Fractional electro-magneto transport of blood modeled with magnetic particles in cylindrical tube without singular kernel

The electro-kinetic transport of blood flow mixed with magnetic particles in the circular channel was investigated. The flow was subjected to an external electric and uniform magnetic field. The fluid was driven by pressure gradient and perpendicular magnetic field to the flow direction. Due to the usefulness and suitability of Caputo–Fabrizio fractional order derivative without singular kernel in fluid flow modeling and mass transfer phenomena, the governing equations were modeled as Caputo–Fabrizio time fractional partial differential equations and solved for a 2 ð0; 1�. The analytical solutions for the velocities of blood flow and magnetic particles were obtained by using Laplace, finite Hankel transforms and Robotnov and Hartley’s functions, respectively. Mathematica software was used to simulate the influences of fractional parameter a, Hartmann number and Reynolds number on the velocities of blood and magnetic particles. The findings are important for controlling bio-liquids in the devices used for analysis and diagnosis in biological and medical applications

Iodide-selective membrane electrode based on salophen complex of cobalt (III)

A highly selective PVC membrane electrode based on a cobalt-salophen complex was prepared. The sensor displays an anti-Hofmeister selectivity sequence with a preference for iodide ion over many common anions. The electrode has a linear dynamic range between 5.0×10-7 to 1.0×10-1 mol L-1, with a Nernstian slope of -58.9 mV decade-1 and a detection limit of 3.0×10-7 mol L-1. The working pH range of the sensor is 3.1-9.8. It exhibits of a fast as 15 s and has a lifetime of about 2 months. The selectivity coefficients for the proposed electrode were improved for some interferences, when compared with those of available iodide membrane electrode. The proposed electrode was successfully applied for the direct determination of iodide in edible salt and as an indicator electrode in potentiometric titration of I- against Ag+

Prediction of Cognitive Decline in Parkinson’s Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems

Background: We aimed to predict Montreal Cognitive Assessment (MoCA) scores in Parkinson’s disease patients at year 4 using handcrafted radiomics (RF), deep (DF), and clinical (CF) features at year 0 (baseline) applied to hybrid machine learning systems (HMLSs). Methods: 297 patients were selected from the Parkinson’s Progressive Marker Initiative (PPMI) database. The standardized SERA radiomics software and a 3D encoder were employed to extract RFs and DFs from single-photon emission computed tomography (DAT-SPECT) images, respectively. The patients with MoCA scores over 26 were indicated as normal; otherwise, scores under 26 were indicated as abnormal. Moreover, we applied different combinations of feature sets to HMLSs, including the Analysis of Variance (ANOVA) feature selection, which was linked with eight classifiers, including Multi-Layer Perceptron (MLP), K-Neighbors Classifier (KNN), Extra Trees Classifier (ETC), and others. We employed 80% of the patients to select the best model in a 5-fold cross-validation process, and the remaining 20% were employed for hold-out testing. Results: For the sole usage of RFs and DFs, ANOVA and MLP resulted in averaged accuracies of 59 ± 3% and 65 ± 4% for 5-fold cross-validation, respectively, with hold-out testing accuracies of 59 ± 1% and 56 ± 2%, respectively. For sole CFs, a higher performance of 77 ± 8% for 5-fold cross-validation and a hold-out testing performance of 82 + 2% were obtained from ANOVA and ETC. RF+DF obtained a performance of 64 ± 7%, with a hold-out testing performance of 59 ± 2% through ANOVA and XGBC. Usage of CF+RF, CF+DF, and RF+DF+CF enabled the highest averaged accuracies of 78 ± 7%, 78 ± 9%, and 76 ± 8% for 5-fold cross-validation, and hold-out testing accuracies of 81 ± 2%, 82 ± 2%, and 83 ± 4%, respectively. Conclusions: We demonstrated that CFs vitally contribute to predictive performance, and combining them with appropriate imaging features and HMLSs can result in the best prediction performance.Medicine, Faculty ofScience, Faculty ofOther UBCNon UBCPhysics and Astronomy, Department ofRadiology, Department ofReviewedFacultyResearcherPostdoctora

Optimization of MHD Flow of Radiative Micropolar Nanofluid in a Channel by RSM: Sensitivity Analysis

These days, heat transfer plays a significant role in the fields of engineering and energy, particularly in the biological sciences. Ordinary fluid is inadequate to transfer heat in an efficient manner, therefore, several models were considered for the betterment of heat transfer. One of the most prominent models is a single-phase nanofluid model. The present study is devoted to solving the problem of micropolar fluid with a single-phase model in a channel numerically. The governing partial differential equations (PDEs) are converted into nonlinear ordinary differential equations (ODEs) by introducing similarity transformation and then solved numerically by the finite difference method. Response surface methodology (RSM) together with sensitivity analysis are implemented for the optimization analysis. The study reveals that sensitivity of the skin friction coefficient (Cfx) to the Reynolds number (R) and magnetic parameter (M) is positive (directly proportional) and negative (inversely proportional) for the micropolar parameter