Eco Grow Pellets are high-porosity plant-growing clay pellets made from ceramic industrial sediment, blended with ground chicken bone to enhance calcium and essential minerals, promoting strong and healthy plant growth. They are suitable for all types of plants, especially those requiring well-aerated soil with good water drainage. Eco Grow Pellets are an innovative clay-based growing medium designed to optimize plant cultivation efficiency. Their high porosity structure allows for excellent air and water circulation, reducing soil compaction and waterlogging—common causes of root rot and stunted growth. Additionally, the pellets are enriched with calcium and essential minerals from ground chicken bones, reinforcing plant structure and enhancing root strength, enabling better nutrient absorption. This product is made from 100% recycled ceramic industrial sediment, aligning with the principles of Zero Waste and the BCG Economy Model. It helps minimize industrial waste while transforming discarded materials into high-value, eco-friendly growing media. Eco Grow Pellets are ideal for vegetables, flowers, and potted plants, offering ease of use, cleanliness, and safety. They contribute to sustainable agriculture by improving both crop productivity and environmental health.
ผลิตภัณฑ์ “Eco Grow Pellet” เป็นเม็ดดินเผาน้ำหนักเบา ซึ่งผลิตจากวัสดุเหลือทิ้งในอุตสาหกรรมเซรามิกส์ และพัฒนาเพื่อเพิ่มมูลค่าสูงสุดให้กับวัสดุเหลือใช้ โดยสอดคล้องกับแนวคิด Zero Waste และหลักการของ BCG Economy Model ที่เน้นการใช้ทรัพยากรให้คุ้มค่าในทุกช่วงของวงจรชีวิต นอกจากนี้ โครงการยังเพิ่มปุ๋ยจากกระดูกไก่ ซึ่งเป็นแหล่งแคลเซียมและฟอสฟอรัสที่สำคัญ เพื่อเสริมคุณค่าทางอาหารสำหรับพืช และช่วยลดการพึ่งพาปุ๋ยเคมี โดยเน้นกระบวนการผลิตที่ยั่งยืนและเป็นมิตรต่อสิ่งแวดล้อม
วิทยาลัยเทคโนโลยีและนวัตกรรมวัสดุ
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คณะวิทยาศาสตร์
With the development of space technology, wide-field sky surveys using telescopes have expanded the range of new data available for time-domain astronomical research. Traditional data analysis methods can no longer respond quickly and accurately enough to the growing volume of data. Thus, classifying time-series data, such as light curves, has become a significant challenge in the era of big data. In modern times, analyzing light curves has become essential for using machine learning techniques to handle and filter through massive amounts of data. Machine learning algorithms can be divided into two categories: shallow learning and deep learning. Numerous researchers have proposed and developed a variety of algorithms for light curve classification. In this study, we experimented with Support Vector Machine (SVM) and XGBoost, which are shallow machine learning algorithms, as well as 1D-CNN and Long Short-Term Memory (LSTM), which are deep learning algorithms, which are branches of deep machine learning, to classify variable stars. The training and testing data used in this study were from the Optical Gravitational Lensing Experiment-III (OGLE-III), consisting of variable star data from the Large Magellanic Cloud (LMC), categorized into five main classes: Classical Cepheids, δ Scutis, eclipsing binaries, RR Lyrae stars, and Long-period variables. The results demonstrate the performance analysis of each machine learning algorithm type applied to light curve data, while also highlighting the accuracy and statistical metrics of the algorithms used in the experiments.
คณะวิศวกรรมศาสตร์
The Diabetes Meal Management Application is a digital health tool designed to empower Type 2 diabetic patients in managing their diet and blood sugar levels more effectively. With features like personalized meal recommendations, nutrient tracking, and seamless integration with wearable blood glucose monitors via Blood sugar measuring device (CGM), the application enables users to monitor glucose fluctuations in real time and adjust dietary choices accordingly. Built with the Flutter framework and supported by a backend of Express.js and MongoDB, the application prioritizes a user-friendly interface, ensuring easy navigation and encouraging consistent engagement with meal planning and health tracking. Preliminary user trials show that the application contributes to more stable blood sugar levels and improved adherence to dietary recommendations, helping users reduce health risks associated with diabetes complications. By offering a proactive approach to diabetes management, the application reduces the need for frequent clinical interventions, thus potentially lowering medical costs over time. This project highlights the promising role of digital health solutions in supporting personalized diabetes care, emphasizing the potential for scalable, user-centered interventions that foster long-term health improvements for diabetic patients.