Search Results (313)

The resilience concept is well established in engineering, but the quantitative studies of vessel impact resilience for bridge structures remain limited. This paper presents an integrated framework for assessing vessel impact resilience under combined rebar corrosion and vessel collision effects. First, a corroded reinforced concrete bridge is considered for nonlinear static analysis to quantify initial corrosion damage and for nonlinear dynamic analysis to evaluate post-impact function loss. Then, recovery for each damage state is modeled by using both negative exponential and triangular recovery functions to estimate restoration times and to obtain a vessel impact resilience index. The results show that increasing corrosion severity markedly reduces resilience capacity. Furthermore, resilience indices obtained from the negative exponential function generally exceed those from the triangular function, and this improvement becomes more significant at lower resilience levels. Resilience indices calculated by using negative exponential and triangular recovery functions show negligible differences when the concrete bridge is in the uncorroded initial state and the vessel impact velocity is below 1.5 m/s. However, as reinforcement corrosion increases, the maximum discrepancy between these two recovery functions also increases, reaching a value of 67% at a corrosion level of 15.0%. From the numerical results obtained from a case study, it is important to select an appropriate recovery model when assessing vessel impact resilience. For rapid initial restoration followed by slower long-term recovery, the negative exponential model yields greater resilience gains compared to the triangular model. The proposed method thus provides an effective tool for engineers and decision makers to evaluate and improve the vessel impact resilience of aging bridges under the combined corrosion and impact effects. This proposes a quantitative metric for resilience-based condition assessment and maintenance planning. Full article

Effective water infrastructure planning and management is key to sustainable water supply globally. This research assesses water infrastructure planning and management in Gauteng, South Africa, amid growing challenges from rapid urbanisation, high water demand, climate change, and resource scarcity. These challenges threaten the achievement of Sustainable Development Goals 6 and 11; hence, an integrated approach is required for water sustainability. The study responds to a gap in the literature, which often treats planning and management separately, by adopting an integrated, multi-institutional approach across the water value chain. A mixed-methods triangulation strategy was employed for data collection whereby surveys provided quantitative data, while two sets of structured interviews were conducted: the first round to determine causal relationships among the critical success factors and the second round to validate the proposed framework. The findings reveal a misalignment between infrastructure planning and implementation, contributing to infrastructure backlogs and a short- to medium-term focus. Infrastructure management is further constrained by inadequate system redundancy, leading to ineffective maintenance. External factors such as delayed adoption of 4IR technologies, lack of climate resilient strategies, and fragmented institutional coordination exacerbate these issues. Using Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis, the study identified Strategic Alignment and a Value-Driven Approach as the most influential critical success factors in water asset management. The research concludes by proposing an integrated water infrastructure and planning framework that supports sustainable water supply. Full article

As the global energy transition accelerates, the integration of solar power and artificial intelligence (AI) technologies offers new pathways for sustainable local development. This study examines four Serbian municipalities—Šabac, Sombor, Pirot, and Čačak—to assess how AI-enabled solar power systems can enhance energy resilience, reduce emissions, and support community-level sustainability goals. Using a mixed-method approach combining spatial analysis, predictive modeling, and stakeholder interviews, this research study evaluates the performance and institutional readiness of local governments in terms of implementing intelligent solar infrastructure. Key AI applications included solar potential mapping, demand-side management, and predictive maintenance of photovoltaic (PV) systems. Quantitative results show an improvement >60% in forecasting accuracy, a 64% reduction in system downtime, and a 9.7% increase in energy cost savings. These technical gains were accompanied by positive trends in SDG-aligned indicators, such as improved electricity access and local job creation in the green economy. Despite challenges related to data infrastructure, regulatory gaps, and limited AI literacy, this study finds that institutional coordination and leadership commitment are decisive for successful implementation. The proposed AI–Solar Integration for Local Sustainability (AISILS) framework offers a replicable model for emerging economies. Policy recommendations include investing in foundational digital infrastructure, promoting low-code AI platforms, and aligning AI–solar projects with SDG targets to attract EU and national funding. This study contributes new empirical evidence on the digital–renewable energy nexus in Southeast Europe and underscores the strategic role of AI in accelerating inclusive, data-driven energy transitions at the municipal level. Full article

In the design of multiprocessor systems, evaluating the reliability of interconnection networks is a critical aspect that significantly impacts system performance and functionality. When quantifying the reliability of these networks, extra connectivity and extra diagnosability serve as fundamental metric parameters, offering valuable insights into the network’s resilience and fault-handling capabilities. In this paper, we investigate the 1-extra connectivity and 1-extra diagnosability of the n-dimensional enhanced folded hypercube-like network. Through analysis, we show that the 1-extra connectivity of this network is $2n+2$. Moreover, for $n>5$, we determine its 1-extra diagnosability under both the PMC model and the MM${}^{\ast }$ model to be $2n+3$. These results show that as the dimension n increases, both the 1-extra connectivity and 1-extra diagnosability of the network approach approximately twice the value of traditional diagnosability metrics. This provides quantitative insights into the reliability properties of the enhanced folded hypercube-like network, contributing to a better understanding of its performance in terms of connectivity and fault diagnosis. Full article

Collaborative business ecosystems (CBEs) are increasingly exposed to disruptive events (e.g., pandemics, supply chain breakdowns, cyberattacks) that challenge organizational adaptability and value creation. Traditional approaches to resilience and robustness often fail to capture the full range of systemic responses. This study introduces a unified mathematical framework to evaluate four crisis response modes—plasticity, resilience, transformative resilience, and antifragility—within complex adaptive networks. Grounded in complex systems and collaborative network theory, our model formalizes both internal organizational capabilities (e.g., adaptability, learning, innovation, structural flexibility) and strategic interventions (e.g., optionality, buffering, information sharing, fault-injection protocols), linking them to pre- and post-crisis performance via dynamic adjustment functions. A composite performance score is defined across four dimensions (Innovation, Contribution, Prestige, and Responsiveness to Business Opportunities), using capability–strategy interaction matrices, weighted performance change functions, and structural transformation modifiers. The sensitivity analysis and scenario simulations enable a comparative evaluation of organizational configurations, strategy impacts, and phase-transition thresholds under crisis. This indicator-based formulation provides a quantitative bridge between resilience theory and practice, facilitating evidence-based crisis management in networked business environments. Full article

Manual assembly remains essential in modern manufacturing, yet the increasing complexity of customised production imposes significant cognitive burdens and error rates on workers. Existing Spatial Augmented Reality (SAR) systems often operate passively, lacking adaptive interaction, real-time feedback and a control system with gesture. In response, we present Gest-SAR, a SAR framework that integrates a custom MediaPipe-based gesture classification model to deliver adaptive light-guided pick-to-place assembly instructions and real-time error feedback within a closed-loop interaction instance. In a within-subject study, ten participants completed standardised Duplo-based assembly tasks using Gest-SAR, paper-based manuals, and tablet-based instructions; performance was evaluated via assembly cycle time, selection and placement error rates, cognitive workload assessed by NASA-TLX, and usability test by post-experimental questionnaires. Quantitative results demonstrate that Gest-SAR significantly reduces cycle times with an average of 3.95 min compared to Paper (Mean = 7.89 min, p < 0.01) and Tablet (Mean = 6.99 min, p < 0.01). It also achieved 7 times less average error rates while lowering perceived cognitive workload (p < 0.05 for mental demand) compared to conventional modalities. In total, 90% of the users agreed to prefer SAR over paper and tablet modalities. These outcomes indicate that natural hand-gesture interaction coupled with real-time visual feedback enhances both the efficiency and accuracy of manual assembly. By embedding AI-driven gesture recognition and AR projection into a human-centric assistance system, Gest-SAR advances the collaborative interplay between humans and machines, aligning with Industry 5.0 objectives of resilient, sustainable, and intelligent manufacturing. Full article

A quantitative analysis of the fault transient is critical for system resilience assessment and protection coordination. Focusing on hybrid modular multilevel converter (MMC)-based HVDC architecture with enhanced fault ride-through (FRT) capability, this study develops a mathematical calculation framework to quantify how controller configurations influence fault current profiles. Unlike conventional static topologies (e.g., RLC or fixed-voltage RL circuits), the proposed model integrates an RL network with a time-variant controlled voltage source, which can emulate closed-loop control response during the FRT transient. Then, the quantitative relationship is established to map the parameters of DC controllers to the fault current across diverse FRT strategies, including scenarios where control saturation dominates the transient response. Simulation studies conducted on a two-terminal MMC-HVDC architecture substantiate the efficacy and precision of the developed methodology. The proposed method enables the evaluation of DC fault behavior for hybrid MMCs, concurrently appraising FRT control strategies. Full article

This paper examines the importance of ‘education outside the classroom’ (EOtC) in an Australian secondary school. The primary aim was to develop a sense of belonging, build resilience, and enhance wellbeing in female students. This study investigated two cohorts of Year 9 students (aged 14–15 yrs) who participated in a four-week residential EOtC pilot program. The first cohort (Wave 1; N = 58) undertook the program alongside (N = 39) boys. The second cohort was single-sex girls (Wave 2; N = 28). A mixed-methods research design was implemented to inform experiences of students, parents, and staff and to triangulate inferences drawn from the data. Quantitative data was gained from pre- and post-program surveys with students and parents, whilst qualitative data was gathered from student focus groups, staff, and parents through semi-structured interviews to assess more nuanced impacts. School belonging was measured using the PISA six-item scale. Academic buoyancy was quantified using the four-item Academic Buoyancy Scale. Self-efficacy, peer relations, and resilience were evaluated by employing the 34-item Adolescent Girls’ Resilience Scale. The findings revealed significant improvements in students’ sense of belonging, including higher levels of school belonging than reported Australia-wide averages for 15-year-olds. Despite students’ mean academic buoyancy scores being more than a point lower than reported baseline scores for Australian high school students, it was promising to see a modest increase following the EOtC program. In conclusion, EOtC is a potent vehicle for developing a sense of belonging, enhancing resilience, and equipping students to deal with academic challenges. Full article

As cyber threats continue to grow in complexity and persistence, resilience has become a critical requirement for cyber–physical systems (CPSs). Resilience quantitative assessment is essential for supporting secure system design and ensuring reliable operation. Although various methods have been proposed for evaluating CPS resilience, major challenges remain in accurately modeling the interaction between cyber and physical domains and in providing structured guidance for resilience-oriented design. This study proposes an integrated CPS resilience assessment framework that combines cyber-layer anomaly modeling based on Markov chains with mathematical modeling of performance degradation and recovery in the physical domain. The framework establishes a structured evaluation process through parameter normalization and cyber–physical coupling, enabling the generation of resilience curves that clearly represent system performance changes under adverse conditions. A case study involving an industrial controller equipped with a diversity-redundancy architecture is conducted to demonstrate the applicability of the proposed method. Modeling and simulation results indicate that the framework effectively reveals key resilience characteristics and supports performance-informed design optimization. Full article

The interconnections among water, energy, and food (WEF) systems are growing increasingly complex, making it essential to understand their evolutionary mechanisms and coordination barriers to enhance regional resilience and sustainability. In this study, we investigated the WEF system in Northeast China by constructing a comprehensive indicator system encompassing resource endowment and utilization efficiency. The coupling coordination degree (CCD) of the WEF system was quantitatively assessed from 2001 to 2022. An obstacle degree model was employed to identify key constraints, while grey relational analysis was used to evaluate the driving influence of individual indicators. Furthermore, a co-evolution model based on logistic growth and competition–cooperation dynamics was developed to simulate system interactions. The results reveal the following: (1) the regional WEF-CCD increased from 0.627 in 2001 to 0.769 in 2022, reaching the intermediate coordination level, with the CCDs of the food, water, and energy subsystems rising from 0.39 to 0.62, 0.38 to 0.60, and 0.40 to 0.55, respectively, highlighting that the food subsystem had the most stable and significant improvement; (2) Jilin Province attained the highest WEF-CCD, 0.850, in 2022, while that for Heilongjiang remained the lowest, at 0.715, indicating substantial interprovincial disparities; (3) key indicators, such as food self-sufficiency rate, electricity generation, and ecological water use, functioned as both core constraints and major drivers of system performance; (4) co-evolution modeling revealed that the food subsystem exhibited the fastest growth, followed by water and energy (${\alpha }_3$ >${ \alpha }_1$ > ${ \alpha }_2$ > 0), with mutual promotion between water and energy subsystems and inhibitory effects from the food subsystem, ultimately converging toward a stable equilibrium state; and (5) interprovincial co-evolution modeling indicated that Jilin leads in WEF system development, followed by Liaoning and Heilongjiang, with predominantly cooperative interactions among provinces driving convergence toward a stable and coordinated equilibrium despite structural asymmetries. This study proposes a transferable, multi-method analytical framework for evaluating WEF coordination, offering practical insights into bottlenecks, key drivers, and co-evolutionary dynamics for sustainable resource governance. Full article

Osteoarthritis and metastatic bone cancers create pathological oxidative environments characterized by elevated reactive oxygen species (ROS). ROS impair bone regeneration by degrading the scaffold and suppressing mineralization. To address these challenges, we fabricated thermoresponsive scaffolds based on poly(N-isopropylacrylamide) (PNIPAAm) incorporating in situ-grown nanohydroxyapatite on graphene oxide nanoscrolls (nHA-GONS) using stereolithography (SLA). Three scaffold formulations were studied: pure PNIPAAm (PNP), PNIPAAm with 5 wt.% nHA-GONS (P5G), and PNIPAAm with 5 wt.% nHA-GONS reinforced with polycaprolactone (PCL) microspheres (PN5GP). Each scaffold was evaluated for (i) swelling and lower critical solution temperature (LCST) using differential scanning calorimetry (DSC); (ii) oxidative degradation assessed using Fourier-transform infrared spectroscopy (FTIR), mass loss, and antioxidant assays; and (iii) mineralization and morphology via immersion in simulated body fluid followed by microscopy. The PN5GP and P5G scaffolds demonstrated reversible swelling, sustained antioxidant activity, and enhanced calcium deposition, which enable redox stability and mineralization under oxidative environments, critical for scaffold functionality in bone repair. PNP scaffolds exhibited copper accumulation, while PN5GP suffered from accelerated mass loss driven by the PCL phase. These findings identify the P5G formulation as a promising scaffold. This study introduces a quantitative framework that enables the predictive design of oxidation-resilient scaffolds. Full article

Urban and regional ecosystems are increasingly challenged by the compounded effects of climate change and intensive land use. In this study, a predictive assessment framework for ecosystem resilience in the Beijing–Tianjin–Hebei region was developed by integrating multi-source remote sensing data, with the aim of quantitatively evaluating the coupled effects of climate change and land use change on future ecosystem resilience. In the first stage of the study, the SD-PLUS coupled modeling framework was employed to simulate land use patterns for the years 2030 and 2060 under three representative combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Building upon these simulations, ecosystem resilience was comprehensively evaluated and predicted on the basis of three key attributes: resistance, adaptability, and recovery. This enabled a quantitative investigation of the spatio-temporal dynamics of ecosystem resilience under each scenario. The results reveal the following: (1) Temporally, ecosystem resilience exhibited a staged pattern of change. From 2020 to 2030, an increasing trend was observed only under the SSP1-2.6 scenario, whereas, from 2030 to 2060, resilience generally increased in all scenarios. (2) In terms of scenario comparison, ecosystem resilience typically followed a gradient pattern of SSP1-2.6 > SSP2-4.5 > SSP5-8.5. However, in 2060, a notable reversal occurred, with the highest resilience recorded under the SSP5-8.5 scenario. (3) Spatially, areas with high ecosystem resilience were primarily distributed in mountainous regions, while the southeastern plains and coastal zones consistently exhibited lower resilience levels. The results indicate that climate and land use changes jointly influence ecosystem resilience. Rainfall and temperature, as key climate drivers, not only affect land use dynamics but also play a crucial role in regulating ecosystem services and ecological processes. Under extreme scenarios such as SSP5-8.5, these factors may trigger nonlinear responses in ecosystem resilience. Meanwhile, land use restructuring further shapes resilience patterns by altering landscape configurations and recovery mechanisms. Our findings highlight the role of climate and land use in reshaping ecological structure, function, and services. This study offers scientific support for assessing and managing regional ecosystem resilience and informs adaptive urban governance in the face of future climate and land use uncertainty, promotes the sustainable development of ecosystems, and expands the applicability of remote sensing in dynamic ecological monitoring and predictive analysis. Full article

The Industrial Internet of Things (IIoT) offers transformative potential for supply chain management by enabling automation, real-time monitoring, and predictive analytics. However, fragmented standardization, interoperability challenges, and cybersecurity risks hinder its sustainable adoption. This study aims to develop and validate an ISO-based framework to optimize IIoT networks for sustainable supply chain operations. A quantitative time-series research design was employed, analyzing 150 observations from 10–15 industrial firms over five years. Analytical methods included ARIMA, structural equation modeling (SEM), and XGBoost for predictive evaluation. The findings indicate a 6.2% increase in system uptime, a 4.7% reduction in operational costs, a 2.8% decrease in lead times, and a 55–60% decline in security incidents following ISO standard implementation. Interoperability improved by 40–50%, and integration cost savings ranged from 35–40%, contributing to a 25% boost in overall operational efficiency. These results underscore the critical role of ISO frameworks such as ISO/IEC 30141 and ISO 50001 in enhancing connectivity, energy efficiency, and network security across IIoT-enabled supply chains. While standardization significantly improves key performance indicators, the persistence of lead time variability suggests the need for additional optimization strategies. This study offers a structured and scalable methodology for ISO-based IIoT integration, delivering both theoretical advancement and practical relevance. By aligning with internationally recognized sustainability standards, it provides policymakers, practitioners, and industry leaders with an evidence-based framework to accelerate digital transformation, enhance operational efficiency, and support resilient, sustainable supply chain development in the context of Industry 4.0. Full article

Time-of-flight sensors have emerged as a viable solution for real-time distance sensing in robotic safety applications due to their compact size, fast response, and contactless operation. This study addresses one of the key challenges with time-of-flight sensors, focusing on how they perceive and evaluate the environment, particularly in the presence of complex geometries and reflective surfaces. Using a Universal Robots UR5e arm in a controlled indoor workspace, two different sensors were tested across eight scenarios involving objects of varying shapes, sizes, materials, and reflectivity. Quantitative metrics including the root mean square error, mean absolute error, area difference, and others were used to evaluate measurement accuracy. Results show that the sensor’s field of view and operating principle significantly affect its spatial resolution and object boundary detection, with narrower fields of view providing more precise measurements and wider fields of view demonstrating greater resilience to specular reflections. These findings offer valuable insights into selecting appropriate ToF sensors for integration into robotic safety systems, particularly in environments with reflective surfaces and complex geometries. Full article

This study examines the role of supervisory frameworks in shaping the risk management behavior of Greece’s four systemic banks during the period of 2015–2024. It explores how regulatory reforms under Capital Requirements Regulation II, Basel III, and European Central Bank oversight influenced capital adequacy, asset quality, and liquidity metrics. Employing a quantitative methodology, this study analyzes secondary data from Pillar III disclosures, annual financial reports, and supervisory statements. Key risk indicators (capital adequacy ratio, non-performing exposure ratio, liquidity coverage ratio, and risk-weighted assets) are evaluated in conjunction with regulatory interventions, such as International Financial Reporting Standards 9 transitional relief, the Hercules Asset Protection Scheme, and European Central Bank liquidity measures. The findings reveal that enhanced supervision contributed to improved resilience and regulatory compliance. International Financial Reporting Standards 9 transitional arrangements were pivotal in maintaining capital thresholds during stress periods. Supervisory flexibility and extraordinary European Central Bank support measures helped banks absorb shocks and improve risk governance. Differences across banks highlight the impact of institutional strategy on regulatory performance. This study offers a rare longitudinal assessment of supervisory influence on bank risk behavior in a high-volatility Eurozone context. Covering an entire decade (2015–2024), it uniquely links institutional strategies with evolving regulatory frameworks, including crisis-specific interventions such as International Financial Reporting Standards 9 relief and asset protection schemes. The results provide insights for policymakers and regulators on how targeted supervisory interventions and transitional mechanisms can enhance banking sector resilience during protracted crises. Full article