ISOLATION AND SCREENING OF ANTAGONISTIC ACTINOMYCETES FROM MANGROVE SOIL

ABSTRACT:Objective: The objective of the present study was to isolate and screen the antagonistic actinomycetes from mangrove soil in Visakhapatnam.Methods: A total of 30 actinomycetes isolates were isolated by serial dilution plate technique, of these 20 isolates showed activity in primary screening against test pathogens used in this study. The active isolates were morphologically discrete on the basis of spore mass, colour, formation of aerial and substrate mycelia, production of diffusible pigment and biochemical characterization. The active isolates were subjected to shake flask fermentation and the secondary metabolites were extracted with ethyl acetate and screened for their antimicrobial activities against selected bacterial and fungal pathogens by agar well diffusion method.Results: Out of 20 isolates, 13 isolates exhibited both antibacterial and antifungal activity and 7 isolates showed only antibacterial activity and did not inhibit fungi used in this study. These isolates were identified as Streptomyces species basing on their morphological, physiological and biochemical characters.Conclusion: The results of the present study evidenced that mangrove source may be beneficial for the discovery of novel antibiotics from actinomycete

A Sign-to-Speech Translation System

This thesis describes sign-to-speech translation using neural networks. Sign language translation is an interesting but difficult problem for which neural network techniques seem promising because of their ability to adjust to the user\u27s hand movements, which is not possible to do by most other techniques. However, even using neural networks and artificial sign languages, the translation is hard, and the best-known system, that of Fels & Hinton (1993), is capable of translating only 66 root words and 203 words including their conjugations. This research improves their results to 790 root signs and 2718 words including their conjugations while preserving a high accuracy (i.e., over 93 %) in translation. The use of matcher neural networks (Revesz 1989, 1990) and asymmetric Hamming distances are the key sources of improvement. This research aims at providing a means of communication for deaf people. Adviser: Peter Z. Reves

Attribute Selection Algorithm with Clustering based Optimization Approach based on Mean and Similarity Distance

With hundreds or thousands of attributes in high-dimensional data, the computational workload is challenging. Attributes that have no meaningful influence on class predictions throughout the classification process increase the computing load. This article's goal is to use attribute selection to reduce the size of high-dimensional data, which will lessen the computational load. Considering selected attribute subsets that cover all attributes. As a result, there are two stages to the process: filtering out superfluous information and settling on a single attribute to stand in for a group of similar but otherwise meaningless characteristics. Numerous studies on attribute selection, including backward and forward selection, have been undertaken. This experiment and the accuracy of the categorization result recommend a k-means based PSO clustering-based attribute selection. It is likely that related attributes are present in the same cluster while irrelevant attributes are not identified in any clusters. Datasets for Credit Approval, Ionosphere, Annealing, Madelon, Isolet, and Multiple Attributes are employed alongside two other high-dimensional datasets. Both databases include the class label for each data point. Our test demonstrates that attribute selection using k-means clustering may be done to offer a subset of characteristics and that doing so produces classification outcomes that are more accurate than 80%

Distributed Dynamic Condition Response Structures

We present distributed dynamic condition response structures as a declarative process model in-spired by the workflow language employed by our industrial partner and conservatively generalizing labelled event structures. The model adds to event structures the possibility to 1) finitely specify re-peated, possibly infinite behavior, 2) finitely specify fine-grained acceptance conditions for (possibly infinite) runs based on the notion of responses and 3) distribute events via roles. We give a graph-ical notation inspired by related work by van der Aalst et al and formalize the execution semantics as a labelled transition system. Exploration of the relationship between dynamic condition response structures and traditional models for concurrency, application to more complex scenarios, and further extensions of the model is left to future work.

EECLA: A Novel Clustering Model for Improvement of Localization and Energy Efficient Routing Protocols in Vehicle Tracking Using Wireless Sensor Networks

Due to increase of usage of wireless sensor networks (WSN) for various purposes leads to a required technology in the present world. Many applications are running with the concepts of WSN now, among that vehicle tracking is one which became prominent in security purposes. In our previous works we proposed an algorithm called EECAL (Energy Efficient Clustering Algorithm and Localization) to improve accuracy and performed well. But are not focused more on continuous tracking of a vehicle in better aspects. In this paper we proposed and refined the same algorithm as per the requirement. Detection and tracking of a vehicle when they are in larges areas is an issue. We mainly focused on proximity graphs and spatial interpolation techniques for getting exact boundaries. Other aspect of our work is to reduce consumption of energy which increases the life time of the network. Performance of system when in active state is another issue can be fixed by setting of peer nodes in communication. We made an attempt to compare our results with the existed works and felt much better our work. For handling localization, we used genetic algorithm which handled good of residual energy, fitness of the network in various aspects. At end we performed a simulation task that proved proposed algorithms performed well and experimental analysis gave us faith by getting less localization error factor

DEVELOPMENT AND VALIDATION OF A STABILITY INDICATING HPLC METHOD FOR THE ESTIMATION OF RABEPRAZOLE IMPURITIES IN PHARMACEUTICAL DOSAGE FORMS BY DESIGN OF EXPERIMENTS

A novel stability-indicating reverse phase liquid chromatographic method was developed for the determination of Rabeprazole impurities in Rabeprazole tablet formulations. One unknown impurity was isolated and characterized by using MS and NMR, which was formed in the formulated drug stability study. Rabeprazole was subjected to the stress conditions like oxidative, acid, base, hydrolytic, thermal and photolytic degradation. Chromatographic separation was achieved on HPLC in gradient elution mode by QbD-approach. The eluted compounds were monitored at 280 nm. All the impurities and degradation products were well resolved from the main peak, proving the stability-indicating power of the method. On the basis of spectral data, the unknown impurity was characterized as 1-(1H -Benzimidazol-2-yl)-4-(3-methoxypropoxy)-3-methylpyridinium-2-carboxylate. The developed method was validated as per International Conference on Harmonization (ICH) guidelines with respect to specificity, limit of detection, limit of quantification, precision, linearity, accuracy, robustness and ruggedness Keywords: Rabeprazole, QbD approach, Degradation Products, Stability-Indicating, ICH Guideline

Clafer: Lightweight Modeling of Structure, Behaviour, and Variability

Embedded software is growing fast in size and complexity, leading to intimate mixture of complex architectures and complex control. Consequently, software specification requires modeling both structures and behaviour of systems. Unfortunately, existing languages do not integrate these aspects well, usually prioritizing one of them. It is common to develop a separate language for each of these facets. In this paper, we contribute Clafer: a small language that attempts to tackle this challenge. It combines rich structural modeling with state of the art behavioural formalisms. We are not aware of any other modeling language that seamlessly combines these facets common to system and software modeling. We show how Clafer, in a single unified syntax and semantics, allows capturing feature models (variability), component models, discrete control models (automata) and variability encompassing all these aspects. The language is built on top of first order logic with quantifiers over basic entities (for modeling structures) combined with linear temporal logic (for modeling behaviour). On top of this semantic foundation we build a simple but expressive syntax, enriched with carefully selected syntactic expansions that cover hierarchical modeling, associations, automata, scenarios, and Dwyer's property patterns. We evaluate Clafer using a power window case study, and comparing it against other notations that substantially overlap with its scope (SysML, AADL, Temporal OCL and Live Sequence Charts), discussing benefits and perils of using a single notation for the purpose