This research examines whether transportation network companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion in major cities. Existing research has produced conflicting results and has been hampered by a lack of data. Using data scraped from the application programming interfaces of two TNCs, combined with observed travel time data, we find that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco. Between 2010 and 2016, weekday vehicle hours of delay increased by 62% compared to 22% in a counterfactual 2016 scenario without TNCs. The findings provide insight into expected changes in major cities as TNCs continue to grow, informing decisions about how to integrate TNCs into the existing transportation system.

We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular edema, macular hole, and age-related macular degeneration, in the OCT slice centered at the fovea. We use a machine learning approach based on global image descriptors formed from a multi-scale spatial pyramid. Our local features are dimension-reduced Local Binary Pattern histograms, which are capable of encoding texture and shape information in retinal OCT images and their edge maps, respectively. Our representation operates at multiple spatial scales and granularities, leading to robust performance. We use 2-class Support Vector Machine classifiers to identify the presence of normal macula and each of the three pathologies. To further discriminate sub-types within a pathology, we also build a classifier to differentiate full-thickness holes from pseudo-holes within the macular hole category. We conduct extensive experiments on a large dataset of 326 OCT scans from 136 subjects. The results show that the proposed method is very effective (all AUC > 0.93).

Background Coronavirus disease (COVID-19) has become a pandemic. The knowledge, attitudes, and practices (KAP) of the public play a major role in the prevention and control of infectious diseases. The objective of the present study was to evaluate the KAP of the Chinese public and to assess potential influencing factors related to practices. Methods A cross-sectional online survey was conducted in China in February 2020 via a self-designed questionnaire comprising 33 questions assessing KAP. Results For the 2136 respondents from 30 provinces or municipalities in China, the accurate response rate for the knowledge section ranged from 72.7 to 99.5%, and the average was 91.2%. Regarding attitude section, the percentage of positive attitudes (“strongly agree” and “agree”) ranged from 94.7 to 99.7%, and the average value was 98.0%. The good practices (“always” and “often”) results ranged from 76.1 to 99.5%, and the average value was 96.8%. The independent samples t-test revealed that gender and ethnic differences had no effect on knowledge, attitude or behaviour (P > 0.05). However, knowledge was associated with age (t = 4.842, p < 0.001), marital status (t = − 5.323, p < 0.001), education level (t = 8.441, p < 0.001), occupation (t = − 10.858, p < 0.001), and place of residence (t = 7.929, p < 0.001). Similarly, attitude was associated with marital status (t = − 2.383, p = 0.017), education level (t = 2.106, p = 0.035), occupation (t = − 4.834, p < 0.001), and place of residence (t = 4.242, p < 0.001). The multiple linear regression analysis results showed that the factors influencing practices were knowledge (t = − 3.281, p = 0.001), attitude (t = 18.756, p < 0.001), occupation (t = − 3.860, p < 0.001), education level (t = 3.136, p = 0.002), and place of residence (t = 3.257, p = 0.001). Conclusions The Chinese public exhibited a good level of knowledge of COVID-19, a positive attitude, and high adherence to good practices. COVID-19-related knowledge, attitudes and practices were affected by age, marital status, education level, occupation, and place of residence to varying degrees. In addition, practices were affected by knowledge and attitudes towards COVID-19.

No abstract