Freshwater scarcity is a global crisis due to limited accessible freshwater resources and rising demand. Seawater desalination is a key solution but is energy-intensive and reliant on fossil fuels, leading to high costs and environmental impacts. This study aims to investigate the use of solar thermal energy from an evacuated tube collector for freshwater production via evaporation and condensation. The focus is on analyzing system efficiency by comparing freshwater yield with energy input. The findings may contribute to the development of sustainable desalination technologies suitable for freshwater-scarce regions.
ปัจจุบันโลกปกคลุมด้วยน้ำถึง 70% ของพื้นที่ทั้งหมด แต่มีทรัพยากรน้ำที่เป็นน้ำจืด (fresh water) ที่สามารถใช้อุปโภค บริโภคได้เพียง 3% ประกอบกับจำนวนประชากรที่เพิ่มสูงขึ้นอย่างต่อเนื่อง จึงเกิดวิกฤตการขาดแคลนน้ำ การแยกเกลือออกจากน้ำทะเลเป็นแนวทางสำคัญในการแก้ไขปัญหานี้ โดยงานวิจัยของเรามุ่งพัฒนาเทคโนโลยีแยกเกลือออกจากน้ำทะเลโดยใช้พลังงานแสงอาทิตย์ ผ่านระบบท่อสุญญากาศ (ETSC) เพื่อลดต้นทุน พึ่งพาพลังงานสะอาด และเพิ่มประสิทธิภาพการผลิตน้ำจืด โดยเฉพาะในพื้นที่แห้งแล้งที่ขาดแคลนน้ำ
คณะครุศาสตร์อุตสาหกรรมและเทคโนโลยี
This research confirms the potential of bamboo fiber as a sustainable raw material for the textile industry, demonstrating exceptional properties that meet both functional requirements and environmental friendliness. The study focuses on integrating sustainability concepts with material innovation, encompassing fiber property analysis, production process development, and product design. The research objectives were to: 1) develop the properties of bamboo fiber for production; 2) study factors in designing environmentally friendly textile products from bamboo fiber; and 3) forecast future prospects for environmentally friendly textile product design using bamboo fiber. The findings revealed that 60-day-old bamboo possessed optimal properties for fiber separation, with an average fiber size of 5.32 μm, smaller than other natural fibers, resulting in superior moisture absorption and ventilation properties. When blended with recycled polyester fiber in a 30:70 ratio, the yarn exhibited strength and unique tactile characteristics. Although the antibacterial properties against Staphylococcus aureus were low, the fibers demonstrated excellent whiteness and softness. Factor analysis identified four key components in product design: Local Materials, Green Products, Healthy, and Sustainability. Consumer satisfaction evaluation of the prototype products showed high levels of acceptance, with the model explaining 84.7% of consumer satisfaction. The developed production process reduced chemical usage and hazardous waste. Furthermore, utilizing fast-growing bamboo minimized long-term environmental impact, contributing to sustainable development in Thailand's rural communities across economic, environmental, and occupational stability dimensions. The research demonstrates that developing bamboo fiber blended with recycled polyester creates sustainable products that meet consumer demands for health consciousness, local material utilization, and green product promotion. Commercial implementation of these products can enhance economic value and promote environmentally friendly product development in the future.
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.
คณะบริหารธุรกิจ
This research aimed to develop the mixed tea from longan peels and seeds. Population studied were longan farmers who planted longan and preserved the longan product in Ampur Wang Nam Yen, Sa Kaeo Province. From the results, it was found that from By-product in the production of dehydrated longan, longan peels and seeds, which can be processed into ready-to-drink powdered tea. This not only helps reduce waste from the production process but also contributes to generating additional income from these by-products.