Water scarcity, particularly in arid and semi-arid regions, is a critical issue affecting forest management. This study investigates the effects of drought stress on the water requirement and morphological characteristics of two important tree species Turkish pine and Chinaberry. Using a factorial design, the study examines the impact of three age stages (one-year-old, three-year-old, and five-year-old plants) and three levels of drought stress on these species. Microlysimeters of varying sizes were employed to simulate different drought conditions. Soil moisture was monitored to show the effect of the various irrigation schedules. The study also calculated reference crop evapotranspiration (ET₀) using the PMF-56 method and developed plant coefficients (Kc) for the species. Results showed that evapotranspiration increased with soil moisture, peaking during summer and decreasing in winter. Turkish pine exhibited higher plant ET than Chinaberry, particularly among one-year-old seedlings. Drought stress significantly reduced evapotranspiration and water uses for both species, highlighting the importance of efficient water management in afforestation projects. The findings underscore the necessity of selecting drought-resistant species and optimizing irrigation practices to enhance the sustainability of green spaces in arid regions. These insights are crucial for improving urban forestry management and mitigating the impacts of water scarcity in Iran and similar climates globally.

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.

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.

Inflammation of the lungs, called pneumonia, is a disease characterized by inflammation of the air sacs that interfere with the exchange of oxygen and carbon dioxide. It is caused by a variety of infectious organisms, including viruses, bacteria, fungus, and parasites. Pneumonia is more common in people who have pre-existing lung diseases or compromised immune systems, and it primarily affects small children and the elderly. Diagnosis of pneumonia can be difficult, especially when relying on medical imaging, because symptoms may not be immediately apparent. Convolutional neural networks (CNNs) have recently shown potential in medical imaging applications. A CNN-based deep learning model is being built as part of ongoing research to aid in the detection of pneumonia using chest X-ray images. The dataset used for training and evaluation includes images of people with normal lung conditions as well as photos of people with pneumonia. Various preprocessing procedures, such as data augmentation, normalization, and scaling, were used to improve the accuracy of pneumonia diagnosis and extract significant features. In this study, a framework for deep learning with four pre-trained CNN models—InceptionNet, ResNet, VGG16, and DenseNet—was used. To take use of its key advantages, transfer learning utilizing DenseNet was used. During training, the loss function was minimized using the Adam optimizer. The suggested approach seeks to improve early diagnosis and enable fast intervention for pneumonia cases by leveraging the advantages of several CNN models. The outcomes show that CNN-based deep learning models may successfully diagnose pneumonia in chest X-ray pictures.

It is important for society to know the actions implemented by companies in the construction sector to reduce the environmental pollution generated by this industry and to contribute to the solution of economic and social problems in their environment; however, the variables that allow identifying their contributions and impacts are not known. Based on this problem, the study focuses on identifying the factors that influence sustainability management within the construction sector in Colombia. The research presents a predictive approach and uses a quantitative methodology, applying statistical modeling techniques. The sample corresponds to 84 Colombian companies. As a result, a system of equations of the form y=mx+b is presented to describe the deviation of the environmental, economic, social, compensation measures, management, indicators and sustainability reports. The analysis of the intersections constitutes a projective tool to evaluate the relationships and balance points between the dimensions analyzed, helping to identify strengths and opportunities for improvement.

This study explores the factors affecting dentists’ willingness to use social media in their practices, examining how consumer behavior influences their adoption decisions. Despite the growing use of social media across industries, its adoption in dentistry remains relatively underexplored. As investments in digital technologies increase, understanding dentists’ intentions to integrate social media becomes crucial, especially considering the evolving consumer behavior patterns in healthcare. Using the Technology Acceptance Model (TAM) and factoring in patient pressures, this study analyzes data from 209 respondents through SPSS and Smart PLS 4.0. The results offer valuable insights for dentists, highlighting the benefits of social media integration, and justifying investments in these platforms to align with changing consumer expectations. The study also discusses its limitations and suggests future research directions to further explore social media adoption in dentistry and its potential to drive economic growth within the sector.