Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

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 NO_x 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, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x 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.     

B-P网络应用于地下水水质监测点优选

以地下水水质评价分级标准作为训练样本,构造B-P网络模型对其进行训练,用训练好的B-P网络对某地的地下水水质监测点进行评判、优选。并与其它方法的结果进行比较,结果表明,B-P网络用于环境测点优选不仅原理直观,而且具有较好的客观性和实用性。     

基于B-P神经网络的环境质量评价方法

提出可将环境质量评价的无论是定量指标还是定性参数转化成"二进制"的"1"或"0",进而将这种二进制数引入B-P网络.通过实例探讨,这种新的B-P网络既适用于定量指标的水质参数又适用于定性指标的水质参数.     

层级化环境质量数据网络直报业务系统的设计和应用

分析了目前环境监测部门的数据传输现状,设计了基于VPN互联网络的环境质量数据层级化直报业务系统,介绍了网络直报系统的业务模型、数据审核和存储策略、系统功能设计及其应用等。     

人工神经网络用于铅的化学形态模拟计算

用前馈线性网络法求解水体系中Ｐｂ（２＋）与ＯＨ－之间的反应常数，不同训练算法对求解结果的精度、收敛速度及权值均有影响．结果表明，批处理算法的精度最好，权值不出现负值，但运算时间最长；在线算法的精度虽不如批处理算法，而比数据变换－在线算法好，权值有时会出现负值．运算时间较长；数据变换－在线算法的优点是运算时间短，但相对误差较大，权值出现负值的机会多。采用反馈网络模拟计算铅的各种化学形态的浓度．用物料核算的方法对反馈网络模型进行检验表明，此种模型用于平衡计算是可行的，详细分析了理论模拟和实验曲线的差异的原因，温度的影响最小，在４＜ｐＨ＜９时，ＣＯ有重要的影响．在国代检验时，ｎ值取整所引入的误差的影响亦不可忽视。从本文的结果可以看到，采用前馈网络和反馈网络相结合的方法考察水体中的化学形态是可行的．从而为解决这一类问题提供了一种可能的途径．     

无盘网络系统的介绍和应用

无盘网络系统是一种很适合在计算机配置较固定、软件程序较划一的网络环境中运行的网络系统,随着电脑教育的规模扩大,网络教室的普及,发展也非常快.本文简述了其产生环境、功能介绍,针对其优势和不足说明了无盘网络系统的应用前景是非常广阔的.     

基团贡献法对取代苯类化合物生物降解性的预测

测定了 47种取代苯类化合物在松花江水中的 5日生化需氧量 (BOD5) .分别采用线性基团贡献法和非线性基团贡献法(人工神经网络法 )对化合物的生物降解性BOD5 ThOD(ThOD :理论需氧量 )进行QSBR研究 .得到不同基团对生物降解性的贡献为 :C6H5>COOH >OH >CH3 O CH3 >NH2 >Cl >NO2 .线性基团贡献法对于训练组和测试组的定性预测正确率分别为72 %和 86 % ;而人工神经网络法的预测正确率分别为 92 %和 86 % .预测结果表明线性和非线性基团贡献法的预测效果都很好 ,相比而言 ,非线性方法对生物降解性的预测更准确     

污水处理厂自动控制系统综述

针对我国污水处理厂自动控制系统的现状 ,简述了污水处理控制系统的控制原理、特点、构成和功能分析了系统的故障原因、干扰因素、智能性及提高系统智能性的方法 ,介绍了模糊控制等新方法在污水处理控制系统中的应用以及系统的功能扩展 ,为建立我国自己完善的污水处理厂集散控制系统提供了一些思路     

综合福利视角下中国生态文明建设绩效评价及国际比较

基于强可持续研究范式,以生态经济学和可持续发展理论为基础,运用两阶段网络Super-EBM模型进行绩效测算和评价,打开生态福利转化过程的“黑箱”,基于综合福利视角将生态文明建设绩效进一步分解为生态经济效率和经济福利效率,实证研究部分选取2006-2015年的时间序列数据对中国“十一五”和“十二五”期间的生态文明建设绩效水平进行纵向分析,与此同时,选取42个国家2014年的截面数据进行横向的国际对比研究。研究表明：（1）“十一五”到“十二五”期间,中国生态文明建设绩效（0.986）整体水平一般,均未实现DEA有效,但“十二五”期间已呈现出逐年改善的利好趋势。国际比较层面,瑞士（1.647）、印度尼西亚（1.365）、爱尔兰（1.039）依次排名前三位,中国（0.297）排名相对靠后,位列倒数第二。各国之间差异较大,总体上呈现出“OECD国家（0.677）最高、G20国家（0.611）次之、金砖国家（0.417）最低”的态势。（2）从分阶段效率来看,无论是纵向分析还是横向对比,均发现第一阶段的生态经济效率明显低于第二阶段的经济福利效率,生态经济效率偏低是导致生态文明建设绩效总体水平偏低的主要原因。（3）与传统的单阶段、径向或非径向DEA模型相比,两阶段网络Super-EBM模型综合考虑了径向和松弛变量问题,测算出来的效率值更为精确,能够更为客观地反映现实。同时,对于其他领域涉及两阶段或多阶段生产系统的效率或绩效评估具有较好的借鉴价值。最后,根据上述结论提出相应的政策建议。     

Detecting neural tube defects by amniocentesis between 11 and 15 weeks' gestation

Forty-two open neural tube defects (NTDs) were identified in our series of 7440 amniocenteses tested between 11 and 15 weeks of gestation. Using a cut-off of ≥2.0 MOM, the detection rate for open NTDs was 95 per cent; 100 per cent each for anencephaly and spina bifida; and 78 per cent for encephalocele. Two encephaloceles had AFP levels less than 2.0 MOM and negative AChEs. Thirty-four (81 per cent) of these NTDs were tested between 13 and 15 weeks and 8 (19 per cent) before 13 weeks. There were 0.6 per cent false positives by AFP (excluding serious abnormalities and fetal death) and 0.1 per cent after AChE. The likelihood of an open NTD after an elevated AFP (≥2.0 MOM) was 24 and 77 per cent for any serious abnormality. These results, when combined with an earlier study, indicate that amniotic fluid AFP appears to be as sensitive a test for open NTDs between 13 and 15 weeks as between 16 and 20 weeks. Additional experience is necessary to determine this before 13 weeks.