Search Results (826)

The increasing digitalization of smart circular economy (CE) systems intensifies reliance on interconnected cyber-physical infrastructures, thereby increasing exposure to cybersecurity risks that may affect operational continuity and regulatory compliance. This study proposes a Fuzzy Analytical Hierarchy Process (Fuzzy AHP)-based framework to systematically assess cybersecurity readiness in alignment with the ISO/IEC 27001:2022 Information Security Management System (ISMS) standard. The framework adopts a structured three-level hierarchy consisting of seven main criteria and 39 sub-criteria, derived from ISO/IEC 27001:2022 clause-based requirements and Annex A control families, and expanded with an additional regulatory criterion based on the Cyber Resilience Act (CRA) Requirements Standards Mapping. Expert judgments from ten specialists in cybersecurity and digital systems were elicited using linguistic assessments and converted into triangular fuzzy numbers to compute priority weights under uncertainty. The results indicate that ISMS governance and organizational context are the most influential determinants of cybersecurity readiness, followed by regulatory and compliance alignment, operational oversight, and technological controls, while organizational, human, and physical controls play supportive roles. Consistency and sensitivity analyses confirm the robustness and stability of the weighting structure. Overall, the framework provides a standards-aligned decision-support tool for prioritizing cybersecurity readiness in digitally intensive CE environments. Full article

Freshwater ecosystems are increasingly threatened by eutrophication and other anthropogenic and climate-driven pressures that undermine ecological functioning and biodiversity. This study evaluates the transferability of a GIS-based multi-criteria decision analysis (GIS–MCDA) framework with Fuzzy Analytic Hierarchy Process (F-AHP), originally developed for a shallow coastal lake, to a morphologically distinct deep upland lake (Lough Tay, Ireland). Monthly in situ measurements at a single monitoring point in 2024 were analysed together with meteorological variables using Spearman rank correlations. Because spatial interpolation of in-lake water quality parameters was not feasible, eutrophication susceptibility was mapped using four external spatial drivers: distance from water resources (River Cloghoge inflows), land-based nitrogen export potential, distance from environmental pollutants represented by the transportation network, and a wind exposure index derived from a DEM and wind-rose analysis. Criteria were standardized with fuzzy membership functions, weighted using F-AHP (consistency index 0.056), and aggregated using weighted linear combination at 25 m resolution. The resulting Eutrophication Susceptibility Index (ESI) ranged from 0.18 to 0.81, indicating generally moderate to good conditions, with higher ESI values concentrated in the northern lake sector near inflow zones. The results demonstrate that GIS–MCDA can be adapted to lakes with limited monitoring by relying on external drivers, providing a spatial proxy for susceptibility rather than measured trophic status. Full article

The normal operational characteristics of power cables are a crucial foundation for developing their protection devices. To analyze the current and voltage characteristics of parallel cable lines, the parameter matrix of the phase-aligned parallel cable lines is first established based on Carson’s formula. The metal sheath is treated as a general line, considering its self-inductance and mutual inductance with the core loop, and its sheath circulating current is calculated. Then, the relationship between line voltage and current is established, and the effect of the sheath circulating current is equivalently incorporated into the line’s phase impedance matrix. A π-type equivalent circuit of the cable line is established, from which the operational parameters of the phase-aligned parallel cables are calculated. Indicators measuring the operational characteristics of phase-aligned parallel operation—sheath circulating current, imbalance, carrying capacity, and voltage deviation—are introduced, and the optimal arrangement is determined using the analytic hierarchy process. This study integrates Carson’s formula for impedance modeling and fuzzy AHP for multi-criteria optimization, addressing gaps in single-indicator approaches. The proposed method identifies the three-phase vertical layout as optimal, improving ampacity by 10% and reducing sheath circulating current by 28%, offering direct guidance for 500 kV cable projects. Full article

This study proposes a data-driven evaluation framework to quantify the impact of artificial intelligence (AI) on industrial process performance and enterprise value creation. The framework integrates enterprise value assessment based on the Feltham–Ohlson model with a multi-level performance evaluation framework that incorporates a hybrid Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) for indicator weighting, together with Fuzzy Comprehensive Evaluation (FCE) for multi-dimensional aggregation. This integrated approach enables systematic analysis of AI-driven effects from the perspectives of intelligent investment input, operational governance environment, and process output performance. Using panel data from 3515 Chinese A-share listed firms (20,076 firm-year observations) during 2014–2022, a Process Performance Index (PI) is constructed to measure AI-enabled operational capability across resource allocation efficiency, coordination effectiveness, and production performance dimensions. Empirical results indicate that PI is positively associated with abnormal earnings and firm profitability, demonstrating that AI-enabled process capability contributes to sustained enterprise value growth. The findings further show increased digital technology investment intensity, knowledge-based human capital accumulation, and improved data governance conditions, accompanied by enhanced production and service performance. By explicitly integrating AHP–EWM weighting and FCE aggregation within the Feltham–Ohlson valuation structure, the proposed framework provides an interpretable quantitative mechanism linking AI adoption, operational capability development, and enterprise value creation. The results offer practical insights for evaluating intelligent transformation strategies in the context of Industry 5.0 and data-driven industrial development. Full article

Background: To address the critical challenges of hazardous material (HAZMAT) incidents in dense urban areas, this study develops a hybrid framework for spatial emergency response optimization tailored for Intelligent Transport Systems (ITSs). Methods: Our approach integrates the Fuzzy Analytic Hierarchy Process (FAHP) with a rigorous technical benchmarking of multiple navigation APIs to improve routing decisions under volatile Bangkok traffic. By employing a normalized cost function (scale 0–1), we evaluated the performance of localized (Longdo Map) versus global (Google Maps and OpenStreetMap) platforms across day and night scenarios. Results: Experimental results, yielding normalized costs between 0.464 and 0.748, identified Bon Kai as the optimal response node, whereas Chan Road showed the lowest efficiency. Interestingly, OpenStreetMap provided the highest temporal consistency for emergency logistics. Conclusions: These findings offer a practical decision-support tool for authorities, proving that integrated API assessment is essential for building resilient and responsive urban mobility infrastructures. Full article

Quality evaluation in production systems represents a significant challenge in the manufacturing industry, particularly in environments where expert judgment plays a key role in managing the inherent uncertainty of the production system. This study proposes a fuzzy multicriteria decision-making control chart, termed Fuzzy Decision-Making Control Chart based on AHP-Extent and Triangular Fuzzy Numbers (FDMCC-AHPE). The method integrates expert knowledge through triangular fuzzy numbers and a Fuzzy Analytic Hierarchy Process supported by Extent Analysis, to define fuzzy decision intervals for quality assessment and subsequently perform a structured analysis to classify the product within a control chart framework. In this framework, expert judgments expressed through linguistic evaluations are systematically translated into triangular fuzzy numbers and processed using FAHP–Extent Analysis, allowing the aggregation of subjective assessments within a structured mathematical decision model. The proposed method was validated in a tannery company, specifically in the retanning process. The industrial case study considers both qualitative criteria, such as surface defects and color uniformity, and quantitative process variables that include bath pH, treatment duration, and processing temperature. The results were compared with an empirical expert-based evaluation and a structured expert assessment supported by a multicriteria decision-making method. The findings demonstrate that the FDMCC-AHPE exhibits greater sensitivity in discriminating between quality states under uncertain evaluation conditions, particularly when samples involve complex evaluation conditions. Full article

The selection of bridge construction systems significantly influences the sustainability of infrastructure projects, encompassing both economic and environmental dimensions. This study presents a comparative assessment of three hybrid fuzzy Multi-Criteria Decision-Making (MCDM) techniques, Fuzzy AHP–VIKOR, Fuzzy TOPSIS, and Fuzzy BWM–VIKOR, for choosing optimum bridge construction system during the preliminary design phases. Each method was applied consistently, integrating project-specific criteria and construction alternatives. The comparison extended beyond the final rankings to assess computational efficiency, sensitivity to input variations, ease of implementation, and stability. Expert opinions were gathered using semi-structured interviews and questionnaires to reflect the practical circumstances of bridge engineering in the field. The results show distinct strengths and trade-offs among the techniques, offering valuable insights for researchers and industry professionals alike. This study contributes to the knowledge base by explaining how different fuzzy MCDM methods are used in real-world bridge construction projects. These outcomes improve the methodological rigor of decision science and support more robust decision-making frameworks in bridge engineering. Full article

Under the dual pressures of urbanization and tourism development, the Historic Centre of Macao, as a World Cultural Heritage site, faces challenges including the inadequate transmission of cultural elements and the excessive commercialisation of tourism development. To systematically identify and safeguard its cultural characteristics, this study introduces the theory of cultural genes, constructing a dual-strand identification model encompassing both tangible and intangible cultural genes. This model integrates architectural function, structure, and ornamentation, alongside indigenous religions, arts, and folklore, thereby achieving a comprehensive extraction of cultural elements. Building upon this foundation, the study employed the AHP-fuzzy comprehensive evaluation method to quantitatively assess the degree of identification with various cultural elements, integrating perspectives from three distinct groups: tourists, residents, and third-party experts. The findings revealed significant disparities in cultural identification across these groups. For instance, residents demonstrated the highest level of identification with beliefs and folk customs, whilst tourists favoured more tangible aspects such as decorative elements and cuisine, exhibiting generally weaker identification with architectural structures. The findings demonstrate that this study, through the establishment of a systematic framework for identifying cultural genes and a multi-stakeholder evaluation system, has accurately discerned the characteristics of various cultural genes and the public’s level of identification with them. This provides a scientific basis for evidence-based, differentiated, and precise governance of the Historic Centre of Macao, offering significant reference value for the conservation and revitalisation of similar cultural heritage sites. Full article

With the interconnection and intercommunication of Internet of Things (IoT) devices, the security risks of Power Distribution Internet of Things (PDIoT) systems have increased accordingly. How to monitor and assess these risks has become a key issue for advancing the construction and implementation of PDIoT. The traditional security evaluation methods mostly adopt a single weighting method and membership function, which are highly susceptible to subjective factors and have the characteristics of fuzziness and uncertainty. To address the problems, this paper proposes a security risk evaluation method of the PDIoT based on a combined weighting and cloud model. We first analyze the factors of security risks in PDIoT systems. A security evaluation index system for PDIoT was established based on factors from three aspects: the perception layer, network layer, and application layer, including 3 first-level indicators and 16 second-level indicators. Then, the analytic hierarchy process (AHP) and entropy weight method (EWM) are adopted for combined weighting to optimize the weight of each index; the cloud model is employed to calculate the standard evaluation and the comprehensive evaluation cloud. Subsequently, validity verification and cloud similarity calculation are performed to get the security state of the PDIoT. Finally the security evaluation level of the PDIoT system is obtained. An empirical test was conducted by taking the PDIoT of Meizhou Power Supply Bureau of Guangdong Power Grid as an example. The experimental results show that our method has better result distinguishability than the other three classical methods, allowing risk levels to be identified more clearly and intuitively. Full article

This study investigates the effective translation of complex biological principles into viable engineering solutions within the field of biomimetic design. A critical challenge in current research is the “fuzzy front end” bridging initial biological observations and practical engineering applications. This gap primarily stems from the lack of intermediary models capable of abstracting complex biomechanical data into manufacturable mechanical paradigms. To address this, we propose a systematic biomimetic translation framework that redefines traditional crafts as “Empirically Optimized Biological Analogues” (EOBAs), serving as a logical bridge between biological inspiration and engineering realization. This study contributes by integrating the Analytic Hierarchy Process (AHP) with the Fuzzy Comprehensive Evaluation (FCE) method to construct a quantitative assessment system. This system evaluates translation feasibility, engineering innovation potential, semantic interaction characteristics, and prototype manufacturability. Applying this framework to four intangible cultural heritages in Guangdong, combined with comprehensive expert and public evaluations, revealed that the Guangdong Lion Dance exhibits the highest biomimetic translation potential in terms of morphological clarity and dynamic behavioral characteristics. Consequently, we extracted the core principle of “embodied kinematics for communication” and developed a conceptual multi-segment biomimetic robotic prototype designated as “Kine-Lion”. Ultimately, this research provides a structured methodological reference for biomimetic robotic design, demonstrating that culturally abstracted biological behaviors can be systematically decoded into functional robotic structures. These findings indicate broad application prospects in the domains of human–robot interaction and biomimetic engineering. Full article

Green supply chain management (GSCM) has become essential for organizations seeking to balance environmental sustainability, regulatory compliance, and economic resilience. However, selecting appropriate green supply chain strategies constitutes a complex multicriteria decision-making (MCDM) problem due to diverse sustainability practices, conflicting objectives, dynamic market conditions, and significant uncertainty in expert evaluations. To address these challenges, this study proposes an intelligent multicriteria group decision-making (MCGDM) framework to assess 15 GSCM strategies across 15 environmental, operational, economic, and regulatory criteria. The framework employs complex fractional orthopair fuzzy sets $\left(C_{FOFS}\right)$ to model uncertainty, expert hesitation, and complex-valued judgments. Expert weights are determined using the analytic hierarchy process (AHP), while criteria weights are derived objectively through the entropy method. A modified technique for order preference by similarity to the ideal solution (TOPSIS) is applied to obtain a robust ranking of alternatives. Evaluations from five multidisciplinary experts ensure practical relevance and validity. The results indicate enhanced uncertainty modeling, improved ranking stability, and greater interpretability compared with conventional fuzzy and deterministic approaches. The proposed framework provides a transparent and effective decision support tool for strategic GSCM planning. Full article

The aim of this study is to identify logistics supply chain criteria by considering the sustainability factor and to conduct a performance evaluation based on these criteria. The application analyzes 15 sub-criteria under the five main criteria of sustainable logistics: procurement logistics, production logistics, reverse logistics, distribution logistics, and disposal logistics. Accordingly, the importance weights of the logistics criteria were determined using the Fuzzy AHP (Analytic Hierarchy Process) method. Based on the determined criterion weights, an integrated model for performance evaluation was proposed using the Spherical Fuzzy MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the Full Multiplicative Form) and Heuristic Fuzzy COPRAS (Complex Proportional Assessment) methods. The application ranked the sustainable logistics performance of three major logistics firms, and the results obtained from both methods were consistent. The findings highlight that the three criteria with the highest importance levels are, in order, as follows: green purchasing strategies (0.356), green design (0.151), and integration of supplier into environmental management processes (0.125). This demonstrates that firms aim to foster environmental responsibility not only in their internal processes but also throughout the supply chain. This study provides a reliable model for evaluating and improving sustainable logistics performance, contributing both to the academic literature and to practical applications in logistics firms. Full article

Energy-efficient retrofit of existing residential buildings is essential for reducing heating energy demand and carbon emissions in severe cold regions. However, the absence of a structured quantitative evaluation approach often limits effective decision-making in practice. This study develops a multi-dimensional evaluation framework integrating the Fuzzy Delphi Method and Analytic Hierarchy Process (AHP) to assess and prioritize building envelope retrofit strategies. A representative non-energy-efficient residential building in Changchun, China, is selected as a case study. Based on expert consultation, a hierarchical indicator system is established, and indicator weights are determined with satisfactory consistency (CR < 0.1). The results indicate that envelope thermal performance and energy–carbon benefits are the dominant factors influencing retrofit decisions. At the parameter level, insulation thermal conductivity and external wall heat transfer coefficient are identified as the most critical variables. The findings suggest that prioritizing improvements in envelope thermal performance can effectively enhance energy-saving and carbon-reduction performance under practical constraints. The proposed framework provides a practical and transferable decision-support tool for energy-efficient retrofit planning for existing residential buildings in severe cold regions. Full article

Traffic signal control at urban intersections is one of the key determinants of the overall efficiency of the transportation system, given its direct impact on travel time, congestion levels, and emissions of exhaust fumes. This study proposes an integrated hybrid model that combines a metaheuristic Genetic Algorithm for generating potential signal timing plans with fuzzy multi-criteria decision-making (MCDM) for their evaluation and selection of the optimal solution. In order to determine the relative importance of criteria, the fuzzy methods F-AHP, F-FUCOM, and F-PIPRECIA were employed, thus providing stable assessments of criteria importance under conditions of uncertainty and expert subjectivity. The ranking of generated alternatives was performed by employing the F-TOPSIS, F-WASPAS, and F-ARAS methods, while the robust decision-making rule approach was employed to develop a robust decision-making rule by integrating multiple MCDM methods. The proposed model was tested using data collected from a real urban intersection. The results show that the integrated hybrid approach enables a significantly more reliable selection of the optimal signal timing plan and achieves higher traffic management efficiency compared to traditional methods. The proposed model provides a flexible and scalable framework that can be adapted to different types of intersections and traffic demand conditions, thereby significantly contributing to the development of modern intelligent traffic management systems. Full article

Given the increasing frequency and intensity of floods, which are mostly caused by continuous climate change and growing human pressures on the environment, accurately identifying areas that are susceptible to flooding is a crucial priority for risk reduction and long-term land use planning. Thus, this research examines multilevel flood susceptibility mapping across North Macedonia, using 328 past flood occurrences, 14 conditioning variables derived from a digital elevation model, simplified lithology, and calculated direct runoff. The methodology integrates fuzzy set theory (Fuzzy), analytic hierarchy process (AHP), weighted linear combination (WLC), and random forest (RF) approaches. The two-stage process employs distinct sets of conditioning factors in sequential flood susceptibility mapping: first, generating Fuzzy/AHP/WLC predictions and pseudo-absence data, and second, producing five RF predictions by varying pseudo-absences and binary cutoffs. Validation results indicate that the very high susceptibility class (0.8–1.0) of the Fuzzy/AHP/WLC model predicted 46.6% of flood pixels within 31.6% of the total area. In comparison, the very high susceptibility class of the RF models predicted 88.5%, 78.3%, 60.6%, 48.5%, and 28.3% of flood pixels within 54.7%, 42.2%, 30.5%, 27.0%, and 25.1% of the total area, respectively. The RF models achieved area under the curve (AUC) values exceeding 0.850, with a maximum of 0.966. Additionally, areas of high and low uncertainty were highlighted using a standard deviation map created from the RF models, highlighting agreement/disagreement and potential locations for methodological improvement and focused sampling. The findings also highlight the potential of the multilevel technique for mapping flood susceptibility and call for more research into its potential for future studies and practical uses. Full article