Antibaryon yields in ultrahigh-energy collisions and the astroparticle implications

Some of the most recent results on antibaryon production in some ultrahigh-energy nuclear collisions are being analysed on the basis of a particle production model which has had some intrinsic features of non-standard nature. It is seen that the model could accommodate the data without any induction of the quark-gluon plasma (QGP) ideas which have, truly speaking, become a fad in the domain of the ultrahigh-energy nuclear collisions with some probable implications for or impact upon astroparticle physics

Stability of strange stars (SS) derived from a realistic equation of state

A realistic equation of state (EOS) leads to realistic strange stars (ReSS) which are compact in the mass radius plot, close to the Schwarzchild limiting line (Dey et al 1998). Many of the observed stars fit in with this kind of compactness, irrespective of whether they are X-ray pulsars, bursters or soft $\gamma$ repeaters or even radio pulsars. We point out that a change in the radius of a star can be small or large, when its mass is increasing and this depends on the position of a particular star on the mass radius curve. We carry out a stability analysis against radial oscillations and compare with the EOS of other strange star (SS) models. We find that the ReSS is stable and an M-R region can be identified to that effect.Comment: 16 pages including 5 figures. Accepted for publication in MPL

Integrated Classifier: A Tool for Microarray Analysis

Microarray technology has been developed and applied in different biological context, especially for the purpose of monitoring the expression levels of thousands of genes simultaneously. In this regard, analysis of such data requires sophisticated computational tools. Hence, we confined ourselves to propose a tool for the analysis of microarray data. For this purpose, a feature selection scheme is integrated with the classical supervised classifiers like Support Vector Machine, K-Nearest Neighbor, Decision Tree and Naive Bayes, separately to improve the classification performance, named as Integrated Classifiers. Here feature selection scheme generates bootstrap samples that are used to create diverse and informative features using Principal Component Analysis. Thereafter, such features are multiplied with the original data in order create training and testing data for the classifiers. Final classification results are obtained on test data by computing posterior probability. The performance of the proposed integrated classifiers with respect to their conventional classifiers is demonstrated on 12 microarray datasets. The results show that the integrated classifiers boost the performance up to 25.90% for a dataset, while the average performance gain is 9.74%, over the conventional classifiers. The superiority of the results has also been established through statistical significance test