varunr14/newsummary huggingface dataset:
Pressure ulcers are considered as one of the most challenging problems that Nursing professionals have to deal with in their daily practice . Nowadays the education on PrUs is mainly based on traditional lecturing seminars and face to face instruction sometimes with the support of photographs of wounds being used as teaching material . This traditional educational methodology suffers from some important limitations which could affect the efficacy of the learning process . This current study has been designed to introduce information and communication technologies in the education on PrU for undergraduate students with the main objective of evaluating the advantages an disadvantages of using ICT by comparing the learning results obtained from using an e learning tool with those from a traditional teaching methodology . In order to meet this major objective a web based learning system named ePULab has been designed and developed as an adaptive e learning tool for the autonomous acquisition of knowledge on PrU evaluation . This innovative system has been validated by means of a randomized controlled trial that compares its learning efficacy with that from a control group receiving a traditional face to face instruction . Students using ePULab gave significantly better learning acquisition scores to post test mean 15.83 than those following traditional lecture style classes to post test mean 11.6 . In this article the ePULab software is described in detail and the results from that experimental educational validation study are also presented and analyzed . 


Summary: A web tool is developed for adaptive self learning of pressure ulcer evaluation. We evaluate the effectiveness of an e learning tool for pressure ulcer diagnosis. The effectiveness of the tool is compared with that from a traditional training. The e learning strategy gives significantly better knowledge acquisition results.
Challenges in the analyses of growth mixture models include missing data outliers estimation and model selection . Four non ignorable missingness models to recover the information due to missing data and three robust models to reduce the effect of non normality are proposed . A full Bayesian method is implemented by means of data augmentation algorithm and Gibbs sampling procedure . Model selection criteria are also proposed in the Bayesian context . Simulation studies are then conducted to evaluate the performances of the models the Bayesian estimation method and selection criteria under different situations . The application of the models is demonstrated through the analysis of education data on children s mathematical ability development . The models can be widely applied to longitudinal analyses in medical psychological educational and social research . 


Summary: Four non ignorable missingness models are proposed. Three robust models to deal with outliers are proposed. A full Bayesian method is implemented. Model selection criteria are proposed in a Bayesian context. Three simulation studies and one real data case study are conducted.
Transfer printing has been reported recently as a viable route for electronics on flexible substrates . The method involves transferring micro macrostructures such as wires or ultra thin chips from Si wafers to the flexible substrates by using elastomeric transfer substrates such as poly . A major challenge in this process is posed by the residues of PDMS which are left over on Si surface after the nanostructures have been transferred . As insulator PDMS residues make it difficult to realize metal connections and hence pose challenge in the way of using nanostructures as the building blocks for active electronics . This paper presents a method for PDMS residues free transfer of Si micro macrostructures to flexible substrates such as polyimide . The PDMS residues are removed from Si surface by immersing the transferred structures in a solution of quaternary ammonium fluoride such as TBAF and non hydroxylic aprotic solvent such as PMA . The residues are removed at a rate which is about five times faster than the traditional dry etch methods . Unlike traditional alternatives the presented method removes PDMS without attacking the flexible PI substrates . 


Summary: PDMS residues free micro macrostructures. Transfer printing the micro nanostructures to ultra flexible substrates. Removing PDMS using chemical etch and dispersion. Disintegrating and dissolving the PDMS.
In this paper the problem of predicting blood glucose concentrations for the treatment of patients with type 1 diabetes is addressed . Predicting BG is of very high importance as most treatments which consist in exogenous insulin injections rely on the availability of BG predictions . Many models that can be used for predicting BG are available in the literature . However it is widely admitted that it is almost impossible to perfectly model blood glucose dynamics while still being able to identify model parameters using only blood glucose measurements . The main contribution of this work is to propose a simple and identifiable linear dynamical model which is based on the static prediction model of standard therapy . It is shown that the model parameters are intrinsically correlated with physician set therapy parameters and that the reduction of the number of model parameters to identify leads to inferior data fits but to equivalent or slightly improved prediction capabilities compared to state of the art models a sign of an appropriate model structure and superior reliability . The validation of the proposed dynamic model is performed using data from the UVa simulator and real clinical data and potential uses of the proposed model for state estimation and BG control are discussed . 


Summary: New glucose prediction model for the treatment of type 1 diabetes. Strongly correlation of model parameters with therapy parameters. Equivalent predictions to models with 50 more parameters. Validation on clinical data as well as UVa simulation data.
Rational curves and surfaces are powerful tools for shape representation and geometric modeling . However the real weights are generally difficult to choose except for a few special cases such as representing conics . This paper presents an extension of rational curves and surfaces by replacing the real weights with matrices . The matrix weighted rational curves and surfaces have the same structures as the traditional rational curves and surfaces but the matrix weights can be defined in geometric ways . In particular the weight matrices for the extended rational B zier NURBS or the generalized subdivision curves and surfaces are computed using the normal vectors specified at the control points . Similar to the effects of control points the specified normals can be used to control the curve or the surface s shape efficiently . It is also shown that matrix weighted NURBS curves and surfaces can pass through their control points thus curve or surface reconstruction by the extended NURBS model no longer needs solving any large system but just choosing control points and control normals from the input data . 


Summary: We extend rational curves and surfaces that have real weights to rational curves and surfaces permitting matrix weights. Matrix weighted rational curves and surfaces maintain many similar properties or algorithms of traditional rational curves and surfaces. The matrix weights permit novel geometric means for shape control of rational curves and surfaces. Matrix weighted NURBS curves and surfaces are suitable for curve or surface reconstruction with no need of solving large systems.
Many children with motor impairments can not participate in games and jokes that contribute to their formation . Currently commercial computer games there are few options of software and sufficiently flexible access devices to meet the needs of this group of children . In this study a peripheral access device and a 3D computerized game that do not require the actions of dragging clicking or activating various keys at the same time were developed . The peripheral access device consists of a webcam and a supervisory system that processes the images . This method provides a field of action that can be adjusted to various types of motor impairments . To analyze the sensitivity of the commands a virtual course was developed using the scenario of a path of straight lines and curves . A volunteer with good ability in virtual games performed a short training with the virtual course and after 15min of training obtained similar results with a standard keyboard and the adapted peripheral device . A 3D game in the Amazon forest was developed using the Blender 3D tool . This free software was used to model the characters and scenarios . To evaluate the usability of the 3D game the game was tested by 20 volunteers without motor impairments and 13 volunteers with severe motor limitations of the upper limbs . All the volunteers could easily execute all the actions of the game using the adapted peripheral device . The majority positively evaluated the questions of usability and expressed their satisfaction . The computerized game coupled to the adapted device will offer the option of leisure and learning to people with severe motor impairments who previously lacked this possibility . It also provided equality in this activity to all the users . 


Summary: Webcam with supervisory system was developed to allow computer access for people with disabilities. The developed system converts webcam images in command to control a computer game. Virtual game that provides fun without requiring click and drag action. The system provides fun for children with motor difficulties without causing discomfort or embarrassment.
Mahalanobis distance may be used as a measure of the disparity between an individual s profile of scores and the average profile of a population of controls . The degree to which the individual s profile is unusual can then be equated to the proportion of the population who would have a larger Mahalanobis distance than the individual . Several estimators of this proportion are examined . These include plug in maximum likelihood estimators medians the posterior mean from a Bayesian probability matching prior an estimator derived from a Taylor expansion and two forms of polynomial approximation one based on Bernstein polynomial and one on a quadrature method . Simulations show that some estimators including the commonly used plug in maximum likelihood estimators can have substantial bias for small or moderate sample sizes . The polynomial approximations yield estimators that have low bias with the quadrature method marginally to be preferred over Bernstein polynomials . However the polynomial estimators sometimes yield infeasible estimates that are outside the 0 1 range . While none of the estimators are perfectly unbiased the median estimators match their definition in simulations their estimates of the proportion have a median error close to zero . The standard median estimator can give unrealistically small estimates and an adjustment is proposed that ensures estimates are always credible . This latter estimator has much to recommend it when unbiasedness is not of paramount importance while the quadrature method is recommended when bias is the dominant issue . 


Summary: We seek an estimate of the population proportion with extreme Mahalanobis index. Nine point estimates are examined in an extensive simulation study. Maximum likelihood estimates have substantial bias. Methods based on polynomial approximations give low bias but can be out of range. An adapted median estimator that always gives sensible estimates is proposed.
In conjunction with the advance in computer technology virtual screening of small molecules has been started to use in drug discovery . Since there are thousands of compounds in early phase of drug discovery a fast classification method which can distinguish between active and inactive molecules can be used for screening large compound collections . In this study we used Support Vector Machines for this type of classification task . SVM is a powerful classification tool that is becoming increasingly popular in various machine learning applications . The data sets consist of 631 compounds for training set and 216 compounds for a separate test set . In data pre processing step the Pearson s correlation coefficient used as a filter to eliminate redundant features . After application of the correlation filter a single SVM has been applied to this reduced data set . Moreover we have investigated the performance of SVM with different feature selection strategies including SVM Recursive Feature Elimination Wrapper Method and Subset Selection . All feature selection methods generally represent better performance than a single SVM while Subset Selection outperforms other feature selection methods . We have tested SVM as a classification tool in a real life drug discovery problem and our results revealed that it could be a useful method for classification task in early phase of drug discovery . 


Summary: Our aim was to classify small molecule compounds as drugs and nondrugs using Support Vector Machines SVM . We used three feature selection methods to improve the performance of the SVM classifier. Our findings revealed that data pre processing and feature selection enhance the performance of the classifiers. Our study and findings will contribute to improvement of our understanding of the early phase drug design.
In this note we study the regularity of generalized Hermite interpolation and compare it to that of classical Hermite interpolation . While every Hermite interpolation scheme is regular in one variable the classical Hermite interpolation schemes in several variables are regular if and only if they are supported at one point . In this note we exhibit some regular generalized Hermite interpolation schemes supported at two points and study some limitation of existence of such schemes . The existence of such schemes provides a class of counterexamples to a conjecture of Jia and Sharma . 


Summary: We compare regularity properties of generalized and classical Hermite interpolation. Classical Hermite interpolation in several variables is regular iff it interpolates at one point. We exhibit regular generalized Hermite interpolation schemes supported at two points. We study some limitation of existence of such schemes. These schemes provide a class of counterexamples to a conjecture of Jia and Sharma.
A Gini based statistical test for a unit root is suggested . This test is based on the well known Dickey Fuller test where the ordinary least squares regression is replaced by the semi parametric Gini regression in modeling the AR process . A residual based bootstrap is used to find critical values . The Gini methodology is a rank based methodology that takes into account both the variate values and the ranks . Therefore it provides robust estimators that are rank based while avoiding loss of information . Furthermore the Gini methodology relies on first order moment assumptions which validates its use for a wide range of distributions . Simulation results validate the Gini based test and indicate its superiority in some design settings in comparison to other available procedures . The Gini based test opens the door for further developments such as a Gini based cointegration test . 


Summary: A Gini based unit root test is developed. The test relies on the semi parametric Gini regression. Includes an in depth numerical comparison of the Gini based test and existing tests. Simulations indicate the superiority of the Gini based test in some design settings.
This paper shows that generic 2D Free Form Deformations of degree can be made birational by a suitable assignment of weights to the B zier or B spline control points . An FFD that is birational facilitates operations such as backward mapping for image warping and Extended Free Form Deformation . While birational maps have been studied extensively in the classical algebraic geometry literature this paper is the first to present a family of non linear birational maps that are flexible enough to be of practical use in geometric modeling . 


Summary: A method is proposed for creating a planar free form deformation of degree that has a rational inverse. The control points can be freely positioned and weights are computed that produce a birational map. Weight computation is simple and can be performed in real time as the control points are moved.
Background and objective Periodontitis involves progressive loss of alveolar bone around the teeth . Hence automatic alveolar bone loss measurement in periapical radiographs can assist dentists in diagnosing such disease . In this paper we propose an effective method for ABL area localization and denote it as ABLIfBm . Method ABLIfBm is a threshold segmentation method that uses a hybrid feature fused of both intensity and texture measured by the H value of fractional Brownian motion model where the H value is the Hurst coefficient in the expectation function of a fBm curve and is directly related to the value of fractal dimension . Adopting leave one out cross validation training and testing mechanism ABLIfBm trains weights for both features using Bayesian classifier and transforms the radiograph image into a feature image obtained from a weighted average of both features . Finally by Otsu s thresholding it segments the feature image into normal and bone loss regions . Results Experimental results on 31 periodontitis radiograph images in terms of mean true positive fraction and false positive fraction are about 92.5 and 14.0 respectively where the ground truth is provided by a dentist . The results also demonstrate that ABLIfBm outperforms the threshold segmentation method using either feature alone or a weighted average of the same two features but with weights trained differently a level set segmentation method presented earlier in literature and segmentation methods based on Bayesian K NN or SVM classifier using the same two features . Conclusion Our results suggest that the proposed method can effectively localize alveolar bone loss areas in periodontitis radiograph images and hence would be useful for dentists in evaluating degree of bone loss for periodontitis patients . 


Summary: Present an effective threshold segmentation method for localizing alveolar bone loss areas in periodontitis images using a hybrid feature 0.15 fBm H 0.85 1 I both fBm H and I are normalized. TPF FPF of 31 tested radiograph images used and unused in weight training 92.5 14 . 14 lower FPF than level set Bayesian KNN SVM classification using the same two features. The method would be useful for dentists in evaluating degree of bone loss for periodontitis patients.
Background and objective Parkinson s disease is the second most common neurodegenerative disease affecting significant portion of elderly population . One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting . There is no objective quantitative method of clinical diagnosis of PD . It is thought that PD can only be definitively diagnosed at postmortem which further highlights the complexities of diagnosis . Methods We exploit the fact that movement during handwriting of a text consists not only from the on surface movements of the hand but also from the in air trajectories performed when the hand moves in the air from one stroke to the next . We used a digitizing tablet to assess both in air and on surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age and gender matched healthy controls . Results By applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls we demonstrated that assessing the in air on surface hand movements led to accurate classifications in 84 and 78 of subjects respectively . Combining both modalities improved the accuracy by another 1 over the evaluation of in air features alone and provided medically relevant diagnosis with 85.61 prediction accuracy . Conclusions Assessment of in air movements during handwriting has a major impact on disease classification accuracy . This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD . 


Summary: We exploit the fact that movement during handwriting of a text consists of on surface and in air movements and use it for differential diagnosis of PD. By applying feature selection algorithms and support vector machine learning methods to separate PD from HC we demonstrated 85 classification accuracy. Decision support system based on handwriting analysis can be complementary method to diagnosis made by clinician or other decision support systems.
The purpose of this study was the development of a clustering methodology to deal with arterial pressure waveform parameters to be used in the cardiovascular risk assessment . One hundred sixteen subjects were monitored and divided into two groups . The first one was analyzed using APW and biochemical parameters while the remaining 93 healthy subjects were only evaluated through APW parameters . The expectation maximization and k means algorithms were used in the cluster analysis and the risk scores the Systematic COronary Risk Evaluation project the Assessing cardiovascular risk using Scottish Intercollegiate Guidelines Network and the PROspective Cardiovascular M nster commonly used in clinical practice were selected to the cluster risk validation . The result from the clustering risk analysis showed a very significant correlation with ASSIGN and a significant correlation with FRS . The results from the comparison of both groups also allowed to identify the cluster with higher cardiovascular risk in the healthy group . These results give new insights to explore this methodology in future scoring trials . 


Summary: We developed a clustering methodology to deal with arterial pressure waveform APW parameters. A new non invasive device is being evaluated. We collect a database of 116 subjects. This study compares different methods used in the APW analysis towards cardiovascular diseases. It is possible to assess potential cardiovascular risk in healthy subjects using cluster analysis.