Changes in fetal heart rate pattern after intrauterine paracentesis in one twin with meconium peritonitis

We report a case of in utero paracentesis of ascites in a fetus with meconium peritonitis due to volvulus at 34 weeks which resulted in the correction of an abnormal fetal heart rate pattern and enabled vaginal delivery by preventing abdominal dystocia. The intrauterine intervention also helped to establish the diagnosis and potentially reduced the respiratory compromise after birth. Copyright © 2001 John Wiley & Sons, Ltd.     

稻田NO3-还原耦合As(III)氧化过程及微生物群落特征

以华南稻田土壤为研究对象通过构建微宇宙体系,研究了淹水稻田自养硝酸盐还原耦合As（III）氧化过程及其微生物群落结构组成.结果表明,NO₃^-的添加促进了稻田土壤中As（III）的氧化,在未添加NO₃^-的处理（Soil+As（III））以及灭菌处理（Sterilized soil+As（III）+NO₃^-）中As（III）未发生明显的氧化;在Soil+As（III）+NO₃^-处理中,NO₃^-有少量被还原,而在Soil+NO₃^-处理中,NO₃^-没有被还原.通过16S rRNA高通量分析在NO₃^-还原耦合As（III）氧化体系中微生物群落结构特征,在Soil+As（III）+NO₃^-处理中shannon指数相对较低为8.19,土壤微生物群落多样性降低,其中在门水平上主要优势菌群为变形菌门Proteobacteria（33%）、绿弯菌门Chloroflexi（11%）、浮霉菌门Planctomycetes（12%）;在属水平上主要的优势菌属为Gemmatimonas（7.4%）以及少量的Singulisphaera、Thermomonas、Bacillus.NO₃^-的添加能够促进稻田土壤中自养As（III）氧化,并且影响着稻田土壤中微生物群落组成.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

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.     

对水体中有机污染综合指标及其相互间的关系探研

通过对水体中各有机污染综合指标的含义及各指标测定方法条件控制的论述,阐明了各指标间的相互关系,对我们在水质监测中的质量控制、数据的综合分析和判断有一定的指导意义。     

如何实施工业企业厂界噪声的规范性监测

"工业企业厂界噪声"监测目的、测量条件、时段选择,规范的监测程序及注意的问题,完整的监测报告。     

湖泊富营养化的形成机理与防治对策

分析了湖泊富营养化的形成机理,并从湖泊治理人才队伍的组建、控制目标的设定、外源性营养物质的控制、内源性营养物质的控制、湖泊生物-生态修复、管理等10个方面探讨了富营养化湖泊的防治对策,指出防治湖泊富营养化是一项复杂的系统工程,必须从控制污染源出发,重点开展生物-生态修复、完善管理对策,有计划分层次地逐步实施.     

梁平县境内高滩河水质的空间分布及其对土地利用方式的响应

以梁平县境内高滩河流域为研究对象,综合应用GIS空间分析、Pearson相关分析和RDA冗余分析方法,研究在子流域尺度上COD、BOD5、氨氮和总磷4种水质要素对土地利用方式的生态响应。结果表明:高滩河流域土地利用类型对水质要素有着重要影响,林地能够改善河道水环境,旱地和城镇村和工矿用地是COD、BOD5和氨氮的主要污染来源,园地、交通运输用地以及水域对污染物的影响较弱,说明农业面源污染和农村居民生活污染是高滩河水质恶化的主要原因。