Water is one of the prime necessities of life. We can hardly live for a few days without water. In a man's body, 70-80% is water. Cell, blood, and bones contain 90%, 75%, and 22% water, respectively. The general survey reveals that the total surface area of earth is 51 crore km2 out of which 36.1 crore km2 is covered sea. In addition to this, we get water from rivers, lakes, tanks, and now on hills. In spite of such abundance, there is a shortage of soft water in the world. Physicochemical parameter of any water body plays a very important role in maintaining the fragile ecosystem that maintains various life forms. Present research paper deals with various water quality parameter, chlorides, dissolved oxygen, total iron, nitrate, water temperature, pH, total phosphorous, fecal coli form bacteria, and adverse effect of these parameters on human being.

Machine learning and data analytics are being increasingly used for quantitative structure property relation (QSPR) applications in the chemical domain where the traditional Edisonian approach towards knowledge-discovery have not been fruitful. The perception of odorant stimuli is one such application as olfaction is the least understood among all the other senses. In this study, we employ machine learning based algorithms and data analytics to address the efficacy of using a data-driven approach to predict the perceptual attributes of an odorant namely the odorant characters (OC) of “sweet” and “musky”. We first analyze a psychophysical dataset containing perceptual ratings of 55 subjects to reveal patterns in the ratings given by subjects. We then use the data to train several machine learning algorithms such as random forest, gradient boosting and support vector machine for prediction of the odor characters and report the structural features correlating well with the odor characters based on the optimal model. Furthermore, we analyze the impact of the data quality on the performance of the models by comparing the semantic descriptors generally associated with a given odorant to its perception by majority of the subjects. The study presents a methodology for developing models for odor perception and provides insights on the perception of odorants by untrained human subjects and the effect of the inherent bias in the perception data on the model performance. The models and methodology developed here could be used for predicting odor characters of new odorants.

The study signifies that a larger volume of lung receives lower doses because of multiple beam arrangement and a smaller volume of lung receives higher doses because of better dose conformity in IMRT plans. Acute pneumonitis correlates more with V30 values, whereas chronic pneumonitis was predominantly seen in patients with higher V20 values.

Some apparently very simple free-body diagrams thrown at the beginner involve subtle distinctions. They are frequently misunderstood - and not just by the novice.

In this study, solid-waste management practices were evaluated in order to find out its link with occurrence of vector-borne disease. Strategies for solid-waste management were employed as practical model to solve the problems regarding pollution which is originated by solid-waste.