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Understanding particle settlement in channeled fluids has wide applications, such as fine particulate matter, coronavirus particle transport, and the migration of solid particles in water. Various factors have been investigated but few studies have acknowledged the channel's effect on settlement dynamics. This study developed a coupled interpolated bounce-back lattice Boltzmann-discrete element model and examined how a channel's width affects particle settlement. A factor k denoting the ratio of the channel's width and the particle diameter was defined. The terminal settling velocity for a single particle is inversely proportional to k, and the time that the particle takes to reach the terminal velocity is positively related to k. When k is greater than 15, the channel width's effects are negligible. For dual particles of the same size, the drafting-kissing-tumbling (DKT) process occurs infinitely in a periodic pattern, with the two particles swapping positions and settling around the channel's centerline. The smaller the k, the sooner the DKT process occurs. The particles collide with the channel wall when k <= 10. For dual particles of different sizes, the DKT process occurs once so that the bigger particle leads the settlement. Both particles settle along the channel's centerline in a steady state. The bigger the k, the bigger the difference in their terminal settling velocities until k = 15. The small particle collides with the channel wall if released under the big particle when k = 6. The findings of this study are expected to inform channeling or pipeline design in relevant engineering practices.
RESUMEN
Rapid social development has led to serious air pollution problems in cities, and air pollutants, including gaseous pollutants and particulate matter, have an important impact on climate, the environment, and human health. This study analyzed the characteristics, potential sources, and causes of air pollution in the Wu-Chang-Shi urban cluster. The results showed that NO2, CO, SO2, PM10, and PM2.5 had a tendency to decrease, while O-3 showed an increasing trend. The concentrations of SO2, NO2, CO, PM2.5, and PM10 showed the highest values in winter and the lowest values in summer, with similar seasonal variations. However, the concentration of O-3 was highest in the summer and lowest in the winter. Compared with the pollutant concentrations in other Chinese cities, PM2.5, PM10, and NO2 are more polluted in the Wu-Chang-Shi urban. Meteorological factors have a greater impact on pollutant concentrations, with higher concentrations of major pollutants observed when wind speeds are low and specific wind directions are observed, and higher secondary pollutant O-3 concentrations observed when wind speeds are low and specific wind directions are observed. The backward trajectory and concentration weighting analysis show that the particulate pollutants in the Wu-Chang-Shi urban in winter mainly come from Central Asia and surrounding cities. O-3 showed an increasing trend before and after the novel coronavirus outbreak, which may be related to changes in NOX, volatile organic compounds, and solar radiation intensity, and the concentrations of SO2, NO2, CO, PM10, and PM2.5 showed an overall decreasing trend after the outbreak and was smaller than before the outbreak, which is related to the reduction of industrial and anthropogenic source emissions during the outbreak.
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To successfully satisfy user needs, software developers need to suitably capture and implement user requirements. A critical and often overlooked characteristic of user requirements are "human aspects", which are personal circumstances affecting the use of software (e.g., age, gender, language, etc.). To better understand how human aspects can impact the use of software, this work presents an empirical study focusing on app reviews of COVID-19 contact tracing apps. We manually analyzed a dataset of 2,611 app reviews sampled from the reviews associated with 57 COVID-19 apps. To analyze the reviews, we performed qualitative and quantitative analyses. The analyses characterize the human aspects contained in the reviews and investigate whether the apps suitably address the human aspects. We identified 716 reviews related to human aspects and grouped these into nine categories. Of these 716 reviews, 8% report bugs, 14% describe future/improvement requests, and 22% detail the user experience. Our analysis of the results reveal that human aspects are important to users and we need better support to account for them as software is developed.
RESUMEN
The research of atmospheric aerosol in mountain glacier areas has attracted more and more people's attention. For the first time, a field observation study of total suspended particles (TSPs) for four seasons from September 2019 to August 2020 was carried out at the Tianshan Glaciological Station in the source area of Urumqi River, East Tianshan Mountains, China. The TSPs presented typical seasonal characteristics of high in autumn and low in winter, with the annual average value of 181 +/- 170 mu g m(-3). Concentrations of Ca2+, SO42-, NO3-, Cl-, NH4+ and K+, OC, EC were elevated in autumn. The influence of stationary source emissions was stronger than mobile sources, which was explained by the average ratio of NO3-/SO42- (0.31 +/- 0.17). The concentration of secondary organic carbon (SOC) was higher in summer and autumn, especially in summer, indicating that secondary formation processes of organic aerosols were frequent in summer. Impact of fossil fuel combustion sources were evident over the Glaciers, corroborated by the diagnostic mass ratios of OC/EC (0-21.4, 3.38) and K+/EC (0-0.31, 0.08). The factor analysis illustrated that aerosols were mainly affected by rock salt, dust, coal combustion, and automobile exhaust. The local sources made significant contributions to TSPs in the source of Urumqi River by the results of Results of Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and potential source contribution function (PSCF).
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Objective: To compare the performances of different time series models in predicting COVID-19 in different countries. Methods: We collected the daily confirmed case numbers of COVID-19 in the USA, India, and Brazil from April 1 to September 30, 2020, and then constructed an autoregressive integrated moving average (ARIMA) model and a recurrent neural network (RNN) model, respectively. We applied the mean absolute percentage error (MAPE) and root mean square error (RMSE) to compare the performances of the two models in predicting the case numbers from September 21 to September 30, 2020. Results: For the ARIMA models applied in the USA, India, and Brazil, the MAPEs were 13.18%, 9.18%, and 17.30%, respectively, and the RMSEs were 6 542.32, 8 069.50, and 3 954.59, respectively. For the RNN models applied in the USA, India, and Brazil, the MAPEs were 15.27%, 7.23% and 26.02%, respectively, and the RMSEs were 6 877.71, 6 457.07, and 5 950.88, respectively. Conclusions: The performance of the prediction models varied with country. The ARIMA model had a better prediction performance for COVID-19 in the USA and Brazil, while the RNN model was more suitable in India.