The global spread of the novel coronavirus is affected by the spread of related misinformation—the so-called COVID-19 Infodemic—that makes populations more vulnerable to the disease through resistance to mitigation efforts. Here, we analyze the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media platforms, Twitter and Facebook. We characterize cross-platform similarities and differences in popular sources, diffusion patterns, influencers, coordination, and automation. Comparing the two platforms, we find divergence among the prevalence of popular (...) low-credibility sources and suspicious videos. A minority of accounts and pages exert a strong influence on each platform. These misinformation “superspreaders” are often associated with the low-credibility sources and tend to be verified by the platforms. On both platforms, there is evidence of coordinated sharing of Infodemic content. The overt nature of this manipulation points to the need for societal-level solutions in addition to mitigation strategies within the platforms. However, we highlight limits imposed by inconsistent data-access policies on our capability to study harmful manipulations of information ecosystems. (shrink)

The trend to implement the monitoring system with a wireless sensor network has been becoming urgent due to guaranteed flight safety and the passengers comfortability in travel. In this paper, a new dynamic routing algorithm is proposed to prolong the lifetime of the monitoring system with a distributed network based on the K-coverage method, and filter algorithm to be used for data fusion. Finally, the simulation results validate the effectiveness of the proposed approach.

Aiming at the high false alarm rate when using single sensor to detect fire in aircraft cabin, a multisensor data fusion method is proposed to detect fire. First, the weights of multiple factors, that is, temperature, smoke concentration, CO concentration, and infrared ray intensity in the event of fire, were calculated by using the improved analytic hierarchy process method on each sensor node of wireless sensor network, and the probability of fire event in the cabin was evaluated by multivariable-weighted fusion (...) method. Second, based on the mutual support among the evaluation data of fire probabilities of each node, the adaptive weight coefficient is assigned to each evaluation value, and the weighted fusion of all evaluation values of each node is conducted to obtain the fire probability. In the end, compared to the threshold of probability, the fire alarm is determined. Comparing the proposed algorithm to the grey fuzzy neural network fusion algorithm and fuzzy logic fusion algorithm in terms of the time consumption for fire detection and sending alarm and the accuracy of fire alarm perspectives, the experiments demonstrate that the proposed fire detection algorithm can detect the fire within 10s and reduce the false alarm rate to less than 0.5%, which verifies the superiority of the algorithm in promptness and accuracy. In the meanwhile, the fault tolerance of the algorithm is proved as well. (shrink)

Floods are one of the main natural disasters, which cause huge damage to property, infrastructure, and economic losses every year. There is a need to develop an approach that could instantly detect flooded extent. Satellite remote sensing has been useful in emergency responses; however, with significant weakness due to long revisit period and unavailability during rainy/cloudy weather conditions. In recent years, unmanned aerial vehicle systems have been widely used, especially in the fields of disaster monitoring and complex environments. This study (...) employs deep learning models to develop an automated detection of flooded buildings with UAV aerial images. The method was explored in a case study for the Kangshan levee of Poyang Lake. Experimental results show that the inundation for the focal buildings and vegetation can be detected from the images with 88% and 85% accuracy, respectively. And further, we can estimate the buildings’ inundation area according to the UAV images and flight parameters. The result of this study shows promising value of the accuracy and timely visualization of the spatial distribution of inundation at the object level for the end users from flood emergency response sector. (shrink)