Unsupervised classification of eclipsing binary light curves through k-medoids clustering.

This paper proposes k-medoids clustering method to reveal the distinct groups of 1318 variable stars in the Galaxy based on their light curves, where each light curve represents the graph of brightness of the star against time. To overcome the deficiencies of subjective traditional classification, we separate the stars more scientifically according to their geometrical configuration and show that our approach outperforms the existing classification schemes in astronomy. It results in two optimum groups of eclipsing binaries corresponding to bright, massive systems and fainter, less massive systems.

Impact of adult mosquito control on dengue prevalence in a multi-patch setting: A case study in Kolkata (2014-2015).

Dengue is one of the deadliest mosquito-borne disease prevalent mainly in tropical and sub-tropical regions. Controlling the spread of this disease becomes a major concern to the public health authority. World Health Organization (WHO) adopted several mosquito control strategies to reduce the disease prevalence. In this work, a general multi-patch non-autonomous dengue model is formulated to capture the temporal and spatial transmission mechanism of the disease and the effectiveness of different adult mosquito control strategies in reducing dengue prevalence is evaluated. During the period (2014-2015) the dengue situation of Kolkata which is one of the most dengue affected city in India is considered in our study. Depending on geographical location, Kolkata is divided into five regions and our model is fitted to the monthly dengue cases of these five regions during the above-mentioned period. By considering control specific characteristics (e.g. efficacy, environment persistence) of the mosquito control strategies, we study the efficiency of three adult mosquito controls and their combined effect in reducing dengue prevalence. From our study, it is observed that control with higher environment persistence performs better in comparison to the controls having low environment persistence. It is also observed that, connectedness between the regions play a key role in the effectiveness of the control strategies.

On application of constitutional descriptors for merging of quinoxaline data sets using linear statistical methods.

The present paper is an attempt for unifying two different quinoxaline data sets with a wide range of substituents in 2, 3, 7, and 8 positions having excellent antitubercular activities with a view to developing robust and reliable structure-activity relationships. The merging has been performed for these two sets of quinoxaline 1,4-di-N-oxides derivatives comprising 29 and 18 compounds, respectively, on the basis of constitutional descriptors, which denotes the structural characterization of the molecules. Principal component analysis was performed to see the distribution of the compounds from two data sets for the constitutional descriptors. The distribution of compounds in score plot based on constitutional descriptors suggests unification of quinoxaline data sets which is useful for the model development. Outlier detection was performed from the standpoint of residual analysis of the partial least squares regression models. The superiority of the constitutional descriptors over other calculated molecular descriptors has been established from the standpoint of leave-one-out cross-validation technique associated with partial least squares regression analysis. Internal validation through the leave-many-out methodology was also performed with good results, assuring the stability of the models. The results obtained from linear partial least squares regression analysis lead to a statistically significant and robust quantitative structure-activity relationship modeling.

An analysis of clustered categorical data--application in dental health.

Quite often in medical studies multiple discrete indicators are used to measure some characters that are defined only conceptually and are difficult to measure directly. Studies of this type exhibit categorical responses of dependent nature. Analysis of such categorical data appears to be extremely difficult (intractable) particularly in the presence of risk (causal) factors. In the present article, our purpose is to develop a latent mixture regression model for analysing such multivariate categorical data. Such a mixture model accommodates correlated and overdispersed data through the incorporation of random effects. Unfortunately, a full likelihood analysis is often hampered by the need for numerical integration. Two different procedures have been considered here. Both involve intensive computations. Numerical investigation has been carried out on the basis of a survey data covering 220 individuals from medical colleges in and around Calcutta (India). The purpose of the study is to compare tooth cleaning efficiency of brushes manufactured by different companies.