Thermal Decomposition of 1,2-Dichloroethane in a Single-Pulse Shock Tube

在单脉冲激波管上,研究了1,2-二氯乙烷的热裂解.实验的激波条件为:温度区间1020 K＜T＜1190 K, 压力: P=0.12 MPa,实验时间τ=0.5 ms;实验气体为1,2-二氯乙烷稀释于Ar气中(3.95 mmol/L).以4-甲基-1-环己烯作为对比速率法实验的内标物,用4-甲基-1-环己烯开环反应的速率常数k=1015.3exp(-33400/T) s-1,以及从其产物的浓度推定出实验温度.经激波加热后的实验气体的终产物用气相色谱分析出主要成分为C2H3Cl,指示出主要反应通道为β消去反应.如把所有产物C2H3Cl都归于β消去反应,则可推定出表观之反应速率常数k1a=5.0×1013exp(-30000/T) s-1.对于由C-Cl键断键反应引发的链反应的可能影响做了分析研究.用了一种简便分析可推知在实验的温度范围内的低端(1020 K)链反应的影响可以忽略,而在其高端(1190 K)链反应将给出10%的终产物C2H3Cl的附加浓度,获得真实的β消去反应速率常数则必须把这部分予以扣除.经过这样的校正之后,最后得到CH2ClCH2Clβ消去反应速率常数为k1c=2.3×1013exp(-29200/T) s-1.

Simulating dynamic covariance structures for testing the adaptive behavior of variable selection algorithms (Invited Paper)

Variable selection for regression is a classical statistical problem, motivated by concerns that too large a number of covariates may bring about overfitting and unnecessarily high measurement costs. Novel difficulties arise in streaming contexts, where the correlation structure of the process may be drifting, in which case it must be constantly tracked so that selections may be revised accordingly. A particularly interesting phenomenon is that non-selected covariates become missing variables, inducing bias on subsequent decisions. This raises an intricate exploration-exploitation tradeoff, whose dependence on the covariance tracking algorithm and the choice of variable selection scheme is too complex to be dealt with analytically. We hence capitalise on the strength of simulations to explore this problem, taking the opportunity to tackle the difficult task of simulating dynamic correlation structures. © 2008 IEEE.

Models and algorithms for detection and tracking of coordinated groups

In this paper, we describe models and algorithms for detection and tracking of group and individual targets. We develop two novel group dynamical models, within a continuous time setting, that aim to mimic behavioural properties of groups. We also describe two possible ways of modeling interactions between closely using Markov Random Field (MRF) and repulsive forces. These can be combined together with a group structure transition model to create realistic evolving group models. We use a Markov Chain Monte Carlo (MCMC)-Particles Algorithm to perform sequential inference. Computer simulations demonstrate the ability of the algorithm to detect and track targets within groups, as well as infer the correct group structure over time. ©2008 IEEE.

Models and algorithms for tracking of maneuvering objects using variable rate particle filters

Standard algorithms in tracking and other state-space models assume identical and synchronous sampling rates for the state and measurement processes. However, real trajectories of objects are typically characterized by prolonged smooth sections, with sharp, but infrequent, changes. Thus, a more parsimonious representation of a target trajectory may be obtained by direct modeling of maneuver times in the state process, independently from the observation times. This is achieved by assuming the state arrival times to follow a random process, typically specified as Markovian, so that state points may be allocated along the trajectory according to the degree of variation observed. The resulting variable dimension state inference problem is solved by developing an efficient variable rate particle filtering algorithm to recursively update the posterior distribution of the state sequence as new data becomes available. The methodology is quite general and can be applied across many models where dynamic model uncertainty occurs on-line. Specific models are proposed for the dynamics of a moving object under internal forcing, expressed in terms of the intrinsic dynamics of the object. The performance of the algorithms with these dynamical models is demonstrated on several challenging maneuvering target tracking problems in clutter. © 2006 IEEE.

Hacia la aldea virtual : evolución de las tecnologías comunicativas y de los modelos antropológicos

Estudiar los medios de comunicación significa estudiar al hombre, la sociedad en la que vive, su evolución y las perspectivas de desarrollo futuro. Después de haber puntualizado brevemente los avances tecnológicos salientes en la historia del hombre y de haber identificado las características más relevantes de las diversas redefiniciones antropológicas que siguieron a la introducción de algunos nuevos medios, nos detendremos principalmente en el estudio de las peculiaridades de los instrumentos de comunicación de masa más modernos, de la fotografía al cine, de la televisión a Internet. El objetivo principal de este aporte, lejos de querer proveer sistemas teóricos definitivos, es trazar un conjunto coherente de temáticas capaces de suscitar nuevas y proficuas cuestiones y de promover un debate abierto a los más diversos aportes, con particular atención a los avances de las comunicaciones telemáticas.

Pattern Recognition With Weighted Complex Networks

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.

Significant Residue Features Revealed By Eigenvalue Decomposition Analysis Of Blosum Matrices

Here we attempt to characterize protein evolution by residue features which dominate residue substitution in homologous proteins. Evolutionary information contained in residue substitution matrix is abstracted with the method of eigenvalue decomposition. Top eigenvectors in the eigenvalue spectrums are analyzed as function of the level of similarity, i.e. sequence identity (SI) between homologous proteins. It is found that hydrophobicity and volume are two significant residue features conserved in protein evolution. There is a transition point at SI approximate to 45%. Residue hydrophobicity is a feature governing residue substitution as SI >= 45%. Whereas below this SI level, residue volume is a dominant feature. (C) 2007 Elsevier B.V. All rights reserved.

Streamline topology and dilute particle dynamics in a Karman vortex street flow

Three types of streamline topology in a Karman vortex street flow are shown under the variation of spatial parameters. For the motion of dilute particles in the Karman vortex street flow, there exist a route of bifurcation to a chaotic orbit and more attractors in a bifurcation diagram for the proportion of particle density to fluid density. Along with the increase of spatial parameters in the flow field, the bifurcation process is suspended, as well as more and more attractors emerge. In the motion of dilute particles, a drag term and gravity term dominate and result in the bifurcation phenomenon.

Proper orthogonal decomposition of oscillatory Marangoni flow in half-zone liquid bridges of low-Pr fluids

Proper orthogonal decomposition (POD) using method of snapshots was performed on three different types of oscillatory Marangoni flows in half-zone liquid bridges of low-Pr fluid (Pr = 0.01). For each oscillation type, a series of characteristic modes (eigenfunctions) have been extracted from the velocity and temperature disturbances, and the POD provided spatial structures of the eigenfunctions, their oscillation frequencies, amplitudes, and phase shifts between them. The present analyses revealed the common features of the characteristic modes for different oscillation modes: four major velocity eigenfunctions captured more than 99% of the velocity fluctuation energy form two pairs, one of which is the most energetic. Different from the velocity disturbance, one of the major temperature eigenfunctions makes the dominant contribution to the temperature fluctuation energy. On the other hand, within the most energetic velocity eigenfuction pair, the two eigenfunctions have similar spatial structures and were tightly coupled to oscillate with the same frequency, and it was determined that the spatial structures and phase shifts of the eigenfunctions produced the different oscillatory disturbances. The interaction of other major modes only enriches the secondary spatio-temporal structures of the oscillatory disturbances. Moreover, the present analyses imply that the oscillatory disturbance, which is hydrodynamic in nature, primarily originates from the interior of the liquid bridge. (C) 2007 Elsevier B.V. All rights reserved.