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Two types of therapeutic agents, which have discrete yet complementary functions, self-assemble into nanofibers in water to formulate a new supramolecular hydrogel as a self-delivery biomaterial to reduce the toxicity of uranyl oxide at the wound sites.

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Vehicle headway distribution is fundamental for several important traffic research and simulation issues. Many headway models have been developed over the past decades. Each has its own strength and weakness. Selection of the most suitable model for a certain traffic condition remains an open issue. A comprehensive study of the performance of typical headway distribution models on urban freeways is presented. With the advanced loop event data analyzer system, many accurate headway observations were obtained from I-5 in the area of Seattle, Washington. These headway data were used to calibrate and examine the performance of various headway models. The goodness of fit for several most commonly used headway distribution models was investigated by using headways observed on regular lanes and high-occupancy-vehicle (HOV) lanes from different time periods of day. To evaluate the performance of these headway models, the analytical Kolmogorov-Smirnov test statistic and visualized comparison curves were used to measure and reflect their overall goodness of fit to the collected headway data. Although each model has its own practicability to a certain extent, the test results showed that the double-displaced negative exponential distribution model provided the best fit to these urban freeway headway data, especially for HOV lanes at wideranging flow levels. The shifted lognormal distribution also fits the general purpose lane headways very well. As a byproduct, a new standard parameter estimation method was developed for calibrating complex multiparameter headway models.

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To control the spread of COVID-19, rigorous restrictions have been implemented in China, resulting in a great reduction in pollutant emissions. In this study, we evaluated the air quality in the Yangtze River Delta during the COVID-19 lockdown period using satellite and ground-based data, including particle matter (PM), trace gases, water-soluble ions (WSIs) and black carbon (BC). We found that the impacts of lockdown policy on air quality cannot be accurately assessed using MODIS aerosol optical depth (AOD) data, whereas the tropospheric nitrogen dioxide (NO 2 ) vertical column density can well reflect the influences of these restrictions on human activities. Compared to the pre-COVID period, the PM 2.5 , PM 10 , NO 2 , carbon monoxide (CO), BC and WSIs during the lockdown in Suzhou were observed to decrease by 37.2%, 38.3%, 64.5%, 26.1%, 53.3% and 58.6%, respectively, while the sulfur dioxide (SO 2 ) and ozone (O 3 ) increased by 1.5% and 104.7%. The WSIs ranked in the order of NO 3 − > NH 4 + > SO 4 2- > Cl − > Ca 2+ > K + > Mg 2+ > Na + during the lockdown period. By comparisons with the ion concentrations during the pre-COVID period, we found that the ions NO 3 − , NH 4 + , SO 4 2− , Cl − , Ca 2+ , K + and Na + decreased by 66.3%, 48.8%, 52.9%, 56.9%, 57.9% and 76.3%, respectively, during the lockdown, in contrast to Mg 2+ , which increased by 30.2%. The lockdown policy was found to have great impacts on the diurnal variations of Cl − , SO 4 2− , Na + and Ca 2+ .

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