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.

Enhancing the emphasis on incorporating sustainable practices reinforces a linear transition towards a circular economy by organizations. Nevertheless, although studies on circular economy demonstrate an increasing trend, the drivers that support circular economy practices towards sustainable business performance in the Small and Medium-Sized Enterprise (SME) sector, especially in developing nations, demand exploration. Accordingly, the study examines circular economy drivers, i.e., green human resource management, in establishing sustainability performance and environmental dynamism as moderating variables. The study engaged 207 SMEs and 621 respondents who were analyzed utilizing structural equation modeling. The analysis indicated that sustainable business performance was affected by green human resource management and a circular economy. Subsequently, the circular economy mediated the linkage between green human resources management and sustainable business performance. The environmental dynamism moderated the linkage between green human resources management and the circular economy.

The WRKY gene family plays a very diverse role in plant growth and development. These genes contained an evolutionarily conserved WRKY DNA binding domain, which shows functional diversity and extensive expansion of the gene family. In this study, we conducted a genome-wide comparative analysis to investigate the evolutionary aspects of the WRKY gene family across various plant species and revealed significant expansion and diversification ranging from aquatic green algae to terrestrial plants. Phylogeny reconstruction of WRKY genes was performed using the Maximum Likelihood (ML) method; the genes were grouped into seven different clades and further classified into algae, bryophytes, pteridophytes, dicotyledons, and monocotyledons subgroups. Furthermore, duplication analysis showed that the increase in the number of WRKY genes in higher plant species was primarily due to tandem and segmental duplication under purifying selection. In addition, the selection pressures of different subfamilies of the WRKY gene were investigated using different strategies (classical and Bayesian maximum likelihood methods (Data monkey/PAML)). The average dN/dS for each group are less than one, indicating purifying selection. Our comparative genomic analysis provides the basis for future functional analysis, understanding the role of gene duplication in gene family expansion, and selection pressure analysis.

This study investigated the impact of social media on purchasing decision-making using data from a questionnaire survey of 257 randomly sampled students from the College of Business at Imam Muhammad Ibn Saud Islamic University. The study items were selected from the study community through a random sample, where several (257) students were surveyed. To achieve its objectives, the study follows the descriptive analytical approach in addressing its topic. The questionnaire was adopted as a tool for collecting data. The questionnaire collected data on the independent variable social media—and the dimensions of the dependent variables representing the stages of purchasing decision-making: Feeling the need for the advertised goods, collecting information about alternatives, evaluating available options, buying decisions, and post-purchase evaluation of the purchase decision. Then, the data were analyzed based on regression analysis using SPSS and AMOS. The important findings are summarized below: Social media use is directly related to feeling the need for and searching for information on advertised goods. Social communication and the evaluation of alternatives to advertised goods, in addition to the existence of a moral effect and a direct correlation between social media use and making the purchasing decision for advertised goods. Providing honest, sufficient, and accurate information via social media to the buyer can help them make the purchasing decision.

In light of the metaverse’s vast expansion, it’s a crucial intellectual platform that’s transforming the video game industry and spurring creative innovation and technological advancement. Considering the distinctive niche that Taiwan occupies within the realm of the video game industry, this study uses a total of 11 video game companies in Taiwan as samples. The study spans a period of 16 years, from 2007 to 2022, and utilizes the random effect regression model for analysis. The study results illustrate that intellectual capital efficiency exerts varying contributions to the creation of value across different corporate value indicators within the video game industry. Among the factors, HCE, SCE, and CEE demonstrate the highest explanatory power for ROE, reaching up to 82.23%. Following this, they account for 73.57% of the variance in market share, but only a meager 13.67% for Tobin’s Q. This study is the empirical evidence that different methods of measuring intellectual capital and various definitions of value creation in an industry may lead to divergent results and managerial implications in intellectual capital research. Hence, it is worthwhile for subsequent studies to continue clarifying and delving deeper into these aspects.

This research investigates the impact of digital academic supervision (DAS) on teacher professionalism (TP), with a focus on the mediating role of personal learning networks (PLNs) and their implication for educational policy. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from 276 teachers in prestigious secondary schools in East Java, Indonesia. The study uses a regression model design to explore direct and mediated effects between DAS, PLNs, and TP. Findings demonstrate that DAS directly impacts both PLNs (0.638) and TP (0.550), while PLNs also directly influence TP (0.293). Mediated analysis indicates that DAS enhances TP through PLNs (0.187). These results underscore the importance of digital tools in academic supervision, fostering collaboration, and promoting teacher professional development. The empirical evidence supports the effectiveness of DAS in enhancing teacher professionalism, suggesting significant implications for educational policy and practice in Indonesia in terms of regulatory framework, such as data privacy and security, standardization, training programs, and certification and accreditation.