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Accurate occupancy is crucial for planning for sustainable buildings. Using massive, passively-collected mobile phone data, we introduce a novel framework to estimate building occupancy at unprecedented scale. We show that, at urban-scale, occupancy differs widely from current estimates based on building types. For commercial buildings, we find typical occupancy rates are 5 times lower than current assumptions imply, while for residential buildings occupancy rates vary widely by neighborhood. Our mobile phone based occupancy estimates are integrated with a state-of-the-art urban building energy model to understand their impact on energy use predictions. Depending on the assumed relationship between occupancy and internal building loads, we find energy consumption which differs by +1% to -15% for residential buildings and by -4% to -21% for commercial buildings, compared to standard methods. This highlights a need for new occupancy-to-load models which can be applied at urban-scale to the diverse set of city building types.

Chatbots are software applications to simulate a conversation with a person. The effectiveness of chatbots in facilitating the recruitment of study participants in research, specifically among racial and ethnic minorities, is unknown. The objective of this study is to compare a chatbot versus telephone-based recruitment in enrolling research participants from a predominantly minority patient population at an urban institution. We randomly allocated adults to receive either chatbot or telephone-based outreach regarding a study about vaccine hesitancy. The primary outcome was the proportion of participants who provided consent to participate in the study. In 935 participants, the proportion who answered contact attempts was significantly lower in the chatbot versus telephone group (absolute difference -21.8%; 95% confidence interval [CI] -27.0%, -16.5%; P < 0.001). The consent rate was also significantly lower in the chatbot group (absolute difference -3.4%; 95% CI -5.7%, -1.1%; P = 0.004). However, among participants who answered a contact attempt, the difference in consent rates was not significant. In conclusion, the consent rate was lower with chatbot compared to telephone-based outreach. The difference in consent rates was due to a lower proportion of participants in the chatbot group who answered a contact attempt. [ABSTRACT FROM AUTHOR]

The rigorous assessment of bleeding symptoms is a key component in establishing a diagnosis in patients suspected of having von Willebrand disease (VWD) and other inherited bleeding disorders. Multiple bleeding questionnaires have been developed and validated to capture bleeding history phenotypes for assessing patients with bleeding disorders. In this study we developed a prediction model based on Naïve Bayes decision tree classifier by analyzing various phenotypic attributes derived from multiple bleeding questionnaires. We evaluated the classification effectiveness derived from the top 25 attributes with highest discriminations on prediction of VWD. We used data from 952 patients and the classifier achieved a precision of 95%, recall of 94%, and a Receiving Operating Curve (ROC) area under the curve of 0.97.