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The more easily available system performance data and advances in data analytics have provided us with opportunities to optimize maintenance programs for engineered systems, for example nuclear power plants. One key task in maintenance optimization is to obtain an accurate model for system degradation. In this research, we propose a Bayesian method to address this problem. Noting that systems usually exhibit multiple states and that the actual state of a system usually is not directly observable, in the method we first model the system degradation process and the observation process based on a hidden Markov model. Then we develop a sequential Bayesian inference algorithm based on importance sampling and the forward algorithm to infer the posterior distributions of the transition rates in the hidden Markov model based on available observations. The proposed Bayesian method allows us to take advantage of evidence from multiple sources, and also allows us to perform Bayesian inference sequentially, without the need to use the entire history of observations every time new observations are collected. We demonstrate the proposed method using both synthetic data for a nuclear power plant feedwater pump and realistic data for a nuclear power plant chemistry analytical device.

Human operators play a critical role in the operation of complex engineered systems, in particular under abnormal conditions. It is important to assess human performance under conditions of interest and to improve upon the performance by taking effective measures. In this paper we present the application of a previously developed cognitive modeling and simulation environment to address these two problems. The developed environment simulates how a human operator dynamically interacts with the external system, with focus on the operator’s cognitive activities. Based on the Three Mile Island accident, specifically the stuck-open failure of the pilot operated relief valve, we demonstrate how the developed environment can be used for human reliability analysis and human performance improvement. For human reliability analysis, besides providing a single human error probability value the proposed environment allows us to examine in detail how an operator reached a decision or missed an action. For human performance improvement, the proposed environment provides an easy approach for investigating the effects of changes, for example, in operator training and system design, on human performance and therefore identifying the most effective changes to improve human performance.

Cognitive modeling and simulation studies how a human dynamically interacts with the external world. Human performance assessment based on this concept has long been researched in both cognitive sciences and engineering disciplines. However, existing methods have difficulties in describing the uncertain relationships in a human’s knowledge and in considering the uncertainties in the cognitive process. To tackle these issues, we propose a novel cognitive modeling and simulation environment (CMS-BN) by introducing Bayesian networks to represent a human’s knowledge and Monte Carlo simulation to account for the uncertainties in the cognitive process. The proposed environment explicitly models information perception, reasoning and response in a human’s cognitive process. Information perception works as a filtering mechanism to downselect signals from the external world. Reasoning and response are modeled as traversing the human knowledge base represented as a Bayesian network to retrieve knowledge and updating human belief and attention distribution accordingly. Uncertainties in the cognitive process are characterized through Monte Carlo simulation. The proposed environment also models the interplay between the cognitive process and two performance shaping factors, stress and fatigue, though additional factors can be further considered. We expect the proposed environment to be useful in human reliability analysis and human performance improvement.

Fault propagation analysis is an important step in determining system reliability and defining fault tolerance strategies. Typically, in the early software design phases, the propagation probability for a fault is assumed to be one. However, the assumption that faults will always propagate highly underestimates reliability, and valuable resources may be wasted on fixing faults that may never propagate. To determine the fault propagation probability, a concept of flat parts is introduced. A flat part is a property of a function; when multiple functions containing flat parts interact with each other, these flat parts undergo a transformation. During this transformation, the flat parts may be killed, preserved, or new flat parts may be generated. Interval arithmetic-based rules to determine such flat part transformations are introduced. A flat part-based propagation analysis can be used to determine the reliability of a software system, or software-driven mechanical system expressed functionally. In addition, the information obtained through flat part-based propagation analysis can be used to add sensors within the flat parts to increase the probability of fault detection, thus increasing the robustness of the system under study.

Warehouse operations are vital for the success of a supply chain. This paper explores fit among warehouse management structure and the context in which the warehouse operates as an important driver of warehouse performance. Warehouse management structure has been operationalised as the extensiveness by which warehouse operations are planned and controlled, and the complexity of the decision rules used for optimisation of the operations. Warehouse performance is measured using data envelopment analysis (DEA). Hypotheses are developed and tested in a survey study among 111 distribution warehouses in the Netherlands and Belgium. Our results show that indeed warehouse management structure should be adapted to context. In order to obtain high warehouse performance more complex warehouse tasks require more and more complex decision rules, and warehouses with more unpredictable demand require fewer tactical plans. These results can help warehouse managers to structure their warehouse management contingent on the context in which the warehouse operates.

Every day, we are faced with the conflict between the temptation to cheat for financial gains and maintaining a positive image of ourselves as being a ‘good person’. While it has been proposed that cognitive control is needed to mediate this conflict between reward and our moral self-image, the exact role of cognitive control in (dis)honesty remains elusive. Here, we identify this role, by investigating the neural mechanism underlying cheating. We developed a novel task which allows for inconspicuously measuring spontaneous cheating on a trial-by-trial basis in the MRI scanner. We found that activity in the Nucleus Accumbens promotes cheating, particularly for individuals who cheat a lot, while a network consisting of Posterior Cingulate Cortex, Temporoparietal Junction and Medial Prefrontal Cortex promotes honesty, particularly in individuals who are generally honest. Finally, activity in areas associated with Cognitive Control (Anterior Cingulate Cortex and Inferior Frontal Gyrus) helped dishonest participants to be honest, whereas it promoted cheating for honest participants. Thus, our results suggest that cognitive control is not needed to be honest or dishonest per se, but that it depends on an individual’s moral default.

Nuclear micro-reactors have drawn increasing attention in the nuclear industry since they can be potentially applied for a variety of purposes such as desalination and hydrogen production. Nuclear micro-reactors have unique characteristics (e.g. wider use of automation) compared with currently operating large nuclear power plants. These characteristics inevitably influence operator performance. To perform human reliability analysis and system design optimization, it is necessary to have a thorough investigation of such effects. To this end, in this paper we propose a method based on literature review and evidence theory to analyze human factor correlations for nuclear micro-reactors. A literature review is performed to identify studies related to characteristic human factors for micro-reactors. We divide human factors into independent variables that are used to characterize microreactors (e.g., level of automation) and dependent variables (e.g., situation awareness) that are influenced by the independent variables. The correlations between the independent and dependent variables are extracted from the reviewed studies. We should note that there may be conflicts between the correlations extracted from different studies. To address this problem, we use evidence theory to aggregate the potentially conflicting information to draw final conclusions about the correlations. The results obtained in this study demonstrate the high complexity of the relations between independent and dependent variables, including the uncertainties in the relations, the non-monotonic relations, and the inconsistent effects of one independent variable on different dependent variables. The potential applications of the results in human reliability analysis for nuclear micro-reactors and system design optimization are discussed.

Contains fulltext : 219546.pdf (Publisher’s version ) (Open Access) This study explored the feasibility of using shared neural patterns from brief affective episodes (viewing affective pictures) to decode extended, dynamic affective sequences in a naturalistic experience (watching movie-trailers). Twenty-eight participants viewed pictures from the International Affective Picture System (IAPS) and, in a separate session, watched various movie-trailers. We first located voxels at bilateral occipital cortex (LOC) responsive to affective picture categories by GLM analysis, then performed between-subject hyperalignment on the LOC voxels based on their responses during movie-trailer watching. After hyperalignment, we trained between-subject machine learning classifiers on the affective pictures, and used the classifiers to decode affective states of an out-of-sample participant both during picture viewing and during movie-trailer watching. Within participants, neural classifiers identified valence and arousal categories of pictures, and tracked self-reported valence and arousal during video watching. In aggregate, neural classifiers produced valence and arousal time series that tracked the dynamic ratings of the movie-trailers obtained from a separate sample. Our findings provide further support for the possibility of using pre-trained neural representations to decode dynamic affective responses during a naturalistic experience. 12 p.

In a context where software has been pervasive in safety critical applications, trust in software safety is challenged by software complexity and lack of systematic methods to assess the effects of remaining faults. To expand the use of digital technology in the nuclear industry, systematic methods are required to assess the effects of remaining faults for software-based Instrumentation & Control (I& C) systems in safety critical applications. In this paper, the effects of the remaining requirements faults are assessed using a probability density function (PDF) of their hazard rates. A hazard-based effect analysis (HEA) method is developed to obtain the probability distribution of the hazard rates of a remaining requirements fault. The HEA method is applied to a Reactor Protection System (RPS) in the case study. The probability density functions for the introduced faults, detected faults and remaining faults in the requirements phase of the RPS system on the domain of hazard degree are obtained.

International audience; Neuromarketing’ designates both a developing industry and an academic research field. This study documents the emergence of neuromarketing through the first mention of the term in traditional and new media until the stabilization of the field. Our main interest is to establish whether neuromarketing developed separately as an academic field and as an industry (with knowledge transfer from the former to the latter), or whether it was an act of co-creation. Based on a corpus gathered from a systematic search on the Web, we trace the multiple forms of engagement between academic and commercial communities, echoed but also shaped by reports in traditional and new media. We find that neuromarketing developed an identity through a set of practices and a series of debates which involved intertwined communities of academic researchers and practitioners. This result offers an alternative to the narrative of ‘knowledge transfer’ between academia and the industry and offers a contribution on how to use new kinds of digital sources in business history.