Water users' association in Dusi Mamandur Tank: Farmers' experience

Water users' associationsTank irrigation

Water users' association in Dusi Mamandur Tank: Farmers' experience

Water users' associationsTank irrigationWater distributionWater allocationMaintenance

Genetic Analysis of Parkinsonism/Dementia and Multiple sclerosis

Parkinson’s disease is a neurodegenerative disorder resulting in balance instability, tremor, and slowness of movement. It affects about two percent of individuals over the age of 50 throughout the world. My project’s goal is to identify genetic factors that contribute to this disease. A large kindred (MEN-1) has been identified segregates Parkinsonism and dementia as a genetic trait. Whole genome sequencing has been used to show that these phenotypes are not caused by any of the known genes associated with Parkinsonism or dementia, and has identified variants in candidate genes that could contribute to the disease phenotypes seen in this kindred. I plan to undertake a linkage analysis to identify the chromosomal regions harboring genes for these disease traits. This will provide evidence to guide the choice of which genetic variants in the MEN-1 Kindred should be pursued in future analyses. This could involve the development of assays to evaluate the frequency of novel variants in disease and non-disease cohorts as well as functional studies in cell-culture and animal models

Water users' association in Dusi Mamandur Tank: farmers' experience

Water users associations, Tank irrigation, Water distribution, Water allocation, Maintenance, Farm Management, Institutional and Behavioral Economics,

Production of protease from biodiesel waste derived semipurified glycerol

387-395Glycerol generated as the by-product of biodiesel production from vegetable oil or animal fat, has increased the availability of crude glycerol in the market. New strategies need to be developed for the utilization of this abundant carbon source. Using biodiesel waste derived semipurified glycerol as the carbon source, Bacillus amyloliquefaciens, Bacillus megaterium and Bacillus cereus were compared for the production of protease, an industrially important enzyme. Results showed that the medium with 1% semipurified glycerol was an effective carbon source for protease production by these organisms. In addition to glycerol concentration, the parameters such as temperature, pH, incubation period and volume of the inoculum were optimized to increase the protease production. Among the three Bacillus species studied, B. amyloliquefaciens was found to be the best producer of protease. A maximum protease production of 543.95 ± 1.84 U/mL occurred at 48 h with the pH of the medium 9.0, and an inoculum density of 2.6 × 108 cells mL-1 at 45°C by B. amyloliquefaciens.</em

Development of an Ensembled Meta-Deep Learning Model for Semantic Road-Scene Segmentation in an Unstructured Environment

Road scene segmentation is an integral part of the Intelligent Transport System (ITS) for precise interpretation of the environment and safer vehicle navigation. Traditional segmentation methods have faced difficulties in meeting the requirements of unstructured and complex image segmentation. Therefore, the Deep-Neural Network (DNN) plays a significant role in effectively segmenting images with multiple classes in an unstructured environment. In this work, semantic segmentation models such as U-net, LinkNet, FPN, and PSPNet are updated to use classification networks such as VGG19, Resnet50, Efficientb7, MobilenetV2, and Inception V3 as pre-trained backbone architectures, and the performance of each updated model is compared with the unstructured Indian Driving-Lite (IDD-Lite) dataset. In order to improve segmentation performance, a stacking ensemble approach is proposed to combine the predictions of a semantic segmentation model across different backbone architectures using a simple grid search method. Thus, four ensemble models are formed and analyzed on the IDD-Lite dataset. The two metrics Intersection over Union (IoU or Jaccard index) and Dice coefficient (F1 score) are used to assess the segmentation performance of each ensemble model. The results show that an ensemble of U-net with different backbone architectures is more efficient than other ensemble models. This model has achieved 73.12% and 76.67%, respectively, in IoU and F1 scores

Development of an Ensembled Meta-Deep Learning Model for Semantic Road-Scene Segmentation in an Unstructured Environment

Road scene segmentation is an integral part of the Intelligent Transport System (ITS) for precise interpretation of the environment and safer vehicle navigation. Traditional segmentation methods have faced difficulties in meeting the requirements of unstructured and complex image segmentation. Therefore, the Deep-Neural Network (DNN) plays a significant role in effectively segmenting images with multiple classes in an unstructured environment. In this work, semantic segmentation models such as U-net, LinkNet, FPN, and PSPNet are updated to use classification networks such as VGG19, Resnet50, Efficientb7, MobilenetV2, and Inception V3 as pre-trained backbone architectures, and the performance of each updated model is compared with the unstructured Indian Driving-Lite (IDD-Lite) dataset. In order to improve segmentation performance, a stacking ensemble approach is proposed to combine the predictions of a semantic segmentation model across different backbone architectures using a simple grid search method. Thus, four ensemble models are formed and analyzed on the IDD-Lite dataset. The two metrics Intersection over Union (IoU or Jaccard index) and Dice coefficient (F1 score) are used to assess the segmentation performance of each ensemble model. The results show that an ensemble of U-net with different backbone architectures is more efficient than other ensemble models. This model has achieved 73.12% and 76.67%, respectively, in IoU and F1 scores