Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making

There are still some problems need to be solved though there are a lot of achievements in the fields of automatic driving. One of those problems is the difficulty of designing a car-following decision-making system for complex traffic conditions. In recent years, reinforcement learning shows the potential in solving sequential decision optimization problems. In this article, we establish the reward function R of each driver data based on the inverse reinforcement learning algorithm, and r visualization is carried out, and then driving characteristics and following strategies are analyzed. At last, we show the efficiency of the proposed method by simulation in a highway environment

Patient-specific fetal radiation dosimetry for pregnant patients undergoing abdominal and pelvic CT imaging

Background: Accurate estimation of fetal radiation dose is crucial for risk-benefit analysis of radiological imaging, while the radiation dosimetry studies based on individual pregnant patient are highly desired. Purpose: To use Monte Carlo calculations for estimation of fetal radiation dose from abdominal and pelvic computed tomography (CT) examinations for a population of patients with a range of variations in patients’ anatomy, abdominal circumference, gestational age (GA), fetal depth (FD), and fetal development. Methods: Forty-four patient-specific pregnant female models were constructed based on CT imaging data of pregnant patients, with gestational ages ranging from 8 to 35 weeks. The simulation of abdominal and pelvic helical CT examinations was performed on three validated commercial scanner systems to calculate organ-level fetal radiation dose. Results: The absorbed radiation dose to the fetus ranged between 0.97 and 2.24 mGy, with an average of 1.63 ± 0.33 mGy. The CTDIvol-normalized fetal dose ranged between 0.56 and 1.30, with an average of 0.94 ± 0.25. The normalized fetal organ dose showed significant correlations with gestational age, maternal abdominal circumference (MAC), and fetal depth. The use of ATCM technique increased the fetal radiation dose in some patients. Conclusion: A technique enabling the calculation of organ-level radiation dose to the fetus was developed from models of actual anatomy representing a range of gestational age, maternal size, and fetal position. The developed maternal and fetal models provide a basis for reliable and accurate radiation dose estimation to fetal organs.</p

Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making

There are still some problems need to be solved though there are a lot of achievements in the fields of automatic driving. One of those problems is the difficulty of designing a car-following decision-making system for complex traffic conditions. In recent years, reinforcement learning shows the potential in solving sequential decision optimization problems. In this article, we establish the reward function R of each driver data based on the inverse reinforcement learning algorithm, and r visualization is carried out, and then driving characteristics and following strategies are analyzed. At last, we show the efficiency of the proposed method by simulation in a highway environment

Synthesis and Biological Evaluation of Novel Allobetulon/Allobetulin–Nucleoside Conjugates as AntitumorAgents

Allobetulin is structurally similar tobetulinic acid, inducing the apoptosis of cancer cells with low toxicity. However, both of them exhibited weak antiproliferation against several tumor cell lines. Therefore, the new series of allobetulon/allobetulin–nucleoside conjugates 9a–10i were designed and synthesized for potency improvement. Compounds 9b, 9e, 10a, and 10d showed promising antiproliferative activity toward six tested cell lines, compared to zidovudine, cisplatin, and oxaliplatin based on their antitumor activity results. Among them, compound 10d exhibited much more potent antiproliferative activity against SMMC-7721, HepG2, MNK-45, SW620, and A549 human cancer cell lines than cisplatin and oxaliplatin. In the preliminary study for the mechanism of action, compound 10d induced cell apoptosis and autophagy in SMMC cells, resulting in antiproliferation and G0/G1 cell cycle arrest by regulating protein expression levels of Bax, Bcl-2, and LC3. Consequently, the nucleoside-conjugated allobetulin (10d) evidenced that nucleoside substitution was a viable strategy to improve allobetulin/allobetulon’s antitumor activity based on our present study

microRNA-144/451 decreases dendritic cell bioactivity via targeting interferon-regulatory factor 5 to limit DSS-induced colitis

The microRNAs miR-144/451 are highly conserved miRNA that is strongly induced during erythropoiesis. Despite the biological functions of miR-144/451 have been extensively studied in erythropoiesis and tumorigenesis, few studies have been conducted in immune responses. In this study, we showed that miR-144/451-/- DCs exhibit increased activation. Mechanistically, the miR-144 directly targets the 3`-UTR of IRF5 and represses the expression of IRF5 in DCs. Ectopic expression of miR-144/451 by lentiviruses downregulates the levels of IRF5 and suppresses DCs function. In addition, knockdown of IRF5 by shRNA significantly inhibits activities of the miR-144/451-/- DCs. Expression of miR144/451 was decreased in DCs from both patients with IBD and mice with DSS-colitis compared with controls. Human PBMC derived DCs were downregulated expression of miR144/451 after LPS stimulation. In the DSS-induced colitis mice model, we showed that ablation of the miR-144/451 gene causes severe colitis, and their DCs from both periphery and MLN expressed higher co-stimulatory molecules and pro-inflammatory cytokines than wild-type mice. In addition, DCs isolated from miR-144/451-/- mice transfusion exacerbates mice colitis. In the bone marrow transplanted chimeric mice model, we show that miR-144/451-/- bone marrow transplantation deteriorated DSS-induced colitis. At last, we treat the mice with miR-144/451 delivered by chitosan nanoparticles revealing protective effects in DSS-induced colitis mice. Thus, our results reveal a novel miR144/451-IRF5 pathway in DCs that protects experimental colitis. The manipulation of miR-144/451 expression and DCs activation in IBD patients may be a novel therapeutic approach for the treatment of inflammatory diseases

Situational Assessments Based on Uncertainty-Risk Awareness in Complex Traffic Scenarios

Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor failure or communication loss. The objective of this study is to assess traffic situations considering uncertainty-risks, including environment predicting uncertainty. According to the stochastic environment model, collision probabilities between multiple vehicles are estimated based on integrated trajectory prediction under uncertainty, which combines the physics- and maneuver-based trajectory prediction models for accurate prediction results in the long term. The SA method considers the probabilities of collision at different predicting points, the masses, and relative speeds between the possible colliding objects. In addition, risks beyond the prediction horizon are considered with the proposition of infinite risk assessments (IRAs). This method is applied and proved to assess risks regarding unexpected obstacles in traffic, sensor failure or communication loss, and imperfect detections with different sensing accuracies of the environment. The results indicate that the SA method could evaluate traffic risks under uncertainty in the dynamic traffic environment. This could help IAVs’ plan motion trajectories and make high-level decisions in uncertain environments

Research on decision-making of autonomous vehicle following based on reinforcement learning method

Purpose: Over the past decades, there has been significant research effort dedicated to the development of autonomous vehicles. The decision-making system, which is responsible for driving safety, is one of the most important technologies for autonomous vehicles. The purpose of this study is the use of an intensive learning method combined with car-following data by a driving simulator to obtain an explanatory learning following algorithm and establish an anthropomorphic car-following model. Design/methodology/approach: This paper proposed car-following method based on reinforcement learning for autonomous vehicles decision-making. An approximator is used to approximate the value function by determining state space, action space and state transition relationship. A gradient descent method is used to solve the parameter. Findings: The effect of car-following on certain driving styles is initially achieved through the simulation of step conditions. The effect of car-following initially proves that the reinforcement learning system is more adaptive to car following and that it has certain explanatory and stability based on the explicit calculation of R. Originality/value: The simulation results show that the car-following method based on reinforcement learning for autonomous vehicle decision-making realizes reliable car-following decision-making and has the advantages of simple sample, small amount of data, simple algorithm and good robustness

Screening and Enzymatic-producing Study of Chitin Deacetylase Producing Bacteria-Lysinibacillus sp.

In order to biodegrade natural chitin, in this study, chitin was used as the sole carbon source, obtain chitin deacetylase strains which could produce chitosan from natural chitin by biological method. Results showed that, the strains were identified by color plate screening and enzyme activity rescreening, morphological characteristics and 16S rRNA sequence analysis. A bacterium X4 with higher activity of chitin deacetylase was obtained, and was identified as Lysinibacillus sp.. The fermentation culture of the strain showed that the activity of chitin deacetylase produced by X4 could reached 8.210 U/mL, and the deacetylation degree of chitin was 8.642%. The results was valuable for the green biological utilization of chitin