Aiming at the dense urban road network vulnerability without structural negative consequences, this paper proposes a novel non-structural road network vulnerability analysis framework. Three aspects of the framework are mainly described: (i) the rationality of non-structural road network vulnerability, (ii) the metrics for negative consequences accounting for variant road conditions, and (iii) ...

Victor M. Preciado∗ and Ali Jadbabaie† Department of Electrical and Systems Engineering University of Pennsylvania, Philadelphia, PA 19104‡ Abstract The intricate structure of many large-scale networked systems has attracted the attention of the scientific community, leading to many results attempting to explain the relationship between network structural properties and dynamical performance. A...

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure ...

The objective of this project was to develop a structural index for use in network-level pavement evaluation to facilitate the inclusion of the pavement’s structural condition in pavement management applications. The primary goal of network-level pavement management is to provide the best service to the users for the available, often limited, resources. Pavement condition can be described in te...

tehran is one of the most polluted metropolises in the world. sink capacity for absorption and assimilation of pollution is reduced generally due to failure in inclusion of ecological aspects in regional land use development planning. the present condition of air and water related issues in tehran are greatly affected by landscape structural alterations at urban level. in this research the focu...

Description A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, p* modeling, and network visualization.

The Bayesian network formalism is becoming increasingly popular in many areas such as decision aid, diagnosis and complex systems control, in particular thanks to its inference capabilities, evenwhen data are incomplete. Besides, estimating the parameters of a fixed-structure Bayesian network is easy. However, very few methods are capable of using incomplete cases as a base to determine the str...