The project of game development to educate about Egyptian civilization This is intended to be a game to promote learning about ancient Egyptian civilization. The original learning may feel boring. Do not attract the attention of learners Therefore saw the presentation in the form of a game on virtual reality technology that inserts knowledge history With the aim of making learning more interesting The organizers can choose to use Unreal Engine 5.1 (Unreal Engine 5.1) and Oculus Quest II (Oculus Quest2) to develop. Within the game, players must find a way to escape from this room within the specified period of solving puzzles in various forms such as statues, traps, etc. In order to solve puzzles and leave this room, players must collect all the information inside the room. In addition, the game shows the life of the player and when the player cannot solve the puzzle correctly.
อารยธรรมอียิปต์โบราณเป็นหนึ่งในอารยธรรมอันยิ่งใหญ่ของโลกยุคโบราณที่เป็นต้นกำเนิดของศาสตร์หลายๆแขนง ชาวอียิปต์โบราณถือเป็นนักสร้างสรรค์อารยธรรมทั้งด้านคณิตศาสตร์ และสถาปัตยกรรมอันโดดเด่น เพื่อศึกษาประวัติศาสตร์ซึ่งเป็นรากเหง้าของศาสตร์หลากหลายแขนงนั้นอาจต้องใช้เวลาและแหล่งข้อมูลจากหลายแหล่ง และมักจะเกิดความเบื่อหน่ายขึ้นได้ง่าย เพื่อที่ทำให้การศึกษาประวัติศาตร์เหล่านั้นไม่น่าเบื่อจนเกินไป ผู้จัดทำจึงได้นำเกมแนวไขปริศนาซึ่งสร้างขึ้นออกมาเป็นเสมือนกลไกในห้องลับเพื่อที่จะเสริมสร้างวิธีการแก้ไขปัญหาและได้ค้นหา เรียนรู้ประวัติศาสตร์ไปพร้อมๆกัน ผู้จัดทำเล็งเห็นว่าเทคโนโลยีความเป็นจริงเสมือน หรือ VR (Virtual Reality) สามารถดึงความสนุก และเพิ่มความน่าสนใจของตัวเกมจากผู้เล่นในการแก้ไขปริศนาเพื่อหาวัตถุโบราณและนำข้อมูลที่ได้พบนั้นมาตอบคำถามเพื่อเรียนรู้ประวัติศาสตร์ของอารยธรรมอียิปต์ได้เป็นอย่างดี
คณะวิทยาศาสตร์
Currently, climate change and human activities are causing rapid deterioration of coral reefs worldwide. Monitoring coral health is essential for marine ecosystem conservation. This project focuses on developing an Artificial Intelligence (AI) model to classify coral health into four categories: Healthy, Bleached, Pale, and Dead using Deep Learning techniques. With pre-trained convolutional neural network (CNN) for image classification. To improve accuracy and mitigate overfitting, 5-fold Cross-Validation is employed during training, and the best-performing model is saved. The results of this project can be applied to monitor coral reef conditions and assist marine scientists in analyzing coral health more efficiently and accurately. This contributes to better conservation planning for marine ecosystems in the future.
คณะแพทยศาสตร์
Background: The RGL3 gene plays a role in key signal transduction pathways and has been implicated in hypertension risk through the identification of a copy number variant deletion in exon 6. Genome-wide association studies have highlighted RGL3 as associated with hypertension, providing insights into the genetic underpinnings of the condition and its protective effects on cardiovascular health. Despite these findings, there is a lack of data that confirms the precise role of RGL3 in hypertension. Additionally, the functional impact of certain variants, particularly those classified as variants of uncertain significance, remains poorly understood. Objectives: This study aims to analyze alterations in the RGL3 protein structure caused by mutations and validate the location of the ligand binding sites. Methods: Clinical variants of the RGL3 gene were obtained from NCBI ClinVar. Variants of uncertain significance and likely benign were analyzed. Multiple sequence alignment was conducted using BioEdit v7.7.1. AlphaFold 2 predicted the wild-type and mutant 3D structures, followed by quality assessment via PROCHECK. Functional domain analysis of RasGEF, RASGEF_NTER, and RA domains was performed, and BIOVIA Discovery Studio Visualizer 2024 was used to evaluate structural and physicochemical changes. Results: The analysis of 81 RGL3 variants identified 5 likely benign and 76 variants of uncertain significance (VUS), all of which were missense mutations. Structural modeling using AlphaFold 2 revealed three key domains: RasGEF_NTER, RasGEF, and RA, where mutations induced conformational changes. Ramachandran plot validation confirmed 79.7% of residues in favored regions, indicating an overall reliable structure. Moreover, mutations within RasGEF and RA domains altered polarity, charge, and stability, suggesting potential functional disruptions. These findings provide insight into the structural consequences of RGL3 mutations, contributing to further functional assessments. Discussion & Conclusion: The identified RGL3 mutations induced physicochemical alterations in key domains, affecting charge, polarity, hydrophobicity, and flexibility. These changes likely disrupt interactions with Ras-like GTPases, impairing GDP-GTP exchange and cellular signaling. Structural analysis highlighted mutations in RasGEF and RA domains that may interfere with activation states, potentially affecting protein function and stability. These findings suggest that mutations in RGL3 could have functional consequences, emphasizing the need for further molecular and functional studies to explore their pathogenic potential.
คณะวิศวกรรมศาสตร์
This research suggested natural hemp fiber-reinforced ropes (FRR) polymer usage to reinforce recycled aggregate square concrete columns that contain fired-clay solid brick aggregates in order to reduce the high costs associated with synthetic fiber-reinforced polymers (FRPs). A total of 24 square columns of concrete were fabricated to conduct this study. The samples were tested under a monotonic axial compression load. The variables of interest were the strength of unconfined concrete and the number of FRRlayers. According to the results, the strengthened specimens demonstrated an increased compressive strength and ductility. Notably, the specimens with the smallest unconfined strength demonstrated the largest improvement in compressive strength and ductility. Particularly, the compressive strength and strain were enhanced by up to 181% and 564%, respectively. In order to predict the ultimate confined compressive stress and strain, this study investigated a number of analytical stress–strain models. A comparison of experimental and theoretical findings deduced that only a limited number of strength models resulted in close predictions, whereas an even larger scatter was observed for strain prediction. Machine learning was employed by using neural networks to predict the compressive strength. A dataset comprising 142 specimens strengthened with hemp FRP was extracted from the literature. The neural network was trained on the extracted dataset, and its performance was evaluated for the experimental results of this study, which demonstrated a close agreement.