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This work aims to explore the application of an improved convolutional neural network (CNN) combined with Internet of Things (IoT) technology in art design education and teaching. The development of IoT technology has created new opportunities for art design education, while deep learning and improved CNN models can provide more accurate and effective tools for image processing and analysis. In order to enhance the effectiveness of art design teaching and students’ creative expression, this work proposes an improved CNN model. In model construction, it increases the number of convolutional layers and neurons, and incorporates the batch normalization layer and dropout layer to enhance feature extraction capabilities and reduce overfitting. Besides, this work creates an experimental environment using IoT technology, capturing art image samples and environmental data using cameras, sensors, and other devices. In the model application phase, image samples undergo preprocessing and are input into the CNN for feature extraction. Sensor data are concatenated with image feature vectors and input into the fully connected layers to comprehensively understand the artwork. Finally, this work trains the model using techniques such as cross-entropy loss functions and L2 regularization and adjusts hyperparameters to optimize model performance. The results indicate that the improved CNN model can effectively acquire art sample data and student creative expression data, providing accurate and timely feedback and guidance for art design education and teaching, with promising applications. This work offers new insights and methods for the development of art design education.

Lightweight magnesium alloys are attractive as structural materials for improving energy efficiency in applications such as weight reduction of transportation vehicles. One major obstacle for widespread applications is the limited ductility of magnesium, which has been attributed to 〈c + a〉 dislocations failing to accommodate plastic strain. We demonstrate, using in situ transmission electron microscope mechanical testing, that 〈c + a〉 dislocations of various characters can accommodate considerable plasticity through gliding on pyramidal planes. We found that submicrometer-size magnesium samples exhibit high plasticity that is far greater than for their bulk counterparts. Small crystal size usually brings high stress, which in turn activates more 〈c + a〉 dislocations in magnesium to accommodate plasticity, leading to both high strength and good plasticity.

Electrodeposition of lithium (Li) metal is critical for high-energy batteries 1 . However, the simultaneous formation of a surface corrosion film termed the solid electrolyte interphase (SEI) 2 complicates the deposition process, which underpins our poor understanding of Li metal electrodeposition. Here we decouple these two intertwined processes by outpacing SEI formation at ultrafast deposition current densities 3 while also avoiding mass transport limitations. By using cryogenic electron microscopy 4 – 7 , we discover the intrinsic deposition morphology of metallic Li to be that of a rhombic dodecahedron, which is surprisingly independent of electrolyte chemistry or current collector substrate. In a coin cell architecture, these rhombic dodecahedra exhibit near point-contact connectivity with the current collector, which can accelerate inactive Li formation 8 . We propose a pulse-current protocol that overcomes this failure mode by leveraging Li rhombic dodecahedra as nucleation seeds, enabling the subsequent growth of dense Li that improves battery performance compared with a baseline. While Li deposition and SEI formation have always been tightly linked in past studies, our experimental approach enables new opportunities to fundamentally understand these processes decoupled from each other and bring about new insights to engineer better batteries. We report the discovery of lithium metal’s intrinsic growth morphology, a rhombic dodecahedron, and leverage these rhombic dodecahedra as nucleation seeds for improved battery performance.

Two and a half years into the COVID-19 pandemic, we have gained many insights into the human antibody response to the causative SARS-CoV-2 virus. In this Review, we summarize key observations of humoral immune responses in people with COVID-19, discuss key features of infection- and vaccine-induced neutralizing antibodies, and consider vaccine designs for inducing antibodies that are broadly protective against different variants of the SARS-CoV-2 virus.Qi et al. provide an in-depth analysis of antibody responses generated in response to SARS-CoV-2.

The genus Calcyopa Stüning, 2000, is briefly reviewed. A new species, Calcyopa hainana Liu, sp. nov. , is described from Hainan Province, China. Within the genus Calcyopa , two species groups are identified, characterized by shared traits yet distinguished by a set of consistent features. The difoveata -group, comprises C. difoveata , C. fansipana and C. hainana sp. nov. , and the rosearia -group, includes C. rosearia , C. prasina and C. subprasina . The relationship of both species groups is discussed, and an identification key of all known Calcyopa species is presented. Illustrations are provided for adult males and females of the difoveata -group, along with their genitalia, except for C. fansipana , which is known only from males. DNA barcodes are provided for the type species and the newly described species.

7XXX series aluminum alloys (Al 7XXX alloys) are widely used in bearing components, such as aircraft frame, spars and stringers, for their high specific strength, high specific stiffness, high toughness, excellent processing, and welding performance. Therefore, Al 7XXX alloys are the most important structural materials in aviation. In this present review, the development tendency and the main applications of Al 7XXX alloys for aircraft structures are introduced, and the existing problems are simply discussed. Also, the heat treatment processes for improving the properties are compared and analyzed. It is the most important measures that optimizing alloy composition and improving heat treatment process are to enhance the comprehensive properties of Al 7XXX alloys. Among the method, solid solution, quenching, and aging of Al 7XXX alloys are the most significant. We introduce the effects of the three methods on the properties, and forecast the development direction of the properties, compositions, and heat treatments and the solution to the corrosion prediction problem for the next generation of Al 7XXX alloys for aircraft structures. The next generation of Al 7XXX alloys should be higher strength, higher toughness, higher damage tolerance, higher hardenability, and better corrosion resistance. It is urgent requirements to develop or invent new heat treatment regime. We should construct a novel corrosion prediction model for Al 7XXX alloys via confirming the surface corrosion environments and selecting the accurate and reliable electrochemical measurements.

Plasmon-mediated chemical reactions (PMCRs) constitute a vibrant research field, advancing such goals as using sunlight to convert abundant precursors such as CO 2 and water to useful fuels and chemicals. A key question in this burgeoning field which has not, as yet, been fully resolved, relates to the precise mechanism through which the energy absorbed through plasmonic excitation, ultimately drives such reactions. Among the multiple processes proposed, two have risen to the forefront: plasmon-increased temperature and generation of energetic charge carriers. However, it is still a great challenge to confidently separate these two effects and quantify their relative contribution to chemical reactions. Here, we describe a strategy based on the construction of a plasmonic electrode coupled with photoelectrochemistry, to quantitatively disentangle increased temperature from energetic charge carriers effects. A clear separation of the two effects facilitates the rational design of plasmonic nanostructures for efficient photochemical applications and solar energy utilization. Confidently separating the photothermal effect from the generation of energetic charge carriers and quantifying their relative contribution to chemical reactions remain a great challenge in plasmon-mediated chemical reactions. Here, authors describe a strategy based on the construction of a plasmonic electrode coupled with photoelectrochemistry to quantitatively disentangle these two effects.

Two new species, Psilalcis subalbibasis Liu, sp. nov. and Psilalcis subconceptaria Liu, sp. nov. , are described from Hainan Island, China. Adult males and females of both species, including their genitalia, are figured and compared to closely related species.

This paper evaluates sea surface temperature (SST) biases of coupled models participating in Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6. Overall, CMIP6 models perform better than CMIP5 ones in reproducing SST climatology, with lower multi‐model ensemble mean (MME) globally averaged absolute bias (1.17 vs. 1.31 K). MME bias in global mean annual SST shifts from cooling (−0.09 ± 0.52 K) to warming (0.23 ± 0.60 K). Regionally, in CMIP6 cooling biases over the Northwest Pacific and North Atlantic are reduced by 20% and 18%, while warming biases over the Northeast Pacific, Southeast Atlantic and Southern Ocean are increased by 25%, 16% and 107% respectively. These changes are mainly attributed to the combined effects from aggravated positive (or alleviated negative) bias in clear‐sky surface downward longwave radiation, and alleviated negative bias in cloud radiative effect, partially reduced by enhanced cooling bias in clear‐sky surface downward shortwave radiation. Plain Language Summary As the primary approach to projecting future climate change, state‐of‐the‐art climate models still suffer pronounced biases in climatological annual mean sea surface temperature (SST), such as cold biases over the Northwest Pacific and North Atlantic, and warm biases over the Northeast Pacific, Southeast Pacific, Southeast Atlantic and Southern Ocean. We have evaluated the changes in mean‐state SST biases between the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6. CMIP6 models perform better in reproducing SST climatology with lower absolute bias, which is attributed to the process‐level improvement. Overall, annual global mean SST bias shifts from cold (−0.09 ± 0.52 K) to warm (0.23 ± 0.60 K), which is mainly due to the regionally alleviated cooling biases or aggravated warming biases. This warmer shift is contributed by the increased positive (or decreased negative) bias in clear‐sky surface downward longwave radiation and decreased negative bias in cloud radiative effect. Key Points Coupled Model Intercomparison Project Phase 6 (CMIP6) models perform better than CMIP5 ones, with significantly lower global‐mean absolute bias in annual sea surface temperature (SST) Global‐mean SST bias is with a warmer shift (+0.32 K) in CMIP6, with salient regional cold biases alleviated and warm biases aggravated Reduced cold bias in cloud radiative effect and positive change in bias in clear‐sky surface downward longwave together account for the shift

Long-read sequencing is promising for the comprehensive discovery of structural variations (SVs). However, it is still non-trivial to achieve high yields and performance simultaneously due to the complex SV signatures implied by noisy long reads. We propose cuteSV, a sensitive, fast, and scalable long-read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and employs a clustering-and-refinement method to implement sensitive SV detection. Benchmarks on simulated and real long-read sequencing datasets demonstrate that cuteSV has higher yields and scaling performance than state-of-the-art tools. cuteSV is available at https://github.com/tjiangHIT/cuteSV .