大孔径冲孔翻边一次成型的设计

此文主要根据冲压件“前顶梁”冲孔翻边一次成型模设计的实践经验,提出大孔径冲孔翻边一次成型的工艺分析和计算及典型结构的设计,突破了冲孔与翻边同时进行只能在Φ8以下圆孔翻边的局限,并成功地一次试模合格,为类似的圆孔翻边在Φ8以上提供了宝贵的经验。     

洗柱废液的分析与再利用

本文通过对医药ＰＧ０５生产过程中产生的洗柱废液进行气相色谱分析，建立了洗柱废液二氯甲烷、甲醇的色谱分析条件，并且进行定性、定量的分析，从而达到重新利用的目的．该方法已在东北制药总厂三分厂应用．     

污染物排放波动特征及对排污许可限值的影响

受生产活动变化的影响,企业的污染物排放具有一定的波动性,制定基于不同时间周期的污染物排污许可限值,是完善企业污染物排放监管的需要。以铁岭市企业在线监测数据为基础,采用短周期排放量与长周期排放量的比值β,研究了企业行业特征和规模对不同周期污染物排放量的影响。结果表明,行业对β值具有重要的影响,铁岭市屠宰行业、煤炭行业和污水处理厂COD日排放量与年排放量的比值β平均值分别为2.82、2.61和1.65,NH-N排放量Cr MDL 3的β平均值分别为2.9、3.49和2.59,表明污水处理厂相比屠宰行业和煤炭行业具有更强的稳定性。从企业规模来看,MDL企业规模较小时,由于生产稳定性差β值波动较大;企业规模较大时,β值趋于稳定。因此,制定不同周期的企业排污许可限值时,应基于不同的行业特征和企业规模给出不同的参考比值。     

Model selection in demographic time series using VC-bounds

The problem of distinguishing density-independent (DI) from density-dependent (DD) demographic time series is important for understanding the mechanisms that regulate populations of animals and plants. We address this problem in a novel way by means of Statistical Learning Theory (SLT); SLT is built around the idea of VC-dimension, a complexity index for classes of parameterized functions. Though VC-dimensions of nonlinear models are generally unknown, in the linear case VC-dimension actually corresponds to the number of free parameters; this allows one to straightforwardly apply the model selection framework developed within SLT, and called Structural Risk Minimization (SRM). We generate noisy artificial time series, both DI and DD, and use SRM to recognize the model underlying the data, choosing among a suite of both density-dependent and independent demographies. We show that SRM significantly outperforms traditional model selection approaches, such as the Schwartz Information Criterion and Final Prediction Error in recognizing both density-dependence and independence.     

Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo

Coral reefs are threatened ecosystems, so it is important to have predictive models of their dynamics. Most current models of coral reefs fall into two categories. The first is simple heuristic models which provide an abstract understanding of the possible behaviour of reefs in general, but do not describe real reefs. The second is complex simulations whose parameters are obtained from a range of sources such as literature estimates. We cannot estimate the parameters of these models from a single data set, and we have little idea of the uncertainty in their predictions.We have developed a compromise between these two extremes, which is complex enough to describe real reef data, but simple enough that we can estimate parameters for a specific reef from a time series. In previous work, we fitted this model to a long-term data set from Heron Island, Australia, using maximum likelihood methods. To evaluate predictions from this model, we need estimates of the uncertainty in our parameters. Here, we obtain such estimates using Bayesian Metropolis-Coupled Markov Chain Monte Carlo. We do this for versions of the model in which corals are aggregated into a single state variable (the three-state model), and in which corals are separated into four state variables (the six-state model), in order to determine the appropriate level of aggregation. We also estimate the posterior distribution of predicted trajectories in each case.In both cases, the fitted trajectories were close to the observed data, but we had doubts about the biological plausibility of some parameter estimates. We suggest that informative prior distributions incorporating expert knowledge may resolve this problem. In the six-state model, the posterior distribution of state frequencies after 40 years contained two divergent community types, one dominated by free space and soft corals, and one dominated by acroporid, pocilloporid, and massive corals. The three-state model predicts only a single community type. We conclude that the three-state model hides too much biological heterogeneity, but we need more data if we are to obtain reliable predictions from the six-state model. It is likely that there will be similarly large, but currently unevaluated, uncertainty in the predictions of other coral reef models, many of which are much more complex and harder to fit to real data.     

Environment-specific elasticity and sensitivity analysis of the stochastic growth rate

For matrix population models, analyses of how sensitive the population growth rate is to changes in vital rates (i.e. perturbations) are important for studies of life history evolution as well as for management and conservation of threatened species. There are two types of sensitivity analyses corresponding to absolute (sensitivity) or relative (elasticity) changes in the vital rates and both types can be applied to both deterministic and stochastic matrix population models. To date, most empirical studies of elasticity and sensitivity of the stochastic growth rate have examined the response to perturbations in the vital rates in a complete set of possible environments. However, it is often of interest to examine the response to perturbations occurring in only a subset of the possible environments. This has been done for periodic time-varying models elsewhere, but here we describe a recently published method for calculating the environment-specific sensitivity and elasticity of the stochastic growth rate and apply this method to data. These environment-specific perturbation analyses provide a logical way of dividing the sensitivity and elasticity among the environments. They give important insight into the selection regime in different environments and can provide valuable information for making management decisions and management evaluations in stochastic environments.     

Long-term trends and short-term variability of water quality in Skive Fjord, Denmark – nutrient load and mussels are the primary pressures and drivers that influence water quality

Nineteen years of monitoring data from the eutrophic Skive Fjord, Denmark were examined for linkages to external pressures and drivers, including nutrient inputs, meteorology and stocks of blue mussels. Linkages were examined by: 1) time-series analysis to document effects of nutrient reduction programs, 2) Pearson Rank correlations, 3) multivariate statistical analysis (PLS) to identify water quality variables with high predictability and their linkages to pressures, and 4) regression analysis to quantify relationships between pressures and water quality. Freshwater input, nitrogen load and phosphorus load showed decreasing trends through the period 1984–2002. The load reductions were only partially translated into trends in water quality: phosphorus decreased in most seasons, while total nitrogen decreased during winter and spring only. Phosphorus concentration had the highest predictability (explained by seasonal temperature variation) followed by transparency, silicate, tot-N, chlorophyll-a, primary productivity, phytoplankton diversity and phytoplankton turnover. The variation in pressures other than nutrient input confounded the relations between loads and water quality. High biomass of mussels led to reduced chlorophyll-a and increased transparency, while short-term variability in water column mixing led to changes in chlorophyll-a due to nutrient entrainment and coupling to benthic mussels.     

北京市环境电磁辐射自动站数据分析及网络建设

选取北京市环境电磁辐射自动监测站点2年多的数据,利用SPSS统计软件,对各站点数据进行了主要电磁辐射源、长期数据趋势、各站点时间代表性分析;并结合网格法监测数据,对自动站点空间代表性进行分析。结果表明,5个环境电磁辐射自动站周围主要电磁辐射源均为移动通信基站;其总体电磁辐射水平为0.69~0.97 V/m,低于北京市1 V/m的电磁辐射环境本底水平,且单站点连续12个月的监测数据可以描述该点位电磁辐射平均水平和变化趋势;经分析,连续5年全市网格法电磁辐射射频电场的监测数据总体具有较好的一致性,但单站点的电磁辐射水平不能代表所在行政区的电磁辐射总体水平。提出了电磁辐射自动监测网络建设建议。     

江河防洪系统年超标洪水风险率模型研究

本文在考虑最近一次历史洪水发生时间这一因素下,提出了防洪系统年洪水风险率模型.模型反映了年洪水风险率随距己发生洪水的间隔时间递增的规律.最后对这种规律对防洪标准的影响进行了讨论.