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http://dx.doi.org/10.1371/journal.ppat.1005858 | DOI Listing |
Sci Rep
December 2024
College of Economics and Management, Huazhong Agricultural University, Wuhan, 430070, Hubei, China.
In light of the Chinese government's dual carbon goals, achieving cleaner production activities has become a central focus, with regional environmental collaborative governance, including the management of agricultural carbon reduction, emerging as a mainstream approach. This study examines 268 prefecture-level cities in China, measuring the carbon emission efficiency of city agriculture from 2001 to 2022. By integrating social network analysis and a modified gravity model, the study reveals the characteristics of the spatial association network of city agricultural carbon emission efficiency in China.
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December 2024
Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China.
The intelligent identification of wear particles in ferrography is a critical bottleneck that hampers the development and widespread adoption of ferrography technology. To address challenges such as false detection, missed detection of small wear particles, difficulty in distinguishing overlapping and similar abrasions, and handling complex image backgrounds, this paper proposes an algorithm called TCBGY-Net for detecting wear particles in ferrography images. The proposed TCBGY-Net uses YOLOv5s as the backbone network, which is enhanced with several advanced modules to improve detection performance.
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December 2024
Department of Informatics, University of Hamburg, Hamburg, Germany.
Central to the development of universal learning systems is the ability to solve multiple tasks without retraining from scratch when new data arrives. This is crucial because each task requires significant training time. Addressing the problem of continual learning necessitates various methods due to the complexity of the problem space.
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December 2024
Department of Physics, Indian Institute of Technology, Patna, 801106, Bihar, India.
A highly effective method for creating a supramolecular metallogel of Ni(II) ions (NiA-TA) has been developed in our work. This approach uses benzene-1,3,5-tricarboxylic acid as a low molecular weight gelator (LMWG) in DMF solvent. Rheological studies assessed the mechanical properties of the Ni(II)-metallogel, revealing its angular frequency response and thixotropic behaviour.
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December 2024
Department of Computer Science, Birzeit University, P.O. Box 14, Birzeit, West Bank, Palestine.
Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming.
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