可持续发展的产业分类理论——立体产业分类理论

本世纪 60年代以来 ,自然资源产业、环保产业、高新技术产业 (包括信息产业 )等新兴产业迅速发展 ,其对可持续发展的促进作用 ,已经为各国经济发展的实践所证明 ,也给产业经济研究提出了现实的问题。传统产业分类理论没有对新兴产业的内涵、归属及其在产业结构中的地位等加以揭示 ,使得建立在传统产业分类理论基础上的产业结构演进理论解释现象、指导经济实践的能力减弱。作者在深入分析传统产业分类理论的局限的基础上 ,提出可持续发展的产业分类理论     

Welfare generosity, pregnancies and abortions among unmarried AFDC recipients

Even before the 1996 overhaul of the U.S. welfare system, a number of states had ended the practice of paying extra benefits to families who have additional children while receiving welfare. Proponents believe that this reform can reduce births to recipients, however many worry that it may encourage women to obtain abortions. Using a sample of unmarried AFDC recipients from the NLSY, we estimate a bivariate probit model of pregnancy and, conditional on becoming pregnant, the probability of abortion. Our results lend some support for the proposition that reducing incremental AFDC benefits will decrease pregnancies without increasing abortions. Received: 16 April 1998/Accepted: 11 March 1999     

中国干群矛盾的类别、发展趋势及缓解机制研究

干群矛盾是中国社会矛盾中最特别、最具有爆炸性的类型,全面解析干群矛盾是社会矛盾预警及治理的重要环节.文章从性质、主体、地域、表现形式、发展阶段、激烈程度等方面对干群矛盾进行分类,把握其发展的现状,概括出具有普遍性、局部激化、振荡调整、危害性大等特征,指出主要诱因包括政治利益冲突、经济利益冲突和情感利益冲突.预测其发展趋势将更为激烈.而且成为其他社会矛盾演化成大规模冲突的催化剂.提出了"一个中心三个层面"、预警机制、综合控制机制等缓解、化解干群矛盾的机制.     

运用核Fisher鉴别分析和MPM分类器的入侵检测

为了提高分类器的正确率和减少训练时间,将特征提取技术与分类算法结合,提出了一种基于核Fisher鉴别分析和最小极大概率机算法的入侵检测算法。利用核Fisher鉴别分析技术提取关键特征,运用最小极大概率机对提取特征后的数据进行分类,采用离线数据集KDDCUP99进行实验。实验结果表明,该算法是可行和有效的,使分类性能和训练时间都得到了提高。     

A comparison of classification models to identify the Fragile X Syndrome

The main models of machine learning are briefly reviewed and considered for building a classifier to identify the Fragile X Syndrome (FXS). We have analyzed 172 patients potentially affected by FXS in Andalusia (Spain) and, by means of a DNA test, each member of the data set is known to belong to one of two classes: affected, not affected. The whole predictor set, formed by 40 variables, and a reduced set with only nine predictors significantly associated with the response are considered. Four alternative base classification models have been investigated: logistic regression, classification trees, multilayer perceptron and support vector machines. For both predictor sets, the best accuracy, considering both the mean and the standard deviation of the test error rate, is achieved by the support vector machines, confirming the increasing importance of this learning algorithm. Three ensemble methods - bagging, random forests and boosting - were also considered, amongst which the bagged versions of support vector machines stand out, especially when they are constructed with the reduced set of predictor variables. The analysis of the sensitivity, the specificity and the area under the ROC curve agrees with the main conclusions extracted from the accuracy results. All of these models can be fitted by free R programs.     

Predicting Severity of Foreign Body Injuries in Children in Upper Airways: An Approach Based on Regression Trees

The entry of a small item into the upper airways is one of the leading causes of injuries in children up to 14 years old. The aim of this study is to characterize types of objects causing choking along with the features of the children involved in the accident and compare results with current standards. The European Survey on Foreign Bodies Injuries Study (ESFBI) collected data on foreign body injuries from 19 European countries. The data from ESFBI were selected according to the ICD-9-CM codes 933 (foreign body in the pharynx and larynx) and 934 (foreign body in the trachea, bronchi, and lungs). Both a classification tree and a linear discriminant analysis (LDA) have been set up to predict the probability that an injured child experiences a hospitalization. The classification tree provides flowchart-type decision rules and allows for analyzing the impact of the item features, the children characteristics, and the circumstances of the accidents on the severity of the foreign body injuries. Results showed that children younger than 3.5 who are involved in an accident have a high probability to experience a hospitalization.     

Demographic change, macroeconomic conditions, and the murder rate: The case of the United States, 1934–2006

Fluctuations in aggregate crime rates contrary to recent shifts in the age distribution of the U.S. population have cast doubt on the predictive power of the age–crime hypothesis. By examining a longer time horizon, back to the early 1930s, we show that the percentage of the young population is a robust predictor of the observed large swings in the U.S. murder rate over time. However, changes in the misery index—the sum of the inflation and unemployment rates—significantly contribute to explaining changes in the murder rate. This applies, in particular, to those changes that are at odds with the long-run trend of the U.S. age distribution, such as the decline in the murder rate in the latter part of the 1970s or its increase starting around the middle of the 1980s.     

Birth order effects on young students’ academic achievement

We examine birth order effects on academic achievement for ten-year-old students using data for the entire population of fifth graders in Norway 2007/2008. The analysis thus adds to a thin empirical literature focusing on birth order effects among young children. We find that being firstborn confers a significant advantage in families with two, three and four children. The analysis makes two other contributions. First, we provide evidence that unless mother's age at childbirth is included among the control variables, only small and imprecise birth order effects are revealed in families with low socioeconomic status. Second, we provide some evidence that the birth order effects differ across families with highly educated and less educated mothers, which lend support to the resource dilution model over the confluence model.     

Are people in the South less intelligent than in the North? IQ and the North–South disparity in Italy

Socioeconomic disparity between North and South Italy has been recently explained by Lynn (2010) as the result of a lower intelligence quotient (IQ) of the Southern population. The present article discusses the procedure followed by Lynn, supplementing his data with new information on school assessments and per head regional income. Genetic North–South differences are then discussed on the basis of the most recent literature on the subject. The results do not confirm the suggested IQ-economy causal link.     

An exploratory analysis of composite choices: Weighing rationality versus irrationality

Humans are engineered neurologically to make rational and irrational choices. This paper introduces a new paradigm for decision making – a composite choice model – in which economic agents are constantly weighing rationality versus irrationality when encountering options. In an exploratory, deterministic, two-period model, an assumption of a two-way cross-embedment (i.e., a two-way interaction between the rational and irrational components) results in a paradoxical phenomenon, an outcome of either tending toward bliss or abyss at the end of the first period. This implies, for instance, a psychological struggle between two selves within the mind. The paradigm proposed is compared to the dual-process theories recently developed by the cognitive sciences. Future research will explore implications for public policy design and implementation.