Enabling a Powerful Marine and Offshore Decision-Support Solution Through Bayesian Network Technique

A powerful practical solution is by far the most desired output when making decisions under the realm of uncertainty on any safety-critical marine or offshore units and their systems. With data and information typically being obtained incrementally, adopting Bayesian network (BN) is shown to realistically deal with the random uncertainties while at the same time making risk assessments easier to build and to check. A well-matched methodology is proposed to formalize the reasoning in which the focal mechanism of inference processing relies on the sound Bayes's rule/theorem that permits the logic. Expanding one or more influencing nodal parameters with decision and utility node(s) also yields an influence diagram (ID). BN and ID feasibility is shown in a marine evacuation scenario and that of authorized vessels to floating, production, storage, and offloading collision, developed via a commercial computer tool. Sensitivity analysis and validation of the produced results are also presented.     

The Role of Family Income in the Intergenerational Association of AFDC Receipt

Daughters from low‐income families who did not receive Aid to Families with Dependent Children (AFDC) are compared to daughters from low‐income families who did receive assistance to better account for family income in the intergenerational association of AFDC. The research models the timing of a daughter's first birth and, for those who become mothers, the number of years a daughter receives any AFDC. Women whose families received AFDC during their childhood are more likely to receive AFDC as adults relative to those women whose families did not receive AFDC. Controlling for family income in a more careful and comprehensive way than past research explains part but not all of the effects of childhood AFDC receipt. Among daughters from chronically poor families, however, parental AFDC use is not associated with additional years of AFDC participation. Although the present research cannot confirm or deny a causal role for parental welfare use, the intergenerational transmission of financial resources does explain part of the intergenerational association of welfare use.     

Testing regression lack of fit without replication: a tutorial around Minitab's XLOF procedures

The tutorial is concerned with two types of test for the general lack of fit of a linear regression model, as found in the Minitab software package, and which do not require replicated observations. They aim to identify non-specified curvature and interaction in predictors, by comparing fits over the predictor region divided into two parts. Minitab's regression subcommand XLOF which gives the tests is only briefly documented in the manual and, unlike virtually all other statistical procedures in the software, it is not standard and cannot be readily found in textbooks. The two types of test are described here; they concern the predictors one-at-a-time and the predictors all-at-once. An example of their use is given. A suite of macros is available which reproduces the results of the XLOF tests in much more detail than is given by the XLOF subcommand.     

“Entering the developmental spiral the work of Michael F. Basch applied to clinical social work

Entering the developmental spiral through affect attunement reactivates and revitalizes the thrust to continue development when it has been fixated, distorted or derailed. Achieving affect attunement is a vital dimension of the healing process. Basch describes how affect stimulates and controls the ordering and pattern matching function of the brain, motivating behavior and relationships. These theories are examined in selected vignettes illustrating the manner in which we seek to free individuals from stalemate in relationships, destructive behavior or depressive states.     

AN ALGORITHM FOR THE CONSTRUCTION OF OPTIMAL OR NEAR-OPTIMAL CHANGE-OVER DESIGNS

This paper presents an algorithm for the construction of optimal or near optimal change-over designs for arbitrary numbers of treatments, periods and units. Previous research on optimality has been either theoretical or has resulted in limited tabulations of small optimal designs. The algorithm consists of a number of steps:first find an optimal direct treatment effects design, ignoring residual effects, and then optimise this class of designs with respect to residual effects. Poor designs are avoided by judicious application of the (M, S)-optimality criterion, and modifications of it, to appropriate matrices. The performance of the algorithm is illustrated by examples.     

ASKING THREATENING QUESTIONS AND SITUATIONAL FRAMING:

Although surveys are extensively used in sociology, there has been a relative neglect of the effect of task definition upon results obtained. The present article examines the effects of one such task variable, length and specificity of the cue, on response sets concerning the frequency and quantity of alcohol consumed. In one survey format, respondents were asked to report the average number of days drinking per month, and the average number of drinks consumed on each of these occasions. In the second format, questions concerning the monthly frequency of alcohol consumption and quantity consumed were decomposed by twelve drinking locations. The results indicate dramatic increases both in terms of frequency and quantity between the two questionnaire formats. Implications of these results are discussed within the framework of the methodological phenomena of telescoping and the social psychological tendency to under-report threatening behaviors.     

An exploratory study of gambling cross addictions

A growing body of empirical and theoretical research has concentrated on the overlaps and commonalities among chemical and non-chemical (behavioural) addictions. Pathological gambling has perhaps been the most widely researched behavioural addiction and some of this research has concerned the co-existing links between pathological gambling and alcohol/drug addiction in both the gambling substance abuser and the substance abusing gambler (i.e. cross addiction). This study was of an exploratory nature and attempted to gather information and data regarding gambling cross addictions in the UK. To achieve this, 456 letters were sent to all drug and alcohol helping agencies in England requesting such information. This yielded 210 returns (46% response rate). Results indicated that gambling cross addictions occur in both adults and adolescents and were almost exclusively a male condition. However, it must be noted only just over half of the responding agencies had encountered gambling cross addiction and reasons for this are speculated. Results also indicated the existence of various cross addicted subgroups including an adolescent subgroup who were addicted to fruit machines and abused solvents. Future research ideas are also discussed.A more detailed version of this paper is available from the author on request.     

On the existence and uniqueness of maximizers of two likelihood functions

While applying theclassical maximum likelihood method for a certain statistical inference problem, Smith and Weissman [5] have noted that there are conditions under which the likelihood function may be unbounded above or may not possess local maximizers. Ariyawansà and Templeton [1] have derived inference procedures for this problem using the theory of structural inference [2,3,4]. Based on numerical experience, and without proof, they state that the resulting likelihood functions possess unique, global maximizers, even in instances where the classical maximum likelihood method fails in the above sense. In this paper, we prove that under quite mild conditions, these likelihood functions that result from the application of the theory of structural inference are well-behaved, and possess unique, global maximizers. This research was supported in part by the Applied Mathematical Sciences subprogram of the U.S. Department of Energy under contract W-31-109-Eng-38 while the author was visiting the Mathematics and Computer Science Division of Argonne National Laboratory, Argonne, Illinois.