Multi-Agent Path Finding (MAPF) is an algorithm designed to identify collision-free paths for multiple agents, commonly used in fields like robotics and drone navigation. Conflict-Based Search with Continuous Time (CCBS) is particularly beneficial for real-world applications due to its capability to find paths in continuous time; however, it often experiences lengthy computation times. Although techniques such as prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics have been implemented to reduce these times, challenges remain. To address these issues, this paper introduces methods to improve space utilization by calculating agent congestion. By optimizing space usage, we can identify paths that avoid potential collisions, even when those paths share the same cost. We propose enhancements to high-level heuristics, conflict prioritization, and low-level heuristics, as well as a method for calculating congestion in continuous time. These improvements lead to a reduction in agent collisions and a decrease in high-level expansions, resulting in a 30% increase in computational success rates compared to the existing CCBS. Incorporating space utilization into the search process significantly enhances MAPF performance.

As modern warfare shifts towards electronic warfare and Intelligence, Surveillance, and Reconnaissance (ISR), there is an increasing demand for stabilized gimbal systems mounted on tracked vehicles to operate reliably in harsh combat environments. However, high-frequency disturbances generated by the vehicle can degrade the quality of the imagery produced by these systems. To mitigate image blur caused by vibration, elastomeric isolators are used; yet, their nonlinear behavior under shock and vibration makes performance prediction challenging. This study aims to model the nonlinear dynamics of these isolators and identify the optimal configuration. Numerical simulations were conducted to pinpoint leading candidate isolators, which were then tested through operational vibration and shock assessments on four isolators with varying stiffness. The selected isolator achieved vibration attenuation of 83% on the X-axis and 72% on the Y-axis. It also met a safety margin of 1.54 for the image sensor and demonstrated durability through endurance testing.

This study assessed the accuracy and reliability of a 2D image-based deep learning algorithm for posture analysis by comparing it with a 3D motion capture system. Twenty healthy adult males participated, and nine balance parameters were measured using both methods: body tilt (ML/AP), shoulder tilt, pelvis tilt (ML/AP), knee tilt, left/right varus/valgus, and forward head posture. We evaluated agreement and reliability using root mean square error (RMSE), mean absolute error (MAE), Pearson correlation coefficients, and intraclass correlation coefficients (ICC). Most parameters exhibited RMSE and MAE within 3°, while forward head posture, pelvis tilt (AP), and varus/valgus had errors below 10°. High correlations were found for shoulder tilt (r = 0.886) and forward head posture (r = 0.681), whereas knee tilt and left varus/valgus showed lower correlations due to methodological differences. Both methods demonstrated high repeatability (3D: ICC > 0.90, 2D: ICC > 0.80), with moderate-to-high agreement between methods (ICC ≥ 0.5 for most parameters). Shoulder tilt (ICC = 0.919) and forward head posture (ICC = 0.799) showed particularly high agreement. These findings indicate that 2D image-based posture analysis can provide accurate and reliable assessments comparable to 3D motion capture, presenting a more accessible and cost-effective alternative for posture evaluation in clinical and research contexts.

This study presents a rolling tribometer designed to quantitatively assess ball-raceway friction in ball-guided bearings, which is critical for applications such as smartphone camera actuators, where friction impacts power consumption. Following ASTM G133 standards, the tribometer was validated using LCP and PC materials under both short-cycle (10K cycles) and long-cycle (1M cycles) tests. Under short-cycle conditions, LCP exhibited an average coefficient of friction (COF) of 0.011, while PC demonstrated a COF of 0.009, both showing low variability at 2.7% and 4.4%, respectively. In long-cycle testing, LCP maintained stable friction coefficients, whereas PC experienced a significant COF increase around 200K cycles due to wear. Confocal microscopy revealed that the wear volume of PC was approximately 10 times greater than that of LCP after 1M cycles. Displacement-friction force analysis indicated increased energy dissipation in PC, attributed to wear-induced surface asperities. This rolling tribometer provides a reliable method for evaluating friction coefficients and long-term durability, yielding valuable data for optimizing actuator design and enhancing efficiency and lifespan in ball-guided mechanisms. The quantitative friction data generated can significantly improve the performance of ball-guided systems.

This study investigated the influence of inlet velocity on the internal flow characteristics and particle separation performance of a cyclone separator. Computational Fluid Dynamics (CFD) coupled with the Discrete Phase Model (DPM) was used to predict particle trajectories and separation efficiencies under different velocity conditions. The results show that increasing the inlet velocity intensifies the swirling flow and strengthens the centrifugal force within the cyclone. As a result, the axial velocity distribution becomes more pronounced, with stronger downward flow near the wall and intensified upward reverse flow at the center. In the bottom outlet region (Z = 4.5D), clear flow asymmetry associated with the Precessing Vortex Core (PVC) effect is observed, and this phenomenon becomes more pronounced as the inlet velocity increases. Particle trajectory analysis indicates that higher velocities shorten particle residence time and promote rapid migration toward the wall, forming compact helical paths and improving separation efficiency. Analysis using an inverse weighted-sum performance index indicates that an inlet velocity of 15 m/s provides the most favorable balance among the evaluated performance parameters and represents the optimal operating condition for cyclone separator performance.

Alcohol acts as a central nervous system depressant and is classified as a psychoactive drug that impairs cognitive alertness and motor coordination. Driving after alcohol consumption slows reaction time in emergency situations and increases the risk of collisions. Although various technologies have been developed to measure alcohol concentration, many suffer from limitations such as sensitivity to environmental factors (e.g., temperature and humidity), hygiene concerns, and the need for periodic calibration. This study proposes an optical method for measuring alcohol concentration using near-infrared (NIR) spectroscopy. Statistical analyses were conducted across multiple wavelength regions to identify wavelengths with significant correlations to alcohol concentration. As a preliminary step, single alcohol solution samples were prepared using distilled water and ethanol. The optical properties of the samples were analyzed in the NIR wavelength range, and statistical indicators including the coefficient of determination (R²), p-value, and coefficient of variation (CV) were evaluated. The results identified specific wavelengths with statistical significance, and the application of mathematical modeling at these wavelengths enabled accurate estimation of alcohol concentration. This approach demonstrates the potential for non-invasive alcohol concentration measurement while minimizing hygiene and infection-related concerns for users.

Wearable sensors are susceptible to degradation from physical wear, moisture, and desiccation, which can result in signal attenuation and unreliable data. This pilot study, conducted in a controlled single-participant setting, introduces a framework to quantify and characterize sensor degradation while restoring corrupted electromyography (EMG) signals. Four types of sensors—polyethylene terephthalate film, parylene film, 3M bioelectrode pads, and microneedle patches—were affixed to the left forearm in a three-electrode EMG configuration. Impedance at 100 Hz was monitored as an indicator of sensor aging, while a one-dimensional convolutional autoencoder was employed to reconstruct degraded EMG signals using a loss function that incorporated both time-domain and frequency-domain error terms. The reconstruction loss showed a correlation with impedance changes, providing a practical metric for assessing sensor health. These findings highlight the feasibility of real-time signal recovery and its potential to extend the lifespan of sensors.

Methane thermal decomposition is a promising technology for producing CO₂-free hydrogen. This study experimentally examines how temperature (1,000–1,400^oC) and residence time affect methane decomposition in a ceramic tubular reactor. The results show that both the methane conversion rate and hydrogen yield increased with temperature, reaching approximately 95% and 45%, respectively, at 1,400^oC. At lower temperatures (1,000–1,200^oC), residence time had a significant impact, while at higher temperatures (1,300–1,400^oC), temperature became the predominant factor. Additionally, the formation of C₂ hydrocarbons, particularly acetylene (C₂H₂), increased as residence time decreased, negatively affecting both methane conversion and hydrogen yield. Analysis of the solid carbon by-products identified two distinct forms: amorphous, spherical carbon black particles and a semi-graphitic, crystalline carbon film. These findings provide essential data for optimizing the conditions of methane thermal decomposition.

The semiconductor industry is experiencing significant growth in production scale and investment, driven by rising demand for generative AI, high-performance computing (HPC), high-bandwidth memory (HBM), and high-performance/high-density chips. As a result, precision inspection and yield management at the wafer stage have become critical challenges. Probe cards, essential for verifying a chip's electrical performance, play a vital role in yield management. However, during repetitive inspection processes, probe cards absorb heat from the underlying chuck, leading to probe tip-pad alignment errors that degrade contact reliability and measurement accuracy. This situation necessitates a quantitative evaluation system based on thermo-structural coupled analysis. Additionally, the modeling process for multiple interposers and interposer housings, along with the preprocessing of contact conditions, adds complexity due to the increasing number of contact surfaces. This complexity can result in convergence issues and reduced accuracy. To address these challenges, this study employs Ansys Parametric Design Language (APDL) to enhance interposer and housing modeling, as well as contact problem resolution. It introduces a novel meshing method that positions nodes at target coordinates using point clouds, providing an effective analysis approach applicable to large, high-density probe cards and thermo-structural problems involving numerous contacts.

The field of tissue engineering requires versatile scaffold fabrication technologies capable of inducing cell proliferation and differentiation to promote functional tissue regeneration. Traditional fabrication methods face inherent trade-offs among production speed, resolution, and cost, which hinder their ability to replicate the intricate hierarchical structures of biological tissues. To address these challenges, we developed a mask projection photolithography system with variable optical magnification. This system allows for precise control of the microscale feature size in the final product using a single mask, by adjusting the optical magnification with interchangeable objective lenses and a relay lens. With this system, we successfully fabricated porous scaffolds with reproducible pore sizes ranging from 25 to 100 μm, exposing a Poly (ethylene glycol) diacrylate (PEGDA, Mn = 700) hydrogel precursor solution through a honeycomb-patterned mask for durations of just 3 to 10 seconds. The mask projection system presented in this study offers a powerful and efficient platform for creating the microstructures essential for various advanced biomedical applications, including tissue engineering, drug delivery, and organoid-on-a-chip, thanks to its unique combination of speed, precision, and cost-effectiveness.