Effect of Continuous Education on Readmission Rates for CHF Patients

Aim: To evaluate if continuing the education of Congest Heart Failure patients post-discharge will decrease the amount of readmissions within 6 months of discharge. Background: Causes for decreased readmission rates in Congestive Heart Failure patients have been evaluated in multiple studies. The evaluation of the current research showed having discharge education and post- discharge follow-ups decreased the rate of readmission within 6 months. There is a sufficient amount of evidence supporting the implementation of education upon discharge and follow-ups of Congestive Heart Failure patients. Data Source: Databases and search engines used included: PubMed, OneSearch, CINAHL, DogPile, and Google. Of 25 articles read, 10 articles were included in the review of literature. Results: Three specific forms of patient education were reviewed. These included a telephone follow up program, six months of continued patient education, and a plan tailored to the individual needs of the patient. All three interventions were effective in showing a decrease in readmission rates. Conclusion: Increased time of continued education is believed to be effective in decreasing the readmission of Congestive Heart Failure patients within 30 days of discharge

On the uniqueness of the surface sources of evoked potentials

The uniqueness of a surface density of sources localized inside a spatial region $R$ and producing a given electric potential distribution in its boundary $B_0$ is revisited. The situation in which $R$ is filled with various metallic subregions, each one having a definite constant value for the electric conductivity is considered. It is argued that the knowledge of the potential in all $B_0$ fully determines the surface density of sources over a wide class of surfaces supporting them. The class can be defined as a union of an arbitrary but finite number of open or closed surfaces. The only restriction upon them is that no one of the closed surfaces contains inside it another (nesting) of the closed or open surfaces.Comment: 16 pages, 5 figure

A Bayesian Approach to Inverse Quantum Statistics

A nonparametric Bayesian approach is developed to determine quantum potentials from empirical data for quantum systems at finite temperature. The approach combines the likelihood model of quantum mechanics with a priori information over potentials implemented in form of stochastic processes. Its specific advantages are the possibilities to deal with heterogeneous data and to express a priori information explicitly, i.e., directly in terms of the potential of interest. A numerical solution in maximum a posteriori approximation was feasible for one--dimensional problems. Using correct a priori information turned out to be essential.Comment: 4 pages, 6 figures, revte

Fast stable direct fitting and smoothness selection for Generalized Additive Models

Existing computationally efficient methods for penalized likelihood GAM fitting employ iterative smoothness selection on working linear models (or working mixed models). Such schemes fail to converge for a non-negligible proportion of models, with failure being particularly frequent in the presence of concurvity. If smoothness selection is performed by optimizing `whole model' criteria these problems disappear, but until now attempts to do this have employed finite difference based optimization schemes which are computationally inefficient, and can suffer from false convergence. This paper develops the first computationally efficient method for direct GAM smoothness selection. It is highly stable, but by careful structuring achieves a computational efficiency that leads, in simulations, to lower mean computation times than the schemes based on working-model smoothness selection. The method also offers a reliable way of fitting generalized additive mixed models

Reproducing Kernels of Generalized Sobolev Spaces via a Green Function Approach with Distributional Operators

In this paper we introduce a generalized Sobolev space by defining a semi-inner product formulated in terms of a vector distributional operator $\mathbf{P}$ consisting of finitely or countably many distributional operators $P_n$, which are defined on the dual space of the Schwartz space. The types of operators we consider include not only differential operators, but also more general distributional operators such as pseudo-differential operators. We deduce that a certain appropriate full-space Green function $G$ with respect to $L:=\mathbf{P}^{\ast T}\mathbf{P}$ now becomes a conditionally positive definite function. In order to support this claim we ensure that the distributional adjoint operator $\mathbf{P}^{\ast}$ of $\mathbf{P}$ is well-defined in the distributional sense. Under sufficient conditions, the native space (reproducing-kernel Hilbert space) associated with the Green function $G$ can be isometrically embedded into or even be isometrically equivalent to a generalized Sobolev space. As an application, we take linear combinations of translates of the Green function with possibly added polynomial terms and construct a multivariate minimum-norm interpolant $s_{f,X}$ to data values sampled from an unknown generalized Sobolev function $f$ at data sites located in some set $X \subset \mathbb{R}^d$. We provide several examples, such as Mat\'ern kernels or Gaussian kernels, that illustrate how many reproducing-kernel Hilbert spaces of well-known reproducing kernels are isometrically equivalent to a generalized Sobolev space. These examples further illustrate how we can rescale the Sobolev spaces by the vector distributional operator $\mathbf{P}$. Introducing the notion of scale as part of the definition of a generalized Sobolev space may help us to choose the "best" kernel function for kernel-based approximation methods.Comment: Update version of the publish at Num. Math. closed to Qi Ye's Ph.D. thesis (\url{http://mypages.iit.edu/~qye3/PhdThesis-2012-AMS-QiYe-IIT.pdf}

Irrigation and drainage in the new millennium

Presented at the 2000 USCID international conference, Challenges facing irrigation and drainage in the new millennium on June 20-24 in Fort Collins, Colorado.Includes bibliographical references.Current global population growth rates require an increase in agricultural food production of about 40-50% over the next thirty to forty years, in order to maintain present levels of food intake. To meet the target, irrigated agriculture must play a vital role, in fact the FAO estimates that 60% of future gains will have to come from irrigation. The practice of controlling drainage involves the extension of on-farm water management to include drainage management. With the integration of irrigation and drainage management, the water balance can be managed to reduce excess water losses and increase irrigation efficiencies. Controlled drainage is relatively new and there are many theoretical and practical issues to be addressed. The technique involves maintaining high water table in the soil profile for extended periods of time, requiring careful management to ensure that crop growth is not affected by anaerobic conditions. A fieldwork programme has been investigated to test controlled drainage in the Nile Delta, where water resources are stretched to the limit. Water saving is essential in the next 20 years. Pressures from the fixed Nile water allocation, population growth, industry and other sectors and the horizontal expansion programme mean that this need is urgent. One crop season has been completed at a site in the Western Nile Delta using simple control devices in the subsurface drainage system. This paper discusses the potential benefits of controlled drainage to save water in agricultural areas such as the Nile Delta, and presents findings from the first crop season

Fast methods for training Gaussian processes on large data sets

Gaussian process regression (GPR) is a non-parametric Bayesian technique for interpolating or fitting data. The main barrier to further uptake of this powerful tool rests in the computational costs associated with the matrices which arise when dealing with large data sets. Here, we derive some simple results which we have found useful for speeding up the learning stage in the GPR algorithm, and especially for performing Bayesian model comparison between different covariance functions. We apply our techniques to both synthetic and real data and quantify the speed-up relative to using nested sampling to numerically evaluate model evidences.Comment: Fixed missing reference

Detecting Generalized Synchronization Between Chaotic Signals: A Kernel-based Approach

A unified framework for analyzing generalized synchronization in coupled chaotic systems from data is proposed. The key of the proposed approach is the use of the kernel methods recently developed in the field of machine learning. Several successful applications are presented, which show the capability of the kernel-based approach for detecting generalized synchronization. It is also shown that the dynamical change of the coupling coefficient between two chaotic systems can be captured by the proposed approach.Comment: 20 pages, 15 figures. massively revised as a full paper; issues on the choice of parameters by cross validation, tests by surrogated data, etc. are added as well as additional examples and figure