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AI for science: Predicting infectious diseases

Journal of Safety Science and Resilience 2024年第2期5卷 130-146页

作者： Alexis Pengfei Zhao Shuangqi Li Zhidong Cao Paul Jen-Hwa Hu Jiaojiao Wang Yue Xiang Da Xie Xi Lu Institute of Automation Chinese Academy of SciencesUniversity of Chinese Academy of SciencesBejing 100090China Systems Engineering Cornll UniversityIthacaNY 14853USA David Eccles School of Business the University of UtahUtahUSA Collge of Electrical Engineering Sichuan UniversityChina Department of Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Electrical Engineering Southeast UniversityNanjing 210096China

The global health landscape has been persistently challenged by the emergence and re-emergence of infectious *** epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease ***,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive *** is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease *** paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological *** harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced *** comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by *** essence,Al4S represents a paradigm shift in infectious disease *** addresses the limitations of traditional models and paves the way for a more proactive and informed response to future *** we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.

关键词： AI for science(AI4S) Data integration Global healthchallenges Infectious disease prediction Predictive modeling real-timemonitoring

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real-time Monitoring of Subsea Gas Pipelines,Offshore Platforms,and Ship Inspection Scores Using an Automatic Identification System

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Journal of Marine Science and Application 2018年第1期17卷 101-111页

作者： K.B.Artana T.Pitana D.P.Dinariyana M.Ariana D.Kristianto E.Pratiwi Department of Marine Engineering Institut Teknologi Sepuluh NopemberSurabaya 60111Indonesia Department of Informatics Institut Teknologi Sepuluh NopemberSurabaya 60111Indonesia

The aim of this research is to develop an algorithm and application that can perform real-time monitoring of the safety operation of offshore platforms and subsea gas pipelines as well as determine the need for ship inspection using data obtained from automatic identification system(AIS).The research also focuses on the integration of shipping database,AIS data,and others to develop a prototype for designing a real-time monitoring system of offshore platforms and pipelines.A simple concept is used in the development of this prototype,which is achieved by using an overlaying map that outlines the coordinates of the offshore platform and subsea gas pipeline with the ship’s coordinates(longitude/latitude)as detected by *** such information,we can then build an early warning system(EWS)relayed through short message service(SMS),email,or other means when the ship enters the restricted and exclusion zone of platforms and *** ship inspection system is developed by combining several ***,decision analysis software is employed to prioritize the vessel’s four attributes,including ship age,ship type,classification,and flag *** show that the EWS can increase the safety level of offshore platforms and pipelines,as well as the efficient use of patrol boats in monitoring the safety of the ***,ship inspection enables the port to prioritize the ship to be inspected in accordance with the priority ranking inspection score.

关键词： Automatic identification system AIS real-timemonitoring Subsea gas pipeline Offshore platform Ship inspection score

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