Identification of drought in Dhalai river watershed using MCDM and ANN Models

An innovative approach for drought identification is developed using Multi-Criteria Decision-Making (MCDM) and Artificial Neural Network (ANN) model from surveyed drought parameters data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters i.e. precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from experts, literature and cultivators survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai river watershed were collected and used to train the ANN model. The trained ANN model has been tested in the same watershed for its calibration. Results indicate that the Limited Memory - Quasi Newton algorithm was better than the commonly used training method. Based on obtained results from ANN model drought index 0.30 to 0.75 were generated for present study area. Overall analysis revealed that, with appropriate training, the ANN model could be used in the areas where the model is calibrated, or other areas where range of input parameters is similar to the calibrated region.by Sainath Aher, Sambhaji Shinde, Shantamoy Guha and Mrinmoy Majumde

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke