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
คณะวิศวกรรมศาสตร์
This project has been developed to address medical challenges related to the process of counting and classifying blood cells from samples, a task that requires both time and high precision. To reduce the workload of medical personnel, the developers have created a platform and an artificial intelligence (AI) system capable of automatically classifying and counting cells from sample images. This system is designed to assist medical laboratory technicians by enabling them to work more efficiently and accurately, reducing the time required for analysis. Furthermore, it promotes the advancement of medical technology, ensuring effective usability from classrooms and laboratories to hospitals.
คณะวิศวกรรมศาสตร์
Design and Development of a Remote Battery Management System This research focuses on the design and development of a battery management system that enables remote monitoring and control, allowing users to customize battery cell properties as needed. The system is specifically designed for use with graphene battery cells and can be effectively applied to alternative energy systems for residential use.
คณะวิศวกรรมศาสตร์
The integration of intelligent robotic systems into human-centric environments, such as laboratories, hospitals, and educational institutions, has become increasingly important due to the growing demand for accessible and context-aware assistants. However, current solutions often lack scalability—for instance, relying on specialized personnel to repeatedly answer the same questions as administrators for specific departments—and adaptability to dynamic environments that require real-time situational responses. This study introduces a novel framework for an interactive robotic assistant (Beckerle et al. , 2017) designed to assist during laboratory tours and mitigate the challenges posed by limited human resources in providing comprehensive information to visitors. The proposed system operates through multiple modes, including standby mode and recognition mode, to ensure seamless interaction and adaptability in various contexts. In standby mode, the robot signals readiness with a smiling face animation while patrolling predefined paths or conserving energy when stationary. Advanced obstacle detection ensures safe navigation in dynamic environments. Recognition mode activates through gestures or wake words, using advanced computer vision and real-time speech recognition to identify users. Facial recognition further classifies individuals as known or unknown, providing personalized greetings or context-specific guidance to enhance user engagement. The proposed robot and its 3D design are shown in Figure 1. In interactive mode, the system integrates advanced technologies, including advanced speech recognition (ASR Whisper), natural language processing (NLP), and a large language model Ollama 3.2 (LLM Predictor, 2025), to provide a user-friendly, context-aware, and adaptable experience. Motivated by the need to engage students and promote interest in the RAI department, which receives over 1,000 visitors annually, it addresses accessibility gaps where human staff may be unavailable. With wake word detection, face and gesture recognition, and LiDAR-based obstacle detection, the robot ensures seamless communication in English, alongside safe and efficient navigation. The Retrieval-Augmented Generation (RAG) human interaction system communicates with the mobile robot, built on ROS1 Noetic, using the MQTT protocol over Ethernet. It publishes navigation goals to the move_base module in ROS, which autonomously handles navigation and obstacle avoidance. A diagram is explained in Figure 2. The framework includes a robust back-end architecture utilizing a combination of MongoDB for information storage and retrieval and a RAG mechanism (Thüs et al., 2024) to process program curriculum information in the form of PDFs. This ensures that the robot provides accurate and contextually relevant answers to user queries. Furthermore, the inclusion of smiling face animations and text-to-speech (TTS BotNoi) enhanced user engagement metrics were derived through a combination of observational studies and surveys, which highlighted significant improvements in user satisfaction and accessibility. This paper also discusses capability to operate in dynamic environments and human-centric spaces. For example, handling interruptions while navigating during a mission. The modular design allows for easy integration of additional features, such as gesture recognition and hardware upgrades, ensuring long-term scalability. However, limitations such as the need for high initial setup costs and dependency on specific hardware configurations are acknowledged. Future work will focus on enhancing the system’s adaptability to diverse languages, expanding its use cases, and exploring collaborative interactions between multiple robots. In conclusion, the proposed interactive robotic assistant represents a significant step forward in bridging the gap between human needs and technological advancements. By combining cutting-edge AI technologies with practical hardware solutions, this work offers a scalable, efficient, and user-friendly system that enhances accessibility and user engagement in human-centric spaces.