Offshore wind energy is considered as one of the most promising resources of clean and renewable energy to replace fossil fuels. Additionally, its cost is expected to be lower than onshore wind energy as the technology matures. Offshore wind turbines (OWTs) normally operate in harsh ocean environments, which could impact their structural integrity. Scour erosion around foundations of OWTs can substantially change the overall stiffness of these structures and shorten their lifetime. Currently, there are a limited number of studies on countermeasures and their engineering requirements for decreasing the scouring effect; this is due to their different hydraulic circumstances, such as their stability, reliability, and resistance capacity. To this end, advancements in scour protection measures in the offshore energy sector are evaluated in this paper through a thorough and critical review following the PRISMA systematic literature mapping approach. This includes 68 papers on scour protection and over 30 scour protection designs for various types of wind turbine foundations. Here, we aim to provide an overview of the latest scouring protection measures and their comprehensive assessment, as well as their prospects and future challenges. The findings of this study will provide key insights into scour protection measures for OWTs and will subsequently contribute to the future growth of the offshore renewable energy sector.

Water Distribution Networks (WDNs) are critical infrastructure for delivering clean and safe drinking water. As modern WDNs increasingly integrate cyber technologies, they evolve into complex cyber–physical systems (CPSs). This connectivity, however, introduces new vulnerabilities, including cyberattacks. Cybersecurity protects systems from unauthorized access, attacks, and data breaches. In this systematic review, we adopted the PRISMA 2020 reporting guideline. Predefined keyword strings were designed to extract relevant articles from Scopus and Web of Science during the period of 2014–2025. In total, 32 peer-reviewed studies were included for narrative synthesis following duplication and eligibility screening. The review protocol was not registered. This review provides a unified perspective on how Artificial Intelligence (AI) contributes to WDNs resilience. The literature is evaluated in terms of detection tasks, data modalities, learning paradigms, and model architecture. The results highlight three key findings: (a) data bias, reflected in significant reliance on specific synthetic datasets and limited use of real-world utility network data; (b) performance, with deep learning architecture, such as long-short-term memory models, achieving commendable levels of accuracy in intrusion detection, however, overall comparison with other models remain scenario-dependent; and (c) future directions, synthesized through an AI-centered perspective that emphasizes resilience and identifies research gaps in adaptive online learning, attack prediction, interpretability, federated learning and topology localization. This study concludes with recommendations for the broader integration of AI tools to support resilient WDN operation.

Extreme weather events (EWEs) including storms, heatwaves, floods, droughts, wildfires, and cold snaps present an increasing threat to protected habitats and species (PHAS) as well as ecosystem services they provide. Under ongoing climate change, the frequency, intensity, and spatial extent of such events are projected to increase, with disproportionately severe impacts on ecologically sensitive and conservation-priority areas. Despite this growing risk, existing vulnerability assessment frameworks for PHAS have primarily focused on long-term, gradual climatic shifts, largely overlooking the episodic and often catastrophic nature of EWEs. Here, we propose a structured methodological framework to assess the vulnerability of PHAS to extreme climatic events, based on the three IPCC vulnerability components including exposure, sensitivity, and adaptive capacity (ExP-SeN-AdC). The framework introduces the Extreme Event Vulnerability Index (EVI), an integrative metric designed to quantify vulnerability of PHAS to EWEs in a transparent, standardized, and scalable manner. The framework follows an eight-step process: (1) defining the assessment scope; (2) selecting ecologically meaningful indicators; (3) evaluating data availability and acquiring data; (4) constructing component indices for ExP-SeN-AdC; (5) computing the composite vulnerability metric; (6) evaluating uncertainties through validation and sensitivity analysis; (7) communicating and refining results through stakeholder engagement; and (8) formulating science-based conservation and adaptation recommendations. Finally, we demonstrate application of the proposed framework through a case study on Atlantic Salt Meadow habitats (Glauco-Puccinellietalia Maritimae) located within Dublin Bay’s Natura 2000 sites, encompassing a mosaic of Special Areas of Conservation (SACs) and Special Protected Areas (SPAs) under the EU Habitat Directive. The framework and its methodological approach lay the foundation for the vulnerability analysis and mapping, advancing adaptation planning, and guiding targeted interventions and conservation efforts, and the development of forward-looking conservation policies in the face of escalating extreme climatic threats.