The increasing legal availability of cannabis has important implications for road safety. This systematic review characterised the acute effects of Δ-THC on driving performance and driving-related cognitive skills, with a particular focus on the duration of Δ-THC-induced impairment. Eighty publications and 1534 outcomes were reviewed. Several measures of driving performance and driving-related cognitive skills (e.g. lateral control, tracking, divided attention) demonstrated impairment in meta-analyses of "peak" Δ-THC effects (p's<0.05). Multiple meta-regression analyses further found that regular cannabis users experianced less impairment than 'other' (mostly occasional) cannabis users (p = 0.003) and that the magnitude of oral (n = 243 effect estimates [EE]) and inhaled (n = 481 EEs) Δ-THC-induced impairment depended on various factors (dose, post-treatment time interval, the performance domain (skill) assessed) in other cannabis users (p's<0.05). The latter model predicted that most driving-related cognitive skills would 'recover' (Hedges' g=-0.25) within ∼5-hs (and almost all within ∼7-hs) of inhaling 20 mg of Δ-THC; oral Δ-THC-induced impairment may take longer to subside. These results suggest individuals should wait at least 5 -hs following inhaled cannabis use before performing safety-sensitive tasks.
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http://dx.doi.org/10.1016/j.neubiorev.2021.01.003 | DOI Listing |
Viruses
November 2024
Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC 27599, USA.
Robust CD8 T cell responses are critical for the control of HIV infection in both adults and children. Our understanding of the mechanisms driving these responses is based largely on studies of cells circulating in peripheral blood in adults, but the regulation of CD8 T cell responses in tissue sites is poorly understood, particularly in pediatric infections. DNA methylation is an epigenetic modification that regulates gene transcription.
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December 2024
Department of Aerospace Engineering, Chosun University, Gwangju 61452, Republic of Korea.
This paper presents a novel control framework for enhancing the attitude stabilization of multirotor UAVs using Control Moment Gyros (CMGs) and a Disturbance Robust Drive Law (DRDL). Due to their lightweight and compact structure, multirotor UAVs are highly susceptible to disturbances such as wind, making it challenging to achieve stable attitude control using rotor thrust alone. To address this issue, we employ CMGs to provide robust attitude control and apply Fast Terminal Sliding Mode Control (FTSMC) to ensure fast and accurate convergence within a finite time.
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December 2024
Department of Information and Electronic Engineering, International Hellenic University, 57001 Thessaloniki, Greece.
Recent advances in emotion recognition through Artificial Intelligence (AI) have demonstrated potential applications in various fields (e.g., healthcare, advertising, and driving technology), with electroencephalogram (EEG)-based approaches demonstrating superior accuracy compared to facial or vocal methods due to their resistance to intentional manipulation.
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December 2024
School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China.
With the growing prominence of autonomous driving, the demand for accurate and efficient lane detection has increased significantly. Beyond ensuring accuracy, achieving high detection speed is crucial to maintaining real-time performance, stability, and safety. To address this challenge, this study proposes the ECBAM_ASPP model, which integrates the Efficient Convolutional Block Attention Module (ECBAM) with the Atrous Spatial Pyramid Pooling (ASPP) module.
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December 2024
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Deep unfolding networks (DUNs) have attracted growing attention in compressed sensing (CS) due to their good interpretability and high performance. However, many DUNs often improve the reconstruction effect at the price of a large number of parameters and have the problem of feature information loss during iteration. This paper proposes a novel adaptive memory-augmented unfolding network for compressed sensing (AMAUN-CS).
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