varunr14/newsummary huggingface dataset:
The properties of electroless films produced from a bath designed for horizontal plating the preferred technology for high production volumes in printed circuit board metallization are reported. Film thickness substrate type and electrolyte temperature were varied. Formation of a continuous layer of copper film is correlated with a change in the visual and spectroscopic appearance. Grain orientation is random in thin films and a 110 texture develops with increasing thickness. The plating solution contains Cu and Ni ions. Nickel co deposits in copper films in the form of Ni hydroxide and its concentration decreases from about 6 in the vicinity of the substrate to about 1 at the film surface. Film stress and strain were measured by substrate curvature and X ray diffraction respectively. Both stress and strain decrease as the film thickness increases. Stress remains tensile throughout during deposition and during relaxation promoting film adhesion by preventing blisters. After deposition stress relaxes first towards compressive and then towards tensile. The stress the stress relaxation and the Ni concentration are high at the base of the film. We attribute this to the higher volume fraction of grain boundaries smaller grain size in this region. 


Summary: A production level electroless copper bath designed for horizontal plating was characterized. In situ stress evolution of the Cu Ni films was studied with XRD and via substrate curvature. Stress of the Cu films depends on substrate choice metal or ABS polymer . Stress and its relaxation are inversely proportional to film thickness. The Ni content of the Cu Ni films is highest at the film substrate interface.
Background Our lives are connected by a thousand invisible threads and along these sympathetic fibers our actions run as causes and return to us as results . It is Herman Melville s famous quote describing connections among human lives . To paraphrase the Melville s quote diseases are connected by many functional threads and along these sympathetic fibers diseases run as causes and return as results . The Melville s quote explains the reason for researching disease disease similarity and disease network . Measuring similarities between diseases and constructing disease network can play an important role in disease function research and in disease treatment . To estimate disease disease similarities we proposed a novel literature based method . Methods and results The proposed method extracted disease gene relations and disease drug relations from literature and used the frequencies of occurrence of the relations as features to calculate similarities among diseases . We also constructed disease network with top ranking disease pairs from our method . The proposed method discovered a larger number of answer disease pairs than other comparable methods and showed the lowest p value . Conclusions We presume that our method showed good results because of using literature data using all possible gene symbols and drug names for features of a disease and determining feature values of diseases with the frequencies of co occurrence of two entities . The disease disease similarities from the proposed method can be used in computational biology researches which use similarities among diseases . 


Summary: We proposed a novel method which calculates disease disease similarity. The proposed method can be used in constructing of disease network. The proposed method discovered the largest number of answer disease pairs and showed lowest p value when compared with other comparable methods. The proposed method can provide an insight of relationship between diseases.
In this study we assess the interchangeability and statistical agreement of two prevalent instruments from the non invasive sniffer method and compare their precision . Furthermore we develop and validate an effective algorithm for aligning time series data from multiple instruments to remove the effects of variable and fixed time shifts from the instrument comparison . The CH4 and CO2 gas concentrations for both instruments were found to differ for population means and intra cow variation and for inter cow variation . The CH4 and CO2 gas concentrations from both instruments can be used interchangeably to increase statistical power for example in genetic evaluations provided sources of disagreement are corrected through calibration and standardisation . Additionally averaging readings of cows over a longer period of time is an effective noise reduction technique which provides phenotypes with considerable inter cow variation . 


Summary: Developed an algorithm for detecting and correcting time shifts between time series. Compared instruments simultaneously to identify and correct sources of disagreement. Demonstrated the effect of averaging readings over a longer period of time to reduce error.
Limited information statistics have been recommended as the goodness of fit measures in sparse contingency tables but the values of these test statistics are computationally difficult to obtain . A Bayesian model diagnostic tool Relative Entropy Posterior Predictive Model Checking is proposed to assess the global fit for latent trait models in this paper . This approach utilizes the relative entropy to resolve possible problems in the original PPMC procedure based on the posterior predictive value . Compared with the typical conservatism of PPP value the RE value measures the discrepancy effectively . Simulated and real data sets with different item numbers degree of sparseness sample sizes and factor dimensions are studied to investigate the performance of the proposed method . The estimates of univariate information and difficulty parameters are found to be robust with dual characteristics which produce practical implications for educational testing . Compared with parametric bootstrapping RE PPMC is much more capable of evaluating the model adequacy . 


Summary: A novel Relative Entropy Posterior Predictive Model Checking method is proposed. RE PPMC with limited information checks the model fitness effectively. Compared with the original PPMC with PPP value RE value is better. Compared with parametric bootstrapping RE PPMC is more feasible. The univariate information is robust with dual characteristics.
The authors consider a dynamic probit model where the coefficients follow a first order Markov process . An exact Gibbs sampler for Bayesian analysis is presented for the model using the data augmentation approach and the forward filtering backward sampling algorithm for dynamic linear models . The authors discuss how our approach can be used for dynamic probit models as well as its generalizations including Markov regressions and models with Student link functions . An approach is presented to compare static and dynamic probit models as well as for Markov order selection in these classes of dynamic models . The developed approach is implemented to some actual data . 


Summary: A dynamic probit model with a first order Markov process was developed. Gibbs sampler using data augmentation approach and forward filtering backward sampling algorithm were presented. The discussion was extended to propose models to generalized models including Student link function. Model fit was compared between static and dynamic probit models.
Hierarchical centering has been described as a reparameterization method applicable to random effects models . It has been shown to improve mixing of models in the context of Markov chain Monte Carlo methods . A hierarchical centering approach is proposed for reversible jump MCMC chains which builds upon the hierarchical centering methods for MCMC chains and uses them to reparameterize models in an RJMCMC algorithm . Although these methods may be applicable to models with other error distributions the case is described for a log linear Poisson model where the expected value includes fixed effect covariates and a random effect for which normality is assumed with a zero mean and unknown standard deviation . For the proposed RJMCMC algorithm including hierarchical centering the models are reparameterized by modeling the mean of the random effect coefficients as a function of the intercept of the model and one or more of the available fixed effect covariates depending on the model . The method is appropriate when fixed effect covariates are constant within random effect groups . This has an effect on the dynamics of the RJMCMC algorithm and improves model mixing . The methods are applied to a case study of point transects of indigo buntings where without hierarchical centering the RJMCMC algorithm had poor mixing and the estimated posterior distribution depended on the starting model . With hierarchical centering on the other hand the chain moved freely over model and parameter space . These results are confirmed with a simulation study . Hence the proposed methods should be considered as a regular strategy for implementing models with random effects in RJMCMC algorithms they facilitate convergence of these algorithms and help avoid false inference on model parameters . 


Summary: We consider a hierarchical centering approach for reversible jump MCMC algorithms. We describe the case for a log linear Poisson model with mixed effects. The zero mean of the random effect is replaced with part of the linear predictor. We apply the methods to point transect data of indigo buntings and simulated data. Our methods improve model mixing and inference on parameters.
The theory of the isoptic curves is widely studied in the Euclidean plane . The analogous question was investigated by the authors in the hyperbolic and elliptic planes but in the higher dimensional spaces there are only few results in this topic . In Csima and Szirmai we gave a natural extension of the notion of the isoptic curves to the n dimensional Euclidean space which is called isoptic hypersurface . Now we develope an algorithm to determine the isoptic surface of a 3 dimensional polyhedron We will determine the isoptic surfaces for Platonic solids and for some semi regular Archimedean polytopes and visualize them with Wolfram Mathematica . 


Summary: Isoptic curves are generalized to isoptic surfaces. Implicit equations of isoptic surfaces for polytopes are given algorithmically. Spherical geometry is useful to determine solid angles.
Hierarchical generating systems that are derived from Zwart Powell elements can be used to generate quadratic splines on adaptively refined criss cross triangulations . We propose two extensions of these hierarchical generating systems firstly decoupling the hierarchical ZP elements and secondly enriching the system by including auxiliary functions . These extensions allow us to generate the entire hierarchical spline space which consists of all piecewise quadratic smooth functions on an adaptively refined criss cross triangulation if the triangulation fulfills certain technical assumptions . Special attention is dedicated to the characterization of the linear dependencies that are present in the resulting enriched decoupled hierarchical generating system . 


Summary: Hierarchical Zwart Powell elements are studied. Sufficient conditions for algebraic completeness are given. Construction uses decoupling and partial chessboard functions. Characterization of linear dependencies is provided.
Volume is one of the most important features for the characterization of a tumour on a macroscopic scale . It is often used to assess the effectiveness of care treatments thus making its correct evaluation a crucial issue for patient care . Similarly volume is a key feature on a microscopic scale . Multicellular cancer spheroids are 3D tumour models widely employed in pre clinical studies to test the effects of drugs and radiotherapy treatments . Very few methods have been proposed to estimate the tumour volume arising from a 2D projection of multicellular spheroids and even fewer have been designed to provide a 3D reconstruction of the tumour shape . In this work we propose Reconstruction and Visualization from a Single Projection an automatic method conceived to reconstruct the 3D surface and estimate the volume of single cancer multicellular spheroids or even of spheroid cultures . As the input parameter ReViSP requires only one 2D projection which could be a widefield microscope image . We assessed the effectiveness of our method by comparing it with other approaches . To this purpose we used a new strategy that allowed us to achieve accurate volume measurements based on the analysis of home made 3D objects built by mimicking the spheroid morphology . The results confirmed the effectiveness of our method for both 3D reconstruction and volume assessment . ReViSP software is distributed as an open source tool . 


Summary: We present a new image based approach to compute multicellular spheroid volume. The reconstruction method is validated by using home made 3D objects. A new strategy to compare different volume estimation methods is discussed. The results obtained by ReViSP overcome those of the other methods tested. ReViSP source code and standalone executable version are freely available.
In non parametric regression analysis the advantage of frames with respect to classical orthonormal bases is that they can furnish an efficient representation of a more broad class of functions . For example fast oscillating functions as audio speech sonar radar EEG and stock market are much more well represented by a frame with similar oscillating characteristic than by a classical orthonormal basis . In this respect a new frame based shrinkage estimator is derived as the Empirical Regularized version of the optimal Shrinkage estimator generalized to the frame operator . An analytic expression of it is furnished leading to an efficient implementation . Results on standard and real test functions are shown . 


Summary: Totally automatic procedure for denoising fast oscillating functions. The procedure is valid for any frame operator. The procedure is a generalization of the empirical Wiener filter.
Mathematical models that predict the complex dynamic behaviour of cellular networks are fundamental in systems biology and provide an important basis for biomedical and biotechnological applications . However obtaining reliable predictions from large scale dynamic models is commonly a challenging task due to lack of identifiability . The present work addresses this challenge by presenting a methodology for obtaining high confidence predictions from dynamic models using time series data . First to preserve the complex behaviour of the network while reducing the number of estimated parameters model parameters are combined in sets of meta parameters which are obtained from correlations between biochemical reaction rates and between concentrations of the chemical species . Next an ensemble of models with different parameterizations is constructed and calibrated . Finally the ensemble is used for assessing the reliability of model predictions by defining a measure of convergence of model outputs that is used as an indicator of confidence . We report results of computational tests carried out on a metabolic model of Chinese Hamster Ovary cells which are used for recombinant protein production . Using noisy simulated data we find that the aggregated ensemble predictions are on average more accurate than the predictions of individual ensemble models . Furthermore ensemble predictions with high consensus are statistically more accurate than ensemble predictions with large variance . The procedure provides quantitative estimates of the confidence in model predictions and enables the analysis of sufficiently complex networks as required for practical applications . 


Summary: We propose a method for quantifying the quality of predictions in dynamic models. The approach helps in deciding which additional measurements are more useful. Meta parameters are used for accelerating parameter estimation and reducing the risk of over fitting. An ensemble of models is built and consensus among their predictions is used as an indication of reliability. The method is demonstrated on a metabolic model of Chinese Hamster Ovary cells CHO .
Solid on liquid deposition techniques are of great interest to the MEMS and NEMS community because of potential applications in biomedical engineering on chip liquid trapping tunable micro lenses and replacements of gate oxides . However depositing solids on liquid with subsequent hermetic sealing is difficult because liquids tend to have a lower density than solids . Furthermore current systems seen in nature lack thermal mechanical or chemical stability . Therefore it is not surprising that liquids are not ubiquitous as functional layers in MEMS and NEMS . However SOLID techniques have the potential to be harnessed and controlled for such systems because the gravitational force is negligible compared to surface tension and therefore the solid molecular precursors that typically condense on a liquid surface will not sediment into the fluid . In this review we summarize recent research into SOLID where nucleation and subsequent cross linking of solid precursors results in thin film growth on a liquid substrate . We describe a large variety of thin film deposition techniques such as thermal evaporation sputtering plasma enhanced chemical vapor deposition used to coat liquid substrates . Surprisingly all attempts at deposition to date have been successful and a stable solid layer on a liquid can always be detected . However all layers grown by non equilibrium deposition processes showed a strong presence of wrinkles presumably due to residual stress . In fact the only example where no stress was observed is the deposition of parylene layers . Using all the experimental data analyzed to date we have been able to propose a simple model that predicts that the surface property of liquids at molecular level is influenced by cohesion forces between the liquid molecules . Finally we conclude that the condensation of precursors from the gas phase is rather the rule and not the exception for SOLID techniques . 


Summary: We studied the condensation of matter on liquid surfaces from the vapor phase. First solid on liquid deposition based devices are presented. A thin polymer film can chemically react with the liquid at condensation. Encapsulation of a liquid with parylene exhibits perfect optical surfaces. We present a large variety of solid on liquid systems.
This paper presents a method for fast computation of Hessian based enhancement filters whose conditions for identifying particular structures in medical images are associated only with the signs of Hessian eigenvalues . The computational costs of Hessian based enhancement filters come mainly from the computation of Hessian eigenvalues corresponding to image elements to obtain filter responses because computing eigenvalues of a matrix requires substantial computational effort . High computational cost has become a challenge in the application of Hessian based enhancement filters . Using a property of the characteristic polynomial coefficients of a matrix and the well known Routh Hurwitz criterion in control engineering it is shown that under certain conditions the response of a Hessian based enhancement filter to an image element can be obtained without having to compute Hessian eigenvalues . The computational cost can thus be reduced . Experimental results on several medical images show that the method proposed in this paper can reduce significantly the number of computations of Hessian eigenvalues and the processing times of images . The percentage reductions of the number of computations of Hessian eigenvalues for enhancing blob and tubular like structures in two dimensional images are approximately 90 and 65 respectively . For enhancing blob tubular and plane like structures in three dimensional images the reductions are approximately 97 75 and 12 respectively . For the processing times the percentage reductions for enhancing blob and tubular like structures in two dimensional images are approximately 31 and 7.5 respectively . The reductions for enhancing blob tubular and plane like structures in three dimensional images are approximately 68 55 and 3 respectively . 


Summary: A method for fast computation of Hessian based enhancement filters is proposed. The method relies on avoiding computation of Hessian eigenvalues. The percentage reductions in processing time can be over 65 .
In this paper we investigate the problem of interpolating a B spline curve network in order to create a surface satisfying such a constraint and defined by blending functions spanning the space of bivariate quadratic splines on criss cross triangulations . We prove the existence and uniqueness of the surface providing a constructive algorithm for its generation . We also present numerical and graphical results and comparisons with other methods . 


Summary: Interpolation of a B spline curve network by a surface based on quadratic B splines on criss cross triangulations. Proof of the existence and uniqueness of the surface and constructive algorithm for its generation. Numerical and graphical results and comparisons with other spline methods.
The latent class model provides an important platform for jointly modeling mixed mode data i.e . discrete and continuous data with various parametric distributions . Multiple mixed mode variables are used to cluster subjects into latent classes . While the mixed mode latent class analysis is a powerful tool for statisticians few studies are focused on assessing the contribution of mixed mode variables in discriminating latent classes . Novel measures are derived for assessing both absolute and relative impacts of mixed mode variables in latent class analysis . Specifically the expected posterior gradient and the Kolmogorov variation of the posterior distribution as well as related properties are studied . Numerical results are presented to illustrate the measures . 


Summary: Two measures are proposed for assessing continuous and discrete variables in LCA. Both measures are either in closed form or straightforward to compute. Both measures perform reasonably well compared to existing measures such LRT and st Both absolute and relative interpretations of one measure are possible.