突发重大疫情网络舆论的生成逻辑､演进特征与治理机制

新形势下突发重大疫情网络舆论不仅给疫情防控带来了严峻挑战，而且给维护国家意识形态安全带来了较大压力。厘清网络舆论的生成逻辑，把握网络舆论发展的演进特征，对做好网络舆论引导与谣言治理意义重大。在现代网络社会，随着交往实践的普遍进行，从现实逻辑、实践逻辑与理论逻辑来看，突发重大疫情网络舆论的生成，是网络议题、网络交往和网络传播相互作用的结果。受网络社会交往实践主体、客体、环体和介体等多种因素影响，突发重大疫情网络舆论又呈现为非线性的发展演进趋势，极易引发“蝴蝶效应”造成舆论秩序失范，甚至导致“塔西佗陷阱”的舆论信任危机，给疫情防控带来复杂变数。必须建立健全网络舆论治理机制，有效管控舆情，提高疫情防控治理水平。     

网络外部性对双渠道供应链信息分享的影响

研究了网络外部性对双渠道供应链信息分享的影响。分别建立了存在网络外部性和不存在网络外部性下的双渠道供应链模型，通过比较零售商信息分享和不分享下其与制造商的期望利润。研究发现：当未考虑产品的网络外部性时，零售商不与制造商分享其市场需求信息，与已有研究一致。当考虑了产品网络外部性且网络外部性较小时，零售商仍不与制造商分享市场需求信息；然而，当网络外部性较大时，零售商与制造商分享其市场需求信息。此外，零售商与制造商共享其市场需求信息有助于增加制造商和供应链利润。     

Inference and Decision Making for 21st-Century Drug Development and Approval

ABSTRACT

The cost and time of pharmaceutical drug development continue to grow at rates that many say are unsustainable. These trends have enormous impact on what treatments get to patients, when they get them and how they are used. The statistical framework for supporting decisions in regulated clinical development of new medicines has followed a traditional path of frequentist methodology. Trials using hypothesis tests of “no treatment effect” are done routinely, and the p-value < 0.05 is often the determinant of what constitutes a “successful” trial. Many drugs fail in clinical development, adding to the cost of new medicines, and some evidence points blame at the deficiencies of the frequentist paradigm. An unknown number effective medicines may have been abandoned because trials were declared “unsuccessful” due to a p-value exceeding 0.05. Recently, the Bayesian paradigm has shown utility in the clinical drug development process for its probability-based inference. We argue for a Bayesian approach that employs data from other trials as a “prior” for Phase 3 trials so that synthesized evidence across trials can be utilized to compute probability statements that are valuable for understanding the magnitude of treatment effect. Such a Bayesian paradigm provides a promising framework for improving statistical inference and regulatory decision making.     

Probabilistic population projections for countries with generalized HIV/AIDS epidemics

In 2015, the United Nations (UN) issued probabilistic population projections for all countries up to 2100, by simulating future levels of total fertility and life expectancy and combining the results using a standard cohort component projection method. For the 40 countries with generalized HIV/AIDS epidemics, the mortality projections used the Spectrum/Estimation and Projection Package (EPP) model, a complex, multistate model designed for short-term projections of policy-relevant quantities for the epidemic. We propose a simpler approach that is more compatible with existing UN projection methods for other countries. Changes in life expectancy are projected probabilistically using a simple time series regression and then converted to age- and sex-specific mortality rates using model life tables designed for countries with HIV/AIDS epidemics. These are then input to the cohort component method, as for other countries. The method performed well in an out-of-sample cross-validation experiment. It gives similar short-run projections to Spectrum/EPP, while being simpler and avoiding multistate modelling.     

Inference and optimal design of multiple constant-stress testing for generalized half-normal distribution under type-II progressive censoring

The generalized half-normal (GHN) distribution and progressive type-II censoring are considered in this article for studying some statistical inferences of constant-stress accelerated life testing. The EM algorithm is considered to calculate the maximum likelihood estimates. Fisher information matrix is formed depending on the missing information law and it is utilized for structuring the asymptomatic confidence intervals. Further, interval estimation is discussed through bootstrap intervals. The Tierney and Kadane method, importance sampling procedure and Metropolis-Hastings algorithm are utilized to compute Bayesian estimates. Furthermore, predictive estimates for censored data and the related prediction intervals are obtained. We consider three optimality criteria to find out the optimal stress level. A real data set is used to illustrate the importance of GHN distribution as an alternative lifetime model for well-known distributions. Finally, a simulation study is provided with discussion.     

基于Petri网的微博网络信息传播模型

微博网络中的信息传播模型是分析用户行为，找出传播路径，确定领袖人物，发现舆情热点等研究的基础。虽然多种不同角度的信息传播模型已经得到广泛研究，但缺乏对信息动态传播过程的直观描述。本文基于Petri网的结构和特征，针对微博网络信息传播过程，提出了一种简单直观的描述模型，该模型首先对微博网络的信息动态传播过程中的对象进行结构化描述。本文根据微博网络的用户结构关系，并利用Petri网的相关理论，形式化解释和定义信息传播基本对象，从而更加直接描述了微博网络中的转发、评论、回复等多种用户行为。在此基础上，本文利用Petri网能够描述信息流动问题的特征，结合颜色Petri网和时延Petri网，从Petri网系统的角度表示信息动态传播路径，并研究网络的动态性质和传播条件，使得信息传播模型更加真实地模拟信息传播情况。最后本文分析信息传播算例和新浪微博真实数据实验，验证了模型的有效性和可行性，为舆情态势分析以及用户行为的研究提供帮助和支持，同时也为其它社交网络信息传播的用户行为描述提供了新的思路。     

汇率货币模型的非线性协整关系检验——基于深度GRU神经网络

本文采用深度门控循环单元（GRU）神经网络探讨三种汇率货币模型（弹性价格、前瞻性和实际利率差模型）的非线性协整关系。GRU技术在深度学习中具有智能记忆、自主学习和强逼近能力等优点。为此，本文运用该技术对6组典型浮动汇率制国别数据进行了非线性Johansen协整检验。结果表明，汇率与宏观经济基本面之间存在非线性协整关系，从而说明了货币模型在非线性条件下的有效性，以及先进的深度学习工具在检验经济理论中的优势。     

考虑零售商质量控制和风险规避的供应链网络均衡研究

研究零售商质量控制和供应链成员风险规避背景下供应链网络均衡问题。应用条件风险值度量风险规避程度，利用变分不等式理论描绘供应链参与者的最优经济行为，设计供应链网络均衡模型，考虑风险规避和质量控制策略对均衡的影响，证明均衡解存在且唯一，并运用Levenberg-Marquardt算法求解均衡模型。最后对重要参数进行数值分析，揭示质量控制和风险规避对供应链均衡解以及参与者条件风险值的影响。研究表明：供应链参与者越规避风险越有利于产品质量水平的提高，风险规避程度的加深会降低制造商或零售商自身的CVaR，同时增大对方的CVaR，对因产品合格率提高带来的利益而言，制造商所得优于零售商。     

Hierarchical Bayesian Modeling of Post-Earthquake Ignition Probabilities Considering Inter-Earthquake Heterogeneity

Post-earthquake fires are high-consequence events with extensive damage potential. They are also low-frequency events, so their nature remains underinvestigated. One difficulty in modeling post-earthquake ignition probabilities is reducing the model uncertainty attributed to the scarce source data. The data scarcity problem has been resolved by pooling the data indiscriminately collected from multiple earthquakes. However, this approach neglects the inter-earthquake heterogeneity in the regional and seasonal characteristics, which is indispensable for risk assessment of future post-earthquake fires. Thus, the present study analyzes the post-earthquake ignition probabilities of five major earthquakes in Japan from 1995 to 2016 (1995 Kobe, 2003 Tokachi-oki, 2004 Niigata–Chuetsu, 2011 Tohoku, and 2016 Kumamoto earthquakes) by a hierarchical Bayesian approach. As the ignition causes of earthquakes share a certain commonality, common prior distributions were assigned to the parameters, and samples were drawn from the target posterior distribution of the parameters by a Markov chain Monte Carlo simulation. The results of the hierarchical model were comparatively analyzed with those of pooled and independent models. Although the pooled and hierarchical models were both robust in comparison with the independent model, the pooled model underestimated the ignition probabilities of earthquakes with few data samples. Among the tested models, the hierarchical model was least affected by the source-to-source variability in the data. The heterogeneity of post-earthquake ignitions with different regional and seasonal characteristics has long been desired in the modeling of post-earthquake ignition probabilities but has not been properly considered in the existing approaches. The presented hierarchical Bayesian approach provides a systematic and rational framework to effectively cope with this problem, which consequently enhances the statistical reliability and stability of estimating post-earthquake ignition probabilities.     

用联想神经网络设计的信道均衡器

研究了联想神经网络在数字通信信道均衡器实现中的应用,讨论了其设计方法和设计步骤,给出了计算机模拟结果,并进行了性能比较。研究结果表明:Hopfield联想神经网络实现的信道均衡器具有比传统线性横向均衡器更优越的性能。