Charge injection, electroluminescence, and ageing of an epoxy resin in high divergent fields

[INTRODUCTION]Most experimental studies of electrical ageing have concentrated on semi-crystalline polymers such as those used in cable insulation and capacitors (see for example [1]). Theoretical models [2-4] for electrical ageing have been developed on the basis of these studies. The consensus is that ageing involves the formation of low-density regions, though the mechanisms responsible are disputed. For example, bond scission by high-energy electrons [2,5], and mechanical deformation have both been suggested. Both of these mechanisms are related to charge injection and the subsequent formation of high local fields. The semi-crystalline polymers studied so far have similar chemistries and almost identical morphologies. They tend, therefore, to show many similarities in, for example, the size of the energy barriers for the ageing reaction, critical ageing levels, and field dependence of ageing [4]. These similarities make it difficult to discriminate between mechanisms. Epoxy resins, however, are network polymers with a different molecular chemistry to that of the semi-crystalline polymers and are thus ideal to evaluate the proposed ageing mechanisms. We have therefore studied an epoxy resin (CY1301) under both uniform field and high divergent field conditions. Uniform field conditions were used to gain baseline characteristics for the properties of the unaged epoxy resin, and also for the effects of electrical ageing in low fields. Studies in high divergent fields were made using an electrode arrangement adapted from that of [6]. A number of wires set approximately 0.5mm apart were embedded, parallel to the flat faces, in thin (290 m ) flat samples. The radius of the wires ranged from 5 m (gold plated tungsten) to 25 m (tungsten). Relatively small voltages applied to the wires (5 kV DC) therefore produced local fields up to 170 kV/mm depending upon the wire radius chosen. These field levels are high enough to inject space-charge [6] without leading to instantaneous failure. This geometry, therefore, may both inject charge and simulate local stress enhancements arising from charge accumulation. The number of wires is large (30) so that the volume affected is big enough to allow changes on ageing to be detectable

Robust multi-fidelity design of a micro re-entry unmanned space vehicle

This article addresses the preliminary robust design of a small-scale re-entry unmanned space vehicle by means of a hybrid optimization technique. The approach, developed in this article, closely couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. The evolutionary part handles the shape parameters of the vehicle and the uncertain objective functions, while the direct transcription method generates an optimal control profile for the re-entry trajectory. Uncertainties on the aerodynamic forces and characteristics of the thermal protection material are incorporated into the vehicle model, and a Monte-Carlo sampling procedure is used to compute relevant statistical characteristics of the maximum heat flux and internal temperature. Then, the hybrid algorithm searches for geometries that minimize the mean value of the maximum heat flux, the mean value of the maximum internal temperature, and the weighted sum of their variance: the evolutionary part handles the shape parameters of the vehicle and the uncertain functions, while the direct transcription method generates the optimal control profile for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are utilized to approximate the aerodynamic forces required by the optimal control solver. The artificial neural networks are trained and updated by means of a multi-fidelity approach: initially a low-fidelity analytical model, fitted on a waverider type of vehicle, is used to train the neural networks, and through the evolution a mix of analytical and computational fluid dynamic, high-fidelity computations are used to update it. The data obtained by the high-fidelity model progressively become the main source of updates for the neural networks till, near the end of the optimization process, the influence of the data obtained by the analytical model is practically nullified. On the basis of preliminary results, the adopted technique is able to predict achievable performance of the small spacecraft and the requirements in terms of thermal protection materials

Clustering data by inhomogeneous chaotic map lattices

A new approach to clustering, based on the physical properties of inhomogeneous coupled chaotic maps, is presented. A chaotic map is assigned to each data-point and short range couplings are introduced. The stationary regime of the system corresponds to a macroscopic attractor independent of the initial conditions. The mutual information between couples of maps serves to partition the data set in clusters, without prior assumptions about the structure of the underlying distribution of the data. Experiments on simulated and real data sets show the effectiveness of the proposed algorithm.Comment: 8 pages, 6 figures. Revised version accepted for publication on Physical Review Letter

Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates

Non-Fermi liquid behavior and scaling of low frequency suppression in optical conductivity spectra of CaRuO3_3

Optical conductivity spectra $\sigma_1(\omega)$ of paramagnetic CaRuO$_3$ are investigated at various temperatures. At T=10 K, it shows a non-Fermi liquid behavior of $\sigma_1(\omega)\sim 1/{\omega}^{\frac 12}$, similar to the case of a ferromagnet SrRuO$_3$. As the temperature ($T$) is increased, on the other hand, $\sigma_1(\omega)$ in the low frequency region is progressively suppressed, deviating from the 1/{\omega}^{\frac 12%}-dependence. Interestingly, the suppression of $\sigma_1(\omega)$ is found to scale with $\omega /T$ at all temperatures. The origin of the $$ scaling behavior coupled with the non-Fermi liquid behavior is discussed.Comment: 4 pages, 3 figure

A Terrestrial Planet in a ~1 AU Orbit Around One Member of a ~15 AU Binary

We detect a cold, terrestrial planet in a binary-star system using gravitational microlensing. The planet has low mass (2 Earth masses) and lies projected at $a_{\perp,ph}$ ~ 0.8 astronomical units (AU) from its host star, similar to the Earth-Sun distance. However, the planet temperature is much lower, T<60 Kelvin, because the host star is only 0.10--0.15 solar masses and therefore more than 400 times less luminous than the Sun. The host is itself orbiting a slightly more massive companion with projected separation $a_{\perp,ch}=$10--15 AU. Straightforward modification of current microlensing search strategies could increase their sensitivity to planets in binary systems. With more detections, such binary-star/planetary systems could place constraints on models of planet formation and evolution. This detection is consistent with such systems being very common.Comment: Published in Science, Main and supplementary material combine

Multiple signals mediate proliferation, differentiation, and survival from the granulocyte colony-stimulating factor receptor in myeloid 32D cells

Granulocyte colony-stimulating factor (G-CSF) regulates neutrophil production through activation of its cognate receptor, the G-CSF-R. Previous studies with deletion mutants have shown that the membrane-proximal cytoplasmic domain of the receptor is sufficient for mitogenic signaling, whereas the membrane-distal domain is required for differentiation signaling. However, the function of the four cytoplasmic tyrosines of the G-CSF-R in the control of proliferation, differentiation, and survival has remained unclear. Here we investigated the role of these tyrosines by expressing a tyrosine 'null' mutant and single tyrosine 'add back' mutants in maturation-competent myeloid 32D cells. Clones expressing the null mutant showed only minimal proliferation and differentiation, with survival also reduced at low G-CSF concentrations. Analysis of clones expressing the add-back mutants revealed that multiple tyrosines contribute to proliferation, differentiation, and survival signals from the G-CSF-R. Analysis of signaling pathways downstream of these tyrosines suggested a positive role for STAT3 activation in both differentiation and survival signaling, whereas SHP-2, Grb2 and Shc appear important for proliferation signaling. In addition, we show that a tyrosine- independent 'differentiation domain' in the membrane-distal region of the G- CSF-R appears necessary but not sufficient for mediating neutrophilic differentiation in these cells

Representing complex data using localized principal components with application to astronomical data

Often the relation between the variables constituting a multivariate data space might be characterized by one or more of the terms: ``nonlinear'', ``branched'', ``disconnected'', ``bended'', ``curved'', ``heterogeneous'', or, more general, ``complex''. In these cases, simple principal component analysis (PCA) as a tool for dimension reduction can fail badly. Of the many alternative approaches proposed so far, local approximations of PCA are among the most promising. This paper will give a short review of localized versions of PCA, focusing on local principal curves and local partitioning algorithms. Furthermore we discuss projections other than the local principal components. When performing local dimension reduction for regression or classification problems it is important to focus not only on the manifold structure of the covariates, but also on the response variable(s). Local principal components only achieve the former, whereas localized regression approaches concentrate on the latter. Local projection directions derived from the partial least squares (PLS) algorithm offer an interesting trade-off between these two objectives. We apply these methods to several real data sets. In particular, we consider simulated astrophysical data from the future Galactic survey mission Gaia.Comment: 25 pages. In "Principal Manifolds for Data Visualization and Dimension Reduction", A. Gorban, B. Kegl, D. Wunsch, and A. Zinovyev (eds), Lecture Notes in Computational Science and Engineering, Springer, 2007, pp. 180--204, http://www.springer.com/dal/home/generic/search/results?SGWID=1-40109-22-173750210-