The study investigated the extraction of astaxanthin-rich oil from shrimp waste biomass, a valuable byproduct rich in functional lipids and proteins. Wet rendering has long been an inexpensive method to extract oil, however the high temperatures and long cooking times negatively affect the amount of astaxanthin. On the other hand, the study looked into employing deep eutectic solvent as a green solvent and combining a wet rendering process with high-shear homogenization and high-frequency ultrasound-assisted extractions. DES-UAE at 60% amplitude and wet rendering at 60 °C were found to be the ideal conditions, as were DES-HAE at 13,000 rpm and wet rendering at 60 °C. With a notable increase in oil yields of 16.80% and 20.12%, respectively, and improved oil quality (lower acid and peroxide values) in comparison to the conventional wet rendering, experimental validation validated the effectiveness of the DES-HAE and DES-UAE procedures. DES-UAE notably raised the amount of astaxanthin. This study demonstrates that DES-HAE and DES-UAE are quicker, lower-temperature substitutes for obtaining premium, astaxanthin-rich shrimp oil, resulting in more effective use of this priceless byproduct.
Sustainability and Waste Utilization: Upcycling shrimp byproducts into valuable oil helps ensure that seafood manufacturing is waste-free. Potential for Nutraceutical & Functional Food Applications
คณะศิลปศาสตร์
"Niyom Thai" represents health-centric footwear adorned with traditional Thai patterns, embodying an innovative approach to sustainable development tailored to the current needs of local communities. These shoes utilize natural materials to mitigate fatigue and integrate safety technologies, including location tracking via a mobile application and heart rate monitoring. This addresses the aspects of convenience and well-being in both daily life and travel
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
The design and construction of a detailed bathroom model with structural components aim to provide a comprehensive understanding of plumbing and electrical systems in bathrooms. This project enables learners to study the intricacies of bathroom infrastructure through a highly detailed model.
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