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
คณะอุตสาหกรรมอาหาร
The Ginbanirose project aims to develop herbal extracts for alleviating menstrual pain using key ingredients: roselle, banana inflorescence, and ginger. These ingredients contain bioactive compounds with anti-inflammatory, antioxidant, and pain-relieving properties. The extracts are enhanced through liposome encapsulation technology, which improves absorption and stability. The production process involves herbal extraction, freeze-drying, and liposome formulation using lecithin and stabilizers. Experimental results demonstrate high phenolic content and antioxidant activity via the DPPH method. Ginbanirose addresses women’s quality of life concerns while offering significant business opportunities in the rapidly growing herbal market, particularly in the Asia-Pacific region.
คณะอุตสาหกรรมอาหาร
This research focuses on the development of mango powder using the foam-mat drying method, which is an effective technique for preserving the quality of fruit and vegetable products. Hydroxypropyl Methylcellulose (HPMC) was used as a foaming agent. The study evaluated the effects of HPMC on the chemical and physical properties, antioxidant activity, and shelf life of mango powder. The findings indicated that HPMC plays a crucial role in improving the foam stability before drying and enhancing the quality of the dried powder. This research provides a valuable approach to adding value to substandard mango yields and reducing agricultural waste. It also contributes to the development of high-nutritional processed food products with extended shelf life.
คณะเทคโนโลยีสารสนเทศ
This research presents a deep learning method for generating automatic captions from the segmentation of car part damage. It analyzes car images using a Unified Framework to accurately and quickly identify and describe the damage. The development is based on the research "GRiT: A Generative Region-to-text Transformer for Object Understanding," which has been adapted for car image analysis. The improvement aims to make the model generate precise descriptions for different areas of the car, from damaged parts to identifying various components. The researchers focuses on developing deep learning techniques for automatic caption generation and damage segmentation in car damage analysis. The aim is to enable precise identification and description of damages on vehicles, there by increasing speed and reducing the work load of experts in damage assessment. Traditionally, damage assessment relies solely on expert evaluations, which are costly and time-consuming. To address this issue, we propose utilizing data generation for training, automatic caption creation, and damage segmentation using an integrated framework. The researchers created a new dataset from CarDD, which is specifically designed for cardamage detection. This dataset includes labeled damages on vehicles, and the researchers have used it to feed into models for segmenting car parts and accurately labeling each part and damage category. Preliminary results from the model demonstrate its capability in automatic caption generation and damage segmentation for car damage analysis to be satisfactory. With these results, the model serves as an essential foundation for future development. This advancement aims not only to enhance performance in damage segmentation and caption generation but also to improve the model’s adaptability to a diversity of damages occurring on various surfaces and parts of vehicles. This will allow the system to be applied more broadly to different vehicle types and conditions of damage inthe future