A connected autonomous vehicle (CAV) network can be defined as a set of connected vehicles including CAVs that operate on a specific spatial scope that may be a road network, corridor, or segment. The spatial scope constitutes an environment where traffic information is shared and instructions are issued for controlling the CAVs movements. Within such a spatial scope, high‐level cooperation among CAVs fostered by joint planning and control of their movements can greatly enhance the safety and mobility performance of their operations. Unfortunately, the highly combinatory and volatile nature of CAV networks due to the dynamic number of agents (vehicles) and the fast‐growing joint action space associated with multi‐agent driving tasks pose difficultly in achieving cooperative control. The problem is NP‐hard and cannot be efficiently resolved using rule‐based control techniques. Also, there is a great deal of information in the literature regarding sensing technologies and control logic in CAV operations but relatively little information on the integration of information from collaborative sensing and connectivity sources. Therefore, we present a novel deep reinforcement learning‐based algorithm that combines graphic convolution neural network with deep Q‐network to form an innovative graphic convolution Q network that serves as the information fusion module and decision processor. In this study, the spatial scope we consider for the CAV network is a multi‐lane road corridor. We demonstrate the proposed control algorithm using the application context of freeway lane‐changing at the approaches to an exit ramp. For purposes of comparison, the proposed model is evaluated vis‐à‐vis traditional rule‐based and long short‐term memory‐based fusion models. The results suggest that the proposed model is capable of aggregating information received from sensing and connectivity sources and prescribing efficient operative lane‐change decisions for multiple CAVs, in a manner that enhances safety and mobility. That way, the operational intentions of individual CAVs can be fulfilled even in partially observed and highly dynamic mixed traffic streams. The paper presents experimental evidence to demonstrate that the proposed algorithm can significantly enhance CAV operations. The proposed algorithm can be deployed at roadside units or cloud platforms or other centralized control facilities.

Autonomous vehicle (AV) stakeholders continue to seek assurance of the safety performance of this new technology through AV testing on in‐service roads, AV‐dedicated road networks, and AV test tracks. However, recent AV‐related fatalities on in‐service roads have exacerbated public skepticism and eroded some public trust in the safety of AV operations. Further, test tracks are unable to characterize adequately the real‐world driving environment. For this reason, driving simulators continue to serve as an attractive means of AV testing. However, in most AV driving simulators, the AV operation is based on commands external to the vehicle and embedded in the code for the driving environment. To address the simulation shortfalls associated with this approach, this paper develops a deep convolutional neural network–long short‐term memory (CNN–LSTM) algorithm for self‐driving simulation. This algorithm observes and characterizes the AV's driving environment, and controls the AV movement in the driving simulation. The CNN part extracts features that use transfer learning to introduce human prior knowledge, and the LSTM part uses temporal information to process the extracted features, and incorporates temporal dynamics to predict driving decisions. The AV may also use an external server with a database containing road environment data as an additional source of information. It is acknowledged that different driving simulators differ in their functions and their capabilities to access driving‐environment data. Therefore, to make it sufficiently flexible to facilitate replication by other researchers that use driving simulators, the algorithm has been designed and demonstrated using only image data of the driving environment as input. This is because roadway image data are easily and readily accessible from the screen of any driving simulator. The proposed algorithm was tested using the open racing car simulator test track platform and was found to be able to mimic human driving decisions with a high degree of accuracy.

Background: Endometriosis is a benign, chronic, gynecological disease which affect the women in reproductive age. The dysfunction of immune system is associated with endometriosis and the diversity of microbiota in genital tract. According to previous studies, microbiota significantly contributes to multi-systemic function, but the evidence of relationship between microbiota and endometriosis remains insufficient.Methods: There are 68 participants were included in this study and 134 samples obtained from the cervical canal, posterior fornix and uterine cavity were analyzed by 16s-rRNA sequencing. The raw data was filtered, analyzed, and visualized, and bio-information methods were used to identify the characteristics of microbiota.Results: Two different locations near the cervix, cervical canal, and posterior fornix, exhibited no differences in alpha diversity. The microbiota profile of adenomyosis with endometriosis patients is different from control group through PCoA. Among the different disease groups, five microbiotas were distinctive in the genus level, and Atopobium presented with the greatest significance in adenomyoisis-endometriosis patients. The LeFSe analysis failed to identify the special biomarkers, while several characteristic functions were identified through PICRUSt.Conclusions: Lactobacillus is the predominant genus in the female lower genital tract, and Atopobium is higher in patients with endometriosis combined with adenomyosis. Several different functions of microbiota were explored, some of them are found to be associated with endometriosis or adenomyosis, other functions are needed to be further verified. These findings may provide a new concept of microbiota/immune system/ endometriosis system.

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Construction projects are often associated with partial or full road closures, which result in user costs and community disruptions in terms of reduced business productivity. A number of studies have addressed the problem of scheduling construction projects based on a variety of stakeholder objectives. Yet still, there seems to exist a few gaps regarding (1) possible tradeoffs between road user cost reduction and business cost reduction associated with optimal scheduling, (2) role of the project type (rehabilitation and capacity expansion) on the solution methodology, and (3) lack of solution algorithm to address the problem complexity by deriving the optimal solution. In addressing these gaps, this article adopts a novel approach for developing an optimal project schedule for multiple road projects within a construction horizon. The goal is to minimize the overall cost of the projects to road users and adjacent businesses over the construction horizon. The project scheduling problem is formulated as a mixed-integer nonlinear program. We solve the problem using a local decomposition method. The methodology is demonstrated using the Sioux Falls city network with two project types: capacity expansion and rehabilitation. The results of the numerical experiment suggest that (1) the solution algorithm converges to optimal solution in finite iterations and (2) a network-wide scheduling of urban road projects using explicit optimization can yield a significant reduction in business disruption costs while incurring a relatively smaller increase in system travel time, and overall, is superior to a schedule developed only considering the total system travel time. 650wileyonlinelibrary.com/journal/mice Comput Aided Civ Inf. 2020;35:650-667.

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