A scenario analysis under epistemic uncertainty in Natech accidents: imprecise probability reasoning in Bayesian Network

The increasing frequency and severity of Natech accidents warn us to investigate the occurrence mechanism of these events. Cascading disasters chain magnifies the impact of natural hazards due to its propagation through critical infrastructures and socio-economic networks. In order to manipulate imprecise probabilities of cascading events in Natech scenarios, this work proposes an improved Bayesian network （BN） combining with evidence theory to better deal with epistemic uncertainty in Natech accidents than traditional BNs. Effective inference algorithms have been developed to propagate system faulty in a socio-economic system. The conditional probability table (CPT) of BN in the traditional probability approach is modified by utilizing an OR/AND gate to obtain the belief mass propagation in the framework of evidence theory. Our improved Bayesian network methodology makes it possible to assess the impact and damage of Natech accidents under the environment of complex interdependence among accidents with insufficient data. Finally, a case study of Guangdong province, an area prone to natural disasters, is given. The modified Bayesian network is carried out to analyze this area’s Natech scenario. After diagnostic analysis and sensitivity analysis of human factors and the natural factor, we are able to locate the key nodes in the cascading disaster chain. Findings can provide useful theoretical support for urban managers of industrial cities to enhance disaster prevention and mitigation ability.

Research on Identification of Power Grid Weakness Based on Bayesian Inference

With the gradual reform and development of the power grid, it is of great significance to study how to effectively identify and evaluate the weak links of the power grid for the actual planning, construction, and operation of the power grid. This paper analyzed the power grid’s historical component data and real-time operation state parameters. We established a weak link identification model based on Bayesian reasoning. Firstly, we constructed the node branch Bayesian network according to the network topology relationship. The power transmission distribution factor is modified according to the historical operation load of the grid components, and the conditional probability table is calculated based on the grid structure; finally, we used the maximum possible explanation algorithm in the Bayesian network. The weakness degree of all components in the network is calculated, and the maximum probability weak link sequence is obtained. The correctness and effectiveness of the proposed method are verified by IEEE 39 bus simulation and regional power grid data.

Refinements of the Pin-Based Pointwise Energy Slowing-Down Method for Resonance Self-Shielding Calculation—I: Theory

The pin-based pointwise energy slowing-down method (PSM), which is a resonance self-shielding method, has been refined to treat the nonuniformity of material compositions and temperature profile in the fuel pellet by calculating the exact collision probability in the radially subdivided fuel pellet under the isolated system. The PSM has generated the collision probability table before solving the pointwise energy slowing-down equation. It is not exact if the fuel pellet has nonuniform material compositions or temperature profile in all the subdivided regions. In the refined PSM-CPM, the pre-generated table is not required for directly calculating the collision probability in all the subdivided regions of the fuel pellet while solving the slowing-down equation. There are an advantage and a disadvantage to the method. The advantage is to exactly consider the nonuniformity of the material compositions and temperature profile in the fuel pellet. The disadvantage is the longer computing time than that of the PSM when the fuel pellet has more than five subdivided regions. However, in the practical use for UO2 pin-cells, it is still comparable for the computation time with the PSM and the conventional equivalence theory methods. In this article, using simple light water reactor 17 × 17 F A problems with a uniform material composition and temperature profile, it is demonstrated that PSMs (PSM and PSM-CPM) exhibit consistent accuracy in calculating the multiplication factor and the pin power distribution with no compromise in the computation time. More detailed accuracy assessments with various test cases, including problems representing the nonuniformity, are presented in the accompanying article.

Genetic Algorithm-Based Hybrid Spectrum Handoff Strategy in Cognitive Radio-Based Internet of Things

In Cognitive radio-based Internet of Things (CR-IoT) systems, the return of the primary user (PU) causes the secondary user (SU) that is communicating to face the spectrum handoff problem. In the process of spectrum handoff, the user terminal can’t get the idle channels in time because of the unknown channel usage state. To solve this problem, a hybrid spectrum handoff algorithm based on the channel idle probability is proposed. The algorithm considers the regularity of PU activities in space and time, defines the idle probability of channels from the perspective of week attributes and time periods, obtains the optimal time period length using genetic algorithm, generates a channel idle probability table, and provides the target channel sequence for SUs in combination with the proposed channel ordering scheme. Simulation results show that the hybrid spectrum switching algorithm based on the channel idle probability can reduce the energy consumption of SUs during spectrum switching, reduce the communication interruption rate, enable SUs to find idle channels faster and reduce the number of sensing times.

Reducing the Complexity of Casual Representation in Bayesian Belief Network

Background: Bayesian Belief Network (BBN) is a well-established causal framework that is widely adopted in various domains and has a proven track record of success in research and application areas. However, BBN has weaknesses in causal knowledge elicitation and representation. The representation of the joint probability distribution in the Conditional Probability Table (CPT) has increased the complexity and difficulty for the user either in comprehending the causal knowledge or using it as a front-end modelling tool. Methods: This study aims to propose a simplified version of the BBN ─ Bayesian causal model, which can represent the BBN intuitively and proposes an inference method based on the simplified version of BBN. The CPT in the BBN is replaced with the causal weight in the range of[-1,+1] to indicate the causal influence between the nodes. In addition, an inferential algorithm is proposed to compute and propagate the influence in the causal model. Results: A case study is used to validate the proposed inferential algorithm. The results show that a Bayesian causal model is able to predict and diagnose the increment and decrement as in BBN. Conclusions: The Bayesian causal model that serves as a simplified version of BBN has shown its advantages in modelling and representation, especially from the knowledge engineering perspective.

Determine the Applicability of Moyers Analysis in the Contemporary Western Australian Adolescent Population

Background: Moyers probability tables have been widely used to determine the mesiodistal dimensions of unerupted canines and premolars for mixed dentition space analysis. Secular, racial, and sexual dimorphism have been reported in the literature and applicability of Moyer analysis has been doubted in many other populations. Objective: The present study was conducted to determine the applicability of Moyers probability tables and develop a more accurate prediction method in a contemporary Western Australian adolescent population if needed. Methods: A retrospective study including 500 participants [323 females, 177 males] between 13-18 years old attending the orthodontic private practice was conducted. Mesiodistal dimensions of selected participants were obtained from pre-orthodontic treatment digital dental records using Invisalign® technology [ Invisalign®, Align Tech¬nology, Santa Clara, CA, USA] Data were analysed using SPSS. New regression equations were derived based on the sum of permanent mandibular incisors, and probability tables were proposed for more accurate prediction. Results: Significant differences were found between the measured sum of permanent canine-premolar segments and those predicted with the Moyers probability table, at all percentile levels, except the 50th percentile, where no significant difference was observed. Conclusion: Moyers probability table can be applied at the 50th percentile for estimation of sum of mesiodistal dimensions of canine-premolars segments. Newly developed regression equations and tables could be considered to provide more accurate mixed dentition space analysis.

The Effect of Serpent 2 Calculation Parameters on Evaluated Spent Nuclear Fuel Source Term

The estimation of spent nuclear fuel source term (decay heat, reactivity, nuclide inventory etc.) has several sources of uncertainty such as uncertainties in nuclear data, uncertainties in the operation history, choice of calculation parameters etc. In this work the effect of calculation parameters is studied by estimating the source term with the built-in burnup capability of Serpent. The effect of the following parameters is considered: depletion zone division, burnup steps, unresolved resonance probability table sampling, Doppler-Broadening Rejection Correction (DBRC) and energy dependent branching ratios. As a test case a 2D BWR fuel assembly was modelled by first running a burnup calculation followed by a decay calculation. The following source term components were considered when investigating the effect of the studied parameters: total decay heat, photon emission rate and spontaneous fission rate. In general the differences resulting from the use of different parameter variations were small for all three studied source term components. For the decay heat largest absolute relative difference was approximately 0.6 % and for the photon emission rate approximately 1.1 %. For the spontaneous fission rate maximum absolute relative difference of nearly 8 % was observed. For all three components the variation of the depletion zone division resulted in the largest relative differences. Clear differences were also observed for burnup step length and DBRC variations. The use of unresolved resonance probability table sampling and energy dependent branching ratios had an insignificant effect on the studied source term components.

MO767CENTRAL VEIN STENOSIS: FROM DIAGNOSTICS TO TREATMENT STRATEGY

Background and Aims To analyze the screening and prognostic value of various diagnostic signs of central vein stenosis (CVS). Method The retrospective study included 549 patients with AVF/AVG dysfunction. 211 patients were diagnosed with CVS, other patients had lesions of peripheral venous segments. In case of vascular access dysfunction, patients were examined according to the local protocol: ultrasound examination of peripheral (to exclude damage of the peripheral AVF segments) and central veins (at an accessible length) was performed, followed by CT angiography or percutaneous angiography, if necessary. Results Among various clinical signs only limb edema and dilated veins on the chest demonstrated high sensitivity. Aneurysmal dilatation of an AVF was more common in peripheral lesions (CVS RR<1). It should be considered that the clinical signs itself are more likely to be associated with the peripheral lesion than CVS (high NPV value) – table 1. “Indirect dialysis signs” also have very low screening efficacy (Se and Sp) and prognostic values – table 2. Ultrasound signs have low screening accuracy, but high prognostic values. In other words, in the presence of ultrasound signs, patient is likely to have CVS with high probability – table 3. When comparing the frequency of correct and incorrect classifications for different types of lesion, ultrasound accuracy exceeds the clinical picture for types of lesion 1C and 1D (according to the types of lesion in Society of Interventional Radiology Reporting Standards for Thoracic Central Vein Obstruction, doi: 10.1016/j.jvir.2017.12.013): RR= 3,433 [95% CI 2,074; 6,132], p<0.0001 and RR= 2,538 [95% CI 1,592; 4,265], p<0.0001, respectively, but not for lesion types 2B and 3: RR=1.583 [95% CI 0.935; 2.779], p=0.0883 and RR=1 [95% CI 0.231; 4.325], p >0.9999, respectively. In total, incorrect CVS type classifications is noted in 30.7% of cases when using ultrasound imaging. CT angiography and percutaneous angiography are almost 100% effective in the diagnosis of CVS. However, CT angiography is not always informative when AVF is functioning and the volume blood flow is high. In turn, percutaneous angiography does not always allow to assess the condition of the veins of the contralateral side. At the same time, correct detection of lesion type is pivotal to determine the future strategy of providing the patient with permanent vascular access. For example, the patient has very little chance to get functional AVF in case of with two-sided subclavian vein stenosis. Conclusion The CVS diagnostics should be comprehensive, using different methods for screening, confirming CVS and determining of it type.

Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network

The safety level of the northern sea route (NSR) is a common concern for the related stakeholders. To address the risks triggered by disruptions initiating from the harsh environment and human factors, a comprehensive framework is proposed based on the perspective of resilience. Notably, the resilience of NSR is decomposed into three capacities, namely, the absorptive capacity, adaptive capacity, and restorative capacity. Moreover, the disruptions to the resilience are identified. Then, a Bayesian network (BN) model is established to quantify resilience, and the prior probabilities of parent nodes and conditional probability table for the network are obtained by fuzzy theory and expert elicitation. Finally, the developed Bayesian networkBN model is simulated to analyze the resilience level of the NSR by back propagation, sensitivity analysis, and information entropy analysis. The general interpretation of these analyses indicates that the emergency response, ice-breaking capacity, and rescue and anti-pollution facilities are the critical factors that contribute to the resilience of the NSR. Good knowledge of the absorptive capacity is the most effective way to reduce the uncertainty of NSR resilience. The present study provides a resilience perspective to understand the safety issues associated with the NSR, which can be seen as the main innovation of this work.

Efficient Decomposition of Bayesian Networks With Non-graded Variables

Elicitation, estimation and exact inference in Bayesian Networks (BNs) are often difficult because the dimension of each Conditional Probability Table (CPT) grows exponentially with the increase in the number of parent variables. The Noisy-MAX decomposition has been proposed to break down a large CPT into several smaller CPTs exploiting the assumption of causal independence, i.e., absence of causal interaction among parent variables. In this way, the number of conditional probabilities to be elicited or estimated and the computational burden of the joint tree algorithm for exact inference are reduced. Unfortunately, the Noisy-MAX decomposition is suited to graded variables only, i.e., ordinal variables with the lowest state as reference, but real-world applications of BNs may also involve a number of non-graded variables, like the ones with reference state in the middle of the sample space (double-graded variables) and with two or more unordered non-reference states (multi-valued nominal variables). In this paper, we propose the causal independence decomposition, which includes the Noisy-MAX and two generalizations suited to double-graded and multi-valued nominal variables. While the general definition of BN implicitly assumes the presence of all the possible causal interactions, our proposal is based on causal independence, and causal interaction is a feature that can be added upon need. The impact of our proposal is investigated on a published BN for the diagnosis of acute cardiopulmonary diseases.