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Rainfall is the most important fundamental physical parameter among the climate, as this parameter determines the environmental condition of the particular region, which affects the agricultural productivity. Global warming or climate change, is one of the most important worldwide issues discussed among scientists and researchers. One of the consequences of climate change is the alteration of rainfall patterns and an increase in temperature. The drastic changes in rainfall pattern showed a significant impact on society, and therefore its up-to-date information is needed to estimate the spatial distribution and variability at all points of the territory. In this paper, a study on trend analysis of rainfall data observed at Anasi, Haliyal, Kadra, and Supa rain gauge stations in Karnataka, India, was carried out. For this purpose, the annual 1-day maximum rainfall (AMR) and annual total rainfall (ATR) series were generated from the daily rainfall data and used in trend analysis. A non-parametric Mann-Kendall (MK) test was applied to evaluate the presence of significant trend in AMR and ATR, while the rate of significant trend was computed by Sen's slope estimator (SSE). The MK test results indicated that there is a decreasing trend in AMR series of Anasi, Haliyal, Kadra, and Supa. The study showed that the rate of decreasing trend in the AMR series of Anasi, Haliyal, Kadra, and Supa is computed as 1.4 mm/year, 0.1 mm/year, 0.6 mm/year, and 0.2 mm/year, respectively. For the ATR series, the rate of decreasing trend for Anasi was computed as 23.8 mm/year, whereas 11.2 mm/year for Supa, whereas the rate of increasing trend was 4.3 mm/year for Haliyal and 2.2 mm/year for Supa. This paper illustrated the application of the MK test and SSE for analyzing the trend in AMR and ATR of Anasi, Haliyal, Kadra, with Supa and the results obtained from the study. [ABSTRACT FROM AUTHOR]

Estimation of design flood is formed as a basis for planning and management of civil engineering construction especially hydraulic structures such as water use and flood control structures. The dimensions and capacity of the buildings depend on flood magnitude for a given return period that may have some uncertainty due to model error. This can be achieved through Flood Frequency Analysis (FFA) that involves fitting probability distribution to the series of Annual Maximum Discharge (AMD) data. In this paper, a study on assessment of uncertainty of error in design flood estimates was carried out by fitting Method of Moments (MoM), Maximum Likelihood Method (MLM) and method of L- Moments (LMO) of 2- parameter Log Normal (LN2) distribution to the series of AMD data for river Tapi at Prakasha barrage. The selection of best fit method of LN2 was made through Goodness-of-Fit (GoF) tests viz., Chi-Square and Kolmogorov-Smirnov, and Model Performance Indicators (MPIs) such as correlation coefficient, root mean absolute error and root mean squared error. GoF tests results confirmed the suitability of all three methods of LN2 for estimation of Peak Flood Discharge (PFD). The results indicated that the predicted PFD by MLM is closer to the observed data. From FFA results, it was witnessed that the standard error on the estimated PFD by MoM is minimum than those values of MLM and LMO. The outcomes of FFA results were weighed with MPIs and found that MLM is better suited amongst three methods of LN2 for estimation of PFD at Prakasha barrage. [ABSTRACT FROM AUTHOR]

Coronavirus 2 virus is responsible for the spread of the infectious disease COVID-19 (also known as Coronavirus disease). People around the globe who got infected with the virus experienced a respiratory illness that could become as serious as leading someone to lose their life. However, the upside of the pandemic is that it has led to numerous types of research and explorations, majorly in the medical science field. Since a systematic survey of previous research activities and bibliometric analysis gives a brief idea about such contributions and acts as a reference to future research, this study aims to cover the research related to COVID-19 in the computer technology domain. It is limited to the works accepted and accessible with the keywords – Covid-19, prediction, and pandemic, in the Scopus search engine to justify the scope of this survey. Further, the paper highlights a few prior works used for predictive analysis and presents a quantitative angle on their algorithms. Earlier works showcase Time Series Analysis using ARIMA/SARIMA models for predicting the vaccination rates, and Extreme Gradient Boosting (XGBoost), Xtremely Boosted Network (XBNet) Regression, and Recurrent Neural Network (RNN) for Confirmed, Cured, and Death cases. Amongst the algorithms used in the latter use case, XBNet regression performed better than XGBoost regressor. [ABSTRACT FROM AUTHOR]

Estimation of rainfall for a given duration and return period is considered as one of the important design parameters in hydrological studies for planning, design and management of civil and hydraulic structures. This can be computed by fitting a probability distribution to the annual 1-day maximum rainfall observed at the rain-gauge site located within the vicinity of the study area. This paper presents an analysis of rainfall estimation at Dahanu using an extreme value family of probability distributions, namely, Generalized Extreme Value (GEV), Extreme Value Type-1, Extreme Value Type-2, and 3-parameter Pareto, wherein the parameters are determined using the Method of Moments, Maximum Likelihood Method, and Method of L-Moments (LMO). The adequacy of fitting probability distributions adopted in rainfall analysis is evaluated by quantitative assessment through Goodness-of-Fit (viz., Chi-Square and Kolmogorov-Smirnov) and diagnostic (viz., D-index) tests, and qualitative assessment by using the fitted curves of the estimated rainfall. The study shows that the estimated 1-day maximum rainfall given by GEV (LMO) distribution could be considered as a design parameter while designing the civil and hydraulic structures at Dahanu. [ABSTRACT FROM AUTHOR]

The day by day new combinations drugs are being launched in market. Then the multiple therapeutic agents which acts at diverse sites are used in the management of various diseases and disorders are done. Thus it is necessary to develop methods for analysis with the help of number of analytical methods which are available for the estimation of the drugs in combination. The analyst were estimate the rapid, selective, specific, simple, RP-HPLC method is developed and validated for simultaneous estimation of Losartan potassium and Hydrochlorthiazide in pharmaceutical tablet dosage form. RP-HPLC method was performed on the HPLC System with UV-VIS detector and Nucleodur,C8 (150 mm × 3.9 mm, 5 μm), using the mobile phase (Acetonitrile: Buffer B (7:93 v/v) pH 7.0 to 7.5 with 0.05% at a flow rate of 1.0 ml/min, injection volume 20 μl and UV detection at 280 nm. This method is validated according to BP, USP and ICH requirements for new methods, which include accuracy, precision, robustness, ruggedness, linearity and range. Linear relationships were obtained in the ranges of 100-300 μg/ml and 50-150 μg/ml with correlation coefficients of 0.9999 and 0.9996 at Rt value of 10.769 min and 21.633 min for Losartan potassium and hydrochlorthiazide respectively. According to ICH guidelines the developed method was validated. The proposed method can be used for estimation of these drugs in combined pharmaceutical dosage forms. [ABSTRACT FROM AUTHOR]

Estimation of rainfall for a given return period is of utmost importance for planning and design of minor and major hydraulic structures. This can be achieved through Extreme Value Analysis (EVA) of rainfall by fitting probability distributions viz., 2-parameters Normal, 2-parameters Log Normal, Pearson Type-3, Log Pearson Type-3, Extreme Value Type-1 (EV1) and Generalized Extreme Value (GEV) to the series of annual 1-day maximum rainfall. Based on the intended applications and the variate under consideration, Method of Moments (MoM), Maximum Likelihood Method (MLM) and L-Moments (LMO) are used for determination of parameters of the distributions. The adequacy of fitting six probability distributions adopted in EVA of rainfall for Afzalpur, Aland and Kalaburagi sites is quantitatively assessed by Goodness-of-Fit (viz., Chi-square and Kolmogorov-Smirnov) and diagnostic test (viz., D-index) tests, and qualitatively assessed by the fitted curves of the estimated rainfall. The outcomes of the study indicates that the GEV (LMO) is better suited amongst six distributions studied in EVA for rainfall estimation for Afzalpur and Kalaburagi whereas EV1 (MLM) for Aland. [ABSTRACT FROM AUTHOR]