• Aerosol transmission is an indispensable route of COVID-19 spread.• Different outbreak sites have different epidemiologic feature.• SRAS-CoV-2 can exist for a long time in aerosol.• SRAS-CoV-2 RNA can be detected in aerosol in diverse places.• Some environmental factors can impact SARS-CoV-2 transportation in aerosol. Patients with COVID-19 have revealed a massive outbreak around the world, leading to widespread concerns in global scope. Figuring out the transmission route of COVID-19 is necessary to control further spread. We analyzed the data of 43 patients in Baodi Department Store (China) to supplement the transmission route and epidemiological characteristics of COVID-19 in a cluster outbreak. Incubation median was estimated to endure 5.95 days (2–13 days). Almost 76.3% of patients sought medical attention immediately upon illness onset. The median period of illness onset to hospitalization and confirmation were 3.96 days (0–14) and 5.58 days (1–21), respectively. Patients with different cluster case could demonstrate unique epidemiological characteristics due to the particularity of outbreak sites. SRAS-CoV-2 can be released into the surrounding air through patient’s respiratory tract activities, and can exist for a long time for long-distance transportation. SRAS-CoV-2 RNA can be detected in aerosol in different sites, including isolation ward, general ward, outdoor, toilet, hallway, and crowded public area. Environmental factors influencing were analyzed and indicated that the SARS-CoV-2 transportation in aerosol was dependent on temperature, air humidity, ventilation rate and inactivating chemicals (ozone) content. As for the infection route of case numbers 2 to 6, 10, 13, 16, 17, 18, 20 and 23, we believe that aerosol transmission played a significant role in analyzing their exposure history and environmental conditions in Baodi Department Store. Aerosol transmission could occur in some cluster cases when the environmental factors are suitable, and it is an indispensable route of COVID-19 spread. 相似文献
The effect of pyrolysis and oxidation characteristics on the explosion sensitivity and severity parameters, including the minimum ignition energy MIE, minimum ignition temperature MIT, minimum explosion concentration MEC, maximum explosion pressure Pmax, maximum rate of pressure rise (dP/dt)max and deflagration index Kst, of lauric acid and stearic acid dust clouds was experimentally investigated. A synchronous thermal analyser was used to test the particle thermal characteristics. The functional test apparatuses including the 1.2 L Hartmann-tube apparatus, modified Godbert-Greenwald furnace, and 20 L explosion apparatus were used to test the explosion parameters. The results indicated that the rapid and slow weight loss processes of lauric acid dust followed a one-dimensional diffusion model (D1 model) and a 1.5 order chemical reaction model (F1.5 model), respectively. In addition, the rapid and slow weight loss processes of stearic acid followed a 1.5 order chemical reaction model (F1.5 model) and a three-dimensional diffusion model (D3 model), respectively, and the corresponding average apparent activation energy E and pre-exponential factor A were larger than those of lauric acid. The stearic acid dust explosion had higher values of MIE and MIT, which were mainly dependent on the higher pyrolysis and oxidation temperatures and the larger apparent activation energy E determining the slower rate of chemical bond breakage during pyrolysis and oxidation. In contrast, the lauric acid dust explosion had a higher MEC related to a smaller pre-exponential factor A with a lower amount of released reaction heat and a lower heat release rate during pyrolysis and oxidation. Additionally, due to the competition regime of the higher oxidation reaction heat release and greater consumption of oxygen during explosion, the explosion pressure Pm of the stearic acid dust was larger in low concentration ranges and decayed to an even smaller pressure than with lauric acid when the concentration exceeded 500 g/m3. The rate of explosion pressure rise (dP/dt)m of the stearic acid dust was always larger in the experimental concentration range. The stearic acid dust explosion possessed a higher Pmax, (dP/dt)max and Kst mainly because of a larger pre-exponential factor A related to more active sites participating in the pyrolysis and oxidation reaction. Consequently, the active chemical reaction occurred more violently, and the temperature and overpressure rose faster, indicating a higher explosion hazard class for stearic acid dust. 相似文献
Understanding complex systems is essential to ensure their conservation and effective management. Models commonly support understanding of complex ecological systems and, by extension, their conservation. Modeling, however, is largely a social process constrained by individuals’ mental models (i.e., a small-scale internal model of how a part of the world works based on knowledge, experience, values, beliefs, and assumptions) and system complexity. To account for both system complexity and the diversity of knowledge of complex systems, we devised a novel way to develop a shared qualitative complex system model. We disaggregated a system (carbonate coral reefs) into smaller subsystem modules that each represented a functioning unit, about which an individual is likely to have more comprehensive knowledge. This modular approach allowed us to elicit an individual mental model of a defined subsystem for which the individuals had a higher level of confidence in their knowledge of the relationships between variables. The challenge then was to bring these subsystem models together to form a complete, shared model of the entire system, which we attempted through 4 phases: develop the system framework and subsystem modules; develop the individual mental model elicitation methods; elicit the mental models; and identify and isolate differences for exploration and identify similarities to cocreate a shared qualitative model. The shared qualitative model provides opportunities to develop a quantitative model to understand and predict complex system change. 相似文献
Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.
Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).
Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.
Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy. 相似文献