To examine the clinical characteristics of patients with asymptomatic novel coronavirus disease 2019 (COVID-19) and compare them with those of patients with mild disease. A retrospective cohort ...study. Multiple medical centers in Wuhan, Hubei, China. A total of 3,263 patients with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection between February 4, 2020, and April 15, 2020. Patient demographic characteristics, medical history, vital signs, and laboratory and chest computed tomography (CT) findings. A total of 3,173 and 90 patients with mild and moderate, and asymptomatic COVID-19, respectively, were included. A total of 575 (18.2%) symptomatic patients and 4 (4.4%) asymptomatic patients developed the severe illness. All asymptomatic patients recovered; no deaths were observed in this group. The median duration of viral shedding in asymptomatic patients was 17 (interquartile range, 9.25-25) days. Patients with higher levels of ultrasensitive C-reactive protein (odds ratio OR = 1.025, 95% confidence interval CI, 1.01-1.04), lower red blood cell volume distribution width (OR = 0.68, 95% CI 0.51-0.88), lower creatine kinase Isoenzyme(0.94, 0.89-0.98) levels, or lower lesion ratio (OR = 0.01, 95% CI 0.00-0.33) at admission were more likely than their counterparts to have asymptomatic disease.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The decline of system inertia due to the increasing displacement of synchronous units by renewable units has introduced a major challenge on the frequency dynamics management of a power system. This ...paper discusses how fast-response battery energy storages can be used to maintain the frequency dynamic security. Immediately following a generation loss, the injections of batteries are adjusted instantly to ensure minimum power imbalance in the system. This control strategy is included in a novel formulation of the frequency dynamics constrained unit commitment, in which the impact of wind uncertainty is dealt with using interval-based optimization. The reformulation-linearization technique is applied to reformulate the original nonlinear model as a mixed-integer linear programming problem. Case studies on a six-bus system and the modified RTS-79 system demonstrate that the proposed method guarantees frequency security while still preserving economy without curtailing wind generation.
Because of its cost-effectiveness, good uniformity, fast deposition rate and simplicity, electrophoretic deposition (EPD) has been widely used to deposit various metal oxide films for different ...applications. As with other coating fabrication processes, the deposition rate and film quality are two crucial criteria to evaluate the effectiveness and suitability of EPD. In this review, we summarize the parameters and discuss the dynamic processes influencing the deposition behavior of ionically charged metal oxide particles. Special focus is also given to the methods to improve the film quality. In addition, the application of EPD in the fabrication of solid oxide fuel cells (SOFCs) is summarized.
Display omitted
•The deposition behavior of metal oxides is influenced by parameters related to the suspension and the operation.•The increasing resistance is mainly resulted from the formation of an ion depletion zone.•The modulated electric fields appear to be particularly promising for the formation of dense green bodies.•This review also summarizes the application of EPD to the fabrication of SOFCs.
This paper proposes a multi-data driven hybrid learning method for weekly photovoltaic (PV) power scenario forecast that is coordinately driven by weather forecasts and historical PV power output ...data. Patterns of historical data and weather forecast information are simultaneously captured to ensure the quality of the generated scenarios. By combining bicubic interpolation and bidirectional long-short term memory (BiLSTM), a super resolution algorithm is first presented to enhance the time resolution of weather forecast data from three hours to one hour and increase the precision of weather forecasting. A weather process-based weekly PV power classification strategy is proposed to capture the coupling relationships between meteorological elements, continuous weather changes and weekly PV power. A gated recurrent unit (GRU)-convolutional neural network (CNN)-based scenario forecast method is developed to generate weekly PV power scenarios. Evaluation indices are presented to comprehensively assess the quality of the generated weekly scenarios of PV power. Finally, the PV power, weather observation and weather forecast data collected from five PV plants located in Northeast Asia are used to verify the effectiveness and correctness of the proposed method.
This paper proposes a probabilistic power flow analysis technique based on the stochastic response surface method. The probability distributions and statistics of power flow responses can be ...accurately and efficiently estimated by the proposed method without using series expansions such as the Gram-Charlier, Cornish-Fisher, or Edgeworth series. The stochastic continuous input variables following normal distributions such as loads or non-normal distributions such as photovoltaic generation and wind power and their multiple correlations can be easily modeled. The correctness, effectiveness and adaptability of the proposed method are demonstrated by comparing the probabilistic power flow analysis results of the IEEE 14-bus and 57-bus standard test systems obtained from the proposed method, the point estimate method, and the Monte Carlo simulation method.
Eukaryotic cells are stimulated by external pressure such as that derived from heat shock, oxidative stress, nutrient deficiencies, or infections, which induce the formation of stress granules (SGs) ...that facilitates cellular adaptation to environmental pressures. As aggregated products of the translation initiation complex in the cytoplasm, SGs play important roles in cell gene expression and homeostasis. Infection induces SGs formation. Specifically, a pathogen that invades a host cell leverages the host cell translation machinery to complete the pathogen life cycle. In response, the host cell suspends translation, which leads to SGs formation, to resist pathogen invasion. This article reviews the production and function of SGs, the interaction between SGs and pathogens, and the relationship between SGs and pathogen-induced innate immunity to provide directions for further research into anti-infection and anti-inflammatory disease strategies.
H2O2 as a well‐known efficient oxidant is widely used in the chemical industry mainly because of its homolytic cleavage into .OH (stronger oxidant), but this reaction always competes with O2 ...generation resulting in H2O2 waste. Here, we fabricate heterogeneous Fenton‐type Fe‐based catalysts containing Fe‐Nx sites and Fe/Fe3C nanoparticles as a model to study this competition. Fe‐Nx in the low spin state provides the active site for .OH generation. Fe/Fe3C, in particular Fe3C, promotes Fe‐Nx sites for the homolytic cleavages of H2O2 into .OH, but Fe/Fe3C nanoparticles (Fe0 as the main component) with more electrons are prone to the undesired O2 generation. With a catalyst benefiting from finely tuned active sites, 18 % conversion rate for the selective oxidation of methane was achieved with about 96 % selectivity for liquid oxygenates (formic acid selectivity over 90 %). Importantly, O2 generation was suppressed 68 %. This work provides guidance for the efficient utilization of H2O2 in the chemical industry.
In a heterogeneous Fenton‐type FeNx/C catalyst, Fe‐Nx sites and graphene‐encapsulated Fe/Fe3C nanoparticles promote the efficient generation of hydroxyl radicals from H2O2 for the highly selective oxidation of methane to formic acid. The reaction mechanism at the active sites has been studied.
Existing deep learning-based remote sensing images semantic segmentation methods require large-scale labeled datasets. However, the annotation of segmentation datasets is often too time-consuming and ...expensive. To ease the burden of data annotation, self-supervised representation learning methods have emerged recently. However, the semantic segmentation methods need to learn both high-level and low-level features, but most of the existing self-supervised representation learning methods usually focus on one level, which affects the performance of semantic segmentation for remote sensing images. In order to solve this problem, we propose a self-supervised multitask representation learning method to capture effective visual representations of remote sensing images. We design three different pretext tasks and a triplet Siamese network to learn the high-level and low-level image features at the same time. The network can be trained without any labeled data, and the trained model can be fine-tuned with the annotated segmentation dataset. We conduct experiments on Potsdam, Vaihingen dataset, and cloud/snow detection dataset Levir_CS to verify the effectiveness of our methods. Experimental results show that our proposed method can effectively reduce the demand of labeled datasets and improve the performance of remote sensing semantic segmentation. Compared with the recent state-of-the-art self-supervised representation learning methods and the mostly used initialization methods (such as random initialization and ImageNet pretraining), our proposed method has achieved the best results in most experiments, especially in the case of few training data. With only 10% to 50% labeled data, our method can achieve the comparable performance compared with random initialization. Codes are available at https://github.com/flyakon/SSLRemoteSensing .
Machine learning‐based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local ...raindrop size distributions, deep learning (DL) can establish an effective mapping from three‐dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL‐based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities.
Plain Language Summary
Ground radars can provide continuous spatial observations over large areas with high spatiotemporal resolutions, so they form the infrastructure for precipitation monitoring and observation in many countries. Recently, deep learning (DL) techniques have shown great potential for use in polarimetric radar‐based precipitation estimates. Nevertheless, the black‐box and turn‐key characteristics of DL models make it difficult for researchers to understand the model decision‐making process and cast doubt on the reliability of the model results. This study introduces a physically explainable polarization radar‐based quantitative precipitation estimation (QPE) system built on DL technology that can explain the causes of the precipitation estimates provided by deep learning models under different rainfall amounts. An experiment indicates that our model achieves better estimates than the conventional methods. Furthermore, the explainability methodology allows for visualization of the microphysical precipitation information. Being the initial attempt to apply explainability learning in the QPE domain, the explainability results may offer valuable guidance for rainfall estimation.
Key Points
A polarimetric radar‐based rainfall estimation system is developed using deep neural networks
The deep learning‐based rainfall estimates generally outperform products derived from traditional parametric relations
The proposed deep learning interpretation method can provide physical and statistical explanations of the model decision‐making process
PDF
