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Individual identification of pigs is a critical component of intelligent pig farming. Traditional pig ear-tagging requires significant human resources and suffers from issues such as difficulty in recognition and low accuracy. This paper proposes the YOLOv5-KCB algorithm for non-invasive identification of individual pigs. Specifically, the algorithm utilizes two datasets—pig faces and pig necks—which are divided into nine categories. Following data augmentation, the total sample size was augmented to 19,680. The distance metric used for K-means clustering is changed from the original algorithm to 1-IOU, which improves the adaptability of the model's target anchor boxes. Furthermore, the algorithm introduces SE, CBAM, and CA attention mechanisms, with the CA attention mechanism being selected for its superior performance in feature extraction. Finally, CARAFE, ASFF, and BiFPN are used for feature fusion, with BiFPN selected for its superior performance in improving the detection ability of the algorithm. The experimental results indicate that the YOLOv5-KCB algorithm achieved the highest accuracy rates in pig individual recognition, surpassing all other improved algorithms in average accuracy rate (IOU = 0.5). The accuracy rate of pig head and neck recognition was 98.4%, while the accuracy rate for pig face recognition was 95.1%, representing an improvement of 4.8% and 13.8% over the original YOLOv5 algorithm. Notably, the average accuracy rate of identifying pig head and neck was consistently higher than pig face recognition across all algorithms, with YOLOv5-KCB demonstrating an impressive 2.9% improvement. These results emphasize the potential for utilizing the YOLOv5-KCB algorithm for precise individual pig identification, facilitating subsequent intelligent management practices. [ABSTRACT FROM AUTHOR]

A three-dimensional hierarchical CdO nanostructure with a novel bio-inspired morphology is reported. The field emission scanning electronic microscopy, transmission electron microscopy and X-ray diffractometer were employed to characterize the as-prepared samples. In gas-sensing measurements, acetone and diethyl ether were employed as target gases to investigate cataluminescence (CTL) sensing properties of the CdO nanostructure. The results show that the as-fabricated CdO nanostructure exhibited outstanding CTL properties such as stable intensity, high signal/noise values, short response and recovery time. The limit of detection of acetone and diethyl ether was ca. 6.5 ppm and 6.7 ppm, respectively, which was below the standard permitted concentrations. Additionally, a principal components analysis method was used to investigate the recognizable ability of the CTL sensor, and it was found that acetone and diethyl ether can be distinguished clearly. The performance of the bio-inspired CdO nanostructure-based sensor system suggested the promising application of the CdO nanostructure as a novel highly efficient CTL sensing material. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: SiO2 capped Fe3O4 microspheres with a diameter of about 500nm, were synthesized by the hydrolyzation of tetraethyl orthosilicate (TEOS). The scanning electronic microscopy (SEM), transmission electron microscopy (TEM) and energy dispersive spectroscopy (EDS) were employed to characterize the as-prepared samples. Ether, a common volatile organic compound, was selected as a model to investigate the cataluminescence sensing properties of the SiO2/Fe3O4 microspheres in the current work. Results indicated that the as-prepared SiO2/Fe3O4 microspheres exhibited outstanding cataluminescence (CTL) properties such as the stable intensity, high signal/noise values, short response and recovery time. After optimization, the ether sensor system could cover a linear detection range of 10–3000ppm (R =0.9967, n =7) and the detection limit of about 6.7ppm (S/N=3), which was below the standard permitted concentrations. Furthermore, the ether sensor system showed outstanding selectivity to ether compared with other eleven kinds of common volatile organic compounds (VOCs). The performance of the SiO2/Fe3O4 microspheres based ether sensor system suggested the promising application of the SiO2/Fe3O4 nanomaterials as a novel and highly efficient CTL sensing material. [Copyright &y& Elsevier]

Enzymes that degrade fungal cell walls and the resulting oligosaccharides are promising weapons to combat plant fungal disease. In this study, we identified a novel endo-chitosanase, Aq CoA, from Aquabacterium sp. A7-Y. The enzyme showed a specific activity of 18 U/mg toward 95% deacetylated chitosan at pH 5.0 and 40 °C. Aq CoA also showed activity toward sodium carboxymethylcellulose, indicating substrate promiscuity. Aq CoA hydrolyzed chitosan into chitooligosaccharides (CoA-COSs) with degrees of polymerization (DPs) of 3-5 but showed no activity toward CoA-COSs with DPs <6, indicating an endo-type activity. At 2.5 μg/mL, Aq CoA inhibited appressorium formation of Magnaporthe oryzae ; the produced CoA-COSs also inhibited the growth of M. oryzae and Fusarium oxysporum . Furthermore, CoA-COSs acted as immune elicitors in rice by inducing the reactive oxygen species burst and the expression of defense genes. These results demonstrated that Aq CoA and its resulting CoA-COSs might be effective tools for protecting plants against pathogenic fungi.

A three-dimensional hierarchical CdO nanostructure with a novel bio-inspired morphology is reported. The field emission scanning electronic microscopy, transmission electron microscopy and X-ray diffractometer were employed to characterize the as-prepared samples. In gas-sensing measurements, acetone and diethyl ether were employed as target gases to investigate cataluminescence (CTL) sensing properties of the CdO nanostructure. The results show that the as-fabricated CdO nanostructure exhibited outstanding CTL properties such as stable intensity, high signal/noise values, short response and recovery time. The limit of detection of acetone and diethyl ether was ca. 6.5 ppm and 6.7 ppm, respectively, which was below the standard permitted concentrations. Additionally, a principal components analysis method was used to investigate the recognizable ability of the CTL sensor, and it was found that acetone and diethyl ether can be distinguished clearly. The performance of the bio-inspired CdO nanostructure-based sensor system suggested the promising application of the CdO nanostructure as a novel highly efficient CTL sensing material., (Copyright © 2014 John Wiley & Sons, Ltd.)