This study outlines a structural design process for a cylindrical superelastic shape memory alloy (SMA) ligation clip. Although polymer-based clips are widely used, they face challenges related to long-term stability and limited radiopacity, highlighting the necessity for metal clips. By systematically modifying two key design variables—the hole offset ratio and the cut-off ratio—the proposed clip effectively reduces excessive stress concentration and enhances superelastic behavior. Finite element analyses indicate that the stress deviation in the two cross-sectional deformation regions decreased by 83.9%, and the martensitic transformation remained confined to a small area, demonstrating robust strain recovery within the superelastic range. In conclusion, the improved SMA clip successfully withstood internal pressures exceeding 15 psi without leakage, showcasing its superior ligation performance and potential for durable, reliable use in minimally invasive surgical procedures.

This study details the development of a semi-active suspension wheel module for small mobile robots and assesses its dynamic characteristics under various driving conditions through simulation. The wheel module features a low-degree-of-freedom mechanical design and includes a semi-active damper to improve adaptability to different environments. To validate the simulation model, a prototype robot equipped with the wheel module was created, and obstacle-crossing experiments were conducted to measure vertical acceleration responses. The model was then refined based on these experimental results. By employing design of experiments and optimization techniques, the effective range of damping coefficients was estimated. Additionally, simulations were carried out at different speeds, payloads, and obstacle heights to identify optimal damping values and examine their trends. The results indicate that the proposed module significantly enhances driving stability and can serve as a foundation for future control strategies in robotic mobility systems.

The rise of electric vehicles (EVs) has led to a reduction in engine noise, making suspension and road noise more noticeable. However, most assessments focus only on air-conducted (AC) pathways and overlook bone-conducted (BC) transmission. This study identifies key sources of vehicle noise and implements a finite-element simulation to replicate real-world driving conditions. A 12-degree-of-freedom (DOF) human body model quantifies how vibrations transmit from the vehicle structure to the head. Additionally, a detailed finite-element model of the human head evaluates basilar-membrane (BM) vibrations for both AC and BC inputs. The results indicate that BC dominates below 10 Hz, producing BM velocities up to 50 dB greater than AC. Above 10 Hz, AC prevails, showing a difference of approximately 40 dB. Notably, at frequencies of 33, 46, 67, and 80 Hz, the AC–BC difference narrows to below 10 dB, highlighting significant BC effects even at higher frequencies. These findings reveal that neglecting bone-conduction pathways can lead to an underestimation of occupant exposure to low-frequency vibrations. Therefore, comprehensive evaluations and control methods for vehicle noise should consider both AC and BC transmission mechanisms to accurately reflect human perception

This study introduces a highway secondary accident prevention system that employs Fast Fourier Transform (FFT) analysis of vehicle collision sounds. The system is designed to identify abnormal acoustic patterns produced during collisions and skidding events, enabling faster and more accurate accident detection than traditional methods. When a crash is detected, visual warning signals are instantly sent to nearby vehicles using LED devices powered by a photovoltaic panel and an energy storage system (ESS). Experimental results showed 100% detection accuracy during independent playback of collision, skidding, and driving sounds, and 80% accuracy during simultaneous playback. These results confirm the system's ability to effectively differentiate accident-related sounds and deliver timely alerts. This research offers an innovative and environmentally sustainable approach to enhancing highway safety and reducing the societal and economic consequences of secondary accidents.