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
คณะเทคโนโลยีการเกษตร
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คณะวิทยาศาสตร์
In today’s rapidly expanding e-commerce environment, the massive volume of product reviews makes it crucial to summarize user opinions in a way that is both comprehensible and practically applicable. This research presents a system for analyzing product reviews using Aspect-Based Sentiment Analysis (ABSA), a Natural Language Processing (NLP) technique that identifies key aspects of a review (such as shipping, product quality, and packaging) and evaluates the sentiment (positive, negative, or neutral) associated with each aspect, allowing both consumers and merchants to gain more efficient access to in-depth insights. This project focuses on developing AI for Thai-language ABSA by utilizing WangchanBERTa, a model trained on Thai data, and comparing it with various standard approaches such as TF-IDF + Logistic Regression, Word2Vec + BiLSTM, and Multilingual BERT (mBERT/XLM-R) to assess their performance in terms of accuracy, speed, and resource usage. Additionally, a dashboard visualization is provided to help users quickly grasp review trends. The expected outcome is to create an AI tool that can be practically employed in the e-commerce industry, enabling consumers to make easier purchasing decisions and assisting merchants in effectively improving their products and services.
คณะวิทยาศาสตร์
Efficient logistics management requires the development of advanced tools to streamline delivery operations. This study aims to optimize vehicle routes for an animal feed store and develop a web-based application for route planning. The research compares two optimization methods: the Branch and Bound Method and Clustering with the Branch and Bound Method. These methods are evaluated against the store’s existing delivery route by analyzing differences in average travel distance across three dependent groups using Repeated Measures ANOVA. The findings reveal a statistically significant difference in average daily travel distances among the three methods at a 0.05 significance level. The Branch and Bound Method yields the shortest average daily travel distance. Additionally, pairwise comparisons of total daily travel distances using the Paired t-test confirm that the Branch and Bound Method produces the most efficient route with statistical significance at 0.05 level. Implementing this method can reduce total travel distance by 957.51, representing a 30.88% reduction, which translates into fuel cost savings of 2,579.45 THB per month. Based on these results, the Branch and Bound Method was selected for implementation in a web-based application. The application features an intuitive user interface, product inventory management, and optimized daily delivery route recommendations for the case study store. Following development, the web application was deployed and tested in real-world operations. The results demonstrate that it effectively provides map-based route recommendations, ensuring ease of use and accessibility on standard mobile devices.