This paper proposes a “Zero-Plastic Model” that focuses on the complete elimination and systematic replacement of plastic products. The model represents the deviation from the “reduce, reuse, recycle” framework, emphasizing prevention over treatment and introducing innovative technological solutions for plastic alternatives. The paper illustrates the limitations of existing recycling systems. The paper presents three aspects, including (1) the systematic reduction of plastic dependency through frameworks and behavioral interventions, (2) the development and implementation of sustainable replacement materials, and (3) the integration of technologies to facilitate this transition. The proposed “Zero Plastic Model” establishes sustainability frameworks while introducing a novel approach focused on complete plastic elimination rather than recycling. The framework integrates circular economy principles and draws inspiration from pre-plastic era solutions to achieve zero residual plastic in the system. The research contributes to the growing body of knowledge on sustainable practices and offers a practical framework for organizations and communities seeking to eliminate their plastic dependency.

With the growth of internet usage, countless educational videos are now available online. However, it can be a significant challenge for learners to identify the videos they need, especially in their preferred language and within their available time. Additionally, not all videos are suitable for subject-specific learning due to variations in length and presentation components. According to Sweller’s Cognitive Load Theory, working memory during the learning process is highly limited. Learners must be selective about which information from sensory memory they choose to focus on. In our proposed Co-Learning model (a model of connective learning where all necessary knowledge is refined and interconnected to support effective learning within cognitive limitations), we leverage NLP approaches to enhance the learning experience. These approaches include video speech refinement, subtitle generation, dubbed video translation, summarization, classification, keyword extraction for word cloud indexing, and quiz generation, thereby creating a multilingual, learner-efficient environment. In our preliminary survey, the generated content was well-received and effectively utilized for class adjustments with an acceptance rate of 93%.

Nurses play a crucial role in healthcare, directly influencing the quality of patient care. Facing a global nursing shortage, there is an urgent need for strategies to enhance nursing efficiency and care quality. This foundational study explores an NLP-based approach to determine NANDA nursing diagnoses, leveraging both subjective and objective patient data recorded by nurses. Employing text data similarity analysis and a prototype predictive model, our research aims to refine the nursing assessment process and pave the way for the potential automation of nursing diagnoses. This work highlights the potential of AI to support nursing practices and sets a platform for future research to fully realize AI’s benefits in addressing the challenges posed by the nursing shortage.

Challenged by data-driven AI limitations in reasoning and knowledge depth, this work presents a novel approach for enhanced conversational understanding. We leverage advanced text analysis to strategically extract key information from FAQs, then utilize AI-generated questions and robust semantic similarity metrics to significantly improve user query matching precision. Through the strategic integration of important sentence extraction in knowledge preparation, coupled with question generation and the application of semantic textual similarity measures, our model achieves a substantial improvement in user query matching precision. We propose a dual-system architecture—augmenting System 1 with additional knowledge akin to System 2 in human cognition. The methodology is exemplified through chatbot correction using FAQs, demonstrating the potential for human-like mind processing. Results showcase improved semantic understanding and reasoning, offering a promising path for advancing AI capabilities in conversational contexts.

The increasing elderly population necessitates increased geriatric care. However, a shortage of caregivers leads to a risk of falls and bedsores in the elderly, both of which result in severe injuries. Whilst wearable devices, and vision sensors have been adopted for monitoring. However, these sensors come with limitations, impacting comfort and privacy for the elderly. To address these challenges, non-intrusive sensing devices integrated into the environment offer promising value for continuous elderly activity monitoring. This study uses a panel sensor embedded with four sensors, consisting of two piezoelectric sensors and two pressure sensors. It is placed beneath the mattress. The position classification encompasses five distinct positions: off-bed, sitting, lying in the center, lying on the left side, and lying on the right side. To find the best position for placing the panel, the positions of the panel and the combination of panel sensors positions are evaluated for five-bed positions classification. As a result, the best position for a sensor panel was in the middle of the bed (position No. 3), with an accuracy of 97.12%. This suggests the panel sensor should be placed at 123.5 cm, measured from the top of the bed. Moreover, in the case of placing two-panel sensors, the most effective arrangement comprises placing one-panel sensor placed at the the bed-top (position No. 1) and the other in the middle of the bed (position No. 3), yielding accuracy 99.93%.

The Thammasat AI City distributed platform is a proposed AI platform designed to enhance city intelligent management. It addresses the limitations of current smart city architecture by incorporating cross-domain data connectivity and machine learning to support comprehensive data collection. In this study, we delve into two main areas, that is, monitoring and visualization of city ambient lighting, and indoor human physical distance tracking. The smart street light monitoring system provides real-time visualization of street lighting status, energy consumption, and maintenance requirement, which helps to optimize energy consumption and maintenance reduction. The indoor camera-based system for human physical distance tracking can be used in public spaces to monitor social distancing and ensure public safety. The overall goal of the platform is to improve the quality of life in urban areas and align with sustainable urban development concepts.

In this paper, we proposed a two-phase project on emotion corpus creation based on multi-knowledge of cognitive semantics, discourse analysis, paralinguistics, and computer science. Data were gathered from Thai lexicon of five main Thai dictionaries and thesaurus, in addition to written and spoken texts of people with depression in Thai and facial expression with speech situation. We found that semantic primes and features of each emotion were needed to serve as a guideline of emotion categorization in Thai context. We introduced the step-by-step methods of the first phase to create Thai emotion corpus entailing both verbal and nonverbal corpora. The way to classify emotion corpus by focusing on the specific text of depression as well as to find the guidelines of labelling facial expression in the situation of specific emotions was explored. Lastly, the step of creating emotion corpus in the second phase was introduced with some suggestions and discussion.

This research proposes an AI platform for data sharing across multiple domains. Since the data in the smart city concept are domain-specific processed, the existing smart city architecture is suffered from cross-domain data interpretation. To go beyond the digital transformation efforts in smart city development, the AI city is created on the architecture of cross-domain data connectivity and transform learning in the machine learning paradigm. In this research, the health and human behavioral data are targeted on human traceability and contactless technologies. To measure the inhabitants quality of life (QoL), the primary emotion expression study is conducted to interpret the emotional states and the mental health of people in the urbanized city. The results of information augmentation draw attention to the immersive visualization of the Thammasat model.

Computer programming is popularized in 21st century education in terms of allowing intensive logical thinking for students. Artificial Intelligent and robotic field is considered to be the most attractive for programming today. However, for the first-time learners and novice programmers, they may encounter a difficulty in understanding the text-based style programming language with its special syntax, sematic, libraries, and the structure of the program itself. In this work, we proposed a visual programming environment for artificial intelligent and robotic application using Google Blockly. The development framework is a web application which is capable of using Google Blockly to create a program and translate the result of visual programming style to conventional text-based programming. This allows almost instant programming capability for learners of programming in such a complex system.