The Integrated Sachs-Wolfe Effect in Time Varying Vacuum Model

The integrated Sachs-Wolfe (ISW) effect is an important implication for dark energy. In this paper, we have calculated the power spectrum of the ISW effect in the time varying vacuum cosmological model, where the model parameter $\beta=4.407$ is obtained by the observational constraint of the growth rate. It's found that the source of the ISW effect is not only affected by the different evolutions of the Hubble function $H(a)$ and the dimensionless matter density $\Omega_m(a)$, but also by the different growth function $D_+(a)$, all of which are changed due to the presence of matter production term in the time varying vacuum model. However, the difference of the ISW effect in $\Lambda(t)\textmd{CDM}$ model and $\Lambda \textmd{CDM}$ model is lessened to a certain extent due to the integration from the time of last scattering to the present. It's implied that the observations of the galaxies with high redshift are required to distinguish the two models

Local regularization assisted split augmented Lagrangian shrinkage algorithm for feature selection in condition monitoring

\ua9 2024 The Author(s)Feature selection plays a vital role in improving the efficiency and accuracy of condition monitoring by constructing sparse but effective models. In this study, an advanced feature selection algorithm named the local regularization assisted split augmented Lagrangian shrinkage algorithm (LR-SALSA) is proposed. The feature selection is realized by solving a l1-norm optimization problem, which usually selects more sparse and representative features at less computational costs. The proposed algorithm operates in two stages, namely variable selection and coefficient estimation. In the stage of variable selection, the primal problem is converted into three subproblems which can be solved separately. Then individual penalty parameters are applied to every coefficient of the model when dealing with the first subproblem. Under the Bayesian evidence framework, an iterative algorithm is derived to optimize these hyperparameters. During the optimization process, redundant variables will be pruned to guarantee model sparsity and improve computational efficiency at the same time. In the second stage, the coefficients for the selected model terms are determined using the least squares technique. The superior performance and efficiency of the proposed LR-SALSA method are validated through two numerical examples and a real-world cutting tool wear prediction case study. Compared with the existing methods, the proposed method can generate a sparse model and ensure a good trade-off between estimation accuracy and computational efficiency

Hybrid intelligence model based on image features for the prediction of flotation concentrate grade

In flotation processes, concentrate grade is the key production index but is difficult to be measured online. The mechanism models reflect the basic tendency of concentrate grade changes but cannot provide adequate prediction precision. The data-driven models based on froth image features provide accurate prediction within well-sampled space but rely heavily on sample data with less generalization capability. So, a hybrid intelligent model combining the two kinds of model is proposed in this paper. Since the information of image features is enormous, and the relationship between image features and concentrate grade is nonlinear, a B-spline partial least squares (BS-PLS) method is adopted to construct the data-driven model for concentrate grade prediction. In order to gain better generalization capability and prediction accuracy, information entropy is introduced to integrate the mechanism model and the BS-PLS model together and modify the model output online through an output deviation compensation strategy. Moreover, a slide window scheme is employed to update the hybrid model in order to improve its adaptability. The industrial practical data testing results show that the performance of the hybrid model is better than either of the two single models and it satisfies the accuracy and stability requirements in industrial applications

A Physical Explanation for Tilde System in Thermo Field Dynamics

For a two-body quantum system, any pure state can be represented by a biorthogonal expression by means of Schmidt decomposition. Using this in the composite system which include a thermodynamic system and its surroundings, it is found that the tilde system in thermo field dynamics is just the surroundings of the real system.Comment: 10 pages. To be published in Modern Physics Letter

Unified nonequilibrium dynamical theory for exchange bias and training effects

We investigate the exchange bias and training effects in the FM/AF heterostructures using a unified Monte Carlo dynamical approach. This real dynamical method has been proved reliable and effective in simulating dynamical magnetization of nanoscale magnetic systems. The magnetization of the uncompensated AF layer is still open after the first field cycling is finished. Our simulated results show obvious shift of hysteresis loops (exchange bias) and cycling dependence of exchange bias (training effect) when the temperature is below 45 K. The exchange bias fields decrease with decreasing the cooling rate or increasing the temperature and the number of the field cycling. With the simulations, we show the exchange bias can be manipulated by controlling the cooling rate, the distributive width of the anisotropy energy, or the magnetic coupling constants. Essentially, these two effects can be explained on the basis of the microscopical coexistence of both reversible and irreversible moment reversals of the AF domains. Our simulated results are useful to really understand the magnetization dynamics of such magnetic heterostructures. This unified nonequilibrium dynamical method should be applicable to other exchange bias systems.Comment: Chin. Phys. B, in pres

Crystal growth and quantum oscillations in the topological chiral semimetal CoSi

We survey the electrical transport properties of the single-crystalline, topological chiral semimetal CoSi which was grown via different methods. High-quality CoSi single crystals were found in the growth from tellurium solution. The sample's high carrier mobility enables us to observe, for the first time, quantum oscillations (QOs) in its thermoelectrical signals. Our analysis of QOs reveals two spherical Fermi surfaces around the R point in the Brillouin zone corner. The extracted Berry phases of these electron orbits are consistent with the -2 chiral charge as reported in DFT calculations. Detailed analysis on the QOs reveals that the spin-orbit coupling induced band-splitting is less than 2 meV near the Fermi level, one order of magnitude smaller than our DFT calculation result. We also report the phonon-drag induced large Nernst effect in CoSi at intermediate temperatures