Safety Ⅱ学术思想的科学传播研究*

为全面了解Safety Ⅱ学术思想的传播情况,采集Web of Science数据库中284篇引用Safety Ⅱ学术著作的论文,采用科学计量和知识网络的方法,分析Safety Ⅱ学术思想的传播时间趋势、传播主体分布、传播领域与期刊以及传播的热点主题分布。研究结果表明:Safety Ⅱ学术思想在传播时间上呈线性增长趋势;核心传播区域为英格兰、美国、澳大利亚、挪威和意大利;核心传播机构为挪威斯塔万格大学、荷兰代尔夫特理工大学以及罗马第一大学;核心传播者为Saurin Tarcisio Abreu、Hollnagel Erik以及Patriarca Riccardo;Safety Ⅱ传播的核心领域为工业工程、运筹管理科学、人机工程学、应用心理学以及健康护理科学与服务等领域;Safety Ⅱ传播的主题为“韧性与功能共振”、“病患安全”、“事故与人的失误”、“安全与风险管理”、“社会技术系统与人的因素”以及“安全风险的评估”6大方向。研究结果对我国安全科学理论与实践的发展有重要作用。     

Bayesian model-based cluster analysis for predicting macrofaunal communities

To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster analysis in step 1 many hard, seemingly arbitrary choices have to be made that nevertheless influence the solution (similarity measure, clustering strategy, number of clusters). The stability of the solution is often of concern, e.g. in clustering by the program. In the discriminant analysis of step 2 it can occur that a water sample is misclassified on the basis of the environmental data but on further inspection happens to be a borderline case in the cluster analysis. One would then rather reclassify such a sample and iterate the two steps. Bayesian latent class analysis is a flexible, extendable model-based cluster analysis approach that recently has gained popularity in the statistical literature and that has the potential to address these problems. It allows the macrofauna and environmental data to be modelled and analyzed in a single integrated analysis. An exciting extension is to incorporate in the analysis prior information on the habitat preferences of the macrofauna taxa such as is available in lists of indicator values. The output of the analysis is not a hard assignment of water samples to clusters but a probabilistic (fuzzy) assignment. The number of clusters is determined on the basis of the Bayes factor. A standard feature of the Bayesian method is to make predictions and to assess their uncertainty. We applied this approach to a data set consisting of 70 water samples, 484 macrofauna taxa and four environmental variables for which previously a five cluster solution had been proposed. The standard for Bayesian estimation, the Gibbs sampler, worked fine on a subset with only 12 selected taxa but did not converge on the full set with 484 taxa. This is due to many configurations in which the assignment probabilities are all very close to either 0 or 1. This convergence problem is comparable with the local optima problem in classical cluster optimization algorithms, including the EM algorithm used in Latent Gold, a Windows program for latent class analysis. The convergence problem needs to be solved before the benefits of Bayesian latent class analysis can come to fruition in this application. We discuss possible solutions.     

磁场对扩散火焰特性的影响

通过磁场对扩散火焰特性影响的研究，可以发现磁场能够改变火焰的燃烧特性，使火焰的温度升高，而火焰的高度有所降低。这能使我们更精确地了解实际火灾过程中火焰的燃烧特性，掌握火灾的特点，为减灾和灭灾提供有益的参考。     

地累积指数法评价多金属环境质量的方法优化探索:以农业发达地区为例

准确合理的土壤污染评价方法是进行区域性土壤污染评价的前提.将三角模糊数引入地质累积指数法,结合α-截集技术以及Latin Hypercube抽样(LHS)随机模拟方法对研究区重金属含量差异较大的5种不同土壤类型(潮土、风沙土、黄绵土、褐土和新积土)分别进行土壤重金属(Cd、Hg、As、Pb和Cr)累积性评价,能够避免因...     

Load Estimation Method Using Distributions with Covariates: A Comparison with Commonly Used Estimation Methods

Load estimates obtained using an approach based on statistical distributions with parameters expressed as a function of covariates (e.g., streamflow) (distribution with covariates hereafter called DC method) were compared to four load estimation methods: (1) flow‐weighted mean concentration; (2) integral regression; (3) segmented regression (the last two with Ferguson's correction factor); and (4) hydrograph separation methods. A total of 25 datasets (from 19 stations) of daily concentrations of total dissolved solids, nutrients, or suspended particulate matter were used. The selected stations represented a wide range of hydrological conditions. Annual flux errors were determined by randomly generating 50 monthly sample series from daily series. Annual and interannual biases and dispersions were evaluated and compared. The impact of sampling frequency was investigated through the generation of bimonthly and weekly surveys. Interannual uncertainty analysis showed that the performance of the DC method was comparable with those of the other methods, except for stations showing high hydrological variability. In this case, the DC method performed better, with annual biases lower than those characterizing the other methods. Results show that the DC method generated the smallest pollutant load errors when considering a monthly sampling frequency for rivers showing high variability in hydrological conditions and contaminant concentrations.     

Statistical Evaluation of Variables Affecting Occurrence of Hydrocarbons in Aquifers Used for Public Supply,California

The variables affecting the occurrence of hydrocarbons in aquifers used for public supply in California were assessed based on statistical evaluation of three large statewide datasets; gasoline oxygenates also were analyzed for comparison with hydrocarbons. Benzene is the most frequently detected (1.7%) compound among 17 hydrocarbons analyzed at generally low concentrations (median detected concentration 0.024 μg/l) in groundwater used for public supply in California; methyl tert‐butyl ether (MTBE) is the most frequently detected (5.8%) compound among seven oxygenates analyzed (median detected concentration 0.1 μg/l). At aquifer depths used for public supply, hydrocarbons and MTBE rarely co‐occur and are generally related to different variables; in shallower groundwater, co‐occurrence is more frequent and there are similar relations to the density or proximity of potential sources. Benzene concentrations are most strongly correlated with reducing conditions, regardless of groundwater age and depth. Multiple lines of evidence indicate that benzene and other hydrocarbons detected in old, deep, and/or brackish groundwater result from geogenic sources of oil and gas. However, in recently recharged (since ~1950), generally shallower groundwater, higher concentrations and detection frequencies of benzene and hydrocarbons were associated with a greater proportion of commercial land use surrounding the well, likely reflecting effects of anthropogenic sources, particularly in combination with reducing conditions.     

基于边界定位法的填埋场渗漏检测

提出了以传输线模型为基础的新型填埋场渗漏检测方法,并且建立了填埋场渗漏检测的非均匀传输线模型.利用模型中边界电气量以及渗漏区域2个分界面电压连续的基本原理推导出测距方程,通过线路两端电气量的采样值,计算模型的特性阻抗和传播系数.结果表明:检测频率为30 MHz时,渗漏和未渗漏区域的衰减常数和相位常数差异性最大;同一频率下,渗漏区域的值高于未渗漏区域;特性阻抗的模值随频率增加而减小,辐角则随频率的增加而增大;测量中不同步采样对测距精度有一定影响.为提高测距精度,应选用较高的检测频率并保证两端电气量同步采样.      

颗粒煤的多扩散系数瓦斯解吸模型及扩散参数反演研究

为研究颗粒煤瓦斯解吸规律,基于Fick定律建立了颗粒煤的多扩散系数瓦斯解吸 模型,完成了颗粒煤瓦斯解吸模型的数值试验。引入了非负约束最小二乘法反演算法（ NNLS）,通过试验数据反演得出颗粒煤的扩散参数的B谱,从而确定出颗粒煤瓦斯扩散 系数D的准确范围。研究结果表明:颗粒煤瓦斯解吸符合Fick扩散定律,颗粒煤的多扩 散系数瓦斯解吸模型能很好地解决单一扩散系数模型的扩散系数随时间衰减的问题,准 确反映了颗粒煤瓦斯解吸规律,单一扩散系数瓦斯解吸模型只是多扩散系数瓦斯解吸模 型的一个特例;NNLS是一种有效的反演算法,利用NNLS方法可以准确反演出颗粒煤瓦斯 解吸过程中的扩散参数的B谱,通过B谱可方便计算出颗粒煤的瓦斯扩散系数。     

Does localized imitation drive technology adoption? A case study on rooftop photovoltaic systems in Germany

The purpose of this paper is to illuminate the spatio-temporal diffusion of rooftop household photovoltaic installations in Germany and to test whether localized imitation drives their adoption. Our study is based on a unique data set of some 576,000 household photovoltaic systems installed in Germany through 2009. We employ an epidemic diffusion model which includes a spatial dimension, and control for temporal and spatial heterogeneity. According to our results, imitative adoption behavior is highly localized and an important factor for the diffusion of household photovoltaic systems.     

有机污染物的被动采样材料-水分配系数的QSAR研究

污染物的被动采样材料-水分配系数(K_(PW))是衡量被动采样器性能和进行优化的一个重要指标,由于实验方法难以逐个测定众多污染物的K_(PW)值,有必要发展其KP W预测方法。本研究选取聚乙烯(PE)、聚丙烯酸酯(PA)和硅橡胶(SR)3类常用的被动采样材料共7种,采用多元线性回归分析方法构建可用于K_(PW)预测的定量构效关系(QSAR)模型。所构建的QSAR模型具有良好的拟合优度(R2a dj介于0.806~0.989)、稳健性(Q2L O O和Q2B O O T分别介于0.786~0.988和0.773~0.801)和预测能力(R_(ext)~2和Q_(ext)~2分别介于0.769~0.989和0.757~0.982),可以用于预测烷烃、烯烃、芳香类、醇类、酮类、酯类、醚类等多种有机污染物的log KP W值。有机污染物的log KP W与分子Mc Gowan体积(Vx)、氯原子个数(n Cl)、环周长(Rperim)、多重键个数(n BM)、N,O极性贡献的拓扑极性表面积(TPSA(NO))、[-N(=)=]结构个数(Ndds N)和羟基个数(nROH)等参数有关。