环境感知的新冠肺炎传播态势的建模与分析

针对现有传播模型没有考虑个体所在环境对个体感染病毒的影响以及经典传播模型无法很好地刻画个体特征的问题,提出了基于环境感知的病毒传播模型(EA-SIR).首先,引入个体接触矩阵来描述个体之间的接触情况,推导基于环境感知的个体染病概率,建立EA-SIR的微分方程组;然后,推导EA-SIR的基本再生数及其上界.EA-SIR的基本再生数上界小于或等于经典SIR模型,说明病毒在EA-SIR中更难传播,实际上人们强烈的自我保护意识有利于阻断病毒的传播,因此理论上EA-SIR更适用于刻画病毒的传播.最后,使用"钻石公主"号以及国内四个城市的疫情数据进行实验,以平均绝对误差(MAE)为评价标准.仿真实验的结果表明,EA-SIR能够较好地刻画新冠肺炎疫情的传播态势.     

新冠肺炎传播动力学建模及预测

为了深入研究新冠肺炎传播趋势和传播风险,根据新冠肺炎的传播特点,考虑政府管控和个人防护等措施,在经典传染病SIR模型的基础上,引入低风险群体,提出一种新冠肺炎传播动力学模型SLIR,并对模型的平衡点、稳定性和分岔等复杂动力学行为进行分析,揭示新冠肺炎传播机理.为了提高该模型的疫情预测精度,以美国新冠肺炎的真实数据为基础,使用最小二乘法对模型参数进行分段估计.最后利用该模型对美国新冠肺炎进行预测和分析,仿真结果表明,相比于传统SIR模型,该模型能较好地对美国疫情发展趋势做出预测,官方公布的实际数据也可进一步验证模型的有效性. SLIR模型可以有效仿真新冠肺炎传播,并为政府选择合适的防控措施提供技术支撑.     

新冠疫情后大型体育赛事重启评估建模研究

提出了一种基于有效再生数的大型体育赛事重启评估的方法。将疾病传播有效再生数[Rt]作为衡量体育赛事重启安全评估的关键系数,并进行安全分级。对比分析引入无症状感染者的SEIAR模型和改进后的引入戴口罩的Wells-Riley模型,采用前者对地区的疫情进行初步评估,采用后者对体育赛事场馆的疾病传播性进行评估。采用Gaussian模型预测无症状感染者,并确定不同时间节点的[q]值（quanta产生率）,作为评估的输入。基于有效再生数对六大体育赛事进行综合动态评估,得出这些赛事重启的时间表。评估可知,LPL电竞是最早全面放开比赛的体育赛事,大概在7月初;中超联赛和CBA估计在7月中旬可安排全部观众戴口罩观赛。     

粒子群可拓的新冠肺炎建模与仿真

本文提出了一种基于粒子群可拓神经网络预测模型.根据国外近段时间每日新增新冠肺炎确诊人数,利用可拓神经网络模型对国外日新增新冠肺炎确诊人数进行预测,并利用粒子群算法(PSO)对权值进行优化,最后与LSSVM、ABC-LSSVM及PSO-LSSVM模型进行比较.结果表明:采用文中提出的粒子群可拓神经网络模型拟合效果较好,精...     

新冠肺炎疫情下的本科线上教学方案与实践

新冠肺炎疫情使得全国高校开展了大规模的线上教学活动。该文阐述了在疫情形势下开展线上教学的方案与实践。     

基于情感特征的新冠肺炎疫情舆情演化分析

针对突发事件的舆情演变态势进行分析,发现社会舆情的演变规律,提出了一种基于情感特征的舆情演化分析方法,该方法包含舆论情感分析模块与舆情演化分析模块.舆论情感分析模块基于B E RT预训练模型和BiGRU模型,其中BERT作为词嵌入模型提取舆情文本特征向量,BiGRU则用于提取文本特征向量的上下文联系实现对舆情数据情感极...     

基于EasyDL的新冠肺炎检测系统研究

通过EasyDL平台搭建基于CT影像的新冠肺炎检测系统,利用人工智能在图像识别上能够自动学习图像特征及区分图像特征之间差异的特点,来检测病人是否患有新冠肺炎.实验结果显示,新冠肺炎识别精确度为100.00％,感染性肺疾病识别精确度为98.06％,非感染性肺疾病识别精确度为93.64％,正常肺部识别精确度为98.03％....     

考虑不同潜伏期的异质传感网络恶意程序传播建模与分析

为了更加准确地揭示恶意程序在异质传感网络中的传播规律,考虑异质传感器节点的移动性,基于扩展经典传染病理论而提出具有不同恶意程序潜伏期的延迟HSEIRD(Heterogeneous Susceptible-Exposed-Infected-Recovered-Dead)模型.计算得出该模型的稳定点,并使用下一代矩阵算法,得到该模型的基本再生数.进行数值模拟,以验证不同恶意程序潜伏期和自由移出率对异质传感网络稳定性的影响.     

Design and analysis of a large-scale COVID-19 tweets dataset

As of July 17, 2020, more than thirteen million people have been diagnosed with the Novel Coronavirus (COVID-19), and half a million people have already lost their lives due to this infectious disease. The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. Since then, social media platforms have experienced an exponential rise in the content related to the pandemic. In the past, Twitter data have been observed to be indispensable in the extraction of situational awareness information relating to any crisis. This paper presents COV19Tweets Dataset (Lamsal 2020a), a large-scale Twitter dataset with more than 310 million COVID-19 specific English language tweets and their sentiment scores. The dataset’s geo version, the GeoCOV19Tweets Dataset (Lamsal 2020b), is also presented. The paper discusses the datasets’ design in detail, and the tweets in both the datasets are analyzed. The datasets are released publicly, anticipating that they would contribute to a better understanding of spatial and temporal dimensions of the public discourse related to the ongoing pandemic. As per the stats, the datasets (Lamsal 2020a, 2020b) have been accessed over 74.5k times, collectively.

     

三线圈无线电能传输系统建模与传输效率分析

以磁耦合谐振理论为基础,建立了三线圈无线电能传输系统的模型,运用等效电路理论和二端口网络理论推导出三线圈无线电能传输系统的传输效率公式。利用该公式对发射-接收线圈之间的交叉耦合系数进行了理论分析。理论分析结果表明,交叉耦合系数较大时系统的传输效率会降低。仿真结果验证了理论分析的正确性。     

Sentiment analysis and topic modeling for COVID-19 vaccine discussions

World Wide Web - The outbreak of the novel coronavirus disease (COVID-19) has been ongoing for almost two years and has had an unprecedented impact on the daily lives of people around the world....     

Sentiment analysis on Hindi tweets during COVID-19 pandemic

A gap among the people has been created due to a lack of social interactions. The physical void has led to an increase in online interaction among users on social media platforms. Sentiment analysis of such interactions can help us analyze the general public psychology during the pandemic. However, the lack of data in non-English and low-resource languages like ‘Hindi’ makes it difficult to study it among native and non-English speaking masses. Here, we create a small collection of ‘Hindi’ tweets on COVID-19 during the pandemic containing 10,011 tweets for sentiment analysis, which is named as sentiment analysis for Hindi (SAFH). In this article, we describe the process of collecting, creating, annotating the corpus, and sentiment classification. The claims have been verified using different word embedding with a deep learning classifier through the proposed model. The achieved accuracy of the proposed model yields up to a permissible rate of 90.9%.     

超级计算支撑的新冠肺炎CT影像综合分析辅助系统应用

目的 新冠肺炎（COVID-19）已经成为全球大流行疾病,在全球范围数百万人确诊。基于计算机断层扫描（computed tomography,CT）数据的影像学分析是临床诊断的重要手段。为了实现快速高效高精度地检测,提出了一种超级计算支撑的新冠肺炎CT影像综合分析辅助系统构建方法。方法 系统整个处理流程依次包括输入处理模块、预处理模块、影像学分析子系统和人工智能（artifiaial intelligence,AI）分析子系统4部分。其中影像学分析子系统通过分析肺实变、磨玻璃影和铺路石等影像学典型特征检测是否有肺炎和典型新冠肺炎特征,给出肺炎影像分析结论;AI分析子系统通过构建深度学习模型来区分普通病毒肺炎与新冠肺炎,增加肺炎的筛查甄别能力。结果 系统发布以来,持续稳定地为国内外超过三十家医院与一百多家科研机构提供了新冠肺炎辅助诊断服务和科研支撑,为抗击疫情提供重要支撑。结论 本文提出的超级计算支撑的新冠肺炎CT影像综合分析辅助系统构建方法,取得了应用效果,是一种有效实现快速部署服务、对突发疫情提供高效支撑的服务方式。     

Using artificial intelligence techniques for COVID-19 genome analysis

Applied Intelligence - The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province,...     

Hotspot analysis of COVID-19 infection using mobile-phone location data

Artificial Life and Robotics - Restrictions on outdoor activities are required to suppress the COVID-19 pandemic. To monitor social risks and control the pandemic through sustainable restrictions,...