A high-pressure gas storage tank made from composite materials, including carbon fiber, resin, and plastic, is designed for storing compressed natural gas (CNG) or hydrogen. This type of tank is classified as a Type IV high-pressure vessel. In this research, it is designed to operate at a pressure of 250 bar for the transportation of compressed natural gas.
การบรรจุก๊าซที่ความดันสูงต้องใช้บรรจุภัณฑ์ที่มีความแข็งแรง การใช้ถังที่ผลิตจากโลหะถูกนำมาใช้ในช่วงแรกๆ แต่ปัญหาที่ตามมาคือน้ำหนักมากทำให้สิ้นเปลืองเชื้อเพลิงในกรณีที่นำไปติดตั้งบนยานพาหนะ จึงเกิดการพัฒนาถังความดันที่พัฒนาจากวัสดุประกอบขึ้นซึ่งจะช่วยลดน้ำหนักของบรรจุภัณฑ์และการกัดกร่อนเกิดขึ้นน้อย
คณะวิศวกรรมศาสตร์
This research aims to investigate the adulteration of Khao Dawk Mali 105 rice based on storage age using Near-Infrared Spectroscopy (NIRS) with Fourier Transform Near-Infrared Spectroscopy (FT-NIR) in the wavenumber range of 12,500 – 4,000 cm-1 (800 – 2,500 nm). Storage duration significantly impacts the quality of cooked rice. This research is divided into two parts: 1) to investigate the feasibility of separating rice according to storage age (1, 2, and 3 years) using the best model created by an Ensemble method combined with Second Derivative, which achieved an accuracy of 96.3%. 2) To investigate adulteration based on storage age by adulterating at 0% (all 2- and 3-year-old rice), 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% (all 1-year-old rice). The best model was created using Gaussian Process Regression (GPR) combined with Smoothing + Multiplicative Scatter Correction (MSC), with coefficients of determination (r²), root mean square error of prediction (RMSEP), bias, and prediction ability (RPD) values of 0.92, 8.6%, 0.9%, and 3.6 respectively. This demonstrates that the adulteration model can be applied to separate rice by storage age (1, 2, and 3 years). Additionally, the color values of rice with different storage ages show differences in L* and b* values.
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
In a highly competitive business, understanding customers is crucial for an organization to determine its success. Effective marketing is not just about offering good products, promotions, or services; it also requires strategies to reach and build strong relationships with customer groups. Segmenting customers is one method that helps businesses deeply understand the needs and behaviors of the customers who use their services In this internship, the objective is to understand the behavior of customers purchasing coffee and tea at a large cafe group by analyzing stored customer data. As a result of this process, customer groups purchasing coffee and tea were segmented using Naive Bayes, Random Forest, and Deep Learning techniques to compare the accuracy and suitability of different Machine Learning methods, and the insights gained from this analysis can be for further development in analyzing other data set in the future