Electroacupuncture lowers high blood pressure

OBJECTIVE: To determine if applying electroacupuncture at ST 36-37 will lower the systolic, diastolic, and mean blood pressures of chronic hypertensive rats. DESIGN: A 12 week study on the effect of electroacupuncture was conducted from September 2014 to December 2014. The total number of rats used in the study was 16 (n=16). The rats were divided into four groups: Electroacupuncture, Sham-EA, Hypertensive control, and Normotensive control. All of the rats, expect for those in the Normotensive group, were housed in the cold room to induce chronic hypertension. After 8 weeks in the cold room, the rats in the Electroacupuncture group received electrical stimulation twice a week for 30 min. Needles were also inserted into the rats in the Sham-EA group, but there was no electric current. The blood pressures of all of the rats were measured once a week for 12 weeks. Lastly, the data was analyzed using SigmaStat to perform One Way ANOVA and T-tests. RESULT: The initial blood pressures between the 4 groups were similar with a difference of less than 5 mmHg. The groups placed in cold rooms showed a significant difference of more than 20 mmHg compared to their initial blood pressures (P≤0.05) at week 7. Finally, the blood pressures of the Sham-EA and Hypertensive control group did not lower at 12 weeks compared to week 7. However, the systolic, mean, and diastolic blood pressures in the EA group lowered with a significant difference of greater than 20 mmHg at week 12 compared to week 7. There was no significant change between the initial and final blood pressures for those in the Normotensive group. CONCLUSION: The data showed that systolic, diastolic, and mean blood pressures in the Electroacupuncture group lowered significantly at week 12 or after 5 weeks of treatment. Thus, we can conclude that electroacupuncture does have a beneficial effect in lowering blood pressure in chronically hypertensive rats

Direct Numerical Simulation of a high-Reynolds-number Homogeneous shear turbulence

The SHEAR code is developed at the School of Aeronautics, Universidad Politécnica de Madrid, for the simulation of turbulent structures of shear flows. The code has been well tested on smaller clusters. This white paper desbribes the work done to scale and optimise SHEAR for large systems like the Blue Gene/Q system JUQUEEN in Jülich

The temporal evolution of the energy flux across scales in homogeneous turbulence

A temporal study of energy transfer across length scales is performed in 3D numerical simulations of homogeneous shear flow and isotropic turbulence. The average time taken by perturbations in the energy flux to travel between scales is measured and shown to be additive. Our data suggests that the propagation of disturbances in the energy flux is independent of the forcing and that it defines a `velocity' that determines the energy flux itself. These results support that the cascade is, on average, a scale-local process where energy is continuously transmitted from one scale to the next in order of decreasing size.Comment: Accepted for publication as a Letter in Physics of Fluid

Direct numerical simulation of statistically stationary and homogeneous shear turbulence and its relation to other shear flows

Statistically stationary and homogeneous shear turbulence (SS-HST) is investigated by means of a new direct numerical simulation code, spectral in the two horizontal directions and compact-finite-differences in the direction of the shear. No remeshing is used to impose the shear-periodic boundary condition. The influence of the geometry of the computational box is explored. Since HST has no characteristic outer length scale and tends to fill the computational domain, long-term simulations of HST are “minimal” in the sense of containing on average only a few large-scale structures. It is found that the main limit is the spanwise box width, Lz, which sets the length and velocity scales of the turbulence, and that the two other box dimensions should be sufficiently large (Lx ≳ 2Lz, Ly ≳ Lz) to prevent other directions to be constrained as well. It is also found that very long boxes, Lx ≳ 2Ly, couple with the passing period of the shear-periodic boundary condition, and develop strong unphysical linearized bursts. Within those limits, the flow shows interesting similarities and differences with other shear flows, and in particular with the logarithmic layer of wall-bounded turbulence. They are explored in some detail. They include a self-sustaining process for large-scale streaks and quasi-periodic bursting. The bursting time scale is approximately universal, ∼20S−1, and the availability of two different bursting systems allows the growth of the bursts to be related with some confidence to the shearing of initially isotropic turbulence. It is concluded that SS-HST, conducted within the proper computational parameters, is a very promising system to study shear turbulence in general

ForensicsForest Family: A Series of Multi-scale Hierarchical Cascade Forests for Detecting GAN-generated Faces

The prominent progress in generative models has significantly improved the reality of generated faces, bringing serious concerns to society. Since recent GAN-generated faces are in high realism, the forgery traces have become more imperceptible, increasing the forensics challenge. To combat GAN-generated faces, many countermeasures based on Convolutional Neural Networks (CNNs) have been spawned due to their strong learning ability. In this paper, we rethink this problem and explore a new approach based on forest models instead of CNNs. Specifically, we describe a simple and effective forest-based method set called {\em ForensicsForest Family} to detect GAN-generate faces. The proposed ForensicsForest family is composed of three variants, which are {\em ForensicsForest}, {\em Hybrid ForensicsForest} and {\em Divide-and-Conquer ForensicsForest} respectively. ForenscisForest is a newly proposed Multi-scale Hierarchical Cascade Forest, which takes semantic, frequency and biology features as input, hierarchically cascades different levels of features for authenticity prediction, and then employs a multi-scale ensemble scheme that can comprehensively consider different levels of information to improve the performance further. Based on ForensicsForest, we develop Hybrid ForensicsForest, an extended version that integrates the CNN layers into models, to further refine the effectiveness of augmented features. Moreover, to reduce the memory cost in training, we propose Divide-and-Conquer ForensicsForest, which can construct a forest model using only a portion of training samplings. In the training stage, we train several candidate forest models using the subsets of training samples. Then a ForensicsForest is assembled by picking the suitable components from these candidate forest models..