ABSTRACT The present study aims to examine stakeholder perceptions of accreditation effectiveness in quality enhancement, with a focus on identifying and ranking the key factors influencing these perceptions. The study used the Fuzzy AHP model to analyze the responses gathered from 368 stakeholders, including academic administrators, accreditation coordinators, faculty members, students, employers, alumni, and parents. The findings of the current study revealed that Improved Institutional Quality Standards (0.2081) have emerged as the most influential factor in making perceptions of stakeholders toward the relevance of accreditation, followed by Implementation Challenges (0.1956) and Trust in Accrediting Bodies (0.1738). Institutional heads must proactively anticipate and address implementation challenges, and accrediting bodies should establish credibility through transparent, consistent, and contextually relevant practices by involving stakeholders, clearly defining standards and upholding impartiality. This study is novel in employing a multi-criteria decision-making approach to systematically prioritize the key factors that significantly influence stakeholder perceptions of accreditation.

Adaptive feature selection based on fuzzy rough set fusion model with class variance

A self-organized fuzzy neural model for the pressurizer system in nuclear power plants

Improved two stage triangular fuzzy STARMA model for drought forecasting in Southern Telangana

A novel multi-criteria sorting method based on the linguistic polyhedral hesitant fuzzy consensus-reaching model

Optimizing robotic sensors in dynamic Industry 4.0 environments under a complex hesitant fuzzy soft model

Intelligent cost-effective bio-medical type-3 fuzzy model and controller

Hybrid biogeography-based optimization and Mamdani fuzzy modelling for physical habitat suitability modelling under limited data conditions

A novel gradient learning algorithm based on zero-order Takagi-Sugeno fuzzy model: the caputo fractional-order gradient descent

This study examines and compares Mamdani, Sugeno, and ANFIS fuzzy reasoning models for evaluating student academic performance using a bench marking framework where the same inputs, membership functions, and evaluation metrics were applied across the models.

Production management is dominant and essential in Taiwan because it confirms and certifies the effective use of timely delivery, resources, and high-quality output in an expert-driven and highly competitive manufacturing sector. With Taiwan being a world-famous player in industries, especially for precision machinery, semiconductors, and electronics. A modern difficulty in production management in Taiwan is the severe deficiency among significant manufacturing organizations, particularly in semiconductors, where over 30,000 posts remain empty, containing roles in maintenance, production, and quality control. For the valuation of the above problems, we consider the following production management systems in Taiwanese enterprises, such as the lean manufacturing system, smart production system, the automated inventory control system, the internet of things integrated manufacturing system, and the sustainable production management system. Therefore, we construct the procedure of the linguistic bipolar complex fuzzy soft multiattribute border approximation area comparison model and linguistic bipolar complex fuzzy soft multi-attribute decisionmaking model based on the proposed operators. For the assessment of the above problem, we resolve some numerical examples based on the above two models and also derive the activity of the comparative analysis between proposed and existing ranking values to enhance the efficiency and rationality of the derived models.

Development of fuzzy logic algorithm for predicting heavy metal content in poultry product.

The enterprise as an economic system at the micro level is characterized by uncertainty in situations when making decisions, a large number of input indicators, as well as the presence of a decision maker of information that is poorly formalized and cannot be taken into account when applying only quantitative methods. The uncertainty of the system leads to an increased risk of ineffective decisions, which may result in negative economic, technical and social consequences. The research considers the main structural elements of the strategy of developing the foreign economic potential of microeconomic systems. On the basis of calculations, the clear meaning of the indicators that are included in the groups defining the basic elements of the external economic potential is obtained. The algorithm of forming the strategy of development of the foreign economic potential of the enterprise is presented in the framework of the general approach to the organization of the process of increasing the foreign economic potential of microeconomic systems on the basis of a unified information system. As a result of the formation of a mechanism for increasing the foreign economic potential of microeconomic systems, a strategy was developed that was based on substantiation of the direction of development of foreign economic potential in accordance with the level of influence of external factors, using the method of fuzzy modeling and modeling the process of organizational interaction.

This study investigates an intermodal routing problem for emergency materials in the early stage of post-disaster recovery, in which the rapid transportation of emergency materials is formulated as the objective. To achieve reliable transportation that can avoid transportation interruption, this study formulates the uncertainty of both emergency materials’ demand and the network capacity by LR triangular fuzzy numbers, and thus explores a reliable routing problem for transporting emergency materials that is further formulated by a fuzzy linear programming model. Considering the decision makers’ cautious attitude on the transportation of emergency materials to avoid transportation interruption, this study adopts chance-constrained programming based on necessity measure to build a solvable reformulation of the proposed model. A numerical case study is carried out to reveal the conflicting relationship between improving the reliability and reducing the time of transporting emergency materials. The decision-makers of the emergency materials transportation organization should select a reasonable confidence level based on the actual decision-making scenario to plan the reliable intermodal route for emergency materials. By comparing with deterministic modeling, this study verifies the feasibility of the modeling the uncertainty of both demand and capacity in avoiding unreliable transportation and enhancing the flexibility of the intermodal routing for emergency materials. By comparing with chance-constrained programming using possibility measure, this study demonstrates the feasibility of the necessity measure in planning the reliable intermodal route. This study further analyzes how the capacity level of the intermodal network, demand level of the emergency materials and stability of the LR triangular fuzzy parameters influence the optimization results. Accordingly, this study emphasizes the importance of objectively evaluating the uncertain demand for emergency materials, and reveals that the enhancement of the capacity level of the intermodal network and stability of LR triangular fuzzy parameters is able to reduce the transportation time of emergency materials and meanwhile maintain a high reliability.

Background: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. Methods: In this study, an integrated decision support framework is presented that combines multi-objective fuzzy modeling and an adaptive digital twin to simultaneously manage logistics costs, product quality degradation, and service time compliance under operational uncertainty. Key uncertain parameters are modeled using triangular fuzzy numbers, and the digital twin dynamically updates the decision parameters based on operational information. The proposed framework is evaluated using real industrial data and comprehensive computational experiments. Results: The results show that the proposed approach is able to produce stable and balanced solutions, provides near-optimal performance in benchmark cases, and is highly robust to demand fluctuations and temperature deviations. Digital twin activation significantly improves the convergence behavior and stability of the solutions. Conclusions: The proposed framework provides a reliable and practical tool for adaptive planning of cold chain distribution in Agri-Biotech industries and effectively reduces the gap between advanced optimization models and real-world operational requirements.

Voltage source converter-based–high voltage direct current (VSC-HVDC) systems exhibit strong nonlinear characteristics that dominate their dynamic behavior under large disturbances, making large-signal stability assessment essential for secure operation. This paper proposes a large-signal stability analysis framework for VSC-HVDC systems. The framework combines a unified Takagi–Sugeno (T–S) fuzzy model with a model-free predictive control (MFPC) scheme to enlarge the estimated domain of attraction (DOA) and bring it closer to the true stability region. The global nonlinear dynamics are captured by integrating local linear sub-models corresponding to different operating regions into a single T–S fuzzy representation. A Lyapunov function is then constructed, and associated linear matrix inequality (LMI) conditions are derived to certify large-signal stability and estimate the DOA. To further reduce the conservatism of the LMI-based iterative search, we embed a genetic-algorithm-based optimizer into the model-free predictive controller. The optimizer guides the improved LMI iteration paths and enhances the DOA estimation. Simulation studies in MATLAB 2023b/Simulink on a benchmark VSC-HVDC system confirm the feasibility of the proposed approach and show a less conservative DOA estimate compared with conventional methods.

ABSTRACT The paper focuses on developing a dissolved oxygen concentration controller based on a fuzzy model for activated sludge wastewater treatment processes. To achieve accurate aeration in the sewage treatment plant and improve its robustness, a new cascade fuzzy control system is introduced in the paper. The control system contains double fuzzy controllers which can separately be responsible for the aeration rate during the air supply and air shut-off processes. The discrete dissolved oxygen concentration model is derived and the effect of the influent load on the aeration demand is also considered in the fuzzy controllers. The method was validated on the benchmark simulation model, no 1 (BSM1) simulation platform and comparisons with some related methods are presented. The system has been put into use in some sewage plants and its steady-state control accuracy can be within 0.3 mg/L. It can maintain good dynamic response even when the influent has many changes.

This article proposes a resilient control framework for securing cyber-physical systems (CPSs), specifically addressing nonlinear descriptor systems operating under communication constraints and subject to sensor and actuator attacks. We integrate Takagi-Sugeno (T-S) fuzzy models with a Q-learning-based event-triggered mechanism (ETM) and adopt a sliding-mode control strategy to establish a resilient security architecture that adaptively balances operational efficiency with robust protection against cyber-physical threats. A major contribution of this work lies in designing an adaptive fuzzy sliding-mode observer (SMO) with mismatched premise variables for the estimation of compromised system states. Additionally, a sliding-mode controller (SMC) is synthesized to maintain closed-loop admissibility and ensure the reachability of sliding surfaces. We advance beyond the existing approaches by employing the secretary bird optimization algorithm (SBOA) to optimize controller and observer gains, thereby solving the nonconvex optimization challenges present in controller and observer design. The effectiveness of the proposed method is validated through extensive Monte Carlo simulations on a truck-trailer system. These simulations demonstrate the efficacy of the approach in maintaining system stability and performance under various attack scenarios, thereby making a significant contribution to the security of nonlinear systems in networked environments.

To address issues such as low safety factors and difficulty in predicting the force variation of support systems in subway station foundation pit construction, this study develops a subway foundation pit support risk management system based on a risk data monitoring method combining Building Information Modeling technology and gated recurrent neural networks. In constructing the risk management system, a fuzzy analytic hierarchy process model is also used to assess and analyze the foundation pit risks. Experimental results show that the hybrid risk data monitoring method records a displacement of 0.28mm at monitoring point 6, while other methods—Cooperative Game Empowerment, Grey Relational Analysis, and Relativistic Conditional Generative Adversarial Network —record displacements of 0.16mm, -0.21mm, and 0.17mm, respectively. Additionally, empirical analysis of the constructed foundation pit support risk management system reveals that the hybrid subway foundation pit support risk management system measures a settlement value of -10.83mm, while the Generative Adversarial Network and Grey Relational Analysis-based systems measure maximum settlements of -10.11mm and -9.23mm, respectively, all of which are less accurate than the hybrid risk management system. These results demonstrate that the hybrid subway foundation pit support risk management system performs well in risk control and monitoring, ensuring the stable operation of subway construction. This research contributes to future control of foundation pit excavation support safety and the improvement of dynamic risk management levels.

This paper introduces a novel output constraint satisfaction mechanism that can be used to supplement controllers employing a control law. This mechanism is dedicated to control systems of nonlinear processes, with this additional feature. It utilizes an easy-to-obtain fuzzy model composed of step responses, which includes values of the operating points at which these step responses were obtained. The mechanism is based on a prediction approach from Model Predictive Control (MPC) algorithms. Despite this, it can be used with relatively simple controllers (e.g., fuzzy analytical MPC, PID, or Internal Model Control ones). The mechanism involves skillfully modifying the control signal generated by the controller. It is designed in such a way that, under favorable circumstances, the output constraints are not violated, but in less favorable circumstances, the constraint violation can be minimized. The performance and advantages of our mechanism are demonstrated in the simulated control system of an example nonlinear control plant.