Search Results (1,057)

Future fusion power plants like DEMO must be remotely maintained for safety, including breeding blankets (BBs) weighing up to 180$\mathrm{t}$. The BB vertical transporter (BBVT), a crane-like redundant robot with 7 joints, has been previously designed for handling the five unique BB segments per sector. This includes grasping, preloading and collision-free spatial manipulation of BB segments in a space-constrained environment, necessitating advanced motion planning and real-time control. To achieve this, the challenge of obtaining accurate and performant inverse kinematic (IK) solutions for the redundant BBVT must be addressed. Therefore, a kinematic model is presented, and the redundant IK probelm is solved analytically for task-relevant cases, including derivation and analysis of the Jacobian. The model is verified by comparison with an MSC Adams model. Meanwhile, the analytical IK is found to be 53× to 84× faster than a gradient projection-based numerical solver in Matlab while providing multiple solutions. The IK and Jacobian are applied to create collision-free waypoints, verified in Matlab, for handling each BB segment while minimizing static joint loads in key configurations. A first-order estimate of the total BB handling time for a maintenance of nine days is calculated. These developments support the feasibility of the BBVT robot for the BB maintenance task in DEMO, and underpin future efforts in modelling dynamics and achieving real-time resilient control. Full article

Point cloud segmentation is a core technology in remote sensing, enabling the extraction of rich semantic information from complex scenes. Existing methods struggle with semantic inconsistency across multiple heterogeneous datasets in complex urban environments. To address semantic inconsistencies, we propose SemAlign3D, a novel multimodal framework for point cloud segmentation that combines learnable class prompts with a multi-scale feature attention module. We integrate five large-scale datasets (SensatUrban, STPLS3D, WHU3D, SemanticKITTI, Semantic3D) to construct a unified training framework, ensuring label consistency by recalibrating semantic labels. The learnable class prompt mechanism dynamically adapts to dataset-specific semantics, enhancing the semantic consistency across multiple datasets of point cloud segmentation. Additionally, the Multi-scale K-Nearest Neighbor Feature Attention Enhancement module integrates local and global features, improving semantic discriminability in complex scenes. Within a single unified training framework, our method effectively aligns semantic labels from multiple heterogeneous datasets, achieving gains of +1.61% mIoU on WHU3D and +0.98% mIoU on SemanticKITTI. These results demonstrate the effectiveness of our framework in improving semantic consistency and robustness across heterogeneous point cloud datasets. Full article

The squat is a fundamental multi-joint movement widely studied in strength training and biomechanics. While numerous experimental and computational studies have examined squat kinematics and joint loading, the mechanisms governing how squat technique adapts to increasing external load remain insufficiently understood. In particular, inverse-dynamics-based approaches often overlook explicit constraints imposed by limited joint moment capacity. This study presents a computational framework for predicting load-dependent adaptations of squat posture. The human body was represented as a multi-segment rigid-body system, with joints modeled as nonlinear rotational elements with bounded moment capacity. A reference squat trajectory was first generated kinematically, and a constrained optimization procedure was then applied at each motion frame to determine a mechanically admissible posture under increasing barbell load. The results show that higher loads lead to systematic posture adaptations, including increased torso inclination and redistribution of rotational demand from the knee toward the hip joint. For the highest load, peak torso pitch increased from 30° to over 40°, while joint utilization exceeded unity, indicating the onset of yielding. These findings identify joint moment capacity as a key constraint governing squat technique and demonstrate the potential of the proposed framework for predictive biomechanical analysis. Full article

Human squat motion is commonly analyzed using inverse dynamics, where joint moments are computed from experimentally measured kinematics. Such analyses typically assume that the observed motion is mechanically feasible and do not explicitly account for limitations of joint moment capacity. In this study, a computational framework is proposed for the load-constrained reconstruction of squat motion that integrates kinematic motion generation with a mechanical model of moment-limited joints. The human body is represented as a multi-segment system consisting of feet, shanks, thighs, pelvis, and torso. Joint behavior is modeled using nonlinear rotational springs with bounded moment capacity, allowing elastic response followed by allowing bounded moment response and redistribution of mechanical demand as critical moment levels are approached. A reference squat trajectory is first generated kinematically, after which a constrained optimization problem is solved at each motion frame to obtain a mechanically admissible posture under external loading. The objective function combines trajectory tracking with joint energy contributions, while gravitational loading from a barbell applied at the shoulders introduces external work. The formulation enables automatic correction of the reference motion when joint moment limits are exceeded, resulting in mechanically admissible squat postures. Numerical examples illustrate the evolution of pelvis trajectory, torso inclination, lower-limb segment angles, and reconstructed body configurations throughout the squat cycle. The results confirm that joint moment capacity directly influences the reconstructed motion and leads to load-dependent adaptation of squat posture. Full article

Achieving precise fruit localization and fine branch segmentation simultaneously in unstructured orchard environments remains challenging due to variable lighting, occlusion, and complex backgrounds. This study proposed a joint detection–segmentation architecture based on an improved YOLOv11 network for collaborative perception of apples and tree branches. First, a dual-task dataset of spindle-type apple orchards was constructed with bounding-box annotations for fruits and pixel-level polygon masks for branches, encompassing diverse illumination and occlusion conditions. Second, Convolutional Block Attention Modules (CBAMs) are strategically embedded into the YOLOv11 backbone to enhance feature discrimination for slender branch structures while preserving high fruit detection accuracy. The enhanced model achieves precision of 0.981, recall of 0.986, and F1-score of 0.983 for apple detection, and precision of 0.803, recall of 0.715, mAP of 0.698, and IoU of 0.6066 for branch segmentation on the validation set. Comparative experiments against YOLOv8 and baseline YOLOv11 confirm improved segmentation continuity and finer branch delineation. The proposed integrated perception framework provides reliable visual guidance for collision-avoidance robotic harvesting and offers a practical reference for multi-task agricultural vision systems. Full article

This paper presents a lightweight method for assessing the resilience of power distribution systems that integrates climate and infrastructure data through impact chains and a probabilistic approach, while minimizing data integration and implementation complexity. The method is demonstrated for heatwave hazards by combining network characteristics, failure probabilities of heat-sensitive components (e.g., medium-voltage cable joints), and location-specific climate projections to generate spatial maps of failure risk and network resilience. These maps support the identification and prioritization of critical components requiring intervention. Critical segments are then further analyzed using probabilistic resilience metrics to compare alternative adaptation strategies. Overall, this work contributes a practically applicable, low-complexity methodology for identifying the weakest portions of distribution networks, along with a more in-depth probabilistic approach for assessing their climate resilience. The comprehensive framework is illustrated through a case study of a representative portion of the Italian electricity distribution system in the urban area of Rome. It is implemented in a test environment that reflects realistic distribution network data structures and automatically integrates climate data from established online repositories. Full article

Addressing the lack of high-fidelity test cycles for fuel cell light-duty trucks, this paper proposes an automated driving cycle construction method that integrates unsupervised clustering and reinforcement learning. Firstly, based on large-sample real-world driving data, four libraries of typical driving pattern segments are extracted through dimensionality reduction via Principal Component Analysis (PCA) and K-means clustering. Subsequently, the cycle construction process is formulated as a sequential decision-making problem, and a framework based on the Proximal Policy Optimization (PPO) algorithm, incorporating an action masking mechanism, is designed. This framework innovatively injects macro-level time budget allocation as a hard constraint into the agent’s policy space via action masking, while utilizing micro-level Markov transition probabilities as a soft guide. This dual approach drives the agent to learn an optimal segment concatenation strategy, thereby simultaneously ensuring both the macro-level statistical representativeness and the micro-level driving logic coherence of the synthesized cycle. Validation results demonstrate that the cycle constructed by the proposed method achieves an average relative error of only 7.53% in key characteristic parameters, and its joint speed-acceleration distribution exhibits a similarity as high as 0.9886 with the original data, significantly outperforming traditional methods such as the clustering method, the Markov chain method, and standard driving cycles. This study provides an effective tool for generating high-fidelity driving cycles and testing energy management strategies for fuel cell commercial vehicles. Full article

Lithium is a rare metal widely used in the renewable energy industry. The Altyn region in Xinjiang, China, contains abundant granitic pegmatite-type lithium resources; however, the deeply incised and complex terrain limits the accuracy of conventional two-dimensional remote sensing approaches for dike identification and segmentation. To address this limitation, a remote sensing segmentation method incorporating terrain information was proposed. A digital elevation model (DEM) derived from LiDAR data, together with its associated topographic factors, was integrated into the Spatial–Spectral Mamba framework to enable the joint utilization of spectral and terrain features. Rather than performing explicit three-dimensional geometric modeling, the proposed approach enhances a two-dimensional segmentation framework by introducing elevation-derived information, allowing the model to capture terrain-related spatial variations of pegmatite dikes. This design enables improved representation of both the planar distribution and terrain-influenced morphological characteristics of dikes under deeply incised conditions. The Xichanggou lithium deposit in the Altyn region is a large-scale, economically valuable pegmatite-type lithium deposit, and was therefore selected as the study area for pegmatite dike segmentation. The results demonstrated that, compared with conventional two-dimensional approaches and representative machine learning methods, the proposed method achieved higher segmentation accuracy in complex terrain. Improvements were also observed in the continuity and spatial consistency of the extracted dike patterns. Field verification indicated that the major pegmatite dikes delineated by the model were highly consistent with their actual surface exposures. Sampling analyses further confirmed the validity and reliability of the identification results. Overall, the terrain-integrated remote sensing segmentation approach exhibited good applicability and robustness under deeply incised and complex geomorphological conditions. Full article

Weak-echo ship target detection in GNSS-based bistatic synthetic aperture radar is severely limited by the coupled effects of burst-type strong windows and polarization mismatch, cross-frequency mis-registration, and long-sequence chain drift in dual-frequency dual-polarization observations. To address these issues, this paper proposes DFDP-QuadDiff, a dual-frequency dual-polarization quad-differential framework for weak-echo ship target detection using B1/B3 × horizontal–horizontal (HH)/vertical–vertical (VV) four-channel complex range-time data. The proposed framework integrates polarization-consistency-driven strong-window suppression, intra-band adaptive polarimetric synthesis, joint delay–Doppler–phase cross-frequency registration, segment-wise Jones drift calibration, and quality-aware final fusion in a unified hierarchical processing chain. In this way, multi-source inconsistencies are progressively constrained and suppressed from the polarization level to the segment level before final accumulation and detection are performed. Experimental results on self-developed four-channel GNSS-S demonstrate that, relative to the best raw single-channel result, the proposed framework increases the median SCR from 6.51 dB to 9.04 dB (+2.53 dB), improves the P10 SCR from −1.76 dB to 3.05 dB (+4.81 dB), and raises the track continuity from 0.85 to 0.97. In addition, the standard deviation of segment-wise delay drift is reduced from 0.97 bin to 0.29 bin, and positive multi-scale accumulation gains are maintained up to the second-long integration range. These results indicate that the proposed framework not only substantially enhances the stability, continuity, and long-time integrability of weak-target responses under low-SNR maritime conditions, but also maintains robust gains under weak-visibility, interference-dominant, and mismatch-sensitive local conditions in the stratified evaluation, thereby establishing a physically interpretable and implementation-ready solution for collaborative weak-target detection in dual-band dual-polarization GNSS-S. Full article

Aiming to reduce muscle fatigue and prevent occupational injuries caused by prolonged lifting in insulator replacement operations, this study presents the design of an upper-limb exoskeleton. Firstly, this study performs kinematic analysis and phase segmentation of the lifting motion in the insulator replacement operation. Based on the analysis, in terms of mechanical structure, the proposed upper-limb exoskeleton adopts a unilateral three-degree-of-freedom shoulder mechanism that biomimics the human glenohumeral joint, which reduces the misalignment between the exoskeleton and the human body. Meanwhile, a waist–back support structure is integrated into the exoskeleton to realize a more reasonable torque transmission path. In terms of the control strategy, based on the operation’s phase segmentation and dynamic modeling of the human upper limb, this study develops a neural network-based assistive control algorithm for insulator replacement operations, enabling the exoskeleton to provide phase-specific torque output. Experimental results demonstrate that, under a simulated insulator replacement operation with a 20 kg load, the exoskeleton significantly reduces the subject’s sEMG activity of the biceps brachii and triceps brachii, effectively alleviating muscle fatigue. Full article

The emergence of variant strains of Avian orthoreovirus (ARV) has caused substantial economic losses in the poultry industry worldwide, but the molecular features of goose-origin strains and viral transmission among different avian species remain poorly understood. Here, we describe a goose-origin avian orthoreovirus strain, SD0407, associated with growth retardation and joint swelling. Complete genome analysis identified ten double-stranded RNA segments. Sequence comparison indicated that SD0407 is closely related to previously reported duck-origin reovirus strains. Phylogenetic and recombination analyses showed that most segments clustered with duck-origin strains, indicating close genetic relatedness among waterfowl-origin orthoreoviruses. Sequence and structural analysis of the σC attachment protein revealed ten unique amino acid substitutions, including D250 within the DE-loop region involved in receptor-binding. Molecular docking suggested that σC interacts with the conserved AnxA2-S100A10 heterotetrameric receptor complex, providing a possible structural basis for receptor compatibility across avian species. Although SD0407 replicated efficiently in goose embryo fibroblasts, it did not induce expression of type I, II or III interferons. Transcriptome profiling revealed weak activation of innate immune signaling and downregulation of metabolic and cytoskeletal genes, consistent with effective suppression of antiviral responses. These findings demonstrate that SD0407 combines structural variability with immune evasion to enhance host adaptability and underscore the importance of sustained ARV surveillance in waterfowl populations. Full article

In harsh marine environments, vessel energy storage systems (VESS) face elevated thermal runaway (TR) risk, yet practical early warning remains difficult because early voltage differences between TR high-risk cells and normal cells are often weak, warning thresholds vary across operating segments, and decisions relying on a single feature are prone to false or missed warnings. To overcome these difficulties, this study develops a four-part early warning strategy for TR high-risk cells in VESS. First, the original cell voltages are denoised through multiscale jump plus mode decomposition and Spearman correlation guided mode reconstruction to suppress irrelevant interference. Second, an improved Sigmoid nonlinear mapping is introduced to enhance subtle inter-cell voltage deviations and improve early separability. Third, sparse representation is used to construct a cell deviation score, and an adaptive threshold is employed to perform primary abnormal-cell screening under varying segment conditions. Finally, multidimensional mutual information value derived from voltage, temperature, and their rates of change is incorporated into a joint assessment methodology to further verify the abnormal state of flagged cells. Validation on 18 independent real operation cases comprising 2483 discharge segments shows that, across the evaluated TR high-risk cases, the shortest confirmed warning lead time achieved by the proposed strategy was 14 days. The proposed strategy also reduced false and missed warnings, outperformed the compared benchmark methods overall, and retained computational feasibility for onboard application in VESS. Full article

This study compared the kinematic and neuromuscular characteristics of the tennis forehand drive between high-performance (HP) and intermediate (INT) players. Eighteen right-handed male players (HP: n = 9; INT: n = 9) performed cross-court forehands while three-dimensional motion capture and surface electromyography (EMG) were recorded from the dominant upper limb and trunk. Kinematic and EMG data were time-normalized to the forward swing. One-dimensional statistical parametric mapping two-sample t-tests were used to compare joint angles, angular and linear velocities, and EMG amplitude waveforms between groups. Bonferroni-corrected significance levels were set at α = 0.0017 for kinematic variables and α = 0.0063 for EMG data. HP players exhibited greater racket linear velocity during the final part of the forward swing, accompanied by higher shoulder, elbow and wrist linear velocities, whereas hip linear velocity did not differ between groups. Joint angles were broadly similar, with SPM revealing only slightly greater early knee flexion in HP players. In contrast, HP players showed higher hip and knee angular velocities and greater wrist angular velocities in both flexion/extension and radial/ulnar deviation towards impact. EMG patterns were generally comparable, but HP players displayed higher biceps brachii activation in two significant clusters during the mid-to-late forward swing and greater triceps brachii activation in the late forward swing. No significant differences were observed for deltoid, pectoralis major, latissimus dorsi, flexor carpi radialis or extensor carpi radialis. These findings indicate that superior forehand performance in HP players is associated primarily with refined segmental coordination, greater lower-limb and distal segment velocities, and locally increased elbow muscle activation, rather than with widespread increases in upper-limb or trunk muscle activity. Full article

Background/Objectives: Prosthesis optimization after transfemoral amputation is often guided by clinical experience, yet quantitative evidence isolating how prosthesis mass, inertial properties, and alignment affect mechanical load transmission remains limited. Musculoskeletal modeling can be used as a controlled framework for examining relative sensitivity rankings of constraint force transmission across prosthetic junctions under fixed gait inputs. Methods: A model was modified to incorporate a transfemoral prosthesis. Experimental walking data from a healthy adult reference subject (Qualisys motion capture, synchronized AMTI force plates) provided kinematics and ground reaction forces for model scaling, inverse kinematics, and loading. These inputs provided a standardized mechanical reference and were not intended to represent transfemoral amputee gait. Prosthesis mass (2.625, 3.50, 4.375 kg), inertia (0.5×, 1.0×, 1.5×), and mediolateral alignment (−10, 0, +10 mm) were varied while keeping kinematics and ground reaction forces identical across conditions. Constraint reaction forces at the socket–residual limb junction and prosthetic ankle were computed and normalized to body weight. Results: Increasing mass produced the largest monotonic increases in peak resultant constraint reactions, most prominently at the socket-level junction (8.51 → 10.48 → 12.29 BW), with smaller changes at the ankle and unchanged peak timing. Inertia caused joint-specific effects, whereas mediolateral alignment minimally affected constraint reaction forces and redistributed force components. Conclusions: This study quantified the one-factor-at-a-time effects of prosthesis mass, inertia, and mediolateral alignment on inter-segment constraint reaction forces. The reported reactions should be interpreted as net rigid-body constraint reactions under fixed inputs, not as physiological joint contact forces or direct interface loads. Full article

Accurate individual tree crown identification is essential for urban forestry, yet existing datasets often lack exhaustive annotations and multi-temporal diversity. To address this limitation, an exhaustively annotated dataset was curated for crown instance segmentation, comprising 47,754 labeled individual crowns from approximately 110 species across three temporal phases. Anchored in a “crown geometry” labeling criterion focusing on upper-canopy individuals visible in the imagery, and the high-resolution imagery captured seasonal variations in shape, color, and texture, providing an empirical basis for within-site robustness. Utilizing this dataset, this study (1) compared five instance segmentation models; (2) evaluated their generalization capabilities across different temporal phases; and (3) tested a multi-temporal joint training strategy and a non-maximum suppression (NMS)-based fusion. The experiments revealed significant overfitting in single-temporal models. While ConvNeXt-V2 achieved a high segmentation mean Average Precision (Segm_mAP) of 0.852 within the same temporal phase, its performance dropped sharply to 0.361 across phases. Bi-temporal joint training significantly mitigated this issue, improving cross-temporal performance to 0.665 and further increasing within-phase accuracy to 0.874. In contrast, tri-temporal training reduced accuracy (0.748), demonstrating that effective generalizability depends on the strategic selection of complementary temporal phases rather than the mere accumulation of data. The multi-temporal training framework provided in this study could serve as a practical reference and a foundational benchmark for further urban forest structural monitoring research. Full article