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1.
A sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Then, the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH4+-N. The results showed that the optimal removal efficiencies would be obtained at FT of 1 h, RT of 6 h, aeration intensity of 0.88 m3/min and SRT of 30 days. In addition, COD and TSS removal efficiencies decreased and TP and NH4+-N removal efficiencies did not change significantly with increases of influent concentration. The TSS, TP, COD and NH4+-N removal efficiencies were 86%, 79%, 94% and 93%, respectively. The training procedures of all contaminants were highly collaborated for both RBFANN and MLPANN models. The results of training and testing data sets showed an almost perfect match between the experimental and the simulated effluent of TSS, TP, COD and NH4+-N. The results indicated that with low experimental values of input data to train ANNs the MLPANN models compared to RBFANN models are more precise due to their higher coefficient of determination (R2) and lower root mean squared errors (RMSE) values. 相似文献
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S. Savas S. Eraslan S. Kantarci B. Karaman D. Acarsoz T. Tükel O. Cogulu F. Ozkinay S. Basaran K. Aydınlı M. Yuksel-Apak B. Kirdar 《黑龙江环境通报》2002,22(8):703-709
Childhood-onset spinal muscular atrophy (SMA) is one of the most common neurodegenerative genetic disorders. SMN1 is the SMA-determining gene deleted or mutated in the majority of SMA cases. There is no effective cure or treatment for this disease yet. Thus, the availability of prenatal testing is important. Here we report prenatal prediction for 68 fetuses in 63 Turkish SMA families using direct deletion analysis of the SMN1 gene by restriction digestion. The genotype of the index case was known in 40 families (Group A) but unknown in the remaining 23 families (Group B). A total of ten fetuses were predicted to be affected. Eight of these fetuses were derived from Group A and two of these fetuses were from Group B families. Two fetuses from the same family in Group A had the SMNhyb1 gene in addition to homozygous deletion of the NAIP gene. One fetus from Group A was homozygously deleted for only exon 8 of the SMN2 gene, and further analysis showed the presence of both the SMN1 and SMNhyb1 genes but not the SMN2 gene. In addition, one carrier with a homozygous deletion of only exon 8 of the SMN1 gene was detected to have a SMNhyb2 gene, which was also found in the fetus. To our knowledge, these are the first prenatal cases with SMNhyb genes. Follow-up studies demonstrated that the prenatal predictions and the phenotype of the fetuses correlated well in 33 type I pregnancies demonstrating that a careful molecular analysis of the SMN genes is very useful in predicting the phenotype of the fetus in families at risk for SMA. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
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黄河三角洲典型地区耕地土壤养分空间预测 总被引:8,自引:2,他引:6
掌握土壤养分的分布特点是实现养分优化管理的重要基础。论文选择黄河三角洲典型地区山东省垦利县为研究区,通过田间采样与实验室化验分析获取了1 278个样本(0~20 cm)的土壤碱解氮、有效磷、速效钾数据。在经典统计分析的基础上,用地统计学方法分析了土壤养分的空间变异特征,并拟合了养分的变异函数模型。利用普通克里格法(OK)、反距离权重法(IDW)、泛克里格法(UK)、径向基函数法(RBF)和局部多项式法(LP)5种方法进行空间插值,并采用独立数据集验证对插值结果进行精度评价,进而分析了各养分空间分布规律。为深入探索各方法的适用性规律,基于AN数据设计了离散、随机、聚集3种空间分布模式的数据,利用各模型的自动优化进行试验,对比分析了不同插值方法在土壤养分空间预测中的自适应性。结果表明:1)研究区碱解氮、有效磷、速效钾均为中等强度的空间变异和中等程度的空间自相关,其变异函数模型分别为球状模型、指数模型和球状模型,决定系数依次为0.951、0.892和0.787;2)在空间分布上,土壤碱解氮、有效磷、速效钾含量与地形和土地利用类型等有关,西南部地势较高,以水浇地和旱田为主,东北部沿黄农田受黄河淡水影响,耕地质量较好,而中部地区地势低平,以水田为主,养分含量偏低;3)相对于块金系数/基台值,Moran’s I是更为稳健有效的衡量土壤养分空间自相关性的方法;4)论文认为,空间分布模式、样本量、空间自相关性和空间聚集程度(最近邻比)均影响插值精度。在离散模式下,各方法自适应性均较差;在随机模式下,IDW与RBF自适应性优于OK和LP;在聚集模式下,各方法自适应性与样本量和空间自相关性有关,直至样本足够多时,4种插值方法精度接近。论文探明了研究区主要土壤养分的最佳插值预测方法,分析了土壤养分的变异特征和空间分布规律,为黄河三角洲典型地区耕地土壤养分利用管理和农业可持续发展提供了理论依据。 相似文献
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大气自净能力指数的气候特征与应用研究 总被引:1,自引:0,他引:1
为了定量地评估污染气象条件对空气污染的作用并实现对空气污染潜势的预报,本文在城市大气污染数值预报系统(CAPPS)预报原理的基础上,定义了大气自净能力指数,并分别给出了采用气象站观测资料和通过数值模拟计算大气自净能力指数的方法.基于气象站观测资料的全国大气自净能力指数分析计算表明,全国大气自净能力最差的地区分布在四川盆地和新疆塔里木盆地,大气自净能力最强的地区分布在青藏高原、蒙古高原、云贵高原、以及东北平原和三江平原、山东半岛和海南岛;1961~2017年,京津冀、长三角和珠三角地区的大气自净能力指数呈下降的变化趋势,全年低自净能力日数呈上升的变化趋势.采用大气自净能力指数评估2014年北京APEC会议期间大气污染防控效果,表明在11月8~10日极端不利扩散气象条件发生时,减排措施使北京市空气质量AQI平均降低77%,使京津冀平原地区11个城市的空气质量AQI平均降低37%.基于国家气候中心月动力延伸气候预测模式(DERF2.0)的预报产品和中尺度模式(WRF),建立了可以预测全国未来40d逐日大气自净能力指数的延伸期-月尺度大气污染潜势预测系统,回报实验表明,在大多数情况下可以提前15d预报出大气重污染过程;月尺度的大气重污染过程预报效果更大程度上取决于月动力延伸气候预测模式(DERF2.0)的预报准确率. 相似文献
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基于成都市2013年6月~2015年5月期间由Mie散射激光雷达探测的大气消光系数廓线资料,发现混合层以上在颗粒物消光和分子消光之间一致存在一个S型的过渡区,利用sigmoid函数对此分布形态进行模拟,通过计算该函数上下曲率最大点所在的高度,据此提出了颗粒物分界层Mie散射激光雷达识别的sigmoid算法.针对该算法模拟效果的分析表明,颗粒物分界层过渡区附近大气消光系数理论廓线和实测廓线保持了高度的相关性,二者在春夏秋冬四季的相关系数(R)分别为0.9971±0.0052、0.9935±0.0167、0.9979±0.0038和0.9990±0.0021(均通过α=0.05的显著性检验).基于sigmoid算法计算的颗粒物分界层过渡区与成都市温江站探空资料得到的逆温层之间存在很好的对应关系. 相似文献
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Consistent estimators of change and state becomes an issue when sample data come from a mix of permanent and temporary observation units. A joint maximum likelihood estimator of state and change creates estimates of state that depend on antecedent viz. posterior survey results and may differ from estimates of state derived from a single-date analysis of the sample data. A constrained estimator of change in relative categorical frequencies that eliminates this potential inconsistency is proposed and a model based estimator of their sampling variance is developed. The performance of the constrained estimator is quantified against six criteria and a joint maximum likelihood estimator in simulated sampling from 15 populations with three combinations of permanent and temporary samples, four to six categorical class attributes, and constant size between sampling dates. Bias of the constrained estimators was negligible but larger than for joint maximum likelihood estimators. Mean absolute deviations and variances of constrained estimators were generally at par with the joint estimators. Constrained estimators of root mean square errors and achieved coverage of nominal confidence intervals of constrained estimators were occasionally better. A generalized variance function for the constrained estimates of change is provided as a computational shortcut. 相似文献
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Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong 总被引:4,自引:0,他引:4
As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NOx and NO2 are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NOx and NO2 concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NOx and NO2 concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods. 相似文献