107 research outputs found
Exploring the Role of Social Media and Individual Behaviors in Flood Evacuation Processes: An Agent-Based Modeling Approach
Flood warnings from various information sources are important for individuals to make evacuation decisions during a flood event. In this study, we develop a general opinion dynamics model to simulate how individuals update their flood hazard awareness when exposed to multiple information sources, including global broadcast, social media, and observations of neighbors' actions. The opinion dynamics model is coupled with a traffic model to simulate the evacuation processes of a residential community with a given transportation network. Through various scenarios, we investigate how social media affect the opinion dynamics and evacuation processes. We find that stronger social media can make evacuation processes more sensitive to the change of global broadcast and neighbor observations, and thus, impose larger uncertainty on evacuation rates (i.e., a large range of evacuation rates corresponding to sources of information). For instance, evacuation rates are lower when social media become more influential and individuals have less trust in global broadcast. Stubborn individuals can significantly affect the opinion dynamics and reduce evacuation rates. In addition, evacuation rates respond to the percentage of stubborn agents in a nonlinear manner, i.e., above a threshold, the impact of stubborn agents will be intensified by stronger social media. These results highlight the role of social media in flood evacuation processes and the need to monitor social media so that misinformation can be corrected in a timely manner. The joint impacts of social media, quality of flood warnings, and transportation capacity on evacuation rates are also discussed.Additional
support was provided by Shenzhen
Municipal Science and Technology
Innovation Committee
(#ZDSY20150831141712549)
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
A Survey of Bayesian Statistical Approaches for Big Data
The modern era is characterised as an era of information or Big Data. This
has motivated a huge literature on new methods for extracting information and
insights from these data. A natural question is how these approaches differ
from those that were available prior to the advent of Big Data. We present a
review of published studies that present Bayesian statistical approaches
specifically for Big Data and discuss the reported and perceived benefits of
these approaches. We conclude by addressing the question of whether focusing
only on improving computational algorithms and infrastructure will be enough to
face the challenges of Big Data
Progress and Tasks of Research in the Area of the Ageing and Stabilisation of Polyvinyl Chloride
REAL TIME ASSESSMENT OF DRINKING WATER SYSTEMS USING A DYNAMIC BAYESIAN NETWORK
This paper presents a methodology for real-time estimation of water distribution system state parameters using a dynamic Bayesian network to combine current observations with knowledge of past system behavior. The dynamic Bayesian network presented here allows the flexibility to model both discrete and continuous variables and represent causal relationships that exist within the distribution system. The posterior belief state can be inferred using a compact approximation algorithm that has been shown to contain inference errors. Simulations over stochastic variables are proposed to define the transition and observation models for the dynamic Bayesian network
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