Evaluation of standardisation parameters and antioxidant activity of Bergenia ligulata

Berginia ligulata is one of the prominent ayurvedic herb for acute and chronic urinary tract infection [UTI].The present study highlighted the botanical as well as phytochemical studies including parameters such as macroscopic, physiochemical, evaluation and preliminary phytochemical studies of roots. On physiochemical analysis it was found that the powder was soluble in methanol, ethanol and water. It was insoluble in petroleum ether. On phytochemical analysis of aqueous and chloroform extract, it contains alkaloids, Saponin and Flavonoid

Accuracy of Mandibular Rami Measurements in Prediction of Sex

Background: Sex of an individual can be determined by means of skeletal indicators when soft tissues are not available for analysis. Also, when entire skull is not available for analysis, mandible plays a vital role in prediction of sex. As various studies have proven the accuracy of panoramic radiographs in providing anatomical measurements, the present study was conducted using digital orthopantomographs (OPGs) in the South Indian population for the same. Aim: To measure and compare various measurements of the ramus in mandible on digital orthopantomographs (OPGs) and to assess the usefulness of such measurements in prediction of sex in an individual. Materials and Methods: A cross-sectional, observational study was carriedout using 500 digital orthopantomographs (OPGs) with five rami measurements taken for each radiograph in the South Indian population. The determination of sex was done by discriminant function analysis with a prediction accuracy being 84.1% amongst females and 76% amongst males. Results: All the variables were found to be good predictors for prediction of sex in the study with Condylar height/Maximum ramus height; Projective height of ramus; and Coronoid height, being highly significant. Conclusion: This study on rami measurements showed that significant sex-related dimorphism is evident in rami of mandibles indicating its potential usage in mass disasters and otherwise in prediction of sex in individuals with disputed identity.Keywords: Mandibular ramus, Digital Orthopantomographs (OPGs), Prediction of se

Entropy based Software Reliability Growth Modelling for Open Source Software Evolution

During Open Source Software (OSS) development, users submit "new features (NFs)", "feature improvements (IMPs)" and bugs to fix. A proportion of these issues get fixed before the next software release. During the introduction of NFs and IMPs, the source code files change. A proportion of these source code changes may result in generation of bugs. We have developed calendar time and entropy-dependent mathematical models to represent the growth of OSS based on the rate at which NFs are added, IMPs are added, and bugs introduction rate.The empirical validation has been conducted on five products, namely "Avro, Pig, Hive, jUDDI and Whirr" of the Apache open source project. We compared the proposed models with eminent reliability growth models, Goel and Okumoto (1979) and Yamada et al. (1983) and found that the proposed models exhibit better goodness of fit

Ab-inito study on different phases of ferromagnetic CeMnNi4

Using first-principles density functional calculations, we study the possible phases of CeMnNi$_{4}$ and show that the ground state is ferromagnetic. We observed the hexagonal phase to be lowest in energy whereas experimentally observed cubic phase lies slightly higher in energy. We optimized the structure in both phases and in all different magnetic states to explore the possibility of the structural and magnetic phase transitions at ground state. We do not find any phase transitions between the magnetic and non-magnetic phases. The calculated structural, magnetic properties of cubic phase are in excellent agreement with experiments. Further, we do not observe half metallic behavior in any of the phases. However, the cubic phase does have fewer density of states for down-spin component giving a possibility of forming half metallic phase artificially, introducing vacancies, and disorder in lattice

A drug utilization study in glaucoma patients in ophthalmology out patient department in a tertiary care hospital

Background: According to World Health Organization (WHO) studies Glaucoma is a chronic progressive symptomatic disease that damages retinal cells and is one of the leading cause of preventable blindness worldwide. Availability of newer topical agents has modernized the management of glaucoma.Methods: A prospective observational study was carried out from August 2016 to December 2016 at ophthalmology Out Patient Department of L.G General Hospital, Ahmedabad by authours after the approval of the Institutional Ethics Committee.Results: Out of total 101 patients, 71 were males and 30 were females. Average age of patient is 54 years. Common variant of Glaucoma was Primary Open Angle Glaucoma in 57.4% of patients. Average number of drugs per prescription was 2 (45%). Most commonly used Fixed Dose Combination was Brimonidine +Timolol Drops which was used in 87 (86.1%) patients. Most commonly used adjuvant drug was Tab. Acetazolamide (60% of patients).Conclusions: Common variant of Glaucoma was Primary Open Angle Glaucoma in 57.4% of patients. Most commonly used Fixed Dose Combination was Brimonidine+Timolol Drops which was used in 87 (86.1%) patients and commonly used Single drug therapy is Tab. Acetazolamide in (60% of patients)

Surface Wear Studies in Some Materials Using α-induced Reactions

The radio-activity produced during the irradiation of 63,65Cu, 59Co, 93Nb and 121,123Sb targets with α-particles have been measured using activation technique. he yields of radioactive isotopic products 66,67,68 Ga, 61 Cu, 96g,mTc and 123,124,126I have been determined in the energy range ≈ 10-40 MeV using stacked foil Technique. Radioactive counting of samples was performed with a high-resolution gamma-spectrometer. As light ion beams produce an extremely narrow layer of activities in the surface of a material, these reactions may be useful for thin layer activation study

Deep Learning Based Forecasting of Indian Summer Monsoon Rainfall

Accurate short range weather forecasting has significant implications for various sectors. Machine learning based approaches, e.g., deep learning, have gained popularity in this domain where the existing numerical weather prediction (NWP) models still have modest skill after a few days. Here we use a ConvLSTM network to develop a deep learning model for precipitation forecasting. The crux of the idea is to develop a forecasting model which involves convolution based feature selection and uses long term memory in the meteorological fields in conjunction with gradient based learning algorithm. Prior to using the input data, we explore various techniques to overcome dataset difficulties. We follow a strategic approach to deal with missing values and discuss the models fidelity to capture realistic precipitation. The model resolution used is (25 km). A comparison between 5 years of predicted data and corresponding observational records for 2 days lead time forecast show correlation coefficients of 0.67 and 0.42 for lead day 1 and 2 respectively. The patterns indicate higher correlation over the Western Ghats and Monsoon trough region (0.8 and 0.6 for lead day 1 and 2 respectively). Further, the model performance is evaluated based on skill scores, Mean Square Error, correlation coefficient and ROC curves. This study demonstrates that the adopted deep learning approach based only on a single precipitation variable, has a reasonable skill in the short range. Incorporating multivariable based deep learning has the potential to match or even better the short range precipitation forecasts based on the state of the art NWP models.Comment: 14 pages, 14 figures. The manuscript is under review with journal 'Transactions on Geoscience and Remote Sensing