Cognitive Maps Research Articles

Evaluation of Vital Parameters Influencing Oxygen Demand with Pythagorean Neutrosophic-Fuzzy Cognitive Map

Objectives: The primary goal is to provide a novel idea known as the Pythagorean neutrosophic fuzzy cognitive map. A multi-objective goal programming issue with a set of objectives and constraints is solved using PNCM. Methods: Linguistic variables are transformed into crisp values using a score function and the Pythagorean neutrosophic quantification scale. The linear combinations of crisp values and factors yield the goal and constraint equations. Findings: To enhance decision-making in emergency situations like COVID-19, the implications of the novel PNCM concept were utilized to examine the variables influencing oxygen demand. Novelty: Fuzzy cognitive mapping combined with Pythagorean Neutrosophic can improve the modelling and analysis of real-world issues with imprecise data. Keywords: Pythagorean Neutrosophic set (PNs), Fuzzy Cognitive Map (FCM), Goal programming, Oxygen Demand, Multi­objective Optimisation

Examining Neural Correlates of Sexual Preferences between Persian Homo- and Heterosexual Males Using Psychological Assessments and Functional Magnetic Resonance Imaging in Specifying Cognitive Map: A Limited and Cross-sectional Study

AbstractAlthough the brain is an important part of a person's sexual life, little is known about the correlations between sexual response and brain activation. This study examines brain responses through clinical testing to reveal several brain structures whose activation is relevant to sexual arousal besides psychological testing according to the common traditional values and standards of Iranian society. Based on an initial self-report, 25 homosexual and 25 heterosexual males participated in this test to elucidate the identity and sexual trends via the Minnesota Multiphasic Personality Inventory (MMPI-2). Also, an fMRI technique—by presenting distinct homo- and heterosexual sex erotic pictures during the test—was employed in neural correlates of sexual arousal via blood oxygen level dependent (BOLD) signal measuring and Statistical Parametric Mapping analysis. In five selected homosexuals by the most appropriate MMPI test score, brain activities were significantly detected in the midbrain, amygdala, anterior cingulate gyrus, frontal cortex, orbitofrontal cortex, globus pallidus, thalamus, and putamen when the participant saw the erotic pictures during the test, which were responsive to sexual arousal. Also, bilateral caudate nucleus, left angular gyrus, and bilateral pallidum were activated but five selected heterosexuals showed no activation in these areas (p < 0.001). Likewise, the MMPI method confirmed that homosexuality was relevant to diverse life positions, and biological and socioeconomic aspects. The maximum positive correlations were pairwise found in the parietal lobule by r = 0.61 (p < 0.05) and in the frontal gyrus by r = 0.64 (p = 0.10), and the minimum negative correlations were in globus pallidus by r = − 0.18 (p < 0.10) and r = − 0.16 (p < 0.10) for homosexuals and heterosexuals, correspondingly. These results may be useful for understanding the different neural mechanisms of personality recognition and sexual orientation changes while incorporating the individual's history and experience simultaneously into the assessment.

A novel dynamic predictive control model for variable air volume air conditioning systems using deep learning

Model-based predictive control has proven effective for managing indoor temperature and humidity in variable air volume (VAV) air conditioning systems. However, delays in temperature and humidity adjustments in response to changes in internal and external environmental conditions can impede high-performance operation. This study introduces a dynamic predictive control model for the multi-zone indoor temperature and humidity of VAV systems, designed to predict and control these parameters in real-time. Utilizing the RC network two-node wall structure method and backward finite difference scheme, a multi-zone building model is developed. Coupled with a deep fuzzy cognitive map (DFCM) for temperature and humidity prediction, and controlled by a PID controller, the model dynamically regulates the opening degrees of terminal air dampers and chilled water valves. Implemented on a networked control platform in a large commercial building, the model consistently maintained indoor temperature and humidity within ±0.5 °C and ±0.1 g/(kg dry air) of the target values under varying conditions, demonstrating substantial improvements in stability and operational efficiency. The research enhances the predictability and control of HVAC systems through advanced algorithmic integration, improving real-time indoor climate management in complex building environments. This method offers a practical improvement in HVAC control technologies, which could be beneficial for applications in commercial building management systems, providing a useful tool for supporting energy efficiency and sustainability in modern infrastructures.

System dynamics simulation follow-up fuzzy cognitive map for investigating the effect of risks on relief in crisis management

Given the significant threat posed by natural disasters like earthquakes to communities, it is important to assess the associated risks beforehand in order to minimize human and financial losses. This study focuses on identifying existing risks caused by an earthquake crisis by designing a network that evaluates its impact on urban infrastructure. The resulting system dynamics simulation model was used to analyze the frequency of different hazards and estimate the amount of relief goods needed in eight earthquake scenarios. The Fuzzy Cognitive Mapping (FCM) technique was also employed to determine the weight, impact, and effectiveness of each hazard, thus allowing for a ranking of the earthquake crisis scenarios based on their severity. FCM evaluation results showed that the most important and influential risks in an earthquake crisis were fire (with a weight value of 0.124), damage to first-class buildings (0.099), lack of hospital capacity (0.096), and damage to the power grid (0.095). Furthermore, it was observed that mitigating or eliminating the most significant risks would lead to a significant decrease in the demand for relief goods in each scenario. In fact, by addressing the top three earthquake risks, the demand for medicine reduced by 147%. Finally, the ranked results of the earthquake scenarios indicate that fire (with an impact of 12%), damage to the water network, and lack of capacity in relief centers (both with an impact of 6%) are the most and least effective risks, respectively.

QAnon and the Epistemic Communities of the Unreal: A Conceptual Toolkit for a Sociology of Grassroots Conspiracism

The messy politics of combating the COVID-19 pandemic, compounded by the confusion caused by the global (dis)infodemic, have propelled conspiracism from the fringes of society into the public mainstream. Despite the growing political impact of digitally enabled conspiracy theories, they are predominantly delegitimized on three fronts – as psychopathology, pseudoscience, and/or parapolitics. In contrast, this article employs three non-pathologizing conceptual counteroffers borrowed from critical theory, deconstructionist historiography, and citizenship studies – namely, cognitive mapping, narrative emplotment, and performative citizenship – to politicize conspiracy theorizing ‘from below’. Using QAnon as a case-in-point, the article introduces two novel concepts that invite a sociological approach to conspiracism: first, the epistemic communities of the unreal, which designates the participatory, interactive, and decentralized nature of collaboratively creating unreal explanations of the real world; and second, grassroots conspiracism, which denotes the ways in which these bottom-up, horizontal, and collective meaning-making practices and knowledge-production processes are expanding spaces of and for politics.

Cognitive issues of mobile map design and use

ABSTRACT Internet-connected mobile devices have changed how people access information. Like other information sources, maps have benefited from and been re-envisioned for mobile devices, and they are used in new contexts. However, these new contexts often generate additional cognitive load. We explored in depth two strategies designers could use to mitigate high cognitive loads associated with mobile map use: offloading cognition and reducing cognitive load by improved design to support the allocation of attention between the map and the environment. In reviewing these strategies, we considered their relevance to several mobile map use cases (navigation, individual and collaborative spatial decision making, information enrichment, and entertainment). Next, we identified recent progress in our understanding of how to measure cognitive load and map use context. Finally, we explored the wider implications of mobile maps for human behaviour and cognition. We identified two important cross-cutting research questions: 1) How can mobile maps be designed to reduce cognitive load by providing what is really needed by users to facilitate their cognitive processes?; 2) How can the intrinsic additional cognitive load created by the characteristics of mobile maps be managed and minimised by supporting the distribution of the user's attention between the map and environment?

Interpretable predictive modeling of non-stationary long time series

Highly accurate and interpretable forecasting of non-stationary long-time series is a challenge. It is often difficult for existing models to capture the trends of non-stationary time series and achieve interpretable forecasts. To this end, by integrating causal learning and high-order fuzzy cognitive graphs (HFCM), this paper proposes the Causal-HFCM framework. Firstly, the Peter-Clark (PC) algorithm is used to obtain the causal relationships of data, so that the relationships between data can be interpretable and causal learning breaks the limitation that the fuzzy cognitive graph only considers the correlation of data. Secondly, to capture the non-stationary property of time series and improve the prediction ability of fuzzy cognitive maps, a method combining the learning weight matrix algorithm of Huber regression with high-order fuzzy cognitive maps is proposed. Finally, the effectiveness of the proposed method is verified by conducting experiments with three different non-stationary time series datasets of traffic, air, and bearings.

Explainability Analysis of the Evaluation Model of the Level of Digital Transformation in MSMEs based on Fuzzy Cognitive Maps

Explainability Analysis of the Evaluation Model of the Level of Digital Transformation in MSMEs based on Fuzzy Cognitive Maps

A Participatory Evaluation of an Urban Garden Project in Ecuador: Exploring Factors That Impact the Recovery of People With Severe Mental Health Problems.

For the past years, Ecuador has been transitioning away from a hospital-based model of mental healthcare to one that is community-centred. However, challenges associated with hospital-based models endure, notably financial burden faced by those with severe mental health problems (SMHPs) due to labour market discrimination. Employment access for this group is often disregarded in policy planning, despite evidence of its benefits on mental health. Huertomanías, an urban garden initiative in Ecuador founded in 2015, works with individuals with SMHPs, providing work, income, and social inclusion. A case study using a participatory approach was carried out to explore factors that impact the recovery of people with SMHPs. Twelve participants engaged in diverse stages of the research, where several participatory activities were conducted including cognitive mapping, a photovoice project, and interviews. The analysis employed a thematic approach leading to four categories of impact within the urban garden: autonomy (financial and personal), interpersonal relations and relation with the environment, mental health, and family dynamics. A final category of impact was established encompassing external factors (family support and public policy and healthcare services) that influence recovery. Findings suggest that the urban garden promotes autonomy and active participation within society, improves mental health, and transforms family dynamics. Further, this study highlights the importance of community-based mental healthcare (CBMHC), emphasising the need of public policies and healthcare in promoting autonomy through employment and community-centred services. Lastly, the study contributes insights into recovery experiences and CBMHC benefits, informing programme development and similar initiatives in Latin America.

Identifying core IoT technologies using ARM and FCM: A comprehensive data-driven method

The internet of things (IoT) technology has garnered significant attention in recent years due to its wide-ranging applications. IoT, with its high connectivity capabilities, integrates various industrial, domestic, and agricultural devices into a smart and remotely controllable software and hardware platform. The field of IoT technology is expansive and encompasses a multitude of sub-technologies. Identifying core technologies in this domain is crucial for guiding research and development efforts by companies. Given the interrelation of these core technologies and their combination with recent decision-making approaches, network-based strategies have recently received special attention. The developed methods are based on static conditions and the assumption of stability, while in emerging technologies like IoT, the pace of changes over time is high. This leads to changes in the importance of technologies under various scenarios.In this study, in order to analyze the extracted patent data, association rule mining (ARM) algorithms were applied to identify the relationships between technologies and social network analysis was used to analyze the relationships between technologies and estimate their initial weights. Finally, fuzzy cognitive map (FCM) were used to estimate the final weights of technologies and rank them. The fcm approach allows for simultaneous modeling of both static and dynamic states of the system and, on the other hand, by calculating under various scenarios, suggests a core technology that is sustainable.The research results show that digital information transmission technologies, digital or electrical data processing, and wireless communication networks are the most important sub-technologies of Internet of things.

The day-of-the-week effect is resilient to routine change.

Temporal landmarks are salient events that structure the way humans think about time. They may be personal events, such as one's birthday, or shared cultural events, such as the COVID-19 pandemic. Due to societal habits, the cyclical weekly structure - for example, working on weekdays, resting on the weekends - helps individuals orient themselves in time. In the "day-of-the-week effect," individuals are faster at reporting which day of the week it is on weekends than they are on weekdays. Herein, we hypothesized that the disruption of social habits during the COVID-19 pandemic lockdowns may have weakened this effect, thereby accounting for the "Blursday" phenomenon. In the current study, speeded responses to the question "What day of the week is it?" were collected online from 1,742 French participants, during and after the lockdown periods. We found that reaction times for days of the weekends remained faster than for weekdays during the lockdown, although the overall reaction times were significantly slower during lockdown. We also found that responses were slower as governmental stringency rules and restrictions in mobility increased. Our results suggest that the weekend landmark remains a stable temporal anchor in French culture despite the experienced temporal distortions induced by the disruption of social habits during the pandemic. We conclude that cultural temporal landmarks shape socially shared temporal cognitive maps.

Parallel vector memories in the brain of a bee as foundation for flexible navigation

Insects rely on path integration (vector-based navigation) and landmark guidance to perform sophisticated navigational feats, rivaling those seen in mammals. Bees in particular exhibit complex navigation behaviors including creating optimal routes and novel shortcuts between locations, an ability historically indicative of the presence of a cognitive map. A mammalian cognitive map has been widely accepted. However, in insects, the existence of a centralized cognitive map is highly contentious. Using a controlled laboratory assay that condenses foraging behaviors to short distances in walking bumblebees, we reveal that vectors learned during path integration can be transferred to long-term memory, that multiple such vectors can be stored in parallel, and that these vectors can be recalled at a familiar location and used for homeward navigation. These findings demonstrate that bees meet the two fundamental requirements of a vector-based analog of a decentralized cognitive map: Home vectors need to be stored in long-term memory and need to be recalled from remembered locations. Thus, our data demonstrate that bees possess the foundational elements for a vector-based map. By utilizing this relatively simple strategy for spatial organization, insects may achieve high-level navigation behaviors seen in vertebrates with the limited number of neurons in their brains, circumventing the computational requirements associated with the cognitive maps of mammals.

Consumer ethnocentrism: bibliometric analysis and literature review through cognitive structure and mapping of research based on web of science (WoS) and scopus

Consumer ethnocentrism: bibliometric analysis and literature review through cognitive structure and mapping of research based on web of science (WoS) and scopus

Cognitive modeling in project management

Subject. This article deals with the issues of project management, risk forecasting and evolutionary economics. Objectives. The article aims to identify systems approaches to management, strengthening digital interactions between various departments of the company and the management department. Methods. For the study, I used a systems analysis. Results. The article presents models of interactions between actors and factors based on dynamic cognitive maps, taking into account inverse functional relationships using Bayesian networks, in particular, a dynamic graph model of propagating perturbations in the project environment, and it proposes an approach to its identification based on the least squares method. Conclusions. The proposed cognitive models and tools will help improve the quality of decision-making in the context of "noise".

Fuzzy Cognitive Maps as a Bridge between Symbolic and Sub-Symbolic Artificial Intelligence

Fuzzy Cognitive Maps as a Bridge between Symbolic and Sub-Symbolic Artificial Intelligence

Adopting a systems-thinking approach to optimise dietary and exercise referral practices for cancer survivors

PurposeService referrals are required for cancer survivors to access specialist dietary and exercise support. Many system-level factors influence referral practices within the healthcare system. Hence, the aim of this study was to identify system-level factors and their interconnectedness, as well as strategies for optimising dietary and exercise referral practices in Australia.MethodsA full-day workshop involving national multidisciplinary key stakeholders explored system-level factors impacting dietary and exercise referral practices. Facilitated group discussions using the nominal group technique identified barriers and facilitators to referral practices based on the six World Health Organisation (WHO) building blocks. The systems-thinking approach generated six cognitive maps, each representing a building block. A causal loop diagram was developed to visualise factors that influence referral practices. Additionally, each group identified their top five strategies by leveraging facilitators and addressing barriers relevant to their WHO building block.ResultsTwenty-seven stakeholders participated in the workshop, including consumers (n = 2), cancer specialists (n = 4), nursing (n = 6) and allied health professionals (n = 10), and researchers, representatives of peak bodies, not-for-profit organisations, and government agencies (n = 5). Common system-level factors impacting on referral practices included funding, accessibility, knowledge and education, workforce capacity, and infrastructure. Fifteen system-level strategies were identified to improve referral practices.ConclusionThis study identified system-level factors and strategies that can be applied to policy planning and practice in Australia.

Retraction Note: Crime activities prediction using hybridization of firefly optimization technique and fuzzy cognitive map neural networks

Retraction Note: Crime activities prediction using hybridization of firefly optimization technique and fuzzy cognitive map neural networks

Digital Marketing Strategies and Profitability in the Agri-Food Industry: Resource Efficiency and Value Chains

Agriculture is essential to any country’s economy. Agriculture is crucial not only for feeding a country’s population but also for its impact on other businesses. The paradox of agri-food companies generating substantial profits despite seemingly high product prices is explored in this article, focusing on the role of digital marketing within the agri-food industry. Enhanced digital marketing performance leads to efficient advertising campaigns, through reduced advertising costs and increased resource efficiency. To do so, the authors collected web analytical data from five established agri-food firms with the highest market capitalization. Then, linear regression and correlation analyses were used, followed by the utilization of fuzzy cognitive mapping (FCM) modeling. The analysis revealed that increased traffic through search sources is associated with reduced advertising costs. Additionally, enhanced website engagement contributes to lower advertising expenses, emphasizing the optimization of the user experience. However, it has been discovered that allocating funds for social media advertising eventually results in higher expenses with higher website-abandoning rate. Ultimately, successful management of the balance between product costs and profitability in the agri-food sector lies on the increased use of search sources and greatly reducing the use of social media sources.

The Economic Dynamics of Desktop and Mobile Customer Analytics in Advancing Digital Branding Strategies: Insights from the Agri-Food Industry

In the agri-food industry, strategic digital branding and digital marketing are essential for maintaining competitiveness. This study examines the economic dynamics and impact of desktop and mobile customer analytics on digital branding strategies within the sector. Through a comprehensive literature review, this research utilizes empirical evidence to validate hypotheses regarding the influence of desktop and mobile analytics metrics on key digital branding metrics and value creation. This study explores various branding indicators by utilizing descriptive statistics, correlation analyses, regression models, and fuzzy cognitive mapping (FCM). The findings reveal significant correlations between desktop and mobile analytics and digital branding outcomes, underscoring the critical role of digital analytics and Decision Support Systems (DSSs) in shaping modern branding strategies in the agri-food industry. This study highlights the economic implications of desktop and mobile customer analytics on digital branding, providing insights to enhance market performance and foster sustainable growth in the agri-food sector.

A novel sequential risk assessment model for analyzing commercial aviation accidents: Soft computing perspective.

Due to the importance of the commercial aviation system and, also, the existence of countless accidents and unfortunate occurrences in this industry, there has been a need for a structured approach to deal with them in recent years. Therefore, this study presents a comprehensive and sequential model for analyzing commercial aviation accidents based on historical data and reports. The model first uses the failure mode and effects analysis (FMEA) technique to determine and score existing risks; then, the risks are prioritized using two multi-attribute decision making (MADM) methods and two novel and innovative techniques, including ranking based on intuitionistic fuzzy risk priority number and ranking based on the vague sets. These techniques are based in an intuitionistic fuzzy environment to handle uncertainties and the FMEA features. A fuzzy cognitive map is utilized to evaluate existing interactions among the risk factors, and additionally, various scenarios are implemented to analyze the role of each risk, group of risks, and behavior of the system in different conditions. Finally, the model is performed for a real case study to clarify its applicability and the two novel risk prioritization techniques. Although this model can be used for other similar complex transportation systems with adequate data, it is mainly employed to illustrate the most critical risks and for analyzing existing relationships among the concepts of the system.