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http://dx.doi.org/10.1016/j.lana.2024.100766 | DOI Listing |
BMC Genomics
January 2025
College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, 712100, China.
Background: Rex rabbit is famous for its silky and soft fur coat, a characteristic predominantly attributed to its hair follicles. Numerous studies have confirmed the crucial roles of mRNAs and non-coding RNAs (ncRNAs) in regulating key cellular processes such as cell proliferation, differentiation, apoptosis and immunity. However, their involvement in the regulation of the hair cycle in Rex rabbits remains unknown.
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January 2025
Department of Electrical Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha, 61421, KSA, Saudi Arabia.
In wind energy generation systems, ensuring high energy quality is critical but is often compromised due to the limited performance and durability of conventional regulators. To address this, this work presents a novel controller for managing the machine-side inverter of a single-rotor large wind turbine system using an induction machine-type generator. The proposed controller is designed using proportional, integral, and derivative error-based mechanisms, which fundamentally differ from traditional proportional-integral (PI) regulators.
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January 2025
School of Food and Pharmacy, Zhejiang Ocean University, Zhoushan, 316022, People's Republic of China.
Accurate and rapid segmentation of key parts of frozen tuna, along with precise pose estimation, is crucial for automated processing. However, challenges such as size differences and indistinct features of tuna parts, as well as the complexity of determining fish poses in multi-fish scenarios, hinder this process. To address these issues, this paper introduces TunaVision, a vision model based on YOLOv8 designed for automated tuna processing.
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January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
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January 2025
Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada.
Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy.
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