Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal

The scenario in a risk analysis can be defined as the propagating feature of specific initiating event which can go to a wide range of undesirable consequences. If we take various scenarios into consideration, the risk analysis becomes more complex than do without them. A lot of risk analyses have been performed to actually estimate a risk profile under both uncertain future states of hazard sources and undesirable scenarios. Unfortunately, in case of considering specific systems such as a radioactive waste disposal facility, since the behaviour of future scenarios is hardly predicted without special reasoning process, we cannot estimate their risk only with a traditional risk analysis methodology. Moreover, we believe that the sources of uncertainty at future states can be reduced pertinently by setting up dependency relationships interrelating geological, hydrological, and ecological aspects of the site with all the scenarios. It is then required current methodology of uncertainty analysis of the waste disposal facility be revisited under this belief.In order to consider the effects predicting from an evolution of environmental conditions of waste disposal facilities, this paper proposes a quantitative assessment framework integrating the inference process of Bayesian network to the traditional probabilistic risk analysis. We developed and verified an approximate probabilistic inference program for the specific Bayesian network using a bounded-variance likelihood weighting algorithm. Ultimately, specific models, including a model for uncertainty propagation of relevant parameters were developed with a comparison of variable-specific effects due to the occurrence of diverse altered evolution scenarios (AESs). After providing supporting information to get a variety of quantitative expectations about the dependency relationship between domain variables and AESs, we could connect the results of probabilistic inference from the Bayesian network with the consequence evaluation model addressed. We got a number of practical results to improve current knowledge base for the prioritization of future risk-dominant variables in an actual site.     

Protein deep profile and model predictions for identifying the causal genes of male infertility based on deep learning

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. Millions of genes have been sequenced in data-driven genomics era, but their causal relationships with disease phenotypes remain limited, due to the difficulty of elucidating underlying causal genes by laboratory-based strategies. Here, we proposed an innovative deep learning computational modeling alternative (DPPCG framework) for identifying causal (coding) genes for a specific disease phenotype. In terms of male infertility, we introduced proteins as intermediate cell variables, leveraging integrated deep knowledge representations (Word2vec, ProtVec, Node2vec, and Space2vec) quantitatively represented as ‘protein deep profiles’. We adopted deep convolutional neural network (CNN) classifier to model protein deep profiles relationships with male infertility, creatively training deep CNN models of single-label binary classification and multi-label eight classification. We demonstrate the capabilities of DPPCG framework by integrating and fully harnessing the utility of heterogeneous biomedical big data, including literature, protein sequences, protein–protein interactions, gene expressions, and gene–phenotype relationships, and effective indirect prediction of 794 causal genes of male infertility and associated pathological processes. We present this research in an interactive ‘Smart Protein’ intelligent (demo) system (http://www.smartprotein.cloud/public/home). Researchers can benefit from our intelligent system by (i) accessing a shallow gene/protein-radar service involving research status and a knowledge graph-based vertical search; (ii) querying and downloading protein deep profile matrices; (iii) accessing intelligent recommendations for causal genes of male infertility and associated pathological processes, and references for model architectures, parameter settings, and training outputs; and (iv) carrying out personalized analysis such as online K-Means clustering.     

A Bayesian Perspective on the Analysis of Unreplicated Factorial Experiments Using Potential Outcomes

Unreplicated factorial designs have been widely used in scientific and industrial settings, when it is important to distinguish “active” or real factorial effects from “inactive” or noise factorial effects used to estimate residual or “error” terms. We propose a new approach to screen for active factorial effects from such experiments that uses the potential outcomes framework and is based on sequential posterior predictive model checks. One advantage of the proposed method is its ability to broaden the standard definition of active effects and to link their definition to the population of interest. Another important aspect of this approach is its conceptual connection to Fisherian randomization tests. Extensive simulation studies are conducted, which demonstrate the superiority of the proposed approach over existing ones in the situations considered.     

因果分析法在电工实习教学中的应用

如何更好地提高当代大学生实践创新和动手以及分析能力,培养学生实习过程中自我创新意识,是目前大学专业教学中共同探讨和深思的一个重要问题。本文以电工实习教学为例,因果分析法的思维对影响大学生实践创新能力的因素进行了系统分析,提出了应根据其结果调整操作技能、技术参数规范、电子元件采购、电子元件特性、生产设备、环境资源等因素,摆脱僵化的教学体系,形成一种科学、合理、有效的实习教学模式。重点突出因果分析法在大学教学中的如何运用,指导学生分析和评估,以达到有效的教学目的。     

Self-regularized causal structure discovery for trajectory-based networks

Trajectory-based networks exhibit strong heterogeneous patterns amid human behaviors. We propose a notion of causal time-varying dynamic Bayesian network (cTVDBN) to efficiently discover such patterns. While asymmetric kernels are used to make the model better adherence to causal principles, the variations of network connectivities are addressed by an adaptive over-fitting control. Compact regularization paths are obtained by approximate homotopy to make the solution tractable. In our experiments, cTVDBN structure discovery has successfully revealed the evolution of time-varying relationships in a ring road system, and provided insights for plausible road structure improvements from a traffic flow dataset.     

Gamma rate theory for causal rate control in source coding and video coding

The rate distortion function in information theory provides performance bounds for lossy source coding. However, it is not clear how to causally encode a Gaussian sequence under rate constraints while achieving R–D optimality. This problem has significant implications in the design of rate control for video communication. To address this problem, we take distortion fluctuation into account and develop a new theory, called gamma rate theory, to quantify the trade-off between rate and distortion fluctuation. The gamma rate theory implies that, to evaluate the performance of causal rate controls in source coding, the traditional R–D metric needs to be replaced by a new GRD metric. The gamma rate theory identifies the trade-off between quality fluctuation and bandwidth, which is not known previously. To validate the gamma rate theory, we design a rate control algorithm for video coding; our experimental results demonstrate the utility of the gamma rate theory in video coding.     

一种因果维纳滤波器的推导方法

维纳滤波是一种以最小均方误差为最优准则的线性滤波器。它利用了平稳随机过程的相关特性和频谱特性对混有噪声的信号进行滤波。它是目前基本的滤波方法之一,也是当今＂现代数字信号处理＂课程的经典内容之一。为了加深对这一内容的理解,利用了单边Z变换的性质,提出一种基于单边Z变换的因果维纳滤波器的推导方法,旨在对维纳滤波器的进一步研究提供了一种新的途径。     

Risk factors for fatal crashes in rural Australia

This paper presents findings from the rural and remote road safety study, conducted in Queensland, Australia, from March 2004 till June 2007, and compares fatal crashes and non-fatal but serious crashes in respect of their environmental, vehicle and operator factors. During the study period there were 613 non-fatal crashes resulting in 684 hospitalised casualties and 119 fatal crashes resulting in 130 fatalities. Additional information from police sources was available on 103 fatal and 309 non-fatal serious crashes. Over three quarters of both fatal and hospitalised casualties were male and the median age in both groups was 34 years. Fatal crashes were more likely to involve speed, alcohol and violations of road rules and fatal crash victims were 2½ times more likely to be unrestrained inside the vehicle than non-fatal casualties, consistent with current international evidence. After controlling for human factors, vehicle and road conditions made a minimal contribution to the seriousness of the crash outcome. Targeted interventions to prevent fatalities on rural and remote roads should focus on reducing speed and drink driving and promoting seatbelt wearing.     

The impact of service level on the acceptance of application service oriented medical records

Service level is considered to be the most important criterion in evaluating application services. In our study we empirically investigated how perceived service level (PSL) influenced healthcare workers’ willingness to use application service oriented medical records. In particular, we extended the technology acceptance model (TAM) by embedding PSL as a causal antecedent. We found that PSL explained 61% of the variation in ease of use, which is twice as much as our current understanding. We also found that TAM was validated when tested in isolation but failed within the larger nomological network. We provided an explanation based on the notion of conditional independence. We further applied TETRAD III to explore the phenomenon and discovered two spurious associations in TAM, successfully confirming the explanation.     

Causality and consistency in economic models

In this paper, a method for causal and consistency analysis of economic models is presented. The method is based on a qualitative algebra together with a disturbance propagation algorithm. Given a set of equations, the corresponding structural model is derived by causal ordering. Based on it, different reasoning strategies over the model are given a graph-oriented interpretation in terms of a graph-search algorithm. One of them, i.e. the one corresponding to long-term economic analysis, is then further developed as a propagation algorithm. It is finally shown how the defined algebra and propagation algorithm may enable several kinds of consistency checks over the model and the policy targets.