The Oued Kert watershed in Morocco is essential for local biodiversity and agriculture, yet it faces significant challenges due to meteorological drought. This research addresses an urgent issue by aiming to understand the impacts of drought on vegetation, which is crucial for food security and water resource management. Despite previous studies on drought, there are significant gaps, including a lack of specific analyses on the seasonal effects of drought on vegetation in this under-researched region, as well as insufficient use of appropriate analytical tools to evaluate these relationships. We utilized the Standardized Precipitation Index (SPI) and the Normalized Difference Vegetation Index (NDVI) to analyze the relationship between precipitation and vegetation health. Our results reveal a very strong correlation between SPI and NDVI in spring (98%) and summer (97%), while correlations in winter and autumn are weaker (66% and 55%). These findings can guide policymakers in developing appropriate strategies and contribute to crop planning and land management. Furthermore, this study could serve as a foundation for awareness and education initiatives on the sustainable management of water and land resources, thereby enhancing the resilience of local ecosystems in the face of environmental challenges.

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

This study examines the impact of state highway construction contracts on state spending efficiency controlling for production structure, service demands, and situational factors. The theoretical argument is that because highway construction projects are relatively large in scale, complex, and can be monitored through objective performance measurement, state highway construction programs may save government production costs through contracts. Contracting helps highway producers achieve efficiency by optimizing production size based on workload and task complexity. The unit of analysis is 48 state governments’ highway construction contracts from 1998 to 2008. Through a two-stage analysis method including a Total Function Productivity (TFP) index and system dynamic panel data analysis, the results suggest that highway construction contracts enhance state highway spending efficiency, especially for large-scale construction projects.

This scholarly article aims at analyze the obstacles encountered by teachers who specialize in the transition from early childhood education to primary education, as well as the possible areas for their growth and progress. Through the utilization of a combination of qualitative and quantitative research methods, along with detailed case studies, we have identified a number of major challenges faced by these teachers. These challenges primarily center around psychological stress, disparities in educational philosophies, and the task of bridging the gap between home and kindergarten education. To promote the professional development of teachers specializing in this transition in the future, it is crucial to prioritize their mental well-being, implement policy reforms, and emphasize the importance of comprehensive qualities and innovative pedagogical approaches.