With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.

The purpose of this study is to investigate whether the strategic orientations of organizations have an impact on the resilience levels of employees in the digitalized world. The sample for this research is based on the convenience sampling method. In this study, the sub-factors of strategic orientation, namely innovation, technological orientation, customer focus, and competitor focus, are examined, and their potential effects on resilience are analyzed. Our research emphasizes the importance of psychological resilience in organizations that undergo continuous changes due to the impact of digital transformation, as it enhances employee performance and has positive implications for firms. The field study associated with this research was conducted on 316 individuals working in the service sector in the context of Turkey. The data collected for the research were analyzed using SPSS 25 software. The research findings indicate that the components of strategic orientation have an impact on the levels of psychological resilience.

The study focuses on the employees’ behavioral intentions towards the usage of disruptive technology in the industry. The digital technology application in consumer, retail, and hospitality, education and training, financial services, the health sector, infrastructure, government, and airports. The study objectives were to explore the possible adoption of innovation and creativity changes and their acceptance by the employees in the organization. To identify the variables impacting behavioral intention and analyze how these variables relate to perceived usefulness, attitude, perceived ease of use, facilitating conditions, and technology optimism. A structured questionnaire was used to collect data from 335 respondents, who were selected based on their relevance to the study objectives. The questionnaires were distributed through the Google Forms application, and the data were collected and analyzed periodically. The findings of the study provide valuable insights into the behavioral intention towards disruptive technologies in Kuala Lumpur and Putrajaya locations in Malaysia and highlight the significance of factors such as perceived usefulness, attitude, perceived ease of use, facilitating conditions, and technology optimism. The research contributes to the existing body of knowledge on Industry 4.0 by providing empirical evidence and practical implications for organizations seeking to leverage disruptive technologies in their operations management.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

This study investigated the students’ perceptions of a self-paced fitness program that is integrated with SitFit, a fitness tracker that measures body inclination during sit-up exercises, and their acceptance of digital innovation in physical education. The data was gathered from a survey of 1001 Thai undergraduates. Results revealed that attitudes toward using the technology and the perceived ease of use were important predictors of behavioral intention to use the sit-up fitness tracker. consistent with previous TAM studies. Subsequently, SitFit was developed based on exercise principles and expert advice to enable users to exercise more effectively while reducing injury risk.

Digital labor, as a new theoretical form of "audience commodity theory" in the digital media era, represents a new form of production and labor. This paper explores the unique features of digital labor in labor form, labor products and labor time, and combining Marx's theory, it further reveals the alienation and exploitation of human social relations, emotional value and social class in the process of digital labor, and finally makes suggestions on the unequal relationship between platform and workers in the process of digital labor.