Dinitrogen Activation in the Gas Phase: Spectroscopic Characterization of C–N Coupling in the V₃C⁺ + N₂ Reaction

We report on cluster-mediated C–N bond formation in the gas phase using N2 as a nitrogen source. The V3C+ + N2 reaction is studied by a combination of ion-trap mass spectrometry with infrared photodissociation (IRPD) spectroscopy and complemented by electronic structure calculations. The proposed reaction mechanism is spectroscopically validated by identifying the structures of the reactant and product ions. V3C+ exhibits a pyramidal structure of C1 symmetry. N2 activation is initiated by adsorption in an end-on fashion at a vanadium site, followed by spontaneous cleavage of the N≡N triple bond and subsequent C−N coupling. The IRPD spectrum of the metal nitride product [NV3(C=N)]+ exhibits characteristic C=N double bond (1530 cm-1) and V-N single bond (770, 541 and 522 cm-1) stretching bands

Effect of Reflux Hole on the Transient Flow Characteristics of the Self-Priming Sewage Centrifugal Pump

The reflux hole has a large effect on the performance of self-priming centrifugal pumps. In order to study the effects of the reflux hole on the performance and transient flow characteristics of the self-priming centrifugal pump, four different areas of the reflux hole inside an external mixed self-priming pump were proposed. The 3D transient flow was numerically simulated under different operating conditions for the investigated pump. The differential pressure, reflux quantity and transient flow characteristics near the reflux hole were analysed, and then the effects of the reflux hole area on the pressure fluctuation characteristics and performance of the pump were further researched. The results show that the differential pressure and reflux quantity is zero around the best-efficiency point. The vorticity magnitude near the exit of the reflux hole is significant, and the unsymmetrical flow structures represent periodic motion over time in the cross-section. The pressure fluctuation intensities of monitoring points P2-P5 upstream of the reflux hole were generally larger than others and decreased with a decrease in reflux hole area. With a decrease of the reflux hole area, the performance of the pump improved to some extent

Improving automatic source code summarization via deep reinforcement learning

© 2018 Association for Computing Machinery. Code summarization provides a high level natural language description of the function performed by code, as it can benefit the software maintenance, code categorization and retrieval. To the best of our knowledge, most state-of-the-art approaches follow an encoder-decoder framework which encodes the code into a hidden space and then decode it into natural language space, suffering from two major drawbacks: a) Their encoders only consider the sequential content of code, ignoring the tree structure which is also critical for the task of code summarization; b) Their decoders are typically trained to predict the next word by maximizing the likelihood of next ground-truth word with previous ground-truth word given. However, it is expected to generate the entire sequence from scratch at test time. This discrepancy can cause an exposure bias issue, making the learnt decoder suboptimal. In this paper, we incorporate an abstract syntax tree structure as well as sequential content of code snippets into a deep reinforcement learning framework (i.e., actor-critic network). The actor network provides the confidence of predicting the next word according to current state. On the other hand, the critic network evaluates the reward value of all possible extensions of the current state and can provide global guidance for explorations. We employ an advantage reward composed of BLEU metric to train both networks. Comprehensive experiments on a real-world dataset show the effectiveness of our proposed model when compared with some state-of-the-art methods

Turbulence and Multiscaling in the Randomly Forced Navier Stokes Equation

We present an extensive pseudospectral study of the randomly forced Navier-Stokes equation (RFNSE) stirred by a stochastic force with zero mean and a variance $\sim k^{4-d-y}$, where $k$ is the wavevector and the dimension $d = 3$. We present the first evidence for multiscaling of velocity structure functions in this model for $y \geq 4$. We extract the multiscaling exponent ratios $\zeta_p/\zeta_2$ by using extended self similarity (ESS), examine their dependence on $y$, and show that, if $y = 4$, they are in agreement with those obtained for the deterministically forced Navier-Stokes equation ($3d$NSE). We also show that well-defined vortex filaments, which appear clearly in studies of the $3d$NSE, are absent in the RFNSE.Comment: 4 pages (revtex), 6 figures (postscript

Interleukin-10 inhibits tumor metastasis through an NK-cell dependent mechanism

Interleukin-10 (IL-10) is a recently described pleiotropic cytokine secreted mainly by type 2 helper T cells. Previous studies have shown that IL-10 suppresses cytokine expression by natural killer (NK) and type 1 T cells, thus down-regulating cell-mediated immunity and stimulating humoral responses. We here report that injected IL-10 protein is an efficient inhibitor of tumor metastasis in experimental (B16-F10) and spontaneous (M27 and Lox human melanoma) metastasis models in vivo at doses that do not have toxic effects on normal or cancer cells. Histological characterization after IL-10 treatment confirmed the absence of CD8+ and CD4+ T cells and macrophages at the sites of tumor growth, but abundant NK cells were localized at these sites. This unexpected finding was confirmed by showing that IL-10 inhibits most B16-F10 and Lox metastases in mice deficient in T or B cells (SCID and nu/nu mice), but not in those deficient in NK cells (beige mice or NK cell-depleted mice). However, IL-10 downregulation of pro-inflammatory cytokine production and/or recruitment of additional effector cells may also be involved in the anti-tumor effect at higher local concentrations of IL-10, since transfected B16 tumor cells expressing high amounts of IL-10 were rejected by normal, nu/nu, or SCID mice at the primary tumor stage, and there was still a 33% inhibition of tumor metastasis in beige mice

A minimum single-band model for low-energy excitations in superconducting Kx_xFe2_2Se2_2

We propose a minimum single-band model for the newly discovered iron-based superconducting K$_x$Fe$_2$Se$_2$. Our model is found to be numerically consistent with the five-orbital model at low energies. Based on our model and the random phase approximation, we study the spin fluctuation and the pairing symmetry of superconducting gap function. The $(\pi/2,\pi/2)$ spin excitation and the $d_{x^2-y^2}$ pairing symmetry are revealed. All of the results can well be understood in terms of the interplay between the Fermi surface topology and the local spin interaction, providing a sound picture to explain why the superconducting transition temperature is as high as to be comparable to those in pnictides and some cuprates. A common origin of superconductivity is elucidated for this compound and other high-T$_c$ materials.Comment: 5 pages, 4 figure

Graphene-based modulation-doped superlattice structures

The electronic transport properties of graphene-based superlattice structures are investigated. A graphene-based modulation-doped superlattice structure geometry is proposed and consist of periodically arranged alternate layers: InAs/graphene/GaAs/graphene/GaSb. Undoped graphene/GaAs/graphene structure displays relatively high conductance and enhanced mobilities at elevated temperatures unlike modulation-doped superlattice structure more steady and less sensitive to temperature and robust electrical tunable control on the screening length scale. Thermionic current density exhibits enhanced behaviour due to presence of metallic (graphene) mono-layers in superlattice structure. The proposed superlattice structure might become of great use for new types of wide-band energy gap quantum devices.Comment: 5 figure