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This paper presents an investigation of the performance of a 22 MnB5 tube after local heat treatment according to a patterning shape under dynamic crash test conditions to propose the patterning shape with the best energy absorption efficiency. Numerical simulations support experimental results to validate the deformation mode during dynamic crash test as well as the strain distribution of the specimen. The helical patterning not only demonstrates the highest axial loading force and energy absorbance in both static and dynamic crash tests, but also can be easily fabricated in a short time. The helical pattern can optimize different pitch sizes according to the thickness and diameter of the cylindrical tube, and it has the highest energy absorption rate with 83.0% in dynamic conditions.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571882 | PMC |
http://dx.doi.org/10.3390/ma15196580 | DOI Listing |
Sci Rep
September 2025
Department of Civil Engineering, Debre Markos University, Debre Markos, Amhara, Ethiopia.
Reducing road traffic accidents and enhancing road safety remain pressing concerns for effective transportation systems worldwide. However, in developing countries like Ethiopia, addressing these issues faces significant challenges. Despite the success of cost-effective safety measures in developed countries, similar strategies are often lacking in Ethiopia.
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August 2025
Beijing Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing 100044, China; School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China. Electronic address:
Traffic crashes remain a critical global public safety concern, exhibiting complex heterogeneity across multiple temporal scales that limits the timeliness and precision of current traffic safety management strategies. Modeling the underlying causation mechanisms across temporal scales presents several challenges, including weak sequential patterns and nonlinear interactions among temporal features. To address these issues, this research categorizes crash data into a macro-scale (annual seasonality) and micro-scale (daily peak intervals and daytime/nighttime variation), and proposes a Multi-Channel Feature Correlation Transformer (MCFformer) to systematically model the influence of multi-scale temporal factors on crash-induced delay.
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August 2025
Swinburne Research, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122, Australia.
The shift from manual to conditionally automated driving, supported by Advanced Driving Assistance Systems (ADASs), introduces challenges, particularly increased crash risks due to human factors like cognitive overload. Driving simulators provide a safe and controlled setting to study these human factors under complex conditions. This study leverages Functional Near-Infrared Spectroscopy (fNIRS) to dynamically assess cognitive load in a realistic driving simulator during a challenging night-time-rain scenario.
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October 2025
Hebei Transportation Planning and Design Institute Co., Ltd., No. 1 Gutai West Road, Shijiazhuang City, Hebei Province 050000, China.
Combined alignment sections of mountainous freeways often feature complex geometric configurations-such as downhill sag/convex curves, slope-changing curves, and uphill curves-that significantly affect crash risk. Existing studies typically apply homogeneous segmentation and broad classifications (e.g.
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August 2025
Geneva School of Economics and Management, University of Geneva, 1205, Geneva, Switzerland.
In complex systems, understanding the nonlinear interactions among risk factors is essential for accurate risk analysis. However, traditional linear models often fail to capture these complex interdependencies, leading to significant gaps in risk prediction. The aim of this study is to present a novel approach for risk analysis of nonlinear risk interactions using Bayesian networks (BNs), thereby providing a broadly applicable method for risk management and mitigation.
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