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Rumen metagenome reveals the mechanism of mitigation methane emissions by unsaturated fatty acid while maintaining the performance of dairy cows

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Animal Nutrition 2024年第3期18卷 296-308页

作者： Zhantao Yang Yuhui Zheng Siyuan Liu Tian Xie Qianqian Wang Zhonghan Wang Shengli Li Wei Wang State Key Laboratory of Animal Nutrition and Feeding Beijing Engineering Technology Research Center of Raw Milk Quality and Safety ControlCollege of Animal Science and TechnologyChina Agricultural UniversityBeijing 100193China

Dietary fat content can reduce the methane production of dairy cows;however,the relevance fatty acid(FA)composition has towards this inhibitory effect is ***,in-depth studies elucidating the effects of unsaturated fatty acids(UFA)on rumen function and the mechanism of reducing methane(CH_(4))production are *** study exposed 10 Holstein cows with the same parity,similar milk yield to two total mixed rations:low unsaturated FA(LUFA)and high unsaturated FA(HUFA)with similar fat *** LUFA group mainly added fat powder(C16:0>90%),and the HUFA group mainly replaced fat powder with extruded *** experiment lasted 26 d,the last 5 d of which,gas exchange in respiratory chambers was conducted to measure gas *** found that an increase in the UFA in diet did not affect milk production(P>0.05)and could align the profile of milk FAs more closely with modern human nutritional ***,we found that increasing the UFA content in the diet lead to a decrease in the abundance of Methanobrevibacter in the rumen(|linear discriminant analysis[LDA]score|>2 and P 2 and P<0.05),which ultimately decreased CH4 production(P<0.05).Our results illustrated the mechanism involving decreased CH4 production when fed a UFA diet in dairy *** believe that our study provides new evidence to explore CH4 emission reduction measures for dairy cows.

关键词： Methane emission Rumen metagenome Dairy cow Unsaturated fatty acid

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Microstructure Transformation of Undercooled Cu-Ni-Co Alloy and Refinement Mechanism under High Undercooling

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Journal of Wuhan University of technology(Materials Science) 2023年第2期38卷 440-444页

作者： AN Hongen Willey Liew Yun Hsien Nancy Julius Siambun Bih-Li Chuab YANG Mengmeng YAN Wei LI Hongtao Faculty of Engineering University Malaysia SabahKota Kinabalu88400Malaysia College of Mechanical and Electronic Engineering Huanghe Jiaotong UniversityJiaozuo454950China Henan Engineering Technology Research Center on Intelligent Manufacturing Technology and Equipment Jiaozuo454950China

The molten Cu60Ni35Co5 ternary alloy was undercooled by fluxing method and solidified by natural cooling *** microstructure of all undercooled samples were *** is found that the rapidly solidified Cu60Ni35Co5 alloy has undergone two grain refinement phenomena within the undercooling range,and its microstructure and morphology change with the undercooling through the transformation process of“coarse dendrite-equiaxed dendrite-oriented fine dendrite-equiaxed crystal”.The refined structure with the maximum undercooling?T of 253 K was selected for electron backscatter diffraction(EBSD)and transmission electron microscopy(TEM)*** results show that the refined structure contains a high proportion of large angle grain boundaries(LAGBs),a large number of randomly oriented grains,and high proportion of twins,while TEM results show that the dislocation density in most areas is relatively *** above characteristics confirm that the solidification structure is refined due to recrystallization behavior under high undercooling.

关键词： microstructure grain refinement recrystallization

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Detection of skin defects on loquat using hyperspectral imaging combining both band radio and improved three-phase level set segmentation method

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Food Quality and Safety 2023年第1期7卷 100-111页

作者： Zhaoyang Han Bin Li Qiu Wang Zhaoxiang Sun Yande Liu School of Mechatronics&Vehicle Engineering East China Jiaotong UniversityNational and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and EquipmentNanchangChina

Background and objectives Skin defects are one of the primary problems that occur in post-harvest grading and processing of *** defects lead to the loquat being easily destroyed during transportation and storage,which causes the risk of other loquats being infected,affecting the selling *** and Methods In this paper,a method combining band radio image with an improved three-phase level set segmentation algorithm(ITPLSSM)is proposed to achieve high accuracy,rapid,and non-destructive detection of skin defects of *** component analysis(PCA)was used to find the characteristic wavelength and PC images to distinguish four types of skin *** best band ratio image based on characteristic wavelength was *** The band ratio image(Q782/944)based on PC2 image is the best segmented *** on pseudo-color image enhancement,morphological processing,and local clustering criteria,the band ratio image(Q782/944)has better contrast between defective and normal areas in ***,the ITPLSSM was used to segment the processing band ratio image(Q782/944),with an accuracy of 95.28%.Conclusions The proposed ITPLSSM method is effective in distinguishing four types of skin ***,it also effectively segments images with intensity inhomogeneities.

关键词： Loquat skin defects hyperspectral imaging multispectral images band ratio improved three-phase level set segmentation.

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FA-MSVNet: multi-scale and multi-view feature aggregation methods for stereo 3D reconstruction

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Multimedia Tools and Applications 2024年 1-23页

作者： Li, Yao Zhou, Yong Zhao, Jiaqi Du, Wen-Liang Yao, Rui School of Computer Science and Technology China University of Mining and Technology Jiangsu Xuzhou221116 China Engineering Research Center of Mine Digitization of the Ministry of Education of the People’s Republic of China Jiangsu Xuzhou221116 China Disaster Intelligent Prevention and Control and Emergency Rescue Innovation Research Center Jiangsu Xuzhou221116 China

Stereo 3D reconstruction is pivotal in computer vision, requiring effective fusion of local detail and global semantic features. This paper introduces a novel multi-scale, multi-view cascading network that enhances both accuracy and completeness in 3D reconstruction. In this paper, we addresse the critical challenge of fusing local detail and global semantic features across multiple views. Firstly, the Multi-Scale Feature Aggregation Module (SFAM) is proposed which integrates features at various scales within each view to capture fine-grained details and improve point cloud resolution. Secondly, the Feature Enhancement Module (FEM) is designed to enhance features representation by aggregating spatial context, improving point cloud boundary integrity and reducing occlusion artifacts. Thirdly, the Multi-View Feature Aggregation Module (VFAM) utilizes rectified linear attention to merge global contextual information across views, ensuring more coherent and semantically accurate reconstructions. Extensive experiments on three benchmark datasets resulted in experimental improvements of 3.1%, 8.0%, and 3.1%, respectively, demonstrating that our approach achieves competitive results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： 3D reconstruction

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Multimodal Interaction Fusion Network for Cloth-Changing Person Re-Identification 22

Multimodal Interaction Fusion Network for Cloth-Changing Per...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Yuan, Caihong Xu, Yuanchen Guan, Zhijie Chen, Xiaopan Zhu, Xiaoke Liang, Wenjuan Henan University School of Computer and Information Engineering Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan Province Spatial Information Processing Engineering Technology Research Center Kaifeng China

ISBN: (纸本)9798331509712

Cloth-Changing Person Re-Identification (CC-ReID) refers to the technology that can identify the same individual in surveillance scenarios, even after changing their clothes. The core of CC-ReID is to exact cloth-irrelevant identity features. Existing methods usually extract them from body contours, skeletons, human faces, etc. However, information extracted from a single modality is often insufficient. Therefore, we propose a novel Multimodal Interaction Fusion (MIF) framework that could facilitate the deep integration of multimodal information, thereby enhancing the generalization ability of CC-ReID. Specifically, we design a triple-interaction collaborative module that strategically integrates features captured from body posture, original RGB images, and clothes-erased images, maximizing the advantages of each modality and overcoming their limitations. Additionally, we develop an Adapt-Blend Pool (ABP) module that effectively improves pooling methods to enhance the model's ability to process images with complex backgrounds and varied features. Following training, our framework effectively harnesses a variety of modal inputs to capture identity representations independent of clothing accurately. Extensive experimental results on PRCC, LTCC, and VC-Clothes datasets demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Image enhancement

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Detection and Characterization of Defects in Additive Manufacturing by Polarization-Based Imaging System

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Chinese Journal of Mechanical engineering 2023年第5期36卷 120-140页

作者： Xing Peng Lingbao Kong Shanghai Engineering Research Center of Ultra-precision Optical Manufacturing School of Information Science and TechnologyFudan UniversityShanghai 200438China College of Intelligent Science and Technology National University of Defense TechnologyChangsha 410003HunanChina

Additive manufacturing (AM) technology such as selective laser melting (SLM) often produces a high refection phenomenon that makes defect detection and information extraction challenging. Meanwhile, it is essential to establish a characterization method for defect analysis to provide sufcient information for process diagnosis and optimization. However, there is still a lack of universal standards for the characterization of defects in SLM parts. In this study, a polarization-based imaging system was proposed, and a set of characterization parameters for SLM defects was established. The contrast, defect contour information, and high refection suppression efect of the SLM part defects were analyzed. Comparative analysis was conducted on defect characterization parameters, including geometric and texture parameters. The experimental results demonstrated the efects of the polarization imaging system and verifed the feasibility of the defect feature extraction and characterization method. The research work provides an efective solution for defect detection and helps to establish a universal standard for defect characterization in additive manufacturing.

关键词： Additive manufacturing Selective laser melting High refection Defect characterization Polarizationbased imaging

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A Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

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IEEE Internet of Things Journal 2025年第11期12卷 17791-17806页

作者： Zeng, Hongjian Xiong, Yonghua She, Jinhua Yu, Anjun China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China Tokyo University of Technology School of Engineering Hachioji Tokyo192-0982 Japan Jiangxi Ganyue Expressway Co. Ltd Jiangxi Nanchang330000 China

Sparse Mobile Crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers' mobility and data collection time during subarea selection, as well as the time constraints of the sensing cycle in task assignment. This may affect the task completion timeliness and data quality. To address these issues, we develop a subarea evaluation method based on deep reinforcement learning, considering both the temporal effectiveness of sensing tasks and the importance of subarea selection for data inference. Using the subarea evaluation values derived from this method, we establish an online urban sensing task assignment model which is subject to constraints of sensing cycle time and cost budget. This model aims to find the task assignment result that minimizes data inference error by maximizing the comprehensive utility value. Considering the characteristics of the task assignment model, we propose an evolutionary algorithm named OTA-EA, which is based on an improved genetic algorithm. Its enhanced evolutionary operators can avoid generating infeasible solutions while maintaining robust search and optimization performance. Lastly, we conduct experimental evaluations of these methods on the real-world datasets. The results demonstrate that our subarea evaluation method can significantly reduce the data inference error, and our evolutionary task assignment algorithm can achieve better task assignment results than the baseline algorithms. © 2014 IEEE.

关键词： Genetic algorithms

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Masked hybrid attention with Laplacian query fusion and tripartite sequence matching for medical image segmentation

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Neural Computing and Applications 2025年第8期37卷 5891-5911页

作者： Ekong, Favour Yu, Yongbin Patamia, Rutherford Agbeshi Sarpong, Kwabena Ukwuoma, Chiagoziem C. Wang, Xiangxiang Ukot, Akpanika Robert Cai, Jingye School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan Chengdu610059 China

Medical image segmentation is pivotal in computer-aided diagnosis systems, demanding high precision and contextual understanding. Vision Transformer-based approaches have gained much attention recently due to their excellent performance and ability to capture long-range dependencies in medical images. However, research shows they suffer from inadequate multi-scale feature integration, poor object localization, and inconsistent mask predictions, leading to sub-optimal segmentation performance. This paper addresses these challenges by redefining semantic medical image segmentation through learnable object queries within an enhanced transformer framework with a masked hybrid attention querying mechanism, optimizing multi-scale feature fusion, object localization, and instance-specific segmentation. First, this study presents a novel transformer-based masked hybrid attention mechanism using Laplacian query fusion on learnable query features and incorporating a novel tripartite sequence matching technique as part of the enhanced decoder block to improve the consistency of mask predictions and optimize decoder queries. The designed hybrid multi-head self- and cross-attention mechanisms aim to selectively integrate multi-scale features, ensuring optimal feature combinations for precise segmentation. Secondly, multiple class tokens are incorporated to improve object localization and capture class-specific characteristics within the transformer framework, leveraging the transformer decoders’ ability to learn distinct instance representations. Experimental results and extensive ablation studies demonstrate the effectiveness of the proposed approach on three publicly available datasets, obtaining better segmentation results compared to various state-of-the-art approaches using various evaluation metrics. Specifically, the proposed model achieves a Dice Score of 95.25%, 92.75%, and 85.25% on LUNA, ISIC, and DRIVE datasets, respectively. © The Author(s), under exclusive licence

关键词： Laplace transforms

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Fault Detection Method of Aircraft Sensor Based on Canonical Variate Analysis 15

Fault Detection Method of Aircraft Sensor Based on Canonical...

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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Han, Xiaojia Wang, Ming Cheng, Libo Wang, Xiaobo Jiang, Bingshan Pei, Chao Research Center for Intelligent Equipment Zhejiang Laboratory Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350354010

Aircrafts heavily rely on sensors for effective control. However, sensor faults are bound to occur over time. It is imperative to devise a dependable sensor fault detection technique for aircrafts. This study presents a data-driven strategy for aircraft fault detection using canonical variate analysis (CVA), a multivariate statistical technique. Through the utilization of CVA, the study establishes optimal correlations in aircraft flight data, facilitating accurate and efficient diagnosis of faults. The T2 statistic and squared prediction error (SPE) are put forward as indicators for detecting faults in this method. Compared to the traditional model-based Kalman filter (KF) fault detection method, this approach involves learning from actual data and using the learned models for decision-making, without the need for prior knowledge. The effectiveness of CVA in accurately identifying abrupt and incipient faults in inertial measurement unit (IMU) sensors has been confirmed through comprehensive simulation and experimental verification. This method lays the foundation for active fault tolerance control of aircrafts and improves the overall reliability and safety of aircrafts. © 2024 IEEE.

关键词： Kalman filters

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High thermoelectric properties of Cu_(2)TeeAg_(2)Te composite with Fe addition and non-stoichiometric Te

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Journal of Materiomics 2024年第1期10卷 37-44页

作者： Wenxiu Li Su Dun Bin Xiao Jun Wang Xinba Yaer Chun Ying Ye Li Xiaohuan Wang Huijun Kang School of Materials Science and Engineering Inner Mongolia University of TechnologyHohhot010051China Research and Development Center HUA Cloud Intelligent Healthcare Co.LtdShenzhen518000China College of Sciences Inner Mongolia University of TechnologyHohhot010051China Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province) School of Materials Science and EngineeringDalian University of TechnologyDalian116024China

Cu_(2)Te-based materials are a type of superionic conductor belonging to the class of phonon-liquid elec-tron-crystal materials and have achieved high ZT values by doping and ***,it is easy to form copper vacancies in Cu_(2)Te which leads to an excessive carrier concentration and then results in a low Seebeck ***,controlling copper ion migration and optimizing carrier concen-tration is essential to improve the thermoelectric performance of Cu_(2)*** paper reports high-performance Cu_(2)TeeAg_(2)Te composite with high application value in the low-middle temperature re-gion,which is achieved by fine tuning the carrier concentration using Fe addition and non-stoichiometric Te,as well as controlling the thermal conductivity of composite.A high ZT of~1.2 is obtained in AgCu_(0.97)Fe_(0.03)Te_(0.96)at a low temperature of 573 ***,the phase transition mechanism of Cu_(2)TeeAg_(2)Te and its effect on the thermoelectric transport performance are revealed that go beyond nanostructuring and single-doping,which provides a strong theoretical basis for research and perfor-mance improvement of thermoelectric materials in this system.

关键词： Thermoelectric AgCuTe Fe addition Non-stoichiometric Te

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