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In order to accurately estimate the distribution of Trachinotus ovatus in marine cages, a novel method was developed using omnidirectional scanning sonar and deep-learning techniques. This method involved differentiating water layers and clustering data layer by layer to achieve precise location estimation. The approach comprised two main components: fish identification and fish clustering. Firstly, omnidirectional scanning sonar was employed to perform spiral detection within marine cages, capturing fish image data. These images were then labeled to construct a training dataset for an enhanced CS-YOLOv8s model. After training, the CS-YOLOv8s model was used to identify and locate fish within the images. Secondly, the cages were divided into water layers with depth intervals of 40 cm. The identification coordinate data for each water layer were clustered using the DBSCAN method to generate location coordinates for the fish in each layer. Finally, the coordinate data from all water layers were consolidated to determine the overall distribution of fish within the cage. This method was shown, through multiple experimental results, to effectively estimate the distribution of Trachinotus ovatus in marine cages, closely matching the distributions detected manually. [ABSTRACT FROM AUTHOR]

Long-term, automated fish detection provides invaluable data for deep-sea aquaculture, which is crucial for safe and efficient seawater aquafarming. In this paper, we used an infrared camera installed on a deep-sea truss-structure net cage to collect fish images, which were subsequently labeled to establish a fish dataset. Comparison experiments with our dataset based on Faster R-CNN as the basic objection detection framework were conducted to explore how different backbone networks and network improvement modules influenced fish detection performances. Furthermore, we also experimented with the effects of different learning rates, feature extraction layers, and data augmentation strategies. Our results showed that Faster R-CNN with the EfficientNetB0 backbone and FPN module was the most competitive fish detection network for our dataset, since it took a significantly shorter detection time while maintaining a high AP50 value of 0.85, compared to the best AP50 value of 0.86 being achieved by the combination of VGG16 with all improvement modules plus data augmentation. Overall, this work has verified the effectiveness of deep learning-based object detection methods and provided insights into subsequent network improvements. [ABSTRACT FROM AUTHOR]

Food safety is closely related to human health. Therefore, named entity recognition technology is used to extract named entities related to food safety, and building a regulatory knowledge graph in the field of food safety can help relevant authorities to regulate food safety issues and mitigate the hazards caused by food safety problems. However, there is no publicly available named entity recognition dataset in the food safety domain. In contrast, the non-standardized Chinese short texts generated from user comments on the web contain rich implicit information that can help identify named entities in specific domains (e.g., food safety domain) where the corpus is scarce. Therefore, in this paper, named entities related to food safety are extracted from these unstandardized texts on the web. However, the existing Chinese named entity identification methods are mainly for standardized texts. Meanwhile, these unstandardized texts have the following problems: (1) their corpus size is small; (2) there are various new and wrong words and noise; (3) and they do not follow strict syntactic rules. These problems make the recognition of Chinese named entities for online texts more challenging. Therefore, this paper proposes the ERNIE-Adv-BiLSTM-Att-CRF model to improve the recognition of food safety domain entities in unstandardized texts. Specifically, adversarial training is added to the model training as a regularization method to alleviate the influence of noise on the model, while self-attention is added to the BiLSTM-CRF model to capture features that significant impact entity classification and improve the accuracy of entity classification. This paper conducts experiments on the public dataset Weibo NER and the self-built food domain dataset Food. The experimental results show that our model achieves a SOTA performance of 72.64% and a good performance of 69.68% for F1 values on the public and self-built datasets, respectively. The validity and reasonableness of our model are verified. In addition, the paper further analyses the impact of various components and settings on the model. The study has practical implications in the field of food safety. [ABSTRACT FROM AUTHOR]

To study the leaching characteristics of a cement-stabilized macadam base with fly ash, a calcium leaching test, using varying cement and fly ash dosages in an ammonium chloride solution, was designed to obtain the rate of calcium ion leaching, porosity, and permeability coefficient of cement-stabilized macadam with leaching time. The results showed that the number of leached calcium ions increased with the cement dosage in the leaching of cement-stabilized macadam. With an increase in the cement dosage, the leaching rate of calcium ions decreased, and the leaching process of the material was delayed. The incorporation of fly ash could effectively slow down the degree of calcium ion leaching. The permeability coefficient increased gradually with the extension of leaching time, and the increase in fly ash content had a more significant effect on the improvement of the permeability coefficient than the increase in cement dosage did. [ABSTRACT FROM AUTHOR]