基于数据分解的AQI的CEEMD-Elman神经网络预测研究

针对Elman神经网络在预测空气质量指数（AQI）时易受到数据非平稳性的影响导致预测趋势良好但准确度较低的问题,提出以互补集合经验模态分解（Complementary Ensemble Empirical Mode Decomposition,CEEMD）为基础的CEEMD-Elman模型.应用CEEMD对AQI序列分解成不同时间尺度上的本征模态函数分量和剩余分量,进而首次将对非平稳的AQI序列的预测研究转化为对多个平稳的本征模态函数分量的研究.分别与Elman单一模型、EMD-Elman模型、BP单一模型及CEEMD-BP模型进行实验对比.结果表明：应用该方法建立的模型的均方误差、平均绝对误差和平均绝对百分比误差分别为4.80、0.71、1.84%,均小于其他模型结果;对应空气质量等级预报正确天数的频率为94.12%.该模型能有效的降低非平稳性对实验预测结果的影响,实现对空气质量等级的准确预报;该研究为进一步预测AQI的走向提供了有效依据,也为政府决策和管理部门制定空气污染控制提供了更充分的参考.     

上海市大气污染对感冒疾病相对危险度的影响

探讨上海市主要大气污染PM₁₀、PM_2.5、NO₂和O₃的日平均浓度变化以及上海市大气污染对感冒疾病的影响.本文汇总了2008年1月1日~2010年12月31日上海市大气污染、气象要素,以及感冒日就诊人数数据,采用时间序列的Possion半参数广义相加模型,通过平滑样条函数控制长期趋势、"星期几效应"及气象因素等混杂因素的影响,分析上海市大气污染物与居民健康的暴露-反应关系,并按年龄进行分层分析,定量评估上海市大气污染对感冒日就诊人数的影响及滞后效应.结果表明,上海市大气污染物PM₁₀、NO₂、O₃、PM_2.5浓度每增加一个IQR,感冒疾病发生的相对危险度为1.0240（1.0233~1.0246）、1.0206（1.0201~1.0212）、0.9393（0.9384~0.9402）、1.0080（1.0069~1.0086）,大气污染物PM₁₀、NO₂、O₃、PM_2.5浓度每增加10 μg·m^-3,感冒日就诊人数分别增加0.5%、1.0%、-2.0%、0.2%.在多污染模型中,在引入其他污染物后,NO₂和PM_2.5的结果较单污染模型是基本减小的,PM₁₀和O₃的结果是基本增大的.上海市大气污染对感冒疾病的发生有影响.     

FNA对短程硝化污泥菌群结构的影响

在SBR反应器增加游离亚硝酸（FNA）预处理单元,投加浓度为1.2mgHNO₂-N/L的FNA进行缺氧搅拌4.5h,连续处理3d,考察短程硝化污泥中FNA对氨氧化菌（AOB）,丝状菌和微生物菌群结构的影响.研究表明,FNA对AOB有短时抑制作用,并能够抑制优势丝状菌Candidatus_Microthrix（微丝菌属）和Cytophagaceae（噬纤维菌）的增殖,分别由5.1%和1.1%下降到0.78%和几乎不可见.SVI从281mL/g降低到100mL/g左右.NAR能够维持在90%左右,短程硝化不受到破坏.高通量结果显示,FNA处理后微生物菌群结构多样性与丰度出现下降,但Thauera（陶厄氏菌属）和Ottowia出现了增殖,分别增加到5.58%和7.82%,同步硝化反硝化（SND）作用明显,这使得即便只有短程硝化,总氮去除率依然能达到60%以上.     

人群的自然和社会属性分类下PM2.5对心血管疾病的影响

为了研究人群在自然属性和社会属性分类下的效应修饰作用,利用2005~2011年北京市大气PM_2.5日均浓度、气象要素及循环系统疾病每日死亡人数数据,根据不同年龄、性别及学历将人群分组,采用时间序列的半参数广义相加模型（GAM）,定量评价大气PM_2.5对居民循环系统疾病死亡人数的影响.单污染物模型和多污染物模型结果表明,PM_2.5为影响循环系统疾病每日死亡人数的主要大气污染物.北京市空气中PM_2.5的浓度每增加10μg/m³,循环系统疾病总死亡人数增加0.50%（95% CI：0.36,0.63）.不同人群的易感程度不同,各人群死亡人数的增加范围为0.23~0.71%,女性、60~74岁人群、文盲人群的健康风险相对较高.在文盲人群中,老年（60+）女性占比为66.2%,为了分离社会属性人群分类中可能存在的自然属性因素干扰,本文去除其中老年、女性这两个混杂因素的影响,文盲人群健康风险仍然大于较高学历的人群.年龄和性别因素叠加在学历因素上,可能加大低学历人群的健康风险,对于这部分叠加了自然因素和社会因素双重敏感性的人群应给与重点关注.     

"一退两还"中的博弈分析与制度创新

“一退两还”即“退耕还林还草”，是我国政府在世纪之交所作出的一项旨在推进国民经济可持续发展的战略举措。本文运用博弈论的方法，通过对“一退两还”中的各个相关主体利益的思考和行为的辨析，结合分析结论，提出了对提高“一退两还”实施效果具有重要意义的制度创新思路。     

Effect on fatality risk of changing from secondary to primary seat belt enforcement

BACKGROUND: Most seat belt use laws originally passed in the United States contained language restricting enforcement to drivers already stopped for some other reason. States that have since removed this secondary enforcement restriction have reported increased seat belt use. The purpose of the present study was to estimate the effect of these law changes on driver fatality rates. METHOD: Trends in passenger vehicle driver death rates per billion miles traveled were compared for 10 states that changed from secondary to primary seat belt enforcement and 14 states that remained with secondary enforcement. RESULTS: After accounting for possible economic effects and other general time trends, the change from secondary to primary enforcement was found to reduce annual passenger vehicle driver death rates by an estimated 7% (95% confidence limits 3.0-10.9). CONCLUSION: The majority of U.S. states still have secondary enforcement laws. If these remaining secondary laws were amended, an estimated 696 deaths per year could be prevented.     

基于偏序集的采空区塌陷危险性评价研究

针对采空区塌陷危险性评价方法中的赋权争议问题,基于偏序集理论提出偏序集评价模型。首先阐明评价指标并确定其分级标准;然后应用偏序集评价模型得到Hasse图,并通过图展现的层集信息判别采空区塌陷危险程度;最后应用该模型对大宝山矿11个采空区的塌陷程度进行判别,评价结果准确合理。研究结果表明:该模型避免了以往研究中的赋权争议问题,克服了样本量不足致使模型无法应用的问题,而且能够应用更多的赋权方法识别排序的稳定程度,体现出样本间的分层信息。     

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.     

Flow Dynamics in Eccentrically Rotating Flasks Used for Dispersant Effectiveness Testing

The evaluation of dispersant effectiveness used for oil spills is commonly done using tests conducted in laboratory flasks. We used a Hot Wire Anemometer (HWA) to characterize mixing dynamics in the Swirling Flask (SF) and the Baffled Flask (BF), the latter is being considered by the EPA to replace the prior to test dispersant effectiveness in the laboratory. Five rotation speeds of the orbital shaker carrying the flasks were considered, Ω = 50, 100, 150, 175 and 200 rpm. The radial and azimuthal water speeds were measured for each Ω. It was found that the flow in the SF is, in general, two-dimensional changing from horizontal at low Ω to axi-symmetric at high Ω. The flow in the BF appeared to be three-dimensional at all rotation speeds. This indicates that the BF is more suitable for representing the (inherently) 3-D flow at sea. In the SF, the speeds and energy dissipation rates ɛ increased gradually as the rotation speed increased. Those in the BF increased sharply at rotation speeds greater than 150 rpm. At 200 rpm, the Kolmogorov scale (i.e., size of smallest eddies) was about 250 and 50 μm in the SF and BF, respectively. Noting that the observed droplet sizes of dispersed oils range from 50 to 400 μm (hence most of it is less than 250 μm), one concludes that the mixing in the SF (even at 200 rpm) is not representative of the vigorous mixing occurring at sea.     

NONLINEAR MODELING AND PREDICTION OF A RIVER FLOW SYSTEM1

ABSTRACT: Model estimation and prediction of a river flow system are investigated using nonlinear system identification techniques. We demonstrate how the dynamics of the system, rainfall, and river flow can be modeled using NARMAX (Nonlinear Autoregressive Moving Average with eXogenuous input) models. The parameters of the model are estimated using an orthogonal least squares algorithm with intelligent structure detection. The identification of the nonlinear model is described to represent the relationship between local rainfall and river flow at Enoree station (inputs) and river flow at Whitmire (output) for a river flow system in South Carolina.