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Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system. 

The COVID-19 pandemic has significantly impacted most countries in the world. Analyzing COVID-19 data from these countries together is a prominent challenge. Under the sponsorship of NSF REU, this paper describes our experience with a ten-week project that aims to guide an REU scholar to develop a physics-guided graph attention network to predict the global COVID- 19 Pandemics. We mainly presented the preparation, implementation, and dissemination of the addressed project. The COVID-19 situation in a country could be dramatically different from that of others, which suggests that COVID-19 pandemic data are generated based on different mechanisms, making COVID-19 data in different countries follow different probability distributions. Learning more than one hundred underlying probability distributions for countries in the world from large scale COVID- 19 data is beyond a single machine learning model. To address this challenge, we proposed two team-learning frameworks for predicting the COVID-19 pandemic trends: peer learning and layered ensemble learning framework. This addressed framework assigns an adaptive physics-guided graph attention network (GAT) to each learning agent. All the learning agents are fabricated in a hierarchical architecture, which enables agents to collaborate with each other in peer-to-peer and cross-layer way. This layered architecture shares the burden of large-scale data processing on machine learning models of all units. Experiments are run to verify the effectiveness of our approaches. The results indicate the proposed ensemble outperforms baseline methods. Besides being documented on GitHub, this work has resulted in two journal papers. 

Human experience involvement in existing operations of airborne Light Detection and Ranging (LIDAR) systems and off-line processing of collected LIDAR data make the acquisition process of airborne LIDAR point cloud less adaptable to environment conditions. This work develops a deep reinforcement learning-enabled framework for adaptive airborne LIDAR point cloud acquisition. Namely, the optimization of the airborne LIDAR operation is modeled as a Markov decision process (MDP). A set of LIDAR point cloud processing methods are proposed to derive the state space, action space, and reward function of the MDP model. A DRL algorithm, Deep Q-Network (DQN), is used to solve the MDP. The DRL model is trained in a flexible virtual environment by using simulator AirSim. Extensive simulation demonstrates the efficiency of the proposed framework. 

Context Temporal prediction of lower extremity (LE) injury risk will benefit clinicians by allowing them to better leverage limited resources and target athletes most at risk. Objective To characterize instantaneous risk of LE injury by demographic factors sex, sport, body mass index (BMI), and previous injury history. Instantaneous injury risk was defined as injury risk at any given point in time following baseline measurement. Design Descriptive epidemiology study. Setting NCAA Division I athletic program. Patients or Other Participants 278 NCAA Division I varsity student-athletes (119 males, 159 females). Main Outcome Measure(s) LE injuries were tracked for 237±235 days. Sex-stratified univariate Cox regression models investigated the association between time to first LE injury and BMI, sport, and previous LE injury history. Relative risk ratios and Kaplan-Meier curves were generated. Variables identified in the univariate analysis were included in a multivariate Cox regression model. Results Females displayed similar instantaneous LE injury risk compared to males (HR=1.29, 95%CI=[0.91,1.83], p=0.16). Overweight athletes (BMI>25 kg/m2) had similar instantaneous LE injury risk compared with athletes with BMI<25 kg/m2 (HR=1.23, 95%CI=[0.84,1.82], p=0.29). Athletes with previous LE injuries were not more likely to sustain subsequent LE injury than athletes with no previous injury (HR=1.09, 95%CI=[0.76,1.54], p=0.64). Basketball (HR=3.12, 95%CI=[1.51,6.44], p=0.002) and soccer (HR=2.78, 95%CI=[1.46,5.31], p=0.002) athletes had higher risk of LE injury than cross-country athletes. In the multivariate model, females were at greater LE injury risk than males (HR=1.55, 95%CI=[1.00,2.39], p=0.05), and males with BMI>25 kg/m2 were at greater risk than all other athletes (HR=0.44, 95%CI=[0.19,1.00], p=0.05). Conclusions In a collegiate athletic population, previous LE injury history was not a significant contributor to risk of future LE injury, while being female or being male with BMI>25 kg/m2 resulted in increased risk of LE injury. Clinicians can use these data to extrapolate LE injury risk occurrence to specific populations.