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.

Heat removal has become an increasingly crucial issue for microelectronic chips due to increasingly high speed and high performance. One solution is to increase the thermal conductivity of the corresponding dielectrics. However, traditional approach to adding solid heat conductive nanoparticles to polymer dielectrics led to a significant weight increase. Here we propose a dielectric polymer filled with heat conductive hollow nanoparticles to mitigate the weight gain. Our mesoscale simulation of heat conduction through this dielectric polymer composite microstructure using the phase-field spectral iterative perturbation method demonstrates the simultaneous achievement of enhanced effective thermal conductivity and the low density. It is shown that additional heat conductivity enhancement can be achieved by wrapping the hollow nanoparticles with graphene layers. The underlying mesoscale mechanism of such a microstructure design and the quantitative effect of interfacial thermal resistance will be discussed. This work is expected to stimulate future efforts to develop light-weight thermal conductive polymer nanocomposites.

Bibliometric analysis is a commonly used tool to assess scientific collaborations within the researchers, community, institution, regions and countries. The analysis of publication records can provide a wealth of information about scientific collaboration, including the number of publications, the impact of the publications, and the areas of research where collaborations are most common. By providing detailed information on the patterns and trends in scientific collaboration, these tools can help to inform policy decisions and promote the development of effective strategies to support and enhance scientific collaborations between countries. This study aimed to analyze and visualize the scientific collaboration between Japan and Russia, using bibliometric analysis of collaborative publications from the Web of Science (WoS) database. The analysis utilized the bibliometrix package within the R statistical program. The analysis covered a period of two decades, from 2000 to 2021. The results showed a slight decrease in co-authored publications, with an annual growth rate of −1.26%. The keywords and thematic trends analysis confirmed that physics is the most co-authored field between the two countries. The study also analyzed the collaboration network and research funding sources. Overall, the study provides valuable insights into the current state of scientific collaboration between Japan and Russia. The study also highlights the importance of research funding sources in promoting and sustaining scientific cooperation between countries. The analysis suggests that more efforts in government funding are needed to increase collaboration between the two countries in various fields.

In today's changing world of work, Strategic Human Resource Management (SHRM)) still focuses on making workers more productive. This study systematically examines the mediating function of incentives both monetary and non-monetary between antecedent characteristics (e.g., leadership, organizational culture) and employee productivity using a systematic literature review (SLR) of papers published from 2010 to 2024. The review adheres to PRISMA principles and integrates 18 peer-reviewed studies chosen through a stringent screening and quality evaluation process from Scopus and Google Scholar. The results show that the success of incentives depends a lot on things like the ideals of the business, the style of leadership, and the demographics of the workforce. Thematic analysis, informed by the Ability-Motivation-Opportunity (AMO) theory and Strategic Human Resource Management (SHRM) frameworks, delineates four principal processes by which incentives affect productivity: goal alignment, perceived equity, motivational pathways, and cultural congruence. The research emphasizes the necessity of customizing incentive systems to specific organizational contexts and offers practical guidance for HR professionals. Recognizing limitations and publishing bias, suggestions for future incentive system design are presented.

Purpose: This research paper aims to justify the need for the Quality of Hire (QOH) construct as a value-adding focus for strategic human resource management (SHRM). The traditional focus on efficiency and cost-oriented recruitment metrics overlooks the importance of QOH in providing a competitive advantage and delivering long-term value. The study expands the economic theory of human resource development and develops a profit-building concept relevant to SHRM by exploring the practices that enable QOH in organizations. Design: The study utilizes a case-study method to examine a target firm’s mechanisms to build QOH in its recruitment process. It applies a structuration theory lens to analyze the behavior of various actors, their agencies, and the continuous interplay between structure and action in enabling QOH. Findings: The findings suggest that assessing and building measures for getting QOH is a complex task for organizations due to the inherent reliance on lag measures such as performance and tenure. The study highlights that QOH can be enabled through changes in the firm’s recruitment practices. Originality: This paper contributes to recruitment research in two significant ways. First, it expands on the under-researched construct of QOH, providing clarity on its definition and importance. Second, it identifies lead practices that organizations can incorporate into their recruitment and selection processes to enable QOH. By using a structuration theory lens, the study explores how actors in the recruitment process adapt and align with new structural rules to enable QOH. Research implications: The research builds on the structuration theory in recruitment and selection and exhorts practitioners in organizations to move beyond efficiency-oriented recruitment practices and focus on practices that contribute to QOH. By considering post-hire outcomes, such as job performance and long-term retention, organizations can improve their talent acquisition and retention strategies, creating long-term value for the organizations.

The emerging growth digital application has driven ecosystems integrating digital banks and e-commerce platforms, enabling seamless, efficient transactions. This study examines the impact of user experience and satisfaction on reuse intention in this integrated environment. Using a mixed-method approach, data were collected through surveys of 471 respondents and interviews with 30 participants. Quantitative data were analyzed using structural equation modeling, while qualitative data were processed through content analysis. Results show that perceived ease of use, usefulness, reliability, value, and risk significantly affect user experience, while perceived security does not. These findings aim to help digital banks and e-commerce platforms design effective CRM strategies to enhance satisfaction and reuse intention.