29,144 research outputs found
Uncertainty in the manufacturing of fibrous thermosetting composites: A review
Composites manufacturing involves many sources of uncertainty associated with material properties variation and boundary conditions variability. In this study, experimental and numerical results concerning the statistical characterization and the influence of inputs variability on the main steps of composites manufacturing including process-induced defects are presented and analysed. Each of the steps of composite manufacturing introduces variability to the subsequent processes, creating strong interdependencies between the process parameters and properties of the final part. The development and implementation of stochastic simulation tools is imperative to quantify process output variabilities and develop optimal process designs in composites manufacturing
Spatial networks with wireless applications
Many networks have nodes located in physical space, with links more common
between closely spaced pairs of nodes. For example, the nodes could be wireless
devices and links communication channels in a wireless mesh network. We
describe recent work involving such networks, considering effects due to the
geometry (convex,non-convex, and fractal), node distribution,
distance-dependent link probability, mobility, directivity and interference.Comment: Review article- an amended version with a new title from the origina
Algorithms of causal inference for the analysis of effective connectivity among brain regions
In recent years, powerful general algorithms of causal inference have been developed. In particular, in the framework of Pearl’s causality, algorithms of inductive causation (IC and IC*) provide a procedure to determine which causal connections among nodes in a network can be inferred from empirical observations even in the presence of latent variables, indicating the limits of what can be learned without active manipulation of the system. These algorithms can in principle become important complements to established techniques such as Granger causality and Dynamic Causal Modeling (DCM) to analyze causal influences (effective connectivity) among brain regions. However, their application to dynamic processes has not been yet examined. Here we study how to apply these algorithms to time-varying signals such as electrophysiological or neuroimaging signals. We propose a new algorithm which combines the basic principles of the previous algorithms with Granger causality to obtain a representation of the causal relations suited to dynamic processes. Furthermore, we use graphical criteria to predict dynamic statistical dependencies between the signals from the causal structure. We show how some problems for causal inference from neural signals (e.g., measurement noise, hemodynamic responses, and time aggregation) can be understood in a general graphical approach. Focusing on the effect of spatial aggregation, we show that when causal inference is performed at a coarser scale than the one at which the neural sources interact, results strongly depend on the degree of integration of the neural sources aggregated in the signals, and thus characterize more the intra-areal properties than the interactions among regions. We finally discuss how the explicit consideration of latent processes contributes to understand Granger causality and DCM as well as to distinguish functional and effective connectivity
High-resolution truncated plurigaussian simulations for the characterization of heterogeneous formations
Integrating geological concepts, such as relative positions and proportions
of the different lithofacies, is of highest importance in order to render
realistic geological patterns. The truncated plurigaussian simulation method
provides a way of using both local and conceptual geological information to
infer the distributions of the facies and then those of hydraulic parameters.
The method (Le Loc'h and Galli 1994) is based on the idea of truncating at
least two underlying multi-Gaussian simulations in order to create maps of
categorical variable. In this manuscript we show how this technique can be used
to assess contaminant migration in highly heterogeneous media. We illustrate
its application on the biggest contaminated site of Switzerland. It consists of
a contaminant plume located in the lower fresh water Molasse on the western
Swiss Plateau. The highly heterogeneous character of this formation calls for
efficient stochastic methods in order to characterize transport processes.Comment: 12 pages, 9 figure
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