Power and reversal power links for binary regressions: An application for motor insurance policyholders

In binary regression, symmetric links such as logit and probit are usually considered as standard. However, in the presence of unbalancing of ones and zeros, these links can be inappropriate and inflexible to fit the skewness in the response curve and likely to lead to misspecification. This is the case of covering some type of insurance, where it can be observed that the probability of a given binary response variable approaches zero at different rates than it approaches one. Furthermore, when usual links are considered, there is not a skewness parameter associated with the distribution chosen that, regardless of the linear predictor, is easily interpreted. In order to overcome such problems, a proposal for the construction of a set of new skew links is developed in this paper, where some of their properties are discussed. In this context, power links and their reversal versions are presented. A Bayesian inference approach using MCMC is developed for the presented models. The methodology is illustrated considering a sample of motor insurance policyholders selected randomly by gender. Results suggest that the proposed link functions are more appropriate than other alternative link functions commonly used in the literature. Copyright © 2016 John Wiley & Sons, Ltd.     

An ensemble learning framework for potential miRNA-disease association prediction with positive-unlabeled data

To explore the pathogenic mechanisms of MicroRNA (miRNA) on diverse diseases, many researchers have concentrated on discovering the potential associations between miRNA and disease using machine learning methods. However, the prediction accuracy of supervised machine learning methods is limited by lacking of experimentally-validated uncorrelated miRNA-disease pairs. Without these negative samples, training a highly accurate model is much more difficult. Different from traditional miRNA-disease prediction models using randomly selected unknown samples as negative training samples, we propose an ensemble learning framework to solve this positive-unlabeled (PU) learning problem. The framework incorporates two steps, i.e., a novel semi-supervised Kmeans (SS-Kmeans) to extract reliable negative samples from unknown miRNA-disease pairs and subagging method to generate diverse training sample sets to make full use of those reliable negative samples for ensemble learning. Combined with effective random vector functional link (RVFL) network as prediction model, the proposed framework showed superior prediction accuracy comparing with other popular approaches. A case study on lung and gastric neoplasms further confirms the framework’s efficacy at identifying miRNA disease associations.     

带有二元连接函数的半参数模型

提出了一种新的带有二元连接函数的广义半参数模型,即二元连接模型(简称为BLM).使用轮廓似然方法估计模型的参数和非参数部分,并给出了计算算法.证明了所得的未知参数的估计量为n~(1/2)-相合,渐近正态且具有渐近最小方差,给出了实际数据分析和模拟研究,最终采用局部功效方法来检验非参数部分的线性性.     

DYNAMICS MODELING,SIMULATION, AND CONTROL OF ROBOTS WITH FLEXIBLE JOINTS AND FLEXIBLE LINKS$^{\bf 1)}$

本文探究了铰柔性对机器人动力学响应和动力学控制的影响. 首先, 建立由$n$个柔性铰和$n$个柔性杆组成的空间机器人模型, 运用递推拉格朗日动力学方法, 得到柔性机器人系统的刚柔耦合动力学方程. 在动力学建模过程中, 除了考虑杆件的拉伸变形、弯曲变形、扭转变形以及非线性耦合变形对机器人系统动力学行为的影响, 还考虑了铰的柔性对机器人动力学响应和控制的影响. 其中, 柔性铰模型是基于Spong的柔性关节简化模型, 将柔性铰看成线性扭转弹簧, 不仅考虑了铰阻尼的存在, 还考虑了柔性铰的质量效应. 其次, 编写了空间柔性铰柔性杆机器人仿真程序, 研究铰的刚度系数和阻尼系数对系统动力学响应的影响. 研究表明: 随着柔性铰刚度系数的增大, 柔性机器人的动态响应幅值减小, 振动频率变大. 随着柔性铰阻尼系数的增大, 柔性机器人的动态响应幅值减小, 振动幅值的衰减速度变快. 可通过调节柔性铰的刚度和阻尼来减小柔性铰柔性杆机器人的振动, 因此铰阻尼的研究具有重要工程意义. 最后, 研究了铰柔性在机器人系统动力学控制中的影响. 在刚性铰机械臂和柔性铰机械臂完成相同圆周运动时, 通过逆动力学方法求解得到两种情况下的关节驱动力矩. 研究表明: 引入柔性铰会使控制所需的驱动力矩变小, 对机器人控制的影响显著.     

二维微裂纹体有效性质之间的关联研究

通过解析和数值的方法研究了二维微裂纹体有效模量和有效导电系数之间的关联，针对二维含有任意取向和平行的微裂纹介质，通过基于象素的有限元方法分别计算了它们的有效杨氏模量和有效导电系数，并建立了两种不同物理量之间的关联．研究结果表明利用简单的解析Mori-Tanaka方法也可以建立上述关联，并且与数值结果吻合很好。     

在线教学的实践与反思*——以有机化学课程为例

以有机化学课程在线教学开展情况为背景,通过与传统教学模式对比,分析了在线教学的优势与不足。在此基础上,将笔者所在课程组开展在线教学以来在教学内容重构、教学环节设计、考核方式改革等方面的探索与尝试进行了总结与分享。     

Cross‐linking and properties of rubber magnetic composites cured with different curing systems

Rubber magnetic composites were prepared by incorporation of strontium ferrite into rubber compounds based on acrylonitrile butadiene rubber and ethylene propylene diene monomer rubber. The sulfur, peroxide, and mixed sulfur/peroxide curing systems were introduced as cross‐linking agents for rubber matrices. The aim was to investigate the influence of curing system composition on curing process and cross‐link density of composite materials. Then, static and dynamic mechanical properties and thermal and magnetic characteristics were investigated in relation to the cross‐link density of rubber magnetic composites and structure of the formed cross‐links. The changes of dynamical and physicomechanical properties were in close correlation with the change of cross‐link density, whereas the tensile strength of magnetic composites showed increasing trend with increasing amount of peroxide in mixed curing systems. On the other hand, thermal conductivity and magnetic characteristics were found not to be dependent on the curing system composition.     

Preparation of Molecule‐Responsive Microsized Hydrogels via Photopolymerization for Smart Microchannel Microvalves

Microdevices designed for practical environmental pollution monitoring need to detect specific pollutants such as dioxins. Bisphenol A (BPA) has been widely used as a monomer for the synthesis of polycarbonate and epoxy resins. However, the recent discovery of its high potential ability to disrupt human endocrine systems has made the development of smart systems and microdevices for its detection and removal necessary. Molecule‐responsive microsized hydrogels with β‐cycrodextrin (β‐CD) as ligands are prepared by photopolymerization using a fluorescence microscope. The molecule‐responsive micro‐hydrogels show ultra‐quick shrinkage in response to target BPA. Furthermore, the flow rate of a microchannel is autonomously regulated by the molecule‐responsive shrinking of their hydrogels as smart microvalves.

     

Co-authorship model with link strength

We generalize a network formation model for co-authorship introducing the possibility of the connections having different link strengths. Different link strengths represent the fact that authors may put different efforts into different collaborations. To evaluate the model, we consider the notions of efficiency and pairwise stability, which are based on a utility function that measures the benefits for an author to belonging to a certain network structure. We divide the analysis in two cases, considering that link strengths are unbounded or bounded. In the first case, we show that if there are more than two authors in the network, then there is no pairwise stable network. In the second case, we show that the pairwise stable networks consist of completely connected disjoint components where essentially all link strengths are maximal. Regarding efficiency, in both cases, if the number of authors is even, then the unique efficient network structure consists of pairs of connected authors.