Making the abstract concrete: The role of norms and values in experimental modeling

Experimental modeling is the construction of theoretical models hand in hand with experimental activity. As explained in Section 1, experimental modeling starts with claims about phenomena that use abstract concepts, concepts whose conditions of realization are not yet specified; and it ends with a concrete model of the phenomenon, a model that can be tested against data. This paper argues that this process from abstract concepts to concrete models involves judgments of relevance, which are irreducibly normative. In Section 2, we show, on the basis of several case studies, how these judgments contribute to the determination of the conditions of realization of the abstract concepts and, at the same time, of the quantities that characterize the phenomenon under study. Then, in Section 3, we compare this view on modeling with other approaches that also have acknowledged the role of relevance judgments in science. To conclude, in Section 4, we discuss the possibility of a plurality of relevance judgments and introduce a distinction between locally and generally relevant factors.     

明代宦官新论

明代的宦官制度一直为人所诟病,甚至很多人都将明代灭亡的原因归结为宦官,尽管牵强,但是明代宦官为害之深是汉唐两代所不曾有的,既要究其制度成因,更需要对其社会背景下的心理因素进行剖析。     

Forecasting key US macroeconomic variables with a factor‐augmented Qual VAR

In this paper, we first extract factors from a monthly dataset of 130 macroeconomic and financial variables. These extracted factors are then used to construct a factor‐augmented qualitative vector autoregressive (FA‐Qual VAR) model to forecast industrial production growth, inflation, the Federal funds rate, and the term spread based on a pseudo out‐of‐sample recursive forecasting exercise over an out‐of‐sample period of 1980:1 to 2014:12, using an in‐sample period of 1960:1 to 1979:12. Short‐, medium‐, and long‐run horizons of 1, 6, 12, and 24 months ahead are considered. The forecast from the FA‐Qual VAR is compared with that of a standard VAR model, a Qual VAR model, and a factor‐augmented VAR (FAVAR). In general, we observe that the FA‐Qual VAR tends to perform significantly better than the VAR, Qual VAR and FAVAR (barring some exceptions relative to the latter). In addition, we find that the Qual VARs are also well equipped in forecasting probability of recessions when compared to probit models.     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.     

基于CF-PCCs模型的维西县滑坡致灾因子敏感性分析

为了深入了解滑坡的成灾背景，减少灾害造成的生命财产损失，以云南省维西县为研究区，选取了高程、坡度、坡向、降水量、距河流距离、工程地质岩组、距断层距离、植被归一化指数（Normalized Difference Vegetation Index,NDVI）和道路密度9项致灾因子，利用确定性系数（Certainty Factor,CF）-皮尔逊积矩相关系数（Pearson Product-moment Correlation Coefficient,PCCs）模型对致灾因子进行敏感性分析，并对分析结果的准确性进行了验证.结果表明：(1)因子敏感性由高到低分别为坡向、河流、高程、降水量、断层、道路、坡度、工程地质岩组和NDVI；(2)敏感性最大的因子类别包括：高程为1 486～2 600 m，坡度为0°～20°，坡向为半阳坡，降水量为782～1 178 mm，距河流距离为0～300 m，工程地质岩组为极软岩，距断层距离为2 400～3 200 m,NDVI为-0.169～0.039，道路密度为80～117 km/km2；(3)CF-PCCs模型精度高，数据分析结果可靠，该方法可为滑坡敏感性分...     

人才吸引力因素与政策工具契合关系研究以河北省为例

人才是第一资源,人才引进培养是经济社会发展的一个关键,近年来我国各地纷纷出台吸引人才的重磅政策.为探求人才政策工具对人才的吸引力情况,在构建人才吸引力因素理论模型基础上,通过对178位人才问卷调查获得人才吸引力关键因素,对河北省163份人才政策文本进行数据挖掘,分析政策工具与人才吸引力因素的契合关系.研究得出创新氛围、人才培训培养、人才激励评价、创新创业支持、人才服务和薪资待遇等方面契合度高,政策效果较佳;工作场所仪器设备条件、配偶子女安置和住房补贴的契合关系为政策不足,应加大政策制定;人才载体和医疗社会保障的契合关系为政策冗余,需加强政策实施.     

基于熵评价模态分解的雷达信号双谱特征识别

针对在低信噪比下雷达信号调制识别准确率低、抗噪性差的问题, 提出一种基于熵评价模态分解和双谱特征提取的识别方法。利用双谱可以抑制高斯噪声的特点, 分析了在低信噪比下进行信号调制识别的可行性并引入了噪声项。由于噪声项的干扰, 双谱在0 dB以下时, 噪声抑制效果变差, 提出了基于信息熵评价的经验模态化分解对信号进行预处理, 提高信噪比。最后, 设计了卷积神经网络分类器, 实现对不同调制类型信号的识别。仿真实验结果表明, 本文方法相比传统方法具有良好的抗噪性, 能够在低信噪比下对不同类型信号进行有效识别。     

空域色噪声背景下双基地MIMO雷达角度估计

空域有色噪声会导致现有多输入多输出(multiple input multiple output, MIMO)雷达算法性能下降, 甚至完全失效。针对空域色噪声背景下双基地MIMO雷达联合波离角(direction of departure, DOD)和波达角(direction of arrival, DOA)估计问题, 分析了现有算法失效的原因。考虑到匹配滤波后无噪协方差矩阵的低秩特性、色噪声协方差矩阵的稀疏特性以及MIMO雷达数据的多维结构特性, 提出一种基于张量分析的角度估计算法。首先, 构造角度估计的协方差张量, 通过去除协方差张量中受噪声协方差影响的元素对色噪声进行抑制。其次，利用张量填充技术对无噪协方差矩阵进行恢复。然后，利用平行因子分解获得目标角度的方向矩阵。最后, 采用最小二乘算法对目标的DOA和DOD进行拟合。仿真结果表明, 所提算法对色噪声不敏感, 且无孔径损失。相比现有矩阵及张量分析算法, 所提算法具有更高的估计精度。     

全球慢性肾病防治药物研究进展

 慢性肾病（chronic kidney disease，CKD）具有病因复杂、治疗难度大等特点，已成为全球性的公共卫生问题，其影响了全球约10%的人群，并可导致更高的心血管疾病和死亡的发生风险等。CKD防治的关键环节之一是阻止或延缓CKD进展。总结了CKD的流行病学、风险因素、研发投入和防治药物，以详细了解CKD的现状和防治新进展。     

文化线路遗产旅游体验影响因素研究——以丝绸之路为例

通过文献分析法以及访谈法拟定和选取了文化线路遗产旅游体验测评的潜变量和相应的测量变量,并基于陕西,甘肃两地丝绸之路世界遗址点景区的游客调研数据对丝绸之路文化线路遗产旅游体验评价维度进行验证性因子分析,构建了丝绸之路旅游体验影响因素的二阶五因素结构模型。结果显示:影响丝绸之路文化线路遗产旅游体验的因素并非是由资源主导,而是综合因素共同导向;遗产地旅游设施服务水平、遗产资源品质、遗产解说、活动参与感知以及遗产地环境氛围均显著影响旅游者体验质量,且以旅游设施服务能力的影响程度最大。