Launching Dreams: A Transcendental Phenomenological Study Describing What It Means to Be an Underrepresented Student in a Collegiate Aviation Program

The purpose of this transcendental phenomenological study was to explore and describe what it means to be an underrepresented student in a collegiate professional pilot degree program. The central research question for this study was: What does it mean to be an underrepresented student in a collegiate professional pilot degree program? The four sub-questions were: (a) What influence do cognitive-person attributes have on meaning that is experienced and described by the underrepresented aviation student?, (b) What influence do gender and race have on meaning that is experienced and described by underrepresented aviation student?, (c) What influence does the contextual environment have on meaning that is experienced and described by the underrepresented aviation student?, and (d) What meaning do underrepresented aviation students ascribe to their experience in terms of social justice and equality as it relates to their academic and career development? The theories guiding this study were critical theory (CT) and social cognitive career theory (SCCT). Participants were 15 female and racial minority students in professional pilot degree programs at three four-year universities. Interviews, a focus group and written questionnaires provided data for phenomenological data analysis and a rich description of the phenomenon. The results provided five themes, which captured the essence of the lived experience of the underrepresented aviation student. Passion for flight was the motivation and drive for the students, and their passion transcends their identity as an underrepresented student. Collegiate aviation programs treat everyone equally and provide welcoming environments for all students to help them in achieving success. Future research can add to the results by comparing the experience of students who are not classified as underrepresented

Clustering in complex networks. II. Percolation properties

The percolation properties of clustered networks are analyzed in detail. In the case of weak clustering, we present an analytical approach that allows to find the critical threshold and the size of the giant component. Numerical simulations confirm the accuracy of our results. In more general terms, we show that weak clustering hinders the onset of the giant component whereas strong clustering favors its appearance. This is a direct consequence of the differences in the $k$-core structure of the networks, which are found to be totally different depending on the level of clustering. An empirical analysis of a real social network confirms our predictions.Comment: Updated reference lis

Effect of Collector Configuration on Test Section Turbulence Levels in an Open-Jet Wind Tunnel

Flow quality studies in the Langley 14- by 22-Foot Subsonic Tunnel indicated periodic flow pulsation at discrete frequencies in the test section when the tunnel operated in an open-jet configuration. To alleviate this problem, experiments were conducted in a 1/24-scale model of the full-scale tunnel to evaluate the turbulence reduction potential of six collector configurations. As a result of these studies, the original bell-mouth collector of the 14- by 22-Foot Subsonic Tunnel was replaced by a collector with straight walls, and a slot was incorporated between the trailing edge of the collector and the entrance of the diffuser

Properties of Random Graphs with Hidden Color

We investigate in some detail a recently suggested general class of ensembles of sparse undirected random graphs based on a hidden stub-coloring, with or without the restriction to nondegenerate graphs. The calculability of local and global structural properties of graphs from the resulting ensembles is demonstrated. Cluster size statistics are derived with generating function techniques, yielding a well-defined percolation threshold. Explicit rules are derived for the enumeration of small subgraphs. Duality and redundancy is discussed, and subclasses corresponding to commonly studied models are identified.Comment: 14 pages, LaTeX, no figure

Emergence of Clusters in Growing Networks with Aging

We study numerically a model of nonequilibrium networks where nodes and links are added at each time step with aging of nodes and connectivity- and age-dependent attachment of links. By varying the effects of age in the attachment probability we find, with numerical simulations and scaling arguments, that a giant cluster emerges at a first-order critical point and that the problem is in the universality class of one dimensional percolation. This transition is followed by a change in the giant cluster's topology from tree-like to quasi-linear, as inferred from measurements of the average shortest-path length, which scales logarithmically with system size in one phase and linearly in the other.Comment: 8 pages, 6 figures, accepted for publication in JSTA

Random acyclic networks

Directed acyclic graphs are a fundamental class of networks that includes citation networks, food webs, and family trees, among others. Here we define a random graph model for directed acyclic graphs and give solutions for a number of the model's properties, including connection probabilities and component sizes, as well as a fast algorithm for simulating the model on a computer. We compare the predictions of the model to a real-world network of citations between physics papers and find surprisingly good agreement, suggesting that the structure of the real network may be quite well described by the random graph.Comment: 4 pages, 2 figure

A dissemination strategy for immunizing scale-free networks

We consider the problem of distributing a vaccine for immunizing a scale-free network against a given virus or worm. We introduce a new method, based on vaccine dissemination, that seems to reflect more accurately what is expected to occur in real-world networks. Also, since the dissemination is performed using only local information, the method can be easily employed in practice. Using a random-graph framework, we analyze our method both mathematically and by means of simulations. We demonstrate its efficacy regarding the trade-off between the expected number of nodes that receive the vaccine and the network's resulting vulnerability to develop an epidemic as the virus or worm attempts to infect one of its nodes. For some scenarios, the new method is seen to render the network practically invulnerable to attacks while requiring only a small fraction of the nodes to receive the vaccine

Self-avoiding walks on scale-free networks

Several kinds of walks on complex networks are currently used to analyze search and navigation in different systems. Many analytical and computational results are known for random walks on such networks. Self-avoiding walks (SAWs) are expected to be more suitable than unrestricted random walks to explore various kinds of real-life networks. Here we study long-range properties of random SAWs on scale-free networks, characterized by a degree distribution $P(k) \sim k^{-\gamma}$. In the limit of large networks (system size $N \to \infty$), the average number $s_n$ of SAWs starting from a generic site increases as $\mu^n$, with $\mu = / - 1$. For finite $N$, $s_n$ is reduced due to the presence of loops in the network, which causes the emergence of attrition of the paths. For kinetic growth walks, the average maximum length, $$, increases as a power of the system size: $\sim N^{\alpha}$, with an exponent $\alpha$ increasing as the parameter $\gamma$ is raised. We discuss the dependence of $\alpha$ on the minimum allowed degree in the network. A similar power-law dependence is found for the mean self-intersection length of non-reversal random walks. Simulation results support our approximate analytical calculations.Comment: 9 pages, 7 figure

Evolution equation for a model of surface relaxation in complex networks

In this paper we derive analytically the evolution equation of the interface for a model of surface growth with relaxation to the minimum (SRM) in complex networks. We were inspired by the disagreement between the scaling results of the steady state of the fluctuations between the discrete SRM model and the Edward-Wilkinson process found in scale-free networks with degree distribution $P(k) \sim k^{-\lambda}$ for $\lambda <3$ [Pastore y Piontti {\it et al.}, Phys. Rev. E {\bf 76}, 046117 (2007)]. Even though for Euclidean lattices the evolution equation is linear, we find that in complex heterogeneous networks non-linear terms appear due to the heterogeneity and the lack of symmetry of the network; they produce a logarithmic divergency of the saturation roughness with the system size as found by Pastore y Piontti {\it et al.} for $\lambda <3$.Comment: 9 pages, 2 figure