Assessing the effectiveness of a DRA versus DRA with an activity schedule procedure on the reduction of problem behaviour

Differential Reinforcement of Alternative behaviour (DRA) (Athens & Vollmer, 2010; Cooper, Heron, & Heward, 2007) is a procedure that consists in withholding reinforcement for the targeted inappropriate behaviour while reinforcing behaviours, i.e., that have the same function, but socially more acceptable topographies. DRA has repeatedly proven to be effective in reducing problem behaviours in individuals with autism (Campbell, 2003). On the other hand, a number of single-subject research studies have provided evidence for the use of activity schedules as a means to decrease aggressive behaviour (Dooley et al., 2001; Flannery & Hemer, 1994; Lalli, Casey, Goh, & Merlinoet al., 1994). The purpose of the present study was to evaluate the effectiveness of DRA in combination with the use of an activity schedule. We compared the impact of the visual activities schedule used in combination with a DRA procedure versus the impact of the DRA procedure used alone on problem behaviour of a boy diagnosed with an Autism Spectrum Disorder. An alternating treatments design was used to compare the rate of behaviour problems in each of the two treatment conditions. DRA was delivered as treatment A, while the combination of the activities schedule and DRA was treatment B.

Emotional development among early school-age children: Gender differences in the role of problem behaviours

There has been an increasing focus on social and emotional development in educational programmes in early childhood as both variables are believed to influence behavioural outcomes in the classroom. However, relationships between social and emotional development and behaviour in early childhood have rarely been explored. This article sets out to investigate the conceptualisation of these variables and their inter-relationships. Structural equation models were used to assess if differences exist between boys and girls in relation to social and emotional competences, which could affect the relative success of such programmes. This article is based on cross-sectional data collected from 749 four- to six-year-olds and their teachers. The findings generally supported the hypothesised relationships between social and emotional development variables and prosocial behaviour (including internalising behaviour) for boys and girls. However, some gender differences were noted in externalising behaviour, which teachers often consider to be most significant due to its potentially disruptive nature in the classroom.

New Results for the MAP Problem in Bayesian Networks

This paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bayesian networks, which is the problem of querying the most probable state configuration of some of the network variables given evidence. First, it is demonstrated that the problem remains hard even in networks with very simple topology, such as binary polytrees and simple trees (including the Naive Bayes structure). Such proofs extend previous complexity results for the problem. Inapproximability results are also derived in the case of trees if the number of states per variable is not bounded. Although the problem is shown to be hard and inapproximable even in very simple scenarios, a new exact algorithm is described that is empirically fast in networks of bounded treewidth and bounded number of states per variable. The same algorithm is used as basis of a Fully Polynomial Time Approximation Scheme for MAP under such assumptions. Approximation schemes were generally thought to be impossible for this problem, but we show otherwise for classes of networks that are important in practice. The algorithms are extensively tested using some well-known networks as well as random generated cases to show their effectiveness.

Is the multiracial congregation an answer to the problem of race? Comparative perspectives from South Africa and the USAF

This paper uses a comparative perspective to analyze how multiracial congregations may contribute to racial reconciliation in South Africa. Drawing on the large-scale study of multiracial congregations in the USA by Emerson et al., it examines how they help transform antagonistic identities and make religious contributions to wider reconciliation processes. It compares the American research to an ethnographic study of a congregation in Cape Town, identifying cross-national patterns and South African distinctives, such as discourses about restitution, AIDS, inequality and women. The extent that multiracial congregations can contribute to reconciliation in South Africa is linked to the content of their worship and discourses, but especially to their ability to dismantle racially aligned power structures. © Koninklijke Brill NV, 2008.

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.

Tackling the problem of blood culture contamination in the intensive care unit using an educational intervention

Blood culture contamination (BCC) has been associated with unnecessary antibiotic use, additional laboratory tests and increased length of hospital stay thus incurring significant extra hospital costs. We set out to assess the impact of a staff educational intervention programme on decreasing intensive care unit (ICU) BCC rates to <3% (American Society for Microbiology standard). BCC rates during the pre-intervention period (January 2006-May 2011) were compared with the intervention period (June 2011-December 2012) using run chart and regression analysis. Monthly ICU BCC rates during the intervention period were reduced to a mean of 3·7%, compared to 9·5% during the baseline period (P < 0·001) with an estimated potential annual cost savings of about £250 100. The approach used was simple in design, flexible in delivery and efficient in outcomes, and may encourage its translation into clinical practice in different healthcare settings.

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.

Forward and backward least angle regression for nonlinear system identification

A forward and backward least angle regression (LAR) algorithm is proposed to construct the nonlinear autoregressive model with exogenous inputs (NARX) that is widely used to describe a large class of nonlinear dynamic systems. The main objective of this paper is to improve model sparsity and generalization performance of the original forward LAR algorithm. This is achieved by introducing a replacement scheme using an additional backward LAR stage. The backward stage replaces insignificant model terms selected by forward LAR with more significant ones, leading to an improved model in terms of the model compactness and performance. A numerical example to construct four types of NARX models, namely polynomials, radial basis function (RBF) networks, neuro fuzzy and wavelet networks, is presented to illustrate the effectiveness of the proposed technique in comparison with some popular methods.

Kernel based approaches to local nonlinear non-parametric variable selection

In this paper, we consider the variable selection problem for a nonlinear non-parametric system. Two approaches are proposed, one top-down approach and one bottom-up approach. The top-down algorithm selects a variable by detecting if the corresponding partial derivative is zero or not at the point of interest. The algorithm is shown to have not only the parameter but also the set convergence. This is critical because the variable selection problem is binary, a variable is either selected or not selected. The bottom-up approach is based on the forward/backward stepwise selection which is designed to work if the data length is limited. Both approaches determine the most important variables locally and allow the unknown non-parametric nonlinear system to have different local dimensions at different points of interest. Further, two potential applications along with numerical simulations are provided to illustrate the usefulness of the proposed algorithms.