体积权重与个数权重晶粒体积分布特征的相互关系

金属多晶体三维晶粒尺寸分布常近似呈Gamma分布或Lognormal分布．基于这2种分布函数,导出了体积权重与个数权重的晶粒体积分布特征多量的相互关系,并利用一种低碳钢奥氏体晶粒组织的实验数据及文献数据对其进行了验证．利用该关系,通过对多晶材料晶粒组织二维截面的标准金相测量,即可计算个数权重的三维晶粒体积分布的特征参量．     

Simulation of Extreme Climate Events over China with Different Regional Climate Models

During phase Ⅱ of the Regional Climate Model Inter-comparison Project (RMIP) for Asia, the Asian climate was estimated from July 1988 to December 1998 using six climate models. In this paper, the abilities of six climate models to simulate several important extreme climate events in China during the last years of the last century were analyzed. The modeled results for the intensity of the precipitation anomaly over the Yangtze-Huaihe Valley during the summers of 1991 and 1998were weaker than the observed values. The positive precipitation anomaly responsible for a catastrophic flood in 1991 was well reproduced in almost all simulation results,but the intensity and range of the precipitation anomaly in 1998 were weaker in the modeled results. The spatial distribution of extreme climate events in 1997, when severe drought affected North China and flood impacted South China, was reproduced by most of the regional models because the anomaly of the large-scale background field was well-simulated, despite poor simulation of high temperature areas in the north during the summer by all models.     

Laboratory Experiments on Frazil-Size Characteristics in a Counterrotating Flume

In an attempt to increase the fundamental understanding of the formation and evolution of frazil ice, laboratory experiments have been conducted in a counterrotating flume at the Hydraulics Research and Testing Facility, University of Manitoba. A high-precision thermometer and recently improved digital image processing system were used to acquire data from these experiments, which will hopefully aid in the development of numerical models. The image processing system is described, and results from a series of experiments are presented. The effects of air temperature, water velocity, and bottom roughness on the formation of frazil ice are discussed. A lognormal distribution appears to provide a reasonable fit to the observed frazil particle size distributions.     

Investigations of the problems of assessing aflatoxin levels in peanuts

In this study, a number of probability distributions that have been used to model the occurrence of aflatoxin in peanuts are compared. Two distributions, the compound gamma and the negative binomial, are shown to have special appeal in that both can be justified by reasoning from the fundamental biological and stochastic processes that generate the aflatoxin. Since method of moments and maximum likelihood give consistent estimates of parameters in both models, practical considerations suggest using the former. One hundred twenty data sets, each consisting of fifty observations, were not sufficient to provide goodness-of-fit tests to establish either as superior to the other as a model. Both models fit the data well, appreciably better than other models examined. An attractive aspect of the compound gamma and the negative binomial distributions is that, as a consequence of their theoretical underpinnings, both involve parameters that have meaningful interpretations. In the compound gamma, the alpha parameter reflects the shape of the kernel-to-kernel aflatoxin content distribution, the lambda parameter reflects the number (or frequency) of contaminated kernels in the sample, and the beta parameter is a scale parameter. In the negative binomial, the two parameters can be used as measures of mean or location and shape.     

Probabilistic Analysis of Soil-Water Characteristic Curves

Direct measurement of the soil-water characteristic curve (SWCC) is costly and time consuming. A first-order estimate from statistical generalization of experimental data belonging to soils with similar textural and structural properties is useful. A simple approach is to fit the data with a nonlinear function and to construct an appropriate probability model of the curve-fitting parameters. This approach is illustrated using sandy clay loam, loam, loamy sand, clay, and silty clay data in Unsaturated Soil Database. This paper demonstrates that a lognormal random vector is suitable to model the curve-fitting parameters of the SWCC. Other probability models using normal, gamma, Johnson, and other distributions do not provide better fit than the proposed lognormal model. The engineering impact of adopting a probabilistic SWCC is briefly discussed by studying the uncertainty of unsaturated shear strength due to the uncertainty of SWCC.     

Quantifying the distribution of inhalation exposure in human populations: distribution of minute volumes in adults and children

Assessments of inhalation exposure to environmental agents necessitate quantitative estimates of pulmonary ventilation rates. Estimating a range of exposures in a given population requires an understanding of the variability of ventilation rates in the population. Distributions of ventilation rates (Ve) were described based on the results of a large study where Ve were measured while subjects performed a variety of physical tasks. Three distinct ventilation levels were identified using cluster analyses of the mean Ve and then various activities were assigned to the three levels using a k-means procedure. Separate distributions were identified for the three Ve levels for adult males, adult females, and children. The variability of Ve was consistent with a lognormal distribution for all groups. An aggregate daily inhalation rate can be estimated based on the distributions of Ve.     

Method for Estimating Size-Specific Particulate Emission Inventories

Increasing evidence on the detrimental health effects of suspended fine particulates has prompted the introduction of new ambient air quality standards for particles with diameters smaller than 2.5 and∕or 10 μm and has created the need for size-specific inventories. To address this need, “generic” cumulative weight fraction data have been fitted with lognormal particle size distributions, and the resulting mean diameter dm and standard deviation σg values compiled for many sources of practical interest. In addition, nomographs are developed for assessing the mass fraction of particles with diameters <2.5, 6, 10, 15, and 30 μm as a function of the applicable dm and σg. The above data and tools allow easy extension of total particulate matter emission inventories for uncontrolled sources into size-specific ones. Supplemented with published typical efficiencies of control systems in the ranges 0–2.5, 2.5–6, and 6–10 μm, they also allow easy compilation of approximate size-specific inventories for controlled sources. In addition, used in conjunction with graphical models for selected control systems that predict the total efficiency and lognormal size distribution of penetrating particles, they offer a unique rigorous approach for size-specific inventories from controlled sources.     

基于Monte Carlo Potts方法的三维大尺度晶粒组织仿真模型及定量表征

为改善三维晶粒组织可视化模型的统计性,采用Monte Carlo Potts方法建立了材料多晶体组织的一种大尺度三维数字化模型,并实现了其定量表征和三维可视化.逾万晶粒的统计结果表明,该模型的平均晶粒面数为13.8±0.1,晶粒尺寸分布和晶粒面数分布均可用Log-normal函数近似拟合,与实际材料晶粒组织情况相近.     

Analysis of genetic structure and dispersal patterns in a population of sea beet

A model of the migration pattern in a metapopulation of sea beet (Beta vulgaris L. ssp. maritima), based on the continuous distributions of seed and pollen movements, is fitted to gene frequency data at 12 isozyme and RFLP loci by maximum likelihood by using an approximation of the simultaneous equilibrium distribution of the gene frequencies generated by the underlying multivariate stochastic process of genetic drift in the population. Several alternative restrictions of the general model are fitted to the data, including the island model, a model of complete isolation, and a model in which the seed and pollen dispersal variances are equal. Several likelihood ratio tests between these alternatives are performed, and median bias in the estimated parameters is corrected by using parametric bootstrapping. To assess the fit of the selected model, the predicted covariances are compared with covariances computed from the data directly. The dependency of estimated parameters on the ratio between effective and absolute subpopulation sizes, which is treated as a known parameter in the analysis, is also examined. Finally, we note that the data also appear to contain some information about this ratio.     

Estimation for Stochastic Soil Models

Although considerable theory exists for the probabilistic treatment of soils, the ability to identify the nature of spatial stochastic soil variation is almost nonexistent. We all know that we could excavate an entire site and there would be no doubt about the soil properties. However, there would no longer be anything to rest our structure on, and so we must live with uncertainty and attempt to quantify it rationally. Twenty years ago the mean and variance was sufficient. Clients are now demanding full reliability studies, requiring more sophisticated models, so that engineers are becoming interested in rational soil correlation structures. Knowing that soil properties are spatially correlated, what is a reasonable correlation model? Are soils best represented using fractal models or finite-scale models? What is the difference? How can this question be answered? Once a model has been decided upon, how can its parameters be estimated? These are questions that this paper addresses by looking at a number of tools that aid in selecting appropriate stochastic models. These tools include the sample covariance, spectral density, variance function, variogram, and wavelet variance functions. Common models, corresponding to finite scale and fractal models, are investigated, and estimation techniques are discussed.     

A Comparison of Sequential Sampling Models for Two-Choice Reaction Time.

The authors evaluated 4 sequential sampling models for 2-choice decisions--the Wiener diffusion, Ornstein-Uhlenbeck (OU) diffusion, accumulator, and Poisson counter models--by fitting them to the response time (RT) distributions and accuracy data from 3 experiments. Each of the models was augmented with assumptions of variability across trials in the rate of accumulation of evidence from stimuli, the values of response criteria, and the value of base RT across trials. Although there was substantial model mimicry, empirical conditions were identified under which the models make discriminably different predictions. The best accounts of the data were provided by the Wiener diffusion model, the OU model with small-to-moderate decay, and the accumulator model with long-tailed (exponential) distributions of criteria, although the last was unable to produce error RTs shorter than correct RTs. The relationship between these models and 3 recent, neurally inspired models was also examined. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Model to Forecast Maximum Flows in On-Demand Irrigation Distribution Networks

A model is developed using data from an on-demand pressurized water distribution network located in sector VIII of the Genil-Cabra irrigation district of Santaella, Córdoba (southern Spain), to simulate an irrigation season and calculate the flows that circulate in the system at any given time during the irrigation day. Using the results obtained by the model, water demand frequencies can be estimated. These results are compared to those attained by Clément’s and Mavropoulos’s formulas. This procedure enables us to determine to what extent it is possible to adjust statistical distributions to the demand obtained by both models and to verify if the hypotheses upon which these models are based are, in fact, fulfilled. Moreover, we are able to study which periods are the most appropriate for determining peak demand. Our results show that the statistical methods slightly underestimate demand because demand tends to be concentrated at two peak times during the day: one at mid-morning and another in the late afternoon. Nonetheless, the design flow obtained by the models is valid for designing the system. After studying the demand frequencies, we concluded that a better fit is achieved when a more flexible distribution such as the gamma distribution is used.     

Adjusting Nodal Demands in SCADA Constrained Real-Time Water Distribution Network Models

This paper describes the proportional demand method and the target demand method, two techniques for adjusting estimated demands in hydraulic models of water distribution networks to produce solutions that are consistent with available supervisory control and data acquisition (SCADA) data. The two techniques assume that pipe resistances and SCADA data are accurate and that the combination of SCADA data and demand estimates produce overdetermined problems. Nodal demands are regarded as stochastic variables which fluctuate about an estimated mean value. The method of weighted least squares is used to obtain solutions that satisfy all of the constraints imposed by SCADA data with adjusted nodal demands that most closely resemble the estimates. The methods are intended for use in real-time modeling but are limited to quasi-steady state flow. The paper demonstrates the methods on two example problems.     

Middle ear overpressure treatment of endolymphatic hydrops in guinea pigs

Changes in sleep state and night-time body temperature were studied in 13 healthy young men. Temperature data showed serial dependence that was removed when considering three main segments of the thermogram: an initial rapid drop between 60 and 140 min after lights off, followed by a slower decrease to the thermal minimum and a final temperature increase until waking. The first segment was fitted by a two-term function, while the other two segments were fitted by linear regression. Temperature data obtained after fitting these models were stationary and used to calculate the fast Fourier transformation and the cross-correlation functions between the hypnogram and the thermogram of each subject. Attaining stationarity has unmasked short oscillations during sleep and some temperature series showed the presence of ultradian oscillations with a period of 90-100 min. Significant cross-correlations between sleep stages and body temperature were found, and the parameters of the two-term function fitted to the first temperature drop were related to the sign of the correlation and to that time series (sleep or temperature) which preceded the other during the night.     

Demand Forecasting for Irrigation Water Distribution Systems

One of the main problems in the management of large water supply and distribution systems is the forecasting of daily demand in order to schedule pumping effort and minimize costs. This paper examines methodologies for consumer demand modeling and prediction in a real-time environment for an on-demand irrigation water distribution system. Approaches based on linear multiple regression, univariate time series models (exponential smoothing and ARIMA models), and computational neural networks (CNNs) are developed to predict the total daily volume demand. A set of templates is then applied to the daily demand to produce the diurnal demand profile. The models are established using actual data from an irrigation water distribution system in southern Spain. The input variables used in various CNN and multiple regression models are (1) water demands from previous days; (2) climatic data from previous days (maximum temperature, minimum temperature, average temperature, precipitation, relative humidity, wind speed, and sunshine duration); (3) crop data (surfaces and crop coefficients); and (4) water demands and climatic and crop data. In CNN models, the training method used is a standard back-propagation variation known as extended-delta-bar-delta. Different neural architectures are compared whose learning is carried out by controlling several threshold determination coefficients. The nonlinear CNN model approach is shown to provide a better prediction of daily water demand than linear multiple regression and univariate time series analysis. The best results were obtained when water demand and maximum temperature variables from the two previous days were used as input data.     

Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets

An integrated understanding of molecular and developmental biology must consider the large number of molecular species involved and the low concentrations of many species in vivo. Quantitative stochastic models of molecular interaction networks can be expressed as stochastic Petri nets (SPNs), a mathematical formalism developed in computer science. Existing software can be used to define molecular interaction networks as SPNs and solve such models for the probability distributions of molecular species. This approach allows biologists to focus on the content of models and their interpretation, rather than their implementation. The standardized format of SPNs also facilitates the replication, extension, and transfer of models between researchers. A simple chemical system is presented to demonstrate the link between stochastic models of molecular interactions and SPNs. The approach is illustrated with examples of models of genetic and biochemical phenomena where the ULTRASAN package is used to present results from numerical analysis and the outcome of simulations.     

Probabilistic Foundation Settlement on Spatially Random Soil

By modeling soils as spatially random media, estimates of the reliability of foundations against serviceability limit state failure, in the form of excessive differential settlements, can be made. The soil’s property of primary interest is its elastic modulus, E, which is represented here using a lognormal distribution and an isotropic correlation structure. Prediction of settlement below a foundation is then obtained using the finite element method. By generating and analyzing multiple realizations, the statistics and density functions of total and differential settlements are estimated. In this paper probabilistic measures of total settlement under a single spread footing and of differential settlement under a pair of spread footings using a two-dimensional model combined with Monte Carlo simulations are presented. For the cases considered, total settlement is found to be represented well by a lognormal distribution. Probabilities associated with differential settlement are conservatively estimated through the use of a normal distribution with parameters derived from the statistics of local averages of the elastic modulus field under each footing.     

Dose-related cardiovascular effects of amrinone and epinephrine in reversing bupivacaine-induced cardiovascular depression

Effective dose, an indicator of the stochastic effect of radiation, has been widely used in dose evaluation in the environment. Though conversion factors have been used to obtain E from the air kerma or air absorbed dose, the variation of the conversion factors due to the change of exposure conditions has not been sufficiently investigated. This report documents an investigation of the variation of the effective dose per air kerma for environmental gamma rays depending on the exposure conditions using anthropomorphic phantoms and Monte Carlo calculations, taking into account the precise angular and energy distributions of the environmental gamma rays incident on the human body. As causes of the variation, posture of human bodies, biases of environmental source distributions, and body size were considered. The variation of effective dose in a prone position compared with that in a standing position was found to be within 30%. The bias of environmental sources causes the effective dose per air kerma to vary by 20% at maximum, but in some cases for low-energy gamma rays the variation was found to be up to 40% due to the change in the energy spectrum. The effective dose for a new born infant was estimated to be higher than that for an adult by a maximum of 80-90% for low-energy gamma rays from anthropogenic sources because of a lower shielding effect of the smaller body. The variation of the effective dose equivalent shows a similar tendency to the effective dose. Consequently, this study made it possible to estimate the uncertainties of effective dose and effective dose equivalent evaluated from air kerma or absorbed dose in air using the standard available conversion factors.     

Mixed Bayesian networks: a mixture of Gaussian distributions

Mixed Bayesian networks are probabilistic models associated with a graphical representation, where the graph is directed and the random variables are discrete or continuous. We propose a comprehensive method for estimating the density functions of continuous variables, using a graph structure and a set of samples. The principle of the method is to learn the shape of densities from a sample of continuous variables. The densities are approximated by a mixture of Gaussian distributions. The estimation algorithm is a stochastic version of the Expectation Maximization algorithm (Stochastic EM algorithm). The inference algorithm corresponding to our model is a variant of junction three method, adapted to our specific case. The approach is illustrated by a simulated example from the domain of pharmacokinetics. Tests show that the true distributions seem sufficiently fitted for practical application.