The present study was carried out to study the antibacterial and antioxidant activities of Commelina nudiflora extracts and to analyze the extracts for secondary metabolites. Chloroform, acetone and ethanolic extracts of Commelina nudiflora were screened for tannins, phlobatannins, saponins, flavonoids, terpenoids, cardiac glycosides, and steroids. Antibacterial activity was carried out by Minimum Inhibitory Concentration (MIC) and antioxidant assay was carried out using 1,1-diphenyl picrylhydrazyl (DPPH) and total antioxidant activity. Flavonoids present in all extracts. Acetone and ethanolic extracts exhibited antibacterial activity. All extracts showed significant antioxidant activity. All extracts showed concentration dependent antioxidant activity. Ethanolic extract showed high antioxidant activity against DPPH with IC50 11.25 mg/ml. The total antioxidant activity was high in chloroform extract with IC50 0.1282 mg/ml. The results show Commelina nudiflora has potential antioxidants that can fight against life threatening diseases like inflammation, cancer etc.
Biomedical image registration, or geometric alignment of twodimensional and /or three-dimensional (3-D) image data, is becoming increasingly important in diagnosis, treatment planning, functional studies, and computer-guided therapies and in biomedical research [1]. Registration is an important problem and a fundamental task in image processing technique. In the medical image processing fields, some techniques are proposed to find a geometrical transformation that relates the points of an image to their corresponding points of another image. In recent years, multimodality image registration techniques are proposed in the medical imaging field. Especially, CT and MR imaging of the head for diagnosis and surgical planning indicates that physicians and surgeons gain important information from these modalities. In radiotherapy planning manual registration techniques performed on MR image and CT images of the brain. Nowadays , physicians segment the volume of interest (VOIs) from each set of slices manually. However, manual segmentation of the object area may require several hours for analysis. Furthermore, MDCT images and MR images contain more than 100 slices. Therefore, manual segmentation and registration method cannot apply for clinical application in the head CT and MR images. Many automatic and semiautomatic image registration methods have been proposed [2]. The main techniques of image registration are performed by the manual operation, using Landmark and using voxel information. In this paper, an automatic intensity based registration of head images by computer has been employed by applying maximization of mutual information. The primary objective of this paper is to increase accuracy of the registration and reduce the processing time. Experiments show our algorithm is a robust and efficient method which can yield accurate registration results.
In this study, we consider a perishable inventory system that has an (s, Q) ordering policy, along with a finite waiting hall. The single server, which provides an item to the customer after completing the required service performance for that item, only begins serving after N customers have arrived. Impatient demand is assumed in that the customers waiting to be served lose patience and leave the system if the server’s idle time overextends or if the arriving customers find the system to be full and will not enter the system. This article analyzes the impatient demands caused by the N-policy server to an inventory system. In the steadystate, we obtain the joint probability distribution of the level of inventory and the number of customers in the system. We analyze some measures of system performance and get the total expected cost rate in the steadystate. We present a beneficial cost function and confer the numerical illustration that describes the impact of impatient customers caused by N-policy on the inventory system’s total expected cost rate.
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, the capital of China’s Hubei province. The objective of this research is to propose a forecasting model using the COVID-19 available dataset from top affected regions across the world using machine learning algorithms. Machine Learning algorithms help us achieve this objective. Regression models are one of the supervised machine learning techniques to classify large-scale data. This research aims to apply Multivariate Linear Regression to predict the number of confirmed and death COVID-19 cases for a span of one and two weeks. The experimental results explain 99\% variability in prediction with the R-squared statistics scores of 0.992. The algorithms are evaluated using the error matrix such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and accuracy for top affected regions across the world.
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