Explaining 750 GeV diphoton excess from top/bottom partner cascade decay in two-Higgs-doublet model extension

In this paper, we interpret the 750 GeV diphoton excess in the Zee-Babu extension of the two-Higgs-doublet model by introducing a top partner ($T$)/bottom partner ($B$). In the alignment limit, the 750 GeV resonance is identified as the heavy CP-even Higgs boson ($H$), which can be sizably produced via the QCD process $pp \to T\bar{T}$ or $pp \to B\bar{B}$ followed by the decay $T\to Ht$ or $B \to Hb$. The diphoton decay rate of $H$ is greatly enhanced by the charged singlet scalars predicted in the Zee-Babu extension and the total width of $H$ can be as large as 7 GeV. Under the current LHC constraints, we scan the parameter space and find that such an extension can account for the observed diphoton excess.Comment: 19 pages, 4 figures; some discussions and references adde

Research on the Equilibrium Speed-Density Relationship Around Flyover Work Zone

Increasing traffic demand has already reached the capacity of existing traffic facilities in most cities. In order to alleviate the traffic pressure and expand the capacity of the road network, constructing flyovers has become an effective way in most cities in China. During the period of the flyover construction, work zones occupy road space, impact traffic flow characteristics and driver behaviour; therefore, this causes a significant reduction of the capacity. Researching of the traffic flow characteristics during the period of flyover construction can improve traffic organization and traffic safety around work zones. This study analyses the traffic flow characteristics around the flyover work zone based on the site data collected in Hohhot City, China. This study shows that the traditional Logistic model for the equilibrium speed-density relationship is not applied to the traffic flow around the flyover work zone. Based on an in-depth analysis of the traffic flow characteristics and specific driver behaviours, this paper proposes an improved Logistic model to depict the equilibrium speed-density relationship around the flyover work zone. To analyse the mathematical characteristics of the speed-density relationship, this paper proposes a method to insert virtual data points into the initial data, which can make the fit curve be continuous.</p

DGR mutagenic transposition occurs via hypermutagenic reverse transcription primed by nicked template RNA.

Diversity-generating retroelements (DGRs) are molecular evolution machines that facilitate microbial adaptation to environmental changes. Hypervariation occurs via a mutagenic retrotransposition process from a template repeat (TR) to a variable repeat (VR) that results in adenine-to-random nucleotide conversions. Here we show that reverse transcription of the Bordetella phage DGR is primed by an adenine residue in TR RNA and is dependent on the DGR-encoded reverse transcriptase (bRT) and accessory variability determinant (Avd ), but is VR-independent. We also find that the catalytic center of bRT plays an essential role in site-specific cleavage of TR RNA for cDNA priming. Adenine-specific mutagenesis occurs during reverse transcription and does not involve dUTP incorporation, indicating it results from bRT-catalyzed misincorporation of standard deoxyribonucleotides. In vivo assays show that this hybrid RNA-cDNA molecule is required for mutagenic transposition, revealing a unique mechanism of DNA hypervariation for microbial adaptation

Low-Complexity Joint Beamforming for RIS-Assisted MU-MISO Systems Based on Model-Driven Deep Learning

Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal. However, optimizing the phase shifts jointly with the beamforming vector at the access point is challenging due to the non-convex objective function and constraints. In this study, we propose an algorithm based on weighted minimum mean square error optimization and power iteration to maximize the weighted sum rate (WSR) of a RIS-assisted downlink multi-user multiple-input single-output system. To further improve performance, a model-driven deep learning (DL) approach is designed, where trainable variables and graph neural networks are introduced to accelerate the convergence of the proposed algorithm. We also extend the proposed method to include beamforming with imperfect channel state information and derive a two-timescale stochastic optimization algorithm. Simulation results show that the proposed algorithm outperforms state-of-the-art algorithms in terms of complexity and WSR. Specifically, the model-driven DL approach has a runtime that is approximately 3% of the state-of-the-art algorithm to achieve the same performance. Additionally, the proposed algorithm with 2-bit phase shifters outperforms the compared algorithm with continuous phase shift.Comment: 14 pages, 9 figures, 2 tables. This paper has been accepted for publication by the IEEE Transactions on Wireless Communications. Copyright may be transferred without notice, after which this version may no longer be accessibl