Search

Dendrobium huoshanense (D. huoshanense) has been used as functional food supplements and herbal medicines for preventing and managing diseases with a long history in China. Due to its endangered natural resources and huge demand, people tend to cultivate D. huoshanense to protect this species. However, the quality of wild and cultivated herbs of the same species may change. This work quantified and compared the main quality traits and chemical components of wild imitating and greenhouse cultivated D. huoshanense with different growth years. As a result, wild and cultivated D. huoshanense had similar chemical composition, but there are significant differences in the content of many ingredients (polysaccharides, flavonoids, nucleosides, bibenzyls, lignans and volatile compounds). And the contents of many of these components increased with growing years. In addition, multivariate statistical analyses have been applied to classify and evaluate samples from different cultivation modes according to these components. In conclusion, our results demonstrated that the overall quality of greenhouse cultivated D. huoshanense was not as good as wild-grown, but this mode can be a promising and sustainable way of producing D. huoshanense. [ABSTRACT FROM AUTHOR]

A new CDF II measurement of the W -boson mass indicated a 7 σ deviation from the Standard Model (SM) prediction, and the previous FNAL and BNL measurement of muon (g -2) showed a 4.2 σ deviation from the SM prediction. As a result, new physics beyond the SM is indicated. In this paper, parameter space is scanned in the μ -term extended Next-to-Minimal Supersymmetric Standard Model (μ NMSSM). We get the surviving samples considering the limitations from the Large Hadron Collider (LHC) experiment and the Dark Matter (DM) direct detection experiment. In addition, we study phenomenon of W -boson mass and muon magnetic moment for them. Finally, we find that it is difficult to obtain samples surviving constraints of both CDF II experiment and muon anomaly magnetic in the μ NMSSM. [ABSTRACT FROM AUTHOR]

Recently, stacked autoencoder (SAE)‐based deep learning has been widely employed for industrial process fault detection, which can extract representative low‐dimensional features. However, SAE fails to take the distribution structure of raw data into consideration in the unsupervised self‐reconstruction learning, and this may lead to limited monitoring performance. Focusing on this issue, this paper proposes a new fault detection method based on entropy estimation density peak clustering and stacked autoencoder (EEDPC‐SAE) for industrial process monitoring. First, an improved automatic clustering algorithm, namely, entropy estimation density peak clustering (EEDPC), is presented to analyze the distribution structure of process data. Then, the data distribution information is integrated into the learning procedure of SAE to capture the data intrinsic structure. EEDPC‐SAE can describe the distribution characteristics of process data, and more informative high‐level features can be learned for fault detection. Finally, two statistics, that is, Hotelling's T‐squared (T2) and squared prediction error (SPE), are established based on the encoder features and residual features generated by EEDPC‐SAE. The effectiveness of the proposed method is evaluated by the Tennessee Eastman (TE) process and fed‐batch fermentation penicillin (FBFP) process. [ABSTRACT FROM AUTHOR]

The Z 3 -invariant next-to-minimal supersymmetric standard model (NMSSM) can provide a candidate for dark matter (DM). It can also be used to explain the hypothesis that the Higgs signal observed on the Large Hadron Collider (LHC) comes from the contribution of the two lightest CP-even Higgs bosons, whose masses are near 125 GeV. At present, XENON1T, LUX, and PandaX experiments have imposed very strict restrictions on direct collision cross sections of dark matter. In this paper, we consider a scenario that the observed Higgs signal is the superposition of two mass-degenerate Higgs in the Z 3 -invariant NMSSM and scan the seven-dimension parameter space composing of λ , κ , tan β , μ , A k , A t , M 1 via the Markov chain Monte Carlo (MCMC) method. We find that the DM relic density, as well as the LHC searches for sparticles, especially the DM direct detections, has provided a strong limit on the parameter space. The allowed parameter space is featured by a relatively small μ ≤ 300 GeV and about tan β ∈ (10 , 20) . In addition, the DM is Higgsino-dominated because of | 2 κ λ | > 1 . Moreover, the co-annihilation between χ ˜ 1 0 and χ ˜ 1 ± must be taken into account to obtain the reasonable DM relic density. [ABSTRACT FROM AUTHOR]

Arsenene has received considerable attention because of its unique optoelectronic and nanoelectronic properties. Nevertheless, the research on van der Waals (vdW) heterojunctions based on arsenene has just begun, which hinders the application of arsenene in the optoelectronic and nanoelectronic fields. Here, we systemically predict the stability and electronic structures of the arsenene/WS2 vdW heterojunction based on first-principles calculations, considering the stacking pattern, electric field, and strain effects. We found that the arsenene/WS2 heterostructure possesses a type-II band alignment. Moreover, the electric field can effectively tune both the band gap and the band alignment type. Additionally, the band gap could be tuned effectively by strain, while the band alignment type is robust under strain. Our study opens up a new avenue for the application of ultrathin arsenene-based vdW heterostructures in future nano- and optoelectronics applications. Our study demonstrates that the arsenene/WS2 heterostructure offers a candidate material for optoelectronic and nanoelectronic devices. [ABSTRACT FROM AUTHOR]

Kernel canonical variate analysis (KCVA) cannot be adopted for monitoring nonlinear time-varying processes because of changes in variance, mean, and correlation between variables. Efficient recursive kernel canonical variate analysis (ERKCVA) is thus proposed to monitor the nonlinear time-varying processes. In a high-dimensional feature space, the covariance matrix can be updated recursively by the exponentially weighted moving average approach. The first-order perturbation theory is introduced to obtain the recursive singular value decomposition of the Hankel matrix, which can significantly reduce the computational cost of the proposed method. Prediction errors and state variables are non-Gaussian; thus, upper control limits can be derived from the estimated probability density function by kernel density estimation. The proposed method is demonstrated by simulating a continuous stirred tank reactor. Simulation results indicate that ERKCVA could efficiently capture the predefined normal and natural changes in nonlinear time-varying processes. In addition, ERKCVA can also identify 4 types of sensor faults. [ABSTRACT FROM AUTHOR]

Canonical variate analysis (CVA) has been applied successfully in process monitoring. This paper proposes an efficient recursive CVA approach to monitor time-varying processes. The exponential weighted moving average approach has been adopted to update the covariance matrix of past observation vectors without the need for recalling past training data. The most important challenge faced by the recursive CVA algorithm is the high computation cost. To reduce the computation cost, the first order perturbation theory was introduced to update the singular value decomposition (SVD) of the Hankel matrix recursively. The computation cost of recursive SVD based on the first order perturbation theory is significantly less compared with conventional SVD. The proposed method is illustrated by the simulation of the continuous stirred tank reactor system. Simulation results indicate that not only can the proposed method effectively adapt to the natural changes of time-varying processes but also the proposed method can also identify two types of abrupt sensor faults. [ABSTRACT FROM AUTHOR]

Although the Higgs boson mass and single production rate have been determined more or less precisely, its other properties may deviate significantly from its predictions in the standard model (SM) due to the uncertainty of Higgs data. In this work we study the Higgs pair production at the LHC in the Manohar-Wise model, which extends the SM by one family of color-octet and isospin-doublet scalars. We first scanned over the parameter space of the Manohar-Wise model considering experimental constraints and performed fits in the model to the latest Higgs data by using the ATLAS and CMS data separately. Then we calculated the Higgs pair production rate and investigated the potential of its discovery at the LHC14. We conclude that: (i) Under current constrains including Higgs data after Run I of the LHC, the cross section of Higgs pair production in the Manohar-Wise model can be enhanced up to even 10 times prediction in the SM. (ii) Moreover, the sizable enhancement comes from the contributions of the CP-odd color-octet scalar $ S_I^A $. For lighter scalar $ S_I^A $ and larger values of |λ|, the cross section of Higgs pair production can be much larger. (iii) After running again of LHC at 14 TeV, most of the parameter spaces in the Manohar-Wise model can be test. For an integrated luminosity of 100 fb at the LHC14, when the normalized ratio R = 10, the process of Higgs pair production can be detected. [ABSTRACT FROM AUTHOR]

Magnetic transport spectroscopy is investigated in quantum point contacts (QPCs) fabricated in Al0.25Ga0.75N/GaN heterostructures. The magnetic field perpendicular to the two-dimensional electron gas (2DEG) is shown to depopulate the quasi-one-dimensional energy levels in the first two-dimensional (2D) subband faster than those in the second one. In GaN based heterostructures, the energy levels in the second 2D subband is generally concealed in the fast course of depletion and hence rarely detected. The perpendicular magnetic field facilitates the observation of the second 2D subband, and provides a method to study the properties of these energy levels. A careful analysis on the rate of the magnetic depletion with respect to the level index and confinement is carried out, from which the profile of the lateral confinement in GaN based QPCs is found to be triangular. The stability diagram at T shows the energy separation between the first and second 2D subband to be in the range of 32 to 42 meV. [ABSTRACT FROM AUTHOR]

The evidence of a new scalar particle X from the 750 GeV diphoton excess, and the absence of any other signal of new physics at the LHC so far suggest the existence of new colored scalars, which may be moderately light and thus can induce sizable Xgg and Xγγ couplings without resorting to very strong interactions. Motivated by this speculation, we extend the Manohar–Wise model by adding one gauge singlet scalar field. The resulting theory then predicts one singlet dominated scalar ϕ as well as three kinds of color-octet scalars, which can mediate through loops the ϕgg and ϕγγ interactions. After fitting the model to the diphoton data at the LHC, we find that in reasonable parameter regions the excess can be explained at 1 σ level by the process g g → ϕ → γ γ , and the best points predict the central value of the excess rate with χ min 2 = 2.32 , which corresponds to a p -value of 0.68. We also consider the constraints from various LHC Run I signals, and we conclude that, although these constraints are powerful in excluding the parameter space of the model, the best points are still experimentally allowed. [ABSTRACT FROM AUTHOR]

This paper investigates the consensus stabilization problem of stochastic multi-agent systems with noises, Markovian switching topology, and communication delays. To solve the consensus problem of the group of interacting agents, it is supposed to use the stochastic approximation type algorithm with the step-size non-decreasing to zero. For stochastic approximation based consensus algorithms with switching topologies, the existing convergence analysis methods may be difficult to guarantee for switching digraphs because these techniques heavily relies on quadratic Lyapunov functions. To overcome the inherent limitations of the existing methods, we develop a new ergodicity approach for backward products of degenerating stochastic matrices via a discrete time dynamical system approach to analyze the consensus stabilization problem of stochastic multi-agent systems with noises, Markovian switching topology, and communication delays; and we attain necessary and sufficient condition of the consensus stabilization. Our approach does not require the double stochasticity condition typically assumed for the existence of a quadratic Lyapunov function, and provides an effective tool for analyzing consensus problems via the existing theory of stochastic matrices and ergodicity of backward products. Finally, numerical simulation is shown to demonstrate theoretical results. [ABSTRACT FROM AUTHOR]