强化污水人工土快滤处理系统除氮效果的研究

传统的人工土处理系统对污水中氮的去除率较低,改进后的污水人工土处理系统由珍珠岩人工土硝化柱和原人工土反硝化柱构成。试验结果表明,硝化柱有较好的通气条件,对NH⁺₄－N的转化去除率为84％～98％;反硝化柱中碳氮比（BOD₅/NO^－_x－N）以3.0左右、水力停留时间为6h左右较适宜。原人工土快滤系统经强化处理后,对污水中氮的去除率有较大幅度的提高,全N去除率由45％提高到91％～96％,出水水质全N和NH⁺₄－N含量小于5mg/L,NO^－_x－N［（NO^－₃＋NO^－₂－N的含量远小于10mg/L。     

环境质量综合评价的径向基函数网络模型

建立了一个环境质量综合评价的径向基函数人工神经网络(RBF-ANN)模型,同时提出了一种简便?快速的RBF-ANN的最小二乘算法(RBF-LS算法)?在城市环境质量综合评价的应用中表明,提出的RBF-ANN模型及其算法是合理?有效?通用的?      

基于BP神经网络的大气污染物浓度预测

利用BP神经网络结合变量筛选的方法建立了SO_2、NO_2、O3、CO、PM_(10)、PM_(2.5)等6种污染物的浓度预测模型,并选取2014-01-01至2015-11-28时段,昆明市区5个环境监测点以上6种污染物浓度的监测数据建立了昆明市污染物日均浓度预测模型.采用平均影响值(Mean Impact Value,MIV)的方法筛选出分别对6种污染物日均浓度值有主要影响的变量,作为BP神经网络的输入变量,利用建立的预测模型分别对6种污染物的日均浓度进行预测.结果表明,在关上监测点利用浓度预测模型对SO_2、NO_2、O3、CO、PM_(10)、PM_(2.5)等6种污染物浓度进行预测,污染物浓度预测值和实测值趋势吻合度较高.变量筛选后SO_2、PM_(2.5)预测效果比变量筛选前的预测效果好.O3的均方根误差和PM_(10)的标准化平均偏差,变量筛选前的预测效果比变量筛选后的预测效果好.变量筛选前的NO_2和CO的预测结果比变量筛选后的预测效果好.其他4个环境监测点的污染物浓度预测结果与关上监测点的结果相似.     

软土中浅埋地铁车站结构的抗震性能分析

两侧采用粘弹性人工边界,底部为全约束,建立合理的土与地下结构相互作用模型,通过有限元软件分析,比较研究土—地下结构相互作用分析中埋深、土层分布和结构类型等因素对地下结构抗震性能的影响。结果表明,埋深对地下结构影响比较大,选择合适的地质土层和结构类型能大大提高结构抗震性能。     

地震作用下土‐深埋地下结构相互作用的高效时程分析方法∗

基于黏弹性边界和将场地地震反应转化为等效荷载的有限元直接法是目前进行地震作用下土-结构相互作用分析的常用时程分析方法之一。当结构埋深较深时,整个土-结构系统的有限元模型自由度数目较多,尤其对于三维问题,计算效率低。提出一种高效分析方法,即一维场地反应分析仍然针对整个深厚土层,在后续的土-结构相互作用分析中将土-结构计算模型的自由表面向下移动、底边界面向上移动到接近结构的位置,通过缩减土-结构相互作用模型尺寸来提高计算效率。采用理论分析与数值算例,通过与采用整个深厚土层的传统土-结构相互作用分析结果对比,说明提出的高效分析方法能够满足精度要求,并且针对不同结构尺寸、结构埋深和围岩等级给出上、下人工边界位置的建议。     

Roadside vehicle particulate matter concentration estimation using artificial neural network model in Addis Ababa, Ethiopia

Currently, vehicle-related particulate matter is the main determinant air pollution in the urban environment. This study was designed to investigate the level of fine (PM_2.5) and coarse particle (PM₁₀) concentration of roadside vehicles in Addis Ababa, the capital city of Ethiopia using artificial neural network model. To train, test and validate the model, the traffic volume, weather data and particulate matter concentrations were collected from 15 different sites in the city. The experimental results showed that the city average 24-hr PM_2.5 concentration is 13%–144% and 58%–241% higher than air quality index (AQI) and world health organization (WHO) standards, respectively. The PM₁₀ results also exceeded the AQI (54%–65%) and WHO (8%–395%) standards. The model runs using the Levenberg-Marquardt (Trainlm) and the Scaled Conjugate Gradient (Trainscg) and comparison were performed, to identify the minimum fractional error between the observed and the predicted value. The two models were determined using the correlation coefficient and other statistical parameters. The Trainscg model, the average concentration of PM_2.5 and PM₁₀ exhaust emission correlation coefficient were predicted to be (R² = 0.775) and (R² = 0.92), respectively. The Trainlm model has also well predicted the exhaust emission of PM_2.5 (R² = 0.943) and PM₁₀ (R² = 0.959). The overall results showed that a better correlation coefficient obtained in the Trainlm model, could be considered as optional methods to predict transport-related particulate matter concentration emission using traffic volume and weather data for Ethiopia cities and other countries that have similar geographical and development settings.     

引信步进应力加速试验贮存寿命预测研究

目的 针对某机电引信加速寿命试验数据,采用传统统计分析方法存在计算量大、寿命预测精度难以保证的问题,开展与智能算法相结合的引信贮存寿命预测研究。方法 针对步进应力加速寿命试验数据,采用贝叶斯理论的环境因子法,对各级应力下的贮存时间进行折合计算。利用进化策略对粒子群算法进行改进,进而对所建立的BP神经网络预测模型的全局参数进行调整和优化,突破传统方法的局限。将折合后的试验时间、样本量、应力水平作为网络输入,失效数作为输出,来预测引信贮存寿命。结果 利用训练好的 BP神经网络预测引信在正常应力水平下的失效数,计算其贮存可靠度。在迭代402次后,模型找到最优解,且预测误差在1%以内。结论 步进应力加速寿命试验与智能算法相结合的方法计算过程简单,预测精度较高,可有效提高引信贮存寿命的预测精度。     

BP神经网络和遗传算法在红发夫酵母培养基优化中的应用

运用BP神经网络对红发夫酵母发酵培养基组成进行建模以及预测类胡萝卜素产量,在此基础上采用遗传算法对此模型进行全局寻优.得到红发夫酵母发酵培养基的最佳配比为:蔗糖45.10 g/L,硫酸铵3.00 g/L,硫酸镁0.80 g/L,磷酸二氢钾1.40 g/L,酵母膏3.00 g/L,氯化钙0.50 g/L,类胡萝卜素产量达到8.20 mg/L,干重达到9.47 g/L.采用上述方法优化后的培养基使类胡萝卜素的产量比起始培养基提高了95.90%.     

Structure of the social network and its influence on transmission dynamics in a honeybee colony

Infectious processes in a social group are driven by a network of contacts that is generally structured by the organization arising from behavioral and spatial heterogeneities within the group. Although theoretical models of transmission dynamics have placed an overwhelming emphasis on the importance of understanding the network structure in a social group, empirical data regarding such contact structures are rare. In this paper, I analyze the network structure and the correlated transmission dynamics within a honeybee colony as determined by food transfer interactions and the changes produced in it by an experimental manipulation. The study demonstrates that widespread transmission in the colony is correlated to a lower clustering coefficient and higher robustness of the social network. I also show that the social network in the colony is determined by the spatial distribution of various age classes, and the resulting organizational structure provides some amount of immunity to the young individuals. The results of this study demonstrates how, using the honeybee colony as a model system, concepts in network theory can be combined with those in behavioral ecology to gain a better understanding of social transmission processes, especially those related to disease dynamics.     

Fish Responses to Experimental Fragmentation of Seagrass Habitat

Abstract: Understanding the consequences of habitat fragmentation has come mostly from comparisons of patchy and continuous habitats. Because fragmentation is a process, it is most accurately studied by actively fragmenting large patches into multiple smaller patches. We fragmented artificial seagrass habitats and evaluated the impacts of fragmentation on fish abundance and species richness over time (1 day, 1 week, 1 month). Fish assemblages were compared among 4 treatments: control (single, continuous 9‐m² patches); fragmented (single, continuous 9‐m² patches fragmented to 4 discrete 1‐m² patches); prefragmented/patchy (4 discrete 1‐m² patches with the same arrangement as fragmented); and disturbance control (fragmented then immediately restored to continuous 9‐m² patches). Patchy seagrass had lower species richness than actively fragmented seagrass (up to 39% fewer species after 1 week), but species richness in fragmented treatments was similar to controls. Total fish abundance did not vary among treatments and therefore was unaffected by fragmentation, patchiness, or disturbance caused during fragmentation. Patterns in species richness and abundance were consistent 1 day, 1 week, and 1 month after fragmentation. The expected decrease in fish abundance from reduced total seagrass area in fragmented and patchy seagrass appeared to be offset by greater fish density per unit area of seagrass. If fish prefer to live at edges, then the effects of seagrass habitat loss on fish abundance may have been offset by the increase (25%) in seagrass perimeter in fragmented and patchy treatments. Possibly there is some threshold of seagrass patch connectivity below which fish abundances cannot be maintained. The immediate responses of fish to experimental habitat fragmentation provided insights beyond those possible from comparisons of continuous and historically patchy habitat.