Donald and Dorothy.

Mode of access: Internet.

The land of pluck; stories and sketches for young folk.

Mode of access: Internet.

The boomerang; a comedy in three acts,

Mode of access: Internet.

Perspectives of carers on medication management in dementia:lessons from collaboratively developing a research proposal

Background: The need for carers to manage medication-related problems for people with dementia living in the community raises dilemmas, which can be identified by carers and people with dementia as key issues for developing carer-relevant research projects.A research planning Public Patient Involvement (PPI) workshop using adapted focus group methodology was held at the Alzheimer's Society's national office, involving carers of people with dementia who were current members of the Alzheimer's Society Research Network (ASRN) in dialogue with health professionals aimed to identify key issues in relation to medication management in dementia from the carer viewpoint. The group was facilitated by a specialist mental health pharmacist, using a topic guide developed systematically with carers, health professionals and researchers. Audio-recordings and field notes were made at the time and were transcribed and analysed thematically. The participants included nine carers in addition to academics, clinicians, and staff from DeNDRoN (Dementias and Neurodegenerative Diseases Research Network) and the Alzheimer's Society. Findings. Significant themes, for carers, which emerged from the workshop were related to: (1) medication usage and administration practicalities, (2) communication barriers and facilitators, (3) bearing and sharing responsibility and (4) weighing up medication risks and benefits. These can form the basis for more in-depth qualitative research involving a broader, more diverse sample. Discussion. The supported discussion enabled carer voices and perspectives to be expressed and to be linked to the process of identifying problems in medications management as directly experienced by carers. This was used to inform an agenda for research proposals which would be meaningful for carers and people with dementia. © 2014 Poland et al.; licensee BioMed Central Ltd.

A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting

Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. urthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. inally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random orest (R), and C4.. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.9% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 4% and 6.% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.