Fuzzy System Dynamics Risk Analysis (FuSDRA) of Autonomous Underwater Vehicle Operations in the Antarctic

With the maturing of autonomous technology and better accessibility, there has been a growing interest in the use of autonomous underwater vehicles (AUVs). The deployment of AUVs for under-ice marine science research in the Antarctic is one such example. However, a higher risk of AUV loss is present during such endeavors due to the extreme operating environment. To control the risk of loss, existing risk analyses approaches tend to focus more on the AUV's technical aspects and neglect the role of soft factors, such as organizational and human influences. In addition, the dynamic and complex interrelationships of risk variables are also often overlooked due to uncertainties and challenges in quantification. To overcome these shortfalls, a hybrid fuzzy system dynamics risk analysis (FuSDRA) is proposed. In the FuSDRA framework, system dynamics models the interrelationships between risk variables from different dimensions and considers the time-dependent nature of risk while fuzzy logic accounts for uncertainties. To demonstrate its application, an example based on an actual Antarctic AUV program is presented. Focusing on funding and experience of the AUV team, simulation of the FuSDRA risk model shows a declining risk of loss from 0.293 in the early years of the Antarctic AUV program, reaching a minimum of 0.206 before increasing again in later years. Risk control policy recommendations were then derived from the analysis. The example demonstrated how FuSDRA can be applied to inform funding and risk management strategies, or broader application both within the AUV domain and on other complex technological systems.     

The distribution of cook's d statistic

Cook (1977) proposed a diagnostic to quantify the impact of deleting an observation on the estimated regression coefficients of a General Linear Univariate Model (GLUM). Simulations of models with Gaussian response and predictors demonstrate that his suggestion of comparing the diagnostic to the median of the F for overall regression captures an erratically varying proportion of the values.

We describe the exact distribution of Cook's statistic for a GLUM with Gaussian predictors and response. We also present computational forms, simple approximations, and asymptotic results. A simulation supports the accuracy of the results. The methods allow accurate evaluation of a single value or the maximum value from a regression analysis. The approximations work well for a single value, but less well for the maximum. In contrast, the cut-point suggested by Cook provides widely varying tail probabilities. As with all diagnostics, the data analyst must use scientific judgment in deciding how to treat highlighted observations.     

Computer-Aided Teaching of Probabilistic Modeling for Biological Phenomena

College students majoring in science and engineering need to learn how to model key features of the driving mechanisms of natural, scientific, and engineering phenomena. A rigorous treatment of these topics requires a thorough understanding of advanced mathematical concepts and probability theory. However, we believe that carefully designed computer simulation software offers a means of conveying fundamental ideas of probabilistic modeling, while minimizing the need for underlying mathematical analyses. Based on this premise we have initiated the development of a software system that will be incorporated into a novel, introductory course in probabilistic modeling for undergraduate students in the biological and environmental sciences. In this paper we describe the preliminary version of our system that implements simulation, real-time animation, and calculations of dynamic statistical summaries for several prototypical stochastic models for a variety of biological systems.     

Reference priors for constrained rate models of count data

We derive reference priors for constrained rate models of count data using the sequential algorithm of Berger and Bernardo (1992b). The event counts for various groups of subjects are modeled as discrete random variables (Poisson, binomial, or negative binomial) with group specific rates. We consider situations in which the groups can be completely ordered according to one covariate. The priors enforce monotonicity (or monotonicity and convexity) of the rates with respect to the ordering. We use the priors to model a data set on mortality rates for men in different age groups assuming that the mortality rates increase with respect to age. We also consider the situation in which the parameter space is augmented to include rates corresponding to unobserved age groups, and the case of a random upper bound on the mortality rates. In addition, we provide an evaluation of the out-of-sample predictive performance of the proposed methods.     

Multiple comparisons to both a negative and a positive control

This paper addresses multiple comparisons in the presence of both a negative and a positive control. The methodology of the three-arm trial is extended to the case of many experimental treatment arms or different doses of a compound. In contrast to the classic three-arm trial, the focus is on the family-wise error type I. Normally distributed data with either homogeneous or heterogeneous group variances are considered. Explicit criteria for an optimal allocation are proposed. Depending on the pattern of heterogeneity, remarkably unbalanced designs are power-optimal. As an example, the method will be applied to a toxicological experiment.     

A qualitative information system for data management

This article traces the path which led to the adaptation of the Information System approach to data management and the use of a Relational Data Base Management System in the Vitrine 2001 project, a research project looking at children's learning activities with computers using qualitative research techniques. The principles which guided the selection and design of the data base, consideration of its advantages, as well as guidelines to help others who may wish to use a similar instrument are also presented.     

Unemployment insurance: Theoretical approaches and empirical evidence

This article analyses the principal theoretical approaches to the effects on the labour market when an unemployment insurance scheme is introduced. The article also reviews the international data derived from such insurance schemes already in use in some European and American economies. The principal conclusion of this study is that an unemployment scheme does not necessarily affect employment decisions adversely, nor does it encourage long-term unemployment. Furthermore, to the extent that this analysis goes beyond the traditional limits of the employment-unemployment relationship and considers the institutional aspects, which can be at least as important as the individual benefits, the effects on employment and on the allocation of resources in the economy can be positive. Finally, the study identifies some criteria to be considered when designing the scheme, such as the level of benefits, the period for which they are granted, the type of beneficiary covered, the type of financing and its relationship to the quality of the information and its complementary role in the process of adapting the labour force to new demands.