Search Results (7,148)

Skilled operation of forestry harvesters is essential for ensuring safety, efficiency, and sustainability in logging practices. However, conventional training methods are often prohibitively expensive and limited by access to specialized equipment. This study delivers one of the first user-centered validations of a low-cost, VR HMD-based forestry harvester simulator, directly addressing access and scalability barriers in training. With 26 participants, we quantify cognitive load, usability, user experience, and simulator sickness using established instruments. An increase in cognitive load was seen from baseline tutorial to each training module (NASA-TLX: $18.65\to 34.26\to 38.43$; rm-ANOVA, p < 0.001). Usability was ‘Good’ (with a mean SUS score: 76.63), hedonic UX ranked in the top decile (UEQ-S), and simulator sickness was moderate (mean SSQ score: 28.91), while task success remained high across all modules. These results indicate early-stage feasibility and usability of a low-cost VR HMD harvester simulator for student-focused introductory instruction, and they provide actionable design guidance (e.g., managing extraneous load, comfort safeguards) advancing evidence-based VR HMD-based training in the forest engineering and harvesting domain. Our findings validate the potential of VR-HMD as a tool for forestry education capable of addressing training accessibility gaps and enhancing learner motivation through immersive experiential learning. Full article

High-spectral-resolution retrievals of nitrogen dioxide (NO₂) provide detailed atmospheric absorption information, but they usually involve large data volume, low computational efficiency, and complex instrument requirements. To address these limitations, we employ a low-spectral-information retrieval strategy for fast atmospheric monitoring. In this study, the Discrete-Wavelength Differential Optical Absorption Spectroscopy (DWDOAS) technique is applied by selecting 14 representative wavelength samples in the 420–450 nm window. Multiple wavelength–resolution configurations are constructed and quantitatively assessed using an entropy-weighting scheme to identify the optimal setup. Using TROPOspheric Monitoring Instrument (TROPOMI) and Environmental Trace Gases Monitoring Instrument (EMI-II) measurements as case studies, we show that at a spectral resolution of ~2 nm, DWDOAS-derived NO₂ vertical column density (VCD) are highly consistent with those from conventional DOAS retrievals (correlation coefficient R > 0.7) and exhibit relative differences of approximately ±30%. Monte Carlo simulations further demonstrate method robustness, yielding mean uncertainties below 2 × 10¹⁴ molecules·cm⁻². The results indicate that DWDOAS effectively suppresses high-frequency spectral noise while preserving key differential absorption structures, thereby achieving a favorable trade-off between information retention and noise robustness. Nevertheless, increased retrieval uncertainty is observed under low-NO₂ background conditions or strong aerosol loading, which reduces sensitivity to weak absorption features. Overall, this study confirms that reliable NO₂ retrieval performance can be maintained while substantially reducing spectral information requirements, offering practical implications for low-resolution spectrometer design, onboard data compression, and rapid, wide-area atmospheric trace-gas monitoring. Full article

Nonintrusive Load Monitoring (NILM) models often suffer from significant performance degradation when deployed across different households and datasets, primarily because of distribution discrepancies. To address this challenge, this study proposes an adversarial hierarchical distribution alignment unsupervised domain adaptation network for nonintrusive load disaggregation. The network aims to reduce the distribution divergence between the source and target domains in both the feature and label spaces, enabling effective adaptation to transfer learning scenarios in which the source domain has limited labeled data and the target domain has abundant unlabeled data. The proposed method integrates adversarial training with a hierarchical distribution alignment strategy that uses Correlation Alignment (CORAL) to align global marginal distributions. It employs Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to constrain the conditional distributions of individual appliances, thereby enhancing cross-domain generalization. Extensive experiments on three public datasets demonstrate that, in both in-domain and cross-domain settings, the proposed method consistently reduces Mean Absolute Error (MAE) and Signal Aggregation Error (SAE), outperforming baseline approaches in cross-domain generalization. Full article

Since a significant part of the Italian territory was not seismically classified until 2003, most existing bridges have been designed—for decades—disregarding earthquake-induced excitations. In fact, this means that load-bearing devices and shear keys of presently in-service infrastructures may not be up to current codes, both in terms of resistance and displacement capacity. Robust investigations are hence required for verifications and possible retrofit. In this study, the seismic behaviour of a case study post-tensioned concrete bridge built in the 1980s is numerically analysed. The examined structure is 440 m long and composed of nine spans, built with precast segments using the balance cantilever construction method. The deck is divided into two parts connected by a hinged joint in the middle of the central span, obtained with three shear keys and originally designed to allow for thermal expansion only. Most importantly, the mid-span hinge, the end joints and the bearing devices were originally designed without considering the effects of seismic action. In order to preliminarily investigate the performance of devices and joints, the case study bridge is analysed by means of non-linear dynamic time history simulations, formulating different hypotheses about the non-linear behaviour of the load bearings. Forces and displacements over time are obtained for a set of seven accelerograms, and maximum values are compared to the capacity of the bridge devices. Results are then critically discussed. Full article

This study investigates the physical and cyber-physical resilience of smart grids with a high share of renewable energy sources (RESs) dominated by permanent magnet synchronous generators (PMSGs). The originality of this work lies in the development and unified evaluation of five integrated control strategies, the PLL with grid following, VSG with grid shaping, VSG+BESS, VSG+STATCOM, and VSG+BESS+STATCOM, implemented within a coherent simulation framework based on Python. Unlike previous works that analyze these methods in isolation, this study provides a comprehensive quantitative comparison of their dynamic characteristics, including frequency root mean square deviation, maximum deviation, and composite resilience index (RI). To extend the analysis beyond static conditions, a multi-generator (multi-PMSG) scenario with heterogeneous inertia constants and variable load profiles is introduced. This dynamic model allows the evaluation of natural inertia diversity and the effects of inter-generator coupling compared to the synthetic inertia emulation provided by VSG-based control. The combined VSG+BESS+STATCOM configuration achieves the highest synthetic resilience, improving frequency and voltage stability by up to 15%, while the multi-PMSG system demonstrates comparable or even higher RI values due to its inherent mechanical inertia and decentralized response behavior. In addition, a cyber-physical scenario is included to evaluate the effect of communication delays and false data injection (FDI) on VSG frequency control. The results show that a communication delay of 50 ms reduces RI by approximately 0.2%, confirming that even minor cyber disturbances can affect synchronization and transient recovery. However, hybrid control architectures with local energy buffering (BESS) show superior resilience under such conditions. The main technical contribution of this work is the establishment of an integrated analytical and simulation framework that enables the joint assessment of synthetic, natural, and cyber-physical resilience in converter-dominated smart grids. This framework provides a unified basis for the analysis of dynamic stability, hybrid control interaction, and the impact of cyber uncertainty, thereby supporting the design of low-inertia, resilient, and secure next-generation power systems. Full article

Background/Objectives: Noncontact knee ligament injuries, including anterior cruciate ligament (ACL) ruptures and medial collateral ligament (MCL) sprains, are prevalent in sports that involve frequent cutting and pivoting. Conventional rigid knee braces can offer stability but often compromise comfort and performance, whereas soft sleeve-type supports provide minimal mechanical protection. The purpose of this study was to evaluate the acute biomechanical effects of a tensioned cable knee bracing system on peak knee valgus angle and external knee abduction moment during a controlled lateral shuffle task. Methods: Ten physically active adults (mean age 21.7 ± 3.8 years) performed submaximal lateral shuffle movements under three conditions: unbraced, sleeve-only (zero-tension), and a novel tensioned cable brace. Three-dimensional knee kinematics and ground reaction forces were collected, and peak knee valgus angle and external abduction moment were calculated during the eccentric phase of each movement. Results: Wearing the knee brace under tension significantly reduced knee valgus angle (4.5° vs. 7.9°) and peak external knee abduction moment (1.6 vs. 2.0–2.1 Nm/kg) compared to the unbraced condition. Conclusions: These findings indicate that the tensioned cable brace effectively reduced frontal plane knee loading during a lateral shuffle task, indicating its potential as an effective bracing approach. Full article

Upper limb rehabilitation exoskeletons form a spatial closed kinematic chain with the human arm, where inevitable joint-center and axis misalignment can generate hyperstatic interaction forces and torques. Passive degrees of freedom (DOF) are widely introduced to improve kinematic compatibility, yet different compatible configurations may exhibit distinct wearable performance. This study experimentally compares three compatible four-degree-of-freedom exoskeleton configurations derived from the synthesis of Li et al. using a single reconfigurable rehabilitation robot. The platform is assembled into each configuration through modular passive units and instrumented with two six-axis force–torque sensors at the upper-arm and forearm interfaces. Interaction forces and torques are measured in passive training mode during eating and combing trajectories. For each configuration, tests are performed with passive joints released and with passive joints locked to quantify the effect of passive motion accommodation. Directional and resultant metrics are computed using mean and peak values over movement cycles. Results show that releasing passive joints consistently reduces interaction loading, and Category 2 achieves the lowest forces and torques with the strongest peak suppression, indicating the best practical compatibility. Full article

Reliable state estimation is critical for the safe operation of second-life battery systems but is severely hindered by significant parameter heterogeneity arising from diverse historical aging conditions. Traditional static models struggle to adapt to such variability, while online identification methods are prone to divergence under dynamic loads. To overcome these challenges, this paper proposes a Dual-Anchor Adaptive Fusion Framework for robust State of Charge (SOC) estimation and State of Health (SOH) soft-sensing. Specifically, to establish a reliable physical baseline, an automated Dynamic Relaxation Interval Selection (DRIS) strategy is introduced. By minimizing the fitting Root Mean Square Error (RMSE), DRIS systematically extracts high-fidelity parameters to construct two “anchor models” that rigorously define the boundaries of the aging space. Subsequently, a residual-driven Bayesian fusion mechanism is developed to seamlessly interpolate between these anchors based on real-time voltage feedback, enabling the model to adapt to uncalibrated target batteries. Concurrently, a novel “SOH Soft-Sensing” capability is unlocked by interpreting the adaptive fusion weights as real-time health indicators. Experimental results demonstrate that the proposed framework achieves robust SOC estimation with an RMSE of 0.42%, significantly outperforming the standard Adaptive Extended Kalman Filter (A-EKF, RMSE 1.53%), which exhibits parameter drift under dynamic loading. Moreover, the a posteriori voltage tracking residual is compressed to ~0.085 mV, effectively approaching the hardware’s ADC quantization limit. Furthermore, SOH is inferred with a relative error of 0.84% without additional capacity tests. This work establishes a robust methodological foundation for calibration-free state estimation in heterogeneous retired battery packs. Full article

The increasing penetration of distributed generation in active distribution networks (ADNs) introduces significant voltage regulation challenges due to the intermittent nature of renewable energy sources. Electric springs (ESs) have emerged as a cost-effective alternative to conventional FACTS devices for voltage regulation, requiring minimal energy storage while providing fast, flexible reactive power compensation. This paper proposes a deep reinforcement learning (DRL)-based approach for voltage regulation in balanced active distribution networks with distributed generation. Electric springs are deployed at selected buses in series with noncritical loads to provide flexible voltage support. The main contributions of this work are: (1) a novel region-based penalized reward function that effectively guides the DRL agent to minimize voltage deviations; (2) a coordinated control strategy for multiple ESs using the Deep Deterministic Policy Gradient (DDPG) algorithm, representing the first application of DRL to ES-based voltage regulation; (3) a systematic hyperparameter tuning methodology that significantly improves controller performance; and (4) comprehensive validation demonstrating an approximately 40% reduction in mean voltage deviation relative to the no-control baseline. Three well-known continuous-control DRL algorithms, Twin Delayed Deep Deterministic Policy Gradient (TD3), Proximal Policy Optimization (PPO), and DDPG, are first evaluated using the default hyperparameter configurations provided by MATLAB R2022b.Based on this baseline comparison, a dedicated hyperparameter-tuning procedure is then applied to DDPG to improve the robustness and performance of the resulting controller. The proposed approach is evaluated through simulation studies on the IEEE 33-bus and IEEE 69-bus test systems with time-varying load profiles and fluctuating renewable generation scenarios. Full article

With the advancement of cotton mechanized topping technology, deep learning-based methods for detecting cotton apical buds have made significant progress in improving detection accuracy. However, existing algorithms generally suffer from complex structures, large parameter counts, and high computational costs, making them difficult to deploy in practical field environments. To address this, this paper proposes a lightweight YOLO-SEW algorithm for detecting cotton apical buds in complex cotton field environments. Based on the YOLOv8 framework, the algorithm introduces Spatial and Channel Reconstruction Convolutions (SCConv) into the C2f module of the backbone network to reduce feature redundancy; embeds an Efficient Multi-scale Attention (EMA) module in the neck network to enhance feature extraction capabilities; and replaces the bounding box loss function with a dynamic non-monotonic focusing mechanism, WIoU, to accelerate model convergence. Experimental results on cotton apical bud data collected in complex field environments show that, compared to the original YOLOv8n algorithm, the YOLO-SEW algorithm reduces parameter count by 40.63%, computational load by 25%, and model size by 33.87%, while improving precision, recall, and mean average precision (mAP) by 1.2%, 2.5%, and 1.4%, respectively. Deployed on a Jetson Orin NX edge computing device and accelerated with TensorRT, the algorithm achieves a detection speed of 48 frames per second, effectively supporting real-time recognition of cotton apical buds and mechanized topping operations. Full article

Introduction: Vitamin D deficiency [25(OH)D < 30 ng/mL] is widely prevalent globally and the efforts to tackle it have been rather unsuccessful to date. Despite different cutoffs used to define it, many clinicians adhere to the 2011 Endocrine Society definition. We present a special treat-to-target protocol aiming to restore and maintain vitamin D sufficiency. Methods: We reviewed the efficacy and safety of our vitamin D supplementation protocol over 5 years, and compared it to a group of patients who self-reported never taking vitamin D supplements. We recorded the baseline, 2-month, and annual 25(OH)D (D) measurements, along with subjects’ age, sex, BMI, history of osteoporosis, nephrolithiasis, nephrocalcinosis, and renal colics. According to our supplementation protocol, replenishment of vitamin D involves cholecalciferol dosing in two steps: a loading dose (LD) for 2 months and a maintenance dose (MD) thereafter. Please refer to the main text for loading and maintenance dose titration. Results: Of 8329 cases with vitamin D measurements, 2248 had adequate follow up data of 3524.5 patient-years and were included in the study: a total of 1575 intervention subjects and 673 controls, with an average follow-up of 18.8 months. Baseline vitamin D concentrations of 22.6 ng/mL (controls) did not change significantly (2 months: 22.2; 1 year: 21.7; 2 years: 22.0; 3 years: 23.8; 4 years: 21.8; and 5 years: 22.1 ng/mL), while concentrations of 21.9 ng/mL (intervention group) reached and remained 40 ng/mL (2 months: 41.0; 1 year: 39.4; 2 years: 39.0; 3 years: 39.3; 4 years: 40.4; and 5 years: 39.4 ng/mL). Vitamin D adequacy was achieved in 91.6% of patients in the intervention arm compared to only 16.9% in controls (p < 0.0001). Mean D and rates of adequacy were significantly higher over time in the intervention arm (p < 0.0001). The incidence of renal adverse events or hypervitaminosis did not differ between groups (p > 0.05). Conclusions: Our intervention protocol appears highly efficient in achieving and maintaining vitamin D adequacy over 5 years, with no increase in adverse events compared with controls, presenting it as an effective long-term strategy. Full article

This study explores the feasibility of estimating tibiofemoral joint contact forces using deep learning models trained on in vivo biomechanical data. Leveraging the comprehensive CAMS-Knee datasets, we developed and evaluated two machine learning network architectures, a bidirectional Long Short-Term-Memory Network with a Multilayer Perceptron (biLSTM-MLP) and a Temporal Convolutional Network (TCN) model, to predict medial and lateral knee contact forces (KCFs) across various activities of daily living. Using a leave-one-subject-out validation approach, the biLSTM-MLP model achieved root mean square errors (RMSEs) as low as 0.16 body weight (BW) and Pearson correlation coefficients up to 0.98 for the total KCF (${\mathrm{F}}_{\mathrm{tot}}$) during walking. Although the prediction of individual force components showed slightly lower accuracy, the model consistently demonstrated high predictive accuracy and strong temporal coherence. In contrast to the biLSTM-MLP model, the TCN model showed more variable performance across force components and activities. Leave-one-feature-out analyses underscored the dominant role of lower-limb kinematics and ground reaction forces in driving model accuracy, while EMG features contributed only marginally to the overall predictive performance. Collectively, these findings highlight deep learning as a scalable and reliable alternative to traditional musculoskeletal simulations for personalized knee load estimation, establishing a foundation for future research on larger and more heterogeneous populations. Full article

Exoskeletons are increasingly used in industrial settings, yet most are designed for structured, repetitive tasks, limiting adaptability to dynamic movements. In construction, frequent locomotion tasks demand continuous lower-limb engagement, and ladder climbing places substantial loads on coordination and flexibility. This study aimed to identify key muscles involved in climbing to support the development of adaptive exoskeletons. Ten healthy male participants (33.8 ± 3.4 years; 178.7 ± 5.0 cm; 87.4 ± 16.1 kg) performed vertical and A-frame ladder ascents in a controlled laboratory setting. Surface electromyography was recorded from eight right-leg muscles and processed using band-pass filtering, rectification, and root mean square smoothing. Two normalization strategies were applied: walking normalization, expressing climbing activity relative to level walking, and maximum voluntary contraction normalization, with amplitudes expressed as a percentage of maximum voluntary contraction. Our results showed that all muscles were more active in climbing than walking, with quadriceps (vastus medialis, vastus lateralis, rectus femoris) exhibiting the greatest increases. Gastrocnemius also approached or exceeded 100%MVC, tibialis anterior averaged 70–80%MVC, and hamstrings contributed 20–40%MVC mainly for stabilization. Vertical and A-frame ladders followed similar patterns with subtle posture-related variations. These findings highlight knee extensors as primary targets for adaptive exoskeleton assistance during ladder climbing tasks commonly performed on construction sites. Full article

To address the limitations of traditional analytical modeling in capturing complex surface topographies, this paper presents comprehensive research on the error sensitivity mechanism, loaded tooth contact analysis (LTCA), and load-bearing contact characteristics of curved face gears based on high-precision point cloud modeling. The primary objectives are threefold: (1) to establish a high-fidelity topological reconstruction framework using Non-Uniform Rational B-Splines (NURBS) to bridge the gap between discrete data and finite element analysis (FEA); (2) to reveal the inherent mechanical response and sensitivity mechanism to spatial installation misalignments; and (3) to evaluate the contact performance and transmission error fluctuations under operational loads. Specifically, an analytical discretization method is proposed for point cloud generation, followed by a dual-path validation system integrating “rigid tooth contact analysis (TCA)” and “loaded FEA”. The results demonstrate that the proposed reconstruction achieves a superior accuracy with a Root Mean Square Error (RMSE) of 2.2 × 10⁻³ mm. Furthermore, shaft angle error is identified as the dominant sensitivity factor affecting transmission smoothness and edge contact, exerting a more significant influence than offset and axial errors. Compared with existing research on arc-tooth and helical face gears, this work provides a more robust closed-loop verification for curved profiles, revealing that material elastic deformation increases transmission error amplitude by 10.1% to 17.2%. These insights offer a theoretical reference for the high-precision assembly and tolerance allocation of helicopter transmission systems. Full article

To address the technical challenge where traditional high-pressure, large-flow emulsion pump stations cannot adapt to the drastic flow rate changes in hydraulic supports due to the fixed displacement of their quantitative pumps—leading to frequent system unloading, severe impacts, and damage—this study proposes an intelligent flow control method based on the digital flow distribution principle for actively perceiving and matching support demands. Building on this method, a compact, electro-hydraulically separated prototype with stepless flow regulation was developed. The system integrates high-speed switching solenoid valves, a piston push rod, a plunger pump, sensors, and a controller. By monitoring piston position in real time, the controller employs an optimized combined regulation strategy that integrates adjustable duty cycles across single, dual, and multiple cycles. This dynamically adjusts the switching timing of the pilot solenoid valve, thereby precisely controlling the closure of the inlet valve. As a result, part of the fluid can return to the suction line during the compression phase, fundamentally achieving accurate and smooth matching between the pump output flow and support demand, while significantly reducing system fluctuations and impacts. This research adopts a combined approach of co-simulation and experimental validation to deeply investigate the dynamic coupling relationship between the piston’s extreme position and delayed valve closure. It further establishes a comprehensive dynamic coupling model covering the response of the pilot valve, actuator motion, and backflow control characteristics. By analyzing key parameters such as reset spring stiffness, piston cylinder diameter, and actuator load, the system reliability is optimized. Evaluation of the backflow strategy and delay phase verifies the effectiveness of the multi-mode composite regulation strategy based on digital displacement pump technology, which extends the effective flow range of the pump to 20–100% of its rated flow. Experimental results show that the system achieves a flow regulation range of 83% under load and 57% without load, with energy efficiency improved by 15–20% due to a significant reduction in overflow losses. Compared with traditional unloading methods, this approach demonstrates markedly higher control precision and stability, with substantial reductions in both flow root mean square error (53.4 L/min vs. 357.2 L/min) and fluctuation amplitude (±3.5 L/min vs. ±12.8 L/min). The system can intelligently respond to support conditions, providing high pressure with small flow during the lowering stage and low pressure with large flow during the lifting stage, effectively achieving on-demand and precise supply of dynamic flow and pressure. The proposed “demand feedforward–flow coordination” control architecture, the innovative electro-hydraulically separated structure, and the multi-cycle optimized regulation strategy collectively provide a practical and feasible solution for upgrading the fluid supply system in fully mechanized mining faces toward fast response, high energy efficiency, and intelligent operation. Full article