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One of the main issues which microelectronics industry encounter is reliability as feature sizes scale down to nano-design level. The purpose of this paper is to provide a probabilistic transfer matrix based to find the accurate and efficient method of finding circuit’s reliability.

The proposed method provides a probabilistic description of faulty behavior and is well-suited to reliability and error susceptibility calculations. The proposed method offers accurate circuit reliability calculations in the presence of reconvergent fanout. Furthermore, a binary probability matrix is used to not only resolve signals correlation problem but also improve the accuracy of the obtained reliability in the presence of reconverging signals.

The results provide the accuracy and computation time of reliability evaluation for ISCAS85 benchmark schemes. Also, simulations have been conducted on some digital circuits involving LGSynth’91 circuits. Simulation results show that proposed solution is a fast method with less complexity and gives an accurate reliability value in comparison with other methods.

The proposed method is the only scheme giving the low calculation time with high accuracy compared to other schemes. The library-based method also is able to evaluate the reliability of every scheme independent from its circuit topology. The comparison exhibits that a designer can save its evaluation time in terms of performance and complexity.

We introduce the idea that informational cascades can explain the observed regularity that emigrants from the same location tend to choose the same foreign location. Thus, informational cascades generate herd behavior. Herd behavior is compared with the network-externalities explanation of the same phenomenon of migration clustering.

In recent years, the mechanical industries began to apply many investments in research and technological development to obtain efficient methods to analyze the integrity of structures and prevent disasters and/or accidents, ensuring people’s lives and preventing economic losses. Any structure, whether mechanical or aeronautical, before being put into use undergoes a structural integrity assessment and testing. In this case, non-destructive evaluations are performed, aiming to estimate the degree of safety and reliability of the structure. For this, there are techniques traditionally used such as ultrasonic inspection, X-ray, acoustic emission tests, among other techniques. The traditional techniques may even have a good instrumental apparatus and be well formulated for structural integrity assessment; however, these techniques cannot meet growing industrial needs, even more so when structures are in motion. The purpose of this paper is to demonstrate artificial immune systems (AISs), ate and strengthen the emergence of an innovative technological tool, the biological immune systems and AISs, and these are presented as computing methods in the field of structural health monitoring (SHM).

The concept of SHM is based on a fault detection mechanism used in industries, and in other applications, involving the observation of a structure or a mechanical system. This observation occurs through the dynamic response of periodic measurements, later related to the statistical analysis, determining the integrity of the system. This study aims to develop a methodology that identifies and classifies a signal in normal signals or in faults, using an algorithm based on artificial immunological systems, being the negative selection algorithm, and later, this algorithm classifies the failures in probabilities of failure and degree of fault severity. The results demonstrate that the proposed SHM is efficient and robust for prognosis and failure detection.

The present study aims to develop different fast access methodologies for the prognosis and detection of failures, classifying and judging the types of failures based on AISs. The authors declare that the present study was neither published in any other vehicle of scientific information nor is under consideration for publication in another scientific journal, and that this paper strictly followed the ethical procedures of research and publication as requested.

This study is original by the fact that conventional structural integrity monitoring methods need improvements, which intelligent computing techniques can satisfy. Intelligent techniques are tools inspired by natural and/or biological processes and belong to the field of computational intelligence. They present good results in problems of pattern recognition and diagnosis and thus can be adapted to solve problems of monitoring and identifying structural failures in mechanical and aeronautical engineering. Thus, the proposal of this study demonstrates and strengthens the emergence of an innovative technological tool, the biological immune system and the AIS, and these are presented as computation methods in the field of SHM in rotating systems – a topic not yet addressed in the literature.

Signal Detection Theory (SDT) has recently been used to evaluate the performance of imperfect inspectors. SDT model is based on a priori probabilities and perceived payoffs and penalties to study inspectors′ behaviour. In this article, Bayes′ theorem is used to compute posterior probabilities of the two types of inspection error. These posterior probabilities give rise to the definition of Receiver Analysis Curves (RAC), which depict the “after the facts” consequences of inspection error. A cost model is also developed that reflects the true benefits and costs of inspection accuracy to the organisation.

The purpose of this paper is to explore the role of accounting discretion in a principal-agent setting, wherein accounting information is used in performance evaluation. The agent may choose one from among several allowable accounting methods. However, limited audit resources allow only for verification of only the method the agent chooses, and this is the only one used to determine the agent's compensation.

This paper uses the model developed by Penno (1990) and construct relatively simple examples that illustrate the effects of accounting discretion on performance evaluation. These examples may make the implications of Penno (1990) clearer to educators and practitioners.

Accounting discretion can be useful in performance evaluation even if the accounting method chosen is not disclosed. Lower limits on compensation reduce the usefulness of accounting discretion. If the principal declares a preferred accounting treatment, it enhances the usefulness of accounting discretion; in fact, it guarantees that accounting discretion is weakly preferred to no discretion.

Accounting discretion is often viewed pejoratively. Those responsible for designing performance evaluation schemes should be aware of the potential benefits of accounting discretion, and those charged with promulgating accounting rules should consider the possibility that rules can be too inflexible.

This paper provides accessible illustrations of the effects of accounting discretion on performance evaluation and offer discussion to place the conclusions drawn from the model into a standard setting context.

We examine the determinants of a current migrant's location choice emphasizing the relative importance and interaction of migrant stocks and flows. We show that both stocks and flow have significant impacts on the migrant's decision of where to locate. The significance and size of the effects vary according to legal status and whether the migrant is a “new” or a “repeat” migrant.

The performance of the measurement matrix directly affects the quality of reconstruction of compressive sensing signal, and it is also the key to solve practical problems. In order to solve data collection problem of wireless sensor network (WSN), the authors design a kind of optimization of sparse matrix. The paper aims to discuss these issues.

Based on the sparse random matrix, it optimizes the seed vector, which regards elements in the diagonal matrix of Hadamard matrix after passing singular value decomposition (SVD). Compared with the Toeplitz matrix, it requires less number of independent random variables and the matrix information is more concentrated.

The performance of reconstruction is better than that of Gaussian random matrix. The authors also apply this matrix to the data collection scheme in WSN. The result shows that it costs less energy and reduces the collection frequency of nodes compared with general method.

The authors design a kind of optimization of sparse matrix. Based on the sparse random matrix, it optimizes the seed vector, which regards elements in the diagonal matrix of Hadamard matrix after passing SVD. Compared with the Toeplitz matrix, it requires less number of independent random variables and the matrix information is more concentrated.

Indicators of financial crises generally do not have a good track record. This chapter presents an early warning system (EWS) for six countries in Asia in which indicators do work. We extract a full list of currency crisis indicators from the literature, apply factor analysis to combine the indicators, and use these factors as explanatory variables in logit models which are estimated for the period 1970:01–2001:12. The quality of the EWS is assessed both in-sample and out-of-sample. We find that money growth (M1 and M2), national savings, and import growth correlate with currency crises.

Based on a synthesis of findings from psychology, neuroscience, and machine learning, we propose a unified theory of curiosity as a form of motivated cognition. Curiosity, we propose, is comprised of a family of mechanisms that range in complexity from simple heuristics based on novelty, salience, or surprise, to drives based on reward and uncertainty reduction and finally, to self-directed metacognitive processes. These mechanisms, we propose, have evolved to allow agents to discover useful regularities in the world – steering them toward niches of maximal learning progress and away from both random and highly familiar tasks. We emphasize that curiosity arises organically in conjunction with cognition and motivation, being generated by cognitive processes and in turn, motivating them. We hope that this view will spur the systematic study of curiosity as an integral aspect of cognition and decision making during development and adulthood.

The purpose of this paper is to introduce a new design of an R chart to catch smaller shifts in the process dispersion as well as maintaining the simplicity so that it may be applied at shopfloor level.

Here a new R chart has been proposed which can overcome the limitations of Shewhart, CUSUM and EWMA range charts. The concept of this R chart is based on chi‐square (χ²) distribution. Although CUSUM and EWMA charts are very useful for catching the small shifts in the mean or standard deviation, they can catch the process shift only when there is a single and sustained shift in process average or standard deviation.

It was found that the proposed chart performs significantly better than the conventional (Shewhart) R chart, CUSUM range schemes proposed by Chang and Gan for most of the process shifts in standard deviation. The ARLs of the proposed R chart is higher than ARLs of CUSUM schemes for only ten cases out of 40. The performance of the proposed R chart has also been compared with the variance chart proposed by Chang and Gan for various shifts in standard deviation. The ARLs of the proposed R chart are compared with Chang's R chart for sample sizes of 3 and it can be concluded from the comparisons that the proposed R chart is much better than Chang's variance chart for all shift ratios for sample size of three. Many difficulties related to the operation and design of CUSUM and EWMA control charts are greatly reduced by providing a simple and accurate proposed R chart scheme. The performance characteristics (ARLs) of the proposed charts are very comparable to a great degree with FIR CUSUM, simple CUSUM and other variance charts. It can be concluded that, instead of considering many parameters, it is better to consider single sample size and single control limits because a control chart loses its simplicity with a greater number of parameters. Moreover, practitioners may also find difficulty in applying it in production processes. On the other hand, CUSUM control charts are not effective when there is a single and sustained shift in the process dispersion.

A lot of effort has been done to develop the new range charts for monitoring the process dispersion. Various assumptions and factors affecting the performance of the R chart have been identified and taken into account. In the proposed design, the observations have been assumed independent of one another but the observations may also be assumed to be auto‐correlated with previous observations and the performance of the proposed R chart may be studied.

The research findings could be applied to various manufacturing and service industries as it is more effective than the conventional (Shewhart) R chart and simpler than CUSUM charts.