Search Results (582)

Skeleton-based human action recognition (HAR) requires models that preserve the local kinematic structure of the human body while capturing long-range spatiotemporal dependencies under noisy or incomplete joint observations. Traditional Graph Convolutional Networks (GCNs) provide topology-aligned inductive bias but are often limited by local information aggregation from neighboring joints. In contrast, attention-based mechanisms capture global interactions, yet they may attend to spurious correlations when skeletal constraints are weakly enforced. This paper proposes Differential Hyperedge Attention-enhanced GCN (DHA-eGCN), a hybrid architecture that couples structure-aware Differential Hyperedge Attention with multi-scale temporal convolution for spatiotemporal skeleton sequence processing. DHA injects skeletal structure into attention via hop-distance relative positional encoding and hyperedge context tokens generated via joint-to-part pooling. It further employs differential attention to suppress shared noisy correlations and enhance interaction selectivity. To strengthen spatial grounding, an explicit GCN branch is added under partial- or full-depth configurations, where the first four or all ten layers are applied with graph convolutions. The model further employs an ensemble strategy that combines predictions from multiple complementary model instances. Our experiments on NTU RGB+D 60 under the X-Sub and X-View protocols, NTU RGB+D 120 under the X-Sub and X-Set protocols, and Northwestern-UCLA demonstrate that DHA-eGCN consistently outperforms or remains competitive with strong graph-based, transformer-based, and hybrid state-of-the-art methods based on the same four-stream architecture. The best configuration achieves 93.7% and 97.0% on NTU RGB+D 60 X-Sub and X-View, respectively; 90.9% and 91.9% on NTU RGB+D 120 X-Sub and X-Set, respectively; and 97.6% on Northwestern-UCLA. Full article

Vegetable mechanized transplanting is a key link bridging industrial seedling raising and field cultivation, whose technical level directly determines operating efficiency and planting standardization. Despite its importance, current transplanting systems still struggle with instability and limited coordination between modules. This review adopts a systematic literature analysis methodology, covering core databases including Web of Science, Scopus, CNKI, and CAB Abstracts. In response to prominent issues in current transplanting equipment, such as continuous seedling supply, low-damage seedling picking, synchronization of conveying and planting actions, and adaptability to high-speed operation, this paper systematically reviews and evaluates the latest research progress in related key technologies worldwide. From the perspective of kinematic chain coupling, the transplanting process is deconstructed into four core stages: “seedling supply—seedling picking—seedling delivery—seedling planting,” with a focus on analyzing the temporal coordination, spatial constraints, state transitions, and their dynamic coupling relationships within the machine-seedling-soil system. Research indicates that vegetable transplanting technology is evolving from localized mechanism optimization toward whole-process collaborative design and system stability control, with typical high-speed operation efficiency reaching 60–140 plants per minute per row. However, significant challenges remain in low-damage seedling picking and planting at high speeds, adaptability to diverse varieties and seedling states, online perception and real-time error correction, as well as engineering reliability. The seedling damage rate under high-speed operation exceeds 8% in most existing equipment, and the planting upright rate drops by more than 5% when the operating speed increases from 60 plants/min to 120 plants/min. Future research should prioritize multi-stage collaborative optimization design, in-depth investigation of machine-seedling-soil interaction mechanisms, innovation in intelligent perception and precise control strategies, and the development of modular, low-cost, and high-performance transplanting equipment. These efforts will drive vegetable mechanized transplanting technology toward greater intelligence, efficiency, and versatility. Full article

Variable-amplitude vibrating screens are widely adopted for screening frozen sea buckthorn berry particles. Investigating their motion characteristics and particle behaviors on the screen surface is essential for optimizing the screening process and improving equipment performance and screening efficiency. In this work, a variable-amplitude vibrating screen is taken as the research subject. Its structural composition and working principle are elaborated, and kinematic simulations are conducted via RecurDyn. The results reveal that the vertical amplitude and velocity of the screen surface increase gradually from the feed end to the discharge end, which facilitates rapid particle penetration. Meanwhile, the horizontal velocity remains stable across all sections of the screen. Specifically, crank length governs the screen amplitude, while crank rotational speed determines the vibration frequency. A dynamic model of particles and the screen surface is established by combining EDEM 2024 and RecurDyn V9R4, and two-way coupling of the discrete element model is realized. Coupled simulation results indicate that the dynamic screening efficiency rises with increasing crank length and rotational speed, reaching the maximum at a crank length of 20 mm and a rotational speed of 208 r/min. Crank parameters exert remarkable effects on the thickness of the particle layer and the quantity of penetrated particles: a thicker particle layer leads to a longer residence time of materials on the screen. Field tests are carried out to verify the model accuracy. It turns out that the simulation results are basically consistent with experimental data. In conclusion, crank length and rotational speed are critical influencing factors for variable-amplitude vibrating screens. Research on the screen’s motion characteristics and particle behaviors can provide a theoretical reference for its efficient operation and optimal design. Full article

Background/Objectives: Alignment philosophy in total knee arthroplasty (TKA) may affect joints beyond the knee. Mechanical alignment (MA) targets a neutral mechanical axis, whereas kinematic alignment (KA) aims to restore native alignment and joint line obliquity (JLO). This study compares the effects of MA and KA on hip and ankle radiographic parameters and investigates the propagation of coronal correction along the lower limb. Methods: A retrospective comparative study evaluated 63 TKAs performed for varus deformity (KA: n = 32; MA: n = 31). Pre- and postoperative full-length standing radiographs were used to calculate changes (Δ), defined as the difference between postoperative and preoperative values, in hip offsets, mechanical and arithmetic hip–knee–ankle angles (mHKA, aHKA), medial proximal tibial angle (MPTA), lateral distal femoral angle (LDFA), JLO, and ankle ground-referenced angles. Between-group differences and correlations were analysed. Interobserver reliability was assessed for all variables. Results: MA produced significantly greater limb correction than KA (ΔmHKA: 8.89° vs. 4.82°, p < 0.001), primarily due to increased tibial valgus correction (ΔMPTA: 6.26° vs. 2.41°, p < 0.001). JLO increased substantially with MA (+4.10°) but was preserved with KA (+0.30°, p < 0.001). MA resulted in significant valgus shifts at the ankle (ground talar dome angle (GTDA) −3.01°, ground tibial plafond angle (GTPA) −3.02°; p = 0.006 for both), whereas KA produced no significant ankle changes. Correlation analysis demonstrated limited knee–ankle biomechanical coupling, with a moderate negative correlation in MA (ΔmHKA vs. ΔGTDA: ρ = −0.479, p = 0.006) and a weak correlation in KA (ΔaHKA vs. ΔGTDA: ρ = −0.360, p = 0.043). Hip parameters remained unchanged in both groups. Conclusions: Mechanical alignment induces larger tibial-driven coronal corrections, increases joint line obliquity, and produces measurable valgus shift at the ankle. In contrast, kinematic alignment preserves native alignment and joint-line obliquity while minimising distal ankle compensatory changes. Full article

This paper develops a generalized kinematic model for a lever-link-type flat pressing mechanism used in food processing applications for compacting the coagulate. The study aims to highlight the influence of the geometric parameter that defines the position of the intermediate coupling on the driving element on the mechanism’s configuration and on the main kinematic variables of the active pressing point. Under an idealized representation—assuming rigid links, perfect joints, and a vertical constraint acting on the active element—general analytical expressions for displacement, velocity, and acceleration were established using the vector-kinematic method. The results show that modifying the position of the intermediate coupling produces nonlinear variations in the length of the connecting element, its spatial orientation, and the vertical motion of the active point. Increased values of this parameter are associated with a greater effective stroke and higher vertical velocities toward the end of the motion, while the calculated accelerations remain relatively low, indicating a smooth kinematic evolution. The model establishes analytical relationships that describe the influence of geometric parameters on the kinematic behavior of the mechanism and can serve as a basis for further developments involving dynamic analysis and experimental validation. Full article

Continuum robots offer inherent compliance and dexterity for operation in confined and unstructured environments; however, achieving hybrid multi-segment functionality typically requires application-specific redesign and tightly coupled architectures. To address this limitation, this study proposes a reconfigurable hybrid continuum robot architecture based around a multi-lumen central integration backbone that supports multiple actuation modalities and robot configurations. The proposed design combines external tendon-driven disk modules for proximal actuation with a pneumatically actuated distal tip, while internal lumens allow routing of pneumatic lines and the insertion of optional stiffening elements without structural interference. The reconfigurability of the architecture is demonstrated through two configurations: Concept-1, a two-segment hybrid system, and Concept-2, a miniaturized three-segment configuration achieved by reducing the disk diameter and extending tendon actuation to the backbone. Experimental evaluations are conducted to characterize segment-wise actuation, coupled deformation behavior, and workspace capabilities, hysteresis response, tip contact force, and phantom-based target reachability. Results show that the integration of tendon-driven and pneumatic actuation significantly expands and reorients the reachable workspace. Additional functional tests showed repeatable loading–unloading behaviour of the tendon-driven segment, a maximum pneumatic tip contact force of approximately 0.45 N, and successful access to five representative targets within a stomach-like phantom using Concept-2. A kinematic model based on a constant-curvature formulation is validated against experimental data, yielding root-mean-square errors (RMSE) of 5.44 mm and 6.12 mm for Concept-1 and Concept-2, respectively. These results demonstrate consistent model accuracy across different configurations and scales. Overall, the proposed architecture enables modular, scalable, and reconfigurable hybrid continuum robots, providing a flexible framework for applications ranging from large-scale manipulation to gastroscopy-inspired minimally invasive procedures. Full article

Optimizing the structural stability of foundations is challenging in modern geotechnical engineering. This study investigated the mechanism of geocell-reinforced foundations through discrete element modeling based on transparent soil model tests. A three-dimensional particle flow code (PFC^3D) model was developed to investigate the micromechanical soil–geocell interactions in both unreinforced and geocell-reinforced foundations under strip loading. Particle displacement, contact force distribution, and structural deformation within the foundation system were analyzed to quantify the performance of geocell reinforcement. The results show that geocell inclusion enhances structural performance by 2.1 times compared to an unreinforced foundation, increasing the bearing capacity from 60.6 to 126.8 kPa at a defined bearing capacity criterion. The geocell walls act as rigid physical boundaries that microscopically intercept the lateral migration and horizontal extrusion of soil particles. The kinematic trajectories of soil particles beneath the loading plate are forced into a downward realignment, decreasing the displacement vector rotation angle from 42° in the unreinforced soil to 27° in the reinforced soil and effectively mitigating the heave of adjacent surfaces. Furthermore, the quasi-rigid three-dimensional network completely interrupts the continuous steep contact force chains inherent in unreinforced foundations. Concentrated vertical stresses are converted into horizontal components through interfacial friction and mechanical interlocking, resulting in the lateral redistribution of the applied load by a distance of approximately 0.06 m. The geocell–soil composite considered as a flexible raft foundation extends load dispersion and reduces average subsoil pressure. A coupled tension and compression stress state in the horizontal plane is developed within the geocell structure. Forces are channeled along rigid paths by elevated bending moments and stress concentrations at the cell junctions. These findings provide micromechanical insights into the performance of geocell-reinforced-foundation systems. Full article

Accurate Unmanned Aerial Vehicle (UAV) position estimation is the cornerstone of urban low-altitude safety management systems. Time Difference of Arrival (TDOA) and Remote Identification (Remote ID) are widely used surveillance technologies with complementary characteristics. TDOA provides high-rate updates but suffers from geometry-induced horizontal–vertical anisotropy and multipath effects, while Remote ID supplies absolute state information yet struggles with intermittent sampling and packet loss. Existing fusion schemes typically address these issues in isolation: sequential filtering manages asynchrony but assumes Gaussian noise, robust estimators suppress outliers at the cost of discarding valid data, and coupled-filter architectures allow vertical anomalies to contaminate horizontal estimates through the Kalman gain cross-coupling. No prior framework jointly handles structural TDOA altitude jumps, stochastic Remote ID timing jitter, and the geometric anisotropy between estimation subspaces within a single coherent pipeline. To bridge this gap, we propose a Hybrid Conditional Kalman Filter (HCKF) framework comprising three integrated modules. First, a kinematics-based temporal alignment module maps asynchronous measurements onto a uniform timeline and predicts missing samples, resolving cross-modal time mismatches. Second, a measurement quality evaluation mechanism detects TDOA altitude steps via robust two-layer stratification and scores Remote ID timing irregularity through a confidence mapping, converting these anomalies into dynamic covariance adjustments and weight caps without discarding observations. Third, a Subspace-Decoupled Fusion strategy exploits the physical insight that TDOA horizontal precision derives from hyperbolic intersection geometry, whereas its vertical estimates suffer from weak observability due to near-coplanar ground-station deployment. By applying entropy-guided weighting in the horizontal plane and a conditional Remote ID-dominant rule in the vertical axis, this design prevents cross-dimensional error propagation. The framework was validated using three real-world flight missions at distinct altitudes (255 m, 345 m, and 440 m) totaling 13.51 km of flight distance, with RTK serving as ground truth. HCKF reduces the Root Mean Square Error by over 40% relative to single-source baselines (95% bootstrap confidence interval: [35.2%, 48.7%]), and paired Wilcoxon signed-rank tests confirm statistically significant improvement ($p<0.01$) over standard EKF, Covariance Intersection, and Iterative CI across all three tracks. Full article

In precision gear manufacturing, longitudinal crowning on tooth flanks is commonly produced by applying diagonal feed in worm-type generating processes using tools such as variable-tooth-thickness hobs and dressable grinding worms. However, precise twist control remains difficult because the geometric parameters of the generating tool are strongly coupled with the machine feed settings in the underlying generating kinematics. In addition, direct numerical optimization becomes unreliable near the standard tool state, where the sensitivity of the diagonal-feed coefficient degenerates and conventional linearized solvers may lose effectiveness. To address these issues, this study proposes a multi-variable optimization framework for twist-constrained worm-type gear generation. An iterative singular value decomposition (SVD) scheme is developed to construct and update the sensitivity matrix, while a warm-start continuation strategy is introduced to overcome the local singularity and improve numerical robustness. Two closed-form expressions for the diagonal-feed coefficient are also proposed as practically useful initial estimates, corresponding respectively to the minimum SVD topographic residual and the minimum tooth-flank twist. Numerical validation over a 60-case parameter sweep shows maximum relative errors below 1.6% within the tested range. The proposed framework coordinates the tool-geometry design and diagonal-feed selection to generate tooth flanks with prescribed crowning characteristics while satisfying a specified twist requirement and limiting the required diagonal shift. Numerical examples show that the iterative framework reduces the root-mean-square (RMS) topographic error from 1.14 μm to 0.027 μm relative to the analytical setting of Hsu and Fong. These results indicate that the proposed method provides a reliable computational basis for twist control and process-parameter design in advanced CNC gear generation. From a manufacturing standpoint, because the three design criteria are accessed by adjusting only the diagonal-feed ratio on the machine, a single generating-tool design can serve a range of crowning and twist requirements without retooling, reducing setup and tooling efforts in production. Full article

Kinematic modeling and parameter identification are essential for achieving high-precision robot calibration. A widely used strategy involves utilizing the end-effector position error for parameter identification. However, the strong coupling between length and angular parameters often impedes calibration accuracy. In addition, substantial differences in their scales further exacerbate this issue. To overcome these limitations, following the variable projection method, this paper reformulates the conventional Modified Denavit–Hartenberg (MDH) model into a separable nonlinear structure. This allows independent identification of the two parameter types. Non-geometric errors such as joint compliance and backlash are also explicitly taken into account. The backlash errors are separated from the angular positions of each joint by modeling their bidirectional positioning errors with Chebyshev polynomials. This method enables the establishment of a comprehensive positioning error model to mitigate the influence of backlash errors. Based on the variable projection method, an improved variable projection with modified Gram–Schmidt (IVPMGS) identification method is proposed, which also eliminates redundant parameters that hinder identification robustness. Simulations indicate that the proposed method achieves faster convergence and higher identification accuracy. Compensation experiments demonstrate that the average absolute positioning error is reduced from 0.1804 mm to 0.0917 mm compared with the traditional MDH model, corresponding to a 49.17% improvement in positioning accuracy. These findings confirm the accuracy and effectiveness of the proposed approach. Full article

Inertial Measurement Units (IMUs) enable portable, multibody motion capture in diverse environments beyond the laboratory, making them a desirable choice for diagnosing mobility disorders and supporting rehabilitation in clinical or home settings. However, challenges associated with IMU measurements, including magnetic distortions and errors due to integration drift, complicate their broader use for motion capture. In this work, we propose a tightly coupled motion-capture approach that directly integrates IMU measurements with multibody dynamic models via an iterated extended Kalman filter to simultaneously estimate the system’s kinematics and kinetics. By enforcing the complete multibody system dynamics and utilizing only accelerometer and gyroscope data, our method accurately estimates joint kinematics and kinetics. Our algorithm is designed to fuse different sensor data, such as optical motion-capture measurements and joint torque readings, to further enhance estimation accuracy. We validated our approach using highly accurate ground-truth data from a 3-degree-of-freedom pendulum and a 6-degree-of-freedom collaborative robot. We demonstrate a maximum root-mean-square difference of 3.75° in the pendulum’s computed joint angles with respect to the marker motion-capture inverse kinematics. For the robot, we observed a maximum joint angle root-mean-square difference of 3.24° with respect to the joint encoders, while the maximum joint angle root-mean-square difference of the optical motion-capture inverse kinematics with respect to the encoders was 1.16°. With regard to kinetic estimates, we report a maximum joint torque root-mean-square difference of 3.02 Nm in the pendulum with respect to the marker motion-capture inverse dynamics and 4.27 Nm in the robot relative to its joint torque sensors. Full article

Near-fault ground motions (NFGMs), characterized by forward-directivity velocity pulses, impose severe kinematic demands that challenge conventional structural systems. As modern civil engineering pivots toward rapid functional recovery, a critical paradigm shift is required: moving from component-centric kinematic vulnerability diagnostics to network-level systemic resilience optimization. This comprehensive review elucidates this transition, conceptualizing an integrated “3C Resilience Framework”—encompassing Coupled-multi-hazard, City-scale, and Carbon-friendly dimensions—as a strategic roadmap for next-generation seismic design. A pivotal focus is the physical evaluation of contemporary regulatory evolutions, specifically the multi-point spectral lower-bound constraints in American Society of Civil Engineers Standard 7-22 (ASCE 7-22) and the site-specific scaling factors in Eurocode 8. We demonstrate that these spectral floors are physically essential for flexible and isolated structures to constrain long-period kinetic energy, thereby mitigating the underestimation of residual drifts that fundamentally dictate repairability. Furthermore, this review explicitly aligns structural performance with the UN Sustainable Development Goals (SDG 9 & 11). By synthesizing advanced mitigation topologies with surrogate-assisted computational paradigms, this roadmap bridges the micro-to-macro scale gap between physical structural degradation and regional functional restoration, providing an actionable blueprint for sustainable urban networks. Full article

Excess zinc (Zn) has an important effect on seed yield and quality, as well as the possibility of subsequent processing. In this study, we investigated the effect of excess Zn concentrations (50, 100, and 150 mg kg⁻¹) in the substrate on the seed yield and selected biochemical parameters of white mustard (Sinapis alba L.) seeds. The following parameters were investigated: seed yield, individual lipid classes were analyzed using high-performance thin-layer chromatography (HPTLC); gas–liquid chromatography with mass-selective detection (GS-MS) was used to analyze the fatty acid (FA) profile; and trace elements were detected using inductively coupled plasma-mass spectrometry (ICP-MS). It was found that Zn at concentrations of 100 and 150 mg kg⁻¹ caused a decrease in the number of S. alba pods, and the 1000-seed weight also decreased at a Zn concentration of 150 mg kg⁻¹. The Zn concentration in seeds from plants grown on contaminated substrate was higher than the control values and government standard thresholds. Zn at concentrations of 100 and 150 mg kg⁻¹ caused a slight increase in the content of triacylglycerols (up to 1.35–1.36% dry weight) and the total content of unsaturated FAs along with a decrease in saturated and total FAs. Zn at a concentration of 50 mg kg⁻¹ stimulated an increase in the total FA content. A high erucic acid content in S. alba seeds was observed in all studied variants. A mathematical model was used to evaluate the physicochemical properties of biofuels. The parameters relating to the FA composition of seeds of plants grown on a Zn-contaminated substrate did not deteriorate, and in the case of the kinematic viscosity coefficient, they were even improved compared to the control plants and complied with American (ASTM D6751) and European (EN 14214) standards for biofuels. The obtained data indicate a negative effect of high Zn concentrations on seed yield. Changes in the FA composition of S. alba seeds may reduce their value for the production of edible oils due to the high content of erucic acid, but they could be used to produce technical oils (biofuel). Full article

Metamaterials with tailored structural architectures enable negative thermal expansion through geometric mechanisms that counteract constituent-level positive expansion. This study evaluates the thermomechanical performance and structural limits of polymer and hybrid NTE lattices. We systematically classify the dominant kinematic mechanisms, including bimetallic bending, rotational squares, and re-entrant honeycombs, and quantify the inherent trade-offs between effective thermal contraction, structural stiffness, and mass efficiency. The analysis demonstrates that reliance on idealized linear–elastic and rigid-lever models leads to significant predictive discrepancies when evaluating the physical response of polymeric and hybrid prototypes. We establish that these deviations are fundamentally governed by localized stress singularities at multi-material interfaces and the profound thermoviscoelastic softening of polymers as they approach the glass transition temperature ($T_g$). We conclude that accurate prediction of the cyclic lifespan and dimensional stability of these systems requires a transition to coupled multiphysics frameworks. Specifically, integrating temperature-dependent cohesive zone modeling and time–temperature superposition principles is essential for capturing interfacial delamination and thermal ratcheting in high-performance polymeric NTE metamaterials. Full article

This work presents a pilot study to analyze the effect of aging on motor performance of young adults (YAs) and older adults (OAs) through wrist movement assessment, using an upper limb rehabilitation robot (ULRR) in passive mode coupled to a maze-solving task serious video game. The proposed approach considers the use of kinematic metrics, such as ROM, path accuracy, and movement smoothness, as quantitative biomarkers that evidence differences between YAs and OAs. An experimental protocol was conducted with 20 participants: 10 OAs and 10 YAs. Standardized wrist movements corresponding to flexion (F), extension (E), radial deviation (R), and ulnar deviation (U) were assessed at each level of the maze. The kinematic analysis was based on metrics for range of motion (ROM), path accuracy, smoothness, and root-mean-square error (RMSE) in trajectory tracking. The results revealed clear differences between the groups: the YAs achieved a greater ROM and made fewer errors on mean (2.167 errors for YAs compared to 6.000 errors for OAs), and showed a lower RMSE, while the OAs showed greater smoothness in their movements, because the YAs exhibit greater variability and disturbances in movement when correcting and controlling their movements to achieve good performance, reflecting more precise motor control and a greater capacity for error correction during movements with trajectory constraints. Full article