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Localization through intricate traffic scenes poses challenges due to their dynamic, light-variable, and low-textured nature. Existing visual Simultaneous Localization and Mapping (SLAM) methods, which are based on static and texture-rich assumptions, struggle with drift and tracking failures in such complex environments. To address this, we propose a visual SLAM algorithm based on semantic information and geometric consistency in order to solve the above issues and further realize autonomous driving applications in road environments. In dynamic traffic scenes, we employ an object detection network to identify moving objects and further classify them based on geometric consistency as dynamic objects or potential dynamic objects. This method permits us to preserve more reliable static feature points. In low-texture environments, we propose a method that employs key object categories and geometric parameters of static scene objects for object matching between consecutive frames, effectively resolving the problem of tracking failure in such scenarios. We conducted experiments on the KITTI and ApolloScape datasets for autonomous driving and compared them to current representative algorithms. The results indicate that in the dynamic environment of the KITTI dataset, our algorithm improves the compared metrics by an average of 29.68%. In the static environment of the KITTI dataset, our algorithm's performance is comparable to that of the other compared algorithms. In the complex traffic scenario R11R003 from the ApolloScape dataset, our algorithm improves the compared metrics by an average of 25.27%. These results establish the algorithm's exceptional localization accuracy in dynamic environments and its robust localization capabilities in environments with low texture. It provides development and support for the implementation of autonomous driving technology applications. [ABSTRACT FROM AUTHOR]

Summary: Light regulates the subcellular localization of plant photoreceptors, a key step in light signaling. Ultraviolet‐B radiation (UV‐B) induces the plant photoreceptor UV RESISTANCE LOCUS 8 (UVR8) nuclear accumulation, where it regulates photomorphogenesis. However, the molecular mechanism for the UV‐B‐regulated UVR8 nuclear localization dynamics is unknown.With fluorescence recovery after photobleaching (FRAP), cell fractionation followed by immunoblotting and co‐immunoprecipitation (Co‐IP) assays we tested the function of UVR8‐interacting proteins including CONSTITUTIVELY PHOTOMORPHOGENIC 1 (COP1), REPRESSOR OF UV‐B PHOTOMORPHOGENESIS 1 (RUP1) and RUP2 in the regulation of UVR8 nuclear dynamics in Arabidopsis thaliana.We showed that UV‐B‐induced rapid UVR8 nuclear translocation is independent of COP1, which previously was shown to be required for UV‐B‐induced UVR8 nuclear accumulation. Instead, we provide evidence that the UV‐B‐induced UVR8 homodimer‐to‐monomer photo‐switch and the concurrent size reduction of UVR8 enables its monomer nuclear translocation, most likely via free diffusion. Nuclear COP1 interacts with UV‐B‐activated UVR8 monomer, thereby promoting UVR8 nuclear retention. Conversely, RUP1and RUP2, whose expressions are induced by UV‐B, inhibit UVR8 nuclear retention via attenuating the UVR8–COP1 interaction, allowing UVR8 to exit the nucleus.Collectively, our data suggest that UV‐B‐induced monomerization of UVR8 promotes its nuclear translocation via free diffusion. In the nucleus, COP1 binding promotes UVR8 monomer nuclear retention, which is counterbalanced by the major negative regulators RUP1 and RUP2. [ABSTRACT FROM AUTHOR]

Localization through intricate traffic scenes poses challenges due to their dynamic, light-variable, and low-textured nature. Existing visual Simultaneous Localization and Mapping (SLAM) methods, which are based on static and texture-rich assumptions, struggle with drift and tracking failures in such complex environments. To address this, we propose a visual SLAM algorithm based on semantic information and geometric consistency in order to solve the above issues and further realize autonomous driving applications in road environments. In dynamic traffic scenes, we employ an object detection network to identify moving objects and further classify them based on geometric consistency as dynamic objects or potential dynamic objects. This method permits us to preserve more reliable static feature points. In low-texture environments, we propose a method that employs key object categories and geometric parameters of static scene objects for object matching between consecutive frames, effectively resolving the problem of tracking failure in such scenarios. We conducted experiments on the KITTI and ApolloScape datasets for autonomous driving and compared them to current representative algorithms. The results indicate that in the dynamic environment of the KITTI dataset, our algorithm improves the compared metrics by an average of 29.68%. In the static environment of the KITTI dataset, our algorithm's performance is comparable to that of the other compared algorithms. In the complex traffic scenario R11R003 from the ApolloScape dataset, our algorithm improves the compared metrics by an average of 25.27%. These results establish the algorithm's exceptional localization accuracy in dynamic environments and its robust localization capabilities in environments with low texture. It provides development and support for the implementation of autonomous driving technology applications. [ABSTRACT FROM AUTHOR]

[Display omitted] • Novel halloysite@YF 3 :Ce3+, Tb3+ was prepared. • Halloysite be endowed with luminescent properties. • The luminescent intensity of YF 3 :Ce3+, Tb3+ was enhanced by SIDMC effect. • The utilization rate of halloysite and YF 3 :Ce3+, Tb3 + can be increased. • This work provides a method of solving the property problem of luminescent materials. A novel halloysite@YF 3 : Ce3+, Tb3+ anocomposite with strong luminescent properties was designed and synthesized by a facile direct precipitation strategy. Owing to the halloysite as a support, it can significantly prevent the aggregation of YF 3 :Ce3+,Tb3+ and the distribution of YF 3 :Ce3+,Tb3+ on halloysite was highly uniform. Importantly, due to the unique surface-interface-dielectric multiple confinement (SIDMC) effects, the as-harvested halloysite@YF 3 :Ce3+,Tb3+ nanocomposite exhibited excellent luminescent performance. Compared with YF 3 :Ce3+,Tb3+, the luminescence intensity of halloysite@YF 3 :Ce3+,Tb3+ nanocomposite is significantly enhanced by about 6 times under 255 nm excitation. However, the fluorescence lifetime of halloysite@YF 3 :Ce3+,Tb3+ nanocomposite (7.21 ms) is shorter than that of YF 3 :Ce3+,Tb3+ nanoparticles (8.34 ms). This finding indicated that halloysite can change the luminescent properties of YF 3 :Ce3+,Tb3+ nanoparticles through an SIDMC effect. The combination of halloysite and YF 3 :Ce3+,Tb3+ nanoparticles not only endowed halloysite with special properties, and effectively tuned the luminescent properties of YF 3 :Ce3+,Tb3+ nanoparticles, thereby improving the utility of halloysite and YF 3 :Ce3+,Tb3+ nanoparticles. The research supplies an insight on the development of natural mineral-based luminescent materials, and hopefully it could promote them application in many fields. [ABSTRACT FROM AUTHOR]