The acquisition and exercise of nephrology nursing expertise : A grounded theory study

Aims and objectives. This purpose of this study was to describe the process of expertise acquisition in nephrology nursing practice. Background. It has been recognized for a number of decades that experts, compared with other practitioners in a number of professions and occupations, are the most knowledgeable and effective, in terms of both the quantity and quality of output. Studies relating to expertise have been undertaken in a range of nursing contexts and specialties; to date, however, none have been undertaken which focus on nephrology nursing. Design. This study, using grounded theory methodology, took place in one renal unit in New South Wales, Australia and involved six non-expert and 11 expert nurses. Methods. Simultaneous data collection and analysis took place using participant observation, semi-structured interviews and review of nursing documentation. Findings. The study revealed a three-stage skills-acquisitive process that was identified as non-expert, experienced non-expert and expert stages. Each stage was typified by four characteristics, which altered during the acquisitive process; these were knowledge, experience, skill and focus. Conclusion. This was the first study to explore nephrology nursing expertise and uncovered new aspects of expertise not documented in the literature and it also made explicit other areas, which had only been previously implied. Relevance to clinical practice. Of significance to nursing, the exercise of expertise is a function of the recognition of expertise by others and it includes the blurring of the normal boundaries of professional practice. �� 2006 Blackwell Publishing Ltd.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

Review of Collaboration and Interdisciplinarity in the Republic of Letters : Essays in Honour of Richard G. Maber, edited by Paul Scott.

This volume brings together a number of essays that seek to explore the nature of early modern scholarship, ostensibly with special regard to the themes of interdisciplinarity and collaboration. As one might expect, the essays thus cover a gamut of topics ��� political manoeuvring, philosophical debates, gift-giving and dramatic performance ��� and each study is important and useful in its own right. As a whole, however, this collection serves more as a starting point for an exploration of its themes, than as an authoritative overview of the subject at hand.

Morphological classification of bovine ovarian follicles

Follicle classification is an important aid to the understanding of follicular development and atresia. Some bovine primordial follicles have the classical primordial shape, but ellipsoidal shaped follicles with some cuboidal granulosa cells at the poles are far more common. Preantral follicles have one of two basal lamina phenotypes, either a single aligned layer or one with additional layers. In antral follicles <5���mm diameter, half of the healthy follicles have columnar shaped basal granulosa cells and additional layers of basal lamina, which appear as loops in cross section (���loopy���). The remainder have aligned single-layered follicular basal laminas with rounded basal cells, and contain better quality oocytes than the loopy/columnar follicles. In sizes >5���mm, only aligned/rounded phenotypes are present. Dominant and subordinate follicles can be identified by ultrasound and/or histological examination of pairs of ovaries. Atretic follicles <5���mm are either basal atretic or antral atretic, named on the basis of the location in the membrana granulosa where cells die first. Basal atretic follicles have considerable biological differences to antral atretic follicles. In follicles >5���mm, only antral atresia is observed. The concentrations of follicular fluid steroid hormones can be used to classify atresia and distinguish some of the different types of atresia; however, this method is unlikely to identify follicles early in atresia, and hence misclassify them as healthy. Other biochemical and histological methods can be used, but since cell death is a part of normal homoeostatis, deciding when a follicle has entered atresia remains somewhat subjective.

Resource allocation and scheduling of multiple composite web services in cloud computing using cooperative coevolution genetic algorithm

In cloud computing, resource allocation and scheduling of multiple composite web services is an important and challenging problem. This is especially so in a hybrid cloud where there may be some low-cost resources available from private clouds and some high-cost resources from public clouds. Meeting this challenge involves two classical computational problems: one is assigning resources to each of the tasks in the composite web services; the other is scheduling the allocated resources when each resource may be used by multiple tasks at different points of time. In addition, Quality-of-Service (QoS) issues, such as execution time and running costs, must be considered in the resource allocation and scheduling problem. Here we present a Cooperative Coevolutionary Genetic Algorithm (CCGA) to solve the deadline-constrained resource allocation and scheduling problem for multiple composite web services. Experimental results show that our CCGA is both efficient and scalable.

Margin-adaptive model selection in statistical learning

A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of model selection, in a general learning framework. Actually, we consider a weaker version of this condition that allows one to take into account that learning within a small model can be much easier than within a large one. Requiring this ���strong margin adaptivity��� makes the model selection problem more challenging. We first prove, in a general framework, that some penalization procedures (including local Rademacher complexities) exhibit this adaptivity when the models are nested. Contrary to previous results, this holds with penalties that only depend on the data. Our second main result is that strong margin adaptivity is not always possible when the models are not nested: for every model selection procedure (even a randomized one), there is a problem for which it does not demonstrate strong margin adaptivity.

Particulate texture image analysis with applications

Texture analysis and textural cues have been applied for image classification, segmentation and pattern recognition. Dominant texture descriptors include directionality, coarseness, line-likeness etc. In this dissertation a class of textures known as particulate textures are defined, which are predominantly coarse or blob-like. The set of features that characterise particulate textures are different from those that characterise classical textures. These features are micro-texture, macro-texture, size, shape and compaction. Classical texture analysis techniques do not adequately capture particulate texture features. This gap is identified and new methods for analysing particulate textures are proposed. The levels of complexity in particulate textures are also presented ranging from the simplest images where blob-like particles are easily isolated from their back- ground to the more complex images where the particles and the background are not easily separable or the particles are occluded. Simple particulate images can be analysed for particle shapes and sizes. Complex particulate texture images, on the other hand, often permit only the estimation of particle dimensions. Real life applications of particulate textures are reviewed, including applications to sedimentology, granulometry and road surface texture analysis. A new framework for computation of particulate shape is proposed. A granulometric approach for particle size estimation based on edge detection is developed which can be adapted to the gray level of the images by varying its parameters. This study binds visual texture analysis and road surface macrotexture in a theoretical framework, thus making it possible to apply monocular imaging techniques to road surface texture analysis. Results from the application of the developed algorithm to road surface macro-texture, are compared with results based on Fourier spectra, the auto- correlation function and wavelet decomposition, indicating the superior performance of the proposed technique. The influence of image acquisition conditions such as illumination and camera angle on the results was systematically analysed. Experimental data was collected from over 5km of road in Brisbane and the estimated coarseness along the road was compared with laser profilometer measurements. Coefficient of determination R2 exceeding 0.9 was obtained when correlating the proposed imaging technique with the state of the art Sensor Measured Texture Depth (SMTD) obtained using laser profilometers.

An economic evaluation of the Safe Motherhood programme in Guangxi, China

Maternal and infant mortality is a global health issue with a significant social and economic impact. Each year, over half a million women worldwide die due to complications related to pregnancy or childbirth, four million infants die in the first 28 days of life, and eight million infants die in the first year. Ninety-nine percent of maternal and infant deaths are in developing countries. Reducing maternal and infant mortality is among the key international development goals. In China, the national maternal mortality ratio and infant mortality rate were reduced greatly in the past two decades, yet a large discrepancy remains between urban and rural areas. To address this problem, a large-scale Safe Motherhood Programme was initiated in 2000. The programme was implemented in Guangxi in 2003. Interventions in the programme included both demand-side and supply side-interventions focusing on increasing health service use and improving birth outcomes. Little is known about the effects and economic outcomes of the Safe Motherhood Programme in Guangxi, although it has been implemented for seven years. The aim of this research is to estimate the effectiveness and cost-effectiveness of the interventions in the Safe Motherhood Programme in Guangxi, China. The objectives of this research include: 1. To evaluate whether the changes of health service use and birth outcomes are associated with the interventions in the Safe Motherhood Programme. 2. To estimate the cost-effectiveness of the interventions in the Safe Motherhood Programme and quantify the uncertainty surrounding the decision. 3. To assess the expected value of perfect information associated with both the whole decision and individual parameters, and interpret the findings to inform priority setting in further research and policy making in this area. A quasi-experimental study design was used in this research to assess the effectiveness of the programme in increasing health service use and improving birth outcomes. The study subjects were 51 intervention counties and 30 control counties. Data on the health service use, birth outcomes and socio-economic factors from 2001 to 2007 were collected from the programme database and statistical yearbooks. Based on the profile plots of the data, general linear mixed models were used to evaluate the effectiveness of the programme while controlling for the effects of baseline levels of the response variables, change of socio-economic factors over time and correlations among repeated measurements from the same county. Redundant multicollinear variables were deleted from the mixed model using the results of the multicollinearity diagnoses. For each response variable, the best covariance structure was selected from 15 alternatives according to the fit statistics including Akaike information criterion, Finite-population corrected Akaike information criterion, and Schwarz.s Bayesian information criterion. Residual diagnostics were used to validate the model assumptions. Statistical inferences were made to show the effect of the programme on health service use and birth outcomes. A decision analytic model was developed to evaluate the cost-effectiveness of the programme, quantify the decision uncertainty, and estimate the expected value of perfect information associated with the decision. The model was used to describe the transitions between health states for women and infants and reflect the change of both costs and health benefits associated with implementing the programme. Result gained from the mixed models and other relevant evidence identified were synthesised appropriately to inform the input parameters of the model. Incremental cost-effectiveness ratios of the programme were calculated for the two groups of intervention counties over time. Uncertainty surrounding the parameters was dealt with using probabilistic sensitivity analysis, and uncertainty relating to model assumptions was handled using scenario analysis. Finally the expected value of perfect information for both the whole model and individual parameters in the model were estimated to inform priority setting in further research in this area.The annual change rates of the antenatal care rate and the institutionalised delivery rate were improved significantly in the intervention counties after the programme was implemented. Significant improvements were also found in the annual change rates of the maternal mortality ratio, the infant mortality rate, the incidence rate of neonatal tetanus and the mortality rate of neonatal tetanus in the intervention counties after the implementation of the programme. The annual change rate of the neonatal mortality rate was also improved, although the improvement was only close to statistical significance. The influences of the socio-economic factors on the health service use indicators and birth outcomes were identified. The rural income per capita had a significant positive impact on the health service use indicators, and a significant negative impact on the birth outcomes. The number of beds in healthcare institutions per 1,000 population and the number of rural telephone subscribers per 1,000 were found to be positively significantly related to the institutionalised delivery rate. The length of highway per square kilometre negatively influenced the maternal mortality ratio. The percentage of employed persons in the primary industry had a significant negative impact on the institutionalised delivery rate, and a significant positive impact on the infant mortality rate and neonatal mortality rate. The incremental costs of implementing the programme over the existing practice were US $11.1 million from the societal perspective, and US $13.8 million from the perspective of the Ministry of Health. Overall, 28,711 life years were generated by the programme, producing an overall incremental cost-effectiveness ratio of US $386 from the societal perspective, and US $480 from the perspective of the Ministry of Health, both of which were below the threshold willingness-to-pay ratio of US $675. The expected net monetary benefit generated by the programme was US $8.3 million from the societal perspective, and US $5.5 million from the perspective of the Ministry of Health. The overall probability that the programme was cost-effective was 0.93 and 0.89 from the two perspectives, respectively. The incremental cost-effectiveness ratio of the programme was insensitive to the different estimates of the three parameters relating to the model assumptions. Further research could be conducted to reduce the uncertainty surrounding the decision, in which the upper limit of investment was US $0.6 million from the societal perspective, and US $1.3 million from the perspective of the Ministry of Health. It is also worthwhile to get a more precise estimate of the improvement of infant mortality rate. The population expected value of perfect information for individual parameters associated with this parameter was US $0.99 million from the societal perspective, and US $1.14 million from the perspective of the Ministry of Health. The findings from this study have shown that the interventions in the Safe Motherhood Programme were both effective and cost-effective in increasing health service use and improving birth outcomes in rural areas of Guangxi, China. Therefore, the programme represents a good public health investment and should be adopted and further expanded to an even broader area if possible. This research provides economic evidence to inform efficient decision making in improving maternal and infant health in developing countries.

Georg Calixtus, Isaac Casaubon, and the Consensus of Antiquity

This article explores the relationship between Georg Calixtus (1586-1656) and Isaac Casaubon (1559-1614). It does this in order to highlight an oversight in the existing literature concerning Calixtus, and to encourage scholars to revisit the work of Early Modern figures who have previously been considered only from modern disciplinary perspectives. By emphasizing the relationship between Calixtus and Casaubon, this article argues that Calixtus was potentially exposed to much broader circles of intellectual debate than has previously been considered, and that a reevaluation of his work in light of these debates is therefore in order.

Scientia temporum & rerum : History or Antiquarianism? The collection of examples in Georg Calixtus' De studio historiarum oratio (1629)

This paper explores an early modern application of the Stoic principle of similitudo temporum to the study of history. In so doing, it highlights the tension between historiography and antiquarianism, suggesting that the collection of remains ��� whether material or immaterial ��� was understood in at least some early modern circles as an integral part of the historiographic process. It also emphasises the evolving meaning of ���history��� during this time, drawing attention to the perceived novelty of such antiquarian approaches to the study of the past, and briefly exploring subtle differences between the example at hand and the work and activities of better-known figures such as Nicolas-Claude Fabri de Peiresc and Justus Lipsius. As such, this paper makes a contribution to our evolving understanding of early modern scholarship, and draws attention to the variegated approaches of its practitioners to contemporary issues.