Stab resistance of flexible composite reinforced with warp-knitted fabric like scale structure at quasi-static loading

The stab-resistant fabric like scale structure has a promising application prospect for areas of stab prevention owing to its great flexibility and excellent stab-resistance performance. In this paper, a kind of novel stab-resistant fabric has been designed by coating with epoxy resin (ER) and silicon carbide (SiC) particles, which was based on the warp-knitted fabric like scale structure (WKFS). The uniformity of dispersion has been investigated with different diameter and mass fraction of SiC, and polymerization degree of polyglycols (PEG); the flexibility and quasi-static properties of different kinds of WKFS treated with different coating solution has been studied, and the coating solution was prepared by mixing SiC particles and ER at different ratios. The results showed that the dispersion uniformity of the dispersion was the best when the diameter of SiC is 1 μm, the content is 50%, and the polymerization degree of PEG is 600. The longitudinal flexibility of the stab-resistant fabric is greater than that of transverse due to the gap between the longitudinal scales, and the addition of SiC particles can increase the transverse and longitudinal flexibility of stab-resistant fabric, and the flexible properties were the greatest when SiC:ER = 50:30. In addition, the WKFS treated with SiC of 16.7% has fatigue resistance; the damage mechanism of the fabric treated with pure ER is thankful to the brittleness of the resin; the added SiC particles hinder the further crack propagation of the resin, and the failure mode is mainly in stretch.

Scalar diversity and ignorance inferences: An experimental study on 'at least' as a modifier of numerals vs. adjectives

This work presents results from an experiment that investigates whether at least as a modifier of gradable adjectives (e.g., at least misleading) triggers speaker ignorance inferences just as has been established for at least as a numeral modifier (e.g., at least two). I find that, while at least gives rise to ignorance inferences with both types of scalar expressions, this happens in varying degrees, contra existing accounts of at least (Geurts & Nouwen 2007; Cohen & Krifka 2014) and in line with experimental evidence on the scalar inferences of unmodified adjectives and numerals (Doran, Baker, McNabb, Larson & Ward 2009), known as scalar diversity. I also find indications that the scale structure of adjectives may affect the availability of ignorance inferences, as in the case of scalar implicature computation for unmodified adjectives (Gotzner, Solt & Benz 2018a), yet in a reverse manner.

MSRCall: A Multi-scale Deep Neural Network to Basecall Oxford Nanopore Sequences

MinION, a third-generation sequencer from Oxford Nanopore Technologies, is a portable device that can provide long nucleotide read data in real-time. It primarily aims to deduce the makeup of nucleotide sequences from the ionic current signals generated when passing DNA/RNA fragments through nanopores charged with a voltage difference. To determine the nucleotides from the measured signals, a translation process known as basecalling is required. However, compared to NGS basecallers, the calling accuracy of MinION still needs to be improved. In this work, a simple but powerful neural network architecture called MSRCall is proposed. MSRCall comprises a multi-scale structure, recurrent layers, a fusion block, and a CTC decoder. To better identify both short-range and long-range dependencies, the recurrent layer is redesigned to capture various time-scale features with a multi-scale structure. The results show that MSRCall outperforms other basecallers in terms of both read and consensus accuracies.

Accelerating Large-Scale-Structure data analyses by emulating Boltzmann solvers and Lagrangian Perturbation Theory

The linear matter power spectrum is an essential ingredient in all theoretical models for interpreting large-scale-structure observables. Although Boltzmann codes such as CLASS or CAMB are very efficient at computing the linear spectrum, the analysis of data usually requires 104-106 evaluations, which means this task can be the most computationally expensive aspect of data analysis. Here, we address this problem by building a neural network emulator that provides the linear theory (total and cold) matter power spectrum in about one millisecond with ≈0.2%(0.5%) accuracy over redshifts z ≤ 3 (z ≤ 9), and scales10-4 ≤ k [h Mpc-1] < 50. We train this emulator with more than 200,000 measurements, spanning a broad cosmological parameter space that includes massive neutrinos and dynamical dark energy. We show that the parameter range and accuracy of our emulator is enough to get unbiased cosmological constraints in the analysis of a Euclid-like weak lensing survey. Complementing this emulator, we train 15 other emulators for the cross-spectra of various linear fields in Eulerian space, as predicted by 2nd-order Lagrangian Perturbation theory, which can be used to accelerate perturbative bias descriptions of galaxy clustering. Our emulators are specially designed to be used in combination with emulators for the nonlinear matter power spectrum and for baryonic effects, all of which are publicly available at http://www.dipc.org/bacco.

Effect of Sand-Frying-Triggered Puffing on the Multi-Scale Structure and Physicochemical Properties of Cassava Starch in Dry Gel

This study revealed the underlying mechanisms involved in the puffing process of dried cassava starch gel by exploring the development of the puffed structure of gel upon sand-frying, chiefly focused on the changes in the multi-scale structure and the physicochemical properties of starch. The results suggested that the sand-frying-induced puffing proceeded very fast, completed in about twenty seconds, which could be described as a two-phase pattern including the warming up (0~6 s) and puffing (7~18 s) stages. In the first stage, no significant changes occurred to the structure or appearance of the starch gel. In the second stage, the cells in the gel network structure were expanded until burst, which brought about a decrease in moisture content, bulk density, and hardness, as well as the increase in porosity and crispness when the surface temperature of gel reached glass transition temperature of 125.28 °C. Upon sand-frying puffing, the crystalline melting and molecular degradation of starch happened simultaneously, of which the latter mainly occurred in the first stage. Along with the increase of puffing time, the thermal stability, peak viscosity, and final viscosity of starch gradually decreased, while the water solubility index increased. Knowing the underlying mechanisms of this process might help manufacturers produce a better quality of starch-based puffed products.