Plant anatomy is patterned early during leaf development which suggests studying the spatial–temporal transcriptomes of primordia will help identify critical regulative and functional genes.We successfully isolated t...Plant anatomy is patterned early during leaf development which suggests studying the spatial–temporal transcriptomes of primordia will help identify critical regulative and functional genes.We successfully isolated the leaf primordia tissues from the C3grass rice and the C4grass foxtail millet by laser capture microdissection(LCM)and studied the gene expression throughout leaf developmental stages.Our data analysis uncovered the conserved expression patterns of certain gene clusters both in rice and foxtail millet during leaf development.We revealed genes and transcription factors involved in vein formation,stomatal development,and suberin accumulation.We identified 79 candidate genes associated with functional regulation of C4anatomy formation.Screening phenotype of the candidate genes revealed that knock-out of a putative polar auxin transport related gene NAL1 resulted significantly reduced veinal space in rice leaf.Our present work provides a foundation for future analyses of genes with novel functions in grasses and their role in leaf development,in particular the role in leaves with a contrasting C3vs.C4biosynthetic pathway.展开更多
This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed ra...This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed rapidly,moving from basic driver-assistance systems(Level 1)to fully autonomous capabilities(Level 5).Central to this advancement are two key functionalities:Lane-Change Maneuvers(LCM)and Adaptive Cruise Control(ACC).In this study,a detailed simulation environment is created to replicate the road network between Nantun andWuri on National Freeway No.1 in Taiwan.The MPC controller is deployed to optimize vehicle trajectories,ensuring safe and efficient navigation.Simulated onboard sensors,including vehicle cameras and millimeterwave radar,are used to detect and respond to dynamic changes in the surrounding environment,enabling real-time decision-making for LCM and ACC.The simulation resultshighlight the superiority of the MPC-based approach in maintaining safe distances,executing controlled lane changes,and optimizing fuel efficiency.Specifically,the MPC controller effectively manages collision avoidance,reduces travel time,and contributes to smoother traffic flow compared to traditional path planning methods.These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios.Future research will focus on validating these results through real-world testing,addressing computational challenges for real-time implementation,and exploring the adaptability of MPC under various environmental conditions.This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation,paving the way for broader adoption of MPC in AV systems.展开更多
基金supported by the National Key Research and Development Program of China(NKRDP)(2022YFF1001700)the Agricultural Science and Technology Innovation Program(2020YFE0202300)the National Natural Science Foundation of China(31871313)。
文摘Plant anatomy is patterned early during leaf development which suggests studying the spatial–temporal transcriptomes of primordia will help identify critical regulative and functional genes.We successfully isolated the leaf primordia tissues from the C3grass rice and the C4grass foxtail millet by laser capture microdissection(LCM)and studied the gene expression throughout leaf developmental stages.Our data analysis uncovered the conserved expression patterns of certain gene clusters both in rice and foxtail millet during leaf development.We revealed genes and transcription factors involved in vein formation,stomatal development,and suberin accumulation.We identified 79 candidate genes associated with functional regulation of C4anatomy formation.Screening phenotype of the candidate genes revealed that knock-out of a putative polar auxin transport related gene NAL1 resulted significantly reduced veinal space in rice leaf.Our present work provides a foundation for future analyses of genes with novel functions in grasses and their role in leaf development,in particular the role in leaves with a contrasting C3vs.C4biosynthetic pathway.
基金National Science and Technology Council,Taiwan,for financially supporting this research(Grant No.NSTC 113-2221-E-018-011)Ministry of Education’s Teaching Practice Research Program,Taiwan(PSK1120797 and PSK1134099).
文摘This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed rapidly,moving from basic driver-assistance systems(Level 1)to fully autonomous capabilities(Level 5).Central to this advancement are two key functionalities:Lane-Change Maneuvers(LCM)and Adaptive Cruise Control(ACC).In this study,a detailed simulation environment is created to replicate the road network between Nantun andWuri on National Freeway No.1 in Taiwan.The MPC controller is deployed to optimize vehicle trajectories,ensuring safe and efficient navigation.Simulated onboard sensors,including vehicle cameras and millimeterwave radar,are used to detect and respond to dynamic changes in the surrounding environment,enabling real-time decision-making for LCM and ACC.The simulation resultshighlight the superiority of the MPC-based approach in maintaining safe distances,executing controlled lane changes,and optimizing fuel efficiency.Specifically,the MPC controller effectively manages collision avoidance,reduces travel time,and contributes to smoother traffic flow compared to traditional path planning methods.These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios.Future research will focus on validating these results through real-world testing,addressing computational challenges for real-time implementation,and exploring the adaptability of MPC under various environmental conditions.This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation,paving the way for broader adoption of MPC in AV systems.
