From the current situation and uncertainty; leads to the concept of food security. It is the application of innovation and technology to create high productivity in a limited area. The unused buildings in urban areas were renovated for planting, created as a learning area for planting in urban area. The different methods of growing plants were presented. There are 35 planting innovations for disseminating knowledge, to create food security, self-reliant, supports sustainable living.
จากสถานการณ์ในปัจจุบัน ปัญหาการเปลี่ยนแปลงสภาพภูมิอากาศ ภัยพิบัติ โรคระบาด การเพิ่มขึ้นของประชากร ขยายตัวของเมืองอย่างรวดเร็ว นำมาสู่แนวคิดความมั่นคงทางอาหาร ข้อจำกัดของการปลูกพืชในเมือง และแนวคิด BCG Economy Model เป็นการนำเอานวัตกรรมและเทคโนโลยีเข้ามาปรับใช้เพื่อให้เกิดการให้ผลผลิตที่มากในพื้นที่จำกัด โดยปรับปรุงอาคารเก่าในพื้นที่เมืองที่ไม่ถูกใช้งานมาปรับปรุงให้เหมาะสมกับการปลูกพืช ในพื้นที่ของสำนักงานการวิจัยแห่งชาติ (วช) เป็นพื้นที่ต้นแบบส จัดทำเป็นพื้นที่เรียนรู้การปลูกพืชในเมือง นำเสนอวิธีการปลูกพืชแบบต่างๆ รวบรวมเป็นนวัตกรรมการปลูกพืชกว่า 35 รายการ สำหรับเผยแพร่ความรู้ให้กับนักเรียนนักศึกษา ชุมชน และผู้ที่สนใจ ไปจนถึงหน่วยงานรัฐ เพื่อสร้างความมั่นคงทางอาหาร พึ่งพาตนเองได้ รองรับการอยู่อาศัยอย่างยั่งยืน
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.
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.
คณะเทคโนโลยีการเกษตร
Durian is a crucial economic crop of Thailand and one of the most exported agricultural products in the world. However, producing high-quality durian requires maintaining the health of durian trees, ensuring they remain strong and disease-free to optimize productivity and minimize potential damage to both the tree and its fruit. Among the various diseases affecting durian, foliar diseases are among the most common and rapidly spreading, directly impacting tree growth and fruit quality. Therefore, monitoring and controlling leaf diseases is essential for preserving durian quality. This study aims to apply image analysis technology combined with artificial intelligence (AI) to classify diseases in durian leaves, enabling farmers to diagnose diseases independently without relying on experts. The classification includes three categories: healthy leaves (H), leaves infected with anthracnose (A), and leaves affected by algal spot (S). To develop the classification model, convolutional neural network (CNN) algorithms—ResNet-50, GoogleNet, and AlexNet—were employed. Experimental results indicate that the classification accuracy of ResNet-50, GoogleNet, and AlexNet is 93.57%, 93.95%, and 68.69%, respectively.