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http://dx.doi.org/10.1016/j.semerg.2019.11.001 | DOI Listing |
Inj Epidemiol
January 2025
Injury Prevention Research Center, University of Iowa, 145 N Riverside Dr., Iowa City, IA, 52242, USA.
Background: Motor vehicle crashes are the second leading cause of injury death among adults aged 65 and older in the U.S., second only to falls.
View Article and Find Full Text PDFPilot Feasibility Stud
January 2025
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Sci Rep
January 2025
Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13120, Republic of Korea.
Network security is crucial in today's digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network security by combining methods for instruction detection from machine learning (ML) and deep learning (DL). Attackers have tried to breach security systems by accessing networks and obtaining sensitive information.
View Article and Find Full Text PDFTransl Psychiatry
January 2025
Laboratory of Exercise and Neurobiology, School of Physical Education and Sports Science, South China Normal University, Guangzhou, 510006, Guangdong, China.
Repeated closed-head injuries (rCHI) from activities like contact sports, falls, military combat, and traffic accidents pose a serious risk due to their cumulative impact on the brain. Often, rCHI is not diagnosed until symptoms of irreversible brain damage appear, highlighting the need for preventive measures. This study assessed the prophylactic efficacy of remote photobiomodulation (PBM) targeted at the lungs against rCHI-induced brain injury and associated behavioral deficits.
View Article and Find Full Text PDFSensors (Basel)
December 2024
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
IoT (Internet of Things) networks are vulnerable to network viruses and botnets, while facing serious network security issues. The prediction of payload states in IoT networks can detect network attacks and achieve early warning and rapid response to prevent potential threats. Due to the instability and packet loss of communications between victim network nodes, the constructed protocol state machines of existing state prediction schemes are inaccurate.
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