There is a rise in car accidents due to human errors on the road. A critical task of self-driving cars that can reduce accidents on the road is traffic sign detection and recognition (TSDR), which is vital in alerting...
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ISBN:
(数字)9798350351767
ISBN:
(纸本)9798350351774
There is a rise in car accidents due to human errors on the road. A critical task of self-driving cars that can reduce accidents on the road is traffic sign detection and recognition (TSDR), which is vital in alerting drivers to the presence of traffic signs in advance. This research will separate the proposed deep ensemble learning algorithm into two methods. First, after the traffic scene process, the algorithm will detect the traffic sign as two categories with the YOLOv5s network. Then, process the traffic sign to recognize the traffic sign into seven classes with the MobileNet network. The detection model was trained with the Taiwan Traffic Sign Detection (TTSD) dataset collected from Taiwan roads. The recognition model was trained with the Taiwan Traffic Sign Recognition (TTSR) dataset. The result of the proposed algorithm showed high performance when experimenting with 95.83% accuracy, 87.34% true prediction, and 191.3 milliseconds (ms) of inference time.
Decentralized Stochastic Gradient Descent (SGD) is an emerging neural network training approach that enables multiple agents to train a model collaboratively and simultaneously. Rather than using a central parameter s...
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Microglia have both protective and pathogenic properties, while polarization plays a decisive role in their functional diversity. Apart from being an energetic organelle, mitochondria possess biological capabilities o...
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Microglia have both protective and pathogenic properties, while polarization plays a decisive role in their functional diversity. Apart from being an energetic organelle, mitochondria possess biological capabilities of signaling and immunity involving mitochondrial dynamics. The N-methyl-D-aspartate (NMDA)-type glutamate receptor displays excitatory neurotransmission, excitatory neurotoxicity and pro-inflammatory properties in a membrane location- and cell context-dependent manner. In this study, we have provided experimental evidence showing that by acting on mitochondrial dynamics, NMDA receptors displayed pro-inflammatory properties, while its non-competitive inhibitor MK801 exhibited anti-inflammatory potential in Lipopolysaccharide (LPS)-challenged BV-2 microglia cells. LPS stimulation increased the protein phosphorylation of cells regarding their NMDA receptor component subunits and Calcium/Calmodulin-dependent Protein Kinase II (CaMKII), along with mobilizing intracellular calcium. Additionally, parallel changes occurred in the activation of Transforming Growth Factor-β (TGF-β)-Activated Kinase 1 (TAK1), NF-κB p65 and NF-κB DNA binding activity, acquisition of pro-inflammatory M1 polarization and expression of pro-inflammatory cytokines. LPS-treated cells further displayed signs of mitochondrial dysfunction with higher expressions of the active form of Dynamin-Related Protein 1 (Drp1), NADPH Oxidase-2 (NOX2) expression and the generation of DCFDA-/MitoSOX-sensitive Reactive Oxygen Species (ROS). NMDA receptor blockade by MK801, along with CaMKII inhibitor KN93, Drp1 inhibitor Mdivi-1 and antioxidant apocynin alleviated LPS-induced pro-inflammatory changes. Other than the reported CaMKII/TAK1/NF-κB axis, our in vitro study revealed the CaMKII/Drp1/ROS/NF-κB axis being an alternative cascade for shaping pro-inflammatory phenotypes of microglia upon LPS stimulation, and MK801 having the potential for inhibiting microglia activation and any associated inflammator
In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occ...
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In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.
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