Water splitting, the process of converting water into hydrogen and oxygen gases, has garnered significant attention as a promising avenue for sustainable energy production. One area of focus has been the development of efficient and cost-effective catalysts for water splitting. Researchers have explored catalysts based on abundant and inexpensive materials such as nickel, iron, and cobalt, which have demonstrated improved performance and stability. These catalysts show promise for large-scale implementation and offer potential for reducing the reliance on expensive and scarce materials. Another avenue of research involves photoelectrochemical (PEC) cells, which utilize solar energy to drive the water-splitting reaction. Scientists have been working on designing novel materials, including metal oxides and semiconductors, to enhance light absorption and charge separation properties. These advancements in PEC technology aim to maximize the conversion of sunlight into chemical energy. Inspired by natural photosynthesis, artificial photosynthesis approaches have also gained traction. By integrating light-absorbing materials, catalysts, and membranes, these systems aim to mimic the complex processes of natural photosynthesis and produce hydrogen fuel from water. The development of efficient and stable artificial photosynthesis systems holds promise for sustainable and clean energy production. Tandem cells, which combine multiple light-absorbing materials with different bandgaps, have emerged as a strategy to enhance the efficiency of water-splitting systems. By capturing a broader range of the solar spectrum, tandem cells optimize light absorption and improve overall system performance. Lastly, advancements in electrocatalysis have played a critical role in water splitting. Researchers have focused on developing advanced electrocatalysts with high activity, selectivity, and stability for the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). These electrocatalysts contribute to overall water-splitting efficiency and pave the way for practical implementation.

This study aims to identify the impact of inheritance literacy, inheritance socialization, inheritance stress, and peer influence on the inheritance behaviors among FELDA communities in Malaysia. Inheritance literacy pertains to individuals’ comprehension of wealth transfer and estate planning, while peer influencer evaluates friends’ impact on inheritance attitudes; inheritance socialization explores family interactions’ role in shaping inheritance attitudes, and inheritance stress measures emotional strain in inheritance matters, with inheritance behaviors encompassing asset management and wealth transfer decisions for future generations by individuals and families. Understanding inheritance behaviors is crucial, as it helps individuals depict their inheritance knowledge and attitudes toward FELDA inheritance better, fostering a more favorable inheritance attitude. Through self-administered survey questionnaires, data related to FELDA communities are obtained using convenience sampling from 413 respondents. This study applies Partial Least Squares Structural Equation Modeling (PLS-SEM) technique to test the research hypotheses. The present study’s outcome confirms that two determinants, which are inheritance literacy and inheritance socialization significantly influence the inheritance behavior of FELDA communities. However, inheritance stress and peer influence determinants have statistically insignificant influence inheritance behavior. This study’s theoretical framework enriches the discussions on wealth management and financial behavior by refining and expanding upon existing financial behavior theories to incorporate inheritance-specific behaviors. The present study is exclusive in its effort to ascertain the relative importance of both inheritance behavior and the FELDA communities. This paper will assist the government, inheritance service providers, and policymakers in offering innovative economic schemes and designing policies that may enhance the inheritance behavior wellbeing of FELDA communities. This article also provides a roadmap to guide future research in this area.

Using data from 31 provinces, municipalities, and autonomous regions in mainland China from 2006 to 2019, we employ a double difference (DID) model and a spatial double difference (SDID) model to estimate the impact of the High-speed Railway (HSR) on the income gap between urban and rural residents, as well as its spatial spillover effects. Our research reveals several key findings. Firstly, the introduction of high-speed railways helps to narrow the income gap between urban and rural residents within local areas, but its spatial effects can lead to an increase in the income gap in neighboring provinces. Secondly, from a spatial perspective, intermediate variables such as industrial structure, education, science and technology, and foreign trade can also contribute to balancing the income gap between urban and rural residents, although the impact of population mobility is not significant. Thirdly, further analysis of the spatial effects demonstrates that education plays a significant role in balancing the income gap both within the local province and neighboring provinces. Additionally, adjustments in industrial structure, advancements in science and technology, and foreign trade have stronger spillover effects in reducing the income gap among neighboring provinces compared to their impact at a local level.

This study explores the potential of digital preservation in the documentation of colonial cultural heritage in Egypt. It also explores the stories behind historical wars to revive these sites and attract different segments of visitors. Documentation of these sites should enhance Egyptian colonial cultural heritage sites, which include battlefields, war memorials, commanders’ palaces, assassination and murder spots, cemeteries, and mausoleums. The purpose of this study was fulfilled through field visits supplemented with in-depth interviews with experts on colonial heritage sites in Egypt. The findings showed that technology could play a key role in implementing the storytelling documentation and interpretation of colonial history and its relevant events at the Egyptian sites. However, to date, these sites have not made the best use of technology for digital preservation and documentation due to many challenges. The study recommends that decision-makers should integrate technological innovation, which can revitalize the communities built on the ruins of colonialism and revive the heritage of popular resistance. Technological innovation could be implemented not only in digital preservation and documentation but also in service and marketing of these colonial heritage sites.

The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.

This study investigated the utilization of Artificial Intelligence (AI) in the Recruitment and Selection Process and its effect on the Efficiency of Human Resource Management (HRM) and on the Effectiveness of Organizational Development (OD) in Jordanian commercial banks. The research aimed to provide solutions to reduce the cost, time, and effort spent in the process of HRM and to increase OD Effectiveness. The research model was developed based on comprehensive review of existing literature on the subject. The population of this study comprised HR Managers and Employees across all commercial banks in Jordan, and a census method was employed to gather 177 responses. Data analysis was conducted using Amos and SPSS software packages. The findings show a statistically significant positive impact of AI adoption in the Recruitment and Selection Process on HR Efficiency, which in turn positively impacted OD Effectiveness. Additionally, the study indicated that the ease-of-use of AI technologies played a positive moderating role in the relationship between the Recruitment and Selection Process through AI and HR Efficiency. This study concludes that implementing AI tools in Recruitment is vital through improving HR Efficiency and Organization Effectiveness.