Nanoscale zero-valent iron (nZVI) is thought to be the most effective remediation material for contaminated soil, especially when it comes to heavy metal pollutants. In the current high-industrial and technologically advanced period, water pollution has emerged as one of the most significant causes for concern. In this instance, silica was coated with zero-valent iron nanoparticles at 650 and 800 ℃. Ferric iron with various counter-ions, nitrate (FN) and chloride (FC), and sodium borohydride as a reducing agent were used to create nanoscale zero-valent iron in an ethanol medium with nitrogen ambient conditions. X-ray diffraction (XRD) and field emission scanning electron microscopy (FE-SEM) techniques were employed to describe the structures of the generated zero-valent iron nanoparticles. Further, we investigated the electrical properties and adsorption characteristics of dyes such as alizarin red in an aqueous medium. As a result, zero-valent nano iron (nZVI), a core-shell environmental functional material, has found extensive application in environmental cleanup. The knowledge in this work will be useful for nZVI-related future research and real-world applications.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.

This study examines the compliance between the accounting standard for Property, Plant and Equipment (PPE) and accountants’ practices in terms of disclosure and measurement, in order to determine its levels and drivers. Based on the assumption that a higher level of compliance is associated with a higher quality of the accounting information system, compliance indices are proposed and econometric regressions are used to analyze the determinants of this accounting compliance for Portuguese firms. The empirical evidence shows that compliance is not high, and that it tends to be higher for disclosing rather than for measuring. Moreover, the results suggest that firm size has a positive impact on compliance, both for measurement and disclosure, consistent with larger firms being subject to greater scrutiny. Liquidity, on the other hand, tends to have a negative effect on compliance, as more liquid firms are less dependent on external financing. Furthermore, while leverage tends to have a positive effect on measurement compliance, profitability has no effect on accounting compliance. Therefore, this study adds evidence straight from the perceptions of practitioners who interpret and apply accounting standards and then influence the quality of financial reporting, providing valuable insights that have the potential to affect confidence in firms.

The study aimed to investigate the concept of workplace equality as experienced and perceived by female librarians of Punjab, Pakistan. Through this investigation, the study aimed to contribute to the broader discourse on creating equitable and inclusive workplaces for women in the field of library and information science. A qualitative research method based on semi-structured interviews was employed to meet the objectives of the study. The interview guide was used to collect data from female librarians working in the Higher Education Commission’s (HEC) recognized public and private sector universities of the Punjab, Pakistan. According to the results, female librarians shared that they have faced gender-based discrimination in job allocation as male librarians were favored for tasks with additional wages or representation at corporate events. Private sector candidates reported issues related to career development opportunities as managers often restrict participation in seminars, conferences, and higher education pursuits. The study also highlighted that inequalities or discriminations affect employees motivation and enthusiasm. This study highlights issues of inequality from a female perspective in the library and information science field, contributing to a deeper understanding of the key factors to ensure equitable workplaces. This study may be a useful contribution to the body of research literature, as well as the findings may help in sensitizing the management and authorities to control the work environment to facilitate females, and to make female-oriented policies.

Purpose: This article explores the adoption of Artificial Intelligence (AI) in Human Resource Management (HRM) in the UAE, focusing on the critical challenges of fairness, bias, and privacy in recruitment processes. The study aims to understand how AI is transforming HR practices in the UAE, highlighting the issues of bias and privacy while examining real-world applications of AI in recruitment, employee engagement, talent management, and learning and development. Methodology: Through case study methodology, detailed insights are gathered from these companies to understand real-world applications of AI in HRM. A comparative analysis is conducted, comparing AI-driven HRM practices in UAE-based organizations with international examples to highlight global trends and best practices. Findings: The research reveals that while AI holds significant potential to streamline HR functions such as recruitment, onboarding, performance monitoring, and talent management, it also discusses challenges and strategies companies face and develop in integrating AI into their HRM processes, reflecting the broader context of AI adoption in the UAE’s HR landscape. Originality: This paper contributes to the growing body of literature on AI in HRM by focusing on the unique context of the UAE, a rapidly developing market with a highly diverse workforce. It highlights the specific challenges and opportunities faced by organizations in the UAE when implementing AI in HRM, particularly regarding fairness, bias, and data privacy.

The purpose of this research is to estimate the differences in sales levels between businesses owned by individuals who self-identify as Indigenous (IE) and those who do not (NIE), as well as between males (ME) and females (WE), and how this intersection may affect their sales levels. To accomplish this, an Analysis of Variance (ANOVA) is used to compare the means between the groups analyzed, and Tukey’s Honestly Significant Differences (HSD) is used to determine the magnitude and direction of these differences. The results of the study show that indigenous-owned businesses have sales that are 26% lower than the general average, while women-owned businesses have sales that are 70.6% lower in the same comparison. In addition, businesses run by indigenous women have sales that are 93.5% lower on average. These findings suggest that the challenges faced by entrepreneurs reflect the structural inequalities observed in other areas of society and highlight the need for public and private policies focused on reducing these gaps.