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
A new colorimetric assay for the rapid detection of tannic acid in beverage samples based on displacement phenomenon of aggregated gallic acid-modified platinum nanoparticles is developed for the first time. PtNPs were functionalized with gallic acid, promoting the formation of the green-hued aggregated nanoparticles. While colorimetry offers a rapid method for identifying tannic acid, challenges remain in sensitivity and accuracy of detection on the PtNPs colorimetric probe, particularly in the presence of anthocyanin interferences. To address this, we developed a sample preparation method to degrade anthocyanin in beverages. Tannic acid was easily displaced onto the gallic acid-coated PtNPs surfaces, causing dispersion and resulting in a visible color change from green to orange−brown. Under the optimal conditions, the colorimetric sensor exhibited a linear response in the range of 1−2,000 µmol L−1 (R2 = 0.9991). The limit of detection (LOD) and the limit of quantification (LOQ) were found at 0.02 and 0.09 µmol L−1, respectively. The proposed sensor expressed superior selectivity over other interfering substances and demonstrated excellent precision with a relative standard deviation (RSD) of 1.00%−3.36%. More importantly, recoveries ranging from 95.0−104.7% were obtained, indicating the capability of proposed colorimetric sensor to detect tannic acid rapidly and accurately in real beverage samples.
คณะวิศวกรรมศาสตร์
This thesis project was conducted to identify the optimal conditions for producing concentrated butterfly pea juice using vacuum evaporation to preserve key compounds in butterfly pea flowers, such as anthocyanins—natural pigments with high antioxidant properties. The study applies a Box-Behnken Design, a statistical method that facilitates analysis of multiple factors. The research focuses on the ratio of dried butterfly pea flowers to water, extraction temperature, and evaporation temperature, each of which has a direct effect on the preservation of key compounds, color, aroma, and flavor. The results indicate that using a dried flower-to-water ratio of 1:15, an extraction temperature of 60°C, and an evaporation temperature of 40°C under low pressure can minimize the loss of essential compounds and best retain the properties of the concentrated butterfly pea juice. Findings from this research provide a foundation for developing an industrial production process for concentrated butterfly pea juice and enhance the potential for creating new products from butterfly pea flowers.