The complex numerical climate models pose a big challenge for scientists in weather predictions, especially for tropical system. This paper is focused on presenting the importance of weather prediction using machine learning (ML) technique. Recently many researchers recommended that the machine learning models can produce sensible weather predictions in spite of having no precise knowledge of atmospheric physics. In this work, global solar radiation (GSR) in MJ/m2/day and wind speed in m/s is predicted for Tamil Nadu, India using a random forest ML model. The random forest ML model is validated with measured wind and solar radiation data collected from IMD, Pune. The prediction results based on the random forest ML model are compared with statistical regression models and SVM ML model. Overall, random forest machine learning model has minimum error values of 0.750 MSE and R2 score of 0.97. Compared to regression models and SVM ML model, the prediction results of random forest ML model are more accurate. Thus, this study neglects the need for an expensive measuring instrument in all potential locations to acquire the solar radiation and wind speed data.

The free energies of hydration of the 20 amino acids in their zwitterionic form, their pK _a shifts and the side-chain free energies of transfer have been calculated using the finite difference Poisson–Boltzmann methodology. A comparison of the results obtained with charge and size parameters from some popular force fields used in modelling biomolecules is presented. The force fields considered include recent versions of AMBER, CHARMM, CVFF, GROMOS and OPLS, PARSE and an ab initio-derived charge set. A general agreement between the theoretical predictions, emerging from each of the parameter sets, and experiment is discernible. A critique on the current status of theoretical studies on amino acids in solution is also advanced.

Large Hydrophobic Residues (LHR) such as phenylalanine, isoleucine, leucine, methionine and valine play an important role in protein structure and activity. We describe the role of LHR in complete set of protein sequences in 15 different species. That is the distribution of LHR in different proteins of different species is reported. It is observed that the proteins prefer to have 27% of large hydrophobic residues in total and all along the sequence. It is also observed that proteins accumulate more LHR in its active sites. A window analysis on these protein sequences shows that the 27% of LHR is more frequent at window length of 45 amino acids. The influenza virus and P. falciparum show a random distribution of LHR in its proteins compared to other model organisms.

Weather forecasting refers to the prediction of atmospheric conditions depending on a given time and location. Weather prediction is essential and it plays a significant role in many sectors namely energy and utililities, marine transportation, aviation, agriculture and forestry to a greater extent. Accurate weather forecast mechanism help the farmers for suitable planning of farming operations that will prevent crop losses. In this work, the weather parameters namely precipitation, relative humidity, wind speed and solar radiation were predicted for few Indian locations using the conventional temperature based empirical models and machine learning algorithms such as linear regression, support-vector machine (SVM) and decision tree. Forecasting of weather parameters, on which agriculture depends, will increase the overall yield and it helps farmers and agricultural-based businesses to plan better. From the current results, it is observed that machine learning (ML) based methods had a better prediction results than the physics based conventional models for weather forecasting with mean square error of 0.1397 and correlation coefficient of 0.9259. The objective of this work is to arrive at an optimized end result and a better weather prediction using the Machine learning models with lesser computational effort.