Search Results (12,070)

This study selected a simplified water intake tower model, simplifying the physical structure into a cantilever model, and MATLAB software (R2010b) was used to develop a rapid seismic response analysis program for the structure. Thirty near-fault pulse and non-pulse ground motions were selected as the input ground motions for this analysis. Peak ground velocity (PGV) was used as the intensity parameter for the ground motions. The acceleration, cross-sectional rotation, and lateral curvature of the simplified water intake tower model were calculated for ground motions modulated with different PGA amplitudes. The acceleration, maximum shear force, and cross-sectional rotation of the simplified water intake tower model were also calculated for ground motions modulated with improved effective peak acceleration (IEPA) and improved effective peak velocity (IEPV). The study showed that the seismic response of the simplified water intake tower model for near-fault ground motions modulated with different intensities of PGV amplitude modulation was closer to the unmodulated response order. PGV as an intensity parameter did not affect the acceleration response amplification factor of the water intake tower and hoist chamber. The AC coefficient indicated that PGV was less suitable for pulse-type earthquake amplitude modulation than PGA. Compared with PGA amplitude modulation, IEPA amplitude modulation is more suitable for pulse-type seismic motion, while IEPV amplitude modulation has less impact on pulse-type seismic motion. Full article

Eye movement classification, particularly the identification of fixations and saccades, plays a vital role in advancing our understanding of neurological functions and cognitive processing. Conventional modalities of data, such as RGB webcams, often face limitations such as motion blur, latency and susceptibility to noise. Neuromorphic Vision Sensors, also known as event cameras (ECs), capture pixel-level changes asynchronously and at a high temporal resolution, making them well suited for detecting the swift transitions inherent to eye movements. However, the resulting data are sparse, which makes them less well suited for use with conventional algorithms. Spiking Neural Networks (SNNs) are gaining attention due to their discrete spatio-temporal spike mechanism ideally suited for sparse data. These networks offer a biologically inspired computational paradigm capable of modeling the temporal dynamics captured by event cameras. This study validates the use of Spiking Neural Networks (SNNs) with event cameras for efficient eye movement classification. We manually annotated the EV-Eye dataset, the largest publicly available event-based eye-tracking benchmark, into sequences of saccades and fixations, and we propose a convolutional SNN architecture operating directly on spike streams. Our model achieves an accuracy of 94% and a precision of 0.92 across annotated data from 10 users. As the first work to apply SNNs to eye movement classification using event data, we benchmark our approach against spiking baselines such as SpikingVGG and SpikingDenseNet, and additionally provide a detailed computational complexity comparison between SNN and ANN counterparts. Our results highlight the efficiency and robustness of SNNs for event-based vision tasks, with over one order of magnitude improvement in computational efficiency, with implications for fast and low-power neurocognitive diagnostic systems. Full article

Hand exoskeletons are used in rehabilitation together with serious games to enhance patient experience and, possibly, therapy outcomes. To achieve good engagement, a realistic virtual representation of hand motion is needed; however, the relationship between exoskeleton joint motion and anatomical finger kinematics is rarely obtained using low-cost procedures. This work introduces a mechanical redesign and modeling pipeline that utilizes temporary sensors to identify the exoskeleton–finger mapping, enabling qualitatively realistic virtual hand motion driven solely by the existing on-board sensor. A recently developed hand exoskeleton prototype was redesigned to host two temporary rotary encoders aligned with the MetaCarpoPhalangeal (MCP) and Proximal InterPhalangeal (PIP) joints, in addition to the actuation encoder. Healthy subjects wore the modified device and performed full flexion–extension cycles. Encoder trajectories were processed; then each cycle was approximated by a third-order polynomial in the normalized actuation angle, and a group-level model was obtained by averaging coefficients across valid cycles. Finally, the encoder-based reconstructions of MCP and PIP motion were evaluated against measurements from a gold-standard optical motion capture system. Results indicate that the proposed polynomial model enables joint-angle estimation with sufficient accuracy for interactive rehabilitation scenarios, supporting its use to drive smooth virtual hand motion from the on-board exoskeleton encoder alone. Full article

We show that the Zitterbewegung of the electron arises as a real internal motion when spin is treated as a classical bivector rather than as a point fermion of the Dirac equation. In the Bivector Standard Model, physically meaningful dynamics reside in the body-fixed frame where two orthogonal internal angular momentum vectors counter-precess about a torque axis. Their rigid rotation generates a time-dependent chord whose magnitude oscillates at twice the Compton frequency, $2{\omega }_C$, and whose orientation precesses at ${\omega }_C$. When projected into a laboratory-fixed frame, this internal rotor produces the characteristic trembling motion of the Zitterbewegung and traces a horn torus envelope without additional assumptions. The internal clock defined by this cyclic bivector motion unifies the origin of spin properties and the de Broglie modulation. It distinguishes complementary parity domains that cannot be related by Lorentz transformations. The Zitterbewegung is therefore not an interference between positive- and negative-energy spinors, but rather the visible shadow of a real, energy-conserving internal rotation inherent to the bivector structure. Full article

Background: Augmented reality (AR) and mixed reality (MR) promise to enhance anatomical understanding, spatial orientation, and workflow in cardiac surgery. Their clinical adoption remains limited and the translational path is incompletely defined. Methods: A PubMed search was conducted by two independent reviewers from database inception through July 2025 and identified peer-reviewed, English-language articles describing peri- or intra-operative AR/MR applications in cardiac surgery. Relevant clinical, preclinical, technical, and review articles were selected for inclusion based on scope and content. Given the narrative approach and heterogeneity across studies, findings were synthesized qualitatively into application domains. Results: Fourteen studies were included. Five domains emerged: (1) preoperative planning and patient-specific modelling—MR enhanced spatial orientation and planning for minimally invasive and valve procedures; (2) intraoperative navigation and visualization—AR improved targeting and interpretation with preclinical overlay errors ≈ 5 mm; (3) physiological/functional guidance—thermographic AR detected ischemia in vivo with strong correlation to invasive thermometry; (4) robotic integration and workflow optimization—AR-guided port placement and stepwise robotic adoption supported the feasibility of totally endoscopic CABG; (5) AR-based early rehabilitation. Conclusions: Early clinical and preclinical evidence supports AR/MR feasibility and utility for visualization and orientation in cardiac surgery. Priorities include deformable, motion-compensated registration, ergonomic integration with robotic platforms, and multicentre trials powered for operative efficiency and patient outcomes. Full article

Continuum robots (CRs) exhibit high compliance and environmental adaptability in confined, tortuous spaces, yet their inherent low stiffness and load capacity limit performance in precise positioning and stable support tasks. To solve the “soft-rigid” paradox, this study proposes and implements a three-segment tendon-driven variable-stiffness CR. Structurally, a segmented constant-curvature model directs the optimization of grid skeletons and notch parameters, enhancing bending consistency and motion predictability. Elongated flat airbag actuators, arranged in annular arrays, enable segment-level stiffness switching through the enhancement of surface properties like axial constraints and friction amplification. A time-sharing drive strategy decouples multi-segment coupling into sequential single-segment subproblems, reducing drivers and kinematic complexity while maintaining dexterity. Experimental results demonstrate that flexible-mode joints maintain near-constant curvature with stable motion (average end-effector trajectory error < 0.9 mm), and in rigid mode, stiffness increases by a factor of 5.77 (rated load: 4.0 N). Shape-locking disturbances during transitions are confined to millimeter levels (remote offset < 1.32 mm), with successful traversal of J/U/S-shaped and irregular paths confirmed in pipeline tests. This work introduces a practical, scalable system for designing variable-stiffness structures and enabling low-complexity multi-segment control, offering valuable insights for minimally invasive devices and industrial endoscopy in confined spaces. Full article

Micro-expressions (MEs) are involuntary facial movements that reveal genuine emotions, holding significant value in fields like deception detection and psychological diagnosis. However, micro-expression recognition (MER) is fundamentally challenged by the entanglement of subtle emotional motions with identity-specific features. Traditional methods, such as those based on Robust Principal Component Analysis (RPCA), attempt to separate identity and motion components through fixed preprocessing and coarse decomposition. However, these methods can inadvertently remove subtle emotional cues and are disconnected from subsequent module training, limiting the discriminative power of features. Inspired by the Bruce–Young model of facial cognition, which suggests that facial identity and expression are processed via independent neural routes, we recognize the need for a more dynamic, learnable disentanglement paradigm for MER. We propose LFD-TCMEN, a novel network that introduces an end-to-end learnable feature disentanglement framework. The network is synergistically optimized by a multi-task objective unifying orthogonality, reconstruction, consistency, cycle, identity, and classification losses. Specifically, the Disentangle Representation Learning (DRL) module adaptively isolates pure motion patterns from subject-specific appearance, overcoming the limitations of static preprocessing, while the Temporal-Complemented Motion Enhancement (TCME) module integrates purified motion representations—highlighting subtle facial muscle activations—with optical flow dynamics to comprehensively model the spatiotemporal evolution of MEs. Extensive experiments on CAS(ME)³ and DFME benchmarks demonstrate that our method achieves state-of-the-art cross-subject performance, validating the efficacy of the proposed learnable disentanglement and synergistic optimization. Full article

Background: Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integrated into sport and exercise through wearable biosensing systems that enable continuous monitoring and data-driven training adaptation. However, their practical value for coaching depends on the validity of biosensor data, the robustness of analytical models, and the conditions under which these systems have been empirically evaluated. Methods: A structured narrative review was conducted using Scopus, PubMed, Web of Science, and Google Scholar (2010–2026), synthesising empirical and applied evidence on wearable biosensing, signal processing, and ML-based adaptive training systems. To enhance transparency, an evidence map of core empirical studies was constructed, summarising sensing modalities, cohort sizes, experimental settings (laboratory vs. field), model types, evaluation protocols, and key outcomes. Results: Evidence from field and laboratory studies indicates that wearable biosensors can reliably capture physiological (e.g., heart rate variability), biomechanical (e.g., inertial and electromyographic signals), and biochemical (e.g., sweat lactate and electrolytes) markers relevant to training load, fatigue, and recovery, provided that signal quality control and calibration procedures are applied. ML models trained on these data can support training adaptation and recovery estimation, with improved performance over traditional workload metrics in endurance, strength, and team-sport contexts when evaluated using athlete-wise or longitudinal validation schemes. Nevertheless, the evidence map also highlights recurring limitations, including sensitivity to motion artefacts, inter-session variability, distribution shift between laboratory and field settings, and overconfident predictions when contextual or psychosocial inputs are absent. Conclusions: Current empirical evidence supports the use of AI-driven biosensor systems as decision-support tools for monitoring and adaptive training, but not as autonomous coaching agents. Their effectiveness is bounded by sensor reliability, appropriate validation protocols, and human oversight. The most defensible model emerging from the evidence is human–AI collaboration, in which ML enhances precision and consistency in data interpretation, while coaches retain responsibility for contextual judgement, ethical decision-making, and athlete-centred care. Full article

Performance diagnostics in alpine skiing increasingly rely on integrated biomechanical and kinematic assessments to support technique optimization under real training conditions; however, many existing approaches address trajectory geometry or biomechanical variables separately, limiting their explanatory power. This study evaluates an integrated analysis framework combining OptiPath, an AI-assisted video-based trajectory analysis tool, with XSensDOT wearable inertial sensors to identify technical inefficiencies during giant slalom skiing. Thirty competitive youth athletes (n = 30; 14–16 years) performed controlled runs with predefined lateral offsets from the gates, enabling systematic examination of the relationship between spatial trajectory deviations, biomechanical execution, and performance outcomes. Skier trajectories were extracted using computer vision-based methods, while lower-limb kinematics, trunk motion, and tri-axial acceleration were recorded using inertial measurement units. Deviations from mathematically defined ideal trajectories were quantified through regression-based calibration and arc-based modeling. The results show that although OptiPath reliably detected trajectory variations, shorter skiing paths did not consistently produce faster run times. Instead, superior performance was associated with more efficient biomechanical execution, reflected by coordinated trunk–lower limb motion, controlled vertical loading, reduced lateral corrections, and higher forward acceleration, even when longer trajectories were followed. These findings indicate that trajectory geometry alone is insufficient to explain performance outcomes and support the integration of wearable biomechanics with trajectory modeling as a practical, low-cost, and field-deployable tool for alpine skiing performance diagnostics. Full article

During the reentry terminal flight of lifting-body Reusable Launch Vehicles (RLVs) propelled by liquid fuel, the sloshing of liquid propellent presents new features that, if neglected, could lead to adverse flight oscillations or even worse. This paper focuses on liquid sloshing coupled flight dynamics, sloshing effect prediction, and the suppression of adverse flight oscillations. First, a transfer function model for unsteady aerodynamics is improved and applied to describe the sloshing force effect, being included in the rigid–liquid control coupled flight dynamics model. The frequency domain analysis results show that liquid sloshing tends to degrade the closed-loop stability margin of the vehicle and even induce less damped oscillations, which can be predicted through the frequency characteristics with the sloshing force effect included. Furthermore, three suppression control measures to mitigate adverse oscillation are addressed, which include enhancing the trajectory-tracking loop damping, separating the frequencies of the rigid body motion and the liquid sloshing, and especially introducing a compensation loop to counteract the sloshing effect. Simulations demonstrate that all the provided approaches help mitigate the sloshing effect, while the compensation control with sloshing frequency characteristics included works best. Full article

Knee disorders are a major indication for musculoskeletal imaging, yet MRI reliability remains constrained by signal nonuniformity, motion artefacts, protocol variability, and reader-dependent effects. This study presents an integrated two-vector framework that couples (i) a fuzzy diagnostic control-risk model with (ii) a quantitative digital-maturity assessment to strengthen MRI-based diagnosis of knee pathology. The vertical vector characterizes organizational readiness through a weighted fuzzy aggregation of six capability agents (technical, information and analytical, mathematical/model, metrological, human resources, and software support). The horizontal vector estimates producer’s and consumer’s risks as misclassification probabilities relative to an acceptance boundary, driven by measurement/interpretation uncertainty, variability of the decision threshold, and the ratio of instrumental to physiological dispersion. Simulation results indicate that error probabilities increase sharply when threshold uncertainty exceeds 20–25% and rise by approximately 15–20% as the standard-deviation ratio approaches unity. To connect diagnostic reliability with downstream mechanics, a FE analysis of the tibial insert in TKA under F = 1150 N at 0° flexion predicts a peak contact pressure of 85.449 MPa and a maximum UHMWPE von Mises stress of 43.686 MPa, identifying wear-critical contact zones. Overall, the proposed framework provides interpretable quantitative targets for QA, protocol refinement, and resource allocation in radiology services undergoing digital transformation, and offers a reproducible pathway for linking imaging reliability to biomechanical risk. Full article

Rapid depressurization and warming during recovery can trigger stress in deep-sea microbes and accelerate RNA degradation. We developed a remotely operated vehicle (ROV)-oriented multi-sequence microbial sampler for 2000 m sampling (20 MPa, 2 °C) that integrates in situ filtration with immediate RNAlater injection (an RNA stabilization reagent), collecting up to 12 samples per dive. A Dirichlet sampling–B-spline–SVM framework was used to optimize the cam profile of the sequence trigger for robust actuation under geometric constraints and realistic tolerances in both manufacturing and assembly. Relative to the baseline 3-4-5 motion law, the optimized design reduces nominal peak driving torque by ~18–20% and lowers the maximum torque under tolerance perturbations; tests show a further ~10–25% reduction using a SiC ball–ZrO₂ block pair versus a MoS₂-lubricated titanium pushrod–ZrO₂ block pair. A Darcy–Forchheimer porous-media computational fluid dynamics (CFD) model predicts earlier clogging on the lower membrane and a fast-to-slow RNAlater displacement process; greater membrane resistance mismatch delays 95% displacement and increases RNAlater loss. Simulations and Rhodamine B tests suggest an RNAlater consumption of 0.9 L per parallel filter (one membrane per side), and 20 MPa chamber tests confirm stable operation and membrane retrieval. Full article

Accurate braking-distance prediction for heavy-duty multi-axle trucks remains challenging due to the large gross vehicle weight, tandem-axle interactions, and strong transient load transfer during emergency braking. Recent studies on tire–road friction estimation, commercial-vehicle braking control (EBS/AEBS), and weigh-in-motion (WIM) sensing have highlighted that unmeasured vertical-load dynamics and time-varying friction are key sources of prediction uncertainty. To address these limitations, this study proposes an integrated sensing–simulation–AI framework that combines real-time axle-load estimation, full-scale robotic braking tests, fused road-friction sensing, and physics-consistent machine-learning modeling. A micro-electro-mechanical systems (MEMS)-based load-angle sensor was installed on the leaf-spring panel linking tandem axles, enabling the continuous estimation of dynamic vertical loads via a polynomial calibration model. Full-scale on-road braking tests were conducted at 40–60 km/h under systematically varied payloads (0–15.5 t) using an actuator-based braking robot to eliminate driver variability. A forward-looking optical friction module was synchronized with dynamic axle-load estimates and deceleration signals, and additional scenarios generated in a commercial ASM environment expanded the operational domain across a broader range of friction, grade, and loading conditions. A gradient-boosting regression model trained on the hybrid dataset reproduced measured stopping distances with a mean absolute error (MAE) of 1.58 m and a mean absolute percentage error (MAPE) of 2.46%, with most predictions falling within ±5 m across all test conditions. The results indicate that incorporating real-time dynamic axle-load sensing together with fused friction estimation improves braking-distance prediction compared with static-load assumptions and purely kinematic formulations. The proposed load-aware framework provides a scalable basis for advanced driver-assistance functions, autonomous emergency braking for heavy trucks, and infrastructure-integrated freight safety management. All full-scale braking tests were carried out at approximately 60% of the nominal service-brake pressure, representing non-panic but moderately severe braking conditions, and the proposed model is designed to accurately predict the resulting stopping distance under this prescribed braking regime rather than to minimize the absolute stopping distance itself. Full article

This study develops a comprehensive workflow for the analytical seismic vulnerability and structural performance assessment of a special-importance steel building located in a region of elevated seismic hazard in southern Spain. The work addresses the need for reliable analytical methodologies for facilities that must remain operational during earthquakes. The proposed framework integrates a probabilistic seismic hazard assessment, including uniform hazard spectra and hazard disaggregation to identify control earthquakes. Additionally, an analytical vulnerability assessment under the Spanish seismic design code, NCSE-02, is performed. Operational modal analysis and nonlinear analysis are combined to retrofit the numerical model of the building and capture the building’s realistic seismic response. The resulting demand spectra are derived from site-specific ground-motion scenarios for Los Barrios (Cádiz, Spain). Retrofitting strategies are designed and assessed to ensure compliance with the code-defined performance requirements. Results indicate that the retrofitted model reproduces the building’s dynamic behaviour with improved reliability, and that the strengthening interventions enhance seismic performance while still allowing moderate damage in specific components. These findings highlight the importance of analytical vulnerability approaches and code-oriented retrofitting when evaluating the seismic performance and vulnerability of essential facilities. The study demonstrates that rigorous analytical methods provide a robust basis for defining seismic vulnerability in special-importance buildings and support improved decision-making for structural safety and resilience. Full article

To achieve high-precision trajectory tracking control for an underwater hexapod robot, this paper proposes a hierarchical control architecture. Firstly, a multi-rigid-body dynamic model for the robot is established based on the Newton-Euler method and reasonably simplified. Secondly, a Central Pattern Generator (CPG) network with the Hopf oscillator as its core is designed to generate stable and coordinated crawling gaits. By introducing a steering parameter, a kinematic model connecting the CPG output is constructed. Furthermore, based on this dynamic and kinematic model, an upper-layer Model Predictive Controller (MPC) is designed. The optimized control quantities output by the MPC are mapped into the rhythmic parameters of the CPG network via a transfer function established by fitting experimental data, thus forming the complete MPC-CPG controller. Finally, the proposed method is validated through simulations of circular trajectory tracking. The results show that even in the presence of initial errors, the controller can converge rapidly, with trajectory position error consistently maintained within −0.1 m~0.1 m, and heading angle error confined to the range of −15~15°. The experiments fully demonstrate the effectiveness of the proposed MPC-CPG controller in ensuring trajectory tracking accuracy, motion smoothness, and system stability. Full article