A Long and Winding Road to Employment?: Disabled young people in Northern Ireland making the transition to adulthood

This paper presents the findings of a qualitative research project that explores the experiences and aspirations of disabled young people in Northern Ireland as they make and deal with the transition to adulthood. The study involved young people with disabilities (n=76) in four areas of Northern Ireland, ensuring a geographical spread, an urban/rural mix and representation of both communities. Young people with learning disabilities were included as well as those with physical and/or sensory impairments. This paper focuses on those who were completing job training or work placements and examines the role of such schemes in assisting young people’s transition to adulthood. The research found that many young people had positive experiences of work placement and job training and that social interaction was important to them. Few young people, however, had made the actual transition from work placement or training to ‘real’ employment.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

Distance between home and work: A multilevel analysis of individual workers, neighbourhoods, and employment sites in Northern Ireland

Abstract. We explore the distances between home and work for employees at twenty-eight different employment sites across Northern Ireland. Substantively, this is important for better understanding the geography of labour catchments. Methodologically, with data on the distances between place of residence (566 wards) and place of work for some 15 000 workers, and the use of multilevel modelling (MLM), the analysis adds to the evidence derived from other census-based and survey-based studies. Descriptive analysis is supplemented with MLM that simultaneously explores individual, neighbourhood, and site variations in travel-to-work patterns using hierarchical and cross-classified model specifications, including individual and ecological predictor variables (and their cross-level interactions). In doing so we apportion variability to different levels and spatial contexts, and also outline the factors that shape spatial mobility. We find, as expected, that factors such as gender and occupation influence the distance between home and work, and also confirm the importance of neighbourhood characteristics (such as population density observed in ecological analyses at ward level) in shaping individual outcomes, with major differences found between urban and rural locations. Beyond this, the analysis of variability also points to the relative significance of residential location, with less individual variability in travel-to-work distance between workers within wards than within employment sites. We conclude by suggesting that, whilst some general ‘rules’ about the factors that shape labour catchments are possible (eg workers in rural areas and in higher occupations travel further than others), the complex variability between places highlighted by the MLM analysis illustrates the salience of place-specific uniqueness.