An efficient BEM for numerical solution of the biharmonic boundary value problem

This paper presents an efficient BEM for solving biharmonic equations. All boundary values including geometries are approximated by the universal high order radial basis function networks (RBFNs) rather than the usual low order interpolations. Numerical results show that the proposed BEM is considerably superior to the linear/quadratic-BEM in terms of both accuracy and convergence rate

Solving high-order partial differential equations with indirect radial basis function networks

This paper reports a new numerical method based on radial basis function networks (RBFNs) for solving high-order partial differential equations (PDEs). The variables and their derivatives in the governing equations are represented by integrated RBFNs. The use of integration in constructing neural networks allows the straightforward implementation of multiple boundary conditions and the accurate approximation of high-order derivatives. The proposed RBFN method is verified successfully through the solution of thin-plate bending and viscous flow problems which are governed by biharmonic equations. For thermally driven cavity flows, the solutions are obtained up to a high Rayleigh number

Numerical analysis of corrugated tube flow using RBFNs

This paper reports the application of neural networks for the numerical analysis of steady-state axisymmetric flow through an indefinitely long corrugated tube. Meshless global radial basis function networks (RBFNs) are employed to represent all dependent variables in the governing differential equations. For a better quality of approximation, the networks used here are constructed based on the integration process rather than the usual differentiation process. Multiple spaces of network weights for each variable are converted into the single space of nodal variable values, resulting in the square system of equations with usual size. The governing equations are discretized in the strong form by point collocation and the resultant nonlinear system is solved with trust-region methods. The corrugated tube flow of a Newtonian fluid, power-law fluid and Oldroyd-B fluid are considered. With relatively low numbers of data points, flow resistance predictions obtained are in good agreement with the benchmark solutions

Simulation of the Elastic Properties of Reinforced Kevlar-Graphene Composites

The compressive strength of unidirectional fiber composites in the form of Kevlar yarn with a thin outer layer of graphene was investigated and modeled. Such fiber structure may be fabricated by using a strong chemical bond between Kevlar yarn and graphene sheets. Chemical functionalization of graphene and Kevlar may achieved by modification of appropriate surface-bound functional (e.g., carboxylic acid) groups on their surfaces. In this report we studied elastic response to unidirectional in-plane applied load with load peaks along the diameter. The 2D linear elasticity model predicts that significant strengthening occurs when graphene outer layer radius is about 4 % of kevlar yarn radius. The polymer chains of Kevlar are linked into locally planar structure by hydrogen bonds across the chains, with transversal strength considerably weaker than longitudinal one. This suggests that introducing outer enveloping layer of graphene, linked to polymer chains by strong chemical bonds may significantly strengthen Kevlar fiber with respect to transversal deformations

Echinococcus multilocularis coproantigen detection by enzyme-linked immunosorbent assay in fox, dog, and cat populations

A sandwich enzyme-linked immunosorbent assay (ELISA) for the detection of Echinococcus multilocularis coproantigens (EM-ELISA) was developed with polyclonal rabbit (solid phase) and chicken egg (catching) antibodies that were directed against E. multilocularis coproantigens and somatic worm antigens, respectively. In experimentally infected dogs and cats, coproantigens were first detectable 6-17 days postinfection (PI) in samples of 8 dogs (worm burdens at necropsy: 6,330-43,200) and from 11 days PI onward in samples of 5 cats infected with 20-6,833 worms. After anthelmintic treatment of 4 dogs and 5 cats at day 20 PI, coproantigen excretion disappeared within 3-5 days. The sensitivity of the ELISA was 83.6% in 55 foxes infected with 4-60,000 E. multilocularis, but reached 93.3% in the 45 foxes harboring more than 20 worms. The EM-ELISA was used in surveys of 'normal' dog and cat populations in Switzerland. Among 660 dogs and 263 cats, 5 dogs and 2 cats exhibited a positive reaction. In 2 of these dogs (0.30%) and 1 cat (0.38%), intestinal E. multilocularis infections were confirmed by necropsy, polymerase chain reaction PCR, or both. The specificities of the ELISA in these groups were found to be 99.5% and 99.6%, respectively, if positive ELISA results that could not be confirmed by other methods were classified as 'false positive' reactions

Silicon Solar Cell Process Development, Fabrication and Analysis, Phase 1

Solar cells from RTR ribbons, EFG (RF and RH) ribbons, dendritic webs, Silso wafers, cast silicon by HEM, silicon on ceramic, and continuous Czochralski ingots were fabricated using a standard process typical of those used currently in the silicon solar cell industry. Back surface field (BSF) processing and other process modifications were included to give preliminary indications of possible improved performance. The parameters measured included open circuit voltage, short circuit current, curve fill factor, and conversion efficiency (all taken under AM0 illumination). Also measured for typical cells were spectral response, dark I-V characteristics, minority carrier diffusion length, and photoresponse by fine light spot scanning. the results were compared to the properties of cells made from conventional single crystalline Czochralski silicon with an emphasis on statistical evaluation. Limited efforts were made to identify growth defects which will influence solar cell performance

Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions

This paper provides a formal evaluation of the predictive performance of a model (and its various updates) developed by the Institute for Health Metrics and Evaluation (IHME) for predicting daily deaths attributed to COVID19 for each state in the United States. The IHME models have received extensive attention in social and mass media, and have influenced policy makers at the highest levels of the United States government. For effective policy making the accurate assessment of uncertainty, as well as accurate point predictions, are necessary because the risks inherent in a decision must be taken into account, especially in the present setting of a novel disease affecting millions of lives. To assess the accuracy of the IHME models, we examine both forecast accuracy as well as the predictive performance of the 95% prediction intervals provided by the IHME models. We find that the initial IHME model underestimates the uncertainty surrounding the number of daily deaths substantially. Specifically, the true number of next day deaths fell outside the IHME prediction intervals as much as 70% of the time, in comparison to the expected value of 5%. In addition, we note that the performance of the initial model does not improve with shorter forecast horizons. Regarding the updated models, our analyses indicate that the later models do not show any improvement in the accuracy of the point estimate predictions. In fact, there is some evidence that this accuracy has actually decreased over the initial models. Moreover, when considering the updated models, while we observe a larger percentage of states having actual values lying inside the 95% prediction intervals (PI), our analysis suggests that this observation may be attributed to the widening of the PIs. The width of these intervals calls into question the usefulness of the predictions to drive policy making and resource allocation