SPECIAL HEAT TRANSFER PHENOMENA FOR SUPERCRITICAL FLUIDS

Present-day knowledge concerning the molecular structure of supercritical fluids is briefly reviewed. It is shown that liquid-like and gas-like phases may coexist at supercritical pressures, although they may not be in equllibrium with each other. it is postulated that on the basis of the coexistence of these two phases a "boiling-like" phenomenon may provide the mechanism of heat transfer to supercritical fluids at high heat fluxes and certain other conditions. An unusual mode of heat transfer was actually observed at supercritical pressures during tests which produced the high heat fluxes and other conditions under which such "boiling" would be expected. The tests and the various conditions are briefly described. An emission of high-frequeney, high-intensity sounds usually accompanied these tests. It is shown that similar screaming sounds were heard during boiling at subcritical pressures, giving further support to the hypothesis that "boiling" may occur at supercritical pressures. A seeond possible explanation for the unusual mode of heat transfer is based on boundarylayer stability considerations. At high heat fluxes large density differences exist between the bulk of the fluid and the fluid in the boundary layer near the wall. A breakdown of the boundary layer may be caused by the build-up of ripples between its low-density fluid and the high-density bulk fluid, in a manner quite similar to the breaking of ocean waves at high wind velocities. It is pointed out that the density variation of supercritical fluids may be used to advantage by certrifuging boundary layers. (auth

Natural linewidth analysis of d-band photoemission from Ag(110)

We report a high-resolution angle-resolved study of photoemission linewidths observed for Ag(110). A careful data analysis yields k$-resolved upper limits for the inverse inelastic lifetimes of$d$-holes at the X-point of the bulk band structure. At the upper$d$-band edge the hole-lifetime is$\tau_h \geq 22 $fs, i.e. more than one order of magnitude larger than predicted for a free-electron gas. Following calculations for$d$-hole dynamics in Cu (I.\ Campillo et al., Phys. Rev. Lett., in press) we interpret the lifetime enhancement by a small scattering cross-section of $d$- and $sp$-states below the Fermi level. With increasing distance to $E_F$ the $d$-hole lifetimes get shorter because of the rapidly increasing density of d-states and contributions of intra-$d$-band scattering processes, but remain clearly above free-electron-model predictions.Comment: 14 pages, 7 figure

Density of bulk trap states in organic semiconductor crystals: discrete levels induced by oxygen in rubrene

The density of trap states in the bandgap of semiconducting organic single crystals has been measured quantitatively and with high energy resolution by means of the experimental method of temperature-dependent space-charge-limited-current spectroscopy (TD-SCLC). This spectroscopy has been applied to study bulk rubrene single crystals, which are shown by this technique to be of high chemical and structural quality. A density of deep trap states as low as ~ 10^{15} cm^{-3} is measured in the purest crystals, and the exponentially varying shallow trap density near the band edge could be identified (1 decade in the density of states per ~25 meV). Furthermore, we have induced and spectroscopically identified an oxygen related sharp hole bulk trap state at 0.27 eV above the valence band.Comment: published in Phys. Rev. B, high quality figures: http://www.cpfs.mpg.de/~krellner

Field-induced charge transport at the surface of pentacene single crystals: a method to study charge dynamics of 2D electron systems in organic crystals

A method has been developed to inject mobile charges at the surface of organic molecular crystals, and the DC transport of field-induced holes has been measured at the surface of pentacene single crystals. To minimize damage to the soft and fragile surface, the crystals are attached to a pre-fabricated substrate which incorporates a gate dielectric (SiO_2) and four probe pads. The surface mobility of the pentacene crystals ranges from 0.1 to 0.5 cm^2/Vs and is nearly temperature-independent above ~150 K, while it becomes thermally activated at lower temperatures when the induced charges become localized. Ruling out the influence of electric contacts and crystal grain boundaries, the results contribute to the microscopic understanding of trapping and detrapping mechanisms in organic molecular crystals.Comment: 14 pages, 4 figures. Submitted to J. Appl. Phy

The time-course of a scrapie outbreak

BACKGROUND: Because the incubation period of scrapie has a strong host genetic component and a dose-response relationship, it is possible that changes will occur during an outbreak, especially in the genotypes of cases, age-at-onset of disease and, perhaps, the clinical signs displayed. We investigated these factors for a large outbreak of natural scrapie, which yielded sufficient data to detect temporal trends. RESULTS: Cases occurred mostly in two genotypes, VRQ/VRQ and VRQ/ARQ, with those early in the outbreak more likely to be of the VRQ/VRQ genotype. As the epidemic progressed, the age-at-onset of disease increased, which reflected changes in the genotypes of cases rather than changes in the age-at-onset within genotypes. Clinical signs of cases changed over the course of the outbreak. As the epidemic progressed VRQ/VRQ and VRQ/ARQ sheep were more likely to be reported with behavioural changes, while VRQ/VRQ sheep only were less likely to be reported with loss of condition. CONCLUSION: This study of one of the largest scrapie outbreaks in the UK allowed investigation of the effect of PrP genotype on other epidemiological parameters. Our analysis indicated that, although age-at-onset and clinical signs changed over time, the observed changes were largely, but not exclusively, driven by the time course of the PrP genotypes of cases

Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks

Data preprocessing is a crucial part of any machine learning pipeline, and it can have a significant impact on both performance and training efficiency. This is especially evident when using deep neural networks for time series prediction and classification: real-world time series data often exhibit irregularities such as multi-modality, skewness and outliers, and the model performance can degrade rapidly if these characteristics are not adequately addressed. In this work, we propose the EDAIN (Extended Deep Adaptive Input Normalization) layer, a novel adaptive neural layer that learns how to appropriately normalize irregular time series data for a given task in an end-to-end fashion, instead of using a fixed normalization scheme. This is achieved by optimizing its unknown parameters simultaneously with the deep neural network using back-propagation. Our experiments, conducted using synthetic data, a credit default prediction dataset, and a large-scale limit order book benchmark dataset, demonstrate the superior performance of the EDAIN layer when compared to conventional normalization methods and existing adaptive time series preprocessing layers

Deep Time-Delay Reservoir Computing: Dynamics and Memory Capacity

The Deep Time-Delay Reservoir Computing concept utilizes unidirectionally connected systems with time-delays for supervised learning. We present how the dynamical properties of a deep Ikeda-based reservoir are related to its memory capacity (MC) and how that can be used for optimization. In particular, we analyze bifurcations of the corresponding autonomous system and compute conditional Lyapunov exponents, which measure the generalized synchronization between the input and the layer dynamics. We show how the MC is related to the systems distance to bifurcations or magnitude of the conditional Lyapunov exponent. The interplay of different dynamical regimes leads to a adjustable distribution between linear and nonlinear MC. Furthermore, numerical simulations show resonances between clock cycle and delays of the layers in all degrees of the MC. Contrary to MC losses in a single-layer reservoirs, these resonances can boost separate degrees of the MC and can be used, e.g., to design a system with maximum linear MC. Accordingly, we present two configurations that empower either high nonlinear MC or long time linear MC

M3C: Monte Carlo reference-based consensus clustering

Genome-wide data is used to stratify patients into classes for precision medicine using clustering algorithms. A common problem in this area is selection of the number of clusters (K). The Monti consensus clustering algorithm is a widely used method which uses stability selection to estimate K. However, the method has bias towards higher values of K and yields high numbers of false positives. As a solution, we developed Monte Carlo reference-based consensus clustering (M3C), which is based on this algorithm. M3C simulates null distributions of stability scores for a range of K values thus enabling a comparison with real data to remove bias and statistically test for the presence of structure. M3C corrects the inherent bias of consensus clustering as demonstrated on simulated and real expression data from The Cancer Genome Atlas (TCGA). For testing M3C, we developed clusterlab, a new method for simulating multivariate Gaussian clusters

M3C: Monte Carlo reference-based consensus clustering.

Genome-wide data is used to stratify patients into classes for precision medicine using clustering algorithms. A common problem in this area is selection of the number of clusters (K). The Monti consensus clustering algorithm is a widely used method which uses stability selection to estimate K. However, the method has bias towards higher values of K and yields high numbers of false positives. As a solution, we developed Monte Carlo reference-based consensus clustering (M3C), which is based on this algorithm. M3C simulates null distributions of stability scores for a range of K values thus enabling a comparison with real data to remove bias and statistically test for the presence of structure. M3C corrects the inherent bias of consensus clustering as demonstrated on simulated and real expression data from The Cancer Genome Atlas (TCGA). For testing M3C, we developed clusterlab, a new method for simulating multivariate Gaussian clusters