新冠肺炎疫情对互联网服务业的影响分析

受2020年新冠肺炎疫情影响,短期内,网络出行、在线旅游、餐饮外卖等互联网服务平台面临收入大幅下降、经营风险加剧、企业用工困难等问题。疫情结束后,线上服务行业仍可能受到商户现金流危机、市场预期严重不足等负面影响,部分行业恢复需要较长时间。同时,疫情将加速融合业务线下流量转向线上、融合应用场景创新、传统服务业转型升级。     

Annotation of miRNAs in the COVID-19 Novel Coronavirus

The coronavirus disease 2019 (COVID-19) coronavirus is a new strain of coronavirus that had not been previously detected in humans. As its severe pathogenicity is concerned, it is important to study it thoroughly to aid in the discovery of a cure. In this study, the microRNAs (miRNAs) of COVID-19 were annotated to provide a powerful tool for the study of this novel coronavirus. We obtained 16 novel coronavirus genome sequences and the mature sequences of all viruses in the microRNA database (miRbase), and then used the miRNA matures sequences of the virus to perform the Basic Local Alignment Search Tool (BLAST) analysis in the coronavirus genome, extending the matched regions of approximately 20 bp to two segments by 200 bp. Six sequences were obtained after deleting redundant sequences. Then, the hairpin structures of the mature miRNAs were determined using RNAfold. The mature sequence on one hairpin arm was selected into a total of 4 sequences, and finally the relevant miRNA precursor prediction tools were used to verify whether the selected sequences are miRNA precursor sequences of the novel coronavirus. The miRNAs of the novel coronavirus were annotated by our newly developed method, which will lay the foundation for further study of this virus.     

Annotation of miRNAs in COVID-19 coronavirus

The COVID-19 coronavirus is a new strain of coronavirus that had not been previously detected in humans. As its severe pathogenicity is concerned, it is important to study it thoroughly to aid in the discovery of a cure. In this study, the microRNAs (miRNAs) of COVID-19 were annotated to provide a powerful tool for the study of this novel coronavirus. We obtained 16 novel coronavirus genome sequences and the mature sequences of all viruses in the microRNA database (miRbase), and then used the miRNA mature sequences of the virus to perform the Basic Local Alignment Search Tool (BLAST) analysis in the coronavirus genome, extending the matched regions of approximately 20 bp to two segments by 200 bp. Six sequences were obtained after deleting redundant sequences. Then, the hairpin structures of the mature miRNAs were determined using RNAfold. The mature sequence on one hairpin arm was selected into a total of 4 sequences, and finally the relevant miRNA precursor prediction tools were used to verify whether the selected sequences are miRNA precursor sequences of the novel coronavirus. The miRNAs of the novel coronavirus were annotated by our newly developed method, which will lay the foundation for further study of this virus.     

COVID-19 Propagation Model Based on Economic Development and Interventions

Wireless Personal Communications - In order to understand the influencing factors affecting the COVID-19 propagation, and analyze the development trend of the epidemic situation in the world,...     

Graphene-Based Technologies for Tackling COVID-19 and Future Pandemics

The COVID-19 pandemic highlighted the need for rapid tools and technologies to combat highly infectious viruses. The excellent electrical, mechanical and other functional properties of graphene and graphene-like 2D materials (2DM) can be utilized to develop novel and innovative devices to tackle COVID-19 and future pandemics. Here, the authors outline how graphene and other 2DM-based technologies can be used for the detection, protection, and continuous monitoring of infectious diseases including COVID-19. The authors highlight the potential of 2DM-based biosensors in rapid testing and tracing of viruses to enable isolation of infected patients, and stop the spread of viruses. The possibilities of graphene-based wearable devices are discussed for continuous monitoring of COVID-19 symptoms. The authors also provide an overview of the personal protective equipment, and potential filtration mechanisms to separate, destroy or degrade highly infectious viruses, and the potential of graphene and other 2DM to increase their efficiency, and enhance functional and mechanical properties. Graphene and other 2DM could not only play a vital role for tackling the ongoing COVID-19 pandemic but also provide technology platforms and tools for the protection, detection and monitoring of future viral diseases.     

COVID-19 pandemic and the cyberthreat landscape: Research challenges and opportunities

Although cyber technologies benefit our society, there are also some related cybersecurity risks. For example, cybercriminals may exploit vulnerabilities in people, processes, and technologies during trying times, such as the ongoing COVID-19 pandemic, to identify opportunities that target vulnerable individuals, organizations (e.g., medical facilities), and systems. In this paper, we examine the various cyberthreats associated with the COVID-19 pandemic. We also determine the attack vectors and surfaces of cyberthreats. Finally, we will discuss and analyze the insights and suggestions generated by different cyberattacks against individuals, organizations, and systems.     

基于XGBoost算法的新冠病毒拉曼光谱感染诊断

新型冠状病毒的出现给全球经济、公共安全等方面带来严重损失,快速且准确地诊断新冠病毒对控制疫情爆发尤为重要.将机器学习与拉曼光谱技术结合,选取157份(包含确诊和健康)血清样品对应的拉曼光谱数据作为样本.首先使用Min-max方法对样本数据归一化、Savitzky-Golay方法对光谱平滑滤波和线性判别分析方法LDA对光...     

基于SEIR模型的COVID-19传播过程及网约车应对策略研究

2019年末突然爆发的新型冠状病毒(COVID-19)肺炎严重影响到居民的日常生活.网约车作为公共交通系统的补充,在大中运量公共交通停止运营期间,为居民的刚性出行需求提供了保障性服务.本文基于SEIR传染病模型模拟病毒传染的传播过程,通过对比分析说明减少人员接触对遏制病毒传播的重要性.健康码作为一个数字抗疫亮点,考虑到健康码为红色的乘客携带病毒的可能性较高,本文提出对其进行一对一接送策略,在保证乘客出行需求的前提下,降低其他乘客感染病毒的风险,并利用二元选择模型中的logit模型来预测网约车司机运送红码乘客的概率.最后基于SEIR模型传播过程,分别从控制车内的人员数量、缩短乘客的出行时间以及车辆的防疫措施这三方面对疫情期间网约车出行提出应对策略.     

The impact of gate-oxide breakdown on SRAM stability

We have investigated the effects of oxide soft breakdown (SBD) on the stability of CMOS 6T SRAM cells. Gate-to-diffusion leakage currents of 20-50 μA at the n-FET source can result in a 50% reduction of noise margin. Breakdown at other locations in the cell may be less deleterious depending on n-FET width. This approach gives targets for tolerable values of leakage caused by gate-oxide breakdown     

基于信息技术的网络众包平台开发与研究

随着信息技术的高速发展,众包以其灵活有效的解决问题的方式得到众多公司的青睐,在很多行业领域得到了应用,但国内众包平台的软件开发还存在诸多问题。研究了众包的实现过程,开发了基于项目管理的众包平台,并采用遗传算法,实现平台中的分包任务,实际的效果较为理想。研究开发的众包平台达到了通过软件规范项目管理的目的。     

科学地应对新冠疫情——洁净室相应技术措施的探讨

新冠肺炎疫情横扫全球,对人类健康和世界经济造成重大伤害。种种迹象表明新冠病毒还将相对长期地与人类共存。国内外的医疗卫生机构及政府主管部门,根据这种形势提出了各种防控疫情的措施。这些规定主要是针对公众场所制定,对其他环境有一定的参考价值。针对洁净室环境的具体情况和设施特点,分析、探讨了疫情之下,洁净环境应予关注的问题,提出了一些洁净室环境管控疫情值得注意的事项,供洁净室相关行业人士参考。     

The relational governance antecedents and loyalty consequence of service quality in crowdsourcing: The moderating role of crowdsourcing experience

This study aims to investigate the antecedents (i.e., relational governance) and consequence (i.e., loyalty) of service quality from the crowdsourcer perspective in the online crowdsourcing context. The hypotheses derived from our research model were empirically validated using an online survey of 240 crowdsourcers from Zhubajie.com in China. Results show that relational governance elements (i.e., information exchange, conflict resolution and trust) positively affect service quality dimensions despite the insignificant effect of trust on interaction quality. All service quality dimensions (i.e., interaction quality, outcome quality, environment quality) significantly affect crowdsourcer loyalty. Crowdsourcing experience positively moderates the effect of environment quality on crowdsourcer loyalty but negatively moderates the effect of outcome quality. This study contributes to deepening the understanding of the approach to enhance service quality and crowdsourcer loyalty.     

采用音质特征和VLAD编码的新冠肺炎检测算法

2020年,世界卫生组织宣布COVID-19疫情为大流行病。为了实现COVID-19快速地、可靠地检测,本研究通过语音信号分析技术来寻找感染COVID-19的语音信号特征,利用咳嗽声片段和语音片段对是否感染COVID-19做出自动判断。在INTERSPEECH 2021 ComParE竞赛提供的相关数据集和baseline的基础上,本文首先利用语音端点检测技术对数据集进行增广,其次在特征集中加入语音质量特征,使相关baseline结果得到了提升,证明了语音质量特征在对COVID-19自动语音检测任务上的有效性。同时,引入局部聚合描述子向量对低级别特征进行编码,当字典大小较小时,有效地提升了系统的分类性能。最后,对多种算法得到的分类结果进行融合,进一步提升分类效果,最终在两个子任务中的验证集上UAR分别取得了73.9%和77.2%。      

“新冠”疫情环境下的随机信号分析实验在线教学设计

在“新冠”疫情爆发的特殊时期，学校无法正常开学，需要为大学生开展在线教学任务。”随机信号分析实验”作为通信工程、信息工程专业的必修课，它的正常实施对学生的培养至关重要。由此，为该课程设计了一种切实可行的在线教学方案，并针对课程的培养目标，提出了多维度的考核方式、评价标准，既保证了教学内容的顺利完成，又完成到了该课程的培养目标，学生综合实践能力能够到达课程预期。     

Broadband and economic growth in China: An empirical study during the COVID-19 pandemic period

This paper proposed an economic model that describes the special role of broadband in the economic growth during the COVID-19 pandemic period. Empirical research was conducted using the data of China to investigate how broadband affected China’s economic growth during this period. The findings showed broadband alleviated China’s economic losses during the first a few months of 2020 when the new coronavirus spread across the country, and broadband affected China’s economic growth to a larger extent during this pandemic period than during the normal time periods. These findings have policy implications for China as well as other countries where the pandemic is still pervasive.     

Virtual reality adoption during the COVID-19 pandemic: A uses and gratifications perspective

The coronavirus disease 2019 (COVID-19) pandemic has impacted all aspects of people's lives, including how we work, play, learn, exercise, and socialize. Virtual reality (VR) technology has the potential to mitigate many of the challenges brought about by the pandemic, which has spurred increased adoption. However, relatively low adoption overall and limited software still restrict the power of VR to address COVID-19 difficulties effectively. This study examines how the perceived impacts of COVID-19 might lead to different VR uses and gratifications and device ownership / variability. Furthermore, we investigate the importance of social interactivity within VR for increasing adoption intentions.We surveyed 298 Amazon Mechanical Turk users during the Fall of 2020. Results indicate that the pandemic's perceived impacts influenced the likelihood of acquiring VR for education, tourism, and work. For VR ownership and variability, those who purchased VR during the pandemic were more likely to report buying it for work. Those with access to high-end VR hardware were more likely to report a broader range of uses, including socializing, health, and telemedicine. Validating the importance of various applications during the pandemic, we found that the desire for social interactivity mediates the impacts of COVID-19 on future adoption intentions. Theoretically, we propose several gratifications sought via the use of VR during the pandemic. Practically, we discuss recommendations for future VR research, marketing, and software design.     

FibVID: Comprehensive fake news diffusion dataset during the COVID-19 period

As the SARS-CoV-2 (COVID-19) pandemic has run rampant worldwide, the dissemination of misinformation has sown confusion on a global scale. Thus, understanding the propagation of fake news and implementing countermeasures has become exceedingly important to the well-being of society. To assist this cause, we produce a valuable dataset called FibVID (Fake news information-broadcasting dataset of COVID-19), which addresses COVID-19 and non-COVID news from three key angles. First, we provide truth and falsehood (T/F) indicators of news items, as labeled and validated by several fact-checking platforms (e.g., Snopes and Politifact). Second, we collect spurious-claim-related tweets and retweets from Twitter, one of the world’s largest social networks. Third, we provide basic user information, including the terms and characteristics of “heavy fake news” user to present a better understanding of T/F claims in consideration of COVID-19. FibVID provides several significant contributions. It helps to uncover propagation patterns of news items and themes related to identifying their authenticity. It further helps catalog and identify the traits of users who engage in fake news diffusion. We also provide suggestions for future applications of FibVID with a few exploratory analyses to examine the effectiveness of the approaches used.     

基于注意力机制和Inf-Net的新冠肺炎图像分割方法

新型冠状病毒肺炎肆虐全球,严重影响了人类社会的生活和健康。CT影像技术是检测新冠肺炎的重要诊断方式,从CT图像中自动准确分割出新冠肺炎病灶区域,对于诊断、治疗和预后都有重要意义。针对新冠肺炎病灶的自动分割,文中提出基于Inf-Net算法改进的自动分割方法,通过引入通道注意力机制加强特征表示,并运用注意力门模块来更好地融合边缘信息。在COVID-19 CT分割数据集上的实验结果表明,文中所提出新冠肺炎图像分割方法的Dice系数、灵敏度、特异率分别为75.1%、75.4%和95.4%,算法性能也优于部分主流方法。     

Consequence and impact of electric utility industry restructuringon transient stability and small-signal stability analysis

The electric utility industry is undergoing unprecedented changes in its structure worldwide. With the advent of an open market environment and competition in the industry, and restructuring of the industry into separate generation, transmission, and distribution entities, new issues in power system operation and planning are inevitable. One of the major consequences of this new electric utility environment is the greater emphasis on reliability and secure operation of the power system. This paper examines the impact of restructuring on power system dynamic analysis. It specifically addresses issues related to transient stability analysis and small-signal stability analysis. Four major topics to examine the effect on the nature of studies conducted are considered. These topics are: (1) system adequacy and security, (2) system modeling data requirements, (3) system protection and control, and (4) system restoration. The consequences and impact of each of these topics on the nature of the studies conducted are examined and discussed. The emphasis on greater reliability has led to a clearer enunciation of standards, measurements, and guides in some countries. These requirements will result in: (1) more measurements on existing systems, (2) rigorous analysis of transient stability and small-signal stability to determine operating limits and plan systems, (3) greater emphasis on studies to verify coordination and proper performance of protection and controls, and (4) development of a detailed plan for system restoration in the case of wide-spread outages