Study on Parasites in Blackchin Tilapia and Value-Added Processing Parasites play a crucial role in affecting fish health and the balance of marine ecosystems. The study of parasites in fish is essential for assessing fish population status and their impact on the ecosystem. This research focuses on a preliminary survey of parasites in Blackchin Tilapia (Sarotherodon melanotheron) found in the waters of Chumphon Province to determine whether this species carries parasitic infections. The findings will provide valuable insights for managing marine resources and developing strategies for processing Blackchin Tilapia into food products to help control its population in the ecosystem. One of the value-added processing approaches for Blackchin Tilapia is the "Nai Hoi Hua Fu" product. This product involves deep-frying the fish to achieve a crispy and fluffy texture before mixing it with mango salad to enhance its flavor and make it more appealing. This processing method not only adds value to the fish but also serves as a practical solution for managing the Blackchin Tilapia population, which may impact the ecosystem. The study results indicate that no parasitic infections were found in either the internal or external organs of the sampled fish, suggesting that the marine environment in the study area is favorable for fish health. However, continuous research is recommended to monitor long-term ecological changes and evaluate the impact of Blackchin Tilapia on ecosystem balance to ensure sustainable resource management.
มีการแพร่ระบาดปลาหมอคางดำ ในพื้นที่จังหวัดชุมพร โดยเฉพาะปากแม่น้ำ ดังนั้น จึงมีความสนใจว่าปลาชนิดนี้แพร่กระจายลงสู่ทะเลหรือไม จึงสำรวจพร้มอศึกาาาการติดเชื้อปรสิต เนื่องจากอยากทราบว่าทำไมถึงแพร่กระจายในระบบนิเวศเร็วมาก และสามารถนำมารับประทานได้หรือไม่
คณะวิทยาศาสตร์
This special problem aims to compare the performance of machine learning methods in time series forecasting using lagged time periods as independent variables. The lagged periods are categorized into three groups: lagged by 10 units, lagged by 15 units, and lagged by 20 units. The study employs four machine learning methods: Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The time series data simulated as independent variables diverse including characteristics: Random Walk data, Trending data, and Non-Linear data, with sample sizes of 100, 300, 500, and 700. The research methodology involves splitting the data into 90% for training and 10% for testing. Simulations and analysis are performed using the R programming language, with 1,000 iterations conducted. The results are evaluated based on the average mean squared error (AMSE) and the average mean absolute percentage error (AMAPE) are calculated to identify the best performing method. The research findings revealed that for Random Walk data, the best performing methods are Random Forest and Support Vector Machine. For Trend data, the best performing methods are Random Forest. For Non-Linear data, the best performing methods are Support Vector Machine. When tested with real-world data, the results show that for the Euro-to-Thai Baht exchange rate, the best methods are Random Forest and Support Vector Machine. For the S&P 500 Index in USD, the best performing methods are Random Forest. For the Bank of America Corp Index in USD, the best performing methods are Support Vector Machine.
คณะวิศวกรรมศาสตร์
A high-pressure gas storage tank made from composite materials, including carbon fiber, resin, and plastic, is designed for storing compressed natural gas (CNG) or hydrogen. This type of tank is classified as a Type IV high-pressure vessel. In this research, it is designed to operate at a pressure of 250 bar for the transportation of compressed natural gas.
วิทยาลัยวิศวกรรมสังคีต
This project explores the therapeutic potential of binaural beats within a 3D soundscape environment, focusing on the effects of left-right (L-R) beating sound positioning. Using Dolby Atmos technology to create immersive auditory experiences, the research aims to investigate how varying spatial beating sound placements in binaural beat therapy influence mental and emotional healing. Binaural beats, a form of auditory brainwave entrainment, have been shown to promote relaxation, reduce anxiety, and enhance cognitive performance. However, there has been limited exploration of how spatial sound technologies, like Dolby Atmos, can enhance the efficacy of these therapies. This study examines how different beating L-R configurations in a 3D soundscape impact the listener’s perception and therapeutic outcomes. Participants will experience binaural beat sessions in various beating L-R orientations, and physiological and psychological measures, such as heart rate variability and self-reported relaxation levels, will be assessed. The results are expected to provide new insights into the interaction between spatial audio environments and sound-based therapies, potentially improving sound therapy practices by leveraging advanced audio technologies.