Several members of family Urticaceae are mainly found in the temperate and subtropical zones of the Northern Hemisphere and are important medicinal plants. Among them, Urtica dioica L. (Urticaceae) is an annual or perennial herb that has been used for feeding and medicinal purposes since long time and is the most exploited species of Urticaceae. Recently, it has received attention to be used as animal feed, as its fresh leaves fed to animals in moderate, dried, and other forms. This review details the advantages of U. dioica as an alternative feed in terms of germplasm specificity, nutritional composition, and feed application status. Its roots, stems, leaves, and seeds are rich in active ingredients. It has also been found to have anticancer effects through antioxidant action and promotion of apoptosis of cancer cells. In shady conditions, U. dioica is highly adaptable while under stressful conditions of drought; it also reduces light absorption and ensures carbon assimilation through light energy conversion efficiency. Therefore, it can be added to animal diets as a suitable feed to reduce costs and improve economic efficiency. This paper investigates the feasibility of using U. dioica as a feed and systematically presents the progress of research and exploitation of U. dioica.

Broccoli is a nutritious vegetable. It is high in protein, minerals, and vitamins. Also, it possesses antioxidant activities and is beneficial to the human body. Due to its active effect, broccoli is widely accepted by people in daily life. However, in terms of current utilization, only its florets are consumed as vegetables, while more than half of its stalks and leaves are not utilized. The stalks and leaves contain not only nutrients but also bioactive substances with physiologically regulating properties. Therefore research into the action and mechanism of its bioactive substances as well as its development and utilization technology will make contributions to the further promotion of its resource development and utilization. As a theoretical foundation for the resource utilization of broccoli stalks and leaves, this report will review the distribution and consumption of broccoli germplasm resources, the mechanism of action of bioactive substances, and innovative methods for their exploitation.

Background: In China, the transportation sector is the main energy consumer and the main source of carbon emissions. Reducing carbon emissions in the transportation sector is an important goal for China, especially during the current period of economic development. Due to the impact of pandemic shocks, the rapid development of green finance is conducive to supporting the transportation sector in achieving a carbon peak. Thus, we examined whether the development of green finance is still effective under the impact of a pandemic and the actual effect of green finance on the reduction of carbon emissions.Methods: In this study, we searched the internet for consumption structure data of vehicles and green finance indices of 30 Chinese provinces and cities from 2016 to 2021. A regression discontinuity model was constructed to test the effect of pandemic shock and green finance development on the reduction of transportation energy carbon emissions.Results: The results show that the impact of the COVID-19 pandemic has helped people change their preference toward more energy-efficient vehicles and reduce carbon emissions in the transportation sector. Green finance can effectively contribute to the reduction of transportation energy carbon emissions; however, the overall mitigation effect is limited.Conclusion: The empirical evidence is not only helpful in assessing the long-term impact of the COVID-19 pandemic but also conducive to the appropriate establishment of policy tools for supporting green finance development, which is further conducive to reducing carbon emissions in the transportation sector.

IntroductionCorn is one of the world's essential crops, and the presence of corn diseases significantly affects both the yield and quality of corn. Accurate identification of corn diseases in real time is crucial to increasing crop yield and improving farmers' income. However, in real-world environments, the complexity of the background, irregularity of the disease region, large intraclass variation, and small interclass variation make it difficult for most convolutional neural network models to achieve disease recognition under such conditions. Additionally, the low accuracy of existing lightweight models forces farmers to compromise between accuracy and real-time.MethodsTo address these challenges, we propose FCA-EfficientNet. Building upon EfficientNet, the fully-convolution-based coordinate attention module allows the network to acquire spatial information through convolutional structures. This enhances the network's ability to focus on disease regions while mitigating interference from complex backgrounds. Furthermore, the adaptive fusion module is employed to fuse image information from different scales, reducing interference from the background in disease recognition. Finally, through multiple experiments, we have determined the network structure that achieves optimal performance.ResultsCompared to other widely used deep learning models, this proposed model exhibits outstanding performance in terms of accuracy, precision, recall, and F1 score. Furthermore, the model has a parameter count of 3.44M and Flops of 339.74M, which is lower than most lightweight network models. We designed and implemented a corn disease recognition application and deployed the model on an Android device with an average recognition speed of 92.88ms, which meets the user's needs.DiscussionOverall, our model can accurately identify corn diseases in realistic environments, contributing to timely and effective disease prevention and control.

The subtropical regions in China are prone to recurrent summer droughts induced by the Western Pacific Subtropical High-Pressure, which has induced the death of tens of millions of culms of Moso bamboo (Phyllostachys edulis (Carriere) J. Houzeau), a widely distributed giant bamboo with high economic and ecological values. In the future, the intensity and frequency of the summer drought are projected to increase in these areas due to global climate change, which may lead to significant age-specific mortality of Moso bamboo. So far, it is still unclear about the age-specific response mechanisms of hydraulic traits and carbon balance of Moso bamboo when it is suffering to an ongoing summer drought. This study aimed to investigate the hydraulic and photosynthetic responses of newly sprouted (1 year old) and established (2-5 years old) culms of Moso bamboo to summer drought, which was manipulated by throughfall reduction in Lin’an of Zhejiang. The results showed that both newly sprouted and established culms had a gradually weakening hydraulic conductivity and photosynthesis during the whole drought process. In the early stage of the manipulated drought, the established culms had more loss of hydraulic conductivity than the newly sprouted culms. However, the newly sprouted culms had significant more loss of hydraulic conductivity and lower photosynthetic rates and stomatal conductance in the middle and late stages of the manipulated drought. The results suggest that the newly sprouted culms were more susceptible to summer drought than established culms due to the combined effects of hydraulic damage and photosynthetic restriction, explaining why the newly sprouted culms have higher mortality than elder culms when subjected to extreme drought. These findings provided insights into the mechanisms of Moso bamboo’s age-specific drought-induced mortality, which will help for the anti-drought management of bamboo.

Pepper leaf disease identification based on convolutional neural networks (CNNs) is one of the interesting research areas. However, most existing CNN-based pepper leaf disease detection models are suboptimal in terms of accuracy and computing performance. In particular, it is challenging to apply CNNs on embedded portable devices due to a large amount of computation and memory consumption for leaf disease recognition in large fields. Therefore, this paper introduces an enhanced lightweight model based on GoogLeNet architecture. The initial step involves compressing the Inception structure to reduce model parameters, leading to a remarkable enhancement in recognition speed. Furthermore, the network incorporates the spatial pyramid pooling structure to seamlessly integrate local and global features. Subsequently, the proposed improved model has been trained on the real dataset of 9183 images, containing 6 types of pepper diseases. The cross-validation results show that the model accuracy is 97.87%, which is 6% higher than that of GoogLeNet based on Inception-V1 and Inception-V3. The memory requirement of the model is only 10.3 MB, which is reduced by 52.31%-86.69%, comparing to GoogLeNet. We have also compared the model with the existing CNN-based models including AlexNet, ResNet-50 and MobileNet-V2. The result shows that the average inference time of the proposed model decreases by 61.49%, 41.78% and 23.81%, respectively. The results show that the proposed enhanced model can significantly improve performance in terms of accuracy and computing efficiency, which has potential to improve productivity in the pepper farming industry.