Search Results (3,374)

To address the issues of high working power consumption and poor structural stability of current ploughing equipment under conditions of straw coverage and heavy clay soil, a 1LFT-450D variable width reversible plough (VWRP) with resistance reduction function is designed. Based on the shark shield scale, a bionic resistance reduction plough body was designed. Through theoretical analysis, the turnover mechanism (TM) and the working width adjustment mechanism (WWAM) were designed, and their main structural parameters were determined. Further research was conducted on key components using simulation software. The discrete element method (DEM) simulation results indicated that arranging bionic ribs on the plough breast achieved the best resistance reduction effect compared with the ploughshare tip and ploughshare. Meanwhile, relative to the conventional plough body, the designed bionic plough body exhibited average reductions in resistance and energy consumption of 12.55% and 12.34%, respectively. The soil bin test further verified the resistance reduction performance of the designed bionic plough body. The kinematic performance of the TM and the WWAM was analyzed using RecurDyn, and their reliability and stability were verified through the mechanism performance test. The results of the field operation performance test showed that under the conditions of forward speed of 8–10 km·h⁻¹ and working width of 1320–2000 mm, the operation performance of the designed VWRP satisfied the requirements of relevant standards. This study can provide a theoretical reference for the resistance reduction optimization of agricultural machinery soil-engaging parts and the design of new ploughs. Full article

Background: Older adults are particularly vulnerable to heat related illness due to impaired thermoregulatory responses. Heat acclimation (HA) strategies can mitigate the negative impacts of high environmental temperatures on physiological and perceptual responses. Whilst active HA strategies may prove problematic for older adults, passive approaches such as hot water immersion (HWI) may be more feasible. Methods: This study investigated the effects of four consecutive days of HWI on physiological and perceptual markers in individuals aged over 65 years during moderate exercise. Nine healthy, recreationally active participants (76 ± 5 years) completed two 30 min cycling bouts at 75–80% age predicted HR_max pre- and post-four days of HWI at 40 °C. Measures of average HR, gastrointestinal temperature, skin temperature, thermal sensation, thermal comfort, rate of perceived exertion, power output, and distance covered were recorded during both exercise bouts. Results: Results showed a significant increase in exercise capacity as measured by power output (p < 0.05, 7.45 W) post-intervention, despite no change in ratings of perceived exertion, and reductions in average heart rate (112 ± 3 vs. 109 ± 4 bpm). There were no alterations in gastrointestinal or skin temperature, and ratings of thermal comfort and sensation remained unchanged post-intervention. Conclusions: These preliminary findings provide important new evidence that four days of passive HWI may be a practical and effective method of inducing physiological adaptations in older individuals, which may be of use in interventions to mitigate the negative impact of high environmental temperatures in this population. Full article

When the Risley prism pair is used for target tracking, the nonlinear relationship between beam deflection and prism rotation makes tracking performance highly dependent on precise and stable motor control over a wide speed range. Although the brushless DC motor serves as the preferred drive source, its inherent commutation torque ripples directly induce beam pointing jitter, severely degrading overall tracking accuracy and stability. To address these issues, this paper proposes a three-vector-based model predictive direct speed control method. This approach establishes a direct speed-to-torque control channel by generating reference active power through dynamic equations, eliminating the need for fitting a constant flux linkage and parameter tuning. Simultaneously, combined with three-vector optimization and seven-segment modulation strategies, it achieves a dynamic balance between high-frequency, instantaneous electromagnetic power fine-tuning and inherent mechanical inertia of the rotor. Simulation results demonstrate that the proposed method exhibits superior speed stability compared to the conventional double-vector-based model predictive power control method and maintains high-precision dynamic tracking over a wide speed range. Ultimately, it leads to an average reduction of over 60% in the time-weighted absolute tracking error integral under various target trajectories, providing an effective solution for drive control of target tracking in Risley prism systems. Full article

The stochastic and intermittent characteristics of renewable energy pose significant challenges to energy utilization and power system stability. The reversible solid oxide cell (RSOC), as an emerging multi-energy conversion technology, exhibits high efficiency in both electrolysis and power generation modes, offering a promising solution to renewable energy integration and energy supply issues. However, RSOC performance degrades over time, and its average efficiency decay rate directly influences capacity investment decisions and day-ahead scheduling strategies. To address this, a comprehensive energy system model considering RSOC capacity is developed, with a detailed representation of each subsystem. A bi-level optimization framework is then proposed, where the upper level minimizes system investment and operation costs, and the lower level optimizes day-ahead scheduling costs. The model explicitly accounts for RSOC efficiency degradation and lifetime attenuation. Particle swarm optimization is applied to determine the optimal capacity configuration. Case studies demonstrate that the proposed model enhances system economics, promotes multi-energy complementarity, and prolongs RSOC lifetime, providing theoretical and technical support for the planning and operation of integrated energy systems with RSOC. Full article

High-power-density LEDs can achieve many functions that are difficult for traditional light sources and conventional LEDs to realize, pushing the semiconductor lighting technology chain to a new level. Increasing the number of chips is an effective approach to improving the light output capability of LED devices. In this study, five high-power-density LED devices with different chip numbers (4, 9, 16, 25, and 64 chips) were fabricated using the same blue LED chips, and the effects of chip number on the light output capability, spatial light distribution characteristics, and spatially correlated color temperature distribution characteristics of high-power-density LED devices were systematically investigated. The temperature distribution characteristics of the internal chips were further analyzed in combination with infrared thermal imaging results. The results show that increasing the chip number significantly enhances the total light output capability of the device; however, due to the influence of thermal coupling among chips, the saturation current and saturated luminous intensity of devices with different chip numbers are not proportional to the chip number. Increasing the number of chips in the device does not alter the intrinsic spatial emission pattern. Under optical saturation conditions, the luminous intensity distribution curves of all five devices exhibit Lambertian spatial light distribution characteristics. In terms of correlated color temperature, the CCT of all devices increases with increasing current per chip, and devices with more chips exhibit higher CCT values and a faster increasing trend. The spatial CCT distribution results show that the correlated color temperature of the device reaches its maximum in the normal direction, decreases with increasing testing angle, and exhibits good spatial symmetry. The thermal imaging results show that devices with more chips exhibit higher average chip temperatures and a relatively more uniform temperature distribution, which improves the spatial CCT uniformity of the device to some extent. Full article

Vocational high school students represent a substantial yet understudied population in school-based mental health research. Drawing on positive psychology and bioecological theory, this study examined whether personal growth initiative (PGI) shows a statistical indirect effect with respect to the relationships between multidimensional school climate and mental health outcomes among Chinese vocational students. Participants were 14,006 students from 112 vocational high schools. Two-level path models simultaneously entered different climate dimensions to estimate their unique associations with PGI, depressive symptoms, and Subjective well-being (SWB) at the within- and between-school levels, controlling for gender and socioeconomic status. Within schools, Safety, Interpersonal Relationships, Rules and Norms/Career Development Support, and Teaching and Learning/Diversity were positively associated with PGI, which in turn was associated with lower depressive symptoms and higher SWB. Wald tests indicated that Safety showed the strongest overall association with depressive symptoms, whereas Interpersonal Relationships showed the strongest overall association with SWB. At the between-school level, school-average climate and school-average PGI were associated with both outcomes, although these findings should be interpreted cautiously given the limited between-school power and substantial overlap among aggregated climate indicators. Overall, the findings are consistent with PGI being an important student-level pathway linking school climate to mental health in vocational education. Full article

Accurate medium-horizon load forecasting and risk-aware unit commitment are critical for high-load power systems. This study develops an integrated prediction–optimization framework that couples 744 h recursive load forecasting with uncertainty-aware scheduling. In the forecasting stage, a CNN-LSTM model is tuned by the Dung Beetle Optimizer (DBO), while Monte Carlo Dropout is retained during inference to generate probabilistic trajectories and time-varying prediction intervals. In the scheduling stage, these forecast-derived intervals are embedded into a mixed-integer linear robust unit commitment model through a dynamic uncertainty budget. Using real-world load data from Southern China, the proposed method achieves average RMSE, MAE, MAPE, and R² values of 2941 kW, 2137 kW, 4.33%, and 0.97, respectively. Relative to SARIMA and Informer, the average RMSE is reduced by 48.1% and 26.0%, respectively, while point-forecasting performance remains competitive with XGBoost. The proposed model also provides the best overall interval quality, with average PINAW and Winkler Score values of 0.19 and 17,049, outperforming XGBoost, CNN-LSTM, and Informer. In the scheduling study, the proposed robust strategy reduces average EENS and LOLH to 68.6 kWh and 0.0454 h, respectively, and yields the lowest average generalized total cost of CNY 97.30 million, compared with 124.69 million CNY for the deterministic benchmark and CNY 99.66 million for the chance-constrained benchmark. These results show that forecast uncertainty can be effectively translated into more reliable and economical scheduling decisions. Full article

Vertical-axis wind turbine (VAWT) clusters have been extensively investigated owing to their positive aerodynamic interactions. However, accurate predictions of the flow field and power output of each rotor in VAWT clusters using high-fidelity computational fluid dynamics (CFD) remain computationally expensive. In this study, we propose a fast computation method for the flow field and operating state of each rotor of VAWT clusters using temporally and spatially averaged velocity data compressed from an unsteady velocity field obtained via a 3D-CFD simulation of an isolated rotor. First, the unsteady 3D flow field in the 3D-CFD simulation is time-averaged over several revolutions. Next, the temporally averaged velocity is spatially averaged in the vertical direction to obtain spatially compressed data. Based on a previously developed fast computation framework, a wind-farm flow field is constructed using condensed two-dimensional velocity data obtained from a single turbine. The proposed method is applied to three-rotor configurations, and the rotational speeds of the turbines are compared with the wind-tunnel measurements. The results show that the proposed method substantially improved the prediction accuracy while maintaining a low computational cost. In addition, it can be used to efficiently design and optimize turbine layouts in VAWT wind farms. Full article

State-of-charge (SOC) estimation for lithium-ion batteries in smartphones is complicated by nonlinear load variation, electro-thermal coupling, aging effects, and heterogeneous user behaviors. This study proposes a multi-physics coupled SOC estimation framework, termed the Multi-Physics Thermo-Electrochemical Coupled SOC Model (MTEC-SOC), to characterize battery behavior under representative user-load conditions within controlled ambient thermal boundaries. The model combines system-level power profiling, thermal evolution, voltage dynamics, and aging-related capacity correction within a unified framework. To support model development and validation, a dual-source dataset is established using laboratory battery characterization data and real-world smartphone behavioral data, from which users are classified into light, heavy, and mixed usage patterns. Comparative results against four benchmark models (M1–M4) show that MTEC-SOC achieves the highest overall accuracy, with average MAE, RMSE, and TTE error values of 0.0091, 0.0118, and 0.08 h, respectively. The results suggest distinct degradation tendencies across user types: calendar aging dominates under prolonged high-voltage dwell in light-use scenarios, whereas, within the tested thermal range, heavy-use scenarios exhibit stronger voltage sag, relative temperature rise, and polarization-related stress; mixed-use scenarios are characterized by transient responses induced by abrupt load switching. Sensitivity analysis further indicates that the predictive behavior of the model is strongly scenario-dependent, with higher-load operation within the calibrated range amplifying parameter perturbations. Overall, the proposed MTEC-SOC framework provides accurate SOC estimation and physically interpretable insight within the evaluated dataset and operating conditions, offering potential guidance for battery management and energy optimization in intelligent mobile terminals. Full article

As the global energy transition accelerates, clean energy development has surged. However, accurately modeling correlations and uncertainties of hydro, wind, and photovoltaic energy remains challenging in long-term scheduling for energy complementarity. This study employs Latin hypercube sampling and Cholesky decomposition to capture the temporal correlations of water runoff, wind, and photovoltaic resources. It generates numerous scenarios for uncertainty simulation. The scenario set is reduced based on probability distance while maintaining a high-fidelity approximation. A stochastic dual-objective model is proposed for long-term multi-energy complementary system scheduling (LMCS), aiming to maximize expected revenue considering carbon emission costs while ensuring minimum power output guarantees. An evolutionary algorithm—namely, an orthogonal multi-population evolutionary (OMPE) algorithm based on orthogonal design and a multi-population search framework—is introduced, along with constraint-handling strategies. Three annual-regulation hydropower stations in the Hongshui River Basin serve as a case study. The experimental results indicate that generated scenarios capture temporal characteristics with high accuracy. The proposed algorithm efficiently solves the LMCS problem, achieving average increases of 5.46% and 3.89% in revenue and minimal output compared to benchmarks. The validation results demonstrate that orthogonalization-based initialization, recombination operators, and dominance rules significantly enhance OMPE performance. Sensitivity analysis indicates that economic efficiency and risk trade-offs can be adjusted by varying scenario numbers. 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

Nuclear energy has emerged as a crucial technological solution for ensuring energy security and achieving carbon neutrality goals, given its ultra-high energy density and near-zero carbon emissions against the backdrop of rapid socioeconomic development, increasing energy demands, and accelerated global transition toward low-carbon energy structures. As the core component for energy conversion in nuclear reactors, fuel elements critically determine reactor efficiency and safety performance, with the fission product retention capability of silicon carbide layers in multilayer-coated fuel particles having been thoroughly validated through high-temperature gas-cooled reactor irradiation tests. The precise sphericity control of large-sized UO₂ fuel kernels represents a fundamental requirement for enhancing tristructural isotropic (TRISO) fuel particle performance and advancing Generation IV nuclear power plant development. This study presents a sphericity control strategy based on sol–gel processing that synergistically integrates physicochemical regulation of gelling media with multi-field washing flow field optimization. By implementing silicone oil-mediated interfacial tension gradient control, we effectively suppressed gel sphere destabilization while developing an innovative three-phase sequential washing technique involving kerosene washing, anhydrous ethanol interfacial transition, and ammonia solution replacement, which significantly enhanced mass transfer diffusion in stagnant liquid films and revolutionized fuel microsphere washing technology with improved efficiency and quality. Experimental results demonstrate that this integrated approach increases kernel sphericity qualification to 99.8%, reduces washing solution consumption by 79%, and achieves an average sphericity of 1.03. The research establishes a coupling mechanism between gelling media and multi-field washing processes, elucidating the synergistic effect between interfacial tension regulation and washing optimization, thereby providing both theoretical foundations and engineering application basis for the precision manufacturing of high-performance nuclear fuels. 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

In Time Division Duplex (TDD) Direct-Sequence Code Division Multiple Access (DS/CDMA) architectures, Pre-Rake filtering serves as a powerful transmitter-side strategy to alleviate receiver hardware constraints by leveraging channel reciprocity. Nevertheless, rapid channel fluctuations induced by high Doppler spreads critically undermine this reciprocity assumption. This failure is primarily driven by the unavoidable latency between uplink reception and downlink transmission, leading to severe performance deterioration. To address these challenges and enhance system robustness in modern high-speed scenarios, we propose an improved hybrid transceiver architecture. This scheme integrates multiplexed Pre-Rake processing with a Matched Filter-based Rake receiver and employs a Minimum Mean Square Error (MMSE) equalizer to suppress the severe Inter-Symbol Interference (ISI) and Multi-User Interference (MUI). Furthermore, we conduct a comparative analysis of channel estimation methods tailored for a 10 Mbps high-speed transmission environment.Our investigation reveals that while complex quadratic interpolation is often prioritized in low-data-rate studies, simple averaging is sufficient and even superior in high-speed communications. This is because the shortened slot duration allows simple averaging to effectively track channel variations while avoiding the noise overfitting associated with higher-order interpolation. The simulation results demonstrate that the proposed MMSE-optimized architecture achieves superior Bit Error Rate (BER) performance, providing a practical and computationally efficient solution for next-generation mobile networks. Full article

High-precision time and frequency references are essential for satellite navigation, deep-space exploration, and space science missions. To address the large size, high power consumption, and limited integration of conventional Passive Hydrogen Maser (PHM) servo electronics based on discrete analog chains, this paper proposes a miniaturized digital servo architecture for PHMs based on software-defined radio (SDR) and a field-programmable gate array (FPGA). The AD9364 is used as an integrated RF front end for microwave interrogation signal generation, receiver down-conversion, and analog-to-digital conversion (ADC), while digital demodulation, discriminator construction, and closed-loop control are implemented in the FPGA. A dual-frequency interrogation and time-division multiplexing scheme is introduced to separate the atomic and cavity responses, and an oversampling-based processing method combining outlier rejection and averaging decimation is adopted to improve the observation accuracy and noise immunity of weak error signals. Experimental results demonstrate stable closed-loop locking of the atomic transition spectrum, achieving a frequency stability of 1.46 × 10⁻¹² at 1 s, while significantly improving the compactness and integration level of the servo electronics. Full article