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The global energy issue and lack of fossil fuels are obviously difficult concerns, prompting experts to look for other solutions. In this paper, the day-ahead operation of the multi-carrier energy system (MCE) to minimize operational costs, emission polluting and maximizing consumers’ comfort is presented. The optimal energy scheduling of consumption by energy demand curtailment strategy (EDCS) in the peak demand of electrical energy is proposed. Also, the operation of the energy storage via an onsite generation strategy (OGS) for consumers is considered by energy storage systems. The EDCS and OGS are modelled based on Demandside Management (DSM). The fuzzy method is taken into account as an optimization approach and objective functions. Finally, two scenarios using numerical simulation are studied to demonstrate the effectiveness of the approach for energy optimization in MCE. The results of the scenarios are studied based on non-participation and participation of the consumers in the scenarios A and B, respectively. The objective functions such as operation costs and emission polluting in scenario B are minimized by 1.63% and 5.35% than scenario A.

This study is aimed at developing an intelligent factory control system for improving environment quality and reducing electricity consumption. By automating intelligent equipment and leveraging internet networks, the system enables the remote monitoring and management of environmental conditions. We combine the fuzzy method and deep reinforcement learning (DRL) to handle complex factory data and optimize decision-making. The fuzzy method uses fuzzy sets and rules to generate accurate outputs from the data. On the other hand, the DRL system learns optimal policies by interacting with the environment using environment quality, central air conditioner (AC), and alarm data. Hardware implementation uses an ESP32-S microcontroller to send data to Google's Firebase cloud for seamless management and monitoring through a mobile app or website. The study involves developing 36 fuzzy rules and creating 10 models with different combinations of hidden layers, epochs, and learning rate values. Among the fuzzy inference system (FIS)-DRL modeling results, the fourth model stands out as the preferred option to proceed with the experiment, as it achieves the highest accuracy of 91.16%. Note that this model also exhibits a loss value of 1.64% and an incredibly short inference time of only 3 ms. The proposed system offers benefits such as enhanced energy efficiency and reduced costs, making it ideal for intelligent factories. By optimizing resource usage, it will contribute to sustainable development in various industries. [ABSTRACT FROM AUTHOR]

Evaluating the performance of city construction not only helps optimize city functions and improve city quality, but it also contributes to the development of sustainable cities. However, most of the scoring rules for evaluating the performance of city construction are overly cumbersome and demand very high data integrity. Moreover, the properties, change scale, and scope of different evaluation indicators of city construction often lead to uncertain and ambiguous results. In this study, a hybrid fuzzy method is proposed to conduct the performance evaluation of city construction in two phases. Firstly, a city performance index (CPI) was developed by combining the means and standard deviations of indicators of city construction to address the volatility of historical statistical data as well as different types of data. Considering the sampling errors in data analysis, the parameter estimation method was used to derive the 100% × (1 − α) confidence interval of the CPI. Buckley's fuzzy approach was then adopted to extend the statistical estimators from the CPI into fuzzy estimators, after which a fuzzy CPI was proposed. To identify the specific improvement directions for city construction, the fuzzy axiom design (fuzzy AD) method was applied to explore the relationship between the targets set by city managers and actual performance. Finally, an example of six cities in China is provided to illustrate the effectiveness and practicality of the proposed method. The results show that the performance of Chongqing on several evaluation indicators is lower than that of other cities. The proposed method takes into account the issues of uniformity and diversity in the performance evaluation of city construction. It can enable a quantitative assessment of the city construction level in all cities and provide theoretical support and a decision-making basis for relevant government departments to optimize city construction planning and scientifically formulate city construction policies. [ABSTRACT FROM AUTHOR]

This paper addresses the fuzzy resilient control of DC microgrids with constant power loads. The DC microgrid is subject to abrupt parameter changes which are described by the Markov jump model. Due to the constant power loads, the DC microgrid exhibits nonlinear dynamics which are characterized by a T-S fuzzy model. According to the parallel distributed compensation principle, mode-dependent fuzzy resilient controllers are designed to stabilize the resultant T-S fuzzy Markov jump DC microgrid. The "resilient" means the controller could cope with the uncertainty caused by the inaccurate execution of the control laws. This uncertainty is governed by a Bernoulli distributed random variable and thus may not occur. Then, the mean square exponential stability is analyzed for the closed-loop system by using the mode-dependent Lyapunov function. Since the stability conditions are not convex, a design algorithm is further derived to calculate the fuzzy resilient controller gains. Finally, simulations are provided to test the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

The submerged arc welding (SAW) process involves a complex relationship between controllable input parameters and measurable output characteristics. Several research works based on statistical methods, nature-inspired algorithms, and Multicriteria decision-making methods have been carried out to optimise submerged arc welding process parameters for effective utilization of resources and improved weld quality. Past researches suffer from limitations like the tendency to get trapped into local minima, lack of repeatability of optimised output, subjective nature of the methods, mathematical complexity, rank reversal, or bias in determining user-dependent weights. In the present work, a hybrid Fuzzy-MABAC approach has been employed that can overcome the difficulties of the previous approaches to optimise submerged arc welding process parameters. The 27 numbers of experiments have been conducted based on 3 level 5 factor L27 Taguchi orthogonal array where arc voltage, welding current, welding speed, electrode stick out, and reuse of flux are considered as input parameters and weld quality has been assessed through the combined effect of measured output characteristics such as, weld hardness (H), weld bead width (BW), and heat affected zone (HAZ) respectively. The triangular Fuzzy method has been used to determine the weights of output parameters. Further, Multi-Attributive Border Approximation Area Comparison (MABAC) utilized the weights to determine optimised SAW output. The highest overall score of 0.6144 indicates optimum weld quality with 84 HRB, 11.1 mm, and 11.4 mm for H, BW, and HAZ respectively. Corresponding input parameters are 175A, 26 V, 1.2 m/min, 45 mm, and 30% for welding current, arc voltage, welding speed, electrode stick-out length, and reuse of flux respectively. Optimised weld quality has been achieved by moderate current, low arc voltage, moderate welding speed, adequate electrode stick out, and percentage reuse of flux. Welding voltage is found to affect the overall weld quality most significantly. The sensitivity analysis has been carried out with adequate details. The performance of the proposed Fuzzy-MABAC method corroborates well with the popular Fuzzy-TOPSIS method. [ABSTRACT FROM AUTHOR]

Maintenance models rely heavily on the failure characteristics and inspection or maintenance actions. This paper introduced a sequentially inspected system whose failure alternated between revealing and non-revealing failures. The distinctive characteristic of the proposed model is that inspections are imperfect, inspection and maintenance time are non-negligible, and corrective maintenance is perfect. The proposed study contributed on the multi-objective availability and cost optimization issue of the aforementioned system, which was lacking in the prior studies. This paper focused on evaluating the general expressions for limiting availability and long-run average cost of the sequentially inspected system and addressed the multi-objective optimization issue using particle swarm optimization to generate the Pareto optimal solutions followed by a fuzzy method to select the best compromise solution. Moreover, reasonable results were achieved using a numerical case study and the findings of the existing studies were found to be the special case of this model. [ABSTRACT FROM AUTHOR]

ABSTRACTSea-Land Segmentation (SLS) of remote sensing images is a meaningful task in the remote sensing and computer vision community. Some tricky situations, such as intraclass heterogeneity due to imaging constraints, inherent interclass similarity of sea-land regions and uncertain sea-land boundaries, still are and continues to be the significant challenges in SLS. In this paper, a fuzzy-embedded multi-scale prototype network, named FMPNet, is proposed to target the above challenges of SLS task. We design a dual-branch joint attention feature extraction module (DAFM) for effective feature extraction. Memory bank (MB) is designed to collect multi-scale prototypes, aiming to obtain discriminative feature representations and guide feature selection. In addition, fuzzy connection (FC) unit is embedded in the network structure to mitigate the uncertain sea-land boundaries through 2D Gaussian fuzzy method. Extensive experimental results on a publicly SLS dataset and real region images captured by the Gaofen-1 satellite demonstrate the superior performance of the proposed FMPNet over the other state-of-the-art methods.

Abstract Dam construction has some qualitative and quantitative effects on groundwater resources. This effect may be in the form of an increasing groundwater table or a changing groundwater system. In this paper, the effect of the Rodbal dam construction on the Darab aquifer was investigated. For this paper water table levels from observation wells and precipitation data was used. The study aims to analyse the behaviour of the water table during precipitation by employing a combination of the standardisation process, Fuzzy Inference System (FIS), and Root Mean Square Error (RMSE) calculations. Fuzzy logic involves the fuzzification of input data, transforming precise values into fuzzy sets. The effectiveness of the FIS is highlighted, particularly in determining the number of membership functions for inputs. The performance of the results is assessed using indicators such as RMSE and Coefficient of Determination (R2). The FIS showed a high level of effectiveness in performance assessment, exhibiting a 70% similarity between the fuzzy and Mann–Kendall methods. Nonetheless, the Fuzzy Purpose Method corresponded more closely with the observed data, indicating a more accurate reflection of reality. The findings indicate that for P3, P7, and P8, the results from the Mann–Kendall method do not show a discernible trend. Conversely, the proposed Fuzzy method accounts for changes in the behaviour of these piezometers.

Dam construction has some qualitative and quantitative effects on groundwater resources. This effect may be in the form of an increasing groundwater table or a changing groundwater system. In this paper, the effect of the Rodbal dam construction on the Darab aquifer was investigated. For this paper water table levels from observation wells and precipitation data was used. The study aims to analyse the behaviour of the water table during precipitation by employing a combination of the standardisation process, Fuzzy Inference System (FIS), and Root Mean Square Error (RMSE) calculations. Fuzzy logic involves the fuzzification of input data, transforming precise values into fuzzy sets. The effectiveness of the FIS is highlighted, particularly in determining the number of membership functions for inputs. The performance of the results is assessed using indicators such as RMSE and Coefficient of Determination (R2). The FIS showed a high level of effectiveness in performance assessment, exhibiting a 70% similarity between the fuzzy and Mann–Kendall methods. Nonetheless, the Fuzzy Purpose Method corresponded more closely with the observed data, indicating a more accurate reflection of reality. The findings indicate that for P3, P7, and P8, the results from the Mann–Kendall method do not show a discernible trend. Conversely, the proposed Fuzzy method accounts for changes in the behaviour of these piezometers. [ABSTRACT FROM AUTHOR]

This study focuses on identifying optimal areas for wheat cultivation in the northwestern part of the Khuzestan province- southwest Iran. This research adopts a multi-method approach that integrates Fuzzy logic, the Analytical Hierarchy Process (AHP), and satellite images to facilitate result-driven analysis. The study employs 86 bands and band ratios from Landsat-9, Sentinel-3, Sentinel-2, and fused Landsat-9_Sentinel-2 satellite images to establish correlations (CR) with key agro-ecological parameters relevant to wheat cultivation. The analysis encompasses climatic data (average, minimum, and maximum temperature, and annual rainfall), topographic data (slope percentage), and soil physical–chemical properties (organic carbon percentage, electrical conductivity, exchangeable sodium percentage, pH, gypsum, and soil texture). The choice of Sentinel-2 bands and band ratios is based on their superior CR values, which enable the development of robust regression models. This approach facilitates the generation of precise maps for all pertinent agro-ecological parameters. By overlaying weighted fuzzy maps, the research unveils distinct suitability degrees for wheat cultivation across various regions. The center and northern parts of the study area exhibit highly and moderately suitable classes, while the southeastern and southwestern areas display marginally and permanently not suitable classes. Out of the total land area, 2.29% is highly suitable, 6.51% is moderately suitable, 48.45% is marginally suitable, 36.75 is marginally not suitable, and 6% is permanently not suitable. To validate the findings, the researchers collect field data, which confirms the accuracy of the generated output map. Furthermore, a comparison between the present research method and traditional approaches highlights the superiority of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper examines the multi-objective availability and cost optimization of a periodically inspected system. A system with K independent failure modes is specifically considered. The principal characteristic of the proposed model is that the inspection and maintenance time is nonnegligible and corrective maintenance results in as good as a new unit. This paper focuses on evaluating the general expressions for limiting availability and long-run average cost of the system undergoing periodic inspections. The multi-objective optimization issue is solved by first generating the Pareto-optimal solutions using particle swarm optimization and then selecting the best compromise solution using a fuzzy method. A numerical illustration is also presented to highlight the applicability of the proposed work. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy-based method for estimating demand in forward and reverse logistics networks (F&RLNs) subject to carbon pricing in the post-COVID era. We investigate the complex relationships between carbon prices, the reverse logistics network (RLN), and the ever-changing product demand by utilizing fuzzy inference systems (FIS) capabilities. The case study from Mexico confirms the effectiveness of the proposed technique. The findings demonstrate the precision and predictive power of our proposed FIS-based method and show how well it can predict the number of items in demand and the number of goods that will be returned and subject to carbon taxation in the post-COVID era. The results illustrate the significant impact that carbon pricing has on the RLN and the associated product demand. For logistics managers seeking to make informed decisions about the establishment and operation of forward and RLNs within the carbon pricing paradigm, this empirical data provides insightful information in the post-COVID era.

To improve the accuracy of fault diagnosis for recoil systems under multiple operating conditions, a fuzzy RBF neural network (Radial Basis Function, RBF) fault diagnosis method based on knowledge and data fusion is proposed. A kinetic model for the recoil system is first established to describe the system's behavior. Next, fuzzy RBF neural network is used to establish the relationship between abnormal operating parameters and fault causes, achieving a fault cause diagnosis accurately based on the integration of expert experience knowledge and system operation data. A study case demonstrate that the algorithm has strong knowledge and data fusion capabilities and can effectively identify faults in recoil system. [ABSTRACT FROM AUTHOR]

Blockchain technology has become a reality and its experimentation promises a future of innovative and beneficial technology. In almost all fields, this technology has seduced the actors because of its potentialities, namely traceability, transparency, security, immutability, etc. However, setting up such a technology adapted to one's needs is far from being a classic project. Indeed, a technical and organizational decision support is very important during a blockchain project to measure its actions and anticipate the risks. In this paper, we will focus on the choice of the public or private blockchain according to technical, functional criteria, and a decision aid module based on the Fuzzy method is proposed to do so. The results obtained prove the effectiveness of our approach used in this paper. [ABSTRACT FROM AUTHOR]

The excellence of Uzbek Universities can be effectively classified by systematic and objective design criteria, which participates in developing the learning outcomes in Uzbekistan. In the first phase of this research, suitable quantitative and qualitative performance evaluation criteria are determined and defined, pairwise comparisons and evaluation forms are designed and exploited in order to get experts opinions/preference on the evaluation criteria that are used to measure the universities and academic staff performance using different types of survey. The research presents a fuzzy logic computational model based on this survey to measure and classify the performance of Uzbek universities, which includes computation of criteria weights and overall evaluation of Uzbek Universities using AHP and TOPSIS techniques. This study presents a Fuzzy Consistency Algorithm (FCA) to check and evaluate the consistency level of expert's judgment. The new algorithm proposes a consistent preference linguistic value(s) as an option to the experts in case of inconsistency judgment in evaluation performance. Based on the proposed algorithm, the research introduces new tool that allows experts to trace and understand the roots of inconsistency and select the relevant consistent option(s). The algorithm allows the degree of consistency to be configured by user. The study also applies the proposed algorithm to the performance evaluation of Uzbek universities as an empirical study. [ABSTRACT FROM AUTHOR]

Ecological flow regime analysis through developing a novel ecohydraulic optimization method is the objective of this study in which three components are linked. Hydrological analysis is the first component in which average monthly flow is assessed in different hydrological conditions by applying a drought index in the selected control points or representative reaches in the river basin. Another component is the ecological model in which field ecological studies are used for selecting the target species, and habitat loss was modelled through the fuzzy method. The outputs of the hydrological analysis and hydraulic habitat simulation were then applied in the structure of the optimization model in which minimizing ecological impacts and water supply loss were defined as the purposes. Different evolutionary algorithms were used in the optimization process. A decision-making system was utilized to finalize ecological flow by selecting the privileged algorithm. According to the outputs, the proposed method can mitigate ecological impacts and water supply losses simultaneously. Either particle swarm optimization or differential evolution algorithm is the best approach for ecological flow in this research work. The outputs of optimization indicated that the reliability of the water supply in dry years is less than 32%, while it is more than 80% in wet years, which means that changing the hydrological condition will increase the portion of ecological flow regime significantly. In other words, the reliability of the water supply can be reduced by more than 50%. Hence, using other water resources such as groundwater is necessary in dry years in the study area. [ABSTRACT FROM AUTHOR]

Abstract Nowadays, researchers are studying the energy discharging of electric vehicles (EVs) in charging stations. Furthermore, these stations can help maintain the balance of electric energy and reduce costs in the energy hub in the discharging mode. In this regard, this article proposes a probability and possibility approach by the Z‐number method to investigate the uncertainty of the EV owners’ behaviour at charging stations during the discharge of electric energy to the energy hub. Also, here, the uncertainty of electric energy demand, thermal energy, drinking water, and the price of electric energy, thermal energy, drinking water, and electric energy production from a solar power plant has been done by the scenario‐base method. The seawater desalination unit has been modelled to meet the drinking water needs. To check the final results of the proposed method, the mixed integer linear programming (MILP) model has been selected for energy management in the energy hub in the CPLEX optimization solver. The final results show that modelling the behaviour of EV owners in the charging stations using the Z‐number method leads to reduced consumption of some energy carriers in comparison with the fuzzy method in the proposed energy hub.

Evaluating the performance of city construction not only helps optimize city functions and improve city quality, but it also contributes to the development of sustainable cities. However, most of the scoring rules for evaluating the performance of city construction are overly cumbersome and demand very high data integrity. Moreover, the properties, change scale, and scope of different evaluation indicators of city construction often lead to uncertain and ambiguous results. In this study, a hybrid fuzzy method is proposed to conduct the performance evaluation of city construction in two phases. Firstly, a city performance index (CPI) was developed by combining the means and standard deviations of indicators of city construction to address the volatility of historical statistical data as well as different types of data. Considering the sampling errors in data analysis, the parameter estimation method was used to derive the 100% × (1 − α) confidence interval of the CPI. Buckley’s fuzzy approach was then adopted to extend the statistical estimators from the CPI into fuzzy estimators, after which a fuzzy CPI was proposed. To identify the specific improvement directions for city construction, the fuzzy axiom design (fuzzy AD) method was applied to explore the relationship between the targets set by city managers and actual performance. Finally, an example of six cities in China is provided to illustrate the effectiveness and practicality of the proposed method. The results show that the performance of Chongqing on several evaluation indicators is lower than that of other cities. The proposed method takes into account the issues of uniformity and diversity in the performance evaluation of city construction. It can enable a quantitative assessment of the city construction level in all cities and provide theoretical support and a decision-making basis for relevant government departments to optimize city construction planning and scientifically formulate city construction policies.

This paper addresses the fuzzy resilient control of DC microgrids with constant power loads. The DC microgrid is subject to abrupt parameter changes which are described by the Markov jump model. Due to the constant power loads, the DC microgrid exhibits nonlinear dynamics which are characterized by a T-S fuzzy model. According to the parallel distributed compensation principle, mode-dependent fuzzy resilient controllers are designed to stabilize the resultant T-S fuzzy Markov jump DC microgrid. The “resilient” means the controller could cope with the uncertainty caused by the inaccurate execution of the control laws. This uncertainty is governed by a Bernoulli distributed random variable and thus may not occur. Then, the mean square exponential stability is analyzed for the closed-loop system by using the mode-dependent Lyapunov function. Since the stability conditions are not convex, a design algorithm is further derived to calculate the fuzzy resilient controller gains. Finally, simulations are provided to test the effectiveness of the proposed results.

Nowadays, researchers are studying the energy discharging of electric vehicles (EVs) in charging stations. Furthermore, these stations can help maintain the balance of electric energy and reduce costs in the energy hub in the discharging mode. In this regard, this article proposes a probability and possibility approach by the Z‐number method to investigate the uncertainty of the EV owners' behaviour at charging stations during the discharge of electric energy to the energy hub. Also, here, the uncertainty of electric energy demand, thermal energy, drinking water, and the price of electric energy, thermal energy, drinking water, and electric energy production from a solar power plant has been done by the scenario‐base method. The seawater desalination unit has been modelled to meet the drinking water needs. To check the final results of the proposed method, the mixed integer linear programming (MILP) model has been selected for energy management in the energy hub in the CPLEX optimization solver. The final results show that modelling the behaviour of EV owners in the charging stations using the Z‐number method leads to reduced consumption of some energy carriers in comparison with the fuzzy method in the proposed energy hub. [ABSTRACT FROM AUTHOR]

Background: In the petrochemical industries, accidents are generally catastrophic which endanger human, environment and economic. In the industries, there is a wide range of flammable and toxic substances that affect health and safety of workers. They have also adverse effects on society. Numerical risk and impact assessment as well as design for protective layers against catastrophic events are necessary for designing process units.Methods: First, the occupational-process and environmental safety hazards were measured by hazard and operability (HAZOP) and environmental failure mode and effects analysis (EFMEA) techniques. Then, the risk was assessed using the layer and operability analysis (LOPA) method.Results: The results showed that a total of 50 safe and health items and 37 environmental risks were identified by HAZOP and EFMEA methods in Imam Khomeini Petrochemical Aromatic Unit. There were 17, 19 and 14 items with low, medium and high level risk, respectively.Conclusion: This study showed that the LOPA method is more comprehensive than hazard identification methods for the analysis of protective layers. The important actions were blockage of the excess gas to the flare and release the H2S gas. Also, evaluation of the environmental aspects of aromatic unit activities showed that air pollutant production in the power supply unit, waste disposal of reactor tank, waste disposal of condensate tank and reactor fire and explosion were at a high level risk.

BackgroundToday, with the development of the industry, the occurrence of accidents caused by the release and explosion of chemical and toxic substances in industrial units has increased, and these accidents sometimes cause irreparable damage to human life and the environment. According to a study by the American Petroleum Institute, of the recent major accidents in the last 30 years, 44% are related to machinery failure and 12% are caused by unknown factors and lack of information. Therefore, equipment risk control is aimed at preventing large and dangerous accident. The present study, the performance of LOPA and fuzzy-LOPA methods was compared toward the risk assessment of Imam Khomeini Petrochemical Company under certainty and uncertainty of data. This comparison was done in order to a conceptual method with high certainty to assess high-level hazards leading to health and safety risks and environmental pollution.MethodsFirst, the health, safety hazards and environmental aspects were identified via the HAZOP method. Then, a risk assessment was performed using the LOPA method. The fuzzification, severity, and likelihood of each risk were considered as an input variable and risk probability as an output variable. Finally, was the methods used in our analysis were compared and the Bow-tie software was used to draw a Bow-tie diagram to control and reduce the risks.ResultsAs a result, a total of 50 safety and health hazards and 37 environmental aspects were identified in the aromatic outlet of the studied company using the HAZOP method. The most critical risks identified were operational activities in feed and product tanks; flammable materials pumping; blocking the flare path; and releasing H2S gas. The results showed that the production of air pollutants in the power supply unit, disposal of waste from reactor tanks, disposal of waste from condensate tanks, and fire and explosion of the reactor are high-level environmental risks.ConclusionIn the conditions of uncertainty or the absence of information related to the probability and severity of the risk scenario, among the mentioned methods. The result showed that errors in the risk assessment were reduced to an acceptable extent by using Fuzzy LOPA method.

The purpose of this paper is to investigate the effect of political capital on GDP per capita. Political capital is the ability of citizens to influence the political system. Increasing economic stability depends on political capital, which improves macroeconomic variables, leading to the efficiency and productivity of production factors, thus increasing GDP per capita. In this study, the political capital index is estimated based on the combination of three indicators of social capital, political participation, and democracy using fuzzy logic method. Then, we measured the impact of political capital on GDP per capita for 38 selected developing and developed countries during 2007–2017 using the dynamic generalized method of moments (GMM). The result shows that political capital has a positive significant effect on GDP per capita. [ABSTRACT FROM AUTHOR]

With the growth of the population, the development of industrial, agricultural and so on, the amount of gas demand has been increasing steadily. Supplying the required gas energy often involves the construction of new power plants. The construction of power plants has social, economic, political and environmental consequences. Given the impact of power plant location in the cost of production and transmission of energy, environment, and so on, the importance of selecting the right place for the construction of the power plant is obvious. Therefore, in this study, geographical information system (GIS) based fuzzy-analytic hierarchy process (AHP) as one of the multicriteria decision methods and Dempster–Shafer theory (DST) methods are used to determine suitable locations for construction of gas power plant in Esfahan province, southwest of Iran. Different layers including distance to pipeline, distance to road, lithology, distance to stream, elevation, slope, and land use are prepared using ArcGIS. Subsequently, the importance of each factor in determining gas power plant location is defined using membership function. In the fuzzy-AHP method, membership function fuzzy maps for each layer are prepared. The DST model in Matlab is also used. The method was applied with three levels of confidence 99.5%, 99%, and 95% with the results compare. The results with the fuzzy method showed that 9.57% of the study area is unsuitable for the gas power location. On the hand, the results of the DST model showed that unsuitable areas for the DST method occupy 10.36%, 13.01%, and 19.55% of study area for levels of confidence of 95%, 99%, and 99.5%, respectively. [ABSTRACT FROM AUTHOR]

With increasing the penetration rate of microgrids in power networks, their control becomes more and more important, and microgrid frequency control in islanded mode is one of the most important recent approaches which are studied. With the aim of fast microgrid frequency adjustment, this study presents a novel robust approach using fuzzy and reset techniques. Therefore, this paper firstly describes the model of an islanded microgrid consisting of various elements such as renewable resources, variable loads, storage resources and distributed energy resources, and then designs and presents a robust hybrid control technique based on switching. The planned control method consists of three parts, which are: 1-Fuzzy control technique, 2- Fuzzy reset control technique, and 3- Switching between the two techniques. Due to the robust feature of the reset technique, which in addition to quickly reducing the frequency error, it is able to overcome the limitations of linear controllers, and also due to the intelligence of the innovative fuzzy technique, the planned technique is able to quickly remove frequency errors and restore the islanded microgrid frequency in the fastest possible time and with the least ups and downs and fluctuations. As the results of simulation and comparison in MATLAB environment show, the planned technique has a high and undeniable ability to overcome the uncertainty of the model and disturbances on the system even of the most severe type. [ABSTRACT FROM AUTHOR]

(1) Background: comprehending market concentration and its implications is one of the most crucial aspects of studying and evaluating competition issues, and this is gaining increasing attention in companies' sustainability research. Sustainability in the manufacturing industry refers to a company's ability to continue operations over the long term. A manufacturing business must be able to ensure that it will have appropriate resources, labor, and consumers for its products well into the distant future in order to remain economically viable. At this time, market competition concentration influence is significant. The purpose of this article is to assess the viability of measuring the HHI in the implementation of business strategies in order to prevent market distortions in the manufacturing industry. (2) Methods: the distinguishing aspect of this paper is that it describes the capabilities of the fuzzy VIKOR approach for evaluating the HHI in order to avoid market distortion by studying the most important economic characteristics of the manufacturing market. (3) Results: the contribution of this study is the compilation of the HHI evaluation system in the manufacturing sector, with the goal of identifying the market conditions of corporate entities and facilitating the attainment of long-term market competitiveness. (4) Conclusions: a comparison of the multicriteria techniques approach and probability theories reveals that the industrial sector's HHI-based performance may be measured. It is more adaptable to the organization's actual decision-making procedure. The operation of manufacturing businesses in the market with the least amount of distortion can be significant for enhancing competitiveness not only at the national level, but also at the global level, particularly by enhancing reputation, reducing costs, responding to market demands, and adapting to market demands. The findings will be relevant for policymakers tasked with controlling market inefficiencies and fostering sustainable economic initiatives. [ABSTRACT FROM AUTHOR]

The purpose of this research is to determine the optimum location to construct gas power plants (GPPs) in semi-arid regions. A combination of ordered weight averaging (OWA) and analytic hierarchy process (AHP) method named OWA-AHP is utilized to prepare land suitability maps with different risk scenarios when fuzzy method is to homogenize inputs. In the proposed method, AHP is to weigh each different parameter while OWA considers risk levels. In order to validate the accuracy of the proposed method, the receiver operating characteristics (ROC) curve is investigated. Besides, the self-organizing map (SOM) algorithm and Pearson correlation are used to determine the most important parameters for constructing GPP. According to obtained results, the areas located in the north and parts of the east and south of the selected case study (about 15%) at all risk levels are the optimum areas to be hosted for GPPs. Furthermore, ROC curve shows that the Area Under the Curve (AUC) values are high for both AHP and OWA-AHP methods (AUC Fuzzy-AHP = 94.0%, AUC OWA = 89.0%). The results of the SOM algorithm and Pearson correlation with high accuracy (RModel1: 0.853 and RModel2: 0.940) also depict that distance to the pipeline and road are the most important parameters to identify suitable locations for GPP.

The broad availability of connected and intelligent devices has increased the demand for Internet of Things (IoT) applications that require more intense data storage and processing. However, cloud-based IoT systems are typically located far from end-users and face several issues, including high cloud server load, slow response times, and a lack of global mobility. Some of these flaws can be addressed with edge computing. In addition, node selection helps avoid common difficulties related to IoT, including network lifespan, allocation of resources, and trust in the acquired data by selecting the correct nodes at a suitable period. On the other hand, the IoT's interconnection of edge and blockchain technologies gives a fresh perspective on access control framework design. This article provides a novel node selection approach for blockchain-enabled edge IoT that provides a quick and dependable node selection. Moreover, fuzzy logic to approximation logic was used to manage numerical and linguistic data simultaneously. In addition, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a powerful tool for examining Multi-Criteria Decision-Making (MCDM) problems, is used. The suggested fuzzy-based technique employs three input criteria to select the correct IoT node for a given mission in IoT-edge situations. The outcomes of the experiments indicate that the proposed framework enhances the parameters under consideration. [ABSTRACT FROM AUTHOR]

This article focuses on a dissipativity and synchronization control method for a class of delayed quaternion-valued memristor-based recurrent neural networks. On the basis of Lyapunov functionals, analytical techniques, and novel control algorithms, sufficient conditions are established to achieve dissipativity and synchronization of fuzzy quaternion-valued memristive systems. Moreover, the lexicographical order method is introduced, and can be used to determine the "magnitude" of two different vectors. Finally, corresponding simulation results are provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Human activities have a significant impact on global ecosystems. Assessing and quantifying ecological vulnerability is a fundamental challenge in the study of the ecosystem's capacity to respond to anthropogenic disturbances. However, little research has been conducted on EVA's existing fuzzy uncertainties. In this paper, an ecological vulnerability assessment (EVA) framework that integrated the Exposure-Sensitivity-Adaptive Capacity (ESC) framework, fuzzy method, and multiple-criteria decision analysis (MCDA), and took into account human impacts, was developed to address the uncertainties in the assessment process. For the first time, we conducted a provincial-scale case study in China to illustrate our proposed methodology. Our findings imply that China's ecological vulnerability is spatially heterogeneous due to regional differences in exposure, sensitivity, and adaptive capacity indices. The results of our ecological vulnerability assessment and cause analysis can provide guidance for further decision-making and facilitate the protection of ecological quality over the medium to long term. The developed EVA framework can also be duplicated at multiple spatial and temporal dimensions utilizing context-specific datasets to assist environmental managers in making informed decisions. [ABSTRACT FROM AUTHOR]

The escalating growth of the global population has led to degraded water quality, particularly in seawater environments. Water quality monitoring is crucial to understanding the dynamic changes and implementing effective management strategies. In this study, water samples from the southwestern regions of Iran were spatially analyzed in a GIS environment using geostatistical methods. Subsequently, a water quality map was generated employing large and small fuzzy membership functions. Additionally, advanced prediction models using neural networks were employed to forecast future water pollution trends. Fuzzy method results indicated higher pollution levels in the northern regions of the study area compared to the southern parts. Furthermore, the water quality prediction models demonstrated that the LSTM model exhibited superior predictive performance (R2 = 0.93, RMSE = 0.007). The findings also underscore the impact of urbanization, power plant construction (2010 to 2020), and inadequate urban wastewater management on water pollution in the studied region. • Investigating spatial distribution of chemical elements in water using geostatistical method • Generating water quality maps utilizing the MCDM method • Assessing the influence of land use changes on water quality • Forecasting water pollution trends to facilitate improved water management [ABSTRACT FROM AUTHOR]

Tool selection is a multi-criteria decision-making problem in the presence of various selection criteria and a set of alternatives, but previous works are limited to evaluating the tools within the workshop tool library. To intelligently select proper inserts across suppliers under the Internet environment, an insert data format based on ISO 513 was established, and a framework was then designed to obtain a set of alternatives from different suppliers based on fuzzy intervals. Then, knowledge was described with convenient language and the simple membership function to build an intelligent system, which would infer the matching degree of insert characteristics to the machining conditions. Furthermore, analytic hierarchy process was applied to sort the alternatives. Finally, the case study shows that compared with previous works and machinists, this work not only obtains a set of alternatives from all suppliers who uploaded their product data with the designed format but comprehensively evaluates the insert (take finishing low-carbon steel as an example, both cemented carbide and cermet are recommended, the nose radius reduces 25%, the environmental index increases 25%, while the rake reduces 11.25%, when compared with machinists who tend to select the larger rake angle foe finishing). A platform was also developed based on Visual Studio 2015 and SQL Server 2012 to improve selection efficiency for inexperienced CNC operators, purchasers, and vendors. [ABSTRACT FROM AUTHOR]

Space launch projects are extremely risky, and any equipment failure or human error may lead to disastrous consequences. Probabilistic risk assessment (PRA) is beneficial to qualitative analysis of risk, but it has not been paid enough attention in risk analysis for space launch systems (SLSs). Compared with most qualitative risk analysis in this field, this paper proposes a risk analysis framework based on Bayesian network (BN) with fuzzy method, which is suitable for probabilistic risk analysis of SLS. This method establishes a risk analysis model of SLS based on statistics and expert experience and reduces the uncertainty of the model by using fuzzy theory. By predicting the system risk probabilities, diagnosing the key risk causes, determining the risk conduction path, and performing a sensitivity analysis, the proposed risk analysis framework is aimed at alleviating this drawback to deal more effectively with the uncertainties in the field of space launches. A case study of space launches demonstrates and verifies the proposed method, and it also provides guidance for similar engineering projects. [ABSTRACT FROM AUTHOR]

This study investigated the capability of the Weather Research and Forecasting (WRF) model to simulate seven different heavy precipitation (PRE) events that occurred across East Africa in the summer of 2020. The WRF model outputs were evaluated against high-resolution satellite-based observations, which were obtained from prior evaluations of several satellite observations with 30 stations' data. The synoptic conditions accompanying the events were also investigated to determine the conditions that are conducive to heavy PRE. The verification of the WRF output was carried out using the area-related root mean square error (RMSE)-based fuzzy method. This method quantifies the similarity of PRE intensity distribution between forecast and observation at different spatial scales. The results showed that the WRF model reproduced the heavy PRE with PRE magnitudes ranging from 6 to >30 mm/day. The spatial pattern from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification-Climate Data Record (PERSIANN-CCS-CDR) was close to that of the WRF output. The area-related RMSE with respect to observation showed that the error in the model tended to reduce as the spatial scale increased for all the events. The WRF and high-resolution satellite data had an obvious advantage when validating the heavy PRE events in 2020. This study demonstrated that WRF may be used for forecasting heavy PRE events over East Africa when high resolutions and subsequent simulation setups are used. [ABSTRACT FROM AUTHOR]

Sustainable urban mobility plans (SUMPs) have become increasingly popular in cities with environmental problems to reduce pollution, often caused by urban transportation. Therefore, this study aims to provide a practical framework for the selection decisions of final measures and policies to be carried out to achieve SUMP workspace goals using a fuzzy multi-criteria decision-making (MCDM) methodology. Alternatives are created with the strategic "pillar", which was first adopted by the Podgorica city council. With the measurements in this pillar, the main criteria and the recommended measures for these measures create sub-criteria. Secondly, a Fuzzy Full Consistency Method (F-FUCOM) was used to determine the weights of the main and sub-criteria. The Fuzzy Combined Compromise Solution (F-CoCoSo) method was then applied to rank the alternatives. "The implementation and assurance of the SUMP" and "establishing a system for regular data collection, monitoring and evaluation of selected mobility indicators" were decided as the most important main and sub-criteria with weights of 0.286 and 0.1079, respectively. The findings suggest that the comprehensive planning for sustainable urban mobility alternative is first and the valorization of cycling potential alternative is second. A comprehensive sensitivity analysis confirms the validity, robustness, and effectiveness of the proposed framework. The applied methodology has the potential to assist decision makers in the process of developing SUMPs. [ABSTRACT FROM AUTHOR]

An improved fuzzy method is proposed to derive a fuzzy number for characterizing uncertain wind power. The input measurement data are firstly converted into nested sets, and the fuzzy number is further obtained based on nested set transformation method. Numerical studies have demonstrated the effectiveness and advantages of the improved fuzzy method.

This paper is on the suggestion of maintenance items for electric railway facility systems.With the recent increase in the use of electric locomotives, the utilization and importance of railroad electrical facility systems are also increasing, but the railroad electrical facility system in Korea is rapidly aging. To solve this problem, various methodologies are applied to ensure operational reliability and stability for railroad electrical facility systems, but there is a lack of detailed evaluation criteria for railroad electrical facility system maintenance. Also, maintenance items must be selected in a scientific and systematic method. Therefore, railroad electrical facility systems are selected for study. Design Structure Matrix (DSM) is utilized to establish considerations tailored to the maintenance characteristics, and the Fuzzy-TOPSIS methodology is utilized for determining the maintenance detail evaluation item baseline weights, a multi-criteria decision-making problem. Studies show that degradation, insulation items have the highest weight of 14.63%, and capacity items have the lowest weight of 5.34%. The results of this may be contributed to the underlying research in carrying out maintenance activities to ensure the reliability and safety of railroad electrical facility systems. [ABSTRACT FROM AUTHOR]