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Sericulture has a pivotal position in the agricultural production of China. The number of silkworms is one of the key indicators in the process of silkworm breeding, which is crucial to the feeding and healthy growth of silkworms. Accurate detection of the number of silkworms can ensure the quality of sericulture products. However, manual counting has not fully met the needs of large-scale production in recent years. There is a high demand to combine AI technology for silkworm counting. In this study, silkworm detection and counting were studied from the perspective of deep learning. Several challenges were solved in the detection of silkworms. Since the silkworms belonged to the small targets, it was difficult to detect the fewer features; When the number of silkworms was high, serious masking occurred to cause interference in the detection of densely distributed silkworms; It was difficult to ensure that the clear images of silkworms were captured from the actual production. The blur images were easy to deteriorate the accuracy of detection. Two tasks were covered as follows. 1) The image dataset of silkworms was constructed; 2) MAM-YOLO, a silkworm detection and counting model was proposed using multi-dimensional perception. The pictures of silkworms were first taken from the Guangxi Sericulture Technology Promotion Station. These pictures of silkworms were preliminarily processed. The polygon annotation was then selected, according to the body and distribution of silkworms. The pictures of silkworms were annotated with the open-source tool (Labelme). Finally, the dataset was expanded using image transformation and noise addition. The silkworm picture dataset was obtained for the subsequent training, verification, and testing. An improved YOLOv8 (MAM-YOLO) was proposed to detect the silkworm, in order to realize the accurate detection and counting. A silkworm detection and counting model was proposed using space-to-depth transformation and image deblurring. The fine-grained detection performance of blurred images was improved in the MAMYOLO model. The space-to-depth transformed convolution was utilized instead of stride convolution for the pooling layer in the feature extraction. The fine-grained features were extracted from the images, in order to further improve the detection of the low-resolution and small targets. In addition, the impact of blurred images on the detection performance was reduced by introducing a deblurring module into the model. RCS-OSA module was added to the YOLOv8 model. The network feature extraction was improved to integrate the information of different sizes in the receptive fields. The detection head of the original network was improved into the multi-dimensional sensing head. The three-dimensional features of size, space, and channel were aggregated to enhance the detection of silkworm targets. The loss function was replaced with the EIOU loss function. The regression accuracy of the detection frame was improved to reduce the object occlusion. Experimental results show that the MAM-YOLO more accurately detected the silkworm targets. The detection speed of mAP@0.5:0.95 reached 74.8% and 22.2 frames per second, indicating the better functions of silkworm detection and counting with high precision and high rate. The detection requirements were fully met in the actual work of the Guangxi sericulture station. The results show that the silkworm detection and counting system of the technical indicators met the application requirements. The finding can provide the detection services for the actual production work of the silkworm breeding station. In the end, a generalization experiment was conducted on the Global Wheat Challenge 2021 dataset. It was found that the MAM-YOLO was also achieved in the better detection performance of wheat ears, particularly on the small targets similar to dense occlusion.Sericulture has a pivotal position in China's agricultural production. The number of silkworms is a key indicator in the process of silkworm breeding, which is crucial to the feeding and healthy growth of silkworms, and accurate detection of the number of silkworms can ensure the quality of sericulture products. The traditional way of counting silkworms manually is inefficient, and it needs to be combined with technology to intelligently complete this kind of heavy work. In this thesis, from the perspective of deep learning, a silkworm detection and counting method is studied, and several problems in silkworm detection are solved: silkworms belong to small targets, which have fewer features so that they are not easy to detect; silkworms are densely distributed, and when the number of silkworms is high, serious masking phenomenon occurs which causes interference in the detection; it is difficult to ensure that the images of silkworms obtained in the actual production are clear, and blur images will make the detection affected. The main research content of this thesis is as follows: This chapter completed two main tasks: 1) Completed the production of silkworm image data set; 2) MAM-YOLO, a silkworm detection and counting model based on multi-dimensional perception, was proposed.In this paper, pictures of silkworms were taken by workers from Guangxi Sericulture Technology Promotion Station. First, these pictures of silkworms were preliminically processed. Then, according to the characteristics of the body and distribution of silkworms, the polygon annotation method was selected, and the pictures of silkworms were annotated with the open-source tool Labelme. Finally, the data set is expanded by means of image transformation and noise addition, and finally the silkworm picture data set is obtained for subsequent training, verification and testing.This chapter proposes an improved silkworm detection method based on YOLOv8, MAM-YOLO, which realizes accurate detection and counting of silkworm. Secondly, aiming at the problems of loss of fine-grained information and poor detection performance of blurred images in the MAM-YOLO model, a silkworm detection and counting model based on spaceto-depth transformation and image deblurring is proposed. The model utilizes space-to-depth transformed convolution instead of stride convolution and pooling layer in the feature extraction process, so that fine-grained features in the image can be extracted, which further improves the detection capability for low-resolution and small targets. In addition, the impact of blurred images on the detection performance is reduced by introducing a deblurring module into the model. By adding RCSOSA module to YOLOv8 model, the capability of network feature extraction is improved, and the information of different size receptive fields is integrated. The detection head of the original network is improved into multi-dimensional sensing head, and the three-dimensional characteristics of size, space and channel are aggregated to enhance the detection ability of silkworm targets. By replacing the loss function with EIOU loss function, the regression accuracy of detection frame is improved and the problem caused by object occlusion is reduced. Experiments show that MAM-YOLO can more accurately detect silkworm targets, and the detection speed of mAP@0.5:0.95 reaches 74.8% and 22.2 frames per second, which realizes the functions of silkworm detection and counting with high precision and high rate, and can meet the detection requirements in the actual work of Guangxi sericulture station. results show that the silkworm detection and counting system of the technical indicators have met the application requirements, and can provide detection services for the actual production work of silkworm breeding station.In the end, a generalization experiment was conducted on the Global Wheat Challenge 2021 data set, and it was found that MAM-YOLO also showed a high improvement in the detection performance of wheat ears, proving that the proposed method can generalize to other small targets similar to dense occlusion. [ABSTRACT FROM AUTHOR]

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In order to the evolution of land use benefits in Gansu Province, we selected land use economic benefits, social benefits and ecological benefits as indicators to evaluate the comprehensive land use benefit data of 14 prefecture-level cities in Gansu Province from 2010 to 2018. The TOPSIS model and logistic regression analysis model were used to evaluate and predict the land use benefits in Gansu Province. The results showed that: (1) The comprehensive benefits of land use in Gansu Province first increased and then gradually decreased along with time. (2) The overall comprehensive benefits of land use in Gansu Province showed 500% increase. (3) The increase range of social benefits was the largest, with an increase of 944.44%. However, some cities such as Longnan and Linxia had lower ecological benefits of land use than other cities. This study took 9 years of data to evaluate the comprehensive benefits of land use in Gansu Province, which could provide reference and theoretical support for the efficient use and optimal allocation of land in Gansu Province for the government. [ABSTRACT FROM AUTHOR]

By analyzing the case of large-scale residents’ credit data leakage in the United States in 2012,the authors find that the external invasion of illegal hackers, the lack of professional ethics of employees, and the lack of awareness of citizen information protection are the causes of credit data leakage. Credit data leakage will infringe personal privacy, affect personal property safety, destroy the peace of life and make citizens lose confidence in the credit market. The imperfect legal system of data protection, the imperfect supervision system and the lack of protection awareness and ability of the information subject are the main problems in China’s credit market. Therefore, we should improve the relevant laws and regulations, strengthen the supervision of citizens’ information security, and improve citizens’ personal credit information protection and rights awareness. [ABSTRACT FROM AUTHOR]

The basic principle of Blockchain technology operation and the types of supply chain finance business models are described. The advantages of Blockchain technology in supply chain finance are analyzed. The problems existing in the supply chain finance business model based on Blockchain technology are studied. Accordingly, some suggestions such as improving the data protection in Blockchain, improving the docking efficiency between banks and enterprises, using intelligent contracts to identify the authenticity of transactions between enterprises, introducing talents and strengthening technological innovation, are put forward. [ABSTRACT FROM AUTHOR]

Though being regarded as the best credit information service agencies in China,the management of the eight pilot individual credit reporting enterprises established in 2015 does not meet the standards of the Peoples’ Bank of China. In this study, the external factors affecting the development of the credit agencies in China were analyzed. By referring to the lessons from the development of the institutions in American, some suggestions are put forward. [ABSTRACT FROM AUTHOR]

Under the new economic normal, the industrial structure of Liaoning province is facing severe challenges under the background of supply-side reform. It is key issue to advance inclusive finance in Liaoning province with digital technology. In this paper, the necessity of developing inclusive finance in Liaoning province is analyzed. Some measures for the province to develop digital inclusive finance are also proposed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Tianjin Polytechnic University / Tianjin Gongye Daxue Xuebao is the property of Journal of Tianjin Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)