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Learning is to use algorithms to enable machines to learn rules from a large amount of historical data, so as to intelligently identify new samples or predict the future. Deep learning can promote students’ understanding of knowledge, conduct in-depth processing of new knowledge, integrate it with the original knowledge, and apply it to new situations, solve intelligent audio–visual listening from the perspective of deep learning, and focus on cultivating students’ in-depth learning ability and individual differences in innovative thinking. As the main position of ecological education, schools should effectively strengthen the publicity and education of ecological ideas and low-carbon concepts, and integrate them into education and teaching to effectively improve students’ awareness of environmental protection. This study aims to explore the effectiveness of flipped classroom teaching model based on deep learning. Therefore, from the perspective of deep learning, this paper combs the theory of deep learning, constructs a new model of smart classroom, and provides ideas and directions for model reform. In this study, the flipped classroom teaching model based on deep learning was applied to English teaching, and an 8-week teaching experiment was conducted. In addition, this paper believes that it is of great practical significance to carry out environmental protection education with the help of English teaching.

MnO2 is one of the most studied cathodes for aqueous neutral zinc‐ion batteries. However, the diverse reported crystal structures of MnO2 compared to δ‐MnO2 inevitably suffer a structural phase transition from tunneled to layered Zn‐buserite during the initial cycles, which is not as kinetically direct as the conventional intercalation electrochemistry in layered materials and thus poses great challenges to the performance and multifunctionality of devices. Here, a binder‐free δ‐MnO2 cathode is designed and a favorable “layered to layered” Zn2+ storage mechanism is revealed systematically using such a “noninterferencing” electrode platform in combination with ab initio calculation. A flexible quasi‐solid‐state Zn–Mn battery with an electrodeposited flexible Zn anode is further assembled, exhibiting high energy density (35.11 mWh cm−3; 432.05 Wh kg−1), high power density (676.92 mW cm−3; 8.33 kW kg−1), extremely low self‐discharge rate, and ultralong stability up to 10 000 cycles. Even with a relatively high δ‐MnO2 mass loading of 5 mg cm−2, significant energy and power densities are still achieved. The device also works well over a broad temperature range (0–40 °C) and can efficiently power different types of small electronics. This work provides an opportunity to develop high‐performance multivalent‐ion batteries via the design of a kinetically favorable host structure. A “layered to layered” mechanism for zinc storage in δ‐MnO2 is revealed based on a binder‐/additive‐free “noninterferencing” electrode platform. A δ‐MnO2‐based flexible quasi‐solid‐state zinc–manganese battery is further designed, which achieves high energy density, high power density, and outstanding cycling stability up to 10 000 times and exhibits good mechanical properties and a low self‐discharge rate.

Photodynamic therapy (PDT) is a promising cancer treatment but has limitations due to its dependence on oxygen and high-power-density photoexcitation. Here, we report polymer-based organic photosensitizers (PSs) through rational PS skeleton design and precise side-chain engineering to generate •O 2 − and •OH under oxygen-free conditions using ultralow-power 808 nm photoexcitation for tumor-specific photodynamic ablation. The designed organic PS skeletons can generate electron-hole pairs to sensitize H 2 O into •O 2 − and •OH under oxygen-free conditions with 808 nm photoexcitation, achieving NIR-photoexcited and oxygen-independent •O 2 − and •OH production. Further, compared with commonly used alkyl side chains, glycol oligomer as the PS side chain mitigates electron-hole recombination and offers more H 2 O molecules around the electron-hole pairs generated from the hydrophobic PS skeletons, which can yield 4-fold stronger •O 2 − and •OH production, thus allowing an ultralow-power photoexcitation to yield high PDT effect. Finally, the feasibility of developing activatable PSs for tumor-specific photodynamic therapy in female mice is further demonstrated under 808 nm irradiation with an ultralow-power of 15 mW cm −2 . The study not only provides further insights into the PDT mechanism but also offers a general design guideline to develop an oxygen-independent organic PS using ultralow-power NIR photoexcitation for tumor-specific PDT. Conventional photodynamic therapy (PDT) is hindered by oxygen-dependent photosensitization pathways and high-power-density photoexcitation. Here, the authors develop polymer-based organic photosensitizers (PSs) through PS skeleton design and side-chain engineering to allow tumor-specific PDT under oxygen-free conditions using ultralow-power 808 nm photoexcitation.

Medical image fusion techniques can fuse medical images from different morphologies to make the medical diagnosis more reliable and accurate, which play an increasingly important role in many clinical applications. To obtain a fused image with high visual quality and clear structure details, this paper proposes a convolutional neural network (CNN) based medical image fusion algorithm. The proposed algorithm uses the trained Siamese convolutional network to fuse the pixel activity information of source images to realize the generation of weight map. Meanwhile, a contrast pyramid is implemented to decompose the source image. According to different spatial frequency bands and a weighted fusion operator, source images are integrated. The results of comparative experiments show that the proposed fusion algorithm can effectively preserve the detailed structure information of source images and achieve good human visual effects.

In this article, the existence and nonexistence of solutions for the quasilinear elliptic equations involving multiple critical terms under Dirichlet boundary conditions on bounded smooth domains are proved by using the variational method and Pohozaev identity, respectively.

Design and fabrication of electrochemical energy storage systems with both high energy and power densities as well as long cycling life is of great importance. As one of these systems, Battery‐supercapacitor hybrid device (BSH) is typically constructed with a high‐capacity battery‐type electrode and a high‐rate capacitive electrode, which has attracted enormous attention due to its potential applications in future electric vehicles, smart electric grids, and even miniaturized electronic/optoelectronic devices, etc. With proper design, BSH will provide unique advantages such as high performance, cheapness, safety, and environmental friendliness. This review first addresses the fundamental scientific principle, structure, and possible classification of BSHs, and then reviews the recent advances on various existing and emerging BSHs such as Li‐/Na‐ion BSHs, acidic/alkaline BSHs, BSH with redox electrolytes, and BSH with pseudocapacitive electrode, with the focus on materials and electrochemical performances. Furthermore, recent progresses in BSH devices with specific functionalities of flexibility and transparency, etc. will be highlighted. Finally, the future developing trends and directions as well as the challenges will also be discussed; especially, two conceptual BSHs with aqueous high voltage window and integrated 3D electrode/electrolyte architecture will be proposed. The fundamental scientific principle, structure, and possible classification of battery‐supercapacitor hybrid devices (BSHs), outlining the recent advances on various existing and emerging BSHs, with the focus on materials and electrochemical performances, and finally providing the future developing trends and directions as well as the challenges are addressed in this review.

Background Circular RNAs (circRNAs) are a class of noncoding RNAs (ncRNAs) and can modulate gene expression by binding to miRNAs; further, circRNAs have been shown to participate in several pathological processes. However, the expression and biological function of circCUL2 in gastric cancer (GC) remains largely unknown. Methods circRNA microarrays and quantitative real-time PCR (qRT-PCR) were used to identify differentially expressed circRNAs in GC tissues and cell lines. circCUL2 knockdown and overexpression were performed to indicate the functional role of circCUL2 in vitro and in vivo. The expression and regulation of circCUL2, miR-142-3p and ROCK2 were evaluated using fluorescence in situ hybridization (FISH), dual-luciferase assays, RNA pull-down assays, RNA immunoprecipitation (RIP) and rescue experiments. Furthermore, the regulation of cisplatin sensitivity and autophagy by circCUL2/miR-142-3p/ROCK2 was demonstrated by cellular apoptosis assays, western blot, immunofluorescence and transmission electron microscopy analyses. Results The level of circCUL2, which is stable and cytoplasmically localized, was significantly reduced in GC tissues and cells. Overexpressed circCUL2 inhibited malignant transformation in vitro and tumorigenicity in vivo. In the AGS and SGC-7901 cell lines, circCUL2 sponged miR-142-3p to regulate ROCK2, thus modulating tumor progression. Furthermore, in the AGS/DDP and SGC-7901/DDP cell lines, circCUL2 regulated cisplatin sensitivity through miR-142-3p/ROCK2-mediated autophagy activation. Conclusion circCUL2 may function as a tumor suppressor and regulator of cisplatin sensitivity through miR-142-3p/ROCK2-mediated autophagy activation, which could be a key mechanism and therapeutic target for GC.

Strong background interference signals from normal tissues have significantly compromised the sensitive fluorescence imaging of early disease tissues with exogenous probes in vivo, particularly for sensitive fluorescence imaging of early liver disease due to the liver’s significant uptake and accumulation of exogenous nanoprobes, coupled with high tissue autofluorescence and deep tissue depth. As a proof-of-concept study, we herein report a near-infrared-II (NIR-II, 1.0-1.7 μm) light-excited “off-on-off” NIR-II fluorescent probe (NDP). It has near-ideal zero initial probe fluorescence but can turn on its NIR-II fluorescence in liver cancer tissues and then turn off the fluorescence again upon migration from cancer to normal tissues to minimize background interference. Due to its low background, a blind study employing our probes could identify female mice with orthotopic liver tumors with 100% accuracy from mixed subjects of healthy and tumor mice, and implemented sensitive locating of early orthotopic liver tumors with sizes as small as 4 mm. Our NIR-II-excited “off-on-off” probe design concept not only provides a promising molecular design guideline for sensitive imaging of early liver cancer but also could be generalized for sensitive imaging of other early disease lesions. Background interference signals from normal tissues compromise the sensitive fluorescence imaging of early disease tissues with exogenous probes in vivo. Here, the authors report a Near-infrared-II excited ‘’off-on-off” fluorescent probe to focus on events occurring on the diffusion of the activated probes from cancer tissues to normal tissues for imaging of early orthotopic liver tumors.

The detection and classification of bone marrow (BM) cells is a critical cornerstone for hematology diagnosis. However, the low accuracy caused by few BM-cell data samples, subtle difference between classes, and small target size, pathologists still need to perform thousands of manual identifications daily. To address the above issues, we propose an improved BM-cell-detection algorithm in this paper, called YOLOv7-CTA. Firstly, to enhance the model’s sensitivity to fine-grained features, we design a new module called CoTLAN in the backbone network to enable the model to perform long-term modeling between target feature information. Then, in order to cooperate with the CoTLAN module to pay more attention to the features in the area to be detected, we integrate the coordinate attention (CoordAtt) module between the CoTLAN modules to improve the model’s attention to small target features. Finally, we cluster the target boxes of the BM cell dataset based on K-means++ to generate more suitable anchor boxes, which accelerates the convergence of the improved model. In addition, in order to solve the imbalance between positive and negative samples in BM-cell pictures, we use the Focal loss function to replace the multi-class cross entropy. Experimental results demonstrate that the best mean average precision (mAP) of the proposed model reaches 88.6%, which is an improvement of 12.9%, 8.3%, and 6.7% compared with that of the Faster R-CNN model, YOLOv5l model, and YOLOv7 model, respectively. This verifies the effectiveness and superiority of the YOLOv7-CTA model in BM-cell-detection tasks.

The development of information technology has led to the rapid development of modern book packaging design. Book packaging design is different from painting. It is a kind of design process that combines artistry and practicality and has double characteristics. With the continuous progress of society, people’s requirements for book packaging design have become higher and higher, and modern book packaging design has become an important topic in the field of art design. To this end, this paper introduces the machine learning algorithms used in this paper, including the AdaBoost algorithm and the SVR algorithm. Specifically, it includes the principles and specific implementation steps for AdaBoost classification algorithm and SVR regression algorithm, as well as evaluation indexes of AdaBoost classification and SVR regression analysis. Realization of physical books reflects artistry, creativity, professionalism, popularity, and vitality of books in the packaging and design of books in the electronic information era. The stimulation effect of this paper starts from packaging design, inspection mechanism, and brand psychology to get the superior design in modern book packaging design.