Standard Gibbs Energies of Transfer for KBPh4 from Water to Water ＋ PrOH and Water ＋ TBA Mixtures and the Primary Medium Effects

The standard Gibbs energiics of transfer (△rG^0) for potassium tetraphenylborate (KBPh4) have been studied in the system of water and water l-propamol (PrOH) as well as water and water t-butyl alcohol (TBA) at 298.15K. The rcsults show that -△rG^0 cxhibits a complicated changing pattern with the mole fraction of TBA(x (TBA)) or PrOH (x (PrOH)), and △rG^0 has the a maximum value at x (TBA) = 0.2 or x (PrOH) = 0.2. Especially, -△rG^0 of KBPh4 changcs unusually with increasing x (TBA) when x (TBA) ＜0.05. The reasons for these changes were analyzed and discussed.     

A Stochastic Neural Model for Fast Identification of Spatiotemporal Sequences

In this Letter, a new approach to build a neural model for the fast identification of spatiotemporal sequences is proposed. Such a model, the Stochastic Neural Sequence Identifier (SNSI), is simple and rapidly learns and identifies a given sequence. The SNSI receives as input several patterns belonging to a particular spatiotemporal sequence and produces as output a label for the sequence identified and a probability of this classification being correct. The SNSI is able to identify a sequence from patterns learned during training or novel ones, i.e., combinations of the sequence items distinct from those belonging to the trained set. The SNSI was tested on a 2D set of both closed and open trajectories with varying levels of complexity. The results suggest that the SNSI is able to recognize all the patterns presented in the training and most of the novel patterns used for testing.     

Fast, On-Line Learning of Globally Consistent Maps

To navigate in unknown environments, mobile robots require the ability to build their own maps. A major problem for robot map building is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of cumulative drift errors. This paper introduces a fast, on-line algorithm for learning geometrically consistent maps using only local metric information. The algorithm works by using a relaxation technique to minimize an energy function over many small steps. The approach differs from previous work in that it is computationally cheap, easy to implement and is proven to converge to a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot.     

Evaluation of a high-volume portable bioaerosol sampler in laboratory and field environments

This study investigated the physical and biological performances of a portable centrifugal sampler for viable bioaerosols, RCS High Flow. The performance of the test sampler in the laboratory and field environments was compared with that of a reference sampler, BioSampler. The laboratory experiments with non-biological particles of KCl, oleic acid, and polystyrene latex showed that the test sampler's collection efficiency is about 22% for 0.5-microm particles, 48% for 1.0-microm particles, and approximately 100% for particles of 2.5 microm and larger. These tests indicated that the sampler's cut-off size (d50) was 1.1 microm. The test sampler's physical performance when collecting the spores and vegetative cells of Bacillus subtilis var. niger (BG) was similar to that when collecting non-biological particles of the same size. In the laboratory tests, the RCS High Flow sampler was found to enumerate approximately 40% of BG spores and cells relative to the reference sampler, BioSampler. A similar ratio was found during testing in an indoor environment. This ratio decreased to below 10% when testing was performed in an outdoor environment. We hypothesize that the test sampler's underperformance compared with the BioSampler could be caused by the damage to sensitive microorganisms during the collection process, test sampler's sensitivity to wind direction and speed as well as break-up of particle aggregates during the impingement process in BioSampler, which resulted in more colony-forming units (CFUs) being counted by the reference sampler than by the test sampler. Overall, when the RCS High Plus is used to sample culturable airborne microorganisms, the results obtained may have to be adjusted to avoid potential underestimation of microorganism concentration in the air. PRACTICAL IMPLICATIONS: The laboratory testing of the RCS High Flow bioaerosol sampler showed that the sampler collects 1 microm particles and larger with an efficiency of 50% and higher; the efficiency reaches approximately 100% for particles of 2.5 microm and larger. When considering this result, most of the airborne fungal spores would be collected with an efficiency between 50 and 100%. The field testing, however, indicated that the RCS High Flow sampler recovered from 41 to 71% of microorganisms collected relative to the reference sampler, Biosampler, and this ratio dropped to below 5% during outdoor testing. Thus, while the RCS High Flow sampler offers certain advantages over other samplers for viable bioaerosols--it is lightweight, battery operated, and collects viable microorganisms at a high flow rate directly on agar media, the results obtained may have to be adjusted to avoid potential underestimation of microorganism concentration in the air, especially if sampling is performed outdoors.     

PAC-Bayesian Stochastic Model Selection

PAC-Bayesian learning methods combine the informative priors of Bayesian methods with distribution-free PAC guarantees. Stochastic model selection predicts a class label by stochastically sampling a classifier according to a posterior distribution on classifiers. This paper gives a PAC-Bayesian performance guarantee for stochastic model selection that is superior to analogous guarantees for deterministic model selection. The guarantee is stated in terms of the training error of the stochastic classifier and the KL-divergence of the posterior from the prior. It is shown that the posterior optimizing the performance guarantee is a Gibbs distribution. Simpler posterior distributions are also derived that have nearly optimal performance guarantees.     

Viscosity of Some Binary Mixtures of Arenes

The viscosity of 12 binary mixtures of benzene+toluene, +ethylbenzene, +isopropylbenzene, +tert-butylbenzene; toluene+ethylbenzene, +isopro- pylbenzene, +tert-butylbenzene; ethylbenzene+isopropylbenzene; isopropylbenzene+tert-butylbenzene; o-xylene+m-xylene; m-xylene+p-xylene; and p-xylene+o-xylene has been measured over the entire range of composition. The viscosity deviations and excess Gibbs energy of activation G ^*E of viscous flow based on Eyring's theory have been calculated. The results have been analyzed in terms of the change in the structure of pure component molecules. The viscosity data have been correlated with the equations of Grunberg and Nissan; Hind, McLaughlin, and Ubbelohde; Tamura and Kurata; Katti and Chaudhri; McAllister; and Heric and Brewer. The Prigogine–Flory–Patterson– Bloomfield–Dewan (PFPBD) theory has been applied to analyze the excess viscosity of the present binary mixtures.     

Bayesian approach to parameter estimation of the generalized pareto distribution

Several methods have been used for estimating the parameters of the generalized Pareto distribution (GPD), namely maximum likelihood (ML), the method of moments (MOM) and the probability-weighted moments (PWM). It is known that for these estimators to exist, certain constraints have to be imposed on the range of the shape parameter,k, of the GPD. For instance, PWM and ML estimators only exist fork>−0.5 andk≤1, respectively. Moreover, and particularly for small sample sizes, the most efficient method to apply in any practical situation highly depends on a previous knowledge of the most likely values ofk. This clearly suggests the use of Bayesian techniques as a way of using prior information onk. In the present work, we address the issue of estimating the parameters of the GPD from a Bayesian point of view. The proposed approach is compared via a simulation study with ML, PWM and also with the elemental percentile method (EPM) which was developed by Castillo and Hadi (1997). The estimation procedure is then applied to two real data sets.     

Viscosities of Nonelectrolyte Liquid Mixtures Containing Acrylonitrile

The measurements of viscosity are reported for seven binary mixtures of acrylonitrile (AN) with ethanenitrile (EN), methyl acetate (MA), ethyl acetate (EA), n-butyl acetate (BA), dimethylformamide (DMF), dimethylacetamide (DMA), and dimethyl sulphoxide (DMSO) at 303.15 K temperature. The viscosity data have been correlated with the equations of Grunberg and Nissan; Hind, McLaughlin, and Ubbelohde; Tamura and Kurata; Katti and Chaudhri; McAllister; Heric and Brewer; and of Auslaender. The relations between the viscosity deviations , excess Gibbs energy of activation G*^E of viscous flow, and the intermolecular interaction in these mixtures are discussed.     

Reconstruction of Piecewise Smooth Functions from Non-uniform Grid Point Data

Spectral series expansions of piecewise smooth functions are known to yield poor results, with spurious oscillations forming near the jump discontinuities and reduced convergence throughout the interval of approximation. The spectral reprojection method, most notably the Gegenbauer reconstruction method, can restore exponential convergence to piecewise smooth function approximations from their (pseudo-)spectral coefficients. Difficulties may arise due to numerical robustness and ill-conditioning of the reprojection basis polynomials, however. This paper considers non-classical orthogonal polynomials as reprojection bases for a general order (finite or spectral) reconstruction of piecewise smooth functions. Furthermore, when the given data are discrete grid point values, the reprojection polynomials are constructed to be orthogonal in the discrete sense, rather than by the usual continuous inner product. No calculation of optimal quadrature points is therefore needed. This adaptation suggests a method to approximate piecewise smooth functions from discrete non-uniform data, and results in a one-dimensional approximation that is accurate and numerically robust.      

醋酸乙烯的NVT-GEMC模拟研究

采用了NVT-Gibbs系综蒙特卡罗(NVT-GEMC)方法,借助于Towhee模拟软件,在常压下,温度为293.15 K～473.15 K条件下,对醋酸乙烯体系进行汽液相平衡模拟计算。采用力场参数迁移的方法将TraPPE-UA力场描述文件中缺少的与CHo=c原子基团相关的键参数和非键参数补全。在此基础上,通过设置合理的模拟参数,计算得到了常压下醋酸乙烯汽液相密度随温度的变化情况。将计算值与实验值进行对比,发现液相密度的计算值与实验值的相对偏差在低温时较小,约在10%以内;随温度的升高相对偏差逐渐变大,在473.15 K时高达25%。这说明GEMC方法模拟计算醋酸乙烯体系相共存性质具有可行性,TraPPE-UA力场中CHo=c原子的力场参数还有待修正。