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The mass table in the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with the PC-PK1 density functional has been established for even-$Z$ nuclei with $8\le Z\le120$, extended from the previous work for even-even nuclei [Zhang $\it{et.~al.}$ (DRHBc Mass Table Collaboration), At. Data Nucl. Data Tables 144, 101488 (2022)]. The calculated binding energies, two-nucleon and one-neutron separation energies, root-mean-square (rms) radii of neutron, proton, matter, and charge distributions, quadrupole deformations, and neutron and proton Fermi surfaces are tabulated and compared with available experimental data. A total of 4829 even-$Z$ nuclei are predicted to be bound, with an rms deviation of 1.477 MeV from the 1244 mass data. Good agreement with the available experimental odd-even mass differences, $\alpha$ decay energies, and charge radii is also achieved. The description accuracy for nuclear masses and nucleon separation energies as well as the prediction for drip lines is compared with the results obtained from other relativistic and nonrelativistic density functional. The comparison shows that the DRHBc theory with PC-PK1 provides an excellent microscopic description for the masses of even-$Z$ nuclei. The systematics of the nucleon separation energies, odd-even mass differences, pairing energies, two-nucleon gaps, $\alpha$ decay energies, rms radii, quadrupole deformations, potential energy curves, neutron density distributions, and neutron mean-field potentials are discussed., Comment: 394 pages, 17 figures, 2 tables, published in Atomic Data and Nuclear Data Tables, data file in the TXT form is available for download under "Ancillary files"

We systematically search for quasiperiodic oscillatory (QPO) signals on the month timescale among the 1525 sources given in the Fermi Large Area Telescope Light Curve Repository. We find a transient QPO of 31.3$\pm$1.8 days in the gamma-ray band light curve of the TeV blazar S5 0716+714, which has seven cycles (MJD 55918-56137) for the first time by weighted wavelet Z-transform and Lomb-Scargle periodogram methods. Monte Carlo simulations based on the power spectral density and probability distribution function were used to evaluate the confidence level of the QPO, and the result is $\sim 4.1\sigma$. Seasonal autoregressive integrated moving average modeling of the light curve revealed it is a significant physical QPO. The physical models to explain the sporadic QPO of the month-timescale QPOs in blazar were discussed. Our studies indicate that the helical jet model and blob move helically in a curved jet model to properly explain this kind of transient QPO., Comment: 13 pages, 5 figures, accepted for publication in ApJ

We study the band structure and transport properties of ferromagnetic tetragonal silicene nanoribbons by using the non-equilibrium Green's function method. The band structure and spin-dependent conductance are discussed under the combined effect of the external electric field, potential energy, exchange field and the spin-orbit coupling. One can easily realize a phase transition from a semimetallic to a semiconducting state by changing the transverse width of the nanoribbon. Separation of spin-dependent conductances arises from the effect of exchange field and the spin-orbit coupling, while zero-conductance behaviors exhibit spin-dependent band gaps induced by the electric field. We propose a device configuration of four-terminal tetragonal silicene nanoribbon with two central channels. It is found that spin current can be controlled by utilizing two switches. The switch with a high potential barrier can block electrons flowing from the central scattering region into other terminals. Interestingly, applying only one switch can realize spin-dependent zero conductance and large spin polarization. Two switches can provide multiple operations for controlling spin-dependent transport properties. The two-channel ferromagnetic tetragonal silicene nanoribbon can realize an effective separation of spin current, which may be a potential candidate for spintronic devices., Comment: 11 pages, 15 figures

The double Weyl semimetal (DWSM) is a newly proposed topological material that hosts Weyl points with chiral charge n=2. The disorder effect in DWSM is investigated by adopting the tight-binding Hamiltonian. Using the transfer matrix method and the noncommutative Kubo formula, we numerically calculate the localization length and the Hall conductivity in the presence of the on-site nonmagnetic disorder or orbital (spin-flip) disorders, and give the corresponding global phase diagrams. For the on-site nonmagnetic disorder, the system undergoes the DWSM-3D quantum anomalous hall (3D QAH) and normal insulator (NI)-DWSM phase transitions, and evolves into the diffusive metal (DM) phase before being localized by strong disorders, which is consistent with the Weyl semimetal. For \sigma_x orbital disorder, we find that increasing disorder can generate a pair of Weyl nodes at the boundary of the Brillouin zone and induce a 3D QAH-DWSM phase transition. Then we investigate the combined effect of orbital disorders for both disordered 3D QAH phase and DWSM phase. The disorder-induced transitions can be well understood in terms of an effective medium theory based on self-consistent Born approximation., Comment: 8 pages, 9 figures

While the advanced machine learning algorithms are effective in load forecasting, they often suffer from low data utilization, and hence their superior performance relies on massive datasets. This motivates us to design machine learning algorithms with improved data utilization. Specifically, we consider the load forecasting for a new user in the system by observing only few shots (data points) of its energy consumption. This task is challenging since the limited samples are insufficient to exploit the temporal characteristics, essential for load forecasting. Nonetheless, we notice that there are not too many temporal characteristics for residential loads due to the limited kinds of human lifestyle. Hence, we propose to utilize the historical load profile data from existing users to conduct effective clustering, which mitigates the challenges brought by the limited samples. Specifically, we first design a feature extraction clustering method for categorizing historical data. Then, inheriting the prior knowledge from the clustering results, we propose a two-phase Long Short Term Memory (LSTM) model to conduct load forecasting for new users. The proposed method outperforms the traditional LSTM model, especially when the training sample size fails to cover a whole period (i.e., 24 hours in our task). Extensive case studies on two real-world datasets and one synthetic dataset verify the effectiveness and efficiency of our method., Comment: *The first two authors contributed equally to this work, and hence are co-first authors of this work. C. Wu is the corresponding author. This work was supported in part by the Shenzhen Institute of Artificial Intelligence and Robotics for Society