Properties of whole saliva and dental plaque in relation to 40-month consumption of chewing gums containing xylitol, sorbitol of sucrose

The concern with nursing research in the future work of nurse graduates. Expectations from the viewpoint of the nurse researcher. Keeping in mind the concerns which occupy nurse researchers now and in the future, expectations are raised of the graduates of degree programmes in nursing science which are now also being established in the Federal Republic of Germany. This takes into account the structurally determined areas of self determined action in new fields of professional practice. Nurse graduates will be seen as "change agents" in their future areas of work and will have three essential aspects of involvement with nursing research: 1. they themselves will conduct research 2. they will receive, put into practice and transmit results of nursing research 3. they will initiate and commission nursing research projects. According to the broad topics of nursing research, requirements for nurses to act as professionals in their new working fields are formulated referring to the given structural conditions in Germany.     

A sufficient condition to test identifiability of nonlinear delayed-differential models with constant delays and multi-inputs

In this paper, an original result in terms of a sufficient condition to test the identifiability of nonlinear delayed-differential models with constant delays and multi-inputs is given. The identifiability is studied for the linearized system and a criterion for linear systems with constant delays is provided, from which the identifiability of the original nonlinear system can be proved. This result is obtained by combining a classical identifiability result for nonlinear ordinary differential systems due to Grewal and Glover (1976) with the identifiability of linear delayed-differential models developed by Orlov, Belkoura, Richard, and Dambrine (2002). This paper is a generalization of Denis-Vidal, Jauberthie, and Joly-Blanchard (2006), which deals with the specific case of nonlinear delayed-differential models with two delays and a single input.     

Feature-based statistical analysis of combustion simulation data

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.     

Improving literacy and metacognition with electronic portfolios: Teaching and learning with ePEARL

Can an electronic portfolio that is both a multimedia container for student work and a tool to support key learning processes have a positive impact on the literacy practices and self-regulated learning skills of students? This article presents the findings of a yearlong study conducted in three Canadian provinces during the 2007–2008 school year initially involving 32 teachers and 388 students. Due to varying levels of implementation our final data set included 14 teachers and 296 students. Using a non-equivalent pre-test/post-test design, we found that grade 4–6 students who were in classrooms where the teacher provided regular and appropriate use of the electronic portfolio tool ePEARL (i.e., medium–high implementation condition, n = 7 classrooms and 121 students), compared to control students (n = 7 classrooms and 175 students) who did not use ePEARL, showed significant improvements (p < .05) in their writing skills on a standardized literacy measure (i.e., the constructed response subtest of the Canadian Achievement Test-4th ed.) and certain metacognitive skills measured via student self-report. The results of this study indicate that teaching with ePEARL has positive impacts on students’ literacy and self-regulated learning skills when the tool is used regularly and integrated into classroom instruction.     

Sources of New Ideas for Charity Fundraising: An Empirical Study

Intense competition in the UK charity fundraising market and the near continuous need for charities to acquire new supporters, reactivate lapsed donors and encourage first time donors to make further gifts has resulted in charities needing repeatedly to initiate novel fundraising campaigns. The starting point for an innovative campaign is an idea or set of ideas that will attract public attention, differentiate a charity from other fundraising organizations and distinguish the charity's current marketing activities from those undertaken in the past. An important but neglected issue in the charity idea creation field is the question of where and how fundraising organizations source their ideas for new campaigns. This empirical study identified (i) the main sources of fresh ideas for fundraising employed by a sample of 417 British charities, and (ii) certain antecedents of a charity's use of various categories of sources. Potential connections between particular types of idea source and managerial satisfaction with both the creative quality of the ideas obtained and the financial revenues derived from these ideas were also examined.     

Visual tracking for the recovery of multiple interacting plant root systems from X-ray $$\upmu $$CT images

We propose a visual object tracking framework for the extraction of multiple interacting plant root systems from three-dimensional X-ray micro computed tomography images of plants grown in soil. Our method is based on a level set framework guided by a greyscale intensity distribution model to identify object boundaries in image cross-sections. Root objects are followed through the data volume, while updating the tracker’s appearance models to adapt to changing intensity values. In the presence of multiple root systems, multiple trackers can be used, but need to distinguish target objects from one another in order to correctly associate roots with their originating plants. Since root objects are expected to exhibit similar greyscale intensity distributions, shape information is used to constrain the evolving level set interfaces in order to lock trackers to their correct targets. The proposed method is tested on root systems of wheat plants grown in soil.     

Enlarging the Margins in Perceptron Decision Trees

Capacity control in perceptron decision trees is typically performed by controlling their size. We prove that other quantities can be as relevant to reduce their flexibility and combat overfitting. In particular, we provide an upper bound on the generalization error which depends both on the size of the tree and on the margin of the decision nodes. So enlarging the margin in perceptron decision trees will reduce the upper bound on generalization error. Based on this analysis, we introduce three new algorithms, which can induce large margin perceptron decision trees. To assess the effect of the large margin bias, OC1 (Journal of Artificial Intelligence Research, 1994, 2, 1–32.) of Murthy, Kasif and Salzberg, a well-known system for inducing perceptron decision trees, is used as the baseline algorithm. An extensive experimental study on real world data showed that all three new algorithms perform better or at least not significantly worse than OC1 on almost every dataset with only one exception. OC1 performed worse than the best margin-based method on every dataset.     

Healthcare foodservice report 1993. Getting ready for reform. Interview by Mitchell Schechter

Hospital foodservice directors & dietitians are facing the greatest challenge of their careers -- to prepare, along with their institutions, for the most thoroughgoing reform of America's healthcare system ever proposed. Healthcare institutions across the country are seeking the means to offer enhanced patient services & gain competitive advantages by forming new alliances, new patient-centered care systems & cost-reduction programs. To aid in these efforts, their foodservices are developing comprehensive cross-training schemes, inter-departmental teams with other service groups & new ways to cut operating subsidies. Here's how five hospital foodservice departments are using cost controls, revenue building, quality management processes & enhanced efficiencies to help their institutions prepare for the changes ahead.