a lack of systematic quantitative analysis of turnback behavior in the high-risk context of deep-buried metro stations. The specific triggers, quantified impact, and key constraints of such behavior remain unclear. To address this gap, this study employs field research and simulation modeling to quantitatively analyze turnback behavior—primarily induced by factors like uneven exit distribution—in deep-buried stations. It investigates the effects of different turnback ratios and locations on efficiency to identify key bottlenecks. The findings aim to provide a theoretical basis and decision support for emergency planning and safety management in these environments. Full article

Full-vehicle crash simulations that account for occupant injury are essential for automobile safety assessment; however, they are computationally intensive and time-consuming. In particular, dash panel deformation plays a key role in transmitting impact loads to an occupant’s lower extremities. To address this issue, this study proposes a two-stage machine learning framework for occupant lower-limb injury assessment. In the first stage, the deformation behavior of the dash panel is predicted using a machine learning model, enabling efficient generation of a wide range of deformation patterns. In the second stage, occupant lower-limb injury metrics are evaluated based on the predicted deformation using a sled model. While the ultimate objective is to establish the complete two-stage framework, the present paper is limited to the first stage. It investigates the feasibility of machine learning-based deformation prediction. Deformation distributions of simplified structural components are predicted using an XGBoost-based machine learning model, in which principal component scores derived from geometric and deformation data serve as input features. The objective is to efficiently generate representative deformation modes from limited training data rather than optimizing prediction accuracy for individual deformation responses. Numerical experiments are conducted to investigate the effectiveness of the proposed prediction framework. The results of the proposed approach show good agreement with crash simulations in overall deformation behavior, while local deformation is not reproduced perfectly. These findings demonstrate the feasibility of machine learning-based dash panel deformation prediction as the first step toward the proposed two-stage framework for lower-limb injury assessment. Full article

Background/Objectives: Shift work is associated with circadian disruption, sleep disturbances, and psychological distress, all of which may influence both reflux-related symptoms and temporomandibular disorder (TMD)-related complaints. However, the relationships among reflux-related symptom burden, TMD severity, oral parafunctional behaviors, and psychological distress in healthcare professionals remain insufficiently understood. To evaluate the association between reflux-related symptom burden, assessed using the Gastroesophageal Reflux Disease Health-Related Quality of Life (GERD-HRQL) questionnaire, and TMD severity among healthcare professionals, and to investigate the potential roles of shift work, oral parafunctional behaviors, and psychological distress in this relationship. Methods: This cross-sectional observational study included healthcare professionals working at a tertiary hospital. Data were collected using validated questionnaires, including the GERD-HRQL, Fonseca Anamnestic Index, Oral Behavior Checklist (OBC), Depression Anxiety Stress Scale-21 (DASS-21), and Epworth Sleepiness Scale (ESS). Participants were categorized as shift workers (≥4 night shifts/month) and non-shift workers. Correlation and multivariable regression analyses were performed. Results: A total of 240 participants were included. Higher GERD-HRQL scores were positively correlated with TMD severity (r = 0.31, 95% CI: 0.19 to 0.42, p < 0.001), oral parafunctional behavior scores (r = 0.28, 95% CI: 0.16 to 0.39, p < 0.001), and DASS-21 stress scores (r = 0.35, 95% CI: 0.23 to 0.46, p < 0.001). Shift workers demonstrated significantly higher GERD-HRQL scores and TMD severity scores than non-shift workers, with small-to-moderate effect sizes. In multivariable analysis, higher TMD severity, OBC score, stress score, and shift-work exposure showed adjusted associations with higher GERD-HRQL scores. The model explained 32% of the variance in reflux-related symptom burden (R² = 0.32; adjusted R² = 0.29). Conclusions: Higher GERD-HRQL scores, reflecting reflux-related symptom burden rather than objectively confirmed GERD, showed weak to small-to-moderate associations with TMD severity, oral parafunctional behaviors, psychological distress, and shift-work exposure among healthcare professionals. These findings indicate co-occurrence of gastrointestinal, temporomandibular, behavioral, and psychosocial symptom domains within this occupational population. Longitudinal studies using objective diagnostic methods are required to clarify the directionality and clinical significance of these associations. Full article

Background: Micromanagement is an extensively prevalent yet relatively under-theorized management process in healthcare organizations. This narrative review synthesizes the literature on micromanagement and related leadership practices in healthcare, focusing on its antecedents, manifestations, consequences, and mitigation strategies. Methods: A structured literature search was conducted on 10 May 2024 across eight electronic databases. Eligible studies included qualitative, quantitative, mixed-methods, and applied studies published between 2003 and 2024. The main outcomes were the underlying causes and behavioral measures of micromanagement, examined directly, or closely related constructs such as excessive supervision, reduced autonomy, authoritarian leadership, toxic leadership, and controlling managerial behavior. The secondary outcomes involved organizational and patient-related effects and their respective interventions. Results: A total of twelve studies were selected. The identified antecedents of micromanagement were authoritarian leadership styles, autocratic and toxic leadership personality traits, overly intrusive supervisory practices, poor employee empowerment, complicated regulation, unclear definition of professional roles, and inherent structural challenges. Micromanagement behavior was seen in authoritative decision-making, transactional supervision, systematic reduction in employee autonomy, and institutionalized distrust. The consequences recorded include high levels of occupational stress, poor organizational productivity, poor quality of healthcare services, high employee turnover rates, and psychological problems. Conclusions: This review represents a preliminary conceptual synthesis of the literature that addresses micromanagement in healthcare. The evidence base is inconsistent, with many studies focusing on constructs that relate to micromanagement while not studying it directly. In future research, validated tools to assess micromanagement should be designed, as well as leadership interventions that benefit both workplace and patient outcomes. Full article

Background/Objectives: Dietary adherence and physical activity are pivotal yet understudied behavioral components of self-management of type 2 diabetes mellitus (T2DM) in the Middle East and North Africa region. This study aimed to quantify dietary adherence and physical activity levels, examine their association, and identify sociodemographic and clinical factors independently associated with these outcomes among adults with T2DM in southwest Saudi Arabia—a region chronically underrepresented in the literature. Methods: A descriptive cross-sectional study (n = 257; December 2023–March 2024) was conducted at a specialist diabetes center. The Perceived Dietary Adherence Questionnaire (PDAQ; 0–56 after removal of the fat-avoidance item with near-zero item-total correlation) and General Practice Physical Activity Questionnaire (GPPAQ) were administered alongside body mass index (BMI) and glycated hemoglobin (HbA1c) extracted from medical records. Bonferroni-corrected non-parametric bivariate tests, multiple linear regression with variance inflation factor diagnostics, and binary logistic regression were applied. Results: Mean 8-item PDAQ was 20.44 ± 10.04/56 (36.5%); carbohydrate spacing was the critical deficit (16.4%). GPPAQ distribution: 10.1% inactive, 28.0% moderately inactive, 49.0% moderately active, and 12.8% active, with sensitivity analysis ranging 28.0–47.5% in the two lowest categories. PDAQ–GPPAQ correlation was weak (Spearman r = 0.18; 95% CI: 0.06–0.29; r² = 0.032). BMI alone accounted for 81.0% of PDAQ score variance (cross-sectional; direction of association not established; full model Adj. R² = 0.826; LOO-CV R² = 0.820, indicating model stability). Employment type showed the strongest cross-sectional association with GPPAQ-derived inactivity classification (housewife OR = 5.77; retired/seeking OR = 4.98 vs. employed), largely driven by the occupational component of the composite score. Conclusions: Dietary adherence was substantially below the maximum achievable score; BMI was the factor most strongly associated with PDAQ scores in cross-sectional analysis, though the direction of this relationship cannot be established. Physical activity levels were substantially associated with occupational patterns; housewives and retired/other participants faced approximately five-fold greater odds of being classified as inactive or moderately inactive compared with employed individuals. The weak PDAQ–GPPAQ correlation (r² = 0.032) suggests these behaviors are not strongly co-determined and points to the potential value of distinct, hypothesis-generating intervention approaches for dietary quality and leisure-time physical activity in T2DM populations. Full article

Background: Fasting plasma glucose, glycated hemoglobin (HbA1c), and oral glucose tolerance testing remain central to the diagnosis and monitoring of dysglycemia, but they mainly reflect the average glycemic exposure or discrete time-point measurements and may not capture intraday and interday glucose fluctuations. Glycemic variability (GV) has been associated with oxidative stress, endothelial dysfunction, inflammation, and diabetes-related complications, although much of the evidence derives from experimental, clinical, and diabetes-care settings rather than occupational cohorts. Aim: This narrative review examines the physiological basis, measurement, and potential occupational relevance of GV and continuous glucose monitoring (CGM) in working populations. Methods: Literature was narratively selected from biomedical databases, major guidelines, consensus statements, and occupational-health sources, prioritizing reviews, clinical guidelines, cohort studies, mechanistic studies, and CGM studies. No systematic search, risk-of-bias assessment, or quantitative synthesis was performed. Main findings: CGM is an established technology in selected diabetes-care contexts and provides metrics such as coefficient of variation, time in range, time above range, and time below range. Its use in occupational medicine, however, remains investigational outside selected clinical circumstances. Work-related factors such as shift work, circadian disruption, sleep loss, psychosocial stress, irregular meal timing, sedentary behavior, and variable physical workload may influence glucose regulation, but direct evidence linking these exposures to CGM-measured GV in workers remains limited. Implications: Potential applications include research on occupational determinants of metabolic health, monitoring of workplace lifestyle interventions, and individualized management of workers with diabetes in safety-sensitive roles, provided that consent, confidentiality, clinical follow-up, equity, and data-governance safeguards are ensured. Conclusions: GV assessment may complement traditional metabolic markers in selected occupational-health contexts, but routine CGM-based surveillance of general worker populations is not currently supported by sufficient evidence. Further longitudinal and interventional studies are required. Full article

As accessibility development in public buildings has gradually shifted from facility compliance toward experience- and performance-oriented evaluation, the quantitative assessment of indoor mobility experiences among blind users still lacks a systematic sensor-supported analytical framework. To address this gap, this study proposes an indoor accessibility assessment approach that integrates multi-sensor data acquisition with explainable machine learning, using a tactile museum as the experimental setting. Sixty-four participants with first-level blindness were recruited to complete a real-world directed walking task. A multimodal database was constructed by integrating objective data collected from an ultra-wideband (UWB) indoor positioning system, an intelligent gait analysis system, and video-based behavioral recording, including spatiotemporal trajectories, gait characteristics, and behavioral events, together with post-task accessibility satisfaction ratings. Based on this dataset, a random forest model was developed using the Overall Accessibility Satisfaction Score (OAS) as the response variable. SHAP, partial dependence analysis, and GAM smoothing were further applied to interpret the associations between key variables and predicted satisfaction. The results showed that walking distance, number of turns, self-reported collision perception, and selected gait indicators made relatively high contributions to the model interpretation, and these variables exhibited certain nonlinear associations with predicted satisfaction. These findings suggest that combining multi-source sensor-based behavioral measurement with explainable machine learning has potential for sensor-supported post-occupancy evaluation of indoor accessibility environments and can provide exploratory references for the quantitative assessment and optimization of accessibility in public buildings. Full article

Background: Behavioral procrastination has been increasingly recognized as a maladaptive self-regulatory pattern associated with unhealthy lifestyle behaviors, psychological stress, and adverse cardiometabolic profiles. However, its relationship with accelerated cardiovascular aging remains poorly understood. This study aimed to evaluate the association between behavioral procrastination and heart age acceleration in a large occupational cohort of Spanish workers. Methods: A multicenter cross-sectional study was conducted including 92,184 actively employed Spanish workers undergoing routine occupational health examinations between 2021 and 2024. Behavioral procrastination was assessed using the Pure Procrastination Scale-9 (PPS-9). Estimated heart age and heart age acceleration were calculated using a cardiovascular risk-factor-based algorithm. Multivariable linear and logistic regression analyses were performed to evaluate associations between procrastination score, continuous heart age acceleration, and accelerated cardiovascular aging phenotypes after adjustment for demographic, lifestyle, anthropometric, and cardiometabolic variables. Restricted cubic spline analyses and sex-stratified analyses were additionally conducted. Results: Higher procrastination levels were associated with progressively worse cardiometabolic and cardiovascular aging profiles. Mean heart age acceleration increased from −3.1 ± 6.0 years in participants with very low procrastination to 14.0 ± 6.4 years in those with very high/chronic procrastination (p < 0.001). The prevalence of accelerated cardiovascular aging (>0 years) increased from 27.2% to 94.2% across increasing procrastination categories, whereas severe accelerated cardiovascular aging (≥10 years) increased from 1.7% to 75.6% (both p < 0.001). In fully adjusted multivariable analyses, each 5-point increase in PPS-9 score was associated with a 0.50-year increase in heart age acceleration (B = 0.50; 95% CI 0.48–0.52; p < 0.001). Participants with very high/chronic procrastination exhibited significantly higher odds of accelerated cardiovascular aging (OR 1.89; 95% CI 1.65–2.18) and severe accelerated cardiovascular aging (OR 2.51; 95% CI 2.16–2.92). Associations were significantly stronger among women (p-interaction < 0.001). Findings remained robust in sensitivity analyses excluding participants with diabetes mellitus. Conclusions: Behavioral procrastination was associated with higher estimated heart age acceleration and less favorable cardiovascular aging profiles in this large occupational cohort. Higher procrastination severity was consistently related to greater estimated heart age acceleration and a higher prevalence of cardiovascular aging phenotypes, even after extensive multivariable adjustment. These findings indicate that higher procrastination levels were associated with less favorable cardiovascular aging profiles beyond traditional biomedical risk factors. However, given the cross-sectional design, no conclusions regarding causality or temporality can be drawn. Full article

The transition toward next-generation energy systems requires advanced computational tools capable of supporting accurate, adaptive, and data-driven energy performance assessment. Within this context, Energy Performance Indicators (EnPIs) established under the ISO 50001 framework remain essential for monitoring energy efficiency and continuous improvement; however, conventional indicators are often based on static or simplified relationships that do not adequately capture the dynamic, nonlinear, and multivariable behavior of modern buildings and energy management systems. This systematic review analyzes the integration of ISO 50001-based EnPIs with intelligent algorithms and artificial intelligence techniques for enhanced energy management. The review follows a PRISMA-inspired methodology, using Scopus as the primary database and Web of Science and Google Scholar as complementary sources. From 5442 initial records, 2691 studies were screened and 283 articles were selected for detailed analysis, supported by a bibliometric keyword co-occurrence analysis using VOSviewer 1.6.20. The results show a clear evolution from traditional energy indicators and normalized baselines toward computational modeling approaches based on regression analysis, machine learning, deep learning, forecasting, anomaly detection, and optimization algorithms. These methods improve the predictive capability, adaptability, and operational relevance of EnPIs by incorporating climatic, occupancy, temporal, and operational variables. The reviewed evidence indicates that intelligent algorithms can strengthen ISO 50001 energy management systems by enabling dynamic baselines, early detection of abnormal consumption patterns, predictive decision-making, and continuous operational optimization. Nevertheless, challenges remain regarding data quality, model interpretability, methodological standardization, and practical integration into certified energy management frameworks. Overall, this review highlights that the future of energy performance assessment does not rely on replacing conventional EnPIs, but on transforming them into intelligent, computationally supported indicators for sustainable, resilient, and next-generation energy management systems. Full article