微观剩余油赋存状态的矿物学机制探讨——以鄂尔多斯盆地中部中‒低渗砂岩储层为例

针对现阶段微观剩余油研究中可视化精度低、微观剩余油与矿物之间的依存关系不明确以及形态分类描述不完善等问题, 利用岩芯观察、铸体薄片分析、X射线衍射分析等手段, 以场发射环境扫描电子显微镜(FE-SEM)图像为基础资料, 联合能谱(EDS)分析资料, 研究鄂尔多斯盆地中部长2油层组和长9油层组的微观剩余油分布规律, 并探讨剩余油赋存状态的矿物学机制。结果表明, 微观剩余油的赋存状态由孔隙的大小和形态以及与孔隙相接触的边缘矿物的性质共同决定, 不同类型孔隙对剩余油的赋存能力取决于孔隙边缘矿物的形态、表面粗糙程度、比重和润湿性等物理化学性质。据此, 可将微观剩余油分为残留油团、半自由油岛、半自由油雾、半自由微油团和束缚油5个类型。     

中国经济增长与杠杆率的非线性关系研究--基于制造业面板数据门限回归分析

传统理论基于有效市场理论(EMH)主要从公司财务视角研究杠杆率问题,忽略了生产环节的决定机制.本文从决定生产效率的全要素生产率视角对杠杆率的决定机制重新认识.并采用制造业29个行业1995-2016年的面板数据,以杠杆率为门限变量构建动态面板门限模型,主要研究结论为:1)经济增长-杠杆率的门限值位于名义杠杆率114,在门限值左侧,杠杆率增加促进经济增长,在门限值右侧,杠杆率增加"拖累"经济增长,杠杆率对经济增长整体表现"倒U"型关系;2)经济增长-杠杆率的泡沫杠杆率门限值位于泡沫杠杆率65,当名义杠杆率包含的泡沫杠杆率低于65时,杠杆率增加对经济具有显著的促进效应,当包含的杠杆率大于65时,杠杆率对经济增长的作用并不明显,说明经济可能进入了滞胀阶段;3)经济增长-足值杠杆率的关于对泡沫杠杆率存在双门门限效应,分别是43、49,当泡沫杠杆率小于43时,增加泡沫杠杆率对经济的增长的促进效应小于泡沫杠杆率介于[43,49]区间的效应,当泡沫杠杆率大于49时,泡沫杠杆率对经济增长的促进作用不再明显.     

考虑关键性的多约束下辐射干扰对消装备随舰备件配置优化方法

可用度是评估备件对装备完好性影响的重要指标,针对备件关键性对装备可用性造成影响而又难以将其考虑在内建立可用度模型的问题,以舰载通信电台辐射干扰对消装备为对象,采用对备件关键性实行专家打分的方法,将备件关键性作为备件配置优化的约束之一,建立以备件费用、备件体积、备件关键性为约束的备件配置优化模型。在备件费用、体积及关键性归一化的基础上,采用权重因子方法将多约束转化为单一约束,并给出了权重因子更新方法。列举案例,分别计算了费用约束、体积约束、关键性约束和多约束下的备件方案,并对比了装备改进前后的备件配置方案,将装备使用过程中的备件保障延伸至装备可靠性设计阶段。案例分析结果表明:备件配置方法在考虑了体积、费用等限制约束下,能同时兼顾备件关键性对装备完好性的影响;装备可靠性设计时,提高装备可靠性低的现场可更换单元(line replaceable unit, LRU)等薄弱点,能有效减少装备维修保障资源。     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Identifying US business cycle regimes using dynamic factors and neural network models

We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Dynamic factors are then extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in-sample and out-of-sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore, using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.     

因子von Neumann代数上的非线性中心化子

设m，n是任意非零整数，且满足(m＋n)(m－n)≠0， M是实或复数域F上的Hilbert空间上的一个因子von Neumann代数.利用代数分解方法证明了M上满足2mφ(AB)＋2nφ(BA)＝mφ(A)B＋mAφ(B)＋nφ(B)A＋nBφ(A)的非线性映射φ为可加中心化子，并刻画出具体形式φ：A→λA(λ∈F， A∈M).     

国际石油价格波动的结构性因素分析

文章将石油供给性冲击、需求性冲击和投机行为冲击3方面结构性因素细分为5个内生变量,构建反映油价波动的结构向量自回归(SVAR)模型,并运用模型对1999—2012年期间油价波动的原因进行了实证分析。结果表明:需求性冲击无论是长期还是短期都是油价波动的最主要因素;其次,投机性冲击对油价波动的影响也较大,不容忽视;短期突发事件和供给冲击短期对油价有些影响,长期影响基本消失。     

人工智能促进服务业生产率了吗

近年来，我国服务业占比呈现持续上涨态势，但服务业的高成本低效率问题仍然存在，随着人工智能和服务业的融合，服务业的全要素生产率是否提高有待进一步验证。我们运用DEA-Malmquist生产率指数验证了“鲍莫尔病”的确存在；加入人工智能因素后的服务业全要素生产率的确提高了，但是进一步的分解发现，全要素生产率的提高主要源于要素配置效率的改善和规模报酬递增效应，并非由于技术进步，人工智能作为技术进步的主要标志在提高服务业全要素生产率中的作用仍然有限，这是由于人工智能作用的滞后性所致。     

Flow and heat transfer behaviour of nanofluids in microchannels

Flow and heat transfer of aqueous based silica and alumina nanofluids in microchannels were experimentally investigated. The measured friction factors were higher than conventional model predictions at low Reynolds numbers particularly with high nanoparticle concentrations. A decrease in the friction factor was observed with increasing Reynolds number, possibly due to the augmentation of nanoparticle aggregate shape arising from fluid shear and alteration of local nanoparticle concentration and nanofluid viscosity. Augmentation of the silica nanoparticle morphology by fluid shear may also have affected the friction factor due to possible formation of a core/shell structure of the particles. Measured thermal conductivities of the silica nanofluids were in approximate agreement with the Maxwell-Crosser model, whereas the alumina nanofluids only showed slight enhancements. Enhanced convective heat transfer was observed for both nanofluids, relative to their base fluids (water), at low particle concentrations. Heat transfer enhancement increased with increasing Reynolds number and microchannel hydraulic diameter. However, the majority of experiments showed a larger increase in pumping power requirements relative to heat transfer enhancements, which may hinder the industrial uptake of the nanofluids, particularly in confined environments, such as Micro Electro-Mechanical Systems (MEMS).     

Methods for backcasting,nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.