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Electrostriction is an important electro-mechanical property in poly (vinylidene fluoride) (PVDF) films, which describes the proportional relation between the electro-stimulated deformation and the square of the electric field. Generally, traditional methods to improve the electrostriction of PVDF either sacrifice other crystalline-related key properties or only influence minimal regions around the surface. Here, we design a unique electret structure to fully exploit the benefits of internal crystal in PVDF films. Through the 3D printing of charged ink, we have obtained the best electrostrictive and ferroelectric properties among PVDF-based materials so far. The optimized electrostrictive coefficient M33 (324 × 10−18 m2 V−2) is 104 times that of normal PVDF films, and the piezoelectric constant d33 (298 pm V−1) is close to 10 times its traditional limit. The proposed 3D electret structure and the bottom-up approach to 'print the charge' open up a new way to design and adapt the electroactive polymers in smart devices and systems. The authors make a high-density charge structure to enhance the electrostriction of pristine poly (vinylidene fluoride), improving its ferroelectric performance to nearly 10 times its traditional limit. [ABSTRACT FROM AUTHOR]

In vertical farms, the supplementation of far-red light has been widely applied to regulate plant growth and development. However, the relative contribution of far-red to photosynthesis and plant growth in indoor production systems is not sufficiently quantified. This study quantify the photosynthesis and growth responses under different levels of supplemental far-red in lettuce using a 3D modelling approach. Lettuce were cultivated under either white light or red to far-red (R:FR) ratio of 1.6 or 0.8. Measurements of plant morphological traits, leaf photosynthesis, and organ fresh and dry mass were taken and the 3D modelling approach was used to simulate plant photosynthesis and biomass accumulation. Results showed that leaf elevation angle, leaf expansion rate, and plant height significantly increased at each growth stage as the R:FR ratio decreased. Far-red light also promoted plant growth, leading to an increase in the dry and fresh weight of lettuce throughout the entire growth period. However, plants cultivated at low R:FR showed reduced maximum Rubisco carboxylation rate and maximum electron transport rate, which indicated that far-red light reduced the photosynthetic capacity in lettuce. Nevertheless, 3D model simulations demonstrated that plants exposed to enhanced far-red light exhibited increased light interception and whole-plant photosynthesis. The integrated analysis of photosynthetic parameters and plant morphological changes on the photosynthetic rate of the whole plant indicated that the positive effects of plant morphological changes outweighed the negative impacts of photosynthetic parameters. These results implied that far-red light-induced morphological changes enhanced light interception and whole-plant photosynthesis, thereby increased lettuce yield. [ABSTRACT FROM AUTHOR]

Understanding photosynthetic acclimation to elevated CO2 (eCO2) is important for predicting plant physiology and optimizing management decisions under global climate change, but is underexplored in important horticultural crops. We grew three crops differing in stomatal density—namely chrysanthemum, tomato, and cucumber—at near‐ambient CO2 (450 μmol mol−1) and eCO2 (900 μmol mol−1) for 6 weeks. Steady‐state and dynamic photosynthetic and stomatal conductance (gs) responses were quantified by gas exchange measurements. Opening and closure of individual stomata were imaged in situ, using a novel custom‐made microscope. The three crop species acclimated to eCO2 with very different strategies: Cucumber (with the highest stomatal density) acclimated to eCO2 mostly via dynamic gs responses, whereas chrysanthemum (with the lowest stomatal density) acclimated to eCO2 mostly via photosynthetic biochemistry. Tomato exhibited acclimation in both photosynthesis and gs kinetics. eCO2 acclimation in individual stomatal pore movement increased rates of pore aperture changes in chrysanthemum, but such acclimation responses resulted in no changes in gs responses. Although eCO2 acclimation occurred in all three crops, photosynthesis under fluctuating irradiance was hardly affected. Our study stresses the importance of quantifying eCO2 acclimatory responses at different integration levels to understand photosynthetic performance under future eCO2 environments. SUMMARY STATEMENT: Three important horticultural crops with contrasting stomatal density (cucumber > tomato > chrysanthemum) acclimated to elevated CO2 with very different strategies: Cucumber mostly acclimated via stomatal conductance responses, whereas chrysanthemum mostly acclimated via photosynthetic biochemistry. [ABSTRACT FROM AUTHOR]

In this study, a novel approach integrating multimodal data processing and attention aggregation techniques is proposed for pear tree disease detection. The focus of the research is to enhance the accuracy and efficiency of disease detection by fusing data from diverse sources, including images and environmental sensors. The experimental results demonstrate that the proposed method outperforms in key performance metrics such as precision, recall, accuracy, and F1-Score. Specifically, the model was tested on the Kaggle dataset and compared with existing advanced models such as RetinaNet, EfficientDet, Detection Transformer (DETR), and the You Only Look Once (YOLO) series. The experimental outcomes indicate that the proposed model achieves a precision of 0.93, a recall of 0.90, an accuracy of 0.92, and an F1-Score of 0.91, surpassing those of the comparative models. Additionally, detailed ablation experiments were conducted on the multimodal weighting module and the dynamic regression loss function to verify their specific contributions to the model performance. These experiments not only validated the effectiveness of the proposed method but also demonstrate its potential application in pear tree disease detection. Through this research, an effective technological solution is provided for the agricultural disease detection domain, offering substantial practical value and broad application prospects. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information." The original publication contained errors in Table 2, Table 3, Table 4, Figure 2, and Figure 7. Additionally, there were mistakes in some equations. The corrected versions of these tables, figures, and equations are provided in the document. The authors state that these corrections do not affect the scientific conclusions of the paper. The document also includes figures and tables related to the paper's content, providing information on computational complexity and derivatives of formulas. The authors of the document are Yugen Yi, Haoming Zhang, Ningyi Zhang, Wei Zhou, Xiaomei Huang, Gengsheng Xie, and Caixia Zheng. [Extracted from the article]

A novel deep learning model, DiffuCNN, is introduced in this paper, specifically designed for counting tobacco lesions in complex agricultural settings. By integrating advanced image processing techniques with deep learning methodologies, the model significantly enhances the accuracy of detecting tobacco lesions under low-resolution conditions. After detecting lesions, the grading of the disease severity is achieved through counting. The key features of DiffuCNN include a resolution enhancement module based on diffusion, an object detection network optimized through filter pruning, and the employment of the CentralSGD optimization algorithm. Experimental results demonstrate that DiffuCNN surpasses other models in precision, with respective values of 0.98 on precision, 0.96 on recall, 0.97 on accuracy, and 62 FPS. Particularly in counting tobacco lesions, DiffuCNN exhibits an exceptional performance, attributable to its efficient network architecture and advanced image processing techniques. The resolution enhancement module based on diffusion amplifies minute details and features in images, enabling the model to more effectively recognize and count tobacco lesions. Concurrently, filter pruning technology reduces the model's parameter count and computational burden, enhancing the processing speed while retaining the capability to recognize key features. The application of the CentralSGD optimization algorithm further improves the model's training efficiency and final performance. Moreover, an ablation study meticulously analyzes the contribution of each component within DiffuCNN. The results reveal that each component plays a crucial role in enhancing the model performance. The inclusion of the diffusion module significantly boosts the model's precision and recall, highlighting the importance of optimizing at the model's input end. The use of filter pruning and the CentralSGD optimization algorithm effectively elevates the model's computational efficiency and detection accuracy. [ABSTRACT FROM AUTHOR]