Membership Functions Research Articles

Cross-gradient joint inversion and clustering of ERT and SRT data on structured meshes incorporating topography

Summary The combination of electrical resistivity and seismic refraction tomography is a common practice for the characterization of subsurface features. Presently, the cross-gradient inversion scheme stands out as one of the most robust joint approaches, and some authors modified it to manage complex topographies on unstructured meshes even if at the expense of introducing additional parameters in the inversion process. We propose in this work a cross-gradient algorithm for jointly inverting electrical and seismic tomographic data on structured meshes in cases with non-flat topography. The proposed approach preserves the benefit of the classical cross-gradient approach without the need to impose physical length scales, as for irregular meshes. The quality of the results is evaluated in comparison with independent inversion through a new standardized cross-gradient index and a fuzzy c-means analysis that provides an assessment of the reconstruction accuracy through the membership function. The proposed method was applied to both synthetic models and field-scale examples located in Central Italy, where an accurate geophysical reconstruction is needed for the rehabilitation of existing dams. For all cases, joint inversion yielded superior results compared to independent inversion, demonstrating better agreement with available borehole data. The effectiveness of the joint approach was also demonstrated by the post-inversion tools, where the new cross-gradient index highlighted changes in structural similarity whilst fuzzy c-means clustering allowed for a quantitative reconstruction (position and shape) of the main units at the sites, facilitating the detection of site layering modifications.

Winter forage crops influence soil properties through establishing different arbuscular mycorrhizal fungi communities in paddy field

Winter planting is promising for improving the utilization rate of fallow paddy fields in southern China by establishing arbuscular mycorrhizal fungi (AMF) communities. However, the effects of different winter forage crops on AMF community construction remain unknown. The AMF community establishment of different winter planting forage crops were conducted in oat, rye, Chinese milk vetch, and ryegrass, with winter fallow as a control. The AMF colonization rate, soil AMF spore density, community structure and diversity, and soil physicochemical properties were determined. The results showed that the total nitrogen and available nitrogen in winter Chinese milk vetch were 11.11% and 16.92% higher than those in winter fallow (P < 0.05). After planting winter forage crops, the AMF spore density in winter oat, rye, Chinese milk vetch, and ryegrass soil were 127.90%, 64.37%, 59.91%, and 73.62% higher than that before planting, respectively (P < 0.05). Claroideoglomus was the dominant AMF genus in the soil of winter planting oat, rye, and ryegrass. The average membership function value of winter Chinese milk vetch was the highest, indicating that it had the best comprehensive effect on soil physicochemical properties, AMF community structure and diversity, and fresh forage yield. Winter forage crops could increase the spore pool of soil AMF and improve the soil AMF community structure and diversity. Winter Chinese milk vetch in paddy field had the best comprehensive effect on soil physicochemical properties and soil AMF community according to the comprehensive evaluation. These findings provide a theoretical basis for sustainable development and utilization of the southern rice paddy ecosystem.

Improving safety in complex systems: A review of integration of functional resonance analysis method with semi‐quantitative and quantitative approaches

AbstractFunctional resonance analysis method (FRAM) is extensively employed in analyzing and managing performance variabilities. Additionally, semi‐quantitative and quantitative methods have been increasingly integrated with the FRAM to analyze complex socio‐technical systems to improve safety levels. This review article presents a comprehensive and updated survey of current literature focused on semi‐quantitative and quantitative methods employed for quantifying performance variabilities and exploring aggregation/propagation rules. A total of 1659 studies published between 2012 and March 2024 from various scientific databases were systematically examined using preferred reporting items for systematic review and meta‐analysis, identifying 29 studies that met inclusion criteria. The identified studies were categorized into four groups based on the quantitative methods employed: Monte Carlo simulation, fuzzy logic, cognitive reliability and error analysis method, and miscellaneous approaches. While different methodologies had unique strengths, they commonly relied on expert judgment for data collection, whether for defining probability distributions in Monte Carlo simulations, membership functions, and fuzzy rule bases in fuzzy inference systems, or selecting common performance conditions, determining their interrelationships, and assigning scores. Addressing bias from expert judgment in assessing performance variabilities can be achieved by using suitable experts' opinions integration techniques, and leading safety indicators in the analysis.

A new method for CBR prediction using fuzzy set theory

California Bearing Ratio (CBR) values of the subgrade soils are key parameters in highway route survey and project design. However, in order to calculate the CBR value, time-consuming and specialized tests must be performed in the laboratory or in the field. In this study, a model based on Fuzzy set theory is proposed to predict the CBR value of soils. The geological characteristics of the fine-sized aggregate in soils are considered as the key parameter in the prediction model.For this purpose, novel Fuzzy membership functions are defined. Logarithmic interpolation approach, which takes into account the Atterberg limits and Proctor parameters of the soils, was used to determine the membership degrees of the gravel and sand sized aggregates forming the soil. The membership degrees of fine-sized aggregates were determined by basic sigmoid membership function according to the type of clay they contain. In order to verify the accuracy of the model, the calculated results were compared with the soaked CBR values obtained from laboratory tests and it was found that there was a high correlation (R2=0.86) between each other. Considering the obtained results together, it was determined that the effect of variables such as the geological class to which the aggregates forming the soil belong, which have the potential to affect the CBR value of the soils, on the results of the CBR prediction models can be eliminated by means of membership functions.

Comfort evaluation of carrier-based aircraft missile handling operation based on fuzzy analytic hierarchy process

The analysis and evaluation of missile handling operation are crucial for ensuring safety and reliability during these processes. This paper proposes a novel assessment method for missile handling motions based on the Fuzzy Analytic Hierarchy Process (FAHP). The methodology comprises several stages: Initially, an analysis of missile handling motions is conducted. Subsequently, joint evaluation indicators influencing operational comfort are selected. Following this, membership functions and a comprehensive evaluation matrix are constructed. Finally, a fuzzy comprehensive evaluation model for the comfort level of carrier-based aircraft ammunition handling operations is established. A typical case study of carrier-based aircraft missile handling operation is presented to illustrate the fuzzy comprehensive evaluation process. The evaluation results are then compared with those obtained from the Rapid Upper Limb Assessment (RULA) and Ovako Working Posture Analysis System (OWAS) models. Comparative analysis indicates that the results derived from the fuzzy comprehensive evaluation model demonstrate high reliability.

Integrated Fault-Tolerant Control Design With Sampled-Output Measurements for Interval Type-2 Takagi-Sugeno Fuzzy Systems.

This article is concerned with the integrated design of fault estimation (FE) and fault-tolerant control (FTC) for uncertain nonlinear systems suffering from actuator faults and external disturbance. The uncertain nonlinear systems are characterized as the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model, and IT2 membership functions are employed to effectively handle uncertainties. A fuzzy observer, utilizing only sampled-output measurements, is applied to simultaneously estimate actuator faults and system states. Based on the estimation, the fault-tolerant controller is designed to ensure the system stability under a predefined H∞ performance. The sampling behavior complicates the system dynamics and makes the integrated FTC design more challenging. To confront this issue, the discontinuous Lyapunov functional technique is exploited to enhance stability results by considering the sampling characteristic, upon which FE and FTC units are co-designed in the linear matrix inequality (LMI) framework. To further relax stability criteria, the analysis process incorporates the bound information of membership functions through the membership-function-dependent (MFD) method. Additionally, the relationship of mismatched premise variables resulting from the sampling scheme is also taken into account. Moreover, considering the imperfect premise matching (IPM) framework, the proposed fault-tolerant controller provides greater flexibility in selecting the shapes of membership functions and number of fuzzy rules that can vary from the counterpart of the fuzzy system. Finally, the efficacy of the proposed FTC technique is validated through a detailed numerical example.

Efficient parameter estimation in biochemical pathways: Overcoming data limitations with constrained regularization and fuzzy inference

In analytical modeling for biochemical pathways, precisely determining unknown parameters is paramount. Traditional methods, reliant on experimental time course data, often encounter roadblocks — limited accessibility and variable quality — that can significantly impact the algorithm’s performance. In this study, we address these hurdles by unveiling a groundbreaking parameter estimation technique, Constrained Regularized Fuzzy Inferred Extended Kalman Filter (CRFIEKF). This innovative approach eliminates the need for experimental time-course measurements and capitalizes on the existing imprecise relationships among the molecules within the network. Our proposed framework integrates a Fuzzy Inference System (FIS) block to encapsulate these approximated relationships. To fine-tune the estimated parameter values, we employ Tikhonov regularization. The selection of Tikhonov regularization and Gaussian membership functions was based on the Mean Squared Error (MSE) values observed during the parameter estimation process, contrasting our results with those of previous studies.We rigorously tested the proposed approach across various pathways, from the glycolytic processes in mammalian erythrocytes and yeast cells to the intricate JAK/STAT and Ras signaling pathways. The results were impressive, showing a significant similarity (p-value < 0.001) to the outcomes of specific prior experiments. The dynamics of the biochemical networks normalized within the [0, 1] range mirrored the transient behavior (MSE < 0.5) of both in vivo and in silico results from previous studies. In conclusion, our findings highlight the effectiveness of CRFIEKF in estimating the kinetic parameter values without prior knowledge of experimental data within a biochemical pathway in the state-space model. The proposed method underscores its potential as a game-changer in biochemical pathway analysis.

Robust Adaptive Fuzzy Control for Second-Order Euler-Lagrange Systems With Uncertainties and Disturbances via Nonlinear Negative-Imaginary Systems Theory.

Ensuring robust and precise tracking control in the presence of uncertain multi-input-multi-output (MIMO) system dynamics and environmental variations is a significant challenge in the field of robust and adaptive control theory. While fuzzy control strategies have demonstrated good tracking performance in normal conditions, designing and tuning fuzzy controllers can be a challenging task in highly uncertain environments. In this study, we investigate a novel approach that combines robust nonlinear negative-imaginary (NI) systems theory with a self-adaptive fuzzy control scheme and the Lyapunov synthesis to develop a robust adaptive negative-imaginary-fuzzy (RANIF) control scheme. We optimize the critical parameters of the proposed fuzzy system using a self-tuning technique with a proportional-derivative sliding manifold. Furthermore, unlike the existing adaptive fuzzy control methods, we propose a small number of membership functions and systematically derive the fuzzy rules by employing Lyapunov, nonlinear NI, and dissipativity theories, which simplify the tuning process, work out the matter of "explosion of complexity," and reduce computational complexity. We demonstrate the global stability of the closed-loop system using nonlinear NI theory. To evaluate the effectiveness of our proposed approach, we present simulation results for two examples involving uncertain MIMO second-order Euler-Lagrange systems. These systems, known for their capacity to represent a diverse range of practical physical systems, serve as suitable testbeds for our methodology. Our results show that RANIF outperforms other control methods, such as nonlinear strictly NI-Fuzzy, fuzzy-logic control, model predictive control, and conventional PID control, in terms of robustness to disturbances and inestimable faults, trajectory tracking performance, and computational complexity.

Green microgrid’s LFC using recursive step-by-step optimized multi-stage fuzzy controller with separated inference systems

Amidst the growing integration of renewable energy sources (RES), the efficient administration of load-frequency control (LFC) in microgrids (MG) continues to be a significant challenge. In response to the complexities of modern MGs operations, this study introduces a novel two-stage fuzzy controller aimed at enhancing the system’s dynamic responses. The proposed controller consists of two levels, each of which contains a separate and autonomous fuzzy inference system (FIS). The proposed controller includes proportional (P) and derivative (D) control operators in the first level, and in the second level, the combination of proportional and integral (I) operators is used. The suggested fuzzy P-fuzzy D multiplied by 1+(fuzzy P - fuzzy I) which is named FPFD-(1+FPFI) controller parameters are tuned by solving an optimization problem to reduce energy wastage and prevent undesirable dynamic responses. The parameters of the controller and the membership functions (MF) at each level are both optimized. The optimization process utilizes an enhanced particle swarm optimization (PSO) algorithm. The decisive superiority of the FPFD-(1+FPFI) controller has been confirmed by evaluating its performance in an all-renewable MG compared to conventional controllers. Reduction of frequency deviation in the face of disturbances in the demand side or production of renewables, better performance in the presence of uncertainty in the parameters of the system model, better dynamic responses against nonlinear factors such as time delays, and also, robustness against cyberattacks are prominent features of the proposed FPFD-(1+FPFI) controller. In addition, the results of the studies show that the controller, with its fast and accurate performance, reduces the dependence on the energy storage systems to maintain the stability of the system by more than 40%. The efficiency of the proposed controller is also verified through laboratory scale evaluation.

Using the Nanogonal Membership Function and Fuzzy Parameters for The Exponential-Rayleigh Distribution of Coved-19

In this paper, we discuss estimating the fuzzy parameters of the Exponential-Rayleigh distribution using the Progressive Censored sample for the Rank Set method to become the proposed Rank Set Sampling estimation Method (RSSEM) in Censoring sample using the Newton-Raphson method to determine the parameter estimate values. Then utilizing Al Karkh General Hospital-Ministry of Health and Environment in Iraq for providing information about the actual data for COVID-19. To ascertain the Chi-square test was used. The distribution of the using sample is the Exponential-Rayleigh distribution. (ER). Then, we estimate the probability density function, hazard function, and survival function. To follow the estimation of the parameters and ER distribution's fuzzy parameters, we use the suggested approach. Moreover, to find the efficiency of the estimators, we use the mean square error of the survival function:

Method for solving distributional problems of mathematical programming under conditions of fuzzy initial data

The object of this study is a large class of mathematical programming problems under conditions of uncertainty of initial data. The formulated object generates a subclass of problems of rational distribution of a limited resource under conditions of initial data described in terms of fuzzy mathematics. The conventional, standardly used method for solving such problems is based on optimization on average. To obtain such a solution, it is sufficient to replace all fuzzy initial data with their modal values in the analytical description of the mathematical model of the corresponding problem. To solve the resulting deterministic problem, one can use known methods of mathematical programming. However, the results of such a solution can be used in practice if the carriers of fuzzy parameters are specified compactly, that is, the intervals of possible values of fuzzy parameters of the problem are small. Otherwise, the implementation of this solution may lead to unpredictably large losses. Other alternative approaches are based on the use of insufficiently informative estimates of the best or worst possible values of fuzzy parameters of the problem. These circumstances make the statement of the problem and the objective of the study relevant: devising a method for solving the problem of rational distribution of a limited resource under conditions of fuzzy initial data. To solve the stated problem of rational distribution of a limited resource, a productive idea of constructing the proposed general optimization method under conditions of uncertainty of the initial data has been constructively implemented. In this case, the initial problem is reduced to a clear problem of optimizing a complex criterion constructed on the basis of the objective function of the initial problem and a set of membership functions of its fuzzy parameters. An example of solving the problem has been considered, leading to a solution that is better than that obtained on the basis of the modal values of the fuzzy parameters of the problem

Development of a fuzzy logic-based model for assessing the reliability of relay protection systems

This article presents a reliability assessment model for relay protection devices using fuzzy logic. It introduces an algorithm for simulating these devices, employing the Mamdani model with an "if-then" rule base. Inputs include the percentage of correct operation and the frequency of correct and incorrect operations. Implemented in Matlab with 21 rules, the fuzzy logic model uses triangular membership functions for input and output variables, with defuzzification via the center of gravity method. The model was tested using statistical data from SIEMENS SIPROTEC terminals, specifically distance protection and differential protection devices for transformers and autotransformers. The assessment considers operation frequencies for 1 to 3 devices, combining statistical and simulation data for a comprehensive analysis. Results show that including operation frequencies improves evaluation accuracy. The proposed model not only assesses current reliability but also predicts future behavior, aiding in the planning and optimization of relay protection systems. This model is valuable for professionals in generating companies, grid organizations, and operational dispatch control entities, helping them analyze relay protection performance and develop strategies to ensure reliable operation. The research focuses on the reliability of relay protection devices in power systems, addressing the need for a more accurate and comprehensive evaluation method. Traditional methods may not fully account for the complexities in modern microprocessor-based protections. This study aims to enhance reliability assessments through a model that integrates statistical data and simulation techniques, ultimately supporting better planning and optimization of relay protection systems

Beyond livestreaming: The rise of social media gifting and paid memberships − A systematic literature review and future research agenda

Paid memberships and gifts are increasingly implemented in social media as content monetization functions. Here, we outline the overarching trend toward gifting and membership functions, develop a theoretical framework of stakeholder dynamics, summarize changes on major platforms, and conduct a systematic review of the literature investigating this development. We analyze 76 articles published across 51 journals using the Theory, Characteristics, Context, and Methodology framework and find that the research has primarily focused on livestreaming gifting but has neglected paid memberships and gift-giving outside of livestreaming platforms. Moreover, the research has largely neglected the role of platforms and how they can optimize gifting and membership functions to increase revenue. Based on our findings, we give an overview of the most pressing research questions and develop a comprehensive research agenda.

Prognostic algorithm for early diagnosis of subcritical conditions as predictors of sudden cardiac death

Aim. To develop a method for early diagnosis of subcritical homeostasis disorders leading to sudden cardiac death (SCD). The basis is to improve the efficiency of predictive algorithms.Material and methods. This pilot, controlled, open-label, randomized, prospective clinical trial included 220 patients at risk of SCD and 150 patients without risk of SCD. Main and control groups was formed according to the global cardiovascular risk score. Based on the informative features proposed by specialized experts using multivariate statistics methods (discriminant analysis), two condition classes were formed. The conducted exploratory analysis confirmed the significance of diagnostic criteria in relation to SCD manifestation (manifestation of cardiac arrest — MCA), which is an integral assessment of a fatal complication. The development of decision rules was carried out on the basis of soft computing technology.Results. Taking into account the priority of clinical research, namely, the identification of subcritical stages of MCA, a classifier is proposed according to basic severity of patients — the severity of critical condition risk (SCCR). The discriminant function and intersection areas between MCA subclasses in the conditions of early SCD diagnosis determine the transition to soft computing technology. Membership functions for severe MCA are formed, followed by their iteration according to E. Shortliffe. The final decision rule, using a fuzzy classifier, differentiates the MCA into stages with different SCCR. In parallel with standard protocols for the management of severe somatic patients (chronic obstructive pulmonary disease, chronic kidney disease, hepatocellular failure), based on the proposed algorithm with an integral assessment of critical conditions, using the MCA decision rule in the main group in 30,5% of cases, subcritical stage was revealed, followed by targeted treatment and preventive support. In the first group, a subcritical condition was detected in 67 patients (30,5%); a critical condition without SCD — in 3 patients (1,4%). In all noted cases, early prevention of SCD was successfully carried out (these patients were transferred to a class with a lower SCD degree). Using conventional prognostic scores in this group, 46 patients (20,9%) were identified with a subcritical condition and 1 (0,4%) with a critical condition. In the control group, subcritical condition was determined in 35 patients (23,3%), of which 17 patients (11,3%) had a moderate risk of SCD. Using conventional prognostic scores, 23 patients (15,3%) with subcritical condition were identified.Conclusion. In the conditions of intensive care unit, general medicine departments, hemodialysis department, cardiac surgery, and organ transplantation department, an algorithm for early diagnosis and risk stratification of SCD with an integral assessment (MCA) should be used. The fuzzy classifier MCA according to SCCR makes it possible to carry out timely correction of treatment measures in addition to standard protocols.

Assessing interpretability of data‐driven fuzzy models: Application in industrial regression problems

AbstractMachine Learning (ML) has attracted great interest in the modeling of systems using computational learning methods, being utilized in a wide range of advanced fields due to its ability and efficiency to process large amounts of data and to make predictions or decisions with a high degree of accuracy. However, with the increase in the complexity of the models, ML's methods have presented complex structures that are not always transparent to the users. In this sense, it is important to study how to counteract this trend and explore ways to increase the interpretability of these models, precisely where decision‐making plays a central role. This work addresses this challenge by assessing the interpretability and explainability of fuzzy‐based models. The structural and semantic factors that impact the interpretability of fuzzy systems are examined. Various metrics have been studied to address this topic, such as the Co‐firing Based Comprehensibility Index (COFCI), Nauck Index, Similarity Index, and Membership Function Center Index. These metrics were assessed across different datasets on three fuzzy‐based models: (i) a model designed with Fuzzy c‐Means and Least Squares Method, (ii) Adaptive‐Network‐based Fuzzy Inference System (ANFIS), and (iii) Generalized Additive Model Zero‐Order Takagi‐Sugeno (GAM‐ZOTS). The study conducted in this work culminates in a new comprehensive interpretability metric that covers different domains associated with interpretability in fuzzy‐based models. When addressing interpretability, one of the challenges lies in balancing high accuracy with interpretability, as these two goals often conflict. In this context, experimental evaluations were performed in many scenarios using 4 datasets varying the model parameters in order to find a compromise between interpretability and accuracy.

Gypsum and organic materials improved soil quality and crop production in saline-alkali on the loess plateau of China

Arable soil and crop productivity are severely affected by salinization. Therefore, soil amendments are an important measure for improving saline-alkali soil for agricultural development. Desulfurized gypsum is a common soil amendment that has been used repeatedly alongside organic materials to improve the biological, physical, and chemical properties of saline soil. This study takes the typical saline-alkali farmland soil in Yulin as the research object, and five treatments were established: a blank treatment (CK), a single application 2.5 t ha−1 of desulfurized gypsum (T), 2.5 t ha−1 of desulfurized gypsum and 1.5 t ha−1 of green manure (TL), application 2.5 t ha−1 of desulfurized gypsum and 1.5 t ha−1 of straw (TS), and 2.5 t ha−1 of desulfurized gypsum and 1.5 t ha−1 of organic fertilizer (TV). The results show that the TV treatment achieved a significant improvement in soil nutrients, organic carbon, enzyme activity, and maize yield. In 2022 (2023), the compared of organic matter, TN, TP, TK, AP, and AK increased significantly compared with the CK treatment when the TV treatment was applied. Soil phosphatase activity (SPA), soil urease activity (SUA) and soil sucrase activity (SSA) significantly higher in the TV treatment compared with the other treatments and increased significantly over the two-year period. Furthermore, soil organic carbon (SOC), easily oxidizable organic carbon (EOC), dissolved organic carbon (DOC) and microbial biomass carbon (MBC) also significantly increased with the 2022 and 2023 TV treatments. Correlation analysis revealed a positive relationship between maize yield and soil nutrients, organic carbon, and enzyme activity (P &amp;lt; 0.05). Thus, the TV treatment was determined to be the optimal treatment for soil improvement. This conclusion was supported by analyses performed using membership function analysis, gray correlation analysis, and entropy TOPSIS model evaluation. Therefore, this method increases soil quality, improves soil fertility, achieves high maize yields, and provides a scientific basis for enhancing and utilizing saline-alkali soil in the Loess Plateau.

Design and Analysis of the Effect of the Number of Membership Functions on the Accuracy of the Fuzzy Controller in a Sun Tracking System

Purpose: To investigate how the number of rules in the fuzzy controller affects the single-axis solar tracking system's orientation accuracy. Methodology: Design and compare FLC49 and FLC25 controllers, each with different organic rules and functions. To improve the system efficiency by accurately measuring the maximum power point, the controller receives comparison results from the light sensors installed on the solar panels. We study and analyze the controllers, selecting the best one based on the accuracy of the solar panel orientation. Using the FLC output to operate a two-phase SM. Findings: This study developed a sun tracking system using Matlab/Simulink and compared FLC49 and FLC25 controllers. FLC49 performed better in simulations, with lower settling time and overshoot. The number of rules significantly influenced accuracy, and it also showed lower transient ripple magnitudes, accelerating time response. Unique Contribution to Theory, Practice and Policy: The research investigates the effectiveness of rules in a fuzzy controller and recommends the optimal number of windings to achieve precision in controlling a stepper motor. The study explores the use of 49-rule and 25-rule fuzzy logic controllers to control a stepper motor based on LDR sensor inputs. The researchers found that while both controllers had similar steady time error and overshoot, they differed in orientation accuracy, with the 49-roll fuzzy controller being more accurate in orientation. This single-axis solar tracking system optimizes solar energy conversion into electricity.

A risk assessment of inhibited cargo operations in maritime transportation: a case of handling styrene monomer

Hazardous cargo handling on tanker ships involves various risks that could lead to accidents such as explosion and fire. Investigating these risks is crucial to safeguard life, property and the marine environment. This paper performs a risk assessment specific to hazardous cargoes with special requirements. It addresses the risks of inhibited cargo operation, which has not been adequately studied in the literature despite its criticality. Styrene monomer is selected as the inhibited cargo type since failures during its handling may lead to polymerisation and catastrophic consequences. Using the Failure Modes, Effects and Criticality Analysis (FMECA) 24 failures are identified, and their occurrence, severity and detection aspects are evaluated by experts familiar with the process. In addition, a rule-based fuzzy logic technique with Gaussian membership functions is employed to support the traditional FMECA application. The integration of methods helps to deal with the limitations of FMECA in the calculation of risk priority number (RPN). Based on the fuzzy RPN results, failure modes are prioritized and preventive actions are presented for the high-ranked failure modes. The outputs of the study could help to increase the awareness of operators and interested individuals concerning the risks of tanker cargo operations with special requirements.

On the local quadratic stability of T–S fuzzy systems in the vicinity of the origin

The main goal of this paper is to introduce new local stability conditions for continuous-time Takagi-Sugeno (T-S) fuzzy systems. These stability conditions are based on linear matrix inequalities (LMIs) in combination with quadratic Lyapunov functions. Moreover, they integrate information on the membership functions at the origin and effectively leverage the linear structure of the underlying nonlinear system in the vicinity of the origin. As a result, the proposed conditions are proved to be less conservative compared to existing methods using fuzzy Lyapunov functions. Moreover, we establish that the proposed methods offer necessary and sufficient conditions for the local exponential stability of T-S fuzzy systems. Discussions on the inherent limitations associated with fuzzy Lyapunov approaches are also given. To illustrate the theoretical results, we provide comprehensive examples that demonstrate the core concepts and validate the efficacy of the proposed conditions.

Refining Environmental Sustainability Governance Reports through Fuzzy Systems Evaluation and Scoring

Environmental, Social, and Governance (ESG) reports have become essential tools for enterprises to showcase their commitment to sustainable development and social responsibility. However, discrepancies persist regarding the criteria, assessments, and ratings disclosed in these reports. Moreover, there is a need for more objective methods to determine the weight distribution of indicator items. This study introduces a novel approach utilizing semantic variables in fuzzy theory and a multiple logic fuzzy inference system to develop an ESG environmental management performance assessment model. Therefore, this paper aims to develop a novel approach utilizing semantic variables and a multiple logic fuzzy inference system to quantitatively evaluate the sustainable performance of an environmental management plan. This research also aims to ensure fair and objective assessment outcomes, providing valuable guidance for enterprises in implementing performance management strategies. Key aspects investigated include the impact of membership functions, the extended utilization of semantic variables and logical rules, a comparative analysis of traditional weight assessments, and the limitations of applying fuzzy theory. Through comprehensive discussions and calculations, it is evident that fuzzy theory offers considerable flexibility in application. By tailoring fuzzy rules and selecting appropriate membership functions, diverse application scenarios can be accommodated. The Fuzzy systems evaluation and scoring EMP model generates EMP evaluation scores ranging from 1.76 to 8.29 for Gaussian membership, 1.80 to 8.19 for Triangular membership-A, 1.92 to 8.00 for Triangular membership-B, and 1.81 to 8.19 for Quadrilateral trapezoidal membership, based on simulated rating scenarios using the semantic variables of completeness and feasibility. This approach successfully incorporates distribution logic from subjective membership degrees to evaluate EMP scores. The findings demonstrate that fuzzy theory enables the consideration of multiple factors and facilitates the provision of objective-level membership, underscoring its potential in addressing complex evaluation challenges. This study illuminates the versatility of the fuzzy system theory, with its applications poised to extend across various domains.