Optical investigation of cavitation erosion by laser-induced bubble collapse

By means of a new force sensor based on optical beam deflection (OBD), the mechanical effects of laser-matter interaction underwater at different incident laser energy are investigated in detail. The experimental results show that a target underwater is impacted in turn by laser-plasma ablation force and high-speed liquid-jet impulse induced by bubbles collapse in the vicinity of a solid boundary. Furthermore, the amplitudes of the two forces increase monotonously with laser energy. According to the ablation force detected by the experiment and the theoretical relationship between laser intensity and ablation pressure, the value of liquid-jet impact against a solid boundary can be easily obtained. In addition, based on the model of a collapsing bubble, some characteristic parameters, such as the liquid-jet impact velocity, the maximum bubble radius, the bubble energy can also be obtained at different laser energy, which are valuable in the corresponding research fields.     

Characterization at a local scale of a laser-shock peened aluminum alloy surface

The influence of a laser shock peening mechanical surface treatment on 2050-T8 aluminum alloy has been investigated, mostly using Scanning Kelvin Probe Force Microscopy. Volta potential difference maps around Al(CuFeMn) precipitates were performed before and after laser-shock peening to determine the influence of laser treatment versus galvanic coupling near precipitates, and resulting pit initiations. It has been shown that laser shock peening either preserves or reduces precipitate-matrix Volta potentials gradients, which in this later case, and correlated to recent corrosion electrochemical investigations, could explain corrosion improvement obtained after laser-shock peening treatments of aluminum alloys. The influence of crystal orientation and plastic deformation, and more specifically the effect of laser-induced compressive residual stresses or work-hardening, on the Volta potential values and on the pitting corrosion behavior was also addressed.     

Analyzing complex networks evolution through Information Theory quantifiers

A methodology to analyze dynamical changes in complex networks based on Information Theory quantifiers is proposed. The square root of the Jensen-Shannon divergence, a measure of dissimilarity between two probability distributions, and the MPR Statistical Complexity are used to quantify states in the network evolution process. Three cases are analyzed, the Watts-Strogatz model, a gene network during the progression of Alzheimer's disease and a climate network for the Tropical Pacific region to study the El Niño/Southern Oscillation (ENSO) dynamic. We find that the proposed quantifiers are able not only to capture changes in the dynamics of the processes but also to quantify and compare states in their evolution.     

Risk estimation of infectious diseases determines the effectiveness of the control strategy

Usually, whether to take vaccination or not is a voluntary decision, which is determined by many factors, from societal factors (such as religious belief and human rights) to individual preferences (including psychology and altruism). Facing the outbreaks of infectious diseases, different people often have different estimations on the risk of infectious diseases. So, some persons are willing to vaccinate, but other persons are willing to take risks. In this paper, we establish two different risk assessment systems using the technique of dynamic programming, and then compare the effects of the two different systems on the prevention of diseases on complex networks. One is that the perceived probability of being infected for each individual is the same (uniform case). The other is that the perceived probability of being infected is positively correlated to individual degrees (preferential case). We show that these two risk assessment systems can yield completely different results, such as, the effectiveness of controlling diseases, the time evolution of the number of infections, and so on.     

Social structure of Facebook networks

We study the social structure of Facebook “friendship” networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes–gender, class year, major, high school, and residence–at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on user characteristics. We thereby examine the relative importance of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further.     

SPR-based Markov chain method for degree distributions of evolving networks

In this paper, we develop a stochastic process rules (SPR) based Markov chain method to calculate the degree distributions of evolving networks. This new approach overcomes two shortcomings of Shi, Chen and Liu’s use of the Markov chain method (Shi et al. 2005 [21]). In addition we show how an SPR-based Markov chain method can be effectively used to calculate degree distributions of random birth-and-death networks, which we believe to be novel. First SPR are introduced to replace traditional evolving rules (TR), making it possible to compute degree distributions in one sample space. Then the SPR-based Markov chain method is introduced and tested by using it to calculate two kinds of evolving network. Finally and most importantly, the SPR-based method is applied to the problem of calculating the degree distributions of random birth-and-death networks.     

Small-world and the growing properties of the Chinese railway network

By using the tools of statistical physics and recent investigations of the scaling properties of different complex networks, the structural and evolving properties of the Chinese railway network (CRN) is studied. It has been verified that the CRN has the same small-world properties of the Indian railway network (IRN). According to the class of small-world networks, we believe the CRN is a single scale. In addition, a novel result is obtained. Measurements on the CRN indicate that the rate at which nodes acquire links depends on the node’s degree and follows a power law.      

酵母蛋白质网络的动力学性质

蛋白质-蛋白质、蛋白质-DNA相互作用网络决定了细胞中各种关键功能的执行．基于芽殖酵母(budding yeast saccharomyces cerevisiae)的蛋白质-蛋白质相互作用网络数据和相关的实验文献，我们建立了调控细胞周期和生命周期(cell cycle and life cycle)的蛋白质网络，并利用离散模型研究了该网络的动力学性质，研究表明：细胞周期网络的动力学性质具有很强的稳定性，约94％的蛋白质初态将演化到对应于生物学G1基态的稳定态，使其成为惟一的全局吸引点；同时，绝大多数的初态的演化路径都通过由G1激发态到G1基态的细胞周期演化路径，使细胞周期路径成为全局性的“吸引”路径。     

Neural networks for prediction of acoustical properties of polyurethane foams

This paper presents a study of neural networks for prediction of acoustical properties of polyurethane foams. The proposed neural network model of the foam uses easily measured parameters such as frequency, airflow resistivity and density to predict multiple acoustical properties including the sound absorption coefficient and the surface impedance. Such a model is quite robust in the sense that it can be used to develop models for many different classes of materials with different sets of input and output parameters. The current neural network model of the foam is empirical and provides a useful complement to the existing analytical and numerical approaches.