Pressure-relieving equipment: promoting its correct use amongst nurses via differing modes of educational delivery

Aims and objectives. To examine the impact of written and verbal education on bed-making practices, in an attempt to reduce the prevalence of pressure ulcers. Background. The Department of Health has set targets for a 5% reduction per annum in the incidence of pressure ulcers. Electric profiling beds with a visco-elastic polymer mattress are a new innovation in pressure ulcer prevention; however, mattress efficacy is reduced by tightly tucking sheets around the mattress. Design. A prospective randomized pre/post-test experimental design. Methods. Ward managers at a teaching hospital were approached to participate in the study. Two researchers independently examined the tightness of the sheets around the mattresses. Wards were randomized to one of two groups. Groups A and B received written education. In addition, group B received verbal education on alternate days for one week. Beds were re-examined one month later. One researcher was blinded to the educational delivery received by the wards. Results. Twelve wards agreed to participate in the study and 245 beds were examined. Before education, 113 beds (46%) had sheets tucked correctly around the mattresses. Following education, this increased to 215 beds (87.8%) (chi(2) = 68.03, P < 0.001). There was no significant difference in the number of correctly made beds between the two different education groups: 100 (87.72%) beds correctly made in group A vs. 115 (87.79%) beds in group B (chi(2) = 0, P 0.987). Conclusions. Clear, concise written instruction improved practice but verbal education was not additionally beneficial. Relevance to clinical practice. Nurses are receptive to clear, concise written evidence regarding pressure ulcer prevention and incorporate this into clinical practice.

Rapid quality assessment of wheat cultivars registered in Poland using the 2-g mixograph and multivariate statistical analysis

Baking and 2-g mixograph analyses were performed for 55 cultivars (19 spring and 36 winter wheat) from various quality classes from the 2002 harvest in Poland. An instrumented 2-g direct-drive mixograph was used to study the mixing characteristics of the wheat cultivars. A number of parameters were extracted automatically from each mixograph trace and correlated with baking volume and flour quality parameters (protein content and high molecular weight glutenin subunit [HMW-GS] composition by SDS-PAGE) using multiple linear regression statistical analysis. Principal component analysis of the mixograph data discriminated between four flour quality classes, and predictions of baking volume were obtained using several selected mixograph parameters, chosen using a best subsets regression routine, giving R-2 values of 0.862-0.866. In particular, three new spring wheat strains (CHD 502a-c) recently registered in Poland were highly discriminated and predicted to give high baking volume on the basis of two mixograph parameters: peak bandwidth and 10-min bandwidth.

Comparing nurses’ and doctors’ views of nurse prescribing: a questionnaire survey

Nurses have successfully adopted the role of prescriber in numerous health care settings in the UK. Existing research has not addressed how Nurse Independent and Nurse Supplementary Prescribers compare with doctors in terms of the perceived advantages and disadvantages of nurse prescribing, nor has the perceived importance of nurses providing patients with an explanation about their medicines been established. The current study utilized a random sample of 31 qualified Nurse Independent and Nurse Supplementary Prescribers and 30 general practitioners who self-completed a written questionnaire in an independent groups design. The study establishes nurses’ and doctors’ perceptions of the advantages and disadvantages of independent and supplementary nurse prescribing and provides some indication of the importance that nurses and doctors place on nurses providing an explanation about medicines, and the categories of information perceived to be important.

Optimization algorithms in FMRF model-based segmentation for LIDAR data and co-registered bands

In this paper, a fuzzy Markov random field (FMRF) model is used to segment land-objects into free, grass, building, and road regions by fusing remotely, sensed LIDAR data and co-registered color bands, i.e. scanned aerial color (RGB) photo and near infra-red (NIR) photo. An FMRF model is defined as a Markov random field (MRF) model in a fuzzy domain. Three optimization algorithms in the FMRF model, i.e. Lagrange multiplier (LM), iterated conditional mode (ICM), and simulated annealing (SA), are compared with respect to the computational cost and segmentation accuracy. The results have shown that the FMRF model-based ICM algorithm balances the computational cost and segmentation accuracy in land-cover segmentation from LIDAR data and co-registered bands.

An effective approach for land-cover classification from airborne lidar fused with co-registered data

Airborne lidar provides accurate height information of objects on the earth and has been recognized as a reliable and accurate surveying tool in many applications. In particular, lidar data offer vital and significant features for urban land-cover classification, which is an important task in urban land-use studies. In this article, we present an effective approach in which lidar data fused with its co-registered images (i.e. aerial colour images containing red, green and blue (RGB) bands and near-infrared (NIR) images) and other derived features are used effectively for accurate urban land-cover classification. The proposed approach begins with an initial classification performed by the Dempster–Shafer theory of evidence with a specifically designed basic probability assignment function. It outputs two results, i.e. the initial classification and pseudo-training samples, which are selected automatically according to the combined probability masses. Second, a support vector machine (SVM)-based probability estimator is adopted to compute the class conditional probability (CCP) for each pixel from the pseudo-training samples. Finally, a Markov random field (MRF) model is established to combine spatial contextual information into the classification. In this stage, the initial classification result and the CCP are exploited. An efficient belief propagation (EBP) algorithm is developed to search for the global minimum-energy solution for the maximum a posteriori (MAP)-MRF framework in which three techniques are developed to speed up the standard belief propagation (BP) algorithm. Lidar and its co-registered data acquired by Toposys Falcon II are used in performance tests. The experimental results prove that fusing the height data and optical images is particularly suited for urban land-cover classification. There is no training sample needed in the proposed approach, and the computational cost is relatively low. An average classification accuracy of 93.63% is achieved.

Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes

Brain activity can be measured with several non-invasive neuroimaging modalities, but each modality has inherent limitations with respect to resolution, contrast and interpretability. It is hoped that multimodal integration will address these limitations by using the complementary features of already available data. However, purely statistical integration can prove problematic owing to the disparate signal sources. As an alternative, we propose here an advanced neural population model implemented on an anatomically sound cortical mesh with freely adjustable connectivity, which features proper signal expression through a realistic head model for the electroencephalogram (EEG), as well as a haemodynamic model for functional magnetic resonance imaging based on blood oxygen level dependent contrast (fMRI BOLD). It hence allows simultaneous and realistic predictions of EEG and fMRI BOLD from the same underlying model of neural activity. As proof of principle, we investigate here the influence on simulated brain activity of strengthening visual connectivity. In the future we plan to fit multimodal data with this neural population model. This promises novel, model-based insights into the brain's activity in sleep, rest and task conditions.