As social growth and educational concepts continue to evolve, college libraries, as hubs of cultural innovation and inheritance, are crucial in advancing the practice of great traditional culture aesthetic teaching. Based on the special status and resource advantages of college libraries, this paper explores the paths and approaches colleges libraries take in advancing the practice of aesthetic education of excellent traditional culture by combining the connotation and characteristics of excellent traditional culture. With a study of the research and case studies that concentrate on the planning of cultural events, the development of collection resources, and the use of digital innovation, it suggests a workable path. The goal is to give university libraries theoretical direction and useful references so they can carry out the aesthetic education of superior traditional culture.

Named Entity Recognition (NER), a core task in Information Extraction (IE) alongside Relation Extraction (RE), identifies and extracts entities like place and person names in various domains. NER has improved business processes in both public and private sectors but remains underutilized in government institutions, especially in developing countries like Indonesia. This study examines which government fields have utilized NER over the past five years, evaluates system performance, identifies common methods, highlights countries with significant adoption, and outlines current challenges. Over 64 international studies from 15 countries were selected using PRISMA 2020 guidelines. The findings are synthesized into a preliminary ontology design for Government NER.

Blockchain technology has increasingly attracted the attention of the financial service sector, customers, and investors because of its distinctive characteristics, such as transparency, security, reliability, and traceability. The paper is based on a Systematic Literature Review (SLR). The study comprehended the literature and the theories. It deployed the technology-organization-environment (TOE) model to consider technological, organizational, and environmental factors as antecedents of blockchain adoption intention. The paper contributes to blockchain literature by providing new insights into the factors that affect the intention to adopt blockchain technology. A theoretical model incorporates antecedents of blockchain adoption intention to direct an agenda for further investigations. Researchers can use the model proposed in this study to test the antecedents of blockchain adoption intention empirically.

Organizations are gradually focusing on creating a healthy workplace for their employees and becoming more people-centric. This occurs because a healthy workforce increases the work performance of the organisation and the personal development of its employees. This study aims to investigate the HR functions that impact employee motivation in the Malaysian banking sector. The three HR functions that were selected were training and development, rewards and recognition, and career management. The study utilised a cross-sectional design, and the research instruments were adapted from a number of past studies. A total of 350 respondents from the Malaysian banking industry were recruited. Using SPSS Version 26.0, the research hypotheses were examined. The results show that rewards and recognition are not significant predictors of employee motivation in the Malaysian banking industry; however, training and development and career management are significant predictors of employee motivation. These results will help the human resources department develop and improve its HR operations.

In today’s fast-paced digital world, generative AI, especially OpenAI’s ChatGPT, has become a game-changing technology with significant effects on education. This study examines public sentiment and discourse surrounding ChatGPT’s role in higher education, as reflected on social media platform X (formerly Twitter). Employing a mixed-methods approach, we conducted a thematic analysis using Leximancer and Voyant Tools and sentiment analysis with SentiStrength on a dataset of 18,763 tweets, subsequently narrowed to 5655 through cleaning and preprocessing. Our findings identified five primary themes: Authenticity, Integrity, Creativity, Productivity, and Research. The sentiment analysis revealed that 46.6% of the tweets expressed positive sentiment, 38.5% were neutral, and 14.8% were negative. The results highlight a general openness to integrating AI in educational contexts, tempered by concerns about academic integrity and ethical considerations. This study underscores the need for ongoing dialogue and ethical frameworks to responsibly navigate AI’s incorporation into education. The insights gained provide a foundation for future research and policy-making, aiming to enhance learning outcomes while safeguarding academic values. Limitations include the focus on English-language tweets, suggesting future research should encompass a broader linguistic and platform scope to capture diverse global perspectives.