区域用电量短期预测的一种方法

本文介绍一种新型的区城用电量短期预测的方法──递推预测法。实例表明，本方法是可行的、准确的。     

Relationships among metabolites influencing ovarian function in the dairy cow

Time series analysis was used to evaluate relationships between the uptake of metabolites, ovarian blood flow, arterial LH concentrations, and the output of steroid hormones by the ovary. There were no significant correlations between cholesterol uptake and progesterone output; therefore, cholesterol uptake was not a factor that immediately limited progesterone output. Although no significant correlations were found between cholesterol and oxygen uptake, significant cross-correlations between the uptake of glucose and oxygen by the ovary at lag 0 indicated some immediate oxidation of glucose. This result strongly indicates that glucose is a major source of energy for the bovine ovary. Arterial LH concentrations had little influence on ovarian metabolism. The examination of interrelationships among factors that influence ovarian function was useful in identifying factors that can limit ovarian activity.     

Neural network-based mean–variance–skewness model for portfolio selection

In this study, a novel neural network-based mean–variance–skewness model for optimal portfolio selection is proposed integrating different forecasts and trading strategies, as well as investors’ risk preference. Based on the Lagrange multiplier theory in optimization and the radial basis function (RBF) neural network, the model seeks to provide solutions satisfying the trade-off conditions of mean–variance–skewness. The feasibility of the RBF network-based mean–variance–skewness model is verified with a simulation experiment. The experimental results show that, for all examined investor risk preferences and investment assets, the proposed model is a fast and efficient way of solving the trade-off in the mean–variance–skewness portfolio problem. In addition, we also find that the proposed approach can also be used as an alternative tool for evaluating various forecasting models.     

A fuzzy multi-objective model for capacity allocation and pricing policy of provider in data communication service with different QoS levels

Data communication service has an important influence on e-commerce. The key challenge for the users is, ultimately, to select a suitable provider. However, in this article, we do not focus on this aspect but the viewpoint and decision-making of providers for order allocation and pricing policy when orders exceed service capacity. It is a multiple criteria decision-making problem such as profit and cancellation ratio. Meanwhile, we know realistic situations in which much of the input information is uncertain. Thus, it becomes very complex in a real-life environment. In this situation, fuzzy sets theory is the best tool for solving this problem. Our fuzzy model is formulated in such a way as to simultaneously consider the imprecision of information, price sensitive demand, stochastic variables, cancellation fee and the general membership function. For solving the problem, a new fuzzy programming is developed. Finally, a numerical example is presented to illustrate the proposed method. The results show that it is effective for determining the suitable order set and pricing policy of provider in data communication service with different quality of service (QoS) levels.     

Developing an SVM-based ensemble learning system for customer risk identification collaborating with customer relationship management

In this study, we propose a support vector machine (SVM)-based ensemble learning system for customer relationship management (CRM) to help enterprise managers effectively manage customer risks from the risk aversion perspective. This system differs from the classical CRM for retaining and targeting profitable customers; the main focus of the proposed SVM-based ensemble learning system is to identify high-risk customers in CRM for avoiding possible loss. To build an effective SVM-based ensemble learning system, the effects of ensemble members’ diversity, ensemble member selection and different ensemble strategies on the performance of the proposed SVM-based ensemble learning system are each investigated in a practical CRM case. Through experimental analysis, we find that the Bayesian-based SVM ensemble learning system with diverse components and choose from space selection strategy show the best performance over various testing samples.     

The sizes and powers of some stochastic dominance tests: A Monte Carlo study for correlated and heteroskedastic distributions

Testing for stochastic dominance among distributions is an important issue in the study of asset management, income inequality, and market efficiency. This paper conducts Monte Carlo simulations to examine the sizes and powers of several commonly used stochastic dominance tests when the underlying distributions are correlated or heteroskedastic. Our Monte Carlo study shows that the test developed by Davidson and Duclos [R. Davidson, J.Y. Duclos, Statistical inference for stochastic dominance and for the measurement of poverty and inequality, Econometrica 68 (6) (2000) 1435–1464] has better size and power performances than two alternative tests developed by Kaur et al. [A. Kaur, B.L.S.P. Rao, H. Singh, Testing for second order stochastic dominance of two distributions, Econ. Theory 10 (1994) 849–866] and Anderson [G. Anderson, Nonparametric tests of stochastic dominance in income distributions, Econometrica 64 (1996) 1183–1193]. In addition, we find that when the underlying distributions are heteroskedastic, both the size and power of the test developed by Davidson and Duclos [R. Davidson, J.Y. Duclos, Statistical inference for stochastic dominance and for the measurement of poverty and inequality, Econometrica 68 (6) (2000) 1435–1464] are superior to those of the two alternative tests.     

Seasonal Phosphate Demand for Lake Erie Plankton

From April to mid-October 1979, in the eastern and central basins of Lake Erie, the pattern for phosphate demand was assessed by four independent methods: conventional monitoring, measurements of polyphosphate, phosphate turnover time, and a newly developed phosphate deficiency index. Thermal stratification and P limitation occurred faster in the central than in the eastern basin and the plankton remained P limited throughout most of the stratified season. This condition relaxed as the thermocline deepened in the central basin, exposing the epilimnion to a greater area of sediments. In September, when mixing of the entire water column occurred, the plankton were no longer P limited. In contrast, as the thermocline deepened in the eastern basin, metalimnetic water was entrained but the plankton remained P limited until mid-October. These observations support the view that a reduction of phosphorus in Lake Erie would probably affect the phytoplankton biomass during the periods of P limitation.     

An integrated data preparation scheme for neural network data analysis

Data preparation is an important and critical step in neural network modeling for complex data analysis and it has a huge impact on the success of a wide variety of complex data analysis tasks, such as data mining and knowledge discovery. Although data preparation in neural network data analysis is important, some existing literature about the neural network data preparation are scattered, and there is no systematic study about data preparation for neural network data analysis. In this study, we first propose an integrated data preparation scheme as a systematic study for neural network data analysis. In the integrated scheme, a survey of data preparation, focusing on problems with the data and corresponding processing techniques, is then provided. Meantime, some intelligent data preparation solution to some important issues and dilemmas with the integrated scheme are discussed in detail. Subsequently, a cost-benefit analysis framework for this integrated scheme is presented to analyze the effect of data preparation on complex data analysis. Finally, a typical example of complex data analysis from the financial domain is provided in order to show the application of data preparation techniques and to demonstrate the impact of data preparation on complex data analysis.     

The Third-Party Logistics Services and Globalization of Manufacturing

Cross-border production chains of manufacturing require coordination and logistics. Third-party logistics services have thus taken this role as a link in the globalization of economic activities. Distriparks, a provider of total logistics to manufacturing, have emerged in Singapore to offer customized distribution and inventory management. Established as a traditional entrepôt by international trading, the city-state has revamped its distribution industry when transnational corporations come to choose Singapore as regional centre and local firms venture into the region. Distribution has regained its vitality in the global economy. It is demonstrated that distriparks have facilitated the restructuring of Singapore manufacturing and altered the internal structure of firms in their space utilization. Thereafter, a changing profile of industrial landscape, high-density industrial land use, and industrial agglomeration ensues.     

人造金刚石合成设备中的恒功率加热系统

温度控制是人造金刚石生产中重要条件之一。为了克服常用的加热系统存在的缺陷，特研制ＨＧＫ型恒功率控制器。本文介绍了目前国内加热系统的概况，恒功率控制器研制过程中制定方案的依据，分析了该控制器的设计原理，简述了它的实验和实际应用情况。