TiEV: The Tongji Intelligent Electric Vehicle in the Intelligent Vehicle Future Challenge of China

TiEV is an autonomous driving platform implemented by Tongji University of China. The vehicle is drive-by-wire and is fully powered by electricity. We devised the software system of TiEV from scratch, which is capable of driving the vehicle autonomously in urban paths as well as on fast express roads. We describe our whole system, especially novel modules of probabilistic perception fusion, incremental mapping, the 1st and the 2nd planning and the overall safety concern. TiEV finished 2016 and 2017 Intelligent Vehicle Future Challenge of China held at Changshu. We show our experiences on the development of autonomous vehicles and future trends

Multi-Robot Autonomous Exploration and Mapping Under Localization Uncertainty with Expectation-Maximization

We propose an autonomous exploration algorithm designed for decentralized multi-robot teams, which takes into account map and localization uncertainties of range-sensing mobile robots. Virtual landmarks are used to quantify the combined impact of process noise and sensor noise on map uncertainty. Additionally, we employ an iterative expectation-maximization inspired algorithm to assess the potential outcomes of both a local robot's and its neighbors' next-step actions. To evaluate the effectiveness of our framework, we conduct a comparative analysis with state-of-the-art algorithms. The results of our experiments show the proposed algorithm's capacity to strike a balance between curbing map uncertainty and achieving efficient task allocation among robots

Decentralized Multi-Robot Navigation for Autonomous Surface Vehicles with Distributional Reinforcement Learning

Collision avoidance algorithms for Autonomous Surface Vehicles (ASV) that follow the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) have been proposed in recent years. However, it may be difficult and unsafe to follow COLREGs in congested waters, where multiple ASVs are navigating in the presence of static obstacles and strong currents, due to the complex interactions. To address this problem, we propose a decentralized multi-ASV collision avoidance policy based on Distributional Reinforcement Learning, which considers the interactions among ASVs as well as with static obstacles and current flows. We evaluate the performance of the proposed Distributional RL based policy against a traditional RL-based policy and two classical methods, Artificial Potential Fields (APF) and Reciprocal Velocity Obstacles (RVO), in simulation experiments, which show that the proposed policy achieves superior performance in navigation safety, while requiring minimal travel time and energy. A variant of our framework that automatically adapts its risk sensitivity is also demonstrated to improve ASV safety in highly congested environments.Comment: The 2024 IEEE International Conference on Robotics and Automation (ICRA 2024

Optimal resource allocation in HIV self-testing secondary distribution among Chinese MSM: data-driven integer programming models

Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several innovative implementations of HIVST have been developed and piloted among some HIV high-risk populations like men who have sex with men (MSM) to meet the global testing target. One innovative strategy is the secondary distribution of HIVST, in which individuals (defined as indexes) were given multiple testing kits for both self-use (i.e.self-testing) and distribution to other people in their MSM social network (defined as alters). Studies about secondary HIVST distribution have mainly concentrated on developing new intervention approaches to further increase the effectiveness of this relatively new strategy from the perspective of traditional public health discipline. There are many points of HIVST secondary distribution in which mathematical modelling can play an important role. In this study, we considered secondary HIVST kits distribution in a resource-constrained situation and proposed two data-driven integer linear programming models to maximize the overall economic benefits of secondary HIVST kits distribution based on our present implementation data from Chinese MSM. The objective function took expansion of normal alters and detection of positive and newly-tested 'alters' into account. Based on solutions from solvers, we developed greedy algorithms to find final solutions for our linear programming models. Results showed that our proposed data-driven approach could improve the total health economic benefit of HIVST secondary distribution. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'

Force-reversible chemical reaction at ambient temperature for designing toughened dynamic covalent polymer networks.

Force-reversible C-N bonds, resulting from the click chemistry reaction between triazolinedione (TAD) and indole derivatives, offer exciting opportunities for molecular-level engineering to design materials that respond to mechanical loads. Here, we displayed that TAD-indole adducts, acting as crosslink points in dry-state covalently crosslinked polymers, enable materials to display reversible stress-responsiveness in real time already at ambient temperature. Whereas the exergonic TAD-indole reaction results in the formation of bench-stable adducts, they were shown to dissociate at ambient temperature when embedded in a polymer network and subjected to a stretching force to recover the original products. Moreover, the nascent TAD moiety can spontaneously and immediately be recombined after dissociation with an indole reaction partners at ambient temperature, thus allowing for the adjustment of the polymer segment conformation and the maintenance of the network integrity by force-reversible behaviors. Overall, our strategy represents a general method to create toughened covalently crosslinked polymer materials with simultaneous enhancement of mechanical strength and ductility, which is quite challenging to achieve by conventional chemical methods

Deciphering a mitochondria-related signature to supervise prognosis and immunotherapy in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is a major public health problem in humans. The imbalance of mitochondrial function has been discovered to be closely related to the development of cancer recently. However, the role of mitochondrial-related genes in HCC remains unclear.MethodsThe RNA-sequencing profiles and patient information of 365 samples were derived from the Cancer Genome Atlas (TCGA) dataset. The mitochondria-related prognostic model was established by univariate Cox regression analysis and LASSO Cox regression analysis. We further determined the differences in immunity and drug sensitivity between low- and high-risk groups. Validation data were obtained from the International Cancer Genome Consortium (ICGC) dataset of patients with HCC. The protein and mRNA expression of six mitochondria-related genes in tissues and cell lines was verified by immunohistochemistry and qRT-PCR.ResultsThe six mitochondria-related gene signature was constructed for better prognosis forecasting and immunity, based on which patients were divided into high-risk and low-risk groups. The ROC curve, nomogram, and calibration curve exhibited admirable clinical predictive performance of the model. The risk score was associated with clinicopathological characteristics and proved to be an independent prognostic factor in patients with HCC. The above results were verified in the ICGC validation cohort. Compared with normal tissues and cell lines, the protein and mRNA expression of six mitochondria-related genes was upregulated in HCC tissues and cell lines.ConclusionThe signature could be an independent factor that supervises the immunotherapy response of HCC patients and possess vital guidance value for clinical diagnosis and treatment

A Machine Learning Model for Identifying Sexual Health Influencers to Promote the Secondary Distribution of HIV Self-Testing Among Gay, Bisexual, and Other Men Who Have Sex With Men in China: Quasi-Experimental Study

BACKGROUND: Sexual health influencers (SHIs) are individuals actively sharing sexual health information with their peers, and they play an important role in promoting HIV care services, including the secondary distribution of HIV self-testing (SD-HIVST). Previous studies used a 6-item empirical leadership scale to identify SHIs. However, this approach may be biased as it does not consider individuals' social networks. OBJECTIVE: This study used a quasi-experimental study design to evaluate how well a newly developed machine learning (ML) model identifies SHIs in promoting SD-HIVST compared to SHIs identified by a scale whose validity had been tested before. METHODS: We recruited participants from BlueD, the largest social networking app for gay men in China. Based on their responses to the baseline survey, the ML model and scale were used to identify SHIs, respectively. This study consisted of 2 rounds, differing in the upper limit of the number of HIVST kits and peer-referral links that SHIs could order and distribute (first round ≤5 and second round ≤10). Consented SHIs could order multiple HIV self-testing (HIVST) kits and generate personalized peer-referral links through a web-based platform managed by a partnered gay-friendly community-based organization. SHIs were encouraged to share additional kits and peer-referral links with their social contacts (defined as "alters"). SHIs would receive US $3 incentives when their corresponding alters uploaded valid photographic testing results to the same platform. Our primary outcomes included (1) the number of alters who conducted HIVST in each group and (2) the number of newly tested alters who conducted HIVST in each. We used negative binomial regression to examine group differences during the first round (February-June 2021), the second round (June-November 2021), and the combined first and second rounds, respectively. RESULTS: In January 2021, a total of 1828 men who have sex with men (MSM) completed the survey. Overall, 393 SHIs (scale=195 and ML model=198) agreed to participate in SD-HIVST. Among them, 229 SHIs (scale=116 and ML model=113) ordered HIVST on the web. Compared with the scale group, SHIs in the ML model group motivated more alters to conduct HIVST (mean difference [MD] 0.88, 95% CI 0.02-2.22; adjusted incidence risk ratio [aIRR] 1.77, 95% CI 1.07-2.95) when we combined the first and second rounds. Although the mean number of newly tested alters was slightly higher in the ML model group than in the scale group, the group difference was insignificant (MD 0.35, 95% CI -0.17 to -0.99; aIRR 1.49, 95% CI 0.74-3.02). CONCLUSIONS: Among Chinese MSM, SHIs identified by the ML model can motivate more individuals to conduct HIVST than those identified by the scale. Future research can focus on how to adapt the ML model to encourage newly tested individuals to conduct HIVST. TRIAL REGISTRATION: Chinese Clinical Trials Registry ChiCTR2000039632; https://www.chictr.org.cn/showprojEN.html?proj=63068. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12889-021-11817-2