varunr14/newsummary huggingface dataset:
Recent study shows that tendon sheath system has great potential in the development of surgical robots for endoscopic surgery . It is able to deliver adequate power in a light weight and compact package . And the flexibility and compliance of the tendon sheath system make it capable of adapting to the long and winding path in the flexible endoscope . However the main difficulties in precise control of such system fall on the nonlinearities of the system behavior and absence of necessary sensory feedback at the surgical end effectors . Since accurate position control of the tool is a prerequisite for efficacy safety and intuitive user experience in robotic surgery in this paper we propose a system modeling approach for motion compensation . Based on a bidirectional actuated system using two separate tendon sheaths motion transmission is firstly characterized . Two types of positional errors due to system backlash and environment loading are defined and modeled . Then a model based feedforward compensation method is proposed for open loop control giving the system abilities to adjust according to changes in the transmission route configuration without any information feedback from the distal end . A dedicated experimental platform emulating a bidirectional TSS robotic system for endoscopic surgery is built for testing . Proposed positional errors are identified and verified . The performance of the proposed motion compensation is evaluated by trajectory tracking under different environment loading conditions . And the results demonstrate that accurate position control can be achieved even if the transmission route configuration is updated . 


Summary: We reviewed the current shortage in the development of robotic endoscopic surgery. Motion transmission of a bidirectional tendon driven system was characterized. Two errors uniquely related to the robotic system setup were identified and modeled. Compensation method was proposed for accurate motion control regardless of any variations in the environment loading. Experimental validation was presented by emulating mechanism transmission in a typical robotic endoscopic surgery setup.
Recently a construction of spline spaces suitable for representing smooth parametric surfaces of arbitrary topological genus and arbitrary order of continuity has been proposed . These splines called RAGS are a direct generalization of bivariate polynomial splines on planar triangulations . In this paper we discuss how to construct parametric splines associated with the three homogeneous geometries and we also consider a number of related computational issues . We then show how homogeneous splines can be used to obtain RAGS . As examples of RAGS surfaces we consider direct analogs of the Powell Sabin macro elements and also spline surfaces of higher degrees and higher orders of continuity obtained by minimizing an energy functional . 


Summary: Piecewise rational functions called rational geometric splines or RAGS are studied. RAGS are suitable for representing surfaces of arbitrary genus and continuity. A construction of parametric homogeneous splines is proposed. Interpolation and approximation methods for constructing smooth splines are presented. It is shown how homogeneous splines can be used to obtain RAGS.
The and distributional family is generated from a relatively simple transformation of the standard normal and can approximate a broad spectrum of distributions . Consequently it is easy to use in simulation studies and has been applied in multiple areas including risk management stock return analysis and missing data imputation studies . A rapidly convergent quantile based least squares estimation method to fit the and distributional family parameters is proposed and then extended to a robust version . The robust version is then used as a more general outlier detection approach . Several properties of the QLS method are derived and comparisons made with competing methods through simulation . Real data examples of microarray and stock index data are used as illustrations . 


Summary: Introduced simple methods to estimate the and distributional parameters. Proved consistency and asymptotic normality. Effective robust version introduced. Robust version used to obtain base distribution for outlier detection. Illustrated use of proposed methods to multiple fields.
Background Gastric cancer is among the most common gastrointestinal cancers worldwide . Patients who have undergone surgery for gastric cancer may suffer from malnutrition and potential consequences such as gastrointestinal complications surgical stress and cancer cachexia . A tablet PC based intervention via a mobile application might enhance the early recovery of postgastrectomy patients . Objective The aim of this study was to develop and test a tablet personal computer assisted intervention to hasten the recovery of postgastrectomy cancer patients with respect to nutritional status . Methods This single arm pilot study investigated a tablet PC application developed to serve the functions of nutritional monitoring medical information management drainage follow up and wound care . All services were delivered by medical professionals . Results Twenty consecutive gastrectomy patients at the National Taiwan University Hospital received perioperative care via this application . During the study period we retrospectively collected an additional 20 demographically matched gastrectomy cases as a control group . The App group had a lower body weight loss percentage relative to the control group during a 6 month follow up period . However the patients in the App group had more outpatient clinic visits than did those in the control group . Conclusions This study supported the feasibility of a tablet PC based application for the perioperative care of gastric cancer subjects to promote a lower body weight loss and the collection of comprehensive surgical records . 


Summary: A gap between hospital care and self care at home. App provides a feasible solution for gastrectomy patients to do self recording of postoperative information. With App support the postgastrectomy patients had a lower proportion of body weight loss compared to traditional care.
The paper addresses the issue of non invasive real time prediction of hidden inner body temperature variables during therapeutic cooling or heating and proposes a solution that uses computer simulations and machine learning . The proposed approach is applied on a real world problem in the domain of biomedicine prediction of inner knee temperatures during therapeutic cooling after anterior cruciate ligament reconstructive surgery . A validated simulation model of the cryotherapeutic treatment is used to generate a substantial amount of diverse data from different simulation scenarios . We apply machine learning methods on the simulated data to construct a predictive model that provides a prediction for the inner temperature variable based on other system variables whose measurement is more feasible i.e . skin temperatures . First we perform feature ranking using the RReliefF method . Next based on the feature ranking results we investigate the predictive performance and time memory efficiency of several predictive modeling methods linear regression regression trees model trees and ensembles of regression and model trees . Results have shown that using only temperatures from skin sensors as input attributes gives excellent prediction for the temperature in the knee center . Moreover satisfying predictive accuracy is also achieved using short history of temperatures from just two skin sensors as input variables . The model trees perform the best with prediction error in the same range as the accuracy of the simulated data . Furthermore they satisfy the requirements for small memory size and real time response . We successfully validate the best performing model tree with real data from in vivo temperature measurement from a patient undergoing cryotherapy after ACL reconstruction . 


Summary: Non invasive real time prediction of inner body temperature variables during cryotherapy. Validated simulation model of the cryotherapeutic treatment. Machine learning methods on the simulated data to construct a predictive model. Feature ranking with the RReliefF method. Using only skin temperatures as input attributes gives excellent prediction.
We present a new method for immersogeometric fluid flow analysis that directly uses the CAD boundary representation of a complex object and immerses it into a locally refined non boundary fitted discretization of the fluid domain . The motivating applications include analyzing the flow over complex geometries such as moving vehicles where the detailed geometric features usually require time consuming labor intensive geometry cleanup or mesh manipulation for generating the surrounding boundary fitted fluid mesh . The proposed method avoids the challenges associated with such procedures . A new method to perform point membership classification of the background mesh quadrature points is also proposed . To faithfully capture the geometry in intersected elements we implement an adaptive quadrature rule based on the recursive splitting of elements . Dirichlet boundary conditions in intersected elements are enforced weakly in the sense of Nitsche s method . To assess the accuracy of the proposed method we perform computations of the benchmark problem of flow over a sphere represented using B rep . Quantities of interest such as drag coefficient are in good agreement with reference values reported in the literature . The results show that the density and distribution of the surface quadrature points are crucial for the weak enforcement of Dirichlet boundary conditions and for obtaining accurate flow solutions . Also with sufficient levels of surface quadrature element refinement the quadrature error near the trim curves becomes insignificant . Finally we demonstrate the effectiveness of our immersogeometric method for high fidelity industrial scale simulations by performing an aerodynamic analysis of an agricultural tractor directly represented using B rep . 


Summary: Immersogeometric analysis that directly uses the B rep CAD model is proposed. A GPU accelerated point membership classification is performed. Distribution of the surface quadrature points is crucial for accuracy. The quadrature error near the trim curves is relatively insignificant. The methodology is found effective on a 3D benchmark and an industrial problem.
In large scale genomic analyses dealing with detecting genotype phenotype associations such as genome wide association studies it is desirable to have numerically and statistically robust procedures to test the stochastic independence null hypothesis against certain alternatives . Motivated by a special case in a GWAS a novel test procedure called Correlation Profile Test is developed for testing genomic associations with failure time phenotypes subject to right censoring and competing risks . Performance and operating characteristics of CPT are investigated and compared to existing approaches by a simulation study and on a real dataset . Compared to popular choices of semiparametric and nonparametric methods CPT has three advantages it is numerically more robust because it solely relies on sample moments it is more robust against the violation of the proportional hazards condition and it is more flexible in handling various failure and censoring scenarios . CPT is a general approach to testing the null hypothesis of stochastic independence between a failure event point process and any random variable thus it is widely applicable beyond genomic studies . 


Summary: A novel procedure called Correlation Profile Test CPT is proposed. A novel permutation test the hybrid permutation test is proposed. CPT fits genomics applications better than common survival analysis methods. CPT is general and can have wide applications beyond genomics.
Model based clustering associates each component of a finite mixture distribution to a group or cluster . Therefore an underlying implicit assumption is that a one to one correspondence exists between mixture components and clusters . In applications with multivariate continuous data finite mixtures of Gaussian distributions are typically used . Information criteria such as BIC are often employed to select the number of mixture components . However a single Gaussian density may not be sufficient and two or more mixture components could be needed to reasonably approximate the distribution within a homogeneous group of observations . A clustering method based on the identification of high density regions of the underlying density function is introduced . Starting with an estimated Gaussian finite mixture model the corresponding density estimate is used to identify the cluster cores i.e . those data points which form the core of the clusters . Then the remaining observations are allocated to those cluster cores for which the probability of cluster membership is the highest . The method is illustrated using both simulated and real data examples which show how the proposed approach improves the identification of non Gaussian clusters compared to a fully parametric approach . Furthermore it enables the identification of clusters which can not be obtained by merging mixture components and it can be straightforwardly extended to cases of higher dimensionality . 


Summary: Clusters are identified as connected components from high density regions. Gaussian finite mixture models are used for density estimation. Identified clusters are not constrained to have a Gaussian shape. Clusters need not be obtained by combining mixture components. A dimension reduction step is used in cases of higher data dimensionality.
We study the dimension of trivariate splines on bipyramid cells that is cells with boundary vertices n of which are coplanar with the interior vertex . We improve the earlier lower bound on the dimension given by J. Shan . Moreover we derive a new upper bound that is equal to the known lower bound in most cases . In the remaining cases our upper bound is close to the known lower bound and we conjecture that the dimension coincides with the upper bound . We use tools from both algebraic geometry and Bernstein B zier analysis . 


Summary: We introduce bipyramid cells in We improve the lower bound on the dimension of splines for bipyramids. We derive a new upper bound that is equal to the known lower bound in most cases. We use tools from algebraic geometry and Bernstein B zier analysis.
Estimation of longitudinal models of relationship status between all pairs of individuals in social networks is challenging due to the complex inter dependencies among observations and lengthy computation times . To reduce the computational burden of model estimation a method is developed that subsamples the always null dyads in which no relationships develop throughout the period of observation . The informative sampling process is accounted for by weighting the likelihood contributions of the observations by the inverses of the sampling probabilities . This weighted likelihood estimation method is implemented using Bayesian computation and evaluated in terms of its bias efficiency and speed of computation under various settings . Comparisons are also made to a full information likelihood based procedure that is only feasible to compute when limited follow up observations are available . Calculations are performed on two real social networks of very different sizes . The easily computed weighted likelihood procedure closely approximates the corresponding estimates for the full network even when using low sub sampling fractions . The fast computation times make the weighted likelihood approach practical and able to be applied to networks of any size . 


Summary: Estimation of statistical models for social networks is challenging. Dyads with no relationship null dyads are common in large social networks. Propose to subsample the always null dyads. Develop weighted likelihood Bayesian estimation method. Method enables large social networks to be analyzed feasibly and accurately.
In the application of the popular maximum likelihood method to factor analysis the number of factors is commonly determined through a two stage procedure in which stage 1 performs parameter estimation for a set of candidate models and then stage 2 chooses the best according to certain model selection criterion . Usually to obtain satisfactory performance a large set of candidates is used and this procedure suffers a heavy computational burden . To overcome this problem a novel one stage algorithm is proposed in which parameter estimation and model selection are integrated in a single algorithm . This is obtained by maximizing the criterion with respect to model parameters and the number of factors jointly rather than separately . The proposed algorithm is then extended to accommodate incomplete data . Experiments on a number of complete incomplete synthetic and real data reveal that the proposed algorithm is as effective as the existing two stage procedure while being much more computationally efficient particularly for incomplete data . 


Summary: We propose a novel algorithm for learning factor analysis with complete and incomplete data. The algorithm is able to determine the number of factors in an automated manner. The algorithm is as effective as the previous two stage procedure. However the algorithm is much more computationally efficient.
Proteins control all biological functions in living species . Protein structure is comprised of four major classes including all class all class and . Each class performs different function according to their nature . Owing to the large exploration of protein sequences in the databanks the identification of protein structure classes is difficult through conventional methods with respect to cost and time . Looking at the importance of protein structure classes it is thus highly desirable to develop a computational model for discriminating protein structure classes with high accuracy . For this purpose we propose a silco method by incorporating Pseudo Average Chemical Shift and Support Vector Machine . Two feature extraction schemes namely Pseudo Amino Acid Composition and Pseudo Average Chemical Shift are used to explore valuable information from protein sequences . The performance of the proposed model is assessed using four benchmark datasets 25PDB 1189 640 and 399 employing jackknife test . The success rates of the proposed model are 84.2 85.0 86.4 and 89.2 respectively on the four datasets . The empirical results reveal that the performance of our proposed model compared to existing models is promising in the literature so far and might be useful for future research . 


Summary: We develop an accurate and high throughput predictor for classification of protein structure classes. It is the combination of Pseudo Average Chemical Shift and SVM. Three datasets were evaluated using jackknife test. Best results are reported so far in the literature.
We used two methods namely stamping and printing to transfer arrays of epitaxial gallium phosphide nanowires from their growth substrate to a soft biodegradable layer of polycaprolactone . Using the stamping method resulted in a very inhomogeneous surface topography with a wide distribution of transferred nanowire lengths whereas using the printing method resulted in an homogeneous substrate topography over several mm2 . PC12 cells were cultured on the hybrid nanowire PCL substrates realized using the printing method and exhibited an increased attachment on these substrates compared to the original nanowire semiconductor substrate . Transferring nanowires on PCL substrates is promising for implanting nanowires in vivo with a possible reduced inflammation compared to when hard semi conductor substrates are implanted together with the nanowires . The nanowire PCL hybrid substrates could also be used as biocompatible cell culture substrates . Finally using nanowires on PCL substrates would enable to recycle the expensive GaP substrate and repeatedly grow nanowires on the same substrate . 


Summary: Vertical nanowires were transferred to polycaprolactone substrates. The hybrid nanowire polymer substrate has a homogeneous topography over millimeters. PC12 cells were successfully cultured on the hybrid substrates. The results are promising for bioimplant applications.
Optimal design and operation of a planned full scale UASB reactor at a dairy farm are determined using optimization algorithms based on steady state simulations of a dynamic AD process model combined with models of the reactor temperature and heat exchanger temperatures based on energy balances . Available feedstock is 6m3 d dairy manure produced by the herd . Three alternative optimization problems are solved Maximization of produced methane gas flow minimization of reactor volume and maximization of power surplus . Constraints of the optimization problems are an upper limit of the VFA concentration and an upper limit of the feed rate corresponding to a normal animal waste production at the farm . The most proper optimization problem appears to be minimization of the reactor volume assuming that the feed rate is fixed at its upper limit and that the VFA concentration is at its upper limit . The optimal result is a power surplus of 49.8MWh y a hydraulic retention time of 6.1d and a reactor temperature of 35.9 C assuming heat recovery with an heat exchanger and perfect reactor heat transfer insulation . In general the optimal solutions are improved if the ratio of the solids retention time to the hydraulic retention time is increased . 


Summary: Optimal design and operation of a UASB reactor at a dairy farm is determined. Brute force optimization is based on a dynamic AD model and temperature models. Solutions provided are maximum gas flow minimum volume and maximum power surplus. The optimal solutions are improved if the biomass retention time is increased.
Structuring or removal of the epoxy based photo sensitive polymer SU 8 by inductively coupled plasma reactive ion etching was investigated as a function of plasma chemistry bias power temperature and pressure . In a pure oxygen plasma surface accumulation of antimony from the photo initiator introduced severe roughness and reduced etch rate significantly . Addition of SF6 to the plasma chemistry reduced the antimony surface concentration with lower roughness and higher etch rate as an outcome . Furthermore the etch anisotropy could be tuned by controlling the bias power . Etch rates up to 800nmmin 1 could be achieved with low roughness and high anisotropy . 


Summary: SU 8 is etched using inductively coupled plasma ICP RIE in O2 with SF6 added. Anisotropic etching at etch rates around 750nmmin 1 is achieved. Etched SU 8 surface roughness is caused by surface antimony acting as a local mask. Sb surface concentration and roughness are reduced significantly by adding SF6.
We consider the adaptive refinement of bivariate quartic smooth box spline spaces on the three directional grid G. The polynomial segments of these box splines belong to a certain subspace of the space of quartic polynomials which will be called the space of special quartics . Given a bounded domain and finite sequence of dyadically refined grids we obtain a hierarchical grid by selecting mutually disjoint cells from all levels such that their union covers the entire domain . Using a suitable selection procedure allows to define a basis spanning the hierarchical box spline space . The paper derives a characterization of this space . Under certain mild assumptions on the hierarchical grid the hierarchical spline space is shown to contain all smooth functions whose restrictions to the cells of the hierarchical grid are special quartic polynomials . Thus in this case we can give an affirmative answer to the completeness questions for the hierarchical box spline basis . 


Summary: We identify the subspace of special quartics generated by quartic box splines. We establish the completeness of hierarchical smooth quartic box splines. We characterize the hierarchical space using special quartic polynomials. We obtain a basis for the hierarchical spline space over type I triangulations.