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The development of lithium-ion batteries largely relies on the cathode and anode materials. In particular, the optimization of cathode materials plays an extremely important role in improving the performance of lithium-ion batteries, such as specific capacity or cycling stability. Carbon coating modifying the surface of cathode materials is regarded as an effective strategy that meets the demand of Lithium-ion battery cathodes. This work mainly reviews the modification mechanism and method of carbon coating, and summarizes the recent progress of carbon coating on some typical cathode materials (LiFePO4, LiMn2O4, LiCoO2, NCA (LiNiCoAlO2) and NCM (LiNiMnCoO2)). In addition, the limitations of the carbon coating on the cathode are also introduced. Suggestions on improving the effectiveness of carbon coating for future study are also presented.

Soybean is one of the world’s most consumed crops. As the human population continuously increases, new phenotyping technology is needed to develop new soybean varieties with high-yield, stress-tolerant, and disease-tolerant traits. Hyperspectral imaging (HSI) is one of the most used technologies for phenotyping. The current HSI techniques with indoor imaging towers and unmanned aerial vehicles (UAVs) suffer from multiple major noise sources, such as changes in ambient lighting conditions, leaf slopes, and environmental conditions. To reduce the noise, a portable single-leaf high-resolution HSI imager named LeafSpec was developed. However, the original design does not work efficiently for the size and shape of dicot leaves, such as soybean leaves. In addition, there is a potential to make the dicot leaf scanning much faster and easier by automating the manual scan effort in the original design. Therefore, a renovated design of a LeafSpec with increased efficiency and imaging quality for dicot leaves is presented in this paper. The new design collects an image of a dicot leaf within 20 s. The data quality of this new device is validated by detecting the effect of nitrogen treatment on soybean plants. The improved spatial resolution allows users to utilize the Normalized Difference Vegetative Index (NDVI) spatial distribution heatmap of the entire leaf to predict the nitrogen content of a soybean plant. This preliminary NDVI distribution analysis result shows a strong correlation (R2 = 0.871) between the image collected by the device and the nitrogen content measured by a commercial laboratory. Therefore, it is concluded that the new LeafSpec-Dicot device can provide high-quality hyperspectral leaf images with high spatial resolution, high spectral resolution, and increased throughput for more accurate phenotyping. This enables phenotyping researchers to develop novel HSI image processing algorithms to utilize both spatial and spectral information to reveal more signals in soybean leaf images.

Emission Trading System (ETS) is an innovative practice under the progress of green development in China. It is also an important method for China to achieve market-oriented environmental governance in ecological civilization construction. The ETS pilot policy has implemented for more than 10 years. However, the co-benefits of ETS pilot policy by the integration of energy consumption, carbon and sulfur dioxide emissions, and wastewater has not been evaluated. In order to fill this gap, we use the 2003–2017 annual data of 30 China’s provinces (municipalities and autonomous regions), and utilize the Difference-in-Differences (DID) model and Propensity Score Matching (PSM-DID) methodology to evaluate the co-benefits of ETS pilot policy on energy conservation and emission reduction. We find that the ETS pilot policy significantly promote energy conservation and emission reduction. Eastern and central China have significantly benefited from the policy, while the western China has not due to the limited technology and innovation as well as an imbalance of the industrial structure. The results provide the policy reference for China’s government and institutions as well as the governments and institutions around the world to fulfill their commitments to save energy and reduce emissions, and early achieve the carbon peaking and carbon neutralization.

Image-based spectroscopy phenotyping is a rapidly growing field that investigates how genotype, environment and management interact using remote or proximal sensing systems to capture images of a plant under multiple wavelengths of light. While remote sensing techniques have proven effective in crop phenotyping, they can be subject to various noise sources, such as varying lighting conditions and plant physiological status, including leaf orientation. Moreover, current proximal leaf-scale imaging devices require the sensors to accommodate the state of the samples during imaging which induced extra time and labor cost. Therefore, this study developed a proximal multispectral imaging device that can actively attract the leaf to the sensing area (target-to-sensor mode) for high-precision and high-throughput leaf-scale phenotyping. To increase the throughput and to optimize imaging results, this device innovatively uses active airflow to reposition and flatten the soybean leaf. This novel mechanism redefines the traditional sensor-to-target mode and has relieved the device operator from the labor of capturing and holding the leaf, resulting in a five-fold increase in imaging speed compared to conventional proximal whole leaf imaging device. Besides, this device uses artificial lights to create stable and consistent lighting conditions to further improve the quality of the images. Furthermore, the touch-based imaging device takes full advantage of proximal sensing by providing ultra-high spatial resolution and quality of each pixel by blocking the noises induced by ambient lighting variances. The images captured by this device have been tested in the field and proven effective. Specifically, it has successfully identified nitrogen deficiency treatment at an earlier stage than a typical remote sensing system. The p-value of the data collected by the device (p = 0.008) is significantly lower than that of a remote sensing system (p = 0.239).

Spectral imaging has been widely applied in plant phenotyping to assess corn leaf nitrogen status. Recent studies indicate that spatial variations within a single leaf’s multispectral image provide stronger signals for corn nitrogen estimation. However, current technologies for corn multispectral imaging cannot capture a large corn leaf segment with high-resolution and simple operation, limiting their efficiency and accuracy in nitrogen estimation. To address this gap, this study developed a proximal multispectral imaging device that can capture high-resolution snapshot multispectral images of a large segment of a single corn leaf. This device uses airflow to autonomously position and flatten the leaf to minimize the noise in images due to leaf curvature and simplify operation. Moreover, this device adopts a transmittance imaging regime by clamping the corn leaf between the camera and the lighting source to block the environmental lights and supply uniform lighting to capture high-resolution and high-precision leaf images within six seconds. A field assay was conducted to validate the effectiveness of the multispectral images captured by this device in assessing nitrogen status by classifying the nitrogen treatments applied to corn. Six nitrogen treatments were applied to 12 plots of corn fields, and 10 images were collected at each plot. By using the average vegetative index of the whole image, only one treatment was significantly different from the other five treatments, and no significant difference was observed among any other groups. However, by extracting the spatial and spectral features from the images and combining these features, the accuracy of nitrogen treatment classification improved compared to using the average index. In another analysis, by applying spatial–spectral analysis methods to the images, the nitrogen treatment classification accuracy has improved compared to using the average index. These results demonstrated the advantages of this high-resolution and high-throughput imaging device for distinguishing nitrogen treatments by facilitating spatial–spectral combined analysis for more precise classification.

Purpose The objective of the study was to explore the effect of periodontally accelerated osteogenic orthodontics (PAOO) in orthodontic patients with bone dehiscence and fenestration in the anterior alveolar region of the mandible. Methods A retrospective study was performed in 42 patients with bone dehiscence and fenestrations in the anterior alveolar region of the mandible who underwent the PAOO technique. The bleeding index (BI), probing depth (PD), keratinized gingiva width (KGW), gingival recession level (GRL), and gingival phenotype were recorded and assessed at baseline and 6 and 12 months postoperatively. Cone-beam computerized tomography was used to measure bone volume in terms of root length (RL), horizontal bone thickness at different levels, and vertical bone height at baseline and 6 months and 12 months after surgery. Results The sample was composed of 42 patients (22 males and 20 females; mean age, aged 25.6 ± 4.8 years) with 81 teeth showing dehiscence/fenestrations and 36 sites presenting gingival recessions. There was no significant difference in BI, PD, or KGW (between baseline and 6 or 12 months postoperatively) based on the clinical evaluations ( P  > 0.05). Gingival recession sites demonstrated a significant reduction in the GRL after surgery ( P  < 0.05). Furthermore, the proportion of teeth with a thick gingival phenotype increased from 33.61% at baseline to 53.13% at the end of the follow-up. In addition, the bone thickness measurements at the mid-root and crestal levels were markedly increased compared with the baseline values ( P  < 0.05), although the increase in thickness at the apical level was not statistically significant ( P  > 0.05). Conclusions Within the limitations of the study, the results show that the PAOO technique is beneficial to periodontal conditions in terms of soft and hard tissue augmentation. The PAOO procedure may represent a safe and efficient treatment for orthodontic patients with bone dehiscence and fenestration. Trial registration This study was approved by the ethics committee of the stomatological hospital affiliated with Xi'an Jiaotong University (xjkqll [2019] No. 016) and registered in the Chinese Clinical Trial Registry (ChiCTR2100053092).

Green finance is an important practice of China’s high-quality economic development in the new era, which is closely related to economic development, environment, and energy conditions. However, few studies systematically analyze the impact of green finance on economic development, environmental pollution, and energy consumption, especially on China which is turning to high-quality economic development. In order to fill the gap, based on the annual data on 30 provinces (autonomous regions and municipalities) in China from 2008 to 2018, we construct a comparatively comprehensive green finance index system and use a panel regression model to explore the impacts of green finance on high-quality economic development, environmental pollution, and energy consumption. We find that green finance can significantly promote high-quality economic development, mitigate environmental pollution, and reduce energy consumption. There is spatial and temporal heterogeneity in the impact of green finance on China’s economic quality, environmental pollution, and energy consumption. In the eastern region, green finance has a remarkable positive impact on high-quality economic development and a significant negative impact on energy consumption, but the impact on environmental pollution is inconspicuous. In the central region, green finance has a prominent effect on reducing environmental pollution, but the impact on high-quality economic development and energy consumption is not significant. In the western region, green finance has not been able to significantly promote high-quality economic development, mitigate environmental pollution, and reduce energy consumption. After the clear proposal of green finance, the role of green finance in promoting a high-quality economy has enhanced, and the role of green finance in reducing environmental pollution and energy consumption has decreased. This study can provide a useful decision-making reference for promoting high-quality economic development, reducing environmental pollution and energy consumption, and spurring sustainable development.

With the development of online shopping platforms, consumers and designers need to choose from a large number of garments when shopping or designing. Quick identification of clothing products can effectively improve the efficiency of designers’ and consumers’ experience. Therefore, this paper used DeepLabV3+ combined with deep separable convolution to improve the network computation speed. To address the problem of low recognition rate of H-shaped silhouette in semantic segmentation, the fuzzy trouser silhouette samples are further analyzed. The trouser silhouette was redefined according to the characteristics of pants, and the dataset labels were optimized with a trouser silhouette classification method. It was found that the accuracy and efficiency of trouser silhouette recognition were significantly improved. The indicators of recall rate, IoU and PA of H silhouette is improved by 6%, 5%, and 1% respectively. After label optimization, the classification prediction accuracy of silhouette V is 100%, the recall of silhouette V is 97%, and the recall of silhouette O is 96%.

Hyperspectral imaging (HSI) is a prevalent method in crop phenotyping. Nevertheless, current HSI remote sensing techniques are compromised by changing ambient lighting conditions, long imaging distances, and comparatively low resolutions. Proximal HSI sensors such as LeafSpec were developed to improve the imaging quality. However, the application of proximal sensors remains contrained by their low throughput and intensive labor costs. Moreover, few automation solutions were available to use LeafSpec in phenotyping dicot plants. In this paper, a novel robotic system is presented as a sensor platform to operate LeafSpec to collect leaf-level hyperspectral images for in vivo phenotyping of soybean. A machine vision algorithm was developed to detect the top mature trifoliate and estimate the poses of the leaflets. A control and motion planning algorithm was developed for an articulated robotic manipulator to grasp the target leaflets. An experiment was conducted in March 2021 in a greenhouse with 64 soybean plants of 2 genotypes and 2 nitrogen treatments. The machine vision detected the target leaflets with a first trial success rate of 84.13% and an overall success rate of 90.66%. The robotic manipulator operated LeafSpec to image the target leaflets with a first trial success rate of 87.30% and an overall success rate of 93.65%. The average cycle time for one soybean plant was 63.20 s. The PLS predictions from the robot-collected data had an R2 of 0.84 with the measured nitrogen content and an R2 of 0.82 with the predictions from human-collected data. The results demonstrated the potential of applying the system for automated in vivo leaf-level HSI for soybean phenotyping in the field.

The global pandemic caused by the outbreak of COVID-19 has posed significant risks to our health. Preventive measures such as closed management have greatly affected the economies, environments and societies of various countries. Economy, air pollution and income are three important interconnected aspects of sustainable development. However, current research lacks systematic quantitative analysis of their relationships. To fill the gap, this study adopts monthly data from January 2016 to April 2022 and constructs both a Simultaneous Equation Model (SEM) and a Time Varying Parameter Stochastic Volatility Vector Autoregressive (TVP-SV-VAR) model to empirically analyze the impact of COVID-19 on China's economy, air pollution and income. This study finds that the COVID-19 has a negative impact on China’s economy and income, and a positive impact on air pollution, and the impact of the COVID-19 is systematic. In addition, there is an inverted-U shaped relationship between air pollution and economics, and a positive correlation between economic and income. The impact of COVID-19 on the economy, air pollution and income show a process of sharp fluctuations to gradual stabilization that gradually stabilized over time. This process is time-varying in the short-term, medium-term and long-term. The impacts are persistent at three different time points (before, during and after the outbreak of COVID-19), but the negative impact on the economy and income is persistent, while the positive impact on air pollution is limited. This study provides a more systematic and dynamic understanding of the COVID-19 preventive and mitigation measures in China and even the world, which helps to provide insights into the formulation of more comprehensive planning strategies in the future.