Siamese fighting fish (Betta splendens) is an ornamental fish that is the first exported economically valuable fish in the country, but there is a limitation to increase the production of betta fish due to climate variability and the shortage of Thai workers. This research aims to develop 2 systems: a betta fish fry nursery system and a market-sized betta fish rearing system by using automated technology to precisely control the water quality in the system and reduce labor costs. Using precise automation consists of two systems: a minimal-waste system, which repurposes some of the waste generated from farming, and a zero-waste system, which treats and recycles all wastewater from farming. These systems aim to address issues related to water quality, animal welfare, and labor requirements in Betta fish farming. Experimental results show that these systems improve Betta fish survival rates by 10-15% compared to traditional methods. When considering net returns, the zero- waste system provides the highest profitability.
ในการเพาะเลี้ยงปลากัดประกอบด้วย ระบบอนุบาลลูกปลากัด เป็นการเลี้ยงลูกปลากัดในบ่อปูนซีเมนต์ขนาดเส้นผ่านศูนย์กลาง 60-80 เซนติเมตร มีการเพิ่มระดับน้ำแทนการเปลี่ยนถ่ายน้ำสัปดาห์ละ 5-10 เซนติเมตรจากระดับน้ำเดิม และการเลี้ยงปลาขนาดตลาด เป็นการเลี้ยงปลากัดในขวดแบนเรียงต่อกันจำนวนมากบนพื้นคอนกรีต มีการให้อาหารหรือเปลี่ยนถ่ายน้ำโดยใช้แรงงานคน 1 คนต่อการเลี้ยงปลากัด 10,000 – 40,000 ตัว ดังนั้นระบบอนุบาลและการเลี้ยงปลากัดขนาดตลาดแบบพัฒนาใหม่โดยใช้เทคโนโลยีในการควบคุมคุณภาพน้ำ การให้อาหารและการเปลี่ยนถ่ายน้ำด้วยระบบอัตโนมัติ จะช่วยแก้ปัญหาอัตราการตายของปลา การใช้แรงงาน และยังสามารถเพิ่มอัตราการรอดของลูกปลากัดได้อีกด้วย
คณะวิศวกรรมศาสตร์
Jaundice, a common condition in infants that results from high bilirubin levels in the blood, often requires early diagnosis and monitoring to prevent severe complications, especially in newborns. Traditional diagnostic methods can be time-consuming and subject to human error. This study proposes an approach for real-time jaundice detection using advanced image processing techniques and machine learning algorithms. By analyzing images captured in RGB color spaces, pixel values are extracted and processed through Otsu’s thresholding and morphological operations to detect color patterns indicative of jaundice. A classifier model is then trained to distinguish between normal and jaundiced conditions, offering an automated, accurate, and efficient diagnostic tool. The system’s potential to operate in real-time makes it particularly suited for clinical settings, providing healthcare professionals with timely insights to improve patient outcomes. The proposed method represents a significant innovation in healthcare, combining artificial intelligence and medical imaging to enhance the early detection and management of jaundice, reducing reliance on manual interventions and improving overall healthcare delivery.
คณะวิทยาศาสตร์
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คณะสถาปัตยกรรม ศิลปะและการออกแบบ
The " Center of Invention for Future and Sustainability Project (Continuing)" serves as a continuation of a pilot initiative focused on the retrofitting of older buildings (Vach. 7), specifically a five-story structure. The primary aim of this project is to develop methodologies for enhancing the sustainability of existing buildings in order to mitigate carbon dioxide emissions. In the execution of the Future and Sustainability Innovation Development Center Project (Continuing), a comprehensive analysis of relevant data and theoretical frameworks has been undertaken, leading to the formulation of a research methodology designed to identify optimal strategies for retrofitting older buildings to reduce carbon dioxide emissions. This approach is structured into three principal phases: the combustion of fuels associated with transportation, labor, and materials; the electricity consumption during the construction process; and the accumulation of greenhouse gases from both existing and new construction materials. The project employs an experimental research design, wherein empirical data is collected to evaluate and quantify the equivalent carbon dioxide emissions arising from the construction of new buildings compared to the retrofitting of the selected case study building. Subsequent analysis of the collected data revealed that retrofitting the existing structure—through the integration of sustainable design principles—resulted in greenhouse gas emissions of 11.88 kgCO2e/sq.m. In contrast, the emissions associated with new building construction amounted to 299.35 kgCO2e/sq.m., indicating a reduction in carbon dioxide emissions by a factor of approximately 26 when compared to the construction of new buildings.