Geographic Information Systems (GIS) is one of the most accurate, analytical, results-based, and decision-making tools. In this context, we will try to apply this technique to the best geographical locations for the construction of industrial units in the Nador region (north-eastern Morocco); But these units vary according to the manufacturing branches. The factors in control of light industries vary compared to heavy industries. considerations in the localization of building materials units vary compared to textile units. Thus, the criteria adopted in the localization of industrial units and their indicators differ. We decided to determine several factors that influence plant location, such as proximity to industrial units, proximity standards to cities, road density, topography nature, ports, raw materials, railways, and other factors. We find that the prime locations for establishing industrial facilities and developing industrial zones are the geographical regions neighboring the town of Selouane. The most significant areas for industrial investment cover approximately 1469 hectares, notably the flatlands to the west of Selouane. Additionally, there are well-suited areas such as Azgangan, Beni Ansar, and Zaio, totaling approximately 14,211 hectares in the outskirts of these town.

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE) often fall short in capturing complex environmental interactions, leading to inaccurate soil loss predictions. This research introduces a novel approach using Convolutional Neural Networks (CNNs) combined with Geographic Information Systems (GISs) to improve the precision and spatial resolution of soil loss risk assessments. High-resolution satellite imagery, soil maps, and climatic data were processed to extract critical factors, such as slope, land cover, and rainfall erosivity, which were then fed into the CNN model. The findings revealed that the CNN model outperformed traditional methods, achieving a low Root Mean Square Error (RMSE) of 2.3 and an R-squared value of 0.92, significantly surpassing the USLE and RUSLE models. The resulting high-resolution soil loss maps identified high-risk erosion areas, particularly in the central and eastern regions of the watershed, with soil loss rates exceeding 40 tons/ha/year. These findings demonstrate the superior predictive capabilities of deep learning, offering valuable insights for targeted soil conservation strategies and highlighting the potential of advanced computational techniques to revolutionize environmental modeling.

The increase in the temperature of photovoltaic (PV) cells is a critical factor that negatively affects the efficiency of converting solar radiation into electrical energy. This phenomenon not only reduces energy conversion efficiency but also causes damage to PV components, thereby preventing the achievement of the intended energy production goals. Moreover, the heating of PV modules has two significant impacts: first, a reduction in energy efficiency, and second, a decrease in the lifespan of the solar cells. Therefore, projects aimed at producing clean electrical energy using PV solar panels must consider the study of installation sites for PV solar plants and the impact of environmental conditions on panel performance. Given that an increase in PV cell temperature reduces their productivity, this study examines the impact of ambient temperature on the maximum temperature reached by a PV solar panel and analyzes the results. The results show that installing solar panels in harsh environments characterized by high temperatures, such as Ouarzazate in Morocco, can cause these panels to reach critical temperature thresholds of up to 115°C under high solar flux, which can lead to solar system failure and thus the failure of the entire project. In addition, the heating of photovoltaic modules has two major impacts: firstly, energy efficiency is reduced by around 0.44% for every 1°C increase, and overall efficiency is reduced from 16% to less than 10% under extreme conditions; secondly, solar cell life is shortened. Finally, this study highlights the importance of carrying out thorough climatic and environmental assessments before establishing solar photovoltaic power plants. It also highlights the importance of employing high-performance cooling systems or innovative technologies to reduce the impact of heat on photovoltaic panels. This approach is essential to ensure the longevity and efficiency of solar photovoltaic installations, and to achieve our ambitions for sustainable, green energy production.

The study presents a comprehensive analysis of industrial localization in Nador Province, northeastern Morocco. By employing a descriptive and analytical methodology, we have thoroughly examined the province's natural and human data, diagnosing all indicators influencing the process of industrial localization. Our comprehensive approach has classified the area based on its suitability for industrial localization, ranging from ideal to unsuitable regions. The ideal areas for manufacturing, estimated to cover approximately 1,469 hectares, represent 0.5% of the province's total area. The suitable areas for establishing industrial units, estimated to cover 14,211 hectares, represent 5.4% of the province's total area. These comprehensive findings, which encompass several municipalities, such as Selouane, Al Aroui, Ouled Settout, Beni Ansar, and Zaio, provide a robust foundation for policymakers, urban planners, and industrial stakeholders. Furthermore, our correlation between the current industrial zones delineated in the direction plan, Nador Horizon 2040, and the outcomes derived from the spatial analysis of industrial localization factors using geographic information systems (GIS), adds to the validity and comprehensiveness of our research.

This study aims to analyse the dynamics of industrial electricity consumption in the Greater Nador region over the period 2007–2016, with a particular focus on trends in electricity sales and the expansion of the industrial customer base. To this end, a descriptive analytical approach was adopted, utilizing official data from the National Electricity Office concerning electricity sales to industrial units and the number of industrial consumers in the cities of Nador and Selouane. The analysis employed Pearson correlation tests and comparative evaluations between the two cities to identify patterns and disparities. The results indicate a substantial upward trend in electricity consumption across the region. In Nador, industrial electricity sales increased from 1,208 megawatt-hours in 2007 to 1,373 megawatt-hours by October 2016. Similarly, in Selouane, sales rose from 717 to 866 megawatt-hours over the same period. Parallel to this growth, the number of industrial clients also expanded considerably, rising from 353 to 611 in Nador, and from 93 to 143 in Selouane.

Thermal management of solar panels is a major challenge, particularly in hot climates, and has attracted growing interest from both researchers and professionals in the photovoltaic (PV) sector. In this study, a two-dimensional numerical analysis was conducted to evaluate the thermal performance of an aluminum box filled with a phase change material (PCM) used for cooling a solar panel. The impact of integrating horizontal fins placed either on one side or on both sides of the PV/PCM system, was also examined to optimize heat transfer and stabilize temperature. The simulations indicate that a configuration with five fins arranged on one side, spaced 18.25 mm apart, provides effective cooling. The use of RT25 PCM maintains the surface temperature at 28 °C for 100 minutes before it gradually increases without exceeding 65 °C. Similarly, RT35 also demonstrates good thermal performance, maintaining the temperature between 35 and 43 °C for 252 minutes before eventually reaching a peak of 61 °C. These results highlight the effectiveness of the fins and the critical role of PCM selection in enhancing the thermal regulation of solar panels.