Kinetic energy dose as a unified metric for comparing ball mills in the mechanocatalytic depolymerization of lignocellulose

Mechanochemistry utilizes mechanical forces to activate chemical bonds. It offers environmentally benign routes for both (bio) organic and inorganic syntheses. However, direct comparison of mechanochemistry results is often very challenging. In mechanochemical synthetic protocols, ball mill setup (mechanical design and grinding vessel geometry) in addition to experimental parameters (milling frequency, duration, ball count and size) vary broadly. This fact poses a severe issue to further progress in this exciting research area because ball mill setup and experimental parameters govern how much kinetic energy is transferred to a chemical reaction. In this work, we address the challenge of comparing mechanochemical reaction results by taking the energy dose provided by ball mills as a unified metric into account. In this quest, we applied kinematic modeling to two ball mills functioning under distinct working principles to express the energy dose as a mathematical function of the experimental parameters. By examining the effect of energy dose on the extent of the mechanocatalytic depolymerization (MCD) of lignocellulosic biomass (beechwood), we found linear correlations between yield of water-soluble products (WSP) and energy dose for both ball mills. Interestingly, when a substrate layer is formed on the grinding jar wall and/or grinding medium, a weak non-linear correlation between water-soluble products yield and energy dose is identified. We demonstrate that the chemical reaction’s best utilization of kinetic energy is achieved in the linear regime, which presents improved WSP yields for given energy doses. In the broader context, the current analysis outlines the usefulness of the energy dose as a unified metric in mechanochemistry to further the understanding of reaction results obtained from different ball mills operating under varied experimental conditions

Application of wavelets to singular integral scattering equations

The use of orthonormal wavelet basis functions for solving singular integral scattering equations is investigated. It is shown that these basis functions lead to sparse matrix equations which can be solved by iterative techniques. The scaling properties of wavelets are used to derive an efficient method for evaluating the singular integrals. The accuracy and efficiency of the wavelet transforms is demonstrated by solving the two-body T-matrix equation without partial wave projection. The resulting matrix equation which is characteristic of multiparticle integral scattering equations is found to provide an efficient method for obtaining accurate approximate solutions to the integral equation. These results indicate that wavelet transforms may provide a useful tool for studying few-body systems.Comment: 11 pages, 4 figure

Comment on ``Solidification of a Supercooled Liquid in a Narrow Channel''

Comment on PRL v. 86, p. 5084 (2001) [cond-mat/0101016]. We point out that the authors' simulations are consistent with the known theory of steady-state solutions in this system

Solution of an infection model near threshold

We study the Susceptible-Infected-Recovered model of epidemics in the vicinity of the threshold infectivity. We derive the distribution of total outbreak size in the limit of large population size $N$. This is accomplished by mapping the problem to the first passage time of a random walker subject to a drift that increases linearly with time. We recover the scaling results of Ben-Naim and Krapivsky that the effective maximal size of the outbreak scales as $N^{2/3}$, with the average scaling as $N^{1/3}$, with an explicit form for the scaling function

Carrapato, tristeza parasitaria e tripanossomose dos bovinos.

Carrapato. O carrapato-do-boi Boophilus microplus: ciclo, biologia e epidemiologia, patogenia e controle. Tristeza parasitaria dos bovinos. Tristeza parasitaria dos bovinos (TPB): conceito, etiologia, transmissao, epidemiologia, diagnostico e controle. Imunidade contra Babesia e Anaplasma. Diagnostico parasitologico da tristeza parasitaria bovina. Diagnostico sorologico da tristeza parasitaria bovina. Diagnostico anatomopatologico da tristeza parasitaria bovina. Trpanossomose Trypanosoma vivax: biologia, diagnostico e controle. Imunidade contra Trypanosoma vivax.bitstream/item/196071/1/Carrapato-tristeza-parasitaria.pd

Precision Measurement of the 29Si, 33S, and 36Cl Binding Energies

The binding energies of 29Si, 33S, and 36Cl have been measured with a relative uncertainty $< 0.59 \times 10^{-6}$ using a flat-crystal spectrometer. The unique features of these measurements are 1) nearly perfect crystals whose lattice spacing is known in meters, 2) a highly precise angle scale that is derived from first principles, and 3) a gamma-ray measurement facility that is coupled to a high flux reactor with near-core source capability. The binding energy is obtained by measuring all gamma-rays in a cascade scheme connecting the capture and ground states. The measurements require the extension of precision flat-crystal diffraction techniques to the 5 to 6 MeV energy region, a significant precision measurement challenge. The binding energies determined from these gamma-ray measurements are consistent with recent highly accurate atomic mass measurements within a relative uncertainty of $4.3 \times 10^{-7}$. The gamma-ray measurement uncertainties are the dominant contributors to the uncertainty of this consistency test. The measured gamma-ray energies are in agreement with earlier precision gamma-ray measurements.Comment: 13 pages, 4 figure

BEAMS: separating the wheat from the chaff in supernova analysis

We introduce Bayesian Estimation Applied to Multiple Species (BEAMS), an algorithm designed to deal with parameter estimation when using contaminated data. We present the algorithm and demonstrate how it works with the help of a Gaussian simulation. We then apply it to supernova data from the Sloan Digital Sky Survey (SDSS), showing how the resulting confidence contours of the cosmological parameters shrink significantly.Comment: 23 pages, 9 figures. Chapter 4 in "Astrostatistical Challenges for the New Astronomy" (Joseph M. Hilbe, ed., Springer, New York, forthcoming in 2012), the inaugural volume for the Springer Series in Astrostatistic

Microscopic Selection of Fluid Fingering Pattern

We study the issue of the selection of viscous fingering patterns in the limit of small surface tension. Through detailed simulations of anisotropic fingering, we demonstrate conclusively that no selection independent of the small-scale cutoff (macroscopic selection) occurs in this system. Rather, the small-scale cutoff completely controls the pattern, even on short time scales, in accord with the theory of microscopic solvability. We demonstrate that ordered patterns are dynamically selected only for not too small surface tensions. For extremely small surface tensions, the system exhibits chaotic behavior and no regular pattern is realized.Comment: 6 pages, 5 figure

Extinction Rates for Fluctuation-Induced Metastabilities : A Real-Space WKB Approach

The extinction of a single species due to demographic stochasticity is analyzed. The discrete nature of the individual agents and the Poissonian noise related to the birth-death processes result in local extinction of a metastable population, as the system hits the absorbing state. The Fokker-Planck formulation of that problem fails to capture the statistics of large deviations from the metastable state, while approximations appropriate close to the absorbing state become, in general, invalid as the population becomes large. To connect these two regimes, a master equation based on a real space WKB method is presented, and is shown to yield an excellent approximation for the decay rate and the extreme events statistics all the way down to the absorbing state. The details of the underlying microscopic process, smeared out in a mean field treatment, are shown to be crucial for an exact determination of the extinction exponent. This general scheme is shown to reproduce the known results in the field, to yield new corollaries and to fit quite precisely the numerical solutions. Moreover it allows for systematic improvement via a series expansion where the small parameter is the inverse of the number of individuals in the metastable state

Deep recurrent neural networks for supernovae classification

We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae (code available at https://github.com/adammoss/supernovae). The observational time and filter fluxes are used as inputs to the network, but since the inputs are agnostic, additional data such as host galaxy information can also be included. Using the Supernovae Photometric Classification Challenge (SPCC) data, we find that deep networks are capable of learning about light curves, however the performance of the network is highly sensitive to the amount of training data. For a training size of 50% of the representational SPCC data set (around 104 supernovae) we obtain a type-Ia versus non-type-Ia classification accuracy of 94.7%, an area under the Receiver Operating Characteristic curve AUC of 0.986 and an SPCC figure-of-merit F 1 = 0.64. When using only the data for the early-epoch challenge defined by the SPCC, we achieve a classification accuracy of 93.1%, AUC of 0.977, and F 1 = 0.58, results almost as good as with the whole light curve. By employing bidirectional neural networks, we can acquire impressive classification results between supernovae types I, II and III at an accuracy of 90.4% and AUC of 0.974. We also apply a pre-trained model to obtain classification probabilities as a function of time and show that it can give early indications of supernovae type. Our method is competitive with existing algorithms and has applications for future large-scale photometric surveys