Search Results (6,825)

Increasing frequency and intensity of extreme precipitation events, together with altered soil saturation dynamics, have significantly increased the occurrence of shallow landslides. These processes are closely linked to climate change and increasingly affect mountainous and hilly regions worldwide, where rainfall-induced pore pressure variations and transient infiltration govern slope instability. Despite growing recognition of climate-driven slope failures, most conventional geomechanical analyses still rely on static assumptions and simplified boundary conditions, which are insufficient to capture the pronounced temporal variability of hydro-climatic forcing. To address this gap, this study introduces a conceptual and methodological framework for a proactive landslide cadaster, developed within the Climate Adaptive Resilience Evaluation (CARE) framework. Rather than serving as a static inventory of past events, the proposed cadaster functions as a structured, updatable repository of climate–geomechanical parameters that directly support advanced landslide analyses. The core innovation lies in the formalization of the climate–geomechanical interface, which enables the transformation of climatic and hydrological variables into parameters directly applicable in geomechanical modeling. These parameters encompass climatic, hydrological, geomechanical, and thermo-hydraulic processes and are assigned to spatially referenced locations, complemented by documented landslide occurrences. Their spatial distribution forms a network of reference points that allows interpolation, continuous updating, and reuse across multiple analyses. In this way, the cadaster becomes a proactive, process-based data infrastructure, serving as the foundational input for scenario-based landslide susceptibility, hazard, and risk assessments within the CARE analytical workflow. The conceptual framework is illustrated through an example from Slovenia, focusing on the Visole area near Maribor, where selected data types and workflow steps are presented for demonstration purposes. Full article

The rapid adoption of artificial intelligence (AI) across public services and critical infrastructure is reshaping digital governance. While AI promises efficiency and innovation, its reliance on large, high-dimensional datasets introduces privacy, bias, transparency and accountability risks that existing frameworks struggle to address. This study evaluates the maturity of current AI governance frameworks and develops an integrated risk-tiering model that connects ethical principles to auditable technical controls, aligning with Sustainable Development Goal 9 on industry, innovation and infrastructure. A systematic literature review of 450 records from major databases was conducted using PRISMA 2020 guidelines; 95 high-quality studies were analyzed using principal component analysis and k-means clustering. The analysis produced a heat map of governance frameworks, a co-occurrence network of themes, a cluster analysis of framework coverage and an integrated governance risk framework supported by a risk-tiering matrix. Findings reveal a fragmented landscape dominated by ethics/privacy-centric and compliance/risk-focused approaches, with few integrated frameworks and evident tension between privacy and security. This synthesis bridges the gap between values and practice, offering a policy-ready model for secure and sustainable AI governance. Full article

Land subsidence has emerged as a critical geohazard affecting major urban centers worldwide, particularly in coastal and deltaic regions where intensive groundwater extraction and rapid urbanization are prevalent. It is estimated that subsidence threatens more than 1.6 billion people globally, with reported subsidence rates exceeding 100 mm/year in several rapidly urbanizing cities and cumulative ground lowering exceeding 10 m in extreme cases such as Mexico City. This review provides a comprehensive synthesis of the hydrogeological drivers, impacts, and sustainable mitigation pathways of land subsidence based on a systematic literature review of 167 peer-reviewed studies following the PRISMA framework and bibliometric network analysis. The findings confirm that groundwater extraction is the dominant driver, causing pore pressure decline and irreversible consolidation of compressible aquitards, while geological conditions, recharge imbalance, and climate variability strongly influence subsidence magnitude and persistence. The consequences are severe and multidimensional, including increased flood risk, infrastructure damage, groundwater storage loss, ecosystem degradation, and significant socio-economic impacts. Global case studies from major subsiding cities demonstrate that subsidence often contributes more to relative sea-level rise and urban flood vulnerability than climate-driven ocean rise alone. Mitigation strategies, including groundwater regulation, managed aquifer recharge, water-sensitive urban design, geotechnical stabilization, and satellite-based monitoring, have shown effectiveness but remain limited when implemented independently. This study proposes an integrated management framework combining continuous monitoring, hydrogeological assessment, sustainable groundwater management, engineering and nature-based solutions, and governance integration. The findings highlight that early intervention, groundwater sustainability, and coordinated policy actions are essential to reduce subsidence and enhance long-term urban resilience. These insights support the achievement of Sustainable Development Goal 11 (Sustainable Cities and Communities), particularly in strengthening disaster risk reduction and climate resilience in subsidence-prone urban areas. Full article

Pharmaceutical regulation plays a central role in protecting public health by governing clinical trials, market authorization, and post-marketing safety monitoring throughout the medicine life cycle. While substantial literature describes established systems, particularly the United States Food and Drug Administration (FDA), Japan’s Pharmaceuticals and Medical Devices Agency (PMDA), and the European medicines regulatory network coordinated by the European Medicines Agency (EMA) together with national competent authorities, comparative analyses that integrate both mature authorities, emerging regulators and transnational harmonization networks remain limited. This narrative review draws on primary regulator/network documentation and targeted peer-reviewed literature to compare core regulatory functions across jurisdictions, including approval pathways and evidentiary expectations, inspection and good manufacturing practice (GMP) oversight, transparency practices, and pharmacovigilance and risk-management approaches. Across regions, we observe increasing convergence in scientific expectations through initiatives such as the International Council for Harmonisation (ICH) and reliance and work-sharing models, alongside persistent differences in legal mandates, resourcing, timelines, and data requirements. These differences are most consequential for complex products (e.g., advanced therapies) and in crisis settings, where emergency or conditional authorizations amplify the need for strong lifecycle monitoring, real-world evidence governance, and cross-border communication. We conclude by outlining opportunities to strengthen regulatory resilience and equity through fit-for-purpose harmonization, investment in enabling infrastructure, and future work on interoperable data systems, signal detection, and coordinated post-marketing evaluation. Full article

Smart energy networks, including electricity distribution and district heating, are increasingly operated as sensor-enabled infrastructures where maintenance decisions must be made under heterogeneous and time-varying operating conditions. In these settings, time-to-event data are rarely complete; preventive actions and limited observation horizons routinely introduce censoring and truncation, so models and validation procedures must account for partially observed lifetimes to avoid biased inference and misleading performance estimates. This review surveys survival models for predictive maintenance (PdM) and remaining useful life (RUL) estimation, spanning non-parametric, semi-parametric, parametric, and learning-based approaches, with emphasis on censoring-aware formulations and the use of static and time-varying covariates derived from sensor, inspection, and contextual information. A structured taxonomy and a systematic mapping of model families to data types, core assumptions (proportional hazards versus parametric distributional structure), and decision-oriented outputs such as risk ranking, horizon failure probabilities, and RUL distributions are presented. Evaluation practice is also synthesized by covering discrimination metrics, censoring-aware RUL accuracy measures, and probabilistic assessment via proper scoring rules, including the time-dependent Brier score and Integrated Brier Score (IBS). The review provides researchers and practitioners with a practical guide to selecting, fitting, and evaluating survival models for risk-informed maintenance planning in smart energy networks. Full article

Although smart-city benchmarking has produced many indices and rankings, cities still lack a practical way to assess whether successful initiatives can be transferred across institutional contexts and converted into implementable urban roadmaps. In this study, we aimed to develop and empirically test a paired donor–recipient “problem–solution” methodology that bridges comparative city analysis with implementation readiness gap assessment, addressing the persistent disconnect between smart-city benchmarking and actionable transfer guidance. The smart-city ecosystem was decomposed into eight functional dimensions covering digital foundations, service platforms, finance and procurement, innovation capacity, governance, legal adaptability, and citizen participation. The method was applied to the Ottawa-Astana pair using a systematic desk-based analysis of publicly available strategic documents, legislation and policy frameworks, and implementation materials (e.g., roadmaps, program guidelines, departmental plans, and monitoring outputs). Data were analyzed using a structured gap analysis algorithm employing a three-level qualitative compliance scale (Full Compliance, Partial Compliance, and Non-compliance) to assess recipient city status against donor benchmarks across all eight functional dimensions. The results reveal Astana’s partial compliance with the Ottawa benchmark, with moderate readiness and pronounced “hard-soft” asymmetry; that is, greater progress in regard to infrastructure and platforms, but persistent gaps in adaptive regulation, experimentation-friendly legal instruments, and participatory governance. These findings suggest that progressing toward a Smart City 2.0 model requires prioritizing regulatory sandboxes, adaptive procurement pathways for pilots, and scalable civic-tech mechanisms alongside continued investment in talent and innovation ecosystems—understood here as interconnected networks of universities, technology parks, civic-tech communities, and incubation infrastructure that collectively sustain capacity for technology absorption and local adaptation. The proposed paired framework is replicable and supports phased, actionable transfer roadmaps for policymakers. Full article

With the increasing penetration of distributed energy resources, distribution networks face elevated risks of power disruptions, which call for rapid and flexible emergency response mechanisms. There are not enough traditional emergency generator vehicles, and they are not highly adaptable when it comes to operations, which makes it hard to meet changing dispatching needs. Electric vehicles (EVs), on the other hand, can be used as distributed emergency resources that can be dispatched through vehicle-to-grid (V2G) interaction. Electric vehicle charging stations (EVCSs), on the other hand, are integrated energy storage units that use existing charging infrastructure to provide on-site grid support. To address this gap, this study proposes a comprehensive V2G-powered pre- and post-disaster coordination framework for enhancing distribution network resilience, with three core novelties: first, a refined individual EV model considering dual power and energy constraints is developed, and the Minkowski summation method is applied to accurately quantify the real-time aggregate regulation potential of EVCSs for the first time; second, a two-stage robust optimization model is formulated for pre-event strategic planning, which jointly optimizes EVCS participant selection and distribution network topology to address photo-voltaic (PV) power generation uncertainties; third, a multi-source collaborative dynamic scheduling model is constructed for post-disaster recovery, which explicitly incorporates the spatiotemporal dynamics of EVs and coordinates EVCSs, gas turbine generators (GTGs) and other resources for the first time. We carried out simulations on a modified IEEE 33-bus system with a 10 h extreme fault scenario. The results show that the proposed strategy raises the average critical load recovery ratio to 97.7% (2% higher than traditional deterministic optimization), lowers the total load shedding power by 0.2 MW and the load reduction cost by 19,797.63 CNY, and gives a net V2G power output of 3.42 MW (86.9% higher than the comparison strategy). The proposed V2G-enabled coordinated pre- and post-disaster fault recovery strategy significantly improves the resilience of distribution networks compared to traditional methods. This makes it easier and faster to recover from extreme disaster scenarios, with the overall load recovery rate reaching 91.8% and the critical load restoration rate staying above 85% throughout the recovery process. Full article

The advent of quantum computing poses significant challenges to traditional cryptographic systems, threatening the confidentiality, integrity and authenticity of digital communications. This paper investigates the integration of post-quantum cryptography (PQC) algorithms into mobile communication systems to address these challenges. The study focuses on evaluating key PQC algorithms shortlisted by the National Institute of Standards and Technology (NIST), including CRYSTALS-Kyber, CRYSTALS-Dilithium, Falcon and SPHINCS+, within the context of 5G and future mobile network architectures. The research encompasses the design and implementation of an experimental framework involving mobile devices, servers, and cloud-based infrastructure to simulate real-world communication scenarios. Performance metrics such as key generation time, signature generation, encryption and decryption speed, and resource consumption were analyzed across various devices to identify algorithms suitable for mobile environments. The findings reveal that lattice-based algorithms, such as Kyber and Dilithium, offer a promising balance between security and efficiency, making them ideal for resource-constrained devices. In contrast, hash-based algorithms like SPHINCS+ exhibit higher computational demands, limiting their practicality in certain applications. This work highlights the importance of algorithm selection and hardware optimization in ensuring secure and efficient communications in the quantum era. By integrating theoretical advancements in PQC with practical applications, this research lays the foundation for quantum-resistant security in mobile networks, ensuring secure and future-ready digital communications. Full article

Maritime heterogeneous wireless networks are characterized by dynamic topology and significant heterogeneity in bandwidth, latency, and coverage across communication paradigms, rendering traditional terrestrial routing protocols inadequate. To address these challenges, this paper proposes a unified multi-radio access fusion infrastructure featuring a gateway that enables protocol conversion and collaborative resource management across heterogeneous systems. Building upon this infrastructure, we introduce CMPGNN-DQN, an intelligent routing algorithm that integrates Contrastive Message Passing Graph Neural Networks with Deep Reinforcement Learning. Specifically, the algorithm employs k-hop neighbor aggregation to expand the receptive field for routing decisions, and utilizes a dual-view contrastive learning mechanism—encompassing both homogeneous and heterogeneous perspectives—to enhance representation robustness against dynamic topology perturbations. By deeply fusing network topology features with real-time state information, including bandwidth, delay, and queue length, the agent makes hop-by-hop routing decisions via an ε-greedy policy within the DQN framework. Extensive simulations conducted across various scales of dynamic maritime communication scenarios demonstrate that CMPGNN-DQN outperforms state-of-the-art benchmark algorithms, including AODV, DQN, and GCN, across key metrics such as packet delivery ratio, transmission latency, and bandwidth utilization. Quantitatively, compared to the best-performing alternative (MPNN-DQN), our algorithm achieves throughput improvements of 2.06–5.04% under standard traffic loads and 6.6–27.1% under partial link failure conditions, while converging within merely 25 training episodes. Notably, under heavy network loads (40% load rate) or partial link failures, the algorithm maintains stable communication performance, demonstrating strong adaptability to complex dynamic environments. Full article

Urban road collapses are characterized by sudden occurrence and strong spatial heterogeneity, posing substantial challenges for proactive infrastructure management. Susceptibility mapping can provide spatially explicit evidence to support targeted inspection and early-warning strategies. Using Futian District, Shenzhen (China) as a case study, a total of 315 road collapse events recorded during 2019–2023 were compiled to develop an integrated framework for urban road collapse relative susceptibility mapping based on multi-source remote sensing and urban spatial data. First, an indicator-based susceptibility index (SI) was constructed using eight conditioning factors, including PS-InSAR-derived deformation, topographic–hydrological conditions, and distance-based infrastructure variables (distance to underground utilities, metro lines, and roads). Factor weights were determined by coupling the Analytic Hierarchy Process (AHP) with the Entropy Weight Method (EWM), producing a comprehensive SI for historical collapse locations. Subsequently, a set of 17 remote-sensing predictors, including Sentinel-2 spectral bands, Sentinel-2 GLCM texture features, and Sentinel-1 SAR backscatter variables, was used to train a Random Forest model to predict SI and generate continuous susceptibility maps at the urban road-network scale. The influence of neighborhood window size on predictive performance was systematically evaluated. Results show that the Random Forest model performed best at the 5 × 5 window scale (R² = 0.70, RMSE = 0.0172, MAE = 0.0122), outperforming both pixel-based inputs (1 × 1) and larger windows. Uncertainty analysis further indicated that the 5 × 5 RF configuration yielded the most stable and spatially coherent predictions, whereas overly small windows and less robust learners produced more fragmented or higher-uncertainty susceptibility patterns. Spatiotemporal analysis indicates that susceptibility patterns remained broadly stable from 2019 to 2023, with moderate susceptibility accounting for 50.82–57.89% and high susceptibility for 21.94–23.30%, while very high susceptibility consistently remained below 1%. Overall, this study demonstrates that integrating multi-source remote sensing with scale-optimized machine learning provides an effective approach for fine-scale susceptibility mapping of urban road collapses, offering practical guidance for differentiated monitoring and risk prevention along critical road corridors. Full article

The global shift toward renewable energy systems raises major challenges related to the variability of solar and wind power and their poor alignment with electricity demand. This paper addresses energy adequacy, defined as the ability of an energy system to reliably meet demand by balancing generation, storage, transmission, and reserves for unforeseen events. Within this framework, energy storage systems are identified as strategic components, requiring a diversified and multi-scale set of solutions-from territorial to building scale-to respond to infrastructural constraints and user behaviour. The study adopts a multi-scalar and interdisciplinary methodology combining deductive and inductive approaches. The deductive analysis examines global, European, and Italian electricity systems, highlighting issues such as overcapacity and grid instability caused by the uncoordinated development of renewable generation and network infrastructures. The inductive approach focuses on existing storage technologies, with particular attention to two types of thermal energy storage selected for their simplicity, scalability, and replicability. Hydropower reservoirs are also considered due to their multifunctional role in energy balancing. Two case studies developed by the research group—a public building energy retrofit in Milan and a modular off-grid housing prototype—demonstrate how integrated storage solutions can enhance system flexibility. The results emphasize the necessity of a systemic design approach that combines storage technologies, adaptable energy use, and active user participation to ensure energy adequacy in scenarios with high renewable penetration. Full article

The transport of dangerous goods (TDG) produces serious risks, particularly in urban areas, due to the high presence of people and sensitive infrastructures from a social, environmental and economic point of view. Transport Risk Assessment combines occurrence, vulnerability and exposure to support urban transport planning aimed at achieving the Sustainable Development Goals. The objective of this paper is to propose a simplified risk calculation method, referring to a single link of the urban transport network, with reference to the occurrence component of the risk. The proposed formulation considers the sequence of factors that determine the overall dangerous event. The specification of the occurrence factors and a quantitative definition of the different parameters for a widespread type of transport of dangerous goods in urban areas is proposed. The results obtained are interesting because (1) the method, with quantitative parameters, can be applied to any urban area, and (2) some of the factors can also be used by replacing and introducing, where known, specific factors and relative parameters calibrated for the area for which it is planned to be implemented. The results indicate the feasibility of the proposed method without significant chemical–physical or electromechanical insights. This work is of potential interest for urban transport planners and public and private decision makers. Full article

With the rapid transition toward modern power systems, ensuring the operational integrity of substation earthing networks has become a critical priority in infrastructure modernisation. This paper investigates the resistive coupling interference between substation earthing grids and adjacent underground metallic pipeline networks within the context of renovation projects. An integrated field–circuit coupling methodology, synergising CDEGS-based electromagnetic field analysis with ETAP-based circuit modelling, is proposed to quantify critical safety performance metrics. Simulation results demonstrate that resistive coupling induces significant fluctuations in key performance parameters, potentially compromising system safety during faults. Based on these findings, a suite of targeted optimisation strategies and protective measures is developed to ensure the stable operation of both the earthing system and the surrounding metallic infrastructure. This study provides a rigorous theoretical framework and practical technical guidance for the design and optimisation of substation earthing systems in complex electromagnetic environments. Full article

The urban ecosystem constitutes a complex adaptive system comprising interdependent subsystems—environment, population, infrastructure, public services, environmental governance, and socio-economic factors. Conventional system dynamics (SD) modeling relies on expert-derived causal assumptions, which have limitations in objectivity, transferability, and adaptability. To solve these, this study develops a data-driven SD modeling framework that infers causal structures from time-series data of 38 sustainability indicators. The framework integrates multiple causal inference techniques to identify causal relationships among variables, then systematically identifies stock variables and constructs an SD simulation model. Applying it to panel data from 41 cities in China’s Yangtze River Delta (2013–2022), the study characterizes the causal network topology, interaction patterns between subsystems, dominant feedback loops, and temporal evolution trajectories of key stock variables. Results show: (1) There is significant cross-city variation in causal network structure due to differences in urban development and institutional configurations; (2) Environmental conditions are the most frequently affected terminal node with an average normalized causal strength of 0.277, higher than other subsystems; (3) Several cross-subsystem positive and negative feedback loops are identified, highlighting potential path dependencies and intervention-sensitive nodes for sustainable urban transitions. This study provides a replicable, comparable, and scalable framework for urban sustainable development analysis, offering data-driven support for smart city management and policy formulation. Full article

Urban network externalities have emerged as a novel impetus for regional economic growth. However, the extent to which inter-urban network connections promote regional economic growth and the associated spatiotemporal heterogeneity remain underexplored. This study constructs a multi-dimensional urban network framework from the perspectives of enterprise linkages, infrastructure connectivity, and innovation collaborations, capturing the multifaceted nature of intercity relationships and their critical role in shaping regional development. Utilizing the Cobb–Douglas production function and the spatial Durbin model, the study quantitatively assesses the impact of urban network externalities on economic growth and examines the spatiotemporal heterogeneity of these impacts. The main findings are as follows: Urban network externalities generally exert a positive influence on regional economic growth, yet this effect exhibits significant regional and city-size heterogeneity. Regions with more developed networks experience stronger growth effects from these externalities. Moreover, large cities benefit more substantially from network integration compared to small and medium-sized cities. Spatial decomposition of effects further reveals that urban network externalities promote economic growth through both local direct effects and spillover effects to neighboring areas. Approximately 70% of the economic growth contribution originates from direct effects within the region, while nearly 30% stems from spillover effects from adjacent regions. Additionally, the spatial spillover effects display clear distance decay, following an inverted U-shaped pattern with a bimodal distribution. Significant spillover effects are observed within 380 km, peaking at 180 km and 340 km. Full article