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Abstract In this study, a fuzzy model is presented for predicting the possibility of degradation due to salt crystallisation cycles. The formalization of the proposed model has been based on the multivariable approach which considers environmental data (such as temperature, solar radiation, wind speed, rain quantity, relative humidity), characteristic inflection points of specific salts and stone features derived from laboratory characterizations (including mechanical properties, porosity, and mineralogical composition). Modeling results have been compared with experimental data elaborations acquired by monitoring a semi-confined archaeological site situated in the city of Cagliari (Munatius Irenaus cubicle), revealing substantial alignment in the degradation kinetics trends. Moreover, the achieved outcomes show the remarkable capability to identify salt crystallisation phenomenon type (efflorescence or subflorescence).

Mathematical models, especially complex nonlinear systems, are always difficult to analyze and synthesize, and researchers need effective and suitable control methods to address these issues. In the present work, we proposed a hybrid method that combines the well-known Takagi-Sugeno fuzzy model with wavelet decomposition to investigate nonlinear systems characterized by the presence of mixed nonlinearities. Here, one nonlinearity is super-linear and convex, and other is sub-linear, concave, and singular at zero, which leads to difficulties in the analysis, as is known in PDE theory. Linear and polynomial fuzzy models were combined with wavelets to ensure an improvement in both methods for investigating such problems. The results showed a high performance compared with existing methods via error estimates and Lyapunov theory of stability. The model was applied to a prototype nonlinear Schrödinger dynamical system.

The relevance of the topic under consideration on the development of universal applied methods for assessing the effectiveness and efficiency of industrial policy in the military-industrial complex (hereinafter - MIC) is due to its high practical significance in the context of the challenges of technological development associated with digitalization and informatization. It is also due to the peculiarities of the current stage of Russia’s economic development in the conditions of financial and technological sanctions from Western states. The purpose of the study is to develop the main provisions of a methodological approach to assessing the effectiveness of industrial policy in the defense industry based on the use of economic-mathematical modeling apparatus, which involves the construction of neural networks based on fuzzy logic. In the course of the research the authors have solved the following problems: mathematically formalized the object of analysis; developed an algorithm for determining the effectiveness of industrial policy in the MIC using neural networks; formalized the model for assessing the effectiveness of such industrial policy based on fuzzy sets; proposed a system of indicators for assessing industrial policy in the MIC; determined the sequence of actions in the construction the fuzzy model for assessing the effectiveness of industrial policy in the MIC by means of Fuzzy Logic on the software platform MatLab.

Groundwater is the main source of drinking water in the northern Dezful-Andimeshk plain, Iran which has become saline in some cases. To check the drinking quality of groundwater, in October 2019, 16 samples were taken from different parts of the plain. The samples were analyzed for the concentration of major cations, anions, and nitrates.Two methods were used to evaluate water quality: the Schoeller deterministic method and the Fuzzy logic. The Schoeller method categorized water quality from good to bad and determined that 56.81, 20.83, 18.77, and 3.57% of the area had good, acceptable, inappropriate, and bad water quality, respectively. On the other hand, the Fuzzy method showed that 21.60% of the area had desirable groundwater with a confidence level of 70-81, 75.23% had acceptable water quality with a confidence level of 32-70, and 3.69% had non-acceptable quality with a confidence level of 20-22%. The Fuzzy method was found to be better in evaluating water quality as it provided a more comprehensive, accurate, and efficient assessment by covering uncertainties better.

In this study, a fuzzy model has been proposed for the control of the degradation process in Cultural Heritage. Specifically, the focus has been on the Nuragic building, Genna Maria, in Villanovaforru, Sardinia. Environmental data and material properties were considered to formalize inferences between different variables of the model. The results highlight the possibility of significant decay processes, such as salt crystallization and freeze-thaw cycles, estimated for all months of the year. A comparison between model elaborations and experimental data (material characterization, environmental data, accelerated ageing) demonstrates the reliability of the proposed procedure.

Abstract Flying Ad hoc Networks have emerged as a promising technology for number of real-time applications. However, the flexible and unstructured characteristics of these networks make them vulnerable to security threats posed by malicious nodes, such as denial of service attacks, node impersonation, and information breaches. Another major issue is the consideration of those nodes being unable to prove their trustworthiness due to factors like hardware or software failure, or by link interruptions, during the processing of detection of false nodes in the network. The existing mechanisms encompassing encryption, authentication, and intrusion detection highlight limitations to secure real-time applications and services due to the high speed of flying nodes and the absence of fixed network structures. To overcome these constraints, this research paper incorporates a novel framework for evaluating and improving the security of network by introducing an innovative cluster-based approach. Moreover, it presents a fuzzy model that dynamically estimates the trust levels of both individual nodes and clusters, by assigning weight to the parameters to address vulnerabilities. Additionally, a trust reconfiguration mechanism is further proposed to address the issue of nodes unable to substantiate their trust by providing them with additional chances based on the collective trust from previous evaluations. Further, the paper incorporates a dynamic reputation system to proficiently identify and separate malicious and selfish nodes from the network. Simulation results indicate a significant improvement in performance metrics, with a considerable reduction in delay and drop ratio by 41.46% and 36.37%, respectively, while the sufficient rise of 54.71% and 46.05% in throughput and coverage, respectively, comparing with the considered state-of-art.

The paper is devoted to the model-theoretic formalization of the semantic model of the object domain. The article discusses the concept of a fuzzy model, which is a model where the truth function exhibits properties of a fuzzy measure. We demonstrate that a fuzzy model is a generalization of the concept of fuzzification of a precedent (semantic) model to include a countable number of precedents. In practice, it is common to have partial expert knowledge about the set of events in the object domain, making it difficult to immediately describe the fuzzy model. Additionally, since expert valuations are subjective, they may be incorrect and inconsistent with any fuzzy model. In the article, we introduce the concepts of coordinated and locally coordinated valuation of a set of sentences, and provide proofs for interval theorems and an analogue of the compactness theorem.

Diabetes is becoming more and more dangerous, and the effects continue to grow due to the population’s ignorance of the seriousness of this phenomenon. The studies that have been carried out have not been able to follow the phenomenon more precisely, which has led to the use of the fractional derivative tool, which has a very great capability to study real problems and phenomena but is somewhat limited on nonlinear models. In this work, we will develop a new fractional derivative model of a diabetic population, the Takagi–Sugeno fractional fuzzy model, which will enable us to study the phenomenon with these nonlinear terms in order to obtain greater precision in the results. We will study the existence and uniqueness of the solution using the Lipschizian theorem and then turn to the new fuzzy model, which leads us to four dynamical systems. The interpretation results show the quality of fuzzy membership in tracking the malleable phenomena of nonlinear terms existing in the system.

A large number of real-world problems from various fields of human activity can be reduced to optimal partitioning-allocation problems with the purpose of minimizing the partitioning quality criterion. A typical representative of such problem is an infinite-dimensional transportation problem and more generalized problems—infinite-dimensional problems of production centers placement along with the partitioning of the area of continuously distributed consumers with the purpose of minimizing transportation and production costs. The relevant problems are characterized by some kind of uncertainty level of a not-probabilistic nature. A method is proposed to solve an optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. The method is based on the synthesis of the methods of fuzzy theory and optimal partitioning-allocation theory, which is a new science field in infinite-dimensional mathematical programming with Boolean variables. A theorem was proved that determines the form of the optimal solution of the corresponding optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. An algorithm for solving fuzzy partitioning-allocation problems is proposed, which is based on the proved theorem and on a variant of Shor’s r-algorithm—a non-differential optimization method. The application of the proposed method is demonstrated on model tasks, where the coefficient of mistrust is integrated to interpret the obtained result—the minimum value of the membership function, which allows each point of the set partition to be assigned to a specific fuzzy subset.

Many systems in the manufacturing industry have spatial distribution characteristics, which correlate with both time and space. Such systems are known as distributed parameter systems (DPSs). Due to the spatiotemporal coupling characteristics, the modeling of such systems is quite complex. The paper presents a new approach for three-dimensional fuzzy modeling using Simultaneous Perturbation Stochastic Approximation (SPSA) for nonlinear DPSs. The Affinity Propagation clustering approach is utilized to determine the optimal number of fuzzy rules and construct a collection of preceding components for three-dimensional fuzzy models. Fourier space base functions are used in the resulting components of three-dimensional fuzzy models, and their parameters are learned by the SPSA algorithm. The proposed three-dimensional fuzzy modeling technique was utilized on a conventional DPS within the semiconductor manufacturing industry, with the simulation experiments confirming its efficacy.

The risk warning for steady-state power quality in the power grid is essential for its prevention and management. However, current risk warning methods fall short in predicting the power quality trend while accounting for potential risks. Consequently, this study introduces a novel steady-state power quality risk warning method utilizing VMD-LSTM and a fuzzy model. Firstly, a power quality index prediction method based on variational mode decomposition (VMD) and long short-term memory (LSTM) is proposed. This approach significantly enhances prediction accuracy. Secondly, a power quality risk warning method incorporating kernel density estimation (KDE) and a fuzzy model is proposed, which systematically addresses the uncertainty associated with power quality risks. To validate the effectiveness and practicality of the proposed method, experiments are conducted using field monitoring data from a residential load in southern China. The results affirm the reliability and applicability of the proposed method. The simulation results show that the median error of prediction of power quality indexes by the proposed method is 5.03 % during the evaluated time period, and the prediction accuracy is mostly maintained above 90 %.

The primary objective of this paper is to address the challenge of designing finite-time fault-tolerant control mechanisms for nonlinear flexible spacecraft systems, which are particularly vulnerable to randomized actuator faults. Diverging from traditional methodologies, our research harnesses the capabilities of the Takagi–Sugeno (T–S) fuzzy framework. A unique feature of our model is the representation of actuator failures as stochastic signals following a Markov process, thereby offering a robust solution for addressing timeliness concerns. In this paper, we introduce a generalized reciprocally convex inequality that includes adjustable parameters, broadening the scope of previous results by accommodating them as special cases. Through the amalgamation of this enhanced inequality and flexible independent parameters, we propose an innovative controller design strategy. This approach establishes a stability standard that guarantees mean-square H∞ performance. In order to validate the efficacy of the suggested strategy, we present a numerical illustration involving a nonlinear spacecraft system, showcasing the practical advantages and feasibility of our proposed technique.

The growth in the number of people suffering from eye diseases determines the relevance of research in the field of diagnosing retinal pathologies. Artificial intelligence models and algorithms based on measurements obtained via electrophysiological methods can significantly improve and speed up the analysis of results and diagnostics. We propose an approach to designing an artificial intelligent diagnosis system (AI diagnosis system) which includes an electrophysiological complex to collect objective information and an intelligent decision support system to justify the diagnosis. The task of diagnosing retinal diseases based on a set of heterogeneous data is considered as a multi-class classification on unbalanced data. The decision support system includes two classifiers—one classifier is based on a fuzzy model and a fuzzy rule base (RB-classifier) and one uses the stochastic gradient boosting algorithm (SGB-classifier). The efficiency of algorithms in a multi-class classification on unbalanced data is assessed based on two indicators—MAUC (multi-class area under curve) and MMCC (multi-class Matthews correlation coefficient). Combining two algorithms in a decision support system provides more accurate and reliable pathology identification. The accuracy of diagnostics using the proposed AI diagnosis system is 5–8% higher than the accuracy of a system using only diagnostics based on electrophysical indicators. The AI diagnosis system differs from other systems of this class in that it is based on the processing of objective electrophysiological data and socio-demographic data about patients, as well as subjective information from the anamnesis, which ensures increased efficiency of medical decision-making. The system is tested using actual data about retinal diseases from the Russian Institute of Eye Diseases and its high efficiency is proven. Simulation experiments conducted in various scenario conditions with different combinations of factors ensured the identification of the main determinants (markers) for each diagnosis of retinal pathology.

Precipitation is one of the main elements of the Earth's hydro-climatic cycle and its variability depends on the complex and non-linear relationships between the climate system and environmental factors. Understanding these relationships and doing environmental planning based on them is difficult. Therefore, classifying data and dividing information into homogeneous and small categories can be helpful in this regard. In the present study, an attempt was made to prepare precipitation, altitude, slope, slope direction, and station density data for 3423 synoptic, climatological, and gauge stations in Iran in the 1961-2015 years’ period. These data were entered into fuzzy (FCM), self-organizing map neural network (SOM-ANN) models and precipitation-spatial zoning. The outputs of the two models were compared in terms of accuracy and efficiency. The results obtained from the output of the models have divided the rainfall conditions of Iran into four zones concerning environmental factors. Evaluations also showed that both models had high accuracy in classifying precipitation parameters; However, the fuzzy model has a relative advantage over the neural network model in the accuracy of results.

African swine fever (ASF) is a contagious and lethal hemorrhagic disease with a high case fatality rate. Since 2007, ASF has been spreading into many countries, especially in Europe and Asia. Given that there is no effective vaccine and treatment to deal with ASF, prevention is an important way for a country to avoid the effects of the virus. Australia is currently ASF-free but the disease has been reported in many neighboring countries, such as Indonesia, Timor-Leste, and Papua New Guinea. Therefore, it is necessary for Australia to maintain hyper-vigilance to prevent the ASF introduction. In this paper, we propose the use of fuzzy concepts to establish a fuzzy risk assessment model to predict the ASF introduction risk in Australia. From the analysis, the international passengers (IP) and international import trade (IIT) are concluded as the two main ASF introduction factors based on transmission features and past research. From the established fuzzy risk assessment model based on the analysis of the 2019 and 2020 data, the risks of ASF introduction into Australia are considered to be low. The model further deduced that the Asian region was the major source of potential risks. Finally, in order to validate the effectiveness of the established fuzzy risk assessment model, the qualitative data from the Department for Environment, Food & Rural Affairs of the United Kingdom was used. From the validation results, it has shown that the results were consistent when the same data is adopted, and thus proved that the functionality of the established fuzzy risk assessment model for assessing the risk in Australia.

Airborne magnetic particles may be harmful because of their composition, morphology, and association with potentially toxic elements that may be observed through relationships between magnetic parameters and pollution indices, such as the Tomlinson pollution load index (PLI). We present a fuzzy-based analysis of magnetic biomonitoring data from four Latin American cities, which allows us to construct a magnetic index of contamination (IMC). This IMC uses four magnetic parameters, i.e., magnetic susceptibility χ, saturation isothermal remanent magnetization SIRM, coercivity of remanence Hcr, and SIRM/χ, and proposes summarizing the information to assess an area based exclusively on magnetic parameters more easily. The fuzzy inference system membership functions are built from the standardization of the data to become independent of the values. The proposed IMC is calculated using the baseline values for each case study, similar to the PLI. The highest IMC values were obtained in sites close to industrial areas, and in contrast, the lowest ones were observed in residential areas far from avenues or highways. The linear regression model between modeled IMC and PLI data yielded robust correlations of R2 > 0.85. The IMC is proposed as a complementary tool for air particle pollution and is a cost-effective magnetic approach for monitoring areas.

Introduction. The relevance of the study lies in the need to support managerial decision-making in the field of interaction between the participants in regional strategic alliances. The purpose of the article is to develop a method for adjusting strategies for interaction with stakeholders for an organization that has joined a regional strategic alliance. Materials and Methods. The proposed method is based on a fuzzy model of choosing strategies for the interaction of an organization with stakeholders before and after the entry into the alliance. The quantitative values of the relationship characteristics within the model framework are estimated for each resource component participating in the resource exchange of stakeholders with the organization, using a base of fuzzy production rules and a fuzzy inference algorithm. Results. It presents the main difference between the model and other well-known models for evaluating the characteristics of relations of an organization with stakeholders. The method is based on the assumption of significant changes in the characteristics of relations with stakeholders due to the entry of an organization into a strategic alliance. The paper assumes that these changes, in turn, lead to significant changes in the weighting coefficients of the feasibility of using various types of strategies for the interaction of an organization with stakeholders. The developed tools are tested on the example of the experience industry of territory – a regional strategic alliance between the Shtykovskie Prudy art park and the Tokyo restaurant chain (Primorye Territory). The paper depicts the change of relations with the stakeholders of the art park due to its joining the alliance. Besides, the paper shows how the expediency of using different types of strategies for the organization’s interaction with stakeholders changes. The paper focuses on identifying further directions of the development. Discussion and Conclusion. The results of the work can be useful to specialists in the field of management of regional strategic alliances, in particular, employees of the tourism administration, the business community, scientific and pedagogical personnel in the relevant field, and can be used in the training of specialists of higher and secondary professional education in strategic management.

The article presents the results of a study on the creation of a fuzzy model for assessing the impact of pricing factors on the cost of primary real estate. The aim of the study is to develop a fuzzy logic-based tool for analyzing and evaluating the complex relationships between various factors that affect value. The article covers important aspects of creating a fuzzy model, including the definition of pricing factors, linguistic variables, and membership functions. The process of forming a rule base is considered, which determines the logical connections between factors and their influence on the price of real estate. The essence of the concept of primary housing and factors influencing pricing based on data on the situation in the real estate market are considered. The results of the study can be a useful tool for developing pricing strategies and decision making in the real estate market. The model developed by the authors can be improved considering the characteristics of a particular market and available data.

The industry and government have recently acknowledged and used virtual machines (VM) to promote their businesses. During the process of VM, some problems might occur. The issues, such as a heavy load of memory, a large load of CPU, a massive load of a disk, a high load of network and time-defined migration, might interrupt the business processes. This paper identifies the migration process among hosts for VM to overcome the problem within the defined time frame of migration. The introduction of VMs migration in a timely manner is to detect a problem earlier. There are workload parameters, such as network, CPU, disk and memory as our parameters. To overcome the issue, we have to follow the Model named Fuzzy rule. The rule follows the basic of tree model for decision-making. The application of the fuzzy Model for the study is to determine VMs allocation from busy VMs to vacant VMs for balancing purposes. The result of the study showed that the use of the fuzzy Model to forecast VMs migration based on the defined rule had 2 positive impacts. The positive impacts are (1) Time frame live migration of VMs can reduce workload by 80 %. This aims to reduce failures in performing a live migration of VMs to increase data center performance. (2) In testing, the fuzzy Model can provide results with an accuracy of 90 %, so this model can perform a live migration of VMs precisely in determining the execution time. Next, the workload could be balanced among VMs. This research could be used further to improve 5G Quality of Experience (QoE) shortly.

Introduction Ecotourism is one of the common types of tourism activities that has attracted a lot of attention in recent years. The ecotourism concept is based on the ideals of environmental protection and sustainable development and refers to a responsible journey to nature with an emphasis on ensuring the improvement of local community life and environmental protection (Seifi and Janbaz, 2017: 479). Ecotourism has a deep connection with sustainable development, which stems from the interactions between tourists and the environment. Proper planning and management in order to develop ecotourism are essential to preserve and maintain the environmental richness of the region as well as the economic improvement of local people (Bunruamkaew and Murayama, 2011: 269). Iran is one of the countries that have a lot of potential for ecotourism development, however, studies show that Iran's natural tourism assets are vast array of scattered, unstabilized resources, and in some cases are on the verge of extinction. The main object of the current paper is to assess the ecotourism potentials and capabilities of the Margavar rural district of Urmia County in northwest of Iran. For this purpose, Analytic Network Process (ANP) and Fuzzy method have been used and ecotourism capabilities zoning map of the study area has been produced. Data and Method The current research is a Multicriteria-based study and the Analytic network process (ANP) method and Geographical Information System (GIS) has been used to analyse the data. Spatial criteria were clustered based on reviewing the background into five main groups including climate, human, topography, geology, and tourism and criteria have divided into these 5 groups. At first, the criteria map was prepared in the ArcGIS environment, then all the maps were reclassified with the Reclassify function. In the next step, the criteria maps are standardized with a Fuzzy linear function and the ANP model was run in Super Decisions software, and pairwise comparisons and related super matrices were calculated for the criteria and the relative weight of each criterion was obtained and the resulting weights were applied to maps. Finally, the weighted maps were combined each other using the 0.9 fuzzy gamma operator, and the Fuzzy ecotourism capability zoning map was produced in value 0 to 1. Results and Discussion After designing the network structure, matrices and related super-matrices were calculated and the relative weights of all criteria were determined. Results showed that temperature, geological structure, slope, rain, and tourism facilities have the most importance and weight in relation to the ecotourism ability of the study area, respectively. According to the ecotourism capabilities zoning map, the study area was divided into four class: completely suitable, relatively suitable, relatively unsuitable, and completely unsuitable. The resulting map analysis shows that areas located in the central and western parts of the region, which have a low slope percentage and also include rich pastures and natural tourist attractions, are in the completely suitable group. Furthermore, some parts of the study area have mountain slopes and difficult topographic conditions that are very difficult to access grouped in completely unsuitable lands for ecotourism activities. The final results of the study show that 14.50% of the study area is in the completely suitable class, 26.32% in the relatively suitable class, 27% in the relatively unsuitable class and 32.15% are in the completely unsuitable class Conclusion Research evaluations show that In general, Margavar rural district, both in terms of potential for the future development of ecotourism and in terms of Its current situation has a lot of potential in this regard in terms of receiving a large number of tourists That comprehensive planning and formulation of effective solutions in this field can be considered an effective way and an important step to achieve sustainable regional development. Application of multi-criteria analysis techniques such as ANP model and Fuzzy model in this research indicates the great flexibility of these methods, which makes it possible to determine different scenarios and combine different criteria with each other. On the other hand, the use of GIS has provided a good platform for feasibility studies, assessment, and identification of the natural environment. Therefore, it can be said that due to the high potential of the Margavar rural district, there is a need to review measures and pay more attention to tourism development plans and studies in this region.

In general, vehicle management is based on the proper maintenance and safety of a vehicle. Based on this the quality of the vehicle is calculated. Most of the older vehicles are currently of poor quality and are producing high levels of pollution. Thus, it is necessary to find information about those vehicles. The number plate is helpful to find the information about the vehicle. In this paper, the number blood detection method is proposed. It is based on the fuzzy model and developed in the way of deep learning. Its main purpose is to provide accurate vehicle details from a given set of data. It has also been upgraded to provide its safety measures to its owner based on the vehicle data. Thus, this proposed model prevents major accidents. These functions can also be very helpful in recovering vehicles based on data from stolen vehicles.

This paper focuses on designing multi-objective biomass supply chain planning models that aim to simultaneously minimize the total cost and the carbon footprint from the transportation. Stochastic and fuzzy models were developed for making strategic (optimal plant locations) and tactical decisions (material flows, truck types, etc.), while capturing the uncertainty of the demand. An epsilon-constraint method was applied to generate optimal solutions from these models. Managerial insights are provided based on a practical case study at a biomass plant in the Lower Northern region of Thailand; nine biomass plant candidates, nine rice husks suppliers and eight trucks were considered in the case study. A sensitivity analysis has been conducted to compare the results from the two models. The stochastic model can take into account all the scenarios of demand, while the fuzzy model can handle only a certain level of demand defined by a centroid value. The stochastic model needs to take into account a large number of variables and constraints, and therefore, requires more runtime to define an optimal solution, as compared to the fuzzy model. The trade-off between the operating cost and carbon emissions from both the models are provided with managerial insights.

Abstract This paper proposes to analyze how the intervention measures such as lockdown, partial lockdown and no-lockdown help to impede the spread of the severe outbreak of COVID-19 in Brazil. A p-fuzzy model, considering as input variables, the infected population and the intervention measures and as output variable the level of infestation, is proposed. The numerical results show that intervention measures play a crucial role in determining the success of COVID-19 eradication programs, while the population is being vaccinated in stages. Therefore, the model proposed assists government decision making in order to minimize the spread of the pandemic.

The response spectra defined in seismic design codes include crisp classifications of seismic parameters, which directly affect the spectra’s shape and greatly alter seismic design loads. The optimum design phase seismic forces have an important role in the efficiency of the construction costs and structural safety. Various parameters are used to calculate the seismic design forces, especially presented in the codes with earthquake design spectra. This study presents a rule-based fuzzy inference model with fuzzy sets to determine these parameters using fuzzy inference system (FIS) modelling, which is the most appropriate approach among the different alternatives because both the input and output variables have numerical and linguistic uncertainties in the earthquake problem. Using the seismic zone factor of the region and shear wave velocity of the soil profile as inputs, the model generates the seismic coefficients and peak ground acceleration values of the response spectra specified in the Uniform Building Code (UBC, 1997). The response spectra in this code can be easily generated with these seismic coefficients after their fuzzification. Response spectra of twenty-five different sample cases with and without the FIS model are generated, which provide comparisons for the model superiority assessment. Significant differences are observed between the crisp logic and the FIS model-generated spectra. It is suggested that the FIS model can be modified and applied to various parameters to generate response spectra in different seismic design codes.

The aim of the article was to build a model based on fuzzy logic describing the level of sustainable development for the individual EU Member States in 2009–2019 and to conduct fuzzy simulations as a result of which scenarios for the prediction of sustainable development acknowledging financial variables (the share of environmental taxes, a deficit, a debt). The original research approach is expressed by including fuzzy models in the analysis of the level of sustainable development with the inclusion of financial variables. The Netherlands achieved the highest value (the highest level of sustainable development) (0.571). Slovenia (0.564) and Denmark (0.559) came second and third. The lowest average value (the lowest level of sustainable development) was recorded for Slovakia (0.423). After 10 years there are evident changes in the distribution of individual typological groups. In 2019, the countries belonging to the first and second typological groups were located primarily in the northern and central Europe. The scenario analysis showed that in the case of most countries, if the analyzed variables remained at the maximum or minimum level throughout the analyzed period, it would not significantly affect the assessment of their level of sustainable development, their ranking positions, and inclusion into typological groups. First published online 03 August 2022

Abstract The COVID-19 pandemic has spread widely through the world, since 2019. Then, the search for vaccines has become fundamental against the disease and has generated great expectations on population. In Brazil, the first case of COVID-19 was registered in February 2020 and the process of vaccination started in early 2021. In this context, considering the advance of vaccination and the behavior of epidemics, we present a study on the effect of population-wide vaccination on the evolution of the COVID-19 epidemic. To this end, we propose a new fuzzy system in order to model the COVID-19 epidemic evolution under the effect of vaccination in Brazil. This proposed model consists of a p-fuzzy system, where the input variables are the infected population and the vaccination rate. The output variable is the level of infection. Results of the proposed fuzzy system show that the level of infestation tends to decrease as the number of people vaccinated increases. Therefore, the population-wide vaccination strategy clearly contributes to suppressing the growth of the disease and to mitigating its impacts on society, including saving lives.

The article deals with the content of the concept of "happiness index". As a result of the literary analysis, some methods and approaches to its research have been identified. The choice and advantage of using the theory of fuzzy sets to evaluate the happiness index in the countries of the world is substantiated. The process of constructing a fuzzy model of the happiness index in the countries of the world in the fuzzyTECH environment is described. The main conclusions and results of the approbation of the constructed model are discussed. It is established that the model allows us to assess the well-being of the population at the current moment, thereby allowing us to adjust the directions of development of the state.

ABSTRACT Bell peppers are one of the most cultivated and consumed vegetables in Brazil, and their high production yield largely depends on the use of high-quality seedlings. Supplementary lighting using light-emitting diodes (LEDs) can help in the production of seedlings and improve their quality. This study aims to develop a fuzzy model to predict the agronomic parameters of bell pepper seedlings under different light intensities. Experimental data on the lighting levels and biometric responses of bell pepper seedlings were used to develop a fuzzy model, and the lighting levels defined in five sets were used as the input variable. The model was used to predict the output variables, namely the plant height, number of leaves, stem diameter, internode number, shoot fresh weight, and root fresh weight. The results indicated that the use of supplemental lighting at a level of 75 μmol m-2s-1 was the most beneficial, producing seedlings with more leaves, higher stem diameters, internode numbers, and fresh weights. The model error rates were satisfactory for the predicted values of all variables, demonstrating the usefulness of the fuzzy model for estimating the best lighting levels in the cultivation of bell pepper seedlings.

The article shows that the depletion of existing oil and gas fields leads to the transfer of the main hydrocarbons production to the Arctic zone of the Globe, where their significant reserves are concentrated. It is stated that the management of geodynamic risks in the territories of oil and gas basins is associated with the use of mathematical modeling methods. Models for assessing geodynamic risks are divided into deterministic, probabilistic, and based on the use of the mathematical apparatus of fuzzy sets. To solve the problem of risk classification of the Arctic zone territories in relation to the location of oil and gas facilities, fuzzy models were used. The method of solving problems of managing geodynamic risks in the territories containing oil and gas complexes allows you to rank objects according to the degree of vulnerability in relation to the manifestation of geodynamic hazards. It is concluded that this approach makes it possible to justify high-quality management decisions in ensuring the safety of oil and gas complexes.

In the Small Island Developing States (SIDS), public sector infrastructure projects (PSIPs) fail to both meet targeted performance metrics and deliver on the intended benefits to society. In terms of the cost performance metric, cost overruns (COs) beyond the initial contract value are more of a norm than a unique occurrence. Therefore, to ensure economic sustainability for SIDS, and value for money on PSIPs, there is a need to investigate and evaluate the risk impacts on COs. The purpose of this research was to identify and evaluate the perceived cost overrun risk factors that are within the primary project stakeholders’ sphere of control, and to reduce the ongoing ambiguities that exist in the prioritization of these risks. This was achieved by extracting critical risk factors from selected comparative studies in developing countries to formulate a closed-ended questionnaire to be administered to construction professionals in Trinidad and Tobago. Thereafter, the process of fuzzy synthetic evaluation (FSE) was used to develop a risk model based on three tiers of risks: 11 critical risk factors, 3 critical risk groupings (CRGs) and an overall risk level (ORL). The results showed that the two highest-ranked critical risks were project funding problems and variations by client. The leading critical risk grouping was client-related risk (5.370), followed by professional-related risk (4.815) and physical risk (4.870). The ORL was 5.068. Based on the FSE’s linguistic scaling, the CRGs and the ORL are perceived to be high risks in PSIPs. This research adds to the CO body of knowledge in primarily three ways. Firstly, the study extends the comparative assessment previously undertaken in scholarship into the context of SIDS to build on the generalizability of this context-specific phenomenon. Secondly, the FSE evaluation undertaken provides a practical tool to be promoted for use in SIDS’ construction industry among practitioners to focus and prioritize the critical risks in the planning phases and improve on contemporary risk practices in the execution phases of projects. Finally, this quantitative model approach is recommended to supplement the traditional qualitative risk management practices adopted in SIDS, thus contributing towards the overall improved economic sustainability and viability of PSIPs.

Soil fertility (SF) assessment is an important strategy for identifying agriculturally productive lands, particularly in areas that are vulnerable to climate change. This research focuses on detecting SF zones in Firozabad district, Uttar Pradesh, India, for agricultural purposes, so that they can be prioritized for future management using the fuzzy technique in the Arc GIS model-builder. The model computing technique was also deployed to determine the different fertility zones, considering 17 soil parameters. The derived fuzzy technique outperformed the traditional method of dividing the sampling sites into clusters to correlate soil fertility classes with the studied soil samples. The prioritization of the soil factors and a spatial analysis of the fertility areas were carried out using the Analytic Hierarchy Process (AHP) and GIS tools, respectively. The AHP analysis outcome indicated that hydraulic properties had the highest weighted value, followed by physical and chemical properties, regarding their influence on SF. The spatial distribution map of physico-chemical properties also clearly depicts the standard classification. A fuzzy priority map was implemented based on all the classes parameters to identify the five fertility classes of the soil, namely very high (0.05%); high (16.59%); medium (60.94%); low (22.34%); and very low (0.07% of total area). This study will be of significant value to planners and policymakers in the future planning and development of activities and schemes that aim to solve similar problems across the country.

Aiming at the problems of low accuracy, low efficiency and low stability of traditional methods and recent developments in advanced technology incite the industries to be in sync with modern technology. With respect to various available techniques, this paper designs a fuzzy comprehensive evaluation model of the manufacturing industry for transferring risk based on economic big-data analytics. The big-data analysis method is utilized to obtain the data source of fuzzy evaluation of the manufacturing industry to transfer risk using data as the basis of risk evaluation. Based on the risk factors, the proposed model establishes the risk index system of the manufacturing industry and uses the expert evaluation method to design the scoring method of the evaluation index system. To ensure the accuracy of the evaluation results, the manufacturing industry's fuzzy comprehensive model is established using the entropy weight method, and the expert evaluation results are modified accordingly. The experimental results show that the highest efficiency of the proposed method is 96%, the highest accuracy of the evaluation result is 75%. The evaluation result's stability is higher than the other existing methods, which fully verifies the effectiveness and can provide a reliable theoretical basis for enterprise risk evaluation research.

The subcontractor selection decision is inherently a multicriterion problem. It is a decision of strategic importance for companies. The nature of this decision is usually complex and unstructured. Management science techniques might be helpful tools for solving these kinds of decision-making problems. In this research, the fuzzy logic method and the analytic hierarchy process were applied for the selection of suitable subcontractors in an apparel supply chain.

During the digital forensic process, different software and hardware tools are used. The devices from which the digital evidences are collected have been varied in parallel with the developments in technology. The issue of identifying the mobile phone owner’s friends and assessing his relationship with them with the help of digital evidences collected from the Android mobile phones has been studied in the literature and it is still under investigation. The software developed in this work enables accessing a variety of data that have evidential value in the court proceedings; these include physical and logical acquisition of images from mobile phones with Android operating system, extracting images for investigations, examining different file types in images, and databases. This software can collect and examine the evidences and then produce reports. At the same time, it can identify criminals or people with potentially have connections to those people whose accounts are under investigations by using developed analysis model which examines the relationships between social media applications` data, phone contacts and calling histories collected from the mobile devices. In this work, the evidences examined by using a novel software developed by the authors which performs multiple tasks using a single interface and the corresponding results are presented.

Adli bilişim sürecinde çok çeşitli yazılımlar ve donanımlar kullanılmaktadır. Teknolojinin hızlı gelişimine paralel olarak dijital delillerin toplandığı cihazlar da hızla çeşitlenmektedir. Android mobil telefonlardan toplanan dijital deliller yardımı ile bu telefona sahip kişinin arkadaşlarının ve bu arkadaşları ile ilişkilerinin derecelendirilerek tespit edilmesi, literatürde çalışılmış ve üzerinde çalışılmaya devam eden konulardan biridir. Bu çalışmada geliştirilen yazılım, Android işletim sistemine sahip mobil cihazlardan fiziksel ve mantıksal imaj alma, imajın incelenmek üzere açılması, imaj içinde farklı dosya türlerinin incelenmesi, veri tabanı incelemeleri gibi dijital delil niteliği taşıyan birçok veriye erişilmesini sağlamaktadır. Delil elde etme, delilleri inceleme ve raporlama işlemlerini yapabilen bu yazılım aynı zamanda geliştirilen analiz modeliyle mobil cihazlardan elde edilen sosyal medya uygulama verileri, telefon rehberi ve görüşme kayıtları arasındaki ilişkileri inceleyerek suçlunun veya hesapları incelenen kişilerin, ilişkili olma ihtimali yüksek kişileri tespit edebilmektedir. Birçok işlemi tek bir ara yüzden yapabilmesi ve veri analiz yöntemi bakımından, özgün bir çalışma olarak gerçekleştirilen yazılımla incelenen deliller ve elde edilen bulgular bu çalışmada sunulmuştur.

In this paper, the fuzzy adaptive finite-time cooperative control with input saturation (FAFTCCIS) is designed to quickly accomplish the cooperation of nonlinear multiagent systems (MASs) without the risk of bumping among agents. At least one agent must communicate with the leader and the information of neighborhood agents is required to accomplish the assigned task. Each agent, including the leader and the followers, is first approximated by N fuzzy-based linear subsystems. To accomplish the null cooperation error in finite time, the proposed adaptive control possesses the switching surface with an integration of fraction order, a time-varying switching gain, and an on-line learning of the upper bound of the uncertainties in each fuzzy subsystem of agent j. The stability of all the cooperative uncertain systems is then verified by the Lyapunov stability theory. Finally, the application to the cooperative control of intelligent chef is presented to confirm the effectiveness, robustness and feasibility of the proposed FAFTCCIS.

Pre-crisis management planning is a matter of paramount importance which nowadays, urban managers have to deal with, especially in the filed of crisis management. In this research, finding locations for multi-purpose crisis management stations in regions 1, 4 and 10 of Tabriz municipality has been studied. At first step, the parameters affecting the selection of locations have been investigated. After gathering and preparing the layers, raster maps were obtained and subsequently, parameter weighing was done throughout the AHP process using Expertchoice software. At next step, data layers were combined together based on IO Boolean and Fuzzy models and the results were utilized to present some locations as desired onse, Due to the Geographic Information System in solving complex problems of urban and ease of analysis and spatial analysis , the ability of the system to prepare , collate and analyze the layers have been applied . The results obtained from this study, the southern part of the region which was set four as the best urban spaces such as green spaces, educational, sportive and etc, and better access conditions for the establishment of these bases is to have the highest priority and the best places for locating the base of the parks Amir Kabir, Anna, Tawhid and Jihad area around the field to 22 Bahman and a few other points is evaluated

Worn-Out urban textures have many problems in the physical, environmental, social and economic fields that have caused a lot of limitation for residents that live in these textures. In the presence of worn-out tissues, due to population growth and urbanization, natural disasters such as earthquakes can cause heavy losses and losses in such places. The need to reduce the vulnerability of cities and especially the worn-out structures against this phenomenon is considered as one of the main goals of urban planning and other related urban areas. Considering that the earthquake vulnerability is one of the most important challenges facing cities in Iran and is the subject of zoning of seismic risk and assessment of the vulnerability of cities to earthquakes in geographic and urban planning issues, this study attempts to measure the magnitude. The vulnerability of the corrosive tissues of Kashan city has been evaluated and solutions to this problem have been proposed. In this research, existing data from different organizations have been analyzed and evaluated using the fuzzy and fuzzy membership in the GIS environment. The results show that Kashan city with 754.3 hectare of worn out texture, located in different places of the city, the most used residential property with low quality, historical texture, ruined lands, long life of buildings and narrow and narrow streets exposed to greater vulnerability due to Earthquakes are more than other urban areas Among the worn-out tissues, the center of the city with the most and the Lather or lowest is the vulnerability.

Objective: The city of Kerman has seen many developments over the past decades. These developments include: physical development, urban population growth, inertial migration, marginalization and horizontal expansion of the city while maintaining the administrative, economic and cultural center of the South East, but building infrastructure, especially in the urban transport sector, in proportion to this developments have not been made. The present study, with the understanding of the importance of the subject and considering the position of Kerman city and the lack of coherent studies in this regard, it has been tried to investigate and analyze the dispersion of accidents in the city of Kerman by using GIS technology and fuzzy model and prioritize the accidental areas using the strategic planning to organize incidental urban areas. Methods: The type of research is operational and considering the nature of the subject, the research method is analytical-expository. The data assessment process is a combination of documentary and survey. Experts opinions and studies survey, show that in addition to cultural issues, the top ten factors affecting the rate of accidents, population density, density of accidents, density of service centers and passageways play the most role in the amount of accidents. Results:Based on the combination of effective factors and fuzzy method, three areas of Azadi Square, Khoyane Motahari and jihad are the most susceptible to intractable accidents, respectively. According to traffic flow projects in Azadi Square, Motahari Avenue (Motahari Park) has three ways Taleghani was designed to organize using strategic planning. Conclusion: The main purpose of this study was to emphasize the total number of accidents in order to describe accident locations and create a model based on the severity and variety of accidents. Conclusions show that central areas of the city, especially Azadi Square, Motahari Street, and Jihad Boulevard, were the most at risk of urban accidents.

Purpose. The development of a model for assessing the level of permissible risk of an emergency situation in the power system in the conditions of fuzzy input data, heterogeneity of the criteria for risk assessment, the absence of analytical links between them and the subjectivity of expert knowledge of decision makers. Methodology. To solve this problem, methods and models of fuzzy logic are used which give satisfactory analytical result in the conditions of uncertainty of the input information and the absence of analytical connections between the individual parameters and characteristics of the object. The proposed fuzzy models are constructed using the Fuzzy Output Algorithm of Mamdani. Findings. The result obtained by the fuzzy model gives the opportunity to assess reliably the acceptable level of risk of an emergency situation in the grid. Based on the magnitude of the risk, it is possible to make informed decisions regarding the expediency (or impracticability) of applying measures to reduce this risk. This enables the organization of preventive management of the risk of an emergency situation in the grid and the use of effective measures to reduce it. Originality. The article is devised a fuzzy model for estimating the permissible risk level of an emergency situation in the electric power system, which takes into account such criteria as the minimum possible level of risk, the influence of meteorological conditions and errors of operational and operational personnel of power systems. In this case, it is possible to take into account the main factors influencing the reliability of the grid functioning, to assess the risk level of an emergency situation.. Practical value. The fuzzy models developed in the article provide an opportunity to carry out an express assessment of the level of permissible risk of an emergency situation in the conditions of limited data and evaluation criteria, to conduct a comparative analysis of the obtained result with the magnitude of the actual risk of an accident in the on-line mode and to make a decision as to the appropriateness of its reduction.

Iran is a dry land with very little rainfall. The average rainfall in Iran is less than a third of the global average. Nowadays, with the increase in population and the subsequent increase in demand for food, the utilization of water resources, and in particular groundwater resources, is considerably increased. Currently the amount of groundwater resources exploitation exceeds its supply and so the lack of groundwater resources should be compensated. The purpose of this study was to locate zones suitable for the artificial recharge of the aquifers with a case study for Lorestan Province. For this purpose, AHP was employed as an efficient techniques and comprehensive system designed for multiple criteria decision-making. Required layers such as the slope, soil and geological structure, land use, rainfall, distribution of Quaternary alluvial and satellite imagery of the area was prepared. Then GIS software was used to overlay the layers and information and with use of Fuzzy logic, distinguishing suitable sites for artificial recharge of groundwater and the flood spreading. Based on the results, form the total area of ​​the province (28294 km²), about 9258.36 km² (32.5%) are suitable for the design of artificial recharge and 5926.36 km² (about 21%) can be considered for flood spreading. Finally, the integration of the two resulting maps led to suitable sites for artificial recharge and flood spreading as about 7682.21 km² (21.79%). Therefore, GIS models are a powerful tool to locate artificial recharge and flood plain sites, because of their ability for analysis of large geographical data and prioritization of scientific decisions.

With the advancement of the marketization process, inventory management has transformed from a single backup protection function to an essential function for enterprises, which helps to survive and develop. Inventory control in supply chain management is the important content of supply chain management. The new management mode makes inventory management present many new characteristics and problems compared with traditional inventory management. From the view of system theory and integration theory, it is imperative to re-examine the problem of inventory control, put forward new inventory management strategies adapted to integrated supply chain management, and improve the integration of the whole supply chain, which can enhance the agility and market response speed of enterprises. Based on the in-depth study of the joint inventory management model, this paper analyzed the current situation of the joint inventory management to optimize the inventory. In view of the achievements and shortcomings of the current research, a more systematic and improved optimization model of the supply chain inventory was proposed by using the basic ideas of ant colony algorithm and fuzzy model.

The departments of transportation worldwide are facing various challenges despite introducing and incorporating various vehicular features. One of such challenges is to make vehicles autonomous, intelligent, and capable of self-learning to evolve their knowledge repository. In this paper, human cognition is proposed to be implemented in vehicles so that they can perform human-like decisions. Therefore, the process of vehicular route decision is debated cognitively in order to provide route information intelligently. The in-vehicle routes provided by the GPS are not optimal and lack on-demand user requirements. GPS connectivity issues, in certain conditions, make it difficult for vehicles to take real-time decisions. This leads to the idea of self-decision by the vehicle controller. We propose a cognitive framework for vehicles to make self-decisions that use cognitive memory for storing route experiences. The framework strengthens the existing in-vehicle route finding capability and its provision in a more realistic manner. The user is provided with all available route-related information that is required for the journey. In addition, the route episodes are learned, stored, and accessed inside the cognitive memory for an optimal route provision. The vehicle learns about the routes and matures with route-experience by itself with the passage of time. In simulations, fuzzy modeling is used to validate the impact of cognitive parameters over static/conventional parameters. Moreover, artificial neural networks are used to minimize the error rate in learning to achieve cognitive route decisions. The proposed in-vehicle cognitive framework outperforms the existing route provision system that is inadequate and provokes the user's anxieties during driving. Besides, the proposed scheme gradually gets mature in delivering optimal as well as latest route-related information.

Background & Aim: One of the vital consequences of the expedite growth of urbanization and the physical development of the cities of the country has been the disruption of the distribution system of the service centers in recent decades, causing the social inequality of citizens in accessing such services. Urban public services structure the citychr('39')s physical, social and spatial nature; thus, irreparable effects are caused on the structure, the nature of the city and the class segregation of the urban neighborhoods due to injustice in its distribution, and faces urban management with serious challenges. The extension of the concepts of justice in the field of geography and urban literature, which began approximately 40 years ago, has recently led to new approaches in the field of epistemology. This study examines the spatial distribution of urban services in Mashhad metropolis with the aim of measuring spatial justice. Methodology: The present research is applied in terms of purpose and descriptive-analytical in terms of method. The data collection method is documentary and as a desk study and the technique used is a fuzzy method. Statistics and research indices were the distributions status of service use intervals at the level of 13 districts of Mashhad metropolis, obtained from a detailed plan. By measuring the research indices, the present research attempts to compare and classify the different districts of Mashhad in terms of the enjoyment rate of the access index, in order to provide a proper route for balancing the city development pillars. Findings: The analysis results indicated that approximately 10,381 hectares, equivalent to 30% of the city area suffers inadequate and relatively inadequate quality in accessing a variety of services, meanwhile districts 1, 11, and Samen were more appropriate than other districts. Also, districts 7, 6, and 2 lack the minimum zones with very good access to the services

The purpose of the study presented in the article was to develop a method for the formation of digital twins for distributed generation plants operating as part of cyber–physical power supply systems. A method of forming a digital twin for a system for automatic regulation of the voltage and rotor speed of a synchronous generator is considered. The structure of a digital twin is presented in the form of a multiply connected model using experimental data. The possibility of using a fuzzy inference system, artificial neural networks, and a genetic algorithm for solving the problem is shown. As a result of the research, neuro-fuzzy models of the elements of the distributed generation plant were obtained, which are an integral part of the digital twin. Based on the simulation results, the following conclusions were drawn: the proposed method for constructing an optimized fuzzy model gives acceptable results when compared with experimental data and shows practical applicability in constructing a digital twin. In the future, in order to simplify the model, it is necessary to solve the problem of optimizing the number of rules in the fuzzy inference system. It is also advisable to direct further research to the formation of a complete hierarchical fuzzy system that connects all elements of the digital twin.

The purpose of this paper is to present the results of a systematic literature review regarding the development of fuzzy-based models for time series forecasting in the period 2017–2021. The study was conducted using a well-established review protocol and a couple of powerful tools for bibliometric analysis to know and analyse the main approaches adopted in the research field of interest. We analysed 118 articles published in peer-reviewed journals indexed in the 2020 Journal Citation Reports of the Web of Science. This allowed us to present an in-depth performance analysis and a science mapping regarding the current situation of fuzzy time series forecasting and modelling. The outputs of this study provide a practical base for further investigations that address this topic from both a methodological point of view and in terms of applicability.

This article aims to formulate proposals for regulatory bodies whose implementation would ensure the effective introduction of civil circulation into electronic signatures, with minimal costs for economic entities. While electronic signatures have been widely discussed in academic literature, there are still gaps in the understanding of similarities and differences between electronic and handwritten signatures, the functional specifics of the relationship between them, and the role of electronic signatures for electronic contract. Our research has allowed us to overcome this gap adopting a functional symmetry approach based on measuring the distance between fuzzy sets and the Mamdani fuzzy inference algorithm. This made it possible to form an estimate of the degree of functional symmetry between different types of signatures in a fuzzy and exact form. Correspondingly, we argue that the signature can be viewed as a set of procedures rather than as a single act in order to achieve functional symmetry with a handwritten signature. The case of online lending was used to test and prove this hypothesis. Therefore, regulating electronic signatures needs to focus on the efficiency of this processes for ex ante identification, capturing the intent, ensuring the inalterability and providing reliable evidence, irrespective of the type of electronic signature that is used. It was also revealed that the proposed functional symmetry approach can be combined with a fuzziness index analysis to provide new prospects for further research.

Every year we see natural disasters causing major financial and human losses to human societies, and unfortunately, our country also has a bitter taste of it every now and then. Floods, earthquakes, droughts, frostbites, storms, and so on ... There are a lot of financial and financial losses to the country, which will require measures to reduce the damage caused by these disasters. Definitely, the first step is to understand and understand these phenomena. The development of the process of urbanization and degradation of vegetation, soil erosion, and global climate change has already paid attention to the importance of addressing the issue of the urban flood. This research was carried out to determine the risk of flooding in Sari using multi-criteria decision-making techniques. Using nine criteria, the distance from the river, runoff coefficient, CN coefficient, population density, residential density, slope, land use, the age of the building and outdoor space were developed. By preparing the required layers, determining the weight of each layer based on their importance in the occurrence of a flood. After the final weighing, the layers were compared in two to two by Expert Choice software and the matrices derived from these comparisons were transferred to Idrisi software and the final coefficient was determined for each layer. Finally, by applying these coefficients, ArcGIS software provided a flood risk zoning map in the city of Sari. The results show that flood risk in the center and south of the city has been the highest. Flood risk zoning map shows that 12.24% of the map area is in a very high danger zone and 37.05% in very low-risk zoning. To reduce the risk of retrofitting buildings around the river to reduce flood damage.

In agriculture with 4.0 technologies, developing a decision model with a 0.0 cost is attractive to small farmers. In water management, if this approach could be used to promote sustainability and optimization, it could become a pathway to reach the sustainable development goal in 2030. The core of this work is the development of a 4.0 mathematical model (based on fuzzy concepts) to verify the benefits of the production of lettuce irrigated with magnetically treated water at different replacement rates. This approach is achieved using computational 4.0 software and manual methods. The aim of mathematical modeling is to understand or explain a natural phenomenon associated with a given area of knowledge, and fuzzy-rule-based systems have been widely used in different types of in-depth research.

Biodiesel is considered to be a promising alternative option to diesel fuel. The main contribution of the current work is to improve compression ignition engine performance, fueled by several biodiesel blends. Three metrics were used to evaluate the output performance of the compression ignition engine, as follows: brake torque (BT), brake specific fuel consumption (BSFC), and brake thermal efficiency (BTE), by varying two input parameters (engine speed and fuel type). The engine speeds were in the 1200–2400 rpm range. Three biodiesel blends, containing 20 vol.% of vegetable oil and 80 vol.% of pure diesel fuel, were prepared and tested. In all the experiments, pure diesel fuel was employed as a reference for all biodiesel blends. The experimental results revealed the following findings: although all types of biodiesel blends have low calorific value and slightly high viscosity, as compared to pure diesel fuel, there was an improvement in both BT and brake power (BP) outputs. An increase in BSFC by 7.4%, 4.9%, and 2.5% was obtained for palm, sunflower, and corn biodiesel blends, respectively, as compared to that of pure diesel. The BTE of the palm oil biodiesel blend was the lowest among other biodiesel blends. The suggested work strategy includes two stages (modeling and parameter optimization). In the first stage, a robust fuzzy model is created, depending on the experimental results, to simulate the output performance of the compression ignition engine. The particle swarm optimization (PSO) algorithm is used in the second stage to determine the optimal operating parameters. To confirm the distinction of the proposed strategy, the obtained outcomes were compared to those attained by response surface methodology (RSM). The coefficient of determination (R2) and the root-mean-square-error (RMSE) were used as comparison metrics. The average R2 was increased by 27.7% and 29.3% for training and testing, respectively, based on the fuzzy model. Using the proposed strategy in this work (integration between fuzzy logic and PSO) may increase the overall performance of the compression ignition engine by 2.065% and 8.256%, as concluded from the experimental tests and RSM.