Search Results (28,922)

Plyometric training is proposed as a method for improving neuromuscular performance in climbing; however, its specific effects on upper-body explosive power and rate of force development (RFD) in male boulderers, ranging from trained to highly trained, remain unclear. This preliminary exploratory trial examined the effects of a 10-week plyometric training program on selected laboratory-based neuromuscular performance outcomes. Eighteen male climbers were assigned to: intervention group (n = 9, 29.7 ± 4.5 yr) or a control group (n = 9, 31.3 ± 5.6 yr). Finger flexor strength and RFD (20-mm grip, 0–200 ms, 20–80%), isometric pull-up strength and RFD (load cell), upper-body power (plyometric push-up, Power Slap), and lower-body power (CMJ) were assessed. The intervention comprised two plyometric sessions/week for 10 weeks. Significant differences were observed in pull-ups (Δ difference = +3.89 repetitions; 95% CI: 0.30, 7.48; p = 0.036; η²p = 0.248), push-up power (Δ difference = +174.25 W; 95% CI: 5.05, 343.46; p = 0.044; η²p = 0.230), isometric pull-up RFD 0–200 ms (Δ difference = +107.85 kg/s; 95% CI: 27.54, 188.16; p = 0.012; η²p = 0.336), and the 20–80% range (Δ difference = +261.78 kg/s; 95% CI: 23.09, 500.47; p = 0.034; η²p = 0.253). No clear between-group differences were observed for finger flexor strength, finger RFD, maximum isometric pull-up strength, Power Slap, or CMJ. Given the exploratory design, small sample size, and low reliability of RFD-derived variables, these findings should be interpreted cautiously as preliminary evidence of movement-specific neuromuscular performance improvements. Full article

The absence of overhead catenary systems in contactless trams imposes stringent requirements on onboard energy efficiency and real-time power management. Hybrid energy storage systems combining batteries and supercapacitors provide an effective solution; however, conventional energy management strategies often suffer from limited global optimality under frequent traction–braking conditions. To address this issue, this paper proposes a variable-horizon model predictive control (MPC)-based energy management strategy for a battery–supercapacitor hybrid power supply system in contactless trams. A power-level-matching method is first adopted for capacity configuration, and the MPC prediction horizon is then dynamically adjusted to cover the entire traction phase, enabling global energy loss optimization while satisfying voltage, current, and SOC constraints. Simulation results obtained in MATLAB/Simulink demonstrate that the proposed strategy effectively suppresses excessive battery current and premature supercapacitor depletion. Compared with the conventional single-step MPC, the total energy loss is reduced by 9.88%, indicating improved energy efficiency and operational performance. Full article

This paper proposes an improved state-of-charge (SOC)-based adaptive droop control strategy for photovoltaic DC microgrids with distributed energy storage units (DESUs). To overcome the slow SOC equalization and limited adaptability of conventional droop control methods, a nonlinear arctangent-based droop coefficient adjustment mechanism is introduced to enhance regulation sensitivity under small SOC deviations. In addition, a capacity compensation factor and an acceleration term are incorporated to improve proportional power sharing and SOC convergence speed among heterogeneous storage units. To further evaluate the engineering significance of SOC balancing performance, a time-integrated SOC deviation index is introduced to analyze the cumulative imbalance effect on battery degradation and lifecycle operation. By reducing the SOC imbalance duration, the proposed strategy contributes to mitigating uneven battery aging and may potentially reduce long-term battery replacement costs. Simulation results under discharging, charging, and irradiance-variation scenarios demonstrate that the proposed strategy significantly improves SOC-balancing performance compared with conventional SOC-based droop control. Across the three operating modes, the average SOC balancing time has been reduced by 52.6%. The proposed method provides an effective and economically sustainable control framework for distributed energy storage coordination in DC microgrids. Full article

Mitochondrial dysfunction is increasingly recognized as a major contributor to central nervous system (CNS) disorders, including neurodegenerative and neuropsychiatric diseases. Animal models are essential for elucidating disease mechanisms and supporting the development of new therapeutic strategies. Among these models, non-mammalian organisms offer distinct advantages, including low cost, rapid life cycles, genetic tractability, and suitability for large-scale, high-throughput studies. Organisms such as Saccharomyces cerevisiae, Dictyostelium discoideum, Caenorhabditis elegans, Drosophila melanogaster, and Danio rerio have substantially advanced the understanding of mitochondrial processes relevant to CNS pathology. Studies using these models have revealed conserved mechanisms involving mitophagy, mitochondrial quality control, respiratory function, bioenergetic signaling, and neurodegenerative pathways. Their strengths, including scalability, live imaging capacity, and efficient genetic manipulation, have accelerated disease modeling and therapeutic discovery. However, simplified physiology, evolutionary distance from humans, and the incomplete representation of complex CNS organization limit their translational relevance and often require validation in higher-order organisms. Nevertheless, integrating these models into CNS research, particularly alongside emerging technologies, provides a powerful strategy for linking fundamental mitochondrial biology with translational neuroscience. This review summarizes the use of non-mammalian models in neuroscience research, with an emphasis on mitochondrial dysfunction in CNS disorders and their potential to support future therapeutic advances. Full article

The accuracy of spectro-temporal features for Makhaarij al-Huroof and Sifaat distinguishes between the ten canonical Qiraát recitation styles of the Holy Quran. However, real-world room reverberations blur formant contours and corrupt inter-word energies, thus making Qiraat discrimination difficult. The current dereverberation methods were designed to work under ordinary speech conditions and are not capable of preserving phonetic qualities for domain-specific purposes. This paper introduces a four-step, phase-consistent signal-processing approach prioritizing phonetic preservation over direct reverberation suppression. The four steps are: (1) adaptive noise-floor attenuation; (2) soft-voice activity detection using power-law boundary decay; (3) application-specific spectral contour adjustment from clean Quranic reference audio; and (4) Griffin–Lim algorithm-based phase correction. A total of 48 real-world room recordings were utilized for the evaluation of this approach based on Energy Ratio (ER), Spectral Contrast (SC), and Spectral Contour Stability (SCS)—measures specific to the Quran audio domain—alongside conventional speech-quality metrics. The proposed approach yielded the highest scores in three of seven metrics, namely SC (+40.11), SCS (+822.94), and PESQ (+1.251), alongside the second-highest Energy Ratio (+19.58 dB), while being superior to Spectral Subtraction, Wiener Filtering, and WPE Dereverberation approaches. Moreover, the perceptual enhancement was verified in a synthetic controlled experiment where the proposed approach scored an improved PESQ metric (+2.495; SNR −1.874 dB). The results illustrate the fact that an optimization for general-purpose metrics does not necessarily ensure phonetic preservation required for specific classification. Full article

Cyber-physical power systems (CPPSs) are a crucial component of smart grids, integrating physical power systems with advanced information and communication technologies to achieve efficient, reliable, and intelligent energy management and control. However, with the widespread deployment of information technology, CPPSs face increasingly severe security threats and privacy protection challenges, such as data leakage, identity forgery, and impersonation, which can compromise the secure and stable operation of CPPSs. To counter these threats and protect privacy, cryptographic technique is developed to provide fundamental and powerful tools, supporting secure authentication, data integrity checking, privacy preservation, and trusted communication between connected devices in CPPSs. We systematically review the research progress on security authentication and privacy protection in CPPSs from a cryptographic perspective. Our survey analyzes the major security threats faced by CPPSs, along with the impact of various attacks on system data. We explore mainstream cryptographic algorithms, including digital signatures, key agreement protocols, signcryption authentication, homomorphic encryption, and blockchain-based security mechanisms that are capable of resisting cyber attacks and ensuring reliable decision-making and control in CPPSs. This work also provides the trends and challenges regarding the intersection of cryptography and networked control, blockchain scalability, and convergence of cryptography and artificial intelligence in CPPSs. Full article

Rough set methodology has gained broad acceptance as a potent mathematical apparatus for feature selection within data mining and machine learning. Yet, classical rough sets hinge on equivalence relations to partition the universe, thereby demanding strict reflexivity, symmetry, and transitivity conditions that are arduous to satisfy in realistic settings. Although fuzzy rough sets have been explored to mitigate this rigidity, the entropy-based uncertainty measures employed in fuzzy approximation spaces remain acutely sensitive to data quality and noise corruption, potentially inducing severe bias in feature evaluation. Moreover, the literature currently lacks noise-tolerant uncertainty measures capable of accommodating a controlled fraction of classification errors while safeguarding the discriminative strength of feature subsets. Inspired by these gaps, this study develops a feature selection framework grounded in variable precision fuzzy entropy within the fuzzy rough set context. To this end, fuzzy decision is adopted to portray the membership degree of samples relative to decision classes, thereby enabling more precise detection and elimination of redundant attributes during approximation. An uncertainty quantifier termed fuzzy relational entropy is then introduced to appraise the distinguishing power of fuzzy similarity relations generated by attribute subsets. Leveraging fuzzy decision, a portfolio of uncertainty measure variants, specifically the variable precision joint discriminant index, the variable precision conditional discriminant index, and the variable precision mutual discriminant index, is developed to counteract noisy data effects. These variable precision discriminant indexes sanction a regulated error proportion and afford a measure of noise resistance. Finally, knowledge reduction for fuzzy decision systems is attacked from the angle of discriminative capability preservation, and a heuristic feature selection algorithm is crafted around the variable precision conditional discriminant index. Evaluation on twelve public UCI datasets reveals that the proposed algorithm effectively prunes redundant features and delivers competitive results against three representative alternatives: classical rough set, neighbourhood-based discriminant index, and fuzzy rough set feature selection. Additionally, it sustains stable classification performance across an extensive sweep of the variable precision parameter. Full article

The rapid adoption of electric vehicles (EVs) has intensified the demand for advanced energy storage technologies capable of delivering high energy density, high power density, enhanced safety, and extended service life. Although lithium-ion batteries remain the dominant energy storage technology for EVs, their limited power capability, thermal degradation, and accelerated aging under high transient loads constrain vehicle performance. Battery–supercapacitor hybrid energy storage systems (HESSs) have emerged as a promising solution by combining the high energy density of batteries with the high-power density and rapid charge–discharge capability of supercapacitors. However, the increasing complexity of HESS architecture requires intelligent energy management strategies to optimize power allocation, battery protection, thermal regulation, and overall system efficiency. Existing review papers primarily address individual aspects of HESS architecture, battery management, or artificial intelligence (AI)-based control, leaving a lack of a unified review integrating these topics. This paper addresses this gap by reviewing 181 publications published between 2020 and 2026, covering HESS architectures, conventional and AI-driven energy management strategies, machine learning, deep learning, reinforcement learning, battery state estimation, diagnostics, prognostics, thermal management, and fault diagnosis. The reviewed studies are critically analyzed to assess the impact of AI on battery lifetime, regenerative braking, charging performance, thermal behavior, and energy efficiency. The review further identifies emerging research directions, including explainable AI, digital twins, federated learning, edge intelligence, vehicle-to-grid integration, and cybersecurity-aware energy management. The findings indicate that AI-based approaches generally demonstrate greater adaptability, predictive capability, and battery protection than conventional methods under dynamic operating conditions, although challenges related to computational complexity, real-time implementation, data availability, explainability, cybersecurity, and standardization remain significant barriers to large-scale deployment. Full article

Background: Children growing up in alcohol-affected families are exposed to chronic stress, adverse childhood experiences (ACEs), emotional insecurity, and environmental instability, all of which may influence neurodevelopmental processes. Numerous EEG markers have been proposed as indicators of attentional regulation, emotional functioning, and stress responsivity; however, their relative diagnostic and practical value remains unclear. The aim of the present study was to verify whether commonly reported EEG markers remain valid indicators of neurofunctional difficulties in children from alcohol-affected families, to establish their hierarchy of importance, and to determine how identified neurofunctional profiles may inform the sequencing of educational interventions and the development of individualized support plans used by educators and social workers. Methods: The study included children aged 6–10 years from alcohol-affected families (n = 20) and a control group from non-dysfunctional family environments (n = 25). Resting-state EEG recordings were conducted under eyes-open and eyes-closed conditions, with analyses focused on the eyes-open condition. Quantitative EEG (qEEG) indices included global, frontal, prefrontal, and midline Theta–Beta Ratio (TBR), frontal alpha asymmetry (FAA), temporal beta stress and parietal beta2 tension. EEG preprocessing was performed using EEGLAB and included artifact rejection, filtering, epoch segmentation, and spectral power analysis. Group differences were analyzed using Welch’s t-tests with Benjamini–Hochberg correction for multiple comparisons. Results: The analyzed EEG markers differed in their ability to distinguish children from alcohol-affected families and controls. The strongest effects were observed for Theta–Beta Ratio (TBR) measures, particularly in frontal and prefrontal regions, indicating impairments in attention regulation, executive functioning, and self-control. Elevated temporal beta stress and parietal beta2 tension reflected increased physiological arousal and chronic stress. In contrast, frontal alpha asymmetry (FAA), commonly associated with depressive emotional processing, was not significant after correction for multiple comparisons. The obtained findings enabled the establishment of a hierarchy of neurofunctional markers, with attentional and executive-function indicators demonstrating greater importance than markers related to depressive symptomatology. Conclusions: The EEG profile of children from alcohol-affected families is characterized primarily by chronic stress, heightened physiological activation, and impaired attention regulation rather than by neurophysiological patterns associated with depression. The results suggest that educational difficulties in this group may stem mainly from deficits in attention control, inhibitory processes, and cognitive flexibility. Consequently, educational interventions should prioritize learning strategies, attentional training, and self-regulated learning skills. The identified hierarchy of EEG markers may also support the development of individualized educational plans and social-support programs, including participation in structured extracurricular activities and interventions aimed at strengthening executive and learning-related competencies. However, given the pilot nature of the present study and the relatively small sample size, these findings should be considered preliminary. Replication in larger, more diverse, and independent cohorts is necessary to confirm the stability, reliability, and generalizability of the identified neurofunctional profile and the proposed hierarchy of qEEG markers before they can be recommended for broader educational and social applications. Full article

Background: Family-based interventions are effective in mitigating the risk for relapse of schizophrenia. However, the accessibility of these interventions is scarce in many clinical settings. A group-based brief cognitive behavioural intervention facilitated by a therapist using videoconferencing may help address this practice gap and improve the high treatment disengagement that occurs in interventions delivered in self-paced web-based forums or mobile applications. Objective: To examine the feasibility, acceptability, and safety of an online group-based cognitive behavioural family intervention for dyads of individuals with schizophrenia-spectrum disorders and caregivers. Methods: This feasibility study adopted a parallel-group, assessor-blind randomised controlled trial with a twelve-week post-intervention follow-up as well as individual semi-structured interviews. Participants were randomly assigned to the intervention group [i.e., tele-group cognitive behavioural family intervention (tgCBFI) group] and the treatment-as-usual group. Both groups also received biweekly brief telephone support. The feasibility and acceptability were assessed by the recruitment rate, intervention completion rate, retention rate and participants’ service satisfaction. Safety was measured by the number of adverse events. Results: Most intervention group participants (85.7%) attended all six online group sessions (six service user-caregiver dyads, corresponding to 12 participants), while 100% of participants attended the per-protocol number of sessions (≥four sessions). The study recruitment rate was 16.4%, while the study retention rate for follow-up assessments was 95.8%. No adverse events were reported throughout the study. Five themes were generated to illustrate the benefits of and recommendations for the tgCBFI programme to complement the quantitative findings. Conclusions: The preliminary results suggested that the use of videoconferencing to deliver group-based cognitive behavioural intervention was partially feasible and provided exploratory estimates suggesting possible improvement in psychiatric symptoms for individuals with schizophrenia-spectrum disorders, warranting a fully powered trial. Trial Registration: prospectively registered at ClinicalTrials.gov NCT05808244. Full article

The startup process of gas turbines driven by the static frequency converter (SFC) exhibits complicated electromechanical coupling characteristics. Conventional simulation methods fail to integrate physical modeling with sequence of event (SOE) data and cannot support co-simulation of multiple startup schemes at the power station level. In this paper, a hierarchical digital twin architecture oriented to gas turbine SFC startup is established to realize intelligent deduction of sequential control and break through the technical limitations of traditional simulations. Relevant waveforms and data of the F-class heavy-duty gas turbine during startup are obtained via the digital twin. The maximum effective value of voltage is 12.07 kV, the maximum effective value of current is 1.6 kA, and the peak output power of the SFC reaches 15.67 MW. The system achieves the rated speed (3000 rpm) within an acceptable start-up duration, demonstrating satisfactory dynamic response. All the above data conform to the preset startup parameters and operation control logic of heavy-duty gas turbines. Full article

Validating wireless power transfer (WPT) systems for electric vehicles (EVs) is a challenge due to efficiency variations caused by coil misalignments and height differences arising from various vehicle designs. Traditional simulation methods, such as finite element analysis (FEM), provide high accuracy but entail significant computational costs and calculation times, limiting the number of case studies and their optimization. This paper presents a methodology that integrates Machine Learning (ML) and Genetic Algorithms (GA) to overcome these limitations. An ML model rapidly and accurately predicts key electromagnetic parameters across a wide range of positions and frequencies. These predictions feed into a GA that optimizes control variables (voltages and frequency) with the objective of maximizing power transfer efficiency, while simultaneously ensuring component integrity at each operating point. Beyond drastically reducing simulation time and experimental effort, this methodology will enable knowledge extraction and its use for formulating design rules. These rules can lay the groundwork for developing simplified, real-time adaptive control strategies, facilitating the reduction of control variables and the narrowing of search ranges. Full article

Wound Field Synchronous Motors (WFSMs) offer precise control over the rotor magnetic field, making them well suited to electric vehicle (EV) traction applications that require adjustable excitation, wide constant-power operation, and freedom from rare-earth permanent magnets. The excitation system (ES) governs the rotor field strength and therefore directly influences motor efficiency, dynamic response, and operational stability. This paper reviews modern excitation strategies for WFSMs in EV traction, with particular emphasis on contactless approaches based on wireless power transfer (WPT). The fundamental principles of inductive power transfer (IPT) and capacitive power transfer (CPT) are presented, together with their design considerations, compensation topologies, power electronic interfaces, control strategies, and practical challenges, followed by a discussion of hybrid IPT–CPT systems. Representative experimental studies in each category are compared on the basis of power level, efficiency, operating frequency, and misalignment tolerance. A capacitive power coupler is also designed for a WFSM, which requires a 6-amp DC field current, where the geometry of the coupler is constrained by the WFSM rotor geometry. The review identifies open challenges—including misalignment sensitivity, electromagnetic interference, thermal constraints, and air-gap variability under rotation—and outlines research directions for compact, efficient, and reliable WPT-based excitation systems for next-generation EV traction motors. Full article

This paper presents a comprehensive review of control techniques, simulation mechanisms, and validation methods applied to DC-DC converters, with a focus on high-step-up topologies used in renewable energy systems such as photovoltaic and wind power applications. Control strategies including classical PID, fuzzy logic, sliding mode, and model predictive control (MPC) are analyzed in terms of performance, robustness, and implementation complexity. Simulation platforms and hardware-in-the-loop (HIL) validation frameworks are also discussed as key enablers for rapid prototyping. The findings reveal a clear trend toward intelligent and hybrid control schemes that combine nonlinear techniques with artificial intelligence to address the inherent nonlinearities and parametric uncertainties of DC-DC converters. However, challenges remain in real-time implementation due to computational demands, which drives the need for future developments focused on the (i) integration of AI-based controllers with low-cost embedded platforms, (ii) standardization of HIL-based validation workflows, and (iii) optimization of converter topologies for specific applications such as electric vehicle charging and photovoltaic grid integration. Looking forward, the convergence of advanced control algorithms, real-time validation platforms, and application-specific converter design is expected to define the next generation of power electronics systems, enabling more efficient, reliable, and scalable renewable energy integration. Full article

Thermal runaway in electric vehicle power battery packs is a key risk in fire prevention and control for electric transportation. Its triggering, propagation, and failure modes are jointly affected by external thermal abuse, material insulation performance, and side reactions inside cells. To identify the temperature response and fire risk of power battery packs under different thermal abuse intensities, this study established a three-dimensional multiphysics thermal runaway simulation model in COMSOL Multiphysics 6.1, coupling solid heat transfer, electrochemical heat generation, and side-reaction heat release. A semi-quantitative risk ranking was then performed using failure mode, effects, and criticality analysis (FMECA). The model considered the low-temperature safe conditions, 120 °C, 140 °C, and 170 °C, as the main ambient temperature conditions, while also analyzing the effects of the heat transfer coefficient on trigger time and peak temperature. The results show that, under the low-temperature safe condition and the 120 °C condition, the battery module mainly exhibits slow heating and does not undergo thermal runaway. Based on the side-reaction characteristics, the temperature near 125 °C can be used as a risk warning threshold for thermal runaway. At 140 °C, the side-reaction heat source increases markedly, and the system enters the thermal runaway risk region. Because the trigger time is strongly affected by the heat transfer coefficient and monitoring position, this condition is interpreted only as a risk-acceleration stage under critical thermal abuse. Approximately 167 °C can be regarded as the critical threshold for irreversible thermal runaway. Under severe thermal abuse at 170 °C, rapid intensification of internal side reactions increases the peak module temperature to 375–385 °C. Temperature field evolution shows that heat is transferred mainly from the exterior to the interior before thermal runaway, forming an outside-high- and inside-low-temperature distribution. After the runaway stage begins, heat release from internal cell side reactions becomes dominant, and the high-temperature region concentrates inside the module, producing a gradient reversal with a higher internal temperature. The FMECA results show that the positive electrode–electrolyte reaction has the highest RPN, with a value of 405. Accelerated SEI decomposition and the negative electrode–electrolyte reaction also form key risk links in the chain heat-release pathway. This study provides a reference for thermal management, fire barrier design, and fire risk classification of power battery packs. Full article