Search Results (2,754)

A self-powered graphene-enhanced hydrogel sensor (SGHS) with high contact-pressure sensitivity and mechanical robustness was developed for precise dynamic biomechanical and material contact sensing. The device generates transient electrical signals via contact electrification and electrostatic induction during contact–separation events, eliminating the need for any external power supply. The optimized SGHS achieves a maximum peak power density of 0.23 mW·m⁻², with contact-pressure sensitivities of 0.6 kPa⁻¹ and 0.26 kPa⁻¹ in the pressure ranges of 0.25–5 kPa and 5–25 kPa, respectively, which is competitive with or exceeds that of other externally powered and self-powered flexible dynamic stress sensors in the low-pressure range. Comprehensive analyses reveal that the pressure response originates from the enhanced piezodielectric effect in the graphene hydrogel layer under compression. The SGHS exhibits excellent mechanical durability, maintaining stable output after 10,000 loading–unloading cycles. Moreover, the pulse intensity, width, and waveform of its self-generated output provide distinctive features for identifying the type and surface characteristics of contacting objects. These results highlight SGHS as a promising candidate for next-generation intelligent, self-powered, and flexible dynamic sensing systems. Full article

This study aimed to investigate how score status and playing position affect the most demanding passages (MDPs) in elite women’s football. Data from ten matches from eighteen outfield players of the Turkish Women’s National Team were collected during UEFA Nations League fixtures in the 2024–2025 seasons. Players were monitored using wearable GPS sensors, and all locomotor variables were segmented into one-minute windows to identify peak demands. The analysed variables included total distance (TD), high-speed running (HSR), sprint distance (SD), high-acceleration distance (HIAccD), high-deceleration distance (HIDecD), high metabolic power distance (HMPD), and player load (PL). Generalised Estimating Equations (GEE) were used to assess the effects of score status and playing position. Wingers (WG) showed the highest TD, HSR, and HMPD values, while centre backs covered less TD and HSR than WG. Full-backs and forwards (FW) also recorded lower TD, although FW exceeded WG in sprinting (p = 0.045, d values = 0.66 [moderate effect]). Score status influenced MDPs, with TD decreasing when the match was tied and further declining when the team was behind; similar reductions occurred in HSR, HIAccD, HIDecD, and HMPD. In conclusion, both score status and position significantly shaped peak locomotor and mechanical demands. These findings may inform individualised training, recovery programmes, and score-dependent tactical planning in elite women’s football. Full article

This paper presents a distributed V2G-enabled multiport DC charging system with a hierarchical charging management strategy. Unlike conventional architectures based on centralized power converter cabinets, the proposed system distributes bidirectional power converters within individual multiport dispensers, each equipped with a local charging power management device. This architecture improves system scalability, fault tolerance, and operational flexibility while enabling vehicle-level charging and V2G services. A hierarchical control framework is introduced, consisting of high-level optimal charging scheduling, mid-level power coordination among distributed dispensers, and low-level converter control. Key elements include modular power units that can be dynamically configured and expanded, providing a cost-effective and adaptable solution for growing EV markets. Experimental results obtained from a 45 kW modular DC charging prototype demonstrate an efficiency improvement of up to 2% at rated power compared to a non-modular charger. In contrast, the optimized charging strategy achieves an overall charging cost reduction of approximately 11% and a peak load demand reduction of up to 31%. Furthermore, stable bidirectional power flow, effective power sharing, and total harmonic distortion within regulatory limits are experimentally validated during both charging and V2G operation. The prototype is implemented to validate the proposed charging system in the laboratory environment. Full article

Cordierite-based ceramics (Mg₂Al₄Si₅O₁₈) were successfully synthesized and comprehensively characterized to evaluate their structural and dielectric behavior for high-temperature electronic applications. Morphological, microstructural and vibrational analyses confirm the high phase purity and structural integrity of the synthesized material. Dielectric measurements reveal high real permittivity (ε′) values at low frequencies and elevated temperatures, mainly attributed to interfacial polarization arising from Schottky-type barriers at grain–grain and surface–volume interfaces, underscoring the crucial influence of heterogeneous interfaces on the dielectric response. The electrical conductivity follows a thermally activated hopping mechanism involving both intra-grain and grain-boundary charge transport. Analysis of the electric modulus formalism provides further insight into relaxation dynamics: the real (M′) and imaginary (M″) components highlight pronounced space-charge effects, with M″ exhibiting a distinct relaxation peak (M″) associated with grain contributions. The systematic shift of this peak toward higher frequencies with increasing temperature indicates enhanced charge-carrier mobility and a strongly thermally activated relaxation process. The frequency-dependent conductivity displays two regimes: a low-frequency plateau corresponding to dc conductivity and a high-frequency dispersive region following a power-law behavior characteristic of hopping conduction, with power-law exponents (α₁ and α₂) markedly lower than unity, confirming the non-Debye character of the relaxation processes. The hopping frequency (ω) increases with temperature, further supporting the thermally activated nature of charge transport. Activation energies extracted from Arrhenius plots of dc conductivity are 0.88 eV for grain boundaries and 0.83 eV for grains, demonstrating that both microstructural regions significantly contribute to the overall conduction process. Full article

This paper proposes a Hybrid-Field DD (HFDD) coupler designed for wireless power transfer (WPT) in mobile robots within smart manufacturing environments, utilizing a dual-coupling mechanism of magnetic and electric fields. The proposed coupler integrates Double-D coils for vertical magnetic field concentration with a split metal plate structure for enhanced electric field coupling in a compact, low-profile design. To evaluate the electromagnetic performance and the impact of inevitable eddy current interference, two distinct configurations—Front Plate Arrangement (FPA) and Back Plate Arrangement (BPA)—are analyzed through both theoretical modeling and 3D full-wave simulations (HFSSs). The comparative results demonstrate that the FPA model reduces the peak induced current intensity by 56.23 A/m compared to the BPA and achieves a peak leakage magnetic field intensity of 1.12 A/m, which is 28% lower than the 1.56 A/m observed in the BPA, offering a superior solution for suppressing leakage magnetic field and contributing to robust coupling stability. The high consistency between the proposed analytical methodology and numerical simulations underscores the theoretical robustness of the HFDD structure, establishing a clear design framework for efficient power transfer in robotic applications. Full article

This study examines a U-shaped rectangular copper busbar under a two-phase short circuit, combining high-power laboratory measurements with a coupled transient finite-element electromagnetic model. Short-circuit currents and forces were recorded using a high-speed acquisition system, while the model coupled an RL circuit with electric currents and magnetic fields to compute flux density and Lorentz forces. Eight test cases (93 V to 125.75 V) produced peak currents up to 35.76 kA and forces exceeding 1 kN. The model accurately reproduces peak currents, while computed forces agree well in magnitude and temporal evolution with measurements. Results show maximum field and force concentrations at inner corners and segment junctions, identifying critical mechanical regions. The study provides validated insight into this busbar configuration and a workflow applicable to other non-standard geometries. Full article

Inter-limb asymmetries are common in soccer players and are frequently monitored in high-performance settings; however, their expression across different flywheel-based strength exercises, movement phases, and over time remains unclear. This study aimed to (i) compare inter-limb power asymmetry magnitudes across multiple unilateral flywheel exercises and between concentric (CON) and eccentric (ECC) phases, and (ii) describe changes in these asymmetries over an 8-week period of routine soccer training, stratified by baseline asymmetry magnitude. The present study was designed as an observational and descriptive study. Twenty-one professional male soccer players completed two testing sessions separated by eight weeks. Players performed six unilateral flywheel exercises targeting hip- and knee-dominant quadriceps (Qhip, Qknee), hip- and knee-dominant hamstrings (Hhip, Hknee), adductors (ADD), and abductors (ABD). For each exercise and limb, the repetition with the highest CON mean power and its consecutive ECC phase were selected for analysis. Inter-limb asymmetry (%) was calculated for mean and peak power in both phases. Across exercises, ECC asymmetries were generally greater than CON asymmetries, with the largest values observed for Qknee peak power (CON: 12.86 ± 11.04%; ECC: 27.60 ± 13.65%) and Hknee peak power (CON: 10.45 ± 11.26%; ECC: 24.01 ± 20.46%). Exercise-specific patterns were evident, with generally weak associations between asymmetries across tasks. Over time, players classified with higher baseline asymmetry (≥10%) presented lower values at follow-up in several outcomes (particularly ECC-related measures), whereas players with lower baseline asymmetry (<10%) showed small increases or remained stable; These between-group patterns should be interpreted cautiously, as they may be more strongly influenced by regression to the mean and measurement variability than by underlying physiological changes. Overall, inter-limb power asymmetries assessed with flywheel technology were phase- and exercise-specific in this professional soccer sample. These descriptive findings may help contextualize phase-specific and multi-exercise asymmetry monitoring in professional soccer settings. Full article

Silicon carbide (SiC) power converters offer superior switching performance but generate severe broadband electromagnetic interference (EMI) that challenges regulatory compliance. Existing prediction methods face a fundamental trade-off between physical fidelity and computational efficiency, while conventional suppression strategies lack adaptability to varying operating conditions. This paper proposes a frequency-band-aware physics-informed generative adversarial network (FBA-PIGAN) that integrates electromagnetic domain knowledge into data-driven generative modeling for joint EMI prediction and adaptive suppression in SiC power converters. The framework employs a Wasserstein GAN with gradient penalty as the adversarial backbone and introduces feature-wise linear modulation (FiLM) to inject converter operating parameters into the generator through learned affine transformations. A hierarchical physics-informed loss function enforces three frequency-dependent constraints, namely, harmonic structure consistency, parasitic resonance characterization, and high-frequency envelope regularization, coordinated by a curriculum-based weight-scheduling strategy. An end-to-end differentiable suppression module maps predicted spectra to optimal passive filter parameters through an analytically embedded transfer function. Experimental validation on a 10 kW SiC inverter platform with 5120 measured spectra across 32 operating conditions demonstrates that FBA-PIGAN achieves a mean spectral error of 2.1 dB, 93.8% peak frequency accuracy, and a physical consistency score of 0.93, improving prediction accuracy by 56% over conventional conditional GANs while maintaining sub-millisecond inference latency. The integrated suppression pipeline attains 19.2 dB average attenuation with 98.5% CISPR 25 compliance, and the framework generalizes to unseen operating conditions with only 19% performance degradation, compared with 56% for data-driven baselines. Full article

The increasing penetration of rooftop photovoltaic (RTPV) systems in low-voltage (LV) distribution networks introduces challenges such as voltage rises, reverse power flow, and reduced hosting capacity, thereby necessitating effective active power regulation (APR) in module-level micro inverters. This paper proposes a dual-layer control framework for a 250 watt-peak (Wp) three switch rooftop PV micro-inverter, integrating quantum-behaved particle swarm optimization with reinforcement learning (QPSO-RL) for accurate maximum power point tracking (MPPT) and a linear quadratic regulator (LQR) for reserve- aware APR. The QPSO-RL algorithm improves available-power estimation under varying irradiance, temperature, and partial-shading conditions, while the LQR-based controller ensures fast, well-damped, and grid-compliant power regulation. The proposed framework was developed and validated using MATLAB/Simulink 2024 for simulation studies and LabVIEW with NI myRIO 2022 for real-time hardware implementation. Both simulation and experimental results confirm that the proposed method achieves 99.5% MPPT accuracy, convergence within 20 ms, grid-injected current total harmonic distortion (THD) below 3%, and a near-unity power factor. In addition, the reserve-based regulation strategy improves feeder compliance and reduces converter stress, thereby supporting reliable rooftop PV integration. These results demonstrate that the proposed QPSO-RL + LQR framework offers a practical and intelligent solution for high-performance, grid-supportive rooftop PV micro-inverter applications. Full article

High-speed operation is a key pathway to higher power density in modern EV traction systems, and multi-parameter optimization is essential for enhancing its high-speed performance. This study investigates a 20,000 r/min interior double-V permanent-magnet flat-wire motor via finite-element simulations to systematically examine the effects of multiple interacting parameters—including flat-wire layer number, stator slot geometry, magnet grade, and rotor magnetic barrier angle—on the electromagnetic performance under high-speed operating conditions. The results indicate that increasing winding layers significantly reduces high-speed torque; an eight-layer design decreases torque by about 50% compared to a four-layer one, while a six-layer arrangement offers a favorable torque-loss trade-off. Wider slots lower the average torque but reduce torque ripple by approximately 27%, whereas deeper slots increase tooth flux density and reduce efficiency. Higher-grade magnets enhance air-gap flux and torque at elevated cost. Rotor magnet angle optimization reveals a trade-off between peak torque and ripple, with a symmetric 100°/100° design achieving balanced performance. These findings clarify structural–control interactions and support the multi-objective design of high-speed flat-wire permanent-magnet motors. Full article

Against the backdrop of the dual-carbon strategy promoting the green and low-carbon transformation of the shipping industry, pollutant emissions generated during vessel berthing operations have become a critical challenge in port environmental governance. To address the combined effects of the priority berthing policy for new energy vessels and time-of-use electricity pricing, a joint optimization model for berth and shore power allocation is developed with the objectives of minimizing the total economic cost of vessels and the environmental tax cost associated with pollutant emissions. An improved Adaptive Large Neighborhood Search algorithm (ALNS-II) is further designed to solve the model. Numerical experiments based on actual port data verify the effectiveness of the proposed model and the superiority of the algorithm. The results indicate that, under time-of-use electricity pricing, the priority berthing policy for new energy vessels can shorten their waiting time at anchorage and encourage fuel-powered vessels to shift toward electrification. When the peak-to-valley electricity price ratio increases from 4.1:1 to 7.5:1, the environmental tax cost of pollutant emissions decreases slightly, whereas the total economic cost of vessels rises by 4.17%, suggesting that the peak-to-valley electricity price ratio should not be set excessively high. In addition, increasing the proportion of new energy vessels to 70% is more conducive to improving the overall economic and environmental performance of ports. The findings provide a theoretical basis and decision support for the optimal allocation of port resources under the coordination of multiple policies. Full article

Understanding salmonid spawning dynamics is critical for conserving cold-water river ecosystems amid increasing human and climate pressures. This study developed and validated a species-specific eDNA (Salmo cf. trutta and Thymallus thymallus) and evaluated its performance for seasonal spawning activity monitoring using droplet digital PCR (ddPCR). Species-specific primers and probes targeting mitochondrial nd5 (S. trutta) and cytb (T. thymallus) genes were designed and optimized as a duplex assay. Performance assessments included in vitro validation, cross-amplification testing, and determining the LOB, LOD, and LOQ. Field validation over a year at two spawning sites in the Gradac River, Serbia, involved seasonal eDNA sampling, filtration, extraction, and ddPCR analysis. Fish community composition was also assessed with electrofishing and metabarcoding. The assay showed high specificity and sensitivity, with LODs of 0.14 cp/µL and LOQs of 0.99 and 1.25 cpµL for S. trutta and T. thymallus. S. trutta eDNA peaked in late autumn during spawning, while T. thymallus remained at or below detection limits, reflecting its lower abundance and different spawning season. Filter type affected filtration efficiency but not eDNA yield. These findings confirm ddPCR-based eDNA as a powerful, non-invasive tool for monitoring salmonid spawning and seasonal changes, supporting adaptive fisheries management and conservation amid environmental changes. Full article

High-frequency endoscopic ultrasound is crucial for the early diagnosis of gastrointestinal tumors. However, achieving high axial resolution, deep tissue signal-to-noise ratio, and real-time data processing simultaneously remains a significant challenge in hardware implementation. This paper proposes a miniaturized real-time high-frequency imaging system based on the Xilinx Artix-7 FPGA. To overcome attenuation limitations of high-frequency signals, we employ a 4-bit Barker code-encoded excitation scheme coupled with a programmable ±100 V high-voltage transmission circuit. This effectively enhances echo energy without exceeding peak voltage safety thresholds. At the receiver end, the system utilizes a multi-channel analog front end integrated with mixed-signal time-gain compensation technology. Furthermore, to address transmission bottlenecks for massive echo data, we designed a Low-Voltage Differential Signaling (LVDS) interface logic based on dynamic phase calibration, ensuring stable, high-speed data transfer to the host computer via USB 3.0. Experimental results with a 20 MHz transducer demonstrate that the system achieves real-time B-mode imaging at 30 frames per second. Phantom testing revealed an axial resolution of 0.13 mm, enabling clear differentiation of 0.1 mm microstructures. Compared to conventional single-pulse excitation, coded excitation technology improved signal-to-noise ratio (SNR) by approximately 4.5 dB at a depth of 40 mm. These results validate the system’s capability for high-precision deep imaging suitable for clinical endoscopy applications, delivered in a compact, low-power form factor. Full article

Distributed acoustic sensing (DAS) has been widely applied to injection–production profile monitoring in horizontal wells because it provides continuous full-wellbore coverage, real-time acquisition, and straightforward long-term deployment. In practical downhole operations, however, DAS measurements are frequently compromised by optical-signal attenuation, loss of fiber–casing/formation coupling, and environmental noise. Meanwhile, the mechanisms governing flow-induced acoustic responses remain insufficiently understood, which continues to impede quantitative diagnosis and interpretation of injection–production profiles based on DAS data. To address these challenges, this study performed controlled laboratory-scale physical simulation experiments of single-phase flow in a horizontal wellbore, systematically investigating DAS acoustic responses under two wellbore diameters (25 mm and 50 mm) and a range of flow velocities. Power spectral density (PSD) was derived using the fast Fourier transform to identify flow-sensitive characteristic frequency bands, and frequency-band energy (FBE) was further used to establish an optimal quantitative relationship with flow velocity. The results show that: (1) DAS energy is dominated by low-frequency components (<100 Hz), with the total energy increasing nonlinearly as flow velocity rises, accompanied by a progressive broadening of the characteristic bands; (2) the feature bands identified using an adaptive method based on energy difference statistics applied to PSD frequency-domain features exhibit a higher signal-to-noise ratio and greater physical clarity than traditional wide frequency bands; furthermore, by employing a feature band merging strategy, the distribution characteristics of flow energy can be captured more comprehensively; and (3) FBE exhibits a strong nonlinear dependence on flow velocity, with a power-law model delivering the best theoretical fit, whereas a cubic model (FBE ∝ V³) achieves high accuracy and robustness for practical applications. The proposed workflow—“PSD peak identification–characteristic band delineation–FBE regression”—establishes a methodological foundation for quantitative DAS-based monitoring of horizontal-well injection–production profiles in both laboratory and field settings, and it provides a basis for subsequent intelligent monitoring and interpretation under multiphase-flow conditions. Full article

A replicable methodology for testing the size and placement of Battery Energy Storage Systems connected to high-voltage transmission networks is presented in this study. The proposed approach involves the power flow analysis inside a Renewable Energy Zone, namely a high-renewable area prone to grid congestion during peak generation periods, based on time-series hourly analysis over a critical month. The model includes detailed operational descriptions such as lines ampacity, battery state of charge limits, round-trip efficiency, self-discharge behavior, and ramp rate restrictions. The methodology distinguishes itself by its simplicity, flexibility, and use of open-source tools, making it a valuable asset for supporting future transmission planning in high-renewable-energy scenarios. The model was developed in Python (version 3.12) using the open-source Pandapower library, introducing an innovative constraint management criterion, and validated against real data provided by the national Transmission System Operator. The approach was then applied to a portion of the Sicilian grid with massive wind and solar penetration. Results show that strategic allocation of batteries leads to a significant reduction in line overloads (up to 13 GWh mitigated in one month), improves the dispatch of renewable energy generated within the Renewable Energy Zone and allows a more sustainable exercise of the power system. Full article