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.

Improving the competitiveness of tourism destinations is crucial for driving local economies and achieving income growth. In light of this evidence, numerous government departments strive to assess specific factors that impact the competitiveness of tourism destinations, enabling them to issue appropriate new tourism policies that promote more effective forms of tourism business. Therefore, the primary objective of this paper is to investigate how various elements such as tourism resources, tourism support, tourism management, location conditions, and tourism demand influence regional competitiveness in the Northern Bay region of Guangxi Province in China. To accomplish this goal, an online survey was conducted to collect data from 420 visitors who had experienced North Gulf Tourism; yielding an impressive response rate of 95 percent. The findings reveal that all aforementioned factors—namely: Tourism resources, tourism support, tourism management, location conditions and tourist demand—significantly impact destination competitiveness. Notably though, it was found that among these factors influencing destination competitiveness; it is primarily determined by effective local-level management (β = 0.345). Following closely behind are tourist demand (β = 0.133) as the second most influential factor affecting destination competitiveness; followed by location conditions (β = 0.116) ranking third; then comes tourist support (β = 0.03) as fourth in line impacting destination competitiveness; finally with least impact being exerted by available tourist resources (β = 0.016). Consequently, highlighting that regional competitiveness within Guangxi’s Northern Bay area predominantly hinges on efficient local-level management practices thus strongly recommending relevant authorities formulate novel work policies aimed at enhancing levels of local-level competitive advantage within the realm of regional touristic offerings.

In the present work, a series of butyl methacrylate/1-hexene copolymers were synthesized, and their efficiency as viscosity index improvers, pour point depressants, and shear stabilizers of lube oil was investigated. The effect of 1-hexene molar ratio, type, and concentration of Lewis acids on the incorporation of 1-hexene into the copolymer backbone was investigated. The successful synthesis of the copolymers was confirmed through FTIR and 1H NMR spectroscopy. Results obtained from quantitative 1H NMR and GPC revealed that an increase in the molar ratio of 1-hexene to butyl methacrylate, along with concentration of Lewis acids led to an increase in 1-hexene incorporation and a reduction in Mn and Ð. Similar trends were observed when the Lewis acid changed from AlCl₃ to organometallic acids. The maximum 1-hexene incorporation (26.4%) was achieved for sample BHY3, with a [1-hexene/BMA] ratio of 4 mol% and a [Yb(OTf)₃/BMA] ratio of 2.5 mol%. Evaluation of the synthesized copolymers as lube oil additives demonstrated that the viscosity index was more significantly influenced by samples with higher molecular weight. Sample BHA13 represents maximum VI of 137. The copolymer containing Yb(OTf)₃ as a catalyst exhibited superior efficiency as a pour point depressant. Furthermore, sample BHY3 showed the lowest shear stability index (6.4).