We investigated the application of artificial intelligence (AI) technology for the inspection of semiconductor process equipment to address key issues such as low production efficiency and high equipment failure rates. The semiconductor industry, being central to modern technology, requires complex and precise processes where even minor defects lead to product failures, negatively impacting yield and increasing costs. Traditional inspection methods are not adequate for modern high-precision, high-efficiency production demands. By integrating advanced AI technologies, such as machine learning, deep learning, and pattern recognition, large volumes of experimental data are collected and analyzed to optimize process parameters, enhance stability, and improve product yield. By using AI, the identification and classification of defects are automated to predict potential equipment failures and reduce downtime and overall costs. By combining AI with automated optical inspection (AOI) systems, a widely used defect detection tool has been developed for semiconductor manufacturing. However, under complex conditions, AOI systems are prone to producing false positives, resulting in overkill rates above 20%. This wastes perfect products and increases the cost due to the need for manual re-inspection, hindering production efficiency. This study aims to improve wafer inspection accuracy using AI technology and reduce false alarms and overkill rates. By developing intelligent detection models, the system automatically filters out false defects and minimizes manual intervention, boosting inspection efficiency. We explored how AI is used to analyze inspection data to identify process issues and optimize workflows. The results contribute to the reduction in labor and time costs, improving equipment performance, and significantly benefitting semiconductor production management. The AI-driven method can be applied to other manufacturing processes to enhance efficiency and product quality and support the sustainable growth of the semiconductor industry.

The effective use of AI-powered ChatGPT in higher education highlights its potential as a knowledge acquisition tool, yet it brings to the fore the less-explored area of user information satisfaction (UIS), a gap addressing the development of the UIS model tailored to ChatGPT. In this foundational research grounded in UIS theory, a model was proposed based on critical factors to guide educational stakeholders in investigating issues such as plagiarism and ethical use. By analyzing responses from a diverse group of Indonesian users employed in higher education and using Structural Equation Modeling with Smart-PLS 4.0, we identified five key determinants: completeness, precision, timeliness, convenience, and format. The determinants affect user satisfaction, while traditionally emphasized factors such as accuracy and reliability do not significantly impact the academic utilization of ChatGPT. The results advocate for reliance on ChatGPT with limitations in regard to accuracy and suggest a change in the academic use of technology. The results also offer a perspective that blends theoretical innovation with practical application in educational settings.

Although Western alien visitors to Taiwan have a unique, well-informed perspective of what appeals to tourists from their countries, little research has been conducted to assess their insights, representing a significant research gap. To fill this gap, this study aims to elucidate how international tourists perceive Taiwan as a tourist destination by collecting detailed qualitative data via in-depth interviews of twenty-four international expatriates. This study illustrated the image differences before and after the visit of international tourists by analyzing the dimensions of tourists’ consensus maps and the changes in their perception of the natural, social, cultural, gourmet food, and Aboriginal image of Taiwan. This study provides a better understanding of how international tourists in Taiwan view its current and potential appeal as a tourist destination, so as to identify ways of attracting new tourists from their cultures. Patterns emerged that suggest new approaches for promoting Taiwan tourism and tourism sustainability which might not occur to Taiwanese tourism professionals due to the interviewees’ unique points of view. This research concludes that the application of cultural promotion strategies, planned tour packages, and improvement of the image of nature, culture, society, food, and Aboriginal people in Taiwan will thereby improve the overall satisfaction of international tourists, building online image, and forming e-word-of-mouth.

In the face of pressing global challenges, coupling the synergy of dance arts, sustainability, and marketing communications emerges as a potent catalyst for advancing sustainable behavior within society. Although prior research has examined the emotional stimulations raised by dance performance, it has not systematically demonstrated the role of traditional dance as a vehicle for sustainability marketing communications. This study fills this gap by using the AIDA model (Attention, Interest, Desire, Action) as a foundation, conceptualizing dance performance elements into seven dimensions, and incorporating emotional engagement and environmental consciousness to foster sustainable behavior. Collaborating with Taiwanese dance troupe, the research produced a performance titled, ‘The New Dragon Palace Chronicle’, which focuses on the themes of destruction and preservation of underwater life, enriched with cultural and sustainability symbolism. The hypothesis were tested on a sample of 315 Taiwanese audience who watching the performance. The results confirmed that the seven dimensions of the dance elements have substantial validity and reliability, significantly drawing the attention of the audience as preliminary stage of awareness. This attention fosters emotional engagement and amplified environmental consciousness and significantly influences interest, desire, and ultimately, sustainable behavior action. The findings underscore the vital role of dance art performance in nurturing actions that contribute to social sustainability.

Integrating low-carbon design into products is crucial for reducing carbon emissions throughout their life cycle and promoting sustainable development. Addressing the uncertainty in the carbon footprint resulting from the unknown choice of product material solutions. This paper considers ABC (activity-based costing) along with the components’ carbon footprint and scrap return issues to illustrate the above challenge in a two-stage production-inventory system with imperfect processes. We determine the optimal production and sales strategies that maximize total profit per unit time. An algorithm is developed to identify these optimal solutions. To illustrate the effectiveness of the proposed model and algorithm, two numerical examples from the Taiwan die casting industry are presented. Additionally, a sensitivity analysis is conducted to provide valuable managerial insights.