This study describes two cases of urolithiasis-related mortality in Asian small-clawed otters (Aonyx cinerea) raised together for over 10 years. Case 1 had multiple renal and bladder stones without clinical signs and died during surgery for stone removal. Case 2, which harbored more extensive renal calculi, also showed no clinical signs but died suddenly from hydronephrosis and renal failure caused by bilateral obstruction of the renal pelvis. In both cases, the uroliths were composed of calcium oxalate. These results highlight the importance of regular examinations in captive otters.

In the production sites of small and medium sized manufacturing enterprises, the increasing proportion of foreign workers has led to frequent difficulties in responding promptly to process defects and equipment setting errors during night and weekend shifts due to the absence of Korean supervisors. If such issues are not addressed in a timely manner, they can lead to large scale defects and reduced production efficiency. In this study, we developed an AI-based defect prediction and prevention system for the bearing machining process to overcome these on site management limitations. Real time machining data, equipment information, and quality inspection results were collected from the production lines of the target company, and the prediction accuracy of three models, RNN(Recurrent Neural Network), LSTM(Long Short-Term Memory), and GRU(Gated Recurrent Unit), was compared. As a result, the LSTM model demonstrated the best performance. The developed system visualizes real time defect prediction results in the form of a dashboard, enabling workers to immediately detect anomalies and adjust the process accordingly. Particularly in bearing machining processes where mass production occurs in short periods, the risk of lot level defects is high, while this system can contribute to improved production quality and efficiency by enabling early defect prediction and immediate response.