Search Results (19,983)

External insulation of coastal power grids transmitting offshore wind power faces significant threats from salt fog flashovers. Current arc monitoring and early warning technologies for flashover are severely inadequate. Research on salt fog discharge processes and determining the threshold at the flashover brink for transmission equipment external insulation is crucial for ensuring the safe operation of coastal grids delivering offshore wind power. Fiber Bragg Grating (FBG), with its advantages of compact size, excellent insulation, and fast response, enables effective discharge monitoring and identification of the critical flashover state on external insulation surfaces. In this study, FBGs were embedded at the interfaces of typical external insulation specimens, including silicone rubber plates and epoxy resin plates, to conduct contaminated AC salt fog discharge tests. Synchronized measurements of visible light images, infrared thermal images, and FBG interface temperature were conducted to investigate the discharge physical processes on silicone rubber insulating surfaces and the flashover threshold based on FBG temperature rise rate. The results indicate that discharge process can be divided into three phases: arc initiation, extension, and flashover based on the characteristics of arc visible light images. By comparing arc locations in infrared and visible light images with the corresponding FBG interface temperature rise, the arc phase criterion of FBG interface temperature rise rate and position were proposed. Furthermore, through multiple experiments, it has been found that flashover occurs when both interface temperatures reached above 4.6 × 10⁻² °C/s. This study provides a novel research methodology for physical process of external insulation discharge and flashover warning in coastal salt fog environments. Full article

Monitoring indoor air quality is vital for health, as CO₂ is a major pollutant. An automated system that accurately forecasts CO₂ levels can optimize HVAC management, preventing sudden increases and reducing energy waste while maintaining occupant comfort. Traditionally, such systems require extensive datasets collected over months to train algorithms, making them computational expensive and inefficient. To address this limitation, an adaptive IoT-based platform has been developed, leveraging a limited set of recent data to forecast CO₂ trends. Tested in a real-world setting, the system analyzed parameters such as physical activity, temperature, humidity, and CO₂ to ensure accurate predictions. Data acquisition was performed using the Smartex WWS T-shirt for physical activity data and the UPSense UPAI3-CPVTHA environmental sensor for other measurements. The chosen sensor devices are wireless and minimally invasive, while data processing was carried out on a low-power embedded PC. The proposed forecasting model adopts an innovative approach. After a 5-day training period, a Generative Adversarial Network enhances the dataset by simulating a 10-day training period. The model utilizes a Generative Adversarial Network with a Long Short-Term Memory network as the generator to predict future CO₂ values based on historical data, while the discriminator, also a Long Short-Term Memory network, distinguishes between actual and generated CO₂ values. This approach, based on Conditional Generative Adversarial Networks, effectively captures data distributions, enabling more accurate multi-step probabilistic forecasts. In this way, the framework maintains a Root Mean Square Error of approximately 8 ppm, matching the performance of our previous approach, while reducing the need for real training data from 10 to just 5 days. Furthermore, it achieves accuracy comparable to other state-of-the-art methods that typically requires weeks or even months of training. This advancement significantly enhances computational efficiency and reduces data requirements for model training, improving the system’s practicality for real-world applications. Full article

The growing environmental concern over waste tire accumulation necessitates innovative recycling strategies in construction materials. Therefore, this study aims to develop and evaluate sustainable concrete by integrating waste tire rubber (WTR) aggregates of different sizes and recycled waste tire steel fibers (WTSFs), assessing their combined effects on the mechanical and microstructural performance of concrete through experimental and analytical approaches. WTR aggregates, consisting of fine (0–4 mm), small coarse (5–8 mm), and large coarse (11–22 mm) particles, were used at substitution rates of 0–20%; WTSF was used at volumetric dosages of 0–2%, resulting in a total of 40 mixtures. Mechanical performance was evaluated using density and pressure resistance tests, while microstructural properties were assessed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). The findings indicate systematic decreases in density and compressive strength with increasing WTR ratio; the average strength losses were approximately 12%, 20%, and 31% at 5%, 10%, and 20% for WTR substitution, respectively. Among the WTR types, the most negative effect occurred in fine particles (FWTR), while the least negative effect occurred in coarse particles (LCWTR). The addition of WTSF compensated for losses at low/medium dosages (0.5–1.0%) and increased strength by 2–10%. However, high dosages (2.0%) reduced strength by 20–40% due to workability issues, fiber clumping, and void formation. The highest strength was achieved in the 5LCWTR–1WTSF mixture at 36.98 MPa (≈6% increase compared to the reference/control concrete), while the lowest strength was measured at 16.72 MPa in the 20FWTR–2WTSF mixture (≈52% decrease compared to the reference/control). A strong positive correlation was found between density and strength (r, Pearson correlation coefficient, ≈0.77). SEM and EDX analyses confirmed the weak matrix–rubber interface and the crack-bridging effect of steel fibers in mixtures containing fine WTR. Additionally, a hybrid prediction model combining physics-informed neural networks (PINNs) and CatBoost, supported by data augmentation strategies, accurately estimated compressive strength. Overall, the results highlight that optimized integration of WTR and WTSF enables sustainable concrete production with acceptable mechanical and microstructural performance. Full article

Background/Objectives: The number of overweight and obese people has significantly increased in the world, and this phenomenon is referred to as globesity. Globally increasing obesity has become one of the major problems to be dealt with for countries, given obesity-related health problems, including nutrition-related noncommunicable diseases and some types of cancer, and the economic and social costs of obesity. Therefore, countries try to combat obesity through diverse strategies related to nutrition, physical activity, and education. In this regard, identifying the factors behind obesity is critical to making progress in the fight against obesity. Methods: This study explores the interplay amongst ICT (information and communication technologies) indicators, including Internet and mobile phone usage, unemployment, and adult obesity in the BRICS states from 1995 to 2022, using recently developed cointegration techniques and causality tests. Results: The outcomes of causality tests uncover an interaction between Internet and mobile phone usage, unemployment, and adult obesity. In addition, the cointegration coefficients reveal that Internet and mobile phone usage positively impact adult obesity, while unemployment has a negative effect on adult obesity. Conclusions: Our outcomes uncover that improper use of the Internet and mobile phones foster adult obesity, but proper utilization of the Internet and mobile phones can be effective instruments in combatting adult obesity through increasing the awareness of healthy lifestyles and online weight loss programs. Full article

Aging involves a progressive decline in physiological functions, increasing vulnerability to disorders, functional decline, and disability. Emphasizing resilience and intrinsic capacity offers a proactive framework for promoting successful aging and quality of life. This narrative review selected significant articles published within the last five years on resilience, especially physical resilience, and intrinsic capacity, along with earlier relevant works. Articles were primarily searched in English using PubMed, Google Scholar, and Scopus, employing relevant terms with Boolean operators (“AND”, “OR”). Inclusion criteria included peer-reviewed conceptual, observational, and interventional studies on resilience and/or intrinsic capacity in adults over 60, published between 2020 and 2025, highlighting how the inclusion of geriatric evaluation improves health outcomes. Studies not focused on older adults, outside the date range, or non-English articles were excluded. Out of 145 references, 43 articles met the inclusion criteria. ResEvidence suggests that resilience (a dynamic response to stressors) and intrinsic capacity (baseline reserves across locomotion, vitality, cognition, sensory, and psychological domains) are interconnected, with resilience being associated with better health outcomes, a lower prevalence of chronic diseases, and greater mental health stability. Incorporating assessments of resilience and intrinsic capacity into clinical workflows could support targeted interventions; routine screening may guide personalized exercise and psychosocial plans to help prevent functional decline. Utilizing brief, validated tools (e.g., Short Physical Performance Battery, handgrip strength, Geriatric Depression Scale, brief cognitive tests, and resilience scales) can inform interventions such as physical activity, nutritional support, deprescribing, and psychosocial engagement, which may support healthier aging trajectories. Full article

Background: Neurofeedback training has emerged as a promising tool for enhancing performance by targeting specific brain activity patterns linked to motor skills, decision-making, and concentration. This study aimed to explore the associations between neurofeedback outcomes and football-specific performance metrics, including anthropometric, physical, technical, and tactical dimensions. Methods: A quasi-experimental design was used to examine the effects of a six-week neurofeedback training program on motor skills, tactical decision-making, and physical performance in young women’s football players (n = 8, aged 14–18). Participants underwent 30-min sessions three times a week targeting sensorimotor rhythms (SMRs) in the 12–15 Hz range within virtual football scenarios. Pre- and post-intervention assessments included anthropometric measures, neurophysiological evaluations, Loughborough Soccer Shooting Test (LSST), and Yo-Yo Intermittent Recovery Test Level 1 (YYIR1). Tactical decision-making was evaluated with a FUT-SAT-based instrument, and biological maturity was estimated using the Mirwald equations. Results: Statistical analyses using Pearson’s correlations revealed significant associations between neurofeedback outcomes, motor efficiency indices (MEIs), decision-making (DM), and football performance metrics. Correlation coefficients ranged from 0.504 to 0.998, with p-values from 0.010 to <0.001, indicating significant associations across physical, technical, and tactical dimensions. Conclusions: This study highlights the beneficial impact of neurofeedback on football performance in young female athletes. Full article

Objective: This pilot study assessed the psychological and physical impact of the COVID-19 pandemic on postpartum women that gave birth during the pandemic, and evaluated the feasibility of remote monitoring for maternal mental health. The study also proposes a conceptual framework to strengthen remote maternal care in future public health emergencies. Methods: Conducted between 2020 and 2021 in Reggio Emilia, one of Italy’s ten hardest-hit provinces during the early COVID-19 outbreak, this study enrolled 21 pregnant women (10 COVID-19-positive at delivery, 11 COVID-19-negative controls). Psychological and physical health were assessed using validated instruments: the Beck Depression Inventory (BDI) and Edinburgh Postnatal Depression Scale (EPDS) for depression, the State-Trait Anxiety Inventory (STAI) for anxiety, the Impact of Event Scale–Revised (IES-R) for trauma-related stress, and the SF-36 for physical functioning. Additional measures included breastfeeding experience and resilience. Remote assessments were conducted between 6 and 12 months postpartum to evaluate psychological recovery and satisfaction with perinatal care. C test was used to compare the two groups of women. Results: COVID-19-positive women reported significantly higher depressive symptoms (BDI: 13.50 ± 8.14 vs. 6.73 ± 4.73; U = 27, p = 0.048), and elevated state anxiety levels (STAI-S: 41.60 ± 10.23 vs. 33.64 ± 10.15; U = 27, p = 0.048) compared to controls. Post-traumatic stress symptoms were also higher among COVID-positive participants (IES-R total: 41.10 ± 19.33 vs. 30.64 ± 7.99; U = 24.5, p = 0.029). No significant differences emerged in EPDS or trait anxiety scores. Conclusions: Remote data collection proved feasible for postpartum women during the pandemic and highlighted elevated depressive, anxiety, and trauma-related symptoms in COVID-19-positive mothers. These findings support the development of flexible digital care frameworks for maternal well-being in crises. The introduction of the “10 Gold Rules for Remote Maternal Healthcare in Critical Situations” offers a forward-looking, expert-informed conceptual framework to guide the development of scalable, trust-based digital care models that go beyond monitoring to include proactive, patient-centred support. Full article

Mitigating ground vibrations from sources like vehicles and construction operations poses significant challenges, often relying on computationally intensive numerical methods such as Finite Element Methods (FEM) or Boundary Element Methods (BEM) for analysis. This study addresses these limitations by developing and evaluating Machine Learning (ML) methodologies for the rapid and accurate prediction of Insertion Loss (IL), a critical parameter for assessing the effectiveness of open trenches as vibration barriers. A comprehensive database was systematically generated through high-fidelity numerical simulations, capturing a wide range of geometric, elastic, and physical configurations of a stratified geotechnical system. Three distinct ML strategies—Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF)—were initially assessed for their predictive capabilities. Subsequently, a Meta-RF stacking ensemble model was developed, integrating the predictions of these base methods. Model performance was rigorously evaluated using complementary statistical metrics (RMSE, MAE, NMAE, R), substantiated by in-depth statistical analyses (normality tests, Bootstrap confidence intervals, Wilcoxon tests) and an analysis of input parameter sensitivity. The results clearly demonstrate the high efficacy of Machine Learning (ML) in accurately predicting IL across diverse, realistic scenarios. While all models performed strongly, the RF and the Meta-RF stacking ensemble models consistently emerged as the most robust and accurate predictors. They exhibited superior generalization capabilities and effectively mitigated the inherent biases found in the ANN and SVM models. This work is intended to function as a proof-of-concept and offers promising avenues for overcoming the significant computational costs associated with traditional simulation methods, thereby enabling rapid design optimization and real-time assessment of vibration mitigation measures in geotechnical engineering. Full article

Introduction: Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease in children. It is believed that children with JIA have lower cardiopulmonary capacity and worse exercise tolerance. The gold standard for assessing physical fitness is aerobic fitness, commonly referred to as the maximum or peak oxygen uptake volume (peakVO₂) measured during a maximum load exercise test. Reduced aerobic fitness may play a key role in predicting the health of JIA patients as it has been associated with cardiovascular diseases and increased adult mortality. Methods: The aim of this study was to assess the oxygen capacity of adolescents with JIA along with other cardiopulmonary parameters in order to determine a group of patients with increased risk of developing cardiovascular diseases in comparison with healthy individuals. Patients were assessed based on parameters such as age, sex, type of JIA, laboratory parameters, physical activity, and treatment. Results: Patients with JIA had lower median values of peakVO₂ (29.05 vs. 38.02 mL/min/kg, p < 0.001), as well as other crucial cardiopulmonary parameters, such as O₂ pulse, minute ventilation, oxygen uptake efficiency slope, and cardiac output than in the healthy control group. The ventilatory anaerobic threshold was achieved earlier and at lower VO₂ values in children with JIA (p = 0.0001). Children with JIA also had lowered respiratory parameters such as maximal voluntary ventilation (p = 0.0031) and tidal volume (p = 0.0002). Patients who were physically active (moderate-intensity physical activity lasting at least 60 min per day) had significantly higher peakVO₂ (p = 0.0099) and ΔVO₂/ΔWR relationship (p = 0.0041) values than JIA patients who were not physically active. Conclusions: Children with JIA show moderate to severe physical impairment. Reduced physical fitness and a low level of activity might be associated with further deterioration of patient’s condition, which might contribute to increased risk of cardiovascular disease, social exclusion and deterioration of quality of life in this group of patients. Exercise programs that improve aerobic fitness and increase muscle strength should be individualized and modified based on the individual needs and capabilities of the patient. Full article

Thermosyphon heat pipes (THPs) are increasingly employed in advanced thermal management applications due to their highly effective thermal conductivity, compact design, and passive operation. In this study, a numerical investigation was conducted on a copper or aluminum thermosyphon charged with different working fluids, with methanol serving as a reference case. A two-dimensional compressible CFD model was implemented in OpenFOAM, coupling the Volume of Fluid (VOF) method with a hybrid phase-change formulation that integrates the Lee and Tanasawa approaches. It provides, indeed, a balance between computational efficiency and physical fidelity. The vapor flow, considered as an ideal gas, was assumed compressible. The isoAdvector algorithm was applied as a reconstruction technique in order to improve interface capturing, to reduce spurious oscillations and parasitic currents, and to ensure more realistic simulation of boiling and condensation phenomena. The performance dependency on operating parameters such as the inclination angle, liquid filling ratio, and thermophysical properties of the working fluid is analyzed. The numerical predictions were validated against experimental measurements obtained from a dedicated test bench, showing discrepancies below 3% under vertical operation. This work provides new insights into the coupled influence of orientation, fluid inventory, and working fluid properties on THP behavior. Beyond the experimental validation, it establishes a robust computational framework for predicting two-phase heat and mass transfer phenomena by linearizing and treating the terms involved in thebalances to be satisfied implicitly. The results reveal a strong interplay between the inclination angle and filling ratio in determining the overall thermal resistance. At low filling ratios, the vertical operation led to insufficient liquid return and increased resistance, whereas inclined orientations enhanced the liquid spreading and promoted more efficient evaporation. An optimal filling ratio range of 40–60% was identified, minimizing the thermal resistance across the working fluids. In contrast, excessive liquid charge reduced the vapor space and degraded the performance due toflow restriction and evaporationflooding. Full article

Runoff simulation and forecasting is of great significance for flood control, disaster mitigation, and water resource management. Alpine regions are characterized by complex terrain, diverse precipitation patterns, and strong snow-and-ice melt influences, making accurate runoff simulation particularly challenging yet crucial. To enhance predictive capability and model applicability, this study takes the Upper Jinsha River as a case study and comparatively evaluates the performance of a physics-based hydrological model BTOP and the data-driven deep learning models LSTM and BiLSTM in runoff simulation and short-term forecasting. The results indicate that for daily-scale runoff simulation, the LSTM and BiLSTM models demonstrated superior simulation capabilities, achieving Nash–Sutcliffe efficiency coefficients (NSE) of 0.82/0.81 (Zhimenda Station) and 0.87/0.86 (Gangtuo Station) during the test period. These values are significantly better than those of the BTOP model, which achieved a validation NSE of 0.57 at Zhimenda and 0.62 at Gangtuo. However, the hydrology-based structure of the BTOP model endowed it with greater stability in water balance and long-term simulation. In short-term forecasting (1–7 d), LSTM and BiLSTM performed comparably, with the bidirectional architecture of BiLSTM offering no significant advantage. When it came to flood events, the data-driven models excelled at capturing peak timing and hydrograph shape, whereas the physical BTOP model demonstrated superior stability in flood peak magnitude. However, forecasts from the data-driven models also lacked hydrological consistency between upstream and downstream stations. In conclusion, the present study confirms that deep learning models achieve superior accuracy in runoff simulation compared to the physics-based BTOP model and effectively capture key flood characteristics, establishing their value as a powerful tool for hydrological applications in alpine regions. Full article

With ongoing climate transformations, reliable Arctic sea ice forecasts are essential for understanding impacts on shipping, ecosystems, and climate teleconnections. This research examines physics-free neural architectures versus physics-informed statistical models for long-term Arctic projections by implementing Fourier Neural Operator (FNO) and Convolutional Neural Network (CNN) alongside a seasonal SARIMAX time series model incorporating physical predictors including temperature anomalies and ice thickness. We test whether neural models trained on historical ice data can match physics-informed SARIMAX reliability, and whether approaches exhibit systematic biases toward specific emission pathways. Using data from January 1979 to December 2024, we conducted forecasts through 2100, with SARIMAX driven by CMIP6 sea ice thickness under SSP2-4.5 and SSP5-8.5 scenarios. Results decisively reject the first hypothesis: both neural models projected ice free Arctic summer by September 2089 regardless of emission scenario, while SARIMAX maintained physically plausible seasonal coverage throughout the century under both pathways. Neural approaches demonstrated systematic bias toward extreme warming exceeding even high-emission projections, revealing fundamental limitations in physics-free deep learning for climate forecasting where physical constraints are paramount. Full article

The aim of this study is to improve the film removal rate of a film removal device on a residual film recovery machine and mitigate the soil compaction caused by film removal operations during the residual film recovery process. We designed a bionic film-lifting shovel by applying the contour curve of the first claw of the North China mole cricket’s front foot to the soil-penetrating portion of the film-lifting tines. Based on agronomic requirements and mechanical analysis of the operation process, the biomimetic blade was developed to break up soil more effectively and lift the residual film more efficiently. The contour features were obtained using high-definition cameras, with the fitting equation guiding the design of the soil-penetrating structure. A three-dimensional model was constructed using SolidWorks. Tensile tests provided the physical parameters of the autumn residual film, enabling the creation of a finite element model using the Mohr–Coulomb yield criterion. Simulation comparisons showed that the biomimetic shovel teeth reduced the operating resistance by 9.3% compared to conventional teeth. Soil trench experiments validated these results, demonstrating a 4.24% higher film-lifting rate and average resistance of 411.49 N for the bionic shovel versus 454.70 N for the conventional one. The close match between the experimental and simulation results confirms the effectiveness of the bionic design in meeting the resistance reduction requirements. Full article

Achieving high-precision clamping force control is crucial for Electro-Mechanical Braking (EMB) systems but remains challenging due to significant nonlinear friction (e.g., static, Coulomb, and viscous friction) within the transmission mechanism. To address this, a comprehensive model integrating the electrical and mechanical dynamics of the EMB actuator is first established. This pressure-oriented model, which explicitly accounts for the nonlinear frictions, is developed and validated in MATLAB/Simulink 2022b. Furthermore, physical experiments under typical braking scenarios are conducted to investigate the system’s friction characteristics, leading to the identification of a displacement–pressure load curve for the actuator. This curve serves as a key reference for braking force observation. Finally, a braking force observer-based controller is designed, implemented via an Auto-Disturbance Rejection Control (ADRC) algorithm. Experimental results from step and sinusoidal braking force tests demonstrate that the proposed controller not only effectively compensates for nonlinear disturbances but also achieves robust and stable clamping force control. Full article

Background: College students face significant stress from academic demands and high pressures, which can contribute to long-term physical and mental health issues. Existing stress-relief strategies are not always immediately available to this population, highlighting the need for accessible, low-cost solutions. Methods: This randomised controlled trial examined the effects of nature exposure on stress and well-being in a sample of 29 healthy college students compared to a healthy control group (n = 28). The intervention group engaged in 30 min walks in a natural environment four times per week over a four-week period. Stress levels and general well-being were assessed using validated self-report questionnaires administered before and after the intervention period, allowing for a comparison of changes in mental health outcomes between an intervention and control group. Eye-tracking analysis during a battery of cognitive tests assessed cognitive functioning. Findings: The intervention was associated with a greater reduction in psychological distress over time (β = −2.98, p = 0.007) and showed a trend toward reduced burnout symptoms (β = −0.12, p = 0.08) compared to the control group. These associations are independent of sex, age, BMI, smoking status, COVID-19 history, and previous diagnosis of mental illness. An increase in the number of saccades during the visual working memory task was observed in the intervention group compared to controls (β = 5.01, p = 0.046), while saccadic activity in other tasks remained unchanged. No significant effects were found for the neurocognitive performance measures. Conclusions: These findings suggest that short-term nature exposure may support psychological well-being and mental engagement in young adults. Our research highlights the use of walking in nature as a realistic and accessible strategy to promote mental health and neurocognitive functioning among students. Full article