കരിമീന്‍ കൃഷി സാധ്യതകള്‍

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Nuclear β−\beta^--decay half-lives for fpfp and fpgfpg shell nuclei

In the present work we calculate the allowed $\beta^-$-decay half-lives of nuclei with $Z = 20 -30$ and N $\leq$ 50 systematically under the framework of the nuclear shell model. A recent study shows that some nuclei in this region belong to the island of inversion. We perform calculation for $fp$ shell nuclei using KB3G effective interaction. In the case of Ni, Cu, and Zn, we used JUN45 effective interaction. Theoretical results of $Q$ values, half-lives, excitation energies, log$ft$ values, and branching fractions are discussed and compared with the experimental data. In the Ni region, we also compared our calculated results with recent experimental data [Z. Y. Xu {\it et al.}, \emph{Phys. Rev. Lett.} \textbf{113}, 032505, 2014]. Present results agree with the experimental data of half-lives in comparison to QRPA.Comment: Accepted in Journal of Physics G: Nuclear and Particle Physic

High-spin structures of 77,79,81,83^{77,79,81,83}As isotopes

In the present work we report comprehensive set of shell model calculations for arsenic isotopes. We performed shell model calculations with two recent effective interactions JUN45 and jj44b. The overall results for the energy levels and magnetic moments are in rather good agreement with the available experimental data. We have also reported competition of proton- and neutron-pair breakings analysis to identify which nucleon pairs are broken to obtain the total angular momentum of the calculated states. Further theoretical development is needed by enlarging model space by including $\pi 0f_{7/2}$ and $\nu 1d_{5/2}$ orbitals.Comment: 16 pages, 8 figures, Accepted for Publication in Modern Physics Letters

DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels

The impact of soiling on solar panels is an important and well-studied problem in renewable energy sector. In this paper, we present the first convolutional neural network (CNN) based approach for solar panel soiling and defect analysis. Our approach takes an RGB image of solar panel and environmental factors as inputs to predict power loss, soiling localization, and soiling type. In computer vision, localization is a complex task which typically requires manually labeled training data such as bounding boxes or segmentation masks. Our proposed approach consists of specialized four stages which completely avoids localization ground truth and only needs panel images with power loss labels for training. The region of impact area obtained from the predicted localization masks are classified into soiling types using the webly supervised learning. For improving localization capabilities of CNNs, we introduce a novel bi-directional input-aware fusion (BiDIAF) block that reinforces the input at different levels of CNN to learn input-specific feature maps. Our empirical study shows that BiDIAF improves the power loss prediction accuracy by about 3% and localization accuracy by about 4%. Our end-to-end model yields further improvement of about 24% on localization when learned in a weakly supervised manner. Our approach is generalizable and showed promising results on web crawled solar panel images. Our system has a frame rate of 22 fps (including all steps) on a NVIDIA TitanX GPU. Additionally, we collected first of it's kind dataset for solar panel image analysis consisting 45,000+ images.Comment: Accepted for publication at WACV 201

Tumor-derived lactate and myeloid-derived suppressor cells: Linking metabolism to cancer immunology

Many malignant cells produce increased amounts of lactate, which promotes the development of myeloid-derived suppressor cells (MDSCs). MDSCs, lactate, and a low pH in the tumor microenvironment inhibit the function of natural killer (NK) cells and T lymphocytes, hence allowing for disease progression. Ketogenic diets can deplete tumor-bearing animals from MDSCs and regulatory T cells, thereby improving their immunological profile