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.

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.

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.

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.

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.