Interpretation of ambiguity in children: a prospective study of associations with anxiety and parental interpretations

Interpretation of ambiguity is consistently associated with anxiety in children, however, the temporal relationship between interpretation and anxiety remains unclear as do the developmental origins of interpretative biases. This study set out to test a model of the development of interpretative biases in a prospective study of 110 children aged 5–9 years of age. Children and their parents were assessed three times, annually, on measures of anxiety and interpretation of ambiguous scenarios (including, for parents, both their own interpretations and their expectations regarding their child). Three models were constructed to assess associations between parent and child anxiety and threat and distress cognitions and expectancies. The three models were all a reasonable fit of the data, and supported conclusions that: (i) children’s threat and distress cognitions were stable over time and were significantly associated with anxiety, (ii) parents’ threat and distress cognitions and expectancies significantly predicted child threat cognitions at some time points, and (iii) parental anxiety significantly predicted parents cognitions, which predicted parental expectancies at some time points. Parental expectancies were also significantly predicted by child cognitions. The findings varied depending on assessment time point and whether threat or distress cognitions were being considered. The findings support the notion that child and parent cognitive processes, in particular parental expectations, may be a useful target in the treatment or prevention of anxiety disorders in children.

Mean-tracking clustering algorithm for radial basis function centre selection

Radial basis functions can be combined into a network structure that has several advantages over conventional neural network solutions. However, to operate effectively the number and positions of the basis function centres must be carefully selected. Although no rigorous algorithm exists for this purpose, several heuristic methods have been suggested. In this paper a new method is proposed in which radial basis function centres are selected by the mean-tracking clustering algorithm. The mean-tracking algorithm is compared with k means clustering and it is shown that it achieves significantly better results in terms of radial basis function performance. As well as being computationally simpler, the mean-tracking algorithm in general selects better centre positions, thus providing the radial basis functions with better modelling accuracy

Selection of funding schemes by a borrowing decision model: a Hong Kong case study

In financial decision-making, a number of mathematical models have been developed for financial management in construction. However, optimizing both qualitative and quantitative factors and the semi-structured nature of construction finance optimization problems are key challenges in solving construction finance decisions. The selection of funding schemes by a modified construction loan acquisition model is solved by an adaptive genetic algorithm (AGA) approach. The basic objectives of the model are to optimize the loan and to minimize the interest payments for all projects. Multiple projects being undertaken by a medium-size construction firm in Hong Kong were used as a real case study to demonstrate the application of the model to the borrowing decision problems. A compromise monthly borrowing schedule was finally achieved. The results indicate that Small and Medium Enterprise (SME) Loan Guarantee Scheme (SGS) was first identified as the source of external financing. Selection of sources of funding can then be made to avoid the possibility of financial problems in the firm by classifying qualitative factors into external, interactive and internal types and taking additional qualitative factors including sovereignty, credit ability and networking into consideration. Thus a more accurate, objective and reliable borrowing decision can be provided for the decision-maker to analyse the financial options.

Parental incarceration, attachment and child psychopathology

Theory and evidence relating parental incarceration, attachment, and psychopathology are reviewed. Parental incarceration is a strong risk factor for long-lasting psychopathology, including antisocial and internalizing outcomes. Parental incarceration might threaten children's attachment security because of parent-child separation, confusing communication about parental absence, restricted contact with incarcerated parents, and unstable caregiving arrangements. Parental incarceration can also cause economic strain, reduced supervision, stigma, home and school moves, and other negative life events for children. Thus, there are multiple possible mechanisms whereby parental incarceration might increase risk for child psychopathology. Maternal incarceration tends to cause more disruption for children than paternal incarceration and may lead to greater risk for insecure attachment and psychopathology. Children's prior attachment relations and other life experiences are likely to be of great importance for understanding children's reactions to parental incarceration. Several hypotheses are presented about how prior insecure attachment and social adversity might interact with parental incarceration and contribute to psychopathology. Carefully designed longitudinal studies, randomized controlled trials, and cross-national comparative research are required to test these hypotheses.

Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks

An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models. The algorithm is an extension of a forward modified Gram-Schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting. The proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice-based modeling algorithms such as radial basis function neurofuzzy networks, enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation. Some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm.

On the utility of input selection and pruning for financial distress prediction models

Analyzes the use of linear and neural network models for financial distress classification, with emphasis on the issues of input variable selection and model pruning. A data-driven method for selecting input variables (financial ratios, in this case) is proposed. A case study involving 60 British firms in the period 1997-2000 is used for illustration. It is shown that the use of the Optimal Brain Damage pruning technique can considerably improve the generalization ability of a neural model. Moreover, the set of financial ratios obtained with the proposed selection procedure is shown to be an appropriate alternative to the ratios usually employed by practitioners.

Variable selection for financial distress classification using a genetic algorithm

This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model and minimizing the collinearity between the model inputs. A case study involving 60 failed and continuing British firms in the period 1997-2000 is used for illustration. The classification model based on ratios selected by the genetic algorithm compares favorably with a model employing ratios usually found in the financial distress literature.

Neural network basis function center selection using cluster analysis

This paper deals with the selection of centres for radial basis function (RBF) networks. A novel mean-tracking clustering algorithm is described as a way in which centers can be chosen based on a batch of collected data. A direct comparison is made between the mean-tracking algorithm and k-means clustering and it is shown how mean-tracking clustering is significantly better in terms of achieving an RBF network which performs accurate function modelling.

Stimulus-driven selection of routes to imitation

Several models have proposed that an action can be imitated via one of two routes: a direct visuospatial route, which can in principle mediate imitation of both meaningful (MF) and meaningless (ML) actions, and an indirect semantic route, which can be used only for MF actions. The present study investigated whether selection between the direct and indirect routes is strategic or stimulus driven. Tessari and Rumiati (J Exp Psychol Hum Percept Perform 30:1107–1116, 2004) have previously shown, using accuracy measures, that imitation of MF actions is superior to imitation of ML actions when the two action types are presented in separate blocks, and that the advantage of MF over ML items is smaller or absent when they are presented in mixed blocks. We first replicated this finding using an automated reaction time (RT), as well as accuracy, measure. We then examined imitation of MF and ML actions in the mixed condition as a function of the action type presented in the previous trial and in relation to the number of previous test trials. These analyses showed that (1) for both action types, performance was worse immediately after ML than MF trials, and (2) even at the beginning of the mixed condition, responding to MF actions was no better than responding to ML items. These results suggest that the properties of the action stimulus play a substantial role in determining whether imitation is mediated by the direct or the indirect route, and that effects of block composition on imitation need not be generated through strategic switching between routes.