Pulse radiolysis study of the initial steps of the polymerization of cyclohexyl acrylate and cyclohexyl methacrylate

Pulse radiolysis with kinetic spectroscopic detection was applied to study the kinetics of the first steps of radiation induced polymerization of cyclohexyl acrylate and cyclohexyl methacrylate in cyclohexane solvent. The reactions were initiated by cyclohexyl radicals produced in the radiolysis of the solvent. The transient absorption spectra of the -carboxyalkyl type radicals produced in addition reaction show maxima around 300 nm. This shifts to longer wavelength with time after the pulse. This phenomenon was explained by the oligomerization reaction. From the kinetic curves average rate coefficients of termination for the oligomer radicals (2k_t) were determined as a function of time elapsed after the electron pulse. The values obtained were compared with those calculated for other (acrylate and methacrylate type) monomers.     

静脉滴注的临床决策

提出了静脉滴注的动力学一般模型，讨论了模型的性质，在给药剂量确定的条件下，探讨了调整滴注速率使治疗效果达到最佳的临床决策方法。     

Kinetics of phenol hydrogenation over supported palladium catalyst

Kinetics of vapor phase hydrogenation of phenol to cyclohexanone over Pd/MgO system has been studied in a flow microreactor under normal atmospheric pressure. The reaction rate is found to be negative order with respect to the partial pressure of phenol and has increased from −0.5 to 0.5 with increasing temperature (473 to 563 K). The apparent activation energy (E_a) of the process is found to be close to 65 kJ per mol. On the basis of kinetic results a surface mechanism is proposed.     

Kalman filtering of a space-time Markov random field

In this paper, finite-dimensional recursive filters for space-time Markov random fields are derived. These filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of the parameters of the model.     

A stochastic algorithm using one sample point per iteration and diminishing stepsizes

A stochastic algorithm for finding stationary points of real-valued functions defined on a Euclidean space is analyzed. It is based on the Robbins-Monro stochastic approximation procedure. Gradient evaluations are done by means of Monte Carlo simulations. At each iteratex _i , one sample point is drawn from an underlying probability space, based on which the gradient is approximated. The descent direction is against the approximation of the gradient, and the stepsize is 1/i. It is shown that, under broad conditions, w.p.1 if the sequence of iteratesx ₁,x ₂,...generated by the algorithm is bounded, then all of its accumulation points are stationary.     

On the optimal allocation of two active redundancies in a two-component series system

Components C₁ and C₂ form a series system. Suppose we can allocate the spare R₁ in parallel with C₁ and the spare R₂ in parallel with C₂, or otherwise, allocate R₁ with C₂ and R₂ with C₁. In this paper, we compare these two options using hazard rate ordering.     

Stochastic techniques for global optimization: A survey of recent advances

In this paper stochastic algorithms for global optimization are reviewed. After a brief introduction on random-search techniques, a more detailed analysis is carried out on the application of simulated annealing to continuous global optimization. The aim of such an analysis is mainly that of presenting recent papers on the subject, which have received only scarce attention in the most recent published surveys. Finally a very brief presentation of clustering techniques is given.     

A priori tests of a stochastic mode reduction strategy

Several a priori tests of a systematic stochastic mode reduction procedure recently devised by the authors [Proc. Natl. Acad. Sci. 96 (1999) 14687; Commun. Pure Appl. Math. 54 (2001) 891] are developed here. In this procedure, reduced stochastic equations for a smaller collections of resolved variables are derived systematically for complex nonlinear systems with many degrees of freedom and a large collection of unresolved variables. While the above approach is mathematically rigorous in the limit when the ratio of correlation times between the resolved and the unresolved variables is arbitrary small, it is shown here on a systematic hierarchy of models that this ratio can be surprisingly big. Typically, the systematic reduced stochastic modeling yields quantitatively realistic dynamics for ratios as large as 1/2. The examples studied here vary from instructive stochastic triad models to prototype complex systems with many degrees of freedom utilizing the truncated Burgers–Hopf equations as a nonlinear heat bath. Systematic quantitative tests for the stochastic modeling procedure are developed here which involve the stationary distribution and the two-time correlations for the second and fourth moments including the resolved variables and the energy in the resolved variables. In an important illustrative example presented here, the nonlinear original system involves 102 degrees of freedom and the reduced stochastic model predicted by the theory for two resolved variables involves both nonlinear interaction and multiplicative noises. Even for large value of the correlation time ratio of the order of 1/2, the reduced stochastic model with two degrees of freedom captures the essentially nonlinear and non-Gaussian statistics of the original nonlinear systems with 102 modes extremely well. Furthermore, it is shown here that the standard regression fitting of the second-order correlations alone fails to reproduce the nonlinear stochastic dynamics in this example.