Sensing behavior of acetone vapors on TiO2_2 nanostructures --- application of density functional theory

The electronic properties of TiO$_2$ nanostructure are explored using density functional theory. The adsorption properties of acetone on TiO$_2$ nanostructure are studied in terms of adsorption energy, average energy gap variation and Mulliken charge transfer. The density of states spectrum and the band structure clearly reveals the adsorption of acetone on TiO$_2$ nanostructures. The variation in the energy gap and changes in the density of charge are observed upon adsorption of acetone on n-type TiO$_2$ base material. The results of DOS spectrum reveal that the transfer of electrons takes place between acetone vapor and TiO$_2$ base material. The findings show that the adsorption property of acetone is more favorable on TiO$_2$ nanostructure. Suitable adsorption sites of acetone on TiO$_2$ nanostructure are identified at atomistic level. From the results, it is confirmed that TiO$_2$ nanostructure can be efficiently utilized as a sensing element for the detection of acetone vapor in a mixed environment.Comment: 13 pages, 14 figures, 3 table

Super Fibonacci Graceful Labeling of Some Special Class of Graphs

A Fibonacci graceful labeling and a super Fibonacci graceful labeling on graphs were introduced by Kathiresan and Amutha in 2006

Functional connectivity in relation to motor performance and recovery after stroke.

Plasticity after stroke has traditionally been studied by observing changes only in the spatial distribution and laterality of focal brain activation during affected limb movement. However, neural reorganization is multifaceted and our understanding may be enhanced by examining dynamics of activity within large-scale networks involved in sensorimotor control of the limbs. Here, we review functional connectivity as a promising means of assessing the consequences of a stroke lesion on the transfer of activity within large-scale neural networks. We first provide a brief overview of techniques used to assess functional connectivity in subjects with stroke. Next, we review task-related and resting-state functional connectivity studies that demonstrate a lesion-induced disruption of neural networks, the relationship of the extent of this disruption with motor performance, and the potential for network reorganization in the presence of a stroke lesion. We conclude with suggestions for future research and theories that may enhance the interpretation of changing functional connectivity. Overall findings suggest that a network level assessment provides a useful framework to examine brain reorganization and to potentially better predict behavioral outcomes following stroke

Lucas Gracefulness of Almost and Nearly for Some Graphs

By a graph, we mean a finite undirected graph without loops or multiple edges

Graphoidal Tree d - Cover

Acharya and Sampathkumar defined a graphoidal cover as a partition of edges into internally disjoint (not necessarily open) paths. If we consider only open paths in the above definition then we call it as a graphoidal path cover

Non-parametric statistical thresholding for sparse magnetoencephalography source reconstructions.

Uncovering brain activity from magnetoencephalography (MEG) data requires solving an ill-posed inverse problem, greatly confounded by noise, interference, and correlated sources. Sparse reconstruction algorithms, such as Champagne, show great promise in that they provide focal brain activations robust to these confounds. In this paper, we address the technical considerations of statistically thresholding brain images obtained from sparse reconstruction algorithms. The source power distribution of sparse algorithms makes this class of algorithms ill-suited to "conventional" techniques. We propose two non-parametric resampling methods hypothesized to be compatible with sparse algorithms. The first adapts the maximal statistic procedure to sparse reconstruction results and the second departs from the maximal statistic, putting forth a less stringent procedure that protects against spurious peaks. Simulated MEG data and three real data sets are utilized to demonstrate the efficacy of the proposed methods. Two sparse algorithms, Champagne and generalized minimum-current estimation (G-MCE), are compared to two non-sparse algorithms, a variant of minimum-norm estimation, sLORETA, and an adaptive beamformer. The results, in general, demonstrate that the already sparse images obtained from Champagne and G-MCE are further thresholded by both proposed statistical thresholding procedures. While non-sparse algorithms are thresholded by the maximal statistic procedure, they are not made sparse. The work presented here is one of the first attempts to address the problem of statistically thresholding sparse reconstructions, and aims to improve upon this already advantageous and powerful class of algorithm

Analysis of neutrosophic multiple regression

The idea of Neutrosophic statistics is utilized for the analysis of the uncertainty observation data. Neutrosophic multiple regression is one of a vital roles in the analysis of the impact between the dependent and independent variables. The Neutrosophic regression equation is useful to predict the future value of the dependent variable. This paper to predict the students' performance in campus interviews is based on aptitude and personality tests, which measures conscientiousness, and predict the future trend. Neutrosophic multiple regression is to authenticate the claim and examine the null hypothesis using the F-test. This study exhibits that Neutrosophic multiple regression is the most efficient model for uncertainty rather than the classical regression model