L-Asparaginase or L-asparagine amido hydrolases are enzymes that catalyze the substrate hydrolysis of L-asparagine. It results in the formation of L-aspartate and ammonia. It has immense application in the treatment of lymphoblastic leukemia as an antineoplastic agent and also finds its use in food technology. Its vast application in the pharmaceutical industry has led to the need for more sources of production of L-asparaginase. The current work is focused on production, purification, and characterization of L-asparaginase. The enzyme is produced using the batch mode of cultivation with critical media components disodium phosphate (0.042M), sodium chloride (0.0854M) and Asparagine (0.060M) respectively. The extracellular Lasparaginase was later purified by the following techniques: salt dialysis, ion-exchange chromatography, and gel filtration chromatography. Meanwhile, protein estimation was done using lowry's method. The molecular weight of the enzyme was found to be at 55KDa as revealed through SDS-PAGE. The biochemical analysis revealed that the species producing the enzyme belonged to Pseudomonas sp. The culture condition favoring the production of the enzyme L-asparaginase was found to be at a temperature of 40 0 C, pH-9, and incubation time of 24hr. Optimization with critical carbon and nitrogen sources with varying concentrations disclosed sucrose and ammonium sulfate at 1.5%(w/w) to maximize the enzyme production. The purified enzyme was characterized by the above parameters (400C, pH-9) and incubation period of 40 minutes was found to be having an enzyme activity 434.10(U/ml). Additionally, to overproduce the enzyme, strain development was performed with the treatment of UV-B rays exposed at different heights and X-rays to yield more amount of enzyme.

India's Karnataka state is home to a vast treasure trove of artefacts, antiquities, and historic and archaeologically significant monuments. Its culture and tradition are linked. In Karnataka, there are numerous Neolithic and Megalithic structures; these historic buildings from illustrious ruling dynasties have endured for thousands of years. They have miracles of their own in their own style, innate sculpture, architecture, technique, immensity, and enormity. However, modern generation is not ready for mining archaeological knowledge regarding empires or ruling dynasties of these ancient Karnataka temples through the archaeological guidance. Hence, a new approach required to bring this valuable information to the modern generation by a proper platform. In this paper both threshold and regional based segmentation methods are applied in order to segment the structural elements of temple. The analysis of segmented structural elements by applying both methods is done in order to provide comparative study. Comparative study on temple structural element shows that regional segmentation is more accurate than threshold method based on VOE and DSC metrics which are used for evaluating the performance of segmentation methods. Further, more efficient segmentation approaches may be applied to improve the efficiency of segmentation and it may be used for classification of viman styles.

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The physicochemical quality of drinking waters of 6 sources from Talakaveri river and different parts of NMAMIT campus( Nitte-Udupi distic) was studied. Drinking water sample was first taken from the filtrate after the water was led into the filter.Second water sample was taken from tap. Ground water sample was taken from well and other three samples were taken from lake, falls and river. The physicochemical quality of drinking water was affected by the origin of raw water. The quality goal was exceeded by all drinking water samples. In general, the physicochemical quality of drinking waters from different sources were rather similar.This project is based on analysis of physical, chemical quality of different water samples. The water samples were collected from different sources. The purpose of our study is to bring an awareness regarding portability of water among people. We can also develop new techniques to eradicate water borne diseases and new equipment’s to prevent water pollution from our studies. The water samples were collected during rainy season in the month of June and July of the year 2018. The parameters used to analyse the quality of water is also called as water quality index. The major properties analysed are as follows: Taste and Odour, Colour, Temperature,Total dissolved solids, Total suspended solids, Determination of pH, Conductivity and Turbidity.

Image Segmentation has played an important role in Computer vision it is used for object tracking and to identify image boundaries. It goals at fetching meaningful objects lying in the digital image of user captured or preprocessed image. Generally there is no unique method or approach for image segmentation. The different algorithms used in Image segmentation are Clustering-based, Region based and Edge based. Image segmentation is the fragmentation technique for an image into multiple fragments i.e. set of pixels, pixels in a specific region are similar according to some attribute. These attributes are such as color, intensity or texture. The proposed paper gives the overall view about the methods in image segmentation specifically thresholding , k-means clustering , grab-cut method , graph-cut method and Feature based segmentation. Every method is discussed along with its advantage and disadvantages which helps us in deciding which the best and efficient method of image segmentation is. The main aim of the paper is to come out with the more efficient method in image segmentation. Index Terms: Thresholding, Clustering, Grab-cut and Graph-cut. ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.177 Volume 7 Issue XII, Dec 2019-Available at www.ijraset.com 167©IJRASET: All Rights are Reserved vectors, which can be trained from the first frame after the change. One important parameter is the required number S of cluster-feature vectors, which is highly scene-depend-ent. For a simple application of the algorithm, S has been fixed to a number of 8 with good results. It is also possible to derive S automatically, e.g. to make it larger until the mean deviation from all cluster centroids reaches some predefined minimum threshold. This can best be applied, if the cluster set M is designed in a treestructured way [4], where a cluster is split when it donates the highest mean deviation. V. CONCLUSIONThis paper deals with different types of techniques used in the segmentation of the image processing. Graph Cut methods usually provides quite good result while compared to other segmentation techniques. Segmentation based on graph cuts works very well for most of the images. Graph cut-based segmentation methods are structured into 3 categories. They are namely as discussed in graph cut segmentation i.e first method based on speed up-based graph cut, second method based on interactive-based graph cut and third method based on shape prior-based graph cut. However, mandate individual execution of these three kind of method is not necessary of the graph-cut approach. These methods are combined to improve the image segmentation result. Many algorithms for image segmentations such as K-mean clustering algorithms, edge based algorithms grab-cut and graph cut algorithms are used. And finally concluding that graph cut method is widely used for image segmentation.