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.
It is important to minimize electric energy consumption of a data center that uses enormous electricity for maintaining an adequate indoor temperature. Most data centers have applied the outdoor air cooling method on account of economic feasibility. However, it is necessary that data centers have an efficient control method in order to achieve extra energy savings. In this paper, we propose an artificial intelligence based real-time optimal control method that minimizes electricity consumption and assures safe operation simultaneously. The main idea of our proposed method is to evolutionary search the optimal range of controlled variable during a normally operative condition. Furthermore, an optimal operating condition can be achieved without requiring large-scale data to learn a model. Experimental results demonstrate that indoor temperature of a data center can be constantly controlled safely and cost effectively based on our proposed methodology.