Justification of maternal serum alphafetoprotein screening in a population with low incidence of neural tube defects

A prospective study of maternal serum alphafetoprotein (α-FP) screening of 9838 women in an area with low prevalence of neural tube defects and predominance of anencephalics revealed that an intervention point of single serum α-FP level above 2·8 times the median was appropriate for this population. Ninety per cent of anencephalics and all fetuses with anterior abdominal wall defects were detected. There was no spina bifida among the population screened. Two per cent of the population screened had serum α-FP level above this cut-off level. Thirty-two per cent of twin pregnancies, 7 per cent of small-for-gestational age infants and 9 per cent of pregnancies which ended in either abortion or perinatal death in the population screened also had one serum α-FP level above this intervention point. The false positive rate was 66 per cent. This false positive rate was only reduced to 63 per cent if instead of one, two serum α-FP level above this intervention point was considered abnormal. Using this strategy there was no significant reduction in the detection rate of fetal anomalies and other pregnancy complications. Because of the predominance of anencephalics in this population the diagnosis of fetal anomaly in women with abnormal serum a-FP level was made by ultrasound examination alone. The reason amniocentesis was not performed in these patients was to avoid unnecessary loss of normal pregnancies which may result from this procedure.     

基于神经网络模型的水质监测与评价系统

对水环境的监测与评价，可以掌握水质现状及其发展趋势，为水资源的开发利用和管理提供科学依据。水质评价是建立在水质监测基础之上的。首先结合我国水环境监测技术规范与标准，合理地选择监测项目与监测仪器，建立水质监测系统。然后在水质监测所获数据基础上，运用人工神经网络的理论和方法，通过BP网络不断的学习与训练，归纳出评价标准与评价结果间复杂的非线性关系，建立水质评价的BP神经网络模型系统。经实际应用表明，该系统具有很强的学习、联想和容错功能。为水质监测与评价提供了一条新的途径。     

Glial fibrillary acidic protein in amniotic fluids from pregnancies with fetal neural tube defects

The glial fibrillary acidic protein (GFAP) is the subunit protein of intermediate filaments in astrocytes and closely related cell types. By means of an enzyme immunoassay we have determined the concentration of GFAP in amniotic fluids from normal pregnancies and from pregnancies complicated by various fetal malformations. The group of 20 cases of fetal anencephaly had a significantly higher mean amniotic fluid GFAP concentration (115 μg/1±133.6 (S.D.), range 6–378 μg/1) than the control group of 117 normal pregnancies (13 μg/1k±5.5 (S.D.), range 0–31 μg/1), (P<0.001). Two cases of fetal encephalocele likewise had very high amniotic fluid GFAP concentrations. None of the other cases of fetal malformations investigated, including 12 cases of spina bifida, had increased amniotic fluid GFAP concentrations. We conclude that determination of the amniotic fluid GFAP concentration may give additional information in the prenatal diagnosis of fetal nervous system malformations.     

珠江口海岸线变化的遥感监测

珠江口、香港及澳门地区海岸线的变化近十几年来十分迅速 ,采用常规方法及时、定量地测量这种变化十分困难。本研究应用多时相Landsat卫星遥感图像 ,采用神经网络分类方法 ,监测海岸线的变化并计算增长的陆地面积。结果表明 ,人为的围海造田 ,香港、澳门机场及相关高速公路、港口等设施的建设是海岸线变化的最主要原因。     

活性污泥系统神经网络建模与仿真研究

采用神经网络技术对松江污水厂污水处理活性污泥系统进行建模试验研究，在对实际运行数据按机理准则和范围准则剔除异常数据后，将样本数据随机分成训练样本、检验样本和测试样本。用试凑法确定合理的神经网络隐层节点数，以避免采用过大或过小的网络结构，在训练过程中用检验样本实时监控从而避免“过训练”现象的影响，较好地解决神经网络方法建模的两大难题，从而建立可靠、有效的活性污泥系统神经网络模型。并应用建立的网络模型对活性污泥系统的运行情况进行了仿真研究。建模研究表明，神经网络技术能较好地应用于活性污泥系统的建模，模型具有较好的泛化能力，有很好的实用价值。     

Probabilistic analysis of aluminium production explosion accidents based on a fuzzy Bayesian network

Explosion accidents of molten aluminium in contact with water during aluminium production often occur and may cause injury and death. In this paper, a fuzzy Bayesian network (BN) was employed to probabilistically analyse the explosion accident of molten aluminium in contact with water. A fault tree-Bayesian network (FT-BN) model was established in the cause-effect analysis of the explosion accident, including three processes: electrolysis, molten aluminium transportation and aluminium casting. Fifty-three nodes were proposed in the model to represent the evolution process of the explosion accident from failure causes to consequences. Furthermore, the occurrence probabilities of basic events (BEs) were determined by expert judgement with weighted treatments based on fuzzy theory. By giving certain occurrence probabilities of each BE, the probability of an explosion accident was estimated. Subsequently, importance measures were assessed for each BE, which could reflect the impact on the occurrence of the top event (TE), and the final ranks were provided. The results indicate that using wet ladles and tools, water on the ground, breakage of the tap hole, damage to the casting mould, and leakage of circulating water are five main problems that cause explosion accidents. Safety advice was provided based on the analysis results. This study can help decision makers improve the safety management of aluminium production.     

储气库注采管柱螺纹失效动态风险评估*

为解决储气库注采管柱螺纹失效问题,识别注采管柱螺纹失效致因与后果,基于蝴蝶结和贝叶斯网络方法构建注采管柱螺纹动态失效风险分析模型,采用模糊集理论计算模型变量先验概率,并评估注采管柱失效后果概率,从而推断注采管柱螺纹失效关键致因因素;引入先兆数据,评估注采管柱螺纹动态失效风险态势。结果表明:气体中携带固体颗粒、上螺纹速度过快、注采温度高、地层断裂等13个因素对螺纹失效风险影响较大;螺纹失效概率逐渐增大,螺纹失效后果也越来越严重,需要监控螺纹失效关键致因以降低螺纹失效的风险。     

基于疲劳模式识别的VDT作业工间休息机制*

为保障作业人员身心健康和作业效率,运用E-Prime软件模拟认知性VDT持续作业,通过方差分析提出作业疲劳综合评价指标体系,并使用客观绩效指标和生理指标作为输入变量,主观疲劳综合指数作为输出变量,训练BP神经网络,对作业疲劳进行模式识别;提出认知性VDT持续作业工间休息机制。结果表明:通过正确反应时间、注视时间、瞳孔直径、眨眼频率4项指标,对VDT持续作业疲劳进行模式识别的结果可信度较高。因此,基于上述4项指标提出的工间休息机制客观有效。     

基于B—P神经网络优化算法的城市环境空气中PM10浓度预测模型

将B—P神经网络应用于西安市环境空气中P10浓度预测,对网络结构和算法进行了优化,建立了PM10浓度预测模型。经验证模型精确度比较高,PM10日平均浓度绝对误差0．015～0．020mg／m^3,相对误差在-20％～20％范围内。     

夹点技术在冶金企业节水减排中的应用研究

水夹点技术可以有效地节约工业新鲜水用量,同时减少废水排放量。尽管水夹点技术在理论上已经比较成熟,国内外在石化、化工等行业应用的报道较多,但在冶金行业鲜有报道。介绍了水夹点技术的原理,提出了将其应用于冶金行业工程实践的实施步骤,并以首钢某公司节水改造为例,给出了用水网络优化方案。实施结果表明节水效果良好。