A spatial approach to network generation for three properties:Degree distribution, clustering coefficient and degree assortativity

Social networks generally display a positively skewed degree distribution and higher values for clustering coefficient and degree assortativity than would be expected from the degree sequence. For some types of simulation studies, these properties need to be varied in the artificial networks over which simulations are to be conducted. Various algorithms to generate networks have been described in the literature but their ability to control all three of these network properties is limited. We introduce a spatially constructed algorithm that generates networks with constrained but arbitrary degree distribution, clustering coefficient and assortativity. Both a general approach and specific implementation are presented. The specific implementation is validated and used to generate networks with a constrained but broad range of property values. © Copyright JASSS.

Microscopy of heat treated titanium alloy BT16

Metallographic characterisation is combined with statistical analysis to study the microstructure of a BT16 titanium alloy after different heat treatment processes. It was found that the length, width and aspect ratio of α plates in this alloy follow the three-parameter Weibull distribution. Increasing annealing temperature or time causes the probability distribution of the length and the width of α plates to tend toward a normal distribution. The phase transformation temperature of the BT16 titanium alloy was found to be 875±5°C.

The VLT-FLAMES tarantula survey:X. Evidence for a bimodal distribution of rotational velocities for the single early B-type stars

Aims. Projected rotational velocities (ve sin i) have been estimated for 334 targets in the VLT-FLAMES Tarantula Survey that do not manifest significant radial velocity variations and are not supergiants. They have spectral types from approximately O9.5 to B3. The estimates have been analysed to infer the underlying rotational velocity distribution, which is critical for understanding the evolution of massive stars. Methods. Projected rotational velocities were deduced from the Fourier transforms of spectral lines, with upper limits also being obtained from profile fitting. For the narrower lined stars, metal and non-diffuse helium lines were adopted, and for the broader lined stars, both non-diffuse and diffuse helium lines; the estimates obtained using the different sets of lines are in good agreement. The uncertainty in the mean estimates is typically 4% for most targets. The iterative deconvolution procedure of Lucy has been used to deduce the probability density distribution of the rotational velocities. Results. Projected rotational velocities range up to approximately 450 kms-1 and show a bi-modal structure. This is also present in the inferred rotational velocity distribution with 25% of the sample having 0 <ve <100 km s^-1 and the high velocity component having v_e ∼ 250 km s^-1. There is no evidence from the spatial and radial velocity distributions of the two components that they represent either field and cluster populations or different episodes of star formation. Be-type stars have also been identified. Conclusions. The bi-modal rotational velocity distribution in our sample resembles that found for late-B and early-A type stars.While magnetic braking appears to be a possible mechanism for producing the low-velocity component, we can not rule out alternative explanations. © ESO 2013.

Optimal day-ahead scheduling of integrated urban energy systems

An optimal day-ahead scheduling method (ODSM) for the integrated urban energy system (IUES) is introduced, which considers the reconfigurable capability of an electric distribution network. The hourly topology of a distribution network, a natural gas network, the energy centers including the combined heat and power (CHP) units, different energy conversion devices and demand responsive loads (DRLs), are optimized to minimize the day-ahead operation cost of the IUES. The hourly reconfigurable capability of the electric distribution network utilizing remotely controlled switches (RCSs) is explored and discussed. The operational constraints from the unbalanced three-phase electric distribution network, the natural gas network, and the energy centers are considered. The interactions between the electric distribution network and the natural gas network take place through conversion of energy among different energy vectors in the energy centers. An energy conversion analysis model for the energy center was developed based on the energy hub model. A hybrid optimization method based on genetic algorithm (GA) and a nonlinear interior point method (IPM) is utilized to solve the ODSM model. Numerical studies demonstrate that the proposed ODSM is able to provide the IUES with an effective and economical day-ahead scheduling scheme and reduce the operational cost of the IUES.

Learning soft computing control strategies in a modular neural network architecture

Modelling and control of nonlinear dynamical systems is a challenging problem since the dynamics of such systems change over their parameter space. Conventional methodologies for designing nonlinear control laws, such as gain scheduling, are effective because the designer partitions the overall complex control into a number of simpler sub-tasks. This paper describes a new genetic algorithm based method for the design of a modular neural network (MNN) control architecture that learns such partitions of an overall complex control task. Here a chromosome represents both the structure and parameters of an individual neural network in the MNN controller and a hierarchical fuzzy approach is used to select the chromosomes required to accomplish a given control task. This new strategy is applied to the end-point tracking of a single-link flexible manipulator modelled from experimental data. Results show that the MNN controller is simple to design and produces superior performance compared to a single neural network (SNN) controller which is theoretically capable of achieving the desired trajectory. (C) 2003 Elsevier Ltd. All rights reserved.

Statistics sunspots and settlement: Influences on sum of probability curves

Large data sets of radiocarbon dates are becoming a more common feature of archaeological research. The sheer numbers of radiocarbon dates produced, however, raise issues of representation and interpretation. This paper presents a methodology which both reduces the visible impact of dating fluctuations, but also takes into consideration the influence of the underlying radiocarbon calibration curve. By doing so, it may be possible to distinguish between periods of human activity in early medieval Ireland and the statistical tails produced by radiocarbon calibration.

Long-period chromospheric oscillations in network bright points

The spatial variation of chromospheric oscillations in network bright points (NBPs) is studied using high-resolution observations in Ca II K3. Light curves and hence power spectra were created by isolating distinct regions of the NBP via a simple intensity thresholding technique. Using this technique, it was possible to identify peaks in the power spectra with particular spatial positions within the NBPs. In particular, long-period waves with periods of 4-15 minutes (1-4 mHz) were found in the central portions of each NBP, indicating that these waves are certainly not acoustic but possibly due to magnetoacoustic or magnetogravity wave modes. We also show that spatially averaged or low spatial resolution power spectra can lead to an inability to detect such long-period waves.

Bounded Rationality and Repeated Network Formation

We define a finite-horizon repeated network formation game with consent and study the differences induced by two levels of individual rationality. Perfectly rational players will remain unconnected at the equilibrium, while nonempty equilibrium networks may form when players are assumed to behave as finite automata of limited complexity. We provide structural properties of the sequences of networks which are likely to form in Nash and subgame perfect Nash equilibria of the repeated game. For instance, players can form totally different connected networks at each period or the sequence of networks can exhibit a total order relationship.

Composites of poly(ε-caprolactone) and Mo6S3I6 Nanowires

Composites of poly(e-caprolactone) (PCL) and molybdenum sulfur iodine (MoSI) nanowires were prepared using twin-screw extrusion. Extensive microscopic examination of the composites revealed the nanowires were well dispersed in the PCL matrix, although bundles of Mo6S3I6 ropes were evident at higher loadings. Secondary electron imaging (SEI) showed the nanowires had formed an extensive network throughout the PCL matrix, resulting in increased electrical conductivity of PCL, by eight orders of magnitude, and an electrical percolation threshold of 6.5T10S3vol%. Thermal analysis (DSC), WAXD, and hot stage polarized optical microscopy (HSPOM) experiments revealed Mo6S3I6 addition altered PCL crystallization kinetics, nucleation density, and crystalline content. A greater number of smaller spherulites were formed via heterogeneous nucleation. The onset of thermal decomposition (TGA) of PCL decreased by 70-C, a consequence of the thermal degradation of Mo6S3I6 to MoO3, which in turn accelerates the formation of volatile gases during the first stage of PCL decomposition.

Revealing differences in gene network inference algorithms on the network level by ensemble methods

Motivation: The inference of regulatory networks from large-scale expression data holds great promise because of the potentially causal interpretation of these networks. However, due to the difficulty to establish reliable methods based on observational data there is so far only incomplete knowledge about possibilities and limitations of such inference methods in this context.

Results: In this article, we conduct a statistical analysis investigating differences and similarities of four network inference algorithms, ARACNE, CLR, MRNET and RN, with respect to local network-based measures. We employ ensemble methods allowing to assess the inferability down to the level of individual edges. Our analysis reveals the bias of these inference methods with respect to the inference of various network components and, hence, provides guidance in the interpretation of inferred regulatory networks from expression data. Further, as application we predict the total number of regulatory interactions in human B cells and hypothesize about the role of Myc and its targets regarding molecular information processing.