Chirality-dependent topological edge states in photonic metacrystal.

Topological edge state, a unique mode for manipulating electromagnetic waves (EMs), has been extensively studied in both fundamental and applied physics. Up to now, the work on topological edge states has focused on manipulating linearly polarized waves. Here, we realize chirality-dependent topological edge states in one-dimensional photonic crystals (1DPCs) to manipulate circularly polarized waves. By introducing the magneto-electric coupling term (chirality), the degeneracy Dirac point (DP) is opened in PCs with symmetric unit cells. The topological properties of the upper and lower bands are different in the cases of left circularly polarized (LCP) and right circularly polarized (RCP) waves by calculating the Zak phase. Moreover, mapping explicitly 1D Maxwell's equations to the Dirac equation, we demonstrate that the introduction of chirality can lead to different topological properties of bandgaps for RCP and LCP waves. Based on this chirality-dependent topology, we can further realize chirality-dependent topological edge states in photonic heterostructures composed of two kinds of PCs. Finally, we propose a realistic structure for the chirality-dependent topological edge states by placing metallic helixes in host media. Our work provides a method for manipulating topological edge states for circularly polarized waves, which has a broad range of potential applications in designing optical devices including polarizers, filters, and sensors with robustness against disorder.

Aircraft Engine Fault Diagnosis Model Based on 1DCNN-BiLSTM with CBAM.

As the operational status of aircraft engines evolves, their fault modes also undergo changes. In response to the operational degradation trend of aircraft engines, this paper proposes an aircraft engine fault diagnosis model based on 1DCNN-BiLSTM with CBAM. The model can be directly applied to raw monitoring data without the need for additional algorithms to extract fault degradation features. It fully leverages the advantages of 1DCNN in extracting local features along the spatial dimension and incorporates CBAM, a channel and spatial attention mechanism. CBAM could assign higher weights to features relevant to fault categories and make the model pay more attention to them. Subsequently, it utilizes BiLSTM to handle nonlinear time feature sequences and bidirectional contextual feature information. Finally, experimental validation is conducted on the publicly available CMAPSS dataset from NASA, categorizing fault modes into three types: faultless, HPC fault (the single fault), and HPC&Fan fault (the mixed fault). Comparative analysis with other models reveals that the proposed model has a higher classification accuracy, which is of practical significance in improving the reliability of aircraft engine operations and for Remaining Useful Life (RUL) prediction.

Omnidirectional defect mode in one-dimensional photonic crystal with a (chiral) hyperbolic metamaterial defect.

The wavelength of defect mode in all-dielectric photonic crystals (PCs) with a dielectric defect are blue-shifted as incident angle increases for both transverse electric and transverse magnetic (TM) polarized waves. The blue-shifted property of defect mode limits the design of some optical devices including omnidirectional optical filters and wide-angle polarization selectors. Here we introduce a hyperbolic metamaterial (HMM) layer as a defect into dielectric one-dimensional photonic crystals (1DPCs) to obtain an omnidirectional defect mode for TM polarized waves at near-infrared regimes. Since only one HMM layer is introduced, omnidirectional defect mode with transmittance as high as 71% can be realized. Because of the unusual angle-dependence of propagating phase in the HMM defect, the total phase for satisfying the resonance condition of defect mode can be unchanged in a wide-angle range at a fixed wavelength, which leads to the omnidirectional defect mode. Moreover, the manipulation of propagating phase can be generalized to the case of circularly polarized waves, and we obtain an omnidirectional defect mode for left-handed circularly polarized waves in 1DPCs with a chiral hyperbolic metamaterial defect. Nevertheless, the defect mode for right-handed circularly polarized waves is still blue-shifted. Such spin-selective omnidirectional defect mode can be utilized to greatly enhance circular dichroism in a wide-angle range up to 64.1°. Our structure facilitates the design of omnidirectional optical filters with a high transmittance and circular polarization selectors working in a wide-angle range.

Giant optical chirality in dielectric metasurfaces induced by toroidal dipole resonances.

Although toroidal dipole (TD) resonance is a highly localized mode with a high quality (Q) factor, in most chiral structures the TD resonance is much weaker than the electric or magnetic dipole resonances and contributes little to the chiral response. In this Letter, we theoretically propose a chiral all-dielectric TD metasurface that possesses giant optical chirality with a certain degree of incident-angle robustness induced by a strong TD resonance. Interestingly, the symmetry of the system can be broken simultaneously at oblique incidence to produce chiral quasi-bound states in the continuum. The nearly unchanged high-Q TD resonance within a certain range of incident angles can avoid the problem of a reduced image quality caused by the incident-angle sensitivity, as demonstrated by the polarization-multiplexed-field image displays. The giant chirality with a certain degree of incident-angle robustness induced by the TD mode would be useful in some applications, including high-quality optical imaging, high-performance asymmetric transmission, and sensing.

On-chip multiple beam splitting assisted by bound states in the continuum.

Usually, on-chip beam splitting can be achieved by manipulating the in-plane iso-frequency curves (IFCs) of the structure, where the confinement of light along the out-of-plane direction is governed by total internal reflection. In this Letter, without needing a high-index dielectric background material for total internal reflection, we achieve on-chip beam splitting in a linear-crossing metamaterial (LCMM) mimicked by a two-dimensional photonic crystal (PhC) slab where the vertical confinement is enabled by a bound state in the continuum (BIC) and totally beyond the light cone. Particularly, the light propagating inside the LCMM can be flexibly controlled by the rotation angle of the rectangular silicon pillars in the PhC slab. On-chip triple beam splitting can further be designed by combining two kinds of LCMM with opposite rotation angles. Such light beam splitting beyond the light cone originates from the combined manipulation of the BIC and the spatial dispersion of LCMMs. Our work promotes the development of optical devices in integrated optics, such as on-chip focusing, switching, and (de)multiplexing.

The Demand Supply Steady-State Process-Based Multi-Level Spare Parts Optimization.

Spare parts are one of the important components of the equipment comprehensive support system. Spare parts management plays a decisive role in achieving the desired availability with the minimum cost. With the equipment complexity increasing, the price of spare parts has risen sharply. The traditional spare parts management makes the contradiction between fund shortage and spare parts shortage increasingly prominent. Based on the analysis of the multi-echelon and multi-indenture spare parts support model VARI-METRIC (vary multi-echelon technology for recoverable item control, VARI-METRIC), which is widely used by troops and enterprises in various countries, the model is mainly used in high system availability scenarios. However, in the case of low equipment system availability, the accuracy and cost of model inventory prediction are not ideal. This paper proposed the multi-level spare parts optimization model, which is based on the demand-supply steady-state process. It is an analytical model, which is used to solve the low accuracy problem of the VARI-METRIC model in the low equipment system availability. The analytical model is based on the multi-level spare parts support process. The article deduces methods for solving demand rate, demand-supply rate, equipment system availability, and support system availability. The marginal analysis method is used in the model to analyze the spare parts inventory allocation strategy's current based cost and availability optimal value. Finally, a simulation model is established to evaluate and verify the model. Then, the simulation results show that, when the low availability of equipment systems are 0.4, 0.6, the relative errors of the analytical model are 3.54%, 3.86%, and its costs are 0.52, 1.795 million ¥ RMB. The experiment proves that the inventory prediction accuracy of the analytical model is significantly higher than that of the VARI-METRIC model in low equipment system availability. Finally, the conclusion and future research directions are discussed.

Effective optical nihility media realized by one-dimensional photonic crystals containing hyperbolic metamaterials.

Owing to the omnidirectional perfect transmission and omnidirectional zero phase accumulation properties, S-type optical nihility media (ONM) have been utilized to design hyperlenses, optical waveguides, field concentrators and field rotators. Under the multiple interference mechanism, for conventional all-dielectric one-dimensional photonic crystals (1DPCs), all the transmittance peaks within the passband will shift towards short wavelengths (blueshift) with the increase in incident angle. Therefore, effective ONM cannot be realized in all-dielectric 1DPCs because the perfect transmission and zero phase accumulation conditions at the wavelength of the transmittance peak can only be satisfied at a specific incident angle. However, in a 1DPC composed of alternating dielectric and hyperbolic metamaterial (HMM) layers, one can realize a stopband of which one band edge is redshifted. At the same time, a transmittance peak in the passband is blueshifted. Therefore, between the redshift band edge and the blueshift transmittance peak, one can obtain an angle-independent transmittance peak. The HMM layer is mimicked by a dielectric/doped semiconductor multilayer. At the wavelength of the angle-independent transmittance peak, perfect transmission and zero phase accumulation conditions can be satisfied at any incident angle. Our work provides a route, under the current experimental conditions, to realize an effective S-type ONM by a simple one-dimensional structure in the near-infrared range.

Wide-angle ultrasensitive biosensors based on edge states in heterostructures containing hyperbolic metamaterials.

Edge states in photonic heterostructures composed of metal layers and all-dielectric one-dimensional photonic crystals (1DPCs) will shift toward short wavelengths (blueshift) with the increase in the incident angle for both transverses magnetic (TM) and transverse electric (TE) polarizations. However, we achieve redshift edge states for TM polarization and blueshift edge states for TE polarization in heterostructures composed of metal layers and 1DPCs containing layered hyperbolic metamaterials. Owing to the opposite wavelength shift of the edge states for two orthogonal polarizations, the ellipsometric phase will change dramatically around the edge state wavelength in a broad angle range. Based on this wide-angle phase singularity property, we propose a biosensor which can work with high refractive index resolution in a broad angle range.

A hybrid approach for named entity recognition in Chinese electronic medical record.

BACKGROUND: With the rapid spread of electronic medical records and the arrival of medical big data era, the application of natural language processing technology in biomedicine has become a hot research topic. METHODS: In this paper, firstly, BiLSTM-CRF model is applied to medical named entity recognition on Chinese electronic medical record. According to the characteristics of Chinese electronic medical records, obtain the low-dimensional word vector of each word in units of sentences. And then input the word vector to BiLSTM to realize automatic extraction of sentence features. And then CRF performs sentence-level word tagging. Secondly, attention mechanism is added between the BiLSTM and the CRF to construct Attention-BiLSTM-CRF model, which can leverage document-level information to alleviate tagging inconsistency. In addition, this paper proposes an entity auto-correct algorithm to rectify entities according to historical entity information. At last, a drug dictionary and post-processing rules are well-built to rectify entities, to further improve performance. RESULTS: The final F1 scores of the BiLSTM-CRF and Attention-BiLSTM-CRF model on given test dataset are 90.15 and 90.82% respectively, both of which are higher than 89.26%, which is the best F1 score on the test dataset except ours. CONCLUSION: Our approach can be used to recognize medical named entity on Chinese electronic medical records and achieves the state-of-the-art performance on the given test dataset.

Lappulaeffusa (Boraginaceae), a new species from Xinjiang, China.

Lappulaeffusa D.H.Liu & W.J.Li, a new species of Boraginaceae from Xinjiang, China, is described and illustrated in this study. The new species is morphologically similar to Lappulahimalayensis and L.tadshikorum. However, it can be distinguished from the compared species by several characteristics, such as: stem single, erect, frequently branched at middle and above, densely spreading hispid, hairs discoid at base; corolla white or blue; fruit compressed, heteromorphic nutlets with two rows of marginal glochids, nutlets acute ovoid, disc narrowly ovate-triangular. The diagnosis of the new species is supported with comprehensive investigation including photographs, detailed description, notes on etymology, distribution and habitat, conservation status, as well as comparisons with morphologically similar species.

Prediction and Screening Model for Products Based on Fusion Regression and XGBoost Classification.

Performance prediction based on candidates and screening based on predicted performance value are the core of product development. For example, the performance prediction and screening of equipment components and parts are an important guarantee for the reliability of equipment products. The prediction and screening of drug bioactivity value and performance are the keys to pharmaceutical product development. The main reasons for the failure of pharmaceutical discovery are the low bioactivity of the candidate compounds and the deficiencies in their efficacy and safety, which are related to the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the compounds. Therefore, it is very necessary to quickly and effectively perform systematic bioactivity value prediction and ADMET property evaluation for candidate compounds in the early stage of drug discovery. In this paper, a data-driven pharmaceutical products screening prediction model is proposed to screen drug candidates with higher bioactivity value and better ADMET properties. First, a quantitative prediction method for bioactivity value is proposed using the fusion regression of LGBM and neural network based on backpropagation (BP-NN). Then, the ADMET properties prediction method is proposed using XGBoost. According to the predicted bioactivity value and ADMET properties, the BVAP method is defined to screen the drug candidates. And the screening model is validated on the dataset of antagonized Erα active compounds, in which the mean square error (MSE) of fusion regression is 1.1496, the XGBoost prediction accuracy of ADMET properties are 94.0% for Caco-2, 95.7% for CYP3A4, 89.4% for HERG, 88.6% for hob, and 96.2% for Mn. Compared with the commonly used methods for ADMET properties such as SVM, RF, KNN, LDA, and NB, the XGBoost in this paper has the highest prediction accuracy and AUC value, which has better guiding significance and can help screen pharmaceutical product candidates with good bioactivity, pharmacokinetic properties, and safety.

Heterogeneous Domain Adaptation by Information Capturing and Distribution Matching.

Heterogeneous domain adaptation (HDA) is a challenging problem because of the different feature representations in the source and target domains. Most HDA methods search for mapping matrices from the source and target domains to discover latent features for learning. However, these methods barely consider the reconstruction error to measure the information loss during the mapping procedure. In this paper, we propose to jointly capture the information and match the source and target domain distributions in the latent feature space. In the learning model, we propose to minimize the reconstruction loss between the original and reconstructed representations to preserve information during transformation and reduce the Maximum Mean Discrepancy between the source and target domains to align their distributions. The resulting minimization problem involves two projection variables with orthogonal constraints that can be solved by the generalized gradient flow method, which can preserve orthogonal constraints in the computational procedure. We conduct extensive experiments on several image classification datasets to demonstrate that the effectiveness and efficiency of the proposed method are better than those of state-of-the-art HDA methods.