树木崇拜与民俗文化

中华民族有着丰富的民俗文化,其中,有许多是与树木有关的,文章就此探讨了树木崇拜及其引发的社会行为。     

A Tabu Search Heuristic for the Location of Multi-Type Protection Devices on Electrical Supply Tree Networks

The problem of determining the number of multi-type protection devices and their locations on electrical supply tree networks with subtree dependency is investigated. The aim is to reduce the amount of inconvenience caused to customers that are affected by any given fault on the networks. An appropriate implementation of tabu search is proposed. We exploit a variable neighborhood and a soft aspiration level, and we embed a data structure and reduction tests into the search to speed up the process. Computational tests are performed on randomly generated electrical tree networks varying in size and branch complexity with encouraging results.     

Soft Classification Trees

An extension of soft classification trees to multinomial outcomes is presented. Estimates of the method's predictive accuracy, as well as average tree size and tree depths, are systematically compared to those of the conventional Classification and Regression Tree (CART) approach by the means of simulations. A similar comparison is performed on real datasets. Results point to an advantage in favor of the soft tree.     

Understanding Community Resilience from a PRA Perspective Using Binary Decision Diagrams

Probabilistic risk assessment (PRA) is a useful tool to assess complex interconnected systems. This article leverages the capabilities of PRA tools developed for industrial and nuclear risk analysis in community resilience evaluations by modeling the food security of a community in terms of its built environment as an integrated system. To this end, we model the performance of Gilroy, CA, a moderate‐size town, with regard to disruptions in its food supply caused by a severe earthquake. The food retailers of Gilroy, along with the electrical power network, water network elements, and bridges are considered as components of a system. Fault and event trees are constructed to model the requirements for continuous food supply to community residents and are analyzed efficiently using binary decision diagrams (BDDs). The study also identifies shortcomings in approximate classical system analysis methods in assessing community resilience. Importance factors are utilized to rank the importance of various factors to the overall risk of food insecurity. Finally, the study considers the impact of various sources of uncertainties in the hazard modeling and performance of infrastructure on food security measures. The methodology can be applicable for any existing critical infrastructure system and has potential extensions to other hazards.     

A reflected feature space for CART

We present an algorithm for learning oblique decision trees, called HHCART(G). Our decision tree combines learning concepts from two classification trees, HHCART and Geometric Decision Tree (GDT). HHCART(G) is a simplified HHCART algorithm that uses linear structure in the training examples, captured by a modified GDT angle bisector, to define splitting directions. At each node, we reflect the training examples with respect to the modified angle bisector to align this linear structure with the coordinate axes. Searching axis parallel splits in this reflected feature space provides an efficient and effective way of finding oblique splits in the original feature space. Our method is much simpler than HHCART because it only considers one reflected feature space for node splitting. HHCART considers multiple reflected feature spaces for node splitting making it more computationally intensive to build. Experimental results show that HHCART(G) is an effective classifier, producing compact trees with similar or better results than several other decision trees, including GDT and HHCART trees.     

现代生态景观视野下城市树木价值计算方法

城市树木是城市有价财产,价值体现在木材产品和生态服务两个方面.为了能够通过货币来表达和评估城市树木的实际价值,通过参考国外城市树木价值计算方法、森林树木价值研究成果、政府官方网站数据、国际木材价格以及咨询城市林业专家的方法,提出城市树木价值计算公式：城市树木的价值=胸高（1.3m）树干断面积×树木基价×树种系数×生长位置系数×生长情况系数×观赏特性系数×生态效益系数,并确定计算公式中的树木基价以及系数赋值的方法和标准.     

Modeling Correlated Discrete Uncertainties in Event Trees with Copulas

Modeling the dependence between uncertainties in decision and risk analyses is an important part of the problem structuring process. We focus on situations where correlated uncertainties are discrete, and extend the concept of the copula‐based approach for modeling correlated continuous uncertainties to the representation of correlated discrete uncertainties. This approach reduces the required number of probability assessments significantly compared to approaches requiring direct estimates of conditional probabilities. It also allows the use of multiple dependence measures, including product moment correlation, rank order correlation and tail dependence, and parametric families of copulas such as normal copulas, t‐copulas, and Archimedean copulas. This approach can be extended to model the dependence between discrete and continuous uncertainties in the same event tree.     

A nonparametric bayesian approach to analysis of drug dependence data

In this paper, a nonparametric Bayesian approach to the analysis of binary response data is considered. Using a Dirichlet process prior, and squared error loss, the Bayes estimators of response probabilities are obtained. Finally, the results obtained are employed to analyze the ARC 090 Trial data.     

从《橘颂》看南国橘和橘文化

屈原是战国时期楚国人,其《橘颂》对橘树的热情礼赞反映了先秦时期橘生南国的情况。从《橘颂》及有关的记载中可窥测到南国橘的种植分为楚国、吴越和巴渝等几大区。     

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.