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 study examines the economic and social impacts of a Southeast Asian multinational company operating in the northwestern region of Hungary, with a particular focus on the local labor market and community responses. The research aims to explore the company’s location choice motivations, its integration process into the local economy, and its cooperation with the local government and communities. The research provides a comprehensive picture of the company’s impacts by employing qualitative and quantitative methodologies—including management interviews and household surveys. The findings indicate that the company has significantly increased employment, enhanced infrastructure, and promoted cultural diversity. However, challenges related to cultural integration persist. The study offers valuable guidance for policymakers and businesses on leveraging the economic benefits of foreign investments and fostering cultural cooperation. Future research could delve deeper into the long-term socio-economic impacts.

As China’s urbanisation continues, the building area is expanding, of which the occupancy of rural residential buildings is also very large. However, most rural buildings have poor thermal performance. This paper analyses the energy-saving potential of green facades for rural buildings in China by simulating typical buildings with different types of facades in rural China. The simulation results show that indirect green façades can achieve good energy savings. Buildings with four types of facades: red brick, rubble, hollow brick, and concrete achieve energy savings of 18.39%, 17.85%, 14.47%, and 11.52%, respectively, after retrofitting with green facades.

Personal branding is a conscious activity that utilizes classic product marketing methods to make a person more marketable. In our study, we employed a quantitative research methodology. Through a survey, we examined the importance respondents assign to visible and non-visible traits and characteristics. During the data analysis, we established a ranking of the most important traits identified by the survey participants, which they believe contribute to a more favorable perception. Among the top five ranked traits—reliability, appearance, charisma, grooming, and authenticity—three are recognizable during the first encounter. Our findings suggest that women place greater emphasis on social perception than men, making them more likely to remain unnoticed. At the same time, younger generations tend to overvalue their presence on social media platforms.

Among contemporary computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are favoured because of their capacity to tackle non-linear modelling and complex stochastic datasets. Nondeterministic models involve some computational intricacies when deciphering real-life problems but always yield better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling power generation/electric power output (EPO) from databases generated in a combined cycle power plant (CCPP). The study presents a comparative study between ANNs and ANFIS to estimate the power output generation of a combined cycle power plant in Turkey. The inputs of the ANN and ANFIS models are ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V), correlated with electric power output. Several models were developed to achieve the best architecture as the number of hidden neurons varied for the ANNs, while the training process was conducted for the ANFIS model. A comparison of the developed hybrid models was completed using statistical criteria such as the coefficient of determination (R²), mean average error (MAE), and average absolute deviation (AAD). The R² of 0.945, MAE of 3.001%, and AAD of 3.722% for the ANN model were compared to those of R² of 0.9499, MAE of 2.843% and AAD of 2.842% for the ANFIS model. Even though both ANN and ANFIS are relevant in estimating and predicting power production, the ANFIS model exhibits higher superiority compared to the ANN model in accurately estimating the EPO of the CCPP located in Turkey and its environment.