This investigation elucidates the intertwined effects of magnetic fields and porous media on the flow of nanofluids towards a stretching sheet, contemplating variable viscosity and convective boundary conditions. A nanofluid model, incorporating the influences of thermophoresis and Brownian motion, is adopted. Via judicious transformations, the fundamental governing coupled non-linear partial differential equations are condensed, and the consequent transformed equations are numerically resolved employing the Finite Element Method (FEM). Paramount emphasis is accorded to parameters embodying notable physical significance, inclusive of the Prandtl number (Pr), Hartmann number, Lewis number (Le), Brownian motion number (Nb), thermophoresis number (Nt), and permeability parameter. The numerical results acquired, as particular instances of the aforementioned study, are found to be congruent with previously reported findings, substantiating the accuracy and reliability of the proposed methodology. A thorough examination of the collective impact of the selected parameters on flow and heat transfer characteristics has been systematically undertaken, revealing intricate dependencies and fostering a deeper understanding of the complex phenomenon under consideration. This study, hence, paves a pathway towards bolstering the comprehension of flow mechanics in porous media under the influence of magnetic fields, contributing valuable insights to the overarching field of fluid dynamics in nano-engineering applications.

In the burgeoning nexus between Industry 4.0 and entrepreneurial ecosystems, a transformative and disruptive dynamic has been observed. Defined by the integration of digital technologies into traditional sectors, Industry 4.0 has been found to alter the way in which entrepreneurs conceptualize, function, and collaborate. This review elucidates salient outcomes and implications of such intersections. Paramount among the findings are the proliferation of companies operating within the Industry 4.0 paradigm, the emergence of hubs centred on collaborative innovation, and the rising prominence of industry-specific ecosystems. Startups have been identified as pivotal in effecting supply chain transformations, embedding sustainability, and fostering digital talent. For practitioners navigating this terrain, it is imperative to champion digital metamorphosis, forge strategic partnerships, underscore the primacy of sustainable practices, and nurture digital expertise. Corporate alliances, ecosystem synergies, and opportunities in the global marketplace have been underscored as potent avenues for expansion and ingenuity. Furthermore, a marked influence of Industry 4.0 on the resilience and adaptability of entrepreneurial ecosystems has been discerned. The confluence of such technologies and ecosystems has been posited as offering an unprecedented juncture for both nascent and established businesses to spur economic progress, tackle pressing global challenges, and cultivate a robust culture of innovation and entrepreneurship.

Purpose: Numerous studies have been conducted on digital finance and financial inclusion. However, there is limited information on the impact of the digital income level divide on the financial inclusion of informal practitioners. Thus, there is a need to examine the area critically from the perspective of a marginalised society. Hence, the current study focused on identifying the components of the digital income level divide and establishing its impact on the financial inclusion of informal traders. Methodology: The study applied a mixed-methods research design whereby interviews and questionnaires were employed to collect data. Quantitative and qualitative data were analysed using inferential statistics and content analysis, respectively. Findings: The findings show that the digital-income level divide has resulted from digital usage, the insignificance of the benefits of digital finance usage, low income levels, and the practical nature of informal traders. Also, informal traders pay high transaction costs, which are not considered beneficial for the services of receiving and sending money. Originality/Value: The paper informs on the set of strategies that enable informal traders to become part of digital financial users and benefit from financial inclusion. This study adds knowledge to the literature on the combined impacts of income level and digital divide challenges associated with informal traders on financial inclusion.

Purpose: During unstable economic conditions, investors are risk averse and rely on fundamental information such as accounting information to make investment decisions. It is reported that during the COVID-19 pandemic, businesses have used accounting discretion in order to cope with difficult economic conditions. The use of discretion in the accounting process may instigate earnings manipulations which reduce earnings quality. This raises the following research question: has earnings quality decreased during the COVID-19 pandemic? This paper aimed at examining earnings quality (EQ) of South African listed firms during the COVID-19 pandemic. Specifically, the study examined the EQ of these firms before and during the COVID-19 pandemic.

Methodology: Weighted least square regression was used to analyze a sample of 132 non-financial firms listed on the Johannesburg Stock Exchange (JSE) over the period of 2018 to 2021. The sampled firms were extracted from the IRESS research domain. Conservatism and accrual quality were used to measure earnings quality because these two measures required the exercise of discretion.

Findings: The results attained were mixed and suggested that, although the sampled firms did not apply accounting conservatism in reported earnings during the COVID-19 pandemic period as compared to the period before the pandemic, there is no evidence of the use of accrual quality to manipulate earnings during the pandemic period as compared to the period before the pandemic.

Originality/Value: The paper will shed light on whether accounting information remains reliable during unstable economic conditions. In addition, it will inform regulators on whether the accounting standards were consistently applied during the COVID-19 pandemic.

The field of educational research increasingly emphasizes predictive modeling of academic performance, focusing on identifying determinants of student success and crafting models to forecast future achievements. This investigation evaluates the efficacy of different feature selection techniques in predicting mathematics performance among Ecuadorian students, based on data from the 2021-2022 cycle of the Ser Estudiante test. Employing supervised logistic regression for classification, the study compares three feature selection methods: selection based on the highest k-scores, recursive feature elimination with cross-validation (RFECV), and recursive feature elimination (RFE). The assessment reveals that both the highest k-scores and RFECV methods are highly effective in isolating the most pertinent features for predicting mathematical prowess. These methods achieved prediction accuracies exceeding 90%, with k-scores attaining 96% and RFECV 92%. Furthermore, they demonstrated remarkable recall (94% and 97%, respectively) and F1-Score (96% and 91%, respectively). Additionally, both methods presented Receiver Operating Characteristic (ROC) curves with an area under the curve (AUC) of 99%, signifying superior discriminatory capability. The findings illuminate the critical role of judicious feature selection in enhancing the precision of predictive models for academic performance, particularly in mathematics. The results advocate for the application of these techniques in pinpointing key factors contributing to student success. This study not only contributes to the methodological discourse in educational data analysis but also provides practical insights for the Ecuadorian education system in leveraging data-driven approaches to enhance student outcomes.

Amidst a transformative economic milieu in China, domestic enterprises are venturing into the global market, exposing them to intensified perils in international trade and investment. This research elucidates the international trade and investment (ITI) context within China, establishing criteria for ITI risk evaluation through an analytical exploration of international trade interactions. A methodology has been developed to quantify ITI risk, employing deep neural networks (DNNs), with a particular focus on the potential impact of edge cloud computing on China's trading economy. Through the utilization of convolutional neural networks (CNN), risks in China's trade and investment are appraised across various dimensions, exhibiting a noteworthy accuracy rate of 90.38%. It is identified that while CNN exhibits exemplary performance in estimating severe and high-risk scenarios, its efficacy diminishes when discerning general investment perils. The analysis underscores that a substantial portion of investments, constituting 14.8%, emanates from The Association of Southeast Asian Nations (ASEAN) and China, with market dynamics and macroeconomic conditions markedly influencing the risk associated with Chinese investments. By extending the utilization of deep learning (DL) in financial investments and integrating edge cloud computing, this investigation augments the capabilities for assessing China's ITI risk, providing a valuable resource for comprehending the ITI landscape within China.

Purpose: Corporate social responsibility (CSR) activities are crucial for the cordial relationship between the business and the community, and despite the cost involved in CSR investments such relationship may have favourable consequence on community patronage and financial outcome. This study investigated the effect of CSR activities on the profitability of oil firms listed in Nigeria by ascertaining how community development costs (CDC) and employee benefits are associated with the financial performance of the firms. Methodology: Data on the study variables from thirteen oil and gas firms were collected over a period of twenty-one years (1998 to 2018), and analysed using a heteroscedasticity and autocorrelation-consistent regression technique to determine the effect of CSR activities on the financial performance of the sampled firms. Findings: The results showed that community development cost (CDC) had a significant positive effect on profitability. Employee benfits also have similar effect on financial performance. These findings indicate that investing in CSR activities ultimately has a favourable impact on corporate financial performance. Accordingly, the study recommended that oil firms should increasingly invest in employee welfare and community development projects in Nigeria. Originality/Value: This paper used a data set drawn from almost all the listed oil firms in Nigeria over a relatively long time span. The results support the usefulness of CSR activities to corporate entities, thereby encouraging oil firms to conduct more CSR investments in Nigeria.

This research delineates a numerical elucidation concerning the flow through an embankment, utilising PLAXIS2D software, and underscores the pivotal influence of soil composition—encompassing gravel, sand, and clay—on the structural resilience of embankments during seismic events. Different material models, incorporating the UBC3D-PLM for sand and the Hardening Soil (HS) small constitutive models for gravel and clay, were strategically employed to replicate embankment behaviours, ensuring a meticulous simulation of distinct soil types. The objective herein was to scrutinise the impact of dynamic loads and soil typologies on pertinent variables: settlements, lateral displacements, and excess pore water pressure engendered within the embankment. A comprehensive series of 2D finite element models, each representative of a specific soil type, were formulated and subsequently subjected to an earthquake record for dynamic analysis. It was discerned that embankments constituted from sand and gravel exhibited a pronounced settlement under dynamic loads, relative to those formulated from clay, primarily attributable to the absence of cohesion forces, augmented porosity, and diminished energy dissipation efficacy. Such factors render sand and gravel more prone to compression and settlement upon exposure to dynamic loads. Moreover, embankments fabricated from sand were identified to generate superior pore pressures compared to their clay or gravel counterparts, a phenomenon attributable to sand’s compressibility which can engender augmented volumetric strains and initiate pumping phenomena, thereby elevating pore pressures. In contrast, gravel and clay materials demonstrated enhanced drainage capabilities and reduced compressibility, facilitating the proficient dissipation of excess pore pressures.

In the face of escalating resource scarcity driven by the consumption of non-renewable resources, the industrial circular economy (ICE) emerges as a vital paradigm shift, pivotal for fostering resource-efficient societies and ensuring national resource security. This integrative review aims to critically assess the evolution and challenges inherent within the ICE over recent years, with a specific focus on the burgeoning role of machine learning (ML) in this domain. By synthesizing extant literature, this examination reveals several key findings. Firstly, the ICE significantly contributes to cost reduction through enhanced recycling and secondary utilization, underscoring its environmental stewardship. Secondly, it is evident that ML exhibits substantial promise in the manufacturing sector, not only augmenting production processes but also elevating product precision, reducing defect rates, and minimizing the likelihood of production mishaps. Most crucially, the application of ML within the ICE is identified as a potent catalyst, driving advancements across various facets - data analysis, model development, technological innovation, and equipment refinement. This analysis further elucidates the intrinsic value of ML in resource recycling and waste management, yielding improvements in resource recycling rates and methodologies, which in turn curtails production costs and amplifies output efficiency. Despite the strides made in replacing traditional industrial models with more sustainable ICE practices, challenges persist, particularly regarding the suboptimal levels of resource recycling and the continued generation of industrial waste. The integration of ML within ICE frameworks is posited as a transformative approach, offering not only enhanced resource recycling capabilities and superior product quality but also a sustainable trajectory for future industrial development. This study, therefore, contributes to the growing discourse on sustainable industrial practices, underscoring the synergistic potential of ML in revolutionizing the ICE, thereby aligning with the broader objectives of sustainable economic development.