Developed countries have successfully implemented various Industry 4.0 (I4.0) initiatives, showcasing their ability to reap the benefits of this new industrial revolution. Active pursuit of excellence in Industry 4.0 is evident in these nations. However, in Africa, many countries still lack a clear understanding of Industry 4.0, with some remaining trapped in Industry 1.0 and others facing challenges in transitioning to Industry 2.0. Moreover, a significant number of these African countries continue to grapple with limited access to reliable electricity. To address the issue, this study examines seven strategies identified as criteria for enhancing the adoption of Industry 4.0 within the East African Community (EAC). These strategies are derived from observations of Industry 4.0 initiatives implemented in developed countries. Subsequently, the criteria are used to evaluate and rank the level of Industry 4.0 adoption in two specific East African countries. To tackle the challenges of complex group decision-making, the study integrates the Weighted Aggregated Sum Product Assessment (WASPAS) technique with the Step-Wise Weight Assessment Ratio Analysis (SWARA) within a spherical fuzzy (SF) framework. The SF-SWARA approach is applied to determine the weight and importance of the criteria, while SF-WASPAS is employed to rank the countries based on the criteria weighted by SF-SWARA. According to the findings, it was revealed that education and training, research, development, and innovation, as well as public-private partnerships and policy innovation, are the three most influential strategies for significantly improving the adoption of Industry 4.0 within the East African community. Furthermore, the results indicate that Rwanda stands out as the leading country in terms of implementing these strategies to enhance the adoption of Industry 4.0 technology. To verify the reliability and suitability of the proposed methodology, a sensitivity analysis was conducted, which affirmed the stability and practicality of the suggested approach.

Water distribution networks are susceptible to abrupt pressure fluctuations and spikes due to rapid adjustments in valve and pump settings. A common occurrence resulting from the sudden closure of a valve, known as water hammer, can potentially cause damage to various components within the network if not adequately addressed. Traditionally, water hammer phenomena have been modeled using a set of hyperbolic partial differential equations (PDEs). This study introduces a simplified model that employs switched differential-algebraic equations (DAEs). Recognized for their capacity to generate infinite peaks in response to sudden structural changes, switched DAEs provide mathematical representations of infinite peaks, manifested as Dirac impulses. This modeling approach offers the potential for more straightforward analyses of complex water networks in future research. To validate the proposed technique, a numerical comparison was conducted between the PDE- and DAE-based models, using a basic configuration consisting of two reservoirs, a pipe, and a valve.

Libya's strategic position at the crossroads of Europe and Africa offers access to abundant raw materials, labor, and extensive land for establishing free trade zones. The primary objective of this research is to determine the key advantages and opportunities that Libya could potentially leverage as a transit trade hub in the Mediterranean region through the establishment of free trade zones. This study investigates the extent to which Libya facilitates the expansion of commerce between Europe and Africa via initiatives related to free trade zones. Six criteria were employed in the present research, including economic, social, financial, environmental, quality, and demand factors. A panel of experts evaluated these criteria. The Full Consistency Method (FUCOM) was utilized to derive the criteria weights, with the economic criterion identified as the most significant. The Grey-CoCoSo (Combined Compromise Solution) methodology was applied to rank the industries eligible for investment within Libya's free zones. According to the findings, the food sector holds the highest importance in relation to investment.

This study aimed to examine the correlation among motivation, organizational culture and employee performance and the mediation role of job satisfaction in higher education. A more productive and positive work environment was created by organizations by understanding the connection among these factors. This study provided valuable insight into how to create a culture of motivation and satisfaction to improve employee performance. 364 participants were selected from Yemeni and Omani universities, including academics and staff. A cross-sectional survey design was employed, with participants selected using stratified random sampling. Questionnaires were contributed online using emails and social media applications and analyzed by PLS-4. Results of Partial Least Squares Structural Equation Modeling (PLS-SEM) revealed that extrinsic motivation had a negative impact on employee performance while organizational culture had a significant positive impact in dynamic environment. Results also highlighted the positive role of job satisfaction represented by supervisor-employee engagement, incentives and promotion in enhancing performance in dynamic environment. Researchers recommended to aggressively increase job satisfaction and employee performance with extrinsic motivation in dynamic environment in Arabic region.

With the expansion of steel production via electric arc furnaces, an increase in dust generation—a by-product of these operations—poses substantial challenges. These difficulties stem from land use restrictions for large-scale dust waste storage and the environmental implications of heavy metal contamination inherent in the dust. In an effort to promote the repurposing of this potentially hazardous solid waste, this study examines the concentration and leachability of various heavy metals in this dust. Digestion of the dust samples was carried out in a controlled laboratory setting, after which the concentrations of iron (Fe), magnesium (Mg), zinc (Zn), manganese (Mn), nickel (Ni), lead (Pb), cadmium (Cd), and cobalt (Co) were determined using flame atomic absorption spectrometry. The mean concentrations of these heavy metals in the dust were found to be in the following descending order (in mg/kg): Fe> Mg> Zn> Mn> Ni> Pb> Cu> Cd> Co. Water leaching tests were subsequently conducted, revealing that Co and Cd exhibited the greatest leachability at varying pH levels. Conversely, Fe and Ni displayed minimal leachability. These findings have significant implications for the reuse and environmental management of electric arc furnace dust.

In this study, an intelligent optimization system for laser micro-machining operations is developed, utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS). The heuristic optimization tool, ANFIS, synergistically combines back-propagation training with gradient descent in a unidirectional manner. A comprehensive training set, incorporating experimental data from the literature, highlights the sensitivity of groove depth and recast layer height to specific critical operating factors during the laser micro-machining process. By optimizing lamp current, pulse width, and frequency, the proposed system aims to achieve superior groove depth and recast layer height outcomes. This novel microscopic research holds the potential to captivate both academic scholars and industry professionals.

This study focuses on the detailed reconstruction of chain wheel geometry utilizing measurement data gathered from the MarSurfXC20 contact system and the iNEXIVE VMA 2520 optical system. Supplementary data were also gathered from a digital micrometer and a caliper to provide a comprehensive data set for the analyzed geometry. The geometric model of the chain wheel was then constructed using Siemens NX software. The reconstructed model was subsequently compared with the original design specifications to assess the fidelity of the reconstructed model. Results demonstrate a high degree of correlation between the model generated by reverse engineering and the original design model. Despite the satisfactory correlation, potential inaccuracies were identified, necessitating further research to mitigate these discrepancies and optimize the procedures for parameters beyond the established tolerance. The study affirms the feasibility of utilizing contact and optical measuring systems in the reverse engineering process of chain wheel geometry, although it underscores the need for additional refinement to improve the model's accuracy.

This investigation elucidates the influence of Administrative Empowerment (AEM) on employee performance within the distribution sector of petroleum products in Torbat Heydarieh, Iran, utilizing a case-study approach to examine the correlational effects of varied AEM factors. A descriptive-analytical methodology was employed, with data collected through a standardized empowerment questionnaire, administered to the entire workforce as the population of interest. The validity of the questionnaire was ensured through the application of the Kolmogorov-Smirnov (K-S) test and Cronbach's alpha, while regression correlation coefficients were used to confirm the legitimacy of the resultant data. A simple random sampling method was employed, yielding a sample size of 45 participants. The principal outcome of this research suggests a consensus regarding the positive influence of AEM on expertise-based outcomes within the Iranian petroleum product distribution sector. Further, the study identified the workplace environment, morale, organizational belongingness, access to knowledge information, and job skills as the most potent determinants influencing human resource motivation. These elements surfaced as critical, feasible, and interesting aspects of work, and were found to be of paramount importance in the empowerment process.

The COVID-19 pandemic emerged over three years ago as a public health crisis, swiftly evolving into a worldwide economic crisis with far-reaching implications for global business enterprises and industries. The unprecedented disruption has varied across sectors, with some experiencing severe consequences while others have thrived. As governments and economies continue to recover from the pandemic's effects, it is crucial to analyze and comprehend these impacts to foster sustainable growth and prepare for future disruptions. This study aims to examine the COVID-19 pandemic's ramifications on Pakistan's software business enterprises by addressing three exploratory research questions: a) the pandemic's influence on Pakistan's software business enterprises, b) actions and initiatives undertaken by these enterprises during the pandemic, and c) the contributions made by these enterprises in combating the pandemic. Employing a mixed-methods approach, a survey research design was developed, incorporating both quantitative and qualitative methods to create a questionnaire grounded in a literature review. The findings of the survey are presented and discussed in-depth. This research contributes to the expanding body of knowledge on the COVID-19 pandemic's effects on business enterprises and industries.

Understanding the response of buildings to wind loads is critical, as these forces can generate significant pressure and suction, potentially leading to structural failure if overlooked. This research was focused on examining the effects of openings on triangular-shaped buildings when subjected to high wind load conditions. Utilizing CAD modeling and Computational Fluid Dynamics (CFD) simulations, the analysis was executed through the ANSYS simulation package. Subsequent Fluid-Structure Interaction (FSI) studies were conducted to ascertain shear stress and lateral deformation. The studies encompassed building models both with and without openings, with the evaluation of induced pressure and velocity. The resultant drag on buildings incorporating openings was discovered to be 6679N lower than those without openings. Furthermore, an analysis employing M25 concrete indicated a 33.13$3 \%$ reduction in lateral deformation in buildings with openings as compared to those without. For buildings constructed with M30 concrete, a 32.17$3 \%$ decrease in lateral deformation was observed. Despite the informative findings, it should be recognized that the investigation was confined to a particular range of wind load conditions and did not consider extreme scenarios. Dynamic wind effects and long-term structural behavior were not included in the current analysis. Therefore, while this study elucidates the importance of wind load analysis and structural reinforcement for maintaining building stability, further research is warranted. Such future investigations should consider broader simulation models, encompassing diverse building shapes and wind load conditions, and account for additional influential factors.

This study aims to optimize the structure of compact Plate-Fin Heat Exchangers (PFHE) by incorporating corrugated fins and validating their improved performance through numerical modeling and simulation. The results provide valuable insights for refining application-specific design guidelines and enhancing the performance of PFHEs. Using Computational Fluid Dynamics (CFD), the PFHE geometry was created in SolidWorks and Ansys Fluent, with fins modeled in three layers inside the heat exchanger both with and without a cover. To investigate the fins' performance, flow field, and heat transfer, fin thickness, entry velocities, and locations of water and air were varied across three wavelengths (10, 20, and 30) during the numerical investigation. The analysis focused on the variations in pressure, temperature, and fluid velocity within the heat exchanger. Key findings include the observation that temperature distribution is influenced by the velocities of both water and air, with the upper layer experiencing a temperature increase due to the warm fluid stream, while the opposite effect is observed near the bottom layer. Furthermore, fluid temperature variation in the depth direction is attributed to conductive heat transfer through side plates and convective heat transfer to the surroundings. The outcomes of this study have the potential to reduce the pressure difference generated during heat exchange and increase the thermal efficiency of PFHEs.

The rapid urbanization accompanying the evolution into “smart” communities presents numerous challenges, not least of which is the significant increase in road vehicles. This proliferation exacerbates congestion and accident rates, posing major barriers to the successful implementation of innovative technologies such as Wireless Sensor Networks (WSNs), surveillance cameras, and the Internet of Things (IoT). Accurate traffic flow prediction, a crucial component of these technological initiatives, requires a reliable and efficient methodology. This research explores the implementation of an intelligent traffic control system that employs a Transferable Texture Convolutional Neural Network (TTCNN). The design of this system eschews the traditional pooling layer, instead incorporating three convolutional layers and a single Energy Layer (EL). This configuration facilitates the provision of real-time traffic updates, which can enhance the utility and efficiency of the smart city infrastructure. A model inspired by the Hybrid Fruit Fly (HFFO) optimizes the system's hyperparameters. The application of HFFO to the TTCNN showcases the potential for improved accuracy in traffic flow prediction. Simulation results suggest that the HFFO provides superior organizational boundaries for the TTCNN, enhancing the overall accuracy of the model's predictions. The hybrid forecasting method discussed herein demonstrates its potential to outperform other established techniques. This investigation sheds light on the potential benefits of applying deep learning algorithms and hybrid models in the context of traffic flow prediction and control, contributing to the ongoing development of smart urban communities.

As fundamental nodal elements in urban spatial structures, the coupling and coordinated development of urban business centers and urban rail transit contributes to the optimization of these structures. Utilizing complex network theory, a model for the urban rail transit network was constructed. The importance and hub nature of urban rail transit stations were evaluated from different angles, including degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. These metrics examined the station's degree, closeness to other nodes, number of shortest paths, and centrality of neighboring nodes. The coupling relationship between urban rail transit and urban business centers was taken into account, leading to the creation of a coupling and coordination degree model for urban rail transit stations and urban business centers. An analysis of the spatio-temporal evolution of the coupling relationship between urban rail transit and business centers in Beijing, Shanghai, and Guangzhou from 2000 to 2020 was conducted. The findings indicated an interactive and mutually influencing coupling relationship between the urban rail transit network and urban business centers. Over time, the coupling and coordination degree of urban rail transit stations and urban business centers trended from being uncoordinated towards preliminary, moderate, and good coordination. Spatial heterogeneity existed in the coupling and coordination status of different circles, with the best coupling and coordination conditions being in the core area. There was a degree of variance in the coupling and coordination development situation of rail transit stations and business centers in the core areas of different cities. Among them, Shanghai's core area had the best spatial coupling and coordination development situation, Beijing's core area lagged in business center development compared to the construction of the urban rail transit network, while Guangzhou's core area saw urban rail transit network development lag behind its mature business centers. The application of these research findings aids in promoting sustainable urban development. While this study primarily measured the importance of urban rail transit network stations from the node centrality perspective, future studies could further examine the spatial coupling of urban rail transit and business centers from the viewpoints of accessibility and passenger flow.