This study begins the conversation on the impact that applicant CSR orientation has on a major phase of workforce development—employer attractiveness. There is also virtually no research that investigates CSRO and workforce development. Meanwhile, this present research effort provides evidence that there is some basic relationship between CSRO and employer attractiveness. The data comes from 280 participants who are interested in joining the hospitality and tourism industries in Pakistan. Structural equation modeling was used to analyze the data. The results showed that all four dimensions are significant predictors of employers‘ attractiveness. More specifically, the ethical aspect of CSR has a stronger impact on employers’ attractiveness, whereas discretionary behavior in CSR has the least impact. The implications for academicians, researchers, and managers in the hospitality industry are given in detail.

Modified chitosan hybrids were obtained via chemical reaction of chitosan with two pyrazole aldehyde derivatives to produce two chitosan Schiff bases, Cs-SB1, and Cs-SB2, respectively. FTIR spectroscopy and scanning electron microscopy confirmed both chemical structures and morphology of these Schiff bases. Thermal gravimetric analysis showed an improvement of thermal properties of these Schiff bases. Both chitosan Schiff bases were evaluated in a batch adsorption approach for their ability to remove Cu(II) ions from aqueous solutions. Energy dispersive X-ray for the Schiff bases adsorbed metal ions in various aqueous solutions was performed to confirm the existence of adsorbed metal ions on the surface substrate and their adsorptive efficiency for Cu(II) ions. Results of the batch adsorption method showed that prepared Schiff bases have good ability to remove Cu(II) ions from aqueous solutions. The Langmuir isotherm equation showed a better fit for both adsorbents with regression coefficients (R² = 0.97 and 0.99, respectively) with maximum adsorption capacity for Cu(II) of 10.33 and 39.84 mg/g for Cs-SB1 and Cs-SB2, respectively. All prepared compounds, pyrazoles and two chitosan Schiff bases, showed good antimicrobial activity against three Gram +ve bacteria, three Gram –ve bacteria and Candida albicans, with varying degrees when compared to the standard antimicrobial agents.

ZnO nanostructures were obtained by electrodeposition on Ni foam, where graphene was previously grown by chemical vapor deposition (CVD). The resulting heterostructures were characterized by X-ray diffraction and SEM microscopy, and their potential application as a catalyst for the photodegradation of methylene blue (MB) was evaluated. The incorporation of graphene to the Ni substrate increases the amount of deposited ZnO at low potentials in comparison to bare Ni. SEM images show homogeneous growth of ZnO on Ni/G but not on bare Ni foam. A percent removal of almost 60% of MB was achieved by the Ni/G/ZnO sample, which represents a double quantity than the other catalysts proved in this work. The synergistic effects of ZnO-graphene heterojunctions play a key role in achieving better adsorption and photocatalytic performance. The results demonstrate the ease of depositing ZnO on seedless graphene by electrodeposition. The use of the film as a photocatalyst delivers interesting and competitive removal percentages for a potentially scalable degradation process enhanced by a non-toxic compound such as graphene.

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.

The banking sector is a pillar of the world’s economic fabric and is today facing a major revolution due to the demands of sustainable development objectives and the evolution of sustainable finance tools. This article analyses the impact of green credit on commercial banks’ performance based on data from 10 commercial banks in China between 2012 and 2022. The study found that in the short term, the implementation of green credit has a positive effect on the income level of commercial banks’ intermediate activities and a moderating effect on their return on total assets and non-performing loan ratio.

In the realm of contemporary business, Business Intelligence (BI) offers significant potential for informed decision-making, particularly among executives. However, despite its global popularity, BI adoption in Malaysia’s service sector remains relatively low, even in the face of extensive data generation. This study explores the factors influencing BI adoption in this sector, employing the Technology Acceptance Model (TAM) as its conceptual framework. Drawing on relevant BI literature, the study identifies key TAM factors that impact BI adoption. Using SEM modelling, it analyses quantitative data collected from 45 individuals in managerial roles within Malaysia’s service sector, particularly in the Klang Valley. The findings highlight the crucial role of Perceived Usefulness in influencing the Behavioral Intention to adopt BI, serving as a mediating factor between Computer Self-efficacy and BI adoption. In contrast, Perceived Ease of Use does not have a direct impact on BI adoption and does not mediate the relationship between Computer Self-efficacy and Behavioral Intention. These insights demonstrate the complex nature of BI adoption, emphasizing the importance of Perceived Usefulness in shaping Behavioral Intentions. The outcomes of the study aim to guide executives in Malaysia’s service sector, outlining key considerations for successful BI adoption.