A Reinforcement-Learning Approach to Power Management

We describe an adaptive, mid-level approach to the wireless device power management problem. Our approach is based on reinforcement learning, a machine learning framework for autonomous agents. We describe how our framework can be applied to the power management problem in both infrastructure and ad~hoc wireless networks. From this thesis we conclude that mid-level power management policies can outperform low-level policies and are more convenient to implement than high-level policies. We also conclude that power management policies need to adapt to the user and network, and that a mid-level power management framework based on reinforcement learning fulfills these requirements.

Numerical Simulation of Electroosmotic Flow with Step Change in Zeta Potential

Electroosmotic flow is a convenient mechanism for transporting polar fluid in a microfluidic device. The flow is generated through the application of an external electric field that acts on the free charges that exists in a thin Debye layer at the channel walls. The charge on the wall is due to the chemistry of the solid-fluid interface, and it can vary along the channel, e.g. due to modification of the wall. This investigation focuses on the simulation of the electroosmotic flow (EOF) profile in a cylindrical microchannel with step change in zeta potential. The modified Navier-Stoke equation governing the velocity field and a non-linear two-dimensional Poisson-Boltzmann equation governing the electrical double-layer (EDL) field distribution are solved numerically using finite control-volume method. Continuities of flow rate and electric current are enforced resulting in a non-uniform electrical field and pressure gradient distribution along the channel. The resulting parabolic velocity distribution at the junction of the step change in zeta potential, which is more typical of a pressure-driven velocity flow profile, is obtained.

Effect of Gas Sparging in Mammalian Cell Bioreactors

One of the major problems in the operations of mammalian cell bioreactors is the detrimental effect of gas sparging. Since the most convenient way to oxygenate any bioreactor is by gas sparging, this adverse effect has often been one of the limiting oxygen transport problems in both laboratory and industrial mammalian cell bioreactors. When one examines the literature on the effect of gas sparging on the death of mammalian cells, a great deal of confusions has been reported. It is not clear from the published literature as to the leading cause for gas-sparged related cell death. These confusions prevent the rational design and operations of mammalian cell bioreactors. In our laboratory, we have attempted to address this problem both fundamentally as well as attempt to obtain a general understanding on the adverse effect of gas sparging. Our analyses first examined the fluid shear associated with the various sections that the gas bubbles encounter during entrance, passage through the bioreactor and the final exit of the gas bubbles. Our analyses showed that the major damage of the mammalian cells by gas bubbles is due to the burst of the bubbles when exiting the bioreactor. It was also our hypothesis that the entrained cells in the liquid boundary layer of the gas bubble upon bursting is the major cause for cell death. We have corroborated this hypothesis by correlating the liquid entrainment with the cell death rate using results from our laboratory as well as other studies. Pluonic F-68, a weak surfactant, has routinely been used in laboratory and industrial bioreactors. In the past, the protective effect of Pluronic F-68 has never been shown as to why it is effective. In our research, we have data using microphotography which clearly demonstrated and corroborated our entrainment hypothesis is the major reason for the effectiveness of Pluronic F-68 in protecting the cells from gas-sparged related cell death.