Search Results (16,691)

Control techniques for neural-network-based charging stations (CSs) are attracting attention worldwide. This popularity is due to the emergent need for alternative intelligent and adaptive control solutions for attaining a CS with stabilized power transfer and voltage control at the point of common coupling. This paper demonstrates novel neural-network-based improved virtual synchronous motor (NN-i-VSM) control through the mechanism of the charging voltage feedback in conjunction with a trained neural network model to adaptively produce field excitation (MN) that mimics a virtual flux model. The MN adaptively generates an electromotive force based on the trained NN output to control the rectifying converter response of the CS for power quality enhancement during multiple-CS operation. Simulation results in the scenario of multiple CSs at 750 kW (5 × 150 kW) with varying capacities showed significant improvement in voltage variable tracking capacity of up to 500 V as well as power response overshot reduction and grid voltage response tracking improvement compared with an i-VSM-based CS model. A comprehensive CS efficiency assessment and plant stability analysis, including Bode plot evaluation, further confirmed the superior dynamic response performance and robustness of the NN-i-VSM model over the i-VSM model. The proposed model offers scalable applicability in smart mobility and wireless CS integration, signifying a new control advancement for future generations of multiple-grid-friendly charging infrastructure for penetration of batteries at varying capacities. 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

This study aims to enhance the corrosion property of 17-4PH martensitic stainless steel, a material commonly used in industrial applications including nuclear power components, to enhance its performance in borate buffer solutions. The study employed plasma-based low-energy nitrogen ion implantation at temperatures ranging from 350 °C to 550 °C for 4 h to modify the steel surface. Microstructural characterization via XRD and TEM revealed the formation of a nanocrystalline nitrided layer, with thickness increasing from 11 to 27 μm and surface nitrogen concentration rising from 29.7 to 33.1% as temperature increased. Correspondingly, the nanocrystalline grains coarsened from an average size of 2 nm to 15 nm. The main findings showed that all nitrided layers significantly improved general corrosion resistance in pH 8.4 borate solution compared to the unmodified steel. An optimal performance with a corrosion potential of −169.4 mV(SCE) and a passive current density of 0.5 μA/cm² was achieved at 450 °C, accompanying the development of a denser passive film with high polarization resistance and lower defect density. It is concluded that the high interstitial nitrogen concentration within the nanocrystalline γ′_N accelerates passivation kinetics and enhances corrosion resistance, with the applied point defect model clarifying the underlying improvement mechanism. Full article

Short-Term Residential Load Forecasting (STRLF) is a core task in smart grid dispatching and energy management, and its accuracy directly affects the economy and stability of power systems. Current mainstream methods still have limitations in addressing issues such as complex temporal patterns, strong stochasticity of load data, and insufficient model interpretability. To this end, this paper proposes an explainable and efficient forecasting framework named KAN+Transformer, which integrates Kolmogorov–Arnold Networks (KAN) with Transformers. The framework achieves performance breakthroughs through three innovative designs: constructing a Reversible Mixture of KAN Experts (RMoK) layer, which optimizes expert weight allocation using a load-balancing loss to enhance feature extraction capability while preserving model interpretability; designing an attention-guided cascading mechanism to dynamically fuse the local temporal patterns extracted by KAN with the global dependencies captured by the Transformer; and introducing a multi-objective loss function to explicitly model the periodicity and trend characteristics of load data. Experiments on four power benchmark datasets show that KAN+Transformer significantly outperforms advanced models such as Autoformer and Informer; ablation studies confirm that the KAN module and the specialized loss function bring accuracy improvements of 7.2% and 4.8%, respectively; visualization analysis further verifies the model’s decision-making interpretability through weight-feature correlation, providing a new paradigm for high-precision and explainable load forecasting in smart grids. Collectively, the results demonstrate our model’s superior capability in representing complex residential load dynamics and capturing both transient and stable consumption behaviors. By enabling more accurate, interpretable, and computationally efficient short-term load forecasting, the proposed KAN+Transformer framework provides effective support for demand-side management, renewable energy integration, and intelligent grid operation. As such, it contributes to improving energy utilization efficiency and enhancing the sustainability and resilience of modern power systems. Full article

Future wireless networks require efficient device-to-device (D2D) communication to meet the demands of increasing connectivity; however, practical challenges such as limited coverage and severe interference persist. This paper addresses these issues by employing simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) equipped with low-resolution phase shifters, thereby enabling full-space coverage while conforming to hardware constraints. To further improve system performance, we propose an irregular STAR-RIS configuration, in which only a subset of elements is activated to enhance spatial diversity without increasing power consumption. Additionally, we introduce a group scheduling strategy that assigns users to different time slots, effectively mitigating interference and improving the overall sum rate. To solve the resulting high-dimensional and non-convex optimization problem, we develop a cross-entropy optimization framework that jointly optimizes element selection, amplitude and phase configurations, and user scheduling. Simulation results demonstrate that the proposed design significantly outperforms existing benchmarks in terms of both the sum rate and scalability, thus providing a practical and efficient solution for STAR-RIS-assisted D2D communication systems. Full article

Magnetically coupled resonant wireless power transfer (MC-WPT) systems with multi-relay coupling structures can significantly extend the transmission distance. However, system performance is highly sensitive to the spatial positions and coupling conditions of the relay coils. Any misalignment can alter the energy transfer path, causing shifts in the optimal operating frequency and reductions in efficiency. This makes conventional single-frequency or static-tuning strategies unsuitable for handling complex variations in coupling states. To address this issue, this paper investigates a three-relay MC-WPT system and proposes an adaptive frequency control and energy routing method that combines clustering and Q-learning for scenarios with severe coil misalignment. First, a physical model based on coupled-mode theory is established to describe the relationships among coupling coefficients, operating frequency, and transmission efficiency. High-dimensional coupling state data are then collected under different relay coil misalignment conditions. Next, principal component analysis (PCA) and clustering algorithms are used to extract representative coupling patterns and identify the system’s optimal efficiency points, forming an offline database that includes mappings of optimal frequencies. Furthermore, Q-learning is introduced to enable adaptive frequency control through online state recognition. Finally, under severe coil misalignment, frequency retuning of non-misaligned coils is applied to actively shield misaligned coils and reconstruct the energy transfer path. Simulation and experimental results show that the proposed method can achieve real-time frequency control and dynamic energy routing in multi-relay MC-WPT systems without additional hardware. The system transmission efficiency is significantly improved under all relay misalignment scenarios, effectively addressing the optimal frequency shift problem in multi-relay coupling structures and providing a new approach for intelligent and efficient MC-WPT systems under complex coupling conditions. Full article

The efficiency of power transfer is a critical issue for wireless charging applications in electric vehicles. The misalignment between the transmitter coil and the receiver coil in wireless charging leads to a significant reduction in efficiency. This article investigates improving coil misalignment performance in wireless power transfer for electric vehicles using magnetic flux density analysis. The objective is to study the effect of the automatic alignment transmitter system’s movement on error distance. The automatic alignment transmitter system was integrated with a wireless power transfer system to realign the transmitter coil whenever lateral misalignment occurred between the transmitter and receiver coils. The experiment was performed with a horizontal misalignment of 0.35 m and was repeated three times. The gap between the coils was held constant at 0.15 m. The wireless charging system was designed according to the Society of Automotive Engineers (SAE) standard. The experimental results demonstrated that the movement error distance was 0.001 m, with an average error of 0.33%. These findings indicate that the automatic alignment transmitter system achieved an operational effectiveness of 99.67%. The maximum wireless charging efficiencies of 75.78% and 75.59% were recorded for the X-axis and Y-axis adjustments, respectively. Full article

Droop control is an effective power regulation method for islanded microgrids to cope with fluctuations in renewable energy and loads. However, its power coordination performance is easily affected by the line impedance. When virtual impedance is introduced to enhance impedance matching, fixed values struggle to adapt flexibly to varying grid conditions. To address this specific limitation, this paper proposes a novel power coordination control strategy based on real-time line impedance identification. The method first analyzes the power distribution principle and equilibrium conditions under droop control. Crucially, it then establishes a dynamic virtual impedance regulation mechanism. By continuously identifying the actual line impedance, the proposed strategy dynamically adjusts the virtual impedance, thereby reshaping the inverter’s output impedance in real-time to match the grid conditions. This approach directly enhances the inverter’s adaptability to impedance variations, which is the core challenge in robust power coordination. Simulation results demonstrate that, compared to methods using fixed virtual impedance, the proposed strategy significantly improves power-sharing accuracy and system robustness under uncertainties such as fluctuating line impedance and load changes. Full article

Proton exchange membrane fuel cells (PEMFCs) are attractive energy sources for clean and efficient power generation; however, their nonlinear characteristics and sensitivity to operating condition variations make maximum power point tracking (MPPT) a challenging control problem. Conventional MPPT techniques often exhibit slow convergence, steady-state oscillations, and degraded performance under dynamic fuel flow variations. This paper proposes a machine learning–driven MPPT control strategy for a PEMFC system integrated with a DC–DC boost converter. The MPPT problem is formulated as a supervised classification task, where machine learning classifiers generate duty-cycle commands to regulate the converter and ensure operation at the maximum power point. A detailed PEMFC–converter model is developed in MATLAB/Simulink-2025b, and a dataset of 3000 labeled samples is generated under varying fuel flow conditions. Several classification algorithms, including decision trees, support vector machines (SVM), k-nearest neighbors (kNN), and ensemble learning methods, are systematically evaluated within an identical simulation framework. Simulation results show that the proposed machine learning-based MPPT controller significantly improves dynamic and steady-state performance. Ensemble Boosted Trees achieve the best overall response with a settling time of approximately 32 ms, peak power overshoot below 4.5%, and steady-state power ripple limited to 1.5%. Quadratic SVM and weighted kNN classifiers also demonstrate stable tracking behavior with power ripple below 2.1%, while overly complex models such as Cubic SVM suffer from large oscillations and reduced accuracy. These results confirm that classification-based machine learning offers an effective, fast, and robust MPPT solution for PEMFC systems under dynamic operating conditions. Full article

As a core power supply component of the more electric aircraft (MEA), the reliability of the permanent magnet synchronous generator (PMSG) is of paramount importance. Phase current reconstruction technology can enhance the redundancy of current sensors, thereby improving system reliability. However, owing to the generally high engine speeds in MEAs, the employment of traditional d-axis current–zero control not only induces DC-link voltage fluctuations but also leads to inaccurate DC-link sampling points and distortion in the reconstructed current. In this paper, a lead-angle flux-weakening control strategy is introduced into the PMSG rectification system. This approach guarantees the normal operation of the current loop when the rotational speed exceeds the rated speed of the PMSG, ensuring the accuracy of the sampling points for phase current reconstruction. To further enhance the reconstruction accuracy, a phase current reconstruction technology based on a linear extended state observer (LESO) is proposed. The LESO not only filters the reconstructed current but also ensures that the observer performance remains robust against PMSG parameter perturbations. Finally, the effectiveness of the proposed method is validated through Hardware-in-the-Loop results. Full article

Background/Objectives: Transcranial direct current stimulation (tDCS) is increasingly used by athletes, yet sport-performance-enhancement findings are mixed and often small, with outcomes depending on stimulation target, timing, and task demands. Aim: This scoping review mapped and synthesized the soccer-specific trial evidence to identify (i) which tDCS targets and application schedules have been tested in soccer players, (ii) which soccer-relevant outcomes show the most consistent immediate (minutes–hours) or training-mediated benefits, and (iii) where evidence gaps persist. Methods: We conducted a scoping review of clinical trials in footballers, following review best-practice guidance (PRISMA-informed) and a preregistered protocol. Searches (August 2025) spanned PubMed/MEDLINE, ResearchGate, Google Scholar, and Cochrane, using combinations of “football/soccer” and “tDCS/transcranial direct current stimulation,” with inclusion restricted to trials from 2008–2025. Dual independent screening was applied. Of 47 records identified, 21 studies met the criteria. Across these, the total N was 593 (predominantly male adolescents/young adults; wide range of levels). Results: Prefrontal protocols—most commonly left-dominant dorsolateral prefrontal cortex (DLPFC) (+F3/−F4, ~2 mA, ~20 min)—most consistently improved post-match recovery status/well-being (e.g., fatigue, sleep quality, muscle soreness, stress, mood), and when repeated and/or paired with practice, shortened decision times and promoted more efficient visual search. Effects on classic executive tests were inconsistent, and bilateral anodal DLPFC under fatigue increased risk-tolerant choices. Motor-cortex targeting (C3/C4/Cz) rarely changed rapid force–power performance after a single session—e.g., multiple well-controlled trials found no immediate CMJ gains—but when paired with multi-week training (core/lumbar stability, plyometrics, HIIT, sling), it augmented strength, jump height, sprint/agility, aerobic capacity, and task-relevant EMG. Autonomic markers (exercise HR, early HR recovery) showed time-dependent normalization without specific tDCS effects in single-session, randomized designs. In contrast, a season-long applied program that added prefrontal stimulation to standard recovery reported significantly reduced creatine kinase. Across studies, protocols and masking were athlete-friendly and rigorous (~2 mA for ~20 min; robust sham/blinding), with only mild, transient sensations reported and no serious adverse events. Conclusions: In soccer players, tDCS shows a qualified pattern of benefits that follows a specificity model: prefrontal stimulation can support post-match recovery status/well-being and decision efficiency, while M1-centered stimulation is most effective when coupled with structured training to bias neuromuscular adaptation. Effects are generally modest and heterogeneous; practitioners should treat tDCS as an adjunct, not a stand-alone enhancer, and align montage × task × timing while monitoring individual responses. Full article

Advancements in concentrating solar power (CSP) plants are essential for the wider adoption of these technologies. Increasing the operating temperature of the plants is one of the most promising ways to achieve further cost reductions and performance improvements. In this context, progress in supporting components—such as molten salt tanks—is critical to enable these advancements. This study compares a novel molten salt tank based on a refractory concrete formulation with a conventional design made from 347H stainless steel over the period 2015–2025. The prices of refractory concrete and stainless steel were analyzed across the decade to estimate the costs of the corresponding TES tanks in 2015 and 2025. The results showed that, while the concrete-based tank was more expensive than the conventional tank in 2015, the situation reversed by 2025, with the conventional stainless steel solution becoming 11% more expensive than the refractory concrete alternative. Additionally, an analysis of the producer price indexes for both materials highlighted that concrete exhibited a more stable price trend compared to stainless steel, which was subject to greater intra- and inter-year fluctuations. Finally, a brief examination of the 347H stainless steel production chain identified key causes of price volatility, such as the high geographic concentration of its main raw material extraction sites worldwide. Full article

Environmental DNA (eDNA) metabarcoding is an innovative tool that is transforming ecological research. It offers a simple and effective method for simultaneously detecting numerous species across a wide range of environments. The method relies on assigning DNA sequences sampled from the environment to taxa, which is straightforward for species that have already been sequenced and are represented in reference databases. However, existing bioinformatics tools often fail to deliver accurate, fine-grained assignments when target species are absent from these databases. This limitation arises from handcrafted classification thresholds that do not account for nucleotide positional information. Here, we propose a deep neural architecture specifically designed to exploit both nucleotide identity and positional patterns in short TELEO sequences. Using an in-silico validation framework based on NCBI genbank sequences, we compare our approach with several state-of-the-art bioinformatics tools (Obitools, Kraken2, Lolo), as well as alternative sequence embedding methods, under controlled conditions. Our approach yields significantly higher classification accuracy at the genus and family levels, achieving average accuracies of 94.7% at the genus level and 86.5% at the family level, substantially outperforming the tested reference-based pipelines. The method remains robust with limited training data and shows improved performance when nucleotide positional information is preserved through sequence alignment. These results demonstrate the potential of AI-powered eDNA metabarcoding to complement existing taxonomic assignment tools, particularly in contexts where reference databases are incomplete or species-level resolution is not achievable, thereby supporting biodiversity monitoring and ecosystem management. Full article

Background: Cardiotoxicity is a major limitation of chemotherapy and radiotherapy for thoracic and systemic cancers, contributing significantly to morbidity and mortality among survivors. Early prediction and prevention are critical to balance oncologic efficacy with cardiovascular safety. Artificial intelligence (AI) offers powerful tools to improve risk stratification, enable earlier detection of subclinical injury, and guide treatment planning in cardio-oncology. Methods: We performed a comprehensive review of the literature on AI applications for cancer therapy-related cardiotoxicity. Evidence was identified from PubMed, Scopus, and Web of Science, focusing on electrocardiography, biomarkers, proteomics, extracellular vesicles, genomics, advanced imaging (echocardiography, cardiac magnetic resonance, computed tomography, nuclear imaging), and radiotherapy dose modeling (dosiomics). Translational insights from animal models and in vitro systems were also included. Methodological quality was appraised with reference to TRIPOD-AI, PROBAST-AI, and CLAIM standards. Results: AI applications span multiple domains. Machine learning models integrating biomarkers, exosomes, and extracellular vesicles show promise for noninvasive early detection. Deep learning enables automated analysis of echocardiographic strain and cardiac MRI mapping, while radiomics and dosiomics approaches combine imaging with cardiac substructure dose maps to predict and prevent late radiation-induced injury. Preclinical studies demonstrate AI-driven advances in small-animal imaging, histopathology quantification, and multi-omics data integration, supporting the discovery of translational biomarkers. Despite encouraging performance, most models remain limited by small cohorts, methodological heterogeneity, and scarce external validation. Conclusions: AI has the potential to transform cardio-oncology by shifting from reactive detection to proactive prevention of cardiotoxicity. Future research should prioritize multimodal integration, harmonized multicenter datasets, prospective validation, and guideline-based clinical trials. As emerging data are incorporated, the field is expanding rapidly—dynamic, complex, and evolving. Full article

Background/Objectives: As part of the European Union’s harmonized monitoring framework, Belgium conducts antimicrobial resistance (AMR) monitoring in commensal bacteria from livestock. The aim of this study was to conduct a cost analysis of the national AMR monitoring in livestock, and to explore sampling size scenarios in relation to their associated costs and statistical performance (power and confidence) of monitoring. Methods: To our knowledge, this is the first published cost evaluation using unit cost aggregation of a national AMR monitoring program in animals. Results: The testing of the different sample size scenarios showed that if the sample size increases, the costs increase linearly. A sample size increase of 10 samples/isolates (e.g., from 170 to 180) can increase the yearly total costs per animal species by 5.2%. Moreover, the testing of the different scenarios showed that if the sample size increases, the power and the confidence level also increase, providing a higher level of trust in the results of the monitoring program. The highest total monitoring costs per animal category were estimated for fattening pigs, broilers and veal calves (over 18% of total costs each, using 2024 data). Among the various monitoring activities, antimicrobial susceptibility testing emerged as the costliest component, representing 50.2% of the total monitoring costs. Conclusions: The approach presented allows it to be used by other countries aiming to estimate the cost of their national AMR monitoring in animals or other similar activities. This economic and scenario testing analysis can be used to suggest informed suggestions to improve AMR monitoring in animals. Full article