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Testing of transition-region models: Test cases and data

Mean flow quantities in the laminar turbulent transition region and in the fully turbulent region are predicted with different models incorporated into a 3-D boundary layer code. The predicted quantities are compared with experimental data for a large number of different flows and the suitability of the models for each flow is evaluated

Exercise Reduce Oxidative Damage in Pregnancy

Pregnancy is a vulnerable condition to all kinds of "stress", resulting in changes of physiological and metabolic functions. This research aims to determine effect of exercise during pregnancy in reducing oxidative demage marked by decrease of malondialdehyde and 8-hydroxy-diguanosine levels. Randomized pre and posttest control group design was employed in this study. A number of 66 pregnant women were recruited in this study and grouped to two groups, i.e 30 of them as control group and the rest as treatment group. Pregnancy exercise was performed to all 36 pregnant women from 20 weeks gestation on treatment group. The exercise was performed in the morning for about 30 minutes, twice a weeks. On the other hand, daily activities was sugested for control group. Student\u27s t-test was then applied to determine the mean different of treatment and control group with 5 % of significant value. This study reveals that there were significantly higher decrease of (MDA) and 8-OHdG about 0.15 nmol/ml and 0.08 ng/ml, respectively, amongs treatment and control groups (p < 0.05). Clinical outcomes, such as strengten of pelvic muscle and quality of life of treatment group were significantly better compared to control group (p < 0.05). This means that exercise during pregnancy ages of 20 weeks decrease MDA and 8-OHdG levels higher compare to control group without exercise

Exercise Reduce Oxidative Damage in Pregnancy

Pregnancy is a vulnerable condition to all kinds of "stress", resulting in changes of physiological and metabolic functions. This research aims to determine effect of exercise during pregnancy in reducing oxidative demage marked by decrease of malondialdehyde and 8-hydroxy-diguanosine levels. Randomized pre and posttest control group design was employed in this study. A number of 66 pregnant women were recruited in this study and grouped to two groups, i.e 30 of them as control group and the rest as treatment group. Pregnancy exercise was performed to all 36 pregnant women from 20 weeks gestation on treatment group. The exercise was performed in the morning for about 30 minutes, twice a weeks. On the other hand, daily activities was sugested for control group. Student's t-test was then applied to determine the mean different of treatment and control group with 5 % of significant value. This study reveals that there were significantly higher decrease of (MDA) and 8-OHdG about 0.15 nmol/ml and 0.08 ng/ml, respectively, amongs treatment and control groups (p < 0.05). Clinical outcomes, such as strengten of pelvic muscle and quality of life of treatment group were significantly better compared to control group (p < 0.05). This means that exercise during pregnancy ages of 20 weeks decrease MDA and 8-OHdG levels higher compare to control group without exercise

Optimal double stopping of a Brownian bridge

We study optimal double stopping problems driven by a Brownian bridge. The objective is to maximize the expected spread between the payoffs achieved at the two stopping times. We study several cases where the solutions can be solved explicitly by strategies of threshold type

Deep Unsupervised Learning using Nonequilibrium Thermodynamics

A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm