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In order to improve the data retrieval accuracy of preschool education major curriculum, this paper designs an auxiliary teaching system for preschool education specialty courses based on artificial intelligence. 2000 preschool education teachers and 3000 students in M city were selected to conduct a questionnaire survey to analyze the application status of artificial intelligence technology, advantages and disadvantages of assisted teaching, obstacles and training intentions of assisted teaching, and the impact on the personal development of teachers and students. Using artificial intelligence technology to improve the shortcomings of existing preschool education professional courses, through the Vue.js technology in the artificial intelligence method to achieve the MVVM mode sharing of preschool education professional courses, using MySQL database to achieve multiuser multithread operation of preschool education system, using MySQL data to complete data storage, C# was used as the programming language to design the learning module of preschool education professional courses, and the artificial intelligence-based preschool education professional course auxiliary teaching system was designed. The experimental results show that when the retrieval quantity of curriculum resources is 600 GB, the response time of resource retrieval of the designed system is 7 s, and the retrieval accuracy of curriculum data of education major can reach 95%. The performance of the system is good.

Offshore and inland river ship detection has been studied on both synthetic aperture radar (SAR) and optical remote sensing imagery. However, the classic ship detection methods based on SAR images can cause a high false alarm ratio and be influenced by the sea surface model, especially on inland rivers and in offshore areas. The classic detection methods based on optical images do not perform well on small and gathering ships. This paper adopts the idea of deep networks and presents a fast regional-based convolutional neural network (R-CNN) method to detect ships from high-resolution remote sensing imagery. First, we choose GaoFen-2 optical remote sensing images with a resolution of 1 m and preprocess the images with a support vector machine (SVM) to divide the large detection area into small regions of interest (ROI) that may contain ships. Then, we apply ship detection algorithms based on a region-based convolutional neural network (R-CNN) on ROI images. To improve the detection result of small and gathering ships, we adopt an effective target detection framework, Faster-RCNN, and improve the structure of its original convolutional neural network (CNN), VGG16, by using multiresolution convolutional features and performing ROI pooling on a larger feature map in a region proposal network (RPN). Finally, we compare the most effective classic ship detection method, the deformable part model (DPM), another two widely used target detection frameworks, the single shot multibox detector (SSD) and YOLOv2, the original VGG16-based Faster-RCNN, and our improved Faster-RCNN. Experimental results show that our improved Faster-RCNN method achieves a higher recall and accuracy for small ships and gathering ships. Therefore, it provides a very effective method for offshore and inland river ship detection based on high-resolution remote sensing imagery.

Jointly using spectral and spatial information has become a mainstream strategy in the field of hyperspectral image (HSI) processing, especially for classification. However, due to the existence of noisy or correlated spectral bands in the spectral domain and inhomogeneous pixels in the spatial neighborhood, HSI classification results are often degraded and unsatisfactory. Motivated by the attention mechanism, this paper proposes a spatial–spectral squeeze-and-excitation (SSSE) module to adaptively learn the weights for different spectral bands and for different neighboring pixels. The SSSE structure can suppress or motivate features at a certain position, which can effectively resist noise interference and improve the classification results. Furthermore, we embed several SSSE modules into a residual network architecture and generate an SSSE-based residual network (SSSERN) model for HSI classification. The proposed SSSERN method is compared with several existing deep learning networks on two benchmark hyperspectral data sets. Experimental results demonstrate the effectiveness of our proposed network.

Conjugated polymeric molecules have been heralded as promising electrode materials for the next-generation energy-storage technologies owing to their chemical flexibility at the molecular level, environmental benefit, and cost advantage. However, before any practical implementation takes place, the low capacity, poor structural stability, and sluggish ion/electron diffusion kinetics remain the obstacles that have to be overcome. Here, we report the synthesis of a few-layered two-dimensional covalent organic framework trapped by carbon nanotubes as the anode of lithium-ion batteries. Remarkably, upon activation, this organic electrode delivers a large reversible capacity of 1536 mAh g −1 and can sustain 500 cycles at 100 mA g −1 . Aided by theoretical calculations and electrochemical probing of the electrochemical behavior at different stages of cycling, the storage mechanism is revealed to be governed by 14-electron redox chemistry for a covalent organic framework monomer with one lithium ion per C=N group and six lithium ions per benzene ring. This work may pave the way to the development of high-capacity electrodes for organic rechargeable batteries. Conjugated polymeric molecules are promising electrode materials for batteries. Here the authors show a two-dimensional few-layered covalent organic framework that delivers a large reversible capacity of more than 1500 mAh g −1 with the storage mechanism governed by 14-electron redox chemistry.

Metallic-phase MoS 2 (M-MoS 2 ) is metastable and does not exist in nature. Pure and stable M-MoS 2 has not been previously prepared by chemical synthesis, to the best of our knowledge. Here we report a hydrothermal process for synthesizing stable two-dimensional M-MoS 2 nanosheets in water. The metal–metal Raman stretching mode at 146 cm −1 in the M-MoS 2 structure, as predicted by theoretical calculations, is experimentally observed. The stability of the M-MoS 2 is associated with the adsorption of a monolayer of water molecules on both sides of the nanosheets, which reduce restacking and prevent aggregation in water. The obtained M-MoS 2 exhibits excellent stability in water and superior activity for the hydrogen evolution reaction, with a current density of 10 mA cm −2 at a low potential of −175 mV and a Tafel slope of 41 mV per decade. Metallic molybdenum disulfide is a metastable phase of the material. Here, the authors synthesize two-dimensional metallic molybdenum disulfide nanosheets, stabilized by adsorbed aqueous monolayers, and evaluate their catalytic hydrogen evolution activity.

Aiming at the problems of high redundancy and slow integration speed in the existing education resource data integration methods, a new preschool language education resource integration method based on metadata warehouse is designed. The metadata warehouse is designed, and the advantages of the integrated database are analyzed. On this basis, the sample data of preschool language education resources are classified with the help of cost matrix, and the constraints of different types of classification are set. The data collector of preschool language education resources is set up by using random forest algorithm to complete the data collection of preschool language education resources. The data of preschool language education resources are processed consistently, and the convergence of the data is calculated by edge function. On this basis, the redundant data in preschool language education data resources are characterized with the help of discourse, and the redundant data are removed to complete the data preprocessing of preschool language education resources. We determine the dimension distance between preschool language education resource data and complete the clustering integration of preschool language education resource data with the help of fuzzy mean clustering algorithm. The experimental results show that the integration method designed in this paper can reduce the redundancy in the integrated data, and the integration speed is fast.

Chemically made colloidal semiconductor quantum dots have long been proposed as scalable and color-tunable single emitters in quantum optics, but they have typically suffered from prohibitively incoherent emission. We now demonstrate that individual colloidal lead halide perovskite quantum dots (PQDs) display highly efficient single-photon emission with optical coherence times as long as 80 picoseconds, an appreciable fraction of their 210-picosecond radiative lifetimes. These measurements suggest that PQDs should be explored as building blocks in sources of indistinguishable single photons and entangled photon pairs. Our results present a starting point for the rational design of lead halide perovskite–based quantum emitters that have fast emission, wide spectral tunability, and scalable production and that benefit from the hybrid integration with nanophotonic components that has been demonstrated for colloidal materials.

Objective Rheumatoid arthritis (RA) is a chronic, progressive autoimmune disease. Over-activation of fibroblast-like synoviocytes is responsible for the hyperplasia of synovium and destruction of cartilage and bone and pyroptosis of FLS plays a key role in those pathological processes during RA. This study investigated the detailed mechanisms that SMAD2 regulates the pyroptosis of FLS and secretion of inflammatory factors in rheumatoid arthritis. Methods We collected synovial tissues of RA patients and FLS-RA and cultured FLS for detection of expression of SMAD2. ASC, NLRP3, cleaved-caspase-1, and GSDMD-N were detected by Western blot after overexpression of SMAD2. Besides, flow cytometry, electron microscope, ELISA, HE staining, and Safranin O staining were performed to further demonstrate that SMAD2 can affect the pyroptosis of FLS-RA. Results The expression of SMAD2 was down-regulated in synovial tissues of RA patients and FLS-RA. Overexpression of SMAD2 can inhibit the expression of ASC, NLRP3, cleaved-caspase-1, and GSDMD-N. Flow cytometry and electron microscope further demonstrated that SMAD2 attenuated pyroptosis of FLS-RA. In addition, overexpression of SMAD2 also inhibited inflammatory factors such as IL-1β, IL-18, IL-6, and IL-8 secretion and release of LDH. Besides, overexpression of SMAD2 can reverse the decrease of p-SMAD2 and TGF-TGF-β induced by nigericin. In vivo experiments on CIA rats further demonstrated that overexpression of SMAD2 by local intra-articular injection of LV-SMAD2 can effectively alleviate joint redness, swelling, and destruction of cartilage and bones. Conclusion SMAD2 inhibited FLS-RA pyroptosis by down-regulating of NLRP3 inflammasomes (NLRP3, ASC, and caspase-1 complex) and eased the secretion of inflammatory factors via the TGF-β signaling pathway, thereby improving the symptom of RA. We hope that this study may provide a new research idea for RA and a potential target for the treatment of RA.

Cobalt-free spinel LiNi 0.5 Mn 1.5 O 4 (LNMO) positive electrodes, promise high energy density when coupled with lithium negative electrodes, due to the high discharge voltage platform. However, the intrinsic dissolution of Mn in positive electrode, electrolyte decomposition at high voltage, and dendrite growth on lithium severely compromise cycling stability, limiting the practical application. Herein, we propose ferrocene hexafluorophosphate as an electrolyte additive to achieve dynamic doping of Fe 3+ in positive electrodes during electrochemical cycling. Furthermore, additive molecule preferentially decomposes at both the positive and negative electrode interfaces, forming thin, dense inorganic positive electrode electrolyte interphase and F, P-rich inorganic solid electrolyte interphase respectively, effectively stabilizing electrode interfaces. Consequently, the Li | |LNMO batteries based on modified electrolytes effectively enhance cycling stability and rate performance at a charge cutoff voltage of 4.9 V and an LNMO pouch cell performs consistently over 160 cycles. Additionally, the efficacy of ferrocene hexafluorophosphate extends beyond LNMO, demonstrating its universal applicability in stabilizing positive electrodes operating at challenging voltages, including LiNi 0.8 Co 0.1 Mn 0.1 O 2 , LiNi 0.6 Co 0.2 Mn 0.2 O 2 , and LiCoO 2 and a 470 Wh kg −1 level Li metal pouch cell was successfully realized. Cobalt-free Mn-based lithium metal batteries suffer from serious Mn dissolution and lithium dendrite problems. Here, authors propose ferrocene hexafluorophosphate as an electrolyte additive to achieve dynamic doping of positive electrode and interphase stabilization of electrodes.

The fusion-based classification of hyperspectral (HS) and light detection and ranging (LiDAR) images has become a prominent research topic, as their complementary information can effectively improve classification performance. The current methods encompass pixel-, feature- and decision-level fusion. Among them, feature- and decision-level fusion have emerged as the mainstream approaches. Collaborative fusion of these two levels can enhance classification accuracy. Although various methods have been proposed, some shortcomings still exist. On one hand, current methods ignore the shared advanced features between HS and LiDAR images, impeding the integration of multimodal features and thereby limiting the classification performance. On the other hand, the existing methods face difficulties in achieving a balance between feature- and decision-level contributions, or they simply overlook the significance of one level and fail to utilize it effectively. In this paper, we propose a novel feature-decision level collaborative fusion network (FDCFNet) for hyperspectral and LiDAR classification to alleviate these problems. Specifically, a multilevel interactive fusion module is proposed to indirectly connect hyperspectral and LiDAR flows to refine the spectral-elevation information. Moreover, the fusion features of the intermediate branch can further enhance the shared-complementary information of hyperspectral and LiDAR to reduce the modality differences. In addition, a dynamic weight selection strategy is meticulously designed to adaptively assign weight to the output of three branches at the decision level. Experiments on three public benchmark datasets demonstrate the effectiveness of the proposed methods.