Threshold Resummation Effects in Neutral Higgs Boson Production by Bottom Quark Fusion at the CERN Large Hadron Collider

We investigate the QCD effects in the production of neutral Higgs bosons via bottom quark fusion in both the standard model and the minimal supersymmetric standard model at the CERN Large Hadron Collider. We include the next-to-leading order (NLO) QCD corrections (including supersymmetric QCD) and the threshold resummation effects. We use the soft-collinear effective theory to resum the large logarithms near threshold from soft gluon emission. Our results show that the resummation effects can enhance the total cross sections by about 5% compared with the NLO results.Comment: 29pages, 14 figures, version to appear in Physical Review

Anomalous Hall effect in type-I Weyl metals beyond the noncrossing approximation

We study the anomalous Hall effect (AHE) in tilted Weyl metals with Gaussian disorder due to the crossed X and {\Psi} diagrams in this work. The importance of such diagrams to the AHE has been demonstrated recently in 2D massive Dirac model and Rashba ferromagnets. It was shown that the inclusion of such diagrams dramatically changes the total AHE in such systems. In this work, we show that the contributions from the X and {\Psi} diagrams to the AHE in tilted Weyl metals are of the same order of the non-crossing diagram we studied in a previous work, but with opposite sign. The total contribution of the X and {\Psi} diagrams cancels the majority of the contribution from the non-crossing diagram in tilted Weyl metals, similar to the 2D massive Dirac model. We also discussed the difference of the contributions from the crossed diagrams between 2D massive Dirac model and the tilted Weyl metals. At last, we discussed the experimental relevance of observing the AHE due to the X and {\Psi} diagrams in type-I Weyl metal Co3Sn2S2.Comment: This paper is a sequential work of our recently published work PRB 107, 125106(2023

Next-to-leading order QCD corrections to the single top quark production via model-independent t-q-g flavor-changing neutral-current couplings at hadron colliders

We present the calculations of the complete next-to-leading order (NLO) QCD effects on the single top productions induced by model-independent $tqg$ flavor-changing neutral-current couplings at hadron colliders. Our results show that, for the $tcg$ coupling the NLO QCD corrections can enhance the total cross sections by about 60% and 30%, and for the $tug$ coupling by about 50% and 20% at the Tevatron and LHC, respectively, which means that the NLO corrections can increase the experimental sensitivity to the FCNC couplings by about 10%$-$30%. Moreover, the NLO corrections reduce the dependence of the total cross sections on the renormalization or factorization scale significantly, which lead to increased confidence on the theoretical predictions. Besides, we also evaluate the NLO corrections to several important kinematic distributions, and find that for most of them the NLO corrections are almost the same and do not change the shape of the distributions.Comment: minor changes, version published in PR

HIPK2 reduces the resistance of gastric cancer cells to cisplatin via p53 pathway

Purpose: To uncover the functional effect of homologous domain-associated protein kinase 2 (HIPK2) on the viability of cisplatin (DDP)-resistant gastric cancer (GC) cells and elucidate the possible mechanism of action.Methods: The effect of DDP on GC viability and apoptotic rate was evaluated using MTT and flow cytometry (FCM) assays. The potential effect of HIPK2 on DDP sensitivity and cell apoptosis was investigated in the presence of cisplatin while the effect of HIPK2 on p53 activation was determined by immunoblot assay.Results: HIPK2 expression was decreased in DDP-resistant GC cell while upregulation of HIPK2 reduced growth, but promoted apoptosis in DDP-resistant GC cells. Further investigations showed that HIPK2 promoted p53 activation, while suppression of p53 weakened the inhibitory effect of HIPK2 on DDP-resistance in GC cells.Conclusion: The results suggest that HIPK2 is a promising and important therapeutic factor for the regulation of the resistance of GC cells to DDP. Thus, may have a role to play in the management of gastric cancer Keywords: Gastric cancer, Cisplatin, HIPK2, Homologous domain-associated protein kinase 2, p53 pathway, Therapeutic targe

Reliability Evaluation of Direct Current Distribution System for Intelligent Buildings Based on Big Data Analysis

In intelligent buildings, the power is distributed in the direct current (DC) mode, which is more energy-efficient than the traditional alternating current (AC) mode. However, the DC distribution system for intelligent buildings faces many problems, such as the stochasticity and intermittency of distributed generation, as well as the uncertain reliability of key supply and distribution devices. To solve these problems, this paper evaluates and predicts the reliability of the DC distribution system for intelligent buildings through big data analysis. Firstly, the authors identified the sources of the big data on DC distribution system for reliability analysis, and constructed a scientific evaluation index system. Then, association rules were mined from the original data on the evaluation indices with MapReduce, and a reliability evaluation model was established based on Bayesian network. Finally, the proposed model was proved valid through experiments. The research provides reference for reliability evaluation of the DC distribution system in various fields

GraphGAN: Graph Representation Learning with Generative Adversarial Nets

The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underlying connectivity distribution in the graph, and discriminative models that predict the probability of edge existence between a pair of vertices. In this paper, we propose GraphGAN, an innovative graph representation learning framework unifying above two classes of methods, in which the generative model and discriminative model play a game-theoretical minimax game. Specifically, for a given vertex, the generative model tries to fit its underlying true connectivity distribution over all other vertices and produces "fake" samples to fool the discriminative model, while the discriminative model tries to detect whether the sampled vertex is from ground truth or generated by the generative model. With the competition between these two models, both of them can alternately and iteratively boost their performance. Moreover, when considering the implementation of generative model, we propose a novel graph softmax to overcome the limitations of traditional softmax function, which can be proven satisfying desirable properties of normalization, graph structure awareness, and computational efficiency. Through extensive experiments on real-world datasets, we demonstrate that GraphGAN achieves substantial gains in a variety of applications, including link prediction, node classification, and recommendation, over state-of-the-art baselines.Comment: The 32nd AAAI Conference on Artificial Intelligence (AAAI 2018), 8 page