Valeri I. Orlov  Pavel S. Vergeles  Eugene B. Yakimov  Xingji Li  Jianqun Yang  Gang Lv  Shangli Dong 《physica status solidi (a)》2019,216(17)

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Jiejie Chen  Zhuo Shi  Shifeng Zhou  Zaijin Fang  Shichao Lv  Haohai Yu  Jianhua Hao  Huaijin Zhang  Jiyang Wang  Jianrong Qiu 《Advanced Optical Materials》2019,7(6)

The control of the optical response of multicomponent photonic glass through short‐ to medium‐range chemistry design has led to the development of high‐performance devices with efficient stimulated radiation, broadband optical amplification, and sensitive optical sensing. However, the success of optical modulation with an all‐fiber configuration is limited by the difficulty in creating smart structural units that can dynamically switch light–matter interactions. Here, a local chemistry design strategy is reported that can help realize dynamic energy storage and its controllable release, based on the simultaneous management of the chemical state and ligand field of transition‐metal dopant through glass crystallization. The theoretical analysis indicates that a four‐level configuration, such as that of tetrahedral Cr⁴⁺, can enable efficient photon–electron–photon conversion. Experimental data further reveal that this configuration can be stable in nanostructured glass. A nanostructured fiber with perfect core‐clad configuration is successfully fabricated by the melt‐in‐tube approach. The optical modulation function in bulk glass with estimated σ_gs and σ_es values of (1.39 ± 0.03) × 10⁻¹⁶ and (1.20 ± 0.02) × 10⁻¹⁶ cm², respectively, is also demonstrated. Therefore, a principle pulse laser device with operation wavelength at 1.06 µm and pulse duration of 176 ns is fabricated for the first time.  相似文献   

    

Lv  Jing  Guo  Chaohui 《Annals of the Institute of Statistical Mathematics》2019,71(5):1163-1199

Quantile regression is a powerful complement to the usual mean regression and becomes increasingly popular due to its desirable properties. In longitudinal studies, it is necessary to consider the intra-subject correlation among repeated measures over time to improve the estimation efficiency. In this paper, we focus on longitudinal single-index models. Firstly, we apply the modified Cholesky decomposition to parameterize the intra-subject covariance matrix and develop a regression approach to estimate the parameters of the covariance matrix. Secondly, we propose efficient quantile estimating equations for the index coefficients and the link function based on the estimated covariance matrix. Since the proposed estimating equations include a discrete indicator function, we propose smoothed estimating equations for fast and accurate computation of the index coefficients, as well as their asymptotic covariances. Thirdly, we establish the asymptotic properties of the proposed estimators. Finally, simulation studies and a real data analysis have illustrated the efficiency of the proposed approach.

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Zhen‐Long Lv  Hong‐Ling Cui  Hui Wang  Xiao‐Hong Li  Guang‐Fu Ji 《physica status solidi b》2016,253(9):1788-1794

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孙添  张世文  王瑞  冯爽  刘洋  吕航  徐海峰 《中国物理快报》2020,(4):31-35

Angular distributions of fragment ions from ionization of several tri-atomic molecules(CO2, OCS, N2O and NO2) by strong 800-nm laser fields are investigated via a time-of-flight mass spectrometer. Anisotropic angular distributions of fragment ions, especially those of atomic ions, are observed for all of the molecules studied.These anisotropic angular distributions are mainly due to the geometric alignment of molecules in the strong field ionization. Distinct different patterns in ionic angular distributions for different molecules are observed. It is indicated that both molecular geometric structure and ionization channels have effects on the angular distributions of strong field ionization/fragmentation.  相似文献   

    

Shengxiang Hu  Bofeng Zhang  Hehe Lv  Furong Chang  Chenyang Zhou  Liangrui Wu  Guobing Zou 《Entropy (Basel, Switzerland)》2022,24(9)

Network data analysis is a crucial method for mining complicated object interactions. In recent years, random walk and neural-language-model-based network representation learning (NRL) approaches have been widely used for network data analysis. However, these NRL approaches suffer from the following deficiencies: firstly, because the random walk procedure is based on symmetric node similarity and fixed probability distribution, the sampled vertices’ sequences may lose local community structure information; secondly, because the feature extraction capacity of the shallow neural language model is limited, they can only extract the local structural features of networks; and thirdly, these approaches require specially designed mechanisms for different downstream tasks to integrate vertex attributes of various types. We conducted an in-depth investigation to address the aforementioned issues and propose a novel general NRL framework called dynamic structure and vertex attribute fusion network embedding, which firstly defines an asymmetric similarity and h-hop dynamic random walk strategy to guide the random walk process to preserve the network’s local community structure in walked vertex sequences. Next, we train a self-attention-based sequence prediction model on the walked vertex sequences to simultaneously learn the vertices’ local and global structural features. Finally, we introduce an attributes-driven Laplacian space optimization to converge the process of structural feature extraction and attribute feature extraction. The proposed approach is exhaustively evaluated by means of node visualization and classification on multiple benchmark datasets, and achieves superior results compared to baseline approaches.  相似文献   

    

Wei Yang  Chunguang Li 《Entropy (Basel, Switzerland)》2022,24(9)

In this work, a unified lattice Boltzmann model is proposed for the fourth order partial differential equation with time-dependent variable coefficients, which has the form . A compensation function is added to the evolution equation to recover the macroscopic equation. Applying Chapman-Enskog expansion and the Taylor expansion method, we recover the macroscopic equation correctly. Through analyzing the error, our model reaches second-order accuracy in time. A series of constant-coefficient and variable-coefficient partial differential equations are successfully simulated, which tests the effectiveness and stability of the present model.  相似文献   

    

Fengbin Wu  Junxing Zhang  Shaobo Li  Dongchao Lv  Menghan Li 《Entropy (Basel, Switzerland)》2022,24(9)

Numerical optimization has been a popular research topic within various engineering applications, where differential evolution (DE) is one of the most extensively applied methods. However, it is difficult to choose appropriate control parameters and to avoid falling into local optimum and poor convergence when handling complex numerical optimization problems. To handle these problems, an improved DE (BROMLDE) with the Bernstein operator and refracted oppositional-mutual learning (ROML) is proposed, which can reduce parameter selection, converge faster, and avoid trapping in local optimum. Firstly, a new ROML strategy integrates mutual learning (ML) and refractive oppositional learning (ROL), achieving stochastic switching between ROL and ML during the population initialization and generation jumping period to balance exploration and exploitation. Meanwhile, a dynamic adjustment factor is constructed to improve the ability of the algorithm to jump out of the local optimum. Secondly, a Bernstein operator, which has no parameters setting and intrinsic parameters tuning phase, is introduced to improve convergence performance. Finally, the performance of BROMLDE is evaluated by 10 bound-constrained benchmark functions from CEC 2019 and CEC 2020, respectively. Two engineering optimization problems are utilized simultaneously. The comparative experimental results show that BROMLDE has higher global optimization capability and convergence speed on most functions and engineering problems.  相似文献   

    

Junjiang Zhu  Jintao Lv  Dongdong Kong 《Entropy (Basel, Switzerland)》2022,24(6)

(1) Background: A typical cardiac cycle consists of a P-wave, a QRS complex, and a T-wave, and these waves are perfectly shown in electrocardiogram signals (ECG). When atrial fibrillation (AF) occurs, P-waves disappear, and F-waves emerge. F-waves contain information on the cause of atrial fibrillation. Therefore it is essential to extract F-waves from the ECG signal. However, F-waves overlap the QRS complex and T-waves in both the time and frequency domain, causing this matter to be a difficult one. (2) Methods: This paper presents an optimized resonance-based signal decomposition method for detecting F-waves in single-lead ECG signals with atrial fibrillation (AF). It represents the ECG signal utilizing morphological component analysis as a linear combination of a finite number of components selected from the high-resonance and low-resonance dictionaries, respectively. The linear combination of components in the low-resonance dictionary reconstructs the oscillatory part (F-wave) of the ECG signal. In contrast, the linear combination of components in the high-resonance dictionary reconstructs the transient components part (QRST wave). The tunable Q-factor wavelet transform generates the high and low resonance dictionaries, with a high Q-factor producing a high resonance dictionary and a low Q-factor producing a low resonance dictionary. The different Q-factor settings affect the dictionaries’ characteristics, hence the F-wave extraction. A genetic algorithm was used to optimize the Q-factor selection to select the optimal Q-factor. (3) Results: The presented method helps reduce RMSE between the extracted and the simulated F-waves compared to average beat subtraction (ABS) and principal component analysis (PCA). According to the amplitude of the F-wave, RMSE is reduced by 0.24–0.32. Moreover, the dominant frequency of F-waves extracted by the presented method is clearer and more resistant to interference. The presented method outperforms the other two methods, ABS and PCA, in F-wave extraction from AF-ECG signals with the ventricular premature heartbeat. (4) Conclusion: The proposed method can potentially improve the accuracy of F-wave extraction for mobile ECG monitoring equipment, especially those with fewer leads.  相似文献   

    

Dan Lv  Xiaoling Zhang  Mengli Xu  Wenyue Cao  Xing Liu  Jinan Deng  Jun Yang  Ning Hu 《Electrophoresis》2022,43(21-22):2165-2174

A microfluidic device was designed and fabricated to capture single microparticles and cells by using hydrodynamic force and selectively release the microparticles and cells of interest via negative dielectrophoresis by activating selected individual microelectrodes. The trap microstructure was optimized based on numerical simulation of the electric field as well as the flow field. The capture and selective release functions of the device were verified by multi-types microparticles with different diameters and K562 cells. The capture efficiencies/release efficiencies were 95.55% ± 0.43%/96.41% ± 1.08% and 91.34% ± 0.01%/93.67% ± 0.36% for microparticles and cells, respectively. By including more traps and microelectrodes, the device can achieve high throughput and realize the visual separation of microparticles/cells of interest in a large number of particle/cell groups.  相似文献