Christ University

About: Christ University is a education organization based out in Bengaluru, India. It is known for research contribution in the topics: Computer science & Convection. The organization has 2267 authors who have published 2715 publications receiving 14575 citations. The organization is also known as: Christ College & Christ University.

Heat transfer enhancement using temperature-dependent effective properties of alumina-water nanoliquid with thermo-solutal Marangoni convection: A sensitivity analysis

Abstract: The sensitivity of the heat transport rate in the thermo-solutal Marangoni convection of $${\mathrm{Al}}_{2}{\mathrm{O}}_{3}-{\mathrm{H}}_{2}\mathrm{O}$$ nanoliquid at 300 K is analyzed. The nanoliquid is modeled using the modified Buongiorno model which incorporates the Brownian motion, effective nanoliquid properties, and thermophoresis effects. The thermophysical models proposed by Khanafer and Vafai are chosen in this analysis as these correlations are in good agreement with the experimental values. External constraining factors like thermal radiation and variable magnetic field are also considered. The basic equations are solved using apposite transformation variables and Finite Difference Method (FDM). The impacts of the effectual parameters on all the profiles are analyzed. Furthermore, the heat transport is analyzed by executing a Response Surface Methodology (RSM) model with the Brownian motion parameter ( $$0.1\le \mathrm{Nb}\le 0.5$$ ), thermophoretic parameter ( $$0.1\le \mathrm{Nt}\le 0.5$$ ), and nanoparticle volume fraction ( $$1\%\le \varphi \le 3\%$$ ). The modified Buongiorno model yields lower temperature and concentration profiles when compared to the conventional Buongiorno model. The heat transfer rate is the most sensitive to the Brownian motion parameter than thermophoresis and nanoparticle (NP) volume fraction parameters. The results of this study would be instrumental in improving the efficiency of the welding process, crystal growth, and coating technologies.

GASP XXIII: A jellyfish galaxy as an astrophysical laboratory of the baryonic cycle

Abstract: With MUSE, Chandra, VLA, ALMA and UVIT data from the GASP programme we study the multiphase baryonic components in a jellyfish galaxy (JW100) with a stellar mass 3.2 X 10^{11} M_sun hosting an AGN. We present its spectacular extraplanar tails of ionized and molecular gas, UV stellar light, X-ray and radio continuum emission. This galaxy represents an excellent laboratory to study the interplay between different gas phases and star formation, and the influence of gas stripping, gas heating, and AGN. We analyze the physical origin of the emission at different wavelengths in the tail, in particular in-situ star formation (related to Halpha, CO and UV emission), synchrotron emission from relativistic electrons (producing the radio continuum) and heating of the stripped interstellar medium (ISM) (responsible for the X-ray emission). We show the similarities and differences of the spatial distributions of ionized gas, molecular gas and UV light, and argue that the mismatch on small scales (1kpc) is due to different stages of the star formation process. We present the relation Halpha--X-ray surface brightness, which is steeper for star-forming regions than for diffuse ionised gas regions with high [OI]/Halpha ratio. We propose that ISM heating due to interaction with the intracluster medium (either for mixing, thermal conduction or shocks) is responsible for the X-ray tail, the observed [OI]-excess and the lack of star formation in the northern part of the tail. We also report the tentative discovery in the tail of the most distant (and among the brightest) currently known ULX, a point-like ultraluminous X-ray source commonly originating in a binary stellar system powered either by an intermediate-mass black hole or a magnetized neutron star.

Modified ceria as a substitute for sulfuric acid in the liquid phase nitration of toluene

Abstract: Ceria, sulfated ceria, ceria–zirconia and sulfated ceria–zirconia catalysts were prepared via the co-precipitation method and calcined at 823 K. The catalysts were characterized by XRD, BET surface area, FTIR, TGA and EDAX. The acidity of the catalysts was studied by pyridine adsorbed FTIR. All the peaks in XRD correspond to the cubic fluorite structure of ceria. The crystallite size of the catalysts was found to be 4–8 nm. Incorporation of zirconia stabilizes the surface sulfate species and thus increases the sulfate content. Sulfation decreases the surface area, but increases the acidity, leading to enhanced catalytic activity. All the catalysts were found to be stable up to 923 K. Catalytic activities were tested towards the liquid phase nitration of toluene. A maximum conversion of about 34 % is achievable for the nitration of toluene to dinitrotoluene. Solid acids effectively play the role of sulfuric acid in the reaction, assisting the formation of nitronium species.

Churn Analysis in Telecommunication Using Logistic Regression

Abstract: Since the beginning of data mining the discovery of knowledge from the Databases has been carried out to solve various problems and has helped the business come up with practical solutions. Large companies are behind improving revenue due to the increase loss in customers. The process where one customer leaves one company and joins another is called as churn. This paper will be discussing how to predict the customers that might churn, R package is being used to do the prediction. R package helps represent large dataset churn in the form of graphs which will help to depict the outcome in the form of various data visualizations. Churn is a very important area in which the telecom domain can make or lose their customers and hence the business/industry spends a lot of time doing predictions, which in turn helps to make the necessary business conclusions. Churn can be avoided by studying the past history of the customers. Logistic Regression is been used to make necessary analysis. To proceed with logistic regression we must first eliminate the outliers that are present, this has be achieved by cleaning the data (for redundancy, false data etc) and the resultant has been populated into a prediction excel using which the analysis has been performed.