Analytical Equations for Compact Dual Frequency Microstrip Antenna

Design equations are presented for calculating the resonance frequencies for a compact dual frequency arrow-shaped microstrip antenna. This provides a fast and simple way to predict the resonant frequencies of the antenna. The antenna is also analyzed using the IE3D simulation package. The theoretical predictions are found to be very close to the IE3D results and thus establish the validity of the design formulae

An analytical study of the power distribution pattterns of silica waveguides with high paraboloic cylindrical deformation

An attempt is made to determine the relative power distribution in a step-index parabolic cylindrical waveguide (PCW) with high deformation across the direction of propagation. The guide is assumed to be made of silica. The scalar field approximation is employed for the analysis under which a vanishing refractive-index (RI) difference in the waveguide materials is considered. Further, no approximation for folds- is used in the analytical treatment. Due to the geometry of such waceguides, PCWs lose the well-defined modal discreteness, and a kind of mode bunching is observed instead, which becomes much more prominent in PCWs with high bends. However, with the increase in cross-sectional size, the mode-bunching tendency is slightly reduced. The general expressions for power in the guiding and nonguiding sections are obtained, and the fractional power patterns in all of the sections are presented for PCWs of various cross-sectional dimensions. It is observed that the confinement of power in the core section is increased for PCWs of larger cross-sectional size. Moreover, a fairly uniform distribution of power is seen over the modes having intermediate values of propagation constants

Analytical Investigations 0n Collapse of Cylindrical Submarine Shells

Submarine hull structure is a watertight envelope, under hydrostatic pressure when in operation. Stiffened cylindrical shells constitute the major portion of these submarine hulls and these thin shells under compression are susceptible to buckling failure. Normally loss of stability occurs at the limit point rather than at the bifurcation point and the stability analysis has to consider the change in geometry at each load step. Hence geometric nonlinear analysis of the shell forms becomes. a necessity. External hydrostatic pressure will follow the deformed configuration of the shell and hence follower force effect has to be accounted for. Computer codes have been developed based on all-cubic axisymmetric cylindrical shell finite element and discrete ring stiffener element for linear elastic, linear buckling and geometric nonIinear analysis of stiffened cylindrical shells. These analysis programs have the capability to treat hydrostatic pressure as a radial load and as a follower force. Analytical investigations are carried out on two attack submarine cylindrical hull models besides standard benchmark problems. In each case, the analysis has been carried out for interstiffener, interdeepframe and interbulkhead configurations. The shell stiffener attachment in each of this configuration has been represented by the simply supported-simply supported, clamped-clamped and fixed-fixed boundary conditions in this study. The results of the analytical investigations have been discussed and the observations and conclusions are described. Rotation restraint at the ends is influential for interstiffener and interbulkhead configurations and the significance of axial restraint becomes predominant in the interbulkhead configuration. The follower force effect of hydrostatic pressure is not significant in interstiffener and interdeepframe configurations where as it has very high detrimental effect on buckling pressure on interbulkhead configuration. The geometric nonlinear interbulkhead analysis incorporating follower force effect gives the critical value of buckling pressure and this analysis is recommended for the determination of collapse pressure of stiffened cylindrical submarine shells.

Recyclable Short Fibre Reinforced Plastics PP/Nylon and HDPE/Nylon composites

The study shows that standard plastics like polypropylene and high density polyethylene can be reinforced by adding nylon short fibres. Compared to the conventional glass reinforced thermoplastics this novel class of reinforced thermoplastics has the major advantage of recyclability. Hence such composites represent a new spectrum of recyclable polymer composites. The fibre length and fibre diameter used for reinforcement are critical parameters While there is a critical fibre length below which no effective reinforcement takes place, the reinforcement improves when the fibre diameter decreases due to increased surface area.While the fibres alone give moderate reinforcement, chemical modification of the matrix can further improve the strength and modulus of the composites. Maleic anhydride grafting in presence of styrene was found to be the most efficient chemical modification. While the fibre addition enhances the viscosity of the melt at lower shear rates, the enhancement at higher shear rate is only marginal. This shows that processing of the composite can be done in a similar way to that of the matrix polymer in high shear operations such as injection moulding. Another significant observation is the decrease in melt viscosity of the composite upon grafting. Thus chemical modification of matrix makes processing of the composite easier in addition to improving the mechanical load bearing capacity.For the development of a useful short fibre composite, selection of proper materials, optimum design with regard to the particular product and choosing proper processing parameters are most essential. Since there is a co-influence of many parameters, analytical solutions are difficult. Hence for selecting proper processing parameters 'rnold flow' software was utilized. The orientation of the fibres, mechanical properties, temperature profile, shrinkage, fill time etc. were determined using the software.Another interesting feature of the nylon fibre/PP and nylon fibre/HDPE composites is their thermal behaviour. Both nylon and PP degrade at the same temperature in single steps and hence the thermal degradation behaviour of the composites is also being predictable. It is observed that the thermal behaviour of the matrix or reinforcement does not affect each other. Almost similar behaviour is observed in the case of nylon fibre/HDPE composites. Another equally significant factor is the nucleating effect of nylon fibre when the composite melt cools down. In the presence of the fibre the onset of crystallization occurs at slightly higher temperature.When the matrix is modified by grafting, the onset of crystallization occurs at still higher temperature. Hence it may be calculated that one reason for the improvement in mechanical behaviour of the composite is the difference in crystallization behaviour of the matrix in presence of the fibre.As mentioned earlier, a major advantage of these composites is their recyclability. Two basic approaches may be employed for recycling namely, low temperature recycling and high temperature recycling. In the low temperature recycling, the recycling is done at a temperature above the melting point of the matrix, but below that of the fibres while in the high temperature route. the recycling is done at a temperature above the melting points of both matrix and fibre. The former is particularly interesting in that the recycled material has equal or even better mechanical properties compared to the initial product. This is possible because the orientation of the fibre can improve with successive recycling. Hence such recycled composites can be used for the same applications for which the original composite was developed. In high temperature recycling, the composite is converted into a blend and hence the properties will be inferior to that of the original composite, but will be higher than that of the matrix material alone.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Novel Approaches to Biodegradable Polymers: Synthesis and Biodegradation Studies

Biodegradable polymers have opened an emerging area of great interest because they are the ultimate solution for the disposal problems of synthetic polymers used for short time applications in the environmental and biomedical field. The biodegradable polymers available until recently have a number of limitations in terms of strength and dimensional stability. Most of them have processing problems and are also very expensive. Recent developments in biodegradable polymers show that monomers and polymers obtained from renewable resources are important owing to their inherent biodegradability, biocompatibility and easy availability. The present study is, therefore, mostly concemed with the utilization of renewable resources by effecting chemical modification/copolymerization on existing synthetic polymers/natural polymers for introducing better biodegradability and material properties.The thesis describes multiple approaches in the design of new biodegradable polymers: (1) Chemical modification of an existing nonbiodegradable polymer, polyethylene, by anchoring monosaccharides after functionalization to introduce biodegradability. (2) Copolymerization of an existing biodegradable polymer, polylactide, with suitable monomers and/or polymers to tailor their properties to suit the emerging requirements such as (2a) graft copolymerization of lactide onto chitosan to get controlled solvation and biodegradability and (2b) copolymerization of polylactide with cycloaliphatic amide segments to improve upon the thermal properties and processability.

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

lntemet utilization and academic activities of faculty members in the universities of Kerala : an analytical study

This thesis Entitled Internet Utilization and Academic Activities of Faculty Members in the Universities of kerala: an analytical study. Today, scientific research is throwing up new discoveries, inventions and vistas by the hour. We are witnessing a veritable knowledge explosion. It is important for members of university faculty members to keep abreast of it for giving up-t-date information to their students about the new development in the subject of their study. The internet is an invaluable tool for achieving it. Most of the universities have sufficient internet facility, but the accessibility to all the faculty members is not adequate. University Libraries also provides standard supplementary service in the internet area. This study indicates differential level of awareness and utilization of the internet services by the faculty members in the areas of teaching, research and publication. However the overall impression is that the awareness and utilization is inadequate. This point to the urgent need to devise programs and schemes to promote internet utilization among the faculty members. The suggestions indicate the key areas that deserve attention by policy makers and administrators. Thanks to the internet, every new development in every field of study is just a click away for faculty members, research scholars and students.

“Development of lymphoid cell culture system from Penaeus monodon and molecular approaches for its transformation

Unveiling the molecular and regulatory mechanisms that prevent in vitro transformation in shrimp remains elusive in the development of continuous cell lines, with an arduous history of over 25 years (Jayesh et al., 2012). Despite presenting challenges to researchers in developing a cell line, the billion dollar aquaculture industry is under viral threat. In addition, the regulatory mechanisms that prevent in vitro transformation and carcinoma in shrimps might provide new leads for the development of anti-ageing and anti-cancer interventions in human (Vogt, 2011) and in higher vertebrates. This highlights the importance of developing shrimp cell lines, to bring out effective prophylactics against shrimp viruses and for understanding the mechanism that induce cancer and ageing in human.. Advances in molecular biology and various gene transfer technologies for immortalization of cells have resulted in the development of hundreds of cell lines from insects and mammals, but yet not a single cell line has been developed from shrimp and other marine invertebrates. With this backdrop, the research described in this thesis attempted to develop molecular tools for induced in vitro transformation in lymphoid cells from Penaeus monodon and for the development of continuous cell lines using conventional and novel technologies to address the problems at cellular and molecular level.

Analytical Studies on Diffusion and lntermittency in Chaotic Maps

The study of simple chaotic maps for non-equilibrium processes in statistical physics has been one of the central themes in the theory of chaotic dynamical systems. Recently, many works have been carried out on deterministic diffusion in spatially extended one-dimensional maps This can be related to real physical systems such as Josephson junctions in the presence of microwave radiation and parametrically driven oscillators. Transport due to chaos is an important problem in Hamiltonian dynamics also. A recent approach is to evaluate the exact diffusion coefficient in terms of the periodic orbits of the system in the form of cycle expansions. But the fact is that the chaotic motion in such spatially extended maps has two complementary aspects- - diffusion and interrnittency. These are related to the time evolution of the probability density function which is approximately Gaussian by central limit theorem. It is noticed that the characteristic function method introduced by Fujisaka and his co-workers is a very powerful tool for analysing both these aspects of chaotic motion. The theory based on characteristic function actually provides a thermodynamic formalism for chaotic systems It can be applied to other types of chaos-induced diffusion also, such as the one arising in statistics of trajectory separation. It was noted that there is a close connection between cycle expansion technique and characteristic function method. It was found that this connection can be exploited to enhance the applicability of the cycle expansion technique. In this way, we found that cycle expansion can be used to analyse the probability density function in chaotic maps. In our research studies we have successfully applied the characteristic function method and cycle expansion technique for analysing some chaotic maps. We introduced in this connection, two classes of chaotic maps with variable shape by generalizing two types of maps well known in literature.

Molecular Approaches For Characterization And Determination Of Pathogenicity Of Vibrios With Special Reference To Vibrio Harveyi From Penaeus Monodon Larval Production Systems

The present study focuses on vibrios especially Vibrio harveyi isolated from shrimp (P. monodon) larval production systems from both east and west coasts during times of mortality. A comprehensive approach has been made to work out their systematics through numerical taxonomy and group them based on RAPD profiling and to segregate the virulent from non- virulent isolates based on the presence of virulent genes as well as their phenotypic expression. The information gathered has helped to develop a simple scheme of identification based on phenotypic characters and segregate the virulent from non virulent strains of V. harveyi.

Polymeric complexesSynthesis, Characterization and Analytical Applications

As the application of polymeric complexes is enormous, there exists a continuing interest in the synthesis and characterization of these complexes. The synthetic and characterization parts are very important in an academic point of view. Further in an application point of view also polymeric ligands/complexes are gaining attention.The thesis is divided in to six chapters, in which the first chapter gives an introduction along with a brief review on polymeric ligands/ complexes. The second chapter explains the different procedure adopted for the whole work along with the details of the reagents/ instruments used. The third chapter gives a report of the detailed study regarding the synthesis and characterization of eighteen complexes. While the fourth chapter is a report of the ion removal studies using three polymeric ligands, the fifth chapter explains the development of a polymeric complex as ion selective electrode material for the fabrication of a CC ion selective electrode. The sixth chapter presents the summary and tables, figures and references are given separately at the end.