Smart City Construction and Management by Digital Twins and BIM Big Data in COVID-19 Scenario

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.

Digital Twins on the Resilience of Supply Chain Under COVID-19 Pandemic

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.

E-commerce fraud risk prediction based on RUSBoost algorithm

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.

Moving Average Method on Big Data in Intercity Transportation in the Post-COVID-19 Era

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.

Identification of Potential Inhibitors Against SARS-CoV-2 Using Computational Drug Repurposing Study

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.

Personalized Situation Adaptive Human-Vehicles-Interaction (HVI) Prediction in COVID-19 Context

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

Sentiment Evolution in Social Network Based on Joint Pre-training Model

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.