滇池入流径流模型

本文以松华坝、滇池两站模型为例，叙述了双变量模型的基本原理，建立了随机模型，模拟生成了径流系列，为滇池的水量及水质模拟模型提供了入流资料。

New empirical model to evaluate groundwater flow into circular tunnel using multiple regression analysis

There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow to tunnels due to their simplicity and practical base theory. Investigations show that the real amount of water infiltrating into jointed tunnels is much less than calculated amount using analytical methods and obtained results are very dependent on tunnel＇s geometry and environmental situations. In this study, using multiple regression analysis, a new empirical model for estimation of groundwater seepage into circular tunnels was introduced. Our data was acquired from field surveys and laboratory analysis of core samples. New regression variables were defined after perusing single and two variables relationship between groundwater seepage and other variables. Finally, an appropriate model for estima- tion of leakage was obtained using the stepwise algorithm. Statistics like R, R2, R2e and the histogram of residual values in the model represent a good reputation and fitness for this model to estimate the groundwater seepage into tunnels. The new experimental model was used for the test data and results were satisfactory. Therefore, multiple regression analysis is an effective and efficient way to estimate the groundwater seeoage into tunnels.

Drought Forecasting in a Semi-arid Watershed Using Climate Signals:a Neuro-fuzzy Modeling Approach

Large-scale annual climate indices were used to forecast annual drought conditions in the Maharlu-Bakhtegan watershed,located in Iran,using a neuro-fuzzy model.The Standardized Precipitation Index(SPI) was used as a proxy for drought conditions.Among the 45 climate indices considered,eight identified as most relevant were the Atlantic Multidecadal Oscillation(AMO),Atlantic Meridional Mode(AMM),the Bivariate ENSO Time series(BEST),the East Central Tropical Pacific Surface Temperature(NINO 3.4),the Central Tropical Pacific Surface Temperature(NINO 4),the North Tropical Atlantic Index(NTA),the Southern Oscillation Index(SOI),and the Tropical Northern Atlantic Index(TNA).These indices accounted for 81% of the variance in the Principal Components Analysis(PCA) method.The Atlantic surface temperature(SST:Atlantic) had an inverse relationship with SPI,and the AMM index had the highest correlation.Drought forecasts of neuro-fuzzy model demonstrate better prediction at a two-year lag compared to a stepwise regression model.

Regression model for daily passenger volume of high-speed railway line under capacity constraint

A non-linear regression model is proposed to forecast the aggregated passenger volume of Beijing-Shanghai high-speed railway(HSR) line in China. Train services and temporal features of passenger volume are studied to have a prior knowledge about this high-speed railway line. Then, based on a theoretical curve that depicts the relationship among passenger demand, transportation capacity and passenger volume, a non-linear regression model is established with consideration of the effect of capacity constraint. Through experiments, it is found that the proposed model can perform better in both forecasting accuracy and stability compared with linear regression models and back-propagation neural networks. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of train planning policies.

Artificial neural network approach for rheological characteristics of coal-water slurry using microwave pre-treatment

Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry(CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa·sn,respectively. This paper presents an artificial neural network(ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm(trainlm) was selected as the controlled ANN. Mean squared error(MSE) of 0.002 and coefficient of multiple determinations(R^2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model.

On the Reproduction Number and a Presentation of Results for Infectious Diseases Models

The classical Kermack-McKendrick homogeneous SIR （susceptible, infected and removed） model is well known, Its general solution is a function of the unique parameter （the reproduction number） that is equal to a mean number of secondary cases produced by a typical infected individual in a completely susceptible population. If the reproduction number is more than one （the threshold value） its value describes an epidemic scope： larger values correspond to more severe epidemics. In the more complex compartment SIR models the population is divided into several non-overlapping groups. It allows us to partly remove assumptions of the classical model. It is well known that for this kind of models, just as for the classical model there is the threshold parameter R0. Usually it is called by the same name--the reproduction number--though the physical meaning of this parameter has changed. The main purpose of the paper is to show that this new parameter is a not unique measure of an epidemic severity for any compartment SIR model. In particular it means that for such models comparison of the severity of two epidemics by simple comparing values of their reproduction numbers is incorrect. For compartment models these statements were proved with the help of the corresponding ODEs analysis. Very popular now individual-based models （IBMs） are more complex in comparison with the compartment ones since they use overlapping groups （school children are members of families also, for example）. In such a case Diekmann＇s calculation method for the reproduction number used in many papers is inapplicable as well as a presentation the simulation results obtained as functions of this parameter.

Application of time–frequency entropy from wake oscillation to gas–liquid flow pattern identification

Gas–liquid two-phase flow abounds in industrial processes and facilities. Identification of its flow pattern plays an essential role in the field of multiphase flow measurement. A bluff body was introduced in this study to recognize gas–liquid flow patterns by inducing fluid oscillation that enlarged differences between each flow pattern. Experiments with air–water mixtures were carried out in horizontal pipelines at ambient temperature and atmospheric pressure. Differential pressure signals from the bluff-body wake were obtained in bubble, bubble/plug transitional, plug, slug, and annular flows. Utilizing the adaptive ensemble empirical mode decomposition method and the Hilbert transform, the time–frequency entropy S of the differential pressure signals was obtained. By combining S and other flow parameters, such as the volumetric void fraction β, the dryness x, the ratio of density φ and the modified fluid coefficient ψ, a new flow pattern map was constructed which adopted S（1–x）φ and （1–β）ψ as the vertical and horizontal coordinates, respectively. The overall rate of classification of the map was verified to be 92.9% by the experimental data. It provides an effective and simple solution to the gas–liquid flow pattern identification problems.

Passenger Flow Status Evaluation in Subway Station Based on Probabilistic Neural Network

This paper select the escalator with large flow in the station as the object, analysing the correlation of the AFC data of the in and out gates and the passenger flow parameters by passenger flow density and the passing time acquired and calculated in the waiting area of the prediction escalator to select the gates related to the predicted the escalator. NARX neural network is used to predict the model of the passenger flow parameters of the escalator waiting area based on the related gates＇ AFC data, then a probabilistic neural network model was established by using the AFC data and predicted passenger flow parameters as input and the passenger flow status in the escalator waiting area of subway station as output.The result shows the predicting model can predict the passenger flow status of the escalator waiting area better by the AFC data in the subway station. Research result can provide decision basis for the operation management of the subway station.

Absorptive Capacity and Knowledge Flows for Large International Finns： A Survey

The main objective of this paper is that of surveying both theoretic and econometric models exploring the existence of knowledge spillovers and quantifying firm＇s ability to identify, assimilate, and exploit existing information （absorptive capacity）. In so doing, we explore different methodologies through which we may analyze the knowledge transmission： both the production function approach and the knowledge function approach. In order to construct the spillover stocks, different dimensions are considered： geographic and technological.

Prediction of the Maximum Flow by the Model SASIS： Sensibility to the Empirical Parameters of the Shape of the Furrow

The model developed in this research presents effective mechanisms in simulations of a discharge strip understood between the minimum and the maximum allowable values, aiming to determine the relationship between discharge and water application efficiency, deep percolation and runoff rates, and consequently to optimize the performance of the furrow irrigation systems with continuous flow. The flow applied in each furrow must be adapted to the length, to the field slope and to the nature of the ground. The authors studied the maximum non erosive flow （Q,,,~）, in function of parameters obtained from the dimensions of the furrow, being Pl and/92, respectively, the linear and exponential parameters of the potential functions that described the relationship between the area of the cross section of flow （or wetted perimeter） and height of flow; in this way, the multiplicative effect of,01 on the area of the cross section of flow is linear, while ,02 is exponential. It verified a conjugated effect of,or and p20n the value of Q,,~. The results of this research point out for the importance of having an estimate of the parameters of the geometry of the cross section of flow （,01 and ,02） the most precise as possible, being known that the dimensions of this section can result in impracticable values of Qmax, outside of the acceptable strip in the literature, that is from 1.2 to 4.0 L.sl. This sensibility analysis was also of great benefit to create an interface in the software SASIS, capable to guide the user of this tool in the input of appropriate values for ,01 and P2 to the process of simulation of the irrigation for furrow with continuous flow and of the optimization of its performance.

Application of ANNs Model with the SDSM for the Hydrological Trend Prediction in the Sub-catchment of Kurau River, Malaysia

The paper describes the application of SDSM （statistical downscaling model） and ANNs （artificial neural networks） models for prediction of the hydrological trend due to the climate-change. The SDSM has been calibrated and generated for the possible future scenarios of meteorological variables, which are temperature and rainfall by using GCMs （global climate models）. The GCM used is SRES A2. The downscaled meteorological variables corresponding to SDSM were then used as input to the ANNs model calibrated with observed station data to simulate the corresponding future streamflow changes in the sub-catchment of Kurau River. This study has discovered the hydrological trend over the catchment. The projected monthly streamflow has shown a decreasing trend due to the increase in the, mean of temperature for overall months, except the month of August and November.

GIS Based Soil Erosion Estimation Using EPM Method, Garmiyan Area, Kurdistan Region, Iraq

Using empirical model is one of the approaches of evaluating sediment yield. This research is aimed at predicting erosion and sedimentation in Garmiyan area at Kurdistan Region, Iraq used EPM （erosion potential model） incorporating into GIS （geographic information system） software. This basin area is about 1,620 km2. It has a range of vegetation, slope, geological, soil texture and land use types. The spatial distribution of gully erosion shows three main zones in the studied area （slight to moderate gully, high gully and sever fluvial erosion）. They form about 10%, 89% and 1% of gully erosion in the studied area respectively. The results of the EPM model show that the values of the coefficient of erosion Z are classified as moderate to high erosion intensity. They increase northward due to increasing of slope, elevation and rate of precipitation that generate Hortonian overland flow, which is due to high discharge and huge fluvial erosion power that cause ground surface erosion to produce large quantity of sediment. The results of GSP （spatial sediment rate） are increasing northward similar to Z due the same reasons, while the value of total sediment rate, shows different values for each watershed because they are mainly affected by the total watershed area.

Comprehensive Evaluation of Sichuan Province Logistics Ability Based On Principal Component Analysis

Regional economy is the foundation for the development of the regional logistics and regional logistics as the support of the regional economic development, both interdependence, coordinated development, become the important factor of regional economic sus-tained and rapid development. This article selects relevant statistical data from 2002 to 2011 in Sichuan province were analyzed, and the quantitative mathematical model was established based on principal component analysis （pca）, and through the model for comprehensive evaluation of regional logistics caoability of Sichuan orovince.

A Systems Approach for Analyzing Vegetative and Soil Degradation in Arnigad Micro-watershed of Indian Himalayan Region

This study analyzes the vegetative and soil degradation,measured as biomass and soil loss,for Arnigad micro-watershed located in Indian Himalayan state of Uttarakhand,in systems framework by using dynamic linear programming bio-economic model.The focus is at investigating the effects of alternate policy regimes,i.e.,introduction of improved energy sources for cooking along with substitution of existing local livestock breeds with improved breed,reduction in human population growth and introduction of high yielding varieties of main crops including paddy,maize and wheat.The model horizon extended over a period of 25 years,i.e.,from 2006 to 2030.It was found that the model scenario incorporating increased use of improved energy sources along with substitution of local cows by improved cows could be the most effective policy option in reducing vegetative and soil degradation.The vegetative biomass density declined to 19.76% compared to 35.24% in the BASE scenario and soil erosion loss was also lowered by 29.13%.Also,the reduction of population growth rate to half of the BASE scenario led to minor improvements in degradation.Introduction of high yielding varieties of main crops slightly increased vegetative degradation but reduced soil loss(8.35%) with respect to the BASE scenario.Such a phenomenon could be explained in terms of changed crop mix resulting in reduced amount of crop by-products requiring increased lopping of tree branches for animal fodder.The policy option of the increased use of improved energy sources along with substitution of improved breed of cows resulted in 9.58% higher income.Introduction of high yielding varieties of crops led to 1.92% increase in income,but the income decreased by 1.25 % when population growth was reduced to half.The usefulness of the model lies in analyzing the systems behavior in its entirety where the results can predict the possible direction of change as a result of manipulation in alternate economic regimes.

Agricultural Land Contract and Management Rights:Transferring Price and Countermeasures in Typical Agricultural Areas of China

Henan is a large agricultural province and one of China’s major grain-producing areas. Agricultural land transfer and large-scale operations will have a profound impact on regional economic development and food security. This is a case study taken in Xiwan village in Qinyang city, Henan. This paper builds a transferring pricing model using land use rights assessment and characteristic correction of land contract and management rights. Results show that the transferring price of land contract and management rights should include the economic price, social price and corrected price. The use of a price model should focus on the distinction of theory and practical application. If there is no change in agricultural project,the actual transferring price should be the sum of the economic price and social security price. If there is a change in agricultural projects,such as cropland into cash crops,the actual transferring price should be the sum of the economic price, social price and corrected price. Because of the complexity of income distribution following the transfer, transferring gains should follow a rational distribution in the outflow side, inflow side, managers and land owner. The government should establish reasonable and orderly contracted management of land circulation transferring and use this to build a land transferring price system.

A global empirical model for estimating zenith tropospheric delay

Tropospheric delay acts as a systematic error source in the Global Navigation Satellite Systems(GNSS) positioning. Empirical models UNB3, UNB3 m, UNB4 and EGNOS have been developed for use in Satellite-Based Augmentation Systems(SBAS). Model performance, however, is limited due to the low spatial resolution of the look-up tables for meteorological parameters. A new design has been established in this study for improving performance of the tropospheric delay model by more effectively eliminating the error produced by tropospheric delay. The spatiotemporal characteristics of the Zenith Tropospheric Delay(ZTD) were analyzed with findings that ZTD exhibits different annual variations at different locations and decreases exponentially with height increasing. Spherical harmonics are utilized based on the findings to fit the annual mean and amplitude of the ZTD on a global scale and the exponential function is utilized for height corrections, yielding the ZTrop model. On a global scale, the ZTrop features an average deviation of ?1.0 cm and Root Mean Square(RMS) of 4.7 cm compared with the International GNSS Service(IGS) ZTD products, an average deviation of 0.0 cm and RMS of 4.5 cm compared with the Global Geodetic Observing System(GGOS) ZTD data, and an average deviation of ?1.3 cm and RMS of 5.2 cm compared with the ZTD data from the Constellation Observing System of Meteorology, Ionosphere, and Climate(COSMIC). The RMS of the ZTrop model is 14.5% smaller than that of UNB3, 6.0% smaller than that of UNB3 m, 16% smaller than that of UNB4, 14.5% smaller than that of EGNOS and equivalent to the sophisticated GPT2+Saas model in comparison with the IGS ZTD products. The ZTrop, UNB3 m and GPT2+Saas models are finally evaluated in GPS-based Precise Point Positioning(PPP), as the models act to aid in obtaining PPP position error less than 1.5 cm in north and east components and relative large error(>5 cm) in up component with respect to the random walk approach.

SO2 Emission Characteristics and BP Neural Networks Prediction in MSW/Coal Co-Fired Fluidized Beds

The SO2 emission characteristics of typical Msw components and their mixtures have been investigated in a φ150mm fluidized bed. Some influencing factors of SO2 emission in MSW fluidized bed incinerator were found out in this study. The SO2 emission is increasing with the growth of the bed temperature, and it is rising with the increasing oxygen concentration at furnace exit. When the weight percentage of auxiliary coal is being raised, the conversion rate of S to SO2 is largely going up. The SO2 emission decreases if the desulfurizing agent （CaCO3） is added during the incineration process, but the desulfurizing efficiency is weakened with the enhancement of the bed temperature. The fuel moisture content has a slight effect on the SO2 emission. Based on these experimental results, a 12 × 6 × 1 three-layer BP neural networks prediction model of SO2 emission in MSW/coal co-fired fluidized bed incinerator was built. The prediction results of this model give good agreement with the experimental results, which indicates that the model has relatively high accuracy and good generalization ability. It was found that BP neural network is an effectual method used to predict the SO2 emission of MSW/coal co-fired fluidized bed incinerator.

Numerical Investigation & Comparison of a Tandem-Bladed Turbocharger Centrifugal Compressor Stage with Conventional Design

Extensive numerical investigations of the performance and flow structure in an unshrouded tandem-bladed centrifugal compressor are presented in comparison to a conventional compressor.Stage characteristics are explored for various tip clearance levels,axial spacings and circumferential clockings.Conventional impeller was modified to tandem-bladed design with no modifications in backsweep angle,meridional gas passage and camber distributions in order to have a true comparison with conventional design.Performance degradation is observed for both the conventional and tandem designs with increase in tip clearance.Linear-equation models for correlating stage characteristics with tip clearance are proposed.Comparing two designs,it is clearly evident that the conventional design shows better performance at moderate flow rates.However;near choke flow,tandem design gives better results primarily because of the increase in throat area.Surge point flow rate also seems to drop for tandem compressor resulting in increased range of operation.