Validated spectophotometric methods for the assay of cinitapride hydrogen tartrate in pharmaceuticals

Three simple, selective and rapid spectrophotometric methods have been established for the determination of cinitapride hydrogen tartrate (CHT) in pharmaceutical tablets. The proposed methods are based on the diazotization of CHT with sodium nitrite and hydrochloric acid, followed by coupling with resorcinol, 1-benzoylacetone and 8-hydroxyquinoline in alkaline medium for methods A, B and C respectively. The formed azo dyes are measured at 442, 465 and 552 nm for methods A, B and C respectively. The parameters that affect the reaction were carefully optimized. Under optimum conditions, Beer’s law is obeyed over the ranges 2.0-32.0, 1.0-24.0 and 1.0-20.0 μg. mL-1 for methods A, B, and C, respectively. The calculated molar absorptivity values are 1.2853 x104, 1.9624 x104 and 3.92 x104 L.mol-1.cm-1 for methods A, B and C, respectively. The results of the proposed procedures were validated statistically according to ICH guidelines. The proposed methods were successfully applied to the determination of CHT in Cintapro tablets without interference from common excipients encountered

Issues of K Means Clustering While Migrating to Map Reduce Paradigm with Big Data: A Survey

In recent times Big Data Analysis are imminent as essential area in the field of Computer Science. Taking out of significant information from Big Data by separating the data in to distinct group is crucial task and it is beyond the scope of commonly used personal machine. It is necessary to adopt the distributed environment similar to map reduce paradigm and migrate the data mining algorithm using it. In Data Mining the partition based K Means Clustering is one of the broadly used algorithms for grouping data according to the degree of similarities between data. It requires the number of K and initial centroid of cluster as input. By surveying the parameters preferred by algorithm or opted by user influence the functionality of Algorithm. It is the necessity to migrate the K means Clustering on MapReduce and predicts the value of k using machine learning approach. For selecting the initial cluster the efficient method is to be devised and united with it. This paper is comprised the survey of several methods for predicting the value of K in K means Clustering and also contains the survey of different methodologies to find out initial center of the cluster. Along with initial value of k and initial centroid selection the objective of proposed work is to compact with analysis of categorical data

Design And Simulation Of Interline Unified Power Quality Conditioner (Iupqc) By Using Fuzzy Logic Controller

This paper proposes anew connection for a unified power quality conditioner (UPQC) to improve the power quality of two feeders in a distribution system. The interline custom power devices named Interline Unified Power Quality Conditioner (IUPQC) is improved for various power quality disturbances and modeled in MATLAB/SIMULINK by using fuzzy logic controller. The developed topology can be used for simultaneous compensation of voltage and current imperfections in a multi bus/multi feeder system.  The proposed IUPQC is designed for medium voltage level (11 kV) and effective Enhanced Phase Locked Loop (EPLL) with Fuzzy based control technique is used to detect and extract the PQ disturbances. The performance of Series Compensator of IUPQC is evaluated through extensive simulations for mitigating unbalanced voltage sags with phase jumps and interruption.             The performance of Shunt Compensator of IUPQC is also tested for harmonic and reactive power compensation that are not investigated before in literature. It is verified that IUPQC which is connected to two feeders, can compensate current and voltage distortions successfully in these feeders according to the results obtained using MATLAB/SIMULINK

MONO, DI and TRI SSRs data extraction & storage from 1403 virus genomes with next generation retrieval mechanism

Now a day׳s SSRs occupy the dominant role in different areas of bio-informatics like new virus identification, DNA finger printing, paternity & maternity identification, disease identification, future disease expectations and possibilities etc., Due to their wide applications in various fields and their significance, SSRs have been the area of interest for many researchers. In the SSRs extraction, retrieval algorithms are used; if retrieval algorithms quality is improved then automatically SSRs extraction system will achieve the most relevant results. For this retrieval purpose in this paper a new retrieval mechanism is proposed which will extracted the MONO, DI and TRI patterns. To extract the MONO, DI and TRI patterns using proposed retrieval mechanism in this paper, DNA sequence of 1403 virus genome data sets are considered and different MONO, DI and TRI patterns are searched in the data genome sequence file. The proposed Next Generation Sequencing (NGS) retrieval mechanism extracted the MONO, DI and TRI patterns without missing anything. It is observed that the retrieval mechanism reduces the unnecessary comparisons. Finally the extracted SSRs provide the useful, single view and useful resource to researchers