Inert Doublet Dark Matter with Strong Electroweak Phase Transition

We reconsider the strength of the electroweak phase transition (EWPT) in the inert doublet dark matter model, using a quantitatively accurate form for the one-loop finite temperature effective potential, taking into account relevant particle physics and dark matter constraints, focusing on a standard model Higgs mass near 126 GeV, and doing a full scan of the space of otherwise unconstrained couplings. We find that there is a significant (although fine-tuned) space of parameters for achieving an EWPT sufficiently strong for baryogenesis while satisfying the Xenon100 constraints from direct detection and not exceeding the correct thermal relic density. We predict that the dark matter mass should be in the range 60-67 GeV, and we discuss possible LHC signatures of the charged and CP-odd Higgs bosons, including a 10% decrease of the h -> 2 photon branching ratio.Comment: 6 pages, 4 figures. v2: added discussion of subdominant IDM DM, and references; v4: improved fig. 1, added discussion of uncertainty in Higgs-nucleon coupling; published version; v5: fixed typo in eq. (5); v6: corrected fig. 2 to account for thermal averaging of cross section near resonance; v7: corrected sign of contribution to Higgs to two photon partial widt

A study on incremental mining of frequent patterns

Data generated from both the offline and online sources are incremental in nature. Changes in the underlying database occur due to the incremental data. Mining frequent patterns are costly in changing databases, since it requires scanning the database from the start. Thus, mining of growing databases has been a great concern. To mine the growing databases, a new Data Mining technique called Incremental Mining has emerged. The Incremental Mining uses previous mining result to get the desired knowledge by reducing mining costs in terms of time and space. This state of the art paper focuses on Incremental Mining approaches and identifies suitable approaches which are the need of real world problem.Keywords: Data Mining, Frequent Pattern, Incremental Mining, Frequent Pattern Minung, High Utility Mining, Constraint Mining

Statistical modeling of wet and dry spell frequencies over North-East India

In this paper an attempt has been made to develop a discrete precipitation model for the daily series of precipitation occurrences over North East India. The point of approach is to model the duration of consecutive dry and wet days i.e. spell, instead of individual wet and dry days. Various distributions viz. uniform, geometric, logarithmic, negative binomial, Poisson and Markov chain of order one and two, Eggenberger-Polya distribution have been fitted to describe the wet and dry spell frequencies of occurrences. The models are fitted to the observed data of seven stations namely Imphal, Mohanbari, Guwahati, Cherrapunji, Silcoorie, North Bank and Tocklai (Jorhat) of North-East India with pronounced attention to summer monsoon season. The goodness of fit of the proposed model has been tested using Kolmogorov-Smirnov test. It is observed that Eggenberger-Polya distribution fairly fits wet and dry spell frequencies and can be used in the future for an estimation of the wet and dry spells in the area under study

A fuzzy based Lagrangian twin parametric-margin support vector machine (FLTPMSVM)

© 2017 IEEE. In the spirit of twin parametric-margin support vector machine (TPMSVM) and support vector machine based on fuzzy membership values (FSVM), a new method termed as fuzzy based Lagrangian twin parametric-margin support vector machine (FLTPMSVM) is proposed in this paper to reduce the effect of the outliers. In FLTPMSVM, we assign the weights to each data samples on the basis of fuzzy membership values to reduce the effect of outliers. Also, we consider the square of the 2-norm of slack variables to make the objective function strongly convex and find the solution of the proposed FLTPMSVM by solving simple linearly convergent iterative schemes instead of solving a pair of quadratic programming problems as in case of SVM, TWSVM, FTSVM and TPMSVM. No need of external toolbox is required for FLTPMSVM. The numerical experiments are performed on artificial as well as well known real-world datasets which show that our proposed FLTPMSVM is having better generalization performance and less training cost in comparison to support vector machine, twin support vector machine, fuzzy twin support vector machine and twin parametric-margin support vector machine

Review of hydrologic and hydraulic studies and unsteady flow routing of the 1993 flood in the Upper Mississippi River system

"December 1997.""A division of the Illinois Department of Natural Resources.

Reference Ranges for Serum Uric Acid among Healthy Assamese People

This study was designed to establish reference ranges for serum uric acid among healthy adult Assamese population. Samples from 1470 aged 35–86 years were used to establish age and sex related reference range by the centile method (central 95 percentile) for serum uric acid level. There were 51% (n=754) males and 49% (n=716) females; 75.9% (n=1115) of them were from urban area and the rest 24.1% (n=355) were from the rural area. Majority of the population were nonvegetarian (98.6%, n=1450) and only 1.4% (n=20) were vegetarian. The mean age, weight, height, and uric acid of the studied group were 53.6±11.3 years, 62.6±10.5 kg, 160±9.4 cm, and 5.5±1.4 mg/dL, respectively. There is a statistically significant difference in the mean value of the abovementioned parameters between male and female. The observed reference range of uric acid in the population is 2.6–8.2 mg/dL which is wider than the current reference range used in the laboratory. Except gender (P<0.0001), we did not find any significant relation of uric acid with other selected factors