Search Results (695)

To improve the control accuracy and address the parameter disturbance issues of joint-driven permanent magnet synchronous motors in intelligent manufacturing, this paper proposes an improved deadbeat predictive current predictive control (DPCC) scheme that eliminates dead zones. This scheme establishes a multi-parameter identification model based on the error equation of the d-q axis predicted current, which improves the problem of not being able to identify all parameters caused by insufficient input signals. It also implements decoupling compensation for the coupling between the d-q axis inductance, stator resistance, and magnetic flux linkage. To meet the anticipated control objectives and account for external disturbances, a low-complexity specified performance tracking controller (LCSPC) based on output target error signals has been designed. This mitigates output delay issues arising from nonlinear components during motor operation. Finally, simulation analysis and experimental testing demonstrate that the proposed control scheme achieves high identification accuracy with minimal delay, thus meeting the transient control performance requirements for motors in intelligent manufacturing processes. Full article

Background/Objectives: This study examines the associations between household socioeconomic status (SES), child nutrition, and developmental status among children aged 24–59 months in the Mont Ngafula health zone in Kinshasa. The primary research question focuses on how SES and stunting affect developmental outcomes in early childhood. Methods: A cross-sectional analysis was conducted involving 348 children, assessing developmental outcomes using the Early Childhood Development Index (ECDI2030). Results: The study found that 70.4% of children were classified as on track, with ONTRACK prevalence increasing across SES tertiles. Children who attended preschool education had higher odds of being on track. The rich tertile had higher odds of being on track than those in the poor tertile, while the middle tertile showed a weaker association. Child age categories and stunting were inversely associated with being developmentally on track. The results are consistent with multiple imputation sensitivity analyses. Conclusions: The study concludes that preschool attendance and a higher household socioeconomic position are strongly associated with better early developmental outcomes, while an age of 48–59 months and stunting are associated with a markedly lower likelihood of being developmentally on track. Integrated policies that reduce household poverty, promote early education, and prevent/treat early faltering growth could improve early childhood developmental trajectories. Full article

Arbitrary zones are commonly used to describe and monitor external load (EL) during training and competitions. However, in recent years, relative speed zones have gained interest as they allow a more detailed description of the demands of each individual player, with their benefits largely unknown. This study aimed to (i) identify differences in EL methodological approaches using arbitrary and relative running speed zones; (ii) examine the effect of the methodological approaches to identify fast and slow basketball players during competition and training; and (iii) determine the effect of the season stage on the methodological approaches. Twelve players from a Spanish fourth-division basketball team were observed for a full season of matches and training using inertial devices with ultra-wideband indoor tracking technology and micro-sensors. Relative velocity zones were based on the maximum velocity achieved during each match quarter and were retrospectively recalculated into four zones. A linear mixed model (LMM) compared fast and slow players based on speed profiles between arbitrary and relative thresholds and during each competition stage. All players surpassed peak speeds of 24 km·h⁻¹ during the season, exceeding typical values reported in elite basketball (20–24.5 km·h⁻¹). Arbitrary thresholds produced greater distances in high-speed running (Zones 3 and 4) and yielded lower values in low-speed activity (Zone 1), with differences of ~100 m and ~120–250 m, respectively (p < 0.001), particularly for fast-profile players. These discrepancies were consistent across most stages of the season, although relative zones better captured variations in Zone 1 across time. Training sessions also elicited +8.7% to +40.7% greater distances > 18 km·h⁻¹ compared to matches. The speed zone methodology substantially influenced EL estimates and affected how player EL was interpreted across time. Arbitrary and relative approaches offer unique applications, with coaches and sport scientists encouraged to be aware that using a one-size-fits-all approach may lead to misrepresentation of individual player demands, especially when tracking changes in performance or managing fatigue throughout a competitive season. Full article

Advances in sensor technology have significantly improved the efficiency and precision of agricultural spraying. Unmanned aerial vehicles (UAVs) are widely utilized for applying plant protection products (PPPs) and fertilizers, offering enhanced spatial control and operational flexibility. This study evaluated the performance of an autonomous UAV-based precision spraying system that applies variable rates based on zone levels defined in a prescription map. The system integrates real-time kinematic global navigation satellite system positioning with a proximity-triggered spray algorithm. Field experiments on a rice field were conducted to assess spray accuracy and fertilization efficacy with liquid fertilizer. Spray deposition patterns on water-sensitive paper showed that the graded strategy distinguished among zone levels, with the highest deposition in high-spray zones, moderate in medium zones, and minimal in no-spray zones. However, entry and exit deviations—used to measure system response delays—averaged 0.878 m and 0.955 m, respectively, indicating slight lags in spray activation and deactivation. Fertilization results showed that higher application levels significantly increased the grain-filling rate and thousand-grain weight (both p < 0.001), but had no significant effect on panicle number or grain count per panicle (p > 0.05). This suggests that increased fertilization primarily enhances grain development rather than overall plant structure. Overall, the system shows strong potential to optimize inputs and yields, though UAV path tracking errors and system response delays require further refinement to enhance spray uniformity and accuracy under real-world applications. Full article

To address the stability control challenges of narrow coal pillar roadways along goaf-sides affected by thick coal seam secondary mining, this study investigates the 51507 track gateway in Liuyuanzi Coal Mine through theoretical analysis, numerical simulation, and field testing. The research focuses on stress evolution and energy distribution characteristics during secondary mining extraction. Key findings include the following: (1) Under the superimposed influence of goaf-side abutment pressure and secondary mining front abutment pressure, roadway surrounding rock exhibits regional asymmetric characteristics in energy dissipation. (2) Within 10 m ahead of the secondary mining face, the coal pillar experiences intense energy dissipation and plastic zone penetration, leading to bearing structure failure. (3) The energy mechanism reveals that asymmetric dissipative energy distribution drives plastic zone expansion. Accordingly, an integrated control strategy combining differentiated support (bolts/cables + tension-type opposite anchor cables + hydraulic props) with coal pillar grouting modification was developed. Field implementation demonstrated effective control of surrounding rock deformation within 200 mm. This study provides theoretical foundations and technical references for roadway stability control under similar mining conditions. Full article

To improve the adaptability of 4WID electric vehicles under various operating conditions, this study introduces a model predictive control approach utilizing a neural network for adaptive weight parameter prediction, which integrates four-wheel steering and longitudinal driving force control. To address the difficulty in adjusting the MPC weight parameters, the neural network undergoes offline training, and the Snake Optimization method is used to iteratively optimize the controller parameters under diverse driving conditions. To further enhance vehicle stability, the real-time stability state of the vehicle is assessed using the $\beta -\dot{\beta }$ phase plane method. The influence of vehicle speed and road adhesion on the instability boundary of the phase plane is comprehensively considered to design a stability controller based on different instability degree zones. This includes an integral sliding mode controller that accounts for both vehicle tracking capability and stability, as well as a PID controller, which calculates the additional yaw moment based on the degree of instability. Finally, an optimal distribution control algorithm coordinates the longitudinal driving torque and direct yaw moment while also considering the vehicle’s understeering characteristics in determining the torque distribution for each wheel. The simulation results show that under various operating conditions, the proposed control strategy achieves smaller tracking errors and more concentrated phase trajectories compared to traditional controllers, thereby improving path tracking precision, vehicle stability, and adaptability to varying conditions. Full article

Inflammatory muscle loss results from excessive inflammatory responses, causing muscle damage and weakness. In the current investigation, we evaluated the protective effects of diphlorethohydroxycarmalol (DPHC) against tumor necrosis factor-alpha (TNF-α)-induced skeletal muscle inflammation and muscle loss and elucidated the underlying mechanisms. Furthermore, the effect of DPHC on swimming performance was confirmed under TNF-α-induced inflammatory muscle loss-conditioned zebrafish by assessing the swimming number, distance moved, time spent swimming, frequency of swimming zebrafishes in an upstream swim track (Zone A). In vivo behavioral endurance test results indicated that TNF-α treatment significantly decreased the number of swimming zebrafish and swimming distance in Zone A compared with the Control. Meanwhile, the DPHC treatment significantly increased the number of swimming zebrafish and swimming distance in Zone A compared to TNF-α-induced zebrafish. These findings indicate that DPHC treatment effectively improved the swimming performance of TNF-α-induced zebrafish. In an additional study, TNF-α significantly induced inflammatory muscle loss by upregulating nuclear factor kappa light chain enhancer of activated B cells (NF-κB) mitogen activated protein kinase (MAPK) associated proteins and MuRF-1 in the skeletal muscle tissues of TNF-α-induced zebrafish. However, DPHC administration significantly counteracted TNF-α-induced inflammation and muscle loss by downregulating NF-Κb and MAPK-associated proteins, as well as the muscle degradation-related proteins MuRF-1 and MAFbx, in the skeletal muscle tissues of TNF-α-induced zebrafish. In summary, our research findings demonstrated that DPHC from Ishige okamurae could be used for the development of nutraceuticals or functional foods targeting inflammatory muscle loss. Full article

Sustainable management of aquatic ecosystems requires effective strategies to monitor and mitigate microplastic pollution, particularly in vulnerable tidal river systems. Microplastic accumulation in these environments poses significant environmental risks, threatening biodiversity, ecosystem health, and long-term water quality. This study employs a three-dimensional hydrodynamic model (TELEMAC-3D—v8p5) coupled with a Lagrangian particle tracking model (CaMPSim-3D—v1.2.1) to simulate microplastic transport dynamics in the lower Fraser River, British Columbia, Canada. The model incorporates tidal forcing, riverine hydrodynamics, and mixing processes, and was validated with good agreement against observed water levels. This model provides a high-resolution representation of microplastic dispersion under varying release scenarios, including emissions from combined sewer overflows (CSOs) and wastewater treatment plants (WWTPs). A novel approach is proposed to identify microplastic accumulation zones using the OPTICS (Ordering Points to Identify the Clustering Structure) clustering algorithm. Accumulation zone locations remain spatially consistent despite variations in release volume. Persistent clusters occurred near channel constrictions and shoreline segments associated with flow deceleration. These findings demonstrate the robustness of the method and provide a systematic framework for prioritizing high-risk areas, supporting targeted monitoring and informing sustainable estuarine management. Full article

Climate wave data, derived from significant wave height (SWH) altimetry, provide accurate information towards nearshore and coastal areas. Their use is crucial to enhance our capabilities of observing, understanding, and forecasting storm waves, even in complex coastal basins. In this study, SWOT nadir data were combined with nine existing altimeters for assessing waves and monitoring their evolution during storms in the English Channel, near UK–French coasts. Validation against wave buoys and numerical models shows high accuracy, with correlations around 95%, decreasing to 85% when buoy track offsets > 50 km, producing the largest errors. The multi-source approach enables depth-resolved monitoring, with SWH mapping revealing ~20–25% modulation in the Channel and ~36% dissipation near the Seine Bay during storms. Spectral analysis of multi-source altimeter-derived merged observations improve time-sampling, resolving high-frequency variability from monthly to daily scales and capturing ~75% of storms. Most storm wave features along altimetry tracks are resolved, with CFOSAT mapping nearshore areas and SWOT capturing coastal zones, both achieving ~80% variance. This temporal and spatial monitoring would be further enhanced with SWOT’s 2D wide swath. This finding provides a complementary, comprehensive understanding of coastal waves and offers valuable input for data assimilation, to improve storm wave estimates in coastal basins. Full article

This study aims to use Computational Fluid Dynamics (CFD) analysis to improve inclusion removal efficiency in tundishes used in the steelmaking industry, with the broader goal of promoting more sustainable steel production and supporting circular economy objectives by producing cleaner steel. Inclusions are non-metallic particles, such as alumina, that enter the tundish with the molten steel and travel through it; if not removed, they can exit through the nozzles and adversely affect the mechanical properties of the final product and process yield. An existing tundish design is modified using three passive techniques, including adding a vertical dam, adding a horizontal baffle, and inclining the side walls, to assess their influence on fluid flow behavior and inclusion removal. Residence time distribution (RTD) analysis is employed to evaluate flow characteristics via key metrics such as dead zone and plug flow volume fractions, as well as plug-to-dead and plug-to-mixed flow ratios. In parallel, a discrete phase model (DPM) analysis is conducted to track inclusion trajectories for particles ranging from 5 to 80 μm. Results show that temperature gradients due to heat losses significantly influence flow patterns via buoyancy-driven circulation, changing RTD characteristics. Among the tested modifications, inclining the side walls proves most effective, achieving average inclusion removal improvements of 8% (Case B1) and 19% (Case B2), albeit with increased heat loss due to greater top surface exposure. Vertical dam and horizontal baffle, despite showing favorable RTD metrics, generally reduce the inclusion removal rate, highlighting a disconnect between RTD-based predictions and DPM-based outcomes. These findings demonstrate the limitations of relying solely on RTD metrics for evaluating tundish performance and suggest that DPM analysis is essential for a more accurate assessment of inclusion removal capability. Full article

Exploration of coalbed methane (CBM) has long been plagued by critical technical challenges, including a low signal-to-noise (S/N) ratio in seismic data, difficulty identifying thin coal seams, and inadequate accuracy in interpreting complex structures. This study presents an innovative methodological framework that integrates artificial intelligence (AI) with advanced seismic processing and interpretation techniques. Its effectiveness is verified through a case study in the North Bowen Basin, Australia. A multi-scale seismic data enhancement approach combining dynamic balancing and blue filtering significantly improved data quality, increasing the S/N ratio by 53%. Using deep learning-driven, multi-attribute fusion analysis, we achieved a prediction error of less than ±1 m for the thickness of thin coal seams (4–7 m thick). Integrating 3D coherence and ant-tracking techniques improved the accuracy of fault identification, increasing the fault recognition rate by 30% and reducing the spatial localization error to below 3%. Additionally, a finely tuned, spatially variable velocity model limited the depth conversion error to 0.5%. Validation using horizontal well trajectories revealed that the rate of reservoir encounters exceeded 95%, with initial gas production in the predicted sweet spots zone being 25–30% higher than with traditional methods. Notably, this study established a quantitative model linking structural curvature to fracture intensity, providing a robust scientific basis for accurately predicting CBM sweet spots. Full article

Under the combined action of bidirectional (longitudinal and transverse) shear loads and corrosive environments, the shear performance of stud connectors in steel–concrete composite track beams of straddle-type monorail transit systems is susceptible to degradation, thereby posing a potential risk to the structural safety of the track girders. This study employs push-out tests and numerical simulations to investigate the influence of bidirectional shear loads and stud corrosion on the shear performance of stud connectors. The results showed that both transverse shear loads and stud corrosion lead to a reduction in the shear capacity of stud connectors, with their coupling effect amplifying the degradation. Transverse shear loads induce an accelerated decay trend in the load-bearing capacity of stud connectors, while an increase in corrosion depth results in a linear degradation of the load-bearing capacity. The corrosion depth at the stud root exerts a more pronounced influence on shear performance compared to the corrosion height. Furthermore, the dominant failure mode of stud connectors manifests as root fracture, while transverse shear loads induce alterations in the concrete damage zone. Based on the verified FE model, a shear capacity reduction factor accounting for the coupling effects of bidirectional shear and stud corrosion was established to improve the Oehlers model. This research provides critical theoretical support for the safe design and durability assessment of monorail track girders. Full article

China’s high-speed railway (HSR) network relies heavily on bridge structures to ensure track regularity, with many lines crossing seismically active near-fault zones. Near-fault ground motions are characterized by significant vertical components (VGMs), which challenge conventional seismic design practices. Although seismic isolation techniques are widely adopted, the effects of VGMs on the dynamic response and damage mechanisms of HSR track–bridge systems remain insufficiently studied. To address this gap, this study develops a refined finite element model (FEM) in OpenSEES that integrates CRTS II slab ballastless tracks, bridge structures, and friction pendulum bearing (FPB). Using nonlinear time-history analyses, the research systematically investigates structural responses and damage degrees under different ratios of vertical-to-horizontal peak ground acceleration (α_VH) and multiple seismic intensity levels (frequent, design, and rare earthquakes). Key findings reveal that α_VH values in near-fault regions frequently range between 0.5 and 1.5, often exceeding current design code specifications. The impact of VGMs intensifies with seismic intensity: negligible under frequent earthquakes but significantly amplifying damage to piers, bearings, and track interlayer components (e.g., sliding layers and CA mortar layers) during design and rare earthquakes. While seismic isolation effectively mitigates structural responses through energy dissipation by bearings, it may increase sliding layer displacements and lead to bearing failure under rare earthquakes. Based on these insights, tiered α_VH values are recommended for seismic design: 0.65 for frequent, 0.9 for design, and 1.2 for rare earthquakes. These findings provide critical references for the seismic design of HSR infrastructure in near-fault regions. Full article

This study investigates visual fatigue in a real-world pharmaceutical packaging environment, where operators perform repetitive inspection and packing tasks under frequently suboptimal lighting conditions. A human-centered methodology was adopted, combining adapted self-report questionnaires, high-frequency eye-tracking data collected with Tobii Pro Glasses 3, and lux-level measurements. Key eye-movement metrics—including fixation duration, visit patterns, and pupil diameter—were analyzed within defined work zones (Areas of Interest). To reduce data complexity and uncover latent patterns of visual behavior, Principal Component Analysis was applied. Results revealed a progressive increase in visual fatigue across the workweek and throughout shifts, particularly during night work, and showed a strong association with inadequate lighting. Tasks involving high physical workload under poor illumination emerged as critical risk scenarios. This integrated approach not only confirmed the presence of visual fatigue but also identified high-risk conditions in the workflow, enabling targeted ergonomic interventions. The findings provide a practical framework for improving operator well-being and inspection performance through sensor-based monitoring and environment-specific design enhancements, in alignment with the goals of Industry 5.0. Full article

This article presents an in-depth study of a dynamic wireless power transfer (DWPT) system used to charge electric vehicles (EVs), with a focus on modeling and controlling a double-D (DD) coil structure. The chosen DD coil design improves energy transfer efficiency and minimizes mutual coupling between adjacent transmit coils, a common problem in dynamic applications. A comprehensive mathematical model is developed to account for the nonlinear dynamics of the system, i.e., when the vehicle is moving and misalignments and coupling variations occur. A robust nonlinear control method based on sliding mode control (SMC) is implemented to ensure stable operation and accurate regulation of the output voltage. The controller is tested in different scenarios where the vehicle speed changes, thus ensuring its robustness and stability under all operating conditions. Particular attention is paid to the critical transition zone, in which the receiver coil is placed between two transmitter coils in order to achieve minimal magnetic coupling. The simulation results demonstrate that the proposed controller offers a fast dynamic response (~0.07 s) and stable voltage tracking, even in the event of significant variations in mutual inductance and different EV movement speeds. These results confirm the effectiveness of the control approach and its potential for real-time charging of electric vehicles in large-scale DWPT applications. Full article