Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network

The operational activities conducted in a shipyard are exposed to high risk associated with human factors. To investigate human factors involved in shipyard operational accidents, a double-nested model was proposed in the present study. The modified human factor analysis classification system (HFACS) was applied to identify the human factors involved in the accidents, the results of which were then converted into diverse components of a fault tree and, as a result, a single-level nested model was established. For the development of a double-nested model, the structured fault tree was mapped into a Bayesian network (BN), which can be simulated with the obtained prior probabilities of parent nodes and the conditional probability table by fuzzy theory and expert elicitation. Finally, the developed BN model is simulated for various scenarios to analyze the identified human factors by means of structural analysis, path dependencies and sensitivity analysis. The general interpretation of these analysis verify the effectiveness of the proposed methodology to evaluate the human factor risks involved in operational accidents in a shipyard.

Supervised linear classification of Gaussian spatio-temporal data

In this article we focus on the problem of supervised classifying of the spatio-temporal Gaussian random field observation into one of two classes, specified by different mean parameters. The main distinctive feature of the proposed approach is allowing the class label to depend on spatial location as well as on time moment. It is assumed that the spatio-temporal covariance structure factors into a purely spatial component and a purely temporal component following AR(p) model. In numerical illustrations with simulated data, the influence of the values of spatial and temporal covariance parameters to the derived error rates for several prior probabilities models are studied.

Update of Prior Probabilities by Minimal Divergence

The present paper investigates the update of an empirical probability distribution with the results of a new set of observations. The update reproduces the new observations and interpolates using prior information. The optimal update is obtained by minimizing either the Hellinger distance or the quadratic Bregman divergence. The results obtained by the two methods differ. Updates with information about conditional probabilities are considered as well.

Quantum Contextual Advantage Depending on Nonzero Prior Probabilities in State Discrimination of Mixed Qubit States

Recently, Schmid and Spekkens studied the quantum contextuality in terms of state discrimination. By dealing with the minimum error discrimination of two quantum states with identical prior probabilities, they reported that quantum contextual advantage exists. Meanwhile, if one notes a striking observation that the selection of prior probability can affect the quantum properties of the system, it is necessary to verify whether the quantum contextual advantage depends on the prior probabilities of the given states. In this paper, we consider the minimum error discrimination of two states with arbitrary prior probabilities, in which both states are pure or mixed. We show that the quantum contextual advantage in state discrimination may depend on the prior probabilities of the given states. In particular, even though the quantum contextual advantage always exists in the state discrimination of two nonorthogonal pure states with nonzero prior probabilities, the quantum contextual advantage depends on prior probabilities in the state discrimination of two mixed states.

Abstract 10617: Antiarrhythmic Use and the Posterior Probability of Neurologically Intact Survival in a Reanalysis of the Amiodarone, Lidocaine or Placebo Study in Out-of-Hospital Cardiac Arrest

Introduction: There are no randomized controlled trials (RCT) demonstrating improvement in neurologically intact survival from antiarrhythmic therapy given during out-of-hospital cardiac arrest (OHCA) from ventricular fibrillation/tachycardia (VF/VT). The Amiodarone, Lidocaine or Placebo Study in Out-of-Hospital Cardiac Arrest (ALPS) was an RCT of amiodarone, lidocaine or placebo whose primary end-point was survival to hospital discharge. We sought to estimate the posterior probability of the absolute risk difference of neurologically intact survival (modified Rankin Score ≤ 3) from antiarrhythmic use (amiodarone or lidocaine) compared to placebo and from the use amiodarone versus lidocaine. Methods: We performed a Bayesian reanalysis on the per-protocol population of the ALPS trial in order to calculate the posterior probability of neurologically intact survival. We derived prior probabilities from the Amiodarone for Resuscitation after Out-of-Hospital Cardiac Arrest Due to Ventricular Fibrillation (ARREST) and Amiodarone Compared with Lidocaine for Shock-Resistant Ventricular Fibrillation (ALIVE) trials. We considered a clinically meaningful absolute difference to be ≥ 1%. Results: The posterior median probability of the absolute difference in neurologically intact survival between antiarrhythmic therapy and placebo was 2.2% (95% credible interval of -0.15% to 4.7%). There is a 96% chance that antiarrhythmic improves neurologic outcome and 86% chance of a clinically meaningful improvement. The posterior median probability of the absolute difference in neurologically intact survival between amiodarone and lidocaine was 1.5% (95% credible interval -1.6% to 4.5%). Conclusion: The results of this Bayesian analysis of the ALPS trial using likely optimistic prior probabilities derived from the ARREST trial may help inform clinicians of the value of antiarrhythmic therapy in OHCA.

Reliability Analysis of Metro Door System Based on Fuzzy Multi-State Bayesian Network

Considering the shortcomings of the fault tree analysis (FTA) method in the reliability analysis of metro door systems, Bayesian network (BN) and fuzzy theory were introduced to establish the failure probability model of a metro door system. A fault tree of the metro door system was established based on the structure of the metro door, the operation data record and the practical experience of relevant engineers. The BN of the metro door system was constructed based on the fault tree. For the problem that the prior probabilities of root nodes with missing data were unavailable, fuzzy theory was introduced to convert the expert language values on these missing data nodes to corresponding prior probabilities, which were substituted into the BN along with the root nodes whose prior probabilities were obtained from the operation fault data to calculate the leaf node probability. Cause analysis of the metro door system was performed with bi-directional reasoning of BN, which provided a way to find the key factors that caused door faults and the metro door system fault probabilities.

Quantum nonlocality without entanglement depending on nonzero prior probabilities in optimal unambiguous discrimination

Nonlocality without entanglement(NLWE) is a nonlocal phenomenon that occurs in quantum state discrimination of multipartite separable states. In the discrimination of orthogonal separable states, the term NLWE is used when the quantum states cannot be discriminated perfectly by local operations and classical communication. In this case, the occurrence of NLWE is independent of nonzero prior probabilities of quantum states being prepared. Recently, it has been found that the occurrence of NLWE can depend on nonzero prior probabilities in minimum-error discrimination of nonorthogonal separable states. Here, we show that even in optimal unambiguous discrimination, the occurrence of NLWE can depend on nonzero prior probabilities. We further show that NLWE can occur regardless of nonzero prior probabilities, even if only one state can be locally discriminated without error. Our results provide new insights into classifying sets of multipartite quantum states in terms of quantum state discrimination.

An early burst in brachiopod evolution corresponding with significant climatic shifts during the Great Ordovician Biodiversification Event

We employ modified tip-dating methods to date divergence times within the Strophomenoidea, one of the most abundant and species-rich brachiopod clades to radiate during the Great Ordovician Biodiversification Event (GOBE), to determine if significant environmental changes at this time correlate with the diversification of the clade. Models using origination, extinction and sampling rates to estimate prior probabilities of divergence times strongly support both high rates of anatomical change per million years and rapid divergences shortly before the clade first appears in the fossil record. These divergence times indicate much higher rates of cladogenesis than are typical of brachiopods during this interval. The correspondence of high speciation rates and high anatomical disparity suggests punctuated (speciational) change drove the high frequencies of early anatomical change, which in turn suggests increased ecological opportunities rather than shifting developmental constraints account for high rates of anatomical change. The pulse of rapid evolution began coincident with cooling temperatures, the start of major oscillations in sea level and increased levels of atmospheric oxygen. Our results suggest that these factors permitted major geographical and ecological expansion of strophomenoids with intervals of geographical isolation, resulting in elevated speciation rates and corresponding elevated frequencies of punctuated change.

Interpreting Wastewater SARS-CoV-2 Results using Bayesian Analysis

Wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS–CoV–2) has proven a practical complement to clinical data for assessing community-scale infection trends. Clinical assays, such as the CDC–promulgated N1, N2, and N3 have been used to detect and quantify viral RNA in wastewater but, to date, have not included estimates of reliability of true positive or true negative. Bayes Theorem was applied to estimate Type I and Type II error rates for detections of the virus in wastewater. Conditional probabilities of true positive or true negative were investigated when one assay was used, or multiple assays were run concurrently. Cumulative probability analysis was used to assess the likelihood of true SARS–CoV–2 detection using multiple samples. Results demonstrate highly reliable positive (>0.86 for priors >0.25) and negative (>0.80 for priors = 0.50) results using a single assay. Using N1 and N2 concurrently caused greater reliability (>0.99 for priors <0.05) when results concurred but generated potentially counterintuitive interpretations when results were discordant. Regional wastewater surveillance data was investigated as a means of setting prior probabilities. Probability of true detection with a single marker was investigated using cumulative probability across all combinations of positive and negative results for a set of three samples. Findings using a low (0.11) and uniformed (0.50) initial prior resulted in high probabilities of detection (>0.95) even when a set of samples included one or two negative results, demonstrating the influence of high sensitivity and specificity values. Analyses presented here provide a practical framework for understanding analytical results generated by wastewater surveillance programs.