Child Protection Work and Family Support Practice in Five Family Centres

Academic interest in the work of family centres in the United Kingdom has largely been concerned with categorising the work of such centres in terms of issues of childcare ideology, working practices and degree of service user control. Meanwhile, the re-focusing of child protection services in order to develop child welfare services has largely dominated childcare social work in recent years, with scant attention paid to the role of family centres in relation to this debate. This study is concerned with examining the perspectives of staff and service users in five 'client focussed' family centres in Northern Ireland in relation to how child protection issues are understood and dealt with. It was found that staff enter into negotiations with both referrers and service users to conceptually reframe child protection work as family support practice. This leads to the development of partnership relationships between staff and service users based upon mutual high regard. The work of such centres leaves them well placed to provide integrated services to children in need in line with current government priorities, but could leave some children vulnerable where child protection issues are not amenable to conceptual reframing along family support lines.

Virtex FPGA Implementation of a Pipelined Adaptive LMS Predictor for Electronic Support Measures Receivers

High-speed field-programmable gate array (FPGA) implementations of an adaptive least mean square (LMS) filter with application in an electronic support measures (ESM) digital receiver, are presented. They employ "fine-grained" pipelining, i.e., pipelining within the processor and result in an increased output latency when used in the LMS recursive system. Therefore, the major challenge is to maintain a low latency output whilst increasing the pipeline stage in the filter for higher speeds. Using the delayed LMS (DLMS) algorithm, fine-grained pipelined FPGA implementations using both the direct form (DF) and the transposed form (TF) are considered and compared. It is shown that the direct form LMS filter utilizes the FPGA resources more efficiently thereby allowing a 120 MHz sampling rate.

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.