Search Results (412)

As a key node in the global supply chain, the resilience of ports is crucial for coping with multiple risks such as increasingly frequent climate change, operational accidents, and geopolitics, and ensuring the smooth flow of trade and sustainable development. This paper takes Ningbo-Zhoushan Port, which leads the world in throughput, as the research object, aiming to construct a comprehensive port resilience assessment model. Through the system dynamics method, the smart port system is deconstructed into three interrelated subsystems: meteorology, production, and economic-politics, and a simulation model including a causal relationship diagram and a system flow diagram is established accordingly. The model is verified to be effective and robust through historical data testing and sensitivity analysis. By setting different scenarios, this paper quantitatively analyzes the impact of single and compound risk shocks such as extreme weather, production accidents, and tariff policies on port throughput, and classifies port resilience into three levels: strong, medium, and weak. The research results show that Ningbo-Zhoushan Port shows strong resilience to the above-mentioned single risks. Even when the risk parameters are increased by 100%, the change rate of port throughput is less than the historical average annual change rate by 5.06%. However, in the extreme scenario of multiple risk couplings, the decline in port throughput is more significant, highlighting the importance of coping with compound risks. Further strategy simulation reveals that accelerating the economic development of the hinterland, increasing investment in port infrastructure, increasing the frequency of equipment maintenance, expanding the proportion of high-quality employees, and strengthening public facility management for accurate risk prediction are all effective ways to enhance port resilience. This research provides a scientific decision-making support tool for port managers, and the proposed resilience enhancement strategies have important theoretical and practical significance for ensuring the long-term stable operation of ports and the sustainable development of the regional economy. Full article

Rapid expansion of electric vehicle adoption has led to increased dependence on a charging infrastructure that is tightly integrated with energy distribution systems and digital communication networks. As electric vehicle charging stations evolve into complex cyber–physical systems, cybersecurity risks pose a growing threat to grid reliability and user trust. This paper presents a hybrid decision-support framework for cybersecurity risk assessment in EV charging infrastructure that advances beyond prior multi-criteria decision-making approaches by combining interpretability with data-driven validation. Specifically, the framework integrates the Analytic Hierarchy Process (AHP) for expert-driven weighting of cybersecurity attributes with PROMETHEE for flexible threat prioritization, enabling transparent and auditable risk rankings. The framework categorizes cybersecurity criteria across four infrastructure layers—transmission, distribution, consumer, and electric vehicle charging stations—and assigns relative weights through expert-driven pairwise comparisons. PROMETHEE is then applied to rank potential cyber threats based on these weights, allowing for flexible prioritization of cybersecurity interventions. The methodology is validated using the real-world WUSTL-IIoT-2018 SCADA dataset, which includes simulated reconnaissance (network scanning), device identification, and exploitation attacks. While this dataset does not natively include OCPP 2.0 or ISO 15118 protocols, the experimental results demonstrate strong discrimination power (AUC = 0.99, recall = 95%) and provide a basis for extension to modern EVSE communication standards. The results identify critical metrics such as anomalous source packet behavior and encryption reliability as key vulnerability markers, aligning with documented EV charging attack scenarios. By bridging expert judgment with empirical traffic data, the proposed framework offers both technical robustness and explainability, supporting grid operators, SOC teams, and infrastructure planners in systematically assessing risks, allocating resources, and enhancing the resilience of EV charging ecosystems against evolving cyber threats. Full article

Urban areas are pivotal to achieving the Sustainable Development Goals (SDGs), yet sustainability monitoring at the municipal level remains fragmented, difficult to operationalize, and weakly comparable across cities. Although the SDGs provide a comprehensive global agenda and ISO 37120 offers a standardized set of city indicators, municipalities still face practical barriers in translating global targets into actionable, jurisdiction-sensitive, and measurable metrics aligned with local responsibilities and available data. This study addresses this gap by presenting the design of an integrated, target-level urban sustainability assessment framework grounded in SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) principles and explicitly tailored to municipalities in developed-country contexts. The framework contributes (i) a structured procedure for disaggregating and reallocating SDG targets according to municipal responsibilities, (ii) a six-dimension architecture that consolidates SDG targets and ISO 37120 themes into a coherent, governance-oriented structure (Government and Economic Development; Civic & Social Infrastructure; Environment and Climate; Infrastructure and Urban Planning; Health; Urban Living Conditions), and (iii) a SMART-based indicator screening logic that prioritizes feasibility, data availability, and benchmarking potential, thus supporting the green transition in Urban Areas. The framework is empirically examined through validation against sustainability reporting practices of the Porto City Council, quantifying indicator coverage, assessing alignment with municipal mandates, and identifying systematic gaps—particularly in cross-cutting areas such as governance transparency, equity monitoring, and long-term climate adaptation. Overall, the results indicate that the proposed approach strengthens coherence, measurability, and comparability in urban sustainability assessment, supporting evidence-based municipal decision-making, performance benchmarking, and more strategically aligned SDG localization. Full article

Accurate characterization of pavement responses under real traffic loading is essential for improving pavement design reliability. This study presents SmartPave, a full-scale embedded monitoring system for measuring multilayer pavement responses under heavy vehicle loading. The system integrates embedded multi-sensors to capture stress, strain, temperature, and moisture within pavement layers. Field experiments were conducted under static and moving loading conditions. The results show that peak vertical stresses in the granular base were approximately 1.7–2.0 times higher than those at the subgrade, indicating stress attenuation with depth, while tensile strains at the bottom of the asphalt layer ranged between 200 and 350 µε. Lower vehicle speeds increased load duration and amplified viscoelastic strain responses. These findings demonstrate the capability of the system to provide reliable field data for mechanistic analysis and model calibration. Full article

A paratransit system is a public transportation mode, commonly operated by private transportation enterprises. In many countries, minibuses are considered paratransit systems and are often criticized for their inability to integrate with smart fare payment systems. Their revenue model is competitive, so it creates security vulnerabilities, and their sustainable service quality, which lags behind other modes of transport in terms of driving characteristics and transport system integration, is a significant disadvantage that sets minibuses apart from other modes of transport. However, given society’s usage habits, minibuses emerge as an indispensable mode of mobility, especially in certain areas and for specific individuals. This situation shows that minibuses need to be made infrastructure-compatible and controllable. This study examines the applicability of smart fare payment systems in minibuses using a multi-criteria evaluation framework. And it considers economic, technological, operational, and legal criteria holistically. Within the scope of the study, a pilot application was evaluated using AHP and TOPSIS techniques, comparing the cash-based structure with the option of using a smart fare payment system from the operators’ perspective. Thus, the system’s transformation potential was analytically assessed. The findings indicate that adopting a smart fare payment infrastructure is perceived to improve sustainable service quality, strengthen control mechanisms, and support revenue transparency. Full article

Urban mobility has undergone a significant transformation in recent years, caused by rapid urbanization, environmental pressures, and technological innovation. Even though digital tools and mobility platforms are increasingly used to address transportation challenges, these challenges remain complex and multidimensional, concerning not only infrastructure, but also user behavior, institutional coordination, trust, and social acceptance. Crowdsourcing has proven effective in leveraging distributed knowledge and accelerating innovation in business and public sectors. However, its application in urban mobility contexts has not yet been sufficiently synthesized in a framework-oriented manner. To address this, the study first conducted a comprehensive literature review of existing crowdsourcing assessment frameworks and their applicability to mobility systems. The results show that current implementations in urban mobility often remain fragmented and limited to unidirectional data extraction, lacking comprehensive approaches that integrate technological, social, and organizational dimensions. In response to this, the authors developed the SMART-CROWD framework for assessing cities’ maturity in using crowdsourcing across six dimensions: Strategy & Leadership (S), Methods & Tools (M), Engagement & Representativeness (A), Responsiveness & Impact (R), Technology & Data (T), and Civic Capital & Sustainability (CROWD). Each dimension includes measurable indicators, providing a structured basis of diagnosing disparities between technological capabilities and socio-institutional readiness. The SMART-CROWD framework is intended to support a transition from one-way data acquisition toward more scalable, reciprocal, and citizen-focused innovation ecosystems. This work contributes to the field of applied systems innovation by proposing a structured framework for assessing and guiding the use of distributed intelligence in smart urban mobility. Full article

We explored the application of spatial information technology in the assessment and optimization of cultural heritage resources within traditional settlements in Meizhou City, a core area of Hakka culture in China. By integrating methods such as geographic information systems and Kernel density estimation, it systematically evaluates the spatial distribution and socioeconomic conditions of these settlements. A multi-criteria evaluation model is constructed to quantify resource endowment across cultural, historical, and ecological dimensions, with particular emphasis on key factors influencing conservation effectiveness, such as infrastructure and economic vitality. Combining field investigations and literature review, we propose adaptive reuse strategies and policy recommendations to enhance settlement resilience and balance cultural preservation with regional development. Their expected outcomes include the engineering of a multidimensional geographic database for traditional settlements, the establishment of a spatial decision-support framework for heritage infrastructure conservation, and the development of systematic optimization protocols integrated with China’s rural revitalization technical policies. These results provide a computational and methodological foundation for interdisciplinary research in sustainable cultural heritage management and smart rural engineering. Full article

Artificial Intelligence (AI) is transforming audit practice by redefining traditional frameworks and enabling the automation of data analysis, risk assessment, substantive testing, and continuous monitoring. This study investigates the effect of AI adoption by audit firms on enterprise risk management (ERM). It further assesses the mediating role of Information Technology (IT) infrastructure flexibility and the moderating roles of technology competencies and organizational culture in this relationship. Data were collected from 355 top managers in Ghana using a judgmental sampling technique based on predefined inclusion and exclusion criteria. The analysis was conducted using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 4.1.1.7. The findings indicate that AI adoption positively and significantly influences ERM and IT infrastructure flexibility. IT infrastructure flexibility also has a positive effect on ERM and partially mediates the relationship between AI adoption and ERM. In addition, technology competencies significantly strengthen the relationship between AI adoption and ERM. Organizational culture positively moderates the relationship between IT infrastructure flexibility and ERM. These insights underscore the need for strategic alignment between AI investments and organizational capabilities. The study contributes to the limited empirical literature on AI-driven ERM in emerging economies and offers insights for policymakers and regulators seeking to promote technology-aided ERM. 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

This research is an analysis of the relationship between sustainable urbanization and economic inequality through smart city initiatives in developing countries such as India. Rapid urbanization in developing countries tends to have a detrimental impact on socioeconomic inequalities, and the effort to build smart cities may inadvertently increase exclusion when it is not planned with inclusiveness in mind. To reach this goal, an integrated Multi-Criteria Decision-Making (MCDM) approach using a combination of AHP, TOPSIS, and DEMATEL is adopted to systematically identify, assess, and identify the key criteria that affect the inclusive urban development. This study’s results show that infrastructure, governance, digital accessibility, and social inclusion play a key role in mitigating urban disparities and facilitating sustainable development. In particular, good governance and the availability of equitable digital infrastructure appear to be one of the critical factors in the reduction in inequalities and long-term urban resilience. This research provides policy-oriented insights for policymakers in designing inclusive smart city policies in accordance with the Sustainable Development Goals, as well as theoretical contributions to urban sustainability research. Full article

The claims resolution process is a cornerstone of the insurance industry, aiming to fairly and accurately determine the economic losses caused by adverse events. Traditionally, adjusters have relied heavily on expert judgment to perform this task. While this approach is essential, it often suffers from subjectivity, inconsistent criteria, and difficulty integrating complex data sources into objective analyses. In this context, Smart Industry tools—such as Artificial Intelligence (AI), Machine Learning (ML), Computer Vision (CV), and the Internet of Things (IoT)—have demonstrated high potential to automate damage detection and assessment; however, their effective integration into loss determination remains uneven across different productive sectors. This study addresses this problem through two objectives. First, we conducted a systematic literature review following PRISMA guidelines to identify which Smart Industry tools are currently used in the insurance sector for loss determination and to analyze their level of maturity in different productive sectors. We searched the Web of Science and Scopus databases, identifying 253 studies, of which 23 met our inclusion criteria. Second, based on the gaps we identified between the construction sector and more advanced industries such as automotive, we propose a methodological framework based on Building Information Modeling (BIM). Our results show that most solutions focus on the detection and technical classification of damage, especially in the automotive sector, while construction lacks methods to convert these technical findings into operational economic estimates. The proposed framework addresses this gap by standardizing technical and economic data from the underwriting stage, enabling more automated, traceable, and objective loss determination for infrastructure claims. Full article

Electric vehicle charging stations (EVCSs) are a critical infrastructure in urban areas. However, because they depend on power grids and digital networks, they are prone to disruptions from grid failures, extreme weather, and cyber threats. Ensuring resilience is therefore essential to minimise service disruptions and ensure reliable transportation in urban areas. While interest in EVCS resilience is growing, current studies are dispersed across technical, environmental, and spatial domains, limiting a consolidated understanding of how resilience is conceptualised and assessed in urban areas. Despite this growing body of research, no prior systematic review has comprehensively synthesised resilience-specific evidence for EVCSs in urban areas. Thus, the objective of the study was to systematically synthesise empirical research on resilient EVCSs in urban areas to identify key factors influencing resilience and how resilience is assessed. A systematic literature review was conducted on 52 empirical articles from Web of Science and Scopus published between 2015 and 2025, following the PRISMA protocol. The review revealed an increasing trend in publications over time, with research geographically concentrated in Asia, the United States of America, and Europe. Results also showed that the resilience of EVCSs in urban areas is influenced by context-related factors (such as location, environment, and governance) and system-related factors (such as operational, technical, and financial), with location and technical issues being the most studied. The resilience of EVCSs is mainly assessed through accessibility, capacity, availability, and vulnerability, using tools such as indices, curves, scenarios, and optimisation models. However, gaps remain in governance, environment, modular design, predictive maintenance, social aspects, and developing economies. Future research should focus on integrating governance and equity into EVCS planning and developing modular, renewable-powered charging systems supported by smart technologies to enhance resilience in urban areas, particularly in developing economies. This review proposes a Factors-Dimensions Implementation framework that operationalises established resilience concepts by linking context- and system-related factors to measurable resilience dimensions of EVCSs in urban areas. Full article

This paper addresses the persistent failure of major digital investments to achieve sustained smart technology adoption in developing countries, limiting productivity and business growth. Although existing research identifies institutional weaknesses as a central barrier, it provides limited guidance on how progress can occur within such constraints. To address this gap, the Institutional Framework for Smart Technology Adoption (IFSTA), pronounced Eye-f-sta, is developed as a contingent institutional framework linking digital transformation theory with practical assessment tools. IFSTA argues that adoption success depends not on technology alone, but on strategic alignment with specific institutional contexts. The framework is built around three core pillars, governance architecture, socio-technical infrastructure, and adaptive capacity, and explains how their interactions generate differentiated adoption outcomes across five institutional contexts. Localization is conceptualized as a cross-cutting mediating mechanism through which governance arrangements, standards, platforms, and capabilities are adapted to domestic realities, shaping both current performance and future transformation potential. Three questions guide the analysis: how institutional contexts moderate the impact of infrastructure investment; what complementarities and compensatory mechanisms enable progress under institutional constraints; and how digital investments can be sequenced according to institutional starting points. To operationalize this logic, the Performance–Knowledge Index (PKI) is introduced as a context-sensitive diagnostic tool that identifies binding constraints and supports sequenced intervention design. The study contributes a contingent institutional model, a methodological bridge between diagnosis and implementation, and a structured, actionable framework for advancing sustainable digital adoption in developing economies. Full article

The rapid convergence of the Internet of Things (IoT) and decentralized finance (DeFi) is reshaping the digital economy by enabling autonomous, trustless, and value-driven interactions among connected devices. This paper provides a comprehensive survey of the emerging paradigm that combines IoT’s pervasive sensing and communication capabilities with DeFi’s programmable financial infrastructure. We first discuss the motivation behind this convergence and explore key opportunities, including autonomous machine-to-machine (M2M) payments, decentralized data marketplaces, and trustless IoT service provisioning. Despite its potential, IoT–DeFi integration introduces significant security and privacy challenges related to smart contract vulnerabilities, consensus protocol risks, oracle manipulation, and constrained device capabilities. We review existing mitigation approaches such as lightweight cryptography, secure contract design, and decentralized identity management, and critically assess their limitations in heterogeneous, resource-limited environments. Building on this analysis, identify research gaps and propose future directions emphasizing formal verification of IoT-integrated smart contracts, robust oracle design, interoperability frameworks, and privacy-preserving trust models. This survey systematically maps opportunities, threats, and open issues. In doing so, it guides researchers and practitioners toward building secure, scalable, and energy-efficient IoT–DeFi ecosystems for next-generation decentralized applications. Full article