A statistical study of the analysis of congested spectra by total spectrum fitting

Heavily overlapped, or congested spectra often display much structure but few individual “lines.” Methods have been devised for analyzing such spectra through nonlinear least-squares fitting of the intensity as a function of wavelength or wavenumber. Such total spectrum fitting (TSF) methods are examined statistically for a simple diatomic model and compared with the standard “measure-assign-fit” (MAF) approach in use since the dawn of spectroscopy. Monte Carlo computations on typically 1000 synthetic spectra at a time verify that the predictions of the variance-covariance matrix are reliable under many circumstances. However in regions where the P and R branches double up, the predicted standard errors in the key spectroscopic constants rise sharply and greatly exceed estimates from the Monte Carlo ensemble statistics. In the same regions, the MAF method actually gives better precision. However, for imperfectly overlapped R and P branches, the MAF standard errors are typically three times larger than the TSF values; moreover, the MAF statistical errors are dwarfed by bias. The TSF approach, while clearly superior in these tests, has a practical drawback: it, too, can give significant bias if the spectra are analyzed with an incorrect model, as illustrated here through calculations employing the wrong function to describe the spectral lineshape.     

Optimal volume-corrected laplace-metropolis method

The article provides a refinement for the volume-corrected Laplace-Metropolis estimator of the marginal likelihood of DiCiccioet al. The correction volume of probability α in DiCiccioet al. is fixed and suggested to take the value α=0.05. In this article α is selected based on an asymptotic analysis to minimize the mean square relative error (MSRE). This optimal choice of α is shown to be invariant under linear transformations. The invariance property leads to easy implementation for multivariate problems. An implementation procedure is provided for practical use. A simulation study and a real data example are presented.     

A Generalized Markov Sampler

A recent development of the Markov chain Monte Carlo (MCMC) technique is the emergence of MCMC samplers that allow transitions between different models. Such samplers make possible a range of computational tasks involving models, including model selection, model evaluation, model averaging and hypothesis testing. An example of this type of sampler is the reversible jump MCMC sampler, which is a generalization of the Metropolis–Hastings algorithm. Here, we present a new MCMC sampler of this type. The new sampler is a generalization of the Gibbs sampler, but somewhat surprisingly, it also turns out to encompass as particular cases all of the well-known MCMC samplers, including those of Metropolis, Barker, and Hastings. Moreover, the new sampler generalizes the reversible jump MCMC. It therefore appears to be a very general framework for MCMC sampling. This paper describes the new sampler and illustrates its use in three applications in Computational Biology, specifically determination of consensus sequences, phylogenetic inference and delineation of isochores via multiple change-point analysis.     

Combined X-ray-electron imaging techniques: Limitations on lateral resolution

Capabilities of the imaging techniques, in which X-rays are converted to electrons and then the emitted electrons are registered by means of an electron microscope, are analyzed, the focus being on the factors limiting lateral resolution at the stage of electron emission. Bearing in mind the tendency to use harder synchrotron X-rays for some combined X-ray-electron microscopy methods, calculations were made for two significantly different X-ray energies: E = 1.828 keV (K-edge of Si) and E = 11.923 keV (L₃-edge of Au). By using Monte Carlo simulations of the electron trajectories beneath the surface of the sample we show that the radius of the spot from which photoelectrons are emitted could be as small as 1 nm. However, when proper account is taken of an entire electron cascade associated with the re-building of electron shells after photoelectron emission, spots more than one order of magnitude larger result, limiting the best lateral resolution to 20–30 nm.     

Molecular scale structure formation by folding

A conception of a structure formation suitable for nano-technology is proposed, which is programmable and suitable for mass production-like lithography. This conception utilizes the controlled folding of chains like the scan-lines of television. Its possibility and property were studied theoretically using the modeled chains consist of beads. By adopting the interaction among the beads which can distinguish the kind of the partner by its polarity and is chiral to break the chiral symmetry of the folded state, the special chains which have the unique ground states could be designed. In these ground states, the chains are folded like the scan-lines of television. The thermodynamic properties of the suggested chains were studied by the Monte Carlo simulations and the suggested chains showed the phase-transition-like behavior which is distinct compared to both the random chains and the chain that has only the non-specific attraction. The size dependence and the effects of adding the non-specific attraction and modifying the border of the folded conformation were also studied.     

BNCT蒙特卡洛剂量计算的混合网格算法研究

在硼中子俘获治疗(BNCT)的蒙特卡洛(MC)剂量计算中,通常使用单一的网格模式,如16mm,8mm,4mm.使用细网格计算资源太大,使用粗网格,计算精度不够,为此,根据粒子穿透深度和计数量的变化梯度,采用混合网格模拟计算,达到了细网格的精度,时间仅为细网格的37%.     

Size dependence of self-diffusion in the hard-square lattice gas

A striking size dependence of the mean-square displacement of diffusing particles in the two-dimensional lattice gas of hard squares has been observed by Monte Carlo simulation. It is shown that the size effect is due to the formation of a stable cage structure in small lattices when the particle concentration is high. The formation of cages is governed by a new type of percolation problem related to bootstrap percolation.     

Monte Carlo simulation of grain growth in polycrystalline materials

Understanding the kinetics of grain growth, under the influence of second phase (such as impurities, voids and bubbles) is fundamental to advances in the control of microstructural evolution. As a precursor to this objective, we have investigated the grain growth kinetics in a polycrystalline material using a standard Q-state Potts’ model under Monte Carlo settings. Based on physical reasoning, new modifications are suggested to circumvent some of the disadvantages in the basic Potts model. The efficacy of these modifications vis-à-vis the basic model is verified. The influence of second phase particles on the impurity loaded grain boundaries is investigated for the study of grain growth kinetics.     

The phase diagram of a single polymer chain: New insights from a new simulation method

We present simulation results for the phase behavior of a single chain for a flexible lattice polymer model using the Wang-Landau sampling idea. Applying this new algorithm to the problem of the homopolymer collapse allows us to investigate not only the high temperature coil–globule transition but also an ensuing crystallization at lower temperature. Performing a finite size scaling analysis on the two transitions, we show that they coincide for our model in the thermodynamic limit corresponding to a direct collapse of the random coil into the crystal without intermediate coil–globule transition. As a consequence, also the many chain phase diagram of this model can be predicted to consist only of gas and crystal phase in the limit of infinite chain length. This behavior is in agreement with findings on the phase behavior of hard-sphere systems with a relatively short-ranged attractive square well. © 2006 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 44: 2542–2555, 2006