The Effect of Recency to Human Mobility

In recent years, we have seen scientists attempt to model and explain human dynamics and, in particular, human movement. Many aspects of our complex life are affected by human movements such as disease spread and epidemics modeling, city planning, wireless network development, and disaster relief, to name a few. Given the myriad of applications it is clear that a complete understanding of how people move in space can lead to huge benefits to our society. In most of the recent works, scientists have focused on the idea that people movements are biased towards frequently-visited locations. According to them, human movement is based on an exploration/exploitation dichotomy in which individuals choose new locations (exploration) or return to frequently-visited locations (exploitation). In this work, we focus on the concept of recency. We propose a model in which exploitation in human movement also considers recently-visited locations and not solely frequently-visited locations. We test our hypothesis against different empirical data of human mobility and show that our proposed model is able to better explain the human trajectories in these datasets

Contrasting social and non-social sources of predictability in human mobility

Social structures influence human behavior, including their movement patterns. Indeed, latent information about an individual’s movement can be present in the mobility patterns of both acquaintances and strangers. We develop a “colocation” network to distinguish the mobility patterns of an ego’s social ties from those not socially connected to the ego but who arrive at a location at a similar time as the ego. Using entropic measures, we analyze and bound the predictive information of an individual’s mobility pattern and its flow to both types of ties. While the former generically provide more information, replacing up to 94% of an ego’s predictability, significant information is also present in the aggregation of unknown colocators, that contain up to 85% of an ego’s predictive information. Such information flow raises privacy concerns: individuals sharing data via mobile applications may be providing actionable information on themselves as well as others whose data are absent

Overview of Hydrological Dynamics and Geomorphological Aspects of the Amazon Region Rivers to Characterize Fluvial Sensitivity to Oil Spills

This chapter presents a collection of studies performed in the Amazon region that includes thematic products portraying its fluvial sensitivity to oil spills. The research addresses the intense Amazonian seasonal dynamics, as well as the environmental peculiarities of this singular ecosystem. Periodic changes caused by natural phenomena have a significant impact on not only flooded alluvial plains and riverine habitats but also on petroleum exploration, production, and transportation activities. Therefore, the implementation of tools to assess the potential impact of oil spills in the Amazonian rivers must be adjusted to the local conditions. The main deliverables of the research are (1) fluvial oil spill sensitivity index maps contemplating each phase of the hydrological cycle (low water, high water, receding water, and rising water), (2) a computational method to represent fluctuations of the seasonal inundation, and (3) a risk analysis method using linguistic rules for the construction of a risk matrix

Probabilistic and Fuzzy Arithmetic Approaches for the Treatment of Uncertainties in the Installation of Torpedo Piles

The “torpedo” pile is a foundation system that has been recently considered to anchor mooring lines and risers of floating production systems for offshore oil exploitation. The pile is installed in a free fall operation from a vessel. However, the soil parameters involved in the penetration model of the torpedo pile contain uncertainties that can affect the precision of analysis methods to evaluate its final penetration depth. Therefore, this paper deals with methodologies for the assessment of the sensitivity of the response to the variation of the uncertain parameters and mainly to incorporate into the analysis method techniques for the formal treatment of the uncertainties. Probabilistic and “possibilistic” approaches are considered, involving, respectively, the Monte Carlo method (MC) and concepts of fuzzy arithmetic (FA). The results and performance of both approaches are compared, stressing the ability of the latter approach to efficiently deal with the uncertainties of the model, with outstanding computational efficiency, and therefore, to comprise an effective design tool

Structure identification and parameter optimization for non-linear fuzzy modeling

International audienc

Complex Networks

X, 266 p.online resource

Fuzzy reasoning in co-operative supervision systems

International audienceThis paper considers a decision support system dedicated to fault detection and isolation from a human–machine co-operation point of view. Detection and isolation are based on different models of the process (non-linear and linear causal local models). Reasoning using real numbers is often used by human beings; fuzzy logic is introduced as a numerical-symbolic interface between the quantitative fault indicators and the symbolic diagnostic reasoning on them; it also provides an effective decision-making tool in imprecise or uncertain environments while managing model uncertainty, sensor imprecision and vague normal behavior limits. Fuzzy rules are modelled geometrically; fuzzy sets are represented as points in a description space. A prototype graphical interface with structural, causal and historical views gives complete information to the human operator. In such an interface, fuzziness is displayed as a colour palette evolving with time