Effect of Different Combustion Modes on the Performance of Hydrogen Internal Combustion Engines under Low Load

Detailed hydrogen–air chemical reaction mechanisms were coupled with the three-dimensional grids of an experimental hydrogen internal combustion engine (HICE) to establish a computational fluid dynamics (CFD) combustion model based on the CONVN1 -https://media.proquest.com/media/hms/PFT/1/iyX6N?_a=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%3D%3D&_s=XxDsfbWeNCPMojxxWroCr%2FH0Al4%3D ERGE software. The effects of different combustion modes on the combustion and emission characteristics of HICE under low load were studied. The simulation results showed that, with the increase in excess hydrogen, the equivalent combustion and excessive hydrogen combustion modes with medium-cooled exhaust gas recirculation (EGR) dilution could improve the intensity of the in-cylinder combustion of HICE, increase the peak values of pressure and temperature in the cylinder, and then improve the indicated thermal efficiency of HICE under low load. However, larger excessive hydrogen combustion could weaken the improvement in performance;therefore, the performance of HICE could be comprehensively improved by the adoption of excessive hydrogen combustion with a fuel–air ratio below 1.2 under low load. The obtained conclusions indicate the research disadvantages in the power and emission performances of HICE under low load, and they are of great significance for the performance optimization of HICE. Furthermore, a control strategy was proposed to improve the stability of HICE under low load.

Prevalence and risk factors of anxiety, depression and sleeping disturbances in china during the COVID-19 outbreak: a web-based cross-sectional study.

The rapid development of the coronavirus disease 2019 (COVID-19) outbreak has brought great harm to physical and mental health of the public. This study aims to investigate the psychological status and sleep quality of the Chinese public during the outbreak of the COVID-19 and its related factors. The survey was conducted from February 17th to February 26th, 2020 in southwestern China. The snowball sampling method was used to invite subjects. Demographic data were collected, and mental status and sleep quality were assessed by the Generalized Anxiety Disorder-7 Scale (GAD-7), the Patient Health Questionnaire-9 (PHQ-9), and the Pittsburgh Sleep Quality Index (PSQI). Descriptive, univariate, and correlation analyses were used to investigate risk factors for psychological status and sleep patterns. A total of 1509 adults (713 males and 796 females) were enrolled in this study. The overall prevalence of anxiety, depression, and decreased sleep quality were 22.3%, 32.2% and 35.4%, respectively. Compared with females, male population has witnessed a higher prevalence of anxiety symptoms (25.1% vs 20.4%, P= 0.007) and depressive symptoms (34.6% vs 30.0%, P= 0.027). In addition, age, marital status, living situation, involvement in anti-pandemic work, basic health status and work status were significant risk factors for anxiety or depression (P< 0.05). During the COVID-19 outbreak, psychological problems and sleep disorders were prevalent among the Chinese public. More attention should be paid to males, the elderly, the solitary, the unemployed, front-line workers in pandemic prevention, and patients with chronic diseases.

ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models (preprint)

Counterfactual examples (CFs) are one of the most popular methods for attaching post-hoc explanations to machine learning (ML) models. However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood, thus they are hard to generalize for complex models and inefficient for large datasets. This work aims to overcome these limitations and introduces ReLAX, a model-agnostic algorithm to generate optimal counterfactual explanations. Specifically, we formulate the problem of crafting CFs as a sequential decision-making task and then find the optimal CFs via deep reinforcement learning (DRL) with discrete-continuous hybrid action space. Extensive experiments conducted on several tabular datasets have shown that ReLAX outperforms existing CF generation baselines, as it produces sparser counterfactuals, is more scalable to complex target models to explain, and generalizes to both classification and regression tasks. Finally, to demonstrate the usefulness of our method in a real-world use case, we leverage CFs generated by ReLAX to suggest actions that a country should take to reduce the risk of mortality due to COVID-19. Interestingly enough, the actions recommended by our method correspond to the strategies that many countries have actually implemented to counter the COVID-19 pandemic.

Exploiting Shared Knowledge from Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation (preprint)

The novel Coronavirus disease (COVID-19) is a highly contagious virus and has spread all over the world, posing an extremely serious threat to all countries. Automatic lung infection segmentation from computed tomography (CT) plays an important role in the quantitative analysis of COVID-19. However, the major challenge lies in the inadequacy of annotated COVID-19 datasets. Currently, there are several public non-COVID lung lesion segmentation datasets, providing the potential for generalizing useful information to the related COVID-19 segmentation task. In this paper, we propose a novel relation-driven collaborative learning model to exploit shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation. The model consists of a general encoder to capture general lung lesion features based on multiple non-COVID lesions, and a target encoder to focus on task-specific features based on COVID-19 infections. Features extracted from the two parallel encoders are concatenated for the subsequent decoder part. We develop a collaborative learning scheme to regularize feature-level relation consistency of given input and encourage the model to learn more general and discriminative representation of COVID-19 infections. Extensive experiments demonstrate that trained with limited COVID-19 data, exploiting shared knowledge from non-COVID lesions can further improve state-of-the-art performance with up to 3.0% in dice similarity coefficient and 4.2% in normalized surface dice. Our proposed method promotes new insights into annotation-efficient deep learning for COVID-19 infection segmentation and illustrates strong potential for real-world applications in the global fight against COVID-19 in the absence of sufficient high-quality annotations.

Rapid Structure-Based Screening Informs Potential Agents for Coronavirus Disease (COVID-19) Outbreak

Coronavirus Disease 2019 (COVID-19), caused by the novel coronavirus, has spread rapidly across China. Consequently, there is an urgent need to sort and develop novel agents for the prevention and treatment of viral infections. A rapid structure-based virtual screening is used for the evaluation of current commercial drugs, with structures of human angiotensin converting enzyme II (ACE2), and viral main protease, spike, envelope, membrane and nucleocapsid proteins. Our results reveal that the reported drugs Arbidol, Chloroquine and Remdesivir may hinder the entry and release of virions through the bindings with ACE2, spike and envelope proteins. Due to the similar binding patterns, NHC (β-d-N4-hydroxycytidine) and Triazavirin are also in prospects for clinical use. Main protease (3CLpro) is likely to be a feasible target of drug design. The screening results to target 3CL-pro reveal that Mitoguazone, Metformin, Biguanide Hydrochloride, Gallic acid, Caffeic acid, Sulfaguanidine and Acetylcysteine seem be possible inhibitors and have potential application in the clinical therapy of COVID-19.