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| http://dx.doi.org/10.1136/bmj.2.5040.355-a | DOI Listing |
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A study on the optimal safety speed and warning sign distance for highways in windy and sandy environments based on driving behavior.
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School of Civil and Hydraulic Engineering, NingXia University, YinChuan, China.
Objective: This study aims to address the issue of driving safety on highways in the desert region of Northwest China during extreme weather conditions such as sandstorms, with the goal of reducing driver risk. It explores driver behavior under extreme conditions of sandstorms and sand accumulation, proposing safety speed recommendations and warning models for different environments to calculate the optimal warning distance in windy and sandy conditions.
Methods: Natural driving simulation experiments were conducted in windy and sandy environments, collecting driving behavior data from 45 drivers under varying visibility and road conditions with or without sand accumulation.
Typhoon localization detection algorithm based on TGE-YOLO.
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College of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, 524088, China.
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View Article and Find Full Text PDFMachine learning and spatio-temporal analysis of meteorological factors on waterborne diseases in Bangladesh.
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Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh.
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View Article and Find Full Text PDFEnhancing Microcalcification Detection in Mammography with YOLO-v8 Performance and Clinical Implications.
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: Microcalcifications in the breast are often an early warning sign of breast cancer, and their accurate detection is crucial for the early discovery and management of the disease. In recent years, deep learning technology, particularly models based on object detection, has significantly improved the ability to detect microcalcifications. This study aims to use the advanced YOLO-v8 object detection algorithm to identify breast microcalcifications and explore its advantages in terms of performance and clinical application.
View Article and Find Full Text PDFApplication of big data technology in enterprise information security management.
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College of Electrical and Information Engineering, Hunan Institute of Traffic Engineering, Hunan, Hengyang, 421001, China.
This study aims to explore the application value of big data technology (BDT) in enterprise information security (EIS). Its goal is to develop a risk prediction model based on big data analysis to enhance the information security protection capability of enterprises. A big data analysis system that can monitor and intelligently identify potential security risks in real-time is constructed by designing complex network analysis algorithms and machine learning models.
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