A Nonlinear Interval Portfolio Selection Model and Its Application in Banks

In classical Markowitz’s Mean-Variance model, parameters such as the mean and covariance of the underlying assets’ future return are assumed to be known exactly. However, this is not always the case. The parameters often correspond to quantities that fall within a range, or can be known ambiguously at the time when investment decision must be made. In such situations, investors determine returns on investment and risks etc. and make portfolio decisions based on experience and economic wisdom. This paper tries to use the concept of interval numbers in the fuzzy set theory to extend the classical mean-variance portfolio selection model to a mean-downside semi-variance model with consideration of liquidity requirements of a bank. The semi-variance constraint is employed to control the downside risk, filling in the existing interval portfolio optimization model based on the linear semi-absolute deviation to depict the downside risk. Simulation results show that the model behaves robustly for risky assets with highest or lowest mean historical rate of return and the optimal investment proportions have good stability. This suggests that for these kinds of assets the model can reduce the risk of high deviation caused by the deviation in the decision maker’s experience and economic wisdom.     

Forecasting China’s Foreign Trade Volume with a Kernel-Based Hybrid Econometric-Ai Ensemble Learning Approach

Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence （AI） models to predict China＇s foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression （EC-VAR） model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear analysis perspective. Then an artificial neural network （ANN） based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert＇s judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression （SVR） for ensemble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China＇s foreign trade volume prediction problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.     

Did speculative activities contribute to high crude oil prices during 1993 to 2008?

By applying two nonlinear Granger causality testing methods and rolling window strategy to explore the relationship between speculative activities and crude oil prices, the unidirectional Granger causality from speculative activities to returns of crude oil prices during the high price phase is discovered. It is proved that speculative activities did contribute to high crude oil prices after the Asian financial crisis and OPEC's output cut in 1998. The unidirectional Granger causality from returns of crude oil prices to speculative activities is significant in general. But after 2000, with the sharp rise in crude oil prices, this unidirectional Granger causality became a complex nonlinear relationship, which cannot be detected by any linear Granger causality test.     

偏心搅拌槽内高浓度浆液颗粒的悬浮特性

以直径为0.34 m的无挡板平底圆筒形搅拌槽为研究对象,对偏心搅拌槽内高浓度浆液中颗粒的悬浮特性进行了数值研究,分析了45°的4斜叶开启涡轮式搅拌桨(PBT)和3窄叶整体板式螺旋桨(ZHX)2种桨型在不同偏心率和转速时,搅拌槽内的流型分布、颗粒体积分数分布、完全离底临界悬浮转速以及功率消耗,并与试验结果进行了对比.结果表明:对于高浓度浆液,偏心搅拌打破了中心搅拌时流场结构的对称性,提高了流体的轴向循环能力,颗粒悬浮效果优于中心搅拌;固体颗粒的悬浮效果与搅拌桨在槽内的偏心位置有关,当偏心率E=0.4时颗粒悬浮效果最佳,但偏心搅拌会增大颗粒的完全离底临界悬浮转速和设备的功率消耗,不利于节能降耗.     

Opposite effects of fear conditioning and extinction on dendritic spine remodelling

It is generally believed that fear extinction is a form of new learning that inhibits rather than erases previously acquired fear memories. Although this view has gained much support from behavioural and electrophysiological studies, the hypothesis that extinction causes the partial erasure of fear memories remains viable. Using transcranial two-photon microscopy, we investigated how neural circuits are modified by fear learning and extinction by examining the formation and elimination of postsynaptic dendritic spines of layer-V pyramidal neurons in the mouse frontal association cortex. Here we show that fear conditioning by pairing an auditory cue with a footshock increases the rate of spine elimination. By contrast, fear extinction by repeated presentation of the same auditory cue without a footshock increases the rate of spine formation. The degrees of spine remodelling induced by fear conditioning and extinction strongly correlate with the expression and extinction of conditioned fear responses, respectively. Notably, spine elimination and formation induced by fear conditioning and extinction occur on the same dendritic branches in a cue- and location-specific manner: cue-specific extinction causes formation of dendritic spines within a distance of two micrometres from spines that were eliminated after fear conditioning. Furthermore, reconditioning preferentially induces elimination of dendritic spines that were formed after extinction. Thus, within vastly complex neuronal networks, fear conditioning, extinction and reconditioning lead to opposing changes at the level of individual synapses. These findings also suggest that fear memory traces are partially erased after extinction.     

道家文化影响下的沈从文创作——从尚“三”特征说起

沈从文创作中喜欢用"三"是一个值得引起注意的问题。道家的宇宙模型是"道生一,一生二,二生三,三生万物。"[1]从尚"三"特征出发,探讨沈从文创作的影响,本文考察道家文化从而进一步挖掘沈从文丰富的文化内涵。     

纯水相SEC法分离水溶性小金纳米簇的研究

以纯水为流动相,用PL aquagel-OH 40色谱柱与自动馏分收集器,在Agilent 1100高效液相色谱仪上,对巯基丁二酸(MSA)单层包裹的粒径约1-5 nm的水溶性小金纳米簇进行尺寸排阻色谱(SEC)分离,环境友好,操作简便,重现性好.TEM表征和粒径统计分析表明:在不同洗脱时间收集的金纳米簇馏分与相应的原始样品相比其粒径分布均得到改善,尺寸分布明显窄化.金纳米簇在所用色谱柱上的分离主要按尺寸排阻机制进行.     

The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence

In this study, we explore the effect of cojumps within the agricultural futures market, and cojumps between the agricultural futures market and the stock market, on stock volatility forecasting. Also, we take into account large and small components of cojumps. We have several noteworthy findings. First, large jumps may lead to more substantial fluctuations and are more powerful than small jumps. The effect of cojumps and their decompositions on future volatility are mixed. Second, a model including large and small cojumps between the agricultural futures market and the stock market can achieve a higher forecasting accuracy, implying that large and small cojumps contain more useful predictive information than cojumps themselves. Third, our conclusions are robust based on various robustness tests such as the realized kernel, expanding forecasts, different forecasting windows, different jump tests, and different threshold values.     

Crosstalk of protein kinase C ε with Smad2/3 promotes tumor cell proliferation in prostate cancer cells by enhancing aerobic glycolysis

Protein kinase C ε (PKCε) has emerged as an oncogenic protein kinase and plays important roles in cancer cell survival, proliferation, and invasion. It is, however, still unknown whether PKCε affects cell proliferation via glucose metabolism in cancer cells. Here we report a novel function of PKCε that provides growth advantages for cancer cells by enhancing tumor cells glycolysis. We found that either PKCε or Smad2/3 promoted aerobic glycolysis, expression of the glycolytic genes encoding HIF-1α, HKII, PFKP and MCT4, and tumor cell proliferation, while overexpression of PKCε or Smad3 enhanced aerobic glycolysis and cell proliferation in a protein kinase D- or TGF-β-independent manner in PC-3M and DU145 prostate cancer cells. The effects of PKCε silencing were reversed by ectopic expression of Smad3. PKCε or Smad3 ectopic expression-induced increase in cell growth was antagonized by inhibition of lactate transportation. Furthermore, interaction of endogenous PKCε with Smad2/3 was primarily responsible for phosphorylation of Ser213 in the Samd3 linker region, and resulted in Smad3 binding to the promoter of the glycolytic genes, thereby promoting cell proliferation. Forced expression of mutant Smad3 (S213A) attenuated PKCε-stimulated protein overexpression of the glycolytic genes. Thus, our results demonstrate a novel PKCε function that promotes cell growth in prostate cancer cells by increasing aerobic glycolysis through crosstalk between PKCε and Smad2/3.