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Metabolische Flexibilität ist die Fähigkeit des Stoffwechsels, sich effizient an das Nahrungsangebot und den Energiebedarf anzupassen. Sie wird durch gesunde Mitochondrien und ein ausgewogenes Hormonsystem gewährleistet und ist für Osteopath*innen von Bedeutung, da sie für ein gesundes Zusammenspiel der Körperfunktionen unerlässlich ist. Ein Verlust der metabolischen Flexibilität führt zu Insulinresistenz und steht in Zusammenhang mit vielen Erkrankungen, die wir in der Praxis täglich behandeln. Durch Lebensstilberatung können Osteopath*innen die Wirkung ihrer Behandlungen erhöhen. Ein angepasster Lebensstil verhindert die Zirkulation von entzündungsfördernden Zytokinen sowie von hohen Konzentrationen von Mastzellen und pathogenen Hormonen und optimiert die Selbstheilungskräfte. Anhand einer prospektiven Studie zeigen wir, dass eine Lebensstilberatung durch Osteopath*innen, die von Lifestyle-Coachs begleitet wird, positive Effekte auf die metabolische Flexibilität und damit auf die metabolische Gesundheit hat. Wir Osteopath*innen können für einen gesunden Stoffwechsel sorgen und sind gut beraten, unsere Patient*innen auf die gesundheitsfördernde Wirkung von Kohlenhydratbeschränkung und Fasten hinzuweisen. Metabolic flexibility is the ability of the metabolism to efficiently adapt to the food supply and energy requirements. It is ensured by healthy mitochondria and a balanced hormone system, and it is important for osteopaths as it is essential for the healthy interplay of bodily functions. A loss of metabolic flexibility leads to insulin resistance and is linked to many conditions that we treat in practice on a daily basis. By means of lifestyle advice, osteopaths can increase the effect of their treatments. An adapted lifestyle prevents the circulation of pro-inflammatory cytokines and obesogenic and pathogenic concentrations of certain hormones, leading to improved self-healing powers. Based on a prospective study, we show that lifestyle counseling by osteopaths, accompanied by lifestyle coaches, has positive effects on metabolic flexibility and thus on metabolic health. We osteopaths can ensure a healthy metabolism and are well advised to point out the health-promoting effects of carbohydrate restriction and fasting to our patients. [ABSTRACT FROM AUTHOR]

Dishonesty is ubiquitous and imposes substantial financial and social burdens on society. Intuitively, dishonesty results from a failure of willpower to control selfish behavior. However, recent research suggests that the role of cognitive control in dishonesty is more complex. We review evidence that cognitive control is not needed to be honest or dishonest per se , but that it depends on individual differences in what we call one's 'moral default': for those who are prone to dishonesty, cognitive control indeed aids in being honest, but for those who are already generally honest, cognitive control may help them cheat to occasionally profit from small acts of dishonesty. Thus, the role of cognitive control in (dis)honesty is to override the moral default. The precise role of cognitive control in dishonesty has been debated for many years, but now important strides have been made to resolve this debate. Recently developed paradigms that allow for investigating dishonesty on the level of the choice rather than on the level of the individual have substantially improved our understanding of the adaptive role of cognitive control in (dis)honesty. These new paradigms revealed that the role of cognitive control differs across people: for cheaters, it helps them to sometimes be honest, while for those who are generally honest, it allows them to cheat on occasion. Thus, cognitive control is not required for (dis)honesty per se but is required to override one's moral default to be either honest or to cheat. Individual differences in moral default are driven by balancing motivation for reward and upholding a moral self-image. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ijresmar.2006.10.005 Byline: Jorna Leenheer (a), Harald J. van Heerde (b), Tammo H.A. Bijmolt (c), Ale Smidts (d) Keywords: Loyalty programs; Grocery retailing; Endogeneity; Tobit-II model; Attraction models Abstract: One of the pressing issues in marketing is whether loyalty programs really enhance behavioral loyalty. Loyalty program members may have a much higher share-of-wallet at the firm with the loyalty program than non-members have, but this does not necessarily imply that loyalty programs are effective. Loyal customers may select themselves to become members in order to benefit from the program. Since this implies that program membership is endogenous, we estimate models for both the membership decision (using instrumental variables) and for the effect of membership on share-of-wallet, our measure of behavioral loyalty. We use panel data from a representative sample of Dutch households who report their loyalty program memberships for all seven loyalty programs in grocery retailing as well as their expenditures at each of the 20 major supermarket chains. We find a small positive yet significant effect of loyalty program membership on share-of-wallet. This effect is seven times smaller than is suggested by a naA[macron]ve model that ignores the endogeneity of program membership. The predictive validity of the proposed model is much better than for the naA[macron]ve model. Our results show that creating loyalty program membership is a crucial step to enhance share-of-wallet, and we provide guidelines on how to achieve this. Author Affiliation: (a) Researcher at SEO Economic Research, University of Amsterdam, Roeterstraat 29, 1018 WB Amsterdam, The Netherlands (b) Professor of Marketing, Waikato Management School, Hamilton, New Zealand (c) Professor of Marketing Research, University of Groningen, The Netherlands (d) Professor of Marketing Research, Erasmus University Rotterdam, The Netherlands

Service managers need to focus more attention on their customers' waiting conditions and be less focused on shortening waiting time since the waiting environment proves to be a stronger factor of service satisfaction than objective waiting time. Objective waiting time impacts satisfaction via both a cognitive and an affective circuit, while the strongest variable, the waiting environment, impacts via the affective route only.

The use of a marketing decision support system (MDSS) by a decisionmaker increases in value when qualitative requirements are considered under both high and low-time pressure environments. Initially, medium-quality MDSS are perceived as more valuable during high-pressure situations but this advantage is negated by the results which are objectively inferior. The qualitative factor is observed to affect more the objective than the subjective determinants of decisionmaking.

Highlights • Method for systematically developing reactor operators' knowledge base is proposed. • Developed knowledge base supports cognitive modeling of human performance. • The knowledge base related to severe accidents in nuclear power plants is developed. • Advantages of the proposed method for knowledge base development are discussed. Abstract Methods based on cognitive modeling have attracted increasing attention in the human reliability analysis community. In such methods, the operator knowledge base plays a central role. This paper proposes a method for systematically developing the knowledge base of nuclear power plant operators. The method starts with a systematic literature review of a predefined topic. Then, the many collected publications are reduced to summaries. Relevant knowledge is then extracted from the summaries using an improved qualitative content analysis method to generate a large number of pieces of knowledge. Lastly, the pieces of knowledge are integrated in a systematic way to generate a knowledge graph consisting of nodes and links. As a case study, the proposed method is applied to develop the knowledge base of reactor operators pertaining to severe accidents in nuclear power plants. The results show that the proposed method exhibits advantages over conventional methods, including reduced reliance on expert knowledge and improved traceability of the process. Generalization of the proposed method to other sources of materials and application of the knowledge base are also discussed. Although this paper is focused on nuclear applications, the proposed method may be extended to other industrial sectors with little additional effort. [ABSTRACT FROM AUTHOR]

Using digital NPP simulator facilities for research in Human Reliability Analysis (HRA) and nuclear power research is increasingly popular. We propose a method for characterizing and quantifying the gap between data collected in a simulator and data that reflect NPP operations. Using novice operators, we demonstrate how to manipulate and measure the impact of the simulator environment on operator actions. A set of biases are proposed to characterize factors introduced by the simulator environment. There are two categories of simulator bias: Environmental Biases (physical differences between the simulator and the control room), and Motivational Biases (cognitive differences between training in a simulator and operating a NPP). This study examines Motivational Bias. A preliminary causal model of Motivational Biases is introduced and tested in a demonstration experiment using 30 student operators. Data from 41 simulator sessions are analyzed. Data include crew characteristics, operator surveys, and time to recognize and diagnose the accident in the scenario. Quantitative models of the Motivational Biases using Structural Equation Modeling (SEM) are proposed. With these models, we estimate how the effects of the scenario conditions are mediated by simulator bias, and we demonstrate how to quantify the strength of these effects. [ABSTRACT FROM AUTHOR]

This paper discusses the nuclear security and safeguards research challenges presented by the development and deployment of nuclear fission batteries. These are defined as easily transportable and deployable nuclear systems which are designed to operate either unattended or autonomously. We start by defining the current landscape of domestic and international safeguards and security and discuss how it can be affected by the introduction of these new nuclear systems. We then specifically discuss the technology gaps and technologies to be developed to facilitate their practical deployment. The discussion is based on a commentary of experts' inputs collected during a workshop held as a part of the fission battery initiative. A commentary of experts' inputs discussing issues and gaps related to security and safeguards of fission batteries is provided. This input is categorized using qualitative coding of the text of the commentary of the experts' inputs but without modifying the basic content. From there we find that, as expected, we can leverage conclusions from existing security and safeguards studies for small modular rectors and develop bespoke target set analyses to inform security postures based on probabilistic risk assessment. We find also that specific fission battery security economic analysis tools are needed and security by design must be applied early. However, the most important finding is that these new systems will require a new comprehensive set of cyber tools covering transportation, installation, operation, maintenance security of the fission battery systems. Overall, we find that the development of the necessary security and safeguards requirements at the design phase of such a technology will be of great benefit in the smooth deployment of this modern nuclear energy system. [ABSTRACT FROM AUTHOR]

Science-based Human Reliability Analysis (HRA) seeks to experimentally validate HRA methods in simulator studies. Emphasis is on validating the internal components of the HRA method, rather than the validity and consistency of the final results of the method. In this paper, we assess the requirements for a simulator study validation of the Technique for Human Error Rate Prediction (THERP), a foundational HRA method. The aspects requiring validation include the tables of Human Error Probabilities (HEPs), the treatment of stress, and the treatment of dependence between tasks. We estimate the sample size, n , required to obtain statistically significant error rates for validating HEP values, and the number of observations, m , that constitute one observed error rate for each HEP value. We develop two methods for estimating the mean error rate using few observations. The first method uses the median error rate, and the second method is a Bayesian estimator of the error rate based on the observed errors and the number of observations. Both methods are tested using computer-generated data. We also conduct a pilot experiment in The Ohio State University’s Nuclear Power Plant Simulator Facility. Student operators perform a maintenance task in a BWR simulator. Errors are recorded, and error rates are compared to the THERP-predicted error rates. While the observed error rates are generally consistent with the THERP HEPs, further study is needed to provide confidence in these results as the pilot study sample size is small. Sample size calculations indicate that a full-scope THERP validation study would be a substantial but potentially feasible undertaking; 40 h of observation would provide sufficient data for a preliminary study, and observing 101 operators for 20 h each would provide data for a full validation experiment. [ABSTRACT FROM AUTHOR]

As digital instrumentation and control systems are being progressively introduced into nuclear power plants, a growing number of related technical issues are coming to light needing to be resolved. As a result, an understanding of relevant terms and basic concepts becomes increasingly important. Under the framework of the OECD/NEA WGRISK DIGREL Task Group, the authors were involved in reviewing definitions of terms forming the supporting vocabulary for addressing issues related to the safety and reliability analysis of digital instrumentation and control (SRA of DI&C). These definitions were extracted from various standards regulating the disciplines that form the technical and scientific basis of SRA DI&C. The authors discovered that different definitions are provided by different standards within a common discipline and used differently across various disciplines. This paper raises the concern that a common understanding of terms and basic concepts has not yet been established to address the very specific technical issues facing SRA DI&C. Based on the lessons learned from the review of the definitions of interest and the analysis of dependency relationships existing between these definitions, this paper establishes a set of recommendations for the development of a consistent terminology for SRA DI&C. [ABSTRACT FROM AUTHOR]

Maintenance policy optimization usually is faced with challenges that arise from an imperfect knowledge of system degradation models and from the partial observability of system degradation states. This paper proposes a reinforcement learning method to address these two challenges for a class of maintenance problems with Markov degradation processes. The reinforcement learning approach consists of a learning component and a planning component. Using sequentially collected observations, at each step of decision-making the learning component improves the knowledge of system degradation in terms of the probability distributions of the transition rates based on sequential Bayesian inference. Using the updated transition rates, at each step of decision-making the maintenance policy optimization problem is then formulated as a partially observable Markov decision problem, and the planning component computes the optimal maintenance policy that maximizes the expected cumulative reward. The proposed method is illustrated using a numerical example with repair and inspection maintenance actions. The result shows that as more observations are collected, the learning component progressively learns the true system degradation process, and the planning component adjusts the optimal maintenance policy accordingly as well, which leads to increased reward. • Maintenance optimization under imperfect knowledge of the system degradation model. • A reinforcement learning approach consisting of learning and planning is proposed. • Learning updates system degradation model knowledge with new observation at each step. • Planning then updates the optimal maintenance policy with the updated knowledge. • A numerical example demonstrates the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: This paper derives the human reliability model requirements for the Next Generation System Code which will be utilized to determine risk-informed safety margins for nuclear power plants through dynamic probabilistic risk analysis. The proposed model is flexible, with the facility to apply a coarse-grain or a fine-grain structure based on the desired resolution level. The varying resolution is achieved by employing human reliability analysis methods with the demonstrated capability of handling human errors that occur during the execution of procedural activities for the coarse-grain structure and the advanced cognitive IDA/IDAC method for the fine-grain structure. The paper proposes improvements to the existing IDA/IDAC model to incorporate functionalities demanded by the NGSC. The improvements are derived for four modules of IDA/IDAC. A Bayesian belief network is constructed for the performance-shaping factors and the conditional probability for existence of each factor is computed from data collected from aviation and nuclear accidents. The influence of the performance-shaping factors on the strategy-selection process of the operator is also depicted. A foundation is laid for the development of mental models with a focus on NPP operation. The research lists the modifications/additions required for the IDA/IDAC method to enable the incorporation of Human Reliability Analysis (HRA) into the Next Generation System Code. [Copyright &y& Elsevier]

Abstract: Dynamic reliability explicitly handles the interactions between the stochastic behaviour of system components and the deterministic behaviour of process variables. While dynamic reliability provides a more efficient and realistic way to perform probabilistic risk assessment than “static” approaches, its industrial level applications are still limited. Factors contributing to this situation are the inherent complexity of the theory and the lack of a generic platform. More recently the increased use of digital-based systems has also introduced additional modelling challenges related to specific interactions between system components. Typical examples are the “intelligent transmitters” which are able to exchange information, and to perform internal data processing and advanced functionalities. To make a contribution to solving these challenges, the mathematical framework of dynamic reliability is extended to handle the data and information which are processed and exchanged between systems components. Stochastic deviations that may affect system properties are also introduced to enhance the modelling of failures. A formalized Petri net approach is then presented to perform the corresponding reliability analyses using numerical methods. Following this formalism, a versatile model for the dynamic reliability modelling of digital-based transmitters is proposed. Finally the framework''s flexibility and effectiveness is demonstrated on a substantial case study involving a simplified model of a nuclear fast reactor. [Copyright &y& Elsevier]

Abstract: This paper presents an overview of QRAS, the Quantitative Risk Assessment System. QRAS is a PC-based software tool for conducting Probabilistic Risk Assessments (PRAs), which was developed to address risk analysis needs held by NASA. QRAS is, however, applicable in a wide range of industries. The philosophy behind the development of QRAS is to bridge communication and skill gaps between managers, engineers, and risk analysts by using representations of the risk model and analysis results that are easy to comprehend by each of those groups. For that purpose, event sequence diagrams (ESD) are being used as a replacement for event trees (ET) to model scenarios, and the quantification of events is possible through a set of quantification models familiar to engineers. An automated common cause failure (CCF) modeling tool further aids the risk modeling. QRAS applies BDD-based algorithms for the accurate and efficient computation of risk results. The paper presents QRAS'' modeling and analysis capabilities. The performance of the underlying BDD algorithm is assessed and compared to that of another PRA software tool, using a case study extracted from the International Space Station PRA. [Copyright &y& Elsevier]

Passive systems, operating on the principle of natural circulation, are adopted extensively in advanced and innovative nuclear reactor designs. Though passive systems are adjudged to be safe, simple and economical, they are often characterized by uncertainties. To ensure the effective and stable operation of these safety-based systems when deployed, it is important to evaluate their reliability using dependable methods. This work presents an extensive coverage of the research contributions to the thermal-hydraulic reliability of passive systems of simple configurations with the different approaches to passive systems reliability evaluation discussed. In addition, the technical issues in the key procedures for implementing thermal-hydraulic reliability methods which consist of systems characterization and reliability mathematical modelling, identification of influencing parameters, uncertainties quantification, sensitivity analysis and setting of failure criteria are extensively discussed. The drawbacks of the existing thermal-hydraulic reliability methods which serve as motivation for future research have been highlighted. The ways to resolve the issues associated with the highlighted procedures in thermal-hydraulic reliability analysis are proposed and efforts being made to improve on thermal-hydraulic reliability methods are discussed in detail. Furthermore, recommendations for further research necessary to ensure the development of robust and consistent thermal-hydraulic reliability methods are enumerated. This paper in its entirety thus serves as a synoptic guide to newcomers in reliability analysis, an up-to-date reference for researchers and also to set future research tracks in passive systems reliability. [ABSTRACT FROM AUTHOR]

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. • We demonstrated applications of CMS-BN to human reliability analysis and improvement. • The stuck open failure of the PORV in the TMI accident is used in the case studies. • Further insights into operator cognition can be drawn in human reliability analysis. • CMS-BN facilitates the comparison of various human performance improvement measures. • Sensitivity analysis was performed to identify the most significant model parameters. [ABSTRACT FROM AUTHOR]

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. • A cognitive modeling and simulation environment, CMS-BN, for human operator performance assessment is developed. • Operator cognitive functions of information perception, reasoning, and response are modeled. • Operator knowledge is represented through Bayesian networks. • Uncertainties in operator cognitive processes are considered and quantified through Monte Carlo simulation. • CMS-BN can be applied to both human reliability analysis and human performance improvement. [ABSTRACT FROM AUTHOR]

The wider use of digital systems in nuclear power plants has raised security concerns to the utilities, the regulatory entities, and the public. These digital systems provide malicious attackers with opportunities to exploit vulnerabilities in these systems to realize physical damages. Replay attacks are a particular type of attacks. They are easy to perform and when combined with other types of attacks can potentially lead to significant consequences. Therefore, timely detection of a replay attack is of vital importance for the operator to take mitigation measures. Besides, it is also important to distinguish between a replay attack and other anomalies, since they are of different nature (one intentional and the other random) and will require different responses by the operator. In this research, we propose a control-theoretic method to address this problem. The method consists of the injection of random noise (i.e. physical watermark) into the control input and two chi-squared tests. The physical watermark is used to excite the system being controlled so that the replay attack, if it exists, can be uncovered. The first chi-squared test is used to detect anomalies in the system, including replay attacks and other anomalies. If the null hypothesis in the first test is rejected, we use the second chi-squared test to determine whether the anomaly is a replay attack or any other anomaly. We demonstrate the proposed method by using a steam generator that can be found in pressurized water reactors. The results of different cases are presented and discussed. [ABSTRACT FROM AUTHOR]

• A Bayesian approach to quantifying performance shaping factors (PSFs) is proposed. • Updates a baseline PSF model with subjective, operator-reported simulator data. • Demonstration experiment uses student operators in a nuclear power plant simulator. • Proposed model incorporates baseline PSFs, simulator bias, and context effects. • The model estimates PSF distributions that capture variation between operators. Traditional human reliability analysis methods consist of two main steps: assigning values for performance shaping factors (PSFs), and assessing human error probability (HEP) based on PSF values. Both steps rely on expert judgment. Considerable advances have been made in reducing reliance on expert judgment for HEP assessment by incorporating human performance data from various sources (e.g., simulator experiments); however, little has been done to reduce reliance on expert judgment for PSF assignment. This paper introduces a data-driven approach for assessing PSFs in Nuclear Power Plants (NPPs) based on contextual information. The research illustrates how to develop a Bayesian PSF network using data collected from student operators in a NPP simulator. The approach starts with a baseline PSF model that calculates PSF values from context information during an accident scenario. Then, a Bayesian model is developed to link the baseline model to the Subjective PSFs. Two additional factors are included: simulator bias and context information. Results and analysis include variation between the results of the proposed model and the training dataset, and the significance of each element in the model. The proposed approach reduces the reliance of PSF assignment on expert judgment and is particularly suitable for dynamic human reliability analysis. [ABSTRACT FROM AUTHOR]

• Dynamic Probabilistic Risk Assessment for cybersecurity of electric grids. • Multiple physical and digital components are considered. • Cybersecurity stakeholders of electric grids are considered. • Multiple types of cyberattacks are supported. • Reconfigurable to perform risk assessments for different grid architectures. Electric grid cybersecurity risk has become a significant concern of industries and governments. This paper proposes a dynamic probabilistic risk assessment method for electric grid cybersecurity risk analysis. The proposed method helps reduce the reliance on expert judgment, capture a broad range of components and system dynamics, and model the interactions between various contributing entities (e.g., attacker, operator). In addition, the scenarios with multiple events, such as the occurrence of both cyberattacks and failures of physical components, the occurrence of both cyberattacks and operators' (in)correct reactions, are considered and analyzed. For each cyberattack scenario, Monte Carlo simulations are used to obtain possible sequences of the system's evolution under study and then derive risk estimates. As an application of the proposed method, the risk assessment method serves as the basis of risk-informed defense resource allocation to improve electric grid cybersecurity. The proposed method is verified using the IEEE 14-bus system by evaluating different security resource allocations for selected cyberattack scenarios. [ABSTRACT FROM AUTHOR]

• Development of a thermal power dispatch (TPD) system to transfer thermal energy from boiling water reactors to hydrogen plants. • TPD system heat exchangers designed with Aspen Exchanger Design and Rating software. • System thermal hydraulics analysis of TPD using Simulink Simscape. • TPD system performance assessed for both transient and steady state operations. Nuclear power plants are crucial to meeting net zero emission goals and achieving energy sustainability. Integrating these plants with clean energy technologies such as high-temperature steam electrolysis (HTSE) may improve the efficiency and economic competitiveness of these plants. The current study investigates the design and operation of a thermal power dispatch (TPD) system for coupling boiling water reactors (BWRs) to HTSE plants. The TPD system extracts a portion of the steam from the reactor's main steam line and transfers its thermal energy to an HTSE plant through a power transport loop. A TPD system for 5 % steam extraction has been designed and the system performance during steady and transient operations has been analyzed. The TPD system dispatched a total of 197 MW thermal energy to the HTSE plant under nominal design conditions. Saturated steam at 7.17 MPa from the BWR plant was condensed and subcooled to a temperature of 168 °C, while a mass flow rate of 91.1 kg/s of superheated steam was dispatched to the HTSE plant. Furthermore, the system performance during transient operation showed a continuous transition from the initial hot standby mode to the nominal power dispatch level. The transient simulation results emphasized the importance of investigating component level performance for the TPD system design. The current results will guide future works on the development of integrated energy systems for hydrogen production or process heat applications. [ABSTRACT FROM AUTHOR]

Abstract: Software plays an increasingly important role in modern safety-critical systems. Although, research has been done to integrate software into the classical probabilistic risk assessment (PRA) framework, current PRA practice overwhelmingly neglects the contribution of software to system risk. Dynamic probabilistic risk assessment (DPRA) is considered to be the next generation of PRA techniques. DPRA is a set of methods and techniques in which simulation models that represent the behavior of the elements of a system are exercised in order to identify risks and vulnerabilities of the system. The fact remains, however, that modeling software for use in the DPRA framework is also quite complex and very little has been done to address the question directly and comprehensively. This paper develops a methodology to integrate software contributions in the DPRA environment. The framework includes a software representation, and an approach to incorporate the software representation into the DPRA environment SimPRA. The software representation is based on multi-level objects and the paper also proposes a framework to simulate the multi-level objects in the simulation-based DPRA environment. This is a new methodology to address the state explosion problem in the DPRA environment. This study is the first systematic effort to integrate software risk contributions into DPRA environments. [Copyright &y& Elsevier]

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. • Multi-state system degradation and observation are described with the hidden Markov model. • A sequential algorithm for Bayesian inference of the transition rates in the hidden Markov model is proposed. • Both maintenance and observations collected at arbitrary times are considered in the algorithm. • The sequential algorithm is demonstrated using both synthetic data and realistic data. [ABSTRACT FROM AUTHOR]

Abstract: Ninety-nine clinical isolates previously identified as Klebsiella oxytoca were evaluated using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). Eight isolates were identified as Raoultella spp., being 5 Raoultella spp. and 3 K. oxytoca, by 16S rRNA sequencing. These isolates were correctly identified by applying the 10% differential rule for the MALDI-TOF MS score values. This approach might be useful to discriminate Raoultella species from K. oxytoca. [Copyright &y& Elsevier]

Piping and Instrumentation Diagrams (P&IDs) are the most commonly used engineering drawings to describe components and their relationships, and they are one of the most important inputs for data analysis in Nuclear Power Plants (NPP). In traditional analysis, the information related to the components is extracted manually from the P&IDs. This usually takes large amounts of effort and is error-prone. With the rapid development in the area of computer vision and deep learning, automatically detecting components and their relationships becomes possible. In this paper, we aim to use the latest neural network models to automatically extract information on components and their identifications, from the P&IDs in NPPs. We use a Faster Regional Convolutional Neural Networks (Faster RCNN) architecture called ResNet-50, to detect the components in the P&IDs. Compared to common object detection, object detection for P&IDs poses unique challenges to these methods. For example, the P&IDs symbols are much smaller than the background, and detecting such small objects remains a challenging task for modern neural networks. To address these challenges, we 1) propose several techniques for data augmentation that effectively solve the problem of training data shortage, and 2) propose a feature grouping strategy for detecting components with distinct features. Besides, we introduce a SegLink model for text detection, which can automatically extract components' identifications from P&IDs. We also develop a method for building a data structure to reflect the relationships between components (e.g., to which pipe a component is connected, or what are the downstream or upstream components of one specific component) based on the extracted information. This data structure can be further used for plant safety analysis, and operation and maintenance cost optimization. Sensitivity analysis and comparison with other Convolutional Neural Networks (CNNs) are performed. The results of these analyses are also discussed in this paper. This analysis framework has been tested on the P&IDs from a commercial NPP. The Average Precision for components, which is used to measure the performance of the proposed method, is about 98%. The success rates of component-text mapping and component-pipe mapping are 270/275 and 319/319, respectively. It is worth noting that this framework is generic and can also be applied to P&IDs of non-nuclear industries. [ABSTRACT FROM AUTHOR]

Cybersecurity has drawn increasing attention in the nuclear industry. To improve the cyber-security posture, it is important to develop effective methods for cyber-attack response and cyber-security risk assessment. In this research, we develop a finite-horizon semi-Markov general-sum game between the defender (i.e., plant operator) and the attacker to obtain the time-sensitive attack response strategy and the real-time risk assessment in nuclear power plants. We propose methods for identifying system states of concern to reduce the state space and for determining state transition probabilities by integrating probabilistic risk assessment techniques. After a proper discretization of the developed continuous-time model, we use dynamic programming to derive the time-varying and state-dependent strategy of the defender based on the solution concept of the mixed-strategy Nash equilibrium. For risk assessment, three risk metrics are considered, and an exact analytical algorithm and a Monte Carlo simulation-based algorithm for obtaining the metrics are developed. Both players' strategies and the risk metrics are illustrated using a digital feedwater control system used in pressurized water reactors. The results show that the proposed method can support plant operators in timely cyber-attack response and effective risk assessment, reduce the risk, and improve the resilience of nuclear power plants to malicious cyber-attacks. [ABSTRACT FROM AUTHOR]

Health monitoring provides opportunities to improve industrial system safety and to reduce system operation and maintenance cost due to more effective condition-based actions. Among the various methods for health monitoring, model-based methods exhibit advantages over data-driven methods in terms of explainability. This advantage is particularly promising for safety-critical systems, for example, nuclear power plants. However, the performance of model-based methods is heavily dependent on the knowledge of the parameters in the model for a system of interest. This knowledge may be lacking or be inaccurate initially, which poses challenges to applications of model-based health monitoring. Various methods for model parameter estimation, in particular, sequential learning, have been proposed in the literature. This research aims to investigate the added value of sequential learning of model parameters to industrial system health monitoring. Case studies based on solenoid-operated valve degradation are performed to illustrate such added values. Results based on synthetic data and experimental data demonstrate that, by considering the sequential learning scheme, the health monitoring accuracy is improved and in certain situations the uncertainty in the health monitoring result is reduced, compared to cases where the sequential learning scheme is not considered. • Sequential model parameter learning for system health monitoring is studied. • Empirical studies demonstrate the value of sequential model parameter learning. • Solenoid-operated valve degradation was investigated in the empirical studies. [ABSTRACT FROM AUTHOR]

The present experiment tested the causal impact of testosterone on human competitive decision-making. According to prevailing theories about testosterone’s role in social behavior, testosterone should directly boost competitive decisions. But recent correlational evidence suggests that testosterone’s behavioral effects may depend on specific aspects of the context and person relevant to social status (win–lose context and trait dominance). We tested the causal influence of testosterone on competitive decisions by combining hormone administration with measures of trait dominance and a newly developed social competition task in which the victory–defeat context was experimentally manipulated, in a sample of 54 female participants. Consistent with the hypothesis that testosterone has context- and person-dependent effects on competitive behavior, testosterone increased competitive decisions after victory only among high-dominant individuals but testosterone decreased competitive decisions after defeat across all participants. These results suggest that testosterone flexibly modulates competitive decision-making depending on prior social experience and dominance motivation in the service of enhancing social status. [ABSTRACT FROM AUTHOR]

• A propagation-based fault detection and discrimination method is proposed. • Fault diagnosis strategies in steady states and transient states are derived. • Optimization of sensor deployment in the design phase is studied. • A brute force algorithm and a genetic algorithm is developed for sensor deployment. • The methods are verified using the GOTHIC model of EBR-II. Industrial processes can be affected by faults having a serious impact on operation when not promptly detected and diagnosed. In this paper, a propagation-basedfaultdetectionanddiscrimination(PFDD) method is proposed to develop a strategy for fault diagnosis while in the design phase of a system. The PFDD method constructs the system model using the IntegratedSystemFaultAnalysis(ISFA) technique. Based on the system model, the propagation of hardware and software faults are simulated qualitatively. Given the results of the simulation, the process by which a fault propagates can be characterized using the qualitative features of system variables including the deviation of the system variables from their expected values, the variation of the system variables over time, and the order in which each variable is influenced during the propagation of the fault. The strategy by which a fault can be detected and discriminated is defined using those features. The PFDD method supports the detection and discrimination of faults in both steady states and transient states. Based on the PFDD method, the optimization of sensor deployment in a system is discussed. A brute force algorithm is developed to examine the system's capability at diagnosing faults and the cost of sensor deployment for all possible configurations of sensors. The optimal sensor deployment strategy can be derived accordingly. However, the brute force method is only applicable to small-scale systems due to its high computational cost. A genetic algorithm is used to optimize sensor deployment in large-scale systems. The PFDD and sensor deployment optimization methods are applied to the Experimental Breeder Reactor II (EBR-II) for verification. [ABSTRACT FROM AUTHOR]

Objective: Written case simulations are increasingly being used to investigate clinical decision making. Our study was designed to determine the validity of written case simulations within a conjoint analysis approach.Study design and setting: We developed a series of 32 written case simulations that differed with respect to nine clinical characteristics. These case simulations represented elderly patients with aortic stenosis. The clinical characteristics varied according to a fractional factorial design. We analyzed retrospectively all consecutive patients of 70 years of age or older with an aortic stenosis in three university hospitals.Results: 34 cardiologists from three Dutch hospitals gave their treatment advice to each of these case simulations on a six-point scale (ranging from ‘certainly no’ to ‘certainly yes’ to surgical treatment). We compared the influence that the clinical characteristics had on the responses to these case simulations with their influence on the actual treatment decision for 147 actual patients in the same three hospitals. We found a strong agreement. This agreement was only slightly affected by the cut-off value used to dichotomize the treatment advice into a recommendation in favor of or against surgical treatment.Conclusion: Written case simulations reflect well how clinicians are influenced by specific clinical characteristics of their patients. [Copyright &y& Elsevier]

This paper proposes an expert-based method to analyze functional failure risk in the fracturing system of unconventional natural gas. The newly proposed Fuzzy-FFIP method integrates functional failure identification and propagation (FFIP) with fuzzy logic. Fuzzy-FFIP is sufficient for dealing with the complexity and correlation of functional failures and the lack of failure data, as well as capturing the fuzziness of functional states of a critical component and entire system. From a configuration-behavior-function aspect, FFIP is a common framework used to represent system-wide logical relationships and study functional failure propagation paths. This framework is an expert system where knowledge base and inference engine are based on behavioral rules (BRs) and functional failure logic (FFL), respectively. Using FFL as a support, fuzzy logic is specifically applied to analyze the fuzzification and defuzzification of functional states – operating, degraded, and lost. The proposed method can clearly provide functional failure modes, effectively reveal failure propagation paths, and quantitatively assess functional failure risk levels in the fracturing system. To illustrate its validity, an on-site pump lubricant subsystem of fracturing unit is selected as a test case. Results show that Fuzzy-FFIP is more detailed and accurate, and contributes to system safety during the whole fracturing period. • An expert-based method, Fuzzy-FFIP, for the fracturing system of unconventional natural gas. • It deals with the complexity, correlation, and fuzziness of functional failures. • Fuzzy-FFIP is detailed and accurate in assessing functional failure risk levels. • Testing and validation of the proposed method using a case study. [ABSTRACT FROM AUTHOR]

With increased adoption of digital systems for instrumentation and control, nuclear power plants have become more vulnerable to cyber-attacks. Such attacks can have very serious implications on a plant's operation, especially if they masquerade as safety events. Thus, it is important that research be focused towards distinguishing cyber-attacks from fault induced safety events for a correct response in a timely manner. In this paper, an event classifier is presented to classify abnormal events in nuclear power plants as either fault induced safety events or cyber-attacks. The process of inferring the hidden (unobservable) state of a system (normal or faulty) through observable physical sensor measurements has been a long-standing industry practice. There has been a recent surge of literature discussing the use of network traffic data for the detection of cyber-attacks on industrial control systems. In the classifier we present, both physical and network behaviors of a nuclear power plant during abnormal events (safety events or cyber-attacks) are used to infer the probabilities of the states of the plant. The nature of the abnormal event in question is then determined based on these probabilities. The Dynamic Bayesian Networks (DBNs) methodology is used for this purpose since it is an appropriate framework for inferring hidden states of a system from observed variables through probabilistic reasoning. In this paper we introduce a DBN based abnormal event classifier and an architecture to implement this classifier as part of a monitoring system. An experimental environment is setup with a two-tank system in conjunction with a nuclear power plant simulator and a programmable logic controller. A set of 27 cyber-attacks and 14 safety events were systematically designed for the experiment. A set of 6 cyber-attacks and 2 safety events were used to manually finetune the Conditional Probability Tables (CPTs) of the 2-timeslice dynamic Bayesian network (2T-DBN). Out of the remaining 33 events, the nature of the abnormal event was successfully identified in all the 33 cases and the location of the cyber-attack or fault was successfully determined in 32 cases. The case-study demonstrates the applicability of the methodology developed. Further research should examine the practicality of implementing the proposed monitoring system on a real-world system and issues associated with cost optimization. [ABSTRACT FROM AUTHOR]