On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models

Nonlinear state-space models have been increasingly applied to study population dynamics and data assimilation in environmental sciences. State-space models can account for process error and measurement error simultaneously to correct for the bias in the estimates of system state and model parameters. However, few studies have compared the performance of different nonlinear state-space models for reconstructing the state of population dynamics from noisy time series. This study compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF) and Bayesian nonlinear state-space models (BNSSM) through simulations. Synthetic population time series were generated using the theta logistic model with known parameters, and normally distributed process and measurement errors were introduced using the Monte Carlo simulations. At higher levels of nonlinearity, the UKF and BNSSM had lower root mean square error (RMSE) than the EKF. The BNSSM performed reliably across all levels of nonlinearity, whereas increased levels of nonlinearity resulted in higher RMSE of the EKF. The Metropolis–Hastings algorithm within the Gibbs algorithm was used to fit the theta logistic model to synthetic time series to estimate model parameters. The estimated posterior distribution of the parameter θ indicated that the 95% credible intervals included the true values of θ (=0.5 and 1.5), but did not include 1.0 and 0.0. Future studies need to incorporate the adaptive Metropolis algorithm to estimate unknown model parameters for broad applications of Bayesian nonlinear state-space models in ecological studies.     

Studying crop sequences with CarrotAge,a HMM-based data mining software

We have developed a knowledge discovery system based on high-order hidden Markov models for analyzing spatio-temporal data bases. This system, named CarrotAge , takes as input an array of discrete data – the rows represent the spatial sites and the columns the time slots – and builds a partition together with its a posteriori probability. CarrotAge has been developed for studying the cropping patterns of a territory. It uses therefore an agricultural drench database, named Ter-Uti , which records every year the land-use category of a set of sites regularly spaced. The results of CarrotAge are interpreted by agronomists and used in research works linking agricultural land use and water management. Moreover, CarrotAge can be used to find out and study crop sequences in large territories, that is a main question for agricultural and environmental research, as discussed in this paper.     

生态供应链与生态型设计

生态供应链是可持续发展思想在运作管理领域的具体应用。生态型设计兼顾了经济效益和生态效益，是生态供应链的核心内容。生态型设计的实质是通过整体优化和局部优化来降低各节点企业的环境影响，借助于生态型设计可以把传统供应链代表的单程经济转化成生态供应链代表的循环经济。生态型设计由生态供应链的整体设计和组成元素的设计两部分组成，具体内容包括产品设计、原材料采购、产品生产、产品营销、产品回收和反向物流。     

Bayesian analysis under ordered functions of parameters

Ordered parameter problems arise in a wide variety of real world situations and are dealt with extensively in the literature. Traditional frequentist methods for dealing with these problems are rather complicated theoretically, especially when sample sizes are small. Bayesian methods are not widely used because high dimensional numerical integration is often required. However, Markov chain Monte Carlo methods provide alternatives to such numerical integration and also deal with ordered parameter problems in a straightforward manner. Little is known about the situation where functions of parameters are ordered. Such problems may seem to be of little practical concern initially, but one can readily see their importance in situations where ordering is placed on the means and variances of several normal or Gamma populations. For the Gamma distribution we will present real examples where we will analyze monthly precipitation data from San Francisco, California and Oakland Mills, Iowa. For the San Francisco data we will simultaneously order both monthly precipitation means and variances. For the Iowa data we will place ordering on seasonal average while still estimating monthly means. Our results show that we would obtain sharper, more accurate inference when order restrictions are employed.     

Neighborhood Level Spatial Analysis of the Relationship Between Alcohol Outlet Density and Criminal Violence

Misuse of alcohol is a significant public health problem, potentially resulting in unintentional injuries, motor vehicle crashes, drownings, and, perhaps of greatest concern, serious acts of violence, including assaults, rapes, suicides, and homicides. Although previous research establishes a link between alcohol consumption increased levels of violence, studies relating the density of alcohol outlets (e.g., restaurants, bars, liquor stores) and the likelihood of violent crime have been less common. In this paper we test for such a relationship at the small area level, using data from 79 neighborhoods in the city of Minneapolis, Minnesota. We adopt a fully Bayesian point of view using Markov chain Monte Carlo (MCMC) computational methods as available in the popular and freely available WinBUGS language. Our models control for important covariates (e.g., neighborhood racial heterogeneity, age heterogeneity) and also account for spatial association in unexplained variability using conditionally autoregressive (CAR) random effects. Our results indicate a significant positive relationship between alcohol outlet density and violent crime, while also permitting easy mapping of neighborhood-level predicted and residual values, the former useful for intervention in the most at-risk neighborhoods and the latter potentially useful in identifying covariates still missing from the fixed effects portion of the model.     

Cluster detection using Bayes factors from overparameterized cluster models

In this paper, we consider the use of a partition model to estimate regional disease rates and to detect spatial clusters. Formal inference regarding the number of partitions (or clusters) can be obtained with a reversible jump Markov chain Monte Carlo algorithm. As an alternative, we consider the ability of models with a fixed, but overly large, number of partitions to estimate regional disease rates and to provide informal inferences about the number and locations of clusters using local Bayes factors. We illustrate and compare these two approaches using data on leukemia incidence in upstate New York and data on breast cancer incidence in Wisconsin.     

人体需要经常补硒

从硒的生物学作用和防治疾病等方面剖析了硒与人体健康的关系；从元素地球化学、生物地球化学和化学地理等方面探讨了我国缺硒区的形成、分布和等级的划分；从环境地球化学物质场和元素生物地球化学营养链等方面．阐明了作物、禽畜和人体缺硒的原因、危害及其人体补硒的途径和方法，提出了在缺硒区运用元素生物地球化学营养链营养结构模式的原理、和技术方法，从土壤中获取富硒有机食品，供居民食用，是进行食物补硒、提高人体摄硒量、提高人体免疫功能和防治缺硒病症的根本措施。     

Estimation of surface area of montmorillonite by ethylene oxide chain adsorption

This study investigates the feasibility of using ethylene oxide chain adsorption to determine the surface area of an expandable clay, montmorillonite. Experimental results indicate that high molecular weight poly(ethylene oxide) or nonionic surfactant with long ethylene oxide chain should be used to provide reasonable estimations for monolayer capacity. The method has advantages over Brunauer, Emmett, and Teller method in that it is readily applicable to a wide range of areas, particularly to 2:1 layer silicates under aqueous conditions.     

河流水质的动态马尔柯夫评价

根据马尔柯夫过程的原理,在水质级别评价结果的基础之上,构造水质变化的概率转移矩阵,并对转移概率赋权计算绝对进步度,然后在此基础上引出相对进步度的概念,用相对进步度来评价河流水质在一段时期内的变化程度.运用该方法计算了中国四大河流2005年的水质相对进步度,并对计算结果进行了分析.     

中国电子废物循环利用空间优化

基于2014年和2015年环保部发布的省级回收数据,分析了现有传统回收体系下,各省的电子废物回收率变化,发现省级尺度电子废物的回收率呈现较大波动,这与电子废物的跨省流动有密切关系.为此,本文基于现有回收渠道的省际流动网络格局,采用网络分析中的层次聚类方法,揭示全国电子废物流动的区域化特征.结果显示根据区域化特征重新划分市场区,可以使主要市场区的整体回收率波动明显降低,从而为制定合理的回收目标提供参考.针对这一研究结果,提出基于市场区域化特征,建立全国一体化的回收利用市场和差异化的区域性回收目标,有助于充分利用市场机制优化中国电子废物循环利用的空间格局.最后,讨论了生产者主导和处理企业主导两种治理机制对空间优化的潜在影响.