Development of Hand Cream from Murraya Extract Using an Eco-Friendly Extraction Process. This research focuses on extracting active compounds from Murraya paniculata using a water-based, environmentally friendly method. The extract exhibits outstanding antibacterial properties and anti-oxidant. It is incorporated into a hand cream formulation.
ต้นแก้ว (Murraya paniculata) เป็นพืชสมุนไพรที่มีการใช้ประโยชน์ในด้านการแพทย์พื้นบ้านและมีรายงานถึงคุณสมบัติทางชีวภาพ เช่น ฤทธิ์ต้านแบคทีเรียและต้านอนุมูลอิสระ อย่างไรก็ตาม การนำสารสกัดจากต้นแก้วมาใช้ในผลิตภัณฑ์เครื่องสำอางยังมีข้อจำกัดด้านวิธีการสกัดที่ปลอดภัยและเป็นมิตรต่อสิ่งแวดล้อม โครงการนี้จึงมุ่งเน้นการพัฒนากระบวนการสกัดด้วยน้ำ และศึกษาคุณสมบัติเพื่อนำไปพัฒนาเป็นครีมบำรุงมือที่มีประสิทธิภาพและปลอดภัยต่อผู้ใช้
คณะวิศวกรรมศาสตร์
ยานยนต์ไฟฟ้าดัดแปลง
คณะเทคโนโลยีสารสนเทศ
Facial Expression Recognition (FER) has attracted considerable attention in fields such as healthcare, customer service, and behavior analysis. However, challenges remain in developing a robust system capable of adapting to various environments and dynamic situations. In this study, the researchers introduced an Ensemble Learning approach to merge outputs from multiple models trained in specific conditions, allowing the system to retain old information while efficiently learning new data. This technique is advantageous in terms of training time and resource usage, as it reduces the need to retrain a new model entirely when faced with new conditions. Instead, new specialized models can be added to the Ensemble system with minimal resource requirements. The study explores two main approaches to Ensemble Learning: averaging outputs from dedicated models trained under specific scenarios and using Mixture of Experts (MoE), a technique that combines multiple models each specialized in different situations. Experimental results showed that Mixture of Experts (MoE) performs more effectively than the Averaging Ensemble method for emotion classification in all scenarios. The MoE system achieved an average accuracy of 84.41% on the CK+ dataset, 54.20% on Oulu-CASIA, and 61.66% on RAVDESS, surpassing the 71.64%, 44.99%, and 57.60% achieved by Averaging Ensemble in these datasets, respectively. These results demonstrate MoE’s ability to accurately select the model specialized for each specific scenario, enhancing the system’s capacity to handle more complex environments.
คณะวิศวกรรมศาสตร์
One of the most important aspects of responding to a medical case is the response time. In general, most fatalities are due to the patient not being able to reach the hands of the doctor in time. This also includes the arrival of medical equipment to the scene. The human brain will start to degrade in function after 3 minutes of oxygen starvation which conventional road transportation method first responders presently use is usually unable to reach the site in this golden 3 minutes, resulting in fatalities during transport or before the arrival of first responders at the scene. Therefore, medical equipment transport by fully autonomous aircraft is explored. This is done through drone deliveries which is much quicker than road methods as the equipment could be flown straight to the site as it is not affected by traffic, road conditions, and navigation. In this project, we will explore an aerial delivery system for medical equipment such as Automatic External Defibrillators (AEDs), First aid equipment, and other small requested medical devices. This will be done through a DJI drone platform and their SDK application. The main goal for this project is to decrease the response time by using an autonomous aerial drone to deliver medical equipment.