The present investigation was carried out using thirty two pigeonpea genotypes during kharif 2013-14 to obtain the knowledge of correlation, path coefficient analysis for the yield components and genetic divergence. The range of GCV was observed from 9.81 to 40.88% for the traits under study which provides information regarding the extent of variability present among the genotypes. Seed yield was significantly and positively correlated with number of secondary branches/plant, pods/plant and 100-seed weight. Path coefficient analysis indicated that number of secondary branches exhibited maximum direct effect followed by number of pods/plant and 100 seed weight. The genotypes were grouped into eight different clusters based on Mahalonobis D 2 statistics. Clusters II and III exhibited maximum inter cluster distance of 8.80. Days to 50% flowering contributed to maximum genetic divergence followed by seed yield. Genotypes in cluster III recorded highest mean value for days to maturity, number of secondary branches and seed yield.
The aim of this paper is to introduce and characterize the vividly (1, 2)-β-irresolute mapping and blurly (1, 2)-β-irresolute mapping. We also define (1, 2)-β-T 2 spaces and (1, 2)-semi-preregular spaces. These spaces are characterized by a new class of open sets, called (1, 2)semipre-θ-open sets.
Summary
Renewable energy sources are useful for sustainable monitoring, but still very limited today due to various implementation constraints. Microbial fuel cells (MFCs) are considered a promising renewable power source for remote monitoring applications. They are used as wireless temperature sensors and biosensors due to their ability in powering environmental sensors. MFCs can provide ultralow and dynamic power, and hence, energy improvement is crucial for self‐powered biosensors. Cloud computing–based IoT framework is proposed for environment monitoring using MFC‐based biosensors. This paper presents the electric energy harvesting from Oryza Sativa plants with bacteria as the catalyst. It adopts the technology of MFC in the plants to extract the maximum energy. An effective power management with IoT cloud framework is presented in this work to independently operate multiple MFCs to generate maximum power. Independently operated MFCs with electrically isolated electrodes have been utilized in the design of a suitable power management system. Cloud computing is utilized in this work to process the data generated in continuous monitoring of environment. Experimental results show that the proposed framework can achieve sustainable power for sensor nodes and achieves maximum performance in environment monitoring using cloud‐based IoT platform.
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