Prediction of the concentration of chlorophyll-α for Liuhai urban lakes in Beijing City

Introduction The eutrophication of fresh w ater has becom e a m ain w ater environm ental problem in the w orld. The m ain negative im pacts of fresh w ater eutrophication are w ater quality deterioration and the decrease of hydrophytes and aquatic specie…     

MATERNAL SERUM ALPHA-FETOPROTEIN SCREENING FOR OPEN NEURAL TUBE DEFECTS IN TWIN PREGNANCIES

Data on maternal serum alpha-fetoprotein (AFP) levels at 13–24 weeks' gestation in 46 twin pregnancies with open neural tube defects (22 with anencephaly, 24 with open spina bifida) and 169 unaffected twins were used to estimate the detection and false-positive rates associated with different cut-off levels. Using the conventional cut-off level of 2·5 multiples of the median (MoM) for unaffected singleton pregnancies of the same gestation and laboratory, the detection rate in twins was 99 per cent for anencephaly and 89 per cent for open spina bifida, with a false-positive rate of 30 per cent. Using a 5·0 MoM cut-off level to maintain a similar false-positive rate to that found among singleton pregnancies at 16–18 weeks' gestation (about 3 per cent), the detection rate was 83 per cent for anencephaly and 39 per cent for open spina bifida. Estimates are provided of the odds of having an affected twin pregnancy given a positive AFP result as well as the odds for individual women with a raised AFP level.     

Iniencephaly is not uniformly fatal

Iniencephaly is an uncommon, but not rare, lesion involving a fusion between the posterior occipital bone and the cervical spine. Many cases are associated with other cranio-cervical abnormalities and anencephaly, and most are associated with other structural abnormalities. The prognosis is thought to be dismal. We describe a male infant, one of twins, who was diagnosed prenatally as having iniencephaly. The child was born without complication and has grown and thrived. This is the fourth patient with long-term survival with iniencephaly. We recommend that non-directive counselling be applied when presenting the findings of iniencephaly to parents.     

Effects of mid-trimester induced abortion on the subsequent pregnancy

The outcome of the pregnancy following (a) a mid-trimester termination of pregnancy (TOP) for fetal neural tube defect (NTD) (77 women=group 1); (b) mid-trimester TOP for fetal Down's syndrome (13 women=group 2); (c) delivery of a baby with NTD (119 women=group 3) was studied. The prenatal fetal loss was relatively high in all groups. In group 1 it was similar to that found in other studies after first trimester TOP, in group 2 it was associated with advanced maternal age and the unexpected finding in group 3 was not attributable to advanced maternal age. It is suggested that a previous NTD per se might increase the risk of fetal loss in the next pregnancy. A previous mid-trimester TOP for NTD was not associated with an increase in premature labour, small for dates babies or congenital abnormality in the next pregnancy, but there was a slight increase in the number of babies weighing less than 2500 g.     

Is maternal alpha-fetoprotein screening still of value in a low-risk area for neural tube defects?

Estimation of maternal serum alpha-fetoprotein (AFP) was used as a screening method for the detection of neural tube defects (NTDs) in 6344 women over three years. Of 88 (1.4 per cent) who had one or more serum AFP levels equal to, or greater than, 2.5 multiples of the median (MoM) for the relevant gestational age, 43 (0.68 per cent) underwent amniocentesis. There were eight NTDs. Four of these were screened by serum AFP, and all cases of spina bifida had serum AFP levels greater than 3.0 MoM, including one small open defect which was not seen on ultrasound. The other four cases of NTD, which were not screened, were identified by ultrasound. Of 64 singleton pregnancies 32 (50 per cent) had serum AFP levels between 2.5 and 3.0 MoM, and low birthweight (⪕2500 g) occurred in 29 per cent. Because of improvements in ultrasound techniques and the apparent falling incidence of NTD, the role of serum AFP as the primary screening procedure should be regularly reviewed. Effective screening is dependent on mothers booking early.     

回采工作面瓦斯涌出量预测的神经网络方法

回采工作面瓦斯涌出量受煤层瓦斯含量、工作面产量和采煤方法等各种因素的影响 ,笔者通过研究得出 :回采工作面瓦斯涌出量与煤层的赋存条件和开采条件之间是一种非线性关系 ,但目前还难以用精确的数学建模来求解。因此 ,提出了一种应用BP人工神经网络模型和算法 ,建立工作面瓦斯涌出量预测模型 ,从而预测不同开采条件下回采工作面瓦斯涌出量。实际应用表明 ,模型精度能满足要求。笔者还对隐含层神经元数目对步长影响作了讨论。     

AN EVALUATION OF THE AVAILABLE TECHNIQUES FOR ESTIMATING MISSING FECAL COLIFORM DATA1

ABSTRACT: This paper presents the findings of a study aimed at evaluating the available techniques for estimating missing fecal coliform (FC) data on a temporal basis. The techniques investigated include: linear and nonlinear regression analysis and interpolation functions, and the use of artificial neural networks (ANNs). In all, seven interpolation, two regression, and one ANN model structures were investigated. This paper also investigates the validity of a hypothesis that estimating missing FC data by developing different models using different data corresponding to different dynamics associated with different trends in the FC data may result in a better model performance. The FC data (counts/100 ml) derived from the North Fork of the Kentucky River in Kentucky were employed to calibrate and validate various models. The performance of various models was evaluated using a wide variety of standard statistical measures. The results obtained in this study are able to demonstrate that the ANNs can be preferred over the conventional techniques in estimating missing FC data in a watershed. The regression technique was not found suitable in estimating missing FC data on a temporal basis. Further, it has been found that it is possible to achieve a better model performance by first decomposing the whole data set into different categories corresponding to different dynamics and then developing separate models for separate categories rather than developing a single model for the composite data set.     

Information Network Topologies for Enhanced Local Adaptive Management

We examined the principal effects of different information network topologies for local adaptive management of natural resources. We used computerized agents with adaptive decision algorithms with the following three fundamental constraints: (1) Complete understanding of the processes maintaining the natural resource can never be achieved, (2) agents can only learn by experimentation and information sharing, and (3) memory is limited. The agents were given the task to manage a system that had two states: one that provided high utility returns (desired) and one that provided low returns (undesired). In addition, the threshold between the states was close to the optimal return of the desired state. We found that networks of low to moderate link densities significantly increased the resilience of the utility returns. Networks of high link densities contributed to highly synchronized behavior among the agents, which caused occasional large-scale ecological crises between periods of stable and high utility returns. A constructed network involving a small set of experimenting agents was capable of combining high utility returns with high resilience, conforming to theories underlying the concept of adaptive comanagement. We conclude that (1) the ability to manage for resilience (i.e., to stay clear of the threshold leading to the undesired state as well as the ability to re-enter the desired state following a collapse) resides in the network structure and (2) in a coupled social–ecological system, the systemwide state transition occurs not because the ecological system flips into the undesired state, but because managers lose their capacity to reorganize back to the desired state. An erratum to this article can be found at .     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

Dynamic Bayesian networks for reliability evaluation of subsea wellhead connector during service life based on Monte Carlo method

The subsea wellhead connector is a critical connection component between subsea Christmas tree and subsea wellhead for preventing the leakage of oil and gas in the subsea production system. Excited by cyclical loadings due to environmental forces and the other support forces, the subsea wellhead connector is prone to the failure, which could lead to the loss of subsea tree or wellhead integrity and even catastrophic accidents. With the Monte Carlo simulation method, this paper presents a reliability analysis approach based on dynamic Bayesian Networks, aiming to assess the failure probability of the subsea wellhead connector during service life. Take the driving ring component of the subsea wellhead connector as an example to demonstrate the reasonability of the proposed model. The generation data is processed by the transform between the numerical value and the state variable. Based on the stress-strength interference theory, the structure reliability of the driving ring with 96.26% is achieved by the proposed model with the consideration the aging of the material strength and the most influential factors are figured out. Meanwhile, the corresponding control measures are proposed effectively reduce the failure risk of the subsea wellhead connector during service life.