Water splitting has gained significant attention as a means to produce clean and sustainable hydrogen fuel through the electrochemical or photoelectrochemical decomposition of water. Efficient and cost-effective water splitting requires the development of highly active and stable catalysts for the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Carbon nanomaterials, including carbon nanotubes, graphene, and carbon nanofibers, etc., have emerged as promising candidates for catalyzing these reactions due to their unique properties, such as high surface area, excellent electrical conductivity, and chemical stability. This review article provides an overview of recent advancements in the utilization of carbon nanomaterials as catalysts or catalyst supports for the OER and HER in water splitting. It discusses various strategies employed to enhance the catalytic activity and stability of carbon nanomaterials, such as surface functionalization, hybridization with other active materials, and optimization of nanostructure and morphology. The influence of carbon nanomaterial properties, such as defect density, doping, and surface chemistry, on electrochemical performance is also explored. Furthermore, the article highlights the challenges and opportunities in the field, including scalability, long-term stability, and integration of carbon nanomaterials into practical water splitting devices. Overall, carbon nanomaterials show great potential for advancing the field of water splitting and enabling the realization of efficient and sustainable hydrogen production.

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 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 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.

In the contemporary landscape characterized by technological advancements and a progressive economic environment, the utilization of currency has undergone a paradigm shift. Despite the growing prevalence of digital currency, its adoption among the Vietnamese population faces several challenges, including limited financial literacy, concerns over security, and resistance to change from traditional cash-based transactions. This research aims to identify these challenges and propose solutions to encourage the widespread use of digital currency in Vietnam. This research adopts a quantitative approach, utilizing Likert scale questionnaires, with a dataset of 330 records. The interrelationships among variables are analyzed using partial least squares structural equation modeling (PLS-SEM). The analysis results substantiate the viability of the research model, confirming the hypotheses. The findings demonstrate a positive relationship and the significance impact of factors such as perceived usefulness (PU), perceived ease of use (PEOU), perceived trust (PT), social influence (SI), openness to innovation (OI), and financial knowledge (FK) to intention to use digital currency (IUDC). Thereby aiming to inform policymakers, industry stakeholders, and the wider community, fostering a deeper understanding of consumer behavior and providing solutions to enhance the adoption of digital currency in the evolving landscape of digital finance.

Due to the gradual growth of urbanization in cities, urban forests can play an essential role in sequestering atmospheric carbon, trapping pollution, and providing recreational spaces and ecosystem services. However, in many developing countries, the areas of urban forests have sharply been declining due to the lack of conservation incentives. While many green city spaces have been on the decline in Thailand, most university campuses are primarily covered by trees and have been serving as urban forests. In this study, the carbon sequestration of the university campuses in the Bangkok Metropolitan Region was analyzed using geoinformatics technology, Sentinal-2 satellite data, and aerial drone photos. Seventeen campuses were selected as study areas, and the dendrometric parameters in the tree databases of two areas at Chulalongkorn University and Thammasat University were used for validation. The results showed that the weight average carbon stock density of the selected university campuses is 46.77 tons per hectare and that the total carbon stock and sequestration of the study area are 22,546.97 tons and 1402.78 tons per year, respectively. Many universities in Thailand have joined the Green University Initiative (UI) and UI GreenMetric ranking and have implemented several campus improvements while focusing on environmental concerns. Overall, the used methods in this study can be useful for university leaders and policymakers to obtain empirical evidence for developing carbon storage solutions and campus development strategies to realize green universities and urban sustainability.