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The decline of the motoric and cognitive functions of the elderly and the high risk of changes in their vital signs lead to some disabilities that inconvenience them. This paper aims to assist the elderly in their daily lives through personalized and seamless technologies.

The authors developed a personalized adaptive system for elderly care in a smart home using a fuzzy inference system (FIS), which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system. Reflexive sensing is obtained from a body sensor and environmental sensor networks. Three methods comprising the FIS generation algorithm – fuzzy subtractive clustering (FSC), grid partitioning and fuzzy c-means clustering (FCM) – were compared to obtain the best prediction accuracy.

The results of the experiment showed that FSC produced the best F₁-score (96 per cent positioning accuracy, 94 per cent reflexive alert accuracy, 96 per cent air conditioning accuracy and 95 per cent lighting conditioning accuracy), whereas others failed to predict some classes and had lower validation accuracy results. Therefore, it is concluded that FSC is the best FIS generation method for our proposed system.

Personalized and seamless technologies for elderly implies life-share awareness, stakeholder awareness and community awareness.

This paper presents a model of personalized adaptive system based on their preferences and medical reference, which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system.

This study aims to explore the influencing factors of online tourism service quality to clarify the relationship between such factors and the degree of influence so that targeted and effective measures to improve service quality can be suggested.

A questionnaire is used to obtain original data, establish the fuzzy cognitive map (FCM) structure chart model and modify the model.

The results reveal that comprehensive service types, comprehensive information provided, true and accurate, upgrade and update, payment security, data and information security, customer rights and interests protection, service friendliness and evaluation processing are the key influencing factors in online tourism service quality. In addition, the FCM can also predict the improvement of service quality.

To establish an FCM model, this study establishes the evaluation framework of influencing factors of online tourism service quality and identifies the cause and effect of 26 indicators. The mechanism of influencing factors of online tourism service quality is explored through the iteration of the model.

Under what conditions do entrepreneurs make the sustainable decisions they need to develop socially and environmentally responsible new businesses? Explanations of sustainable decision-making have involved various cognitive features; however, it is not yet clear how they play a role in empirical terms and, moreover, how they combine to induce business decisions based on social, environmental and economic considerations. The purpose of this paper is to explore how five cognitive factors combine and causally connect to produce sustainable decision-making in entrepreneurship.

This study uses fuzzy-set qualitative comparative analysis to examine the decision-making of 37 sustainable entrepreneurs. It focuses on a substantive conception of entrepreneurial behaviour to uncover the cognitive antecedents underlying entrepreneurial decisions that involve the explicit development and implementation of measures, targets and strategies aimed at improving its impact on people and the environment.

The configurational analysis reveals a typology comprising five combinations of cognitive factors constituting a comprehensive cognitive map of sustainable decision-making in entrepreneurship, namely: purpose-driven, determined; value-based, vacillating; value-based, unintended; single motive, single solution; and purpose-driven, hesitant.

This study demonstrates that no single condition is necessary nor sufficient for triggering decision-making involving social and environmental concerns, revealing five mental models leading to sustainable decision-making. In doing so, this paper responds to recent calls that stress the need for studies capable of uncovering the complex constellation of cognitive factors underlying entrepreneurial sustainable behaviour. Theoretical and practical implications are discussed.

This paper provides a systematic characterization of the cognitive underpinnings of sustainable decision-making and offers a basis for organizing the study of sustainable outcomes and configurations of cognitive antecedents. It reconciles prior efforts aimed at characterizing sustainability decisions in the context of SMEs and new enterprises, challenging current models based on awareness, experience and ethical normative frameworks.

Double auctions are widely used market mechanisms on the world. Communication technologies such as internet increased importance of this market institution. The purpose of this study is to develop novel bidding strategies for dynamic double auction markets, explain price formation through interactions of buyers and sellers in decentralized fashion and compare macro market outputs of different micro bidding strategies.

In this study, two novel bidding strategies based on fuzzy logic are presented. Also, four new bidding strategies based on price targeting are introduced for the aim of comparison. The proposed bidding strategies are based on agent-based computational economics approach. The authors performed multi-agent simulations of double auction market for each suggested bidding strategy. For the aim of comparison, the zero intelligence strategy is also used in the simulation study. Various market outputs are obtained from these simulations. These outputs are market efficiencies, price means, price standard deviations, profits of sellers and buyers, transaction quantities, profit dispersions and Smith’s alpha statistics. All outputs are also compared to each other using t-tests and kernel density plots.

The results show that fuzzy logic-based bidding strategies are superior to price targeting strategies and the zero intelligence strategy. The authors also find that only small number of inputs such as the best bid, the best ask, reference price and trader valuations are sufficient to take right action and to attain higher efficiency in a fuzzy logic-based bidding strategy.

This paper presents novel bidding strategies for dynamic double auction markets. New bidding strategies based on fuzzy logic inference systems are developed, and their superior performances are shown. These strategies can be easily used in market-based control and automated bidding systems.

The concept of smart city has recently become more topical in academic and policy discussions. This idea is considered a complex, non-consensual subject since its definition has not yet been agreed upon by most authors in the relevant fields. The need to identify and measure smart city indicators has also given rise to many different evaluation procedures. However, the available frameworks have yet to overcome challenges in structuring and measuring all the evaluation parameters of the cities in question. Thus, methods still need to be developed and applied that can structure criteria used to assess smart city success.

This study sought to show cognitive mapping's tangible usefulness as an expedient tool for strategic analysis, using smart cities as a complex object of study. To this end, various cognitive maps were constructed and compared using the Strategic Options Development and Analysis (SODA) approach.

Cognitive mapping's advantages and limitations in the strategic visualization research context are analyzed and discussed.

The authors know of no prior work reporting comparative analysis of this methodological approach in the same research context.

The purpose of this research is to present an integrated methodological framework to aid in performance stewardship of management institutions according to their strategies based on a holistic evaluation encompassing social, economic and environmental dimensions.

A Mamdani fuzzy inference system (FIS) approach was adopted to design the quantitative models with respect to balanced scorecard (BSC) perspectives to demonstrate dynamic capability. Individual models were developed for each perspective of BSC using Mamdani FIS. Data was collected from subject matter experts in management education.

The proposed methodology is able to successfully compute the scores for each perspective. Effective placement, teaching learning process, faculty development and systematic feedback from the stakeholders were found to be the key drivers for revenue generation. The model is validated as the results were well accepted by the head of the institution after implementation.

The model resulting from this study will assist the institution to cyclically assess its performance, thus enabling continuous improvement. The strategy map provides the causality of the objectives across the four perspectives to aid the practitioners to better strategize. Also this study contributes to the literature of BSC as well to the applications of multi-criteria decision-making (MCDM) techniques.

Mamdani FIS integrated BSC model is a significant contribution to the academia of management education to quantitatively compute the performance of institutions. This quantified model reduces the ambiguity for practitioners to decide the performance levels for each metric and the priorities of metrics.

To propose a generic method to simplify the fuzzy logic‐based failure mode and effect analysis (FMEA) methodology by reducing the number of rules that needs to be provided by FMEA users for the fuzzy risk priority number (RPN) modeling process.

The fuzzy RPN approach typically requires a large number of rules, and it is a tedious task to obtain a full set of rules. The larger the number of rules provided by the users, the better the prediction accuracy of the fuzzy RPN model. As the number of rules required increases, ease of use of the model decreases since the users have to provide a lot of information/rules for the modeling process. A guided rules reduction system (GRRS) is thus proposed to regulate the number of rules required during the fuzzy RPN modeling process. The effectiveness of the proposed GRRS is investigated using three real‐world case studies in a semiconductor manufacturing process.

In this paper, we argued that not all the rules are actually required in the fuzzy RPN model. Eliminating some of the rules does not necessarily lead to a significant change in the model output. However, some of the rules are vitally important and cannot be ignored. The proposed GRRS is able to provide guidelines to the users which rules are required and which can be eliminated. By employing the GRRS, the users do not need to provide all the rules, but only the important ones when constructing the fuzzy RPN model. The results obtained from the case studies demonstrate that the proposed GRRS is able to reduce the number of rules required and, at the same time, to maintain the ability of the Fuzzy RPN model to produce predictions that are in agreement with experts' knowledge in risk evaluation, ranking, and prioritization tasks.

The proposed GRRS is limited to FMEA systems that utilize the fuzzy RPN model.

The proposed GRRS is able to simplify the fuzzy logic‐based FMEA methodology and make it possible to be implemented in real environments.

The value of the current paper is on the proposal of a GRRS for rule reduction to enhance the practical use of the fuzzy RPN model in real environments.

Limited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues.

A Mamdani fuzzy logic inference system is coupled with a traditional, categorical risk assessment framework to understand a facilities’ consequence of failure and its effect on an organization’s strategic objectives. Model performance is evaluated using the US Air Force’s facility portfolio, which has been previously assessed, treating facility replicability and interruptability as minimization objectives. The fuzzy logic inference system is built to account for these objectives, but as proof of ease-of-adaptation, facility dependency is added as an additional risk assessment criterion.

Results of the fuzzy logic-based approach show a high degree of consistency with the traditional approach, though the value of the information provided by the framework developed here is considerably higher, as it creates a continuous set of facility prioritizations that are unbiased. The fuzzy logic framework is likely suitable for implementation by diverse, spatially distributed organizations in which decision-makers seek to balance risk assessment complexity with an output value.

This paper fills the identified need for portfolio management strategies that focus on prioritizing projects by risk to organizational operations or objectives.

As the global freight transport network has experienced high vulnerability and threats from both natural and man-made disasters, as a result, a huge amount of data is generated in freight transport system in form of continuous streams; it is becoming increasingly important to develop sustainable and resilient transport system to recover from any unforeseen circumstances quickly and efficiently. The aim of this paper is to develop a stream processing data driven decision-making model for higher environmental performance and resilience in sustainable logistics infrastructure by using fifteen dimensions with three interrelated domains.

A causal and hierarchical stream processing data driven decision-making model to evaluate the impact of different attributes and their interrelationships and to measure the level of environmental performance and resilience capacity of sustainable logistics infrastructure are proposed. This work uses fuzzy cognitive maps (FCMs) and fuzzy analytic hierarchy process (FAHP) techniques. A real-life case under a disruptive event scenario is further conducted.

The result shows which attributes have a greater impact on the level of environmental performance and resilience capacity in sustainable logistics infrastructure.

In this paper, causal and hierarchical stream processing data decision and control system model was proposed by identified three domains and fifteen dimensions to assess the level of environmental performance and resilience in sustainable logistics infrastructure. The proposed model gives researchers and practitioners insights about sustainability trade-offs for a resilient and sustainable global transport supply chain system by enabling to model interdependencies among the decision attributes under a fuzzy environment and streaming data.

The objective of this research was to develop a new and highly accurate approach based on a fuzzy inference system (FIS) for the evaluation of usability based on ISO 9241-210:2019. In this study, a fully automated method of usability evaluation is used for interactive systems with a special look at interactive social robots.

Fuzzy logic uses as an intelligent computing technique to deal with uncertainty and incomplete data. Here this system is implemented using MATLAB fuzzy toolbox. This system attempted to quantify four criteria that correlate highly with ISO 9241-210:2019 criteria for the evaluation of interactive systems with maximum usability. Also, the system was evaluated with standard cases of computer interactive systems usability evaluation. The system did not need to train various data and to check the rules. Just small data were used to fine-tune the fuzzy sets. The results were compared against experimental usability evaluation with the statistical analysis.

It is found that there was a high strong linear relation between the FIS usability assessment and System Usability Scale (SUS) based usability assessment, and authors’ new method provides reliable results in the estimation of the usability.

In human-robot systems, human performance plays an important role in the performance of social interactive systems. In the present study, the proposed system has considered all the necessary criteria for designing an interactive system with a high level of user because it is based on ISO 9241-210:2019.

For future research, the system could be expanded with the training of historical data and the production of rules through integrating FIS and neural networks.

This system considered all essential criteria for designing an interactive system with a high level of usability because it is based on ISO 9241-210:2019. For future research, the system could be expanded with the training of historical data and the production of rules through integrating FIS and neural networks.