Analysis of Regional Scale Risk of Whirling Disease in Populations of Colorado and Rio Grande Cutthroat Trout Using a Bayesian Belief Network Model

Introduction and spread of the parasite Myxobolus cerebralis, the causative agent of whirling disease, has contributed to the collapse of wild trout populations throughout the intermountain west. Of concern is the risk the disease may have on conservation and recovery of native cutthroat trout. We employed a Bayesian belief network to assess probability of whirling disease in Colorado River and Rio Grande cutthroat trout (Oncorhynchus clarkii pleuriticus and Oncorhynchus clarkii virginalis, respectively) within their current ranges in the southwest United States. Available habitat (as defined by gradient and elevation) for intermediate oligochaete worm host, Tubifex tubifex, exerted the greatest influence on the likelihood of infection, yet prevalence of stream barriers also affected the risk outcome. Management areas that had the highest likelihood of infected Colorado River cutthroat trout were in the eastern portion of their range, although the probability of infection was highest for populations in the southern, San Juan subbasin. Rio Grande cutthroat trout had a relatively low likelihood of infection, with populations in the southernmost Pecos management area predicted to be at greatest risk. The Bayesian risk assessment model predicted the likelihood of whirling disease infection from its principal transmission vector, fish movement, and suggested that barriers may be effective in reducing risk of exposure to native trout populations. Data gaps, especially with regard to location of spawning, highlighted the importance in developing monitoring plans that support future risk assessments and adaptive management for subspecies of cutthroat trout.     

An access network design problem with end-to-end QoS constraints

In this paper, we present an access network design problem with end-to-end quality of service (QoS) requirement. The problem can be conceptualized as a two-level hierarchical location-allocation problem on the tree topology with nonlinear side constraints. The objective function of the nonlinear mixed integer programming model minimizes the total cost of switch and fiber cable, while satisfying demand within the prescribed level of QoS. By exploiting the inherent structure of the nonlinear QoS constraints, we develop linearization techniques for finding an optimal solution. Also, we devise an effective exact optimal algorithm within the context of disjunctive constraint generation. We present promising computational results that demonstrate the effectiveness of the proposed solution procedure.     

Studying Complexity of Model-based Clustering

Cluster analysis is a popular statistics and computer science technique commonly used in various areas of research. In this article, we investigate factors that can influence clustering performance in the model-based clustering framework. The four factors considered are the level of overlap, number of clusters, number of dimensions, and sample size. Through a comprehensive simulation study, we investigate model-based clustering in different settings. As a measure of clustering performance, we employ three popular classification indices capable of reflecting the degree of agreement in two partitioning vectors, thus making the comparison between the true and estimated classification vectors possible. In addition to studying clustering complexity, the performance of the three classification measures is evaluated.     

Measuring Social Tension from Income Class Segregation

We develop an index that effectively measures the level of social tension generated by income class segregation. We adopt the basic concepts of between-group difference (or alienation) and within-group similarity (or identification) from the income [bi]polarization literature; but we allow for asymmetric degrees of between-group antagonism in the alienation function, and construct a more effective identification function using both the relative degree of within-group clustering and the group size. To facilitate statistical inference, we derive the asymptotic distribution of the proposed measure using results from U-statistics. As the new measure is general enough to include existing income polarization indices as well as the Gini index as special cases, the asymptotic result can be readily applied to these popular indices. Evidence from the Panel Study of Income Dynamics data suggests that, while the level of social tension shows an upward trend over the sample period of 1981 to 2005, government’s taxes and transfers have been effective in reducing the level of social tension significantly.     

基于自组织的大型建筑企业控制力协同进化研究

针对大型建筑企业控制力动态协同的定量化仿真问题,基于复杂系统视角,应用自组织理论分析大型建筑企业控制力自组织耦合网络作用关系,选择有效朗之万方程构建大型建筑企业控制力系统的自组织协同进化模型,通过线性近似法的性态稳定求解非线性方程的稳定性,得出大型建筑企业控制力系统协同进化演化过程的四个决定性参数,并通过MATLAB软件进行参数枚举取值,具体分析企业控制力协同演化。案例验证该模型可对大型建筑企业控制力协同演化最佳状态实现更加精确的参数取值,并发现大型建筑企业控制力协同演化最佳状态的充要条件,以及一种新的大型建筑企业控制力协同演化状态。     

曲线分类建模方法及其在GDP预测中的应用

传统方法解决大规模时序曲线的预测建模问题,需要对每条曲线逐一建模,使得建模工作量相当庞大,在实际应用中缺乏可操作性。文章提出一种解决此问题的新方法——曲线分类建模方法。该方法先减少曲线的模型种类,再进行曲线分类和分类建模,在尽可能保留原始信息的前提下较大程度地降低了建模的工作量。文章阐述了该方法的原理和计算过程,并以应用于多地区GDP曲线的预测案例说明该方法的实用性和有效性。     

Modeling Demand For Unionization With Nontraditional Data Analysis Methods

Upon reviewing the extant literature on determinants of unionism, it becomes clear that many areas that have had a plethora of research attention do not converge upon singularly directional findings. This study explores a potential cause of such an apparent anomaly: nonlinearity of data. An exploratory examination of correlation coefficients among typical union determinant variables seems to show different patterns of relationships at different levels of union demand. Thus, a break from traditional linear data analysis techniques is explored in the interest of explaining more variance with typical, theoretically derived variables by using neural network analysis. Results of analyses on industry level data reveal that using neural network analysis to model union demand explained over four times as much variance as multiple regression analysis.     

The coevolution of trade agreements and investment treaties: Some evidence from network analysis

Regional trade agreements (RTAs) and bilateral investment treaties (BITs) are expected to promote trade and investment relationships. One critical feature of such agreements is the network, so the multiplex coevolution of RTAs and BITs should be captured by the dynamics of their two networks. Although many studies have examined the roles of RTAs and BITs, most studies do not account for crucial network properties. This study explores how RTAs and BITs coevolve by applying a stochastic actor-oriented model of multiplex network evolution. In particular, we examine the roles of (i) cross-network dyadic interinfluences and (ii) within- and cross-network preferential attachments to discuss the dynamic relationships between RTAs and BITs. The results are as follows. First, our estimation supports cross-network dyadic interinfluences. Countries that sign a BIT are willing to establish an RTA, while those that sign an RTA are reluctant to establish a BIT. Second, concerning preferential attachments, countries prefer to sign BITs with partners that have more RTA and BIT links. However, countries tend to form RTAs with partners that have more BIT links but are reluctant to form RTAs with those that have more RTA links. We discuss possible justifications for these results, including arguments regarding the benefits and costs associated with the formation of RTAs and BITs.     

数字化时代网络信息传播秩序重构的内在逻辑与行动策略

数字技术使信息传播渠道从单一向“网络立体化”转变,信息传播主体从“中心化”向“分散化”转变,信息传播环境从“真实化”向“拟态化”转变,信息传播出现了“混沌”与“失序”现象。“坚持人本理念”是网络信息传播秩序重构的基本逻辑,“信息资源与要素的再整合”是网络信息传播秩序重构的基础条件,“智能化信息平台决策机制”是网络信息传播秩序重构的保障。在信息传播秩序重构的过程中,需要强化技术体系与网络体系的链接,搭建多层次信息流动与传播的网络空间;需要加强对社会生活空间与产业的数字化改造,促使现实世界向虚拟世界迁移;需要催生全新的元宇宙信息场域,构建与现实世界映射与交互的虚拟世界。     

网络意识形态安全研究的整体概貌

以CNKI核心论文数据库中的文献为研究对象,梳理网络意识形态安全相关研究成果,可以发现,目前学界对网络意识形态安全的研究成果多以理论分析为主,在研究范式上注重宏观和微观相结合,部分研究成果缺乏精准性和针对性,实证分析较少,研究方法单一,缺乏多学科融合视角。因此,未来应深化研究内容,拓宽研究视角,构建研究话语,以提高研究水平和增加理论供给。