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Traffic flowprediction has always been the focus of research in the field of Intelligent Transportation Systems, which is conducive to the more reasonable allocation of basic transportation resources and formulation of transportation policies. The spread of COVID-19 has seriously affected the normal order in the transportation sector. With the increase in the number of infected people and the government's anti-epidemic policy, human outgoing activities have gradually decreased, resulting in increasingly obvious discreteness and irregularities in traffic flow data. This article proposes a deep-space time traffic flow prediction model based on discrete wavelet transform (DSTM-DWT) to overcome the highly discrete and irregular nature of the new crown epidemic. First, DSTM-DWT decomposes traffic flow into discrete attributes, such as flow trend, discrete amplitude, and discrete baseline. Second, we design the spatial relationship of the transportation network as a graph and integrate the new crown pneumonia epidemic data into the characteristics of each transportation node. Then, we use the graph convolutional network to calculate the spatial correlation of each node, and the temporal convolutional network to calculate the temporal correlation of the data. In order to solve the problem of high discreteness of traffic flow data during the epidemic, this article proposes a graph memory network (GMN), which is used to convert discrete magnitudes separated by discrete wavelet transform into highdimensional discrete features. Finally, use DWT to segment the predicted traffic data, and then perform the inverse discrete wavelet transform between the newly segmented traffic trend and discrete baseline and the discrete model predicted by GMN to obtain the final traffic flow prediction result. In simulation experiments, this work was compared with the existing advanced baselines to verify the superiority of DSTM-DWT.
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With the rapid development of information technology and the spread of Corona Virus Disease 2019 (COVID-19), the government and urban managers are looking for ways to use technology to make the city smarter and safer. Intelligent transportation can play a very important role in the joint prevention. This work expects to explore the building information modeling (BIM) big data (BD) processing method of digital twins (DTs) of Smart City, thus speeding up the construction of Smart City and improve the accuracy of data processing. During construction, DTs build the same digital copy of the smart city. On this basis, BIM designs the building's keel and structure, optimizing various resources and configurations of the building. Regarding the fast data growth in smart cities, a complex data fusion and efficient learning algorithm, namely Multi-Graphics Processing Unit (GPU), is proposed to process the multi-dimensional and complex BD based on the compositive rough set model. The Bayesian network solves the multi-label classification. Each label is regarded as a Bayesian network node. Then, the structural learning approach is adopted to learn the label Bayesian network's structure from data. On the P53-old and the P53-new datasets, the running time of Multi-GPU decreases as the number of GPUs increases, approaching the ideal linear speedup ratio. With the continuous increase of K value, the deterministic information input into the tag BN will be reduced, thus reducing the classification accuracy. When K = 3, MLBN can provide the best data analysis performance. On genbase dataset, the accuracy of MLBN is 0.982 +/- 0.013. Through experiments, the BIM BD processing algorithm based on Bayesian Network Structural Learning (BNSL) helps decision-makers use complex data in smart cities efficiently.
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As the COVID-19 continues threatening public health worldwide, when to vaccinate the booster shots becomes the hot topic. In this paper, based on the characteristics of COVID-19 and its vaccine, an SAIR model associated with temporary immunity is proposed to study the effect on epidemic situation. Second, we theoretically analyze the existence and stability of equilibrium and the system undergoes Hopf bifurcation when delay passes through some critical values. Third, we study the dynamic properties of Hopf bifurcation and derive the normal form of Hopf bifurcation to determine the stability and direction of bifurcating periodic solutions. After that, numerical simulations are carried out to demonstrate the application of the theoretical results. Particularly, in order to ensure the validity, statistical analysis of data is conducted to determine the values for model parameters. Next, we study the impact of the infection rates on booster vaccination time to simulate the mutants, and the results are consistent with the facts. Finally, we predict the mean time of completing a round of vaccination worldwide with the help fitting and put forward some suggestions by comparing with the critical time of booster vaccination.
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To test for arbitrage opportunities and market efficiency in the Hong Kong money, stock, and real estate markets, we find that the money market stochastically dominates both the stock and real estate markets. Furthermore, the real estate market dominates the stock market, the money market dominates nearly all the efficient frontier portfolios, none of the efficient portfolios dominates the money market, and the money market also dominates the equal-weighting portfolio. This infers that in some cases investors could achieve higher expected ex-ante utility by investing in an individual asset rather than a portfolio. Our conclusions drawn from the pre-COVID-19 period are the same as those drawn from the entire period and the conclusions drawn from the COVID-19 period are the same as those drawn from the entire period except that the money market only stochastically dominates some of the efficient frontier portfolios. Our findings question diversification benefits in the Hong Kong capital market during our sample period, including both the pre-COVID-19 and COVID-19 periods. © 2022 Taylor & Francis Group, LLC.
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Objective: To identify the moderating effects of cognitive reappraisal (CR) and expressive suppression (ES) on the relationship between posttraumatic stress (PTS) symptoms and posttraumatic growth (PTG) in university students. Methods: The survey included 1 987 Chinese university students who completed questionnaires on PTS symptoms in February 2020, with three follow-up surveys at two-month intervals until August 2020. We assessed CR and ES at February 2020 and PTG at August 2020. Growth mixture modeling was used to classify the PTS symptom trajectories. Multinomial logistic regression was used to recognize the predictors of class membership. The relationships among PTS symptoms, CR, ES, and PTG were examined using multi-group path analysis. Results: Sex, SARS-CoV-2 infection of a family member or friend, number of siblings, CR, and ES were significantly associated with PTS symptoms. Three latent classes were identified: 'Increasing PTS' (n=205, 10.0%) who had rapid deterioration of PTS symptoms, 'Moderate PTS' (n=149, 8.0%) who had a high level of PTS symptoms at the beginning and slightly increasing, and 'Persistent Minimal PTS' (n=1 633, 82.0%), who had slow resolution of PTS symptoms over time. Male, SARS-CoV-2 infection of a family member or friend, and having a lower CR and a higher ES, were more likely to have 'Increasing PTS'. PTS at February 2020 predicted PTG only in 'Increasing PTS' class, and both CR and ES had moderating effects on the conversion between them. Conclusions: Most students recovered from posttraumatic stress of COVID-19 pandemic, but a small proportion expeienced increasing PTS symptoms, and those with this condition may benefit from emotional regulation intervention.
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The coronavirus disease 2019 (COVID-19) has put enormous pressure on the global supply chain. This work aims to solve supply chain interruption caused by public health emergencies in real life through the resilient supply chain based on digital twins (DTs). The research example used here is the disruption of the supply chain of N95 medical masks under the COVID-19 epidemic. First, the resilient supply chain's emergency decision cost and profit model is established under the manufacturer-supplier shared mode. The supply chain of M company of N95 medical masks in Hubei under the COVID-19 pandemic is selected to discuss the cost of emergency decision-making in the resilient supply chain. Moreover, a product supply chain model is built, including H suppliers, J manufacturers, K distributors, and L retailers. Supply failures result in lower supplier capacity ratios. Accordingly, the supply chain will adopt emergency strategies to reduce operating costs and increase profits. Activating alternative suppliers and distributors can mitigate the loss caused by partial supply chain disruption in emergencies. The elasticity of supply chains based on DTs discussed here is of significant value in helping the automation of critical links of the supply chain. The resilient supply chain combined with the capacity recovery strategy can significantly improve the traditional supply chain's response to supply disruption events.
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A second wave of coronavirus disease 2019 (COVID-19) infections emerged in Beijing in summer 2020, which provided an opportunity to explore the response of air pollution to reduced human activity. Proton-transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) coupled with positive matrix factorization (PMF) source apportionment were applied to evaluate the pollution pattern and capture the detailed dynamic emission characteristics of volatile organic compounds (VOCs) during the representative period, with the occurrence of O-3 pollution episodes and the Beijing resurgence of COVID-19. The level of anthropogenic VOC was lower than during the same period in previous years due to the pandemic and emission reduction measures. More than two thirds of the days during the observation period were identified as high-O-3 days and VOCs exhibited higher mixing ratios and faster consumption rates in the daytime on high-O-3 days. The identified VOC emission sources and the corresponding contributions during the whole observation period included: vehicle + fuel (12.41 +/- 9.43%), industrial process (9.40 +/- 8.65%), solvent usage (19.58 +/- 13.46%), biogenic (6.03 +/- 5.40%), background + long-lived (5.62 +/- 11.37%), and two groups of oxygenated VOC (OVOC) factors (primary emission and secondary formation, 26.14 +/- 15.20% and 20.84 +/- 14.0%, respectively). Refined dynamic source apportionment results show that the 'stay at home' tendency led to decreased emission (-34.47 +/- 1.90%) and a weakened morning peak of vehicle + fuel during the Beijing resurgence. However, a growing emission of primary OVOCs (+51.10 +/- 8.28%) with similar diurnal variation was observed in the new outbreak and afterwards, which might be related to the enhanced usage of products intended to clean and disinfect. The present study illustrated that more stringent VOC reduction measures towards pandemic products should be carried out to achieve the balanced emission abatement of NO (x) and VOC when adhering to regular epidemic prevention and control measures.
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“Without the health of the whole people, there will be no comprehensive well-off society” that has formed a social consensus. The effective interaction between the “COVID-19” prevention and control and the “Healthy China” strategy has become the key to the construction of a community health information management big data platform. The article analyzes through literature, logical analysis, and other research methods in China. Existing are the main problems in community health information management. On the basis, based on the current situation, put forward the idea of constructing big data of health information management, design and plan the overall framework of health information management platform, make full use of and integrate the advantages of technical resources, serve the health of community residents, improve the health awareness of community residents, and protect and satisfy the community. Residents pursue reasonable needs for health and improve the health of community residents. Furthermore, it provides ideas for the construction of a community health information management big data platform and also provides a theoretical basis for the later development and promotion of platform functions. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Predicting the population density in certain key areas of the city is of great importance. It helps us rationally deploy urban resources, initiate regional emergency plans, reduce the spread risk of infectious diseases such as Covid-19, predict travel needs of individuals, and build intelligent cities. Although current researches focus on using the data of point-of-interest (POI) and clustering belonged to unsupervised learning to predict the population density of certain neighboring cities to define metropolitan areas, there is almost no discussion about using spatial-temporal models to predict the population density in certain key areas of a city without using actual regional images. We 997 key areas in Beijing and their regional connections into a graph structure and propose a model called Word Embedded Spatial-temporal Graph Convolutional Network (WE-STGCN). WE-STGCN is mainly composed of three parts, which are the Spatial Convolution Layer, the Temporal Convolution Layer, and the Feature Component. Based on the data set provided by the Data Fountain platform, we evaluate the model and compare it with some typical models. Experimental results show that the Spatial Convolution Layer can merge features of the nodes and edges to reflect the spatial correlation, the Temporal Convolution Layer can extract the temporal dependence, and the Feature Component can enhance the importance of other attributes that affect the population density of the area. In general, the WE-STGCN is better than baselines and can complete the work of predicting population density in key areas. © 2021 SPIE.
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The COVID-19 problem has not gone away with the passing of the seasons. Even though most countries have achieved remarkable results in fighting against epidemic diseases and preventing and controlling viruses, the general public is still far from understanding the new crown virus and lacks imagination on its transmission law. In this paper, we propose MeetDurian: a cross-platform mobile application that exploits a location-based game to improve users' hygiene habits and reduce virus dispersal. We present its main features, its architecture, and its core technologies. Finally, we report a set of experiments that prove the acceptability and usability of MeetDurian. An illustrative demo of the mobile app features is shown in the following video: https://youtu.be/Vqg7nFDQuOU. © 2021 IEEE.
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ASIC Design Principle (ASICDP) is a compulsory course for undergraduate majors in microelectronics and integrated circuits, and the focus of this paper is the teaching methods of online theoretical teaching and offline experimental teaching of this course. As is well known, in order to prevent and control COVID-19, the use of online platforms to carry out online teaching has attracted worldwide attention. In this paper, the teaching strategy "Online-MOOC + Offline Inexpensive FPGA Board"in ASICDP in the Spring 2020 semester is demonstrated, in where MOOC means Massive Open Online Course. The theoretical teaching content of ASICDP is entirely replicated from Hardware Acceleration Design Methodology (HADM) released by the present authors on "China University MOOC,"the largest MOOC platform in China. Meanwhile, with the support of the "Xilinx & Ministry of Education University-Industry Collaborative Education Program,"an FPGA development board called the "Spartan Edge Accelerator Board"(SEA Board) designed by the authors was used in the experimental teaching of the ASICDP. This method can be used to establish the linkage between online courses and offline experiments, and cultivate students' practical VLSI design and FPGA prototype verification skills. It is believed that for educators that want to improve courses related to ASIC design and FPGA prototype verification, Online-MOOC + Offline-Inexpensive FPGA Board is an effective method with lower cost that is easily promotable and replicated. © 2021 ACM.
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In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI's research in the medical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Among them, the hot deep learning field has shown greater potential in applications such as disease prediction and drug response prediction. From the initial logistic regression model to the machine learning model, and then to the deep learning model today, the accuracy of medical disease prediction has been continuously improved, and the performance in all aspects has also been significantly improved. This article introduces some basic deep learning frameworks and some common diseases, and summarizes the deep learning prediction methods corresponding to different diseases. Point out a series of problems in the current disease prediction, and make a prospect for the future development. It aims to clarify the effectiveness of deep learning in disease prediction, and demonstrates the high correlation between deep learning and the medical field in future development. The unique feature extraction methods of deep learning methods can still play an important role in future medical research. © 2021 Tech Science Press. All rights reserved.
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The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an unprecedented public health crisis. There are no approved vaccines or therapeutics for treating COVID-19. Here we report a humanized monoclonal antibody, H014, that efficiently neutralizes SARS-CoV-2 and SARS-CoV pseudoviruses as well as authentic SARS-CoV-2 at nanomolar concentrations by engaging the spike (S) receptor binding domain (RBD). H014 administration reduced SARS-CoV-2 titers in infected lungs and prevented pulmonary pathology in a human angiotensin-converting enzyme 2 mouse model. Cryo-electron microscopy characterization of the SARS-CoV-2 S trimer in complex with the H014 Fab fragment unveiled a previously uncharacterized conformational epitope, which was only accessible when the RBD was in an open conformation. Biochemical, cellular, virological, and structural studies demonstrated that H014 prevents attachment of SARS-CoV-2 to its host cell receptors. Epitope analysis of available neutralizing antibodies against SARS-CoV and SARS-CoV-2 uncovered broad cross-protective epitopes. Our results highlight a key role for antibody-based therapeutic interventions in the treatment of COVID-19.
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Coronavirus disease 2019 (COVID-19) has caused a global pandemic associated with substantial morbidity and mortality. Nasopharyngeal swabs and sputum samples are generally collected for serial viral load screening of respiratory contagions, but temporal profiles of these samples are not completely clear in patients with COVID-19. We performed an observational cohort study at Renmin Hospital of Wuhan University, which involved 31 patients with confirmed COVID-19 with or without underlying diseases. We obtained samples from each patient, and serial viral load was measured by real-time quantitative polymerase chain reaction. We found that the viral load in the sputum was inclined to be higher than samples obtained from the nasopharyngeal swab at disease presentation. Moreover, the viral load in the sputum decreased more slowly over time than in the nasopharyngeal group as the disease progressed. Interestingly, even when samples in the nasopharyngeal swab turned negative, it was commonly observed that patients with underlying diseases, especially hypertension and diabetes, remained positive for COVID-19 and required a longer period for the sputum samples to turn negative. These combined findings emphasize the importance of tracking sputum samples even in patients with negative tests from nasopharyngeal swabs, especially for those with underlying conditions. In conclusion, this work reinforces the importance of sputum samples for SARS-CoV-2 detection to minimize transmission of COVID-19 within the community.
Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/diagnosis , Nasopharynx/virology , Pneumonia, Viral/diagnosis , Sputum/virology , Viral Load , Adult , COVID-19 , China , Humans , Pandemics , Retrospective Studies , SARS-CoV-2ABSTRACT
The 2019 novel coronavirus (2019-nCov) has caused increasing number of infected cases globally. This study was performed to analyze information regarding the transmission route and presence of viral nucleic acids on several clinical samples. Confirmed 2019-nCov-infected cases were identified in Dongyang and were treated according to guidelines for the diagnosis of 2019-nCov infection released by the National Health Commission. Information regarding the contacts that the infected people had was collected to determine whether it caused clustered cases. A series of successive nucleic acid examination of feces, oropharyngeal swabs, and sputum was also performed, and the results were analyzed. A total of 19 confirmed cases of 2019-nCov infection were identified in Dongyang, Zhejiang Province, China. Five cases showed severe symptoms, and the remaining ones showed mild manifestations. Ten cases infected from two asymptomatic individuals were clustered into two groups. Among 14 cases with consecutive nucleic acid test results, four patients showed positive results in feces after their negative conversion in oropharyngeal swabs. Asymptomatic individuals with the virus could cause 2019-nCov clustered cases, and the clustered cases may differ from sporadic cases on age and length of hospitalization. In addition, nucleic acids in feces last longer than those in oropharyngeal swabs.