Axial concentration in narrow tube flow for various RBC suspensions as function of wall shear stress.

The flow of RBC suspensions (Hct 25 and 30%) in a constricted section (of length 35 mm and diameter 0.20 mm) in a glass tube was photographed under dark field illumination. The wall shear stress ranged from 0 to 150 dynes/cm2. The following supensions were investigated: normal RBC in plasma (highly deformable); osmotically crenated RBC in hypertonic plasma (hardly deformable); RBC in a 4:1 mixture of plasma and Rheomacrodex (non aggregating). The thickness δ of the cell free medium at the wall was plotted as function of wall shear stress E(w). It was observed that: on increasing E(w) from zero, δ increases to a maximum for values of E(w) between 1.5 and 5.0 dynes/cm2 (maximum axial concentration), then decreases to zero at very high E(w) (no axial concentration); osmotic crenation as well as inhibition of aggregation result in decreased axial concentration for all E(w)

Hybrid Multigrid for Adaptive Fourth Order Cut Cells:

We present a hybrid geometric-algebraic multigrid approach for solving Poisson's equation on domains with complex geometries. The discretization uses a novel fourth-order finite volume cut cell representation to discretize the Laplacian operator on a Cartesian mesh. This representation is based on a weighted least-squares fit to a cell-averaged discretization, which is used to provide a conservative and accurate framework for the multi-resolution discretization, despite the presence of cut cells. We use geometric multigrid coarsening with an algebraic multigrid bottom solver, so that the memory overhead of algebraic coarsening is avoided until the geometry becomes under-resolved. With tuning, the hybrid approach has the simplicity of geometric multigrid while still retaining the robustness of algebraic multigrid. We investigate at what coarse level the transition should occur, and how the order of accuracy of the prolongation operator affects multigrid convergence rates. We also present some converged solutions as examples of how the use of adaptivity and a cell connectivity graph can affect performanc

A fourth-order cartesian grid embedded boundary method for poisson's equation

In this paper, we present a fourth-order algorithm to solve Poisson's equation in two and three dimensions. We use a Cartesian grid, embedded boundary method to resolve complex boundaries. We use a weighted least squares algorithm to solve for our stencils. We use convergence tests to demonstrate accuracy and we show the eigenvalues of the operator to demonstrate stability. We compare accuracy and performance with an established second-order algorithm. We also discuss in depth strategies for retaining higher-order accuracy in the presence of nonsmooth geometries

Surface acoustic wave enabled pipette on a chip

Mono-disperse droplet formation in microfluidic devices allows the rapid production of thousands of identical droplets and has enabled a wide range of chemical and biological studies through repeat tests performed at pico-to-nanoliter volume samples. However, it is exactly this efficiency of production which has hindered the ability to carefully control the location and quantity of the distribution of various samples on a chip – the key requirement for replicating micro well plate based high throughput screening in vastly reduced volumetric scales. To address this need, here, we present a programmable microfluidic chip capable of pipetting samples from mobile droplets with high accuracy using a non-contact approach. Pipette on a chip (PoaCH) system selectively ejects (pipettes) part of a droplet into a customizable reaction chamber using surface acoustic waves (SAWs). Droplet pipetting is shown to range from as low as 150 pL up to 850 pL with precision down to tens of picoliters. PoaCH offers ease of integration with existing lab on a chip systems as well as a robust and contamination-free droplet manipulation technique in closed microchannels enabling potential implementation in screening and other studies

Brain tumor segmentation and prediction on MRI images using deep learning network

Brain Tumor is caused when the anomalousl cells that form within the brain and these could be of any size, shape in nature, so it is one of the difficult tasks to detect the presence of tumor. This could be found using MRI scans. In this paper, suitable algorithms have been developed to detect the MRI image has a brain tumor or not. The dataset used here has been taken from kaggle competition. Data augmentation is performed to maximize the data in dataset and this could results in having huge data. Since tumor area can overlap with non-tumor area of the MRI image, preprocessing steps is used to differentiate the images. So the proposed idea is to recognize tumors, this utilizes pre-processing strategies like filters, image enhancements, cropping, dilation, erosion, etc and for image classification pre-trained model InceptionResNetv2 is used as an CNN algorithm to detect whether the tumor is present or not. Various combination of pre-processing steps has been performed to find the effective pipeline for the classification. With the image pre processing techniques like  cropped , median filter and CLAHE is gives a accuracy of 98.03% after the classification

Investigation of physical and mechanical properties of nano-pulverized cellulose nanofiber preform sheets for CNF thermoset nanocomposites application

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