Amid the relentless grip of the COVID-19 pandemic, sustainability has emerged as a paramount concern across global economies. As businesses grapple with unprecedented challenges, the imperative for sustainable practices in corporate finance becomes increasingly evident. Throughout this crisis, companies have faced staggering financial strains, with diminished turnovers and escalating operational costs pushing many to the brink of collapse. In response, governments worldwide have provided vital support, albeit often insufficient, underscoring the necessity for sustainable mechanisms of intervention. Central to this discourse is an examination of how companies have adapted their financing policies amidst the pandemic’s tumult. Government-backed credit facilities have served as a critical lifeline for numerous businesses, emphasizing the need for sustainable financial instruments readily deployable in times of crisis. Concurrently, moratoriums on existing credit obligations have offered temporary relief, albeit with looming concerns regarding heightened corporate indebtedness. Moreover, the pandemic’s aftermath has witnessed a pronounced uptick in corporate borrowing, compounded by surging interest rates. This confluence underscores the exigency for companies to adopt sustainable financial strategies, mindful not only of short-term exigencies but also the enduring ramifications on financial stability. In navigating these challenges, a holistic approach to sustainability is imperative. Governments must ensure robust support mechanisms, while companies must proactively seek sustainable financing solutions. Concurrently, stakeholders must meticulously weigh the long-term repercussions of financial policy adjustments, thereby fortifying corporate resilience against future crises while safeguarding the stability of the global economy. In essence, the COVID-19 pandemic has underscored the critical imperative for sustainability in corporate finance. By heeding this call and embracing sustainable practices, businesses can navigate crises with greater resilience, ensuring not only their survival but also the enduring stability of the economic landscape.

Graphene has been ranked among one of the most remarkable nanostructures in the carbon world. Graphene modification and nanocomposite formation have been used to expand the practical potential of graphene nanostructure. The overview is an effort to highlight the indispensable synthesis strategies towards the formation of graphene nanocomposites. Consequently, graphene has been combined with useful matrices (thermoplastic, conducting, or others) to attain the desired end material. Common fabrication approaches like the in-situ method, solution processing, and melt extrusion have been widely involved to form the graphene nanocomposites. Moreover, advanced, sophisticated methods such as three- or four-dimensional printing, electrospinning, and others have been used to synthesize the graphene nanocomposites. The focus of all synthesis strategies has remained on the standardized graphene dispersion, physical properties, and applications. However, continuous future efforts are required to resolve the challenges in synthesis strategies and optimization of the parameters behind each technique. As the graphene nanocomposite design and properties directly depend upon the fabrication techniques used, there is an obvious need for the development of advanced methods having better control over process parameters. Here, the main challenging factors may involve the precise parameter control of the advanced techniques used for graphene nanocomposite manufacturing. Hence, there is not only a need for current and future research to resolve the field challenges related to material fabrication, but also reporting compiled review articles can be useful for interested field researchers towards challenge solving and future developments in graphene manufacturing.

Purpose: This research examines the intricate interplay between Business Intelligence (BI), Big Data Analytics (BDA), and Artificial Intelligence (AI) within the realm of Supply Chain Management (SCM). While the integration of these technologies has promised improved operational efficiency and decision-making capabilities, concerns about complexities and potential overreliance on technology persist. The study aims to provide insights into achieving a balance between data-driven insights and qualitative factors in SCM for sustained competitiveness. Design/methodology/approach: The research executed interviews with ten Arab Gulf-based consulting firms. These companies’ ability to successfully complete BI projects is well recognised. Findings: Through examining the interplay of human judgement and data-driven strategies, addressing integration challenges, and understanding the risks of excessive data reliance, the research enhances comprehension of the modern SCM landscape. It underscores BI’s foundational role, the necessity of balanced human input, and the significance of customer-centric strategies for lasting competitive advantage and relationships. Practical implications: The research provided information for organizations seeking to effectively navigate the complexities of integrating data-driven technologies in SCM. The research is a foundation for future studies to delve deeper into quantitative measurement methodologies and effective data security strategies in the SCM context. Originality: The research highlights the value of integrating BI, BDA, and AI in SCM for improved efficiency, cost reduction, and customer satisfaction, emphasising the need for a balanced approach that combines data-driven insights, human judgement, and customer-centric strategies to maintain competitiveness.

Over the course of many years, the Mekong Delta region has experienced relatively low and inconsistent levels of business attraction and low quality of the enterprise environment compared to other regions in Vietnam. To delve into whether this discrepancy reflects a negative perception of the business environment in the area, this study employs a dataset comprising the aggregate Provincial Competitiveness Index (PCI) and nine of its component scores, alongside other significant control variables, to analyze business attraction trends spanning from 2010 to 2020. It based on the modeling analysis for the panel data that includes Pool-OLS, FEM and REM models. The findings indicate that PCI serves as an important indicator influencing the quality of the business environment and plays a role in determining the location preferences of businesses. It is observed that public investment has exerted an impact on enticing new businesses to the region throughout this period. These research outcomes carry several policy implications, suggesting that public policy interventions can positively shape the business environment, consequently bolstering the appeal of business investments in the region.

In rural areas, land use activities around primary arterial roads influence the road section’s traffic characteristics. Regulations dictate the design of primary arterial roads to accommodate high speeds. Hence, there is a mix of traffic between high-speed vehicles and vulnerable road users (pedestrians, bicycles, and motorcycles) around the land. As a result, researchers have identified several arterial roads in Indonesia as accident-prone areas. Therefore, to improve the road user’s safety on primary arterial roads, it is necessary to develop models of the influence of various factors on road traffic accidents. This research uses binary logistic regression analysis. The independent variables are carelessness, disorderliness, high speed, horizontal alignment, road width, clear zone, road shoulder width, signs, markings, and land use. Meanwhile, the dependent variable is the frequency of accidents, where the frequency of accidents consists of multi-accident vehicles (MAV) and single-accident vehicles (SAV). This study collects data for a traffic accident prediction model based on collision frequency in accident-prone areas. The results, road shoulder width, and road sign factor all have an impact on the frequency of traffic accidents. According to a realistic risk analysis, MAV and SAV have no risk difference. After validation, this model shows a confidence level of 92%. This demonstrates that the model generates estimations that accurately reflect reality and are applicable to a wider population. This research has the potential to assist engineers in improving road safety on primary arterial roads. In addition, the model can help the government measure the impact of implemented policies and engage the public in traffic accident prevention efforts.

Improving the practical skills of Science, Technology, Engineering and Mathematics (STEM) students at a historically black college and university (HBCU) was done by implementing a transformative teaching model. The model was implemented on undergraduate students of different educational levels in the Electrical Engineering (EE) Department at HBCU. The model was also extended to carefully chosen high and middle schools. These middle and high school students serve as a pipeline to the university, with a particular emphasis on fostering growth within the EE Department. The model aligns well with the core mission of the EE Department, aiming to enhance the theoretical knowledge and practical skills of students, ensuring that they are qualified to work in industry or to pursue graduate studies. The implemented model prepares students for outstanding STEM careers. It also increases enrolment, student retention, and the number of underrepresented minority graduates in a technology-based workforce.