https://espace.library.uq.edu.au/view/UQ:8925/pr-t.pdf

The use of the area under the ROC curve in the evaluation of machine learning algorithms

Artificial neural networks: fundamentals, computing, design, and application

Maternal depression and parenting behavior: a meta-analytic review

A large body of literature documents the adverse effects of maternal depression on the functioning and development of offspring. Although investigators have identified factors associated with risk for abnormal development and psychopathology in the children, little attention has been paid to the mechanisms explaining the transmission of risk from the mothers to the children. Moreover, no existing model both guides understanding of the various processes' interrelatedness and considers the role of development in explicating the manifestation of risk in the children. This article proposes a developmentally sensitive, integrative model for understanding children's risk in relation to maternal depression. Four mechanisms through which risk might be transmitted are evaluated: (a) heritability of depression; (b) innate dysfunctional neuroregulatory mechanisms; (c) exposure to negative maternal cognitions, behaviors, and affect; and (d) the stressful context of the children's lives. Three factors that might moderate this risk are considered: (a) the father's health and involvement with the child, (b) the course and timing of the mother's depression, and (c) characteristics of the child. Relevant issues are discussed, and promising directions for future research are suggested.

Risk for psychopathology in the children of depressed mothers: a developmental model for understanding mechanisms of transmission.

Objective To qualitatively review the literature of the past decade covering the epidemiology, clinical characteristics, natural course, biology, and other correlates of early-onset major depressive disorder (MDD) and dysthymic disorder (DD). Method A computerized search for articles published during the past 10 years was made and selected studies are presented. Results Early-onset MDD and DD are frequent, recurrent, and familial disorders that tend to continue into adulthood, and they are frequently accompanied by other psychiatric disorders. These disorders are usually associated with poor psychosocial and academic outcome and increased risk for substance abuse, bipolar disorder, and suicide. In addition, DD increases the risk for MDD. There is a secular increase in the prevalence of MDD, and it appears that MDD is occurring at an earlier age in successive cohorts. Several genetic, familial, demographic, psychosocial, cognitive, and biological correlates of onset and course of early-onset depression have been identified. Few studies, however, have examined the combined effects of these correlates. Conclusions Considerable advances have been made in our knowledge of early-onset depression. Nevertheless, further research is needed in understanding the pathogenesis of childhood mood disorders. Toward this end, studies aimed at elucidating mechanisms and interrelationships among the different domains of risk factors are needed.

Childhood and adolescent depression: a review of the past 10 years. Part I.

This paper develops operational methods for the minimization of the energy consumption of manufacturing equipment. It is observed that there can be a significant amount of energy savings when non-bottleneck (i.e. underutilized) machines/equipment are turned off when they will be idle for a certain amount of time. Using this fact, several dispatching rules are proposed. A detailed performance analysis indicates that the proposed dispatching rules are effective in decreasing the energy consumption of especially underutilized manufacturing equipment. In addition, a multi-objective mathematical programming model is proposed to minimize the energy consumption and total completion time. Using this approach, a production manager will have a set of non-dominated solutions (i.e. the set of efficient solutions) which he/she can use to determine the most efficient production sequence which will minimize the total energy consumption while optimizing the total completion time.

/pdf/operational-methods-for-minimization-of-energy-consumption-13ofq50f9p.pdf

Operational methods for minimization of energy consumption of manufacturing equipment

• First-degree (N = 195) and second-degree (N = 785) adult relatives of prepubertal children with major depression (N = 48), children with nonaffective psychiatric disorders (N = 20), and normal children (N = 27) were assessed by the Family History—Research Diagnostic Criteria method (FH-RDC), except for the adult informant (usually the mother), who was directly interviewed. Compared with normal controls, prepubertal children with major depressive disorder (MDD) had significantly higher familial rates of psychiatric disorders in both first- and second-degree relatives, especially MDD, alcoholism, and "other" (mostly anxiety) diagnoses. Relatives of children in the nonaffective psychiatric control (PC) group had low rates of alcoholism, high rates of other (anxiety) disorder diagnoses, and intermediate rates of MDD (accounted for by those children with separation anxiety). This suggests that prepubertal onset of major depression may be especially likely in families with a high aggregation of affective disorders when these families also have a high prevalence of alcoholism, and that a proportion of children without affective disorder but with separation anxiety disorder in this study were at high risk for the development of affective illness later in life. These results support the validity of prepubertal-onset depressive illness as a diagnostic category, and are consistent with high familial rates of MDD and other psychiatric disorders found in family studies of adolescent and early-onset adult probands with major affective disorders, and with studies of the offspring of parents with major affective disorders. Within the child MDD group substantial heterogeneity was found. Low familial rates of MDD were associated with suicidality and comorbid conduct disorder in the child probands. The highest familial rates of MDD, approximately threefold those in the normal controls, and all the bipolar relatives, were found in the families of prepubertal probands with MDD who never had a concrete suicidal plan or act and who were without comorbid conduct disorder. A useful nosological continuum in which to classify prepubertal MDD may be to place at one end those patients with comorbid conduct disorder and at the other end those patients with manifestations related to bipolarity, including hypomania, mania, and psychotic subtype.

https://jamanetwork.com/HttpHandlers/ArticlePdfHandler.ashx?journal=PSYCH&articleId=494545&pdfFileName=archpsyc_46_5_005.pdf

A Controlled Family History Study of Prepubertal Major Depressive Disorder

Fiber-reinforced polymer composites are engineered materials commonly used for many structural applications because of the high strength-to-weight and stiffness-to-weight ratios. Although the service life of these materials in various applications is usually between 15 and 20 years, these often keep the physical properties beyond this time. Recycling composites using chemical, mechanical, and thermal processing is reviewed in this article. In this review of carbon, aramide, and glass fiber composites, we provide, as of 2011, a complete view of each composite recycling technology, highlight the possible energy requirements, explain the product outputs of recycling, and discuss the quality (fiber strength) of recyclates and how each recyclate fiber could be used in the market for sustainable composite manufacturing. This article also includes the new concept of ‘direct structural composite recycling’ and the use of these products in the same or different applications as low-cost composite materials after sm...

Recycling of fiber-reinforced composites and direct structural composite recycling concept

Machining of fiber reinforced composites is an important activity in the integration of these advanced materials into engineering applications. Machining damage due to excessive cutting forces may result in rejecting the composite components at the last stages of their production cycle. Therefore, the ability to predict the cutting forces is essential for selecting process parameters that would result in minimum machining damage. This work utilizes mechanistic modeling techniques for simulating the cutting of carbon fiber-reinforced polymers (CFRP) with a helical end mill. A methodology is developed for predicting the cutting forces by transforming specific cutting energies from orthogonal cutting to oblique cutting. It is shown that the method developed is capable of predicting the cutting forces in helical end milling of unidirectional and multidirectional composites and over the entire range of fiber orientations from 0° to 180°. This is a significant improvement over previous models that were only capable of addressing orthogonal cutting and/or a limited range of fiber orientations. Model predictions were compared with experimental data and were found to be in good agreement in cutting unidirectional laminate, but with lesser agreement in the case of a multidirectional laminate.

Prediction of cutting forces in helical end milling fiber reinforced polymers

(1)We will regard MSE as essentially analogous to RMSE, and concentrate on comparisonsbetween RMSE and MAE. While the choice between these two metrics may seeminconsequential, there are cases where they would lead to conflicting interpretations duringvalidation, especially for pattern classification networks. The following example is from(Twomey and Smith 1993a, 1995). The pattern classification problem is a well known two classclassification problem depicted in Figure 1. Two Gaussian distributions are classes A and B,where class A has a mean of 0 and a standard deviation of 1 and class B has a mean of 0 and astandard deviation of 2. There is considerable overlap between the classes, making this is astraightforward “hard” pattern classification task. Both training and testing sets contained equalnumbers of observations from each class.Figure 1 here.Typically, networks are trained to decreasingly lower training tolerances in an attempt toachieve the “best” performing network. This assumes that lower training tolerances equate withimproved performance, which is certainly not always the case. To examine the relativedifferences between RMSE and MAE, several networks were trained and tested in order tomeasure the effects of lowering the training tolerance for the two pattern classification problemwhere the correct output for class A is 0 and the correct output for class B is 1.A total of 13 different networks were trained, each to a different training tolerance.Training tolerances ranged from 0.08 to 0.6 for normalized input between 0 and 1. Duringtraining, a response was considered correct if the output was within a pre-specified absolutetraining tolerance of the target value (0 or 1). Training iterations continued for each networkMSEn

Janet M. Twomey

Papers

Operational methods for minimization of energy consumption of manufacturing equipment

A Controlled Family History Study of Prepubertal Major Depressive Disorder

Recycling of fiber-reinforced composites and direct structural composite recycling concept

Prediction of cutting forces in helical end milling fiber reinforced polymers

Validation and Verification