Search Results (81)

Age-related declines in balance and cognitive function increase fall risk and reduce quality of life in older adults and people with neurological disorders. Studying these changes in unimpaired adults provides a normative reference for identifying pathological deviations. However, most dual-task studies focus on single cognitive tasks and static conditions, specifically during gait, limiting understanding of how cognitive demand interacts with postural control while standing and during dynamic challenges. This study identified cognitive and motor outcomes most sensitive to age-related differences during motor–cognitive dual tasks of varying complexity across static and dynamic balance conditions, accounting for minimal detectable change. Sixty healthy adults performed dual-tasks ranging from simple motor activities to complex cognitive challenges (Stroop Test) while standing on a robotic platform. Cognitive performance (reaction time) and balance outcomes, including trunk and center of pressure (CoP) sway area, were assessed. Reaction time was sensitive to aging, with standardized estimates ranging from 0.014 to 0.036. The highest values occurred in the most demanding dual-task condition, enabling detection of meaningful change over short timeframes. Age effects on balance were modest under static conditions but amplified during dynamic perturbations across all dual tasks. In the SCWT 3 condition, standardized estimates for CoP sway area increased from 0.006 in the static condition to 0.047 in the passive condition, reflecting an approximately eightfold increase in age sensitivity. Trunk sway primarily reflected cognitive load, whereas CoP sway was most sensitive to balance perturbations and exceeded minimal detectable thresholds over only a couple of years. These findings support sensitive task–condition combinations for early detection and monitoring of age-related cognitive and balance decline. Full article

The widespread adoption of electric vehicles (EVs) has posed significant challenges to the security of distribution grid loads. To address issues such as increased grid load fluctuations, rising user charging costs, and rapid load surges around midnight caused by uncoordinated nighttime charging of household electric vehicles in communities, this paper first models electric vehicle charging behavior as a Markov Decision Process (MDP). By improving the state-space sampling mechanism, a continuous space mapping and a priority mechanism are designed to transform the charging scheduling problem into a continuous decision-making framework while optimizing the dynamic adjustment between state and action spaces. On this basis, to achieve synergistic load forecasting and charging scheduling decisions, a forecast-augmented deep reinforcement learning method integrating Gated Recurrent Unit and Twin Delayed Deep Deterministic Policy Gradient (GRU-TD3) is proposed. This method constructs a multi-objective reward function that comprehensively considers time-of-use electricity pricing, load stability, and user demands. The method also applies a single-objective pre-training phase and a model-specific importance-sampling strategy to improve learning efficiency and policy stability. Its effectiveness is verified through extensive comparative and ablation validation. The results show that our method outperforms several benchmarks. Specifically, compared to the Deep Deterministic Policy Gradient (DDPG) and Particle Swarm Optimization (PSO) algorithms, it reduces user costs by 11.7% and the load standard deviation by 12.9%. In contrast to uncoordinated charging strategies, it achieves a 42.5% reduction in user costs and a 20.3% decrease in load standard deviation. Moreover, relative to single-objective cost optimization approaches, the proposed algorithm effectively suppresses short-term load growth rates and mitigates the “midnight peak” phenomenon. Full article

The optimal allocation of Photovoltaic (PV) and wind-based renewable energy sources and Battery Energy Storage System (BESS) capacity is an important issue for efficient operation of a microgrid network (MGN). The impact of the unpredictability of PV and wind generation needs to be smoothed out by coherent allocation of BESS unit to meet out the load demand. To address these issues, this article proposes an efficient Energy Management System (EMS) and Demand Side Management (DSM) approaches for the optimal allocation of PV- and wind-based renewable energy sources and BESS capacity in the MGN. The DSM model helps to modify the peak load demand based on PV and wind generation, available BESS storage, and the utility grid. Based on the Real-Time Market Energy Price (RTMEP) of utility power, the charging/discharging pattern of the BESS and power exchange with the utility grid are scheduled adaptively. On this basis, a Jellyfish Search Algorithm (JSA)-based bi-level optimization model is developed that considers the optimal capacity allocation and power scheduling of PV and wind sources and BESS capacity to satisfy the load demand. The top-level planning model solves the optimal allocation of PV and wind sources intending to reduce the total power loss of the MGN. The proposed JSA-based optimization achieved 24.04% of power loss reduction (from 202.69 kW to 153.95 kW) at peak load conditions through optimal PV- and wind-based DG placement and sizing. The bottom level model explicitly focuses to achieve the optimal operational configuration of MGN through optimal power scheduling of PV, wind, BESS, and the utility grid with DSM-based load proportions with an aim to minimize the operating cost. Simulation results on the IEEE 33-node MGN demonstrate that the 20% DSM strategy attains the maximum operational cost savings of €ct 3196.18 (reduction of 2.80%) over 24 h operation, with a 46.75% peak-hour grid dependency reduction. The statistical analysis over 50 independent runs confirms the sturdiness of the JSA over Particle Swarm Optimization (PSO) and Osprey Optimization Algorithm (OOA) with a standard deviation of only 0.00017 in the fitness function, demonstrating its superior convergence characteristics to solve the proposed optimization problem. Finally, based on the simulation outcome of the considered bi-level optimization problem, it can be concluded that implementation of the proposed JSA-based optimization approach efficiently optimizes the PV- and wind-based resource allocation along with BESS capacity and helps to operate the MGN efficiently with reduced power loss and operating costs. Full article

Background/Objectives: Pediatric cancer survivors frequently experience neuromuscular sequelae related to chemotherapy-induced neurotoxicity. Agents such as vincristine, methotrexate, and platinum compounds can lead to persistent gait alterations and sensorimotor deficits that impair mobility and quality of life. This study aimed to objectively assess gait in pediatric cancer survivors after the completion of oncological pharmacological treatment to identify specific spatiotemporal, kinematic, and kinetic alterations and characterize neuromechanical patterns associated with neurotoxic exposure. Methods: A cross-sectional observational study was conducted including pediatric cancer survivors (6–18 years) who had completed chemotherapy and age- and sex-matched healthy controls. Gait was analyzed using a Vicon^®3D motion capture system, with reflective markers placed on standardized anatomical landmarks. Spatiotemporal, kinematic, and kinetic variables were compared between groups using parametric tests and statistical parametric mapping (SPM) with Holm–Bonferroni correction (α = 0.05). Results: Pediatric cancer survivors showed slower gait velocity (Mean Difference (MD) = 0.17, p = 0.018, Confidence Interval CI95% = 0.04; 0.4), shorter step (MD = 0.1, p = 0.015, CI95% = 0.01; 0.19) and stride length (MD = 0.17, p = 0.018, CI95% = 0.03; 0.31), as well as reduced single support time (MD = 0.1, p = 0.043, CI95% = 0.01; 0.19), along with significant alterations in pelvic, hip, knee, and ankle kinematics compared with controls. Increased pelvic elevation (MD = 0.92, p = 0.018, CI95% = 0.25; 1.58), reduced hip extension during stance (MD = −2.99, p = 0.039, CI95% = −5.19; −0.74), knee hyperextension in mid-stance (MD = −3.84, p < 0.001, CI95% = −6.18; −0.72), and limited ankle dorsiflexion (MAS MD = −4.04, p < 0.001, CI95% = −6.79; −0.86, LAS MD = −3.16, p < 0.001) and plantarflexor moments in terminal stance (MAS MD = −149.65, p = 0.018, CI95% = −259.35; −48.25, LAS MD = −191.81, p = 0.008, CI95% = −323.81; −57.31) were observed. Ground reaction force peaks during loading response (MAS MD = −16.86, p < 0.001, CI95% = −26.12; −0.72 LAS MD = −11.74, p = 0.001, CI95% = −19.68; −3.94) and foot-off (MAS MD = 10.38, p = 0.015, CI95% = 0.41; 20.53, LAS MD = 11.88, p = 0.01, CI95% = 3.15; 22.38) were also reduced. Conclusions: Children who have completed chemotherapy present measurable gait deviations reflecting persistent neuromechanical impairment, likely linked to chemotherapy-induced neurotoxicity and deconditioning. Instrumented gait analysis allows early detection of these alterations and may support the design of targeted rehabilitation strategies to optimize functional recovery and long-term quality of life in pediatric cancer survivors. Full article

The sustained growth of electricity demand and the global transition toward low-carbon energy systems have intensified the need for efficient, flexible, and reliable operation of electrical distribution networks. In this context, the coordinated integration of distributed renewable energy resources and demand-side flexibility has emerged as a key strategy to improve technical performance and economic efficiency. This work proposes an integrated optimization framework for active power supply in a radial, distribution-like network through the optimal siting and sizing of photovoltaic (PV) units and wind turbines (WTs), combined with a real-time pricing (RTP)-based demand-side response (DSR) program. The problem is formulated using the branch-flow (DistFlow) model, which explicitly represents voltage drops, branch power flows, and thermal limits in radial feeders. A multiobjective function is defined to jointly minimize annual operating costs, active power losses, and voltage deviations, subject to network operating constraints and inverter capability limits. Uncertainty associated with solar irradiance, wind speed, ambient temperature, load demand, and electricity prices is captured through probabilistic modeling and scenario-based analysis. To solve the resulting nonlinear and constrained optimization problem, an Improved Whale Optimization Algorithm (I-WaOA) is employed. The proposed algorithm enhances the classical Whale Optimization Algorithm by incorporating diversification and feasibility-oriented mechanisms, including Cauchy mutation, Fitness–Distance Balance (FDB), quasi-oppositional-based learning (QOBL), and quadratic penalty functions for constraint handling. These features promote robust convergence toward admissible solutions under stochastic operating conditions. The methodology is validated on a large-scale radialized network derived from the IEEE 118-bus benchmark, enabling a DistFlow-consistent assessment of technical and economic performance under realistic operating scenarios. The results demonstrate that the coordinated integration of PV, WT, and RTP-driven demand response leads to a reduction in feeder losses, an improvement in voltage profiles, and an enhanced voltage stability margin, as quantified through standard voltage deviation and fast voltage stability indices. Overall, the proposed framework provides a practical and scalable tool for supporting planning and operational decisions in modern power distribution networks with high renewable penetration and demand flexibility. Full article

Transmission tower guy wires are critical flexible tension members ensuring the stability and safe operation of overhead power transmission networks. However, these components are vulnerable to external impacts from falling rocks, ice masses, and other natural hazards, which can cause excessive deformation, anchorage loosening, and catastrophic failure. Current design standards primarily consider static loads, lacking comprehensive models for predicting dynamic impact responses. This study presents a theoretical model for predicting the peak impact response of guy wires by modeling the impact process as a point mass impacting a nonlinear spring system. Using an energy-based elastic potential method combined with cable theory, analytical solutions for axial force, displacement, and peak impact force are derived. Newton–Cotes numerical integration solves the implicit function to obtain closed-form solutions for efficient prediction. Validated through finite element simulations, deviations of peak displacement, peak impact force, and peak axial force between theoretical and numerical results are within ±4%, ±18%, and ±4%, respectively. Using the validated model, parametric studies show that increasing the inclination angle from 15° to 55° slightly reduces peak displacement by 2–4%, impact force by 1–13%, and axial force by 1–10%. Higher prestress (100–300 MPa) decreases displacement and impact force but increases axial force. Longer lengths (15–55 m) cause linear displacement growth and nonlinear force reduction. Impacts near anchorage points help control displacement risks, and impact velocity generally has a more significant influence on response characteristics than impactor mass. This model provides a scientific basis for impact-resistant design of power grid infrastructure and guidance for optimizing de-icing strategies, enhancing transmission system safety and reliability. Full article

This paper introduces an advanced control method for a Shunt Active Power Filter (SAPF), engineered specifically for the compensation of non-active current and power management in DC-powered systems. Non-active current components frequently arise in practical DC systems due to power electronics and dynamic loads. Their presence leads to increased current draw from the source, higher losses, and accelerated deterioration of DC energy providers, such as fuel cells and batteries. The proposed SAPF control strategy is based on the concept of an equivalent conductance signal, which dynamically reflects the load’s active power consumption and the SAPF’s internal losses. A key feature of this method is the derivation of the conductance signal primarily from the DC-link capacitor voltage, effectively eliminating the need for additional current or power sensors and thereby simplifying the control hardware and software. This methodology enables efficient buffering of energy flow through user-defined time constants, significantly reducing both the average value and the variability range of the current required to transmit the demanded power (as measured by the RMS parameter and standard deviation of the source current, respectively). As a result, the degradation process of energy sources can be mitigated. Furthermore, the conductance signal’s ability to assume negative values allows for effective management of generative loads, enabling power flow back into the system or directing it to specific loads. The flexibility of tuning the SAPF’s functionality—by adjusting the time constant and imposing limits on the conductance signal’s variation range—is demonstrated in the presented results. Simulation examples, including the potential for direct energy exchange with the DC-link capacitor without affecting the upstream source, validate the effectiveness and versatility of the proposed control method in improving power quality and extending the lifespan of DC energy storage systems. Full article

As offshore wind power continues to develop, with increased capacity and ability to function in deeper waters, jacket-type offshore wind turbines (OWTs) are becoming increasingly challenged by complex environmental loads and significant structural vibration issues. This study focuses on a 10 MW jacket foundation OWT and proposes an optimization approach for tuned mass damper (TMD) parameters based on the artificial bee colony (ABC) algorithm. A fully coupled model of the OWT and TMD system is developed, and the TMD parameters are optimized through frequency-domain analysis and time-domain simulations. The vibration control performance of the optimized TMD is then evaluated under combined wind, wave, and seismic excitations. The results show that the passive TMD achieves substantially greater vibration suppression under seismic loading compared to combined wind and wave conditions. In addition, the optimized TMD reduces the standard deviations of tower-top displacement and tower-base bending moment by more than 50%, significantly enhancing the dynamic response of the structure and contributing to an extended fatigue life. Full article

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article

Distribution System Operators (DSOs) increasingly need planning tools that coordinate utility-influenced assets—such as electric-vehicle charging stations (EVCS) and voltage-support resources—with customer-sited distributed generation (DG). We present a Nash-equilibrium-based Iterative Best Response Algorithm (IBRA-NE) for joint planning of DG and EVCS in radial distribution networks. The framework supports two applicability modes: (i) a DSO-plannable mode that co-optimizes EVCS siting/sizing and utility-controlled reactive support (DG operated as VAR resources or functionally equivalent devices), and (ii) a customer-sited mode that treats DG locations as fixed while optimizing DG reactive set-points/sizes and EVCS siting. The objective minimizes network losses and voltage deviation while incorporating deployment costs and EV charging service penalties, subject to standard operating limits. A backward/forward sweep (BFS) load flow with Monte Carlo simulation (MCS) captures load and generation uncertainty; a Bus Voltage Deviation Index (BVDI) helps identify weak buses. On the EEU 114-bus system, the method reduces base-case losses by up to 57.9% and improves minimum bus voltage from 0.757 p.u. to 0.931 p.u.; performance remains robust under a 20% load increase. The framework explicitly accommodates regulatory contexts where DG siting is customer-driven by treating DG locations as fixed in such cases while optimizing EVCS siting and sizing under DSO planning authority. A mixed scenario with 5 DGs and 3 EVCS demonstrates coordinated benefits and convergence properties relative to PSO, GWO, RFO, and ARFO. Additionally, the proposed algorithm is also tested on the IEEE 69-bus system and results in acceptable performance. The results indicate that game-theoretic coordination, applied in a manner consistent with regulatory roles, provides a practical pathway for DSOs to plan EV infrastructure and reactive support in networks with uncertain DER behavior. Full article

Objectives: The purpose of the present study was to investigate the relationship between acute and chronic loads, and the fatigue response within male elite professional football players. Design: 40-week longitudinal study across the 2021–2022 season in the English Championship. Methods: Twenty-three outfield football players had workload measured using global positioning system (Distance, High-Intensity Distance and Sprint Distance) and perceived exertion. Load-response was measured via a perceived wellbeing questionnaire, counter-movement jump (CMJ) and salivary immunoglobulin A. Results: General estimating equation models identified 18 significant interactions between workload and load-response markers. Thirteen significant interactions were found between acute and chronic workloads and CMJ variables, jump height, eccentric duration and flight contraction time. A poor CMJ was observed when acute sprint workload was >+1 standard deviation and chronic distance increased. However, when chronic perceived exertion increased, and acute sprint workload was >+1 standard deviation an advantageous response was detected on counter movement jump variables. The S-IgA response to acute and chronic workload was more variable; when chronic loads were >+1 standard deviation above mean values and acute workload increased, salivary immunoglobulin A was both suppressed and elevated depending on the interacting acute variable. Higher chronic workload was associated with better perceived wellbeing, even when acute workload was >+1 standard deviation above the mean. Conclusion: In general, low chronic workloads and acute spikes in workload were associated with poorer neuromuscular and immune function. Furthermore, CMJ performance and perceived wellbeing improved when chronic workloads were higher, despite the occurrence of acute spikes in workload. Full article

This study introduces a novel dual-battery architecture with intelligent auto-switching control, designed to ensure uninterrupted operation of agricultural sensing systems in environments with unpredictable energy availability. The proposed system integrates Lithium-Sulphur (Li-S) and Lithium-Ion (Li-Ion) batteries with advanced switching algorithms—specifically, the Dynamic Load Balancing–Power Allocation Optimisation (DLB–PAO) and Dynamic Load Balancing–Genetic Algorithm (DLB–GA)—tailored to maximise sensor operational longevity. By optimizing the dual-battery configuration for real-world deployment and conducting comparative evaluations across multiple system designs, this work advances an innovative engineering solution with significant practical implications for sustainable agriculture and remote sensing applications. Unlike conventional single-battery systems or passive redundancy approaches, the architecture introduces active redundancy, adaptive energy management, and fault tolerance, substantially improving operational continuity. A functional prototype was experimentally validated using realistic load profiles, demonstrating seamless battery switching, extended uptime, and enhanced energy reliability. To further assess long-term performance under continuous Internet of Things (IoT) operation, a simulation framework was developed in MATLAB/Simulink, incorporating battery degradation models and empirical sensor load profiles. The experimental results reveal distinct performance improvements. A baseline single-battery system sustains 28 h of operation with 31.2% average reliability, while a conventional dual-battery configuration extends operation to 45 h with 42.6% reliability. Implementing the DLB–PAO algorithm elevates the average reliability to 91.7% over 120 h, whereas the DLB–GA algorithm achieves near-perfect reliability (99.9%) for over 170 h, exhibiting minimal variability (standard deviation: 0.9%). The integration of intelligent auto-switching mechanisms and metaheuristic optimisation algorithms demonstrates a marked enhancement in both reliability and energy efficiency for soil nutrient monitoring systems. This method extends the lifespan of electronic devices while ensuring reliable energy storage over time. It creates a practical foundation for sustainable IoT agricultural systems in areas with limited resources. Full article

To enable accurate and efficient real-time detection of rail fastener defects under resource-constrained environments, we propose DP-YOLO, an advanced lightweight algorithm based on YOLOv5s with four key optimizations. First, we design a Depthwise Separable Convolution Stage Partial (DSP) module that integrates depthwise separable convolution with a CSP residual connection strategy, reducing model parameters while enhancing recognition accuracy. Second, we introduce a Position-Sensitive Channel Attention (PSCA) mechanism, which calculates spatial statistics (mean and standard deviation) across height and width dimensions for each channel feature map. These statistics are multiplied across corresponding dimensions to generate channel-specific weights, enabling dynamic feature recalibration. Third, the Neck network adopts a GhostC3 structure, which reduces redundancy through linear operations, further minimizing computational costs. Fourth, to improve multi-scale adaptability, we replace the standard loss function with Alpha-IoU, enhancing model robustness. Experiments on the augmented Roboflow Universe Fastener-defect-detection Dataset demonstrate DP-YOLO’s effectiveness: it achieves 87.1% detection accuracy, surpassing the original YOLOv5s by 1.3% in mAP0.5 and 2.1% in mAP0.5:0.95. Additionally, the optimized architecture reduces parameters by 1.3% and computational load by 15.19%. These results validate DP-YOLO’s practical value for resource-efficient, high-precision defect detection in railway maintenance systems. Full article

Design standards for manual handling equipment tend to measure maximal loads and moving forces using a smooth, flat, horizontal steel plate; yet, in everyday use, such equipment is used on floor coverings. Such test methods therefore overestimate the maximal loads acceptable for operators, which increases the risk of injury including the development of musculoskeletal disorders. This study presents a new approach for calculating the pushing force for manually handled equipment moving longitudinally on resilient floor coverings from the pushing force measured on a steel plate. This method combines corrective forces with the pushing force model presented in this study. Corrective force abaci, which describe corrective forces as functions of the hardness of the floor covering’s base foam, are provided for each type of tread and bearing in the cart’s wheels. These abaci have been elaborated from pushing force measurements obtained with 44 wheel designs (of varying diameters, treads and bearings) tested on five different floors on a custom-built test bench. A mean deviation between experimental results and model predictions of 5.1% is obtained for pushing forces. These results permit us to account for the real conditions in which manual handling equipment is used and help in reducing the incidence of musculoskeletal disorders. Full article

Background/Objectives: Total knee arthroplasty (TKA) is the gold-standard treatment for advanced knee arthritis, offering pain relief and improved joint function. However, tibial component malalignment, malrotation, and improper biomechanical loading remain critical factors contributing to implant failure, instability, and revision surgeries. This review systematically examines the impact of biomechanical loading on the tibial insert following primary TKA, with a focus on alignment, posterior tibial slope (PTS), and load distribution. Methods: A systematic literature search was conducted across the PubMed, Google Scholar, and Web of Science databases following the PRISMA guidelines. Studies investigating the effects of tibial component alignment, varus/valgus deviations, PTS, and load distribution on tibial inserts post-TKA were included. Seven studies meeting the inclusion criteria were analyzed and described narratively. Results: The reviewed studies highlighted that varus and valgus malalignment significantly alter tibiofemoral contact pressures and ligament strains, increasing the risk of aseptic loosening and implant failure. Excessive PTS was associated with posterior femoral translation, altered ligament tension, and increased contact stresses on polyethylene (PE) inserts. Kinematically aligned TKA demonstrated reduced tibial force imbalances and improved functional outcomes compared to mechanically aligned TKA. Computational and cadaveric studies revealed that even minor malalignments (e.g., 3° varus or valgus) can cause significant biomechanical changes. Conclusions: Biomechanical loading on tibial inserts after primary TKA is highly sensitive to the alignment and PTS. Optimal alignment and controlled biomechanical forces are essential. Kinematically aligned TKA has shown promising effects, preventing aseptic loosening and ensuring long-term implant survival. Further in vivo studies are needed to validate these findings and optimize surgical techniques. Full article