1.Introduction The 21st century has witnessed a concerning escalation of viral threats,ranging from seasonal influenza to global pandemics such as COVID-19.These outbreaks,including SARS,Ebola,and MERS,have highlighte...1.Introduction The 21st century has witnessed a concerning escalation of viral threats,ranging from seasonal influenza to global pandemics such as COVID-19.These outbreaks,including SARS,Ebola,and MERS,have highlighted critical weaknesses in traditional public health systems:delayed outbreak detection and identification due to fragmented data surveillance,inadequate preparedness for zoonotic spillovers,and inequitable distribution of resources,all challenges necessitating innovative solutions.The persistent increase in global infectious disease threats,particularly the COVID-19 pandemic,has prompted the integration of artificial intelligence(AI)into modern public health systems.By leveraging its unparalleled capabilities in data processing and pattern recognition,AI technologies have proven to be powerful tools for predicting,detecting,and mitigating the spread of infectious diseases,offering transformative pathways for their prevention and control.Key advantages span six key domains:pathogen identification,infection risk assessment,therapeutic development,and containment strategies.展开更多
Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a re...Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a reasonable option. The size of the fragments is critical to transmission efficiency and should be adaptable to the communication capability of a network. We propose a novel communication capacity calculation model of opportunistic network based on the classical random direction mobile model, define the restrain facts model of overhead, and present an optimal fragment size algorithm. We also design and evaluate the methods and algorithms with video data fragments disseminated in a simulated environment. Experiment results verified the effectiveness of the network capability and the optimal fragment methods.展开更多
基金supported by grants from the National Natural Science Foundation of China(82330103).
文摘1.Introduction The 21st century has witnessed a concerning escalation of viral threats,ranging from seasonal influenza to global pandemics such as COVID-19.These outbreaks,including SARS,Ebola,and MERS,have highlighted critical weaknesses in traditional public health systems:delayed outbreak detection and identification due to fragmented data surveillance,inadequate preparedness for zoonotic spillovers,and inequitable distribution of resources,all challenges necessitating innovative solutions.The persistent increase in global infectious disease threats,particularly the COVID-19 pandemic,has prompted the integration of artificial intelligence(AI)into modern public health systems.By leveraging its unparalleled capabilities in data processing and pattern recognition,AI technologies have proven to be powerful tools for predicting,detecting,and mitigating the spread of infectious diseases,offering transformative pathways for their prevention and control.Key advantages span six key domains:pathogen identification,infection risk assessment,therapeutic development,and containment strategies.
基金supported by the Shaanxi Natural Science Foundation Research Plan (No. 2015JQ6238)the China Scholarship Council+3 种基金the National Natural Science Foundation of China(Nos. 61373083 and 61402273)the Fundamental Research Funds for the Central Universities of China (No. GK201401002)the Program of Shaanxi Science and Technology Innovation Team of China (No. 2014KTC18)the 111 Programme of Introducing Talents of Discipline to Universities (No. B16031)
文摘Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a reasonable option. The size of the fragments is critical to transmission efficiency and should be adaptable to the communication capability of a network. We propose a novel communication capacity calculation model of opportunistic network based on the classical random direction mobile model, define the restrain facts model of overhead, and present an optimal fragment size algorithm. We also design and evaluate the methods and algorithms with video data fragments disseminated in a simulated environment. Experiment results verified the effectiveness of the network capability and the optimal fragment methods.
