RESUMEN
Eco-driving has garnered considerable research attention owing to its potential socio-economic impact, including enhanced public health and mitigated climate change effects through the reduction of greenhouse gas emissions. With an expectation of more autonomous vehicles (AVs) on the road, an eco-driving strategy in hybrid traffic networks encompassing AV and human-driven vehicles (HDVs) with the coordination of traffic lights is a challenging task. The challenge is partially due to the insufficient infrastructure for collecting, transmitting, and sharing real-time traffic data among vehicles, facilities, and traffic control centers, and the following decision-making of agents involved in traffic control. Additionally, the intricate nature of the existing traffic network, with its diverse array of vehicles and facilities, contributes to the challenge by hindering the development of a mathematical model for accurately characterizing the traffic network. In this study, we utilized the Simulation of Urban Mobility (SUMO) simulator to tackle the first challenge through computational analysis. To address the second challenge, we employed a model-free reinforcement learning (RL) algorithm, proximal policy optimization, to decide the actions of AV and traffic light signals in a traffic network. A novel eco-driving strategy was proposed by introducing different percentages of AV into the traffic flow and collaborating with traffic light signals using RL to control the overall speed of the vehicles, resulting in improved fuel consumption efficiency. Average rewards with different penetration rates of AV (5%, 10%, and 20% of total vehicles) were compared to the situation without any AV in the traffic flow (0% penetration rate). The 10% penetration rate of AV showed a minimum time of convergence to achieve average reward, leading to a significant reduction in fuel consumption and total delay of all vehicles.
RESUMEN
Single-nucleotide polymorphism (SNP) markers and high-density genetic maps are important resources for marker-assisted selection, mapping of quantitative trait loci (QTLs) and genome structure analysis. Although linkage maps in certain catfish species have been obtained, high-density maps remain unavailable in the economically important southern catfish (Silurus meridionalis). Recently developed restriction site-associated DNA (RAD) markers have proved to be a promising tool for SNP detection and genetic map construction. The objective of the present study was to construct a high-density linkage map using SNPs generated by next-generation RAD sequencing in S. meridionalis for future genetic and genomic studies. An F1 population of 100 individuals was obtained by intraspecific crossing of two wild heterozygous individuals. In total, 77 634 putative high-quality bi-allelic SNPs between the parents were discovered by mapping the parents' paired-end RAD reads onto the reference contigs from both parents, of which 54.7% were transitions and 45.3% were transversions (transition/transversion ratio of 1.2). Finally, 26 714 high-quality RAD markers were grouped into 29 linkage groups by using de novo clustering methods (Stacks). Among these markers, 4514 were linked to the female genetic map, 23 718 to the male map and 6715 effective loci were linked to the integrated map spanning 5918.31 centimorgans (cM), with an average marker interval of 0.89 cM. High-resolution genetic maps are a useful tool for both marker-assisted breeding and various genome investigations in catfish, such as sequence assembly, gene localization, QTL detection and genome structure comparison. Hence, such a high-density linkage map will serve as a valuable resource for comparative genomics and fine-scale QTL mapping in catfish species.
