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Civil aero-engine is the scientific and technological highland of modern industry and a symbol of the comprehensive national strength of a country; it is also crucial for constructing a new development pattern of dual circulation in China. The development of civil aero-engine industry in China started late and now it is in a critical period of development. Therefore, it is necessary to explore a strategic path for the independent development of the industry, thereby strengthening the transportation, manufacturing, and aviation sectors of China. Through field investigation and further discussion, experiences of other countries regarding aero-engine development are summarized and development status of aero-engines in China is analyzed. Moreover, existing problems of the aero-engine industry in China are examined while considering the development situation in China and abroad and focusing on market demand. The independent development of China’s aero-engine industry can be achieved by three steps and the three-step targets can be realized by promoting scientific and technological innovation capabilities, improving the industrial and supply chains, and encouraging international cooperation. Furthermore, the following suggestions are proposed: promoting the research and development of green power, establishing an aircraft – engine coordination mechanism, developing a multiple support mode, and strengthening policy support and guidance.

Scintillation-based X-ray imaging can provide convenient visual observation of absorption contrast by standard digital cameras, which is critical in a variety of science and engineering disciplines. More efficient scintillators and electronic postprocessing derived from neural networks are usually used to improve the quality of obtained images from the perspective of optical imaging and machine vision, respectively. Here, we propose to overcome the intrinsic separation of optical transmission process and electronic calculation process, integrating the imaging and postprocessing into one fused optical–electronic convolutional autoencoder network by affixing a designable optical convolutional metasurface to the scintillator. In this way, the convolutional autoencoder was directly connected to down-conversion process, and the optical information loss and training cost can be decreased simultaneously. We demonstrate that feature-specific enhancement of incoherent images is realized, which can apply to multi-class samples without additional data precollection. Hard X-ray experimental validations reveal the enhancement of textural features and regional features achieved by adjusting the optical metasurface, indicating a signal-to-noise ratio improvement of up to 11.2 dB. We anticipate that our framework will advance the fundamental understanding of X-ray imaging and prove to be useful for number recognition and bioimaging applications.

The pansharpening method based on spatial injection is prone to spectral distortion due to the spatial detail injection amount mismatch. For this reason, this paper proposes a method that combines adaptive intensity component extraction and spatial detail injection optimisation. Firstly, a new method for extracting the I component of multispectral (MS) image is proposed, which decomposes each band of MS image and panchromatic (PAN) image according to the structure-energy mechanism through the joint bilateral filter and then determines the weight coefficient of each band of MS image to extract the I component by solving the minimum optimisation problem of the structure layer of PAN image and the energy layer of MS image. Secondly, a spatial detail extraction method combining local spatial frequency and local energy is proposed, and the method can fully extract the fine detail features of PAN images and MS images. In this paper, many experiments are conducted on QuickBird, GeoEye-1 and WorldView-4 datasets, and quantitative and qualitative comparisons are made with eight advanced methods, and qualitative and quantitative analyses of the experimental results show that the proposed method has certain superiority. [ABSTRACT FROM AUTHOR]

BackgroundSynchrotron radiation experimental methods have unique advantages in studying the structure and physical properties of materials, but it is a challenge for many experimental methods to achieve synchrotron radiation in situ high temperature conditions, especially above 2 000 K. Laser heating methods can achieve rapid, micro-region extreme high temperature conditions, and have become an important tool for the study of high temperature physical properties.PurposeThis study aims to develop a portable laser heating device for Shanghai Synchrotron Radiation Facility (SSRF) in situ experiments in the field of extreme high temperature research, such as high entropy alloys, turbine blades, aviation materials, etc.MethodsA 100 W continuously tunable near-infrared fiber laser was used as the heating souce, the sample was heated up by laser through the focusing lens and generated thermal radiation. The radiation spectrum was collected through the spectral collection focusing lens and measured by spectrometers. The temperature gradient and temperature stability of the sample were fitted by the blackbody radiation method. Finally, the melting experiment of pure tungsten sheets in vacuum was conducted to verify its maximum heating temperature, and the temperature gradient and stability measurement of the device were calibrated with platinum samples.ResultsWe Experimental results show that melting point of about 3 695 K for tungsten sheets in vacuum is achieved using this device, and X-ray diffraction patterns of MoS2 and CTAB-MoS2 materials under 1 608 K in situ are obtained at the surface diffraction beamline station of SSRF.ConclusionsThe laser heating method developed expands the extreme experimental conditions in SSRF, and provides an important means to study high temperature physics for materials.

At present, as a research hotspot for time series data (TSD), the deep clustering analysis of TSD has huge research value and practical significance. However, there still exist the following three problems: (1) For deep clustering based on joint optimization, the inevitably mutual interference existing between deep feature representation learning progress and clustering progress leads to difficult model training especially in the initial stage, the possible feature space distortion, inaccurate and weak feature representation; (2) Existing deep clustering methods are difficult to intuitively define the similarity of time series and rely heavily on complex feature extraction networks and clustering algorithms. (3) Multidimensional time series have the characteristics of high dimensions, complex relationships between dimensions, and variable data forms, thus generating a huge feature space. It is difficult for existing methods to select discriminative features, resulting in generally low accuracy of methods. Accordingly, to address the above three problems, we proposed a novel general two-stage multi-dimensional spatial features based multi-view deep clustering method 1DCAE-TSSAMC (One-dimensional deep convolutional auto-encoder based two-stage stepwise amplification multi-clustering). We conducted verification and analysis based on real-world important multi-scenario, and compared with many other benchmarks ranging from the most classic approaches such as K-means and Hierarchical to the state-of-the-art approaches based on deep learning such as Deep Temporal Clustering (DTC) and Temporal Clustering Network (TCN). Experimental results show that the new method outperforms the other benchmarks, and provides more accurate, richer, and more reliable analysis results, more importantly, with significant improvement in accuracy and spatial linear separability. [ABSTRACT FROM AUTHOR]

Conversion‐type tellurium‐based cathodes have attracted great attention in aqueous zinc‐ion batteries (AZIBs) due to potentially high capacity with impressive Te4+/Te0/Te2− conversion. However, impeded by the low‐conductivity Te4+ species and unpreferred kinetics, realizing deep zinc‐tellurium redox remains a major challenge. Herein, energetic NiTe2 nanosheets are first proposed and implemented as preprepared tellurium‐based deep redox cathodes in AZIBs with accelerated electron transport and favorable kinetics. Operando synchrotron X‐ray diffraction and comprehensive ex situ characterizations demonstrate that Te4+ species from direct oxidation of NiTe2 in dilute mild‐aqueous electrolyte undergo a thermodynamic two‐step TeO2→Te→ZnTe discharge process, while the intertwined tellurium oxides and high‐conductivity nickel monomers derived in situ from NiTe2 nanosheets promote a kinetics‐preferred one‐step ZnTe→TeO2 charging conversion with significantly boosted reversibility. Therefore, a high capacity of 510 mAh g−1 at 50 mA g−1 and 93% capacity retention over 400 cycles at 2000 mA g−1 are established. Operando pH tracking and electrode‐electrolyte engineering analyses define that the mild electrolyte environment rich in moisture and electrode conductivity optimization are both critical for achieving reversible tellurium deep redox conversion. [ABSTRACT FROM AUTHOR]

Dendrite‐free Zn anode is the key to avoiding battery failure, which is largely determined by the crystal growth during the electrodeposition process. Therefore, controlling the specific crystal orientation growth of Zn is imperative for high‐performing aqueous zinc ion batteries. Herein, an electrocrystallization orientation regulation strategy is proposed to achieve near‐unity stacked Zn (002) texture growth. Featuring the zincophilic nature and high electronegative carboxylate radical, disodium maleate molecules tend to selectively adsorb on the Zn (002) crystal plane, forming a dynamic protection layer. This adsorption layer regulates Zn2+ diffusion along [100] and [101] orientations with the lowest deposition rate on the (002) plane, homogenizes Zn2+ flux, and keeps away water from Zn surface, constructing the flattened and horizontally arranged Zn deposition layer with dominantly Zn (002) texture and inhibited side‐reaction. Consequently, the Zn anode with near‐unity stacked (002) texture exhibits a 40‐fold enhancement in running lifetime beyond 3200 h and improved coulombic efficiency of 99.81% over 3000 cycles than that with bare ZnSO4 electrolyte. Even at harsh plating/stripping conditions of 30 and 30 mAh cm−2, the Zn anode still sustains state‐of‐the‐art stability over 120 h, enabling a substantial advance in the long‐term stability of the battery. [ABSTRACT FROM AUTHOR]

Conversion‐type electrodes offer a promising multielectron transfer alternative to intercalation hosts with potentially high‐capacity release in batteries. However, the poor cycle stability severely hinders their application, especially in aqueous multivalence‐ion systems, which can fundamentally impute to anisotropic ion diffusion channel collapse in pristine crystals and irreversible bond fracture during repeated conversion. Here, an amorphous bismuth sulfide (a‐BS) formed in situ with unprecedentedly self‐controlled moderate conversion Cu2+ storage is proposed to comprehensively regulate the isotropic ion diffusion channels and highly reversible bond evolution. Operando synchrotron X‐ray diffraction and substantive verification tests reveal that the total destruction of the Bi─S bond and unsustainable deep alloying are fully restrained. The amorphous structure with robust ion diffusion channels, unique self‐controlled moderate conversion, and high electrical conductivity discharge products synergistically boosts the capacity (326.7 mAh g−1 at 1 A g−1), rate performance (194.5 mAh g−1 at 10 A g−1), and long‐lifespan stability (over 8000 cycles with a decay rate of only 0.02 ‰ per cycle). Moreover, the a‐BS Cu2+‖Zn2+ hybrid ion battery can well supply a stable energy density of 238.6 Wh kg−1 at 9760 W kg−1. The intrinsically high‐stability conversion mechanism explored on amorphous electrodes provides a new opportunity for advanced aqueous storage. [ABSTRACT FROM AUTHOR]

Zhaotong City in Yunnan Province is one of the largest apple growing bases in China. However, the terrain of Zhaotong City is complicated, and the rainy weather is more frequent, which brings difficulties to the identification of apple orchards by remote sensing. In this paper, an improved spectral angle feature is proposed by combining the Spectral Angle Mapper and Sentinel-1 data. Based on the Google Earth Engine and Sentinel image, a random forest classifier was used to extract apple orchards in the Ganhe Reservoir area, Zhaoyang District, Zhaotong City, which provides a theoretical basis for extracting the spatial distribution and sustainable development of the local apple industry. The classification results show that the improved spectral angle characteristics can improve the overall accuracy and F1 score of apple orchards. The RGB band combined with NDVI, GLCM, and improved spectral angle features obtained the most favorable results, and the F1 score and overall accuracy were 88.89% and 84.44%, respectively, which proved the reliability of the method in identifying apple orchards in Zhaotong City. [ABSTRACT FROM AUTHOR]