au:"He, Z." (50)
WHO COVID (4)
ScienceDirect (1)
Students, Medical (1)
General Surgery (1)
Cysticercosis (1)
Cysticercus (1)
Biomedical Research (1)
Randomized controlled trials (10)
Chinese Traditional and Herbal Drugs (3)
Impact Of Covid-19 On Asian Economies And Policy Responses (3)
8th International Conference on Computing and Artificial Intelligence, ICCAI 2022 (2)
Value in Health Regional Issues (2)
16th Dahe Fortune China Forum and Chinese High-Educational Management Annual Academic Conference, DFHMC 2020 (1)
2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021 (1)
2021 International Conference on Environmental Engineering and Energy, ICEEE 2021 (1)
2022 International Joint Conference on Neural Networks, IJCNN 2022 (1)
2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021 (1)
3rd International Conference on Frontiers of Biological Sciences and Engineering, FBSE 2020 (1)
Analysis of 394 COVID-19 cases infected with Omicron variant in Shenzhen: impact of underlying diseases to patient's symptoms
Zhang, P.; Cai, Z.; He, Z.; Chen, P.; Wu, W.; Lin, Y.; Feng, S.; Peng, L.; Li, J.; Yuan, J.; Yang, L.; Wang, F.; Liu, Y.; Lu, H..
Eur J Med Res ; 27(1):291, 2022.
Article in English | PubMed | ID: covidwho-2162426
OBJECTIVES: The emergence of new variants of SARS-CoV-2 is continuously posing pressure to the epidemic prevention and control in China. The Omicron variant of SARS-CoV-2 having stronger infectivity, immune escape ability, and capability causing repetitive infection spread to many countries and regions all over the world including South Africa, United States and United Kingdom etc., in a short time. The outbreaks of Omicron variant also occurred in China. The aim of this study is to understand the epidemiological characteristics of Omicron variant infection in Shenzhen and to provide scientific basis for effective disease control and prevention. METHODS: The clinical data of 394 imported COVID-19 cases infected with Omicron variant from 16 December 2021 to 24 March 2022 admitted to the Third People's hospital of Shenzhen were collected and analyzed retrospectively. Nucleic acid of SARS-CoV-2 of nasopharyngeal swabs and blood samples was detected using 2019-nCoV nucleic acid detection kit. Differences in Ct values of N gene were compared between mild group and moderate group. The specific IgG antibody was detected using 2019-nCoV IgG antibody detection kit. Statistical analysis was done using SPSS software and graphpad prism. RESULTS: Patients were categorized into mild group and moderate group according to disease severity. The data on the general conditions, underlying diseases, COVID-19 vaccination and IgG antibody, viral load, laboratory examination results, and duration of hospitalization, etc., were compared among disease groups. Mild gorup had higher IgG level and shorter nucleic acid conversion time. Patients with underlying diseases have 4.6 times higher probability to progress to moderate infection. CONCLUSION: In terms of epidemic prevention, immunization coverage should be strengthened in the population with underlying diseases. In medical institutions, more attention needs to be paid to such vulnerable population and prevent further deterioration of the disease.
Reflections on science, technology and innovation on the aspirations of the Sendai framework for disaster risk reduction
Adu-Gyamfi, B.; Ariyaningsih, He, Z. Q.; Yamazawa, N.; Kato, A.; Shaw, R..
International Journal of Disaster Resilience in the Built Environment ; 2022.
PurposeThe Sendai framework for disaster risk reduction (DRR) 2015-2030 offers guidelines to reduce disaster losses and further delivers a wake-up call to be conscious of disasters. Its four priorities hinge on science, technology and innovations as critical elements necessary to support the understanding of disasters and the alternatives to countermeasures. However, the changing dynamics of current and new risks highlight the need for existing approaches to keep pace with these changes. This is further relevant as the timeline for the framework enters its mid-point since its inception. Hence, this study reflects on the aspirations of the Sendai framework for DRR through a review of activities conducted in the past years under science, technology and innovations. Design/methodology/approachMultidimensional secondary datasets are collected and reviewed to give a general insight into the DRR activities of governments and other related agencies over the past years with case examples. The results are then discussed in the context of new global risks and technological advancement. FindingsIt becomes evident that GIS and remote sensing embedded technologies are spearheading innovations for DRR across many countries. However, the severity of the Covid-19 pandemic has accelerated innovations that use artificial intelligence-based technologies in diverse ways and has thus become important to risk management. These notwithstanding, the incorporation of science, technology and innovations in DRR faces many challenges. To mitigate some of the challenges, the study proposes reforms to the scope and application of science and technology for DRR, as well as suggests a new framework for risk reduction that harnesses stakeholder collaborations and resource mobilizations. Research limitations/implicationsThe approach and proposals made in this study are made in reference to known workable processes and procedures with proven successes. However, contextual differences may affect the suggested approaches. Originality/valueThe study provides alternatives to risk reduction approaches that hinge on practically tested procedures that harness inclusivity attributes deemed significant to the Sendai framework for DRR 2015-2030.
A Dual Arrhythmia Classification Algorithm Based on Deep Learning and Attention Mechanism Incorporating Morphological-temporal Information
Shan, C.; Yuan, Z.; Qiu, Z.; He, Z.; An, P..
2022 International Joint Conference on Neural Networks, IJCNN 2022 ; 2022-July, 2022.
In the context of increasing medical resource constraints and the global pandemic of COVID-19, the acquisition and automatic diagnosis of electrocardiogram (ECG) signal at home is becoming more and more important. In this paper, we propose a dual arrhythmia classification algorithm for edge-cloud collaboration. We first design a lightweight single-lead ECG signal binary classification model incorporating RR intervals that can be deployed at the edge, which achieves lightweight ECG feature extraction by using depthwise separable convolution and positional attention, and fuses RR interval features to the fully connected layer to achieve normal or abnormal classification of ECG heartbeats. For heartbeats classified as abnormal using the above model, we design a dual-branch arrhythmia multi-classification model with channel and spatial dual attention that integrates simple convolutional neural network (CNN) modules that can be deployed in a cloud artificial intelligence (AI) server to perform accurate classification of abnormal ECG heartbeats, where the input of one branch is a heartbeat signal and the input of the other branch is an ECG segment containing adjacent R-peaks. The experimental results based on the MIT-BIH arrhythmia database demonstrate that our binary classification model achieves an average accuracy of 99.80% and the multi-classification model achieves an average accuracy of 99.71%, and our method ensures a high enough accuracy while performing dual analysis to make the analysis results more reliable. © 2022 IEEE.
Theoretical Study on Raman Characteristic Peaks of Coronavirus Spike Protein Based on Deep Learning
Ni, S.; Wen, J. X.; Zhou, M. J.; Huang, J. L.; Le, W.; Chen, G.; He, Z. B.; Li, B.; Zhao, S. N.; Zhao, Z. Q.; Du, K..
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis ; 42(9):2757-2762, 2022.
Article in Chinese | Scopus | ID: covidwho-2090458
COVID-19, which has lasted for a year, has caused great damage to the global economy. In order to control COVID-19 effectively, rapid detection of COVID-19 (SARS-CoV-2) is an urgent problem. Spike protein is the detection point of Raman spectroscopy to detect SARS-CoV-2. The construction of spike protein Raman characteristic peaks plays an important role in the rapid detection of SARS-CoV-2 using Raman technology. In this paper, we used Deep Neural Networks to construct the amide I and III characteristic peak model of spike proteins based on simplified exciton model, and combined with the experimental structures of seven coronaviruses (HCoV-229E, HCoV-HKUl, HCoV-NL63, HCoV-OC43, MERS-CoV, SARS-CoV, SARS-CoV-2) spike proteins, analyzed the differences of amide I and III characteristic peaks of seven coronaviruses. The results showed that seven coronaviruses could be divided into four groups according to the amide I and III characteristic peaks of spike proteins: SARS-CoV-2, SARS-CoV, MERS-CoV form a group;HCoV-HKUl, HCoV-NL63 form a group;HCoV-229E and HCoV-OC43 form a group independently. The frequency of amide I and III in the same group is relatively close,and it is difficult to distinguish spike proteins by the frequency of amide I and III ;the characteristic peaks of amide I and III in different groups are quite different, and spike proteins can be distinguished by Raman spectroscopy. The results provide a theoretical basis for the development of Raman spectroscopy for rapid detection of SARS-CoV-2. © 2022 Science Press. All rights reserved.
Analysis of the world oil and gas exploration situation in 2021
Dou, L.; Wen, Z.; Wang, J.; Wang, Z.; He, Z.; Liu, X.; Zhang, N..
Petroleum Exploration and Development ; 49(5):1195-1209, 2022.
The global exploration investment, new oil and gas discoveries, exploration business adjustment strategies of oil companies in 2021, and future favorable exploration domains are systematically analyzed using commercial databases such as IHS and public information of oil companies. It has been found that the world oil and gas exploration situation in 2021 has continued the downturn since the outbreak of COVID-19. The investment and drilling workload decreased slightly, but the success rate of exploration wells, especially deepwater exploration wells, increased significantly, and the newly discovered reserves increased slightly compared with last year. Deep waters of the passive continental margin basins are still the leading sites for discovering conventional large and medium-sized oil and gas fields. The conventional oil and gas exploration in deep formations of onshore petroliferous basins has been keeping a good state, with tight/shale oil and gas discoveries made in Saudi Arabia, Russia, and other countries. While strengthening the exploration and development of local resources, national, international, and independent oil companies have been focusing on major overseas frontiers using their advantages, including risk exploration in deep waters and natural gas. Future favorable exploration directions in the three major frontiers, the global deep waters, deep onshore formations, and unconventional resources, have been clarified. Four suggestions are put forward for the global exploration business of Chinese oil companies: first, a farm in global deepwater frontier basins in advance through bidding at a low cost and adopt the "dual exploration model" after making large-scale discoveries;second, enter new blocks of emerging hot basins in the world through farm-in and other ways, to find large oil and gas fields quickly;third, cooperate with national oil companies of the resource host countries in the form of joint research and actively participate exploration of deep onshore formations of petroliferous basins;fourth, track tight/shale oil and gas cooperation opportunities in a few countries such as Saudi Arabia and Russia, and take advantage of mature domestic theories and technologies to farm in at an appropriate time. © 2022 Research Institute of Petroleum Exploration & Development, PetroChina
Reflections on pandemic governance in China and its implications to future 5G strategy
He, Z.; Hua, J.; Zhang, Y.; Deng, J.; Adu-Gyamfi, B.; Shaw, R..
Pandemic Risk, Response, and Resilience: COVID-19 Responses in Cities around the World ; : 173-189, 2022.
COVID-19 pandemic and its repercussions came as a surprise to nations including China. However, the unique central governance system of the country enhanced its ability to promulgate laws and guidelines which caused rapid changes across all aspects of its development. The result from this was a swift implementation of initiatives that ensured strict safety protocols to reduce the spread of COVID-19, generated advanced analytical technological systems to control the virus, and created new markets for some new technologies. Hence, the enormous growth of 5G contributed a lot to the economic recovery of China. Given its potential shown during the pandemic, the 5G strategy was considered as the most important attempt to face the challenges in post-COVID-19 by the Chinese government. This chapter outlines some of the structures, policy outcomes, and results during the pandemic and makes recommendations for curtailing future challenges. © 2022 Elsevier Inc. All rights reserved.
Prediction of Monthly PM2.5 Concentration in Liaocheng in China Employing Artificial Neural Network
He, Z. F.; Guo, Q. C.; Wang, Z. S.; Li, X. Z..
Atmosphere ; 13(8), 2022.
Fine particulate matter (PM2.5) affects climate change and human health. Therefore, the prediction of PM2.5 level is particularly important for regulatory planning. The main objective of the study is to predict PM2.5 concentration employing an artificial neural network (ANN). The annual change in PM2.5 in Liaocheng from 2014 to 2021 shows a gradual decreasing trend. The air quality in Liaocheng during lockdown and after lockdown periods in 2020 was obviously improved compared with the same periods of 2019. The ANN employed in the study contains a hidden layer with 6 neurons, an input layer with 11 parameters, and an output layer. First, the ANN is used with 80% of data for training, then with 10% of data for verification. The value of correlation coefficient (R) for the training and validation data is 0.9472 and 0.9834, respectively. In the forecast period, it is demonstrated that the ANN model with Bayesian regularization (BR) algorithm (trainbr) obtained the best forecasting performance in terms of R (0.9570), mean absolute error (4.6 mu g/m(3)), and root mean square error (6.6 mu g/m(3)), respectively. The ANN model has produced accurate results. These results prove that the ANN is effective in monthly PM2.5 concentration predicting due to the fact that it can identify nonlinear relationships between the input and output variables.
Phage display technology and its application in antivirals discovery
Xu, S. Q.; He, Z. H.; Tao, B. Z.; Qin, X..
Yaoxue Xuebao ; 57(7):1937-1945, 2022.
Article in Chinese | EMBASE | ID: covidwho-2006568
The COVID-19 outbreak has drawn attention to viral infectious diseases once again, and the development of antiviral drugs for both known and potentially emerging viruses is of great significance. In recent years, peptides and protein drugs are becoming a hot spot in the field of antiviral drug research and development. Phage display technology, as a powerful tool for screening peptides and protein drugs, has been increasingly concerned in the academic and industrial fields. The present review introduced the basic principle of phage display technology, summarized phage display libraries often used in antiviral drug discovery and their applications, discussed the challenges and future direction of antiviral drug research and development based on phage display technology.
Molecular biological characteristics of the 2019 novel Coronavirus in Shijiazhuang
Gao, H.; Yang, L.; Guo, Y.; Wang, Y.; Liu, Y.; Tang, Y.; He, Z.; Liang, X.; Huang, S.; Gao, P.; Huang, Y.; Dai, M.; Zhang, Z.; Hu, Q.; Wang, Y.; Chen, F.; Dai, E.; Jiang, P.; Du, Y..
Chinese Journal of Laboratory Medicine ; 45(6):637-641, 2022.
Objective To analyze the molecular epidemiological characteristics of the Corona virus disease 2019 (COVID‑19) cases in Shijiazhuang, which can reveal the origin of the outbreak and provide a scientific basis for COVID‑19 prevention and control. Methods From January 2 to January 8, 2021, a total of 404 samples from 170 COVID‑19 cases were collected from the Shijiazhuang Fifth Hospital. The consensus sequence of 2019 novel Coronavirus(2019‑nCoV) was obtained through multiplex polymerase chain reaction‑based sequencing. The sequences of 170 COVID‑19 cases were analyzed by the PANGOLIN, and the data were statistically analyzed by T‑test. Results Among the 404 COVID‑19 samples, a total of 356 samples obtained high quality genome sequences (>95%, 100×sequencing depth). The whole genome sequences of 170 COVID‑19 cases were obtained by eliminating repeated samples. All 170 sequences were recognized as lineage B1.1 using PANGOLIN. The number of single nucleotide polymorphism arrange from 18-22 and most of the single nucleotide polymorphism were synonymous variants. All of 170 genomes could be classified into 48 sub‑groups and most of the genomes were classified into 2 sub‑groups (66 and 31, respectively). Conclusions All cases in this study are likely originated from one imported case. The viruses have spread in the community for a long time and have mutated during the community transmission.
Design and Implementation of Epidemic Prevention and Control Measures Recommendation System Based on K-means Clustering Algorithm
Zhang, Y.; He, Z.; Tao, Y..
8th International Conference on Computing and Artificial Intelligence, ICCAI 2022 ; : 301-306, 2022.
Aiming at the problem that it is difficult for countries to make appropriate judgment on the epidemic situation and what effective measures should be taken, this paper designed and implemented an epidemic prevention and control measures recommendation system based on K-means cluster analysis. Firstly, after normalizing the daily number of newly infected and cured people in various countries, the density based local anomaly factor algorithm was used to detect outliers. Excluding the impact of individual abnormal data on all the data, and then renormalizing the data, the data set was divided into three categories by K-means clustering method, which was respectively corresponded to the three stages of the epidemic situation. By comparing the clustering results of China with the actual situation, the three stages of transformation were roughly consistented with the actual situation. Finally, by referring to the epidemic prevention plan adopted by China in the same period, the epidemic prevention measures that should be taken in each stage were recommended. The results showed that the system has broad application prospects and practical significance for countries to quickly formulate effective control measures to control the spread of the epidemic. © 2022 ACM.
Analysis of public sentiment tendency in COVID-19 period based on GA-BiLSTM
Tao, Y.; He, Z.; Zhang, Y..
As the Internet becomes the main source of information for the public, grasping the emotional polarity of online public opinion is particularly important for relevant departments to supervise online public opinion. In order to more accurately determine the emotional polarity of public opinion in the epidemic, this paper proposes a public sentiment analysis model based on Word2vec, genetic algorithm and Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm. The Word2vec model converts the comment text into an n-dimensional vector, uses the Bi-LSTM algorithm to analyze the sentiment polarity, and uses the genetic algorithm to analyze the number of Bi-LSTM layers and the number of fully connected layers and the number of neurons in each layer of Bi-LSTM optimization. The experimental results show that the accuracy of the above model is compared with the accuracy of the Word2vec model and the LSTM model separately, and the accuracy is increased by 11.0% and 7.7%, respectively. © 2022 ACM.
[Evaluation of efficiency of different anti-cysticercus antibody test kits for serodiagnosis of cysticercosis].
Zhang, Y L; Jiang, T T; Ji, P H; He, Z Q; Chen, X; Hong, Y; Zhao, D Y; Deng, Y; Chen, W Q; Zhang, H W.
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi ; 34(1): 36-40, 2022 Feb 23.
Article in Chinese | MEDLINE | ID: covidwho-1893447
OBJECTIVE: To evaluate the diagnostic efficiency of four anti-cysticercus IgG, IgG4 or IgM antibody test kits (enzyme-linked immunosorbent assay, ELISA) by different manufacturers, so as to provide insights into the epidemiological investigation and clinical detection of cysticercosis. METHODS: Forty serum samples from cerebral cysticercosis patients, 100 serum samples from healthy volunteers, 30 serum samples from paragonimiasis skrjabini patients, 17 serum samples from cystic echinococcosis and 19 serum samples from subcutaneous or cerebral sparganosis patients were collected and detected using anti-cysticercus IgG, IgG4 or IgM antibody test kits (brand A) and the anti-cysticercus IgG antibody test kit (brand B). The sensitivity, specificity and false negative rate of the four kits for detection of cysticercosis were estimated. RESULTS: The anti-cysticercus IgG, IgG4 or IgM antibody test kits (brand A) showed 95.00% (38/40), 87.50% (35/40), 7.50% (3/40) sensitivities and 98.00% (98/100), 100.00% (100/100) and 100.00% (100/100) for detection of cysticercosis, while the anti-cysticercus IgG antibody test kit (brand B) presented a 75.00% (30/40) sensitivity and 100.00% (100/100) specificity for detection of cysticercosis. The sensitivity for detection of cysticercosis was significantly higher by the anti-cysticercus IgG antibody test kit (brand A) than by the anti-cysticercus IgG antibody test kit (brand B) (χ2 = 6.28, P < 0.05); however, no significant difference was seen in the specificity by two kits (χ2 = 2.01, P > 0.05). The four ELISA kits showed overall false positive rates of 37.88% (25/66), 22.73% (15/66), 62.12% (41/66) and 15.15% (10/66) for detection of paragonimiasis, echinococcosis and sparganosis (χ2 = 37.61, P < 0.05), and the anti-cysticercus IgG antibody test kit (brand A) presented the highest overall false positive rate for detection of paragonimiasis, echinococcosis and sparganosis (χ2 = 7.56, P' < 0.008), while a higher overall false positive rate was seen for detection of paragonimiasis, echinococcosis and sparganosis by the anti-cysticercus IgG antibody test kit (brand A) than by the anti-cysticercus IgG antibody test kit (brand B) (χ2 = 8.75, P' < 0.008). The four ELISA kits showed false positive rates of 40.00% (12/30), 16.67% (5/30), 76.67% (23/30) and 13.33% (4/30) for detection of paragonimiasis (χ2 = 32.88, P < 0.05) and 21.05% (4/19), 26.32% (5/19), 73.68% (14/19) and 15.79% (3/19) for detection of sparganosis (χ2 = 19.97, P < 0.05), and the highest false positive rates were found by the anti-cysticercus IgM antibody test kit (brand A) for detection of paragonimiasis and sparganosis (all P' < 0.008). However, the four ELISA kits showed comparable false positive rates of 52.94% (9/17), 29.41% (5/17), 23.53% (4/17) and 17.65% (3/17) for detection of echinococcosis (χ2 = 8.24, P > 0.05). In addition, the anti-cysticercus IgM anti-body test kit (brand A) showed false positive rates of 76.67% (23/30), 23.53% (4/17) and 73.68% (14/19) for detection of paragonimiasis, echinococcosis and sparganosis (χ2 = 14.537, P < 0.05), with the lowest false positive rate seen for detection of echinococcosis (χ2 = 14.537, P' < 0.014), while no significant differences were seen in the false positive rate for detection of paragonimiasis, echinococcosis and sparganosis by other three ELISA kits (all P > 0.05). CONCLUSIONS: The four anti-cysticercus IgG, IgG4 or IgM antibody test kits exhibit various efficiencies for serodiagnosis of cysticercosis. The anti-cysticercus IgG antibody test kit (brand A) has a high sensitivity for serodiagnosis of cysticercosis; however, it still needs to solve the problems of cross-reaction with other parasitic diseases and stability.
Cysticercosis , Cysticercus , Animals , Antibodies, Helminth , Cysticercosis/diagnosis , Enzyme-Linked Immunosorbent Assay , Humans , Reagent Kits, Diagnostic , Sensitivity and Specificity , Serologic Tests
Characteristics of Traditional Chinese Medicine Syndromes and Treatments of COVID-19 Patients from Two Hospitals Based on "Treatment of Disease in Accordance with Three Conditions"
Xu, X. H.; Weng, H.; He, Z. H.; Wang, H. T.; Li, L.; Liu, Y. T.; Tang, L. J.; Yin, X.; Ding, B. H.; Guo, J. W.; Zhang, Z. D..
Chinese Journal of Experimental Traditional Medical Formulae ; 28(4):172-180, 2022.
Objective: To explore the guidance value of "treatment of disease in accordance with three conditions" theory in the prevention and treatment of corona virus disease 2019 (COVID-19) based on the differences of syndromes and traditional Chinese medicine (TCM) treatments in COVID-19 patients from Xingtai Hospital of Chinese Medicine of Hebei province and Ruili Hospital of Chinese Medicine and Dai Medicine of Yunnan province and discuss its significance in the prevention and treatment of the unexpected acute infectious diseases. Method: Demographics data and clinical characteristics of COVID-19 patients from the two hospitals were collected retrospectively and analyzed by SPSS 18.0. The information on formulas was obtained from the hospital information system (HIS) of the two hospitals and analyzed by the big data intelligent processing and knowledge service system of Guangdong Hospital of Chinese Medicine for frequency statistics and association rules analysis. Heat map-hierarchical clustering analysis was used to explore the correlation between clinical characteristics and formulas. Result: A total of 175 patients with COVID-19 were included in this study. The 70 patients in Xingtai, dominated by young and middle-aged males, had clinical symptoms of fever, abnormal sweating, and fatigue. The main pathogenesis is stagnant cold-dampness in the exterior and impaired yin by depressed heat, with manifest cold, dampness, and deficiency syndromes. The therapeutic methods highlight relieving exterior syndrome and resolving dampness, accompanied by draining depressed heat. The core Chinese medicines used are Poria, Armeniacae Semen Amarum, Gypsum Fibrosum, Citri Reticulatae Pericarpium, and Pogostemonis Herba. By contrast, the 105 patients in Ruili, dominated by young females, had atypical clinical symptoms, and most of them were asymptomatic patients or mild cases. The main pathogenesis is dampness obstructing the lung and the stomach, with obvious dampness and heat syndromes. The therapeutic methods are mainly invigorating the spleen, resolving dampness, and dispersing Qi with light drugs. The core Chinese medicines used are Poria, Atractylodis Macrocephalae Rhizoma, Glycyrrhizae Radix et Rhizoma, Coicis Semen, Platycodonis Radix, Lonicerae Japonicae Flos, and Pogostemonis Herba. Conclusion: The differences in clinical characteristics, TCM syndromes, and medication of COVID-19 patients from the two places may result from different regions, population characteristics, and the time point of the COVID-19 outbreak. The "treatment of disease in accordance with three conditions" theory can help to understand the internal correlation and guide the treatments. © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.
Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings
He, Z.; Mokhberian, N.; Câmara, A.; Abeliuk, A.; Lerman, K..
2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021 ; : 2102-2118, 2021.
Growing polarization of the news media has been blamed for fanning disagreement, controversy and even violence. Early identification of polarized topics is thus an urgent matter that can help mitigate conflict. However, accurate measurement of topic-wise polarization is still an open research challenge. To address this gap, we propose Partisanship-aware Contextualized Topic Embeddings (PaCTE), a method to automatically detect polarized topics from partisan news sources. Specifically, utilizing a language model that has been finetuned on recognizing partisanship of the news articles, we represent the ideology of a news corpus on a topic by corpus-contextualized topic embedding and measure the polarization using cosine distance. We apply our method to a dataset of news articles about the COVID19 pandemic. Extensive experiments on different news sources and topics demonstrate the efficacy of our method to capture topical polarization, as indicated by its effectiveness of retrieving the most polarized topics. © 2021 Association for Computational Linguistics.
Deep Reinforcement Learning-based Vaccine Distribution Strategies
Wei, X.; Pu, C.; He, Z.; Lio, P..
2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021 ; : 427-436, 2021.
The rapid development of artificial intelligence techniques is significantly promoting the resolution of various important decision-making issues such as material distribution, generation line optimization scheduling, and path planning. Currently, SARS-CoV-2 is raging over the world, and it is valuable to propose a vaccine distribution strategy to utilize limited vaccine resources rationally. In this paper, we aim to propose an optimal vaccine distribution strategy based on deep reinforcement learning(DRL) approaches. An End-to-End vaccine distribution model is proposed by combining the Deep Reinforcement Learning model and LinUCB algorithm to get an optimistic strategy of allocation. Experiment results demonstrated that vaccine distribution strategies based on this model show a strong capacity to control the epidemic and ensure stable government revenue compared with baseline strategies. © 2021 IEEE.
A new type of engagement between China and Europe amidst the COVID pandemic
He, Z.; Li, Y..
Journal of Chinese Economic and Business Studies ; 2022.
The impact of the pandemic on the international economic and normative order has accelerated power struggles between the East and the West. Especially, the intensification of conflicts between China and Europe in terms of values may even affect the existing cooperation in the fields of economics, trade, and multilateralism. Despite divergences between China and Europe, including queries over the so-called mask diplomacy and cultural discrimination, as well as pressures from the US, some in Europe hope to strengthen engagement with China and keep the door open for cooperation and dialogue, thereby committing to the fundamentals of win-win cooperation. Cooperation is the backbone of a new type of engagement between China and Europe, which should not be affected by some occasional discord. Based on this new type of engagement, China and Europe build mutual trust, strengthen communication and coordination on global issues in a positive and pragmatic manner. © 2022 The Chinese Economic Association–UK.
Evidence of aircraft activity impact on local air quality: A study in the context of uncommon airport operation
Xu, H.; Xiao, K.; Pan, J.; Fu, Q.; Wei, X.; Zhou, J.; Yu, Y.; Hu, X.; Ren, H.; Cheng, J.; Peng, S.; Hong, N.; Ye, Y.; Su, N.; He, Z.; Hu, T..
Journal of Environmental Sciences (China) ; 125:603-615, 2023.
Wuhan Tianhe International Airport (WUH) was suspended to contain the spread of COVID-19, while Shanghai Hongqiao International Airport (SHA) saw a tremendous flight reduction. Closure of a major international airport is extremely rare and thus represents a unique opportunity to straightforwardly observe the impact of airport emissions on local air quality. In this study, a series of statistical tools were applied to analyze the variations in air pollutant levels in the vicinity of WUH and SHA. The results of bivariate polar plots show that airport SHA and WUH are a major source of nitrogen oxides. NOx, NO2 and NO diminished by 55.8%, 44.1%, 76.9%, and 40.4%, 33.3% and 59.4% during the COVID-19 lockdown compared to those in the same period of 2018 and 2019, under a reduction in aircraft activities by 58.6% and 61.4%. The concentration of NO2, SO2 and PM2.5 decreased by 77.3%, 8.2%, 29.5%, right after the closure of airport WUH on 23 January 2020. The average concentrations of NO, NO2 and NOx scatter plots at downwind of SHA after the lockdown were 78.0%, 47.9%, 57.4% and 62.3%, 34.8%, 41.8% lower than those during the same period in 2018 and 2019. However, a significant increase in O3 levels by 50.0% and 25.9% at WUH and SHA was observed, respectively. These results evidently show decreased nitrogen oxides concentrations in the airport vicinity due to reduced aircraft activities, while amplified O3 pollution due to a lower titration by NO under strong reduction in NOx emissions. © 2022
The Common Ancestor Time Estimation of SARS-CoV-2 Based on Phylogenetic Analysis
Lu, Y.; Niu, K.; He, Z..
7th International Conference on Computer and Communications, ICCC 2021 ; : 1783-1789, 2021.
At the end of 2019, a novel coronavirus SARS-CoV-2 was first identified in Wuhan, Hubei Province, China. So far, the virus has spread globally. The rapid transmission and mutation have undoubtedly increased the difficulty of exploring the source of SARS-CoV-2, and it has made the origins study meaningful. This study uses 40,280 SARS-CoV-2 genetic sequences from the world for phylogenetic analysis. It is inferred that the time to Most Recent Common Ancestor (tMRCA) of global SARS-CoV-2 is roughly in early December 2019. Similarly, the tMRCA of SARS-CoV-2 in each continent is estimated, and sort out the timeline of the virus's entry into human society. Also, it is found that some regions outside Asia have COVID-19 cases in December 2019. © 2021 IEEE.
Masked Face Recognition Datasets and Validation
Huang, B. J.; Wang, Z. Y.; Wang, G. C.; Jiang, K.; He, Z.; Zou, H.; Zou, Q.; Soc, Ieee Comp.
IEEE/CVF International Conference on Computer Vision (ICCVW) ; : 1487-1491, 2021.
In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This nearly makes conventional facial recognition technology ineffective in many scenarios, such as face authentication, security check, community visit check-in, etc. Therefore, it is very urgent to boost performance of existing face recognition systems on masked faces. Most current advanced face recognition approaches are based on deep learning, which heavily depends on a large number of training samples. However, there are presently no publicly available masked face recognition datasets. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Synthetic Masked Face Recognition Dataset (SMFRD). As far as we know, we are the first to publicly release large-scale masked face recognition datasets that can be downloaded for free at: https://github.com/X-zhangyany/Real-World-Masked-Face-Dataset.
Research on the Planning and Design of Forest Recreation Base Based on GIS and RMP Analysis - Take the Example of Forest Recreation Base above the Clouds in Heshun County, Shanxi Province
Lu, J.; He, Z..
2021 International Conference on Environmental Engineering and Energy, ICEEE 2021 ; 966, 2022.
After the Novel coronavirus pneumonia, it is imperative to revitalize and restore the industry. Promoting the forest recreation industry in Heshun County to find the right positioning and integrating the local ecological and cultural connotation is the most effective channel and way. This paper uses GIS to spatially analyze the forest recreation base above the clouds and combines RWP analysis to analyze and summarize its current resources and development status, analyze the dilemmas and shortcomings in the process of base construction, and use it to make some suggestions on the path and benefits of integrating forest recreation and ecological culture in Heshun County. In this way, we can make reference to the integration of forest recreation and ecological culture in other regions and build a sustainable forest recreation industry in the new era. © 2022 Institute of Physics Publishing. All rights reserved.
au:"He, Z."