Working of Women’s Industrial Co-operative Societies in Kerala

This study deals with the working of Women’s Industrial Co-operative Societies (WICS) in Kerala. The formation of women’s co-operatives was identified as a lucrative enterprise and a feasible proposition for empowerment of women through encouraging and ensuring their active participation in the process of social and economic development. The problem of unemployment of Women and poverty in India can be tackled effectively only through suitable and appropriate self-employment schemes. WICS help to supplement the income of families and thus raise the standard of living. WICS in Kerala have a significant role in the elimination of industrial backwardness and mounting employment. This study focuses its attention on the performance aspects of WICS. It also gives an introduction to the co-operative movement and review of literature on industrial co-operatives in general and women’s industrial co-operatives in particular.

Dopamine Receptor Subtypes,IP3, cAMP and cGMP Functional Regulation in Parkinsonism Induced by Unilateral Infusion of Rotenone in Rats: Recovery with Bone Marrow Cells,Serotonin and GABA Supplementation

In the present study, the effects of 5-HT, GABA and Bone Marrow Cells infused intranigrally to substantia nigra individually and in combinations on unilateral rotenone infused Parkinsonism induced rats. Scatchard analysis of DA, DA D1 and D2 receptors in the corpus striatum, cerebral cortex, cerebellum, brain stem and hippocampus showed a significant increase in the Brain regions of rotenone infused rat compared to control. Real Time PCR amplification of DA D1, D2, Bax and ubiquitin carboxy-terminal hydrolase were up regulated in the brain regions of rotenone infused rats compared to control. Gene expression studies of -Synuclien, cGMP and Cyclic AMP response element-binding protein showed a significant down regulation in Rotenone infused rats compared to control. Behavioural studies were carried out to confirm the biochemical and molecular studies.Our study demonstrated that BMC administration alone cannot reverse the above said molecular changes occurring in PD rat. 5-HT and GABA acting through their specific receptors in combination with bone marrow cells play a crucial role in the functional recovery of PD rats. 5-HT, GABA and Bone marrow cells treated PD rats showed significant reversal to control in DA receptor binding and gene expression. 5-HT and GABA have co-mitogenic property. Proliferation and differentiation of cells re-establishing the connections in Parkinson's disease facilitates the functional recovery. Thus, it is evident that 5-HT and GABA along with BMC to rotenone infused rats renders protection against oxidative, related motor and cognitive deficits which makes them clinically significant for cellbased therapy. The BMC transformed to neurons when co-transplanted with 5-HT and GABA which was confirmed with PKH2GL and nestin. These newly formed neurons have functional significance in the therapeutic recovery of Parkinson’s disease.

A Study Of The Marketing Channels Of Natural Rubber With Special Reference To Co-Operative Marketing In Kerala

The study is designed to gather, record, analyse and critically evaluate data on natural rubber production, consumption and marketing in Kerala. The scope of the study also covers the processing of natural rubber by rubber growers, especially small growers. Taking into consideration Kerala’s supremacy in natural rubber cultivation, a detailed study of marketing channels of natural rubber in Kerala, by giving special reference to co-operative rubber marketing has been given importance. Attempt has also been made to collect secondary data of the last 15 years

A study of industrial co-operatives in kerala with special reference to modern small scale sector

The report of the Steering Committee of the Kerala State Planning Board for the preparation of the VIII five Year Plan on Industry and Hiningzslisted several factors inhibiting and promoting growth of small industrial units. Kerala's educated manpower, the native intelligence of the Keralites best suited for taking up logic—based professions and availability of a Hell—developed and broad~based physical infrastructure particularly in the transport and communications sectors, are a few of the positive factors identified. On the negative side, may be mentioned high wage rates, preference on the part of the educated for white collar Jobs, lack of entrepreneurship, paucity of essential resources for industrial use, high density of population and the distance factor which places Kerala away from the major domestic markets. In this context, it would appear that the industrial co-operative movement could possibly play a vital role in accelerating-the momentum of small industrial development of Kerala which has a Health of highly educated and skilled manpower. However, in spite of the encouragement extended by the Government, the movement does not seem to have.been picking up in the modern small scale sector. The present study is an attempt to analyse the factors that have affected the performance of the industrial co—operatives in the small scale industrial sector of Kerala.

CBIR Using Local and Global Properties of Image Sub-blocks

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation and finding the corner density in each partition. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). Euclidean distance measure is used for computing the distance between the features of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods

Texture Description of low grade and high grade Glioma using Statistical features in Brain MRIs

Low grade and High grade Gliomas are tumors that originate in the glial cells. The main challenge in brain tumor diagnosis is whether a tumor is benign or malignant, primary or metastatic and low or high grade. Based on the patient's MRI, a radiologist could not differentiate whether it is a low grade Glioma or a high grade Glioma. Because both of these are almost visually similar, autopsy confirms the diagnosis of low grade with high-grade and infiltrative features. In this paper, textural description of Grade I and grade III Glioma are extracted using First order statistics and Gray Level Co-occurance Matrix Method (GLCM). Textural features are extracted from 16X16 sub image of the segmented Region of Interest(ROI) .In the proposed method, first order statistical features such as contrast, Intensity , Entropy, Kurtosis and spectral energy and GLCM features extracted were showed promising results. The ranges of these first order statistics and GLCM based features extracted are highly discriminant between grade I and Grade III. In this study which gives statistical textural information of grade I and grade III Glioma which is very useful for further classification and analysis and thus assisting Radiologist in greater extent.

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing