Breast is Best? Reasons Why Mothers Decide to Breastfeed or Bottlefeed their Babies and Factors Influencing the Duration of Breastfeeding.

Breastfeeding is known to confer benefits, both in the short term and long term, to the child and also to the mother. Various health-promotion initiatives have aimed to increase breastfeeding rates and duration in the United Kingdom over the past decade. In order to assist in these endeavours, it is essential to understand the reasons why women decide whether to breastfeed and the factors that influence the duration of breastfeeding. This study reports breastfeeding initiation and duration rates of mothers participating in the Growth, Learning and Development study undertaken by the Child Health & Welfare Recognised Research Group. Although this study cannot provide prevalence data for all mothers in Greater Belfast, it can provide useful information on trends within particular groups of the population. In addition, it examines maternally reported reasons for choosing to breastfeed and for breastfeeding cessation. The likelihood of mothers initiating breastfeeding is influenced by factors such as increased age, higher educational attainment and higher socio-economic grouping. The most common reason cited for breastfeeding is that it is “best for baby”. Returning to work is the most important factor in influencing whether mothers continued to breastfeed. Women report different reasons for cessation depending on the age of their child when they stopped breastfeeding. This information should inform health-promotion initiatives and interventions.

Increasing Staff and Student Capacity to Use the Communication Facility in Queen's Online in Order to Support Learning and Teaching on a BSW Programme

In this article the authors explore and evaluate developments in the use of information and communications technologies (ICT) within social work education at Queen's University Belfast since the inception of the new degree in social work. They look at the staff development strategy utilised to increase teacher confidence and competence in use of the Queen's Online virtual learning environment tools as well as the student experience of participation in modules involving online discussions. The authors conclude that the project provided further opportunity to reflect on how ICT can be used as a platform to support a whole course in a systematic and coordinated way and to ensure all staff remained abreast of ongoing developments in the use of ICT to support learning which is a normative expectation of students entering universities. A very satisfying outcome for the leaders is our observation of the emergence of other 'experts' in different aspects of use of ICT amongst the staff team. This project also shows that taking a team as opposed to an individual approach can be particularly beneficial

Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

Extending BPM Environments of Your Choice with Performance Related Decision Support

What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools process optimizations or a combination Of Such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.