Search Results (4,553)

Training load monitoring in women’s volleyball is a challenge for optimizing performance and mitigating injury risk. Non-invasive monitoring technologies and machine learning (ML) can support decision-making, but the evidence remains heterogeneous. This scoping review mapped and integrated the evidence on training load management, fatigue, and performance in women’s volleyball and identified gaps. The PRISMA Extension for Scoping Reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI) framework were followed. A systematic search was conducted in Scopus, Web of Science, and PubMed, covering January 2020 to September 2025. We included studies in female players at any competitive level, including mixed-sex studies meeting a minimum threshold of female participation, that evaluated external and/or internal load, neuromuscular or perceptual fatigue, and/or performance, using standardized data extraction and narrative/thematic synthesis. Fifty-three studies were included. Inertial measurement units (IMUs), force platforms, heart rate (HR) and heart rate variability (HRV), wellness questionnaires, and global/local positioning systems (GPSs/LPSs) were most prevalent. External-load intensity indicators (e.g., high-intensity jumps and accelerations) were reported as more sensitive to fatigue-related changes than accumulated volume. Machine learning models were less frequent and were mainly applied to multi-source integration and fatigue/readiness prediction, with recurring limitations in external validation and interpretability. Women-specific biological moderators, such as the menstrual cycle, were rarely addressed. Full article

Flexible barrier systems are widely used for landslide and debris flow mitigation due to their ability to dissipate impact energy through large deformations. Conventional systems, however, rely on steel mesh components, which are associated with high environmental impact and durability concerns. This study examines the feasibility of a sustainable coir geotextile composite barrier as an alternative flexible barrier for mitigating small-to-moderate landslides. A woven geotextile barrier was developed using multi-strand coir ropes and evaluated through a comprehensive experimental program involving physical and mechanical characterization, accelerated degradation testing, incremental static loading, vertical drop impact tests, and sustained load retention tests. The developed barrier exhibited a high mass per unit area of approximately 3750 g/m² and tensile capacities exceeding 2 kN at the rope level. Accelerated weathering tests revealed a limited reduction in tensile strength of approximately 5% after three years of exposure, whereas prolonged exposure of five years led to strength losses exceeding 70%, underscoring durability as a key design consideration. Static loading tests confirmed stable behavior up to 550 kg, and sustained loading of approximately 1700 kg was maintained over 48 h without loss of structural integrity. Vertical drop tests demonstrated impact resistance in the range of 6–51 kN, depending on the drop height, mass, and connection density. The results demonstrate that coir geotextile barriers can function as flexible, energy-dissipating composite systems suitable for sustainable landslide mitigation in moderate hazard scenarios. Full article

With the increasing integration of high-proportion renewable energy, power systems are exhibiting low-inertia and low-damping characteristics, posing severe challenges to frequency stability. This paper proposes a coordinated supplementary frequency regulation strategy utilizing electrolytic aluminum (EA) loads and a hybrid energy storage system (HESS). Firstly, a system frequency response model is established, incorporating EA, electrochemical energy storage, pumped hydro storage, and conventional generation units. Secondly, an improved variable filter time constant controller is designed, supplemented by fuzzy logic, to achieve adaptive power allocation under different disturbance magnitudes. Concurrently, regulation intervals are defined based on the area control error (ACE), enabling a tiered response from source-grid-load resources. Simulation results demonstrate that under a severe disturbance of 0.05 p.u., the proposed strategy reduces the maximum frequency deviation from 0.198 Hz to 0.054 Hz, achieving a 72.7% performance improvement, and shortens the system settling time by 59.5%. Furthermore, the state of charge (SOC) of the electrochemical storage is successfully maintained within the range of [0.482, 0.505], effectively balancing frequency regulation performance and device lifespan. The findings demonstrate the effectiveness of the proposed strategy in enhancing the frequency resilience of low-inertia power grids. Full article

The primary objective of the current study is to establish and validate for the first time a method to determine and quantify praziquantel (PZQ) and its main degradation products loaded in poly(methyl methacrylate–co-2-(diethylamino)ethyl methacrylate) P(MMA-co-DEAEMA) microparticles. A high-performance liquid chromatography (HPLC) approach was developed and validated in accordance with the United States Pharmacopeia (USP) guidelines, addressing parameters such as accuracy, linearity, solution stability, precision, specificity, robustness, sensitivity, and system suitability. The method employed a gradient mobile phase consisting of ultrapure water and acetonitrile, flowing at a rate of 1 mL/minute over a Phenomenex Kinetex^® C18 column (5 µm, 100 Å, 250 × 4.6 mm) maintained at 35 °C. Detection was performed at the wavelength of 210 nm using a DAD/UV detector. Samples of the active pharmaceutical ingredient (API) praziquantel, microencapsulated praziquantel, placebo, and a mixture of related substances (A, B, and C) were prepared with 0.5% formic acid in water/ethanol, 45:55 v/v as the diluent, and injected at 20 °C. The method demonstrated a limit of quantification (LOQ) of 0.20 µg/mL for praziquantel and related substances. The method exhibited an excellent linear response, with all correlation coefficients (R²) values exceeding 0.998, which is well above the recommended specified limit of R² > 0.995. Percent recoveries fell within the acceptable range of (95.0–105.0%), and all results indicated a percentage of relative standard deviation (%RSD) ≤ 2.0, indicating a robust methodology. Thus, the proposed HPLC technique proved to be selective, accurate, sensitive, and consistent in analyzing both the material content and its main degradation products. Full article

Microbiological contamination in public buildings is closely linked to human presence, such as airborne bacteria, fungi, and particulate matter, which strongly influence indoor air quality (IAQ). This study examined the distribution of microorganisms in a museum building in relation to time of day, air-handling unit (AHU) type, and ventilation operating mode. Exhibition rooms without natural light relied entirely on a central heating, ventilation and air conditioning (HVAC) system. Microbiological contamination was assessed using Koch’s passive sedimentation method over a 24 h cycle for two AHUs (I and III) and selected rooms, while CO₂ levels were monitored as indicators of occupancy and ventilation demand in line with EN 16798-1:2019 and ASHRAE 62.1-2022. Although the demand-controlled ventilation system increased the outdoor air fraction from 40% to 70–100% during peak visitor density, localized increases in microbial contamination occurred. AHU I showed higher loads of Staphylococcus sp. and fungi, while AHU III exhibited pronounced fungal peaks influenced by elevated humidity from an open water reservoir. Psychrophilic bacteria reached 140–230 CFU·m⁻³, mesophilic bacteria 230–320 CFU·m⁻³, and fungi up to 740 CFU·m⁻³. Most CFU values remained below commonly referenced upper limits (<1000 CFU·m⁻³), but several peaks exceeded lower recommended thresholds, indicating a need for improvements. Enhanced filtration, humidity control, increased airflow during high occupancy, and reducing moisture sources in AHUs may mitigate microbial growth and improve IAQ in public buildings. Full article

Smart microgrid is promising in providing a more affordable, efficient, and sustainable energy solution with increasing energy production from distributed renewable sources and diverse household electricity usage with large amounts of connected smart devices. Accurate prediction of the household electricity load and renewable energy production plays a significant role in achieving optimized efficiency of the microgrid. Meanwhile, the privacy and security of data sharing over the smart grid are crucial. This paper proposes a blockchain-enabled microgrid Internet of Things (MIoT) with accurate predictions of renewable energy production and household electricity load. The blockchain framework can guarantee the privacy and security of data sharing over the microgrid. An improved model by stacking long short-term memory (LSTM) and gated recurrent units (GRUs) is proposed for energy generation and electricity load predictions using historical data in the microgrid and the weather forecasting data. The sparrow search algorithm optimized by Levy flights (LevySSA) is used to optimize the hyperparameters of the stacked LSTM-GRU method. The experimental results verify the accuracy and robustness of the proposed method in the prediction of electricity load and renewable energy production for effective smart microgrid operation. For PV forecasting, the proposed LevySSA-LSTM-GRU achieves nRMSE = 0.0535, nMAE = 0.0455, and R² = 0.9898, outperforming the strongest baseline. For load forecasting, averaged over four test intervals, it yields nRMSE = 0.1034, nMAE = 0.0836, with R² = 0.9340, demonstrating consistent superiority compared with conventional baseline models. Overall, the proposed framework enables secure data sharing and high-accuracy forecasting, offering strong potential to support real-time energy management and operational optimization in smart microgrids. Full article

Background/Objectives: Knee osteoarthritis (KOA) is associated with pain, functional decline, and altered biomechanics. The Step-Up and Down-Forwards (StUD-F) task provides an ecologically relevant assessment of challenging movements. This study investigated neuromuscular activation and lower-limb kinematics of leading and trailing-limbs during the StUD-F in individuals with KOA. Methods: Forty participants with KOA (65.3 ± 7.68 years; 21M/19F; BMI 28.9 ± 4.52 kg/m²) completed a 25 cm box StUD-F task. Surface electromyograph recorded bilateral activation of the vastus medialis (VM), vastus lateralis (VL), bicep femoris (BF), and semitendinosus (ST). Triplanar hip, knee, and ankle joint angles were estimated using inertial measurement units. StUD-F events (initial stance; step contact; ascent completion; descent preparation; step-down touchdown; and descent completion) were identified using custom algorithms. Pain was assessed using visual analogue scales and the Knee Injury and Osteoarthritis Outcome Score (KOOS). Limb differences were analysed for leading or trailing roles using paired samples t-tests or non-parametric equivalents; waveforms were visually inspected. Results: Distinct neuromuscular and kinematic asymmetries were observed when affected and contralateral limbs were compared within each role (leading/trailing). During step-up, the affected leading limb demonstrated higher quadriceps activation at initial stance (VM: p = 0.035; VL: p = 0.027) and reduced trailing-limb activation at step contact (VM: p = 0.015; VL: p = 0.018), with sagittal-plane ankle differences (p = 0.004). During step-down, when the affected limb initiated ascent, trailing limb activation was higher at descent completion (VL: p < 0.001; VM: p = 0.003; BF: p = 0.009), with coronal-plane hip deviations (p < 0.001). When the contralateral limb-initiated ascent, trailing-limb muscles activation differences (VM: p < 0.001; VL: p = 0.015; BF: p = 0.007) and ankle/coronal-plane asymmetries (p ≤ 0.049) persisted. Conclusions: The StUD-F task elicits altered strategies in KOA, including elevated quadriceps–hamstring co-activation and altered sagittal/coronal alignment, and habitual limb choice across ascent and descent. These adaptations may enhance stability and joint protection but could increase medial compartment loading. The findings support rehabilitation focused on dynamic control, alignment, and shock absorption. Full article

Against the backdrop of high-proportion renewable energy grid integration, modeling accuracy for substations incorporating wind and solar power is critical. Traditional modeling methods rely on theoretical parameters and lack sufficient accuracy. This study uses the 154 kV/23 kV Yeonggwang Substation in Jeollanam-do, South Korea (connected to three wind farms and three solar power plants, with 35 Micro-Phasor Measurement Unit (μPMU) measurement points deployed) as a case study. It investigates three-phase detailed modeling using Power System Computer Aided Design (PSCAD) and μPMU data-driven calibration. Based on substation topology and equipment parameters, a simulation model encompassing main transformers, transmission lines, renewable energy units, and loads was established. A hierarchical calibration system of “data preprocessing—parameter identification—iterative correction” was constructed, employing an iterative optimization strategy of “main grid layer—renewable energy layer—load layer.” A multi-objective optimization function centered on voltage, current, and power was developed. Verification results show that after calibration, the mean relative error rates (MRE) for voltage, current, active power and reactive power are 2.46%, 2.57%, 2.52% and 3.96% respectively, with mean error reduction rates (MERRs) of 80%, 82.75%, 81.33%, and 74.94% compared to pre-calibration values. The uniqueness of the calibration method proposed in this study lies in its use of actual μPMU measurement data to drive PSCAD model parameter calibration, achieving precise matching with the actual characteristics of the substation. This provides a reference method for modeling and digital twin construction of similar substations, demonstrating significant engineering application value. Full article

As wind penetration rises, the share of synchronous generation declines, reducing system inertia and increasing uncertainty in frequency stability; wind-output disturbances, power-electronic control characteristics, and stochastic load variations can further amplify frequency deviations caused by power imbalance. To improve frequency security under high wind penetration, this study optimizes BESS control parameters and evaluates their impact on system dynamic stability using a PSS®E V34 dynamic model of the IEEE New England 39-bus system that includes three wind turbines and two BESS units under four disturbance scenarios: (i) derating one turbine to 50%, (ii) tripping one turbine, (iii) derating all three turbines to 50%, and (iv) an N-1 contingency corresponding to the tripping of the largest conventional generator in the system. Morris sensitivity analysis is first applied to identify key parameters affecting frequency response and reduce the optimization dimension, and the selected parameters are then tuned using an improved genetic algorithm (IGA) and grey wolf optimization (GWO). Simulation results show the minimum frequency improves from 59.957 Hz (baseline) to 59.961 Hz with IGA and to 59.966 Hz with GWO, while the maximum equivalent power-angle difference in the BESS unit relative to the center of inertia decreases from 266.3° to 250.1° (IGA) and 251.2° (GWO), indicating that the proposed approach strengthens BESS frequency support and enhances dynamic stability under various wind-power and N-1 contingency disturbance conditions. Full article

The integration of new energy into the grid has significantly intensified power grid operational pressure, posing higher demands on hydropower system regulation. As a key unit for power grid load tracking and stability maintenance, parameter mismatch of the PID governor is prone to inducing system bifurcation, thus leading to oscillatory instability, which has emerged as a critical challenge affecting the reliable consumption and sustainable supply of new energy. To address this challenge, a hydroelectric power generation system (HPGS) model in the infinite-bus power system is established. Bifurcation analysis is employed to quantitatively identify the critical thresholds of PID parameters that cause HPGS instability. Based on this, system dynamic response processes under critical thresholds are clarified using time-domain analysis. Furthermore, the potential oscillation instability mechanism is revealed using eigenvalue analysis, and suggestions for PID parameter selection are provided. Key quantitative results indicate that variations in proportional gain, kp, induce five limit point bifurcations. The system enters an unstable region when k_p exceeds 2.467, whereas operation within the range below 0.891 is conducive to system stability. A supercritical Hopf bifurcation arises when integral gain k_i reaches 0.925, so strict restrictions should be imposed on k_i to avoid operating around this critical value. Two supercritical Hopf bifurcations that may trigger system oscillatory instability are identified during differential gain k_d changing, and it should be regulated to a level below 5.188 to ensure system stability. By integrating bifurcation analysis, time-domain analysis, and eigenvalue analysis, this study effectively improves the accuracy of characterizing system dynamic behaviors, providing a clear quantitative basis for PID parameter optimization and bifurcation suppression, as well as laying a theoretical foundation for hydropower system stable operation and the efficient absorption of new energy. Full article

Nonlinear loads are widely applied, making the generation mechanism of grid harmonics increasingly intricate. However, high-precision monitoring devices suffer from high deployment costs and limited coverage. This poses a major challenge to directly acquiring harmonic voltages at some nodes. To solve this problem, this paper proposes a harmonic state estimation method based on a Dual-Stream Adaptive Fusion Generative Adversarial Network (DSAF-GAN), with an innovative design in its generator architecture. A dual-path generator is developed to extract multi-scale features through heterogeneous network branches collaboratively. The ResNet-GRU path integrates convolutional residual modules with Bidirectional Gated Recurrent Units (Bi-GRUs). It effectively captures local spatial patterns and temporal dynamic characteristics of time-series data. The multi-layer perceptron (MLP) path focuses on mining global nonlinear correlations, thereby enhancing the overall feature-expressing capability. An adaptive weight fusion module (Attention Weight Net) fuses the outputs of the two paths. It dynamically allocates contribution weights, improving the model’s flexibility and generalization performance. Experimental results show that the proposed DSAF-GAN can accurately reconstruct the harmonic voltage component content rate of missing nodes. Full article

Bio-inspired flow-sensing and actuation mechanisms offer a promising path for enhancing the stability of flapping-wing flying robots (FWFRs) operating in dynamic and noisy environments. This study introduces a bio-inspired sensing and actuation feather unit (SAFU) that mimics the covert feathers of falcons and serves simultaneously as a distributed flow sensor and an adaptive actuation element. Each electromechanical feather (EF) passively detects airflow disturbances through deflection and actively modulates its flaps through an embedded actuator, enabling real-time aerodynamic adaptation. A reduced-order bond-graph model capturing the coupled aero-electromechanical dynamics of the FWFR wing and SAFU is developed to provide a physics-based training environment for a proximal policy optimization (PPO) based reinforcement learning controller. Through closed-loop interaction with this environment, the PPO policy autonomously learns control actions that regulate feather displacement, reduce airflow-induced loads, and improve dynamic stability without predefined control laws. Simulation results show that the PPO-driven SAFU achieves fast, well-damped responses with rise times below 0.5 s, settling times under 1.4 s, near-zero steady-state error across varying gust conditions and up to 50% alleviation of airflow-induced disturbance effects. Overall, this work highlights the potential of bio-inspired sensing-actuation architectures, combined with reinforcement learning, to serve as a promising solution for future flapping-wing drone designs, enabling enhanced resilience, autonomous flow adaptation, and intelligent aerodynamic control during operations in gusts. Full article

The liquid oxygen subcooler is a key unit for the deep cooling, storage, and transportation of liquid oxygen. Its frequent start–stop operation under liquid nitrogen bath conditions introduces potential risks to service reliability. This study employs a thermo-structural sequential coupling approach to evaluate the stress behavior of ABS components in a flat plate-fin heat exchanger during the pre-cooling, heat-exchange, and recovery stages. Based on the maximum shear stress (Tresca) criterion, the evolution of principal stresses in the brazed layer under liquid nitrogen bath conditions was analyzed, and a conservative assessment of the material’s fatigue behavior was conducted. The results indicate that the equivalent stress is governed by the third principal stress, originating from the thermal compression effect induced by low-temperature constraint shrinkage. During the heat exchange phase (2700 s), the inlet equivalent stress reached 93.49 MPa, which is below the 258 MPa limit, falling within Region 1. Local stress concentration is primarily driven by thermal loading, with brazing layer thickness, curvature radius, and liquid oxygen pressure serving as key control variables. Under a safety factor of 1.15 (107 MPa), fatigue testing exceeding 1.5 million cycles has confirmed the static safety and operational reliability of the ABS. Full article

Performance diagnostics in alpine skiing increasingly rely on integrated biomechanical and kinematic assessments to support technique optimization under real training conditions; however, many existing approaches address trajectory geometry or biomechanical variables separately, limiting their explanatory power. This study evaluates an integrated analysis framework combining OptiPath, an AI-assisted video-based trajectory analysis tool, with XSensDOT wearable inertial sensors to identify technical inefficiencies during giant slalom skiing. Thirty competitive youth athletes (n = 30; 14–16 years) performed controlled runs with predefined lateral offsets from the gates, enabling systematic examination of the relationship between spatial trajectory deviations, biomechanical execution, and performance outcomes. Skier trajectories were extracted using computer vision-based methods, while lower-limb kinematics, trunk motion, and tri-axial acceleration were recorded using inertial measurement units. Deviations from mathematically defined ideal trajectories were quantified through regression-based calibration and arc-based modeling. The results show that although OptiPath reliably detected trajectory variations, shorter skiing paths did not consistently produce faster run times. Instead, superior performance was associated with more efficient biomechanical execution, reflected by coordinated trunk–lower limb motion, controlled vertical loading, reduced lateral corrections, and higher forward acceleration, even when longer trajectories were followed. These findings indicate that trajectory geometry alone is insufficient to explain performance outcomes and support the integration of wearable biomechanics with trajectory modeling as a practical, low-cost, and field-deployable tool for alpine skiing performance diagnostics. Full article

Short-term electricity load forecasting has become increasingly challenging due to growing demand volatility, nonlinear load patterns, and the dynamic penetration of renewable energy sources. Conventional forecasting models often suffer from sensitivity to hyperparameter settings and limited capability in capturing long-term temporal dependencies. To address these issues, this paper proposes a hybrid forecasting framework that integrates an Improved Snow Ablation Optimizer (ISAO) with a Dilated Bidirectional Gated Recurrent Unit (Dilated BiGRU). The proposed ISAO enhances the original Snow Ablation Optimizer through three key strategies to improve performance in high-dimensional optimization problems: (i) a subgroup cooperative mechanism to alleviate cross-dimensional interference, (ii) a learning-automata-based adaptive dimension assignment strategy to dynamically allocate optimization resources, and (iii) a t-distribution-based adaptive step size mechanism to balance global exploration and local exploitation. Extensive experiments on the CEC2017 benchmark suite demonstrate that ISAO achieves superior convergence speed and optimization accuracy, with average rankings of 1.60, 1.77, and 2.03 on 30-, 50-, and 100-dimensional problems, respectively, significantly outperforming the original SAO and several state-of-the-art metaheuristic algorithms. Building upon this optimization capability, ISAO is employed to automatically tune the key hyperparameters of the Dilated BiGRU model. Experiments conducted on the Kaggle electricity load dataset show that the proposed ISAO-Dilated BiGRU model achieves MAE, MAPE, and RMSE values of 20.003, 1.711%, and 25.926, respectively, corresponding to reductions of 16.6%, 15.6%, and 17.7% compared with the baseline model, along with an R² of 0.97841. Comparative results against RNN, LSTM, Random Forest, and the original Dilated BiGRU confirm the robustness and superior long-term dependency modeling capability of the proposed framework. Overall, the proposed ISAO effectively enhances hyperparameter optimization quality and significantly improves the predictive accuracy and stability of the Dilated BiGRU model, providing a reliable and practical solution for short-term electricity load forecasting in modern power systems. Full article