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Nonheading Chinese cabbage is an important leafy vegetable, and quantitative identification and automated analysis of nonheading Chinese cabbage leaves are crucial for cultivating new varieties with higher quality, yield, and resistance. Traditional leaf phenotypic analysis relies mainly on phenotypic observation and the practical experience of breeders, leading to issues such as time consumption, labor intensity, and low precision, which result in low breeding efficiency. Considering these issues, a method for the extraction and analysis of phenotypes of nonheading Chinese cabbage leaves is proposed, targeting four qualitative traits and ten quantitative traits from 1500 samples, by integrating deep learning and OpenCV image processing technology. First, a leaf classification model is trained using YOLOv8 to infer the qualitative traits of the leaves, followed by the extraction and calculation of the quantitative traits of the leaves using OpenCV image processing technology. The results indicate that the model achieved an average accuracy of 95.25%, an average precision of 96.09%, an average recall rate of 96.31%, and an average F1 score of 0.9620 for the four qualitative traits. From the ten quantitative traits, the OpenCV-calculated values for the whole leaf length, leaf width, and total leaf area were compared with manually measured values, showing RMSEs of 0.19 cm, 0.1762 cm, and 0.2161 cm2, respectively. Bland–Altman analysis indicated that the error values were all within the 95% confidence intervals, and the average detection time per image was 269 ms. This method achieved good results in the extraction of phenotypic traits from nonheading Chinese cabbage leaves, significantly reducing the personpower and time costs associated with genetic resource analysis. This approach provides a new technique for the analysis of nonheading Chinese cabbage genetic resources that is high-throughput, precise, and automated. [ABSTRACT FROM AUTHOR]

In intelligent process monitoring and fault detection of the modern process industry, conventional methods mostly consider singular characteristics of systems. To tackle the problem of suboptimal incipient fault detection in nonlinear dynamic systems with non-Gaussian distributed data, this paper proposes a methodology named Gap-Mixed Kernel-Dynamic Canonical Correlation Analysis. Initially, the Gap metric is employed for data preprocessing, followed by fault detection utilizing the Mixed Kernel-Dynamic Canonical Correlation Analysis. Ultimately, fault identification is conducted through a contribution method based on the T 2 statistic. Furthermore, a comparative analysis was conducted using Canonical Variate Analysis, Dynamic Canonical Correlation Analysis, and Mixed Kernel-Dynamic Canonical Correlation Analysis on the Tennessee Eastman process. Experimental results indicate varying degrees of improvements in the detection rate, false alarm rate, missed detection rate, and detection time compared to the comparative methods, demonstrating the industrial value and academic significance of the method. [ABSTRACT FROM AUTHOR]

Incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of mixed kernel principal components analysis and dynamic canonical correlation analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient faults in nonlinear and dynamic systems. Meanwhile, fault identification experiments were conducted, validating the high accuracy of the fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance. [ABSTRACT FROM AUTHOR]

As solvent-based coatings are gradually phasing out in China, the volatile organic compounds (VOCs) emission characteristics of surface-coating industries have changed rapidly. Sector-based field measurements were conducted to build VOCs emission factors and source profiles of surface-coating industries in Jiangsu Province. A VOCs emission inventory was developed, and the projections for 2020 to 2030 were set. It was found that VOCs content in water-based coatings is 50.8% of solvent-based coatings on average. VOCs emission factors of solvent-based coatings ranged from 0.40 to 0.51 kg kg−1, while those of water-based coatings ranged from 0.14 to 0.24 kg kg−1. Compared to solvent-based coatings, the proportion of aromatics emitted from water-based coatings was 44.2% lower, while the proportion of oxygenated VOCs (OVOCs) was 11.6% higher. The results showed that VOCs emissions were about 134 Gg in Jiangsu Province in 2020, of which the solvent-based coating sources contributed 79.6% of the total. Aromatics were the main species contributing 52.9% of VOCs emissions and 85.9% of ozone formation potential (OFP). According to emission prediction results of four scenarios, the emission reduction of implementing low-content VOCs coating substitution is 8.7% higher than that of adopting the best available end-of-pipe treatment measures by 2030. [ABSTRACT FROM AUTHOR]

Eyelid tumors are tumors that occur in the eye and its appendages, affecting vision and appearance, causing blindness and disability, and some having a high lethality rate. Pathological images of eyelid tumors are characterized by large pixels, multiple scales, and similar features. Solving the problem of difficult and time-consuming fine-grained classification of pathological images is important to improve the efficiency and quality of pathological diagnosis. The morphology of Basal Cell Carcinoma (BCC), Meibomian Gland Carcinoma (MGC), and Cutaneous Melanoma (CM) in eyelid tumors are very similar, and it is easy to be misdiagnosed among each category. In addition, the diseased area, which is decisive for the diagnosis of the disease, usually occupies only a relatively minor portion of the entire pathology section, and screening the area of interest is a tedious and time-consuming task. In this paper, deep learning techniques to investigate the pathological images of eyelid tumors. Inspired by the knowledge distillation process, we propose the Multiscale-Attention-Crop-ResNet (MAC-ResNet) network model to achieve the automatic classification of three malignant tumors and the automatic localization of whole slide imaging (WSI) lesion regions using U-Net. The final accuracy rates of the three classification problems of eyelid tumors on MAC-ResNet were 96.8%, 94.6%, and 90.8%, respectively. [ABSTRACT FROM AUTHOR]

Ni nano-micro structures have been synthesized via a solution reduction route in the presence of ethylenediamine (EDA) under strong alkaline conditions. The phase composition, morphology, and microstructure of the resulting products are investigated by X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. The presence of EDA plays an important role in the formation of Ni nano-micro structures, and microflowers or microspheres assembled from nanosized horns can be produced by changing the amount of EDA. The size of Ni nano-micro structures is dependent on the NaOH concentration, and long chains assembled from Ni nano-micro structured microspheres can be obtained by reducing the amount of NaOH solution used. The role of both EDA and NaOH in the reduction of Ni (II) to Ni, as well as in the growth of Ni nano-micro structures, has been discussed, and a possible formation mechanism of these Ni nano-micro structures has been proposed based on the experimental results. [ABSTRACT FROM AUTHOR]