Use of the child health utility and strengths and difficulties outcome measures in economic evaluations of school-based interventions: data from a cluster-randomised controlled trial in Northern Ireland

Background There is growing evidence linking early social and emotional wellbeing to later academic performance and various health outcomes including mental health. An economic evaluation was designed alongside the Roots of Empathy cluster-randomised trial evaluation, which is a school-based intervention for improving pupils’ social and emotional wellbeing. Exploration of the relevance of the Strengths and Diffi culties Questionnaire (SDQ) and Child Health Utility 9D (CHU9D) in school-based health economic evaluations is warranted. The SDQ is a behavioural screening questionnaire for 4–17-year-old children, consisting of a total diffi culties score, and also prosocial behaviour,
which aims to identify positive aspects of behaviour. The CHU9D is a generic preference-based health-related quality of life instrument for 7–17-year-old children.

Gaussian pulse mixing by stacked semiconductor layers

The nonlinear scattering of two Gaussian pulses with different central frequencies incident at slant angles on the periodic stack of binary semiconductor layers has been modelled in the self-consistent problem formulation taking into account the dynamics of charges. The effects of the pump pulse length and central frequencies, and the stack physical and geometrical parameters on the properties of the emitted combinatorial frequency waveforms are analysed and discussed.

Insight Extraction for Semiconductor Manufacturing Processes

In the semiconductor manufacturing environment it is very important to understand which factors have the most impact on process outcomes and to control them accordingly. This is usually achieved through design of experiments at process start-up and long term observation of production. As such it relies heavily on the expertise of the process engineer. In this work, we present an automatic approach to extracting useful insights about production processes and equipment based on state-of-the-art Machine Learning techniques. The main goal of this activity is to provide tools to process engineers to accelerate the learning-by-observation phase of process analysis. Using a Metal Deposition process as an example, we highlight various ways in which the extracted information can be employed.

On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. The prediction models for VM can be from a large variety of linear and nonlinear regression methods and the selection of a proper regression method for a specific VM problem is not straightforward, especially when the candidate predictor set is of high dimension, correlated and noisy. Using process data from a benchmark semiconductor manufacturing process, this paper evaluates the performance of four typical regression methods for VM: multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), neural networks (NN) and Gaussian process regression (GPR). It is observed that GPR performs the best among the four methods and that, remarkably, the performance of linear regression approaches that of GPR as the subset of selected input variables is increased. The observed competitiveness of high-dimensional linear regression models, which does not hold true in general, is explained in the context of extreme learning machines and functional link neural networks.

The Cluster Effect

No abstract available

A cluster randomised controlled trial evaluation and cost-effectiveness analysis of the Roots of Empathy schools-based programme for improving social and emotional wellbeing outcomes among 8-9 year olds in Northern Ireland

A substantial body of evidence suggest that well designed school based prevention programmes can be effective in improving a variety of social, health and academic outcomes for children and young people. This poster presents the methodology for evaluating the Roots of Empathy (ROE) programme. ROE is a universal programme delivered on a whole-class basis for one academic year. It consists of 27 lessons that run over a school year and is based around a monthly classroom visit by an infant and parent, typically recruited from the local community, whom the class 'adopts' at the start of the school year. The evaluation aims to evaluate the immediate and longer term impact of ROE on social and emotional wellbeing outcomes among 8-9 year old pupils, as well as evaluate the cost-effectiveness of the programme.

Pulse mixing by Fibonacci and Thue-Morse ordered semiconductor layers

The properties of combinatorial frequency generation by two-tone Gaussian pulses incident at oblique angles on quasiperiodic (Fibonacci and Thue-Morse) stacks of binary semiconductor layers are discussed. The analysis has been performed using the self-consistent model taking into account the nonlinear dynamics of mobile charges in the layers. The effects of the stack arrangements and constituent layer parameters on the combinatorial frequency waveforms are presented for the specific structures of both types

The Application of Semiconductor Photocatalysis for the Removal of Cyanotoxins from Water and Design Concepts for Solar Photocatalytic Reactors for Large Scale Water Treatment

There has been a significant increase in the occurrence of cyanobacterial blooms in freshwaters over the past few decades due to escalating nutrient levels. These cyanobacteria release a range of toxins, for example microcystins which are chemically very stable. Many cyanotoxins are consequently very difficult to remove from water using existing treatment technologies. Semiconductor photocatalysis, however, has proven to be a very effective process for the removal of these compounds from water. In this chapter we consider the application of this highly versatile and exciting technology for the decomposition of cyanotoxins. Furthermore design concepts for solar photocatalytic reactors that could be utilized for the removal of these toxins are also considered