国外政府高新技术产业化需求创造的经验研究

需求创造是影响高新技术产业化和新经济发展的重要因素。新经济在发达国家率先崛起与发达国家政府高新技术产业化需求创新的行为创新一一对应。因此，本文研究了国外政府高新技术产业优需求创造的经验。     

过程强化的若干新进展

本文从现代过程工业发展的需求出发，介绍了过程强化的历史和现状，分析了近年来过程强化的特点。本文还结合实例对化工过程强化的作用和存在的问题等进行了讨论。     

DARPA及其项目管理方略与启示

美国高级研究计划局（DARPA）是组织美国国防领域高技术项目研发的重要机构。自成立以来，DARPA已为美军研发成功了大量的先进武器系统，同时为美国积累了雄厚的科技资源储备，并且引领着美国乃至世界军民高技术研发的潮流。本文系统分析了DARPA名称和组织结构的历史变迁、当前在研项目结构体系以及DARPA研发高技术项目的成功经验，最后总结了DARPA经验对我国高技术项目管理的启示。     

科技创新中的新型举国体制实施路径

举国体制在我国“两弹一星”、人工合成结晶牛胰岛素、青蒿素等重大成就中发挥了至为重要的作用。近年来，在科技攻关中采用新型举国体制受到越来越多的重视，但在新形势下如何更高效地实施，需要进一步的探索。为解析这一问题，我们从历史逻辑、理论逻辑和实践逻辑出发，就国内外如何在国家层面调动创新资源、突破重大科学技术难题的方法和案例进行了比较，就我国不同历史时期的科技创新形势进行了对比，在此基础上提出了新型举国体制实施的“弓箭模式”。该模式表明新型举国体制是“自上而下”与“自下而上”的有机结合，前者侧重于确定大目标和大方向，后者则侧重保持各任务的适应性和灵活性。一方面，“自上而下”的路径通过对各解决方案的内在关联梳理，使得整体谋划瞄准战略重点来展开；另一方面，“自下而上”的路径则用于协同子任务团队各尽所能地围绕大目标来攻关。因而，新型举国体制或可在科技团队的攻关任务协同中发挥重要作用。     

美国科学技术政策的历史沿革

美国目前是全世界的经济、军事超级大国，其经济和军事的发展，最根本、最重要的原因是科学和技术，是美国历届政府对科学和技术的重视和善用，是有一套适时调整的正确并有效的科技政策。美国的国家技术政策有着比较久远的历史根源。从美国独立到目前两百多年，美国国家科技政策经历了几次历史性的变革。分析美国国家科技政策的沿革、现行科技政策，对我国的科技决策的科学化和先进化具有十分重要的借鉴意义。     

韩国攻克半导体关键技术的组织管理模式及启示

项目的组织管理模式在关键核心技术攻关中发挥着重要作用,是技术研发成功的基础保障。韩国半导体DRAM领域从跟跑、并跑再到领跑的跨越,离不开其组织管理模式的贡献。分析韩国DRAM攻关组织管理模式发现,对于面向国家战略需求或服务国民经济主战场的关键技术,韩国采用了由国家牵头政府与企业共同出资、产学研合作攻克"卡脖子"技术的项目管理模式;针对关键核心技术设置两种技术路线的竞争技术攻关模式;面向目标明确的产品采用跳跃式研发模式;在世界技术动向的情报监测与分析方面采取技术攻关全周期实时监测模式;并注重海外人才引进。最后,分析韩国DRAM领域组织管理模式特点的基础上,对我国开展核心技术领域攻关过程中制定合理的组织管理模式提出启示。     

中美两国风险投资行业比较分析及启示

作为打造新动能资本的重要手段,风险投资的作用日益凸显。随着风险投资业的快速发展,近年来我国已经成为仅次于美国的风险投资大国。然而,繁荣的背后掩盖了中国风险投资发展的短板与不足。对此,本文通过对中美两国在行业内生结构、生态系统构建、政策监管等多方面进行对标研究后发现,我国风险投资行业仍然存在行业内部管理水平有限、缺少长期投资、退出渠道受限,以及监管设计不完善等问题,亟待完善。     

铁路网扩张与铁路货运需求的关系分析

本文通过对经济发达国家铁路货运需求与经济发展之间的关系和铁路网里程增长与工业化过程之间的关系分析，得出铁路网扩张与铁路货运需求和国民经济发展之间不存一一对应关系，提出今后我国铁路网增长建设的理想完成时间。     

金融租赁在商业地产中的应用探究

为了解决我国宏观调控下商业地产项目的融资难问‘题,总结了金融租赁的特点和运作模式,多角度论证了金融租赁在商业地产项目中应用的适用性。借鉴金融租赁在房地产和其他行业的成功运用经验,结合商业地产项目的自身特点,对金融租赁应用于商业地产的操作方法进行创新设计,提出了金融租赁在商业地产项目中运用的具体操作模式。并结合我国政策和金融制度,针对应用中存在的问题和困难提出了相应的对策建议,以期拓宽商业地产项目的融资渠道,促进商业地产的良性发展。     

广东省的核电发展现状和瞻望

本文介绍了广东省的电力需求，能源结构，环境污染，核电发展的必要性，现状和瞻望。     

The Role of Financial Variables in predicting economic activity

Previous research found that the US business cycle leads the European one by a few quarters, and can therefore be useful in predicting euro area gross domestic product (GDP). In this paper we investigate whether additional predictive power can be gained by adding selected financial variables belonging to either the USA or the euro area. We use vector autoregressions (VARs) that include the US and euro area GDPs as well as growth in the Rest of the World and selected combinations of financial variables. Out‐of‐sample root mean square forecast errors (RMSEs) evidence that adding financial variables produces a slightly smaller error in forecasting US economic activity. This weak macro‐financial linkage is even weaker in the euro area, where financial indicators do not improve short‐ and medium‐term GDP forecasts even when their timely availability relative to GDP is exploited. It can be conjectured that neither US nor European financial variables help predict euro area GDP as the US GDP has already embodied this information. However, we show that the finding that financial variables have no predictive power for future activity in the euro area relates to the unconditional nature of the RMSE metric. When forecasting ability is assessed as if in real time (i.e. conditionally on the information available at the time when forecasts are made), we find that models using financial variables would have been preferred in several episodes and in particular between 1999 and 2002. Copyright 2011 John Wiley & Sons, Ltd.     

Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods

Financial distress prediction (FDP) has been widely considered as a promising approach to reducing financial losses. While financial information comprises the traditional factors involved in FDP, nonfinancial factors have also been examined in recent studies. In light of this, the purpose of this study is to explore the integrated factors and multiple models that can improve the predictive performance of FDP models. This study proposes an FDP framework to reveal the financial distress features of listed Chinese companies, incorporating financial, management, and textual factors, and evaluating the prediction performance of multiple models in different time spans. To develop this framework, this study employs the wrapper-based feature selection method to extract valuable features, and then constructs multiple single classifiers, ensemble classifiers, and deep learning models in order to predict financial distress. The experiment results indicate that management and textual factors can supplement traditional financial factors in FDP, especially textual ones. This study also discovers that integrated factors collected 4 years prior to the predicted benchmark year enable a more accurate prediction, and the ensemble classifiers and deep learning models developed can achieve satisfactory FDP performance. This study makes a novel contribution as it expands the predictive factors of financial distress and provides new findings that can have important implications for providing early warning signals of financial risk.     

Combination of forecasts in electricity demand prediction

Accurate demand prediction is of great importance in the electricity supply industry. Electricity cannot be stored, and generating plant must be scheduled well in advance to meet future demand. Up to now, where online information about external conditions is unavailable, time series methods on the historical demand series have been used for short-term demand prediction. These have drawbacks, both in their sensitivity to changing weather conditions and in their poor modelling of the daily/weekly business cycles. To overcome these problems a framework has been constructed whereby forecasts from different prediction methods and different forecasting origins can be selected and combined, solely on the basis of recent forecasting performance, with no a priori assumptions of demand behaviour. This added flexibility in univariate forecasting provides a significant improvement in prediction accuracy.     

Predicting multistage financial distress: Reflections on sampling,feature and model selection criteria

This research proposes a prediction model of multistage financial distress (MSFD) after considering contextual and methodological issues regarding sampling, feature and model selection criteria. Financial distress is defined as a three‐stage process showing different nature and intensity of financial problems. It is argued that applied definition of distress is independent of legal framework and its predictability would provide more practical solutions. The final sample is selected after industry adjustments and oversampling the data. A wrapper subset data mining approach is applied to extract the most relevant features from financial statement and stock market indicators. An ensemble approach using a combination of DTNB (decision table and naïve base hybrid model), LMT (logistic model tree) and A2DE (alternative N dependence estimator) Bayesian models is used to develop the final prediction model. The performance of all the models is evaluated using a 10‐fold cross‐validation method. Results showed that the proposed model predicted MSFD with 84.06% accuracy. This accuracy increased to 89.57% when a 33.33% cut‐off value was considered. Hence the proposed model is accurate and reliable to identify the true nature and intensity of financial problems regardless of the contextual legal framework.     

A stereospecific approach to diterpene alkaloids with a bridge across ring B

Zusammenfassung Die Herstellung eines Schlüsselprodukts für die Synthese von Alkaloiden des Songorin- und Delphinin-Typs wird beschrieben.

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We wish to thank the Hoffmann La Roche Company Inc., Nutley, New Jersey, and specifically Dr.A. Brossi, not only for financial support but also for collaboration and help. Financial support by the National Research Council of Canada is gratefully acknowledged.     

Forecasting commercial paper rates

A model previously developed by Lackman (C. L. Lackman, Forecasting commercial paper rates. Journal of Business Finance and Accounting 15 (1988) 499–524) for the period 1960 to 1985 is updated to include the 1990s and incorporate statistical techniques relating to tests for stationary conditions not available in 1988. As in the previous model, the demand for commercial paper by each institution (Households (HH), Life Insurance Companies (LIC), Non‐Financial Corporations (CRP) and Finance Corporations (FC)) and the total demand is simulated. Simulations of the commercial paper rate are also generated—using just the demand equations (total supply exogenous) and then employing the entire model (supply endogenous) to determine the rate. Simulation periods are from 1960:2 to 2001:4 for all demand simulations. The dynamic simulation of the total demand for commercial paper performs well. The resulting root mean square error, 3.485, compares favourably with the Federal Reserve Boston–Massachusetts Institute of Technology (FRB–MIT) estimate of the commercial paper rate (deLeeuw and Granlich, 1968). Copyright © 2004 John Wiley & Sons, Ltd.     

Geopolitical risk and global financial cycle: Some forecasting experiments

In this study, we investigate the connection between geopolitical risk (GPR) and global financial cycle (GFCy) as well as whether the former has predictive value for the out-of-sample predictability of the latter. We utilize both the historical and recent GPR data and their variants, namely, GPR act covering all “acts” that constitute GPR such as war, nuclear invasion and terrorism, and GPR threat, which represents threats of these acts. We construct a predictive model that accommodates the salient features of the predicted and predictor series while the forecast evaluation is conducted for both in-sample and out-of-sample periods. Our findings reveal that a rise in GPR discourages investments in risky assets and by implication worsens GFCy. The impact is more severe after the global financial crisis (gfc), and the GPR threat exerts more adverse effect on GFCy compared with GPR act regardless of whether historical GPR or recent GPR is used. Meanwhile, the predictive model of GFCy that accommodates the GPR data outperforms the benchmark model that ignores it both in the in-sample and out-of-sample estimates albeit with improved forecast performance during the post-gfc period and at a longer forecast horizon. However, the recent GPR data, which are broader in scope, offer better forecast accuracy than the historical GPR data. Additional analyses involving the vulnerability of global economic conditions reveal similar outcomes as GFCy.     

The role of sex steroid in two avian song behaviours differing in ontogenetic process

The male Bengalese finch,Lonchura striata, has two types of song behaviour (directed song, DS and undirected song, US). DS and US share a basically identical syllable repertoire, sequence pattern and tempo, but differ in the time course of appearance during the maturational process. In order to examine whether this results from a difference in testosterone (T) dependency, we studied developmental changes in the fecal T level and the amounts of DS and US during the 2–4 month period (N=7). DS appeared between 83 and 94 days of age, 4–16 days after a rise in the fecal T level. In contrast, US appeared earlier and at high frequency even when T was still at a very low level. These results suggest that DS is more dependent on the T level than US, and is not activated until the T level rises during the maturational process.     

Changes in IAA oxidase activity in rooting hypocotyl cuttings ofPhaseolus mungo L.

Summary IAA oxidase activity increased concomitant with root initiation during 16–52 h, in all cultures, thus showing the necessity of an active IAA oxidase for root initiation inPhaseolus mungo hypocotyl cuttings. The increase being very sharp in IAA alone, but less in all other cultures. The role of sucrose in root initiation via IAA oxidase activity has been discussed.The research has been partly financed by a grant from the US Department of Agriculture. One of us (R.N.C.) is thankful to the CSIR, India, for financial assistance.     

Modeling and forecasting municipal solid waste generation in the US energy supply

This paper describes an economic and statistical approach to modeling and forecasting municipal solid waste generation in the US energy supply. It begins with a discussion of the historical developments in the waste to energy industry over the last 25 years. Then a model is developed to provide energy policy makers with an analytical framework for understanding the relationships between the solid waste industry and the waste to energy industry. The model is tested empirically using data at the national level. The model's forecasts are compared with projections made by the US Environmental Protection Agency