A Simple Method to Improve the Accuracy of Advection in Discontinuous Galerkin Methods for Navier-Stokes Simulations

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140426/1/6.2014-1276.pd

A Novel Type of Neuron Within the Dorsal Striatum

The striatum is predominantly composed of medium spiny projection neurons, with the remaining neurons consisting of several types of interneurons. Among the interneurons are a group of cells that express tyrosine hydroxylase (TH). Although the intrinsic electrical properties of these TH-expressing interneurons have been characterized, there is no agreement on the number of TH-expressing cell types and their electrical properties. Here, we have used transgenic mice in which YFP-tagged channelrhodopsin-2 (ChR2) was expressed in potential TH-expressing cells in a Cre-dependent manner. We found that the YFP+ neurons in the striatum were heterogeneous in their intrinsic electrical properties; unbiased clustering indicated that there are three main neuronal subtypes. One population of neurons had aspiny dendrites with high-frequency action potential (AP) firing and plateau potentials, resembling the TH interneurons (THIN) described previously. A second, very small population of labeled neurons resembled medium-sized spiny neurons (MSN). The third population of neurons had dendrites with an intermediate density of spines, showed substantial AP adaptation and generated prolonged spikes. This type of striatal neuron has not been previously identified in the adult mouse and we have named it the Frequency-Adapting Neuron with Spines (FANS). Because of their distinctive properties, FANS may play a unique role in striatal information processing

Cluster-based feedback control of turbulent post-stall separated flows

We propose a novel model-free self-learning cluster-based control strategy for general nonlinear feedback flow control technique, benchmarked for high-fidelity simulations of post-stall separated flows over an airfoil. The present approach partitions the flow trajectories (force measurements) into clusters, which correspond to characteristic coarse-grained phases in a low-dimensional feature space. A feedback control law is then sought for each cluster state through iterative evaluation and downhill simplex search to minimize power consumption in flight. Unsupervised clustering of the flow trajectories for in-situ learning and optimization of coarse-grained control laws are implemented in an automated manner as key enablers. Re-routing the flow trajectories, the optimized control laws shift the cluster populations to the aerodynamically favorable states. Utilizing limited number of sensor measurements for both clustering and optimization, these feedback laws were determined in only $O(10)$ iterations. The objective of the present work is not necessarily to suppress flow separation but to minimize the desired cost function to achieve enhanced aerodynamic performance. The present control approach is applied to the control of two and three-dimensional separated flows over a NACA 0012 airfoil with large-eddy simulations at an angle of attack of $9^\circ$, Reynolds number $Re = 23,000$ and free-stream Mach number $M_\infty = 0.3$. The optimized control laws effectively minimize the flight power consumption enabling the flows to reach a low-drag state. The present work aims to address the challenges associated with adaptive feedback control design for turbulent separated flows at moderate Reynolds number.Comment: 32 pages, 18 figure

Evaluation of changes of Mean Arterial pressure measured by non invasive oscillometric readings (NIBP) with passive leg raise as an index of fluid responsiveness in patients with shock

OBJECTIVES : 1 .a) To determine the sensitivity and specificity of non invasive Mean arterial pressure change (MAP) with passive leg raise (PLR) compared against a gold standard of more than or equal to 15 % increase in stroke volume. b) To determine MAP change with best cut –off 2. a) To determine the sensitivity and specificity of non invasive Systolic blood pressure change (SBP) , Pulse pressure change (PP), heart rate change (HR) with passive leg raise compared against a a gold standard b) To determine SBP change, PP change, HR change with best cut off METHODS : This is a prospective observational study of diagnostic accuracy conducted in the Medical ICU/HDU of CMC Vellore. We included adult patients who were admitted with shock and clinical deemed to require a fluid bolus. We excluded patients who had a contra indication to passive leg raise (PLR) or a poor echo window or persistent arrhythmia. A specially created wedge was constructed for standardising the PLR at 45 degree. The principal investigator then measured various Non invasive oscillometric measurements before and after the passive leg raise. These measurements were compared to stroke volume change, measured before and after a fluid bolus, which was deemed responsive if the change was >= 15 %. The sample size was calculated to include 140 observations with 70 in each arm. RESULTS : Out of 176 observations 106 observations were in the responder arm and 70 observations in the non responder arm. MAP change of 3 % co related with a sensitivity of 50 % and specificity of 83 %. AUC of ROC curve was 0.64. SBP had an AUC of 0.636 ,with a change of 2 % co relating with a sensitivity of 48 % and specificity of 75 %. PP change had an AUC of 0.668, with a change of 5 % co relating with 48 % sensitivity and 75 % specificity. HR had an AUC of 0.771, with a change of 5 % co relating with 97 % sensitivity and 3 % specificity. CONCLUSIONS : ∆MAP of 3 % co related with a sensitivity of 50 % and specificity of 83 %. AUC of ROC curve was 0.64. ∆SBP ,with a change of 2 % co relating with a sensitivity of 48 % and specificity of 75 %. AUC of 0.636. ∆PP with a change of 5 % co relating with 48 % sensitivity and 75 % specificity. AUC of 0.668. ∆HR with a change of 5 % co relating with 97 % sensitivity and 3 % specificity. AUC of 0.771. We found in our study that Non invasive blood pressure measurements were poor predictors of fluid responsiveness as indicated by the ROC mentioned and sensitivity and specificity obtained for the variables studied