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.

This study investigates the core competencies essential for product designers to excel in cross-cultural global markets, with particular emphasis on implications for human resource development and organizational leadership. As design practices increasingly transcend cultural and geographical boundaries, designers are required to integrate advanced technical proficiency, creative problem-solving, technological adaptability, and cultural intelligence to create inclusive, socially responsible, and market-relevant products. Employing a mixed-methods approach—including focus groups and surveys with design professionals, industry executives, and academic leaders—the research identifies key competencies such as flexibility, intercultural communication, ethical integrity, and systems thinking. The findings underscore the necessity of balancing technical expertise with emotional intelligence and transformational leadership capabilities to effectively lead diverse, cross-functional teams. These competencies contribute significantly to fostering innovation, enhancing employee well-being and job satisfaction, and strengthening organizational resilience, thereby supporting sustainable human resource strategies. Furthermore, the study highlights the importance of continuous professional development and lifelong learning in cultivating culturally competent and ethically driven design talent. The insights offer strategic guidance for human resource professionals, organizational leaders, and educational institutions aiming to develop adaptive, inclusive, and future-ready design capabilities aligned with evolving global demands.

The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI's capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.

Creating products and services that satisfy individual and community needs is impossible without raw materials. This study takes a novel approach by integrating the economic dynamics and raw material consumption indicators of the European Union (EU). The study uses different econometric methods to analyze the relationship between GDP (gross domestic product) and the EU’s raw material consumption (RMC) from 2014–2023. Among the results, the panel data analysis model shows that the resource productivity of the EU improved during the period under review, whereas the material intensity decreased significantly. These trends significantly contributed to the relative decoupling of material consumption from GDP in the last decade. The results of the K-means cluster analysis highlight the regional economic differences within the EU. According to the results of the correlation analysis, EU member countries differ significantly in the efficiency of raw material use. Nevertheless, five member countries are robustly vulnerable to large-scale raw material use. The divergence calculation results show that while some countries use raw materials extremely efficiently to produce GDP, others achieve low efficiency. This unique approach and the resulting findings provide a new perspective on the complex relationship between economic growth and raw material use in the EU.

This research explores the critical influence of corporate culture on small and medium-sized enterprises’ (SMEs) crisis response abilities under varied cross-cultural environments. Amid the disruptive backdrop of the COVID-19 pandemic, SMEs globally have faced unprecedented challenges. This study addresses a gap in the existing literature by conducting a cross-cultural analysis of SMEs in China, Thailand, and Germany to understand how corporate culture affects crisis management. Utilizing a competitive cultural value model, the research categorizes corporate culture into four dimensions: group culture, development culture, hierarchy culture, and rational culture. These cultural dimensions are investigated in relation to their impact on crisis response abilities. Additionally, national cultural dimensions such as individualism and uncertainty avoidance are examined as moderating variables. The findings reveal that group and development cultures positively influence crisis response abilities, enhancing organizational resilience and adaptability. Conversely, hierarchy culture negatively affects crisis management, hindering flexible response strategies. Rational culture supports structured crisis response through goal-oriented practices. National culture significantly moderates these relationships, with individualism and high uncertainty avoidance impacting the effectiveness of organizational cultural dimensions in crisis scenarios. This study offers theoretical advancements by integrating cultural dimensions with crisis response strategies and provides practical implications for SMEs striving to enhance their resilience and adaptability in a globalized business environment.

Consumers, particularly women, pursue beauty and health in order to uphold their image within society, which has contributed to consistent demand for cosmetics. The cosmetics market, driven by globalization and cultural exchange, sees Thai cosmetics gaining popularity among Chinese women. There has been a significant rise in the popularity of Thai cosmetics, known for their natural ingredients and innovative formulations. With a growing interest in cross-cultural consumer behaviour, particularly in the context of skincare and make-up products, understanding how different age groups perceive and choose Thai cosmetics is crucial for effective marketing strategies. The main issue is the development of consumer preferences over time among Chinese women who have only recently been given the opportunity to choose among many brands. This qualitative study explores the intergenerational differences in Chinese female consumers’ preferences for Thai cosmetics, aiming to uncover rich insights into their perceptions, attitudes, and behaviours. The target population is female Chinese who have visited Thailand and purchased or used Thai-branded cosmetics. Key themes emerge regarding the perception of product efficacy, the cultural authenticity and the role of digital media and trends in influencing product choices. Findings highlight nuanced generational preferences, with older cohorts emphasizing trust and familiarity with established brands, while younger cohorts prioritize innovation, sustainability, and personalized beauty experiences. These insights provide valuable implications for marketers seeking to tailor strategies and product offerings to engage effectively diverse generational segments within the competitive cosmetics market.