Emergence, Evolution and Scaling of Online Social Networks

This work was partially supported by AFOSR under Grant No. FA9550-10-1-0083, NSF under Grant No. CDI-1026710, NSF of China under Grants Nos. 61473060 and 11275003, and NBRPC under Grant No. 2010CB731403. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Analysis of the strong vertices of ΣcND∗\Sigma_cND^{*} and ΣbNB∗\Sigma_bNB^{*} in QCD sum rules

The strong coupling constant is an important parameter which can help us to understand the strong decay behaviors of baryons. In our previous work, we have analyzed strong vertices $\Sigma_{c}^{*}ND$, $\Sigma_{b}^{*}NB$, $\Sigma_{c}ND$, $\Sigma_{b}NB$ in QCD sum rules. Following these work, we further analyze the strong vertices $\Sigma_{c}ND^{*}$ and $\Sigma_{b}NB^{*}$ using the three-point QCD sum rules under Dirac structures $q\!\!\!/p\!\!\!/\gamma_{\alpha}$ and $q\!\!\!/p\!\!\!/p_{\alpha}$. In this work, we first calculate strong form factors considering contributions of the perturbative part and the condensate terms $\langle\overline{q}q\rangle$, $\langle\frac{\alpha_{s}}{\pi}GG\rangle$ and $\langle\overline{q}g_{s}\sigma Gq\rangle$. Then, these form factors are used to fit into analytical functions. According to these functions, we finally determine the values of the strong coupling constants for these two vertices $\Sigma_{c}ND^{*}$ and $\Sigma_{b}NB^{*}$.Comment: arXiv admin note: text overlap with arXiv:1705.0322

A Modified KNN Algorithm for Activity Recognition in Smart Home

Nowadays, more and more elderly people cannot take care of themselves, and feel uncomfortable in daily activities. Smart home systems can help to improve daily life of elderly people. A smart home can bring residents a more comfortable living environment by recognizing the daily activities automatically. In this paper, in order to improve the accuracy of activity recognition in smart homes, we conduct some improvements in data preprocess and recognition phase, and more importantly, a novel sensor segmentation method and a modified KNN algorithm are proposed. The segmentation algorithm employs segment sensor data into fragments based on predefined activity knowledge, and then the proposed modified KNN algorithm uses center distances as a measure for classification. We also conduct comprehensive experiments, and the results demonstrate that the proposed method outperforms the other classifiers

2-[6,8-Dibromo-3-(4-hy­droxy­cyclo­hex­yl)-1,2,3,4-tetra­hydro­quinazolin-2-yl]phenol methanol 0.25-solvate

The title compound, C20H22Br2N2O2·0.25CH4O, was synthesized by the condensation reaction of salicyl­aldehyde with 4-(2-amino-3,5-dibromo­benzyl­amino)­cyclo­hexa­nol in methanol. There are four independent main mol­ecules and two half-occupied methanol solvent mol­ecules in the asymmetric unit. The dihedral angles between the two benzene rings in the four mol­ecules are 87.8 (6), 86.6 (6), 89.3 (6) and 83.1 (6)°. Each mol­ecule features an intra­molecular O—H⋯N hydrogen bond and a short N—H⋯Br link. In the crystal components are linked by O—H⋯O hydrogen bonds

Oxonium 2-carb­oxy-3-(2-fur­yl)acrylate

In the title compound, H3O+·C8H5O5 −, neighbouring cations and anions are linked by O—H⋯O hydrogen bonds, forming a one-dimensional chain framework along [001]. The crystal structure is further stabilized by π–π inter­actions, with centroid–centroid distances of 3.734 (3) Å

2-(Chloro­meth­yl)benzimidazolium chloride

The structure of title compound, C8H8ClN2 +·Cl−, comprises discrete ions which are inter­connected by N—H⋯Cl hydrogen bonds, leading to a neutral one-dimensional network in [001]. This hydrogen bonding appears to complement π–π stacking inter­actions [centroid–centroid distances 3.768 (2) and 3.551 (2) Å] and helps to stabilize the structure further