最优货币区不同标准对欧元区内在不稳定性的量化分析对比——基于指数化模型

本文以最优货币区成员国宏观经济之间的差异性为出发点,以指数函数为基础,通过建立模型,对欧元区的大量统计数据进行处理,以量化分析的方法对欧元区的内在不稳定性进行研究,并通过纵向比较和横向比较,对蒙代尔、弗莱明、凯南的最优货币区建立标准进行了对比分析,认为欧债危机的爆发只是欧元区不稳定性引发问题的一种形式,得出了最优货币区危机爆发是周期性的以及可以通过减小成员国之间的差异性来预防危机的结论。

基于指数化模型下的国家投资风险动态分析——以土库曼斯坦为例

伴随着中国与中亚国家联系的日趋紧密,中国对土库曼斯坦的投资需求也不断的增大。考虑到土库曼斯坦特殊的地域环境,对该国的国家投资风险进行有效的评估分析对我国今后的对外投资选择有着极其重要的作用。本文以风险函数为基础,采用指数化模型对土库曼斯坦的投资风险进行数字化计量,并结合相关的风险波动系数对土库曼斯坦的投资风险进行分析,最后根据相关数据对该国的投资风险进行预测估计,并以此验证模型的可行性及其应用价值。

无线通信中信道建模中两个基扩展模型的比较

本文研究的是移动通信中信号传输问题。建立基扩展模型,给出最小二乘法拟合复指数基系数方法,通过仿真验证该模型在减少测试数据方面的效果。又建立了泛化复指数基扩展模型同样对信道进行估计,将两种方法从不同方面进行比较,得出各模型的优点。

非侵入性肝纤维化诊断模型对肝癌DEB-TACE术后急性肝功能恶化的预测与生存分析

目的探讨非侵入性肝纤维化诊断模型对肝细胞癌(HCC)载药微球化疗栓塞(DEB-TACE)术后发生急性肝功能恶化(ALFD)风险的预测及生存分析。方法回顾性分析288例经DEB-TACE治疗的HCC患者的临床资料。按照文中标准将患者分为ALFD组与对照组,对比两组患者的基线资料及术前肝功能指标,采用受试者工作特征(ROC)曲线分析术前Child-Pugh评分、APRI、FIB-4对DEB-TACE术后ALFD的预测价值。利用Kaplan-Meier对数秩检验及Cox风险回归模型分析生存率,通过多因素Cox比例风险回归模型筛选出独立预后影响因素。结果除丙氨酸转氨酶水平外,两组间其他肝功能指标差异均有统计学意义(P<0.05)。术前Child-Pugh评分、APRI、FIB-4的曲线下面积分别为0.763、0.735、0.740,均无显著性差异,APRI、FIB-4最佳截断点值分别为1.42和4.47。分层分析提示,APRI>1.42或FIB-4>4.47是DEB-TACE术后ALFD的危险因素。生存分析表明,APR≤1.42与APRI>1.42时的中位生存期分别为32.5个月、19个月(P=0.005);FIB-4≤4.47与FIB-4>4.47时中位生存期分别为32.5个月、20.5个月(P=0.097);多因素Cox风险回归模型分析得到最大肿瘤直径和门静脉侵犯为独立预后影响因素。结论基于APRI、FIB-4的非侵入性肝纤维化模型对评估肝细胞癌DEB-TACE术后发生ALFD的风险及生存具有一定的临床应用价值。

基于神经网络无线通信中时变信道建模应用

对移动通信中时变信道进行建模,采取BP神经网络Levenberg-Marquardt算法对多径时变信道进行建模,同时根据泛化复指数基扩展模型,拟合出训练数据,通过仿真来模拟信噪比与误比特率之间的关系,最后验证了该模型具有很好的预测效果。

Plastic flow behavior of superalloy GH696 during hot deformation

In order to investigate the hot deformation behavior of superalloy GH696, isothermal compression experiments were carried out at deformation temperatures of 880?1120 °C and strain rates of 0.01?10 s?1. And the deformation amount of all the samples was 50%. The strain rate sensitivity exponent (m) and strain hardening exponent (n) under different deformation conditions were calculated, meanwhile the effects of the processing parameters on the values ofm andn were analyzed. The results show that the flow stress increases with the increase of strain rate and the decrease of deformation temperature. The value ofm increases with the increase of deformation temperature and decreases with the increase of strain rate, while the value ofn decreases with the increase of deformation temperature. A novel flow stress model during hot deformation of superalloy GH696 was also established. And the calculated flow stress of the alloy is in good agreement with the experimental one.

Spatial and temporal variation of drought index in a typical steep alpine terrain in Hengduan Mountains

This study describes the spatial and temporal variation of a drought index and makes inferences regarding the environmental factors that influence this variability in the Hengduan Mountains. A drought index is typically used to determine the moisture conditions and the magnitude of water deficiency in a given area. Based on data from 26 meteorological stations over the period 1960-2012, the spatial and temporal variations of the drought index were analyzed using a thin plate smoothing splines method that considered elevation as a covariate. The drought index was estimated based on the potential evapotranspiration(E0) as defined by the Penman Monteith model modified by FAO(1998). The results of the reported analysis showed that the drought index in the Hengduan Mountains has been decreasing since 1960 at a rate of-0.008/a. This represented a progressive shift from the "sub-humid" class, which typified the wider area in the Hengduan Mountains, toward the "humid" class, which appeared in the Hengduan Mountains areas. The drought index was relatively high in the north and low in the south and the variation of the drought index varied with seasons. The drought index showed increasing trends in summer and autumn and it is greater in autumn than in summer, while it showed a decreasing trend in spring and winter. Drought index is inversely proportional to the soil relative humidity and Normalized Difference Vegetation Index(NDVI).

A Study on the Effects of the Surrounding Faults on Water Loss in the Zoige Wetland,China

The Zoige wetland is the biggest alpine wetland in the world,and an important water resource of the Yellow River.Due to natural and human factors,the Zoige wetland has been seriously degraded.Existing studies on the Zoige wetland mainly focus on the macro features of the wetland,while the influence of the surrounding faults on the Zoige wetland degradation is rarely studied.This study uses terrain data to analyze the cover change and the water loss caused by the Wqie-Seji fault based on the distributed hydrological model.The simulated water loss demonstrates that the Normalized Difference Vegetation Index(NDVI) is the most important factor for inducing water loss.The fault is also a factor that cannot be neglected,which has caused 33% of the wetland water loss.Therefore,it is of importance to study the influence of the fault on the wetland degradation.

A Model-based Phenomenological Investigation of Char Combustion Kinetics through Thermogravimetry

Five coal char samples were burnt in thermobalance with ramp heating rate of 30 K/min. The pore structure of these char samples was studied through mercury intrusion method. Combined with the kinetic theory of gases, the data of surface area was used in fitting the results. As a result, the kinetic triplet was given. The analysis showed that five char samples share almost the same intrinsic activation energy of the overall reaction. The phenomenological implication of the derived combustion rate equation was given.

Examining Forest Net Primary Productivity Dynamics and Driving Forces in Northeastern China During 1982–2010

Forest net primary productivity （NPP） is a key parameter for forest monitoring and management. In this study, monthly and annual forest NPP in the northeastern China from 1982 to 2010 were simulated by using Carnegie-Ames-Stanford Approach （CASA） model with normalized difference vegetation index （NDVI） sequences derived from Advanced Very High Resolution Radiometer （AVHRR） Global Invento y Modeling and Mapping Studies （GIMMS） and Terra Moderate Resolution Imaging Spectroradiometer （MODIS） products. To address the problem of data inconsistency between AVHRR and MODIS data, a per-pixel unary linear regres- sion model based on least ~;quares method was developed to derive the monthly NDVI sequences. Results suggest that estimated forest NPP has mean relative error of 18.97% compared to observed NPP from forest inventory. Forest NPP in the northeastern China in- creased significantly during the twenty-nine years. The results of seasonal dynamic show that more clear increasing trend of forest NPP occurred in spring and awmnn. This study also examined the relationship between forest NPP and its driving forces including the climatic and anthropogenic factors. In spring and winter, temperature played the most pivotal role in forest NPR In autumn, precipitation acted as the most importanl factor affecting forest NPP, while solar radiation played the most important role in the summer. Evaportran- spiration had a close correlation with NPP for coniferous forest, mixed coniferous broadleaved forest, and broadleaved deciduous forest. Spatially, forest NPP in the Da Hinggan Mountains was more sensitive to climatic changes than in the other ecological functional re- gions. In addition to climalie change, the degradation and improvement of forests had important effects on forest NPP. Results in this study are helpful for understanding the regional carbon sequestration and can enrich the cases for the monitoring of vegetation during long time series.

Effect of climate change on seasonal water use efficiency in subalpine Abies fabri

Abies fabri is a typical subalpine dark coniferous forest in southwestern China. Air temperature increases more at high elevation areas than that at low elevation areas in mountainous regions,and climate change ratio is also uneven in different seasons. Carbon gain and the response of water use efficiency(WUE) to annual and seasonal increases in temperature with or without CO_2 fertilization were simulated in Abies fabri using the atmospheric-vegetation interaction model(AVIM2). Four future climate scenarios(RCP2.6,RCP4.5,RCP6.0 and RCP8.5) from the Coupled Model Intercomparison Project Phase 5(CMIP5) were selectively investigated. The results showed that warmer temperatures have negative effects on gross primary production(GPP) and net primary production(NPP) in growing seasons and positive effects in dormant seasons due to the variation in the leaf area index. Warmer temperatures tend to generate lower canopy WUE and higher ecosystem WUE in Abies fabri. However,warmer temperature together with rising CO_2 concentrations significantlyincrease the GPP and NPP in both growing and dormant seasons and enhance WUE in annual and dormant seasons because of the higher leaf area index(LAI) and soil temperature. The comparison of the simulated results with and without CO_2 fertilization shows that CO_2 has the potential to partially alleviate the adverse effects of climate warming on carbon gain and WUE in subalpine coniferous forests.

Assimilating the LAI Data to the VEGAS Model Using the Local Ensemble Transform Kalman Filter: An Observing System Simulation Experiment

Information on the spatial and temporal patterns of surface carbon flux is crucial to understanding of source/sink mechanisms and projection of future atmospheric CO2 concentrations and climate. This study presents the construction and implementation of a terrestrial carbon cycle data assimilation system based on a dynamic vegetation and terrestrial carbon model Vegetation-Global-Atmosphere-Soil(VEGAS) with an advanced assimilation algorithm, the local ensemble transform Kalman filter(LETKF, hereafter LETKF-VEGAS). An observing system simulation experiment(OSSE) framework was designed to evaluate the reliability of this system, and numerical experiments conducted by the OSSE using leaf area index(LAI) observations suggest that the LETKF-VEGAS can improve the estimations of leaf carbon pool and LAI significantly, with reduced root mean square errors and increased correlation coefficients with true values, as compared to a control run without assimilation. Furthermore, the LETKF-VEGAS has the potential to provide more accurate estimations of the net primary productivity(NPP) and carbon flux to atmosphere(CFta).

Prediction model for permeability index by integrating case-based reasoning with adaptive particle swarm optimization

To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning （CBR） with adaptive par- ticle swarm optimization （PSO）. The nmnber of nearest neighbors and the weighted features vector are optimized online using the adaptive PSO to improve the prediction accuracy of CBR. The adaptive inertia weight and mutation operation are used to overcome the premature convergence of the PSO. The proposed method is validated a compared with the basic weighted CBR. The results show that the proposed model has higher prediction accuracy and better performance than the basic CBR model.

Probability Models of Fire Risk Based on Forest Fire Indices in Contrasting Climates over China

Fire weather indices have been widely applied to predict fire risk in many regions of the world. The objectives of this study were to establish fire risk probability models based on fire indices over different climatic regions in China. We linked the indices adopted in Canadian, US, and Australia with location, time, altitude, vegetation and fire characteristics during 1998-2007 in four regions using semi- parametric logistic （SPL） regression models. Different combinations of fire risk indices were selected as explanatory variables for specific regional probability model. SPL regression models of probability of fire ignition and large fire events were established to describe the non-linear relationship between fire risk indices and fire risk probabilities in the four regions. Graphs of observed versus estimated probabilities, fire risk maps, graphs of numbers of large fire events were produced from the probability models to assess the skill of these models. Fire ignition in all regions showed a significant link with altitude and NDVI. Indices of fuel moisture are important factors influencing fire occurrence in northern China. The fuel indices of organic material are significant indicators of fire risk in southern China. Besides the well skill of predicting fire risk, the probability models are a useful method to assess the utility of the fire risk indices in estimating fire events. The analysis presents some of the dynamics of climate-fire interactions and their value for management systems.

Land Use Dynamics and Landscape Patterns in Shanghai,Jiangsu and Zhejiang

Land use change and landscape patterns have a large effect on land productivity and ecosystem biodiversity. Based on geographical information system technology and remote sensing data related to land use and land cover of Jiangsu and Zhejiang provinces and Shanghai （Jiang-Zhe-Hu area）, we analyzed patterns of landscape change and predicted land use dynamics using the CA-MARKOV model. We also analyzed the conversion rate and area among landscape classes using the CA-Markov model. We found that from 1980 to 2005, there was a significant decrease in the area of farmland, and much of this landscape was transformed into settlements. Most of the landscape classes have become fragmented and isolated. The areas of farmland, settlement land and water tend to be complex in their shape and spatial clustering. The shapes of other land class patches have become simpler, and overall landscape fragmentation has increased. Landscape diversity and heterogeneity have increased. The CAMARKOV model predicted that settlement land will continue to grow from 2005 to 2015, but the speed of conversion will be reduced. The speed of the reduction in farmland and forest has increased, and increased settlement areas are clustered along the Yangtze River. Land use dynamics and change in the landscape pattern have affected land productivity and made the ecosystem more sensitive and fragile in this study region.

Separating impacts of vegetation change and climate variability on streamflow using hydrological models together with vegetation data

Vegetation information is seldom considered in lumped conceptual rainfall-runoff models.This paper uses two modified rainfall-runoff models,the Xinanjiang-ET and SIMHYD-ET models in which vegetation leaf area index is incorporated,to investigate impacts of vegetation change and climate variability on streamflow in a Southern Australian catchment,the Crawford River experimental catchment,where Tasmanian blue gum plantations were introduced gradually from 1998 till 2005.The Xinanjiang-ET and SIMHYD-ET models incorporate remotely-sensed leaf area index(LAI) data obtained from the Advanced Very High Resolution Radiometer(AVHRR) on board NOAA polar orbiting satellites.Compared to the original versions,the Xinanjiang-ET and SIMHYD-ET models show marginal improvements in runoff simulations in the pre-plantation period(1882-1997).The calibrated Xinanjaing-ET and SIMHYD-ET models are then used to simulate plantation impact on streamflow in the post-plantation period.The total change in streamflow between the pre-plantation and post-plantation periods is 32.4 mm/a.The modelling results from the two models show that plantation reduces streamflow by 20.5 mm/a,and climate variability reduces streamflow by 11.9 mm/a.These results suggest that increase in plantations can reduce streamflow substantially,even more than climate variability.

Testing for the parametric parts in a single-index varying-coefficient model

Single-index varying-coefficient models (SIVCMs) are very useful in multivariate nonparametric regression.However,there has less attention focused on inferences of the SIVCMs.Using the local linear method,we propose estimates of the unknowns in the SIVCMs.In this article,our main purpose is to examine whether the generalized likelihood ratio (GLR) tests are applicable to the testing problem for the index parameter in the SIVCMs.Under the null hypothesis our proposed GLR statistic follows the chi-squared distribution asymptotically with scale constant and degree of freedom independent of the nuisance parameters or functions,which is called as Wilks' phenomenon (see Fan et al.,2001).A simulation study is conducted to illustrate the proposed methodology.