This study aims to investigate the co-fermentation process between lactic acid bacteria (LAB) and Saccharomyces cerevisiae in the production of sour beer, with a focus on its impact on product quality, including pH, organic acid content, sugar content, and sensory characteristics. In this experiment, selected LAB strains and S. cerevisiae were utilized under controlled fermentation conditions. The microbial ratio was optimized to enhance growth and the production of key compounds. The findings indicate that co-fermentation significantly reduces pH compared to fermentation with yeast alone. Furthermore, an increase in lactic acid was observed due to sugar consumption by LAB, contributing to the distinctive flavor profile of sour beer.
เบียร์เปรี้ยว (Sour Beer) เป็นเบียร์ประเภทหนึ่งที่มีรสเปรี้ยวเฉพาะตัว ซึ่งเกิดจากกระบวนการหมักที่ต่างจากเบียร์ทั่วไป ความเปรี้ยวของ เบียร์เปรี้ยวเกิดจากการใช้แบคทีเรียกรดแลกติก (Lactic acid bacteria) และยีสต์เช่น Lactobacillus และ Pediococcus,ในการหมักเพิ่มทางเลือกให้กับผู้บริโภค Sour Beer เป็นตัวเลือกที่น่าสนใจสำหรับคนที่ต้องการประสบการณ์รสชาติใหม่ๆ แตกต่างจากเบียร์รสขมทั่วไป โดยมีทั้งรสเปรี้ยวที่เบาและรสเข้มข้น การหมักและกระบวนการผลิตเฉพาะทาง การทำ Sour Beer ต้องใช้ความรู้และเทคนิคพิเศษทำให้ต้องมีการควบคุมกระบวนการหมักอย่างใกล้ชิดซึ่งส่งเสริมการพัฒนาทักษะและนวัตกรรมในอุตสาหกรรมการผลิตเบียร์ ผู้ผลิตต้องเข้าใจลักษณะของจุลินทรีย์ที่ใช้และรู้จักการควบคุมรสชาติ ทำให้ Sour Beerมีความสำคัญในการพัฒนาอุตสาหกรรมเบียร์ไปสู่ความสร้างสรรค์ใหม่ๆ
คณะเทคโนโลยีสารสนเทศ
This research presents a deep learning method for generating automatic captions from the segmentation of car part damage. It analyzes car images using a Unified Framework to accurately and quickly identify and describe the damage. The development is based on the research "GRiT: A Generative Region-to-text Transformer for Object Understanding," which has been adapted for car image analysis. The improvement aims to make the model generate precise descriptions for different areas of the car, from damaged parts to identifying various components. The researchers focuses on developing deep learning techniques for automatic caption generation and damage segmentation in car damage analysis. The aim is to enable precise identification and description of damages on vehicles, there by increasing speed and reducing the work load of experts in damage assessment. Traditionally, damage assessment relies solely on expert evaluations, which are costly and time-consuming. To address this issue, we propose utilizing data generation for training, automatic caption creation, and damage segmentation using an integrated framework. The researchers created a new dataset from CarDD, which is specifically designed for cardamage detection. This dataset includes labeled damages on vehicles, and the researchers have used it to feed into models for segmenting car parts and accurately labeling each part and damage category. Preliminary results from the model demonstrate its capability in automatic caption generation and damage segmentation for car damage analysis to be satisfactory. With these results, the model serves as an essential foundation for future development. This advancement aims not only to enhance performance in damage segmentation and caption generation but also to improve the model’s adaptability to a diversity of damages occurring on various surfaces and parts of vehicles. This will allow the system to be applied more broadly to different vehicle types and conditions of damage inthe future
คณะวิศวกรรมศาสตร์
This Project has been undertaken to address the need for skill development and knowledge enhancement in pneumatic systems and automation control, which are crucial in today’s manufacturing industry. Pneumatic systems play a vital role in various production processes, including machine control, automated devices, and assembly lines. However, the Department of Measurement and Control Engineering currently lacks a laboratory dedicated to the study and experimentation of pneumatic systems due to the deterioration and lack of maintenance of the previously used equipment. This has resulted in students missing the opportunity to practice essential skills required in the industrial sector. The authors of this thesis recognize the necessity of reviving and developing a pneumatic laboratory that can effectively support teaching, learning, and research activities. This project focuses on studying and developing industrial robotic arm control systems and pneumatic systems, integrating modern technologies such as Programmable Logic Controllers (PLC) and AI Vision. These systems are intended to be applicable to real-world industrial contexts. The outcomes of this project are expected to not only enhance the understanding of relevant technologies but also aim to transform the laboratory into a vital learning hub for current and future students. Furthermore, this initiative seeks to improve the competitiveness of students in the job market and support the development of innovations in the manufacturing industry in the years to come.
คณะวิทยาศาสตร์
Air pollution, particularly PM2.5, is a major environmental and public health concern in Bangkok. Instead of predicting PM2.5 levels, this project aims to identify the most significant factors influencing PM2.5 concentration. By analyzing historical air quality, weather, and other environmental data, we will determine which variables—such as temperature, humidity, wind speed, or other pollutants—have the greatest impact on PM2.5 fluctuations.