Noether symmetry in f(T)f(T) teleparallel gravity

Hao Wei et.al. has claimed in $\mathrm{Phys. Lett. \textbf{B707}, 298 (2012)}$ that Noether symmetry in the context of teleparallel $f(T)$ theory of gravity admits $f(T)\propto T^{n}$, (where $n$ is an arbitrary) in matter domain era in Friedmann- Robertson universe. But, it has been shown that the conserved current obtained under the process does not satisfy the field equations in general. Here, it is shown that Noether Symmetry admits $f(T)\propto T^\frac{3}{2}$ along with a conserved current $a \dot a T^\frac{1}{2}$ in teleparallel $f(T)$ gravity. Thus, their claim is not correct.Comment: Appear in Physics Letters B, 9 Page

Effect of the strength of attraction between nanoparticles on Wormlike micelle-nanoparticle system

The nanoparticle-Equilibrium polymer (or Wormlike micellar) system shows morphological changes from percolating network-like structures to non-percolating clusters with a change in the minimum approaching distance (EVP-excluded volume parameter) between nanoparticles and the matrix of equilibrium polymers. The shape anisotropy of nanoparticle clusters can be controlled by changing the polymer density. In this paper, the synergistic self-assembly of nanoparticles inside equilibrium polymeric matrix (or Wormlike micellar matrix) is investigated with respect to the change in the strength of attractive interaction between nanoparticles. A shift in the point of morphological transformation of the system to lower values of EVP as a result of a decrease in the strength of the attractive nanoparticle interaction is reported. We show that the absence of the attractive interaction between nanoparticles leads to the low packing of nanoparticle structures, but does not change the morphological behaviour of the system. We also report the formation of the system spanning sheet-like arrangement of nanoparticles which are arranged in alternate layers of matrix polymers and nanoparticles

Magnetotransport properties of 8-Pmmn borophene: effects of Hall field and strain

The polymorph of ${\rm 8-Pmmn}$ borophene is an anisotropic Dirac material with tilted Dirac cones at two valleys. The tilting of the Dirac cones at two valleys are in opposite direction, which manifests itself via valley dependent Landau levels in presence of an in-plane electric field (Hall field). The sensitivity of the Landau levels on valley index causes valley polarized magnetotransport properties in presence of a Hall field, which is in contrast to the monolayer graphene with isotropic non-tilted Dirac cones. The longitudinal conductivity and Hall conductivity are evaluated by using linear response theory in low temperature regime. An analytical approximate form of longitudinal conductivity is also obtained to reveal how the tilting of the Dirac cones affects the frequency of longitudinal conductivity oscillation (Shubnikov-de Hass oscillation). On the other hand, Hall conductivity exhibits graphene-like plateau excepts the appearance of valley dependent steps which is purely attributed to the Hall field induced lifting valley degeneracy in Landau levels. Another noticeable point is that if the real magnetic field is replaced by the strain induced pseudo magnetic field then the electric field looses its ability to cause valley polarized transport.Comment: 11 pages, 11 figures. To appear in Journal of Physics: Condens. Matte

Fighting Zoonotic, Rabies and Public Health in Colonial India

Rabies is the oldest Zoonotic diseases in the world and one of the most important Zoonotic diseases in India. It was one of the most difficult problems confronted both by the medical and veterinary authorities in colonial India. The disease is transmitted from animal to animal and from animal to man through saliva. More than 90 per cent of cases of human rabies are transmitted by dogs which was a major concern of public health. A few British officials and soldiers were bitten by dogs during the colonial period. As a result, they suffered from rabies. As ownerless dogs were infested all through the country, the disease prevailed largely at that time. Gradually, rabies became a problem to the colonial Government. Louis Pasteur obtained his first success against rabies through vaccination in 1885. At that time, Dr. Lingard, Bacteriologist, had proposed to introduce a system of anti-rabies vaccination in the Bacteriological laboratory at Poona. A similar proposal was also submitted by J.H.B. Hallen, a civil Surgeon in 1890. Later, five institutes were established in India for anti-rabies treatment. Gradually rabies patients were treated at pasture institute. Thus, this paper examines impact of rabies, treatment and veterinary public health policy in colonial India. The great Zoonotic waves of rabies that suffered public health in colonial India are also focus of this paper

On construction of robust composite indices by linear aggregation

In this paper we construct thirteen different types of composite indices by linear combination of indicator variables (with and without outliers/data corruption). Weights of different indicator variables are obtained by maximization of the sum of squared (and, alternatively, absolute) correlation coefficients of the composite indices with the constituent indicator variables. Seven different types of correlation are used: Karl Pearson, Spearman, Signum, Bradley, Shevlyakov, Campbell and modified Campbell. Composite indices have also been constructed by maximization of the minimal correlation. We find that performance of indices based on robust measures of correlation such as modified Campbell and Spearman, as well as that of the maxi-min based method, is excellent. Using these methods we obtain composite indices that are autochthonously sensitive and allochthonously robust. This paper also justifies a use of simple mean-based composite indices, often used in construction of human development index.Composite index; linear aggregation; principal components; robust correlation; Spearman, Signum; Bradley; Shevlyakov; Campbell; Hampel; outliers; mutilation of data

Estimation under Multicollinearity: Application of Restricted Liu and Maximum Entropy Estimators to the Portland Cement Dataset

A high degree of multicollinearity among the explanatory variables severely impairs estimation of regression coefficients by the Ordinary Least Squares. Several methods have been suggested to ameliorate the deleterious effects of multicollinearity. In this paper we aim at comparing the Restricted Liu estimates of regression coefficients with those obtained by applying the Maximum Entropy Leuven (MEL) family of estimators on the widely analyzed dataset on Portland cement. This dataset has been obtained from an experimental investigation of the heat evolved during the setting and hardening of Portland cements of varied composition and the dependence of this heat on the percentage of four compounds in the clinkers from which the cement was produced. The relevance of the relationship between the heat evolved and the chemical processes undergone while setting takes place is best stated in the words of Woods et al.: "This property is of interest in the construction of massive works as dams, in which the great thickness severely hinder the outflow of the heat. The consequent rise in temperature while the cement is hardening may result in contractions and cracking when the eventual cooling to the surrounding temperature takes place." Two alternative models have been formulated, the one with an intercept term (non-homogenous) that exhibits a very high degree of multicollinearity and the other with no intercept term (extended homogenous) that characterizes perfect multicollinearity. Our findings suggest that several members of the MEL family of estimators outperform the OLS and the Restricted Liu estimators. The MEL estimators perform well even when perfect multicollinearity is there. A few of them may outperform the Minimum Norm LS (OLS+) estimator. Since the MEL estimators do not seek extra information from the analyst, they are easy to apply. Therefore, one may rely on the MEL estimators for obtaining the coefficients of a linear regression model under the conditions of severe (including perfect) multicollinearity among the explanatory variables.Multicollinearity; Estimator; Restricted Liu; Maximum Entropy Leuven estimator; MEL family; Modular Maximum Entropy Leuven estimator; Least Absolute Deviation; Minimum Norm Least Squares; Moore-Penrose inverse; Portland cement dataset