varunr14/newsummary huggingface dataset:
Telecare medicine information systems provide a communicating platform for accessing remote medical resources through public networks and help health care workers and medical personnel to rapidly making correct clinical decisions and treatments . An authentication scheme for data exchange in telecare medicine information systems enables legal users in hospitals and medical institutes to establish a secure channel and exchange electronic medical records or electronic health records securely and efficiently . This investigation develops an efficient and secure verified based three party authentication scheme by using extended chaotic maps for data exchange in telecare medicine information systems . The proposed scheme does not require server s public keys and avoids time consuming modular exponential computations and scalar multiplications on elliptic curve used in previous related approaches . Additionally the proposed scheme is proven secure in the random oracle model and realizes the lower bounds of messages and rounds in communications . Compared to related verified based approaches the proposed scheme not only possesses higher security but also has lower computational cost and fewer transmissions . 


Summary: We develop an efficient and secure verifier based three party authentication scheme. Our scheme is based on extended chaotic maps and suitable for data exchange in TMIS. Our scheme proven secure in the random oracle model. Our scheme avoids time consuming modular exponents and scalar multiplications on ECC. Our scheme has lower computational cost fewer transmissions and higher security.
In this study we developed an integrated hospital associated urinary tract infection surveillance information system based on existing electronic medical records systems for improving the work efficiency of infection control professionals in a 730 bed tertiary care teaching hospital in Taiwan . The iHAUTISIS can automatically collect data relevant to HAUTI surveillance from the different EMR systems and provides a visualization dashboard that helps ICPs make better surveillance plans and facilitates their surveillance work . In order to measure the system performance we also created a generic model for comparing the ICPs work efficiency when using existing electronic culture based surveillance information system and iHAUTISIS respectively . This model can demonstrate a patient s state and corresponding time spent on surveillance tasks performed by ICPs for the patient in that state . The study results showed that the iHAUTISIS performed better than the eCBSIS in terms of ICPs time cost . It reduced the time by 73.27s when using iHAUTISIS and eCBSIS for each patient on average . With increased adoption of EMR systems the development of the integrated HAI surveillance information systems would be more and more cost effective . Moreover the iHAUTISIS adopted web based technology that enables ICPs to online access patient s surveillance information using laptops or mobile devices . Therefore our system can further facilitate the HAI surveillance and reduce ICPs surveillance workloads . 


Summary: We develop an integrated surveillance system called iHAUTISIS for improving the work efficiency of infection control. The iHAUTISIS can automatically collect surveillance data from EMRs and detect infected cases. Compare to the existing surveillance system the iHAUTISIS can reduce 39 of an ICP s surveillance time for each patient.
The main goal of this study was to numerically quantify risk of duodenal stump blowout after Billroth II gastric resection . Our hypothesis was that the geometry of the reconstructed tract after BII resection is one of the key factors that can lead to duodenal dehiscence . We used computational fluid dynamics with finite element simulations of various models of BII reconstructed gastrointestinal tract as well as non perfused ex vivo porcine experimental models . As main geometrical parameters for FE postoperative models we have used duodenal stump length and inclination between gastric remnant and duodenal stump . Virtual gastric resection was performed on each of 3D FE models based on multislice Computer Tomography DICOM . According to our computer simulation the difference between maximal duodenal stump pressures for models with most and least preferable geometry of reconstructed GI tract is about 30 . We compared the resulting postoperative duodenal pressure from computer simulations with duodenal stump dehiscence pressure from the experiment . Pressure at duodenal stump after BII resection obtained by computer simulation is 4 5 times lower than the dehiscence pressure according to our experiment on isolated bowel segment . Our conclusion is that if the surgery is performed technically correct geometry variations of the reconstructed GI tract by themselves are not sufficient to cause duodenal stump blowout . Pressure that develops in the duodenal stump after BII resection using omega loop only in the conjunction with other risk factors can cause duodenal dehiscence . Increased duodenal pressure after BII resection is risk factor . Hence we recommend the routine use of Roux en Y anastomosis as a safer solution in terms of resulting intraluminal pressure . However if the surgeon decides to perform BII reconstruction results obtained with this methodology can be valuable . 


Summary: The main goal of this study was to numerically quantify risk of duodenal stump blowout after Billroth II BII gastric resection. Our hypothesis was that the geometry of the reconstructed tract after BII resection is one of the key factors that can cause increased duodenal pressure and consequently duodenal dehiscence. If surgeon decide to perform BII type of reconstruction findings obtained by this preoperative simulation are very useful in terms of minimizing the risk of duodenal dehiscence.
This survey paper describes the role of splines in geometry and topology emphasizing both similarities and differences from the classical treatment of splines . The exposition is non technical and contains many examples with references to more thorough treatments of the subject . The goal of this survey paper is to describe how splines arise in geometry and topology . Geometric splines usually appear under the name GKM theory after Goresky Kottwitz MacPherson who developed them to compute what is called the cohomology ring of a geometric object . Geometers and analysts ask many of the same questions about splines what is their dimension can we identify a basis can we find explicit formulas for the elements of the basis However geometric constraints can change the tone of these questions the splines may satisfy various symmetries or have a basis satisfying certain conditions . And some questions are specific to geometric splines geometers particularly care about the multiplication table with respect to a given basis . 


Summary: Many varieties X have a graph that describes their essential geometric features. The splines  form the equivariant cohomology ring and encode key data about X. We describe geometric tools to analyze splines that differ from classical tools. We give an algebraic and combinatorial generalization called generalized splines.
The influence of cathode agitation on the residual stress of electroplated gold has been investigated . Using a custom built plating cell a periodic reciprocating motion was applied to silicon substrates that were electroplated with soft gold . A commercially available gold sulfite solution was used to deposit the 0.6 m thick gold films using a current density of 3.0mA cm2 and a bath temperature of 50 C. By increasing the speed of cathode agitation from 0 to 5cm s the magnitude of the compressive stress decreased from 64 to 9MPa . The results suggest that cathode agitation significantly alters the mass transport within the electrolytic cell and can be used as a method of stress control in gold electroplating . This finding is potentially significant for plating applications in microelectronics and microsystems that require precise stress control . 


Summary: We measure experimentally cathode agitation versus stress in electroplated gold. The stress for gold on silicon substrates is measured using microcantilevers. We demonstrate the feasibility of cathode agitation in gold film stress control. Cathode agitation reduced the compressive stress in the gold films.
High aspect ratio GaN based nanostructures are of interest for advanced photonic crystal and core shell devices. Nanostructures grown by a bottom up approach are limited in terms of doping geometry and shape which narrow their potential application areas. In contrast high uniformity and a greater diversity of shape and design can be produced via a top down etching approach. However a detailed understanding of the role of etch process parameters is lacking for creating high aspect ratio nanorods and nanopores. Here we report a systematic analysis on the role of temperature and pressure on the fabrication of nanorod and nanopore arrays in GaN. Our results show a threshold in the etch behaviour at a temperature of 125 C which greatly enhances the verticality of the GaN nanorods whilst the modification of the pressure enables a fine tuning of the nanorod profile. For nanopores we show that the use of higher temperatures at higher pressures enables the fabrication of nanopores with an undercut profile. Such a profile is important for controlling the optical field in photonic crystal based devices. Therefore we expect the ability to create such nanostructures to form the foundation for new advanced LED designs. 


Summary: Impact of the temperature and the pressure on nanorods profile Identification of critical temperature of 125 C for etching vertical nanorods Discussion on the impact of the temperature pressure on the etching mechanisms in nanorod and nanopore arrays Identification and control of the key parameters needed to control the nanostructure profiles for the fabrication of advanced photonic crystal devices.
Taiwan is an area where chronic hepatitis is endemic . Liver cancer is so common that it has been ranked first among cancer mortality rates since the early 1980s in Taiwan . Besides liver cirrhosis and chronic liver diseases are the sixth or seventh in the causes of death . Therefore as shown by the active research on hepatitis it is not only a health threat but also a huge medical cost for the government . The estimated total number of hepatitis B carriers in the general population aged more than 20 years old is 3 067 307 . Thus a case record review was conducted from all patients with diagnosis of acute hepatitis admitted to the Emergency Department of a well known teaching oriented hospital in Taipei . The cost of medical resource utilization is defined as the total medical fee . In this study a fuzzy neural network is employed to develop the cost forecasting model . A total of 110 patients met the inclusion criteria . The computational results indicate that the FNN model can provide more accurate forecasts than the support vector regression or artificial neural network . In addition unlike SVR and ANN FNN can also provide fuzzy IF THEN rules for interpretation . 


Summary: FNN is employed to develop the cost forecasting model for acute hepatitis patients in emergency room. The FNN model can provide more accurate forecast than SVR and ANN. Unlike SVR and ANN FNN can also provide fuzzy IF THEN rules for interpretation.
Background Chronic hypoxemia has deleterious effects on psychomotor function that can affect daily life . There are no clear results regarding short term therapy with low concentrations of O2 in hypoxemic patients . We seek to demonstrate by measuring the characteristics of drawing these effects on psychomotor function of hypoxemic patients treated with O2 . Methods Eight patients M F age 69.5 yr mean with hypoxemia mmHg performed two drawings of pictures . Tests were performed before and after 30min breathing with O2 . Results Stroke velocity increased after O2 for the house drawing mm s basal 30.9 mm s with O2 mean p 0.025 Wilcoxon test . The drawing time down or fraction time the pen is touching the paper during the drawing phase decreased s basal 17.4 s with O2 p 0.017 Wilcoxon test . Conclusions This study shows that in patients with chronic hypoxemia a short period of oxygen therapy produces changes in psychomotor function that can be measured by means of drawing analysis . 


Summary: First paper devoted to analyze oxygen therapy effects measured by handwritten tasks. This study shows that in patients with chronic hypoxemia a short period of oxygen therapy produces changes in psychomotor function. We show that this changes can be measured by means of drawing analysis.
Breast cancer is the most deadly disease affecting women and thus it is natural for women aged 40 49 years to assess their personal risk for developing familial breast cancer . Besides as each individual woman possesses different levels of risk of developing breast cancer depending on their family history genetic predispositions and personal medical history individualized care setting mechanism needs to be identified so that appropriate risk assessment counseling screening and prevention options can be determined by the health care professionals . The presented work aims at developing a soft computing based medical decision support system using Fuzzy Cognitive Map that assists health care professionals in deciding the individualized care setting mechanisms based on the FBC risk level of the given women . The FCM based FBC risk management system uses NHL to learn causal weights from 40 patient records and achieves a 95 diagnostic accuracy . The results obtained from the proposed model are in concurrence with the comprehensive risk evaluation tool based on Tyrer Cuzick model for 38 40 patient cases . Besides the proposed model identifies high risk women by calculating higher accuracy of prediction than the standard Gail and NSAPB models . The testing accuracy of the proposed model using 10 fold cross validation technique outperforms other standard machine learning based inference engines as well as previous FCM based risk prediction methods for BC . 


Summary: Development of a familial breast cancer risk assessment model using Fuzzy Cognitive Map FCM methodology. The proposed methodology concentrates its efforts on personalized individual decision making including the family history and the demographic risk factors to elicit the hidden risk of developing breast cancer. Hebbian based learning capabilities of FCM were used to improve the modeling issue and contribute to risk prediction. The accuracy of the system is compared with benchmark machine learning methods showing its superiority.
A mixture of latent trait models with common slope parameters for model based clustering of high dimensional binary data a data type for which few established methods exist is proposed . Recent work on clustering of binary data based on a dimensional Gaussian latent variable is extended by incorporating common factor analyzers . Accordingly this approach facilitates a low dimensional visual representation of the clusters . The model is further extended by the incorporation of random block effects . The dependencies in each block are taken into account through block specific parameters that are considered to be random variables . A variational approximation to the likelihood is exploited to derive a fast algorithm for determining the model parameters . Real and simulated data are used to demonstrate this approach . 


Summary: A method for clustering high dimensional binary data. The model is extended to include random block effects for repeatedly sampled data. A variational EM algorithm is developed for parameter estimation.
We propose an automatic method for fast reconstruction of indoor scenes from raw point scans which is a fairly challenging problem due to the restricted accessibility and the cluttered space for indoor environment . We first detect and remove points representing the ground walls and ceiling from the input data and cluster the remaining points into different groups referred to as sub scenes . Our approach abstracts the sub scenes with geometric primitives and accordingly constructs the topology graphs with structural attributes based on the functional parts of objects . To decompose sub scenes into individual indoor objects we devise an anchor guided subgraph matching algorithm which leverages template graphs to partition the graphs into subgraphs which is capable of handling arbitrarily oriented objects within scenes . Subsequently we present a data driven approach to model individual objects which is particularly formulated as a model instance recognition problem . A Randomized Decision Forest is introduced to achieve robust recognition on decomposed indoor objects with raw point data . We further exploit template fitting to generate the geometrically faithful model to the input indoor scene . We visually and quantitatively evaluate the performance of our framework on a variety of synthetic and raw scans which comprehensively demonstrates the efficiency and robustness of our reconstruction method on raw scanned point clouds even in the presence of noise and heavy occlusions . 


Summary: We propose a functional part guided modeling method for cluttered indoor scenes. We design an anchor guided graph matching algorithm for scene decomposition. We devise a data driven approach for object modeling based on RDF.
Background and objectives Rule based classification is a typical data mining task that is being used in several medical diagnosis and decision support systems . The rules stored in the rule base have an impact on classification efficiency . Rule sets that are extracted with data mining tools and techniques are optimized using heuristic or meta heuristic approaches in order to improve the quality of the rule base . In this work a meta heuristic approach called Wind driven Swarm Optimization is used . The uniqueness of this work lies in the biological inspiration that underlies the algorithm . Methods WSO uses Jval a new metric to evaluate the efficiency of a rule based classifier . Rules are extracted from decision trees . WSO is used to obtain different permutations and combinations of rules whereby the optimal ruleset that satisfies the requirement of the developer is used for predicting the test data . The performance of various extensions of decision trees namely RIPPER PART FURIA and Decision Tables are analyzed . The efficiency of WSO is also compared with the traditional Particle Swarm Optimization . Results Experiments were carried out with six benchmark medical datasets . The traditional C4.5 algorithm yields 62.89 accuracy with 43 rules for liver disorders dataset where as WSO yields 64.60 with 19 rules . For Heart disease dataset C4.5 is 68.64 accurate with 98 rules where as WSO is 77.8 accurate with 34 rules . The normalized standard deviation for accuracy of PSO and WSO are 0.5921 and 0.5846 respectively . Conclusion WSO provides accurate and concise rulesets . PSO yields results similar to that of WSO but the novelty of WSO lies in its biological motivation and it is customization for rule base optimization . The trade off between the prediction accuracy and the size of the rule base is optimized during the design and development of rule based clinical decision support system . The efficiency of a decision support system relies on the content of the rule base and classification accuracy . 


Summary: Wind driven Swarm Optimization WSO is used for optimizing the rule base of a Clinical Decision Support System. WSO is a bio inspired approach inspired from the flight of birds. WSO uses Jval to evaluate the efficiency of a rule based CDSS. Experiments with six medical datasets show that WSO provides efficient rulesets. The novelty of WSO lies in its biological motivation and customization for rule base optimization.
Patients who suffer from chronic renal failure tend to suffer from an associated anemia as well . Therefore it is essential to know the hemoglobin levels in these patients . The aim of this paper is to predict the hemoglobin value using a database of European hemodialysis patients provided by Fresenius Medical Care for improving the treatment of this kind of patients . For the prediction of Hb both analytical measurements and medication dosage of patients suffering from chronic renal failure are used . Two kinds of models were trained global and local models . In the case of local models clustering techniques based on hierarchical approaches and the adaptive resonance theory were used as a first step and then a different predictor was used for each obtained cluster . Different global models have been applied to the dataset such as Linear Models Artificial Neural Networks Support Vector Machines and Regression Trees among others . Also a relevance analysis has been carried out for each predictor model thus finding those features that are most relevant for the given prediction . 


Summary: Different prediction algorithms were used to predict Hb levels in CRF patients. Prediction errors in the validation cohorts of patients were around 0.6g dl. Difficulty to obtain lower errors due to the measuring machine precision 0.2g dl . Relevance analysis of features have been applied for each predictor.
We propose Bayesian shrinkage methods for coefficient estimation for high dimensional vector autoregressive models using scale mixtures of multivariate normal distributions for independently sampled additive noises . We also suggest an efficient selection procedure for the shrinkage parameter as a computationally feasible alternative to the traditional MCMC sampling methods for high dimensional data . A shrinkage parameter is selected at the minimum point of a newly proposed score function which is asymptotically equivalent to the mean squared error of the model coefficients . The selected shrinkage parameter is presented in a closed form as a function of sample size level of noise and non normality in data and it can be efficiently estimated by using a suggested variation of cross validation . Consistency of both of the cross validation estimator and proposed shrinkage estimator is proved . The competitiveness of the proposed methods is demonstrated based on comprehensive experimental results using simulated data and high dimensional plant gene expression data in the context of coefficient estimation and structural inference for VAR models . The proposed methods are applicable to high dimensional stationary time series with or without near unit roots . 


Summary: The proposed methods are useful for high dimensional VAR models with small data. A formula for an optimal shrinkage parameter is derived under a Bayes framework. The proposed methods are superior in computation time and performance.
The huge amount of textual data on the Web has grown in the last few years rapidly creating unique contents of massive dimension . In a decision making context one of the most relevant tasks is polarity classification of a text source which is usually performed through supervised learning methods . Most of the existing approaches select the best classification model leading to over confident decisions that do not take into account the inherent uncertainty of the natural language . In this paper we pursue the paradigm of ensemble learning to reduce the noise sensitivity related to language ambiguity and therefore to provide a more accurate prediction of polarity . The proposed ensemble method is based on Bayesian Model Averaging where both uncertainty and reliability of each single model are taken into account . We address the classifier selection problem by proposing a greedy approach that evaluates the contribution of each model with respect to the ensemble . Experimental results on gold standard datasets show that the proposed approach outperforms both traditional classification and ensemble methods . 


Summary: A novel ensemble learning methodology is proposed for polarity classification task. A selection strategy is studied to reduce the search space of candidate ensembles. The proposed model has been shown to be effective and efficient in several domains.