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It is not the first time in human history, nor will it be the last for that matter, that a collective problem calls for a collective response. Climate change fueled by greenhouse emissions affects humankind alike. Despite the disagreement among policymakers and scientists on the severity of the issue, the truth is that the problem remains. A broad look at different technologies being used today in different fields has led to the idea of bringing them together in an attempt to offer a viable solution to reducing anthropogenic CO2. The following paper describes how the nanotechnologies, available or soon to be available, would make CO2 capture, cache, and conversion (coined the three Cs) a valid way for achieving a more sustainable energy society. Authors also set out to highlight with this work how knowledge transfer is instrumental in the development of technology and how methodical assessment of crossovers can expedite research when time plays against us. [ABSTRACT FROM AUTHOR]

The importance of agricultural crops in India has been understated in terms of production during the last two decades, owing to global warming and other factors. Policymakers and farmers would benefit from imagining crop yields well before harvest to help them make appropriate marketing and storage decisions. Crop yields will benefit from such estimates as well. Several systems for predicting and modelling agricultural yields have been developed in the past, with varying degrees of effectiveness, due to the fact that they do not objectively account for meteorological aspects and seasonal climate variations. The importance of agricultural crops in India has been understated in terms of production during the last two decades, owing to global warming and other factors. Policymakers and farmers would benefit from imagining crop yields well before harvest to help them make appropriate marketing and storage decisions. Crop yields will benefit from such estimates as well. Several systems for predicting and modelling agricultural yields have been developed in the past, with varying degrees of effectiveness, due to the fact that they do not objectively account for meteorological aspects and seasonal climate variations. The method makes use of climate data from Thanjavur, India's soils. To choose the optimal crop for a particular set of input and climate conditions, the system leverages real-time input of location-specific soil attributes. The model is been tested with various machine learning techniques such as NB, KNN, SVM, and decision tree. Among all these methods, the SVM gives the good results. The accuracy of the model was determined using LSTM, SVM, and Tabu search optimization. TSO with SVM has an 89% accuracy value. [ABSTRACT FROM AUTHOR]