The Department of Engineering Education at KMITL offers courses in power electronics laboratory practices, which require the use of expensive imported training kits. This results in a loss of national revenue due to the purchase of these imported kits. Therefore, the developers propose a power electronics training kit that offers equivalent or superior functionality to the imported ones while being more cost-effective, making it suitable for student experiments.
การเรียนการสอนรายวิชาอิเล็กทรอนิกส์กำลังในปัจจุบัน มีการใช้การจำลองการทำงานด้วยโปรแกรมคอมพิวเตอร์ เนื่องจากขาดแคลนชุดทดลองจริง ซึ่งการทดลองกับอุปกรณ์จริงยังมีจำเป็นเป็นอย่างยิ่ง ดังนั้นจึงมีการริเริ่มในการสร้างด้วยตนเองเพื่อให้สามารถนำมาใช้ในการเรียนการสอนได้
วิทยาลัยเทคโนโลยีและนวัตกรรมวัสดุ
The development of skin-on-a-chip models plays a crucial role in research for drug and cosmetic development. Traditional approaches often utilize two-dimensional (2D) methods that rely on culturing cells on flat surfaces, resulting in a lack of complexity in skin structure and realistic cell interactions. Moreover, traditional methods have limitations in mimicking fluid flow and nutrient circulation, which affects the accuracy of pharmaceutical testing and the prediction of drug effects. This has led to the advancement of three-dimensional (3D) skin models using new microfluidic technology, enhancing the realism of skin structure by replicating both the epidermis and dermis layers, as well as simulating fluid flow similar to physiological conditions in the human body. The design of 3D systems allows for more realistic cell arrangement and interactions, enabling better simulation of skin functions and increasing the accuracy in evaluating the effects of various substances on cell responses, including absorption, inflammation, and wound healing. Therefore, the development of three-dimensional (3D) skin models not only addresses the limitations of traditional methods but also represents a significant step forward in creating models that can be effectively applied in drug testing and pharmaceutical product development.
คณะอุตสาหกรรมอาหาร
Spent coffee grounds (SCG) are a byproduct of the coffee brewing process, and their quantity continues to increase due to the growing global coffee consumption. SCG contain beneficial compounds such as polysaccharides, dietary fibers, and antioxidants, which can be utilized in various applications, including prebiotic extraction. This study focuses on extracting prebiotics from SCG using acid hydrolysis and enzymatic hydrolysis methods to evaluate their potential in promoting the growth of beneficial gut microorganisms. The expected results of this research include adding value to coffee industry waste, reducing organic waste, and providing a sustainable approach to developing prebiotic products for use in the food and health industries. Furthermore, this study aligns with sustainable resource utilization and environmentally friendly practices.
คณะวิทยาศาสตร์
This special problem aims to study and compare the performance of predicting the air quality index (AQI) using five ensemble machine learning methods: random forest, XGBoost, CatBoost, stacking ensemble of random forest and XGBoost, and stacking ensemble of random forest, SVR, and MLP. The study uses a dataset from the Central Pollution Control Board of India (CPCB), which includes fifteen pollutants and nine meteorological variables collected between January, 2021 and December, 2023. In this study, there were 1,024,920 records. The performance is measured using three methods: root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination. The study found that the random forest and XGBoost stacking ensemble had the best performance measures among the three methods, with the minimum RMSE of 0.1040, the minimum MAE of 0.0675, and the maximum of 0.8128. SHAP-based model interpretation method for five machine learning methods. All methods reached the same conclusion: the two variables that most significantly impacted the global prediction were PM2.5 and PM10, respectively.