Search Results (1,230)

An integrated approach for enhancing microparticle separation efficiency in acoustofluidic lab-on-a-chip systems is presented, combining numerical modeling in COMSOL 6.2 Multiphysics^® with reinforcement learning techniques implemented in Python 3.10.14. The proposed method addresses the limitations of traditional parameter tuning, which is time-consuming and computationally intensive. A simulation framework based on LiveLink™ for COMSOL–Python integration enables the automatic generation, execution, and evaluation of particle separation scenarios. Reinforcement learning algorithms, trained on both successful and failed experiments, are employed to optimize control parameters such as flow velocity and acoustic frequency. Experimental data from over 100 numerical simulations were used to train a neural network, which demonstrated the ability to accurately predict and improve sorting efficiency. The results confirm that incorporating failed outcomes into the reward structure significantly improves learning convergence and model accuracy. This work contributes to the development of intelligent microfluidic systems capable of autonomous adaptation and optimization for biomedical and analytical applications, such as label-free separation of microplastics from biological fluids, selective sorting of soot and ash particles for environmental monitoring, and high-precision manipulation of cells or extracellular vesicles for diagnostic assays. Full article

This paper presents a kinematics-constrained grid-based path planning algorithm that generates real-time, safe, and executable trajectories, thereby enhancing the performance and reliability of autonomous vehicle parking systems. The grid resolution adapts to the minimum turning radius and steering limits, ensuring feasible motion primitives. The cost function integrates path efficiency, direction-switching penalties, and collision risk to ensure smooth and feasible maneuvers. A cubic spline refinement produces curvature-continuous trajectories suitable for vehicle execution. Simulation and experimental results demonstrate that the proposed method achieves collision-free and curvature-bounded paths with significantly reduced computation time and improved maneuver smoothness compared with conventional A* and Hybrid A*. In both structured and dynamic parking environments, the planner consistently maintained safe clearance and stable tracking performance under variations in vehicle geometry and velocity. These results confirm the robustness and real-time feasibility of the proposed approach, effectively unifying kinematic feasibility, safety, and computational efficiency for practical autonomous parking systems. Full article

The present study aims to compare the feasibility of using ultrasound techniques and image processing to obtain comprehensive experimental results on the dynamics of solid–liquid fraction changes during the melting and solidification of coconut oil as a phase change material (PCM). The discussion will focus on the advantages and limitations of various ultrasonic techniques and image data analysis for inspecting materials during phase transitions. Ultrasound enables the detection of phase changes in materials by analysing variations in their acoustic properties, such as wave velocity and amplitude, during transitions. This method is not only cost-effective compared to traditional non-destructive techniques, such as X-ray tomography, but also offers the potential for real-time monitoring in thermal energy storage systems. Furthermore, it can provide valuable information about internal mechanical parameters and the material’s structure. A detailed analysis of the melting and solidification dynamics has been conducted, confirming the feasibility of using ultrasound parameters to assess the reconstruction of material structures during phase changes. This study paves the way for more efficient and cost-effective monitoring of phase change materials in various applications. Full article

Low Earth orbit (LEO) satellites, characterized by low altitudes, high velocities, and strong ground signal reception, have become an essential and dynamic component of modern global navigation satellite systems (GNSS). However, orbit decay induced by atmospheric drag poses persistent challenges to maintaining stable trajectories. Frequent orbit maneuvers, though necessary to sustain nominal orbits, introduce significant difficulties for precise orbit determination (POD) and navigation augmentation, especially under complex operational conditions. Unlike most existing methods that rely on Two-Line Element (TLE) data—often affected by noise and limited accuracy—this study directly utilizes onboard GNSS observations in combination with real-time precise ephemerides. A novel time-series indicator is proposed, defined as the geometric root-mean-square (RMS) distance between reduced-dynamic and kinematic orbit solutions, which is highly responsive to orbit disturbances. To further enhance robustness, a sliding window-based adaptive thresholding mechanism is developed to dynamically adjust detection thresholds, maintaining sensitivity to maneuvers while suppressing false alarms. The proposed method was validated using eight representative maneuver events from the GRACE-FO satellites (May 2018–June 2022), successfully detecting seven of them. One extremely short-duration maneuver was missed due to the limited number of usable GNSS observations after quality-control filtering. To examine altitude-related applicability, two Sentinel-3A maneuvers were also analyzed, both successfully detected, confirming the method’s effectiveness at higher LEO altitudes. Since the thrust magnitudes and durations of the Sentinel-3A maneuvers are not publicly available, these cases primarily serve to verify applicability rather than to quantify sensitivity. Experimental results show that for GRACE-FO maneuvers, the proposed method achieves near-real-time responsiveness under long-duration, high-thrust conditions, with an average detection delay below 90 s. For Sentinel-3A, detections occurred approximately 7 s earlier than the reported maneuver epochs, a discrepancy attributed to the 30 s observation sampling interval rather than methodological bias. Comparative analysis with representative existing methods, presented in the discussion section, further demonstrates the advantages of the proposed approach in terms of sensitivity, timeliness, and adaptability. Overall, this study presents a practical, efficient, and scalable solution for real-time maneuver detection in LEO satellite missions, contributing to improved GNSS augmentation, space situational awareness, and autonomous orbit control. Full article

The prediction of seismic intensity in an accurate and timely manner is needed to provide scientific guidance for disaster relief. Traditional seismic intensity prediction methods rely on seismograph equipment, which is limited by slow response times and high equipment costs. In this study, we introduce a low-computational-cost transformer-based (LCCTV) visual tracking method to predict seismic intensity in surveillance videos. To this end, an earthquake video dataset is proposed. It is captured in the laboratory environment, where the seismic level is obtained through seismic station simulation. With the proposed dataset, a low-computational-cost transformer-based visual tracking method is first proposed to estimate the movement trajectory of the calibration board target in videos in real time. In order to further improve the recognition accuracy, we then utilize a Butterworth filter to smooth the generated movement trajectory so as to remove low-frequency interference signals. Finally, the seismic intensity is predicted based on the velocity and acceleration derived from the smoothed movement trajectory. Experimental results demonstrated that the LCCTV outperformed other state-of-the-art approaches. The findings confirm that the proposed LCCTV can serve as a low-cost, scalable solution for seismic intensity analysis. Full article

Total internal reflection fluorescence (TIRF) microscopy excites fluorophores within a few hundred nanometers of the sample–substrate interface, enabling high-contrast imaging near the cell membrane. When cultured cells differentiate, the membrane in contact with the coverslip generally acquires basal characteristics, while the opposite membrane develops apical features. Consequently, conventional TIRF microscopy is limited to imaging the basal surface. We developed an immersed-prism TIRF (IP-TIRF) microscope, in which a prism immersed in the culture medium generates TIR at the cell/medium–prism interface, illuminating the apical membrane and reducing cytosolic background. In proof-of-principle experiments, we imaged fluorescent beads and 3xmNeonGreen-tagged intraflagellar transport (IFT) particles in cilia, and compared the performance with confocal microscopy. In cellular regions where both methods can be applied (such as the IFT base pool), on average, IP-TIRF achieved approximately 1.8 times the contrast-to-noise ratio (CNR~31) compared to confocal microscopy. Furthermore, IFT-particle motion was detected in IP-TIRF image sequences and Kymographs of cilia, with adequate spatial resolution. Kymograph analysis revealed an average anterograde IFT velocity of 0.156 ± 0.071 µm/s and an average retrograde velocity of 0.020 ± 0.007 µm/s, approximately one-quarter and one-twentieth, respectively, of the values reported for mammalian primary cilia, which we attribute to acquisition at room temperature rather than physiological conditions. Therefore, these velocity measurements should be regarded as proof-of-principle demonstrations obtained at room temperature, not as validated physiological transport rates. Our IP-TIRF method provides a high-resolution, cost-effective, and broadly accessible approach for imaging the apical membrane in live cells. Full article

Background/Objectives: Children with myelomeningocele (MMC) often experience lower extremity muscular contractures, which can impact their functional mobility. While standing programs have demonstrated benefits for children with other neuromuscular conditions, there is limited evidence on their use in ambulatory children with MMC who have joint deformities. This single-subject design study examined the impact of a home-based standing program on two ambulatory children with MMC, focusing on lower extremity muscle flexibility, functional movement quality, gait velocity, and participation in daily activities. Methods: Two children participated in a multi-phase single-subject (ABABA) withdrawal design beginning with the baseline phase and then alternating between the intervention and withdrawal phases. The intervention consisted of 60-minute standing sessions, five days a week, using a sit-to-stand stander (STSS) with support and supervision from a physical therapist (PT) and the parent. Primary outcomes included goniometric passive range of motion (PROM) and 10-Meter Walk Test (10 MWT). Secondary outcomes included the Pediatric Neuromuscular Recovery Scale (Peds NRS) and the Pediatric Evaluation of Disability Inventory Computer Adaptive Test (PEDI-CAT). Results: Improvements in hip and knee muscle flexibility were observed during the intervention phases, with some loss during the withdrawal phase. Functional movement quality improved in both children. Gait velocity and participation in daily activity scores remained stable during intervention phases. Parental feedback reflected increased independence and high engagement with the home program. One child discontinued due to a heel injury, highlighting the need for individualized support. Conclusions: Personalized standing programs may improve muscle flexibility and functional movement quality in ambulatory children with MMC. Further research is warranted to determine the optimal dosing regimen, ensure safety, and assess long-term functional outcomes. 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

The impact freezing of supercooled water droplets poses a significant threat to the safety of aircraft and power transmission equipment. In recent years, extensive research has been conducted using numerical methods to investigate this phenomenon. However, existing models often incorrectly predict premature freezing near the droplet–air contact line during the early stage of impact, thereby unreasonably suppressing the spreading process in these regions. To address this limitation, this study proposes a velocity-gate-based activation control strategy for the Darcy momentum source, enabling its dynamic adjustment during simulation. The methodology integrates the Volume of Fluid (VOF) model, the solidification model, and the dynamic contact angle (DCA) model with the proposed dynamic Darcy source, while accounting for the influence of supercooling on physical properties. The numerical simulations are performed using COMSOL Multiphysics 6.3 and validated against experimental spreading factor data. The results demonstrate that the proposed methodology effectively eliminates nonphysical freezing during the initial spreading stage, and the predicted spreading factors agree well with experiments, with a maximum relative deviation of up to 11.7% across all simulated cases. The proposed approach improves consistency with real-world behavior and enhances the reliability of existing numerical tools for aircraft icing prediction and anti-icing design. Full article

Unmanned surface vehicles (USVs) rely on multi-sensor perception, such as radar, LiDAR, GPS, and vision, to ensure safe and efficient navigation in complex maritime environments. Traditional ant colony optimization (ACO) for path planning, however, suffers from premature convergence, slow adaptation, and poor smoothness in cluttered waters, while the dynamic window approach (DWA) without global guidance can become trapped in local obstacle configurations. This paper presents a sensor-oriented hybrid method that couples an improved ACO for global route planning with an enhanced DWA for local, real-time obstacle avoidance. In the global stage, the ACO state–transition rule integrates path length, obstacle clearance, and trajectory smoothness heuristics, while a cosine-annealed schedule adaptively balances exploration and exploitation. Pheromone updating combines local and global mechanisms under bounded limits, with a stagnation detector to restore diversity. In the local stage, the DWA cost function is redesigned under USV kinematics to integrate velocity adaptability, trajectory smoothness, and goal-deviation, using obstacle data that would typically originate from onboard sensors. Simulation studies, where obstacle maps emulate sensor-detected environments, show that the proposed method achieves shorter paths, faster convergence, smoother trajectories, larger safety margins, and higher success rates against dynamic obstacles compared with standalone ACO or DWA. These results demonstrate the method’s potential for sensor-based, real-time USV navigation and collision avoidance in complex maritime scenarios. Full article

Shell-like housing structures for motors and compressors can be found in everyday products. Consumers significantly evaluate acoustic emissions during the first usage of products. Unpleasant sounds may raise concerns and cause complaints to be issued. A prevention strategy is a holistic acoustic design, which includes predicting the emitted sound power as part of end-of-line testing. The hybrid experimental-simulative sound power prediction based on laser scanning vibrometry (LSV) is ideal in acoustically harsh production environments. However, conducting vibroacoustic testing with laser scanning vibrometry is time-consuming, making it difficult to fit into the production cycle time. This contribution discusses how the time-consuming sampling process can be accelerated to estimate the radiated sound power, utilizing adaptive sampling. The goal is to predict the acoustic signature and its uncertainty from surface velocity data in seconds. Fulfilling this goal will enable integration into a product assembly unit and final acoustic quality control without the need for an acoustic chamber. The Gaussian process regression based on PyTorch 2.6.0 performed 60 times faster than the preliminary reference implementation, resulting in a regression estimation time of approximately one second for each frequency bin. In combination with the Equivalent Radiated Power prediction of the sound power, a statistical measure is available, indicating how the uncertainty of a limited number of surface velocity measurement points leads to predictions of the uncertainty inside the acoustical signal. An adaptive sampling algorithm reduces the prediction uncertainty in real-time during measurement. The method enables on-the-fly error analysis in production, assessing the risk of violating agreed-upon acoustic sound power thresholds, and thus provides valuable feedback to the product design units. Full article

Parkinson’s disease (PD) is characterized by motor symptoms, with key diagnostic features, such as rigidity, traditionally assessed through subjective clinical scales. This study proposes a novel hybrid framework integrating model-driven biomechanical features (damping ratio, decay rate) and data-driven statistical features (maximum detail coefficient) from wearable sensor data during a modified pendulum test to quantify shoulder girdle rigidity objectively. Using weak supervision, these features were unified to generate robust labels from limited data, achieving a 10% improvement in PD/healthy control classification accuracy (0.71 vs. 0.64) over data-driven methods and matching model-driven performance (0.70). The damping ratio and decay rate, aligning with Wartenberg pendulum test metrics like relaxation index, revealed velocity-dependent aspects of rigidity, challenging its clinical characterization as velocity-independent. Outputs correlated strongly with UPDRS rigidity scores (r = 0.78, p < 0.001), validating their clinical utility as novel biomechanical biomarkers. This framework enhances interpretability and scalability, enabling remote, objective rigidity assessment for early diagnosis and telemedicine, advancing PD management through innovative sensor-based neurotechnology. Full article

Many transportation structures collapse or sustain severe damage as a result of natural disasters such as earthquakes, floods, wars, and similar attacks. These collapsed or severely damaged structures must be rebuilt and returned to service as quickly as possible. Water is used in the mix for cement-bound concrete roads. It is known that drought problems are emerging due to climate change and that water resources are rapidly depleting. Significant amounts of water are used in concrete production, further depleting water resources. In order to contribute to the elimination of these two problems, the usability of polyurethane resin binder in road pavement construction was investigated. Polyurethane resin binder road pavement is a new type of pavement that does not contain cement or bitumen as binders and does not contain water in its mixture. This new type of road pavement can be opened to traffic within 5–15 min. After determining the aggregate and binder mixture ratios, four different curing methods were applied to the created samples. After the curing, the samples were subjected to compression test, flexural test, Bohme abrasion test, freeze–thaw test, bond strength by pull-off test, ultrasonic pulse velocity (UPV) test, SEM-EDX analysis, XRD analysis, and FT-IR analysis. The new type of road pavement created within the scope of this study exhibited a compression strength of 41.22 MPa, a flexural strength of 25.32 MPa, a Bohme abrasion value of 0.99 cm³/50 cm², a freeze–thaw test mass loss per unit area of 0.77 kg/m², and an average bond strength by pull-off value of 4.63 MPa. It was observed that these values ensured the road pavement specification limits. Full article

To address the limited adaptability of traditional fixed-parameter strategies for hexapod robots operating in multi-material terrain environments, this study proposes a terrain-aware gait optimization method based on an improved particle swarm optimization algorithm that incorporates foot-end sinking perception. This method establishes a ground sinking detection mechanism based on foot-end position sensors, constructs a dynamic weight allocation strategy based on ground bearing capacity, and develops a Terrain-aware Ground Particle Swarm Optimization algorithm (TGPSO) that integrates Latin hypercube sampling, linearly decreasing inertia weights, and stagnation exploration mechanisms. Furthermore, it establishes a unified terrain-based reward function framework to achieve dynamic adjustment of weights for velocity, stability, and transportation efficiency. Simulink simulation verification demonstrates that TGPSO achieves superior optimization performance compared to traditional strategies across three typical terrain types, while exhibiting faster convergence speed and enhanced stability. The research findings provide theoretical foundations and technical support for intelligent motion control of hexapod robots across varying material properties, achieving targeted optimization of locomotion performance under diverse terrain conditions. Full article

Finding the time-optimal parameterization of a given path subject to kinodynamic constraints is a critical topic in many robotic applications. However, designing a real-time motion planning algorithm for specified trajectories subject to physical constraints is challenging due to the high nonlinearity in robotic systems. Additionally, moving along a given path may include three types of motion—pure translation, pure orientation, and composite motion—which will further complicate finding the best solution in these applications. To cope with this difficulty, this paper proposes a complete, real-time quaternion-based velocity scheduling algorithm (QBVSA) that takes physical constraints such as joint velocity, joint acceleration, and joint torque into account. The proposed QBVSA is designed to efficiently handle various types of motion subject to physical constraints in real-time. The completeness of the proposed QBVSA is proved mathematically. By exploiting the idea of the initial velocity limit, the search for switching points—which is essential to the conventional numerical integration method—is not required in the proposed approach. Simulations and experiments are performed to validate the proposed motion planning approach. Full article