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In the field of education, the application of generative artificial intelligence is gradually becoming a focus. This article explores the potential, challenges, and coping strategies of generative artificial intelligence in education. Firstly, generative artificial intelligence demonstrates enormous potential in education. It can provide personalized teaching assistance and offer customized learning resources to students through intelligent recommendation systems. Meanwhile, it can also simulate complex situations and assist students in practical operations and problem-solving. In addition, generative artificial intelligence can also perform intelligent evaluations, providing teachers with an analysis of students’ learning progress and effectiveness. However, the application of generative artificial intelligence in education also faces many challenges. Firstly, the immaturity of technology may lead to errors or inaccuracies in the generated content, affecting the quality of teaching. Secondly, excessive reliance on artificial intelligence may weaken the role of teachers and students’ ability to learn independently. In addition, data privacy and security issues are also significant challenges that cannot be ignored. To address these challenges, this article proposes the following strategies: firstly, it is necessary to continuously optimize the technology to ensure the accuracy and reliability of the generated content. Secondly, the government should introduce relevant policies to guide and regulate the application of generative artificial intelligence in education. At the same time, the participation of all sectors of society, including parents, teachers, and students, is necessary to jointly supervise and evaluate the effectiveness of artificial intelligence applications in education. Finally, it is necessary to guide and cultivate the public’s correct understanding and reasonable expectations of generative artificial intelligence from a cultural perspective. In summary, the application of generative artificial intelligence in education is both full of potential and faces challenges. Through technological optimization, policy guidance, social participation, and cultural guidance, the rational and healthy development of the educational environment can be promoted.

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

The nanocomposite of half-fin anchovy hydrolysates (HAHp) and zinc oxide nanoparticles (ZnO NPs) (named as HAHp(3.0)/ZnO NPs) demonstrated increased antibacterial activity compared to either HAHp(3.0) or ZnO NPs as per our previous studies. Also, reactive oxygen species (ROS) formation was detected in Escherichia coli cells after treatment with HAHp(3.0)/ZnO NPs. The aim of the present study was to evaluate the acute toxicity of this nanocomposite and to investigate its effect on intestinal microbiota composition, short-chain fatty acids (SCFAs) production, and oxidative status in healthy mice. The limit test studies show that this nanoparticle is non-toxic at the doses tested. The administration of HAHp(3.0)/ZnO NPs, daily dose of 1.0 g/kg body weight for 14 days, increased the number of goblet cells in jejunum. High-throughput 16S ribosomal RNA gene sequencing of fecal samples revealed that HAHp(3.0)/ZnO NPs increased Firmicutes and reduced Bacteriodetes abundances in female mice. Furthermore, the microbiota for probiotic-type bacteria, including Lactobacillus and Bifidobacterium, and SCFAs-producing bacteria in the Clostridia class, e.g., Lachnospiraceae_unclassified and Lachnospiraceae_UCG-001, were enriched in the feces of female mice. Increases of SCFAs, especially statistically increased propionic and butyric acids, indicated the up-regulated anti-inflammatory activity of HAHp(3.0)/ZnO NPs. Additionally, some positive responses in liver, like markedly increased glutathione and decreased malonaldehyde contents, indicated the improved oxidative status. Therefore, our results suggest that HAHp(3.0)/ZnO NPs could have potential applications as a safe regulator of intestinal microbiota or also can be used as an antioxidant used in food products.

Drought stress has serious effects on the growth and yield of wheat in both productivity and quality and is an abiotic factor. Traditional methods of crop drought stress monitoring have some deficits. This work has been conducted in order to enhance these conventional methods by proposing a new deep learning approach. This paper has presented a deep learning-based model customized for monitoring drought stress in winter wheat during the critical growth stages. Drought-afflicted winter wheat images were captured at three crucial phases: rising–jointing, heading–flowering, and flowering–maturity. These images are correlated against soil moisture data to construct a comprehensive dataset. DenseNet121 was chosen as the network model since it extracts features from images relating to phenotypes. Several factors, like training methods, learning rate adjustment, and addition of the attention mechanism, are optimized in eight sets of experiments. This provided the final DenseNet-121 model with an average recognition accuracy of 94.67% on the test set, which means that monitoring drought stress during wheat growth’s key periods is feasible and effective.

Maize is one of the most important global crops. It is highly susceptible to diseases during its growth process, meaning that the timely detection and prevention of maize diseases is critically important. However, simple deep learning classification tasks do not allow for the accurate identification of multiple diseases present in a single leaf, and the existing RT-DETR (Real-Time Detection Transformer) detection methods suffer from issues such as excessive model parameters and inaccurate recognition of multi-scale features on maize leaves. The aim of this paper is to address these challenges by proposing an improved RT-DETR model. The model enhances the feature extraction capability by introducing a DAttention (Deformable Attention) module and optimizes the feature fusion process through the symmetry structure of spatial and channel in the SCConv (Spatial and Channel Reconstruction Convolution) module. In addition, the backbone network of the model is reconfigured, which effectively reduces the parameter size of the model and achieves a balanced symmetry between the model precision and the parameter count. Experimental results demonstrate that the proposed improved model achieves an mAP@0.5 of 92.0% and a detection precision of 89.2%, representing improvements of 7.3% and 8.4%, respectively, compared to the original RT-DETR model. Additionally, the model’s parameter size has been reduced by 18.9 M, leading to a substantial decrease in resource consumption during deployment and underscoring its extensive application potential.

After laying the groundwork by suggesting that ancient texts that offered supernatural explanations for natural phenomena demonstrated curiosity about the natural world as well as scientific imagination, authors Yao Jianbin and Wu Yan give an overview of major Chinese science fiction authors and trends since the late nineteenth century, when science fiction per se was introduced into China. Tying the development of science fiction in China to the nineteenth-century importation of Western science and science fiction, Yao and Wu trace the fortunes of this kind of literature through the turbulent decades of the twentieth century. They further connect the resurgence and international success of science fiction from China to China's rise as a technological innovator and world power.

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

Metal sulfides for hydrogen evolution catalysis typically suffer from unfavorable hydrogen desorption properties due to the strong interaction between the adsorbed H and the intensely electronegative sulfur. Here, we demonstrate a general strategy to improve the hydrogen evolution catalysis of metal sulfides by modulating the surface electron densities. The N modulated NiCo 2 S 4 nanowire arrays exhibit an overpotential of 41 mV at 10 mA cm −2 and a Tafel slope of 37 mV dec −1 , which are very close to the performance of the benchmark Pt/C in alkaline condition. X-ray photoelectron spectroscopy, synchrotron-based X-ray absorption spectroscopy, and density functional theory studies consistently confirm the surface electron densities of NiCo 2 S 4 have been effectively manipulated by N doping. The capability to modulate the electron densities of the catalytic sites could provide valuable insights for the rational design of highly efficient catalysts for hydrogen evolution and beyond. The hydrogen evolution reaction is a promising route to produce clean hydrogen fuel; however, its efficient electrolytic generation relies on expensive platinum. Here, the authors show how modulating electron density in a metal sulfide, NiCo 2 S 4 , boosts hydrogen desorption to achieve high catalytic activity.

The existence and magnitude of the recently suggested global warming hiatus, or slowdown, have been strongly debated 1 – 3 . Although various physical processes 4 – 8 have been examined to elucidate this phenomenon, the accuracy and completeness of observational data that comprise global average surface air temperature (SAT) datasets is a concern 9 , 10 . In particular, these datasets lack either complete geographic coverage or in situ observations over the Arctic, owing to the sparse observational network in this area 9 . As a consequence, the contribution of Arctic warming to global SAT changes may have been underestimated, leading to an uncertainty in the hiatus debate. Here, we constructed a new Arctic SAT dataset using the most recently updated global SATs 2 and a drifting buoys based Arctic SAT dataset 11 through employing the ‘data interpolating empirical orthogonal functions’ method 12 . Our estimate of global SAT rate of increase is around 0.112 °C per decade, instead of 0.05 °C per decade from IPCC AR5 1 , for 1998–2012. Analysis of this dataset shows that the amplified Arctic warming over the past decade has significantly contributed to a continual global warming trend, rather than a hiatus or slowdown. The Arctic is under-represented in surface temperature datasets and this could affect estimates of global warming. A new dataset with greater coverage of the Arctic shows a higher warming rate of 0.112 °C per decade compared to 0.005 °C from IPCC AR5.