As it tries to incorporate computer-based technology into the public services provided by businesses or organisations, the digital transformation process is currently regarded one of the most prominent topics in circulation. Establishing fundamentals and points is necessary for digital transformation, as is relying on a specific set of employee talents and actively incorporating customers in the process's evolution. Cybersecurity has become an increasingly important issue for governments and businesses worldwide, and this shift towards digital services is a top priority for all of them. Accelerating the digital transformation process and the usage of its services are the vulnerabilities to cyberspace, the advancement of technology and gadgets, and the employment of artificial intelligence in the development of current apps. In order to build cybersecurity governance that can be relied upon and is useful in accomplishing duties without hacking and tampering with data and information, it is necessary to implement simple programmes and tactics. This article emphasises the significance of cybersecurity governance in delivering secure and efficient technical means that can meet all risks and challenges and protect persons' data across all industries.

Digital Twin (DT) technology has emerged as a transformative tool in urban flood risk management (UFRM), enabling real-time data integration, predictive modeling, and decision support. This systematic review synthesizes existing literature to evaluate the scientific impact, technological advancements, and practical applications of DTs in UFRM. Using the PRISMA 2020 framework, we retrieved 1085 records (Scopus = 85; Web of Science = 1000), merged and deduplicated them using DOI and fuzzy-matched titles, screened titles/abstracts, and assessed full texts. This process yielded 85 unique peer-reviewed studies published between 2018 and 2025. Key findings highlight the role of remote sensing (e.g., satellite imagery, IoT sensors) in enhancing DT accuracy, the integration of machine learning for predictive analytics, and case studies demonstrating reduced flood response times by up to 40%. Challenges such as data interoperability and computational demands are discussed, alongside future directions for scalable, AI-driven DT frameworks. This review identifies key technical and governance challenges while recommending the development of modular, AI-driven DT frameworks, particularly tailored for resource-constrained regions.

The Internet of Things (IoT) as Wireless Sensor Networks (WSNs) holds a significant role in various areas: Military surveillance, Industrial automation, Smart housing, Security Systems, Intelligent Vehicular Traffic control, as well as Healthcare monitoring. So, the sensor nodes performance and life-time with limited power, memory and processing capabilities are significant issues to face in the network. To overcome these limitations, a wide range of energy-aware packet forwarding mechanisms and routing protocols have been proposed to optimize network throughput and lifetime. A WSN's performance is strongly dictated by its node deployment approaches, energy consumption profiles, communication latency, and data aggregation techniques. Furthermore, as the aspects of the network topology (chain-based or cluster-based hierarchical) and the criteria for choosing aggregator nodes to further transmit the sensed data to the sink node strongly affect delivery time and energy balance. Existing hierarchical routing protocols are well studied by several surveys, for example LEACH, PEGASIS, PDCH, CHIRON, CCBRP, CCM, TSCP, DLRP and DCBRP. To do so, it underlines why the addition of Artificial Intelligence (AI) to mixed hierarchical topologies is improving decision-making processes, and adaptive clustering while making WSNs more efficient, scalable and resilient in their performance. We further provide a comparative analysis, primarily emphasizing the advantages and limitations of different chain-based and cluster-based AI-assisted routing solutions.

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.

Urbanization markedly influences the microclimatic conditions in semi-arid regions by elevating land surface temperatures (LST) and contributing to ecological degradation. This study examined the spatial and temporal evolution of LST and urban heat island (UHI) effects in Benguerir, Morocco, over a 30-year period (1994–2024), employing high-resolution satellite imagery and in situ sensor data. Urban expansion was quantified using thermal bands from Landsat imagery, the Normalized Difference Built-up Index (NDBI), and the Built-up Index (BU), whereas thermal comfort was evaluated through the Universal Thermal Climate Index (UTCI) and Predicted Mean Vote (PMV) using air temperature and humidity data collected via spatial sensor and the Sniffer Bike mobile sensor network. These urban transformations have intensified the UHI effect, resulting in a 29.34 °C increase in mean LST to 41.82 °C in 2024 across built-up areas. Statistical modeling revealed strong linear relationships between LST and urban indices, with R² values ranging from 0.93 to 0.96, and correlation coefficients around 0.98 (all p-values < 0.001), indicating a reliable model fit. Furthermore, the analysis of thermal comfort trends underscores urbanization’s impact on human well-being. In 1994, 34.2% of the population experienced slight warmth and 65.8% experienced hot conditions. By 2024, conditions had shifted dramatically, with 76.7% experiencing hot conditions and 16.2% exposed to very hot conditions, leaving only 7.1% in the slight warmth category. These findings highlight the urgent need for adaptive urban planning strategies. The implementation of urban greening initiatives, the use of reflective materials, and the integration of data-driven planning approaches are essential to mitigate thermal stress and enhance urban resilience. Leveraging climate modeling and spatial analytics can support the identification of high-risk zones and inform targeted interventions to effectively address the escalating UHI phenomenon.