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The Russian–Ukrainian conflict is considered as a dynamic system, whose variables are factors affecting the losses of the Russian army and the threat of the use of nuclear weapons. A fuzzy cognitive map (FCM) is used for modeling, that is, a directed graph whose vertices are model variables, and the weights of arcs are the degrees of positive and negative influences of variables on each other. The following factors influencing the losses of the Russian army and the threat of a nuclear strike were selected: resistance of the Ukrainian army, support of Ukraine with weapons, economic sanctions against Russia, opposition to the Russian government and its self-preservation instinct. The degrees of the influence of factors on each other and on the possibility of using nuclear weapons are evaluated by experts using fuzzy terms, which correspond to numeric values. To adjust the FCM, a genetic algorithm is used to select the degrees of influence of factors that minimize the discrepancy between the simulation results and expert estimations. The obtained FCM is used for scenario modeling of the conflict according to the "what if" scheme and ranking of factors according to their degree of influence on the level of nuclear threat. [ABSTRACT FROM AUTHOR]

A driving authority interaction decision model based on fuzzy cognitive map (FCM) was proposed to address the dynamic transition from autonomous driving to driver takeover in human-machine co-driving scenarios. Firstly, dynamic risk factors influencing driving risks for both the vehicle and the driver were separately analyzed. Relevant influencing indicators such as vehicle spacing, acceleration, steering wheel entropy, driver eye movement information, and electroencephalogram (EEG) signals were selected to construct FCM-based models for dynamic vehicle risk and driver risk. Subsequently, these models were integrated with environmental factors, vehicle conditions, and driver proficiency to establish an FCM-based driver interaction decision-making model. To reduce reliance on expert knowledge, the model was optimized using a real coded genetic algorithm (RCGA) based on real number encoding, thereby enhancing the accuracy of the model's decision-making. The simulation results indicate that the model, after optimization using RCGA, achieves an adaptation fitness value of 0. 979. This suggests that the model is capable of effectively addressing dynamic and complex driving environments. [ABSTRACT FROM AUTHOR]

The paper is devoted to experimental studies of new methods of cognitive modeling by testing them on a variety of artificially created cognitive models. The features of the process of obtaining such models are studied, and the concepts of a formal cognitive map and a formal cognitive model are introduced. An approach to generating a formal cognitive map with given characteristics, based on the use of random graph models, is proposed and partially formalized. A modification of the configuration model has been carried out, and examples of generating formal fuzzy cognitive maps based on it are given. The architecture of a software solution aimed at supporting experimental research in cognitive modeling is described. Directions for further research, both theoretical and applied in nature, are formulated. [ABSTRACT FROM AUTHOR]

Projects involve inter-organizational relationship cognition, which is central to collaborative engineering project value added. Interest in value added in the project lifecycle is mounting and gaining increasing attention in the research literature. However, little is known about how inter-organizational relationship cognition facilitates value added and how such cognition pushes a project toward higher end-states of value. The existing literature mainly analyzes and studies value added on functional analysis and cost control. There are predominantly static analyses of the factors that influence value added in studies. The guiding role of value added has not been adequately explored in the studies on the influencing factors of value added. Utilizing a combination of Structural Equation Modeling (SEM) and Fuzzy Cognitive Maps (FCMs), this study addresses how inter-organizational relationship cognition influences engineering project value added, identifying complex structures of interaction and cognition dynamics. Results indicate that: (1) A hybrid SEM–FCM method can be able to model dynamic interactions between inter-organizational relationship cognition and value added; (2) trust and shared vision have positive effects on in-role behavior and extra-role behavior. Shared vision has a negative effect on opportunistic behavior. In-role behavior and extra-role behavior have a positive impact on value added, while opportunistic behavior has a negative impact. Organizational behavior is an important mediating variable to explain the interaction between inter-organizational relationship cognitions and value added. This hybrid method explores the potential mechanisms of inter-organizational relationship cognition on project value added from novel perspectives on construction project management practices, proposing practical advice for further project management. [ABSTRACT FROM AUTHOR]

Febrile diseases are highly prevalent in tropical regions due to elevated humidity and high temperatures. These regions, mainly comprising low- and middle-income countries, often face challenges related to inadequate medical infrastructure and a lack of skilled personnel for accurately diagnosing febrile diseases. Distinguishing one febrile illness from another posed a significant challenge, adding to the complexity of accurate diagnoses. This study developed a multi-symptom multi-disease model to address this challenge, leveraging exploratory data analysis of patient datasets from field studies and the expertise of medical practitioners specializing in tropical diseases. The research investigated the most effective modeling approach for differentiating among 11 febrile illnesses that are prevalent in Nigeria using three intelligent techniques: Extreme Gradient Boost (XGBoost), Fuzzy Cognitive Map (FCM), and Analytic Hierarchy Process (AHP). Comparative analysis demonstrates that AHP surpassed the others, achieving a precision of 84%, recall of 83%, and an F1-score of 84%. Consequently, the AHP technique was integrated into the development of “Febra Diagnostica,” an app aimed at enhancing febrile disease diagnosis in resource-constrained settings. The app was then deployed and utilized in select Nigerian states, offering scalability and empowering frontline health workers in primary health facilities. Febra Diagnostica featured user-friendly interfaces, automated diagnosis and treatment suggestions, streamlined referrals, and provisions for further investigations. Encryption, access control, and multi-factor authentication were some of the security and privacy considerations in the app which gained acceptance from medical experts and adapted to regulatory and ethical policies for smart healthcare systems.

Purposes Aiming at the problem that Granger Causality (GC) analysis in EEG emotion recognition does not consider the interaction between nodes when constructing brain network, a method of constructing brain network by combining fuzzy cognitive map (FCM) and GC analysis is proposed. Methods First, on the basis of the correspondence between the structure of FCM and GC brain network, the GC brain network is modeled and improved by using the causal attributes among FCM nodes, and the FCM-GC brain network is constructed, by taking into account the cooperative interaction among nodes. Furthermore, in order to deeply integrate FCM with GC brain network, the spatial position information of EEG electrode channel is added to FCM training, and a new IFCM-GC brain network is constructed. On the basis of DEAP emotional EEG database, the features of IFCM-GC brain network are extracted, and the support vec tor machine is used as the recognition model. The average recognition rates in valence dimension and incentive dimension are 97.10% and 97.00%, respectively, which are more than 8% higher than the existing research on GC improvement. Findings The GC brain network constructed by this method takes into account the cooperative interaction among multiple nodes, and effectively improves the performance of emotion recognition system.

Introduction and objectives: Industry 4.0 has brought significant changes to industries and businesses. This transformation, involving the interconnection of devices, processes, and systems through smart networks and the use of data-driven technologies and artificial intelligence, has created a comprehensive approach in businesses. This development is crucial and has substantial impacts on various areas, including production, services, supply chain, and marketing. Given the increasing stability and future competitiveness of the production sector, the new technologies of the fourth industrial revolution (Industry 4.0) have gained considerable attention from the academic and industrial communities in recent years. However, manufacturers face numerous factors in implementing Industry 4.0, which need to be identified and analyzed. The purpose of this research is to identify the organizational factors influencing the adoption of Industry 4.0 and to present a fuzzy cognitive model.Methods: This research is considered applied-developmental in terms of its purpose and hybrid (exploratory mixed) in terms of the research type. The statistical population of this research comprised small and medium-sized manufacturing companies, with 12 experts from small and medium-sized manufacturing companies active in the industrial town of Yazd province selected purposefully. Initially, to identify the organizational determinants of Industry 4.0, the meta-combination method was used, and then the relationships of these factors were determined using the fuzzy cognitive mapping method and the Fcmappear and Mental Modeler software.Findings: The findings of the meta-combination method indicate 36 primary codes and 11 determining factors affecting the acceptance of Industry 4.0 technologies in small and medium-sized manufacturing companies operating in Yazd Industrial Town. These factors include absorption capacity, commercial assets, technical competence in digitization, digitalization knowledge and expertise, participation in the implementation process, strategic management competence for Industry 4.0, organizational culture, organizational structure, availability of resources, social capital, and top management characteristics. Among these factors, participation in the implementation process, absorption capacity, and social capital have the highest influencing capacity, while top management characteristics, digitalization knowledge and expertise, technical competence in digitization, and strategic management competence have the highest impact. Ultimately, the factors of technical competence in digitization, digitalization knowledge and expertise, top management characteristics, and participation in the implementation process obtained the most central indicators.Conclusion: Industry 4.0 is a vital strategic option for small and medium-sized manufacturing companies, enabling them to keep pace with the digitization race. Small and medium-sized manufacturing companies are significantly behind large organizations in leveraging Industry 4.0 technologies. Additionally, these companies are still grappling with early adoption decisions regarding digital transformation under Industry 4.0, which is a concerning issue. The results identified various organizational determinants that may explain these conditions and provide efficient solutions for small and medium manufacturing companies. This study created an organizational digitalization roadmap that describes the necessary conditions to facilitate the digitalization of small and medium-sized manufacturing companies under Industry 4.0.

The authors propose an approach to setting and solving the problem of optimal selection of product quality indicators, taking into account the interests of both the producer and consumer. The problem is formulated in terms of mathematical programming. The optimization criterion is the maximum proximity between the product attractiveness and the desire to purchase it; the controlled variables are the levels of producer- and consumer-specific indicators; the constraints are agreements regarding the necessary levels of indicators common to the producer and the consumer. Fuzzy cognitive maps are used to construct the dependencies that appear in the objective function, and optimal solutions are found using a genetic algorithm. The approach is illustrated by the example of a robot vacuum cleaner, which is one of the best-selling household applications of artificial intelligence. [ABSTRACT FROM AUTHOR]

Introduction and objectives: Industry 4.0 has brought significant changes to industries and businesses. This transformation, involving the interconnection of devices, processes, and systems through smart networks and the use of data-driven technologies and artificial intelligence, has created a comprehensive approach in businesses. This development is crucial and has substantial impacts on various areas, including production, services, supply chain, and marketing. Given the increasing stability and future competitiveness of the production sector, the new technologies of the fourth industrial revolution (Industry 4.0) have gained considerable attention from the academic and industrial communities in recent years. However, manufacturers face numerous factors in implementing Industry 4.0, which need to be identified and analyzed. The purpose of this research is to identify the organizational factors influencing the adoption of Industry 4.0 and to present a fuzzy cognitive model. Methods: This research is considered applied-developmental in terms of its purpose and hybrid (exploratory mixed) in terms of the research type. The statistical population of this research comprised small and medium-sized manufacturing companies, with 12 experts from small and medium-sized manufacturing companies active in the industrial town of Yazd province selected purposefully. Initially, to identify the organizational determinants of Industry 4.0, the metacombination method was used, and then the relationships of these factors were determined using the fuzzy cognitive mapping method and the Fcmappear and Mental Modeler software. Findings: The findings of the meta-combination method indicate 36 primary codes and 11 determining factors affecting the acceptance of Industry 4.0 technologies in small and mediumsized manufacturing companies operating in Yazd Industrial Town. These factors include absorption capacity, commercial assets, technical competence in digitization, digitalization knowledge and expertise, participation in the implementation process, strategic management competence for Industry 4.0, organizational culture, organizational structure, availability of resources, social capital, and top management characteristics. Among these factors, participation in the implementation process, absorption capacity, and social capital have the highest influencing capacity, while top management characteristics, digitalization knowledge and expertise, technical competence in digitization, and strategic management competence have the highest impact. Ultimately, the factors of technical competence in digitization, digitalization knowledge and expertise, top management characteristics, and participation in the implementation process obtained the most central indicators. Conclusion: Industry 4.0 is a vital strategic option for small and medium-sized manufacturing companies, enabling them to keep pace with the digitization race. Small and medium-sized manufacturing companies are significantly behind large organizations in leveraging Industry 4.0 technologies. Additionally, these companies are still grappling with early adoption decisions regarding digital transformation under Industry 4.0, which is a concerning issue. The results identified various organizational determinants that may explain these conditions and provide efficient solutions for small and medium manufacturing companies. This study created an organizational digitalization roadmap that describes the necessary conditions to facilitate the digitalization of small and medium-sized manufacturing companies under Industry 4.0. [ABSTRACT FROM AUTHOR]

An approach to the formulation and solution of the problem of optimal selection of quality indicators for products, taking into account the interests of both the producer and the consumer, is proposed. The problem is formulated in terms of mathematical programming. The optimization criterion is the maximum closeness between the attractiveness level of the product and the desire to purchase it; the controlled variables are the levels of indicators specific to the manufacturer and the consumer; the constraints are agreements regarding the necessary levels of indicators shared by both the manufacturer and the consumer. Fuzzy cognitive maps are used to construct the dependencies that appear in the objective function, and optimal solutions are found using a genetic algorithm. The approach is illustrated by the example of a robotic vacuum cleaner, which is one of the best-selling household applications of artificial intelligence. [ABSTRACT FROM AUTHOR]

The classification of musical emotion is a challenging problem due to the subjectivity of human perception. In order to analyse the emotion contained in music from the perspective of product supply chain management, the psychological model and characteristics of music emotion are introduced. Additionally, the current product supply chain and the composition of the two contracts are analysed based on the characteristics of online music products. Finally, a speech emotion recognition model based on a Fuzzy Cognitive Map (FCM) is proposed. Two corpora containing five emotions (sadness, anger, happiness, surprise, and boredom) are selected to test the performance and characteristics of the network. In order to evaluate the system performance, different acoustic features are extracted, including prosodic features, formant features, marginal spectral features, and Mel‐Frequency Cepstral Coefficients (MFCC). The results show that the average recognition rate of MFCC features on the Berlin emotional speech database is 70.36%, which is 0.75% higher than the second‐highest edge spectrum features and achieves better results. Meanwhile, on this database, the Musical Emotion Recognition System based on FCM (MERS‐FCM) is improved by 20.36%, 7.34%, and 4.12% compared with Back Propagation, K‐nearest neighbour, and support vector machine, respectively, which shows that MERS‐FCM has better performance. The research of the music emotion recognition system based on FCM can provide more reference paths for the use and development of music emotion information. [ABSTRACT FROM AUTHOR]

The components of the logistics and supply chain are analysed to meet the needs of supply chain management and decision support, aiming at the complexity and uncertainty of the supply chain system. First, the characteristics of logistics demand and logistics service types of some enterprises are analysed. The factors affecting supplier selection are obtained through the questionnaire survey of some enterprises. Second, the mutual feedback relationship between the influencing factors of logistics service demand is analysed, and a fuzzy cognitive graph model of the selection process of enterprise logistics service providers is established. Finally, the fuzzy cognitive map (FCM) model is used to conduct an empirical study on the choice of logistics service providers. The results show that the ranking results of the three candidate logistics service companies based on FCM are WL1 (0.7233) > WL3 (0.6823) > WL2 (0.6764). When each node of the concept does not affect each other, the ranking result of the three candidate logistics service companies based on analytic hierarchy process (AHP) is WL1 (0.7265) > WL2 (0.6949) > WL3 (0.6920). The final evaluation result will be affected by the mutual influence relationship between the concept nodes. Overall, the lowest evaluation score is WL2, and the highest is WL1. The most suitable logistics service provider for the enterprise is WL1. The FCM model selected by the manufacturing enterprise logistics service provider is reasonable and feasible. The mutual feedback between evaluation factors affects the selection of logistics service providers in a company. The FCM can get a more accurate evaluation weight value and can realize the optimal choice of logistics suppliers. It provides reference and decision support for other enterprises. [ABSTRACT FROM AUTHOR]

This study introduces a pioneering framework for modeling students' cognitive processes in mathematics education through Fuzzy Cognitive Maps (FCMs). By integrating key educational theories—Duval's Semiotic Representation Theory, Niss's Mathematical Competencies, Marton's Variation Theory, and the broad Engagement, Motivation, and Participation framework— the model offers a comprehensive and holistic understanding of students' cognitive landscapes. This research underscores the necessity of a multidimensional approach to capturing the intricate interplay of cognitive, affective, and behavioral factors in students' mathematical learning experiences. The novelty lies in its methodological innovation, employing FCMs to transcend traditional qualitative analyzes and facilitate quantitative insights into students' cognitive processes. This approach is particularly relevant in the current era dominated by digital learning environments and artificial intelligence, where real-time, automated analysis of student interactions is increasingly vital. The proposed FCM has been developed over the years with a data-driven approach; the concepts and relationships in it have been derived from the literature and refined by the author's experience in the field. Illustrated through case studies, the framework's utility is demonstrated in diverse contexts, highlighting how the quantitative data obtained are confirmed by qualitative approach: analyzing the impact of remote learning during the Covid-19 pandemic on student engagement and exploring Augmented Reality's role in enhancing mathematical conceptualization. These applications show the framework's adaptability and its potential to integrate new technologies in educational practices. However, the transition from qualitative to quantitative methodologies poses a challenge, given the prevalent use of qualitative approaches in mathematics education research. Additionally, the technological implementation of the FCM model in educational software presents practical hurdles, necessitating further development to ensure ease of integration and use in real-time educational settings. Future work will focus on bridging these methodological gaps and overcoming technological challenges to broaden the FCM model's applicability and enhance its contribution to advancing mathematics education.

The supply chain of the food industry is crucial for countries, yet it is vulnerable to disruptions caused by natural disasters like floods, frost, and heatwaves, as well as operational shutdowns. These disruptions can trigger a ripple effect throughout the food supply chain, posing significant challenges for the country. Therefore, it is imperative to identify and analyze strategies to mitigate the ripple effect. This research has been conducted in two stages: qualitative and quantitative. The qualitative stage aimed to identify coping strategies, employing thematic analysis. The quantitative stage involved scenario modeling and analysis using fuzzy cognitive maps. The findings revealed 84 primary codes grouped into 21 sub-categories and 4 main categories: "Strategic Management," "Operations Management," "Compilation and Correct Implementation of Laws," and "Supply Chain Management." Analysis of backward scenarios underscored the importance of "supplier relationship management," "cooperation and coordination in the supply chain," and "contingency plans." Conversely, analysis of forward scenarios highlighted the significance of "monitoring environmental changes" and "strategic planning." Focusing on short-term plans, enhancing managers' decision-making and problem-solving skills, refining supplier selection criteria, optimizing supply network design with backup locations, and maintaining safety stock for critical goods are recommended actions for industry stakeholders.IntroductionThe growth of supply chains and their increasing interdependence raise concerns about vulnerability and the likelihood of supply chain failure (Kek et al., 2022). One significant contributor to supply chain failure is the propagation of disruption, commonly known as the ripple effect (Ghadge et al., 2022). The ripple effect exerts various negative impacts on the agricultural supply chain (Wei & Chen, 2010), with factors such as climate change exacerbating these effects on the agricultural sector and food supply chain (Galli et al., 2023). A prominent example of the ripple effect is the COVID-19 pandemic, which led to crises in the food supply chain, including human resource shortages, transportation disruptions, and input cost escalations (Waris et al., 2022). In Iran, the pandemic significantly disrupted the food supply chain, resulting in decreased profitability, sales rates, flexibility, and investment returns (Afzali and Zare Mehrjardi, 2020). Thus, investigating this issue in Iran's food supply is imperative. The objectives of the research are:Identifying strategies to cope with the ripple effect in Iran's food product supply chain.Presenting a fuzzy cognitive map of strategies to cope with the ripple effect in Iran's food product supply chain.Conducting scenario analysis of strategies to cope with the ripple effect in Iran's food product supply chain.Materials and MethodsThis research adopts a mixed-method approach, comprising qualitative and quantitative stages. In the qualitative stage, participants include experts and managers with a minimum of 10 years of experience in the food processing supply chain, possessing academic qualifications, and experience with supply chain disruptions. The statistical population for the quantitative stage encompasses the participants from the qualitative stage, supplemented by university professors with publications in the field of supply chain ripple effects. Thematic analysis is employed in the qualitative part to analyze the data. Subsequently, based on the qualitative findings, a researcher-designed questionnaire is developed for the quantitative phase. The fuzzy cognitive map method is then utilized to analyze the quantitative data gathered.ResultsSemi-structured interviews were conducted with experts to identify strategies for coping with the ripple effect in Iran's food supply chain. From these interviews, 84 primary codes were identified, which were then organized into 21 sub-categories and 4 main categories: "strategic management," "operations management," "drafting and correct implementation of laws," and "supply chain management." Notably, nearly half of the obtained codes were attributed to the "supply chain management" category, indicating its significant importance in addressing the ripple effect. In the second stage of the research, a questionnaire was designed based on the findings of the previous stage and administered to 10 experts for completion. In this questionnaire, experts were asked to assess the importance of each of the 21 sub-categories. Subsequently, FCMapper software was employed to construct a fuzzy cognitive map depicting coping strategies.Table 1: Analysis of strategies to cope with the ripple effectTypeCentralityOutdegreeIndegreeStrategyTotal Componentsordinary17٫295٫7311٫56121ordinary12٫32٫459٫852Total Connectionsdriver10٫1110٫1103191ordinary11٫128٫972٫154Densityreceiver9٫6409٫6450.45ordinary8٫282٫985٫36Connections per Componentordinary16٫914٫8712٫0479.09ordinary10٫278٫911٫368Number of Driver Componentsordinary17٫646٫9110٫7393ordinary10٫586٫434٫1510Number of Receiver Componentsordinary5٫192٫552٫64111driver5٫815٫81012Number of Ordinary Componentsdriver8٫98٫901317ordinary16٫336٫331014Complexity Scoreordinary16٫397٫379٫02150.33ordinary8٫897٫641٫2516ordinary15٫816٫369٫4517ordinary14٫184٫849٫3418ordinary11٫644٫197٫4519ordinary4٫723٫261٫4620ordinary11٫487٫134٫3521As shown in Table 1, 'Environmental change monitoring,' 'Strategic planning,' and 'Technology upgrade' strategies have the highest degree of effectiveness, while 'Inventory management,' 'Contingency programs,' and 'Production flexibility' strategies also exhibit high effectiveness. Furthermore, 'Production flexibility,' 'Contingency plans,' and 'Inventory management' demonstrate the highest degree of centrality. Figure 1 depicts the fuzzy cognitive mapping of strategies to cope with the ripple effect in the supply chain of Iran's food products.Figure 1: Fuzzy cognitive mapping of strategies to cope with the ripple effect To examine the scenarios, three backward and three forward scenarios were designed. In the backward scenario, the most effective variables were selected. Figure 2: The first backward scenario of coping strategiesCooperation and CoordinationSupplier Relationship ManagementContingency PlanningInventory ManagementFigure 3: Second backward scenario of coping strategiesSupplier Relationship ManagementCooperation and CoordinationContingency PlanningFigure 4: The third scenario backward coping strategiesCooperation and CoordinationSupplier Relationship ManagementContingency PlanningProduction FlexibilityFigure 5: Overlap of the backward scenarios of coping strategiesCooperation and Coordination Supplier Relationship Management Production Flexibility Contingency Planning Inventory Management To draw forward scenarios, strategies No. 3, 4, and 8, which represent 'monitoring environmental changes,' 'strategic program,' and 'technology improvement,' respectively, were selected.Figure 6: First forward scenario of coping strategiesMulti-Skilled WorkforceShort Term PlanningHRMTechnology UpgradeMonitoring Environmental Changes Figure 7: Second forward scenario of coping strategiesHRMMulti-skilled WorkforceShort Term Planning Horizontal IntegrationStrategic Planning Figure 8: The third forward scenario of coping strategiesMulti-Skilled Workforce Short Term PlanningHRMTechnology Upgrade Figure 9: Overlap of the forward scenario of coping strategiesMulti-skilled Workforce Short Term Planning HRMTechnology Upgrade Monitoring Environmental changes Horizontal IntegrationStrategic PlanningConclusionsFood product supply chain managers should consider long-term factors, price flexibility, and contract support clauses in contracts with suppliers. For foreign products, it is recommended to contract with companies that have active agencies in the country, as other companies may quickly cease their services due to new sanctions. The purchase of critical parts of the supply chain, known as vertical integration, is recommended to reduce risk. Contingency plans are necessary to cope with the ripple effect, but to develop suitable contingency plans, environmental and political issues must be carefully monitored. As a result, it is necessary to create management teams in food products to investigate environmental issues.

Purpose: The purpose of this research was to identify, modeling and scenario analysis of the acceptance factors of the Internet of Things in the supply chain of Iranian businesses. Methodology: To conduct this research, we used both qualitative and quantitative strategies combined. The statistical population in both stages consisted of experts; besides, judgmental and snowball sampling methods were used. In the first stage, acceptance factors were identified through semi-structured interviews. In the second stage, using fuzzy cognitive mapping approach, the acceptance factors were modeled, and the scenario was analyzed. Findings: The results of the first phase show 58 factors of Internet of Things acceptance, which are categorized into 17 sub-themes and 5 main themes. The second-stage results show that the theme of expertise enjoys the highest degree of influence, then the theme of technology readiness comes next. Also, the overlapping of forward and backward scenarios indicates the importance of expertise and technological readiness. Originality: Since there is little understanding of the factors affecting the acceptance of the Internet of Things at the supply chain level of Iranian businesses, this research has been able to add to the literature of this field and to fill the gap by identifying, modeling, and analyzing the scenario of these factors. Implications: Changing recruitment and hiring policies to find and hire qualified personnel, using in-service training tools, and having access to a technological roadmap are the most important strategies of the supply chain, which can lead to organizational expertise and readiness.

Abstract This study explores the integration of artificial intelligence (AI) teaching assistants in sports tennis instruction to enhance the intelligent teaching system. Firstly, the applicability of AI technology to tennis teaching in schools is investigated. The intelligent teaching system comprises an expert system, an image acquisition system, and an intelligent language system. Secondly, employing compressed sensing theory, a framework for learning the large-scale fuzzy cognitive map (FCM) from time series data, termed compressed sensing-FCM (CS-FCM), is devised to address challenges associated with automatic learning methods in the designed AI teaching assistant system. Finally, a high-order FCM-based time series prediction framework is proposed. According to experimental simulations, CS-FCM demonstrates robust convergence and stability, achieving a stable point with a reconstruction error below 0.001 after 15 iterations for FCM with various data lengths and a density of 20%. The proposed intelligent system based on high-order complex networks significantly improves upon the limitations of the current FCM model. The advantages of its teaching assistant system can be effectively leveraged for tennis instruction in sports.

Proper and systematic management of food industry failures can improve the quality of products and save a lot on the costs of organizations and people's health. One of the conventional methods for risk assessment is the Failure Modes and Effects Analysis (FMEA) which is often performed in a phase or stage. Compared to the combined methods, this method is less accurate due to similar priorities of failure in the evaluation and the lack of consideration of the interaction between risks. The current research has applied an integrated approach based on two techniques, FMEA and Fuzzy Cognitive Map (FCM), in a multi-stage manner to increase assessment accuracy and ranking of failures. By considering the risks of an industry in an uncertain environment and the causal relationships between failures, this approach can evaluate the industry's risks better than conventional methods. In the research method, the initial prioritization of failures by the FMEA method is used as the input of the multi-stage FCM. The cause-and-effect relationship between the failures is determined by experts and the functional records of the processes, and the FCM is prepared. Since no research evaluates the risks of the malting industry step by step and considers the causal relationships between the risks, the present study has improved risk evaluation in the malting industry by using a multi-stage FCM. The ranking results with the proposed hybrid approach and its comparison with the conventional methods showed that the rating became more accurate, and the multiple priorities were improved. Managers of the malt beverage industry can make effective investment decisions to reduce or better control the risks of this industry by using the results of applying the proposed approach. [ABSTRACT FROM AUTHOR]

Smart technologies have brought changes in the supply chain. This study was conducted with the aim of investigating the impact of the Internet of Things on the intelligent management of the supply chain, which evaluates the relationships between variables and their impact and effectiveness with the fuzzy cognitive mapping method. The statistical population is academic experts and active experts in the drug distribution company in Bushehr province. After identifying the components from the background of the research, an interview was conducted. Then the questionnaire was presented to 10 experts and experts and it was analyzed in several stages, and finally, the main factors of the use of Internet of Things in the supply chain were determined in 9 categories of criteria and 41 sub-criteria. The criteria include: intelligent management of inventory and warehousing, intelligent management of operations, intelligent management of information, intelligent management of products, intelligent management of costs, intelligent management of corporate productivity, intelligent management of customers and drug suppliers, intelligent management of sales and marketing, and intelligent management of the environment. The results showed that intelligent information management was obtained as the most important indicator; Because it affects all indicators. intelligent management of customers, intelligent management of sales and marketing, and intelligent management of operations are the second most influential. Therefore, managers of the drug distribution industry should use Internet of Things technology to intelligently manage information in their organization, improve relationships with customers, improve operations and focus on the sales process, and optimize supply chain processes and profitability. [ABSTRACT FROM AUTHOR]

With the increasing global disaster risks, constructing more inclusive, flexible, and resilient communities has become crucial for effectively carrying out disaster prevention, mitigation, and relief work. However, existing research on community resilience mostly focuses on the selection of key factors and the assessment of community resilience, lacking in-depth exploration of the interactions between factors and simulation studies of key paths. Therefore, this paper applies the Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) method to select important factors of community resilience. Based on this, the maximum average difference entropy method is used to analyze the relationships and influence mechanisms among different factors, thus identifying the key factors and key paths affecting community resilience. The Fuzzy Cognitive Map (FCM) is then used to simulate the paths. The study finds that factors of community resilience can be categorized as input, intermediary, and output types, and further analysis of their influence mechanisms reveals four key paths and four key factors. Through pathway simulation, different improvement states of community resilience are observed when triggering the input-type factors of the key paths. Therefore, under limited resources, a phased and systematic approach to enhancing community resilience should be adopted. The contribution of this study lies in providing a comprehensive analysis of factors and pathway selection methods, and through pathway simulation, it offers a scientific basis and decision support for improving and constructing community resilience in practice. [ABSTRACT FROM AUTHOR]

This study aims to measure the quality of distance university higher education in the Peruvian department of Junín, as well as to seek strategies of improvement. To meet this objective, a survey was carried out on students from the 4 universities located in this department, all with an institutional license. An intentional non-probabilistic sampling was used. The quality variable in university higher education is described in nine dimensions derived from the educational policies imposed by the National Superintendency of Higher University Education (SUNEDU in Spanish), which are: academic objectives, educational offer, infrastructure and equipment, research lines, qualified teacher, complementary services, job bank, transparency, and satisfaction. Among the results, it stands out that 59% of the students on average show satisfaction with the education received, which allows us to conclude that quality should be directed by a unit with its own name and established transversally in all administrative, academic, and academic procedures, investigative, social projection, and university extension. Transparency avoids any act of corruption, therefore it is important to associate it with the ethics of the authorities and workers of the university entity, strengthening timely communication with the student body. On the other hand, Kosko's Hidden Patterns Method was used to process a fuzzy cognitive map designed by education quality experts' opinions and in this way conclusions were reached about the strategies to follow for improving the quality of Peruvian non-face-to-face education in the department. [ABSTRACT FROM AUTHOR]

This study explores the integration of artificial intelligence (AI) teaching assistants in sports tennis instruction to enhance the intelligent teaching system. Firstly, the applicability of AI technology to tennis teaching in schools is investigated. The intelligent teaching system comprises an expert system, an image acquisition system, and an intelligent language system. Secondly, employing compressed sensing theory, a framework for learning the large-scale fuzzy cognitive map (FCM) from time series data, termed compressed sensing-FCM (CS-FCM), is devised to address challenges associated with automatic learning methods in the designed AI teaching assistant system. Finally, a high-order FCM-based time series prediction framework is proposed. According to experimental simulations, CS-FCM demonstrates robust convergence and stability, achieving a stable point with a reconstruction error below 0.001 after 15 iterations for FCM with various data lengths and a density of 20%. The proposed intelligent system based on high-order complex networks significantly improves upon the limitations of the current FCM model. The advantages of its teaching assistant system can be effectively leveraged for tennis instruction in sports. [ABSTRACT FROM AUTHOR]

Fuzzy cognitive maps (FCMs) learning is a hot topic in recent years. However, as the number of concepts increases in FCMs, it is difficult to learn the sparse and robust FCMs from a small amount of data, especially from noise data. In this paper, a new large-scale FCMs learning method based on the sparse regression of adaptive loss function is presented, marked as AQP-FCM. Adaptive loss function and L 1 -norm are introduced in the model to deal with noise data. We solve the model by ADMM method and quadratic programming method to learn the FCMs better. Moreover, the convergence of model is proved. We did a series of experiments under the synthetic data of time series and noise synthesis data. AQP-FCM is also applied to reconstruct gene regulatory network (GRNs). The results of the experiments show that the proposed AQP-FCM method has good performance. [ABSTRACT FROM AUTHOR]

Supply chain resilience is essential for companies to survive in today's competitive market, as they face environmental and unforeseeable challenges in their supply chain. This paper aims to model and manage the factors and activities that influence supply chain resilience and how they relate to each other. This will help us devise plans for enhancing the resilience of a supply chain. By taking into account the factors and activities and their interrelationships, organizations can use their limited resources more efficiently to improve their supply chain resilience. We use a management matrix to rank the factors based on how they affect and contribute to the supply chain resilience. We conduct an empirical study in a pharmaceutical company to demonstrate the proposed management approach and provide improvement scenarios based on the ranking of the factors. The results show that the most important factors are “the cooperation and trust between supply chain members”, “Visibility & Agility”, and “Leadership Support and Commitment”. The ranking of the factors may vary in different companies. Therefore, other companies can apply the method described in this paper and perform different improvement scenarios according to the ranking of the factors to effectively allocate their limited management efforts.

Classification applications in order to obtain the desired information from satellite images are one of the increasing areas of study. Remote sensing satellite images are very difficult to obtain, but nevertheless, they can be used in many different areas. In this context, satellite images have become an important data source for land use and land cover analysis. In this study, the public EuroSAT dataset, which is compatible with land cover and land use classification, has been used. There are a total of 27000 images in this dataset, with ten different class labels, such as industry, forest, and lake, and approximately 3000 images in each. In order to perform a more precise and efficient classification study, the images have first been subjected to data augmentation using various techniques and have been divided into a training, test, and validation set. In terms of classification, six different convolutional neural network (CNN) architectures and two different vision transformer architectures have been used. Three experiments have been carried out for the proposed system, and the selected models have been trained with three different loss functions, and the results have been evaluated. The first of the loss functions used is cross-entropy, which is one of the most well-known and basic functions; the second is label smoothing cross-entropy; and the third is a new custom loss function that has been improved and consists of a special combination of both loss functions. The eight proposed models have been trained for all three loss functions; performance evaluations have been made by analyzing metrics such as accuracy, precision, and recall; and comparisons have been realized. After the proposed models have been evaluated in terms of these metrics, a Fuzzy Cognitive Map (FCM) has been created to integrate the model outputs to obtain the final classification result. For this construction, the accuracy of each model has been used in the neighborhood matrix of FCM. The accuracy of the proposed FCM method has been calculated as 99.97%. When the results of the proposed different models have been examined, it has been clearly seen that the new loss function improved in this study has been more successful than the other two loss functions in all models. In addition, the simulation results of FCM-based integration indicate that our approach has very high accuracy.

As a soft computing method, applying fuzzy cognitive map (FCM) to time series prediction has become a timely issue pursued by numerous researchers. Although many FCM construction methods have emerged, most of them exhibit obvious limitations in weight learning especially for long-term or complex time series. Either the weight calculation is computationally expensive, or it cannot achieve gratifying accuracy. In this paper, a new method for constructing FCM is proposed which extracts concepts from data by exploiting triangular membership function, and the weights of high-order FCM are subtly obtained by transforming the learning problem of FCM into a convex optimization problem with constraints. Since then, FCM with optimized weights is used to represent fuzzy logical relationships of time series and implement prediction further. Fifteen benchmark time series,such as Soybean Price time series, Yahoo stock time series, Condition monitoring of hydraulic systems time series etc. are applied to verify prediction performance of the proposed method. Accordingly, experiment results show that the proposed numerical prediction method of time series is effective and can acquire better prediction accuracy with lower computation time than other recent advanced methods. In addition, the influence of parameters of the method is analyzed individually.

The business cluster development model is a new method that has been used in the last two decades to develop and empower small and medium enterprises. Many countries, both developed and developing, have taken advantage of this development model to achieve regional development goals and improve the quality and quantity of their advantageous businesses located in a geographical area. In this research, the factors influencing the performance of business cluster development in Iran have been investigated using theme analysis and fuzzy cognitive map techniques. The data was collected using open interview tools and questionnaires. The present research encompasses the period from 2018 to 2021 and focuses on Iran, specifically examining all provinces where business cluster development plans have been enacted. Based on the model obtained from the fuzzy cognitive map method, the most important factor that affects the performance of business cluster development initiatives is financing of the plans and projects. On the other hand, the most crucial factor for effectiveness is the augmentation of collaborative efforts. Finally, based on the identification of the most important affected and influencing factors, backward and forward scenarios related to the performance of business cluster development plans were examined.

Projects involve inter-organizational relationship cognition, which is central to collaborative engineering project value added. Interest in value added in the project lifecycle is mounting and gaining increasing attention in the research literature. However, little is known about how inter-organizational relationship cognition facilitates value added and how such cognition pushes a project toward higher end-states of value. The existing literature mainly analyzes and studies value added on functional analysis and cost control. There are predominantly static analyses of the factors that influence value added in studies. The guiding role of value added has not been adequately explored in the studies on the influencing factors of value added. Utilizing a combination of Structural Equation Modeling (SEM) and Fuzzy Cognitive Maps (FCMs), this study addresses how inter-organizational relationship cognition influences engineering project value added, identifying complex structures of interaction and cognition dynamics. Results indicate that: (1) A hybrid SEM–FCM method can be able to model dynamic interactions between inter-organizational relationship cognition and value added; (2) trust and shared vision have positive effects on in-role behavior and extra-role behavior. Shared vision has a negative effect on opportunistic behavior. In-role behavior and extra-role behavior have a positive impact on value added, while opportunistic behavior has a negative impact. Organizational behavior is an important mediating variable to explain the interaction between inter-organizational relationship cognitions and value added. This hybrid method explores the potential mechanisms of inter-organizational relationship cognition on project value added from novel perspectives on construction project management practices, proposing practical advice for further project management.

The amalgamation of fuzzy model and deep learning has become one hot topic in today's fuzzy community. However, with the model goes deeper, a pivotal aspect for performance enhancement, the interpretability of the model will deteriorate. To enhance the classification accuracy of classifiers while ensuring interpretability, we propose a stacked architecture-based fuzzy classifier named PT-SAFC. Borrowing the hierarchically stacked thought originated from deep learning, the PT-SAFC is composed by stacking two distinct fuzzy systems, implemented by fuzzy neuro-networks. Here, we propose an improved Takagi-Sugeno-Kang (TSK) model (PTFS) for data transfer by incorporating fuzzy cognitive maps (FCM). It imparts the TSK model with the data processing capability akin to deep learning models, thereby mitigating the interpretability loss arising from an increase in model depth. Furthermore, the multi-prototypes fuzzy system for decision making (MPDFS) is constructed to map data onto classes. An enhanced gradient descent method with restriction mechanism of prototype position is designed for parameter optimization. The experiments underscore PT-SAFC's achievement of a harmonious equilibrium between interpretability and classification accuracy. And, PT-SAFC maintains an advantage in classification performance even compared to deep learning methods. Furthermore, experiments validate PT-SAFC's capability to manipulate data distribution to augment classification efforts. [ABSTRACT FROM AUTHOR]

Introduction: Digitalization has become a crucial factor driving innovation in companies, research centers, and governmental organizations. This study aimed to identify the drivers of digital innovation, map the relationships between them, and analyze the effective routes of these drivers within governmental hospitals in Rafsanjan. Methods: This research is pragmatic in its purpose and employs a descriptive survey for data collection, conducted in 1402. The study's population comprised experts from governmental hospitals in Rafsanjan, with a sample of 12 experts selected through the snowball method. The data's validity was confirmed using the content validity ratio index. Data analysis was performed using the fuzzy cognitive mapping method, supported by Fcmapper, Pajek, and Excel software. Results: The study identified 12 key factors driving digital innovation in governmental hospitals. Fuzzy cognitive map analysis revealed that senior management support, with a coefficient of 1.57, exerted the most influence. Meanwhile, the organization's digital capacity, with a coefficient of 3.24, was found to have the greatest influence. Additionally, the organization's digital capacity, with a coefficient of 3.89, exhibited the highest centrality. This research contributes by identifying key digital innovation factors, establishing causal relationships among these factors, and determining the primary factors influencing others through various digital innovation scenarios within governmental hospitals. Conclusion: Based on scenario analysis findings encompassing 12 drivers and understanding the causal relationships among them, it is recommended that hospital authorities prioritize the "needs of citizens" driver to foster digital innovation. [ABSTRACT FROM AUTHOR]

COVID-19 has been declared a pandemic and countries are tackling this disease either through preventative measures such as lockdown and sanitization or through curative ones such as medication, isolation, and so on. Some people believe that vaccination is the best way to prevent this disease, while others disagree. Society's attitudes toward vaccination can be influenced by a variety of factors such as misunderstanding, ambiguity, lack of knowledge. The proposed study's goal is to better understand people's attitudes regarding vaccination by focusing on key topics related to COVID-19 anti-vaccine tweets. Tweets are obtained over a period based on the number of COVID-19 cases by utilizing the "anti-vaccine" keyword rather than the "vaccine" keyword. Furthermore, in addition to people perceptions and attitudes toward anti-vaccination, the causal relationship between each topic is investigated. As a result, latent dirichlet allocation (LDA), fuzzy association rule mining (FARM), fuzzy cognitive map (FCM), and fuzzy c-means are used to conduct a complete study. Topics are analyzed independently using clustering and scenario analysis. The findings demonstrate the most common topics in anti-vaccination tweets, as well as the influence of each topic on the others. [ABSTRACT FROM AUTHOR]

Agrarian growth is becoming increasingly important to many countries as the global demand for food rises, natural resources become scarcer, and environmental problems deepen. Herein, I propose a mechanism for designing agricultural growth management strategies that is based on fuzzy cognitive logic. The research presented is built on three main findings. First, it integrates established theories of economic growth, economic cyclicality, and sectoral market theories into a model of agricultural growth management. This enables the identification of main growth factors and the determination of the nature of their effects on agricultural dynamics. Second, I develop an algorithm for cognitive analysis of agricultural growth management and justify both this mathematical apparatus and the tools it uses. And third, I conduct a computational experiment that applies cognitive technologies to generate what I believe is the best agricultural economic growth strategy for Russia. [ABSTRACT FROM AUTHOR]

Copyright of Neutrosophic Computing & Machine Learning is the property of Multimedia Larga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cryptocurrencies, with their decentralized nature, are gaining rapid international adoption as a means of payment or a valuable digital asset, independent of the economic policies of governments and without the need for a supervisory institutions such as banks. However, limited research has been conducted on the adoption of cryptocurrencies, most of which employ a general technology acceptance/ adoption model with a positivist approach. The main problem with previous studies is that they have been limited to the structure of general adoption models and only examined a few constructs due to the increasing complexity of the model. On the other hand, due to cryptocurrencies' unique nature and rapid developments, it is necessary to create new comprehensive models that include different dimensions. This paper aims to identify influential factors in the adoption of cryptocurrency technology, understand their interrelationships, and ultimately develop a comprehensive model. With a constructivist approach, this study uses the most important research of the past decade in the field of cryptocurrency adoption and creates a cognitive model of their constructs through a systematic approach. The focal point of our approach is constructivism, accompanied by considering the impact of constructs on each other using fuzzy cognitive maps, which has not been previously done in cryptocurrency adoption. The results of the proposed model indicate that perceived usefulness, attitude, financial value, and perceived ease of use are the most significant constructs that influence the creation of positive intention toward the use and adoption of cryptocurrencies. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Psicoterapia is the property of Revista de Psiquiatria y Psicologia Humanista, S.L. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The pandemic situation created by COVID’19 is ridiculous. It has made even the blood relations hide themselves from the infected person. The whole world was stunned by this situation. This is because of the uncertainty in the way in which this disease is spread. As an advancement of this disease, a few other variants like delta, omicron etc. also got spread. It is essential to find a solution to this situation . The variants Omicron and Delta are taken into consideration here. Though both the vibrant colours look alike, the symptoms and prevention methods changes for each of these vibrants. This work aims to make a study of the parameters responsible for these variants. As a result of this study, the parameters involved in the spread of these diseases are identified, and the prevention parameters are concluded. The major benefit of this comparatively study is to identify the parameters that are inconclusive, applying the concepts of fuzzy cognitive maps and neutrosophic cognitive maps is applied to bring out the result.

Although most studies showed the positive impact of climate change adaptation on farmers’ livelihoods, not all strategies are effective. Based on a qualitative study of smallholder farmers in central Ethiopia, this study examined the roles of contextual factors in contributing to or constraining the effectiveness of adaptation strategies. The results show that (i) strategies heavily reliant on rainfall were less effective; (ii) farmers with fatalistic attitudes utilized survival-oriented and low-return strategies, which were less effective to protect them from the effects of climate change and variability (CCV); (iii) farmers who had access to livelihood assets effectively responded to CCV through diversification of adaptation strategies and involvement in high-return strategies; (iv) Inadequate extension services, inefficient supply of farm inputs, and limited inclusivity of poor farmers undermined the roles of formal institutions in supporting effective adaptation. The findings attest to the importance of thoroughly understanding the wider contexts in which adaptation strategies are implemented to ensure their effectiveness. Building the adaptive capacity of farmers through pro-poor measures opens opportunities for engagement in high-return adaptation strategies, thereby promoting the building of sustainable livelihoods by all farmers.

Area business continuity management (Area-BCM) is introduced to enhance sustainable economic growth by building public–private partnerships. It is implemented in pilot industrial zones in disaster-prone regions to tackle problems beyond a single organization's capacity. The framework emphasizes multiple stakeholders in the decision-making process, but participation and implementation remain major challenges for many practitioners in the search for potential pathways. Therefore, this study presents a model of causal relationships between concepts to achieve the implementation of Area-BCM. To capture expert perceptions and visualize relationships, a fuzzy cognitive map (FCM) is deployed. The use of fuzzy logic facilitates the integration of diverse viewpoints and the representation of ambiguous and complex scenarios. Initially, 28 appropriate concepts were identified by reviewing the literature on practical Area-BCM cases, which were then scrutinized by experts, including eight driving causes, eleven required actions, and nine outcome variables. Subsequently, FCMs were constructed through individual interviews. Since the FCMs had been aggregated, a scenario analysis was performed under five different conditions to evaluate potential strategies. The simulation results present promising concepts that could improve Area-BCM implementation. The findings emphasize that these strategies will have a positive influence when top management is committed, government support is achieved, and workshops exist. [ABSTRACT FROM AUTHOR]