Search Results (1,547)

Accurate station-scale weather forecasts support renewable-energy and transportation operations, yet gridded numerical weather prediction (NWP) is affected by representativeness errors and regional biases when transferred to irregularly distributed stations. We propose TF-STNet, a time–frequency dual-branch spatiotemporal network for NWP-to-station bias correction. A station-centered K-nearest-neighbor (KNN) operator retains multiple local NWP trajectories. The time-domain pathway separately encodes recent observations and future NWP, aligns them over the forecast horizon using gated dilated causal convolutions, and propagates lead-resolved states through a coordinate-conditioned directed station graph. The frequency-domain pathway learns spectral weights and applies separate attention to amplitude and phase across neighboring NWP cells. Prediction-level fusion combines the two station forecasts by variable, station, and lead time. The evaluation uses hourly data for wind speed, pressure, relative humidity, and temperature from 455 stations in Hebei, Shandong, Fujian, and Sichuan. Across five independent runs on 16 region–variable tasks, TF-STNet achieves the lowest mean absolute error (MAE) in 15 tasks and the highest Pearson correlation coefficient (PCC) in 15 tasks; its pressure MAE reduction relative to the strongest learned comparator ranges from 16.2% to 57.4% across the four regions. It has lower MAE than raw NWP in seven of eight high-wind or rapid-change event tests and than simple pressure model-output-statistics corrections in all four regions. The Shandong–temperature task and the Hebei–high-wind case illustrate the limits of the present point-forecast formulation. Full article

Tractor rollover is a persistent worldwide problem that does not yet have a fundamental solution. Adjusting the tractor’s attitude or configuration may enhance mobility under complex terrain conditions to prevent rollover. Therefore, in this study, an attitude adjustment method for offsetting the height difference between the uphill and downhill sides is proposed to adjust a tractor’s posture. Kinematic models are established for front-wheel, four-wheel, and articulated body steering modes. Steering mathematical models are developed for the three modes to describe the effects of posture change on tractor steering instability. This method predicts the steering stability by analyzing tire contact forces. Both the critical slope angle and steering speed are derived and used to predict instability while taking the steering radius into consideration. Considering a tractor’s attitude, simulations are conducted under two conditions; namely, attitude adjustment and level attitude. The results show that attitude adjustment is an effective method to enhance a tractor’s steering stability to avoid overturning. Furthermore, the models presented here provide theoretical references and optimization directions to prevent lateral overturning during tractor steering. Full article

This paper investigates stable and compact diagnostic feature signatures for faults related to demagnetization in brushless direct-current (BLDC) and permanent-magnet synchronous motor (PMSM) drives. Discovery analysis on the public DUDU-BLDC v1 benchmark—where DUDU is the project name derived from the Polish Diagnostyka Uszkodzeń i Degradacji Urządzeń—compares current, speed, and combined representations under explicit top-k budgets using ReliefF, minimum-redundancy maximum-relevance (mRMR), least absolute shrinkage and selection operator (LASSO), and Bayesian automatic relevance determination (ARD) logistic ranking. The revision is accompanied by DUDU-BLDC 1.5, a new and previously unpublished March 2026 dataset comprising 50 recordings from five physical motors under altered acquisition conditions. On this second campaign, leakage-free nested five-fold recording-grouped validation with three deterministic repeats reached balanced accuracies of 0.760 and 0.758 for the two confirmatory within-motor tasks. Motor-held-out balanced accuracies fell to 0.500 and 0.554, with four of five physical-motor estimates at chance and one estimate at 0.663, exposing substantial between-motor heterogeneity. A paired experiment that quantized the original raw current signals to the approximately 0.08 A resolution of DUDU-BLDC 1.5 produced a mean absolute balanced-accuracy change of 0.0046, although the maximum change was 0.0725 and individual ranking-stability changes were larger. The results support compact signatures for repeated monitoring within an established or calibrated motor population and show aggregate robustness to reduced current resolution. They do not establish universal transfer to unseen motor instances; broader deployment requires motor-specific calibration or more diverse multi-motor training data. Full article

Objective: This systematic review and meta-analysis evaluated the comparative effects of unilateral versus bilateral training on physical performance outcomes relevant to team sport athletes. The review addresses strength, power, speed, agility, reactive strength, and core stability rather than direct match performance or sport-specific skill execution. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, nine electronic databases were searched from inception through 10 August 2025, for randomized controlled trials directly comparing unilateral and bilateral training. Methodological quality was assessed using the Cochrane Risk of Bias 2.0 tool, and certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. Results: Twenty studies involving 539 unique participants were included. Compared with bilateral training, unilateral training showed potential advantages for agility (standardized mean difference [SMD] = −0.73), sprint performance (SMD = −0.30), and muscular strength (SMD = 0.29). However, effect magnitude and certainty varied across outcomes, and several analyses showed substantial heterogeneity or limited certainty. The primary vertical-jump estimate favored unilateral training (SMD = 0.38), but the association was no longer statistically significant after trim-and-fill adjustment. Moderator analyses were exploratory and should not be interpreted as confirming optimal training parameters. In the one-effect-per-sample sensitivity analysis, the vertical-jump and muscular-strength estimates were no longer statistically significant, whereas excluding the two acute crossover studies did not change any affected outcome conclusion. Conclusions: Unilateral training may be used as a complementary strategy for selected physical performance qualities relevant to team sport participation, particularly agility and sprint performance. These findings remain preliminary because of heterogeneity, potential publication bias, methodological limitations, and moderate-to-very-low GRADE certainty. The evidence does not justify replacing bilateral training or prescribing a specific unilateral program across sports. Full article

UAVs are being employed for inspection, mapping, emergency response, and low-altitude activities. However, propeller fractures, edge wear, tip loss, imbalance, motor-bearing deterioration, eccentric shafts, and operating circumstances might damage their propulsion systems. Acoustic health monitoring is promising for this task because propulsion failures may change pressure fluctuations, blade-passing tones, broadband aerodynamic noise, and vibration-radiated sound without structural changes. Wind, reverberation, interference from surrounding rotors, speed variations, sensor-direction effects, limited datasets, and the gap between laboratory precision and field robustness in operational situations prevent acoustic UAV diagnostics from being widely used. Acoustic diagnostics of small UAV propulsion systems are the focus of this article. Acoustic emission monitoring, microphone-array and beamforming, wavelet and time–frequency feature extraction, normalized feature design, machine learning, lightweight deep learning, and noise-resilient deployment evaluation criteria are included. The reviewed studies reported promising results, including on-board microphone-array isolation of damaged rotors, acoustic-camera propeller-tip identification, and deep learning on propeller-anomaly datasets. Standardization of sensor locations, publicly accessible multi-condition acoustic datasets, cross-UAV validation, systematic selection of wavelet bases and decomposition levels, and clear computational cost reporting are still lacking. A deployment-oriented roadmap emphasizes multi-condition benchmarking, sensor placement optimization, wavelet-based noise robustness, hybrid acoustic-vibration validation, domain adaptation, uncertainty-aware decision-making, and edge AI for real-time UAV propulsion health monitoring. Full article

To improve the yaw-stability tracking accuracy and torque smoothness of distributed-drive electric vehicles (DDEVs) under high-speed double-lane-change maneuvers and crosswind disturbances, this paper proposes a multi-agent-system (MAS)-based direct yaw moment control (DYC) method using full-order terminal sliding mode (FOTSM) control. First, based on the vehicle yaw dynamics model and the vector superposition principle, the whole-vehicle yaw-rate and sideslip-angle responses are decomposed into the local contributions of four wheel agents. A leader–follower MAS tracking framework is then established, in which the yaw-stability reference model acts as the virtual leader, and the four wheel agents act as followers. Second, the yaw-rate error and sideslip-angle error are combined into an aggregated tracking error, thereby transforming yaw-stability control into a second-order nonlinear MAS tracking problem. A FOTSM DYC law is designed, and Lyapunov analysis proves that the closed-loop error system reaches the sliding surface and converges within finite time. Finally, hardware-in-the-loop experiments are conducted under double-lane-change maneuvers with and without crosswind disturbance. Compared with the uncontrolled case and a conventional MAS-based linear sliding mode controller, the proposed method reduces yaw-rate and sideslip-angle deviations, improves trajectory-tracking performance, maintains yaw stability under crosswind disturbance, and suppresses wheel-driving-torque chattering. Full article

Urban elevated-road exits concentrate deceleration, merging and surface-road control within a short transition, yet their emission responses are difficult to resolve from routine vehicle records. This study analyzed six dates of 1 Hz GPS and on-board diagnostics from one China VI diesel commercial vehicle operating in Nanjing. Sequence map matching linked the trajectories to a frozen OpenStreetMap network and identified 92 stable layer 1 → 0 exit occurrences. Leave-one-date-out telemetry models estimated the downstream OBD-derived NOx indicator expected from recorded vehicle, engine and aftertreatment states without using road variables. The trip-balanced post-minus-pre residual contrast was 0.084 log units (95% confidence interval, 0.020–0.151), whereas same-trip non-exit controls balanced on recorded operating changes showed a smaller contrast. The direction persisted across alternative models, map-matching settings, timing falsification, continuity thresholds, low-speed inclusion and an analysis restricted to recurring sites. Twenty sites were observed on multiple dates. These findings identify a recurrent, map-aligned OBD-derived NOx response signature within the observed operation and provide a practical basis for prioritizing targeted field measurements of emissions, traffic and road geometry at complex urban transitions. Full article

Freeway connected and automated vehicle (CAV) priority lanes (CPLs) provide dedicated or preferential road space for CAVs, but access-rule transitions require CAVs, connected vehicles (CVs), human-driven vehicles (HDVs), and trucks to reorganize within a finite control zone. This paper develops a three-layer framework that links robust target-lane assignment, online executability screening, and continuous control through a common state and surrogate-safety-measure interface. A six-slot robust mixed-integer program supplies a feasible incumbent, robustness-enhanced beam search retains alternative lane sequences, and local integer-programming repair corrects restricted conflicts. The middle layer checks retained assignments using gap time, time to collision, deceleration rate to avoid a collision, and post-encroachment time. The lower layer uses extended-Kalman-filter-assisted control-barrier-function model predictive control (EKF-CBF-MPC). Across 180 matched closed-loop scenarios per upper-layer method, the complete framework achieves 98.2% target-lane completion and 100.0% planned-PET pass, compared with 89.5% and 49.8% for direct six-slot robust MIP. Mean access-rule satisfaction is 65.4% and 67.9%, respectively. In 100 matched parameter-mismatch runs, EKF initialized from the present HDV prior reduces one-second speed and net-gap prediction RMSE by 74.5% and 79.6% relative to the matching fixed-parameter predictor; a published highD prior gives similar online errors. Communication-fault experiments further evaluate state prediction, command expiry, lane-change deferral, and local fallback control. The results show improved execution reliability and conflict screening, with a modest rule-compliance and computation-time trade-off relative to robust MIP. Full article

Regional 10 m wind-speed products extend coverage beyond sparse station networks, but their errors vary with wind regime and location. This study evaluates a station-trained LightGBM correction of a 5 km WRF/HNR wind-speed product over Hainan Island. Raw and corrected fields were collocated with stations using a four-neighbour inverse-distance operator. A spatially separated 2022 holdout retained 67 stations after five coordinate-overlap exclusions. Across 582,610 common hourly pairs, corrected RMSE decreased from 3.198 to 1.211 m s⁻¹ and Pearson R increased from 0.368 to 0.651. The paired RMSE reduction was 1.986 m s⁻¹ [95% CI 1.839, 2.122]. Improvement was largest below 3.4 m s⁻¹ (2.203 m s⁻¹) and remained positive for 3.4–7.9 m s⁻¹ (0.566 m s⁻¹). In contrast, corrected RMSE increased by 0.990 m s⁻¹ for 8.0–10.7 m s⁻¹ and by 1.656 m s⁻¹ for ≥10.8 m s⁻¹. A time-only adjustment reduced pooled RMSE to 1.870 m s⁻¹ but also degraded the two upper strata. The 2016–2022 maps show a lower corrected product climatology, with a mean corrected-minus-raw increment of −2.273 m s⁻¹ across the Hainan buffer. Temporal partitions and AWS-selected station groups show variation across season, time of day, and setting. The correction reduces station-sampled errors for light-to-moderate wind speeds; it changes speed magnitude while retaining the raw-product wind direction. Full article

The stable boundary layer (SBL) over land exhibits pronounced vertical decoupling and structural heterogeneity that remain challenging to diagnose using routine operational monitoring. Here, we utilize a three-year continuous multi-level tower dataset ($N=\mathrm{40,630}$ quality-controlled 15-min records at 2, 10, 20, and 50 m) over the Horqin Grassland to characterize stability-dependent modifications of surface-layer wind structure and near-surface kinetic energy. Using the bulk Richardson number ($R{i}_b$) and raw thermal gradients ($\Delta T_v/\Delta z$), we identify a stability-driven reduction in vertical wind coupling, with a statistical change-point cluster centered near $R{i}_b\approx 0.30$ (95% CI: 0.09–0.52). Cross-level correlation analysis empirically localizes a structural transition zone to the 10–20 m interval, separating a faster, shear-driven upper layer from a dynamically suppressed near-surface flow under strong stability. Concurrently, near-surface horizontal kinetic energy ($HK{E}_{02}$) undergoes a 47% (nighttime) to 55% (full-record) median reduction, while horizontal wind-direction variability (${\sigma }_{\theta \_02}$) broadens due to low-wind meandering, and sub-hourly vertical velocity variance (${\sigma }_{w,\mathrm{LF}}^2$) is suppressed by 57–72% across all heights. Multidimensional scaling (MDS) reveals a statistically significant phase-space compaction under high stability (${\tilde{d}}_{\mathrm{high}}=2.13$ vs. ${\tilde{d}}_{\mathrm{low}}=2.67$, $p<0.001$), with adjusted odds ratios highlighting thermal gradient ($\mathrm{OR}=5.80$), wind shear ($\mathrm{OR}=1.52$), and directional variability ($\mathrm{OR}=1.34$) as dominant positive predictors. These empirical patterns remain robust under temporal-holdout validation (Year-1 calibration vs. Years 2–3 validation; consensus breakpoint $R{i}_b=0.161$ vs. $0.156$) and seasonal/wind-speed stratifications. The results demonstrate that routine 15-min multi-level tower networks provide valuable observational constraints on surface-layer structural transitions, informing boundary-layer parameterizations in numerical weather prediction models and near-surface dispersion assessments. Full article

The development of structured protein gel matrices with meat-like fibrous properties is a key challenge in the design of high-moisture meat analogs (HMMA). This study incorporated brewer’s yeast into a soy protein-wheat gluten-corn starch matrix and evaluated the stage-specific effects of moisture content (MC), barrel temperature (BT), and screw speed (SS) on the fibrous appearance, integrity index, nitrogen solubility index (NSI), texture, and cutting strength of yeast-enhanced meat analogs (Y-MA), alongside pasting, rheological, Fourier transform infrared spectroscopy (FTIR), and Raman analyses of pre-extrusion blends (0% and 10% yeast). Among the individual factors examined under their respective fixed processing conditions, moisture content produced the largest changes in several measured responses. Raising MC from 55% to 70% weakened the fibrous structure, reduced chewiness from 6211 to 867 g, and lowered cutting strength in both directions. Raising BT from 140 to 170 °C produced more distinct fibrous features, higher NSI, and greater firmness. Increasing SS from 150 to 300 rpm was associated with decreased chewiness. The 10% yeast blend showed lower pasting viscosities, a higher pasting temperature, and lower terminal G′ and G″ than the yeast-free blend, while FTIR and Raman spectra remained unchanged. These findings indicate that extrusion parameters influenced the fibrous appearance, matrix retention, and mechanical properties of Y-MA under the fixed conditions tested. Full article

Background: While the biomechanical, physiological, and nutritional determinants of table tennis (TT) performance have already been summarized, a dedicated synthesis of players’ anthropometric and motor characteristics is still lacking. This review aimed to synthesize current evidence across different ages, genders, and competitive levels. Methods: BASE, PubMed, ScienceDirect, Scopus, Semantic Scholar, and Web of Science databases were searched for study selection. Cross-sectional studies investigating the anthropometric and motor characteristics of healthy TT players aged 7–40 years were considered eligible. Results: Thirty-one studies were included. Eighteen studies focused on motor characteristics, seven on anthropometric traits, and six examined both. Conclusions: Adult male TT players predominantly exhibit an endomorph–ectomorph profile with greater lean mass, whereas females show an endomorph–ectomorph somatotype. Moreover, higher-ranked athletes generally display greater lean mass. Compared to specific strength-based sports athletes, TT players tend to have lower limb circumferences and lower lean and muscle mass, while showing higher body fat than specific speed- and agility-based sports. Higher-level players tend to demonstrate faster reaction and movement times, as well as superior change-of-direction abilities. Compared to other racket sports, they tend to exhibit superior coincidence-anticipation timing under high stimulus velocities and distinctive motor coordination. In terms of strength, TT players show proficiency in horizontal jumps and dynamic back strength, despite lower vertical explosive power. The identified characteristics provide a descriptive profile of table tennis players and may inform future longitudinal research investigating their potential relevance for talent identification and development. Full article

The wake effect inside wind farms reduces the inflow wind speed and increases the turbulence intensity of downstream turbines, resulting in power loss and increased fatigue loads. Active yaw control can mitigate wake interference through collaborative optimization of turbine yaw angles. However, most existing methods rely on steady-state wake models, which fail to capture the dynamic delay characteristics of wakes and usually lead to excessive yaw actuation losses. To address these issues, this paper constructs a dynamic wake model suitable for real-time control based on the OFF dynamic wake framework (OnWARDS, FLORIDyn, and FLORIS), adopting an improved three-dimensional analytical wake model at the lowest level. On this basis, a finite control set model predictive control (MPC) active yaw controller is designed. Aiming to maximize power generation and minimize yaw loss, the controller realizes rolling optimization of yaw actions combined with ARIMA-based wind direction prediction and particle swarm optimization. Simulations on the 4 × 4 turbine array of the Horns Rev I wind farm show that the proposed method increases the total power by 2.25%, which is 0.79% higher than that obtained by the deadband controller. It results in lower power loss for upstream turbines and higher power gain for downstream turbines, reduces the total yaw travel by nearly 1000° compared with the deadband controller, and produces smaller power fluctuations under sharply changing wind directions. Full article

This paper investigates the influence of coarse particle size on pressure characteristics through horizontal pipe transportation experiments of coarse particles and sand-laden water. As particle size increases, the thin and long grain accumulation layer forming at the pipe bottom and particle clusters above the layer vanish, most grains roll and bounce more quickly, the instantaneous pressure fluctuation intensity increases exponentially, and the time-averaged pressure decreases linearly along the flow direction. The fluctuating pressure probability density tends to obey a Gaussian distribution, and the turbulence power below 16 Hz increases. After adding fine sand constituting sand-laden water flows, the instantaneous pressure fluctuation intensity, transportation energy loss speed and fluctuating pressure amplitudes increase. With increasing particle size, the fitted Gaussian distribution of the fluctuating pressure probability density changes from thin to short and wide, and the spectral power of the measured pressure fluctuations increases within the resolvable frequency range, especially in the range from 8 to 10 Hz. Full article

This paper presents the Post-Stimulus Time-Dependent Event Descriptor (P-TED), a novel “pure event” feature descriptor designed for neuromorphic vision data. Unlike conventional frame-based approaches or hybrid methods that transform event data into intermediate representations, P-TED operates directly on asynchronous event streams, thereby preserving the intrinsic low-latency and high-temporal-resolution advantages of event-based sensors. The descriptor integrates two complementary feature sets: a motion feature vector, which aggregates spatial relationships within a Moore neighbourhood to quantify stimulus direction, and a pattern feature vector, which employs rate encoding to capture temporal excitation signatures. The efficacy of the P-TED framework is validated through three distinct experiments: object and character recognition (MNIST-DVS and CIFAR10-DVS), mobile robot movement analysis, and complex non-rigid robotic hand gesture recognition (RoShamBo). Experimental results demonstrate that the P-TED achieves a significant reduction in classification latency, requiring only 2.7 ms compared to the 10.3 ms recorded by the state-of-the-art Distribution-Aware Retinal Transform (DART) framework. Additionally, P-TED exhibits superior robustness in disambiguating symmetric and mirrored motions, as well as in maintaining stability under non-linear fluctuations in event density caused by changing scale. This work establishes P-TED as a high-speed, computationally efficient, and explainable solution for real-time neuromorphic robotic vision systems. Full article