Space use by two arboreal rodent species in a Neotropical cloud forest

Studying animal space use patterns can help increase our understating of ecological processes such as competition and community dynamics. To quantify space and habitat use in an isolated and patchy cloud forest community in Mexico, we evaluate the vertical stratification, home range and habitat selection of two arboreal rodents: Habromys schmidlyi and Reithrodontomys microdon. Using live-traps at ground level and different forest strata, we radio-equipped nine individuals of H. schmidlyi and seven of R. microdon, and evaluated fine-scale space use and broad-scale habitat selection between cloud forest and oak forest. We found an average home range of 0.24 ha for R. microdon males and 0.72 ha for females, with a preference for higher canopy in the cloud forest. For H. schmidlyi the home range was 0.83 ha for males and 0.29 ha for females, with a preference for the understory level in the cloud forest. Home range is three-dimensional for these rodents, so we estimate that on average, individuals of both species used eight trees in the time they were tracked. We characterised the vegetation at the trap sites, and used recursive partitioning to relate the presence of different plants with the probability of finding these two species and Peromyscus aztecus, a third rodent species also present in the area and considered in our analysis of habitat use. The highest probability of finding R. microdon (96%) was related to the presence of Brachythecium occidentale and Renauldia mexicana, while H. schmidlyi (95%) was found in close proximity to Fabronia ciliaris and Everniastrum. We highlight the importance of arboreal trapping in biodiversity assessments, and the role of arboreal rodents in maintaining tropical forest ecosystems. We suggest that these rodent species could avoid or reduce competition by using the vertical strata differentially, and that H. schmidlyi and R. microdon can be biological indicators for cloud forest management and conservation.     

Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.     

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Four methods of model selection—equally weighted forecasts, Bayesian model‐averaged forecasts, and two models produced by the machine‐learning algorithm boosting—are applied to the problem of predicting business cycle turning points with a set of common macroeconomic variables. The methods address a fundamental problem faced by forecasters: the most useful model is simple but makes use of all relevant indicators. The results indicate that successful models of recession condition on different economic indicators at different forecast horizons. Predictors that describe real economic activity provide the clearest signal of recession at very short horizons. In contrast, signals from housing and financial markets produce the best forecasts at longer forecast horizons. A real‐time forecast experiment explores the predictability of the 2001 and 2007 recessions. Copyright © 2015 John Wiley & Sons, Ltd.     

不等跨柱网混凝土肋梁楼盖平面选型问题初探

通过计算分析和比较,论证了柱网在两方向跨度相差甚大的情况下,楼盖沿长跨方向单向布置次梁的结构方案具有较好的经济性和抗震性,但在设计和施工中须注意竖向振动、长单向板混凝土开裂和结构挠度偏大等不利影响.     

马尾松种子园优良家系选择研究

对广西南宁地区林科所种子园,藤县大芒界种子园和贵港市覃塘种子园的440个马尾松(P inusm asson ianaL am b.)自由授粉子代进行年度和地点重复的半胞子代测定试验,以本地马尾松等11个对照来分析马尾松自由授粉子代的遗传变异规律,并根据遗传稳定性和材积生长快慢,采用性状表现水平分析法从中选择出优良家系。结果表明,440个参试家系中有20.0%~98.0%的家系树高比对照大8.3%~67.7%,有5.5%~99.0%的家系胸径比对照大11.4%~106.9%、有9.1%~99.0%的家系材积比对照大29.2%~484.6%。评选出适合在广西普遍推广的广普性优良家系64个,局部推广的局部性优良家系48个,需淘汰的家系83个;选择出的优良家系在11年生子代林中,材积生长量比对照大64.71%~94.51%。用子代林数据估算马尾松各性状的家系遗传力,树高遗传力为0.306~0.724,胸径为0.174~0.686,单株材积为0.286~0.677。     

改进的模糊一致矩阵决策方法及其应用

对基于模糊一致矩阵的决策方法目前尚存在的不足之处,如模糊优先关系矩阵的元素不能体现相应两个对象之间优劣的差异程度、满足条件a的最小值不容易确定、计算优度值进行单指标排序的方法有待改进等进行了分析,从模糊优先关系矩阵的构造、模糊一致矩阵的转化、排序方法等方面对基于模糊一致矩阵的决策方法进行了改进.将改进后的决策方法用于方案优选的实际问题,进行实证分析.分析结果表明,改进后的决策方法能更好地反映参评对象的实际情况.     

一种改进的嵌入式特征选择算法及应用

针对非线性多分类问题,提出了一个改进的嵌入最小-最大值特征选择算法,并与支持向量机算法结合,提出了针对复杂的组合优化问题的启发式算法.为验证方法的有效性,在钢板缺陷识别工程数据集上进行了实验,表明所提出的方法具有较高的求解速度和预测准确度.     

基于元学习的航空电子设备特征选择算法推荐方法

为了对航空电子设备的测试数据进行有效约简, 去除冗余信息和不相关特征, 基于机器学习领域现有的特征选择算法, 提出了一种元学习框架下的航空电子设备特征选择算法推荐方法。所提方法旨在根据不同航空电子设备测试数据所蕴含的信息, 推荐合适的特征选择算法。首先, 分析了数据集特征的描述方法。然后, 介绍了采用综合度量指数的算法性能评价方法。最后, 给出了特征选择算法推荐方法的框架。使用42个航空电子设备的测试数据和13个过滤型特征选择算法建立了元数据库, 采用留一法进行交叉验证, 推荐命中率达到了90%以上, 推荐性能比例达到97%以上。     

基于特征选取与树状Parzen估计的入侵检测

针对目前网络空间安全形势快速变化带来的新风险和新挑战, 提出一种基于相关性分析的特征选取和树状Parzen估计优化的入侵检测方法。首先, 通过基于相关性分析的数据特征选取方法对数据维度进行压缩。其次, 对原始数据集进行特征筛选, 生成新的特征子集。最终, 使用序列模型优化算法中的树状Parzen估计算法对随机森林算法进行模型优化。实验结果表明, 相比其他应用机器学习算法的入侵检测方法, 所提方法在提升综合性能的同时拥有更高的检测效率, 有效地提升了入侵检测技术的实用性。     

多目标投资组合模型的模糊两阶段解法

对多目标证券组合投资模型进行了研究,模型以绝对偏差和代替方差作为投资组合的风险度量.该模型是一个多目标线性优化问题,采用两阶段模糊算法对模型进行求解,最后给出了该模型的一个实例的最优解.