谢尔宾斯基地毯中基于扩散聚集生长的复杂网络及其分析

本文提出了谢尔宾斯基地毯中一种基于次近邻扩散聚集生长的复杂网络模型生成法则。通过细致的计算机仿真模拟,初步研究了该网络的一些典型数字特征度分布、网络直径、平均路径长度、平均聚类系数、度关联系数等。研究结果表明,按照本文提出的方法产生的复杂网络具有短的平均路径长度、较高的平均聚类系数、正的度关联系数、具备明显的齐次网络特性。同时,在变动跳动概率之差时,生成网络直径、平均路径长度、网络平均聚类系数、度关联系数等呈现规律性变化;且生成网络直径、平均路径长度、度关联系数与网络平均聚类系数呈负相关。

Analysis of Coupling between Soil and Water Conservation and Economic-social Development

The coupling relation exists in water and soil conser-vation and economic-social development. The article analyses the relation of soil and water conservation and economic-social development stages as well as the coupling analytical method. Then calculates the expecting income by dispersing Markov decision and calculates the correlation coefficient and the re-lationship degree. The article obtains the relationship of soil and water conservation investments and all kinds of incomes. Finally, it analyzes the important meaning in socio-economic development of water and soil conservation.

EVALUATION OF CORRECTIONS ON TURBULENT FLUXES OBTAINED BY EDDY COVARIANCE METHOD IN HIGH WINDS

This paper investigates processing of fast-response data and corrections of turbulent fluxes obtained by using eddy covariance method based on data collected at an offshore observation tower during three Cold-intrusion(CI)events in the South China Sea in 2010. This study presents the data processing procedure in detail and compares frictional velocities(u*), sensible heat fluxes(H) and latent heat fluxes(LE) yielded by using different averaging periods and different coordinate rotation methods; evaluates the sonic temperature correction for sensible heat flux and the Webb correction for latent heat flux as a function of 10 m wind speed(u10) during the CIs. The results show(1) that the different averaging periods of 30 min and 10 min cause biases of u*(H, LE) within 5%(15%, 62%). The values of u*(H,LE) averaged from 30 mins are mostly larger than those averaged from 10 mins. We suggest that the averaging period of 10 min is not sufficiently long to capture all scale eddies and recommend 30 min averaging period in calculating turbulent fluxes using eddy covariance method during CIs;(2) that the values of u*(H, LE) obtained from double rotation(DR2) and those obtained from planar fit rotation(PF) have good agreements and correlation coefficients between them are larger than 0.99. Because PF method requires unchanged environment and it is easier to apply DR2 method, we suggest DR2 coordinate rotation method in processing fast-response data; and(3) that the median values of frictional velocity(sensible heat flux and latent heat flux) binned according to 2 m s^(-1) intervals of u_(10) increase(decrease,increase) by less than 9%(4%, 10%) by Coriolis corrections(sonic temperature corrections, Webb corrections), which decreases(decreases, increases) with increasing u10 when u10 are 5-17 m s^(-1).

Evaluating the Algorithm for Correction of the Bright Band Effects in QPEs with S-, C-and X-Band Dual-Polarized Radars

The bright band, a layer of enhanced radar reflectivity associated with melting ice particles, is a major source of signifi- cant overestimation in quantitative precipitation estimation （QPE） based on the Z-R （reflectivity factor-rain rate） relationship. The effects of the bright band on radar-based QPE can be eliminated by vertical profile of reflectivity （VPR） correction. In this study, we applied bright-band correction algorithms to evaluate three different bands （S-, C- and X-band） of dual-polarized radars and to reduce overestimation errors in Z-R relationship-based QPEs. After the reflectivity was corrected by the algo- rithms using average VPR （AVPR） alone and a combination of average VPR and the vertical profile of the copolar correlation coefficient （AVPR＋CC）, the QPEs were derived. The bright-band correction and resulting QPEs were evaluated in eight precipitation events by comparing to the uncorrected reflectivity and rain-gange observations, separately. The overestimation of Z-R relationship-based QPEs associated with the bright band was reduced after correction by the two schemes for which hourly rainfall was less than 5 mm. For the verification metrics of RMSE （root-mean-square error）, RMAE （relative mean absolute error） and RMB （relative mean bias） of QPEs, averaged over all eight cases, the AVPR method improved from 2.28, 0.94 and 0.78 to 1.55, 0.60 and 0.40, respectively, while the AVPR＋CC method improved to 1.44, 0.55 and 0.30, respectively. The QPEs after AVPR＋CC correction had less overestimation than those after AVPR correction, and similar conclusions were drawn for all three different bands of dual-polarized radars.

Models for Analyzing the Driving Force of Cultivated Land Supply Change

This paper focuses on a series of quantitative an al ysis models, such as grey relational analysis model, hierarchical cluster an alysis model, principal component analysis model, linear regression model and elastic coefficient model. These models are used to analyze the comprehensive function and effect of driving forces systemically, including analysis on featur es, analysis for differentiating the primary and the secondary, analysis on comp rehensive effects, analysis of elasticity, analysis of prediction. The primary a nd characteristic factors can be extracted by analysis of features and analysis for differentiating the primary and the secondary. Analysis on prediction an d elasticity can predict the area of cultivated land in the future and find out which factors exert great influence on the cultivated land supply.

Damage Identification in Beam-Type Structures Using Pseudo Strain Energy Density and Grey Relation Coefficient

Based on pseudo strain energy density (PSED) and grey relation coefficient (GRC), an index is proposed to locate the damage of beam-type structures in time-domain. The genetic algorithm (GA) is utilized to identify the structural damage severity of confirmed damaged locations. Furthermore, a systematic damage identification program based on GA is developed on MATLAB platform. ANSYS is employed to conduct the finite element analysis of complicated civil engineering structures, which is embedded with interface technique. The two-step damage identification is verified by a finite element model of Xinxingtang Highway Bridge and a laboratory beam model based on polyvinylidens fluoride (PVDF). The bridge model was constructed with 57 girder segments, and simulated with 58 measurement points. The damaged segments were located accurately by GRC index regardless of damage extents and noise levels. With stiffness reduction factors of detected segments as variables, the GA program evolved for 150 generations in 6 h and identified the damage extent with the maximum errors of 1% and 3% corresponding to the noise to signal ratios of 0 and 5%, respectively. In contrast, the common GA-based method without using GRC index evolved for 600 generations in 24 h, but failed to obtain satisfactory results. In the laboratory test, PVDF patches were used as dynamic strain sensors, and the damage locations were identified due to the fact that GRC indexes of points near damaged elements were smaller than 0.6 while those of others were larger than 0.6. The GA-based damage quantification was also consistent with the value of crack depth in the beam model.

The Effectiveness of Shallow Surface Geophysical Methods in Shear Wave Velocity Derivation

Shear wave velocity Vs is measured by the surface geophysical survey like MASW （multi-channel surface wave analysis） or RWM （refraction wave method） and by the subsurface method like PS logging. PS logging and RWM are direct methods to derive shear wave velocity and MASW retrieves shear wave through the inversion of the surface wave. In this work, the effectiveness of surface methods （MASW and RWM） is compared with PS logging in determining shear wave velocity. For this purpose, shear wave velocity results Vs30 of 12 PS logging and MASW surveys conducted in Mymensingh Municipality in Bangladesh have been utilized. Additionally, the shear wave velocity results of three PS logging have been compared with the refraction profiles of RWM survey conducted in Rooppur nuclear power plant site in Bangladesh. The relative discrepancy between RWM and PS logging is found less （ranges from -3.92 to 0.93） compared to MASW and PS logging （＋/-0.88 to 33.92）. The correlation coefficient of Vs30 derived from RWM and PS logging is observed much better （0.60） compared to MASW and PS logging （0.40）. The result is good considering the lateral lithologic variability and inherent differences among techniques. It is evident from the comparison that the RWM can be used as a cost-effective alternative to traditional borehole PS logging method for Vs30 determination and thus the number of down-hole logging tests might be significantly reduced.

Validation results of NmF2 and hmF2 derived from ionospheric density profiles of GNOS on FY-3C satellite

The first Global Navigation Satellite System Occultation Sounder(GNOS) which is compatible of both Bei Dou System(BDS)and Global Positioning System(GPS) was successfully launched into orbit onboard the Feng Yun 3 C satellite(FY-3 C) on September 23, 2013, and it has already gathered a large amount of ionosphere radio occultation(RO) data so far. However, the detailed analysis and validation of GPS ionosphere RO data have not been done up to now. Therefore, this paper discusses the configuration of the FY-3 C GNOS, the methods and results of GPS ionosphere occultation processment, the quality analysis of the GPS ionosphere RO products, and the precision consistency between the GNOS GPS ionosphere RO product and ionosonde data. The peak electron density(Nm F2) correlation coefficient is 0.97, the corresponding standard deviation is 16.08%. The peak value altitude(hm F2) correlation coefficient is 0.89, and the corresponding standard deviation is 23.79 km. The in-orbit operation of GNOS provides a basis data set for the monitoring, forecasting and research of ionosphere's space weather.

New scaling for compressible wall turbulence

Classical Mach-number(M) scaling in compressible wall turbulence was suggested by van Driest(Van Driest E R.Turbulent boundary layers in compressible fluids.J Aerodynamics Science,1951,18(3):145-160) and Huang et al.(Huang P G,Coleman G N,Bradshaw P.Compressible turbulent channel flows:DNS results and modeling.J Fluid Mech,1995,305:185-218).Using a concept of velocity-vorticity correlation structure(VVCS),defined by high correlation regions in a field of two-point cross-correlation coefficient between a velocity and a vorticity component,we have discovered a limiting VVCS as the closest streamwise vortex structure to the wall,which provides a concrete Morkovin scaling summarizing all compressibility effects.Specifically,when the height and mean velocity of the limiting VVCS are used as the units for the length scale and the velocity,all geometrical measures in the spanwise and normal directions,as well as the mean velocity and fluctuation(r.m.s) profiles become M-independent.The results are validated by direct numerical simulations(DNS) of compressible channel flows with M up to 3.Furthermore,a quantitative model is found for the M-scaling in terms of the wall density,which is also validated by the DNS data.These findings yield a geometrical interpretation of the semi-local transformation(Huang et al.,1995),and a conclusion that the location and the thermodynamic properties associated with the limiting VVCS determine the M-effects on supersonic wall-bounded flows.

An intuitive general rank-based correlation coefficient

Correlation analysis is an effective mechanism for studying patterns in data and making predictions.Many interesting discoveries have been made by formulating correlations in seemingly unrelated data. We propose an algorithm to quantify the theory of correlations and to give an intuitive, more accurate correlation coefficient.We propose a predictive metric to calculate correlations between paired values, known as the general rank-based correlation coefficient. It fulfills the five basic criteria of a predictive metric： independence from sample size,value between-1 and 1, measuring the degree of monotonicity, insensitivity to outliers, and intuitive demonstration.Furthermore, the metric has been validated by performing experiments using a real-time dataset and random number simulations. Mathematical derivations of the proposed equations have also been provided. We have compared it to Spearman＇s rank correlation coefficient. The comparison results show that the proposed metric fares better than the existing metric on all the predictive metric criteria.