Transport of a dilute active suspension in pressure-driven channel flow

Confined suspensions of active particles show peculiar dynamics characterized by wall accumulation, as well as upstream swimming, centerline depletion and shear-trapping when a pressure-driven flow is imposed. We use theory and numerical simulations to investigate the effects of confinement and non-uniform shear on the dynamics of a dilute suspension of Brownian active swimmers by incorporating a detailed treatment of boundary conditions within a simple kinetic model where the configuration of the suspension is described using a conservation equation for the probability distribution function of particle positions and orientations, and where particle-particle and particle-wall hydrodynamic interactions are neglected. Based on this model, we first investigate the effects of confinement in the absence of flow, in which case the dynamics is governed by a swimming Peclet number, or ratio of the persistence length of particle trajectories over the channel width, and a second swimmer-specific parameter whose inverse measures the strength of propulsion. In the limit of weak and strong propulsion, asymptotic expressions for the full distribution function are derived. For finite propulsion, analytical expressions for the concentration and polarization profiles are also obtained using a truncated moment expansion of the distribution function. In agreement with experimental observations, the existence of a concentration/polarization boundary layer in wide channels is reported and characterized, suggesting that wall accumulation in active suspensions is primarily a kinematic effect which does not require hydrodynamic interactions. Next, we show that application of a pressure-driven Poiseuille flow leads to net upstream swimming of the particles relative to the flow, and an analytical expression for the mean upstream velocity is derived in the weak flow limit. In stronger imposed flows .....

On the distribution and swim pressure of run-and-tumble particles in confinement

The spatial and orientational distribution in a dilute active suspension of non-Brownian run-and-tumble spherical swimmers confined between two planar hard walls is calculated theoretically. Using a kinetic model based on coupled bulk/surface probability density functions, we demonstrate the existence of a concentration wall boundary layer with thickness scaling with the run length, the absence of polarization throughout the channel, and the presence of sharp discontinuities in the bulk orientation distribution in the neighborhood of orientations parallel to the wall in the near-wall region. Our model is also applied to calculate the swim pressure in the system, which approaches the previously proposed ideal-gas behavior in wide channels but is found to decrease in narrow channels as a result of confinement. Monte-Carlo simulations are also performed for validation and show excellent quantitative agreement with our theoretical predictions

Factors affecting motivation of adolescent learners in central Durban.

Thesis (M.Ed.)-University of KwaZulu-Natal, 2006.The aim of the study was to gain an understanding of factors that affect motivation of adolescent learners in the classroom. The pilot and main research study was conducted with 42 adolescent learners in Grades 8 to 12 at Sunflower Secondary School1 in Durban. The theoretical frameworks that underpinned this study were the ecosystemic perspective, the systems theory and the humanistic theory. These frameworks guided my focus on the interactions that learners, peers, and educators and the whole system of education have with each other as well as on the role they play in motivating each other. The study used a qualitative research methodology. This approach allowed the researcher to interview the participants of Sunflower Secondary to ascertain rich data as to what factors motivate adolescent learners in the classroom. A semi structured interview schedule was used. The interviews were tape recorded, transcribed and analysed. Central themes emerged, revealing that educators and peers motivated adolescent learners to learn in the classroom. A list of guidelines was also developed to assist educators in motivating adolescent learners to learn in the classroom

Mathematical Modeling of Reverse Flow Oxidation Catalysts

A theoretical model and a computer simulation on methane (CH4) reduction in a simulated natural gas exhaust mixture are performed for a Reverse-Flow Oxidation Catalyst. This theoretical model is to predict the conversion of methane flowing through an oxidation catalyst with periodic reversal of flow direction. The model developed for this purpose is a transient, 1-Dimensional plug flow model with gas phase reactions and surface reactions. The derivation of the model resulted in the mole balance equation and the energy balance equation for the gas phase and the solid phase. The momentum equation for this model is neglected as it is assumed that there is no pressure drop across the catalyst. A FORTRAN code was developed to simulate the forward flow and the reverse flow of the gas species through the catalyst. This code can have a symmetrical or an asymmetrical switching according to the user. It also gives an option of running the code either in the forward direction or with periodic switching to analyze the effect of switching. With this code, the optimum switching time for the maximum conversion of methane was found. The effect of various parameters such as the length of the catalyst, the concentration of the gas species, pre-exponential term and the activation energy was also analyzed. The results show that the optimum switching frequency is 25 seconds for all space velocities for a 10 cm long catalyst with 2000 ppm of inlet methane. The increase in the conversion of methane when compared to the unidirectional flow was found to be 47% at 450oC for a gas hourly space velocity of 50,000 hr-1. It was also found that, at 450oC for a gas hourly space velocity of 50,000 hr-1, the pre-exponential factor and the length of the catalyst had negligible effect on the conversion of methane. The activation energy and the inlet concentration had a significant effect on the methane conversion which is discussed in further chapters. It was also found that symmetric switching had increased solid temperature profile and methane conversion efficiency when compared to the asymmetric switching frequency

Priority Based Power Management and Reduced Downtime in Data Centers

The project deals successfully with software that performs priority based power management and reduced downtime for virtual machines running in data centers. The software deals with power management only at the processor level. The software automatically performs load distribution among servers in data centers to save power. In addition, the software also lets administrator of data centers to mark certain virtual machines, which run user applications, as critical to minimize downtimes for these virtual machines. The software reveals that energy consumption can be minimized while maintaining high runtime availability for the mission critical applications. The software operates in Green mode and in regular mode while maintaining high runtime availability. The experimental results show that Green mode minimizes energy usage by as much as 35%

Hybrid machine learning architecture for automated detection and grading of retinal images for diabetic retinopathy

Purpose: Diabetic retinopathy is the leading cause of blindness, affecting over 93 million people. An automated clinical retinal screening process would be highly beneficial and provide a valuable second opinion for doctors worldwide. A computer-aided system to detect and grade the retinal images would enhance the workflow of endocrinologists. Approach: For this research, we make use of a publicly available dataset comprised of 3662 images. We present a hybrid machine learning architecture to detect and grade the level of diabetic retinopathy (DR) severity. We also present and compare simple transfer learning-based approaches using established networks such as AlexNet, VGG16, ResNet, Inception-v3, NASNet, DenseNet, and GoogLeNet for DR detection. For the grading stage (mild, moderate, proliferative, or severe), we present an approach of combining various convolutional neural networks with principal component analysis for dimensionality reduction and a support vector machine classifier. We study the performance of these networks under different preprocessing conditions. Results: We compare these results with various existing state-of-the-art approaches, which include single-stage architectures.We demonstrate that this architecture is more robust to limited training data and class imbalance. We achieve an accuracy of 98.4% for DR detection and an accuracy of 96.3% for distinguishing severity of DR, thereby setting a benchmark for future research efforts using a limited set of training images. Conclusions: Results obtained using the proposed approach serve as a benchmark for future research efforts. We demonstrate as a proof-of-concept that an automated detection and grading system could be developed with a limited set of images and labels. This type of independent architecture for detection and grading could be used in areas with a scarcity of trained clinicians based on the necessity