A model explaining neutrino masses and the DAMPE cosmic ray electron excess

We propose a flavored $U(1)_{e\mu}$ neutrino mass and dark matter~(DM) model to explain the recent DArk Matter Particle Explorer (DAMPE) data, which feature an excess on the cosmic ray electron plus positron flux around 1.4 TeV. Only the first two lepton generations of the Standard Model are charged under the new $U(1)_{e\mu}$ gauge symmetry. A vector-like fermion $\psi$, which is our DM candidate, annihilates into $e^{\pm}$ and $\mu^{\pm}$ via the new gauge boson $Z'$ exchange and accounts for the DAMPE excess. We have found that the data favors a $\psi$ mass around 1.5~TeV and a $Z'$ mass around 2.6~TeV, which can potentially be probed by the next generation lepton colliders and DM direct detection experiments.Comment: 7 pages, 3 figures. V2: version accepted by Physics Letters

Molecular docking via quantum approximate optimization algorithm

Molecular docking plays a pivotal role in drug discovery and precision medicine, enabling us to understand protein functions and advance novel therapeutics. Here, we introduce a potential alternative solution to this problem, the digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA), which utilizes counterdiabatic driving and QAOA on a quantum computer. Our method was applied to analyze diverse biological systems, including the SARS-CoV-2 Mpro complex with PM-2-020B, the DPP-4 complex with piperidine fused imidazopyridine 34, and the HIV-1 gp120 complex with JP-III-048. The DC-QAOA exhibits superior performance, providing more accurate and biologically relevant docking results, especially for larger molecular docking problems. Moreover, QAOA-based algorithms demonstrate enhanced hardware compatibility in the noisy intermediate-scale quantum era, indicating their potential for efficient implementation under practical docking scenarios. Our findings underscore quantum computing's potential in drug discovery and offer valuable insights for optimizing protein-ligand docking processes.Comment: 10 pages, 5 figures, All comments are welcom

CHARACTERISTICS OF BOARD OF DIRECTORS AND PERFORMANCE OF GEM LISTED COMPANIES FROM THE PERSPECTIVE OF VENTURE CAPITAL PARTICIPATION

Using the difference-in-differences (DID) model and taking the sample of companies listed from 2009 to 2014 on the Growth Enterprises Market (GEM) of the Shenzhen Stock Exchange in China, this paper studies the impact of Venture Capital (VC) participation on board characteristics, which is measured by the chief executive officer (CEO) duality, the scale of the board of directors and the proportion of independent directors, and it also studies how the board characteristics influence the company performance. The findings are as follows: VC-backed GEM listed companies are more inclined to choose the mode of CEO duality and to have a larger board of directors and a higher proportion of independent directors, all of which are conducive to improving company performance