Kevin Macon

Bio: Kevin Macon is an academic researcher from Louisiana State University. The author has contributed to research in topics: Physics & Modularity (networks). The author has an hindex of 3, co-authored 3 publications receiving 1829 citations. Previous affiliations of Kevin Macon include University of North Carolina at Chapel Hill.

Community Structure in Time-Dependent, Multiscale, and Multiplex Networks

Abstract: Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows studies of community structure in a general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.

Community Structure in the United Nations General Assembly

Abstract: We study the community structure of networks representing voting on resolutions in the United Nations General Assembly. We construct networks from the voting records of the separate annual sessions between 1946 and 2008 in three different ways: (1) by considering voting similarities as weighted unipartite networks; (2) by considering voting similarities as weighted, signed unipartite networks; and (3) by examining signed bipartite networks in which countries are connected to resolutions. For each formulation, we detect communities by optimizing network modularity using an appropriate null model. We compare and contrast the results that we obtain for these three different network representations. We thereby illustrate the need to consider multiple resolution parameters and explore the effectiveness of each network representation for identifying voting groups amidst the large amount of agreement typical in General Assembly votes.

Community structure in the United Nations General Assembly

Abstract: We study the community structure of networks representing voting on resolutions in the United Nations General Assembly. We construct networks from the voting records of the separate annual sessions between 1946 and 2008 in three different ways: (1) by considering voting similarities as weighted unipartite networks; (2) by considering voting similarities as weighted, signed unipartite networks; and (3) by examining signed bipartite networks in which countries are connected to resolutions. For each formulation, we detect communities by optimizing network modularity using an appropriate null model. We compare and contrast the results that we obtain for these three different network representations. We thereby illustrate the need to consider multiple resolution parameters and explore the effectiveness of each network representation for identifying voting groups amidst the large amount of agreement typical in General Assembly votes.

Measurement of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mmultiscripts><mml:mi>Ne</mml:mi><mml:mprescripts /><mml:none /><mml:mn>18</mml:mn></mml:mmultiscripts><mml:mo>(</mml:mo><mml:mi>α</mml:mi><mml:mo>,</mml:mo><mml:mi>p</mml:mi><mml:mo>)</mml:mo><mml:mmultiscri

Abstract: The $^{18}\mathrm{Ne}(\ensuremath{\alpha},p)^{21}\mathrm{Na}$ reaction plays a significant role in Type-I X-ray bursts. It is a major path in the breakout from the hot-CNO cycles to the synthesis of heavier elements in the $\ensuremath{\alpha}p$- and $rp$-processes. An experiment to determine the cross section of this reaction was performed with the ANASEN active-target detector system, determining the cross section at energies between 2.5 and 4 MeV in the center-of-mass frame. The measured cross sections for reactions populating the ground state in $^{21}\mathrm{Na}$ are consistent with results obtained from the time-inverse reaction, but significantly lower than the previously published experimental data of direct measurements. The total cross sections are also compared with those derived from indirect methods and statistical-model calculations. This experiment establishes a new experimental data set on the excitation function of the $^{18}\mathrm{Ne}(\ensuremath{\alpha},p)^{21}\mathrm{Na}$ reaction, revealing the significance of the excited states' contributions to the total reaction cross section and allowing us to separate the contribution of the $(\ensuremath{\alpha},2p)$ reaction. The impact of the measured cross section on thermal reaction rates is discussed.

Investigation of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mmultiscripts><mml:mi mathvariant="normal">B</mml:mi><mml:mprescripts /><mml:none /><mml:mn>10</mml:mn></mml:mmultiscripts><mml:mo>(</mml:mo><mml:mi>p</mml:mi><mml:mo>,</mml:mo><mml:mi>α</mml:mi><mml:mo>)</

Abstract: Background: A multitude of broad interfering resonances characterize the B10(p,α)Be7 cross section at low energies. The complexity of the reaction mechanism, as well as conflicting experimental measurements, have so far prevented a reliable prediction of the cross section over the energy ranges pertinent for a boron-proton fusion reactor environment. Purpose: To improve the evaluated cross section of the B10(p,α)Be7 reaction, this study targets the proton energy region from 0.8 to 2.0 MeV, where kinematic overlap of the scattered protons and reaction α particles have made past measurements very challenging. Method: New detailed studies of the reaction have been performed at the Edwards Accelerator Laboratory at Ohio University and the Nuclear Science Laboratory at the University of Notre Dame using time-of-flight and degrader foil techniques, respectively. Results: Proton and α-particle signals were clearly resolved using both techniques, and 16 point differential cross sections were measured over an angular range of θlab=45∘ and 157.5∘. A comprehensive R-matrix analysis of the experimental data, including data from previous low-energy studies of the B10(p,α)Be7, B10(p,p)B10, and B10(p,γ)C11 reactions, was achieved over the region of measurement. Using a representative set of previous data, the fit was extended to very low energies. Conclusions: On the basis of this data and R-matrix analysis, a more reliable and consistent description of the B10(p,α)Be7 cross section has been established. The uncertainty over the energy range of this study has been reduced from ≈20% to ≈10%, and the level structure over this region has been clarified considerably.8 MoreReceived 18 January 2022Accepted 26 April 2022DOI:https://doi.org/10.1103/PhysRevC.105.055802©2022 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasDirect reactionsNuclear astrophysicsNuclear fusionNuclear powerNuclear reactionsNuclear reactorsNuclear structure & decaysRadiative captureResonance reactionsProperties6 ≤ A ≤ 19Nuclear PhysicsPlasma Physics

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Temporal Networks

Abstract: A great variety of systems in nature, society and technology -- from the web of sexual contacts to the Internet, from the nervous system to power grids -- can be modeled as graphs of vertices coupled by edges The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems In many cases, however, the edges are not continuously active As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts In some cases, edges are active for non-negligible periods of time: eg, the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks

Multilayer Networks

Abstract: In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications Such systems include multiple subsystems and layers of connectivity, and it is important to take such "multilayer" features into account to try to improve our understanding of complex systems Consequently, it is necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks, and many others We also survey and discuss existing data sets that can be represented as multilayer networks We review attempts to generalize single-layer-network diagnostics to multilayer networks We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions, and various types of dynamical processes on multilayer networks We conclude with a summary and an outlook

Proceedings of the National Academy of Sciences

Abstract: where A represents the magnetic vector potential, is an integral of the hydromagnetic equations. This -integral made it possible to formulate a variational principle for the force-free magnetic fields. The integral expresses the fact that motions cannot transform a given field in an entirely arbitrary different field, if the conductivity of the medium isconsidered infinite. In this paper we shall show that the full set of hydromagnetic equations admit five more integrals, besides the energy integral, if dissipative processes are absent. These integrals, as we shall presently verify, are I2 =fbHvdV, (2)