Search Results (657)

Logistics parks are increasingly acting as coordination hubs in cross-border agricultural supply chains (CASCs), yet evidence on how park-enabled governance mechanisms translate into sustainability remains limited. This study examines the drivers of CASC sustainability within the context of logistics parks in Henan, China, and assesses whether the dominant park type conditions these effects. A total of 385 valid questionnaire responses were analyzed using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). SEM results show that symbiotic environment cultivation is the strongest predictor of sustainability, while interface mediation efficiency and safety also significantly support cross-border circulation. The moderating role of dominant park type is supported only for the interface and sustainability link. fsQCA further identifies three equifinal configurations leading to high sustainability, indicating that strong environmental cultivation and interface efficiency can compensate for weaker elements under certain combinations. These findings clarify how logistics parks enable economic, environmental, and social value creation in CASCs and provide actionable levers for park management and policy design. Full article

Much of the patient data recorded in electronic health records is stored as unstructured free text. Extracting medication information from such data is essential, particularly for antidiabetic drugs such as sodium–glucose cotransporter-2 (SGLT2) inhibitors, but remains challenging due to spelling variability, abbreviations, and non-standard documentation practices. This study compared four text-mining approaches, simple keyword search, regular expression–based matching, fuzzy string matching, and a transformer-based token classification baseline, for detecting SGLT2 inhibitor mentions in Hungarian clinical narratives. Clinical documents were obtained from the University of Debrecen Clinical Centre and covered patients with type 2 diabetes mellitus (ICD-10: E11) from 2018 and 2019. Searches targeted both generic and brand names and SGLT-related abbreviations. In the 2019 dataset (n = 5383), simple keyword search identified 1.49% of documents as containing an SGLT2 inhibitor mention, compared with 7.21% using regular expressions, 8.55% using fuzzy matching, and 0.71% using the transformer-based baseline. Mean execution times were 0.07 s, 1.64 s, 5.13 s, and 34.71 s, respectively. Method performance was further evaluated against a manually annotated reference set from 2018 using confusion matrices and standard classification metrics. Fuzzy string matching achieved the highest recall and F1-score, while regular expression-based matching provided a strong balance between precision and recall. The transformer-based baseline showed high precision but substantially lower recall in the absence of domain-specific fine-tuning. Overall, similarity-based fuzzy matching offered the most favorable balance between detection performance and computational efficiency for identifying SGLT2 inhibitor mentions in unstructured Hungarian clinical text. Full article

Metaheuristic optimization algorithms have demonstrated their effectiveness in solving complex optimization tasks, such as those related to Intrusion Detection Systems (IDSs). It was widely used to enhance the detection rate of various types of cyber attacks by reducing the feature space or tuning the model’s hyperparameters. The Artificial Rabbit Optimizer (ARO) mimics rabbits’ intelligent foraging and hiding behavior. The ARO algorithm has seen widespread adoption in the optimization field. The widespread use of the ARO algorithm occurs due to its simple design and ease of implementation. However, ARO can get trapped in local optima due to its limited diversity in population dynamics. Although the transition between phases is managed via an energy shrink factor, fine-tuning this balance remains challenging and unexplored. These limitations could limit the ARO algorithm’s effectiveness in high-dimensional space, as with IDS systems. This paper introduces a novel enhancement of the original ARO by integrating Fuzzy Rule Interpolation (FRI) to compute the energy factor during the optimization process dynamically. In this work, we integrate the FRI along with the ARO algorithm to improve solution accuracy, maintain population diversity, and accelerate convergence, particularly in high-dimensional and complex problems such as IDS. The integration of the FRI and ARO aimed to control the exploration-exploitation balance in the IDS application area. To validate our proposed hybrid approach, we tested it on a diverse set of intrusion datasets, covering eight different benchmark intrusion detection datasets. The suggested hybrid approach has been demonstrated to be effective in handling various intrusion classification tasks. For binary intrusion classification tasks, it achieved accuracy rates ranging from 96% to 99.9%. In the case of multiclass intrusion classification tasks, the accuracy was slightly more consistent, falling between 91.6% and 98.9%. The suggested approach effectively reduced the number of feature spaces, achieving reduction rates from 56% up to 96%. Furthermore, the proposed approach outperformed other state-of-the-art methods in terms of detection rate. Full article

This study evaluates the feasibility of estimating stiffness-related parameters and pulse wave velocity (PWV) in a controlled in vitro circulatory setup using artificial silicone vessels with systematically varied Shore A hardness and wall thickness. From synchronized pressure and capacitive waveforms, fiducial points and engineered features are extracted, together with pump settings (stroke volume and heart rate). A Sugeno-type adaptive neuro-fuzzy inference system (ANFIS) is used for hardness-level prediction and benchmarked against linear regression and contemporary machine-learning/deep-learning baselines using stratified cross-validation. PWV estimates derived via hardness-to-elasticity conversion models and the Moens–Korteweg formulation are evaluated against a reference PWV obtained within the same experimental configuration. Under these controlled conditions, the proposed pipeline shows strong agreement with reference labels and measurements. The results should be interpreted as an in vitro validation step; translation to biological tissues or in vivo data will require external validation, calibration of material-property mapping, and robustness testing under physiological variability and measurement noise. Full article

To facilitate the successful achievement of the goals outlined in the 2030 Agenda for Sustainable Development, it is imperative to accelerate the advancement of green technological innovation effectiveness (GTIE). This study aims to synthesize three types of drivers and seven concurrent driving factors of green technological innovation effectiveness identified in existing theories, constructing a multiple concurrent mechanism model for such effectiveness. The fuzzy-set Qualitative Comparative Analysis (fsQCA) method is employed to identify the configurational conditions leading to high green technological innovation effectiveness. Furthermore, the robustness of these configurations is verified through panel decomposition, while Necessary Condition Analysis (NCA) is applied to test the necessity of the factors within these configurations and to conduct further examination. The results reveal that high green technological innovation effectiveness is driven by three types of multiple concurrent mechanisms: the “Demand–Pull and Technology–Push and Porter Effect-Driven” configuration type, the “Demand–Pull & Technology–Push-Driven” type, and the “Demand–Pull & Porter Effect-Driven” type. This paper’s contributions are threefold. First, it investigates the configurational drivers of green technological innovation effectiveness. Second, it uses Necessary Condition Analysis (NCA) to identify necessary conditions within these multiple concurrent effects, deepening insight into the drivers. Third, it reveals three patterns driving green innovation in industries and proposes corresponding sustainable manufacturing policy recommendations. Full article

In this article, a new similarity measure is discussed on interval-valued intuitionistic fuzzy values (IVIFVs). Here, the proposed similarity measure has been derived based on transformed intervals and its probability density functions, mean values, and standard deviations of IVIFVs. Based on the proposed similarity measure, several essential properties have been illustrated in this paper. Additionally, a new algorithm has been developed using the similarity measure of interval-valued intuitionistic fuzzy values (IVIFVs) to solve multi-attribute decision-making (MADM) problem. The proposed method is highly effective for solving various types of MADM problems. To demonstrate the effectiveness of the proposed similarity measure, a car selection problem has been considered, where the objective is to choose a suitable car for a decision maker from a set of alternatives evaluated under multiple criteria. In car selection, different features often involve conflicting criteria with imprecise data. Therefore, the proposed similarity measure of interval-valued intuitionistic fuzzy values assists in determining the best alternative among these conflicting criteria. Full article

Autonomous mobile robots (AMRs) require robust, efficient path planning to operate safely in complex, often dynamic environments (e.g., logistics, transportation, and healthcare). This systematic review focuses on advanced metaheuristic and learning- and reasoning-based (cognitive) techniques for AMR path planning. Drawing on approximately 230 articles published between 2018 and 2025, we organize the literature into two prominent families, metaheuristic optimization and AI-based navigation, and introduce and apply a unified taxonomy (planning scope, output type, and constraint awareness) to guide the comparative analysis and practitioner-oriented synthesis. We synthesize representative approaches, including swarm- and evolutionary-based planners (e.g., PSO, GA, ACO, GWO), fuzzy and neuro-fuzzy systems, neural methods, and RL/DRL-based navigation, highlighting their operating principles, recent enhancements, strengths, and limitations, and typical deployment roles within hierarchical navigation stacks. Comparative tables and a compact trade-off synthesis summarize capabilities across static/dynamic settings, real-world validation, and hybridization trends. Persistent gaps remain in parameter tuning, safety, and interpretability of learning-enabled navigation; sim-to-real transfer; scalability under real-time compute limits; and limited physical experimentation. Finally, we outline research opportunities and open research questions, covering benchmarking and reproducibility, resource-aware planning, multi-robot coordination, 3D navigation, and emerging foundation models (LLMs/VLMs) for high-level semantic navigation. Collectively, this review provides a consolidated reference and practical guidance for future AMR path-planning research. Full article

The Citarum River Basin (DAS Citarum) in Indonesia faces significant challenges in waste management, necessitating a circular economy-based approach to reduce land-based pollution, which is critical for achieving the sustainability goals of the blue economy in the basin. This study addresses the complexity and inherent uncertainty in decision-making processes related to this challenge by developing a novel hybrid model, namely the Weighted Fuzzy N-Soft Set combined with the COmbinative Distance-based Assessment (CODAS) method. The model synergistically integrates the weighted 10R strategies in the circular economy, obtained via the Analytical Hierarchy Process (AHP), the capability of Fuzzy N-Soft Sets to represent uncertainty granularly, and the robust ranking mechanism of CODAS. Applied to a case study covering 16 types of waste in the Citarum River Basin, the model effectively processes expert assessments that are ambiguous regarding the 10R criteria. The results indicate that single-use plastics, particularly plastic bags (HDPE), styrofoam, transparent plastic sheets (PP), and plastic cups (PP), are the top priorities for intervention, in line with the high AHP weights for upstream strategies such as Refuse (0.2664) and Rethink (0.2361). Comparative analysis with alternative models, namely Fuzzy N-Soft Set-CODAS, Weighted Fuzzy N-Soft Set with row-column sum ranking, and Weighted Fuzzy N-Soft Set-TOPSIS, confirms the superiority of the proposed hybrid model in producing ecologically rational priorities, free from purely economic value biases. Further sensitivity analysis shows that the model remains highly robust across various weighting scenarios. This study concludes that the WFN-SS-CODAS framework provides a rigorous, data-driven, and reliable decision support tool for translating circular economy principles into actionable waste management priorities, directly supporting the restoration and sustainability goals of the blue economy in river basins. The findings suggest that targeting the high-priority waste types identified by the model addresses the dominant fraction of riverine pollution, indicating the potential for significant waste volume reduction. This research was conducted to directly contribute to achieving multiple targets under SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water). Full article

This paper introduces a novel analytical and numerical framework for studying nonlinear fractional Volterra integral equations by employing an intuitionistic fuzzy metric structure over a Banach algebra. The principal contribution of this work is the development of fixed-point theory for a new class of intuitionistic fuzzy Z-contractions in $\mathcal{IFM}$-spaces over $\mathcal{BA}$, which extends existing fuzzy and algebra-valued metric frameworks. Within this setting, we established existence, uniqueness, and convergence results for solutions of fractional integral equations of the Caputo type by proving that the associated fractional integral operator satisfies the proposed contractive conditions. Furthermore, we demonstrated how the algebra-valued intuitionistic fuzzy structure enhances the analytical flexibility and robustness of the model. To support the theoretical findings, a numerical simulation based on a discretized iterative scheme is presented, illustrating the rapid convergence of the approximating sequence together with the monotone behavior of intuitionistic fuzzy nearness and non-nearness measures. The numerical results are consistent with the analytical theory and confirm the effectiveness of the proposed $\mathcal{IFM}$-spaces over the $\mathcal{BA}$ approach for fractional dynamical systems. Full article

The selection of a final operating point from a Pareto front set of marine diesel engine configurations relies on the critical task of translating operator priorities into quantitative criterion weights. This study isolates this pivotal weighting step and introduces an operator-defined fuzzy weighting module that maps linguistic importance ratings to normalized weights. This module systematically maps important ratings for Specific Fuel Consumption (SFC), Nitrogen Oxides (NOx), and Particulate Matter (PM) into a set of normalized weights for the Multi-Criteria Decision-Making method. The module’s core is a Mamdani-type fuzzy logic module that utilizes triangular membership functions and centroid defuzzification. These fuzzy weights are integrated with the TriMetric Fusion algorithm to generate a robust consensus ranking. Validation on a Pareto front from a two-stroke diesel engine demonstrates the framework’s efficacy: a Fuel-Economy priority selected a configuration with SFC advantage, while a Strict Environmental Compliance priority correctly identified dual emissions strengths. Furthermore, the system effectively mediated trade-offs in a high-competition scenario. Rank correlation analysis confirmed that while the Pareto front nature of the alternatives leads to inherent similarities in rankings, the fuzzy weights induce significant and logical divergences. Future work will focus on validation with real operator feedback and comparative studies with traditional weighting methods. Full article

Currently, factors such as geopolitical conflicts, frequent extreme weather events, and power struggles among major countries are threatening the stability of the global supply chain. Building a more resilient supply chain has received international consensus. Today, new quality productivity (NQP), spawned by disruptive innovation, is an important way for China to enhance its agricultural product supply chain resilience (SCR). However, studies often overlook the “time lag” problem of the panel data adopted, and their empowering paths require further investigation. Therefore, this study firstly constructs NQP and agricultural product SCR indicators. Based on the panel data produced by 31 Chinese provinces from 2011 to 2022, we solved the “time lag” problem by integrating a Backpropagation Neural Network (BPNN) with an Autoregressive Integrated Moving Average (ARIMA) model to predict the NQP level. Subsequently, the empowering paths through NQP-enhancing agricultural product SCR were explored via entropy weight TOPSIS and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) method. Foundations: China’s agricultural product SCR exhibits a spatial differentiation characteristic of “prominent in the central region and weak in the western region”. A single factor is not a necessary condition for high resilience, and its improvement depends on the synergy of multiple factors. Three differentiated driving paths have been identified: “autonomous endogenous driving type”, “environment-enabled driving type”, and “system architecture driving type”. NQMP has become the bottleneck for improving agricultural product SCR, and the threshold of each factor has increased significantly as the resilience target is raised. High resilience stems from the synergy and functional compensation of core factors, while low resilience is mostly caused by the concurrent absence of key conditions or structural mismatch, showing distinct “multiple concurrencies” and “causal asymmetry” characteristics. Full article

Efforts to reduce food waste in restaurants are crucial for business efficiency, environmental sustainability, and social responsibility. Food waste varies by restaurant type, operations, menu offerings, and customer behavior, yet research on effective reduction strategies remains limited, particularly in Greece and Armenia. This study aims to identify key approaches to minimizing food waste in these countries. Using fuzzy-set Qualitative Comparative Analysis (fsQCA), a method for examining complex causal relationships, we analyzed multiple cases to determine conditions that lead to reduced food waste. Four main paths emerged: (1) digital inventory management combined with educational programs, excluding customer choice enhancement; (2) digital inventory management with flexible dining options, without customer choice enhancement; (3) educational programs with flexible dining, excluding customer choice enhancement; and (4) the combination of digital inventory management, educational programs, and flexible dining. Most cases demonstrating these paths were observed in Greece, indicating more advanced food waste management practices. Interviews highlighted recurring themes such as overordering, portion control, supplier challenges, and the importance of education and policy grounded in social responsibility. The findings provide actionable insights for restaurants and policymakers seeking effective strategies to reduce food waste and promote sustainable practices. Full article

As product complexity and customization levels continue to rise in high-end manufacturing, optimizing and controlling multistage manufacturing processes (MMPs) presents growing challenges. However, existing MMP research has largely focused on optimizing relatively fixed process routes, while limited attention has been paid to the route selection problem itself, particularly the global selection of process routes under real-world conditions where MMPs stages are mutually coupled and characterized by uncertainty. Therefore, the present study focuses on the fundamental challenge of process route decision-making for complex products within MMPs. A hybrid decision model is developed that incorporates expert knowledge and explicitly quantifies uncertainty arising from decision inconsistency and linguistic ambiguity. The proposed model consists of three main components: expert weighting, criterion weighting, and comprehensive ranking of process schemes. Expert and criterion weights are derived using the Enhanced Analytic Hierarchy Process (EAHP) to address inconsistency in expert judgments, while the ranking of alternatives is performed using a novel Combined Compromise Solution (CoCoSo) rule within an Interval Type-2 Fuzzy Sets (IT2FS) linguistic environment. Furthermore, the effectiveness of the proposed framework is validated through a case study on the multistage manufacturing process of compact aerospace heat exchangers. The results demonstrate that the proposed approach provides effective decision support for selecting robust process schemes during the initial planning phase of MMPs. Full article

Background: Type 2 Diabetes Mellitus (T2DM) continues to rise rapidly in Indian communities, affecting millions and posing a major public health challenge. Early identification of risk and timely lifestyle intervention are crucial for prevention. This study aims to develop a lifestyle-driven, fuzzy-enhanced Artificial Neural Network (ANN) model for early T2DM prediction and to design a personalized recommendation framework tailored to the North Indian population. Methods: A comprehensive exploratory data analysis, including statistical significance testing and age-cohort assessment, was conducted to evaluate data quality and identify key lifestyle associations. The ANN model was trained on 1939 lifestyle profiles and classified individuals into four risk categories: low, moderate, high-risk, and diabetic. A monotonic spline-based calibration method was used to refine predicted probabilities. Additionally, a web-based system, the Personalized Care and Intelligence System for Early Diabetes Assessment (PCISEDA), was developed to deliver individualized diet and physical activity recommendations. Cost-effective lifestyle options were curated via a structured web-scraping pipeline. Results: The proposed fuzzy-enhanced ANN model achieved an accuracy of 93.64%, precision of 94.00%, recall of 93.50%, F1-score of 93.50%, and a multiclass ROC–AUC of 94.07%, demonstrating strong discriminative performance. Feature importance analysis revealed age, weight, urination frequency, and thirst as the most influential lifestyle predictors of T2DM risk. The PCISEDA system successfully generated personalized and economically feasible lifestyle recommendations for each risk category. Conclusions: This lifestyle-based AI framework demonstrates substantial potential for early T2DM risk stratification and tailored lifestyle management. The integration of fuzzy calibration and personalized recommendations offers an accurate, scalable, and cost-effective solution that may support diabetes prevention and management in resource-constrained healthcare settings. Full article

In this paper, we consider the family of parameterized Mandelbrot-like sets generated as any point $c\in \mathbb{C}\setminus \left\{0\right\}$ of the complex plane belongs to any member of this family for a real parameter $t\ge 1$, provided that its corresponding orbit of 0 does not escape to infinity under iteration $f_c^n\left(0\right)={\left[f_c^{n-1}\left(0\right)\right]}^2+log{c}^t$; otherwise, it is not a member of this set. This classically means there is only a binary membership possibility for all points. Here, we call this type of fractal set a Mandelblog set, and then we introduce a membership function that assigns a degree to each c to be an element of a fuzzy Mandelblog set under the iterations, even if the orbits of the points are not limited. Moreover, we provide numerical examples and gray-scale graphics that illustrate the membership degrees of the points of the fuzzy Mandelblog sets under the effects of iteration parameters. This approach enables the formation of graphs for these fuzzy fractal sets by representing points that belong to the set as white pixels, points that do not belong as black pixels, and other points, based on their membership degrees, as gray-toned pixels. Furthermore, the membership function facilitates the direct proofs of the symmetry criteria for these fractal sets. Full article