Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Illuminating themes of ecovillages by leveraging participatory modeling: A convergent Gen-AI and Fuzzy Cognitive Maps approach

Ecovillages, as defined by the United Nations, are communities that focus primarily on aspects of sustainability. The research on ecovillages is vital, as it helps to uncover actionable artefacts of sustainable practices. However, much of the extant literature has examined individual ecovillages; thereby leading us to investigate broader themes of ecovillages holistically. This data-driven study aimed to uncover themes of ecovillages and attempted to furnish a comprehensive ecovillage model for quantitative assessment. We compiled ecovillage descriptions from the Global Ecovillage Network (GEN) to create the Comprehensive Ecovillage Description Dataset (CEDD). A generative AI model, GPT-4o, was then used to semantically extract key terms from CEDD, and the results were further processed using the topic modeling technique BERTopic to identify prominent themes. These themes were shared with 18 stakeholders to undertake the development of a Unified Ecovillage Map (UEM), using Participatory Modeling (PM) and Fuzzy Cognitive Maps (FCMs). To understand these themes, we conducted scenario analyses of UEM using Mental Modeler. This structured "what-if" simulation approach, allowed us to assess the potential impact of various sustainability initiatives. This data-driven scenario analyses revealed numerous insights-for instance, initiatives such as permaculture design and renewable energy adoption enhance ecological balance and energy optimization, while measures like incoming visitors may bolster financial liquidity but simultaneously challenge cultural preservation and communal harmony. This research seeks to offer a nuanced, stakeholder-centric approach by examining ecovillage themes which can effectively delineate managerial recommendations and policy implications for enhancing ecovillage management.

A multi-criteria model of supply chain sustainability assessment and improvement for sugarcane agroindustry

The sustainability of the sugarcane agro-industry supply chain plays a crucial role in providing economic benefits, minimizing social and environmental impacts, and optimizing resource utilization. This research aims to analyze the sustainability performance of the sugarcane agro-industry supply chain using multi-criteria assessment and formulate strategies for sustainability improvement. The study proposes a multi-criteria assessment model with twenty-eight indicators and four dimensions of sustainability: economic, social, environmental, and resources, which were developed based on previous research. The fuzzy inference system (FIS) and multi-dimensional scaling (MDS) methods were utilized to analyze the multi-criteria indicators of sustainability performance in each dimension and overall supply chain. The Adaptive Neuro Fuzzy Inference System (ANFIS) model was used to aggregate multi-dimension sustainability to achieve overall sustainability performance. A fuzzy cognitive map (FCM) framework was developed to formulate strategies for improving the sustainability performance of the supply chain. The research was verified at two sugar mill locations in Java Island, Indonesia. The FIS and MDS models successfully analyzed the sustainability performance of the two sugar agro-industries, showing an average value of "quite sustainable". The overall sustainability performance using the ANFIS model for mill A and B were 57.2 and 61.9, respectively. Series of FGDs combined with the FCM model successfully formulated five clusters of strategies as initiatives in improving the sustainability performance, namely raw material provision, harvesting and post-harvest activities, production process optimization, IT-based technology implementation, and institutional aspects. This present work seeks to contributes to the development of multi-criteria of sustainability performance for the food industry's supply chain. It also proposes a comprehensive framework for analyzing and improving sustainable supply chain performance under uncertainty using a combination of conventional and fuzzy assessment modeling approach. A practical initiative strategy in sustainability improvement is revealed for the sugarcane agroindustry's supply chain.

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored.

A fuzzy cognitive map of the quality of user experience determinants in mobile application design

 The users of mobile phone are exponentially increasing. The applications are developed every day in a variety of domains to enhance the Quality of User Experience (QoUE) along with utility determinants. The design of the mobile application impacts the QoUE. QoUE in mobile applications is a measure that describes the appropriateness of the purpose of the application and the need for user retention. However, the challenge is to identify, understand, focus and interconnect the variety of determinants influencing the QoUE based on mobile application design. These determinants are based on the diversity of users and the related functional needs, user-specific needs, and background functioning of the application. The modelling and analysis help mobile application developers to improve, increase and retain user engagement on the app based on improved QoUE. To do so, a qualitative analytical method is employed in the following steps. The first ever Fuzzy Cognitive Map (FCM) is proposed to show the causal-effect links of the interdependent determinants in mobile applications based on QoUE. In our model, the existence of relationships between determinants relies on a thorough literature review. The weight of these links is estimated by users of different ages and lines of work. This is performed by an empirical study based on a questionnaire filled by experts. The questionnaire is based on the formal utility and perceived QoUE-based topics. Finally, scenario-based analysis on formed FCM based on these inputs is performed. We show that small changes in cases using different direct determinants can be used to enhance QoUE. These changes can be studied before launching an application for the user, thereby limiting the need to rework the improvements based on QoUE and providing useful guidance for the possible increase in user base and behaviour change.

Probabilistic linguistic fuzzy cognitive maps: applications to the critical factors affecting the health of rural older adults

Background

Achieving healthy ageing has become the only way for China to alleviate the pressure of ageing, especially in rural areas. However, the factors affecting the health of rural older adults are numerous and complex. It is important to identify the critical factors that affecting the health of older adults in rural areas and provide decision-making support for targeted health interventions.

Methods

To overcome some limitations of existing works, an extended probabilistic linguistic fuzzy cognitive map model is proposed in this paper as a useful tool for modeling the cause-effect relationship between factors. The proposed model integrates the advantages of probabilistic linguistic term sets and fuzzy cognitive maps. In the end, to rank and identify the critical factors affecting the health, a novel similarity measure based on Euclidean distance and Z-mapping function is proposed.

Results

The proposed model can effectively deal with the uncertainty of experts and reflect different opinions of groups well. In terms of representing uncertainty and ambiguity, the proposed method outperforms other models in modeling complex systems. In the real-world case analysis, we find that education is the most important factor affecting the health of rural older adults, followed by previous occupational experiences, psychology, and physical exercise, among other things. Intergenerational relationship has become another important factor affecting the health of rural older adults in China as the development of Chinese society.

Conclusions

From a macro perspective, social economic status, living environment, lifestyle, and health management, are the variables that have the greatest impact on the health of rural older adults. As a result, providing more precise health interventions with the characteristics of factors influencing health is a crucial guarantee for preserving and improving the health of rural older adults in China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-02028-9.

Optimization of Residential Landscape Design and Supply Chain System Using Intelligent Fuzzy Cognitive Map and Genetic Algorithm

This work intends to optimize residential landscape design and Supply Chain (SC) network systems. First, Fuzzy Cognitive Map (FCM) intelligent assistance and genetic algorithm (GA) are used to study residential landscape design and its integration with SC deeply. Weight matrix interactions are employed to implement iterative inference for FCM. The functions are transformed to unify variables of different scopes. Subsequently, a weighting method is proposed to deal with the disadvantage of the simple average method being too general. In addition, the Hebbian learning algorithm is used to adjust the state nodes and the connection weights. Finally, according to the fitness function of the GA and logistic regression (LR) model, residential landscape design and SC are combined. The simulation experiment results show that the causal relationship analysis between SC networks under fuzzy cognition shows that the state errors of each specific situation are 0.21, 0.16, and 0.24, respectively. The total average error is 0.21 in the case of multiple iterations. The average error of the result vector under fuzzy cognition and the operation of the actual result is 0.20, 0.15, and 0.24, respectively, and the error value is much reduced. The simulation accuracy of the GA-LR method for residential landscape design is improved from 77% to 84.7%. The “kappa coefficient” is also improved to 82.3%. The conclusion shows that the weight matrix is used to analyze the high-quality performance of landscape design according to the specific situation of SC. For each specific case, FCM is effective in reducing errors over multiple iterations. Under the GA-LR method, fewer geographic location types and larger accuracy deviations can improve the simulation accuracy.

How to Promote Urban Intelligent Transportation: A Fuzzy Cognitive Map Study

As an important part of smart city, intelligent transportation is an critical breakthrough to solve urban traffic congestion, build an integrated transportation system, realize the intelligence of traffic infrastructure and promote sustainable development of traffic. In order to investigate the construction of intelligent transportation in cities, 20 initial affecting variables were determined in this study based on literature analysis. A questionnaire collected from professionals in intelligent transportation was conducted, and a total of 188 valid responses were received. Then the potential grouping was revealed through exploratory factor analysis. Finally, a causal model containing seven concepts was established using the practical experience and knowledge of the experts. A root cause analysis method based on fuzzy cognitive map (FCM) was also proposed to simulate intelligent transportation construction (ITC). The results indicate:(1) The 20 variables can be divided into six dimensions: policy support (PS), traffic sector control (TSC), technical support (TS), communication foundation (CF), residents’ recognition (RR), and talent quality (TQ); and (2) In the FCM model, all six concept nodes (PS, TSC, TS, CF, RR, and TQ) have a significant positive correlation with the target concept node ITC. The rank of the six dimensions according to correlation strength is TS, CF, PS, TSC, RR, and TQ. The findings of this paper can help academics and practitioners understand the deep-seated determinants of urban intelligent transportation construction more comprehensively, and provide valuable suggestions for policy makers. And thus, the efficiency of intelligent transportation construction can be improved.

Analyzing Greece 2010 Memorandum’s Impact on Macroeconomic and Financial Figures through FCM

The financial crisis of 2008 has caused a series of drawbacks to economies around the world. Greek economy has been hit twice at 2009, since its credibility worsened, provoking the implication of harsh fiscal measures from the 2010 Memorandum of Understanding (MoU). The effects of these measures to Greek macroeconomic figures have been widely criticized. Authors aim to estimate these effects at the macroeconomic figures of Greece through utilization of Decision Support Systems, and propose accurate insights regarding their efficacy. By capitalizing on regression analysis and Fuzzy Cognitive Mapping processes, specific results from 2010 Memorandum’s measures arise. It has been calculated that measures implied by 2010 Memorandum have been harsh and posed a negative effect on key Greek macroeconomic figures like GDPR, public debt, etc., especially with the ongoing 2008 financial crisis.

Barriers and Driving Factors for Sustainable Development of CO2 Valorisation

Mitigating CO2 emissions has become a top question in international and national arenas, likewise on the city level. To initiate and maintain transformative policies related to climate neutrality, an evident-based multi-sectoral forecasting model needs to be timely and effectively deployed. Decarbonisation solutions should be considered from the economic, environmental, and social perspectives. The resulting complexity constitutes an essential barrier to implementing CO2 valorisation projects. This study aims to analyse barriers and driving factors for the sustainable development of CO2 valorisation options. In order to reach the research goal, a methodological approach based on the combination of strengths, weaknesses, opportunities, and threats analysis, Geographical Information System and Fuzzy Logic Cognitive Analysis method was used. The method has been applied to a case study in Latvia

Safety and Reliability Analysis of an Ammonia-Powered Fuel-Cell System

Recently, the shipping industry has been under increasing pressure to improve its environmental impact with a target of a 50% reduction in greenhouse gas emissions by 2050, compared to the 2008 levels. For this reason, great attention has been placed on alternative zero-carbon fuels, specifically ammonia, which is considered a promising solution for shipping decarbonisation. In this respect, a novel ammonia-powered fuel-cell configuration is proposed as an energy-efficient power generation configuration with excellent environmental performance. However, there are safety and reliability concerns of the proposed ammonia-powered system that need to be addressed prior to its wider acceptance by the maritime community. Therefore, this is the first attempt to holistically examine the safety, operability, and reliability of an ammonia fuel-cell-powered ship, while considering the bunkering and fuel specifications. The proposed methodology includes the novel combination of a systematic preliminary hazard identification process with a functional and model-based approach for simulating the impact of various hazards. Furthermore, the critical faults and functional failures of the proposed system are identified and ranked according to their importance. This work can be beneficial for both shipowners and policymakers by introducing technical innovation and for supporting the future regulatory framework.

Robustness of Cyber-Physical Supply Networks in Cascading Failures

A cyber-physical supply network is composed of an undirected cyber supply network and a directed physical supply network. Such interdependence among firms increases efficiency but creates more vulnerabilities. The adverse effects of any failure can be amplified and propagated throughout the network. This paper aimed at investigating the robustness of the cyber-physical supply network against cascading failures. Considering that the cascading failure is triggered by overloading in the cyber supply network and is provoked by underload in the physical supply network, a realistic cascading model for cyber-physical supply networks is proposed. We conducted a numerical simulation under cyber node and physical node failure with varying parameters. The simulation results demonstrated that there are critical thresholds for both firm's capacities, which can determine whether capacity expansion is helpful; there is also a cascade window for network load distribution, which can determine the cascading failures occurrence and scale. Our work may be beneficial for developing cascade control and defense strategies in cyber-physical supply networks.

A medical decision support system for predicting the severity level of COVID-19

The main assay tool of COVID-19, as a pandemic, still has significant faults. To ameliorate the current situation, all facilities and tools in this realm should be implemented to encounter this epidemic. The current study has endeavored to propose a self-assessment decision support system (DSS) for distinguishing the severity of the COVID-19 between confirmed cases to optimize the patient care process. For this purpose, a DSS has been developed by the combination of the data-driven Bayesian network (BN) and the Fuzzy Cognitive Map (FCM). First, all of the data are utilized to extract the evidence-based paired (EBP) relationships between symptoms and symptoms' impact probability. Then, the results are evaluated in both independent and combined scenarios. After categorizing data in the triple severity levels by self-organizing map, the EBP relationships between symptoms are extracted by BN, and their significance is achieved and ranked by FCM. The results show that the most common symptoms necessarily do not have the key role in distinguishing the severity of the COVID-19, and extracting the EBP relationships could have better insight into the severity of the disease.