Time Delay Study of Ultrasonic Gas Flowmeters Based on VMD-Hilbert Spectrum and Cross-Correlation.

The accuracy of ultrasonic flowmeter time delay measurement is directly affected by the processing method of the ultrasonic echo signal. This paper proposes a method for estimating the time delay of the ultrasonic gas flowmeter based on the Variational Mode Decomposition (VMD)-Hilbert Spectrum and Cross-Correlation (CC). The method improves the accuracy of the ultrasonic gas flowmeter by enhancing the quality of the echo signal. To denoise forward and reverse ultrasonic echo signals collected at various wind speeds, a Butterworth filter is initially used. The ultrasonic echo signals are then analyzed by Empirical Mode De-composition (EMD) and VMD analysis to obtain the Intrinsic Mode Function (IMF) containing distinct center frequencies, respectively. The Hilbert spectrum time-frequency diagram is used to evaluate the results of the VMD and EMD decompositions. It is found that the IMF decomposed by VMD has a better filtering performance and better anti-interference performance. Therefore, the IMF with a better effect is selected for signal reconstruction. The ultrasonic time delay is then calculated using the Cross-Correlation algorithm. The self-developed ultrasonic gas flowmeter was tested on the experimental platform of the gas flow standard devices using this signal processing method. The results show a maximum indication error of 0.84% within the flow range of 60-606 m3/h, with a repeatability of no more than 0.29%. These results meet the 1-level accuracy requirements as outlined in the national ultrasonic flowmeters calibration regulation JJG1030-2007.

Optimization of Screening Strategies for COVID-19: Scoping Review.

BACKGROUND: COVID-19 screening is an effective nonpharmaceutical intervention for identifying infected individuals and interrupting viral transmission. However, questions have been raised regarding its effectiveness in controlling the spread of novel variants and its high socioeconomic costs. Therefore, the optimization of COVID-19 screening strategies has attracted great attention. OBJECTIVE: This review aims to summarize the evidence and provide a reference basis for the optimization of screening strategies for the prevention and control of COVID-19. METHODS: We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. We conducted a scoping review of the present publications on the optimization of COVID-19 screening strategies. We searched the PubMed, Web of Science, and Elsevier ScienceDirect databases for publications up to December 31, 2022. English publications related to screening and testing strategies for COVID-19 were included. A data-charting form, jointly developed by 2 reviewers, was used for data extraction according to the optimization directions of the screening strategies. RESULTS: A total of 2770 unique publications were retrieved from the database search, and 95 abstracts were retained for full-text review. There were 62 studies included in the final review. We summarized the results in 4 major aspects: the screening population (people at various risk conditions such as different regions and occupations; 12/62, 19%), the timing of screening (when the target population is tested before travel or during an outbreak; 12/62, 19%), the frequency of screening (appropriate frequencies for outbreak prevention, outbreak response, or community transmission control; 6/62, 10%), and the screening and detection procedure (the choice of individual or pooled detection and optimization of the pooling approach; 35/62, 56%). CONCLUSIONS: This review reveals gaps in the optimization of COVID-19 screening strategies and suggests that a number of factors such as prevalence, screening accuracy, effective allocation of resources, and feasibility of strategies should be carefully considered in the development of future screening strategies.

Acoustic Characterization of Transmitted and Received Acoustic Properties of Air-Coupled Ultrasonic Transducers Based on Matching Layer of Organosilicon Hollow Glass Microsphere.

An air-coupled transducer was developed in this study, utilizing hollow glass microsphere-organosilicon composites as an acoustically matching layer, which demonstrated outstanding acoustic performance. Firstly, a comparison and analysis of the properties and advantages of different substrates was carried out to determine the potential application value of organosilicon substrates. Immediately after, the effect of hollow glass microspheres with different particle sizes and mass fractions on the acoustic properties of the matching layer was analyzed. It also evaluated the mechanical properties of the matching layer before and after optimization. The findings indicate that the optimized composite material attained a characteristic acoustic impedance of 1.04 MRayl and an acoustic attenuation of 0.43 dB/mm, displaying exceptional acoustic performance. After encapsulating the ultrasonic transducer using a 3D-printed shell, we analyzed and compared its emission and reception characteristics to the commercial transducer and found that its emission acoustic pressure amplitude and reception voltage amplitude were 34% and 26% higher, respectively. Finally, the transducer was installed onto a homemade ultrasonic flow meter for practical application verification, resulting in an accuracy rate of 97.4%.

Optimizing the nucleic acid screening strategy to mitigate regional outbreaks of SARS-CoV-2 Omicron variant in China: a modeling study.

BACKGROUND: The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads rapidly and insidiously. Coronavirus disease 2019 (COVID-19) screening is an important means of blocking community transmission in China, but the costs associated with testing are high. Quarantine capacity and medical resources are also threatened. Therefore, we aimed to evaluate different screening strategies to balance outbreak control and consumption of resources. METHODS: A community network of 2000 people, considering the heterogeneities of household size and age structure, was generated to reflect real contact networks, and a stochastic individual-based dynamic model was used to simulate SARS-CoV-2 transmission and assess different whole-area nucleic acid screening strategies. We designed a total of 87 screening strategies with different sampling methods, frequencies of screening, and timings of screening. The performance of these strategies was comprehensively evaluated by comparing the cumulative infection rates, the number of tests, and the quarantine capacity and consumption of medical resource, which were expressed as medians (95% uncertainty intervals, 95% UIs). RESULTS: To implement COVID-19 nucleic acid testing for all people (Full Screening), if the screening frequency was four times/week, the cumulative infection rate could be reduced to 13% (95% UI: 1%, 51%), the miss rate decreased to 2% (95% UI: 0%, 22%), and the quarantine and medical resource consumption was lower than higher-frequency Full Screening or sampling screening. When the frequency of Full Screening increased from five to seven times/week (which resulted in a 2581 increase in the number of tests per positive case), the cumulative infection rate was only reduced by 2%. Screening all people weekly by splitting them equally into seven batches could reduce infection rates by 73% compared to once per week, which was similar to Full Screening four times/week. Full Screening had the highest number of tests per positive case, while the miss rate, number of tests per positive case, and hotel quarantine resource consumption in Household-based Sampling Screening scenarios were lower than Random Sampling Screening. The cumulative infection rate of Household-based Sampling Screening or Random Sampling Screening seven times/week was similar to that of Full Screening four times/week. CONCLUSIONS: If hotel quarantine, hospital and shelter hospital capacity are seriously insufficient, to stop the spread of the virus as early as possible, high-frequency Full Screening would be necessary, but intermediate testing frequency may be more cost-effective in non-extreme situations. Screening in batches is recommended if the testing capacity is low. Household-based Sampling Screening is potentially a promising strategy to implement.

A dataset of a proxy variable for the poultry trade flows in China.

Understanding the intercity poultry trading network is crucial for assessing the risk of avian influenza prevalence. Unfortunately, the poultry trading network in China has rarely been described. Here, with a modified radiation model, we obtain values for a proxy variable for poultry trade flows among 318 prefecture-level cities in China in 2015 utilizing the product capacity and demand quantity of poultry of the cities. The results are validated by comparing the proxy variable values with the trade volumes investigated in the literature, and it is found that the modified radiation model can accurately predict the main poultry trade flows among cities. This is the first dataset on China's poultry trade pattern, and it can be used to analyze the production and consumption structure of poultry in prefecture-level cities within China. The dataset can be a tool for avian influenza epidemic risk assessment as well as a basis to develop prevention and control measures during an epidemic.

Optimization of COVID-19 prevention and control measures during the Beijing 2022 Winter Olympics: a model-based study.

BACKGROUND: The continuous mutation of severe acute respiratory syndrome coronavirus 2 has made the coronavirus disease 2019 (COVID-19) pandemic complicated to predict and posed a severe challenge to the Beijing 2022 Winter Olympics and Winter Paralympics held in February and March 2022. METHODS: During the preparations for the Beijing 2022 Winter Olympics, we established a dynamic model with pulse detection and isolation effect to evaluate the effect of epidemic prevention and control measures such as entry policies, contact reduction, nucleic acid testing, tracking, isolation, and health monitoring in a closed-loop management environment, by simulating the transmission dynamics in assumed scenarios. We also compared the importance of each parameter in the combination of intervention measures through sensitivity analysis. RESULTS: At the assumed baseline levels, the peak of the epidemic reached on the 57th day. During the simulation period (100 days), 13,382 people infected COVID-19. The mean and peak values of hospitalized cases were 2650 and 6746, respectively. The simulation and sensitivity analysis showed that: (1) the most important measures to stop COVID-19 transmission during the event were daily nucleic acid testing, reducing contact among people, and daily health monitoring, with cumulative infections at 0.04%, 0.14%, and 14.92% of baseline levels, respectively (2) strictly implementing the entry policy and reducing the number of cases entering the closed-loop system could delay the peak of the epidemic by 9 days and provide time for medical resources to be mobilized; (3) the risk of environmental transmission was low. CONCLUSIONS: Comprehensive measures under certain scenarios such as reducing contact, nucleic acid testing, health monitoring, and timely tracking and isolation could effectively prevent virus transmission. Our research results provided an important reference for formulating prevention and control measures during the Winter Olympics, and no epidemic spread in the closed-loop during the games indirectly proved the rationality of our research results.

Compartmental structures used in modeling COVID-19: a scoping review.

BACKGROUND: The coronavirus disease 2019 (COVID-19) epidemic, considered as the worst global public health event in nearly a century, has severely affected more than 200 countries and regions around the world. To effectively prevent and control the epidemic, researchers have widely employed dynamic models to predict and simulate the epidemic's development, understand the spread rule, evaluate the effects of intervention measures, inform vaccination strategies, and assist in the formulation of prevention and control measures. In this review, we aimed to sort out the compartmental structures used in COVID-19 dynamic models and provide reference for the dynamic modeling for COVID-19 and other infectious diseases in the future. MAIN TEXT: A scoping review on the compartmental structures used in modeling COVID-19 was conducted. In this scoping review, 241 research articles published before May 14, 2021 were analyzed to better understand the model types and compartmental structures used in modeling COVID-19. Three types of dynamics models were analyzed: compartment models expanded based on susceptible-exposed-infected-recovered (SEIR) model, meta-population models, and agent-based models. The expanded compartments based on SEIR model are mainly according to the COVID-19 transmission characteristics, public health interventions, and age structure. The meta-population models and the agent-based models, as a trade-off for more complex model structures, basic susceptible-exposed-infected-recovered or simply expanded compartmental structures were generally adopted. CONCLUSION: There has been a great deal of models to understand the spread of COVID-19, and to help prevention and control strategies. Researchers build compartments according to actual situation, research objectives and complexity of models used. As the COVID-19 epidemic remains uncertain and poses a major challenge to humans, researchers still need dynamic models as the main tool to predict dynamics, evaluate intervention effects, and provide scientific evidence for the development of prevention and control strategies. The compartmental structures reviewed in this study provide guidance for future modeling for COVID-19, and also offer recommendations for the dynamic modeling of other infectious diseases.

Early signs of geographic spread of COVID-19: lessons learnt from outbreaks in Wuhan 2020 and Nanjing 2021.

BACKGROUND: Knowing the spatiotemporal pattern of the early geographic spread of coronavirus disease 2019 (COVID-19) would inform the preparedness for a possible recurrence of COVID-19. METHODS: We ascertained the number of confirmed cases during the early spread of COVID-19 during the Wuhan outbreak in 2020 and the Nanjing outbreak in 2021. RESULTS: We observed a speeding-up pattern of geographic spread, in particular to cities of no particular orientation then outflowing to commercial cities during the first month of both the Wuhan and Nanjing outbreaks. CONCLUSION: Re-emergence of COVID-19 indicates it is becoming endemic, with new outbreaks and a risk of increased transmission remaining a challenge to local public health institutions. Social distancing and lockdowns should continue in response to any potential widespread and focal outbreaks.

Could COVID-19 pandemic be stopped with joint efforts of travel restrictions and public health countermeasures? A modelling study.

OBJECTIVE: We aim to explore and compare the effect of global travel restrictions and public health countermeasures in response to COVID-19 outbreak. DESIGN: A data-driven spatio-temporal modelling to simulate the spread of COVID-19 worldwide for 150 days since 1 January 2020 under different scenarios. SETTING: Worldwide. INTERVENTIONS: Travel restrictions and public health countermeasures. MAIN OUTCOME: The cumulative number of COVID-19 cases. RESULTS: The cumulative number of COVID-19 cases could reach more than 420 million around the world without any countermeasures taken. Under timely and intensive global interventions, 99.97% of infections could be avoided comparing with non-interventions. The scenario of carrying out domestic travel restriction and public health countermeasures in China only could contribute to a significant decrease of the cumulative number of infected cases worldwide. Without global travel restriction in the study setting, 98.62% of COVID-19 cases could be avoided by public health countermeasures in China only compared with non-interventions at all. CONCLUSIONS: Public health countermeasures were generally more effective than travel restrictions in many countries, suggesting multinational collaborations in the public health communities in response to this novel global health challenge.

Does lock-down of Wuhan effectively restrict early geographic spread of novel coronavirus epidemic during chunyun in China? A spatial model study.

BACKGROUND: Prior to Wuhan lock-down in 2020, chunyun, the largest population mobility on this planet, had begun. We quantified impact of Wuhan lock-down on COVID-19 spread during chunyun across the nation. METHODS: During the period of January 1 to February 9, 2020, a total of 40,278 confirmed COVID-19 cases from 319 municipalities in mainland China were considered in this study. The cross-coupled meta-population methods were employed using between-city Baidu migration index. We modelled four scenarios of geographic spread of COVID-19 including the presence of both chunyun and lock-down (baseline); lock-down without chunyun (scenario 1); chunyun without lock-down (scenario 2); and the absence of both chunyun and lock-down (scenario 3). RESULTS: Compared with the baseline, scenario 1 resulted in 3.84% less cases by February 9 while scenario 2 and 3 resulted in 20.22 and 32.46% more cases by February 9. The geographic distribution of cases revealed that chunyun facilitated the COVID-19 spread in the majority but not all cities, and the effectiveness of Wuhan lock-down was offset by chunyun. Impacts of Wuhan lock-down during chunyun on the COVID-19 spread demonstrated heterogenetic geographic patterns. CONCLUSION: Our results strongly supported the travel restriction as one of the effective responses and highlighted the importance of developing area-specific rather than universal countermeasures.

Risk factors spatial-temporal detection for dengue fever in Guangzhou.

Dengue fever (DF) has been a growing public-health concern in China since its emergence in Guangdong Province in 1978. Of all the regions that have experienced dengue outbreaks in mainland China, the city of Guangzhou is the most affected. This study aims to investigate the potential risk factors for dengue virus (DENV) transmission in Guangzhou, China, from 2006 to 2014. The impact of risk factors on DENV transmission was qualified by the q-values calculated using a novel spatial-temporal method, the GeoDetector model. Both climatic and socioeconomic factors were considered. The impacts on DF incidence of each single factor and the interaction of two factors were analysed. The results show that the number of days with rainfall of the month before last has the highest determinant power, with a q-value of 0.898 (P < 0.01); the q-values of the other factors related to temperature and precipitation were around 0.38-0.50. Integrating a Pearson correlation analysis, nonlinear associations were found between the DF incidence in Guangzhou and the climatic factors considered. The coupled impact of the different variables considered was enhanced compared with their individual effects. In addition, an increased number of tourists in the city were associated with a high incidence of DF. This study demonstrates that the number of rain days in a month has great influence on the DF incidence of the month after next; the temperature and precipitation have nonlinear impacts on the DF incidence in Guangzhou; both the domestic and overseas tourists coming to the city increase the risk of DENV transmission. These findings are useful in the risk assessment of DENV transmission, to predict DF outbreaks and to implement preventive DF reduction strategies.

Modeling the Heterogeneity of Dengue Transmission in a City.

Dengue fever is one of the most important vector-borne diseases in the world, and modeling its transmission dynamics allows for determining the key influence factors and helps to perform interventions. The heterogeneity of mosquito bites of humans during the spread of dengue virus is an important factor that should be considered when modeling the dynamics. However, traditional models generally assumed homogeneous mixing between humans and vectors, which is inconsistent with reality. In this study, we proposed a compartmental model with negative binomial distribution transmission terms to model this heterogeneity at the population level. By including the aquatic stage of mosquitoes and incorporating the impacts of the environment and climate factors, an extended model was used to simulate the 2014 dengue outbreak in Guangzhou, China, and to simulate the spread of dengue in different scenarios. The results showed that a high level of heterogeneity can result in a small peak size in an outbreak. As the level of heterogeneity decreases, the transmission dynamics approximate the dynamics predicted by the corresponding homogeneous mixing model. The simulation results from different scenarios showed that performing interventions early and decreasing the carrying capacity for mosquitoes are necessary for preventing and controlling dengue epidemics. This study contributes to a better understanding of the impact of heterogeneity during the spread of dengue virus.

Modeling Heterogeneity in Direct Infectious Disease Transmission in a Compartmental Model.

Mathematical models have been used to understand the transmission dynamics of infectious diseases and to assess the impact of intervention strategies. Traditional mathematical models usually assume a homogeneous mixing in the population, which is rarely the case in reality. Here, we construct a new transmission function by using as the probability density function a negative binomial distribution, and we develop a compartmental model using it to model the heterogeneity of contact rates in the population. We explore the transmission dynamics of the developed model using numerical simulations with different parameter settings, which characterize different levels of heterogeneity. The results show that when the reproductive number, R0, is larger than one, a low level of heterogeneity results in dynamics similar to those predicted by the homogeneous mixing model. As the level of heterogeneity increases, the dynamics become more different. As a test case, we calibrated the model with the case incidence data for severe acute respiratory syndrome (SARS) in Beijing in 2003, and the estimated parameters demonstrated the effectiveness of the control measures taken during that period.

Factors affecting the performance of microbial fuel cells for sulfide and vanadium (V) treatment.

Sulfide and vanadium (V) are pollutants commonly found in wastewaters. A novel approach has been investigated using microbial fuel cell (MFC) technologies by employing sulfide and V(V) as electron donor and acceptor, respectively. This results in oxidizing sulfide and deoxidizing V(V) simultaneously. A series of operating parameters as initial concentration, conductivity, pH, external resistance were carefully examined. The results showed that these factors greatly affected the performance of the MFCs. The average removal rates of about 82.2 and 26.1% were achieved within 72 h operation for sulfide and V(V), respectively, which were accompanied by the maximum power density of about 614.1 mW m(-2) under all tested conditions. The products generated during MFC operation could be deposited, resulting in removing sulfide and V(V) from wastewaters thoroughly.

Electricity generation using a baffled microbial fuel cell convenient for stacking.

To make sure that microbial fuel cells (MFCs) are more convenient to stack, a baffled single-chambered MFC with two groups of electrodes sharing only one anode chamber was designed and the performance was examined. The experiments showed that the prototype MFC generated electrical power (maximum of 133 mW/m(2)) while removing up to 88% of chemical oxygen demand (COD) in 91 h. Volumetric power increased as electrode area per anode compartment volume increased, indicating that the MFC with two groups of electrodes was better than that with one group. Power density as a function of wastewater concentration was modeled according to saturation kinetics, with a maximum power density of P(max)=164 mW/m(2) (fixed 100 Omega resistor) and half-saturation concentration of K(s)=259 mg/l. The hydraulic retention time (HRT) was examined as a factor influencing the power generation. When it was 15.5h, the voltage and the power density reached the maximum 0.413 V and 108 mW/m(2).