MPFEM Modeling on the Compaction of Al/SiC Composite Powders with Core/Shell Structure

Uniaxial die compaction of two-dimensional (2D) Al/SiC core/shell (core: SiC; shell: Al) composite powders with different initial packing structures was numerically reproduced using DEM-FEM coupled MPFEM modeling from particulate scale. The effects of external pressure, initial packing structure, and SiC content on the packing densification were systematically presented. Various macro and micro properties such as relative density and distribution, stress and distribution, particle rearrangement (e.g. sliding and rolling), deformation and mass transfer, and interfacial behavior within composite particles were characterized and analyzed. The results show that by properly controlling the initial packing structure, pressure, and SiC content, various anisotropic and isotropic Al/SiC particulate composites with high relative densities and uniform density/stress distributions can be obtained. At early stage of the compaction, the densification mechanism mainly lies in the particle rearrangement driven by the low interparticle forces. In addition to sliding, accompanied particle rolling also plays an important role. With the increase of the compaction pressure, the force network based on SiC cores leads to extrusion on Al shells between two cores, contributing to mass transfer and pore filling. During compaction, the debonding between the core and shell of each composite particle appears and then disappears gradually in the final compact

Computation with Advice

Computation with advice is suggested as generalization of both computation with discrete advice and Type-2 Nondeterminism. Several embodiments of the generic concept are discussed, and the close connection to Weihrauch reducibility is pointed out. As a novel concept, computability with random advice is studied; which corresponds to correct solutions being guessable with positive probability. In the framework of computation with advice, it is possible to define computational complexity for certain concepts of hypercomputation. Finally, some examples are given which illuminate the interplay of uniform and non-uniform techniques in order to investigate both computability with advice and the Weihrauch lattice

IGFBPL1 Regulates Axon Growth through IGF-1-mediated Signaling Cascades

Activation of axonal growth program is a critical step in successful optic nerve regeneration following injury. Yet the molecular mechanisms that orchestrate this developmental transition are not fully understood. Here we identified a novel regulator, insulin-like growth factor binding protein-like 1 (IGFBPL1), for the growth of retinal ganglion cell (RGC) axons. Expression of IGFBPL1 correlates with RGC axon growth in development, and acute knockdown of IGFBPL1 with shRNA or IGFBPL1 knockout in vivo impaired RGC axon growth. In contrast, administration of IGFBPL1 promoted axon growth. Moreover, IGFBPL1 bound to insulin-like growth factor 1 (IGF-1) and subsequently induced calcium signaling and mammalian target of rapamycin (mTOR) phosphorylation to stimulate axon elongation. Blockage of IGF-1 signaling abolished IGFBPL1-mediated axon growth, and vice versa, IGF-1 required the presence of IGFBPL1 to promote RGC axon growth. These data reveal a novel element in the control of RGC axon growth and suggest an unknown signaling loop in the regulation of the pleiotropic functions of IGF-1. They suggest new therapeutic target for promoting optic nerve and axon regeneration and repair of the central nervous system

Research on Speech Emotion Recognition Based on AA-CBGRU Network

Speech emotion recognition is an emerging research field in the 21st century, which is of great significance to human–computer interaction. In order to enable various smart devices to better recognize and understand the emotions contained in human speech, in view of the problems of gradient disappearance and poor learning ability of the time series information in the current speech emotion classification model, an AA-CBGRU network model is proposed for speech emotion recognition. The model first extracts the spectrogram and its first and second order derivative features of the speech signal, then extracts the spatial features of the inputs through the convolutional neural network with residual blocks, then uses the BGRU network with an attention layer to mine deep time series information, and finally uses the full connection layer to achieve the final emotion recognition. The experimental results on the IEMOCAP sentiment corpus show that the model in this paper improves both the weighted accuracy (WA) and the unweighted accuracy (UA)

Research on Speech Emotion Recognition Based on AA-CBGRU Network

Speech emotion recognition is an emerging research field in the 21st century, which is of great significance to human–computer interaction. In order to enable various smart devices to better recognize and understand the emotions contained in human speech, in view of the problems of gradient disappearance and poor learning ability of the time series information in the current speech emotion classification model, an AA-CBGRU network model is proposed for speech emotion recognition. The model first extracts the spectrogram and its first and second order derivative features of the speech signal, then extracts the spatial features of the inputs through the convolutional neural network with residual blocks, then uses the BGRU network with an attention layer to mine deep time series information, and finally uses the full connection layer to achieve the final emotion recognition. The experimental results on the IEMOCAP sentiment corpus show that the model in this paper improves both the weighted accuracy (WA) and the unweighted accuracy (UA)

Pharmacogenetic study of drug-metabolising enzyme polymorphisms on the risk of anti-tuberculosis drug-induced liver injury: a meta-analysis.

Three first-line antituberculosis drugs, isoniazid, rifampicin and pyrazinamide, may induce liver injury, especially isoniazid. This antituberculosis drug-induced liver injury (ATLI) ranges from a mild to severe form, and the associated mortality cases are not rare. In the past decade, many investigations have focused the association between drug-metabolising enzyme (DME) gene polymorphisms and risk for ATLI; however, these studies have yielded contradictory results.PubMed, EMBASE, ISI web of science and the Chinese National Knowledge Infrastructure databases were systematically searched to identify relevant studies. A meta-analysis was performed to examine the association between polymorphisms from 4 DME genes (NAT2, CYP2E1, GSTM1 and GSTT1) and susceptibility to ATLI. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Heterogeneity among articles and their publication bias were also tested.38 studies involving 2,225 patients and 4,906 controls were included. Overall, significantly increased ATLI risk was associated with slow NAT2 genotype and GSTM1 null genotype when all studies were pooled into the meta-analysis. Significantly increased risk was also found for CYP2E1*1A in East Asians when stratified by ethnicity. However, no significant results were observed for GSTT1.Our results demonstrated that slow NAT2 genotype, CYP2E1*1A and GSTM1 null have a modest effect on genetic susceptibility to ATLI

Dynamic modelling on the confined crystallization of mono-sized cubic particles under mechanical vibration

The dynamic crystallization of cubic granular particles under three-dimensional mechanical vibration is numerically investigated by the discrete element method. The effects of operational conditions (vibration, container shape and system size) and particle properties (gravity and friction) on the formation of crystals and defects are discussed. The results show that the formation and growth of clusters with face-to-face aligned cubic particles can be easily realized under vibrations. Especially, a single crystal with both translational and orientational ordering can be reproduced in a rectangular container under appropriate vibrations. It is also found that the gravitational effect is beneficial for the ordering of a packing; the ordering of frictional particles can be improved significantly with an enlarged gravitational acceleration. The flat walls of a rectangular container facilitate the formation of orderly layered structures. The curved walls of a cylindrical container contribute to the formation of ring-like structures, whereas they also cause distortions and defects in the packing centers. Finally, it is shown that the crystallization of inelastic particles is basically accomplished by the pursuit of a better mechanical stability of the system, with decreasing kinetic and potential energies

DEM study of crystallization of monosized spheres under mechanical vibrations

The crystallization (disorder-order transition) of monosized spheres under three-dimensional (3D) mechanical vibrations is studied using discrete element method (DEM). The crystallization dynamics and final structure are analyzed for two selected conditions: i.e. the packing of rough spheres (glass beads) with interval vibration and batch-wise feeding (Case I) and the packing of smooth spheres with continuous vibration and total feeding (Case II). The final packing densities are 0.728 and 0.712 for Cases I and II, respectively, higher than that of random close packings. Partial crystallization characterized by the {111}-oriented face centered cubic (FCC) structure can be observed in both packings, which is further confirmed from the analyses of coordination number, radial and angular distribution functions, and Q6 bond order. Through the tracing of the particles (e.g. the evolutions of velocity and force fields), two crystallization mechanisms are identified: engulfed growth of two adjacent small crystals and epitaxial growth from existing ordered structures (nuclei)