Search Results (3,225)

Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremor, bradykinesia, rigidity, and postural instability. In the absence of disease-modifying therapies, exercise remains one of the few interventions shown to effectively reduce fall risk and improve mobility. However, it remains unclear whether skeletal muscle ATP metabolism is impaired in PD, and whether the benefits of exercise arise primarily from improvements in central motor control or peripheral metabolic adaptations. Methods: Fourteen individuals with PD and five healthy controls underwent kinetic ³¹P Magnetic Resonance Spectroscopy (MRS) to assess resting muscle ATP synthesis and dynamic ³¹P MRS during in-magnet exercise to evaluate oxidative phosphorylation in active muscle. Results: At rest, ATP synthesis rates mediated by ATPase and creatine kinase (CK) were on average 46 ± 23% and 24 ± 9% lower, respectively, in the PD group compared to controls (p < 0.005), suggesting peripheral mitochondrial dysfunction. During plantar flexion exercise at 15% of lean body mass, range of motion (ROM) was reduced by 22 ± 5% in PD participants (p = 0.01). Despite this, post-exercise recovery of phosphocreatine (PCr) and inorganic phosphate (Pi) was similar between groups. Recovery time constants for PCr and Pi correlated with participants’ total weekly exercise time, indicating a metabolic adaptation to regular physical activity. Modest ROM improvements were observed in both groups following calf-raise exercise training. Conclusions: Reduced skeletal muscle ATP metabolism may contribute to peripheral weakness in PD. Regular exercise appears to promote adaptive metabolic responses, highlighting the need for therapeutic strategies targeting both central and peripheral components of PD. Full article

During an unpredictable side-cut, this study examined how a sport-specific neuromuscular training program (NMTP) influenced electromyography responses in the lower limb posterior muscles, leg movement angles, maximum vertical ground reaction force (vGRF), and the rate of force development of vGRF. Thirty-eight adult female recreational hockey players were randomly allocated into an intervention group (INT) or a control group (CON). Before beginning training or matches, the INT carried out the NMTP three times per week for eight weeks, whereas the CON performed their routine warm-up. A 45° sidecut (dominant leg only) was performed at baseline and after eight-weeks and recorded with a motion capture system. The effect of group and time, and their interaction, was investigated using a mixed-design ANOVA. After landing, the participants in the INT had greater activation of their gastrocnemius lateralis, gastrocnemius medialis, and gluteus maximus muscles than those in the CON. INT participants showed significantly lower amounts of maximum knee abduction and knee excursion, while there was an increase in these variables for the CON. At week eight, the vGRF RFD decreased for the INT but increased for the CON. Although non-significant, the overall muscle activity showed an increasing trend for the INT when it came to supervised NMTP for eight weeks compared to the effect seen in the CON. This activity caused greater alterations in the motion and forces of the lower body for the INT than the CON. Full article

This paper addresses the challenges of inadequate trajectory tracking accuracy and limited parameter adaptability encountered by hip joints in lower-limb exoskeletons operating across multi-terrain environments. To mitigate these issues, we propose a hybrid control strategy that synergistically combines the slime mould algorithm (SMA) with fuzzy PID control, thereby improving the trajectory tracking performance in such diverse conditions. Initially, we established a dynamic model of the hip joint in the sagittal plane utilizing the Lagrange method, which elucidates the underlying motion mechanisms involved. Subsequently, we designed a fuzzy PID controller that facilitates dynamic parameter adjustment. The integration of the slime mould algorithm (SMA) allows for the optimization of both the quantization factor and the proportional factor of the fuzzy PID controller, culminating in the development of a hybrid control framework that significantly enhances parameter adaptability. Ultimately, we performed a comparative analysis of this hybrid control strategy against conventional PID, fuzzy PID, and PSO-fuzzy PID controls through MATLABR2023b/Simulink simulations as well as experimental tests across a range of multi-terrain scenarios including flat ground, inclines, and stair climbing. The results indicate that in comparison to PID, fuzzy PID, and PSO-fuzzy PID controls, our proposed strategy significantly reduced the adjustment time by 15.06% to 61.9% and minimized the maximum error by 39.44% to 72.81% across various terrains including flat ground, slope navigation, and stair climbing scenarios. Additionally, it lowered the steady-state error ranges by an impressive 50.67% to 90.75%. This enhancement markedly improved the system’s response speed, tracking accuracy, and stability, thereby offering a robust solution for the practical application of lower-limb exoskeletons. Full article

In ocean engineering, path following serves as a fundamental capability for autonomous underwater vehicles (AUVs), enabling essential operations such as environmental exploration and inspection. However, for robotic dolphins employing dorsoventral undulatory propulsion, the periodic pitching induces strong coupling between propulsion and attitude, posing significant challenges for precise path following in disturbed environments. In this paper, a real-time robust path-following control framework is proposed for robotic dolphins to address these challenges. First, a novel robotic dolphin platform is presented by integrating a dorsoventral propulsion mechanism with a passive peduncle joint, followed by the systematic formulation of a full-state dynamic model. Then, a minimum-snap-based path optimizer is constructed to generate smooth and dynamically feasible trajectories, improving path quality and motion safety. Subsequently, a robust model predictive controller is developed, which incorporates control surface dynamics, a nonlinear disturbance observer, and a Sigmoid-based disturbance-grading mechanism to ensure fast attitude response and precise tracking performance. Finally, extensive simulations under various environmental disturbances validate the effectiveness of the proposed approach in both trajectory optimization and robust path following. The proposed framework not only demonstrates strong robustness in path following and disturbance rejection, but also provides practical guidance for future underwater missions such as long-term environmental monitoring, inspection, and rescue. Full article

Gacha games, particularly those using Free-to-Play (F2P) models, have become increasingly popular yet controversial due to their addictive mechanics, often likened to gambling. This study investigates the inherent addictive mechanisms of Gacha games, focusing on Genshin Impact, a leading title in the genre. We analyze the interplay between reward frequency, game attractiveness, and player addiction using the Game Refinement theory and the Motion in Mind framework. Our analysis identifies a critical threshold at approximately 55 pulls per rare item ($N\approx 55$), with a corresponding gravity-in-mind value of 7.4. Beyond this point, the system exhibits gambling-like dynamics, as indicated by Game Refinement and Motion in Mind metrics. This threshold was measured using empirical gacha data collected from Genshin Impact players and analyzed through theoretical models. While not claiming direct causal evidence of player behavior change, the results highlight a measurable boundary where structural design risks fostering addiction-like compulsion. The study contributes theoretical insights with ethical implications for game design, by identifying critical thresholds in reward frequency and game dynamics that mark the shift toward gambling-like reinforcement. The methodologies, including quantitative analysis and empirical data, ensure robust results contributing to responsible digital entertainment discourse. Full article

As the world is technologically advancing, the integration of FSO communication in non-terrestrial platforms is transforming the landscape of global connectivity. By enabling high-data-rate inter-satellite links, secure UAV–ground channels, and efficient HAPS backhaul, FSO technology is paving the way for sustainable 6G non-terrestrial networks. However, the stringent requirement for precise line-of-sight (LoS) alignment between the optical transmitter and receivers poses a hindrance in practical deployment. As non-terrestrial missions require continuous movement across the mission area, the platform is subject to vibrations, dynamic motion, and environmental disturbances. This makes maintaining the LoS between the transceivers difficult. While fine-pointing mechanisms such as fast steering mirrors and adaptive optics are effective for microradian angular corrections, they rely heavily on an initial coarse alignment to maintain the LoS. Coarse pointing modules or gimbals serve as the primary mechanical interface for steering and stabilizing the optical beam over wide angular ranges. This survey presents a comprehensive analysis of coarse pointing and gimbal modules that are being used in FSO communication systems for non-terrestrial platforms. The paper classifies gimbal architectures based on actuation type, degrees of freedom, and stabilization strategies. Key design trade-offs are examined, including angular precision, mechanical inertia, bandwidth, and power consumption, which directly impact system responsiveness and tracking accuracy. This paper also highlights emerging trends such as AI-driven pointing prediction and lightweight gimbal design for SWap-constrained platforms. The final part of the paper discusses open challenges and research directions in developing scalable and resilient coarse pointing systems for aerial FSO networks. Full article

In this study, a nonlinear dynamic model is developed to examine the stability and vibration behavior of a self-balancing electric Segway operating over irregular stochastic terrains. The Segway is treated as a three-degrees-of-freedom cart–inverted pendulum system, incorporating elastic and damping effects at the wheel–ground interface. Road irregularities are generated in accordance with international standard using high-order filtered noise, allowing for representation of surface classes from smooth to highly degraded. The governing equations, formulated via Lagrange’s method, are transformed into a Lorenz-like state-space form for nonlinear analysis. Numerical simulations employ the fourth-order Runge–Kutta scheme to compute translational and angular responses under varying speeds and terrain conditions. Frequency-domain analysis using Fast Fourier Transform (FFT) identifies resonant excitation bands linked to road spectral content, while Kernel Density Estimation (KDE) maps the probability distribution of displacement states to distinguish stable from variable regimes. The Lyapunov stability assessment and bifurcation analysis reveal critical velocity thresholds and parameter regions marking transitions from stable operation to chaotic motion. The study quantifies the influence of the gravity–damping ratio, mass–damping coupling, control torque ratio, and vertical excitation on dynamic stability. The results provide a methodology for designing stability control systems that ensure safe and comfortable Segway operation across diverse terrains. Full article

Micro-expression recognition (MER) aims to detect brief and subtle facial movements that reveal suppressed emotions, discerning authentic emotional responses in scenarios such as visitor experience analysis in museum settings. However, it remains a highly challenging task due to the fleeting duration, low intensity, and limited availability of annotated data. Most existing approaches rely solely on either appearance or motion cues, thereby restricting their ability to capture expressive information fully. To overcome these limitations, we propose a lightweight multi-modal fusion network, termed M³ENet, which integrates both motion and appearance cues through early-stage feature fusion. Specifically, our model extracts horizontal, vertical, and strain-based optical flow between the onset and apex frames, alongside RGB images from the onset, apex, and offset frames. These inputs are processed by two modality-specific subnetworks, whose features are fused to exploit complementary information for robust classification. To improve generalization in low data regimes, we employ targeted data augmentation and adopt focal loss to mitigate class imbalance. Extensive experiments on five benchmark datasets, including CASME I, CASME II, CAS(ME)², SAMM, and MMEW, demonstrate that M³ENet achieves state-of-the-art performance with high efficiency. Ablation studies and Grad-CAM visualizations further confirm the effectiveness and interpretability of the proposed architecture. Full article

With the rapid advancement of Autonomous Mobile Robots (AMRs) in industrial automation and intelligent logistics, achieving efficient and safe path planning in dynamic environments has become a critical challenge. These environments require robots to perceive complex scenarios and adapt their motion strategies accordingly, often under real-time constraints. Existing methods frequently struggle to balance efficiency, responsiveness, and safety, especially in the presence of continuously changing dynamic obstacles. While Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL) have each shown promise in this domain, they also face limitations when applied individually—such as high computational demands or insufficient environmental exploration. To address these challenges, we propose a hybrid path planning framework that integrates an optimized DRL algorithm with MPC. We replace the Actor’s output with a learnable noisy linear layer whose mean and scale parameters are optimized jointly with the policy via backpropagation, thereby enhancing exploration while preserving training stability. TD3 produces stepwise control commands that evolve into a short-horizon reference trajectory, while MPC refines this trajectory through constraint-aware optimization to ensure timely obstacle avoidance. This complementary process combines TD3′s learning-based adaptability with MPC’s reliable local feasibility. Extensive experiments conducted in environments with varying obstacle dynamics and densities demonstrate that the proposed method significantly improves obstacle avoidance success rate, trajectory smoothness, and path accuracy compared to traditional MPC, standalone DRL, and other hybrid approaches, offering a robust and efficient solution for autonomous navigation in complex scenarios. Full article

This study examines the forced vibration behavior of a hydroelastic system composed of a functionally graded material (FGM) plate, a barotropic compressible Newtonian viscous fluid, and an adjacent rigid wall. The fluid occupies the gap between the plate and the wall. A time-harmonic force, applied in and along the free surface of the FGM plate, excites vibrations within the system. The plate’s motion is modeled using the exact equations of elastodynamics, while the fluid dynamics are described by the linearized Navier–Stokes equations for compressible viscous flow. The governing equations, which feature variable coefficients, are solved using a discrete analytical approach. Boundary conditions enforce impermeability at the rigid wall and continuity of both forces and velocities at the fluid–plate interface. The investigation focuses on the plane strain state of the plate coupled with the corresponding two-dimensional fluid flow. Numerical analyses are conducted to evaluate normal stresses and velocity distributions along the interface. The primary objective is to assess how the graded material properties of the plate influence the frequency-dependent responses of stresses and velocities at the plate–fluid boundary. Full article

This paper presents the results of seakeeping tests conducted on the Tritor, a remotely controlled autonomous unmanned surface vehicle (USV) featuring a trimaran hull design known as the Three Slender Cylinders Hull (3SCH) and equipped with electric propulsion. Previous research focused on the vehicle’s design, prototype development, and initial functional testing. Tritor is characterised by its simple design and construction, reliable propulsion system, and excellent stability and manoeuvrability. Its control and navigation systems have demonstrated effective performance in both remote-controlled and fully autonomous modes. In the present study, seakeeping tests were carried out in a towing tank, with repeated trials conducted at various speeds and wavelengths. The selected wavelengths were close to the vehicle’s length, where the most significant responses were expected. Test speeds ranged from 1.0 to 2.5 m per second, based on prior operational experience with the vehicle. Due to the constraints of the towing tank, all wave directions were limited to head seas. Measurements included heave and pitch motions. Vertical accelerations at the vehicle’s centre of gravity were derived from the heave data and used as a key indicator of seakeeping performance. The results were evaluated against established seakeeping criteria related to vessel operability and structural safety. Full article

The upper limb stretching device plays a key role in enhancing physical function. Current commercial upper limb stretching devices often suffer from limited functionality and are poorly aligned with the biomechanics of the human arm. To address these limitations, this paper presents the design of an ergonomic device for upper limb stretching. Firstly, the development of a regression model for the upper limb force test was carried out through the Box–Behnken Design (BBD) response surface methodology. Secondly, the Denavit-Hartenberg (D-H) method was adopted for the kinematic analysis of the human upper limb stretching mechanism. Subsequently, a kinematic model was established by coupling the data from Creo Parametric and ADAMS models. The kinematic characteristics were then investigated throughout the entire range of motion, yielding the corresponding kinematic parameter curves. Next, the finite element method was employed within ABAQUS to model the upper limb stretching mechanism, to allow for a detailed strength analysis of its key components. Finally, a prototype was manufactured and tested through upper limb stretching experiments to validate its performance. The results demonstrate that the designed stretching mechanism achieved the desired range of motion, with its angular velocity and angular acceleration exhibiting smooth variations. The maximum stress observed is 195.2 MPa, which meets the design requirements. This study provides a valuable reference for the development of future human stretching devices. Full article

With the development of offshore wind power progressing towards larger-scale and deeper-water projects, the float-over installation of offshore converter stations has become a mainstream solution due to its high carrying capacity, efficiency and cost-effectiveness. This study addresses the dynamic positioning (DP) challenges during this operation, where traditional PID controllers often struggle with performance under complex environmental loads. An Observer-Based Robust Controller (OBRC) is proposed and integrated with a constant parameter time-domain model (CPTDM) to simulate the DP process of a novel T-U barge. Time-domain simulations for both standby and entry phases were conducted under various wave directions and periods. The results demonstrate that the OBRC significantly outperforms the conventional PID controller in maintaining positioning accuracy. The findings provide critical insights into motion responses and control strategies, offering valuable guidance for the design and safe operation of future float-over installations. Full article

Currently, maritime navigation safety risks—particularly those related to ship navigation—are primarily assessed through traditional rule-based methods and expert experience. However, such approaches often suffer from limited accuracy and lack real-time responsiveness. As maritime environments and operational conditions become increasingly complex, traditional techniques struggle to cope with the diversity and uncertainty of navigation scenarios. Therefore, there is an urgent need for a more intelligent and precise risk prediction method. This study proposes a ship risk prediction framework that integrates a deep learning model based on Long Short-Term Memory (LSTM) networks with Bayesian risk evaluation. The model first leverages deep neural networks to process time-series trajectory data, enabling accurate prediction of a vessel’s future positions and navigational status. Then, Bayesian inference is applied to quantitatively assess potential risks of collision and grounding by incorporating vessel motion data, environmental conditions, surrounding obstacles, and water depth information. The proposed framework combines the advantages of deep learning and Bayesian reasoning to improve the accuracy and timeliness of risk prediction. By providing real-time warnings and decision-making support, this model offers a novel solution for maritime safety management. Accurate risk forecasts enable ship crews to take precautionary measures in advance, effectively reducing the occurrence of maritime accidents. Full article

Horizontal vibrations in high-speed elevators induced by building sway degrade ride comfort and compromise operational safety. Developing an accurate and robust dynamic model is essential for effective vibration control. To address this, this study develops a comprehensive dynamic model of the coupled traction rope–car–guide shoe–guide rail system under multi-support excitations, incorporating nonlinear contact between the guide shoe and rail, guide rail vibration characteristics, and the time-varying length of traction rope. Using this model, the dynamic responses of the system under stationary and operating conditions are analyzed in detail. The results demonstrate that the proposed model accurately captures the dynamic behavior of the coupled system. In addition, the traction rope’s dynamics are a dominant factor in the system’s response, particularly when the elevator is stationary at a landing, producing a resonant condition with the building sway. Furthermore, a strong coupling between vertical motion and horizontal vibration is identified, which significantly amplifies the system response. By linking elevator dynamics with the sway characteristics of high-rise buildings, this work provides a robust analytical framework for predicting the dynamic response of high-speed elevators due to building sway and contributes to the safety assessment of high-rise reinforced concrete (RC) structures. Full article