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Abstract Colorful circularly polarized luminescence materials are desired for 3D displays, information security and asymmetric synthesis, in which single-emitted materials are ideal owing to self-absorption avoidance, evenly entire-visible-spectrum-covered photon emission and facile device fabrication. However, restricted by the synthesis of chiral broad-luminescent emitters, the realization and application of high-performing single-emitted full-color circularly polarized luminescence is in its infancy. Here, we disclose a single-emitted full-color circularly polarized luminescence system (spiral full-color emission generator), composed of whole-vis-spectrum emissive quantum dots and chiral liquid crystals. The system achieves a maximum luminescence dissymmetry factor of 0.8 and remains an order of 10−1 in visible region by tuning its photonic bandgap. We then expand it to a series of desired customized-color circularly polarized luminescence, build chiral devices and further demonstrate the working scenario in the photoinduced enantioselective polymerization. This work contributes to the design and synthesis of efficient chiroptical materials, device fabrication and photoinduced asymmetric synthesis.

The traditional template matching strategy of optical and synthetic aperture radar (SAR) is sensitive to the nonlinear transformation between two images. In some cases, the optical and SAR image pairs do not conform to the affine transformation condition. To address this issue, this study presents a novel template matching strategy which uses the One-Class Support Vector Machine (SVM) to remove outliers. First, we propose a method to construct the similarity map dataset using the SEN1-2 dataset for training the One-Class SVM. Second, a four-step strategy for optical and SAR image registration is presented in this paper. In the first step, the optical image is divided into some grids. In the second step, the strongest Harris response point is selected as the feature point in each grid. In the third step, we use Gaussian pyramid features of oriented gradients (GPOG) descriptor to calculate the similarity map in the search region. The trained One-Class SVM is used to remove outliers through similarity maps in the fourth step. Furthermore, the number of improve matches (NIM) and the rate of improve matches (RIM) are designed to measure the effect of image registration. Finally, this paper designs two experiments to prove that the proposed strategy can correctly select the matching points through similarity maps. The experimental results of the One-Class SVM in dataset show that the One-Class SVM can select the correct points in different datasets. The image registration results obtained on the second experiment show that the proposed strategy is robust to the nonlinear transformation between optical and SAR images.

Traditional feature matching methods of optical and synthetic aperture radar (SAR) used gradient are sensitive to non-linear radiation distortions (NRD) and the rotation between two images. To address this problem, this study presents a novel approach to solving the rigid body rotation problem by a two-step process. The first step proposes a deep learning neural network named RotNET to predict the rotation relationship between two images. The second step uses a local feature descriptor based on the Gaussian pyramid named Gaussian pyramid features of oriented gradients (GPOG) to match two images. The RotNET uses a neural network to analyze the gradient histogram of the two images to derive the rotation relationship between optical and SAR images. Subsequently, GPOG is depicted a keypoint by using the histogram of Gaussian pyramid to make one-cell block structure which is simpler and more stable than HOG structure-based descriptors. Finally, this paper designs experiments to prove that the gradient histogram of the optical and SAR images can reflect the rotation relationship and the RotNET can correctly predict them. The similarity map test and the image registration results obtained on experiments show that GPOG descriptor is robust to SAR speckle noise and NRD.

Abstract In coal mining, coal rock fracturing damage and leakage pose significant challenges. To study the relationship between piezoelectric signals and seepage characteristics during uniaxial compression, and to achieve leakage monitoring based on piezoelectric signals, a coal rock fracturing damage and leakage monitoring experiment was carried out using coal from the Zhaogu Mine in Henan Province. The wavelet packet energy was introduced to investigate the piezoelectric characteristics. The results indicate the relative variation in wavelet packet energy server as a damage index during the uniaxial compression of coal, correlating closely with permeability changes. The wavelet packet energy at the end of the elastic deformation stage was 0.26 V2, similar to the pre-compression value of 0.29 V2. Therefore, the overall damage in the compression and elastic deformation stages was approximately 0. Permeability values remained stable, fluctuating from 0.26 × 10−16 m2 to 0.11 × 10−16 m2. In the plastic deformation stage, wavelet packet energy decreased while the damage index and permeability rose. Prior to peak stress, wavelet packet energy and damage index plateaued, signaling a precursor to a sudden increase in permeability. Post-peak stress, a marked decline in wavelet packet energy and an increase in the damage index and permeability were observed. This study offers a method for monitoring coal rock leakage, contributing valuable insights for efficient coalbed methane extraction and timely safety alerts.

Abstract Carbapenem‐resistant Acinetobacter baumannii (CRAB) has become a major threat in the treatment of bacterial infection, and immunotherapy in a non‐antibiotic‐dependent manner is an effective way to overcome CRAB infection. However, the role of the innate immune response in CRAB infection is poorly understood. Here, it is reported that CRAB infection induced a cytosolic DNA‐sensing signaling pathway and significant IFN‐β production in mice post‐CRAB infection. The knockout of STING reduced bacterial burden, the production of inflammatory cytokines, and lung injury in mice post CRAB infection. The cytosolic DNA sensor cyclic GMP‐AMP synthase (cGAS) and the adaptor protein stimulator of interferon genes (STING) are required for CRAB‐induced IFN‐β expression in macrophages. Intriguingly, CRAB utilized outer membrane vesicles (OMVs) to transport outer membrane protein 38 (OMP38) into mitochondria, triggering mitochondrial DNA (mtDNA) release into the cytosol through the mitochondrial permeability transition pore (mPTP) and activating the cGAS‐STING signaling. Finally, epigallocatechin gallate (EGCG) is demonstrated to block the activation of the cGAS‐STING pathway and ameliorate CRAB‐induced excessive inflammatory response. These results demonstrated that the early innate immune response to CRAB infection is activated in a cGAS‐STING‐dependent manner, which could be a potential therapeutic target for CRAB infection.

Abstract The EphA family belongs to a large group of membrane receptor tyrosine kinases. Emerging evidence indicates that the EphA family participates in tumour occurrence and progression. Nonetheless, the expression patterns and prognostic values of the nine EphAs in non-small cell lung cancer (NSCLC) have rarely been studied before. In the current study, we comprehensively analysed the expression and prognostic role of EphA family members by different means. The Cancer Genome Atlas and Gene Expression Profiling Interactive Analysis databases were used to investigate the expression of EphAs in NSCLC. The cBioPortal database was applied to analyse the prognostic values and genetic mutations of EphAs.We discovered that the expression of EphA10 was significantly higher in NSCLC tissues than in adjacent noncancerous tissues, and survival analyses showed that a higher level of EphA10 predicted poor prognosis. Further exploration into the role of EphA10 by ESTIMATE, CIBERSORT, and ssGSEA analysis found that it was also related to immune infiltration and higher expression of targets of ICI targets. In conclusion, this study revealed that among the EphA family members, EphA10 played an oncogenic role and was a promising biomarker for poor prognosis and better immunotherapy response in NSCLC.

Abstract A scalable, reusable, and broad-coverage unified material knowledge representation shows its importance and will bring great benefits to data sharing among materials communities. A knowledge graph (KG) for materials terminology, which is a formal collection of term entities and relationships, is conceptually important to achieve this goal. In this work, we propose a KG for materials terminology, named Materials Genome Engineering Database Knowledge Graph (MGED-KG), which is automatically constructed from text corpus via natural language processing. MGED-KG is the most comprehensive KG for materials terminology in both Chinese and English languages, consisting of 8,660 terms and their explanations. It encompasses 11 principal categories, such as Metals, Composites, Nanomaterials, each with two or three levels of subcategories, resulting in a total of 235 distinct category labels. For further application, a knowledge web system based on MGED-KG is developed and shows its great power in improving data sharing efficiency from the aspects of query expansion, term, and data recommendation.

Monitoring the temperature to determine the fire source locations is essential for controlling the spontaneous combustion in the goaf. Optical fiber sensors are employed to measure the temperature distribution in the goaf. However, due to changes in the geological conditions and the influence of the falling rocks in the goaf, only sensors on the upper side of the uncompacted goaf, due to inclination and coal pillar, may remain. Unilateral sensors are located on the upper side of the goaf, while fire occurs in the center. To investigate the issue with linear unilateral sensors, a two-dimensional inverse method has been developed to determine the location of fire sources by considering heat transfer after a fire inside the goaf. The equations were theoretically solved using Green's function method to obtain the internal temperature distribution of the physical model of the goaf. Sensitivity analysis identified the most crucial parameters in the process of spontaneous heating at different temperature. The fire source location can be determined using a loop method based on the model calculations. We considered a case to validate the model. Accurately identifying the fire source location in the goaf using the unilateral sensors has an essential theoretical and practical significance for fire prevention and fighting.

Abstract The rupture of coal pillar can lead to spontaneous combustion or collapse of goaf, which endangers the safety of workers. To explore the relationship between the crack depth of the coal structure and the signal received by the piezoelectric ceramic sensor, the output data of coal samples were analyzed by using the piezoelectric effect, combined with the experiment and ABAQUS simulation. Based on the signal amplitude, the output signal characteristics of the coal model with different crack depths were analyzed, and the evaluation index of coal crack cracking degree (D c ) was defined. The results show that the piezoelectric fluctuation method can effectively identify the local cracks of coal. When the distance between the lead Piezoelectric Transducer (PZT) patch and crack position is constant, the amplitude of the PZT patch output signal will decay with the deepening of the crack depth, while the value of increases with the increase of crack depth. This study provides a theoretical basis for mine disaster prevention and control.

Abstract Beginning on December 31, 2019, the large-scale novel coronavirus disease 2019 (COVID-19) emerged in China. Tracking and analysing the heterogeneity and effectiveness of cities’ prevention and control of the COVID-19 epidemic is essential to design and adjust epidemic prevention and control measures. The number of newly confirmed cases in 25 of China’s most-affected cities for the COVID-19 epidemic from January 11 to February 10 was collected. The heterogeneity and effectiveness of these 25 cities’ prevention and control measures for COVID-19 were analysed by using an estimated time-varying reproduction number method and a serial correlation method. The results showed that the effective reproduction number (R) in 25 cities showed a downward trend overall, but there was a significant difference in the R change trends among cities, indicating that there was heterogeneity in the spread and control of COVID-19 in cities. Moreover, the COVID-19 control in 21 of 25 cities was effective, and the risk of infection decreased because their R had dropped below 1 by February 10, 2020. In contrast, the cities of Wuhan, Tianmen, Ezhou and Enshi still had difficulty effectively controlling the COVID-19 epidemic in a short period of time because their R was greater than 1.

Failure Mode and Effects Analysis (FMEA) has been regarded as an effective analysis approach to identify and rank the potential failure modes in many applications. However, how to determine the weights of team members appropriately, with the impact factor of domain experts’ uncertainty in decision-making of FMEA, is still an open issue. In this paper, a new method to determine the weights of team members, which combines evidence theory, intuitionistic fuzzy sets (IFSs) and belief entropy, is proposed to analyze the failure modes. One of the advantages of the presented model is that the uncertainty of experts in the decision-making process is taken into consideration. The proposed method is data driven with objective and reasonable properties, which considers the risk of weights more completely. A numerical example is shown to illustrate the feasibility and availability of the proposed method.