The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies. Deep learning has proven successful in ML tasks such as speech processing and computational vision, with a performance that scales with the amount of available data. The lack of large datasets inhibits the flourish of deep learning applications in wireless communications. This paper presents a methodology that combines a vehicle traffic simulator with a raytracing simulator, to generate channel realizations representing 5G scenarios with mobility of both transceivers and objects. The paper then describes a specific dataset for investigating beamselection techniques on vehicle-to-infrastructure using millimeter waves. Experiments using deep learning in classification, regression and reinforcement learning problems illustrate the use of datasets generated with the proposed methodology.
Bistable display has been a long-awaited goal due to its zero energy cost when maintaining colored or colorless state and electrochromic material has been highly considered as a potential way to achieve bistable display due to its simple structure and possible manipulation. However, it is extremely challenging with insurmountable technical barriers related to traditional electrochromic mechanisms. Herein a prototype for bistable electronic billboard and reader with high energy efficiency is demonstrated with excellent bistability (decay 7% in an hour), reversibility (10
4
cycles), coloration efficiency (430 cm
2
C
−1
) and very short voltage stimulation time (2 ms) for color switching, which greatly outperforms current products. This is achieved by stabilization of redox molecule via intermolecular ion transfer to the supramolecular bonded colorant and further stabilization of the electrochromic molecules in semi-solid media. This promising approach for ultra-energy-efficient display will promote the development of switching molecules, devices and applications in various fields of driving/navigation/industry as display to save energy.
Summary
The flag leaf and grain belong to the source and sink, respectively, of cereals, and both have a bearing on final yield. Premature leaf senescence significantly reduces the photosynthetic rate and severely lowers crop yield. Cytokinins play important roles in leaf senescence and determine grain number. Here, we characterized the roles of the rice (Oryza sativa L.) cytokinin oxidase/dehydrogenase OsCKX11 in delaying leaf senescence, increasing grain number, and coordinately regulating source and sink. OsCKX11 was predominantly expressed in the roots, leaves, and panicles and was strongly induced by abscisic acid and leaf senescence. Recombinant OsCKX11 protein catalysed the degradation of various types of cytokinins but showed preference for trans‐zeatin and cis‐zeatin. Cytokinin levels were significantly increased in the flag leaves of osckx11 mutant compared to those of the wild type (WT). In the osckx11 mutant, the ABA‐biosynthesizing genes were down‐regulated and the ABA‐degrading genes were up‐regulated, thereby reducing the ABA levels relative to the WT. Thus, OsCKX11 functions antagonistically between cytokinins and ABA in leaf senescence. Moreover, osckx11 presented with significantly increased branch, tiller, and grain number compared with the WT. Collectively, our findings reveal that OsCKX11 simultaneously regulates photosynthesis and grain number, which may provide new insights into leaf senescence and crop molecular breeding.
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