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The world is in need of food, water, Shelter and without any of these there is no possibility to live. Agriculture is backbone of our nation which indeed supplies food for the entire people but the producers of food are facing many problems like moisture content, fertility of soil which affects the farming. Because of this reason cultivation and production of food grains are decreased. Current scenario is that the farmers need to go research center to test the fertility content of soil and to predict kind of plants can be cultivable in that land. In this paper perfect cultivable plants can be detected properly without approaching soil research centers which consumes more money as well time. By the application of pH sensors the fertility of soil can be measured. By measuring the pH soil nutrient content can be measured and so the suitable cultivable crop for different soil varieties can be predicted. In this view to give a healthful support to farmers, soil nutrients are analyzed and report the requirement using advanced sensors.

Herein we report the synthesis, PDE-4B and TNF-α inhibitory activities of a few dibenzo[b,d]furan-1-yl-thiazole derivatives. The hydroxycyclohexanol amide derivatives 14, 18, 24, 29, 31 and 33 exhibited promising in vitro PDE-4B and TNF-α inhibitory activities. Compound 24 showed good systemic availability in preclinical animal models and was also found to be non-toxic (exploratory mutagenicity test). Further it exhibited promising results in in vivo asthma/COPD and Uveitis models.

This paper describes LSE method for improving Takagi-Sugeno neuro-fuzzy model for a multi-input and multi-output system using a set of data (Mackey-Glass chaotic time series). The performance of the generated model is verified using certain set of validation / test data. The LSE method is used to compute the consequent parameters of Takagi-Sugeno neuro-fuzzy model while mean and variance of Gaussian Membership Functions are initially set at certain values and will be updated using Back Propagation Algorithm. The simulation using Matlab shows that the developed neuro-fuzzy model is capable of forecasting the future values of the chaotic time series and adaptively reduces the amount of error during its training and validation.