The project focuses on designing and creating Art Toy Mascots that reflect the identities of the 12 departments in the Faculty of Agricultural Technology. It combines the concepts of art and agricultural technology to promote better understanding and easy recognition of each department. The project utilizes creative design and artistic toy production techniques.
ในยุคปัจจุบันที่เทคโนโลยีมีบทบาทสำคัญในการพัฒนาภาคการเกษตร คณะเทคโนโลยีการเกษตรจึงมุ่งเน้นการปลูกฝังองค์ความรู้ควบคู่ไปกับการพัฒนาทักษะความคิดสร้างสรรค์ เพื่อให้นักศึกษาสามารถประยุกต์ใช้ความรู้ทางวิทยาศาสตร์และเทคโนโลยีในการแก้ปัญหาและพัฒนาภาคการเกษตรได้อย่างมีประสิทธิภาพ โครงการออกแบบและสร้างสรรค์ Art Toy Mascot จึงเกิดขึ้นจากแนวคิดที่ต้องการ ผสมผสานศิลปะและเทคโนโลยีเข้าด้วยกัน โดยใช้ Art Toy ซึ่งเป็นผลงานศิลปะที่กำลังได้รับความนิยมในกลุ่มคนรุ่นใหม่ เป็นสื่อกลางในการนำเสนออัตลักษณ์ของ 12 สาขาวิชาในคณะเทคโนโลยีการเกษตร
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.
คณะวิทยาศาสตร์
This research focuses on the fabrication of graphene oxide (GO) composite membranes using the Phase-Inversion Method, which transforms polymers from liquid to solid through phase separation. This process creates a porous membrane structure, making it highly adaptable, cost-effective, and suitable for wastewater treatment, separation processes, and industrial filtration applications. Graphene oxide, with its nano-layered structure, offers excellent molecular sieving properties, high water permeability, and chemical and mechanical stability, making it an ideal additive for membrane fabrication. The GO-based membrane demonstrates efficient removal of nanoparticles, heavy metal ions (Pb²⁺, Cr⁶⁺, Hg²⁺), organic pollutants, and microorganisms while exhibiting antifouling properties and high hydrophilicity due to oxygen-functional groups. Applications of this membrane include industrial wastewater treatment, desalination, and the removal of pharmaceutical contaminants, such as antibiotics and hormones. The incorporation of GO enhances membrane performance, providing a sustainable and energy-efficient solution for water purification.
คณะเทคโนโลยีการเกษตร
The Public Park Project: Bubbledel Park is a new-style public park located at Suan Phra Nakhon in Lat Krabang District, Bangkok. Designed to be modern and entertaining, the park incorporates the concept of using bubbles to add vibrancy and create a unique connection with nature, unlike any other place.