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With the object of translating some concepts and results from classical Reliability Theory into particular types of multistate systems, this article starts by presenting some basic definitions of fuzzy systems. The concepts of fuzzy coherent and fuzzy crisp‐based systems are examined and some results on fuzzy coherent structures are presented. Both static and non‐static models are considered.

This paper aims to identify the big data analytics (BDAs) based enablers of supply chain capabilities (SCCs) and competitiveness of firms. This paper also models the interaction among identified enablers and thus projects the relationship strength of these enablers with SCC and a firm's competitiveness.

In order to achieve the research objectives of this paper, we employed fuzzy total interpretive structural modeling (TISM), an integrated approach of an interpretive structural model and TISM.

Results suggest that BDA-based enablers namely, IT infrastructure for BDA; leadership commitment; people skills for use of BDA and financial support for BDA significantly enable SCC and enhance firm competitiveness.

Results of the present study have implications for researchers and practitioners; the results will enable them to design policies around identified enablers of BDA initiatives.

The present paper is one of a few early efforts that address the role of BDA in augmenting SCC and subsequently a firm's competitiveness from a resource-dynamic capability perspective.

Statement on Auditing Standards (SAS) No. 53 requires that the audit be designed to provide a reasonable assurance of detecting management fraud. Traditionally auditors have utilized personal, business, and economic red flags in risk analysis and audit planning. Touche Ross (1974), Coopers and Lybrand (1977), Price Waterhouse (1985), and SAS Nos. 6, 16, 17, and 53 discuss various red flags associated with management fraud. However, the authoritative literature does not provide any guidance on how to measure and combine red flags. The extant literature primarily measures red flags as “yes” or “no” type binary variables. However, red flags are fuzzy in nature and fuzzy set approach can be used to measure and combine red flags. The purpose of this paper is to provide a framework for the application of the theory of fuzzy sets to the problem of assessing the risk of management fraud using red flags. This approach can be used to capture the beliefs of one or several auditors concerning red flags and combine these beliefs to estimate the risk of management fraud. This approach can be extended to build fuzzy reasoning systems that assess the risk of management fraud.

It is common that junior researchers struggle in finding their own way of doing research. The purpose of this paper is therefore to use the theory of “Muddling through” in order to theorize about the junior vs senior researcher collaboration process.

The theory of “Muddling through” is used in order to reflect on the qualities of shared collaboration between a junior and senior research colleague.

The research process share the characteristics of policy making where goals many times are fuzzy, and the relationship between means to end is far from self-evident. The research process therefore demands from the senior colleague to act firm, fair and friendly in order to support and inspire junior research colleagues.

It is a personalized single case study; still it provides advice for both junior and senior research colleagues that are in the process of research collaboration.

Junior colleagues need to have the courage of actively suggesting research topics to senior colleagues; they also need to actively reflect on the quality of their shared research collaboration process.

The study provides the research community with a sensemaking example of coaching, inspiration and mentoring in the research collaboration process.

This paper aims to deal with the problem of the global stabilization for a class of tension leg platform (TLP) nonlinear control systems.

It is well-known that, in general, the global asymptotic stability of the TLP subsystems does not imply the global asymptotic stability of the composite closed-loop system.

An effective approach is proposed to control chaos via the combination of fuzzy controllers, fuzzy observers and dithers.

If a fuzzy controller and a fuzzy observer cannot stabilize the chaotic system, a dither, as an auxiliary of the controller and the observer, is simultaneously introduced to asymptotically stabilize the chaotic system.

Thus, the behavior of the closed-loop dithered chaotic system can be rigorously predicted by establishing that of the closed-loop fuzzy relaxed system.

General Systems Theory postulates the existence of many general theories that serve to describe isomorphisms across systems. The theory of Fuzzy Sets can be considered as one particular general theory which describes the phenomenon of ambiguity across all systems displaying this property and its consequences. Fuzzy Set Theory is a mathematical development that holds great promise in becoming the metalanguage of ambiguity, in a way parallel to Statistics and Probability Theory which represent the metalanguage of uncertainty. Fuzzy Sets appear particularly well suited to model ambiguity in the context of the systems paradigm which has been offered as a counterpart to the traditional science paradigm. A decision model is used to discuss the differences between these two paradigms and to show the role which Fuzzy Sets can play in resolving some of the epistemological problems in the domain of the social sciences.

This paper aims to address the robust controller design problem for a class of fuzzy C-means clustering algorithm that is robust against both the plant parameter perturbations and controller gain variations. Based on Takagi–Sugeno (T-S) fuzzy model description, the stability and control problems of nonlinear systems are studied.

A recently proposed integral inequality is selected based on the free-weight matrix, and the less conservative stability criterion is given in the form of linear matrix inequalities (LMIs).

Under the premise that the controller and the system share the same, the method does not require the number of membership functions and rules.

Furthermore, the modified controller in a large-scale nonlinear system is utilized as a stability criterion for a closed-loop T-S fuzzy system obtained by LMI, and is rearranged by a machine learning membership function.

The closed-loop controller criterion is derived by energy functions to guarantee the stability of systems. Finally, an example is given to demonstrate the results.

To prove the effectiveness of the proposed design method, this study aims to propose the Fisher equation and temperature cooling fins that control high-speed aerospace vehicles.

A new approach whereby the control of aerospace vehicles can be achieved by fuzzy controller and appropriate Navier–Stokes equations in this article. The design of the controller based on models of Navier–Stokes equations simplified complex mathematical simulations and approximations.

If the fuzzy controller cannot stabilize the system, the Navier–Stokes fuzzy function is injected into the system as a controller tool, and the system is asymptotically stabilized by adjusting the fuzzy parameters.

The simulation results show that if the tuning frequency is high enough, the fuzzy controller and fuzzy observer can create chaotic movements by adjusting the dither amplitude appropriately. The demonstration of the Fisher equation and the temperature-cooled fin control problem for high-speed aerospace vehicles has displayed the benefits of combining fuzzy control with the Navier–Stokes equation.

The purpose of this paper is to provide a comprehensive overview of technical integrity management (TIM) and propose a methodology for assessing to which extent the technical integrity (TI) performance is balanced in terms of goal awareness among the personnel responsible for TIM, and the degree to which high level goals are implemented in maintenance strategies.

The study involved a comprehensive literature survey as well as information and data collected in the Norwegian oil and gas (O&G) industry. Several discussion rounds were carried out with the industrial maintenance management experts to recognize the existing practices and to confirm the suggested model.

The literature review indicated that there is a need for a methodology for assessing to which extent the technical integrity (TI) performance is balanced in terms of goal awareness among the personnel responsible for TIM, and the degree to which high‐level goals are implemented in maintenance strategies. The suggested model can incorporate industrial data as well as intentions, intuitions and experiences of industrial experts who are making decisions sensitive to TI of a production installation.

The study has been conducted in relation to TIM of production installations on the Norwegian Continental Shelf (NCS). The study is limited to measuring TI performance in relations to experts' awareness.

The study stresses the need for improving synergy between the TI and maintenance management function. It proposes a methodology to measure to which extent organizational priorities are balanced, while addressing financial as well as health, safety and environmental (HSE) interests when selecting a maintenance strategy.

This paper addresses a problem that is not given enough attention in the currently available literature. Available approaches suggest models and frameworks for measuring integrity performance. However, the operationalization of most of the existing models and frameworks, remains vague. Instead a formal mechanism is needed to analyze the gap between the present performance and performance targets.

This paper aims to propose a composite form of the robust disturbance fuzzy control scheme. The proposed method uses the benefits of fuzzy system modeling and Lyapunov direct methods.

The effciency of the control technique was demonstrated with assistance of numerical simulation in control problems of unmanned aerial vehicle (UAV) simulation.

To evaluate the control performance, the comparison of the proposed controller was made with conventional control techniques. The Lyapunov stability theorem has been used to testify for asymptotic stability and convergence of the closed loop system.

Implementation of the control law in the real world environment can be easier due to significant reduction in the fuzzy rules, tuning parameters and computation stages.

Simulation results confirm that the proposed control scheme performs remarkably well in terms of the robustness and disturbance attenuation.