Static atomic charges affect key ground-state parameters of boron quasi-planar clusters Bn, n ≤ 20, which serve as building blocks of borophenes and other two-dimensional boron-based materials promising for various advanced applications. Assuming that the outer valence shells partial electron density of the constituent B atoms are shared between them proportionally to their coordination numbers, the static atomic charges in small boron planar clusters in the electrically neutral and positively and negatively singly charged states are estimated to be in the ranges of –0.750e (B70) to +0.535e (B200), –0.500e (B7+, B8+, and B9+) to +0.556e (B17+), and –1.000e (B7–) to +0.512e (B20–), respectively.

Flood risk analysis is the instrument by which floodplain and stormwater utility managers create strategic adaptation plans to reduce the likelihood of flood damages in their communities, but there is a need to develop a screening tool to analyze watersheds and identify areas that should be targeted and prioritized for mitigation measures. The authors developed a screening tool that combines readily available data on topography, groundwater, surface water, tidal information for coastal communities, soils, land use, and precipitation data. Using the outputs of the screening tool for various design storms, a means to identify and prioritize improvements to be funded with scarce capital funds was developed, which combines the likelihood of flooding from the screening tool with a consequence of flooding assessment based on land use and parcel size. This framework appears to be viable across cities that may be inundated with water due to sea-level rise, rainfall, runoff upstream, and other natural events. The framework was applied to two communities using the 1-day 100-year storm event: one in southeast Broward County with an existing capital plan and one inland community with no capital plan.

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 significance of infrastructure development as a determinant of economic growth has been widely studied by economists and policymakers. Though there is no much debate about the importance of infrastructure on growth, the extent to which infrastructure affects growth in the long run is often debated among researchers. This paper aims to examine the effect of infrastructure development on economic growth in ten sub-Saharan Africa. This study uses balanced panel data of ten African countries, particularly sub-Saharan Africa over the period of 2010–2020 by analyzing a set of independent variables with relation to the dependent, which is GDP per capita. The study has found that water supply & sanitation index and electricity index have positive and significant relationship with economic growth, while transport index and Information & Communications (ICT) have negative relationship with economic growth in these countries.

This article explores the landscape of entrepreneurship education in Indonesia amid the wave of digital transformation. The research method uses Systematic Literature Review (SLR) to review research results sourced from journals indexed in Sinta or nationally accredited journals in Indonesia which can be accessed on Google Scholar. The conclusion, (i) Digital transformation-based entrepreneurship education creates a new learning model in colleges with the aim of developing entrepreneurial attitudes and values among young people, especially students, so as to produce entrepreneurial intentions. (ii) Higher education as an entrepreneur education provider must follow the progress of digital transformation in the teaching process of entrepreneurship education so that digital literacy among lecturers and students is getting better. (iii) The participation of stakeholders, the Government, college and the business world, is expected to provide support in policy making, especially curriculum changes in accordance with current circumstances in creating new business actors or entrepreneurial intentions.

This study systematically examines the literature of electric vehicle (EV) purchase intention and consumer behavior using a bibliometric method to unveil three main research questions: 1) identifying influential publications, authors, and journals; 2) analyzing the thematic evolution of research over time; and 3) identifying emerging research directions. The main objective is to provide a comprehensive understanding of the current state of knowledge and to guide future research in this evolving field. A comprehensive bibliometric analysis was conducted, using Scopus statistics analysis, R-Studio Biblioshiny and VOSviewer, comprising 687 publications authored by 1743 researchers representing 34 different countries with the dataset sourced from the Scopus database from 2010 to 2023. To achieve a nuanced understanding of the research landscape, a multifaceted approach was adopted, including detailed citation analysis, author co-citation analysis, keyword analysis, and thematic mapping. Through meticulous analysis, this study identifies the most influential publications, authors, and journals in the domain of EV purchase intentions and consumer behaviors. It also traces the evolution of themes over time and identifies emerging research directions, providing valuable insights into the trajectory and future avenues of inquiry within this field. The findings contribute to a deeper understanding of the dynamics shaping research in the realm of EVs. The insights gained contribute significantly to advancing knowledge in this crucial domain, offering theoretical insights and practical implications for policymakers, businesses, manufacturers, and academics.