A Fault Diagnosis Scheme for Gearbox Based on Improved Entropy and Optimized Regularized Extreme Learning Machine

The performance of a gearbox is sensitive to failures, especially in the long-term high speed and heavy load field. However, the multi-fault diagnosis in gearboxes is a challenging problem because of the complex and non-stationary measured signal. To obtain fault information more fully and improve the accuracy of gearbox fault diagnosis, this paper proposes a feature extraction method, hierarchical refined composite multiscale fluctuation dispersion entropy (HRCMFDE) to extract the fault features of rolling bearing and the gear vibration signals at different layers and scales. On this basis, a novel fault diagnosis scheme for the gearbox based on HRCMFDE, ReliefF and grey wolf optimizer regularized extreme learning machine is proposed. Firstly, HRCMFDE is employed to extract the original features, the multi-frequency time information can be evaluated simultaneously, and the fault feature information can be extracted more fully. After that, ReliefF is used to screen the sensitive features from the high-dimensional fault features. Finally, the sensitive features are inputted into the optimized regularized extreme learning machine to identify the fault states of the gearbox. Through three different types of gearbox experiments, the experimental results confirm that the proposed method has better diagnostic performance and generalization, which can effectively and accurately identify the different fault categories of the gearbox and outperforms other contrastive methods.</p

Research Progress of Cholesteric Liquid Crystals with Broadband Reflection

Cholesteric liquid crystal (ChLC) materials with broadband reflection are witnessing a significant surge in interest due to their unique ability to self-organize into a helical supra-molecular architecture and their excellent selective reflection of light based on the Bragg relationship. Nowadays, by the virtue of building self-organized nanostructures with pitch gradient or non-uniform pitch distribution, extensive work has already been performed to obtain ChLC films with a broad reflection band. This critical review systematically summarizes the optical background of the ChLCs with broadband reflection characteristics, methods to obtain broadband reflection of ChLCs, as well as the application in this area. Combined with the research status and the advantages in the field, the challenges and opportunities of applied scientific problems in the research direction are also introduced.</jats:p

Research Progress of Cholesteric Liquid Crystals with Broadband Reflection

Cholesteric liquid crystal (ChLC) materials with broadband reflection are witnessing a significant surge in interest due to their unique ability to self-organize into a helical supra-molecular architecture and their excellent selective reflection of light based on the Bragg relationship. Nowadays, by the virtue of building self-organized nanostructures with pitch gradient or non-uniform pitch distribution, extensive work has already been performed to obtain ChLC films with a broad reflection band. This critical review systematically summarizes the optical background of the ChLCs with broadband reflection characteristics, methods to obtain broadband reflection of ChLCs, as well as the application in this area. Combined with the research status and the advantages in the field, the challenges and opportunities of applied scientific problems in the research direction are also introduced

Research Progress on Blue-Phase Liquid Crystals for Pattern Replication Applications

Blue-Phase Liquid Crystals (BPLCs) are considered to be excellent 3D photonic crystals and have attracted a great deal of attention due to their great potential for advanced applications in a wide range of fields including self-assembling tunable photonic crystals and fast-response displays. BPLCs exhibit promise in patterned applications due to their sub-millisecond response time, three-dimensional cubic structure, macroscopic optical isotropy and high contrast ratio. The diversity of patterned applications developed based on BPLCs has attracted much attention. This paper focuses on the latest advances in blue-phase (BP) materials, including applications in patterned microscopy, electric field driving, handwriting driving, optical writing and inkjet printing. The paper concludes with future challenges and opportunities for BP materials, providing important insights into the subsequent development of BP.</jats:p

Research Progress on Blue-Phase Liquid Crystals for Pattern Replication Applications

Blue-Phase Liquid Crystals (BPLCs) are considered to be excellent 3D photonic crystals and have attracted a great deal of attention due to their great potential for advanced applications in a wide range of fields including self-assembling tunable photonic crystals and fast-response displays. BPLCs exhibit promise in patterned applications due to their sub-millisecond response time, three-dimensional cubic structure, macroscopic optical isotropy and high contrast ratio. The diversity of patterned applications developed based on BPLCs has attracted much attention. This paper focuses on the latest advances in blue-phase (BP) materials, including applications in patterned microscopy, electric field driving, handwriting driving, optical writing and inkjet printing. The paper concludes with future challenges and opportunities for BP materials, providing important insights into the subsequent development of BP

A Super-Resolution Network for High-Resolution Reconstruction of Landslide Main Bodies in Remote Sensing Imagery Using Coordinated Attention Mechanisms and Deep Residual Blocks

The lack of high-resolution training sets for intelligent landslide recognition using high-resolution remote sensing images is a major challenge. To address this issue, this paper proposes a method for reconstructing low-resolution landslide remote sensing images based on a Super-Resolution Generative Adversarial Network (SRGAN) to fully utilize low-resolution images in the process of constructing high-resolution landslide training sets. First, this paper introduces a novel Enhanced Depth Residual Block called EDCA, which delivers stable performance compared to other models while only slightly increasing model parameters. Secondly, it incorporates coordinated attention and redesigns the feature extraction module of the network, thus boosting the learning ability of image features and the expression of high-frequency information. Finally, a residual stacking-based landslide remote sensing image reconstruction strategy was proposed using EDCA residual blocks. This strategy employs residual learning to enhance the reconstruction performance of landslide images and introduces LPIPS for evaluating the test images. The experiment was conducted using landslide data collected by drones in the field. The results show that compared with traditional interpolation algorithms and classic deep learning reconstruction algorithms, this approach performs better in terms of SSIM, PSNR, and LPIPS. Moreover, the network can effectively handle complex features in landslide scenes, which is beneficial for subsequent target recognition and disaster monitoring

Integrating Future Multi-Scenarios to Evaluate the Effectiveness of Ecological Restoration: A Case Study of the Yellow River Basin

Ecological restoration is an important strategy for mitigating environmental degradation, and the effectiveness evaluation of ecological restoration is of profound significance for the scientific implementation of restoration projects. This study improved the Patch-generating Land Use Simulation (PLUS) model. It was used to simulate the land use patterns under multi-scenarios such as natural development (ND), economic priority (EP), and ecological restoration (ER) in 2030. An evaluation framework covering ecological “Restoration–Monitoring–Effectiveness” (RME) was proposed. Based on 30 m high-resolution remote-sensing data from 2000 to 2020, the land use distribution, landscape pattern changes, and ecosystem services under different scenarios were evaluated and predicted in the Yellow River Basin of Sichuan to verify the effectiveness of the evaluation framework. The results showed the following: (1) Under the ER scenario, the transfer of land use types in 2020–2030 was mainly characterized by an increase in the area of wetlands and a decrease in the area of built-up land. (2) There were obvious differences in land use and landscape patterns under different scenarios. Compared with the ND and EP scenarios, the growth of the construction rate was suppressed in the ER scenario, and the coverage of grassland and wetlands increased significantly. (3) The mean values of ecosystem services in the ER scenario were higher than those in the ND and EP scenarios. These findings clearly indicate that the RME evaluation system can accurately evaluate the ecological restoration effects under multi-scenarios in the future, providing a new perspective for ecological restoration evaluation in other regions

Acridine-based dyes as high-performance near-infrared Raman reporter molecules for cell imaging

A surface-enhanced Raman scattering (SERS) nanoprobe has been proven to be a promising tool for near-infrared (NIR) biomedical imaging and diagnosis because of its high sensitivity and selectivity.</jats:p