Highway Visibility Estimation in Foggy Weather via Multi-Scale Fusion Network.

Poor visibility has a significant impact on road safety and can even lead to traffic accidents. The traditional means of visibility monitoring no longer meet the current needs in terms of temporal and spatial accuracy. In this work, we propose a novel deep network architecture for estimating the visibility directly from highway surveillance images. Specifically, we employ several image feature extraction methods to extract detailed structural, spectral, and scene depth features from the images. Next, we design a multi-scale fusion network to adaptively extract and fuse vital features for the purpose of estimating visibility. Furthermore, we create a real-scene dataset for model learning and performance evaluation. Our experiments demonstrate the superiority of our proposed method to the existing methods.

Switching from offline to online health consultation in the post-pandemic era: the role of perceived pandemic risk.

Introduction: Due to its effectiveness and various benefits, the use of online health consultation (OHC) has dramatically increased in recent years, especially since the outbreak of the COVID-19 pandemic. However, underlying mechanism whereby the pandemic impacted OHC usage is still unclear. Methods: Via an online survey (N=318), the present paper measures the users' perceptions towards both offline and online services, their intention to switch to OHC, and the perceived pandemic risks. The relationships among these factors are conceptualized by the push-pull-mooring framework, and tested via structural equation modelling. Results: Dissatisfaction with offline service (process inefficiency and consultation anxiety), the attractiveness of OHC (perceived benefits and perceived ease of use), and users' behavioral inertia (switching cost and habit) jointly influence the intention to switching to OHC. The significant role of the perceived pandemic risk of going to medical facilities is particularly addressed. On the one hand, the perceived pandemic risk is found with an indirect impact on the switching intention by enlarging the dissatisfaction with offline service and the attractiveness of OHC. On the other hand, a high perceived pandemic risk induces more actual switching behavior and also amplifies the transition from switching intention to behavior. Discussion: The study provides novel insights into the understanding of OHC usage in the post-pandemic era, and also informs medical facilities, OHC platforms, and policymakers on managing and balancing the online and offline healthcare provision.