Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning

Compared to the remarkable progress in risk analysis of normal accidents, the risk analysis of major accidents has not been so well‐established, partly due to the complexity of such accidents and partly due to low probabilities involved. The issue of low probabilities normally arises from the scarcity of major accidents’ relevant data since such accidents are few and far between. In this work, knowing that major accidents are frequently preceded by accident precursors, a novel precursor‐based methodology has been developed for likelihood modeling of major accidents in critical infrastructures based on a unique combination of accident precursor data, information theory, and approximate reasoning. For this purpose, we have introduced an innovative application of information analysis to identify the most informative near accident of a major accident. The observed data of the near accident were then used to establish predictive scenarios to foresee the occurrence of the major accident. We verified the methodology using offshore blowouts in the Gulf of Mexico, and then demonstrated its application to dam breaches in the United Sates.     

Choosing summary statistics by least angle regression for approximate Bayesian computation

Bayesian statistical inference relies on the posterior distribution. Depending on the model, the posterior can be more or less difficult to derive. In recent years, there has been a lot of interest in complex settings where the likelihood is analytically intractable. In such situations, approximate Bayesian computation (ABC) provides an attractive way of carrying out Bayesian inference. For obtaining reliable posterior estimates however, it is important to keep the approximation errors small in ABC. The choice of an appropriate set of summary statistics plays a crucial role in this effort. Here, we report the development of a new algorithm that is based on least angle regression for choosing summary statistics. In two population genetic examples, the performance of the new algorithm is better than a previously proposed approach that uses partial least squares.     

The Roman Catholic Church: A Centuries Old History of Awareness of Clerical Child Sexual Abuse (from the First to the 19th Century)

Debates in international forums and in mainstream media on the role, responsibility, liability, and response of ecclesiastical authorities of the Roman Catholic Church (RCC) toward clerical child sexual abuse (cCSA) fail to take into account the historical roots and awareness of the problem. Reports also fail to mention the historic organizational laws RCC developed over centuries. In contrast, RCC documents evidence that the Catholic Church not only carried century’s old history of cCSA, but also repeatedly condemned cCSA by successive papal authorities, organizational laws, and institutional management mechanisms. During the first millennium, however, church laws remained confined to the bookshelves and were not converted into appropriate management policies and infrastructural models. This was largely due to the absence of a central administrative organizational structure, which developed later in the 12th century, following the Second Council of Lateran (1139) when the Papacy asserted its authority to establish administrative control over the organizational church. It was only then that management policies started to be framed and institutional structures enacted to deal more appropriately with cCSA from the 14th to 20th centuries. Despite this, RCC developed a culture of secrecy using clandestine organizational management models and institutional laws prescribed in 1568, 1622, 1741, 1866, 1922, and 1962 which aimed to manage cCSA. The current study traces reported cCSA as far back as the first century and critically examines the organizational laws, and institutional policies developed by RCC to address clerical sexual misconduct up to the end of the 19th century.     

Total edge irregularity strength of accordion graphs

An edge irregular total k-labeling \(\varphi : V\cup E \rightarrow \{ 1,2, \dots , k \}\) of a graph \(G=(V,E)\) is a labeling of vertices and edges of G in such a way that for any different edges xy and \(x'y'\) their weights \(\varphi (x)+ \varphi (xy) + \varphi (y)\) and \(\varphi (x')+ \varphi (x'y') + \varphi (y')\) are distinct. The total edge irregularity strength, tes(G), is defined as the minimum k for which G has an edge irregular total k-labeling. We have determined the exact value of the total edge irregularity strength of accordion graphs.     

Selective Maintenance in System Reliability with Random Costs of Repairing and Replacing the Components

In this paper we consider the problem of determining the optimum number of repairable and replaceable components to maximize a system's reliability when both, the cost of repairing the components and the cost of replacement of components by new ones, are random. We formulate it as a problem of non-linear stochastic programming. The solution is obtained through Chance Constrained programming. We also consider the problem of finding the optimal maintenance cost for a given reliability requirement of the system. The solution is then obtained by using Modified E-model. A numerical example is solved for both the formulations.     

Minority Adolescents' Future Orientation: The Case of Arabs Living in Israel

This study examines Arab adolescents' future orientation toward their education, family plans, economic status, and social adjustment. Arab adolescents' future orientation is of interest since, presently, Israel is experiencing major internal and external political changes. The Arabs in Israel are a non-assimilating minority. They differ from the Jewish majority in language, religion, and nationality. Previous research has shown that Arab adolescents express hope for peace and fear of war between the two peoples living in Israel. With the new peace initiatives in Israel, the future is being painted in different colors, and it is interesting to explore how Arab adolescents perceive their future, both as adolescents and as Arabs. The sample consists of 662 twelfth-grade students from seven high schools throughout Israel. The questionnaire used is a version of the Offer Self-image Questionnaire (Offer, Ostrov, & Howard, 1981), but only the findings on future orientation are analyzed in the present study. These findings show significant differences in Arab adolescents' future orientation across gender, family size, level of religiosity, and father's education. The significance of the findings is discussed in relation to the literature pertaining to adolescents' future orientation, particularly among minority groups, as well as the social, cultural, and educational transformation which the Arab population living in Israel is currently undergoing.     

Contraceptive utilization: profile of the Muslim Arab women in rural Israel

The contraceptive habits of the Arab population in rural Israel was explored by means of a cohort ( n =429) of rural Muslim women, with the aim to compose a profile of the women who practice modern contraception. Self-reported information revealed that only one third of the women apply modern contraceptive devices. Multivariate analysis showed the following independent variables to bear a contributory and predictive value with respect to use or non-use of modern contraceptive means by Muslim village women in Israel: degree of religiosity of the woman, male offspring, extent of modernity within the nuclear family, number of children, occupation of the husband, and adherence to traditional norms by the woman.     

Urban growth boundary policy and residential suburbanization: Riyadh, Saudi Arabia

The process of suburbanization of Riyadh, a city of 4.5 million inhabitants, offers a unique situation in which the urban morphology of the city can be examined in light of socio-political and economic conditions. Central government ordinances helped create ‘dysfunctional’ sprawl by mandating big lots and overly wide streets. Fifty percent of the city’s urban plan of approximately 1300 km² represents a pattern of untimely, undeveloped subdivisions. Much of this premature sub-divisioning was a result of speculative land deals. The current area of undeveloped subdivided land is approximately 650 km², which is roughly the equivalent to the city’s land currently developed.The author argues that government policies and inefficient urban planning practices have encouraged the transformation of the peripheral desert landscape into unchecked land sub-divisioning championed by land developers and speculators. Using a comparative approach, the paper attempts to highlight sociopolitical and other cultural factors that underlie this inefficient sprawl at the fringe. It closes with recommendations to alleviate such a costly pattern that can be generalized to other developing world cities.     

A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents

Safety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation‐based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical Bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source‐to‐source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry.     

Domino Effect Analysis Using Bayesian Networks

A new methodology is introduced based on Bayesian network both to model domino effect propagation patterns and to estimate the domino effect probability at different levels. The flexible structure and the unique modeling techniques offered by Bayesian network make it possible to analyze domino effects through a probabilistic framework, considering synergistic effects, noisy probabilities, and common cause failures. Further, the uncertainties and the complex interactions among the domino effect components are captured using Bayesian network. The probabilities of events are updated in the light of new information, and the most probable path of the domino effect is determined on the basis of the new data gathered. This study shows how probability updating helps to update the domino effect model either qualitatively or quantitatively. The methodology is applied to a hypothetical example and also to an earlier‐studied case study. These examples accentuate the effectiveness of Bayesian network in modeling domino effects in processing facility.