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The application of BiVO4 in photoelectrochemical water splitting for efficient clean hydrogen energy production encounters challenges arising from the sluggish kinetics of water oxidation. Motivated by the synergistic interplay of metal sites and ligands on the catalyst surface, we utilized the photoelectric deposition technique to introduce amorphous nanothin layers of cobalt–iron double hydroxide (referred to as CoFe-LDH) onto the Fe-doped BiVO4 surface. Fe dopants lead to a size reduction of BiVO4 nanoparticles while enlarging the specific surface area and pore volume, thus increasing the reaction sites, which is favorable for photoelectrochemical water splitting. The unique dual-layered structure of CoFe-LDH not only enhances the mobility of charge carriers but also addresses surface defects through passivation. Additionally, it optimizes the exposure of active sites on the surface and expedites the flow of charge carriers, effectively mitigating recombination. The CoFe/Fe-BiVO4 photoanode demonstrates outstanding photocatalytic performance, achieving a substantial photocurrent of 2.56 mA cm–2 (at 1.23 V vs RHE) and an impressive incident photon current conversion efficiency (IPCE) of 52.1% at 400 nm, which is approximately a 270% increment in photocurrent and a remarkable 2.2-fold improvement in IPCE compared to those of the unmodified sample. In addition, the charge surface transport efficiency increases from 16.8% to 62.5% at 1.23 V vs RHE after modification of the cobalt–iron hydroxide bilayer. This study not only emphasizes the promising results of employing binary polymetallic co-catalysts but also provides a strategic pathway to improve semiconductor-based photoelectrodes in various photoelectrochemical applications. [ABSTRACT FROM AUTHOR]

In this study, a deep learning-based system for apple defect detection and quality grading was developed, integrating various advanced image-processing technologies and machine learning algorithms to enhance the automation and accuracy of apple quality monitoring. Experimental validation demonstrated the superior performance of the proposed model in handling complex image tasks. In the defect-segmentation experiments, the method achieved a precision of 93%, a recall of 90%, an accuracy of 91% and a mean Intersection over Union (mIoU) of 92%, significantly surpassing traditional deep learning models such as U-Net, SegNet, PSPNet, UNet++, DeepLabv3+ and HRNet. Similarly, in the quality-grading experiments, the method exhibited high efficiency with a precision of 91%, and both recall and accuracy reaching 90%. Additionally, ablation experiments with different loss functions confirmed the significant advantages of the Jump Loss in enhancing model performance, particularly in addressing class imbalance and improving feature learning. These results not only validate the effectiveness and reliability of the system in practical applications but also highlight its potential in automating the detection and grading processes in the apple industry. This integration of advanced technologies provides a new automated solution for quality control of agricultural products like apples, facilitating the modernization of agricultural production. [ABSTRACT FROM AUTHOR]

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]

Long-term exposure to high concentrations of fine particles can cause irreversible damage to people's health. Therefore, it is of extreme significance to conduct large-scale continuous spatial fine particulate matter (PM2.5) concentration prediction for air pollution prevention and control in China. The distribution of PM2.5 ground monitoring stations in China is uneven with a larger number of stations in southeastern China, while the number of ground monitoring sites is also insufficient for air quality control. Remote sensing technology can obtain information quickly and macroscopically. Therefore, it is possible to predict PM2.5 concentration based on multi-source remote sensing data. Our study took China as the research area, using the Pearson correlation coefficient and GeoDetector to select auxiliary variables. In addition, a long short-term memory neural network and random forest regression model were established for PM2.5 concentration estimation. We finally selected the random forest regression model (R2 = 0.93, RMSE = 4.59 μg m−3) as our prediction model by the model evaluation index. The PM2.5 concentration distribution across China in 2021 was estimated, and then the influence factors of high-value regions were explored. It is clear that PM2.5 concentration is not only related to the local geographical and meteorological conditions, but also closely related to economic and social development. [ABSTRACT FROM AUTHOR]

Components play a pivotal role in component-based software, with certain components being crucial in realizing the software's overall functionality. The importance of component is usually characterized using the weight value of component. How to get the weight value is a problem that needs to be studied. Traditionally, the weight value can be denoted by an expert's evaluation. However, the importance of components can vary depending on their interactions within different combination structures. Therefore, it becomes necessary to consider the mutual influence resulting from component interactions when determining component weights, in addition to expert evaluations. In this study, we first establish an influence value model that captures the interactions between components within various combinations. Subsequently, we propose a comprehensive weight allocation method for components by integrating expert evaluations with the influence values obtained from the component interactions, using an evidence-based information fusion approach. Finally, we validate the effectiveness of our method by implementing a scenic spot ticket purchase system. This approach is more reasonable than the single method and can provide software developers with a more comprehensive analysis of component importance, enabling them to make informed decisions. [ABSTRACT FROM AUTHOR]