Ranking the spreading influence of nodes in complex networks: An extended weighted degree centrality based on a remaining minimum degree decomposition

Ranking the spreading influence of nodes is crucial for developing strategies to control the spreading process on complex networks. In this letter, we define, for the first time, a remaining minimum degree (RMD) decomposition by removing the node(s) with the minimum degree iteratively. Based on the RMD decomposition, a weighted degree (WD) is presented by utilizing the RMD indices of the nearest neighbors of a node. WD assigns a weight to each degree of this node, which can distinguish the contribution of each degree to the spreading influence. Further, an extended weighted degree (EWD) centrality is proposed by extending the WD of the nearest neighbors of a node. Assuming that the spreading process on networks follows the Susceptible-Infectious-Recovered (SIR) model, we perform extensive experiments on a series of synthetic and real networks to comprehensively evaluate the performance of EWD and other eleven representative measures. The experimental results show that EWD is a relatively efficient measure in running efficiency, it exposes an advantage in accuracy in the networks with a relatively small degree heterogeneity, as well as exposes a competitive performance in resolution.     

D − center in quantum well with strong magnetic field

We consider a system composed of two electrons moving in an infinite quantum well with an ionized donor in the presence of a strong magnetic field. The binding energies of the ground state and some low-lying excited states are calculated with a variational method. The effects of the well-width on the binding energies of this system are investigated. Our results are suitable for both two-dimensional and three-dimensional conditions. When the well-width goes to infinite, the results go to the results for three-dimensional case. And when the wellwidth goes to zero, the results are in agreement with that for two-dimensional condition.     

Macrocyclic Pyrrolizidine Alkaloids and Silphiperfolanol Angelate Esters from Solanecio mannii

Three new compounds, the silphiperfolanol angelate ester umutagarananol ( 1 ), the macrocyclic pyrrolizidine alkaloids umutagarinine A and B ( 2 – 3 ), and five known secondary metabolites ( 4 – 8 ) were isolated from the CH₂Cl₂−MeOH (1 : 1) extract of the roots and the stem bark of Solanecio mannii (Hook. f.) (Asteraceae). The isolated compounds were characterized by NMR and IR spectroscopic, and mass spectrometric analyses, whereas the relative stereochemistry of 4 was established by NAMFIS-based combined computational and solution NMR analysis. Synthetic modification of 5 provided two new compounds, 2-angeloyloxy-4,8-epoxypresilphiperfolane ( 9 ) and 2-angeloyloxy-4,8-epoxypresilphi-perfolane ( 10 ). The crude extracts and the isolated constituents showed weak antibacterial activities (EC₅₀ 0.7–13.3 mM) against the Gram-negative Escherichia coli and the Gram-positive Bacillus subtilis. Compounds 2 , 3 and 4 exhibited strong cytotoxicity against MCF-7 human breast cancer cells, with EC₅₀ values of 35.6, 21.7 and 12.5 μM, respectively.     

Prediction of gas chromatographic retention indices by the use of radial basis function neural networks

A new method for the prediction of retention indices for a diverse set of compounds from their physicochemical parameters has been proposed. The two used input parameters for representing molecular properties are boiling point and molar volume. Models relating relationships between physicochemical parameters and retention indices of compounds are constructed by means of radial basis function neural networks. To get the best prediction results, some strategies are also employed to optimize the topology and learning parameters of the RBFNNs. For the test set, a predictive correlation coefficient R=0.9910 and root mean squared error of 14.1 are obtained. Results show that radial basis function networks can give satisfactory prediction ability and its optimization is less-time consuming and easy to implement.     

试谈多媒体技术与物理实验的整合

阐述了引进多媒体技术辅助物理实验教学的必要性及其优势,分析了多媒体技术与物理实验整合过程中存在的一些问题,以及结合笔者调查数据分析教师的信息技术水平和多媒体资源整合情况,最后提出一些建议.     

稀土烧绿石型催化剂上CO2重整CH4制合成气反应研究

通过溶胶-凝胶法合成了La-Zr-O和Ni-La-Zr-O体系催化剂,采用XRD,BET和TPR技术对其物理化学性质进行了表征,以CO2重整CH4制合成气反应为探针反应对其重整活性进行了研究.结果表明,在空气中1100 ℃焙烧后,La-Zr-O体系催化剂形成了单相烧绿石结构复合氧化物,对CO2重整CH4制合成气反应表现出了优良的抗积炭能力,而以镍修饰后XRD方法只测量到了单相烧绿石结构La2Zr2O7衍射峰,说明NiO很有可能以无定形的细小晶粒均匀分散在La2Zr2O7表面.值得指出的是镍修饰后的烧绿石型催化剂对CO2重整CH4制合成气反应表现出了更加优良的催化活性,同时具有一定的抗积炭能力,优于相同方法制备的Ni-ZrO2和Ni-La2O3催化剂.     

Influence of growth and anneal conditions on the surface roughness of L10 Fe50PdxPt50−x thin films

We deposit Fe₅₀Pd_xPt_50−x alloy thin films by magnetron sputtering onto a TiN seed layer. Chemically ordered L1₀ films are obtained which display large perpendicular magnetic anisotropy. We find that the surface roughness of the film depends strongly on the growth and anneal conditions as well as the Pd composition of the film. Smooth films are obtained by deposition above the chemical ordering temperature and by removing Pd from the alloy.     

超声波对鱼卵孵化影响的研究

研究了一定频率与强度的超声波对金鱼(Carassius auratus)和大鳞副泥鳅(Parami-sgurnus dabryanus(Sauvagl))受精卵孵化的刺激作用,结果表明超声可以促进受精卵的发育,使胚体提前出膜,时间集中,并且仔鱼初期饲养成活率高。可以提高较低水温下的大鳞副泥鳅受精卵的孵化率,达50%。     

全硫取代三苯甲基自由基酯基衍生物与牛血清白蛋白的相互作用

采用荧光光谱、电子顺磁共振（EPR）波谱、紫外-可见吸收光谱和分子对接等技术研究了全硫取代三苯甲基（TAM）自由基酯基衍生物ET-03与牛血清白蛋白（BSA）的相互作用,发现ET--03与BSA能自发发生结合作用;主要以疏水作用力结合在BSA亚结构域ⅡA（位点Ⅰ）和亚结构域ⅢA（位点Ⅱ）上;ET-03对BSA的荧光猝灭效应为动态、静态混合猝灭机制,且可能存在非辐射能量转移.研究结果表明,酯基衍生化TAM自由基与白蛋白能自发结合,有望用于蛋白构效关系研究;同时也提示将TAM自由基酯基衍生物用于活体成像或自旋标记物时应考虑其与蛋白相互作用的影响.