Online discussion forums allow learners to maintain discussions about their learning process at any time and any place; where they can express their concerns and ideas, and organize their thoughts. Indeed, online discussion forums are used as an indicator of learner's performance. In this context, this paper presents the Social Learning Analytics (SLA) process to describe the interactions between learners in an online discussion forum in Moodle using the Pajek visualization tool.

This study employs a logistic regression model to offer an in-depth understanding of disparities in the access to essential urban services, specifically focusing on electricity and Internet services, in Nouakchott, Mauritania. Through a comprehensive analysis of demographic, geographic, and socioeconomic data, we identify key determinants of access to these utilities. Our findings reveal that the geographic location within the city, particularly in the western regions, and home ownership, significantly bolster the likelihood of having electricity. Conversely, women are found to be disadvantaged in this regard. For Internet access, income level and education, particularly at the Bachelor’s level, emerged as significant predictors. This research not only sheds light on the intricate landscape of service provision in Nouakchott but also offers actionable insights for equitable development. These results empower both policymakers and citizens, marking a step toward transforming Nouakchott into a smarter, more inclusive urban environment.

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco’s Oum Er-Rbia (OER) watershed from 2002 to 2022, using the newly developed Watershed Integrated Multi-Drought Index (WIMDI), through Google Earth Engine (GEE). WIMDI integrates several drought indices, including SMCI, ESI, VCI, TVDI, SWI, PCI, and SVI, via a localized weighted averaging model (LOWA). Statistical validation against various drought-type indices including SPI, SDI, SEDI, and SMCI showed WIMDI’s strong correlations (r-values up to 0.805) and lower RMSE, indicating superior accuracy. Spatiotemporal validation against aggregated drought indices such as VHI, VDSI, and SDCI, along with time-series analysis, confirmed WIMDI’s robustness in capturing drought variability across the OER watershed. These results highlight WIMDI’s potential as a reliable tool for effective drought monitoring and management across diverse ecosystems and climates.

The advancement to Urban 4.0 requires urban digitization and predictive maintenance of infrastructure to improve efficiency, durability, and quality of life. This study aims to integrate intelligent technologies for the predictive maintenance of photovoltaic panel systems, which serve as essential smart city renewable energy sources. In addition, we employ vision transformers (ViT), a deep learning architecture devoted to evolving image analysis, to detect anomalies in PV systems. The ViT model is pre-trained on ImageNet to exploit a comprehensive set of relevant visual features from the PV images and classify the input PV panel. Furthermore, the developed system was integrated into a web application that allows users to upload PV images, automatically detect anomalies, and provide detailed panel information, such as PV panel type, defect probability, and anomaly status. A comparative study using several convolutional neural network architectures (VGG, ResNet, and AlexNet) and the ViT transformer was conducted. Therefore, the adopted ViT model performs excellently in anomaly detection, where the ViT achieves an AUC of 0.96. Finally, the proposed approach excels at the prompt identification of potential defects detection, reducing maintenance costs, advancing equipment lifetime, and optimizing PV system implementation.

This article aims to present a new method of evaluating learners by communities on Facebook groups which based on their interactions. The objective of our study is to set up a community learning structure according to the learners' levels. In this context, we have proposed a new algorithm to detect and evaluate learning communities. Our algorithm consists of two phases. The first phase aims to evaluate learners by measuring their degrees of ‘Safely’. The second phase is used to detect communities. These two phases will be repeated until the best community structure is found. Finally, we test the performance of our proposed approach on five Facebook groups. Our algorithm gives good results compared to other community detection algorithms.