A Multivent System for Non-invasive Ventilation: Solving the Problem of Ventilator Shortage during the COVID-19 Pandemic

The COVID-19 pandemic has caused a severe global problem of ventilator shortage. Placing multiple patients on a single ventilator (ventilator sharing) or dual patient ventilation has been proposed and conducted to increase the cure efficiency for ventilated patients. However, the ventilator-sharing method needs to use the same ventilator settings for all the patients, which cannot meet the ventilation needs of different patients. Therefore, a novel multivent system for non-invasive ventilation has been proposed in this study. The close loop system consists of the proportional valve and the flow-pressure sensor can regulate the airway pressure and flow for each patient. Multiple ventilation circuits can be combined in parallel to meet patients’ventilation demands simultaneously. Meanwhile, the mathematical model of the multivent system is established and validated through experiments. The experiments for different inspired positive airway pressure (IPAP), expired positive airway pressure (EPAP), inspiratory expiratory ratio (I:E), and breath per minute (BPM) have been conducted and analyzed to test the performance of the multivent system. The results show that the multivent system can realize the biphasic positive airway pressure (BIPAP) ventilation mode in non-invasive ventilation without interfering among the three ventilation circuits, no matter the change of IPAP, EPAP, I:E, and BPM. However, pressure fluctuation exists during the ventilation process because of the exhaust valve effect, especially in EPAP control. The control accuracy and stability need to be improved. Nevertheless, the novel designed multivent system can theoretically solve the problem of ventilator shortage during the COVID-19 pandemic and may bring innovation to the current mechanical ventilation system. Author

Clinical Efficacy of Modified Sanxiaoyin in Treatment of Mild or Moderate COVID-19 Patients:Based on Retrospective Analysis.

Objective: To retrospectively analyze the clinical data of 52 patients with coronavirus disease-2019 COVID-19 and explore the clinical efficacy of modified Sanxiaoyin on mild/moderate COVID-19 patients. Method(s): The propensity score matching method was used to collect the clinical data of mild or moderate COVID-19 patients enrolled in the designated hospital of the Second Hospital of Jingzhou from December 2019 to May 2020. A total of 26 eligible patients who were treated with modified Sanxiaoyin were included in the observation group,and the 26 patients treated with conventional method were the regarded as the control. The disappearance of clinical symptoms,disappearance time of main symptoms,efficacy on traditional Chinese medicineTCMsymptoms,hospitalization duration,laboratory test indicators,and CT imaging changes in the two groups were compared. Result(s): The general data in the two groups were insignificantly different and thus they were comparable. After 7 days of treatment,the disappearance rate of fever,cough, fatigue,dry throat,anorexia,poor mental state,and poor sleep quality in the observation group was higher than that in the control groupP<0.05,and the difference in the disappearance rate of expectoration and chest distress was insignificant. For the cases with the disappearance of symptoms,the main symptomsfever, cough,fatigue,dry throat,anorexia,chest distressdisappeared earlier in the observation group than in the control groupP<0.01. After 7 days of treatment,the scores of the TCM symptom scale of both groups decreasedP<0.01,and the decrease of the observation group was larger that of the control groupP<0.01. All patients in the two groups were cured and discharged. The average hospitalization duration in the observation group12.79+/-2.68dwas shorter than that in the control group15.27+/-3.11dP<0.01. The effective rate in the observation group92.31%,24/26was higher than that in the control group76.92%,20/26. After 7 days of treatment,the lymphocyteLYMcount increasedP<0.05,and white blood cellWBCcount and neutrophilNEUTcount decreased insignificantly in the two groups. Moreover,levels of C-reactive protein CRP,erythrocyte sedimentation rateESR,and procalcitoninPCTreduced in the two groups after treatmentP<0.01and the reduction in the observation group was larger than that in the control group P<0.01. Through 7 days of treatment,the total effective rate on pulmonary shadow in the observation group 90.00%,18/20was higher than that in the control group77.27%,17/22P>0.05and the improvement of lung shadow in the observation group was better than that in the control groupP<0.01. Conclusion(s):Modified Sanxiaoyin can significantly alleviate fever,cough,fatigue,anorexia,chest distress,poor sleep quality,and other symptoms of patients with mild or moderate COVID-19,improve biochemical indicators,and promote the recovery of lung function. This paper provides clinical evidence for the application of modified Sanxiaoyin in the treatment of mild or moderate COVID-19.Copyright © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

Federated Learning for Medical Imaging Segmentation via Dynamic Aggregation on Non-IID Data Silos

A large number of mobile devices, smart wearable devices, and medical and health sensors continue to generate massive amounts of data, making edge devices' data explode and making it possible to implement data-driven artificial intelligence. However, the "data silos” and other issues still exist and need to be solved. Fortunately, federated learning (FL) can deal with "data silos” in the medical field, facilitating collaborative learning across multiple institutions without sharing local data and avoiding user concerns about data privacy. However, it encounters two main challenges in the medical field. One is statistical heterogeneity, also known as non-IID (non-independent and identically distributed) data, i.e., data being non-IID between clients, which leads to model drift. The second is limited labeling because labels are hard to obtain due to the high cost and expertise requirement. Most existing federated learning algorithms only allow for supervised training settings. In this work, we proposed a novel federated learning framework, MixFedGAN, to tackle the above issues in federated networks with dynamic aggregation and knowledge distillation. A dynamic aggregation scheme was designed to reduce the impact of current low-performing clients and improve stability. Knowledge distillation was introduced into the local generator model with a new distillation regularization loss function to prevent essential parameters of the global generator model from significantly changing. In addition, we considered two scenarios under this framework: complete annotated data and limited labeled data. An experimental analysis on four heterogeneous COVID-19 infection segmentation datasets and three heterogeneous prostate MRI segmentation datasets verified the effectiveness of the proposed federated learning method. © 2023 by the authors.

A fast point-of-care PCR test for infectious diseases, based on aerosol capturing

The COVID-19 pandemic quickly revealed the limitations of existing monitoring and diagnostic capabilities. While rapid antigen tests are not sufficiently reliable, PCR turn-around-time (TAT) typically ranges from hours to days. Standard swab-based tests are also cumbersome and invasive and, worse yet, they detect infection and not transmissibility. A reliable diagnostic test able to discern the infectious phase of COVID-19 could interrupt transmission while limiting isolation requirements. We developed a non-invasive, impaction-based method for capturing aerosols from human breath in one minute of sampling. A proof-of-principle system was used for the detection of viral RNA in breath samples from confirmed positive subjects (=29). A lab setup demonstrated compatibility with on-chip PCR, reducing the TAT to 15-20 minutes. Positive percentage agreement (PPA) between a breath- and nasopharyngeal PCR is 75% overall and 92% in the first 7 days of infection, after which the breath does not contain measurable virus anymore. Breath positivity corresponds to the infectious window. No false positives were noted. Diagnostic accuracy is superior to nasopharyngeal rapid antigen tests. This novel concept of aerosol capturing combined with ultra-fast PCR is proven to be effective to detect SARS-CoV-2 in breath, rivalling the standard nasopharyngeal PCR tests. Combined with a TAT on par with rapid antigen tests, the technology has the potential to become a standard test in the coming years, for COVID-19 or other infectious diseases. A validation study with an advanced setup is currently ongoing, first data should be available during the presentation.

COVID-19 and systemic anti-cancer therapy.

The corona virus disease 2019 (COVID-19) pandemic continues to severely impact healthcare systems around the world, and patients with cancer are even worse affected owing to compromised immune status and greater exposure risk. In the present review, we retrieved the relevant literature including guidelines and consensuses directly related to the purpose of this study from the PubMed database, and then summarized the research data on cancer and COVID-19, aiming to discuss the personal protection, systemic anti-cancer therapy, outcome of co-infection, and the clinical management strategy in this population. We found that patients with malignant tumors had a higher chance of suffering COVID-19, co-infection of whom had an even worse clinical prognosis, especially for those with lung cancer or hematologic cancers. Systemic chemotherapy may delay the clearance of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) virus of human body, and thus have a negative impact on the clinical outcomes of COVID-19, while certain endocrine therapy and targeted drugs having limited or no impact. There has been no sufficient evidence for the impact of immune checkpoint therapy on the outcomes of COVID-19 till now. It is of great value to strengthen the personal protection of patients, adjust the anti-tumor treatments rationally and optimize the clinical management processes.Copyright © 2022, Editorial Office of China Oncology. All rights reserved.

Prevalence and risk factors of erectile dysfunction in COVID-19 patients: a systematic review and meta-analysis.

PURPOSE: Studies have found that erectile dysfunction (ED) may be a short-term or long-term complication in coronavirus disease 2019 (COVID-19) patients, but no relevant studies have completed a pooled analysis of this claim. The purpose of the review was to comprehensively search the relevant literature, summarize the prevalence of ED in COVID-19 patients, assess risk factors for its development, and explore the effect of the COVID-19 infection on erectile function. METHODS: Medline, Embase, and the Cochrane Library was performed from database inception until April 14, 2022. Heterogeneity was analyzed by χ2 tests and I2 was used as a quantitative test of heterogeneity. Subgroup analyses, meta-regression, and sensitivity analyses were used to analyze sources of heterogeneity. RESULTS: Our review included 8 studies, 4 of which functioned as a control group. There were 250,606 COVID-19 patients (mean age: 31-47.1 years, sample size: 23-246,990). The control group consisted of 10,844,200 individuals (mean age: 32.76-42.4 years, sample size 75-10,836,663). The prevalence of ED was 33% (95% CI 18-47%, I2 = 99.48%) in COVID-19 patients. The prevalence of ED based on the international coding of diseases (ICD-10) was 9% (95% CI 2-19%), which was significantly lower than the prevalence of ED diagnosed based on the International Index of Erectile Function (IIEF-5) (46%, 95% CI 22-71%, I2 = 96.72%). The pooling prevalence of ED was 50% (95% CI 34-67%, I2 = 81.54%) for articles published in 2021, significantly higher than that for articles published in 2022 (17%, 95% CI 7-30%, I2 = 99.55%). The relative risk of developing ED was 2.64 times in COVID-19 patients higher than in non-COVID-19 patients (RR: 2.64, 95% CI 1.01-6.88). The GRADE-pro score showed that the mean incidence of ED events in COVID-19 patients was 1,333/50,606 (2.6%) compared with 52,937/844,200 (0.4%) in controls; the absolute impact of COVID-19 on ED was 656/100,000 (ranging from 4/100,000 to 2352/100,000). Anxiety (OR: 1.13, 95% CI 1.03-1.26, I2 = 0.0%) in COVID-19 patients was a risk factor for ED. CONCLUSION: COVID-19 patients have a high risk and prevalence of ED, mainly driven by anxiety. Attention should be paid to patient's erectile functioning when treating COVID-19.

Nanotechnology for ultrafast nucleic acid amplification

Nucleic acid detection has been one of the most valued tools in point-of-care diagnostics from life science, agriculture, food safety and environmental surveillance, because of its high sensitivity, great specificity and simple operation. Since polymerase chain reactions (PCR) were discovered, more and more researchers attach importance to exploring ultrafast nucleic acid amplification methods for further expediting the process of detection and curbing infectious diseases' high spread rate, especially after the coronavirus disease 2019 (COVID-19) worldwide pandemic event. Nowadays, nanotechnology as one of the most cut-ting-edge technologies has aroused growing attention. In this review, we describe new advances in na-notechnology research for ultrafast nucleic acid amplification. We have introduced commonly used nanotechnologies, namely nanofluidics, nanoporous materials, nanoparticles and so on. Recent advances in these nanotechnologies for ultrafast sample pretreatments, accelerated enzymatic amplification and rapid heating/cooling processes was summarized. Finally, challenges and perspectives for the future applications of ultrafast nucleic acid amplification are presented.(c) 2022 Elsevier Ltd. All rights reserved.

BAYESIAN CLUSTERING OF SPATIAL FUNCTIONAL DATA WITH APPLICATION TO A HUMAN MOBILITY STUDY DURING COVID-19

The coronavirus (COVID-19) global pandemic has made a significant impact on people's social activities. Cell phone mobility data provide unique and rich information on studying this impact. The motivating dataset of this study is the daily leaving-home index data at Harris County in Texas provided by SafeGraph. To study changes in daily leaving-home index and how they relate to public policy and sociodemographic variables, we propose a new Bayesian wavelet model for modeling and clustering spatial functional data, where domain partitioning is achieved by operating on the spanning trees. The resulting clusters can have arbitrary shapes and are spatially contiguous in the input domain. An efficient tailored reversible jump Markov chain Monte Carlo algorithm is proposed to implement the model. The method is applied to the spatial functional data of the daily percentages of people who left home. We focus on the time period covering both lockdown and phased reopening in Texas during the COVID-19 pandemic and study the changing behaviors of those functional curves. By linking the clustering results with the sociodemographic information, we identify several covariates of census blocks that have a noticeable impact on the clustering patterns of people's mobility behaviors. © Institute of Mathematical Statistics, 2023.

Study on Measuring Method of Vehicle Carbon Emission in Expressway Network

Under the background of dual-carbon policy, it is imperative to maintain ecological balance and reduce carbon emissions. Nowadays, it is very important to accurately measure the carbon emission index of vehicles on a wide range of road networks. Therefore, this study proposed a measuring method of vehicle carbon emission in expressway network based on multi-source data fusion. Firstly, a basic data cleaning method for carbon emission statistics was proposed to clean the basic data required for subsequent carbon emission calculation. Secondly, the highway carbon emission calculation model was established, and then the related calculation process was designed. Finally, taking the whole highway network of Guangdong province as an example, this paper calculated the vehicle carbon emission from September 2020 to June 2021, and compared the calculation results with China's carbon accounting database. Through this method, the proposed method was proved to be scientific and reliable. The research shows that the average carbon emission of mini buses in Guangdong province is small, but the total carbon emission accounts for the largest proportion of all types of vehicles, up to 52.1%;the total carbon emission of gasoline vehicles accounts for 49.8%, which is higher than that of diesel vehicles (45.4%) and of other energy vehicles (4.8%). Vigorously promoting new energy vehicles can effectively reduce the carbon emission of expressways. In addition, the study finds that there are significant differences in the travel patterns of different vehicles under COVID-19, but the overall impact on the transportation economy is limited. © 2022, Editorial Department, Journal of South China University of Technology. All right reserved.

Audio Feature Ranking for Sound-Based COVID-19 Patient Detection

Audio classification using breath and cough samples has recently emerged as a low-cost, non-invasive, and accessible COVID-19 screening method. However, a comprehensive survey shows that no application has been approved for official use at the time of writing, due to the stringent reliability and accuracy requirements of the critical healthcare setting. To support the development of Machine Learning classification models, we performed an extensive comparative investigation and ranking of 15 audio features, including less well-known ones. The results were verified on two independent COVID-19 sound datasets. By using the identified top-performing features, we have increased COVID-19 classification accuracy by up to 17% on the Cambridge dataset and up to 10% on the Coswara dataset compared to the original baseline accuracies without our feature ranking. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Evaluation and Improvement Strategy of Tourism Industrial Park Competitiveness from the Perspective of New Urbanization: Case Study of the Suining Tourism Industrial Park

Since China's reform and opening up, the domestic economic structure has undergone significant changes, and the industrial system has gradually developed to the tertiary industry. As the core of the tertiary sector and service industry, tourism has, therefore, entered a stage of vigorous development. At the same time, in the context of promoting new-type urbanization, industrial parks created by the theory of industry-urban integration have entered a new stage of development. Due to the respective characteristics of industrial parks and tourism, tourism industrial parks with comprehensive coverage and a strong driving ability have gradually formed under the promotion of industry-urban integration. As a new carrier of the tourism industry, tourism industrial parks are being built all over China. However, due to the differences in the development level of each region, how to improve the competitiveness in the construction of tourism industrial parks has become a problem worth studying. Based on the diamond model in the competitiveness theory, this paper first improves the model according to the actual situation and constructs the competitiveness evaluation index system of tourism industrial parks from the perspective of new-type urbanization. Second, the weight of each index is calculated using the Analytic Network Process (ANP), and then the competitiveness evaluation model is constructed by the matter-element method. Finally, the evaluation model is verified by taking the Suining city Tourism Industrial Park as an example, and corresponding improvement suggestions are put forward for this case. At the same time, the feedback from this evaluation process also provides a scientific method and theoretical basis for enhancing the competitiveness of tourism industrial parks and provides a new idea for the future development of such parks. © 2022 American Society of Civil Engineers.

COVID-19 and systemic anti-cancer therapy

［］ The corona virus disease 2019 (COVID-19) pandemic continues to severely impact healthcare systems around the world, and patients with cancer are even worse affected owing to compromised immune status and greater exposure risk. In the present review, we retrieved the relevant literature including guidelines and consensuses directly related to the purpose of this study from the PubMed database, and then summarized the research data on cancer and COVID-19, aiming to discuss the personal protection, systemic anti-cancer therapy, outcome of co-infection, and the clinical management strategy in this population. We found that patients with malignant tumors had a higher chance of suffering COVID-19, co-infection of whom had an even worse clinical prognosis, especially for those with lung cancer or hematologic cancers. Systemic chemotherapy may delay the clearance of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) virus of human body, and thus have a negative impact on the clinical outcomes of COVID-19, while certain endocrine therapy and targeted drugs having limited or no impact. There has been no sufficient evidence for the impact of immune checkpoint therapy on the outcomes of COVID-19 till now. It is of great value to strengthen the personal protection of patients, adjust the anti-tumor treatments rationally and optimize the clinical management processes. © 2022, Editorial Office of China Oncology. All rights reserved.

Clinical Efficacy of Modified Sanxiaoyin in Treatment of Mild or Moderate COVID-19 Patients：Based on Retrospective Analysis

［］ Objective： To retrospectively analyze the clinical data of 52 patients with coronavirus disease-2019 （COVID-19） and explore the clinical efficacy of modified Sanxiaoyin on mild/moderate COVID-19 patients. Method： The propensity score matching method was used to collect the clinical data of mild or moderate COVID-19 patients enrolled in the designated hospital of the Second Hospital of Jingzhou from December 2019 to May 2020. A total of 26 eligible patients who were treated with modified Sanxiaoyin were included in the observation group，and the 26 patients treated with conventional method were the regarded as the control. The disappearance of clinical symptoms，disappearance time of main symptoms，efficacy on traditional Chinese medicine（TCM）symptoms，hospitalization duration，laboratory test indicators，and CT imaging changes in the two groups were compared. Result： The general data in the two groups were insignificantly different and thus they were comparable. After 7 days of treatment，the disappearance rate of fever，cough， fatigue，dry throat，anorexia，poor mental state，and poor sleep quality in the observation group was higher than that in the control group（P<0.05），and the difference in the disappearance rate of expectoration and chest distress was insignificant. For the cases with the disappearance of symptoms，the main symptoms（fever， cough，fatigue，dry throat，anorexia，chest distress）disappeared earlier in the observation group than in the control group（P<0.01）. After 7 days of treatment，the scores of the TCM symptom scale of both groups decreased（P<0.01），and the decrease of the observation group was larger that of the control group（P<0.01）. All patients in the two groups were cured and discharged. The average hospitalization duration in the observation group［（12.79±2.68）d］was shorter than that in the control group［（15.27±3.11）d］（P<0.01）. The effective rate in the observation group（92.31%，24/26）was higher than that in the control group（76.92%，20/26）. After 7 days of treatment，the lymphocyte（LYM）count increased（P<0.05），and white blood cell（WBC）count and neutrophil（NEUT）count decreased insignificantly in the two groups. Moreover，levels of C-reactive protein （CRP），erythrocyte sedimentation rate（ESR），and procalcitonin（PCT）reduced in the two groups after treatment（P<0.01）and the reduction in the observation group was larger than that in the control group （P<0.01）. Through 7 days of treatment，the total effective rate on pulmonary shadow in the observation group （90.00%，18/20）was higher than that in the control group（77.27%，17/22）（P>0.05）and the improvement of lung shadow in the observation group was better than that in the control group（P<0.01）. Conclusion：Modified Sanxiaoyin can significantly alleviate fever，cough，fatigue，anorexia，chest distress，poor sleep quality，and other symptoms of patients with mild or moderate COVID-19，improve biochemical indicators，and promote the recovery of lung function. This paper provides clinical evidence for the application of modified Sanxiaoyin in the treatment of mild or moderate COVID-19. © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

Moderating Effect of a Cross-Level Social Distancing Policy on the Disparity of COVID-19 Transmission in the United States

Currently, coronavirus disease 2019 (COVID-19) remains a global pandemic, but the prevention and control of the disease in various countries have also entered the normalization stage. To achieve economic recovery and avoid a waste of resources, different regions have developed prevention and control strategies according to their social, economic, and medical conditions and culture. COVID-19 disparities under the interaction of various factors, including interventions, need to be analyzed in advance for effective and precise prevention and control. Considering the United States as the study case, we investigated statistical and spatial disparities based on the impact of the county-level social vulnerability index (SVI) on the COVID-19 infection rate. The county-level COVID-19 infection rate showed very significant heterogeneity between states, where 67% of county-level disparities in COVID-19 infection rates come from differences between states. A hierarchical linear model (HLM) was adopted to examine the moderating effects of state-level social distancing policies on the influence of the county-level SVI on COVID-19 infection rates, considering the variation in data at a unified level and the interaction of various data at different levels. Although previous studies have shown that various social distancing policies inhibit COVID-19 transmission to varying degrees, this study explored the reasons for the disparities in COVID-19 transmission under various policies. For example, we revealed that the state-level restrictions on the internal movement policy significantly attenuate the positive effect of county-level economic vulnerability indicators on COVID-19 infection rates, indirectly inhibiting COVID-19 transmission. We also found that not all regions are suitable for the strictest social distancing policies. We considered the moderating effect of multilevel covariates on the results, allowing us to identify the causes of significant group differences across regions and to tailor measures of varying intensity more easily. This study is also necessary to accomplish targeted preventative measures and to allocate resources.

COVID-19 Vaccine Sensing: Sentiment Analysis from Twitter Data

The COVID-19 outbreak a pandemic, which poses a serious threat to global public health and lead to a tsunami of online social media. Individuals frequently express their views, opinions and emotions about the events of the pandemic on Twitter, Facebook, etc. Many researches try to analyze the sentiment of the COVID-19-related content from these social networks. However, they have rarely focused on the vaccine. In this paper, we study the COVID-19 vaccine topic from Twitter. Specifically, all the tweets related to COVID-19 vaccine from December 15th, 2020 to February 10th, 2021 are collected by using the Twitter API, then the unsupervised learning VADER model is used to judge the emotion categories (positive, neutral, negative) and calculate the sentiment value of the dataset. Based on the interaction between users, a communication topological network is constructed and the emotional direction is explored. We find that people had different sentiments between Chinese vaccine and those in other countries. The sentiment value might be affected by the number of daily news cases and deaths, the nature of key issues in the communication network. And revealing that the key nodes in the social network can produce emotional contagion to other nodes. © 2021 IEEE.

Evaluation of the VOC pollution pattern and emission characteristics during the Beijing resurgence of COVID-19 in summer 2020 based on the measurement of PTR-ToF-MS

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.

Classification of COVID-19 in CT Scans Using Image Smoothing and Improved Deep Residual Network

The global spread of coronavirus disease has become a major threat to global public health. There are more than 137 million confirmed cases worldwide at the time of writing. The spread of COVID-19 has resulted in a huge medical load due to the numerous suspected examinations and community screening. Deep learning methods to automatically classify COVID-19 have become an effective assistive technology. However, the current researches on data quality and the use of CT data to diagnose COVID-19 with convolutional neural networks are poor. This study is based on CT scan data of COVID-19 patients, patients with other lung diseases, and healthy people. In this work, we find that data smoothing can improve the quality of CT images of COVID-19 and improve the accuracy of the model. Specifically, an interpolation smoothing method is proposed using the bilinear interpolation algorithm. Besides, we propose an improved ResNet structure to improve the model feature extraction and fusion by optimizing the structure of the input stem and downsampling parts. Compared with the baseline ResNet, the model improves the accuracy of the three-class classification by 3.8% to 93.83%. Our research has particular significance for research on the automatic diagnosis of COVID-19 infectious diseases. © 2021, Springer Nature Switzerland AG.

Towards Robust Cross-domain Image Understanding with Unsupervised Noise Removal

Deep learning has made a tremendous impact on various applications in multimedia, such as media interpretation and multimodal retrieval. However, deep learning models usually require a large amount of labeled data to achieve satisfactory performance. In multimedia analysis, domain adaptation studies the problem of cross-domain knowledge transfer from a label rich source domain to a label scarce target domain, thus potentially alleviates the annotation requirement for deep learning models. However, we find that contemporary domain adaptation methods for cross-domain image understanding perform poorly when source domain is noisy. Weakly Supervised Domain Adaptation (WSDA) studies the domain adaptation problem under the scenario where source data can be noisy. Prior methods on WSDA remove noisy source data and align the marginal distribution across domains without considering the fine-grained semantic structure in the embedding space, which have the problem of class misalignment, e.g., features of cats in the target domain might be mapped near features of dogs in the source domain. In this paper, we propose a novel method, termed Noise Tolerant Domain Adaptation (NTDA), for WSDA. Specifically, we adopt the cluster assumption and learn cluster discriminatively with class prototypes (centroids) in the embedding space. We propose to leverage the location information of the data points in the embedding space and model the location information with a Gaussian mixture model to identify noisy source data. We then design a network which incorporates the Gaussian mixture noise model as a sub-module for unsupervised noise removal and propose a novel cluster-level adversarial adaptation method based on the Generative Adversarial Network (GAN) framework which aligns unlabeled target data with the less noisy class prototypes for mapping the semantic structure across domains. Finally, we devise a simple and effective algorithm to train the network from end to end. We conduct extensive experiments to evaluate the effectiveness of our method on both general images and medical images from COVID-19 and e-commerce datasets. The results show that our method significantly outperforms state-of-the-art WSDA methods. © 2021 Owner/Author.