Growth response of phytoplankton exposed to industrial effluents in river Periyar

The thesis entitled Growth Response of Phytoplankton Exposed to Industrial Effluents in River Periyar. The present investigation has been conducted in two phases: field observation and algal assays. The monthly distribution of hydrographic features is represented graphically. The sampling year has been divided into three seasons: monsoon (June to September), postmonsoon (October to January) and premonsoon (February to May). The data were analysed using Student's t-test to find whether there was any significant difference between surface and bottom samples. The spatial variation of the variables was assessed by Page's L (trend) test (Ray Meddis, 1975). The standard procedure for algal toxicity test (Ward and Parrish, 1982) was followed throughout the study. Statistical analysis (Page's L (trend) test) showed that there was no significant difference in Secchi disc transparency between the stations. The field observations as well as the laboratory assays confirm that the rate of discharge in river Periyar during premonsoon is insufficient to effect dilution of wastewater received in the industrial zone.

Discrete commutative difference operator theory

The object of this thesis is to formulate a basic commutative difference operator theory for functions defined on a basic sequence, and a bibasic commutative difference operator theory for functions defined on a bibasic sequence of points, which can be applied to the solution of basic and bibasic difference equations. in this thesis a brief survey of the work done in this field in the classical case, as well as a review of the development of q~difference equations, q—analytic function theory, bibasic analytic function theory, bianalytic function theory, discrete pseudoanalytic function theory and finally a summary of results of this thesis

Biclustering Gene Expression Data using MSR Difference Threshold

Biclustering is simultaneous clustering of both rows and columns of a data matrix. A measure called Mean Squared Residue (MSR) is used to simultaneously evaluate the coherence of rows and columns within a submatrix. In this paper a novel algorithm is developed for biclustering gene expression data using the newly introduced concept of MSR difference threshold. In the first step high quality bicluster seeds are generated using K-Means clustering algorithm. Then more genes and conditions (node) are added to the bicluster. Before adding a node the MSR X of the bicluster is calculated. After adding the node again the MSR Y is calculated. The added node is deleted if Y minus X is greater than MSR difference threshold or if Y is greater than MSR threshold which depends on the dataset. The MSR difference threshold is different for gene list and condition list and it depends on the dataset also. Proper values should be identified through experimentation in order to obtain biclusters of high quality. The results obtained on bench mark dataset clearly indicate that this algorithm is better than many of the existing biclustering algorithms