The research on improving the strength of solid electrolytes aims to enhance the properties of solid electrolyte materials produced from cement and additives that help develop the cement structure to generate electricity. The main components include sodium chloride (NaCl) and graphite, which contribute to the material’s ability to generate a weak electrical current. The objective is to develop an electricity-generating flooring material. This study involves preparing a mixture of cement, water, sodium chloride (NaCl), and graphite to enhance the material’s electrical conductivity. It is highly anticipated that this research will lead to the development of concrete flooring capable of generating electricity and can be further expanded for future applications.
ในปัจจุบัน ความต้องการใช้พลังงานไฟฟ้าเพิ่มขึ้นนอย่างต่อเนื่อง ส่งผลให้เกิดการพัฒนาเทคโนโลยีและนวัตกรรมใหม่ ๆ เพื่อเพิ่มแหล่งพลังงานทางเลือกที่มีความยั่งยืนและเป็นมิตรต่อสิ่งแวดล้อม หนึ่งในแนวทางที่ได้รับความสนใจ คือการพัฒนาวัสดุที่สามารถผลิตและกักเก็บพลังงานไฟฟ้าได้ในตัวเอง ซึ่งสามารถนำไปใช้ในโครงสร้างพื้นฐานต่าง ๆเช่น พื้นทางเดิน อาคาร หรือพื้นที่สาธารณะ ดังนั้น งานวิจัยนี้จึงมีเป้าหมายเพื่อพัฒนาและปรับปรุงคุณสมบัติของเซลล์อิเล็กโทรไลต์ชนิดแข็งที่มีโครงสร้างพื้นฐานจากซีเมนต์ โดยมุ่งเน้นการเพิ่มความแข็งแรงของวัสดุควบคู่ไปกับการรักษาคุณสมบัติการนำไฟฟ้า เพื่อให้สามารถนำไปใช้งานเป็นวัสดุปูพื้นที่สามารถผลิตกระแสไฟฟ้าได้ งานวิจัยนี้คาดหวังว่าจะเป็นแนวทางสำคัญในการ พัฒนาวัสดุก่อสร้างสามารถต่อยอดไปสู่การประยุกต์ใช้ในอนาคตได้อย่างมีประสิทธิภาพ
คณะวิทยาศาสตร์
Sugar production from sugarcane is a complex process that requires precise control. One of the major issues is sugar loss, which can result from various factors, particularly "burnt cane," before being sent to the mill. This affects the quality of the sugarcane and the efficiency of sugar extraction, along with the performance of the machinery and the properties of the cane, which impact the amount of sugar extracted. This study aims to analyze the factors that influence sugar loss in the sugar production process, using quantitative data from a sugar factory. Nine variables were examined, including mechanical efficiency, machine downtime per day, cane waiting time per day, sand content in cane juice, pol extraction efficiency, overall working time efficiency, cane juice purity, cane sugar content (C.C.S.), and burnt cane. The data were analyzed using correlation analysis to examine relationships between variables and regression modeling to predict sugar loss. The results showed that mechanical efficiency, cane sugar content, and the amount of sand or impurities in the cane juice were significantly correlated with sugar loss. Mechanical efficiency had a direct relationship with the amount of cane milled, which improved sugar production. On the other hand, burnt cane, or cane that was burnt before harvesting, resulted in reduced sugar extraction and impacted the quality of the sugar. Therefore, reducing sugar loss in the production process can be achieved by improving machine efficiency, reducing impurities in cane juice, and managing burnt cane, which will improve sugar production efficiency in the future.
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.
วิทยาเขตชุมพรเขตรอุดมศักดิ์
This research focuses on the design and development of a prototype Artificial Intelligence of Things (AIoT) system for monitoring and controlling irrigation using weather information. The system consists of four main components: 1) Weather Station – This component includes various sensors such as air temperature, relative humidity, wind speed, and sunlight duration, among others, to collect real-time weather data. 2) Controller Unit – This unit is equipped with machine learning algorithms or models to estimate the reference evapotranspiration (ETo) and calculate the plant’s water requirement by integrating the crop coefficient (Kc) with other plant-related data. This enables the system to determine the optimal irrigation amount based on plant needs automatically. 3) User Interface (UI) and Display – This section allows farmers or users to input relevant information, such as plant type, soil type, irrigation system type, number of water emitters, planting distance, and growth stages. It also provides a display for monitoring and interaction with the system. 4) Irrigation Unit – This component is responsible for controlling the water supply and managing the irrigation emitters to ensure efficient water distribution based on the calculated requirements.