Microphytoplankton community structure in the North Eastern Arabian Sea during Winter Monsoon

The overall attempt of the study was aimed to understand the microphytoplankton community composition and its variations along a highly complex and dynamic marine ecosystem, the northern Arabian Sea. The data generated provides a first of its kind knowledge on the major primary producers of the region. There appears significant response among the microphytoplankton community structure towards the variations in the hydrographic conditions during the winter monsoon period. Interannually, variations were observed within the microphytoplankton community associated with the variability in temperature patterns and the intensity of convective mixing. Changing bloom pattern and dominating species among the phytoplankton community open new frontiers and vistas towards more intense study on the biological responses towards physical processes. The production of large amount of organic matter as a result of intense blooming of Noctiluca as well as diatoms aggregations augment the particulate organic substances in these ecosystem. This definitely influences the carbon dynamics of the northern Arabian Sea. Detailed investigations based on time series as well as trophodynamic studies are necessary to elucidate the carbon flux and associated impacts of winter-spring blooms in NEAS. Arabian sea is considered as one among the hotspot for carbon dynamics and the pioneering records on the major primary producers fuels carbon based export production studies and provides a platform for future research. Moreover upcoming researches based on satellite based remote sensing on productivity patterns utilizes these insitu observations and taxonomic data sets of phytoplankton for validation of bloom specific algorithm development and its implementation. Furthermore Saurashtra coast is considered as a major fishing zone of Indian EEZ. The studies on the phytoplankton in these regions provide valuable raw data for fishery prediction models and identifying fishing zones. With the Summary and Conclusion 177 baseline data obtained further trophodynamic studies can be initiated in the complex productive North Eastern Arabian Seas (NEAS) ecosystem that is still remaining unexplored.

Intraseasonal oscillation of total precipitable water over North Indian Ocean and its application in the diagnostic study of coastal rainfall

TRMM Microwave Imager (TMI) is reported to be a useful sensor to measure the atmospheric and oceanic parameters even in cloudy conditions. Vertically integrated specific humidity, Total Precipitable Water (TPW) retrieved from the water vapour absorption channel (22GHz.) along with 10m wind speed and rain rate derived from TMI is used to investigate the moisture variation over North Indian Ocean. Intraseasonal Oscillations (ISO) of TPW during the summer monsoon seasons 1998, 1999, and 2000 over North Indian Ocean is explored using wavelet analysis. The dominant waves in TPW during the monsoon periods and the differences in ISO over Arabian Sea and Bay of Bengal are investigated. The northward propagation of TPW anomaly and its coherence with the coastal rainfall is also studied. For the diagnostic study of heavy rainfall spells over the west coast, the intrusion of TPW over the North Arabian Sea is seen to be a useful tool.

Propagation of Microwaves in the Troposphere with Potential Application to GPS based Navigation and Meteorology with emphasis on Indian region

Global Positioning System (GPS), with its high integrity, continuous availability and reliability, revolutionized the navigation system based on radio ranging. With four or more GPS satellites in view, a GPS receiver can find its location anywhere over the globe with accuracy of few meters. High accuracy - within centimeters, or even millimeters is achievable by correcting the GPS signal with external augmentation system. The use of satellite for critical application like navigation has become a reality through the development of these augmentation systems (like W AAS, SDCM, and EGNOS, etc.) with a primary objective of providing essential integrity information needed for navigation service in their respective regions. Apart from these, many countries have initiated developing space-based regional augmentation systems like GAGAN and IRNSS of India, MSAS and QZSS of Japan, COMPASS of China, etc. In future, these regional systems will operate simultaneously and emerge as a Global Navigation Satellite System or GNSS to support a broad range of activities in the global navigation sector.Among different types of error sources in the GPS precise positioning, the propagation delay due to the atmospheric refraction is a limiting factor on the achievable accuracy using this system. The WADGPS, aimed for accurate positioning over a large area though broadcasts different errors involved in GPS ranging including ionosphere and troposphere errors, due to the large temporal and spatial variations in different atmospheric parameters especially in lower atmosphere (troposphere), the use of these broadcasted tropospheric corrections are not sufficiently accurate. This necessitated the estimation of tropospheric error based on realistic values of tropospheric refractivity. Presently available methodologies for the estimation of tropospheric delay are mostly based on the atmospheric data and GPS measurements from the mid-latitude regions, where the atmospheric conditions are significantly different from that over the tropics. No such attempts were made over the tropics. In a practical approach when the measured atmospheric parameters are not available analytical models evolved using data from mid-latitudes for this purpose alone can be used. The major drawback of these existing models is that it neglects the seasonal variation of the atmospheric parameters at stations near the equator. At tropics the model underestimates the delay in quite a few occasions. In this context, the present study is afirst and major step towards the development of models for tropospheric delay over the Indian region which is a prime requisite for future space based navigation program (GAGAN and IRNSS). Apart from the models based on the measured surface parameters, a region specific model which does not require any measured atmospheric parameter as input, but depends on latitude and day of the year was developed for the tropical region with emphasis on Indian sector.Large variability of atmospheric water vapor content in short spatial and/or temporal scales makes its measurement rather involved and expensive. A local network of GPS receivers is an effective tool for water vapor remote sensing over the land. This recently developed technique proves to be an effective tool for measuring PW. The potential of using GPS to estimate water vapor in the atmosphere at all-weather condition and with high temporal resolution is attempted. This will be useful for retrieving columnar water vapor from ground based GPS data. A good network of GPS could be a major source of water vapor information for Numerical Weather Prediction models and could act as surrogate to the data gap in microwave remote sensing for water vapor over land.

Synthesis,characterization and application studies of some polymer supported metal complexes

Polymer supports are efficient reagents,substrates and catalysts and they are extensively used for carrying out reactions at controlled rates.Tailor-made polymer supports are highly versatile which have opened an excellent area of research.Now polymer supported chemistry is being exploited at an amazing rate and it seems to join the routine world of organic synthesis.Polymer supported ligands are found to be efficient complexing agents whose high selectivity enables the analysis and removal of heavy metal ions which are toxic to all the living organisms of land and sea.polymer supported membranes function as ion selective potentiometric sensors which allow the exchange of specific ions among other ions of the same charge.In this investigation three series of polymeric schiff bases and three series of metal complexes have been prepared.An attempt is done to develop optimum conditions for the removal of heavy metal ions using polymeric schiff bases.A novel copper sensor electrode have also been prepared from polymer supported metal complex.

Application of bacterins and yeast Acremonium dyosporii to protect the larvae of Macrobrachium rosenbergii from vibriosis

The fresh water prawn, Macrobrachium rosenbergii, has proven potential for use as an aquaculture species (Hanson & Goodwin, 1997; Kurup, 1984). In India alone, culture of this species of prawn in low saline areas requires about 200 million seed per year (Kurup, 1984). In hatcheries poor survival rate has been associated with vibriosis at di#erent stages of the larval cycle. Members of the family Vibrionaceae associated with the larvae of M. rosenbergii were shown to be pathogenic under laboratory conditions (Bhat et al., 2000, in press). Vibrios have been associated with mortality of penaeid prawns by several workers (Aquacop, 1977; Hameed, 1993; Karunasagar et al., 1994). Two methods have been suggested to protect both the larvae and juveniles from vibriosis; one is the administration of bacterins prepared from pathogenic strains (Itami et al., 1989, 1991; Adams, 1991; Song & Sung, 1990; Sung et al., 1991) and the other is the utilization of yeast 1-3 and 1-6 glucans as immunostimulants for enhancing the non-specific defense system (Sung et al., 1994; Song et al., 1997). In the light of these observations it was hypothesised that bacterins and yeast glucans may also be e#ective in protecting the larvae of M. rosenbergii from vibriosis as has been achieved in the case of penaeids. To examine this hypothesis, the ability of bacterins and an extracellular glucan-producing yeast to increase the overall survival and metamorphosis of larvae in a hatchery, as well as to protect against an experimental challenge under laboratory conditions, was evaluated

A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining

Lipase from marine Aspergillus awamori BTMFW032: Production, partial purification and application in oil effluent treatment

Marine fungus BTMFW032, isolated from seawater and identified as Aspergillus awamori, was observed to produce an extracellular lipase, which could reduce 92% fat and oil content in the effluent laden with oil. In this study, medium for lipase production under submerged fermentation was optimized statistically employing response surface method toward maximal enzyme production. Medium with soyabean meal- 0.77% (w/v); (NH4)2SO4-0.1 M; KH2PO4-0.05 M; rice bran oil-2% (v/v); CaCl2-0.05 M; PEG 6000-0.05% (w/v); NaCl-1% (w/v); inoculum-1% (v/v); pH 3.0; incubation temperature 35 8C and incubation period-five days were identified as optimal conditions for maximal lipase production. The time course experiment under optimized condition, after statistical modeling, indicated that enzyme production commenced after 36 hours of incubation and reached a maximum after 96 hours (495.0 U/ml), whereas maximal specific activity of enzyme was recorded at 108 hours (1164.63 U/mg protein). After optimization an overall 4.6- fold increase in lipase production was achieved. Partial purification by (NH4)2SO4 precipitation and ion exchange chromatography resulted in 33.7% final yield. The lipase was noted to have a molecular mass of 90 kDa and optimal activity at pH 7 and 40 8C. Results indicated the scope for potential application of this marine fungal lipase in bioremediation.

A Multi-Objective Antenna Placement Genetic Algorithm for Matched Array Synthesis on Complex Platforms

A Multi-Objective Antenna Placement Genetic Algorithm (MO-APGA) has been proposed for the synthesis of matched antenna arrays on complex platforms. The total number of antennas required, their position on the platform, location of loads, loading circuit parameters, decoupling and matching network topology, matching network parameters and feed network parameters are optimized simultaneously. The optimization goal was to provide a given minimum gain, specific gain discrimination between the main and back lobes and broadband performance. This algorithm is developed based on the non-dominated sorting genetic algorithm (NSGA-II) and Minimum Spanning Tree (MST) technique for producing diverse solutions when the number of objectives is increased beyond two. The proposed method is validated through the design of a wideband airborne SAR