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Facts about standardization of herbal medicine: a review
Vandana Garg
Zhong Xi Yi Jie He Xue Bao , 2012,
Abstract: Natural products, either as pure compounds or as standardized plant extracts, provide extensive opportunities for new drug leads because of the unmatched availability of chemical diversity. In contrast to modern medicines, herbal medicines are frequently used to treat chronic diseases. Standardization guarantees the content of one or more active constituents and marker compounds. The plant environment and genetic factors could significantly affect the biochemical components of the plant extract, in which plants are still the most abundant and cost-effective resource for drug innovation. Production of botanical drugs requires genetically uniform monocultures of the source plant in fully standardized conditions, to assure the biochemical consistency and to optimize the safety and efficacy of every crop. The present review article illustrates about the methods for standardization of herbal medicine and how the goal of preparing herbal medicines of consistent quality and effects can be achieved.
Antianxiety Activity of Methanol Extract of Gelsemium sempervirens (Linn.) Ait.
Garg Vandana,V.J. Dhar,Anupam Sharma,Rohit Dutt
Journal of Stress Physiology & Biochemistry , 2012,
Abstract: Background: Despite significant advances in understanding and management of neuropsychiatric disorders during past few decades, anxiety and depression, still remains the leading cause of deaths, primarily for want of effective and safe treatment of these ailments. Approximately, two third of the anxious or depressed patients respond to the currently available treatment but the magnitude of improvement is still disappointingPurpose of Study: The aim of the present study was to investigate the antianxiety activity of Gelsemium sempervirens (Linn.) Ait. Various doses (50,100, 150, 200mg/kg) of plant extracts viz., of petroleum ether, chloroform, methanol and water were administered orally to Swiss Albino Mice before evaluating their behavioural pattern. Diazepam (2.5 mg/kg) was used as standard drug.Result: The methanol extract of G. sempervirens (150 mg/kg) increased the mean time spent, mean number of arms entries in the open arms of elevated plus maze (EPM) and decreased the mean time spent in the closed arms. The locomotor activity of methanol extract was not affected to the same extent as observed for diazepam.Conclusion: The results suggested that methanol extract of G. sempervirens possess anxiolytic effects with no sedative activity when compared to diazepam.
Total laparoscopic management of large complicated jejunal diverticulum
Garg Niraj,Khullar Rajesh,Sharma Anil,Soni Vandana
Journal of Minimal Access Surgery , 2009,
Abstract: Jejunoileal diverticulae, also referred to as non-Meckelian diverticulae, are very uncommon. These diverticulae are considered to be acquired pulsion diverticulae and they mostly occur in older people. Their prevalence increases with age. About 80% of these diverticulae occur in jejunum and are usually multiple. Patients with jejunoileal diverticulae present with nonspecific symptoms. The clinical picture of a complicated jejunoileal diverticulae can be confused with other intra-abdominal acute conditions such as appendicitis, cholecystitis, perforated ulcer, etc. Nonmechanical or pseudoobstruction is related to the dyskinesia associated with this condition. The diagnosis is made by a small bowel contrast study, enteroclysis, endoscopy or computed tomography. A surgical approach is the best form of treatment for complicated jejunoileal diverticulae. Laparoscopy is very useful in diagnosing and treating this condition. The current report is about a patient who presented with recurrent subacute intestinal obstruction and was managed by laparoscopy.
A Survival Approach to Prediction of Default Drivers for Indian Listed Companies  [PDF]
Vandana Gupta
Theoretical Economics Letters (TEL) , 2017, DOI: 10.4236/tel.2017.72011
Abstract: The objective of the research study is to identify the key predictors that can explain default risk for Indian listed companies using survival analysis. The author has applied the semi-parametric Cox proportional hazard model to test the impact of financial ratios, capital market ratios, macro-economic variables, size and age of companies, and the ownership structure of promoters to a dataset of 859 companies panning across 10 sectors. Unlike traditional models on default prediction, survival models focus on time to default as the dependent variable. The empirical findings reveal that return on capital employed (ROCE), return on net worth (ROE), interest coverage ratio, exchange rate volatility, GDP growth rate, stock index, promoters holdings % and the percent of shares pledged are all significant predictors of default. Among the market variables, it is seen that beta and the ratio of market value of equity/book value of debt are statistically significant variables in explaining default risk. The empirical findings also generate the hazard ratio for each covariate which examines the predicted change in the hazard for a unit increase in the predictor. The author extends the research by applying the market-based KMV structural model to obtain continuous observations of default probability and regressing the same against all the?covariates (Gupta et al.,
Open Market Repurchases and Signaling Hypothesis  [PDF]
Vandana Gupta
Theoretical Economics Letters (TEL) , 2018, DOI: 10.4236/tel.2018.83041
Abstract: This paper analyzes the impact of open market repurchase (OMR) route of buyback on the stock prices of a data set of 30 Indian listed firms which had gone for buyback in the FY16. The author has applied event study methodology to calculate abnormal returns and cumulative abnormal returns on stocks using BSE 500 as market index. The returns are calculated for 20 days prior and post buyback announcement to test for information signaling hypothesis. The analysis shows that average abnormal return (AAR) on the date of announcement is -0.23 percent while cumulative abnormal return (CAR) is about 5.72 percent on the announcement date with an overall CAR 3.44 percent for 40-day event window. The research findings reveal that unlike tender offers, OMR does not lead to a signaling effect as there is the insignificant impact on stock prices. Market reaction to buyback offer is in contradiction to signaling hypothesis predictions. The results of the study imply that the information related to the announcement of the buyback is already reflected in the share price. This also throws light on the growing maturity and efficiency of the stock market of India. Analyzing the signaling effect through OMR reveals that rather than signaling hypothesis, market reaction to buybacks is better explained by free cash flow hypothesis.
Predicting Accuracy of Valuation Multiples Using Value Drivers: Evidence from Indian Listed Firms  [PDF]
Vandana Gupta
Theoretical Economics Letters (TEL) , 2018, DOI: 10.4236/tel.2018.85052
Abstract: The objective of this research study is twofold: 1) to evaluate the prediction accuracy of four valuation multiples across three sectors for Indian listed firms and 2) to identify the fundamental drivers for these multiples. The valuation multiples identified for this study are: price to earnings (P/E), price to book value (P/BV), price to sales (P/S) and enterprise value to earnings before interest, depreciation, tax and amortization (EV/EBIDTA) and the sectors taken are steel, banking and automobile. Multiple regression methodology is followed with the valuation multiple as dependent variable and the value drivers as independent variables, to get predicted multiples on 470 firm observations. By regressing the multiples on fundamental variables, the best suited multiple for each sector and the key drivers of the multiple are obtained. The empirical findings based on root mean square error (RMSE) and Theil coefficient reveal that least prediction errors are observed in P/S and EV/EBIDTA for the automobile sector, EV/EBIDTA for the steel sector and P/BV for the banking sector. It is also observed that the significant variables that explain these multiples are beta, return on equity (ROE), return on capital employed (ROC), dividend payout ratio (D/P) and net profit margins (NPM). These findings are in line with the derivation of fundamental drivers for each multiple as explained in Gordon model. Damodaran: 2007 [1]. The present work contributes to emerging market literature on equity valuations and attempts to compare valuations based on market approach using value drivers. A comparison of forecasts with actuals helps in recommendations to buy/sell/accumulate/hold for equity investors and is also pertinent for market participants and financial regulators.
Evaluating the Accuracy of Valuation Multiples on Indian Firms Using Regularization Techniques of Penalized Regression  [PDF]
Vandana Gupta
Theoretical Economics Letters (TEL) , 2019, DOI: 10.4236/tel.2019.91015

This research study is conducted on companies in three prominent sectors: Automobile, Banking and Steelall three diverse and affected by different economic, fiscal and financial policies. The author Gupta [1] attempts to extend the scope of study done earlier using simple linear regression for valuation of companies. Highlighting the limitations of linear regression: multicollinearity and normality, the present study is conducted by applying regularization techniques of machine learning. Ridge regression, LASSO and elastic net techniques are employed to underscore this commonality of the set of valuation multiples. These regularization techniques are tested on data of Indian listed firms spanning across twelve years from FY 07 to FY 2018 and the four multiples identified for the study are 1) price to earnings (P/E), 2) price to sales (P/S), 3) enterprise value to earnings before interest tax depreciation and amortization (EV/EBIDTA) and 4) price to book value (P/BV). The empirical findings are based on root mean square errors and learning curves, which corroborate the least prediction errors in P/S for auto sector, EV/EBIDTA for steel sector and P/BV for banking sector. As a byproduct, the author has also been able to pinpoint which one of the variables among them is the most important. The study concludes that, in spite of differing sectors, a certain set of common variables can be used across them to effectively assess company valuation (valuation multiples). The present work contributes to emerging market literature by evaluating the key multiples that drive sectors to apply non-traditional regression techniques.

A Rare Fatal Case of Internal Hernia Caused by Meckel’s Diverticulum in a Paediatric Patient  [PDF]
Vandana Jain, Sanjay Sahi
Open Journal of Pediatrics (OJPed) , 2011, DOI: 10.4236/ojped.2011.12005
Abstract: We describe a very rare case of an internal hernia associated with a Meckel’s diverticulum, which lead to the death of a young 3 year old boy. The case describes symptoms of abdominal pain and vomiting, on a background of previous intermittent abdominal pain. The possibility of small bowel obstruction was suspected, and appropriate imaging was performed. This case illustrates the need for a high index of suspicion for small bowel obstruction, with appropriate investigations and review. It also highlights the limitations of imaging modalities in identifying complications of Meckel’s diverticulum. It is important to raise awareness of this fatal cause for small bowel obstruction and to help identify suggestive imaging features, which may point towards a possible complicated Meckel’s diverticulum. Earlier recognition and diagnosis could reduce morbidity and mortality.
Projective Tensor Products of C*-Algebras  [PDF]
Ajay Kumar, Vandana Rajpal
Advances in Pure Mathematics (APM) , 2014, DOI: 10.4236/apm.2014.45023

For C*-algebras A and B, the constant involved in the canonical embedding of \"\"into \"\"is shown to be \"\" . We also consider the corresponding operator space version of this embedding. Ideal structure of \"\" is obtained in case A or B has only finitely many closed ideals.

Artificial Intelligence in the Estimation of Patch Dimensions of Rectangular Microstrip Antennas  [PDF]
Vandana Vikas Thakare, Pramod Singhal
Circuits and Systems (CS) , 2011, DOI: 10.4236/cs.2011.24046
Abstract: Artificial Neural Network (ANNs) techniques are recently indicating a lot of promises in the application of various micro-engineering fields. Such a use of ANNs for estimating the patch dimensions of a microstrip line feed rectangular microstrip patch antennas has been presented in this paper. An ANN model has been developed and tested for rectangular patch antenna design. The performance of the neural network has been compared with the simulated values obtained from IE3D EM Simulator. It transforms the data containing the dielectric constant (εr), thickness of the substrate (h), and antenna’s dominant-mode resonant frequency (fr) to the patch dimensions i.e length (L) and width (W) of the patch. The different variants of back propagation training algorithm of MLFFBP-ANN (Multilayer feed forward back propagation Artificial Neural Network) and RBF –ANN (Radial basis function Artificial Neural Network) has been used to implement the network model. The results obtained from artificial neural network when compared with simulation results, found satisfactory and also it is concluded that RBF network is more accurate and fast as compared to different variants of back propagation training algorithms of MLPFFBP. The ANNs results are more in agreement with the simulation findings. Neural network based estimation has the usual advantage of very fast and simultaneous response of all the outputs.
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