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 objectives of the study are to assess the impact of green human resources management (GHRM) policies and knowledge on the environmental performance of a public transportation company employees. Data from 1130 respondents were analyzed using SmartPLS modeling. The findings that GRHM affected employees of a public transportation company mediated by roles of green human resources management policies and knowledge. GRHM affected public transportation employees’ environmental performance significantly. Employees in the public transportation industry can use the study’s results to their advantage by developing plans to increase their sense of belonging to the company and their impact on the environment. Therefore, many companies understand the value of public transportation employees as the forefront ‘agent of change’ towards a significant positive environmental change in the community.

The research issue at hand pertains to the intricate mechanisms of state regulation that govern the economy of Kazakhstan, particularly in the context of the international sanctions that have been instituted by the nations comprising the Eurasian Economic Union. In order to thoroughly investigate this complex subject matter, this scholarly paper employs a variety of sophisticated methodologies grounded in bibliometric analyses of the most recent 90 academic papers that focus on the various mechanisms of state regulation pertinent to the economic landscape of Kazakhstan. As a subsequent phase in this research endeavor, the modeling of higher-order moments is undertaken with the express aim of delineating the multifaceted ramifications that stem from a singular and isolated perturbation affecting one of the key variables encapsulated within the higher-order moments model. This detailed analytical approach facilitates an in-depth exploration of both the immediate outcomes and the subsequent values of the endogenous variables that are under scrutiny. The innovative aspect of this article’s findings lies in the comprehensive analysis dedicated to the state regulation of Kazakhstan’s economy, which is significantly influenced by the international sanctions that have been imposed by member countries of the Eurasian Economic Union. The outcomes of this research provide a methodical and scientifically rigorous framework for understanding the overarching system of state regulation, which is of paramount importance for cultivating sustainable development within the socio-economic dynamics that characterize the nation of Kazakhstan.

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.