Human migration is based on pull factors that individuals evaluate when it comes to moving to a different territory. Likewise, employee attrition is a phenomenon that represents the tendency to a reduction in employees within an organization. This research paper aims to develop and evaluate machine learning algorithms, namely Decision Tree, Random Forest, and Binary Logistic Regression, to predict employee attrition using the IBM dataset available on Kaggle. The objective is to provide organizations with a proactive approach to employee retention and human resource management by creating accurate predictive models. Employee attrition has significant implications for an organization's reputation, profitability, and overall structure. By accurately predicting employee attrition, organizations can identify the factors contributing to it and implement data-driven human resources management practices. This study contributes to improving decision-making processes, including hiring and firing decisions, and ultimately enhances an organization's capital. The IBM dataset used in this study consists of anonymized employee records and their employment outcomes. It provides a comprehensive HR data representation for analysis and prediction. Three machine learning algorithms, Decision Tree, Random Forest, and Binary Logistic Regression, were utilized in this research. These algorithms were selected for their potential to improve accuracy in predicting employee attrition. The Logistic Regression model yielded the highest accuracy of 87.44% among the tested algorithms. By leveraging this study's findings, organizations can develop predictive models to identify factors contributing to employee attrition. These insights can inform strategic decisions and optimize human resource management practices.

Methyl α-D-mannopyranoside (MAM) is a naturally occurring carbohydrate derivative that has gained attention in drug discovery due to its potential therapeutic applications, particularly as an antifungal agent. In this study, we employed a computational approach to investigate the interactions between MAM and two Candida albicans antifungal proteins, 1IYL and 1AI9, through molecular docking simulations. Furthermore, we performed a PASS (Prediction of Activity Spectra for Substances) analysis to predict MAM potential biological activities, explored the pharmacokinetic properties and ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles, and optimized the MAM using the density functional theory (DFT) method. The molecular docking results revealed favorable binding interactions between MAM and the active sites of the 1IYL and 1AI9 proteins, suggesting potential antifungal activity. Additionally, the ADMET profiles indicated low toxicity and suitable drug-like properties, such as moderate metabolic stability and minimal risk of adverse effects. Furthermore, DFT optimization was performed to investigate the molecular geometry and electronic properties of MAM. The optimization results provided valuable information on the stability and reactivity of MAM, enabling a better understanding of its chemical behavior and potential modifications for enhanced activity. Finally, PASS prediction was employed to evaluate MAM's potential biological activities beyond its antifungal properties. The analysis revealed several potential activities, including antibacterial, antiviral, and immunomodulatory effects, expanding the scope for future research and therapeutic applications. In conclusion, this computational study sheds light on the molecular interactions, pharmacokinetic properties, ADMET profiles, DFT optimization, and PASSES predictions of MAM. These findings highlight the potential of MAM as a promising antifungal agent with favorable pharmacological properties.

The nitrovinyl moiety is surely one of the most reactive fragments in organic chemistry, continuously employed to explore original pathways and reach new targets. One of the most important classes of chemicals containing this fragment are nitro-functionalised analogues of 1,3-butadiene. In this paper a comprehensive review study of these compounds is performed. The manuscript includes an analysis of physicochemical properties, spectral characteristic, synthesis, biological activity, and possible transformations of nitro substituted analogues of 1,3-butadiene. The analysis was divided into several parts, depending on the number of nitro groups included in the structure of a 1,3-butadiene analogue. In addition, the work includes information about hazards and safety principles when working with nitro compounds. Based on the analysis of the literature, it can be concluded that nitro-functionalised analogues of 1,3-butadiene exhibit diversified profile of properties, and an outstanding potential as valuable reagents in organic synthesis useful to obtain heterocycles that can be applied in medicine.

This study explores the influence of two key factors, namely communication and disposition, on policy implementation in the educational environment. The main objective of the research is to investigate the impact of communication and disposition on policy implementation, with a specific emphasis on the moderating role of disposition in the relationship between communication and policy implementation. The method used in this research is partial least squares structural equation modelling (PLS-SEM), analysing data from 232 research samples obtained from nine schools in Indonesia. The research results indicate that communication has a significant and positive influence on policy implementation, while disposition also has a significant and positive impact on policy implementation. A more interesting finding is that disposition, in the context of this research, proves to play a crucial role as a moderating variable, enhancing the positive influence between communication and policy implementation. This finding contributes significantly to our understanding of the complexity of factors influencing policy implementation in the educational environment, particularly from the interaction perspective between communication and disposition. The implications of this research can form the basis for the formulation of more effective and contextual policy strategies in the future.

Android malware is widespread and it is considered as one of the most threatening attacks recently. The threat is targeting to damage access data or information or leaking them; in general, malicious software consists of viruses, worms, and other malware. Current malware attempts to prevent being detected by any software or anti-virus. This paper describes recent Android malware detection static and interactive approaches as well as several open-source malware datasets. The paper also examines the most current state-of-the-art Android malware identification techniques including identifying by comparative evaluation the gaps between these techniques. As a result, an API-based dynamic malware detection framework is proposed for Android to provide a dynamic paradigm for malware detection. The proposed framework was closely inspected and checked for reliability where meaningful API packages and methods were discovered.

The global water crisis requires effective wastewater treatment methods where conventional approaches often face challenges related to cost, recyclability, efficiency, and overall effectiveness. In this regard, the significantly small size, large surface area, and enhanced photocatalytic properties of recently developed nanoparticles have opened new avenues for wastewater treatment. This comprehensive review focuses on recent advancements in the synthesis methods for different types of nanoparticles and nanocomposites based on metals, carbon, polymers, waste materials, and zeolites which have highly sustainable and innovative results in wastewater treatment. The introduction of silver and gold nanoparticles has enhanced photocatalytic and biological activities. Similarly, zeolite and seaweed composites have exhibited efficient dye degradation capabilities. Eco-friendly carbon soot nanoparticles have shown promising biological activities, while nitrogen-doped carbon quantum dots have exhibited long-term stability for various applications. Additionally, waste material-based calcium oxide nanoparticles and carbon quantum dot-carbon nanotube nanocomposites have also shown enhanced dye degradation activities.

Metal-organic frameworks (MOFs) are a relatively new class of materials of unique porous structures and exceptional properties. Currently, more than 110,000 types of MOFs have been reported among the countless possibilities. In this study, we have synthesised a novel MOF using zirconium chloride as the metal source and 4,4'-dicarboxy-2,2'-biquinoline (bicinchoninic acid disodium salt) as the linker, which reacted in N,N-Dimethylformamide (DMF) solvent. Three preparation methods were employed to prepare five types of the MOF, and they were compared to optimize the synthesis conditions. The resulting MOFs, named Zr-BADS, were characterised using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), microscopy, and X-ray diffraction (XRD). By incorporating methanol into the preparation solvent, the surface area was increased to 396 m2/g. Additionally, the prepared MOFs exhibited amorphous shapes, with variations in size depending on the synthesis method. This research demonstrates the significance of the preparation method in controlling resulting particles’ properties.

Considering the increasing demand for cosmetic surgery and the number of private cosmetic surgery clinics, it is essential to measure and manage the quality of services provided by these clinics. Obtaining sufficient knowledge about the content perceived by the clients of the quality of services provided by specialized clinics can affect identifying improvement opportunities and criteria that will cause their competitive advantage, and on the other hand, it also prevents wasting resources. For this purpose, this study aims to identify, evaluate, and prioritize the criteria for quality improvement in cosmetic surgery clinics. First, the effective criteria focus on the quality of medical services have been identified by reviewing the research background. Then, the identified criteria in the case study are customized by the Delphi method, and then the DEMATEL-based analytic network process method (DANP) is applied to reveal their causal relationships between criteria and sub-criteria to determine the direct and indirect influences, and finally, all of them are prioritized. In the end, based on the obtained results and knowledge of experienced medical experts in the case study, some managerial solutions are proposed to improve the quality of the provided medical services.

The inhibiting effect of chloroquine compounds (ChCs) on the SARS-CoV-2 virus represents a highly debated form of study owing to the emerging proposals of mechanistic implications for exploring the mode of action of ChCs on the virus. Keeping in view the emerging significance of chloroquine derivatives, the present study was performed to screen one hundred and ninety known chloroquine derivatives for their interaction with several SARS-CoV-2 target proteins by molecular docking and molecular dynamics simulations to obtain an in-depth understanding of the potential hits of these compounds against SARS-CoV-2. A total of 190 molecules from the chloroquine family were screened for the identification of potential new inhibitors of the three therapeutic target proteins of SARS-CoV-2, namely Mpro (master protease), PLpro (papain-like protease) and SGp-RBD (spike glycoprotein receptor binding domain). The ChCs bound to SARS-COV-2 Mpro, PLpro, and SGp-RBD were generated by molecular docking and molecular dynamics simulations. Herein, a comparative analysis of chloroquine family products and a well-known drug against SARS-CoV-2, called Remdesivir, has also been presented. This investigation is intended to study the mechanism of interaction between ChCs and the SARS-CoV-2 virus and explore the unprecedented areas associated with the inhibitory activity of ChCs against this virus.

The efficiency of different sorbents (bulk soil, sand, silt, clay and humic acid-HA) for chlorantraniliprole (CAP), dinotefuran (DNF), bispyribac-sodium (BPS), and metribuzin (MBZ) adsorption was described and compared. The adsorption of CAP, DNF, BPS and MBZ were significantly greater on clay and HA fractions than the bulk soils. Also, the tested pesticides sorption was greater on HA fraction compared to clay fraction except in the case of BPS. The mass unit of bulk soil adsorbed of CAP, DNF, BPS and MBZ approximately 15, 20, 10 and 5 % of the same of HA and 40, 60, 95 and 35 % of that of clay fraction, respectively. The actual amounts adsorbed on bulk soil ranged from (88.63 to 93.93 %) of the calculated amounts adsorbed on all soil components indicating high efficiency of the tested soils to adsorb tested pesticides. All adsorption data fitted well to the linear Freundlich equation. The adsorption of tested pesticides in soil and their different constituents is S-type according to 1/n (except some cases).