Application of LIBS in Detection of Antihyperglycemic Trace Elements in Momordica charantia

The present study exploits the information based on concentration of trace elements and minerals in understanding the role/mechanism of action of freeze-dried fruit powder suspended in distilled water of Momordica charantia (family: Cucurbitaceae) in diabetes treatment. Laser-induced break down spectroscopy (LIBS) spectra of plant product was recorded under optimized experimental conditions and analyzed. Several atomic lines such as Na, K, Mg, Ca, Fe, Al, etc. have been observed in the LIBS spectra of the above plant product. The concentrations of these minerals are determined by using calibration-free LIBS method. Correlation between the concentration of these elements/minerals and their defined role in diabetes management was studied in normal as well as diabetic animal model

LIBS-Based Detection of Antioxidant Elements in Seeds of Emblica officinalis

The aim of the study was to determine the effect of the elements of the extract of seed from Emblica officinalis on antioxidant enzymes and osmotic fragility of erythrocytes membrane in normal as well as streptozotocin-induced severely diabetic albino Wister rats. The results revealed that the untreated diabetic rats exhibited increase in oxidative stress as indicated by significantly diminished activities of free radical scavenging enzymes such as catalase (CAT) and superoxide dismutase (SOD) by 37.5% (p

The glycemic elemental profile of trichosanthes dioica: a LIBS-based study

The scientific evaluation of the antidiabetic efficacy of aqueous extract of Trichosanthes dioica fruits on streptozotocin-induced diabetic rats is being presented. The graded doses of the extract, viz., 500, 750, 1,000, and 1,250 mg/kg body weight (bw), were administered orally, and it was observed that the blood glucose concentration decreased in a dose-dependent manner. The dose of 1,000 mg/kg bw showed the maximum fall of 23.8% and 19.1% in blood glucose level (BGL) during fasting BGL and glucose tolerance test (GTT) studies, respectively, of nondiabetic rats. Whereas in the case of subdiabetic and mild diabetic models, the same dose showed reduction in BGL of 22.0% and 31.4% during GTT. The study also involves the first use of laser-induced breakdown spectroscopy as a sensitive analytical tool to detect the elemental profile responsible for the antidiabetic activity of aqueous extract of T. dioica fruits that exhibits the antidiabetic activity. High intensities of Ca, Mg, and Fe indicate large concentrations of these elements in the extract, since according to Boltzmann’s distribution law, intensities are directly proportional to concentrations. The higher concentrations of these glycemic elements, viz. Ca, Mg, and Fe, are responsible for the antidiabetic potential of T. dioica as well as other plant already reported by our research group

A Quantum Optical Spring

We study the dynamics of the quantum optical spring, i.e., a spring whose spring constant undergoes discreet jumps depending on the quantum state of another system. We show the existence of revivals and fractional revivals in the quantum dynamics reminiscent of similar dynamical features in cavity QED. We recover in the semi classical limit the results for an oscillator whose frequency undergoes a sudden change. The quantum optical spring is conceivable for example by a micromirror under the influence of radiation pressure by a field which is strictly quantum. Our work suggests that driven systems would in general exhibit a very different dynamics if the drive is replaced by a quantum source.Comment: 5 figure

Deep Kernels for Optimizing Locomotion Controllers

Sample efficiency is important when optimizing parameters of locomotion controllers, since hardware experiments are time consuming and expensive. Bayesian Optimization, a sample-efficient optimization framework, has recently been widely applied to address this problem, but further improvements in sample efficiency are needed for practical applicability to real-world robots and high-dimensional controllers. To address this, prior work has proposed using domain expertise for constructing custom distance metrics for locomotion. In this work we show how to learn such a distance metric automatically. We use a neural network to learn an informed distance metric from data obtained in high-fidelity simulations. We conduct experiments on two different controllers and robot architectures. First, we demonstrate improvement in sample efficiency when optimizing a 5-dimensional controller on the ATRIAS robot hardware. We then conduct simulation experiments to optimize a 16-dimensional controller for a 7-link robot model and obtain significant improvements even when optimizing in perturbed environments. This demonstrates that our approach is able to enhance sample efficiency for two different controllers, hence is a fitting candidate for further experiments on hardware in the future.Comment: (Rika Antonova and Akshara Rai contributed equally

Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion

Learning policies for bipedal locomotion can be difficult, as experiments are expensive and simulation does not usually transfer well to hardware. To counter this, we need al- gorithms that are sample efficient and inherently safe. Bayesian Optimization is a powerful sample-efficient tool for optimizing non-convex black-box functions. However, its performance can degrade in higher dimensions. We develop a distance metric for bipedal locomotion that enhances the sample-efficiency of Bayesian Optimization and use it to train a 16 dimensional neuromuscular model for planar walking. This distance metric reflects some basic gait features of healthy walking and helps us quickly eliminate a majority of unstable controllers. With our approach we can learn policies for walking in less than 100 trials for a range of challenging settings. In simulation, we show results on two different costs and on various terrains including rough ground and ramps, sloping upwards and downwards. We also perturb our models with unknown inertial disturbances analogous with differences between simulation and hardware. These results are promising, as they indicate that this method can potentially be used to learn control policies on hardware.Comment: To appear in International Conference on Humanoid Robots (Humanoids '2016), IEEE-RAS. (Rika Antonova and Akshara Rai contributed equally

Does Microcredit Reach the Poor and Vulnerable? Evidence from Northern Bangldesh.

The Grameen Bank's success in Bangladesh has made microcredit the hot new idea for reducing poverty. This paper uses panel data from two Bangladeshi villages to test if loan recipients are poorer and more vulnerable than non-recipients. Poverty is measured by levels of consumption. Vulnerablitiy is measured as fluctuations in consumption (associated with inefficient risk sharing). We find that loan recipients are poorer than non-recipients in both villages, but are more vulnerable than non-recipients only in the richer and more diversified village. Though microcredit programs target the landless, there is substantial leakage to the landed. Landlessness is not significangly associated with either poverty or vulnerablitiy, but female headship is. Female headed households may be a more appropriate target group for anti-poverty credit programs.POVERTY ; RISK ; ECONOMIC GROWTH