Search Results (1,487)

During freeway reconstruction and expansion, median crossover sections where traffic is maintained during construction are vulnerable to changes in lane configuration, abrupt geometric changes, and construction interference. These factors may lead to safety risks and operational efficiency losses. Existing studies have mainly relied on microscopic traffic simulation to evaluate speed limit schemes, while engineering costs, environmental impacts, driver responses, and policy constraints have rarely been considered in an integrated manner. This study proposes a two-stage evaluation framework that integrates VISSIM microscopic traffic simulation, the Entropy Weight Method–Technique for Order Preference by Similarity to an Ideal Solution (EWM–TOPSIS), and the Fuzzy Analytic Hierarchy Process (FAHP). A four to eight-lane freeway expansion project in a plain area of northern China is used as the case study. Field speed data from a representative median crossover section are used for model calibration and speed-pattern analysis. A total of 27 simulation scenarios is then constructed by combining three bottleneck types, three traffic saturation levels, and three speed limit schemes. The EWM–TOPSIS results show that the 80→70 km/h scheme achieves the highest relative closeness in all scenarios. The FAHP evaluation, based on six criteria and 21 indicators, also ranks this scheme first. Its ranking remains unchanged under ±10% criteria weight perturbations. Field speed comparison indicates that vehicles exhibit a deceleration–recovery pattern when passing through the crossover opening. Overall, the 80→70 km/h gradual speed reduction scheme can be regarded as a candidate scheme for work zones with similar median crossover configurations. Under localized calibration conditions, it can provide decision-making support for reducing operational disturbances, fuel consumption, and external impacts associated with crash risk. Full article

This study evaluates the environmental performance of 34 single-engine light utility helicopters across five operational phases: ground idle departure, ground idle arrival, takeoff, approach, and landing-takeoff (LTO). A hybrid multi-criteria decision-making (MCDM) framework integrating interval type-2 fuzzy sets with the Analytic Hierarchy Process (AHP), VIKOR, and TOPSIS was applied to ensure robust and reliable assessment. Six criteria: shaft horsepower (SHP), fuel flow, hydrocarbon (HC), carbon monoxide (CO), particulate matter (PM), and nitrogen oxides (NOx) were considered to capture both engine performance and environmental impact, with relative importance determined through AHP. VIKOR generated a compromise ranking, while TOPSIS validated the results. The analysis revealed that the HUGHES 500 (DDA250-C18, A34), HUGHES 501 (DDA250-C20B, A29), and BELL 206B-1 (DDA250-C20, A32) engines achieved the best environmental performance due to low fuel consumption and reduced emissions across NOx, PM, HC, and CO. In contrast, engines such as K-1200 (T53 17A-1, A1) and BELL UH-1H (T53 L13, A2) performed the poorest, with high fuel flow and elevated emissions. Sensitivity analysis showed minimal changes in rankings when the NOx weight was varied, confirming the robustness of the framework. These results highlight that emissions and fuel efficiency are more critical than engine power in determining environmental sustainability. Full article

The growing complexity of modern decision-making environments, characterized by multi-dimensional data, uncertainty, and dynamic behavior, demands advanced mathematical frameworks for effective information aggregation. Although fractional fuzzy tensor (FFT) models provide a powerful tool for representing such complex systems by integrating fuzzy logic, tensor structures, and fractional dynamics, the lack of suitable aggregation mechanisms significantly limits their practical applicability. To address this challenge, this paper proposes a novel family of Bonferroni mean-based aggregation operators within the fractional fuzzy tensor environment. The proposed framework extends the classical Bonferroni mean to multi-dimensional fractional fuzzy settings, enabling the effective modeling of interrelationships among criteria while preserving the structural and dynamic properties of FFTs. Specifically, four aggregation operators—namely, the fractional fuzzy tensor Bonferroni mean (FFT-BM), weighted Bonferroni mean (FFT-WBM), ordered Bonferroni mean (FFT-OBM), and hybrid Bonferroni mean (FFT-HBM)—are systematically developed. A comprehensive theoretical analysis is conducted to investigate fundamental properties such as idempotency, monotonicity, boundedness, commutativity, and stability, thereby establishing the mathematical consistency and reliability of the proposed operators. Furthermore, a structured multi-criteria decision-making (MCDM) algorithm is formulated, incorporating tensor construction, aggregation, evaluation, and sensitivity analysis phases to handle complex uncertain information effectively. To demonstrate the practical applicability of the proposed framework, a real-world case study related to disaster management decision-making is presented. The results are further validated through quantitative comparative analysis with classical and recent aggregation operators, revealing improved discrimination power, robustness, and ranking consistency. Additionally, sensitivity analysis confirms the stability of the proposed approach under varying parameters. The findings indicate that the proposed Bonferroni mean-based aggregation framework significantly enhances the capability of FFT models in handling high-dimensional, uncertain, and dynamic decision-making problems. This study not only strengthens the theoretical foundation of aggregation in tensor-based fuzzy environments but also provides a flexible and reliable decision-support tool for complex real-world applications. Full article

As global urban centers transition into “Super-Aged Societies,” the paradigm of urban sustainability has shifted from mere housing provision to the development of Sustainable Care Retirement Communities (SCRCs). This study addresses a critical gap in the urban aging literature: the lack of culturally sensitive frameworks for social resilience in non-Western contexts. By integrating Hofstede’s Cultural Dimensions Theory, this research investigates how national culture influences the prioritization of community attributes within the “15 min city” framework. Methodologically, a hierarchical evaluation framework comprising 4 dimensions and 26 indicators was established. It employed the Fuzzy Delphi Method (FDM) to achieve expert consensus among stakeholders in Taiwan’s Long-term Care 3.0 ecosystem. Analysis using Double Triangular Fuzzy Numbers identified the “Charging Model,” “Staff-to-Resident Ratio,” and “Zoning with Care Continuity” as the highest-priority factors ($G^i$ ≥ 7.8). These results indicate that in cultures with high uncertainty avoidance, institutional financial stability and human-centric staffing are perceived as the structural bedrock of social resilience. Furthermore, the study highlights the emergence of AI-driven “Active Sensing” environments as a pivotal component of technical resilience, mitigating the loneliness epidemic while maintaining institutional efficiency. The findings suggest that social resilience in SCRCs is not merely a product of physical accessibility but is theoretically inferred by experts to be deeply rooted in the synergy of Bonding and Bridging Social Capital, rather than being a directly measured outcome. This research provides urban planners and policy-makers with a robust, evidence-based toolkit to design inclusive, resilient, and culturally aligned aging-in-place environments in the face of unprecedented demographic challenges. Full article

Efficient storm drainage systems (SDSs) play a pivotal role in sustainable urban development. In arid regions, urban SDS often underperform during prolonged dry periods, leaving them inoperable due to sediment buildup and clogging from the intrusion of sprawling waste. Municipalities either rely on emergency response to flooding complaints or inspect storm sewers individually to handle flash floods and conserve high-value rainwater. The present study developed a risk-based decision-analysis framework for resource-efficient inspection and maintenance (I&M) planning of SDS to prioritize geographically clustered sub-zones. The study applied the framework to a case study of three urban zones with varying population densities and land use distributions in Buraydah, Qassim, Saudi Arabia. The framework integrates fuzzy synthetic evaluation (FSE) to address data limitations and subjective expert knowledge, with geographic information system (GIS)-based spatial analysis to assess three risk factors: likelihood, consequences, and detectability of sewer clogging potential. In addition to traditional likelihood-based evaluation of the susceptibility of smaller sewers to sediment accumulation due to performance anomalies, the consequence analysis augmented the process by considering land-use characteristics, exemplified by commercial areas exhibiting higher socio-economic losses than open spaces that buffer excess runoff. The detectability factor consolidated the decision analysis by incorporating the impacts of past delayed inspections, deep manholes, and scattered construction-related waste on clogging potential. The analysis identified sub-zones with aged sewers, deep manholes, long-awaited inspections, and high population densities, resulting in a high risk. GIS maps showing distinct impacts of the three factors on overall flood risk facilitate municipalities facing unique urban flooding challenges arising from sediment accumulation during long dry periods, followed by short-duration, high-intensity rainfall. Full article

In recent years, Intelligent Transportation Systems (ITSs) have emerged as a solution to mitigate the problem of traffic congestion. Understanding human driving styles such as aggressive, normal, and cautious is crucial for safe driving. In particular, predicting lane-change manoeuvres may be further supported by combining vehicle state information with driving style information. However, existing vehicle trajectory datasets lack driving style information, making classification challenging. To address this limitation, this paper proposes a fuzzy logic-based driving style classification framework in a Vehicle-to-Everything (V2X) environment. The model uses vehicle state information, including speed, longitudinal acceleration, lateral acceleration, and distance headway to classify style as cautious, normal, or aggressive. The proposed system is interpretable, aligns with human reasoning, and remains computationally efficient for real-time applications. The performance of the proposed work has been evaluated through comprehensive experiments on highway data. Results show a separation of driving styles, achieving 77% accuracy on a balanced dataset, showing moderate agreement with deterministic labelling while maintaining interpretability. In V2X-enabled lane-change prediction scenarios, computational latency is essential, as Roadside Units (RSUs) must understand driving style and update prediction models. Since lane-change intentions should be predicted around 3 s before manoeuvre, delays in inference reduce reaction time. The proposed classifier achieves an inference latency of approximately 8 ms, ensuring that it does not become a bottleneck in real-time systems. Furthermore, the usefulness of driving style information is tested by integrating it into a lane-change prediction task. Experimental results demonstrate that incorporating driving style enhances prediction accuracy from 75% to 84%. Lastly, the proposed method provides a balanced result between interpretability, computational efficiency, and predictive performance, supporting RSUs to issue timely warnings and support safer decision-making in highway environments. Full article

This paper assesses the challenges and policy responses for the adoption of electric vehicles (EVs) in Africa. We applied a decision support system framework comprising a new integration of the RANking COMparison Method (RANCOM) and Root Assessment Method (RAM) for the first time in the literature to address the multi-criteria decision analysis (MCDA) problems based on expert opinions. Six experts evaluated five criteria along with ten policy responses. While the weights of criteria are computed via the RANCOM method, the RAM approach ranks the policy responses. Moreover, the Compromise Fuzzy Ranking (CFR) method defines the consensus rankings via both positional ranks and preference scores. Furthermore, a three-stage comparative analysis is carried out for criteria weighting, policy responses ranking, and alternative consensus ranking. A sensitivity analysis is carried out including the consideration of experts’ significance according to their experience and their omission. The findings indicated the most critical challenges were the scarcity in charging infrastructure and the affordability and accessibility issues. The resilient charging infrastructure is the most appropriate policy response. The findings direct planners and EVs policymakers across the continent toward a policy that will ensure a clean and sustainable transportation system. Full article

This study addresses the challenge of prioritizing Virtual Reality (VR) and Augmented Reality (AR) applications in Product Lifecycle Management (PLM) under multiple conflicting criteria. A comprehensive fuzzy Multi-Criteria Decision-Making (FMCDM) framework is proposed to support robust and unbiased decision-making. The methodology integrates multiple objective weighting techniques, including Entropy, Criteria Importance Through Intercriteria Correlation (CRITIC), Method based on the Removal Effects of Criteria (MEREC), and Standard Deviation, which are aggregated using the Bonferroni operator to obtain balanced criterion weights. The Fuzzy Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method is employed as the primary ranking approach, supported by comparative methods such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VIšekriterijumsko KOmpromisno Rangiranje (VIKOR), Evaluation based on Distance from Average Solution (EDAS), Weighted Aggregated Sum Product Assessment (WASPAS), and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) for validation. The results indicate that Virtual Reality Digital Prototyping and Design Review (A3) is the most preferred alternative, achieving the highest utility value (0.95267), followed by Augmented Reality-Assisted Assembly and Inspection Guidance (A1) and Augmented Reality-Supported Maintenance and Operator Training (A4). A high Stability Index of 0.9133 confirms robustness, and sensitivity analysis shows stable rankings. The framework provides a reliable and scalable decision-support system for smart manufacturing. Full article

The use of large language models (LLMs) in higher education has increased significantly, and their potential for supporting teaching and learning is considerable. However, their reliability and suitability for generating educational content remain open questions, particularly in technically demanding fields such as software engineering. This paper proposes a multi-criteria framework for assessing the quality of educational content generated by LLMs. The framework is based on existing open educational resource (OER) evaluation rubrics, which were adapted for the assessment of LLM-generated content and further refined based on expert evaluation and consultation. The evaluation was conducted by a panel of eight experts from software engineering, artificial intelligence, education, and related fields, using predefined criteria and pairwise comparisons. The framework was applied to five contemporary LLMs across three selected topics in software engineering. The relative importance of the criteria was determined using the Analytic Hierarchy Process (AHP) and its fuzzy extension (FAHP). The results show that accuracy and professional correctness represent the most important criterion, while visual presentation and language style have the least influence. The findings also indicate differences across models and a high level of agreement between AHP and FAHP rankings. Full article

High penetration of converter-based wind generation reduces system inertia. It poses challenges to frequency stability in modern distribution networks, particularly in doubly fed induction generator (DFIG)-based wind-energy-conversion systems (WECSs), where frequency regulation is coupled with point-of-common-coupling (PCC) voltage and power factor (PF) dynamics. This study presents a multi-objective comparative evaluation of proportional–integral (PI), proportional–integral–derivative (PID), fractional-order PID (FOPID), and adaptive neuro-fuzzy inference system (ANFIS) controllers for a DFIG-based WECS connected to a radial distribution feeder. Controller parameters are tuned using multi-objective optimisation, considering frequency deviation, overshoot, settling time, disturbance robustness, control smoothness, and computational cost, while maintaining PCC voltage and PF within acceptable limits. MATLAB/Simulink simulations are conducted under turbulent wind conditions, load variations, voltage disturbances, and measurement noise. The results indicate that conventional PI and PID controllers exhibit limited performance under low-inertia conditions, whereas FOPID improves damping and voltage/PF behaviour. ANFIS achieves the best overall performance, providing reduced frequency deviation, faster settling time (below 3 s), improved disturbance rejection, and significantly lower integral absolute error (up to ~90%) compared to PI control. These findings offer practical guidance for selecting and tuning controllers to enhance frequency-centric stability in wind-integrated distribution networks. Full article

Over the past few decades, extensive attention has been given by researchers and practitioners to the development and application of multi-criteria decision-making (MCDM) methods within intuitionistic fuzzy environments across a wide range of fields and disciplines. This challenging research area has emerged as one of the most prominent topics, and its importance and popularity are expected to continue growing in the future. The elliptic intuitionistic fuzzy set (EIFS) addresses complex, multidimensional, non-symmetrical vagueness and uncertainty more effectively than other traditional intuitionistic fuzzy sets (IFSs). Sustainable renewable energy source selection is a critical decision-making (DM) process aiming to identify the most suitable energy alternative. The process of selecting sustainable renewable energy sources necessitates a comprehensive assessment of numerous criteria, which encompass environmental ramifications, economic feasibility, and societal acceptance. Contemporary research suggests novel methodologies to enhance this selection process, highlighting the need for an MCDM framework that integrates a variety of factors. This study presents an innovative DM framework for sustainable renewable energy source selection based on EIFS and a newly developed aggregation operator, the Elliptic Intuitionistic Fuzzy Weighted Muirhead Mean Aggregation (EIFWMMA) operator. These mechanisms expand upon conventional intuitionistic fuzzy frameworks by employing an elliptical portrayal of membership and non-membership degrees, facilitating a more accurate and lifelike representation of uncertainty and hesitation in evaluations by experts. To enhance computational efficiency, the framework weaves together machine learning-driven dimensionality reduction and weight optimization strategies of principal component analysis (PCA) for DM. The suggested operators are employed in an MCDM scenario centered around the selection of sustainable renewable energy sources, where the hierarchy of alternatives is established through score values derived from EIFWMMA. A comparative exploration of Circular Intuitionistic Fuzzy Sets (C-IFSs) and Interval-Valued Intuitionistic Fuzzy Sets (IVIFSs) uncovers that the elliptical formulation yields consistently reliable, precise, and geometrically comprehensible results. The findings affirm that EIFS-based operators offer a resilient, adaptable, and broadly applicable strategy for tackling MCDM challenges amidst uncertainty. The Min–Max normalization method is employed to validate our proposed methodology for identifying alternatives within the MCDM paradigm. It also improves accuracy, stability, and scalability in comparison to conventional approaches. Full article

This study evaluated the multi-scale spatial heterogeneity of soil fertility in walnut orchards in the middle and lower reaches of the Yarlung Zangbo River valley. The investigation focused on Jiacha, Lang, and Milin counties, covering four river terrace levels and three soil depths within the 0–60 cm layer, and further examined the effects of such heterogeneity on walnut fruit quality. Using integrated multivariate statistical approaches and fuzzy comprehensive evaluation, 321 paired soil and fruit samples collected in September and October of 2023 were analyzed. Overall soil fertility was moderate (0.4 ≤ IFI < 0.6) with a mean integrated fertility index (IFI) of 0.527, but showed pronounced spatial variation. PCA-based composite scores indicated the highest fertility in Milin County, followed by Lang County, with Jiacha County ranking lowest. Soil fertility across 11 towns was classified into five grades. Cluster analysis based on ten standardized soil fertility indicators revealed clear regional aggregation patterns, where close towns exhibited similar fertility conditions. Third-level river terraces exhibited significantly higher fertility than other terrace levels. Available phosphorus was widely deficient, while exchangeable magnesium and available zinc were also low, representing key limiting nutrients with strong regional variability. Spatial differences in soil enzyme activities reflected variation in microbially mediated nutrient cycling, with phosphatase activity negatively correlated with available phosphorus, suggesting potential microbial responses to phosphorus-stressed environments. Soil fertility significantly influenced walnut fruit quality, with alkaline hydrolyzable nitrogen, phosphorus, potassium, and exchangeable calcium and magnesium identified as key drivers. These findings provide a theoretical basis for suggesting a zoned precision fertilization strategy, where prioritizing P, Zn, and Mg inputs in deficient areas could be considered alongside organic fertilisation. Such site-specific management strategies are suggested to support the sustainable development of the walnut industry along the Yarlung Zangbo River valley. Full article

This research presents an integrated framework for supply chain finance in which digital twin, blockchain, and multi-objective fuzzy optimization are used in synergy to improve financial decision-making in dynamic and uncertain environments. In this framework, the digital twin acts as a real-time monitoring and forecasting layer, blockchain acts as a trust and transparency infrastructure, and the optimization model acts as the decision-making core. To evaluate the proposed framework, a scenario-based mathematical model was developed and analyzed using a combination of real-world and simulated data. The results showed that the proposed framework was able to reduce the total cost by 18.6% and increase the return on investment to 12.4%. Also, the use of the digital twin framework significantly reduced financial risks and delays, while the integration of blockchain improved the transparency, traceability, and reliability of transactions and reduced operational errors. Overall, the findings show that this framework has high potential for developing smart, transparent, and resilient financial systems in the supply chain context. Full article

While the circular economy offers a new perspective for achieving sustainability goals, digital technologies have become key enablers of this transformation. However, few studies in the literature address the identification and prioritisation of critical success factors for digitalisation that support the transition to a circular economy, particularly for agri-food SMEs operating in developing countries. This study proposes an integrated PESTEL-based and Spherical Fuzzy AHP (SF-AHP) framework to identify and prioritise critical success factors for digitalisation in the circular economy transition of agri-food SMEs. First, the literature-derived critical success factors were identified and structured according to the PESTEL framework. The TOE framework was then employed as a theoretical lens to interpret these factors at the firm level in terms of technological, organisational, and environmental dimensions. A five-member expert panel evaluated the factors in the context of Türkiye, and their relative importance was analysed using a weighted SF-AHP approach. Quantitative results reveal that ‘Data analytics to boost agricultural output’ is the most significant factor (w = 0.128), followed by ‘High investment costs’ (w = 0.123) and ‘Efficient technology for the CE process’ (w = 0.114). To ensure the robustness of the findings, a comparative analysis was performed; the results revealed a strong alignment between SF-AHP and Fuzzy AHP (r = 0.986), as well as a high degree of consistency with AHP (r = 0.910), validating the methodological stability of the proposed framework. This study contributes to the identification of strategic priorities for digitalisation in the transition to a circular economy among agri-food SMEs in developing countries and provides policymakers and practitioners with a guiding framework. Full article

As the construction industry faces increasing complexity and uncertainty, multi-criteria decision-making (MCDM) methods have been widely adopted in construction and project management. However, their application in the specific context of livelihood-related building projects during public health emergencies remains insufficiently explored. Existing MCDM approaches lack an integrated framework that combines qualitative factor identification with quantitative evaluation under emergency conditions. To address this gap, this study proposes an extended hybrid decision-making system based on multi-criteria decision-making theory, integrating grounded theory, the Fuzzy DEMATEL method, the CRITIC method, and the PFHWD-TOPSIS evaluation approach. Taking a hospital project in China during the COVID-19 pandemic as a case study, an evaluation indicator system tailored to livelihood-related building construction under public health emergencies is developed and a systematic analysis of the key influencing factors and scheme rankings is conducted. The results show that, besides traditional evaluation criteria, factors such as epidemic prevention and safety management play a critical role in construction decision-making under emergency conditions. Furthermore, the proposed hybrid MCDM framework significantly enhances the scientific rigor and robustness of scheme prioritization. This study not only provides theoretical support and practical guidance for livelihood-related building construction during public health emergencies but also offers valuable insights for optimizing decision-making in similar high-uncertainty contexts. Full article