CALCULATION OF CUMULATIVE PERCENTAGE AT SELECTED PARTICLE SIZES FROM BOTTOM WITHDRAWAL TUBE ANALYSIS

ABSTRACT: A new computation method is developed for the bottom withdrawal tube particle size analysis. A transformation on time scale is introduced so that the cumulative percentage of a selected particle size can be determined from a single continuous Oden cum.     

人工神经网络在水环境质量评价中的应用

为了将人工神经网络应用于水环境质量评价，应用了人工神经网络Ｂ—Ｐ算法，构造了水环境质量评价模型，该模型应用于实例评价结果表明，人工神经网络用于环境质量评价具有客观性，通用性和实用性。     

Left-sided gastroschisis and bilateral multicystic dysplastic kidneys: a rare combination of anomalies

Left-sided gastroschisis is very rare. We report a case of left-sided gastroschisis associated with bilateral multicystic dysplastic kidneys. This combination of anomalies is unknown. The pathogenesis of gastroschisis is not well understood. It is now viewed as a malformation rather than disruption. The findings in this case support this view. The combination of dysplastic kidneys with ventral body wall defect suggests an early developmental defect. Copyright © 2007 John Wiley & Sons, Ltd.     

基于BP神经网络的苯储罐泄漏事故风险评价模型研究

为了在事故发生之前对苯储罐进行风险评价，提出1种基于BP神经网络的泄漏事故风险评价方法，利用该方法构建了苯储罐的风险评价模型，并对模型进行了训练及验证。研究结果表明:BP神经网络成功完成了建模任务，且模型训练结果较好，可利用基于BP神经网络所构建的苯泄漏事故风险评价模型对苯储罐发生泄漏事故的风险进行评价。     

热水锅炉水冷壁管爆管事故综合分析

对在同一单位的三台热水锅炉发生多起水冷壁管爆管事故进行了综合分析,提出了相应的改进措施。     

Prenatal diagnosis of complete atrioventricular septal defect with truncus arteriosus

Truncus arteriosus (TA) is a rare cardiac anomaly constituting less than 1% of all congenital heart defects. Its association with complete atrioventricular septal defect (AVSD) is extremely unusual and only 12 cases diagnosed postnatally or postmortem have been reported so far. We describe the first case of truncus arteriosus with AVSD to be diagnosed prenatally by fetal echocardiography. Copyright © 2007 John Wiley & Sons, Ltd.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

L型钢管混凝土芯柱耐火极限研究

火灾高温对结构安全有显著影响,为研究等肢L形钢管混凝土芯柱的耐火极限及其影响因素,利用ABAQUS软件建立合理的高温反应分析模型,在验证模型可靠性基础上,分析了荷载比、截面边长、长细比、荷载偏心率、含钢率等对L形钢管混凝土芯柱耐火极限的影响。研究结果表明:在一定参数范围内,荷载比和截面边长是构件耐火极限的主要影响参数,荷载比越小,截面边长越大,构件的耐火极限越高;长细比和荷载偏心率对其影响较大,长细比和荷载偏心率越小,构件的耐火极限越高;含钢率对其影响不显著。结果可为异形钢管混凝土芯柱的抗火安全设计提供参考。     

基于人工神经网络建模的PTC功率智能检测技术

PTC是家用空调器中主要器件之一,具有耗电量大、非线性的特征。目前PTC常用的功率检测方法是通过"专用电量计量芯片结合电压、电流采样电路"来实现,存在成本高和可靠性低问题。提出一种基于人工神经网络的PTC功率纯软件计算方法,无需增加任何硬件成本。通过实验验证,精度达±3%,满足产品化需求,成熟可靠。     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.