Air Rack is a product designed to address businesses with limited space and budget constraints for server rooms, cooling systems, and noise management. This system enables efficient use of IT equipment in open spaces, supporting both On-premise and On-cloud operations. It converts sensor data into digital information and displays it via a Dashboard, allowing users to monitor, analyze, and control the system remotely. Additionally, Air Rack significantly reduces power consumption and the costs associated with traditional server room management.
Air Rack เป็นผลิตภัณฑ์ที่ออกแบบมาเพื่อธุรกิจจำนวนมากไม่มีพื้นที่ หรืองบประมาณในการสร้างห้องเซริฟ์เวอร์, ระบบระบายความร้อน และการจัดการเสียงรบกวน ซึ่งเป็นผลมาจากการนำอุปกรณ์ไอทีออกมาในที่โล่ง ใช้งานรวมกับระบบแสดงผลและเก็บข้อมูลแบบออนไลน์ ระบบถูกออกแบบมาให้ทำหน้าที่เป็นเครื่องมือที่สามารถเก็บข้อมูลได้อย่างละเอียดและแม่นยำ ทั้งแบบ On-premise และ On Cloud ก่อนจะประมวลผลโดยแปลงสัญญาณจากเซ็นเซอร์ให้เป็นข้อมูลดิจิตอล เพื่อส่งตรงสู่หน้าจอคอมพิวเตอร์ในรูปแบบของ Dashboard ที่ประกอบไปด้วยแผนภูมิ (Charts) เกจ (Gauges) LEDs ตาราง และอื่นๆ ให้คุณสามารถควบคุมกระบวนการ ติดตาม ตรวจสอบ วิเคราะห์ และสั่งงานได้จากระยะไกล
คณะวิศวกรรมศาสตร์
The Thai Sign Language Generation System aims to create a comprehensive 3D modeling and animation platform that translates Thai sentences into dynamic and accurate representations of Thai Sign Language (TSL) gestures. This project enhances communication for the Thai deaf community by leveraging a landmark-based approach using a Vector Quantized Variational Autoencoder (VQVAE) and a Large Language Model (LLM) for sign language generation. The system first trains a VQVAE encoder using landmark data extracted from sign videos, allowing it to learn compact latent representations of TSL gestures. These encoded representations are then used to generate additional landmark-based sign sequences, effectively expanding the training dataset using the BigSign ThaiPBS dataset. Once the dataset is augmented, an LLM is trained to output accurate landmark sequences from Thai text inputs, which are then used to animate a 3D model in Blender, ensuring fluid and natural TSL gestures. The project is implemented using Python, incorporating MediaPipe for landmark extraction, OpenCV for real-time image processing, and Blender’s Python API for 3D animation. By integrating AI, VQVAE-based encoding, and LLM-driven landmark generation, this system aspires to bridge the communication gap between written Thai text and expressive TSL gestures, providing the Thai deaf community with an interactive, real-time sign language animation platform.
คณะวิทยาศาสตร์
Bacteriocins are microbial peptides that demonstrate potency against pathogens. This study evaluated the inhibitory effects on pathogens and characterized the bacteriogenomic profile of strain TKP1-5, isolated from the feces of Anas platyrhynchos domesticus. Strain TKP1-5 was characterized using phenotypic traits, 16S rRNA sequencing, and Whole-Genome Sequencing (WGS). It exhibited growth in the presence of 2-6% NaCl, temperatures of 25-45°C, and pH levels ranging from 3 to 9. Based on ANIb, ANIm, and dDDH values, strain TKP1-5 was identified as Lactococcus lactis. Whole genome analysis revealed that strain TKP1-5 harbors the Nisin Z peptide gene cluster with a bit-score of 114.775. The antimicrobial spectrum of bacteriocin TKP1-5 showed inhibitory effects against pathogenic bacteria including Pediococcus pentosaceus JCM5885, Listeria monocytogenes ATCC 19115, Enterococcus faecalis JCM 5803T, Salmonella Typhimurium ATCC 13311ᵀ, Aeromonas hydrophila B1 AhB1, Streptococcus agalactiae 1611 and Streptococcus cowan I. Genomic analysis confirmed L. lactis TKP1-5 as a non-human pathogen without antibiotic resistance genes or plasmids. Furthermore, L. lactis TKP1-5 contains potential genes associated with various probiotic properties and health benefits. This suggests that L. lactis TKP1-5, with its antibacterial activity and probiotic potential, could be a promising candidate for further research and application in the food industry.
คณะวิทยาศาสตร์
The purpose of this study was to examine and analyze the factors influencing household energy expenditures in Thailand. With sample group of 57,600 households. The findings reveal that the majority of the sample population is male, with an average age of 54.31 years, and most are married. The majority have an education level of primary or secondary school and are primarily Own-account worker (without employee), Private company employee or engaged in other job. In terms of social characteristics, the average household size is 2.71 people. Most residences are located in the Central, Northeastern, and Northern regions with similar proportions, followed by the Southern region and Bangkok, respectively. Most type of dwelling in detached houses, with materials of construction being cement or brick, followed by half concrete and wood. Regarding tenure, almost own dwelling and land, with an average of 2.88 rooms per household. Electricity is available in all households, with an average of 2.30 vehicles per household and an average of 22 electrical appliances per household. Regarding economic characteristics, most respondents have government/state enterprise welfare and receive benefits from the government programs. The majority have never borrow money from government funds. The average communication services of respondents amount to 788.46 THB, while the average household debt stands at 4,760.74 THB. At a significance level of 0.05, the factors influencing household energy expenditures in Thailand include gender, education level, marital status, job, household size, residential region, type of dwelling, material of construction, tenure, number of rooms, number of vehicles, number of electrical appliances, welfare of medical services, receive benefits from the government programs, borrow money from government funds, communication services, and household debt. However, age does not affect household energy expenditures in Thailand. The results of multiple linear regression analysis indicate that six quantitative independent variables—communication services, number of household electrical appliances, number of vehicles in the household, household debt, number of rooms, and household size—explain variations in household energy expenditures, with an Adjusted R Square value of 0.561.