临床检验质量控制预测值模型的建立和应用

临床检验质量控制预测值模型的建立和应用卫生部临床检验中心王治国临床检验统计质量控制方法的选择或设计应该考虑所监测测定方法的误差发生率。如果测定方法稳定时，则此方法除了本身固有的随机误差外，不受任何其他分析误差的影响，这时使用简单的控制规则和较少的控制...

改进BP神经网络模型在索塔变形预测中的应用

通过分析温度、风速气压、日照和拉索拉力等因素,建立函数关系模型,结合南京长江五桥钢壳-混凝土索塔变形监测实例,采用基于小波阈值去噪、岭回归、BP神经网络的多算法组合模型,进行不同组合算法的实验,预测索塔监测点的坐标,与全站仪观测的索塔变形数据进行比较,得到小波-岭回归-BP神经网.

人工神经网络技术在混凝土本构模型中的应用

利用反向传播前馈神经网络 (BP网络 )的模拟能力来代替传统的方法 ,建立了在三轴单调比例加载情况下混凝土的全量型和增量型神经网络本构模型。通过对比模拟结果 ,对这些模型的差别进行了分析。从模型预测值和试验值逼近程度可以看出 ,训练后的模型具有很高的学习精度和良好的泛化能力 。

Effect Research of Immobilized Algae-bacteria Removal Ammonia Nitrogen of Aquaculture Wastewater and Proposed Model

Applied Immobilized algae bacteria (ABI) to remove ammonia of freshwater aquaculture wastewater. Temperature (T),PH,light intensity (I),dissolved oxygen (DO) and filling rate five factors plays important role in the process of ammonia nitrogen removal ,related data between ammonia removal and five factors was received through multi-factor orthogonal test,and established relations model between the five factor and nitrogen removal. The results show that five-factors had significant effect on AR,and the best combinations for removing AR was temperature 30 ℃,pH=7.0,light intensity 6 000 lux,dissolved oxygen 5.0 mg/L and the fill rate 10%. According to the experimental data,equation model was proposed and coefficient of determination R2 =0.864 8,P<0.05. Samples T-test was done between the model predictions and the actual measured values.Test results showed that the significant difference of overall mean value sig. (2-tailed) was 0.978 (P>0.05),it Shows that had no significant difference between model predictions and the actual measured value,and model had a high degree of fitting.

Modeling of degradation kinetics of Salvianolic acid B at different temperatures and pH values

In this work,the effects of degradation time,temperature,and pH value on the degradation of Salvianolic acid B in aqueous solution were determined.Higher pH values,higher extraction temperature,and longer extraction time led to more degradation of Salvianolic acid B.Danshensu concentration increased as Salvianolic acid B degraded.A mechanism model was developed considering the degradation of Salvianolic acid E and lithospermic acid,which were two degradation products of Salvianolic acid B.The reverse reactions of Salvianolic acid B degradation were also considered.Degradation kinetic constants were calibrated.The degradation kinetics of Salvianolic acid B,lithospermic acid,and Danshensu in a Salvia miltiorrhiza extract aqueous solution were predicted using the mechanism model.The predicted concentrations agreed well with the experimental results.This model was developed using degradation data obtained from simple composition systems,but it can be applied in a complex botanical mixture with high prediction accuracy.

Evaluation of High-Resolution WRF Model Simulations of Surface Wind over the West Coast of India

This paper presents results from a statistical validation of the hindcasts of surface wind by a high-reso-ution-mesoscale atmospheric numerical model Advanced Research WRF （ARW3.3）, which is set up to force the operational coastal ocean forecast system at Indian Na- tional Centre for Ocean Information Services （INCOIS）. Evaluation is carried out based on comparisons of day-3 forecasts of surface wind with in situ and remote-sensing data. The results show that the model predicts the surface wind fields fairly accurately over the west coast of India, with high skill in predicting the surface wind during the pre-monsoon season. The model predicts the diurnal variability of the surface wind with reasonable accuracy. The model simulates the land-sea breeze cycle in the coastal region realistically, which is very clearly observed during the northeast monsoon and pre-monsoon season and is less prominent during the southwest monsoon season.

基于火灾数据的城市火灾时间特征研究

通过数据拟合认为2007-2016年济南市各季度及24 h时段的过火面积-火灾频率符合幂律分布,并计算得到拟合参数。将ARIMA模型的差分方程形式与传递形式结合,同时引入记忆函数进行优化,构建实时自修正预测模型,研究城市火灾的时间变化规律。研究结果认为,新构建的模型能够更好地预测城市火灾。

Mechanical Behavior of a Glass-fiber Reinforced Composite to Steel Joint for Ships

The use of a glass-fiber reinforced composite in marine structures is becoming more common, particularly due to the potential weight savings. The mechanical response of the joint between a glass-fiber reinforced polymer （GRP） superstructure and a steel hull formed is examined and subsequently modified to improve performance through a combined program of modeling and testing. A finite-element model is developed to predict the response of the joint. The model takes into account the contact at the interface between different materials, progressive damage, large deformation theory, and a non-linear stress-strain relationship. To predict the progressive failure, the analysis combines Hashin failure criteria and maximum stress failure criteria. The results show stress response has a great influence on the strength and bearing of the joint. The Balsawood-steel interface is proved to be critical to the mechanical behavior of the joint. Good agreement between experimental results and numerical predictions is observed.

Prediction of the Mooring Force of a 2-D Floating Oil Storage Tank

A Constrained Interpolation Profile （CIP）-based model is developed to predict the mooring force of a two-dimensional floating oil storage tank under wave conditions, which is validated against to a newly performed experiment. In the experiment, a box-shaped floating oil storage apparatus is used. Computations are performed by an improved CIP-based Cartesian grid model, in which the THINC/SW scheme （THINC： tangent of hyperbola for interface capturing; SW： Slope Weighting）, is used for interface capturing. A multiphase flow solver is adopted to treat the water-air-body interactions. The Immersed Boundary Method （IBM） is implemented to treat the body surface. Main attention is paid to the sum force of mooring line and velocity field around the body. It is found that the sum force of the mooring line increases with increasing wave amplitude. The body suffers from water wave impact and large body motions occur near the free surface. The vortex occurs near the sharp edge, i.e., the sharp bottom comers of the float- ing oil storage tank and the vortex shedding can be captured by the present numerical model. The present model could be further improved by including turbulence model which is currently under development. Comparison between the computational mooring forces and the measured mooring forces is presented with a reasonable agreement. The developed numerical model can predict the mooring line forces very well.

Prediction of plastic zone size around circular tunnels in non-hydrostatic stress field

This paper discusses the calculation of plastic zone properties around circular tunnels to rock-masses that satisfy the Hoek–Brown failure criterion in non-hydrostatic condition,and reviews the calculation of plastic zone and displacement,and the basis of the convergence–confinement method in hydrostatic condition.A two-dimensional numerical simulation model was developed to gain understanding of the plastic zone shape.Plastic zone radius in any angles around the tunnel is analyzed and measured,using different values of overburden(four states)and stress ratio(nine states).Plastic zone radius equations were obtained from fitting curve to data which are dependent on the values of stress ratio,angle and plastic zone radius in hydrostatic condition.Finally validation of this equation indicate that results predict the real plastic zone radius appropriately.

Introduction to marine emergency forecasting and early-warning system(MEFES)

Marine emergencies especially oil spill may bring irreversible harm to the marine environment,and will cause immeasurable economic losses.In recent years,the demand for crude oil is increasing year by year in China with the high-speed economic development,leading to the high risk of marine oil spill.Therefore,it is necessary that promoting emergency response on marine oil spill in China and improving oil spill forecasting and early-warning techniques.This paper introduces the Marine Emergency Forecasting and Early-warning System(MEFES)developed by National Marine Data and Information Service(NMDIS).The system consists of one database,two modelling subsystems and a GIS platform.The database is the marine emergency database,and two subsystems include the marine environmental forecasting subsystem and the oil spill behaviour forecasting subsystem.MEFES has been applied in the emergency response of some major oil spill accidents occurred in recent years.The operational applications of the system can provide some theoretical basis and reference for marine oil spill emergency response.

An International Journalism Model of Professionalism in News Production： The Concepts and the Measurements

This study aimed to contribute in establishing an international journalism model of professionalism in the production of the news. The main purpose is to explore the degree to which this model predicts the professional values in the media content. In particular, this model was tested on the content of a leading news organization in the Middle East, AI Jazeera, to identify whether or not AI Jazeera reflected professional values in news production or other non-professional values. A total of 592 news stories--234 from AJE and 358 from AJA--published from January I, 2014, to April 30, 2014, were analyzed. The findings of this study indicate that AI Jazeera reflects professional values to a substantial degree. The professional values were reflected highly and nearly two thirds of the stories had professional values in the content. The chi square tests shows there are frequency/percentage differences, but overall the patterns are similar, with no statistically significant differences in the AJA and AlE. Scholarly implications, future studies and limitations were presented in this study.

Wear characteristics and prediction of wheel profiles in high-speed trains

Wheel/rail relationship is a fundamental problem of railway system. Wear of wheel profiles has great effect on vehicle performance. Thus, it is important not just for the analysis of wear characteristics but for its prediction. Actual wheel profiles of the high-speed trains on service were measured in the high-speed line and the wear characteristics were analyzed which came to the following results. The wear location was centralized from-15 mm to 25 mm. The maximum wear value appeared at the area of 5 mm from tread center far from wheel flange and it was less than 1.5 mm. Then, wheel wear was fitted to get the polynomial functions on different locations and operation mileages. A binary numerical prediction model was raised to predict wheel wear. The prediction model was proved by vehicle system dynamics and wheel/rail contact geometry. The results show that the prediction model can reflect wear characteristics of measured profiles and vehicle performances.

The Study on Agricultural Economy Forecasting using Grey Relationship Model

Thinking of grey group model is the improvement on the traditional grey model. It does not merely use a grey model as the ultimate basis, but takes full account of the traditional GM （1,1） model and the GM （1, n） model and join two predictions to form a prediction interval. So, the results are more reasonable and more realistic requirements and have strong guidance and reference. The farther the forecast period is, the worse the forecast is. The forecasts in the forecast period of 1-3 Years are the best, but the results of long-term are only as a reference value and the guidance data. Therefore, as the forecast period goes on, rolling grey model is used to increase accuracy.

Extracting predictable components and forecasting techniques in extended-range numerical weather prediction

This paper refers to the CNOP-related algorithms and formulates the practical method and forecast techniques of extracting predictable components in a numerical model for predictable components on extended-range scales.Model variables are divided into predictable components and unpredictable chaotic components from the angle of model prediction error growth.The predictable components are defined as those with a slow error growth at a given range.A targeted numerical model for predictable components is established based on the operational dynamical extended-range forecast(DERF)model of the National Climate Center.At the same time,useful information in historical data are combined to find the fields for predictable components in the numerical model that are similar to those for the predictable components in historical data,reducing the variable dimensions in a similar judgment process and further correcting prediction errors of predictable components.Historical data is used to obtain the expected value and variance of the chaotic components through the ensemble forecast method.The numerical experiment results show that this method can effectively improve the forecast skill of the atmospheric circulation field in the 10–30 days extended-range numerical model and has good prospects for operational applications.

Using Digital Elevation Model to Improve Soil pH Prediction in an Alpine Doline

Among spatial interpolation techniques,geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled locations.A doline of approximately 15 000 m 2 at 1 900 m above sea level (North Italy) was selected as the study area to estimate a digital elevation model (DEM) using geostatistics,to provide a realistic distribution of the errors and to demonstrate whether using widely available secondary data provided more accurate estimates of soil pH than those obtained by univariate kriging.Elevation was measured at 467 randomly distributed points that were converted into a regular DEM using ordinary kriging.Further,110 pits were located using spatial simulated annealing (SSA) method.The interpolation techniques were multi-linear regression analysis (MLR),ordinary kriging (OK),regression kriging (RK),kriging with external drift (KED) and multi-collocated ordinary cokriging (CKmc).A cross-validation test was used to assess the prediction performances of the different algorithms and then evaluate which methods performed best.RK and KED yielded better results than the more complex CKmc and OK.The choice of the most appropriate interpolation method accounting for redundant auxiliary information was strongly conditioned by site specific situations.

Prediction of stall inception in multi-stage compressors based on an eigenvalue approach

A stability model for multi-stage compressor is developed on the basis of the eigenvalue approach.This model assumes that the unsteady flow field can be decomposed into pressure,vortex and entropy waves.Besides,a linear cascade of blades is modeled by three-dimensional semi-actuator disk theory and the characteristics of steady flow field are also considered in the present model.The connection between the analytical solution for stator,rotor and gap can be established by applying mode matching approach,the relevant stability equation can be expressed in the form of matrix,while the compressor system stability can be judged by the imaginary part of the matrix eigenvalue.The capacity of the stall inception model to predict the stall inception point of multi-stage compressor is assessed against the experimental data of National Aeronautics and Space Administration(NASA) two stage fan.The theoretical results show that this model can predict the stall onset points of a two-stage fan at different operating speeds with a reasonable accuracy.

Forecast scheme and strategy for extended-range predictable components

Although extended-range forecasting has exceeded the limit of daily predictability of weather,there are still partially predictable characteristics of meteorological fields in such forecasts.A targeted forecast scheme and strategy for extended-range predictable components is proposed.Based on chaotic characteristics of the atmosphere,predictable components and unpredictable random components are separated by using the standpoint of error growth in a numerical model.The predictable components are defined as those with slow error growth at a given range,which are not sensitive to small errors in initial conditions. A numerical model for predictable components(NMPC)is established,by filtering random components with poor predictability.The aim is to maintain predictable components and avoid the influence of rapidly growing forecast errors on small scales. Meanwhile,the analogue-dynamical approach(ADA)is used to correct forecast errors of predictable components,to decrease model error and statistically take into account the influence of random components.The scheme is applied to operational dynamical extended-range forecast(DERF)model of the National Climate Center of China Meteorological Administration (NCC/CMA).Prediction results show that the scheme can improve forecast skill of predictable components to some extent, especially in high predictability regions.Forecast skill at zonal wave zero is improved more than for ultra-long waves and synoptic-scale waves.Results show good agreement with predictability of spatial scale.As a result,the scheme can reduce forecast errors and improve forecast skill,which favors operational use.