基于GPR-TOPSIS方法的充填配比多目标多属性优化

为获得某金矿尾砂胶结充填材料最优配比,基于试验结果,以海水比例、灰砂比和料浆质量浓度为输入参数,以充填体强度、塌落度及泌水率为输出参数,建立了充填配比与其响应量的高斯过程回归模型,分析了不同因素对充填性能的影响程度;采用遗传算法对高斯过程回归模型进行多目标参数优化,获得了Pareto非劣解,在此基础上,引入多属性决策的TOPSIS法对Pareto非劣解进行方案优选,确定了充填最优配比。研究结果表明:高斯过程回归模型相对误差值均小于6%,可靠性高;灰砂比及料浆质量浓度对充填性能影响较为显著,采用海水作为充填水源将降低充填体的强度;经优化后的充填配比与试验结果相符。     

公路隧道火灾发生位置与人群疏散通道仿真研究

利用EXODUS建立隧道的仿真场景,确定疏散人群仿真参数,并将出口工效OPS作为评价疏散效率的指标,分析不同火灾发生位置对疏散时间的影响。采用K-means算法分别对火灾发生位置和人群疏散通道位置进行聚类,并建立基于两者影响下的出口工效模型,通过对该模型求偏导,得出隧道火灾发生位置和人群疏散通道选择之间的关系。研究结果表明:火灾发生位置越接近人行横通道,疏散时间波动越大,人行横通道和隧道入口的疏散时间随人群疏散区域分界线变化,当火灾发生距人行横通道20~110 m时,变化明显,当距140~350 m时,变化平稳;出口工效OPS总体呈现不稳定的阶梯状变化趋势;火灾发生位置和人群疏散通道的最优位置呈线性递减关系。研究结果可应用于隧道应急指挥中心管理人员制定有效疏散路径和采取诱导分流人群措施。     

Regression analysis of forest inventory data with time and space dependencies

In this paper the data of a forest health inventory are analyzed. Since 1983 the degree of defoliation, together with various explanatory variables (covariates) concerning stand, site, soil and weather, are recorded by the second of the two authors, in the forest district of Rothenbuch (Spessart, Bavaria). The focus is on the space and time dependencies of the data. The mutual relationship of space-time functions and the set of covariates is evaluated. For this we use generalized linear models (GLMs) for ordinal response variables and semiparametric estimation approaches. By using goodness-of-fit measures it turns out that (i) the contribution of space-time functions is quantitatively comparable with that of the set of covariates, (ii) the contribution of space-time functions is small compared with the contribution of a set of variables describing the last-year and neighboring response values. By applying appropriate residual methods a detailed analysis of the individual sites in the area can be carried out. This analysis reveals where the predictive power of the covariates fail to explain the observed defoliation.     

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.     

耗散结构理论在土壤科学上应用途径的探索

本文根据耗散结构的基本理论，结合生产实践经验，提出了表征土壤肥力的非线性微分方程：＝（Ｋ－ｂ）ｘ－βｘ２Ｘ─土壤生物活性，ｔ─时间，Ｋ─生物繁殖系数，ｂ─生物死亡系数，β─生物饱和系数．解这个并不复杂的非线性微分方程，便可知道土壤是处于热力学分枝，还是耗散结构分枝．这对研究土壤肥力的动态变化、土壤肥力的分类、土壤的管理、土壤退化的防止等方面有着无法预测的应用前景．     

Sparse regression for analyzing the development of foliar nutrient concentrations in coniferous trees

Analyzing and predicting the development of foliar nutrient concentrations are important and challenging tasks in environmental monitoring. This article presents how linear sparse regression models can be used to represent the relations between different foliar nutrient concentration measurements of coniferous trees in consecutive years. In the experiments the models proved to be capable of providing relatively good and reliable predictions of the development of foliage with a considerably small number of regressors. Two methods for estimating sparse models were compared to more conventional linear regression models. Differences in the prediction accuracies between the sparse and full models were minor, but the sparse models were found to highlight important dependencies between the nutrient measurements better than the other regression models. The use of sparse models is, therefore, advantageous in the analysis and interpretation of the development of foliar nutrient concentrations.     

Statistical inference on patch-specific survival and movement rates from marked animals

Empirical estimates of patch-specific survival and movement rates are needed to parametrize spatially explicit population models, and for inference on the effects of habitat quality and fragmentation on populations. Data from radio-marked animals, in which both the fates and habitat locations of animals are known over time, can be used in conjunction with continuous-time proportional hazards models to obtain inferences on survival rates. Discrete-time conditional logistic models may provide inference on both survival and movement rates. We use Monte Carlo simulation to investigate accuracy of estimates of survival from both approaches, and movement rates from conditional logistic regression, for two habitats. Bias was low (relative bias < 0.04) and interval coverage accurate (close to the nominal 0.95) for estimates of habitat effect on survival based on proportional hazards. Bias was high ( relative bias 0.60) and interval coverage poor ( = 0.26 vs. nominal 0.95) for estimates of habitat effect based on conditional logistic regression; bias was especially influenced by heterogeneity in survival and the shape of the hazard function, whereas both bias and coverage were affected by ‘memory’ effects in movement patterns. Bias estimates of movement rate was low ( relative bias < 0.05), but interval coverage was poor ( = 0.48–0.80), possibly as a result of poor performance of a Taylor series estimate of variance. An example is provided from a radio-telemetry study of 47 wintering American woodcock (Scolopax minor), illustrating practical difficulties in field studies to parametrize these models. We also discuss extensions of continuous-time models to explicitly include a movement process, and further examine tradeoffs between continuous and discrete models.     

Soil Lead and Children’s Blood Lead Levels in Syracuse, NY, USA

A geo-referenced data set of 12 228 first-time blood lead screening values for Syracuse, NY, children was established for the 4-year period 1992–1996. Soil lead values were measured in a 600 m by 600 m tessellation grid covering the city. The two data sets were merged for evaluation of relationships between them. Strong seasonal variation in blood lead levels suggests the importance of contaminated soils as an exposure source. When the data were aggregated at a large geographic scale (3 km²), a logarithmic model showed an R² of > 0.65 for the regression of geometric mean blood lead on median soil lead values. Results showed a striking similarity to those obtained by Mielke et al. (1999) for a study in New Orleans, LA, USA.