预格及其性质的研究

引入了预格和预格同态的概念,研究了预格同态与序同态的关系,得到了预格同态是序同态的结论,并给出了预格同构的等价刻画,证明了预格间的映射是预格同构当且仅当它是序同构.定义了预格的强理想与强滤子,研究了其性质,讨论了预格与理想及滤子的联系和区别.基于强理想的概念,引入了强并不可约元、强余素元等概念,讨论了其与并不可约元、余素元的关系.

预线性剩余格与逻辑代数

本文首先讨论了剩余格与FI代数之间、FI代数与MV代数之间的关系,对已有结果进行了改进。随后提出了预线性剩余格的概念,证明了预线性剩余格是BR_0代数与BL代数的基础,从而也就是著名的MV代数、R_0代数、G代数与Ⅱ代数的公共基础。

预拓扑分子格以及它们之间的开映射和闭映射

引入了预拓扑分子格的概念(它是拓扑分子格的推广),并证明了完备格上的闭预拓扑和伪闭包算子是一一对应的.另外,定义了两个预拓扑分子格之间的连续映射、开映射、闭映射和同胚映射并证明了若干特征定理.

一种针对减格矢量预编码的可调量化误差校正方法

基于减格矢量预编码的量化误差问题,提出了一种可调量化误差校正方法.通过大量仿真实验发现,在减格矢量预编码中量化误差数目的概率分布是有规律的,利用实际量化误差数目的概率分布规律,通过设定不同的调节值控制量化误差校正的数目,获得一种可调的误差校正方法.仿真表明,该方法在量化误差校正中,能够取得性能和复杂度的折衷.

预拓扑分子格上的连通性

定义了预拓扑分子格上的连通性的概念,并给出了它的两个等价刻画,最后讨论了此连通性的一些基本性质,得出如它是同胚不变的,也是可积的等重要结论.

Applications of nonferrous metal price volatility to prediction of China's stock market

The aim of the present work is to examine whether the price volatility of nonferrous metal futures can be used to predict the aggregate stock market returns in China. During a sample period from January of 2004 to December of 2011, empirical results show that the price volatility of basic nonferrous metals is a good predictor of value-weighted stock portfolio at various horizons in both in-sample and out-of-sample regressions. The predictive power of metal copper volatility is greater than that of aluminum. The results are robust to alternative measurements of variables and econometric approaches. After controlling several well-known macro pricing variables, the predictive power of copper volatility declines but remains statistically significant. Since the predictability exists only during our sample period, we conjecture that the stock market predictability by metal price volatility is partly driven by commodity financialization.

Spatial Interpolation of Soil Nutrients Based on BP Neural Network

With Zengcheng City, Guangdong Province, as the object of study, 200 soil sampling points were col ected for the spatial interpolation prediction of soil properties by using Kriging method and BP neural network method. After comparing the interpolation results with the measured values, the root mean square error of the prediction data was obtained. The results showed that the interpolation accuracy of BP neural network was higher than that of Kriging method under the same cir-cumstances, and there was no smoothness in using BP neural network method when there were few sample points. In addition, with no requirement on the distri-bution of sample data, BP neural network method had stronger generalization ability than traditional interpolation method, which was an alternative interpolation method.

A novel resource co-allocation model with constraints to budget and deadline in computational grid

To address the issue of resource co-allocation with constraints to budget and deadline in grid environments, a novel co-allocation model based on virtual resource agent was proposed. The model optimized resources deployment and price scheme through a three-side co-allocation mechanism, and applied queuing system to model the work of grid resources for providing quantitative deadline guarantees for grid applications. The validity and solutions of the model were presented theoretically. Extensive simulations were conducted to examine the effectiveness and the performance of the model by comparing with other co-allocation policies in terms of deadline violation rate, resource benefit and resource utilization. Experimental results show that compared with the three typical co-allocation policies, the proposed model can reduce the deadline violation rate to about 3.5% for the grid applications with constraints to budget and deadline. Also, the system benefits can be increased by about 30% compared with the those widely-used co-allocation policies.

Variable-grid reverse-time migration of different seismic survey data

With an increasing demand for high-resolution imaging of complex subsurface structures, thin layers and hidden reservoirs, borehole and cross-well seismic migration methods have become important. However, large differences are observed in the frequency bandwidth between the surface, borehole, and cross-well surveys. Thus, variable-grid- based algorithms have been adapted to reverse-time migration. Further, we introduce Lanczos filtering to ensure the stability of wavefield calculations as well as to decrease the artificial reflections that are caused due to the variable grid size. Finally, we observe that the application of this method to surface survey, borehole, and cross-well seismic data suggests improvements in the delineation of minor fractures and steeply dipping faults.

Prediction of the Mooring Force of a 2-D Floating Oil Storage Tank

A Constrained Interpolation Profile （CIP）-based model is developed to predict the mooring force of a two-dimensional floating oil storage tank under wave conditions, which is validated against to a newly performed experiment. In the experiment, a box-shaped floating oil storage apparatus is used. Computations are performed by an improved CIP-based Cartesian grid model, in which the THINC/SW scheme （THINC： tangent of hyperbola for interface capturing; SW： Slope Weighting）, is used for interface capturing. A multiphase flow solver is adopted to treat the water-air-body interactions. The Immersed Boundary Method （IBM） is implemented to treat the body surface. Main attention is paid to the sum force of mooring line and velocity field around the body. It is found that the sum force of the mooring line increases with increasing wave amplitude. The body suffers from water wave impact and large body motions occur near the free surface. The vortex occurs near the sharp edge, i.e., the sharp bottom comers of the float- ing oil storage tank and the vortex shedding can be captured by the present numerical model. The present model could be further improved by including turbulence model which is currently under development. Comparison between the computational mooring forces and the measured mooring forces is presented with a reasonable agreement. The developed numerical model can predict the mooring line forces very well.

Prediction of metal futures price volatility and empirical analysis based on symbolic time series of high-frequency

The metal futures price fluctuation prediction model was constructed based on symbolic high-frequency time series using high-frequency data on the Shanghai Copper Futures Exchange from July 2014 to September 2018,and the sample was divided into 194 histogram time series employing symbolic time series.The next cycle was then predicted using the K-NN algorithm and exponential smoothing,respectively.The results show that the trend of the histogram of the copper futures earnings prediction is gentler than that of the actual histogram,the overall situation of the prediction results is better,and the overall fluctuation of the one-week earnings of the copper futures predicted and the actual volatility are largely the same.This shows that the results predicted by the K-NN algorithm are more accurate than those predicted by the exponential smoothing method.Based on the predicted one-week price fluctuations of copper futures,regulators and investors in China’s copper futures market can timely adjust their regulatory policies and investment strategies to control risks.

预应变土工格栅与砂垫层界面特性试验研究

采用拉拔试验研究了预应变土工格栅与砂垫层的界面特性,通过对比分析试验结果,对法向应力、垫层厚度和预应变初值对筋土界面特性的影响进行了研究,结果显示:砂垫层厚度为5 cm时,低应力条件下预应变土工格栅与普通格栅的拉拔阻力峰值相差不大,随着法向应力的增大,预应变土工格栅的抗拔性逐渐优于普通格栅,而应变软化现象也愈明显;法向应力为50 kPa时,垫层厚度的增大对格栅抗拔性有一定提高,当厚度达到15 cm后提高效果明显减弱,且高预应变水平的土工格栅随着垫层厚度的增大会出现应变软化现象。最后通过数据拟合得到了界面抗剪强度包络线,预应变加筋法能显著提高格栅与砂垫层界面似摩擦系数,减小界面似粘聚力,可为预应变加筋法的推广提供参考。

Prevention of core cell damage in isolated islets of Langerhans by low temperature preconditioning

AIM: To study the core cell damage in isolated islets of Langerhans and its prevention by low temperature preconditioning (26 ℃).METHODS: Islets were cultured at 37 ℃ for 7-14 d after isolation, and then at 26 ℃ for 2, 4 and 7 d before additional culture at 37 ℃ for another 7 d. Core cell damage in the isolated islets was monitored by video-microscopy and analyzed quantitatively by use of a computer-assisted image analysis system. The analysis included daily measurement of the diameter and the area of the isolated islets and the area of the core cell damage that developed in those islets over time during culture. Histology and TdT-mediated dUTP-biotin nick end labeling (TUNEL) assay were used to characterize the cell damage and to monitor islet function.RESULTS: Microscopic analysis showed that during the 7 to 14 d of culture at 37 ℃, core cell damage occurred in the larger islets with diameters ＞200 μm, which included both necrotic and apoptotic cell death. Low temperature (26 ℃) culture could prevent core cell damage of isolated islets. The 7-d culture procedure at 26 ℃ could inhibit most of the core cell (excluding diameters＞300 μm) damages when the islets were re-warmed at 37 ℃.CONCLUSION: Our results indicate that core cell damage within isolated islets of Langerhans correlates with the size of islets. Low temperature (26 ℃) culture can prevent core cell damage in isolated islets, and successfully precondition these islets for incubation at 37 ℃. These novel findings may help to understand the pathophysiology of early loss of islet tissue after transplantation, and may provide a new strategy to improve graft function in the clinical setting of islet transplantation.

Seat inventory control methods for Chinese passenger railways

Railway seat inventory control strategies play a crucial role in the growth of profit and train load factor. The railway passenger seat inventory control problem in China was addressed. Chinese passenger railway operation features and seat inventory control practice were analyzed firstly. A dynamic demand forecasting method was introduced to forecast the coming demand in a ticket booking period. By clustering, passengers' historical ticket bookings were used to forecast the demand to come in a ticket booking period with least squares support vector machine. Three seat inventory control methods: non-nested booking limits, nested booking limits and bid-price control, were modeled under a single-fare class. Different seat inventory control methods were compared with the same demand based on ticket booking data of Train T15 from Beijing West to Guangzhou. The result shows that the dynamic non-nested booking limits control method performs the best, which gives railway operators evidence to adjust the remaining capacity in a ticket booking period.

Science Letters:Peak-power reduction by the lattice-reduction-aided closest point search for MIMO broadcast channels

In this letter, we present a modified vector-perturbation precoding scheme for the multiple-input multiple-output broadcast channel, where a perturbation vector is chosen to take into account both the instantaneous power and the instantaneous peak power of the transmitted signal. This perturbation vector is obtained by using the closest point search, with the aid of the lattice-reduction algorithm. Simulation results show that the proposed scheme yields a tradeoff among power efficiency, peak-to-average power ratio reduction, and complexity.

The Spatial Patterns of Initial Errors Related to the “Winter Predictability Barrier” of the Indian Ocean Dipole

In this study, using the Geophysical Fluid Dynamics Laboratory Climate Model version 2pl （GFDL CM2pl） coupled model, the winter predictability barrier （WPB） is found to exist in the model not only in the growing phase but also the Indian Ocean dipole （IOD） decaying phase of positive events due to the effect of initial errors. In particular, the WPB is stronger in the growing phase than in the decaying phase. These results indicate that initial errors can cause the WPB. The domi- nant patterns of the initial errors that cause the occurrence of the WPB often present an eastern-western dipole both in the surface and subsurface temperature components. These initial errors tend to concentrate in a few areas, and these areas may represent the sensitive areas of the predictions of positive IOD events. By increasing observations over these areas and eliminating initial errors here, the WPB phenomenon may be largely weakened and the forecast skill greatly improved.

Electricity Price Forecasting Based on AOSVR and Outlier Detection

Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, mis paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market

A novel deadline and budget constrained scheduling heuristics for computational grids

The conventional deadline and budget constrained （DBC） scheduling heuristics for economic-based computational grids does not take the inconsistency of grid heterogeneity into account, which can lead to decline of application completion ratios. Motivated by this fact, a novel DI3C scheduling heuristics was proposed to deal with sequential workflow applications. In order to valuate the inconsistency, the relative cost （RC） metric was introduced, which was used to indicate the task-starving degree for resources. The new algorithm assigns tasks to resources, considering completion time, budget and RC together. The GridSim toolkit and the benchmark suites of the standard performance evaluation corporation （SPEC） were used to simulate the heterogeneous grid environment and applications. The experimental results show that the task and workflow completion ratios of the new heuristics are higher than those of the conventional heuristics.

Wind Speed Forecasting Based on ARMA-ARCH Model in Wind Farms

Wind speed forecasting is signif icant for wind farm planning and power grid operation. The research in this paper uses Eviews software to build the ARMA (autoregressive moving average) model of wind speed time series, and employs Lagrange multipliers to test the ARCH (autoregressive conditional heteroscedasticity) effects of the residuals of the ARMA model. Also, the corresponding ARMA-ARCH models are established, and the wind speed series are forecasted by using the ARMA model and ARMA-ARCH model respectively. The comparison of the forecasting accuracy of the above two models shows that the ARMA-ARCH model possesses higher forecasting accuracy than the ARMA model and has certain practical value.

Fast Prediction of Acoustic Radiation from a Hemi-capped Cylindrical Shell in Waveguide

In order to predict acoustic radiation from a structure in waveguide, a method based on wave superposition is proposed, in which the free-space Green＇s function is used to match the strength of equivalent sources. In addition, in order to neglect the effect of sound reflection from boundaries, necessary treatment is conducted, which makes the method more efficient. Moreover, this method is combined with the sound propagation algorithms to predict the sound radiated from a cylindrical shell in waveguide. Numerical simulations show the effect of how reflections can be neglected if the distance between the structure and the boundary exceeds the maximum linear dimension of the structure. It also shows that the reflection from the bottom of the waveguide can be approximated by plane wave conditionally. The proposed method is more robust and efficient in computation, which can be used to predict the acoustic radiation in waveguide.