智慧医疗在抗击新冠肺炎中的应用

面对突发的新型冠状病毒肺炎疫情,我院充分利用智慧医疗的远程、专业、便捷等特点,第一时间开通了互联网新冠肺炎咨询门诊、图文和视频咨询门诊、外延处方等功能。门诊筛查登记、物联网辅助发热筛查、医生站和护士站肺炎危急值提醒、新冠肺炎AI智能诊断系统等信息系统智能辅助登记和决策,实现了分诊筛查、智能提醒,从而提高了诊断效率。远程会诊和会议系统、全市新冠肺炎CT影像诊断云平台的使用大大提高了会诊效率、避免了交叉感染的风险。     

陕西“硬核”科技助力抗击疫情 为复工复产筑起“安全屏障”

1月23日,陕西省首次出现新冠肺炎输入病例,即刻启动了Ⅳ级应急响应(1月25日,启动突发公共卫生事件I级应急响应)。1月24日,陕西省科技厅启动“新型冠状病毒感染的肺炎疫情应急防治”专项计划,并于2月3日启动首批8个应急科技攻关项目。西安市科技局也迅速启动“疫情防控应急科技项目”15个。利用在基础研究、生物医药、人工智能、大数据、人脸识别、机器人等领域的技术优势,快速研发出了新冠肺炎诊断治疗技术、定量检测工具、人脸识别测温可视化系统等产品,为复工复产带来的人员流动和聚集筑起了一道“安全屏障”。     

基于Arduino的COVID-19检测系统设计

目前由新型冠状病毒引发的新冠肺炎疫情仍在世界各地蔓延,如何快速准确地进行病毒检测是遏制疫情传播的关键。本文针对目前新型冠状病毒检测繁琐、低效,缺乏方便快捷的检测仪器等问题,设计了一种基于Arduino和ESP32-CAM的便携式荧光分析仪,用于新型冠状病毒的检测。仪器采用荧光免疫层析技术,通过OV2640高清摄像头模块...     

海南：推进卫生行业信息化应用建设

日前,海南联通与海南省卫生厅在海口市签署战略合作框架协议。以推动海南省卫生信息化工作快速发展为目标,双方将携手在海南省卫生网、远程会诊、医院和农村卫生室信息化、应急与救援行动等方面合作,并开展卫生行业信息化应用项目建设。     

到2030年 AI或将取代人类在网络安全领域的地位

新冠肺炎疫情的发生给整个社会的生产生活带来了冲击.为降低疫情影响,企业纷纷将业务从线下转移到线上,与此同时,攻击者在疫情期间的攻击活动也日益活跃.     

创泽机器人总动员:疫情期间五款产品上前线

新冠肺炎疫情爆发以来,医疗领域对于机器人的应用需求日益凸显。哪里有需求,哪里就是服务机器人的目的地。作为一家具备规模化生产智能商用服务机器人的企业,创泽机器人采取积极行动,将针对疫情研发的多款新品快速投放至一线,为抗击疫情增添力量。面对新型冠状病毒感染的肺炎(以下简称新冠肺炎)疫情,军地医务工作者紧急驰援武汉,志愿服务人员无私坚守岗位。     

信息通信产业助力复工复产有序推进

新型冠状病毒肺炎疫情发生以来,在以习近平同志为核心的党中央坚强领导下,举国上下积极抗击疫情,疫情防控形势出现积极变化。为有效应对新冠肺炎疫情、保证经济社会平稳运行,党中央国务院积极研判出台政策,在做好疫情防控前提下有力有序推动企业复工复产、稳产保供。信息通信产业作为新型数字基础设施的支柱产业,在助力各行业平稳有序恢复运行方面正在发挥重要作用,"线上复工"、"智能复产"等新机制、新模式、新工具应加速推广。     

新冠肺炎疫情大数据可视化平台设计与实现

介绍了新冠肺炎疫情大数据可视化平台的搭建:以大量的新冠肺炎疫情数据为基础,借助Echarts技术以及交互组件,通过可视化图形与地图集成的方式对疫情数据进行可视化展示.实现从多个角度展示国内疫情发展趋势,丰富了疫情数据的展示内容,为广大人民群众了解新冠肺炎疫情提供了便利.     

工业互联网助力抗击疫情

疫情之下,临时医院的搭建、医疗防护物资的生产,让制造业迎来大考。工人复工难、物资调配紧张等问题也让制造业产能恢复面临着诸多挑战。工业互联网作为工业经济全要素、全价值链、全产业链,全面连接新型基础设施,发挥其在万物互联、信息汇聚、优化调度、异地协同、远程服务等方面的优势,助力抗击疫情工作。     

直面疫情,共克时艰 自动化企业在行动

新年伊始,新冠肺炎在全国各地蔓延。本该阖家欢乐的春节,笼罩着一层阴云。一方有难,八方支援,各方力量全力以赴,展开了一场全国总动员的疫情防控阻击战。创新驱动,用科技抗击疫情。在这场战“疫”中,自动化、人工智能、工业互联网等技术发挥着重要作用。开展大数据分析,监测疫情,精准施策;AI多人体温快速检测,安全可靠且高效;5G远程会诊,减少医患直接接触;基于工业互联网平台,精准配置资源,让医院建设更加高效;巡逻机器人、消毒机器人、送餐机器人等自动化新技术也成为抗击疫情的重要“武器”。     

New IT driven rapid manufacturing for emergency response

COVID-19, which is rampant around the world, has seriously disrupted people's normal work and living. To respond to public urgent needs such as COVID-19, emergency supplies are essential. However, due to the special requirements of supplies, when an emergency occurs, the supply reserve mostly cannot cope with the high demand. Given the importance of emergency supplies in public emergencies, rapid response manufacturing of emergency supplies is a necessity. The faster emergency supplies and facilities are manufactured, the more likely the pandemic can be controlled and the more human lives are saved. Besides, new generation information technology represented by cloud computing, IoT, big data, AI, etc. is rapidly developing and can be widely used to address such situations. Therefore, rapid response manufacturing enabled by New IT is presented to quickly meet emergency demands. And some policy suggestions are presented.     

In-host Mathematical Modelling of COVID-19 in Humans

COVID-19 pandemic has underlined the impact of emergent pathogens as a major threat to human health. The development of quantitative approaches to advance comprehension of the current outbreak is urgently needed to tackle this severe disease.Considering different starting times of infection, mathematical models are proposed to represent SARS-CoV-2 dynamics in infected patients. Based on the target cell limited model, the within-host reproductive number for SARS-CoV-2 is consistent with the broad values of human influenza infection. The best model to fit the data was including immune cell response, which suggests a slow immune response peaking between 5 to 10 days post-onset of symptoms. The model with the eclipse phase, time in a latent phase before becoming productively infected cells, was not supported. Interestingly, model simulations predict that SARS-CoV-2 may replicate very slowly in the first days after infection, and viral load could be below detection levels during the first 4 days post infection.A quantitative comprehension of SARS-CoV-2 dynamics and the estimation of standard parameters of viral infections is the key contribution of this pioneering work. These models can serve for future evaluation of control theoretical approaches to tailor new drugs against COVID-19.     

视频会商系统在防汛抗旱工作的应用

视频会商系统在水利行业中已得到一定程度的推广,主要实现异地视频会议、培训等功能。随着IP视频会商技术的飞速发展,对视频会商的互联互通、联动指挥和信息共享提出更高的要求。本文通过分析海河下游管理局视频会商系统,针对出现的问题和弊端,提出一套改进方案,在实现高清视频功能同时,增加移动视频和实时调度功能,极大提高防汛决策指挥、应急联动的工作效率,在防汛抗旱中应急响应、防汛演练、会商、会议等方面发挥重要作用。     

基于Android云计算消息框架(C2DM)的FoxNews_ MID 手持移动系统的研究

FoxNews_ MID网络新闻应急管理系统利用Android云计算消息框架(C2DM)技术,实现了对手持移动终端远程典情等应急信息的推送,对当前社会维稳有实用性。论述了该系统FoxNews_ MID的功能、特点,讨论了其关键技术并给出了实现的关键程序。经试运行后表明,FoxNews_ MID系统可以实现基于C2 DM的对手持移动终端远程信息的推送、基于Andriod的手持终端信息的接收和当地典情新闻信息及数字媒体数据的上传。     

Learning Patterns from COVID-19 Instances

Coronavirus disease, which resulted from the SARS-CoV-2 virus, has spread worldwide since early 2020 and has been declared a pandemic by the World Health Organization (WHO). Coronavirus disease is also termed COVID-19. It affects the human respiratory system and thus can be traced and tracked from the Chest X-Ray images. Therefore, Chest X-Ray alone may play a vital role in identifying COVID-19 cases. In this paper, we propose a Machine Learning (ML) approach that utilizes the X-Ray images to classify the healthy and affected patients based on the patterns found in these images. The article also explores traditional, and Deep Learning (DL) approaches for COVID-19 patterns from Chest X-Ray images to predict, analyze, and further understand this virus. The experimental evaluation of the proposed approach achieves 97.5% detection performance using the DL model for COVID-19 versus normal cases. In contrast, for COVID-19 versus Pneumonia Virus scenario, we achieve 94.5% accurate detections. Our extensive evaluation in the experimental section guides and helps in the selection of an appropriate model for similar tasks. Thus, the approach can be used for medical usages and is particularly pertinent in detecting COVID-19 positive patients using X-Ray images alone.     

Real-time communication for Kinect-based telerehabilitation

As chronic diseases and demographic changes alter the trends in world population, more pressure is put on health management to deal efficiently with a new scenario of longer life expectancy and chronic disabilities. Advances in telecommunication technology and miniaturization of sensors in combination with medical information technology provide a viable solution to reduce healthcare costs and deliver remote medical services through connected devices. Although remote consultation via video-conferencing has been well established, many of the chronic or long-term musculoskeletal conditions require pro-active management and therapy. There is a need to develop more advanced interactive telerehabilitation systems that support real-time remote delivery of physical therapy sessions into patients’ homes. In this paper, we introduce KinectRTC, an innovative framework that can be used for Kinect-based telerehabilitation with efficient real-time transmission of video, audio, depth, and skeletal data. By taking advantage of the Web Real-Time Communication (WebRTC) technology, the proposed framework is able to manage video and audio streams based on the state of the network and the available bandwidth to guarantee the real-time performance of the communication.     

Analyzing the Implications of COVID-19 Pandemic through an Intelligent-Computing Technique

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2 virus or COVID-19) disease was declared pandemic by the World Health Organization (WHO) on March 11, 2020. COVID-19 has already affected more than 211 nations. In such a bleak scenario, it becomes imperative to analyze and identify those regions in Saudi Arabia that are at high risk. A preemptive study done in the context of predicting the possible COVID-19 hotspots would facilitate in the implementation of prompt and targeted countermeasures against SARS-CoV-2, thus saving many lives. Working towards this intent, the present study adopts a decision making based methodology of simulation named Analytical Hierarchy Process (AHP), a multi criteria decision making approach, for assessing the risk of COVID-19 in different regions of Saudi Arabia. AHP gives the ability to measure the risks numerically. Moreover, numerical assessments are always effective and easy to understand. Hence, this research endeavour employs Fuzzy based computational method of decision making for its empirical analysis. Findings in the proposed paper suggest that Riyadh and Makkah are the most susceptible regions, implying that if sustained and focused preventive measures are not introduced at the right juncture, the two cities could be the worst afflicted with the infection. The results obtained through Fuzzy based computational method of decision making are highly corroborative and would be very useful for categorizing and assessing the current COVID-19 situation in the Kingdom of Saudi Arabia. More specifically, identifying the cities that are likely to be COVID-19 hotspots would help the country’s health and medical fraternity to reinforce intensive containment strategies to counter the ills of the pandemic in such regions.     

面向新冠新闻的三阶段篇章级事件抽取方法

事件抽取是信息抽取领域的一个研究热点。在新冠肺炎疫情常态化下,利用事件抽取技术可以筛选出有价值的信息。然而事件抽取领域缺乏精标注的新冠新闻训练数据集,且因部分事件的复杂性,论元不只存在于一句话中,需要多个句子才能完整描述一个事件。因此,首先构建新冠肺炎新闻数据集,接着提出一种三阶段的管道方法实现从篇章中抽取新冠肺炎事件。该方法对数据集进行事件类型分类;进行事件句的抽取;实现篇章级论元抽取。实验结果表明提出的方法能够减少事件分类时间,抽取两个事件句的条件下,对数据通报类论元识别效果最好,准确率、召回率和F1值达到75.0%、73.0%,和74.0%,证明方法能有效抽取新冠肺炎相关篇章级事件。