Search

Highlights: Fe3+, Co2+, H3BTC, and collagen peptide are used to achieve a one-step assembly of stable FeCo-MOG/CP by manipulating the complexation effect and solution polarity. By optimizing pyrolysis, two kinds of nitrogen-doped carbon aerogels loaded with virus-shaped and nanospherical magnetic particles are obtained. FeCo/Fe3O4/NC and FeCo/NC aerogels exhibit excellent electromagnetic wave absorbing and radar stealth performances. Metal–organic gel (MOG) derived composites are promising multi-functional materials due to their alterable composition, identifiable chemical homogeneity, tunable shape, and porous structure. Herein, stable metal–organic hydrogels are prepared by regulating the complexation effect, solution polarity and curing speed. Meanwhile, collagen peptide is used to facilitate the fabrication of a porous aerogel with excellent physical properties as well as the homogeneous dispersion of magnetic particles during calcination. Subsequently, two kinds of heterometallic magnetic coupling systems are obtained through the application of Kirkendall effect. FeCo/nitrogen-doped carbon (NC) aerogel demonstrates an ultra-strong microwave absorption of − 85 dB at an ultra-low loading of 5%. After reducing the time taken by atom shifting, a FeCo/Fe3O4/NC aerogel containing virus-shaped particles is obtained, which achieves an ultra-broad absorption of 7.44 GHz at an ultra-thin thickness of 1.59 mm due to the coupling effect offered by dual-soft-magnetic particles. Furthermore, both aerogels show excellent thermal insulation property, and their outstanding radar stealth performances in J-20 aircraft are confirmed by computer simulation technology. The formation mechanism of MOG is also discussed along with the thermal insulation and electromagnetic wave absorption mechanism of the aerogels, which will enable the development and application of novel and lightweight stealth coatings. [ABSTRACT FROM AUTHOR]

At the same time that my country has shifted from high-speed development to high-quality development, my country has also put forward new requirements for education development. Due to the limited study time during college, each student's study habits and learning process are also different, and the degree of connection between tennis lessons is high, so there will be polarization when learning tennis. With the development of science and technology, more and more technological innovations are integrated into the classroom, and traditional teaching methods can no longer keep up with the pace of the times. Tennis teaching is a subject of equal proportion between theory and practice. The traditional teaching method simplifies the theory, which makes students to have some bad phenomena when they practice. Aiming at this series of problems, this paper uses algorithms such as softmax function and threshold function to construct an application model of virtual image technology based on the artificial neural network in tennis teaching. The research results of the article show that: (1) the average accuracy rate of the method in this paper is 97.22%, and the highest accuracy rate is 99.17%. The average accuracy rate also tends to increase with the increase of sample size; the recall rate is the highest, and the highest recall rate is 99.36%. The average recall rate is 96.77%; the highest correct rate is close to 100% and is significantly higher than the other three methods; the average correct rate reaches 98.8%; the response time is the shortest; the average response time is 33 ms; and the response time increases with the increase of the sample size. (2) After using this model, tennis skills have been improved, with an average of 12 in situ flips, an average of 7 in situ rackets, an average of 5 in situ forehand draws, and an average of 3 in situ backhand draws. (3) The average forehand and backhand scores of the class after the experiment were 90 and 86; the average forehand and backhand stability were 8 and 7; and the average forehand and backhand accuracy were 31 and 29, respectively. The average depth of forehand and backhand is 36 and 32. (4) Most of the students are satisfied with this model, and they all choose to strongly agree and relatively agree, and the percentage of very agree that helps stimulate learning has reached 60.52%, and no students choose to disagree very much. [ABSTRACT FROM AUTHOR]

Using these experimental conditions, the chemical composition of the essential oil of I M. glauca i was determined. The essential oil yield from the leaves of I M. glauca i was 0.90%. As shown in Table 1, monoterpene and sesquiterpene compounds comprised the main constituents of the essential oil extracted from I M. glauca i , with I i -cadinene (28.5%), I i -caryophyllene (15.2%), I i -pinene (8.8%), I i -copaene (7.0%), I i -pinene (5.3%), and I i -humulene (5.2%) being the six main constituents. [Extracted from the article]

Background: Diabetes mellitus (DM) is a complex and prevalent metabolic disorder worldwide. Strong evidence has emerged that DM is a risk factor for the accelerated rate of cognitive decline and the development of dementia. Though traditional pharmaceutical agents are efficient for the management of DM and DM-related cognitive decrement, long-term use of these drugs are along with undesired side effects. Therefore, tremendous studies have focused on the therapeutic benefits of natural compounds at present. Ample evidence exists to prove that polyphenols are capable to modulate diabetic neuropathy with minimal toxicity and adverse effects. Purpose: To describe the benefits and mechanisms of polyphenols on DM-induced cognitive dysfunction. In this review, we introduce an updated overview of associations between DM and cognitive dysfunction. The risk factors as well as pathological and molecular mechanisms of DM-induced cognitive dysfunction are summarized. More importantly, many active polyphenols that possess preventive and therapeutic effects on DM-induced cognitive dysfunction and the potential signaling pathways involved in the action are highlighted. Conclusions: The therapeutic effects of polyphenols on DM-related cognitive dysfunction pave a novel way for the management of diabetic encephalopathy. [ABSTRACT FROM AUTHOR]

With the exploration and development of marine resources, deep learning is more and more widely used in underwater image processing. However, the quality of the original underwater images is so low that traditional semantic segmentation methods obtain poor segmentation results, such as blurred target edges, insufficient segmentation accuracy, and poor regional boundary segmentation effects. To solve these problems, this paper proposes a semantic segmentation method for underwater images. Firstly, the image enhancement based on multi-spatial transformation is performed to improve the quality of the original images, which is not common in other advanced semantic segmentation methods. Then, the densely connected hybrid atrous convolution effectively expands the receptive field and slows down the speed of resolution reduction. Next, the cascaded atrous convolutional spatial pyramid pooling module integrates boundary features of different scales to enrich target details. Finally, the context information aggregation decoder fuses the features of the shallow network and the deep network to extract rich contextual information, which greatly reduces information loss. The proposed method was evaluated on RUIE, HabCam UID, and UIEBD. Compared with the state-of-the-art semantic segmentation algorithms, the proposed method has advantages in segmentation integrity, location accuracy, boundary clarity, and detail in subjective perception. On the objective data, the proposed method achieves the highest MIOU of 68.3 and OA of 79.4, and it has a low resource consumption. Besides, the ablation experiment also verifies the effectiveness of our method. [ABSTRACT FROM AUTHOR]