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The purpose of this paper is to consider a nonlinear problem of minimizing the cost of providing reliable systems. The authors assume that the system consists of several components in series, and for each such component, the cost of the component increases exponentially with its reliability.

In order to solve this nonlinear optimization problem, the authors propose two approaches. The first approach is based on the concept of adjusting the reliability of a pair of components to minimize the cost of the system. The authors call this procedure as reliability adjustment routine (RAR). Proofs of optimality and convergence for the proposed model are also provided. The second approach solves the problem by using a Lagrangian multiplier. A procedure is developed to obtain the maximum step size to achieve the desired optimal solution in minimum iterations. Proposed approaches are efficient and give exact solutions.

Proposed methods enable a decision maker to allocate reliability to the components in series while minimizing the total cost of the system. The developed procedures are illustrated using a numerical example. Although an exponential relationship between the component cost and reliability is assumed, this can be extended to various other nonlinear distributions.

This cost optimization problem, subject to system component reliability values, assumes the near practical nonlinear pattern of cost vs reliability. Such problems are complex to solve. The authors provide a unique approach called RAR to solve such convoluted problems. The authors also provide an approach to solve such problems by using a Lagrangian multiplier method. Various proofs have been worked out to substantiate the work.

The purpose of this paper was to examine whether a less precise (or imprecise) estimate may increase investors’ confidence and improve investors’ perceptions of fair value reliability. The main criticism of fair value accounting has been its lack of reliability perceived by investors.

A 2 × 3 randomized experiment was used where management incentive and information precision are manipulated.

The results from this study indicate that perceived reliability is jointly affected by management’s incentives and information precision. Reliability rating is the highest for fair value stated as a point estimate with a specified confidence level attached to it. Further analysis indicates that higher perceived reliability is related to its representational faithfulness because participants perceive that a point estimate with a specified confidence level better matches uncertainty in measuring future cash flows.

This is the first study to examine whether a less precise (or imprecise) estimate may increase investors’ confidence and improve investors’ perceptions of fair value reliability. Because of the subjectivity and uncertainty in fair value estimates, less precise fair value estimates may not be viewed as less reliable. In fact, using a precise format to represent fair value estimates may not be appropriate (neither reliable nor credible), because a precise point estimate fails to capture its underlying uncertainty in future cash flows. A less precise format could represent a credible choice for fair value because it reflects uncertainty and subjectivity and effectively communicates management’s assessments of variability in future cash flows.

The purpose of this paper is to examine the value relevance of intangible assets, including goodwill and other types of intangibles in the pre- and post-adoption periods of International Financial Reporting Standards (IFRS). Most importantly, this paper investigates whether the value relevance of reported intangible assets is associated with their value reliability. Furthermore, this paper reports whether the adoption of IFRS improves the value relevance of intangible assets and alters the relationship between value relevance and reliability.

Both price and return models based on Ohlosn theory (1995) are employed to test the value relevance and value reliability of intangibles. Australian-listed firms with capitalised intangibles from 2001 to 2009 are selected in this study. The sample includes 6,650 firm-year observations.

The main result shows that capitalised intangible assets are value relevant in Australia, in both the pre- and post-adoption of IFRS periods. Value relevance is higher in firms with more reliable information on intangible assets. This study finds that the value relevance of intangibles has declined in the post-adoption period of IFRS. However, the positive relationship between the value relevance and the reliability of intangibles has remained unchanged in the post-adoption period.

The paper contributes a new measurement of value reliability of accounting information about intangibles. This paper is one of few studies on the relationship between value relevance and reliability of intangible assets. The results show that value relevance is positively associated with value reliability. This suggests that, when accounting standard setters assess whether the existing IFRS of intangibles should be improved in the future, they need to think not only in terms of whether the standard can provide more relevant information of intangibles to investors but also whether the standard can make the information of intangibles more reliable.

This study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.

The emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.

A real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.

This study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.

Our contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

The lubricating oil is a non‐renewable source of energy and its useful life is limited due to deterioration during its usage. It is desirable to maximize its use to conserve this scarce resource. At present, continuation or change of the engine oil is decided, based on the manufacturer's recommendation and experience. The suggested engine oil change period is conservative and results in non‐efficient usage of engine oil. This practice needs refinement to include all possible properties/attributes of engine oil and use of appropriate procedure to assess its realistic performance. The paper aims to analyze the procedure.

Oil reliability polygraph is used to analyze the engine oil performance during operation. Reliability analysis of the engine oil is carried out by comparing the area of oil reliability polygraph at a given operation time with the area for the fresh engine oil. The suggested procedure is illustrated by means of an example.

Physical and chemical properties responsible for performance degradation of the engine oil are considered as engine oil reliability attributes. The value of these attributes from time to time, obtained by analyzing samples drawn from the system, is analyzed through oil reliability polygraph. In this approach, the engine oil reliability attributes at a given operation time are represented in terms of reliability value to obtain the “oil reliability polygraph”.

The suggested procedure will be helpful for maintenance personnel in taking planned maintenance action.

The aim of this paper is to propose efficient models and algorithms for reliability value analysis of complex repairable systems linking reliability and losses from failures.

The conventional reliability analysis is based on the premise that increasing the reliability of a system will decrease the losses from failures. In this paper it is demonstrated that a system with larger reliability does necessarily mean a system with smaller losses from failures. In other words, a system reliability improvement, which is disconnected from the losses from failures does not necessarily reduce the losses from failures. An efficient discrete‐event simulation model and algorithm are proposed for tracking the losses from failures for systems with complex topology. A new algorithm is also proposed for system reliability analysis related to productions systems based on multiple production units where the absence of a critical failure means that at least m out n production units are working.

A model for determining the distribution of the net present value (NPV) characterising the production systems is developed. The model has significant advantages compared to models based on the expected value of the losses from failures. The model developed in this study reveals the variation of the NPV due to variation of the number of critical failures and their times of occurrence during the entire life‐cycle of the systems.

The proposed models have been successfully applied and tested for reliability value analysis of productions systems in deepwater oil and gas production.

The proposed approach has been demonstrated by comparing the losses from failures and the NPVs of two competing design topologies: one based on a single‐channel control and the other based on a dual‐channel control.

Reliability assessment does require an effective structural modelling approach for systems, in general and manufacturing systems are no exception. This paper aims to develop it for large manufacturing systems using graph models, a systems approach.

Structural graph models for reliability at various hierarchical levels are developed by considering a CNC cam shaft grinding machine. The system reliability expression is obtained by converting the reliability graphs into equivalent matrices, which helps to evaluate and analyse system.

Using the obtained reliability expressions at various hierarchical levels of the system, it is possible not only to evaluate its reliability from structure point of view but also to identify weak structural elements from reliability point of view.

The approach can be extended to include the influence of other parameters, such as human, component and environment, etc., on the system reliability.

The approach helps to design and develop manufacturing systems from reliability consideration by assessing their possible alternatives among these.

The suggested methodology is useful for reliability evaluation of large and complex manufacturing systems.

The purpose of this paper is to attempt to shift away from an exclusive probabilistic viewpoint or a pure network theory-based perspective for vulnerability assessment of infrastructure networks (INs), toward an integrated framework that accounts for joint considerations of the consequences of component failure as well as the component reliability.

This work introduces a fuzzy inference system (FIS) model that deals with the problem of vulnerability analysis by mapping reliability and centrality to vulnerability. In the presented model, reliability and centrality are first fuzzified, then 16 different rules are defined and finally, a defuzzification process is conducted to obtain the model output, termed the vulnerability score. The FIS model developed herein attempts to explain the linkage between reliability and centrality so as to evaluate the degree of vulnerability for INs elements.

This paper compared the effectiveness of the vulnerability score in criticality ranking of the components against the conventional vulnerability analysis methods. Comparison of the output of the proposed FIS model with the conventional vulnerability indices reveals the effectiveness of the vulnerability score in identifying the criticality of components. The model result showed the vulnerability score decreases by increasing reliability and decreasing centrality.

Two key practical implications for vulnerability analysis of INs can be drawn from the suggested FIS model in this research. First, the maintenance strategy based on the vulnerability analysis proposed herein will provide an expert facilitator that helps infrastructure utilities to identify and prioritize the vulnerabilities. The second practical implication is especially valuable for designing an effective risk management framework, which allows for least cost decisions to be made for the protection of INs.

As part of the first contribution, we propose a novel fuzzy-based vulnerability assessment model in building a qualitative and quantitative picture of the vulnerability of INs. The second contribution is especially valuable for vulnerability analysis of INs by virtue of offering a key to understanding the component vulnerability principle as being constituted by the component likely behavior as well as the component importance in the network.

The purpose of this study is to assess the pricing effects of financial reporting decision usefulness in terms of its constituent elements of relevance and reliability. Although it has long been intuitively appealing to believe in the decision usefulness of more relevant and reliable disclosures, they have been troublesome to demonstrate empirically. The mixed results have often been attributed to the richness of operating company settings.

This study addresses that problem by using 363 firm years of data from US market‐priced mutual funds (termed closed end funds in some countries and investment trusts in others), whose assets are comprised almost entirely of investment securities.

The results are consistent with the principal hypotheses – both relevance and reliability are valued by the market.

Overall, these findings provide a basis not only for reconciling prior, conflicting results, but in adding to our understanding of how disclosure characteristics are valued by investors, a particularly pertinent topic given the IASB's and the FASB's projects in this area.

The simplicity and elegance of the market‐priced mutual fund setting facilitates development of a model that simultaneously distinguishes between the relevance and reliability. Cost (less relevant) and fair‐value (more relevant) disclosures are gathered for both restricted (less reliable) and unrestricted (more reliable) securities for each firm year. Both levels and returns type methods are used to assess the effects of these separate elements of decision usefulness on securities valuation.

A lot of triaxial compressive models for different concrete types and different concrete strength classes were proposed to be used in structural analyses. The existence of so many models creates conflicts and confusions during the selection of the models. In this study, reliability analyses were carried out to prevent such conflicts and confusions and to determine the most reliable model for normal- and high-strength concrete (NSC and HSC) under combined triaxial compressions. The paper aims to discuss these issues.

An analytical model was proposed to estimate the strength of NSC and HSC under different triaxial loadings. After verifying the validity of the model by making comparisons with the models in the literature, reliabilities of all models were investigated. The Monte Carlo simulation method was used in the reliability studies. Artificial experimental data required for the Monte Carlo simulation method were generated by using artificial neural networks.

The validity of the proposed model was verified. Reliability indexes of triaxial compressive models were obtained for the limit states, different concrete strengths and different lateral compressions. Finally, the reliability indexes were tabulated to be able to choose the best model for NSC and HSC under different triaxial compressions.

Concrete compressive strength and lateral compression were taken as variables in the model.

The reliability indexes were tabulated to be able to choose the best model for NSC and HSC under different triaxial compressions.

A new analytical model was proposed to estimate the strength of NSC and HSC under different triaxial loadings. Reliability indexes of triaxial compressive models were obtained for the limit states, different concrete strengths and different lateral compressions. Artificial experimental data were obtained by using artificial neural networks. Four different artificial neural networks were developed to generate artificial experimental data. They can also be used in the estimations of the strength of NSC and HSC under different triaxial loadings.