内螺纹变截面铜管拉伸工艺的研究

此文提出了内螺纹变截面铜管分段拉仲一次成形新工艺,并就工艺因素对该管拉伸成形的影响进行了试验研究,从而为合理制订成形工艺提供了依据。     

Trophoblast-like cells sorted from peripheral maternal blood using flow cytometry: A multiparametric study involving transmission electron microscopy and fetal dna amplification

Three monoclonal antibodies (MAbs) against trophoblast (GB17, GB21, and GB25) and flow cytometry were used to sort trophoblast-like cells (TLCs) from peripheral blood of pregnant women. Sorted TLCs were processed for electron microscopy and fetal DNA amplification of the Y-specific sequences from mothers carrying male fetuses. At the ultra-structural level, most of the nucleated cells had the morphology of leucocytes, suggesting maternal contaminants, and we did not find the characteristic features of the free inter-villous trophoblast cells. Nevertheless, polymerase chain reaction (PCR) analysis showed an amplification of Y-specific sequences in two out of three samples of sorted TLCs. These results suggest that besides the maternal leucocytes, sufficient trophoblast nucleated fetal cells can be obtained using cell enrichment by sorting. This sensitive method holds promise for non-invasive prenatal diagnosis of fetal sex and if sufficient Y(positive) nuclei are found, for the diagnosis of selected numerical chromosome abnormalities.     

Phosphate recovery with granular acid-activated neutralized red mud: Fixed-bed column performance and breakthrough curve modelling

Granular acid-activated neutralized red mud (AaN-RM) has been successfully prepared with good chemical stability and physical strength. However, its potential for industrial application remains unknown. Therefore, the performance of granular AaN-RM for phosphate recovery in a fixed-bed column was investigated. The results demonstrated that the phosphate adsorption performance of granular AaN-RM in a fixed-bed column was affected by various operational parameters, such as the bed depth, flow rate, initial solution pH and initial phosphate concentration. With the optimal empty-bed contact time (EBCT) of 24.27 min, the number of processed bed volumes and the phosphate adsorption capacity reached 496.95 and 84.80 mg/g, respectively. Then, the saturated fixed-bed column could be effectively regenerated with a 0.5 mol/L HCl solution. The desorption efficiency remained as high as 83.45% with a low weight loss of 3.57% in the fifth regeneration cycle. In addition, breakthrough curve modelling showed that a 5-9-1 feed-forward artificial neural network (ANN) could be effectively applied for the optimization of the fixed-bed adsorption system; the coefficient of determination (R²) and the root mean square error (RMSE) evaluated on the validation-testing data were 0.9987 and 0.0183, respectively. Therefore, granular AaN-RM fixed-bed adsorption exhibits promising potential for phosphate removal and recovery from polluted water.     

基于SOM-SVM组合分类器的涂层防护性能研究

目的为避免EIS,EN技术可能出现的问题,建立一个准确、高效的评价模型,以探究现役军用有机涂层防护性能。方法利用电化学阻抗谱(EIS)、电化学噪声(EN)技术分析了两种军车有机涂层在循环暴露试验中的腐蚀行为,提取低频阻抗模值|Z|_(0.1 Hz)与涂层噪声电阻R_n两种电化学评价参数作为自组织神经网络(SOM)的输入训练样本,同时结合支持向量机(SVM)方法建立涂层防护性能组合分类器。结果将涂层失效过程自适应地分为涂层防护性能良好、防护性能下降、基本失效三个阶段。结论所建立的SOM-SVM组合分类器对于辅助分析涂层防护性能具有可行性。     

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.     

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.     

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     

开口圆管非对称弯曲的一种方法

开口圆管非对称弯曲时,在尺寸、形状均有较高精度要求的情况下弯曲有一定的难度。此文介绍了一种成功的开口圆管非对称弯曲冷态成形方法。     

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.