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1.
Ieee Transactions on Engineering Management ; 2022.
Article in English | Web of Science | ID: covidwho-2005240

ABSTRACT

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.

2.
2021 International Conference on Electronic Information Engineering and Computer Communication, EIECC 2021 ; 12172, 2022.
Article in English | Scopus | ID: covidwho-1923084

ABSTRACT

In the context of the era of big data, the emergence of e-commerce platforms has brought many opportunities and risks. Due to the COVID-19, e-commerce has achieved unprecedented development, and e-commerce fraud has severely damaged the healthy economic environment. This paper uses the RUSBoost algorithm to build an e-commerce fraud risk prediction model, and verifies the predictive performance of the model through data experiments. The results show that it has a high accuracy rate for identifying e-commerce fraud. If the model is applied to e-commerce, the losses caused by ecommerce fraud could be avoided in time. At present, there are fewer e-commerce fraud risk prediction models and have a wide development prospection. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

3.
Current Bioinformatics ; 16(10):1320-1327, 2021.
Article in English | EMBASE | ID: covidwho-1639643

ABSTRACT

Background: SARS-Cov-2 is a newly emerged coronavirus and causes a severe type of pneumonia in the host organism. So, it is an urgent need to find some inhibitors against SARS-Cov-2. Therefore, drug repurposing study is an effective strategy for treating pneumonia to find the inhibitors of SARS-Cov-2 proteins. Methods: For this purpose, a library of 2500 verified drug chemical compounds was generated and the compounds were docked against Nucleocapsid, Membrane and Envelope protein structures of SARS-Cov-2 to determine the binding affinity of the chemical compounds against targeting binding pockets. Moreover, cheminformatics properties and ADMET of these compounds were assessed to find the druglikeness behavior of compounds. The chemical compounds with the lowest S-score were identified as potential inhibitors. Results: Our findings showed that the compound ids 1212, 1019 and 1992 could interact inside the active sites of membrane protein, nucleocapsid protein and envelope protein. Conclusion: This in silico knowledge will be helpful for the design of novel, safe and less expensive drugs against the SARS-Cov-2.

4.
21st COTA International Conference of Transportation Professionals: Advanced Transportation, Enhanced Connection, CICTP 2021 ; : 691-702, 2021.
Article in English | Scopus | ID: covidwho-1628028

ABSTRACT

In 2020, the outbreak of COVID-19 pneumonia has had a great impact on China's economic and social life. The construction and transportation industries have been greatly impacted and suffered from its high mobility. This paper studies the big data of migration between Xi'an and Chengdu from January 1, 2020 to March 15, 2020 and divides the epidemic situation into four stages according to the introduction of the elastic coefficient according to the development of the epidemic situation. In each stage, the elastic coefficient of index change is introduced in combination with the decreasing impact of epidemic prevention and control measures on transportation. Finally, a modified moving average method is formed, which is compared with the ordinary moving average method. The results show that the modified moving average method combined with Hadoop big data platform can improve the accuracy and efficiency of the intercity transportation flow prediction under the epidemic situation. © 2021 CICTP 2021: Advanced Transportation, Enhanced Connection - Proceedings of the 21st COTA International Conference of Transportation Professionals. All rights reserved.

5.
IEEE Transactions on Intelligent Transportation Systems ; 2021.
Article in English | Scopus | ID: covidwho-1537784

ABSTRACT

The purposes are to investigate the personalized situation adaptive Human-Computer Interaction (HCI) in the COVID-19 context, achieve accurate predictions for HCI in different urban transportation situations, and solve the urban intelligent transportation problems. Problems of Human-Vehicles-Interaction (HVI) in context awareness are analyzed. Historical traffic flow in three different situations, including novice user situation, mid user situation, and expert user situation, are taken as the data sources. The HVI data are preprocessed afterward. Next, Dilated Convolution (DC) and Long-Short Term Memory (LSTM) are integrated (DC-LSTM) to build an HVI model based on situation adaptive. The proposed model is simulated to analyze its performance. Simulation experiments suggest that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed model are 4.64%, 5.34%, and 7.82%, respectively. Although these three metrics increase under the mid user and expert user situations, the proposed model can still provide a higher accuracy than LTSM, Convolutional Neural Network (CNN), Simple Recurrent Network (SRN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Besides, the prediction velocity can maintain about 60 Frame-Per-Second (FPS) under all three user situations. Regarding the path guidance performance, the proposed model can suppress the traffic congestion and dredge the congested sections effectively. Hence, the HVI model based on situational adaptation constructed has high prediction accuracy and traffic congestion evacuation performance, which can provide an experimental basis for the later intelligent transportation field and improving situational self-adaptability. IEEE

6.
IEEE Transactions on Intelligent Transportation Systems ; 2021.
Article in English | Scopus | ID: covidwho-1515175

ABSTRACT

The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced. A UAV DTs information forecasting model is constructed based on improved AlexNet, whose performance is analyzed through simulation experiments. As end-users and task proportion increase, the proposed model can provide smaller transmission delays, lesser energy consumption in throughput demand, shorter task completion time, and higher resource utilization rate under reduced transmission power than other state-of-art models. Regarding forecasting accuracy, the proposed model can provide smaller errors and better accuracy in Signal-to-Noise Ratio (SNR), bit quantizer, number of pilots, pilot pollution coefficient, and number of different antennas. Specifically, its forecasting accuracy reaches 95.58%and forecasting velocity stabilizes at about 35 Frames-Per-Second (FPS). Hence, the proposed model has stronger robustness, making more accurate forecasts while minimizing the data transmission errors. The research results can reference the precise input of medical resources for COVID-19 prevention and control. IEEE

7.
24th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021 ; : 1093-1098, 2021.
Article in English | Scopus | ID: covidwho-1276421

ABSTRACT

Sentiment analysis is one of the key tasks of natural language understanding. Most of sentiment analysis researches revolve around sentiment classification of subjective texts. However, research in the field of sentiment evolution analysis for complex interactive texts are notable. Sentiment evolution models the dynamics of sentiment orientation over time, it can predict the stage of event development. In this paper, we propose a sentiment evolution method based on a joint model to analyze the dynamics and interactions of individual sentiment on social media such as Weibo. The model contains two modules, sentiment encoder module based on pre-training model and time series prediction module based on Long Short-Term Memory(LSTM). We conducted experiments on real-world datasets which were crawled from Weibo. The experiment demonstrated a case study that analyzed the sentiment dynamics of topics related to COVID-19. Experimental results show that our method achieve an accuracy of 88.0%, which are about 14.7% higher than the existing methods. © 2021 IEEE.

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