Search Results (893)

This study attempted to develop and validate a data-driven simulation model that integrates field-measured data to assess the power requirement and fuel consumption characteristics of a self-propelled collector. The collector is a hydrostatic transmission-based, crawler-type platform designed for garlic and onion harvesting, equipped with multiple hydraulic subsystems for collection and sorting. During field experiments, the power requirements of each subsystem and fuel flow rate were recorded, and Willans line method was applied to estimate engine power and subsystem power transmission efficiencies. Because many small agricultural machines do not support electronically instrumented engines (e.g., CAN-bus/ECU-based measurements), the proposed model was formulated as a data-driven, low-order representation derived from on-site measurements rather than a full physics-based model. Using the identified parameters, the simulation framework predicts engine power and fuel efficiency under various operating conditions. The simulation results exhibited high agreement with field data, achieving R² and mean absolute percentage error values of 0.935–0.981 and 1.79–4.18%, respectively, confirming reliable reproduction of real field performance. A comprehensive analysis of the simulation results revealed that both engine speed and travel speed significantly influence power distribution and fuel rate, while also indicating that hydraulic working power is the dominant contributor to total power demand at higher engine speeds. These findings provide practical guidance for improving the fuel efficiency of compact self-propelled collectors. Full article

Background: This study explored facilitators and barriers to return to work (RTW) after acute cardiovascular events or elective cardiac surgery, integrating clinical, functional, and occupational factors. Methods: A prospective cohort study was conducted at the Occupational Medicine and Cardiac Rehabilitation Units of the Maggiore Hospital in Trieste, Italy. Employed adults (18–67 years) admitted for acute coronary syndrome, valve replacement, or thoracic aortic surgery between January 2024 and July 2025 were enrolled. Sociodemographic, clinical, and occupational data were collected alongside functional and psychosocial assessments, including the Work Ability Index (WAI) and EQ-5D-5L. Predictors of RTW were analyzed with Cox regression models. Results: Among 103 patients (mean age 56.8 years; 92.2% male), 77.7% returned to work after a mean of 58.9 days. Independent predictors of earlier RTW were self-employment (HR 5.08, 95% CI 2.52–10.27), occupational responsibility (HR 2.12, 95% CI 1.01–4.45), and percutaneous coronary intervention (HR 2.72, 95% CI 1.47–5.06). Higher job-related physical demands, arrhythmias, and cardiac rehabilitation participation were associated with delayed RTW. Mean WAI (37.2 ± 5.1) and EQ-5D index (0.92 ± 0.09; EQ-VAS 77.4 ± 12.9) indicated preserved function and quality of life. Conclusions: RTW after cardiovascular events is multifactorial. Integrating occupational medicine into cardiac rehabilitation is key to ensuring safe, sustainable reintegration. Full article

The increasing demand for reliable and efficient power electronic systems in critical applications—such as renewable energy, electric vehicles, and aerospace—has intensified the need to understand and predict failure mechanisms in power devices. This work focuses on the reliability assessment and lifetime modeling of medium-voltage power electronic components under realistic mission profiles. By combining accelerated aging tests, failure analysis, and physics-of-failure modeling, we identify dominant degradation mechanisms such as thermal cycling, partial discharge, and dielectric break-down. A hybrid methodology is proposed, integrating experimental data and simulation to predict the evolution of key parameters (e.g., on-state resistance, threshold voltage) over time. The study also explores the impact of packaging, thermal management, and environmental stresses on device robustness. The results provide valuable insights into the design of more durable power electronic converters and for the implementation of condition monitoring strategies in real-time applications. Full article

Background/Objectives: Nursing professionals were among the most affected groups during the COVID-19 pandemic, exposed to simultaneous physical demands and emotional strain. This study examined the interplay between work engagement, compassion fatigue, and musculoskeletal symptoms among frontline nurses in a Brazilian public hospital. Methods: A cross-sectional study (n = 77) was conducted between February and April 2022 using validated instruments (Work Stress Scale, ProQoL-BR, Nordic Musculoskeletal Questionnaire, and UWES-9). Descriptive and inferential analyses were performed (p ≤ 0.05). Results: Most participants did not report occupational stress (84.4%). No profiles of compassion fatigue were identified, although notable rates of burnout (26.0%) and secondary traumatic stress (23.4%) were observed. Engagement scores were very high in vigor and dedication. Musculoskeletal symptoms were prevalent, especially in the lumbar region (chronic: 60.0%). Female sex, statutory employment, and lack of physical activity were associated with a higher prevalence of symptoms and sick leave. Work engagement (vigor and overall score) showed negative correlations with absenteeism. Conclusions: The coexistence of high engagement and emotional vulnerability, in the absence of compassion fatigue, suggests that higher levels of engagement may be associated with lower occupational stress. These findings highlight the importance of integrated strategies, including ergonomic interventions, health promotion, and organizational support, to preserve the physical and mental health of frontline nursing professionals. This study provides new evidence of engagement as a potential protective factor that may mitigate physical and emotional burden among nurses in resource-limited settings. Full article

Online adaptive radiotherapy (ART) is rapidly transforming clinical radiation oncology by enabling adaptation of treatment plans based on patient-specific anatomical and biological changes. However, most medical physics training programs lack structured education in ART. To address this critical gap, the Medical Physics Adaptive Radiotherapy (MPART) Fellowship was established at our center to train post-residency or practicing physicists in advanced adaptive technologies and workflows. The MPART Fellowship is a two-year program that provides immersive, platform-specific training in CBCT-guided (Varian Ethos), MR-guided (Elekta Unity), and PET-guided (RefleXion X1) radiotherapy. Fellows undergo modular clinical rotations, hands-on training, and dedicated research projects. The curriculum incorporates competencies in imaging, contouring, online planning, quality assurance, and team-based decision-making. Evaluation is based on the Accreditation Council for Graduate Medical Education competency domains and includes milestone tracking, mentor reviews, and structured presentations. The fellowship attracted applicants from both domestic and international institutions, reflecting strong demand for formal ART training. Out of 22 applications, two fellows have been successfully recruited into the program since 2024. Fellows actively participate in all phases of adaptive workflows and are expected to function at near-attending levels by the second year of their training. Each fellow also leads at least one translational or operational research project aimed at improving ART delivery. Fellows contribute to clinical coverage and lead developmental projects, resulting in presentations and publications at the national and international levels. The MPART Fellowship addresses a vital educational need by equipping medical physicists with the advanced competencies necessary for implementing and leading ART. This program offers a replicable framework for other institutions seeking to advance precision radiation therapy through structured post-residency training in adaptive radiotherapy. As this fellowship program is still in its early phase of establishment, the primary goal of this paper is to introduce the structure, framework, and implementation model of the program. Comprehensive outcome analyses—such as quantitative assessments, fellow feedback, and longitudinal competency evaluations—will be incorporated in future work as additional cohorts complete training. Full article

Background/Objectives: Paramedics are routinely exposed to high psychosocial strain due to the demanding and unpredictable nature of emergency medical work. This study aimed to examine psychosocial and behavioral factors—working hours, stress, burnout, and physical activity—associated with self-reported work ability among paramedics in Poland. Methods: A cross-sectional online survey was conducted between July 2023 and January 2024 among paramedics—whether active in Emergency Medical Services or holding a second degree and employed as a nurse—using the Polish version of the Work Ability Index and a stress and burnout assessment tool recommended by the European Commission. Statistical analyses, including Spearman correlation and group comparisons, were performed with a significance level of α = 0.05. Results: Work ability correlated positively with physical activity and negatively with age, stress, and burnout (p < 0.05). The strongest association was observed between stress and burnout (ρ = 0.837). Paramedics working in ambulance services reported significantly higher stress and burnout levels than hospital personnel (p = 0.001 and p = 0.002), although work ability did not differ by workplace. Conclusions: These findings indicate that psychosocial stress, burnout, and low physical activity substantially reduce work ability among paramedics, emphasizing the need for targeted preventive strategies—such as stress management, promotion of physical activity, and regulation of working hours—to support the health and sustainability of the emergency medical workforce. Full article

Bike-sharing services contribute to reducing emissions and conserving natural resources within urban transportation systems. They also promote public health by encouraging physical activity and generate economic benefits through shorter travel times, lower transportation costs, and decreased demand for parking infrastructure. This paper examines the use of shared micro-mobility services in the Italian cities of Florence and Bologna, based on an analysis of GPS origin–destination data and associated temporal coordinates provided by the RideMovi company. Given the still-limited number of studies on free-floating and electric-scooter-sharing systems, the objective of this work is to quantify the performance of electric bikes and e-scooters in bike-sharing schemes and compare it to traditional, muscular bikes. Trips were reconstructed starting from GPS data of origin and destination of the trip with a shortest path criteria that considers the availability of bike lanes. Results show that e-bikes are from 22 to 26% faster on average with respect to muscular bikes, extending trip range in Bologna but not in Florence. Electric modes attract more users than traditional bikes, e-bikes have from 40 to 128% higher daily turnover in Bologna and Florence and e-scooters from 33 to 62% higher in Florence with respect to traditional bikes. Overall, turnover is fairly low, with less than two trips per vehicle per day. The performance is measured in terms of trip duration, speed, and distance. Further characteristics such as daily turnover by transport mode are investigated and compared. Finally, spatial analysis was conducted to observe demand asymmetries in the two case studies. The results aim to support planners and operators in designing and managing more efficient and user-oriented services. Full article

This study investigates how technostress and work intensification jointly influence employee health harm through two distinct stressor-strain pathways within Pakistan’s manufacturing sector. The proposed model specifies two mechanisms, (1) technostress induces IT strain that contributes to health harm, moderated by user satisfaction; and (2) work intensification heightens emotional exhaustion that similarly leads to health harm, moderated by perceived organizational support. Grounded in Conservation of Resources (COR) theory, the framework explains how cumulative digital and organizational demands deplete employee resources, amplifying both psychological and physical harm. A cross-sectional quantitative design was employed, utilizing a structured self-administered questionnaire administered to mid and senior-level employees across manufacturing firms. A total of 252 valid responses were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) using Smart PLS 4. Results revealed that both IT strain and emotional exhaustion significantly mediated the effects of technostress and work intensification, respectively, on health harm. Moreover, user satisfaction significantly moderated the IT strain-health harm relationship, indicating that higher satisfaction with digital tools mitigates the adverse impact of technological stress. Similarly, organizational support weakened the association between emotional exhaustion and health harm, underscoring its protective role in high-pressure work settings. This study offers theoretical advancement by integrating fragmented stressor-strain models and offers practical recommendations to foster digital well-being and supportive organizational work cultures in evolving industrial contexts. Full article

Drilling in aerospace one-up assembly demands high positional accuracy, strong clamping forces, and precise angular compensation to ensure quality in multi-layered stacks. Existing robotic solutions achieve these requirements but are costly, bulky, and unsuitable for flexible or collaborative environments. This work introduces the Advanced Collaborative Multifunctional End-Effector (ACME), a lightweight robotic drilling end-effector designed for integration with collaborative robots (cobots). ACME incorporates vacuum-assisted clamping capable of generating high forces, a passive self-normalization mechanism for angular alignment on double-curvature surfaces, and a compact 5-DoF positioning system for precise positioning and orientation. The system’s kinematics and dynamics were modeled and experimentally verified through frequency response function (FRF) testing, enabling precise behavior prediction. The tool is integrated within a cyber–physical system (CPS) featuring an interactive digital twin that, unlike passive monitoring systems, allows operators to configure workpieces, select drilling locations directly from rendered CAD, and supervise execution without programming expertise. Experiments demonstrated average positional errors of 0.19 mm and normality deviations of 0.29°, both within aerospace standards. The results confirm that ACME effectively extends cobot capabilities for aerospace-grade drilling while improving flexibility, safety, and operator accessibility. Full article

Background/Objectives: Emergency healthcare professionals are continually exposed to high clinical and organizational demands that compromise their mental, physical, and occupational health. This systematic review and meta-analysis examined the prevalence and interrelations of biopsychosocial and work-related health outcomes among emergency personnel, providing an integrated synthesis of recent empirical evidence. Methods: A systematic search of PubMed, Scopus, Web of Science, and CINAHL identified 6214 records, of which 50 studies met inclusion criteria and were analyzed (total n = 278,000 emergency professionals). Eligible studies (2020–2025) evaluated biopsychosocial outcomes (burnout, depression, stress, resilience, sleep quality) and occupational indicators (workplace violence, job satisfaction, effort-reward imbalance, engagement, turnover intention). Meta-analyses were conducted using random-effects models (DerSimonian-Laird method), producing pooled prevalence estimates for each outcome based on the number of studies that reported the corresponding variable. Risk of bias was assessed using the Joanna Briggs Institute tools, with most studies rated as moderate-to-high quality. Results: Pooled estimates showed fair self-perceived health in 44.0%, severe burnout in 10.7%, depressive symptoms in 35.1%, moderate-to-severe stress in 74.6%, and poor sleep quality in 40.1% of staff. Workplace violence affected 76.9% of professionals. Job satisfaction averaged 68.1%, turnover intention 62.1%, and effort-reward imbalance 61.9%. Resilience was predominantly moderate (33.9%). Considerable heterogeneity was observed; however, patterns were consistent across regions and professional roles. Conclusions: Emergency healthcare personnel face substantial biopsychosocial strain and occupational risks, driven by persistent structural pressures. Health systems should implement integrated organizational strategies to reduce violence, enhance psychological support, ensure safe staffing, and protect rest and recovery. Improving staff well-being is essential for maintaining a resilient and effective emergency care workforce. Full article

High-voltage (HV) bushings are critical hub components in power systems, whose operational reliability is paramount to the safety and stability of transmission and distribution infrastructure. Conventional management paradigms are hampered by challenges such as information silos, reactive maintenance, and imprecise condition assessment, rendering them in-adequate for the evolving demands of modern power systems. Digital twin technology, by creating a high-fidelity, re-al-time interplay between physical entities and their virtual counterparts, provides a revolutionary pathway toward the intelligent full-life-cycle management (FLCM) of HV bushings. This paper presents a review of the current state of research in this domain. It begins by reviewing research on the construction a five-dimensional digital twin framework that encompasses the entire lifecycle: design, manufacturing, operation and maintenance (O&M), and decommissioning. Subsequently, it delves into the application paradigms of digital twins across typical scenarios, including external insulation design, intelligent condition assessment, insulation defect identification, fault diagnosis, and predictive maintenance. The paper then examines the core technological underpinnings, such as multi-physics coupled modeling, multi-source heterogeneous data fusion, and data-driven model updating and condition assessment. Finally, it identifies current challenges related to data, models, standards, and costs, and offers a forward-looking perspective on future research directions, including group digital twins, deep integration with artificial intelligence, edge-side deployment, and standardization initiatives. This work aims to provide a theoretical reference and technical guidance for advancing the intelligent O&M of HV bushings and bolstering grid security. Full article

Energy storage is vital for on-demand electricity generation from renewable sources like wind and solar. Besides employing batteries, retrofitting conventional fossil-fired power plants with thermal energy storage might present a highly cost-effective solution. State-of-the-art molten salt storage systems currently operate at a maximum temperature of 565 °C. At a higher permanent temperature, nitrate salts start to decompose. The actual wall temperatures of power components for heating, such as solar receivers and electrical heaters, may exceed temperature limits. To date, there is no clear threshold identified up to which heating surfaces in contact with nitrate salt can be operated without leading to the degradation of the salt, which is inevitably followed by increased corrosivity. In this study, possible mechanisms affecting the maximum permissible wall temperature of heating surfaces are identified. The local production of oxygen and nitrite at hot surfaces and its accumulation in the entire system is looked at in an experiment with 9.3 kg of nitrate salt. The effect of high wall temperatures on the evolution of oxygen and nitrite content over time is monitored and analyzed. Parametric studies with an experimentally validated physical model focusing on the nitrate/nitrite equilibrium reveal major influencing factors, with wall temperatures significantly exceeding current design limits. These findings potentially allow for more compact and cost-effective heating components. This work supports the advancement of high-temperature thermal energy storage systems essential for the scalability and economic competitiveness of renewable energy infrastructure. Full article

Sensor networks generate high-dimensional temporally dependent data across healthcare, environmental monitoring, and energy management, which demands robust machine learning for reliable forecasting. While gradient boosting methods have emerged as powerful tools for sensor-based regression, systematic evaluation under realistic deployment conditions remains limited. This work provides a comprehensive review and empirical benchmark of boosting algorithms spanning classical methods (AdaBoost and GBM), modern gradient boosting frameworks (XGBoost, LightGBM, and CatBoost), and adaptive extensions for streaming data and hybrid architectures. We conduct rigorous cross-domain evaluation on continuous glucose monitoring, urban air-quality forecasting, and building-energy prediction, assessing not only predictive accuracy but also robustness under sensor degradation, temporal generalization through proper time-series validation, feature-importance stability, and computational efficiency. Our analysis reveals fundamental trade-offs challenging conventional assumptions. Algorithmic sophistication yields diminishing returns when intrinsic predictability collapses due to exogenous forcing. Random cross-validation (CV) systematically overestimates performance through temporal leakage, with magnitudes varying substantially across domains. Calibration drift emerges as the dominant failure mode, causing catastrophic degradation across all the static models regardless of sophistication. Importantly, feature-importance stability does not guarantee predictive reliability. We synthesize the findings into actionable guidelines for algorithm selection, hyperparameter configuration, and deployment strategies while identifying critical open challenges, including uncertainty quantification, physics-informed architectures, and privacy-preserving distributed learning. Full article

Diverse application scenarios demand frequency-selective surfaces (FSSs) with tailored center frequencies and bandwidths. However, their design traditionally relies on iterative full-wave simulations using tools such as the High-Frequency Structure Simulator (HFSS) and Computer Simulation Technology (CST), which are time-consuming and labor-intensive. To overcome these limitations, this work proposes an octagonal fractal frequency-selective surface (OF-FSS) composed of a square ring resonator and an octagonal fractal geometry, where the fractal configuration supports single-band and multi-band resonance. A physics-informed reinforcement learning (PIRL) algorithm is developed, enabling the RL agent to directly interact with CST and autonomously optimize key structural parameters. Using the proposed PIRL framework, the OF-FSS achieves both single-band and dual-band responses with desired frequency responses. Full-wave simulations validate that the integration of OF-FSS and PIRL provides an efficient and physically interpretable strategy for designing advanced multi-band FSSs. Full article

Using an attack-simulation module is a well-recognized approach to improving the robustness of end-to-end neural-network-based data-hiding schemes. However, most proposed attack simulators are limited in the types of attacks they cover, usually handling only a basic set of digital transformations. Real, in-demand use cases for data-hiding methods may involve modifications that cannot be modeled by basic digital transformations such as filtering, noise, or compression. In the screen-cam scenario, when an image containing hidden data is displayed on a screen and captured by a camera, the distortions are much more complex and typically require manual experiments that manipulate physical objects in order to replicate. This hinders both the process of creating applicable data-hiding schemes for this scenario and evaluating their effectiveness. In this work, we propose a generator neural network to simulate screen-cam distortions that can replace the manual, time-consuming operations of replicating this attack in the real world, and we show how it can be used to improve the robustness of an existing data-hiding scheme. In our example, we increased robustness by 15% in terms of bit error rate. Full article