The sign of the wave speed for the Lotka-Volterra competition-diffusion system

[[abstract]]In this paper, we study the traveling front solutions of the Lotka-Volterra competition-diffusion system with bistable nonlinearity. It is well-known that the wave speed of traveling front is unique. Although little is known for the sign of the wave speed. In this paper, we first study the standing wave which gives some criteria when the speed is zero. Then, by the monotone dependence on parameters, we obtain some criteria about the sign of the wave speed under some parameter restrictions.[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]US

On a free boundary problem for a two-species weak competition system

[[abstract]]We study a Lotka–Volterra type weak competition model with a free boundary in a one-dimensional habitat. The main objective is to understand the asymptotic behavior of two competing species spreading via a free boundary. We also provide some sufficient conditions for spreading success and spreading failure, respectively. Finally, when spreading successfully, we provide an estimate to show that the spreading speed (if exists) cannot be faster than the minimal speed of traveling wavefront solutions for the competition model on the whole real line without a free boundary.[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control

Traffic congestion is a persistent problem in urban areas, which calls for the development of effective traffic signal control (TSC) systems. While existing Reinforcement Learning (RL)-based methods have shown promising performance in optimizing TSC, it is challenging to generalize these methods across intersections of different structures. In this work, a universal RL-based TSC framework is proposed for Vehicle-to-Everything (V2X) environments. The proposed framework introduces a novel agent design that incorporates a junction matrix to characterize intersection states, making the proposed model applicable to diverse intersections. To equip the proposed RL-based framework with enhanced capability of handling various intersection structures, novel traffic state augmentation methods are tailor-made for signal light control systems. Finally, extensive experimental results derived from multiple intersection configurations confirm the effectiveness of the proposed framework. The source code in this work is available at https://github.com/wmn7/Universal_LightComment: 18 pages, 9 figure

ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement Learning

Traffic signal control has the potential to reduce congestion in dynamic networks. Recent studies show that traffic signal control with reinforcement learning (RL) methods can significantly reduce the average waiting time. However, a shortcoming of existing methods is that they require model retraining for new intersections with different structures. In this paper, we propose a novel reinforcement learning approach with augmented data (ADLight) to train a universal model for intersections with different structures. We propose a new agent design incorporating features on movements and actions with set current phase duration to allow the generalized model to have the same structure for different intersections. A new data augmentation method named \textit{movement shuffle} is developed to improve the generalization performance. We also test the universal model with new intersections in Simulation of Urban MObility (SUMO). The results show that the performance of our approach is close to the models trained in a single environment directly (only a 5% loss of average waiting time), and we can reduce more than 80% of training time, which saves a lot of computational resources in scalable operations of traffic lights

LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments

Traffic congestion in metropolitan areas presents a formidable challenge with far-reaching economic, environmental, and societal ramifications. Therefore, effective congestion management is imperative, with traffic signal control (TSC) systems being pivotal in this endeavor. Conventional TSC systems, designed upon rule-based algorithms or reinforcement learning (RL), frequently exhibit deficiencies in managing the complexities and variabilities of urban traffic flows, constrained by their limited capacity for adaptation to unfamiliar scenarios. In response to these limitations, this work introduces an innovative approach that integrates Large Language Models (LLMs) into TSC, harnessing their advanced reasoning and decision-making faculties. Specifically, a hybrid framework that augments LLMs with a suite of perception and decision-making tools is proposed, facilitating the interrogation of both the static and dynamic traffic information. This design places the LLM at the center of the decision-making process, combining external traffic data with established TSC methods. Moreover, a simulation platform is developed to corroborate the efficacy of the proposed framework. The findings from our simulations attest to the system's adeptness in adjusting to a multiplicity of traffic environments without the need for additional training. Notably, in cases of Sensor Outage (SO), our approach surpasses conventional RL-based systems by reducing the average waiting time by $20.4\%$. This research signifies a notable advance in TSC strategies and paves the way for the integration of LLMs into real-world, dynamic scenarios, highlighting their potential to revolutionize traffic management. The related code is available at https://github.com/Traffic-Alpha/LLM-Assisted-Light.Comment: 20 pages, 11 figure

Traffic Signal Cycle Control with Centralized Critic and Decentralized Actors under Varying Intervention Frequencies

Traffic congestion in urban areas is a significant problem, leading to prolonged travel times, reduced efficiency, and increased environmental concerns. Effective traffic signal control (TSC) is a key strategy for reducing congestion. Unlike most TSC systems that rely on high-frequency control, this study introduces an innovative joint phase traffic signal cycle control method that operates effectively with varying control intervals. Our method features an adjust all phases action design, enabling simultaneous phase changes within the signal cycle, which fosters both immediate stability and sustained TSC effectiveness, especially at lower frequencies. The approach also integrates decentralized actors to handle the complexity of the action space, with a centralized critic to ensure coordinated phase adjusting. Extensive testing on both synthetic and real-world data across different intersection types and signal setups shows that our method significantly outperforms other popular techniques, particularly at high control intervals. Case studies of policies derived from traffic data further illustrate the robustness and reliability of our proposed method.Comment: 26 pages, 17 figure

Evaluating the emotion regulation of positive mood states among people with bipolar disorder using hierarchical clustering

BACKGROUNDPeople with bipolar disorder (BD) frequently struggle with the recurrence of affective symptoms. However, the interplay between coping mechanism and positive mood state remains under-researched.AIMTo explore the associations among behavioral approach system (BAS) sensitivity level, coping, and positive mood states among people with BD.METHODSUsing a cross-sectional study design, 90 participants with BD were presented with four BAS-activating life event scenarios and assessed with regard to their BAS trait sensitivity, coping flexibility, and mood states. A hierarchical clustering method was used to identify different groups with different styles of coping. Multiple hierarchical regression analyses were conducted to examine the mediating and moderating roles of different components of coping on moodstates.RESULTSA three-cluster solution was found to best fit the present data set. The findings showed that a low mass of coping combined with low BAS sensitivity level protects people with BD from detrimentally accentuating mood states when they encounter BAS-activating life events. Moreover, coping flexibility is demonstrated to mediate and moderate the relationships between BAS sensitivity level and mood states. Specifically, subduing the perceived controllability and reducing the use of behavioral-activation/emotion-amplifying coping strategies could help buffer the effect of positive affect.CONCLUSIONThe judicious use of coping in emotion regulation for people with BD when encountering BAS-activating life events was indicated. Practical applications and theoretical implications are highlighted

Genetic Variation and De Novo Mutations in the Parthenogenetic Caucasian Rock Lizard Darevskia unisexualis

Unisexual all-female lizards of the genus Darevskia that are well adapted to various habitats are known to reproduce normally by true parthenogenesis. Although they consist of unisexual lineages and lack effective genetic recombination, they are characterized by some level of genetic polymorphism. To reveal the mutational contribution to overall genetic variability, the most straightforward and conclusive way is the direct detection of mutation events in pedigree genotyping. Earlier we selected from genomic library of D. unisexualis two polymorphic microsatellite containg loci Du281 and Du215. In this study, these two loci were analyzed to detect possible de novo mutations in 168 parthenogenetic offspring of 49 D. unisexualis mothers and in 147 offspring of 50 D. armeniaca mothers . No mutant alleles were detected in D. armeniaca offspring at both loci, and in D. unisexualis offspring at the Du215 locus. There were a total of seven mutational events in the germ lines of four of the 49 D. unisexualis mothers at the Du281 locus, yielding the mutation rate of 0.1428 events per germ line tissue. Sequencing of the mutant alleles has shown that most mutations occur via deletion or insertion of single microsatellite repeat being identical in all offspring of the family. This indicates that such mutations emerge at the early stages of embryogenesis. In this study we characterized single highly unstable (GATA)n containing locus in parthenogenetic lizard species D. unisexualis. Besides, we characterized various types of mutant alleles of this locus found in the D. unisexualis offspring of the first generation. Our data has shown that microsatellite mutations at highly unstable loci can make a significant contribution to population variability of parthenogenetic lizards