Performance of in-duct bipolar ionization devices on pollutant removal and potential byproduct formation in indoor environments

The COVID-19 pandemic has highlighted the importance of indoor air quality (IAQ) since SARS-CoV-2 may be transmitted through virus-laden aerosols in poorly ventilated spaces. Multiple air cleaning technologies have been developed to mitigate airborne transmission risk and improve IAQ. In-duct bipolar ionization technology is an air cleaning technology that can generate ions for inactivating airborne pathogens and increasing particle deposition and removal while without significant byproducts generated. Many commercial in-duct ionization systems have been developed but their practical performance on pollutant removal and potential formation of byproducts have not been investigated comprehensively. The results in this study showed that the in-duct bipolar ionization technology can significantly improve the particle removal efficiency of the regular filter, while no significant ozone and ion were released to the indoor air. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Examining gains and pains of a new virtual internship design

This study investigated how student effort and the course design influenced an online internship in China. A cohort of 95 postgraduate students became distance learners in a credit-bearing internship course due to COVID-19. The course leader applied the action learning framework to prompt student online collaboration and group inquiry. The framework assumes the importance of self-reliant learner autonomy in virtual internships. After the course, researchers analyzed the effects of self-directed learning with technology on a multidimensional community of inquiry in a virtual environment. The study also identified students' narratives that explain how self-directed learning with technology interacted with three elements of virtual communities of inquiry: social, cognitive, and teaching. Findings explain how virtual internships can be facilitated through a community of inquiry model. Educators and practitioners may consider the model to demonstrate student-staff partnerships (Fitzgerald et al., 2020) to achieve quality transformation of internships from face-to-face mode to distance education. © 2023 Open and Distance Learning Association of Australia, Inc.

Bibliometric analysis and system review of vehicle routing optimization for emergency material distribution

Determining the optimal vehicle routing of emergency material distribution (VREMD) is one of the core issues of emergency management, which is strategically important to improve the effectiveness of emergency response and thus reduce the negative impact of large-scale emergency events. To summarize the latest research progress, we collected 511 VREMD-related articles published from 2010 to the present from the Scopus database and conducted a bibliometric analysis using VOSviewer software. Subsequently, we cautiously selected 49 articles from these publications for system review;sorted out the latest research progress in model construction and solution algorithms;and summarized the evolution trend of keywords, research gaps, and future works. The results show that do -mestic scholars and research organizations held an unqualified advantage regarding the number of published papers. However, these organizations with the most publications performed poorly regarding the number of literature citations. China and the US have contributed the vast majority of the literature, and there are close collaborations between researchers from both countries. The optimization model of VREMD can be divided into single-, multi-, and joint-objective models. The shortest travel time is the most common optimization objective in the single-objective optimization model. Several scholars focus on multiobjective optimization models to consider conflicting objectives simultaneously. In recent literature, scholars have focused on the impact of uncertainty and special events (e.g., COVID-19) on VREMD. Moreover, some scholars focus on joint optimization models to optimize vehicle routes and central locations (or material allocation) simultaneously. So-lution algorithms can be divided into two primary categories, i.e., mathematical planning methods and intelligent evolutionary algorithms. The branch and bound algorithm is the most dominant mathematical planning algorithm, while genetic algorithms and their enhancements are the most commonly used intelligent evolutionary algorithms. It is shown that the nondominated sorting genetic algorithm II (NSGA-II) can effectively solve the multiobjective model of VREMD. To further improve the algorithm's performance, re-searchers have proposed improved hybrid intelligent algorithms that combine the ad-vantages of NSGA-II and certain other algorithms. Scholars have also proposed a series of optimization algorithms for specific scenarios. With the development of new technologies and computation methods, it will be exciting to construct optimization models that consider uncertainty, heterogeneity, and temporality for large-scale real-world issues and develop generalized solution approaches rather than those applicable to specific scenarios.(c) 2022 Periodical Offices of Chang'an University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Globalization, population flow and the spatial diffusion of COVID-19

The COVID-19 pandemic has swept the world since December 2019. The spread of COVID-19 has much to do with population flow and close human contacts. This paper demonstrates that the distribution of COVID-19 cases has close relation with the population flow and migration flow in the case of China. Rapid globalization has increased the volumes of migration and travelers in the world since the 1970s. If we reduce the number of air passengers to the level of 0.31 billion in 1970 by 13.6 times in the world, this may delay the same level of infections from being reached by about 3.5 weeks with reduced number of virus export and diffusion. But various authorities may only begin to take systematic and restrictive actions after the case number reaching certain "alarming level", above "saved time" may not be effectively used as the "alarming level" may simply emerge later. The global production network is not able to meet unexpected surging demand of personal protective equipment and other medical essentials in the early stage of pandemic. Emergency plans are need to expand production capacity quickly to deal with future pandemic.

Offerings and challenges of demand response ventilation under covid-19 scenarios

Owning to the outbreak of COVID-19, individuals have to spend more time indoor. It is therefore essential to prepare for a long-term healthy indoor working environment in the transition of post COVID-19 pandemic. However, there is no relevant research so far in investigating such crisis impacts around indoor environmental quality and economic-health issues while home offices are expected becoming common practice soon. Therefore, a case of single-family house in Sweden is specially investigated using IDA ICE. By comparing four predominant ventilation approaches, three operational schedules are proposed, covering different confinement for occupants. Main results show that the demand response ventilation (DRV) generally should sacrifice in remarkable performance in energy saving, and emission reduction to better confront with more challenges in indoor air quality, occupied thermal dissatisfaction fraction and air stagnation under the challenge of COVID-19 pandemic scenario. Altered ventilation strategy should be customized from increased outdoor air supply, various demand-control signal, displacement method towards a heathier homeworking environment. © International Building Performance Simulation Association, 2022

Family literacies during the COVID-19 lockdown: Semiotic assemblages and meaning making at home

When home became the primary place for children's learning during the COVID-19 lockdown, a dominant rhetoric emerged about a literacy-skills crisis, especially involving learners from low-income and culturally and linguistically diverse families. By documenting the literacies practiced and the literacy-learning opportunities created in and among households during the lockdown in the spring and summer of 2020, this study turns this deficit-oriented rhetoric on its head. Conducted by parents with their children (aged 2-15), this collective biography found that during the lockdown households were forced into spaces that were physically constrained yet replete with a wide range of semiotic resources. Parents and children used these resources, which included multiple modes, media, and languages, to produce expansive literacies and literacy-learning opportunities. The present study offers suggestions about how to recognize and build on learners' linguistic, cultural, and semiotic repertoires in the creation of literacy curricula. © 2023 Elsevier Inc.

Pattern dynamics and bifurcation in delayed SIR network with diffusion network

The spread of infectious diseases often presents the emergent properties, which leads to more difficulties in prevention and treatment. In this paper, the SIR model with both delay and network is investigated to show the emergent properties of the infectious diseases' spread. The stability of the SIR model with a delay and two delay is analyzed to illustrate the effect of delay on the periodic outbreak of the epidemic. Then the stability conditions of Hopf bifurcation are derived by using central manifold to obtain the direction of bifurcation, which is vital for the generation of emergent behavior. Also, numerical simulation shows that the connection probability can affect the types of the spatio-temporal patterns, further induces the emergent properties. Finally, the emergent properties of COVID-19 are explained by the above results. © 2023 World Scientific Publishing Company.

Analysis of medication regularities of epidemic disease prescription in Treatise on Febrile Diseases and mechanism of core drugs in treatment of COVID-19

Objective To analyze the medication rules of related epidemic disease prescription in Treatise on Febrile Diseases based on data mining, and the mechanism of "Chaihu (Bupleuri Radix)-Huangqin (Scutellariae Radix)” as the core drugs in the treatment of coronavirus disease 2019 (COVID-19) by network pharmacology, in order to explore the contemporary value of classical prescriptions in the treatment of epidemic diseases. Methods The prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were screened, and the medication rules such as drug frequency, flavor and meridian tropism as well as correlation, apriori algorithm were analyzed by using software such as R language. The mechanism of the core drugs in the medication pattern in the treatment of COVID-19 was explored by the network pharmacology. A "disease-drug-ingredient-target” network was constructed on the selected components and targets with Cytoscape. The key targets were introduced into String database for network analysis of protein-protein interaction (PPI), and gene ontology (GO) functional analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were conducted in R language. Results A total of 61 prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were included, including 52 traditional Chinese medicines (TCMs). In the top 20 high-frequency drugs, warm drugs, spicy drugs and qitonifying drugs were mainly used, mostly in the spleen and lung meridian. Chaihu (Bupleuri Radix) and Huangqin (Scutellariae Radix) herb pair had the strongest correlation. A total of five clusters were excavated: supplemented formula of Xiaochaihu Decoction (小柴胡汤), Sini Decoction (四逆汤), supplemented formule of Maxing Shigan Decoction (麻杏石甘汤), Fuling Baizhu Decoction (茯苓白术汤) and Dachengqi Decoction (大承气汤). A total of 45 active ingredients, 189 action targets of Bupleuri Radix-Scutellariae Radix herb pair, and 543 targets of COVID-19 were obtained from TCMSP and Genecards, and 64 intersection targets were generated. The results of the network analysis showed that the main components of core drugs pair against COVID-19 may be quercetin, wogonin, kaempferol baicalein, acacetin etc., and the core targets may be VEGFA, TNF, IL-6, TP53, AKT1, CASP3, CXCL8, PTGS2, etc. A total of 1871 related entries and 164 pathways were obtained by GO and KEGG enrichment analysis, respectively. Conclusion In Treatise on Febrile Diseases, the treatment of epidemic diseases mainly chose pungent, warm, spleen-invigorating and qi-tonifying herbs, such as Xiaochaihu Decoction, Sini Decoction and Dachengqi Decoction, etc. It was found that Bupleuri Radix-Scutellariae Radix core herb pair prevent and treat COVID-19 through multi-target targets such as PTGS2, IL-6 and TNF. The ancient prescriptions for treating epidemic disease in Treatise on Febrile Diseases may have significant reference value for the prevention and treatment of new epidemic diseases today. © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Estimation of infection risk through airborne transmission in large open spaces with different air distributions

Respiratory diseases such as COVID-19 can be spread through airborne transmission, which is highly dependent on the airflow pattern of the studied room. Indoor air is typically not perfectly mixed even using a mixing ventilation, especially in large spaces. Airflow patterns in large open spaces such as hotel banquet rooms and open plan offices, are of particular concern, as these spaces usually accommodate more occupants and thus have the potential to spread diseases more rapidly leading to outbreaks. Therefore, understanding airflow patterns in large open spaces can help to estimate the detailed infection risk at certain locations in the space, which can prevent the spread of virus and track the potential new infections. This study estimated airflow patterns in a typical banquet room under theatre and banquet scenarios, and a large open plan office using computational fluid dynamics (CFD) simulations. Typical ventilation and air distribution approaches, as well as room layouts and occupant configurations in these scenarios were studied and applied in simulations. According to current results, the air distribution in a typical hotel banquet room with mixing ventilation can be very complicated, particularly for the banquet scenario. For a typical theatre scenario, under typical ventilation design, people sitting in the middle and lateral area were exposed to the highest infection risk. The front rows may be exposed to short-range transmission as well. For a banquet scenario, people sitting on the same table were more likely to be cross contaminated. But cross-table infection was still possible. The results can provide guidance on designing ventilation and air distribution approaches in large spaces with similar settings. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)

Customized High-Sensitivity Plasmonic Metasensing Towards Immunodetection of Single Bio-Nanoparticles

Plasmonic metasurface biosensing has shown great potential in label-free detection of bio-nanoparticles with various sizes, such as cancer antigens, exosomes and SARS-CoV-2 virus. It typically relies on the immunoassay, but current studies usually neglect the perfect size matching between each target bio-nanoparticle and the surface near-field domain, which should be very critical for the enhancement of detection performance. In order to maximize the immunodetection capability for each bio-nanoparticle, we propose a plasmonic meta-biosensor based on the field-customized mechanism. Our design overcomes the serious interference of biofunctionalization and accomplishes a sensitivity of 27 times higher than the conventional nanoplasmonic counterpart. Our method also builds the important basis of single bio-nanoparticle immunodetection by a plasmonic metasurface. The customized plasmonic metasensing study implies a promising way towards ultra-low concentration biosensing or even single bio-nanoparticle detection for high-performance point-of-care-testing in the near future. © 1995-2012 IEEE.

Design and Optimize of Campus Nucleic Acid Tests Queuing System

Covid-19 is more likely to spread in campus than it in other places because students live together without masks. In this case, it is necessary to take nucleic acid tests in a unified time regularly. To make nucleic acid tests efficient and convenient to manage students and the testing time, this article would apply queuing theory to design a nucleic acid tests queuing system by using the data from Sanda University in April 2022. According to the special conditions on campus, such as course schedule, students' daily activities, and campus management, students would be grouped by several management styles. The system would calculate the start time and waiting time for each group and would strive to take nucleic acid tests in an orderly manner with minimal waiting time. © 2022 IEEE.

Trifluoroacetic Anhydride as Acylating Agent and Dehydrant in One-pot Synthesis of Nirmatrelvir

Nirmatrelvir is an effective ingredient in the anti COVID-19 drug Paxlovid. There were two key steps in the original synthetic route, which involved trifluoroacetylation and dehydration. A facile and efficient synthesis of nirmatrelvir is described in this work. Intermediate 7 was converted to nirmatrelvir in one-pot synthesis with trifluoroacetic anhydride. In addition, the condensation and deprotection conditions were optimized. The yield of nirmatrelvir produced from 1raised from 51.6% to 72.5%. © 2023,Cleveland Clinic Journal of Medicine.All Rights Reserved.

Monte Carlo Tree Search improved Genetic Algorithm for unmanned vehicle routing problem with path flexibility

With the gradual normalization of the COVID-19, unmanned delivery has gradually become an important contactless distribution method around China. In this paper, we study the routing problem of unmanned vehicles considering path flexibility and the number of traffic lights in the road network to reduce the complexity of road conditions faced by unmanned vehicles as much as possible. We use Monte Carlo Tree Search algorithm to improve the Genetic Algorithm to solve this problem, first use Monte Carlo Tree Search Algorithm to compute the time-saving path between two nodes among multiple feasible paths and then transfer the paths results to Genetic Algorithm to obtain the final sequence of the unmanned vehicles fleet. And the hybrid algorithm was tested on the actual road network data around four hospitals in Beijing. The results showed that compared with normal vehicle routing problem, considering path flexibility can save the delivery time, the more complex the road network composition, the better results could be obtained by the algorithm.

Analysis of medication regularities of epidemic disease prescription in Treatise on Febrile Diseases and mechanism of core drugs in treatment of COVID-19

Objective To analyze the medication rules of related epidemic disease prescription in Treatise on Febrile Diseases based on data mining, and the mechanism of "Chaihu (Bupleuri Radix)-Huangqin (Scutellariae Radix)" as the core drugs in the treatment of coronavirus disease 2019 (COVID-19) by network pharmacology, in order to explore the contemporary value of classical prescriptions in the treatment of epidemic diseases. Methods The prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were screened, and the medication rules such as drug frequency, flavor and meridian tropism as well as correlation, apriori algorithm were analyzed by using software such as R language. The mechanism of the core drugs in the medication pattern in the treatment of COVID-19 was explored by the network pharmacology. A "disease-drug-ingredient-target" network was constructed on the selected components and targets with Cytoscape. The key targets were introduced into String database for network analysis of protein-protein interaction (PPI), and gene ontology (GO) functional analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were conducted in R language. Results A total of 61 prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were included, including 52 traditional Chinese medicines (TCMs). In the top 20 high-frequency drugs, warm drugs, spicy drugs and qitonifying drugs were mainly used, mostly in the spleen and lung meridian. Chaihu (Bupleuri Radix) and Huangqin (Scutellariae Radix) herb pair had the strongest correlation. A total of five clusters were excavated: supplemented formula of Xiaochaihu Decoction (), Sini Decoction (), supplemented formule of Maxing Shigan Decoction (), Fuling Baizhu Decoction () and Dachengqi Decoction (). A total of 45 active ingredients, 189 action targets of Bupleuri Radix-Scutellariae Radix herb pair, and 543 targets of COVID-19 were obtained from TCMSP and Genecards, and 64 intersection targets were generated. The results of the network analysis showed that the main components of core drugs pair against COVID-19 may be quercetin, wogonin, kaempferol baicalein, acacetin etc., and the core targets may be VEGFA, TNF, IL-6, TP53, AKT1, CASP3, CXCL8, PTGS2, etc. A total of 1871 related entries and 164 pathways were obtained by GO and KEGG enrichment analysis, respectively. Conclusion In Treatise on Febrile Diseases, the treatment of epidemic diseases mainly chose pungent, warm, spleen-invigorating and qi-tonifying herbs, such as Xiaochaihu Decoction, Sini Decoction and Dachengqi Decoction, etc. It was found that Bupleuri Radix-Scutellariae Radix core herb pair prevent and treat COVID-19 through multi-target targets such as PTGS2, IL-6 and TNF. The ancient prescriptions for treating epidemic disease in Treatise on Febrile Diseases may have significant reference value for the prevention and treatment of new epidemic diseases today. Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Predicting next 7-day Discharges of Hospitalized COVID-19 Patients Using Ensemble Learning

Background. COVID-19 pandemic, especially during resurgences of cases in hard-hit areas, led to significant shortage of hospital beds. Such shortages may be alleviated through timely and effective forecasting of hospital discharges. The objective of this study is to predict next 7-day discharges of hospitalized COVID-19 patients using daily-based electronic health records (EHR) data. Methods. Using EHR data of hospitalized COVID-19 patients from 03/2020-08/ 2021, we employed ensemble learning to predict next 7-day discharges of individual patients. We used both baseline and daily inpatient features for model training, validation, and test. Baseline features include demographic and clinical characteristics, and comorbidities. The daily inpatient features were vital signs, laboratory tests, medications administered, acute physiological scores, use of ventilator, and use of intensive care unit. 1832 hospitalized patients were identified (12,397 hospital days). Samples were randomly split at patient level (7:2:1) into training set (N=1,283 patients with 8,704 hospital days), validation set (N=366 patients with 2,524 days), and test/ holdout set (patient N=183, and 1,169 days). Prediction models were trained on the training set and the validation set. We conducted the model training separately on the samples of admission day and the samples of days after admission day. The predictions were based on the ensemble learning from decision tree, XGBoost, logistic regression, and multilayer perceptron, long short-term memory (LSTM), bi-directional LSTM, and convolutional neural network. The combination of ensemble learning on the test/holdout set was used for final next 7-day predictions based on 'hard' voting (by majority). Where there was a tie, we used 'soft' voting (sum of probabilities) to break the tie. (Figure Presented) Results. The overall average hospital length of stay was 8.7 (SD=10.5) days. The ensemble learning accuracies for admission-day samples and after-admission-day samples were 0.781 and 0.793, and the F1-scores for were 0.761 and 0.789, respectively. Conclusion. EHR data of hospitalized COVID-19 patients can be used to predict next 7-day hospital discharges. Additional inpatient features and more advanced machine learning techniques are needed for prediction accuracy improvement.

Mask Wearing Detection Algorithm Based on Improved SSD

In recent years, the COVID-19, which involves a highly infectious virus, has spread worldwide.Wearing masks in public areas can reduce the transmission and hence the spread of the virus.Additionally, using computer vision technology to detect mask wearing behavior in public areas is crucial.To prevent and control epidemics, the correct form of wearing face masks must be identified.In an actual environment, the detection of mask wearing is complex and diverse.The scale of a face wearing a mask is different;furthermore, the difference between the correct and wrong forms of wearing a mask is subtle and hence difficult to detect.Therefore, a mask wearing detection algorithm based on an improved Single Shot Multibox Detector(SSD) algorithm is proposed herein.Based on the SSD network, the algorithm introduces a feature fusion network and an attention coordination mechanism, reconstructs the feature extraction network, and enhances the ability of learning and processing detailed information.In addition, the classification prediction score and IoU score of the algorithm are combined, whereas the Quality Focal Loss(QFL) function is used to adjust the weight of positive and negative samples.An experiment is performed on acustom-developed mask wearing test dataset.Experimental results show that the average accuracy of the algorithm is 96.28%, which is 5.62% higher than that of the original algorithm.The improved algorithm offers good accuracy and practicability for mask wearing detection, as well assatisfies the requirements for epidemic prevention and control. © 2022, Editorial Office of Computer Engineering. All rights reserved.

[Spatiotemporal changes of COVID-19 outbreak in Shanghai].

Objective: To clarify the epidemiological characteristics and spatiotemporal clustering dynamics of COVID-19 in Shanghai in 2022. Methods: The COVID-19 data presented on the official websites of Municipal Health Commissions of Shanghai during March 1, 2022 and May 31, 2022 were collected for a spatial autocorrelation analysis by GeoDa software. A logistic growth model was used to fit the epidemic situation and make a comparison with the actual infection situation. Results: Pudong district had the highest number of symptomatic and asymptomatic infectants, accounting for 29.30% and 35.58% of the total infectants. Differences in cumulative attack rates and infection rates among 16 districts (P<0.001) were significant. The rates were significantly higher in Huangpu district than in other districts. The attack rate of COVID-19 from March 1, 2022 to May 31, 2022 had a global spatial positive correlation (P<0.05). Spatial distribution of COVID-19 attack rate was different at different periods. The global autocorrelation coefficient from March 16 to March 29, April 6 to April 12 and May 18 to May 24 had no statistical significance (P>0.05). Our local autocorrelation analysis showed that 22 high-high clustering areas were detected in eight periods.The high-risk hot-spot areas have experienced a "less-more-less" change process. The growth model fitting results were consistent with the actual infection situation. Conclusion: There was a clear spatiotemporal correlation in the distribution of COVID-19 in Shanghai. The comprehensive prevention and control measures of COVID-19 epidemic in Shanghai have effectively prohibited the growth of the epidemic, not only curbing the spatially spread of high-risk epidemic areas, but also reducing the risk of transmission to other cities.

Adapting New Learners and New Resources to Micro Open Learning via Online Computation

Since the outbreak of COVID-19, an alternative way to keep students on the track, meanwhile, prevent them from being at the risk of infection is in highly demand. Many education providers had made a move in trial of delivering knowledge and learning materials remotely. Along with this trend, learning management systems, open educational resources (OERs) and OER platforms, mini applications in social media and video-conference software were combined in a rush to create a multi-channel delivery mode to make learning resources openly available round-the-clock. Learning activities in this fast migration to online were regularly found to be carried out in gradual and fragmented time spans. Due to the little-known learner information along with the continuously released new OERs, the cold start problem still hinders the innovative mode of delivery and adaptive micro learning. To overcome the data sparsity, an online computation is proposed to benefit OER providers and instructors. A lightweight learner-micro-OER profile and two algorithmic solutions are provided to tackle the new user and new item cold start problem, respectively. Learning paths are generated and optimized in terms of heuristic rules to form the initial recommendation list. By adopting the same set of rules, newly released micro OERs are inserted into established learning paths to increase their discoverability. IEEE

Robust Cough Feature Extraction and Classification Method for COVID-19 Cough Detection Based on Vocalization Characteristics

A fast, efficient and accurate detection method of COVID-19 remains a critical challenge. Many cough-based COVID-19 detection researches have shown competitive results through artificial intelligence. However, the lack of analysis on vocalization characteristics of cough sounds limits the further improvement of detection performance. In this paper, we propose two novel acoustic features of cough sounds and a convolutional neural network structure for COVID-19 detection. First, a time-frequency differential feature is proposed to characterize dynamic information of cough sounds in time and frequency domain. Then, an energy ratio feature is proposed to calculate the energy difference caused by the phonation characteristics in different cough phases. Finally, a convolutional neural network with two parallel branches which is pre-trained on a large amount of unlabeled cough data is proposed for classification. Experiment results show that our proposed method achieves state-of-the-art performance on Coswara dataset for COVID-19 detection. The results on an external clinical dataset Virufy also show the better generalization ability of our proposed method. Copyright © 2022 ISCA.

Numerical Simulations of the Effects of the Radiant Floor Combined with the Displacement Ventilation of the Spread of Exhaled Contaminants in the Confined Space

The COVID-19 pandemic has seen the importance of confined space ventilation to reduce the risks of cross infection. To evaluate and compare the relative impacts of different mitigation strategies is important in order to reduce the risk of infection in a given situation. Using CFD methods, this study aimed to modulate the spread of exhaled contaminants in a floor-heated and ventilated space. Three different inlet velocities and four floor temperatures were used to assess the effect of the radiant floor combined with the displacement ventilation (RFDV) on room airflow and pollutant spread. Results show that RFDV reduced exposure to infection from 87% to 50% compared to the reference case. The inlet velocity is required to increase when the floor temperature is higher to decrease the contaminant exposure risk to in the room. This research provides a timely and necessary study of the ventilation and heating systems. These findings are expected to be useful for designing future of RFDV. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.