This research is concerned with the problem of obstacle avoidance for the underactuated unmanned marine vessel under unknown environmental disturbance. A concise deep reinforcement learning obstacle ...avoidance (CDRLOA) algorithm is proposed with the powerful deep Q-networks architecture to overcome the usability issue caused by the complicated control law in the traditional analytic approach. Furthermore, a comprehensive reward function is specifically designed for obstacle avoidance, target approaching, speed modification, and attitude correction. Compared to the analytic methods, the proposed algorithm based on reinforcement learning shows notable advantages in utility and extendibility. With the same CDRLOA system, the targets and the constraints are highly customizable for various of special requirements. Extensive experiments conducted have demonstrated the effectiveness and conciseness of the proposed algorithm.
This paper focuses on the design of an adaptive second-order fast nonsingular terminal sliding mode control (ASOFNTSMC) scheme for the trajectory tracking of fully actuated autonomous underwater ...vehicles (AUVs) in the presence of dynamic uncertainties and time-varying external disturbances. First, a second-order fast nonsingular terminal sliding mode (SOFNTSM) manifold is designed to achieve a faster convergence rate than the existing second-order nonsingular terminal sliding mode (SONTSM) manifold. Then, by using this SOFNTSM manifold, an ASOFNTSMC scheme is developed for the fully actuated AUVs to track the desired trajectory. The designed SOFNTSM manifold yields local exponential convergence of the position and attitude tracking errors to zero, and the developed ASOFNTSMC scheme ensures that the error trajectories always move toward the SOFNTSM manifold and once they hit the manifold, remain on it in the presence of dynamic uncertainties and time-varying external disturbances. By deriving the expression of the bounding function of the system uncertainty and using adaptive technique to estimate the unknown parameters of the system uncertainty bounds, the ASOFNTSMC scheme does not require the prior knowledge of the upper bound of the system uncertainty. Meanwhile, through involving the discontinuous sign function into the time derivative of the control input, the ASOFNTSMC scheme eliminates the chattering without reducing the tracking precision. Compared with the existing adaptive SONTSM control (ASONTSMC) scheme, the proposed ASOFNTSMC scheme offers a faster convergence rate for the trajectory tracking control of fully actuated AUVs. Two comparative simulation cases performed respectively on two fully actuated AUVs demonstrate the superiority of the ASOFNTSMC scheme over the ASONTSMC scheme.
•This study reports COVID-19 vaccine effectiveness (VE) in real-world settings.•COVID-19 VE for fully and partially vaccinated individuals.•COVID-19 VE for healthcare workers, the elderly, and ...adults.•The effectiveness of different COVID-19 vaccine brands.
To estimate the coronavirus disease 2019 (COVID-19) vaccine effectiveness (VE) against concerned outcomes in real-world settings.
Studies reporting COVID-19 VE from August 6, 2020 to October 6, 2021 were included. The summary VE (with 95% confidence intervals (95% CI)) against disease related to COVID-19 was estimated. The results were presented in forest plots. Predefined subgroup analyses and sensitivity analyses were also performed.
A total of 51 records were included in this meta-analysis. In fully vaccinated populations, the VE against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, COVID-19-related hospitalization, admission to the intensive care unit, and death was 89.1% (95% CI 85.6–92.6%), 97.2% (95% CI 96.1–98.3%), 97.4% (95% CI 96.0–98.8%), and 99.0% (95% CI 98.5–99.6%), respectively. The VE against infection in the general population aged ≥16 years, the elderly, and healthcare workers was 86.1% (95% CI 77.8–94.4%), 83.8% (95% CI 77.1–90.6%), and 95.3% (95% CI 92.0–98.6%), respectively. For those fully vaccinated against infection, the observed effectiveness of the Pfizer-BioNTech vaccine was 91.2% and of the Moderna vaccine was 98.1%, while the effectiveness of the CoronaVac vaccine was found to be 65.7%.
The COVID-19 vaccines are highly protective against SARS-CoV-2-related diseases in real-world settings.
In this paper, we propose a wireless positioning method based on Deep Learning. To deal with the variant and unpredictable wireless signals, the positioning is casted in a four-layer Deep Neural ...Network (DNN) structure pre-trained by Stacked Denoising Autoencoder (SDA) that is capable of learning reliable features from a large set of noisy samples and avoids hand-engineering. Also, to maintain the temporal coherence, a Hidden Markov Model (HMM)-based fine localizer is introduced to smooth the initial positioning estimate obtained by the DNN-based coarse localizer. The data required for the experiments is collected from the real world in different periods to meet the actual environment. Experimental results indicate that the proposed system leads to substantial improvement on localization accuracy in coping with the turbulent wireless signals.
The low Coulombic efficiency of the lithium metal anode is recognized as the real bottleneck to practical high‐efficiency lithium metal batteries with limited Li excess. The grain size and ...microstructure of deposited lithium strongly influences the lithium plating/stripping efficiency. Here, a solubilizer‐mediated carbonate electrolyte that can realize grain coarsening of lithium deposits (>20 µm in width) with oriented columnar morphology, which is in sharp contrast with conventional nanoscale dendrite‐like lithium deposits in carbonate electrolytes, is reported. It exhibits improved Li Coulombic efficiency to 98.14% at a high capacity of 3 mAh cm−2 over 150 cycles, because the colossal lithium deposition with minimal tortuosity can maintain the bulk Li with continuous electron conducting pathway during the stripping process, thus enabling efficient Li utilization. Li/NMC811 full batteries, composed of thin Li anode (45 µm) and a high‐capacity NMC811 cathode (16.7 mg cm−2), can achieve at least 12 times longer lifespan (200 cycles).
A grain‐coarsening behavior of lithium deposits with oriented columnar morphology can be realized in a solubilizer‐mediated carbonate electrolyte. Nanowave‐structured solid electrolyte interphases derived from the Sn2+–NO3– coordination‐solvation structure promote a significant improvement in the lifespan (200 cycles) of Li/NMC811 full batteries (45 µm thin Li anode and 16.7 mg cm−2 NMC811 cathode).
Prediction of air pollutant levels plays an important role in the regulatory plans aimed at the control and reduction of airborne pollutants such as fine particulate matter (PM). Deterministic ...photochemical air quality models, which are commonly used for regulatory management and planning, are computationally intensive and also expensive for routine predictions. Compared to deterministic photochemical air quality models, data-driven statistical models are simpler and may be more accurate. In this paper, hidden Markov models (HMM) are used to forecast daily average PM2.5 concentrations 24h ahead. In conventional HMM applications, observation distributions emitted from certain hidden states are assumed as having Gaussian distributions. However, certain key meteorological factors and most PM2.5 precursors exhibit a non-Gaussian distribution in reality, which would degrade the HMM performance significantly. In order to address this problem, in this paper, HMMs with log-normal, Gamma and generalized extreme value (GEV) distributions are developed to predict PM2.5 concentration at Concord and Sacramento monitors in Northern California. Results show that HMM with non-Gaussian emission distributions is able to predict PM2.5 exceedance days correctly and reduces false alarms dramatically. Compared to HMM with Gaussian distributions, HMM with log-normal distributions can improve the true prediction rate (TPR) by 37.5% and reduce the false alarms by 78% at Concord. And HMM with GEV distribution can improve TPR by 150% and reduce false alarms by 63.62% at Sacramento Del Paso Manor. Comparisons between different distributions used in HMM show that the closer the distribution employed in HMM is to the observation sequence, the better the model prediction performance.
► A hidden Markov model with different non-Gaussian distributions is developed to match data characteristics. ► The method is applied to the prediction of PM2.5 exceedance days in Concord, CA and Sacramento, CA. ► Results show that the HMM can predict most exceedances correctly and reduce false alarms significantly.
Traditional medicine has been practiced for thousands of years in China and some Asian countries. Traditional Chinese Medicine (TCM) is characterized as multi-component and multiple targets in ...disease therapy, and it is a great challenge for elucidating the mechanisms of TCM.
Comprehensively summarize the application of metabolomics in biomarker discovery, stratification of TCM syndromes, and mechanism underlying TCM therapy on metabolic diseases.
This review systemically searched the publications with key words such as metabolomics, traditional Chinese medicine, metabolic diseases, obesity, cardiovascular disease, diabetes mellitus in “Title OR Abstract” in major databases including PubMed, the Web of Science, Google Scholar, Science Direct, CNKI from 2010 to 2019.
A total of 135 papers was searched and included in this review. An overview of articles indicated that metabolic characteristics may be a hallmark of different syndromes/models of metabolic diseases, which provides a new perspective for disease diagnosis and therapeutic optimization. Moreover, TCM treatment has significantly altered the metabolic perturbations associated with metabolic diseases, which may be an important mechanism for the therapeutic effect of TCM.
Until now, many metabolites and differential biomarkers related to the pathogenesis of metabolic diseases and TCM therapy have been discovered through metabolomics research. Unfortunately, the biological role and mechanism of disease-related metabolites were largely unclarified so far, which warrants further investigation.
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An updated role of astragaloside IV in heart failure Zang, Yibei; Wan, Jingjing; Zhang, Zhen ...
Biomedicine & pharmacotherapy,
June 2020, 2020-Jun, 2020-06-00, 20200601, 2020-06-01, Volume:
126
Journal Article
Peer reviewed
Open access
Multi-effect of AS- IV in HF.
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Heart failure (HF) has become a worldwide public health problem that seriously threatens human’s health. Due to "multi-target" effect, traditional ...Chinese medicine (TCM) has unique advantages in this field. Chinese herb-derived active components would provide valuable candidate compounds for new drug development. Astragaloside IV(AS-IV) is a main effective ingredient of Astragalus membranaceus, a commonly used Chinese patent medicine for the patients with chronic heart failure(CHF). Our aim is to review the recent progresses of AS-IV in HF, and provide potential evidence for its clinical application. Data showed that AS-IV could protect myocardial ischemia, regulate sarcoplasmic reticulum Ca2+ pump, promote angiogenesis, improve energy metabolism, inhibit cardiac hypertrophy and fibrosis, reduce myocardial cell apoptosis, etc, which are direct or indirect involved in the beneficial effects of AS-IV in rodents or cellular models of HF. AS-IV or its derivatives may act as a potential therapeutic drug in the clinical treatment of HF.
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