Two-phase numerical model for thermal conductivity and convective heat transfer in nanofluids

Due to the numerous applications of nanofluids, investigating and understanding of thermophysical properties of nanofluids has currently become one of the core issues. Although numerous theoretical and numerical models have been developed by previous researchers to understand the mechanism of enhanced heat transfer in nanofluids; to the best of our knowledge these models were limited to the study of either thermal conductivity or convective heat transfer of nanofluids. We have developed a numerical model which can estimate the enhancement in both the thermal conductivity and convective heat transfer in nanofluids. It also aids in understanding the mechanism of heat transfer enhancement. The study reveals that the nanoparticle dispersion in fluid medium and nanoparticle heat transport phenomenon are equally important in enhancement of thermal conductivity. However, the enhancement in convective heat transfer was caused mainly due to the nanoparticle heat transport mechanism. Ability of this model to be able to understand the mechanism of convective heat transfer enhancement distinguishes the model from rest of the available numerical models

In vitro and ex vivo measurement of the biophysical properties of blood using microfluidic platforms and animal models

Haemorheologically impaired microcirculation, such as blood clotting or abnormal blood flow, causes interrupted blood flows in vascular networks. The biophysical properties of blood, including blood viscosity, blood viscoelasticity, haematocrit, red blood bell (RBC) aggregation, erythrocyte sedimentation rate and RBC deformability, have been used to monitor haematological diseases. In this review, we summarise several techniques for measuring haemorheological properties, such as blood viscosity, RBC deformability and RBC aggregation, using in vitro microfluidic platforms. Several methodologies for the measurement of haemorheological properties with the assistance of an extracorporeal rat bypass loop are also presented. We briefly discuss several emerging technologies for continuous, long-term, multiple measurements of haemorheological properties under in vitro or ex vivo conditions.11Ysciescopu

Effective method for drug injection into subcutaneous tissue

Subcutaneous injection of drug solution is widely used for continuous and low dose drug treatment. Although the drug injections have been administered for a long time, challenges in the design of injection devices are still needed to minimize the variability, pain, or skin disorder by repeated drug injections. To avoid these adverse effects, systematic study on the effects of injection conditions should be conducted to improve the predictability of drug effect. Here, the effects of injection conditions on the drug permeation in tissues were investigated using X-ray imaging technique which provides realtime images of drug permeation with high spatial resolution. The shape and concentration distribution of the injected drug solution in the porcine subcutaneous and muscle tissues are visualized. Dynamic movements of the wetting front (WF) and temporal variations of water contents in the two tissues are quantitatively analyzed. Based on the quantitative analysis of the experimental data, the permeability of drug solution through the tissues are estimated according to permeation direction, injection speed, and tissue. The present results would be helpful for improving the performance of drug injection devices and for predicting the drug efficacy in tissues using biomedical simulation.112Ysciescopu

Asymptotics and computations for approximation of method of regularization estimators

Inverse problems arise in many branches of natural science, medicine and engineering involving the recovery of a whole function given only a finite number of noisy measurements on functionals. Such problems are usually ill-posed, which causes severe difficulties for standard least-squares or maximum likelihood estimation techniques. These problems can be solved by a method of regularization. In this dissertation, we study various problems in the method of regularization. We develop asymptotic properties of the optimal smoothing parameters concerning levels of smoothing for estimating the mean function and an associated inverse function based on Fourier analysis. We present numerical algorithms for an approximated method of regularization estimator computation with linear inequality constraints. New data-driven smoothing parameter selection criteria are proposed in this setting. In addition, we derive a Bayesian credible interval for the approximated method of regularization estimators