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.

This investigation derives formulas to predict the mixed convective surface conductance of a flat isotropic surface roughness having a convex perimeter in a Newtonian fluid with a steady forced flow in the plane of that roughness. Heat transfer measurements of a 30.5 cm square rough plate with forced air velocities between 0.1 m/s and 2.5 m/s were made by the present apparatus in two inclined and all five orthogonal orientations. The present work’s formulas are compared with 104 measurements in twelve data-sets. The twelve data-sets have root-mean-square relative error (RMSRE) values between 1.3% and 4% relative to the present theory. The present work’sformulas are also compared with 78 measurements in 28 data-sets on five vertical rough surfaces in horizontal flow from prior work. The five stucco data-sets have RMSRE values between 2.5% and 6.5%; the other data-sets have RMSRE values between 0.2% and 5%. 

In response to the challenges of climate change, this study explores the use of moringa pod powder as reinforcement in the manufacture of compressed earth bricks to promote sustainable building materials. The objective is to evaluate the impact of African locust bean pod powder on the mechanical properties of the bricks. Two types of soils from Togo were characterized according to geotechnical standards. Mixtures containing 8% African locust bean pod powder at various particle sizes (0.08 mm, 2 mm, and between 2 and 5 mm) were formulated and tested for compression and tensile strength. The results show that the addition of African locust bean pod reduces the mechanical strength of the bricks compared to the control sample without pods, with strengths ranging from 0.697 to 0.767 MPa, compared to 0.967 to 1.060 MPa for the control. However, the best performances for the mixtures were obtained with a fineness of less than 2 mm. This decrease in performance is attributed to several factors, including inadequate water content and suboptimal preparation and compaction methods. Optimizing formulation parameters is necessary to maximize the effectiveness of African locust bean pods. This work highlights the valorization of agro-industrial waste, paving the way for a better understanding of bio-based materials and future research for sustainable construction.

Soil salinity is a major abiotic stress that drastically hinders plant growth and development, resulting in lower crop yields and productivity. As one of the most consumed vegetables worldwide, tomato (Solanum lycropersicum L.) plays a key role in the human diet. The current study aimed to explore the differential tolerance level of two tomato varieties (Rio Grande and Agata) to salt stress. To this end, various growth, physiological and biochemical attributes were assessed after two weeks of 100 mM NaCl treatment. Obtained findings indicated that, although the effects of salt stress included noticeable reductions in shoots’ and roots’ dry weights and relative growth rate as well as total leaf area, for the both cultivars, Rio Grande performed better compared to Agata variety. Furthermore, despite the exposure to salt stress, Rio Grande was able to maintain an adequate tissue hydration and a high leaf mass per area (LMA) through the accumulation of proline. However, relative water content, LMA and proline content were noticeably decreased for Agata cultivar. Likewise, total leaf chlorophyll, soluble proteins and total carbohydrates were significantly decreased; whereas, malondialdehyde was significantly accumulated in response to salt stress for the both cultivars. Moreover, such negative effects were remarkably more pronounced for Agata relative to Rio Grande cultivar. Overall, the current study provided evidence that, at the early growth stage, Rio Grande is more tolerant to salt stress than Agata variety. Therefore, Rio Grande variety may constitute a good candidate for inclusion in tomato breeding programs for salt-tolerance and is highly recommended for tomato growers, particularly in salt-affected fields.