Research on the Digital Workshop Layout Based on Steel Material Processing Workshop

AbstractAiming at the layout optimization of the steel structure machining workshop in modern ship manufacturing industry, a digital optimum solution is proposed. First optimize the production flow and enterprise resources and build the 3D visual parametric model. Second using optimization algorithm build the optimum layout model. Third apply estimation method on the optimum model. As for the initial layout plan, build the layout optimum model using the Improved Genetic Algorithm, and find out the minimization solution of the optimum. This paper is helpful for the digital manufacture workshop's layout optimization research

Complete Assembly of the Genome of an Acidovorax citrulli Strain Reveals a Naturally Occurring Plasmid in This Species

Acidovorax citrulli is the causal agent of bacterial fruit blotch (BFB), a serious threat to cucurbit crop production worldwide. Based on genetic and phenotypic properties, A. citrulli strains are divided into two major groups: group I strains have been generally isolated from melon and other non-watermelon cucurbits, while group II strains are closely associated with watermelon. In a previous study, we reported the genome of the group I model strain, M6. At that time, the M6 genome was sequenced by MiSeq Illumina technology, with reads assembled into 139 contigs. Here, we report the assembly of the M6 genome following sequencing with PacBio technology. This approach not only allowed full assembly of the M6 genome, but it also revealed the occurrence of a ∼53 kb plasmid. The M6 plasmid, named pACM6, was further confirmed by plasmid extraction, Southern-blot analysis of restricted fragments and obtention of M6-derivative cured strains. pACM6 occurs at low copy numbers (average of ∼4.1 ± 1.3 chromosome equivalents) in A. citrulli M6 and contains 63 open reading frames (ORFs), most of which (55.6%) encoding hypothetical proteins. The plasmid contains several genes encoding type IV secretion components, and typical plasmid-borne genes involved in plasmid maintenance, replication and transfer. The plasmid also carries an operon encoding homologs of a Fic-VbhA toxin-antitoxin (TA) module. Transcriptome data from A. citrulli M6 revealed that, under the tested conditions, the genes encoding the components of this TA system are among the highest expressed genes in pACM6. Whether this TA module plays a role in pACM6 maintenance is still to be determined. Leaf infiltration and seed transmission assays revealed that, under tested conditions, the loss of pACM6 did not affect the virulence of A. citrulli M6. We also show that pACM6 or similar plasmids are present in several group I strains, but absent in all tested group II strains of A. citrulli

Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control

Traffic signal control (TSC) is a complex and important task that affects the daily lives of millions of people. Reinforcement Learning (RL) has shown promising results in optimizing traffic signal control, but current RL-based TSC methods are mainly trained in simulation and suffer from the performance gap between simulation and the real world. In this paper, we propose a simulation-to-real-world (sim-to-real) transfer approach called UGAT, which transfers a learned policy trained from a simulated environment to a real-world environment by dynamically transforming actions in the simulation with uncertainty to mitigate the domain gap of transition dynamics. We evaluate our method on a simulated traffic environment and show that it significantly improves the performance of the transferred RL policy in the real world.Comment: 8 pages, 3 figure

LLM Powered Sim-to-real Transfer for Traffic Signal Control

Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks aiming to provide efficient transportation and mitigate congestion waste. In recent, promising results have been attained by Reinforcement Learning (RL) methods through trial and error in simulators, bringing confidence in solving cities' congestion headaches. However, there still exist performance gaps when simulator-trained policies are deployed to the real world. This issue is mainly introduced by the system dynamic difference between the training simulator and the real-world environments. The Large Language Models (LLMs) are trained on mass knowledge and proved to be equipped with astonishing inference abilities. In this work, we leverage LLMs to understand and profile the system dynamics by a prompt-based grounded action transformation. Accepting the cloze prompt template, and then filling in the answer based on accessible context, the pre-trained LLM's inference ability is exploited and applied to understand how weather conditions, traffic states, and road types influence traffic dynamics, being aware of this, the policies' action is taken and grounded based on realistic dynamics, thus help the agent learn a more realistic policy. We conduct experiments using DQN to show the effectiveness of the proposed PromptGAT's ability in mitigating the performance gap from simulation to reality (sim-to-real).Comment: 9 pages, 7 figure