Nitrifying Bioreactors Integrated into Shrimp and Prawn Hatchery Systems: Molecular Characterization of the Nitrifying Consortia,Reactor Kinetics, Modeling and Validation

National Centre for Aquatic Animal Health, Cochin University of Science and Technology

Tropical Mesoscale Convective Systems and Associated Energetics : Observational and Modeling Studies

The main purpose of the thesis is to improve the state of knowledge and understanding of the physical structure of the TMCS and its short range prediction. The present study principally addresses the fine structure, dynamics and microphysics of severe convective storms.The structure and dynamics of the Tropical cloud clusters over Indian region is not well understood. The observational cases discussed in the thesis are limited to the temperature and humidity observations. We propose a mesoscale observational network along with all the available Doppler radars and other conventional and non—conventional observations. Simultaneous observations with DWR, VHF and UHF radars of the same cloud system will provide new insight into the dynamics and microphysics of the clouds. More cases have to be studied in detail to obtain climatology of the storm type passing over tropical Indian region. These observational data sets provide wide variety of information to be assimilated to the mesoscale data assimilation system and can be used to force CSRM.The gravity wave generation and stratosphere troposphere exchange (STE) processes associated with convection gained a great deal of attention to modem science and meteorologist. Round the clock observations using VHF and UHF radars along with supplementary data sets like DWR, satellite, GPS/Radiosondes, meteorological rockets and aircrafl observations is needed to explore the role of convection and associated energetics in detail.

A Diagnostic and Modeling Study on the Onset of Summer Monsoon over South Asia

This study focuses on the onset of southwest monsoon over Kerala. India Meteorological Department (IMD) has been using a semi-objective method to define monsoon onset. The main objectives of the study are to understand the monsoon onset processes, to simulate monsoon onset in a GCM using as input the atmospheric conditions and Sea Surface Temperature, 10 days earlier to the onset, to develop a method for medium range prediction of the date of onset of southwest monsoon over Kerala and to examine the possibility of objectively defining the date of Monsoon Onset over Kerala (MOK). It gives a broad description of regional monsoon systems and monsoon onsets over Asia and Australia. Asian monsoon includes two separate subsystems, Indain monsoon and East Asian monsoon. It is seen from this study that the duration of the different phases of the onset process are dependent on the period of ISO. Based on the study of the monsoon onset process, modeling studies can be done for better understanding of the ocean-atmosphere interaction especially those associated with the warm pool in the Bay of Bengal and the Arabian Sea.

Development and Evaluation of Blind Identification Techniques for Nonlinear Systems

Identification and Control of Non‐linear dynamical systems are challenging problems to the control engineers.The topic is equally relevant in communication,weather prediction ,bio medical systems and even in social systems,where nonlinearity is an integral part of the system behavior.Most of the real world systems are nonlinear in nature and wide applications are there for nonlinear system identification/modeling.The basic approach in analyzing the nonlinear systems is to build a model from known behavior manifest in the form of system output.The problem of modeling boils down to computing a suitably parameterized model,representing the process.The parameters of the model are adjusted to optimize a performanace function,based on error between the given process output and identified process/model output.While the linear system identification is well established with many classical approaches,most of those methods cannot be directly applied for nonlinear system identification.The problem becomes more complex if the system is completely unknown but only the output time series is available.Blind recognition problem is the direct consequence of such a situation.The thesis concentrates on such problems.Capability of Artificial Neural Networks to approximate many nonlinear input-output maps makes it predominantly suitable for building a function for the identification of nonlinear systems,where only the time series is available.The literature is rich with a variety of algorithms to train the Neural Network model.A comprehensive study of the computation of the model parameters,using the different algorithms and the comparison among them to choose the best technique is still a demanding requirement from practical system designers,which is not available in a concise form in the literature.The thesis is thus an attempt to develop and evaluate some of the well known algorithms and propose some new techniques,in the context of Blind recognition of nonlinear systems.It also attempts to establish the relative merits and demerits of the different approaches.comprehensiveness is achieved in utilizing the benefits of well known evaluation techniques from statistics. The study concludes by providing the results of implementation of the currently available and modified versions and newly introduced techniques for nonlinear blind system modeling followed by a comparison of their performance.It is expected that,such comprehensive study and the comparison process can be of great relevance in many fields including chemical,electrical,biological,financial and weather data analysis.Further the results reported would be of immense help for practical system designers and analysts in selecting the most appropriate method based on the goodness of the model for the particular context.

Some Aspects of Domain Specific Conceptual Modeling for Simulation of Logistic Terminal Operations

When simulation modeling is used for performance improvement studies of complex systems such as transport terminals, domain specific conceptual modeling constructs could be used by modelers to create structured models. A two stage procedure which includes identification of the problem characteristics/cluster - ‘knowledge acquisition’ and identification of standard models for the problem cluster – ‘model abstraction’ was found to be effective in creating structured models when applied to certain logistic terminal systems. In this paper we discuss some methods and examples related the knowledge acquisition and model abstraction stages for the development of three different types of model categories of terminal systems

Reliability Estimation of Open Source Software based Computational Systems

Software systems are progressively being deployed in many facets of human life. The implication of the failure of such systems, has an assorted impact on its customers. The fundamental aspect that supports a software system, is focus on quality. Reliability describes the ability of the system to function under specified environment for a specified period of time and is used to objectively measure the quality. Evaluation of reliability of a computing system involves computation of hardware and software reliability. Most of the earlier works were given focus on software reliability with no consideration for hardware parts or vice versa. However, a complete estimation of reliability of a computing system requires these two elements to be considered together, and thus demands a combined approach. The present work focuses on this and presents a model for evaluating the reliability of a computing system. The method involves identifying the failure data for hardware components, software components and building a model based on it, to predict the reliability. To develop such a model, focus is given to the systems based on Open Source Software, since there is an increasing trend towards its use and only a few studies were reported on the modeling and measurement of the reliability of such products. The present work includes a thorough study on the role of Free and Open Source Software, evaluation of reliability growth models, and is trying to present an integrated model for the prediction of reliability of a computational system. The developed model has been compared with existing models and its usefulness of is being discussed.