假设检验策略研究进展述评

在解决问题的过程中,假设检验策略是一种非常重要的启发式思维策略。在心理学领域,对它的探讨最早起源于有关概念形成、规则发现与命题检验的实验研究。有关假设检验的过程一直以来都存在着证真与证伪的激烈争论;近来有学者认为,人们在假设检验的过程中不仅仅只产生一个假设,而是同时去验证两个甚或多个假设的真伪;也有学者认为假设检验过程应当包含三个阶段:提出假设、设计实验和检验假设。对假设检验思维策略的深入研究将有助于提高人们解决问题和探索未知的能力。     

邮区中心局选址的一种优选方法

本文利用图论的理论与方法对邮区中心局的选址问题进行优化。首先对其原理做了较详细的描述,然后运用选址算法对邮区中心局的选址进行了优化。     

安钢渣铁站计算机联锁控制方案优化分析

针对安钢渣铁站铁路信号计算机控制系统的现场要求,通过对国内三种典型的微机联锁方案进行分析,提出了适合我国国情的铁路站场系统优化控制模型,并在安钢渣铁站站场信号自动控制设计中得到实际运用.     

一类差分方程的回归点集

本文讨论了周期点的周期为2的方幂的一类差分方程的回归点的性质,定义这类差分方程的第i个分量仅依赖于前面i个独立变量     

用变长染色体遗传算法演化实验公式

提出用变长染色体遗传算法自动地遗传演化出任何一个实验（包括物理,化学,工程等方面）的近似函数解析式     

吴筠：道士诗人与道教诗

吴筠作为一位道士诗人,无论在唐诗史还是在宗教文学史上都具有不同寻常的地位。他曾有过。朝避地的经历,又处文人世俗化的“天使”地位。其诗在内容上以道为体;以儒为用,是列仙之趣的代表,多近乎迷醉的幻想,虽不乏社会批判,但主要为全身远害。他的诗作在形式上偏爱五言、古体;喜爱联章组诗;具浪漫的幻想色彩。     

新信息技术驱动的制造服务化价值创造过程:基于徐工的探索性案例研究

近年来物联网等新信息技术的快速兴起有力地推动了制造服务化的发展,为制造企业实现价值创造提供了新的机遇.然而,新信息技术环境下制造服务化的价值创造过程尚不明确.为此,本文采用探索性单案例研究方法,借用MAXQDA12.0软件,使用扎根理论的编码方法处理数据,构建了新信息技术驱动的制造企业服务化价值创造过程模型,包括架构支撑体系构建、新信息技术功能应用、价值创造活动实现和价值创造方式实现四部分.由于价值创造是通过价值创造活动实现的,同时,新信息技术环境下价值创造活动多需要新信息技术功能支撑,因此,本文又进一步探讨了新信息技术驱动的制造服务化价值创造过程中价值创造活动的变化及其对价值创造方式的影响,以及新信息技术功能应用中数据、信息和知识的传递、集成、处理和共享对价值创造活动的支撑作用.本文为新信息技术环境下制造企业开展服务化转型提供理论支持,并为制造服务化价值创造研究提供理论借鉴.     

Investing Profitably in China: is it Getting Harder?

Using information from the Toyo Keizai, this article studies the performance of 2,962 foreign subsidiaries across the period 1985-1999 to show a picture of declining profitability from foreign direct investment by MNE’s in China. Despite the influence of macro-level factors, such as the historically fluctuating performance of the Chinese economy, we observed that of the many factors that may affect profitability, subsidiary-specific factors had the greater influence. The findings suggest that there are significant benefits for early entrants into the market, but caution against the use of high majority ownership control. Other evidence showed that larger subsidiaries tended to perform better. Managerial implications for MNEs and the future prospects of foreign direct investment in China are discussed.Since China opened up to the outside world in 1979, it has been attracting increasing amounts of foreign direct investment (FDI), and after 1993 became the second largest recipient of FDI flows in the world. Meanwhile, a major participant in the upsurge in global foreign direct investment in the 1990s was Japan. By the mid-1990s, Japan became the second largest FDI source country in the world behind the United States. China, in particular, has become a major destination for Japan’s direct investments, absorbing one-ninth of all Japanese foreign investments at the end of 1996.¹Studying Japan’s worldwide direct foreign investment, we found that the proportion of profitable Japanese subsidiaries in China has been declining. In fact, this was the only part of the world where this was the case. While over 71 percent of Japanese subsidiaries in China claimed profits in 1992, the percentage steadily slipped to around 50 percent by 1999 (see Table 1). This poses two interesting questions. Is it really getting harder to invest profitably in China? And what could have influenced the profit performance of foreign subsidiaries in China?     

Optimal prediction in finite populations under matrix loss

Optimal prediction problems in finite population are investigated. Under matrix loss, we provide necessary and sufficient conditions for the linear predictor of a general linearly predictable variable to be the best linear unbiased predictor (BLUP). The essentially unique BLUP of a linearly predictable variable is obtained in the general superpopulation model. Surprisingly, the both BLUPs under matrix and quadratic loss functions are equivalent to each other. Next, we prove that the BLUP is admissible in the class of linear predictors. Conditions for optimality of the simple projection predictor (SPP) are given. Furthermore, the robust SPP and the robust BLUP are characterized on the misspecification of the covariance matrix.     

A Cross-Validation Analysis of Neural Network Out-of-Sample Performance in Exchange Rate Forecasting

Econometric methods used in foreign exchange rate forecasting have produced inferior out-of-sample results compared to a random walk model. Applications of neural networks have shown mixed findings. In this paper, we investigate the potentials of neural network models by employing two cross-validation schemes. The effects of different in-sample time periods and sample sizes are examined. Out-of-sample performance evaluated with four criteria across three forecasting horizons shows that neural networks are a more robust forecasting method than the random walk model. Moreover, neural network predictions are quite accurate even when the sample size is relatively small.