741 research outputs found
Wind-tunnel free-flight investigation of a model of a forward-swept-wing fighter configuration
A wind-tunnel free-flight investigation was conducted to study the dynamic stability characteristics of a model of a forward-swept-wing fighter-airplane configuration at high angles of attack. Various other wind-tunnel techniques employed in the study included static- and dynamic- (forced-oscillation) force tests, free-to-roll tests, and flow-visualization tests. A unique facet of the study was the extreme level of static pitch instability (in excess of negative 32-percent static margin) inherent in the airframe design which precluded free-flight testing without stability augmentation in pitch. Results are presented which emphasize the high-angle-of-attack aerodynamics and the vehicle-component contributions to these characteristics. The effects of these aerodynamic characteristics on the high-angle-of-attack flying qualities of the configuration are discussed in terms of results of the wind-tunnel free-flight tests
Robust Detection of Dynamic Community Structure in Networks
We describe techniques for the robust detection of community structure in
some classes of time-dependent networks. Specifically, we consider the use of
statistical null models for facilitating the principled identification of
structural modules in semi-decomposable systems. Null models play an important
role both in the optimization of quality functions such as modularity and in
the subsequent assessment of the statistical validity of identified community
structure. We examine the sensitivity of such methods to model parameters and
show how comparisons to null models can help identify system scales. By
considering a large number of optimizations, we quantify the variance of
network diagnostics over optimizations (`optimization variance') and over
randomizations of network structure (`randomization variance'). Because the
modularity quality function typically has a large number of nearly-degenerate
local optima for networks constructed using real data, we develop a method to
construct representative partitions that uses a null model to correct for
statistical noise in sets of partitions. To illustrate our results, we employ
ensembles of time-dependent networks extracted from both nonlinear oscillators
and empirical neuroscience data.Comment: 18 pages, 11 figure
Spectral mapping of brain functional connectivity from diffusion imaging.
Understanding the relationship between the dynamics of neural processes and the anatomical substrate of the brain is a central question in neuroscience. On the one hand, modern neuroimaging technologies, such as diffusion tensor imaging, can be used to construct structural graphs representing the architecture of white matter streamlines linking cortical and subcortical structures. On the other hand, temporal patterns of neural activity can be used to construct functional graphs representing temporal correlations between brain regions. Although some studies provide evidence that whole-brain functional connectivity is shaped by the underlying anatomy, the observed relationship between function and structure is weak, and the rules by which anatomy constrains brain dynamics remain elusive. In this article, we introduce a methodology to map the functional connectivity of a subject at rest from his or her structural graph. Using our methodology, we are able to systematically account for the role of structural walks in the formation of functional correlations. Furthermore, in our empirical evaluations, we observe that the eigenmodes of the mapped functional connectivity are associated with activity patterns associated with different cognitive systems
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