Search Results (928)

A consistent computational framework is presented that integrates Markov chain modeling, decision optimization, and cost–benefit analysis for the life-cycle management of engineering assets. The approach combines deterioration modeling with a complete economic evaluation and optimization of maintenance decisions. Each condition state of the system is associated with possible actions such as do-nothing, preventive maintenance, major repair, and replacement, each defined by its own transition matrix or generator describing state changes. The expected one-step reward is formulated as the difference between benefits and all relevant costs including operating, action, and failure costs. The optimization problem is expressed as a discounted Markov decision process and solved by linear programming. The resulting stationary policy specifies the optimal decision rule for every state. Both discrete-time and continuous-time variants are implemented. Transition matrices and generator matrices are linked using a matrix exponential mapping for the selected step length. Under state-dependent policies, the discrete step model and the continuous-time feedback model may lead to different long-run state shares. This is caused by different decision timing. The continuous-time variant also provides reliability indicators such as survival and hazard. It can also provide mean time to absorption under an absorbing failure interpretation. Simulation under the optimal policy yields state trajectories, present values of benefits and costs, and key economic indicators such as net present value, benefit–cost ratio, equivalent annual cost, and equivalent annual net benefit. The framework forms a unified and practical tool that connects reliability analysis, Markov optimization, and life-cycle cost–benefit evaluation for long-term infrastructure management. Full article

Green and Blue Infrastructure (GBI) is increasingly addressed within climate adaptation and mitigation policies as a strategic operational measure for reducing climate-related impacts in urban environments. However, GBI effectiveness relies strictly on the biophysical condition of natural assets and on the capacity to monitor and interpret ecosystem processes over time, specifically vegetation quality and its physiological response to climatic stressors within complex urban fabrics. This variability emphasizes the need to integrate ecosystem performance into decision-making through digital frameworks capable of quantifying and accounting for ecological resources across temporal scales. In this context, Remote Sensing (RS) technologies provide a structured informational basis for assessing vegetation health and surface thermal patterns in relation to climatic stress thresholds and human exposure. This paper presents a policy-aligned geospatial evidence framework to bridge the gap between environmental monitoring and urban climate action. By integrating high-resolution multispectral remote sensing with heterogeneous spatial datasets and climate models, the framework enables the multitemporal assessment of ecological conditions to inform where GBI measures can support SECAP implementation, project refinement, and monitoring activities. Developed within the Horizon Europe KNOWING project and applied to the Naples East district, Italy, the framework was operationalized within the city’s Sustainable Energy and Climate Action Plan (SECAP). The application produces scenario-oriented outputs for interpreting the potential contribution of GBI and NbS measures to outdoor heat-stress reduction under SECAP conditions. Its practical value lies in translating biophysical data into reusable GIS/WMS layers that connect ecological performance, climate exposure, socio-energetic vulnerability, and planned urban transformations, thereby supporting SECAP implementation, project refinement, and monitoring. Full article

Toll-road operators must coordinate pavement rehabilitation and toll pricing because surface condition and tolls jointly affect route choice, realized demand, deterioration, and long-term revenue. Motivated by infrastructure REIT asset-operation requirements, this study develops a stochastic bi-level multi-period model for joint pavement maintenance and toll pricing under demand feedback. The upper level selects annual tolls and rehabilitation intensities for tolled links subject to budget and service constraints. The lower level solves an elastic-demand user equilibrium based on generalized travel disutility. The operator objective extends discounted-profit maximization by adding revenue coefficient of variation, maximum drawdown, and terminal pavement value. Budget availability and deterioration uncertainty are represented by scenario multipliers, and the model is solved by a real-coded genetic algorithm coupled with the method of successive averages (GA-MSA). Experiments on the Li-Sheng benchmark and a semi-empirical Nanjing toll-road REIT corridor show that stochastic coordinated decisions retain more than 97% of the NPV achieved by the GA-MSA profit-oriented benchmark while improving revenue stability and limiting downside risk. Supplementary comparisons with PSO-MSA and DE-MSA show that alternative upper-level search rules identify different points on the normalized risk–return surface. The findings support treating maintenance and pricing as an integrated asset-operation problem for long-horizon toll-road assets. Full article

The rapid growth of electric vehicle (EV) adoption presents significant challenges for power system stability while creating new opportunities for decentralized energy management. Peer-to-peer (P2P) energy networks have emerged as a promising approach for transforming EVs from passive loads into coordinated grid assets. This paper presents a comprehensive review of EV-P2P integration through a three-layer architectural framework that systematically connects physical infrastructure, market mechanisms, and intelligent control strategies. The Physical Layer reviews how V2X technologies and bidirectional charging enable EVs to operate as flexible storage resources and ancillary service providers. The Transactional Layer reviews on blockchain-based platforms, auction mechanisms, and game-theoretic models for secure energy trading. The Intelligence Layer reviews advanced control strategies, including decentralized optimization methods such as the Alternating Direction Method of Multipliers (ADMM) and Deep Reinforcement Learning. Collectively, the reviewed studies demonstrate that these approaches enable EVs to operate as flexible loads, distributed storage resources, and ancillary service providers, while improving energy trading efficiency, reducing operating costs, and alleviating network congestion under simulated operating conditions. Despite these promising results, a substantial gap remains between simulation-based studies and practical implementation. Future research should prioritize integrated pilot projects to evaluate scalability, interoperability, cybersecurity, and regulatory compliance under realistic operating conditions. Full article

Precision livestock farming (PLF) is hindered by high costs, infrastructure demands, and complex deployment. To address these barriers, we developed CABRA, an open-hardware wearable system for real-time behaviour monitoring in pasture-based livestock. The collar-mounted device integrates a 6-axis IMU, a GPS, and a low-power ESP32 microcontroller within a modular architecture, using the routerless ESP-NOW protocol to transmit data directly to a base station—eliminating reliance on network infrastructure or cloud connectivity. The system supports both synchronised data logging for video annotation and real-time embedded behaviour classification via an optimized decision-tree pipeline deployed directly on the microcontroller. Field trials with dairy goats confirmed robust hardware performance, minimal animal disturbance, and reliable communication over 100 m. A two-stage evaluation revealed that while the extracted IMU features are highly discriminative (achieving F1 > 0.99 under window-level validation), cross-animal generalization remains challenging (macro F1 = 0.31 under rigorous animal-level partitioning), primarily due to the “sensor placement effect” and domain shift between individuals. These results honestly quantify the current limitations of uncalibrated wearable livestock sensing while validating the functional feasibility of edge-based inference. All design assets—CAD files, schematics, firmware, and data pipelines—are openly released to ensure full reproducibility and community-driven adaptation for diverse PLF applications. Full article

Urban infrastructure systems increasingly depend on textual reports generated by citizens, inspection teams, maintenance units, emergency platforms, and smart city services. Accurate identification of critical risks in these reports is essential for enhancing urban resilience and enabling timely decision-making. Nevertheless, urban infrastructure risk classification is challenging due to the brevity, noise, domain specificity, and context dependence of these reports. This study introduces RAFE-XAI, a retrieval-augmented feature engineering and explainable natural language processing framework for urban infrastructure risk classification. The term retrieval-augmented is used here in a classification-oriented sense: retrieved reports are used to construct additional features and evidence, not to generate output text as in Retrieval-Augmented Generation systems. The proposed framework incorporates semantic sentence embeddings, retrieval-based evidence, neighborhood-derived label distributions, domain-specific risk indicators, infrastructure asset cues, location indicators, and evidence-based explainability. The framework does not construct an explicit graph, adjacency matrix, graph neural network, or message-passing mechanism. Instead, retrieval is used to derive neighbor label-distribution features, which are combined with semantic embeddings and interpretable keyword, asset, and location indicators. To assess the effectiveness of this approach, UIR-Text, a semi-synthetic urban infrastructure risk dataset with scenario-level group splitting to mitigate data leakage, was constructed. Experimental results on UIR-Text show that fine-tuned DistilBERT achieves the strongest predictive performance, with Macro-F1 scores of 0.8278 for category classification, 0.9120 for binary critical-risk detection, and 0.3379 for four-level severity classification. Among the explainable feature-engineering models, RAFE-XAI with Random Forest achieves the strongest category classification performance, with Accuracy 0.8400, Macro-F1 0.8043, Weighted-F1 0.8444, and MCC 0.8062. These results suggest that fine-tuned transformers provide the highest predictive performance on this benchmark, while RAFE-XAI offers a transparent retrieval-augmented alternative that exposes retrieved evidence, neighbor label distributions, and domain cues. Four-level severity classification remains challenging, even with fine-tuned DistilBERT, indicating the need for richer impact-aware variables. Full article

AI technologies are becoming operational components of urban infrastructure systems, including transport networks, structural health monitoring platforms, water utilities, energy systems, and public facilities. These systems support prediction, diagnosis, control, maintenance planning, and asset-management decisions across long service periods. However, much of the existing literature still evaluates infrastructure AI at the model-design or deployment-performance stage, with limited attention to post-deployment validity, operational degradation, update control, and end-of-life management. This survey examines AI applications in urban infrastructure from a lifecycle-management perspective, covering deployment, runtime monitoring, maintenance and adaptation, governance, and retirement. The review applies a PRISMA-guided search and screening protocol to classify retained studies by lifecycle phase, infrastructure domain, deployment evidence, monitoring strategy, adaptation mechanism, governance control, and benchmark support. The cross-domain analysis indicates that deployment-stage accuracy alone is not sufficient for long-term reliability assessment, because sensor wear, environmental variation, asset aging, data drift, maintenance intervention, topology change, and operating-regime shifts can alter model behavior after deployment. The findings further show that current research provides limited support for linking model outputs to maintenance actions, validating model updates under operational constraints, documenting governance evidence, estimating lifecycle cost, defining retirement criteria, and building shared lifecycle benchmarks. The survey concludes that urban infrastructure AI should be managed as a long-term socio-technical asset, with continuous validation, model-health monitoring, controlled adaptation, audit-ready governance, and retirement planning integrated into infrastructure operations. Full article

Southern Australia supports a high diversity of fishes in the family Syngnathidae, which is recognised as globally threatened, facing threats from coastal development, over-exploitation and bycatch. In southwestern Australia, Cockburn Sound houses a large industrial port and naval defence assets with plans to substantially expand the port facilities but also sits within a recognised syngnathid ‘hotspot’. Using a broad array of data sources, including historical museum vouchers, published records, historical and contemporary observations from notebooks and diving surveys, historical and contemporary trawl surveys, citizen science observations and environmental DNA, we compiled 4839 records of syngnathid fishes from the embayment. Twenty-one species of seadragons, seahorses or pipefishes were confirmed from an area of around 200 km², representing a high diversity by area on a global scale. This remarkable diversity exists despite the bay being a highly modified system. Many species are likely to face conservation concerns within the bay, but others may benefit from the addition of artificial structures. There is clearly a need to protect the diversity and mitigate detrimental impacts from development and ongoing infrastructure operations, including direct and indirect interaction, habitat loss, water quality, turbidity, noise and introduced pests. Full article

Climate risk assessments for critical infrastructure are essential to identifying and predicting vulnerabilities early in the asset life cycle, enabling proactive mitigation through the implementation of technical and nature-based solutions (NbS) before impacts occur. However, such assessments often rely on dense quantitative indices that are difficult for non-technical stakeholders to interpret. To address this challenge, this paper presents an open-source decision support platform that combines OpenStreetMap site characterization, qualitative pre-screening, a quantitative IPCC AR6-aligned risk chain, and a downstream NbS recommendation layer. The approach deploys Large Language Models (LLMs) to translate analytical outputs into accessible narrative explanations. End-to-end site-characterization processing across three European demonstration sites took between 29 and 70 s. An exploratory ablation study investigated the faithfulness of the AI-generated explanations using three complementary metrics, demonstrating that the generated hazard assessments remained factually grounded and free from fabricated numerical values. Introducing example reports (exemplars) into the prompt context further stabilized the reliability of the output for complex risk indicators. Finally, a small blind expert evaluation with six researchers from adjacent technical domains provided convergent evidence: five of six raters independently rated with-exemplar Hazard Reports higher on completeness; among the five raters who expressed a directional preference, all five favored the with-exemplar condition (sign test, p = 0.031). Furthermore, seven of eight aggregate dimension-level comparisons confirmed that with-exemplar reports scored at least as high as their ablated counterparts. Full article

This study analyses the evolution of the financial literature on climate risk, examining the integration of artificial intelligence techniques into its measurement and management. To this end, a bibliometric approach is employed based on 221 articles indexed in the Web of Science Core Collection, using the Bibliometrix package. Moving beyond existing descriptive bibliometric reviews on ESG and green finance, the novelty of this paper lies in its analytical focus on how financial science operationalises quantitative AI mechanisms to price and integrate climate transition risk into asset and portfolio valuation. The structural analysis reveals that natural language processing (NLP) and digital transformation acting as driving motor themes, suggesting that the reviewed literature associates AI innovation policies with the mitigation of corporate greenwashing and enhance information transparency. Furthermore, while machine learning algorithms establish the cross-cutting predictive foundation for risk assessment, empirical evidence unveils a critical academic shift of traditional ‘financial performance’ towards a declining quadrant, indicating that empirical studies frequently find that that multi-phase investments in risk technologies do not yield immediate financial returns. Finally, the study maps a persistent geographical gap where emerging markets lack the data infrastructure of advanced economies, alongside isolated high-dimensional causal econometric niches like double machine learning. This analytical mapping provides key implications for global risk management and future quantitative research avenues. Full article

This paper develops a literature-grounded review on connecting circular economy (CE) principles and urban mobility to examine how smart city transportation can be reshaped through durable design, reuse, service-based models, and materials stewardship. A structured review of indexed documents (2010–April 2026) from Scopus and Web of Science identified a set of papers that were screened, thematically grouped into six clusters (design, digital infrastructures, shared mobility, materials management, social inclusion, decision support), and synthesized via textual analysis to generate an integrative, evidence-based conceptual framework, identify empirical and methodological gaps, and propose policy-relevant research priorities informed by the identified topics. The literature documents CE interventions and analytical methods that can lower embodied impacts, raise asset utilization, and recover secondary materials when backed by clear public policies, coordinated governance, and digital platforms. Key limitations include fragmented governance, optimistic recovery rate assumptions, limited integration of life cycle analyses, and weak empirical evidence linking participatory processes to measurable outcomes. The findings are relevant to researchers, planners, policymakers, and practitioners, and indicate that operationalizing circular approaches in urban mobility requires cross-sectoral policy alignment, data governance, financing reform, inclusive stakeholder engagement, interoperable platforms, and longitudinal place-based pilots with multi-metric monitoring aligned with the Sustainable Development Goals. Full article

This study examines whether recognized intangible assets carry information about bank performance in an emerging market, and whether their information value is conditioned by the maturity of macro digital infrastructure. Using a balanced panel of 28 Indonesian commercial banks over 2015–2024 (280 firm-year observations), we estimate two-way fixed-effects models with macro digital infrastructure, an economy-wide principal component index of internet penetration, mobile and broadband subscriptions, and electronic payment volume as a cross-layer moderator. Intangible investment intensity, proxied by the ratio of reported intangible assets to total assets, shows weak direct associations with performance; only the operating efficiency ratio displays a marginally significant short-run cost, consistent with transition-cost dynamics. The central result is conditional: the interaction between intangible intensity and macro digital maturity is strongly significant for operating efficiency (β = −2.587, p = 0.005), with the implied efficiency cost contracting by a model-implied 88 percent across the observed range of digital maturity (an estimate computed from the estimated coefficients over the observed sample variation, not a structural causal magnitude). Heterogeneity is pronounced across regulator-defined bank tiers (KBMI): the four largest banks realize positive profitability effects, whereas mid-tier banks bear transition costs. Results are robust to Driscoll–Kraay standard errors, system GMM, sub-sample splits, and outlier exclusion. The findings show that the information value of recognized intangibles in banking is state-contingent, extending the intangible-asset and digitalization literature to emerging-market banking. Full article

Mining operations generate continuous sensor data across heterogeneous repositories with no unified access layer. Existing integration platforms either require centralizing data into new infrastructure or demand extensive pipeline reconfiguration when sources change. We present a metadata-driven execution model in which integration behavior is resolved at runtime from executable metadata rather than encoded in static workflows, preserving existing infrastructure while enabling unified access across heterogeneous repositories. An Asset Cataloging registry stores executable specifications, including connector identifiers, connection parameters, and routing rules, which select and invoke the appropriate connector at runtime without workflow coding or redeployment. Evaluation on large-scale real mining sensor datasets spanning heterogeneous formats (JSON, CSV, Parquet) and repositories (Kafka, MongoDB, external REST APIs) confirmed zero message loss and bit-exact binary reconstruction across all scenarios under at-least-once delivery with idempotent writes. Connector dispatch overhead fell below the 1 ms measurement resolution, confirming that integration latency is dominated by storage I/O rather than orchestration cost. Following evaluation, four pilot sites deployed the platform in production, spanning from active underground operations to post-mining waste management, under the EU Horizon Europe MINE.IO project, demonstrating viability at industrial scale. Full article

The transition towards decarbonised energy systems, often characterised by high photovoltaic penetration, imposes crucial challenges for operational security, flexibility and grid resilience. In this landscape, meteorological-data reliability has emerged as a strategic pillar to mitigate systemic risks arising from forecasting uncertainty, including grid imbalances, electricity-market volatility, and structural asset safety during extreme weather. This study provides a comparative analysis of four forecasting providers, evaluating their accuracy across atmospheric and solar irradiance parameters through heterogeneous datasets spanning diverse climatic zones and seasons. The analysis is performed by employing a dual-source validation framework that benchmarks every forecast against independent, real-world references rather than the providers’ own model-derived observations: first, the atmospheric variables are compared with real surface-station measurements; second, plane-of-array irradiance is benchmarked directly against on-site sensors at operational photovoltaic plants. This dual-source approach isolates systematic model biases relative to real-world environmental conditions, yielding a provider-independent estimate of accuracy against the true atmospheric and irradiance state. This work therefore proposes not merely a comparative analysis but a validation methodology that addresses a specific limitation of existing forecast-comparison approaches, offering actionable insights to minimise financial and operational risks while fostering a secure, resilient, and sustainable energy infrastructure. Full article

Digital twins (DTs) have become a central paradigm for modeling cyber–physical systems and digital infrastructures, yet the term is applied to very different representations—from physical assets to operational processes and service environments. This ambiguity obscures how the various DT interpretations relate to one another and at which level knowledge can be represented and extracted. This paper develops a conceptual and mathematical framework that treats asset-centric, process-centric, and service-centric DTs as successive levels of system abstraction. DTs are modeled as mappings between real-world entities and their digital representations, and the three paradigms are connected through explicit cross-layer dependencies, with service-centric twins shown to form a distinct level that cannot be reduced to asset and process descriptions alone; the framework is then extended to the ecosystem level as a digital service ecosystem twin. Because each level fixes the entities, features, and relations available to data-driven methods, the framework also specifies where machine-learning and knowledge-extraction tasks operate within layered DT architectures. The approach is illustrated and validated for structural and cross-layer consistency through a smart-city electricity ecosystem, providing a unified basis for interpreting the evolution of DTs toward service-oriented digital ecosystems. Full article