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Substitution of ramie (Boehmeria nivea) for alfalfa in improving the carcass and meat quality of Liuyang Black goats

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Animal Nutrition 2021年第3期7卷 688-694页

作者： Shaoxun Tang Yao He Peihua Zhang Jinhe Kang Qiongxian Yan Xuefeng Han Zhiliang Tan Hongrong Wang Duanqin Wu Lihuai Yu Min Wang Chuanshe Zhou Jinzhen Jiao CAS Key Laboratory for Agro-Ecological Processes in Subtropical Region National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry ProductionHunan Provincial Key Laboratory of Animal Nutritional Physiology and Metabolic ProcessSouth-Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of AgricultureInstitute of Subtropical AgricultureThe Chinese Academy of SciencesChangshaHunan410125China College of Animal Science and Technology Yangzhou UniversityYangzhouJiangsu225009China Institute of Bast Fiber Crops Chinese Academy of Agricultural SciencesChangshaHunan410205China College of Animal Science and Technology Hunan Agricultural UniversityChangshaHunan410128China

Ramie(Boehmeria nivea)is noted for the production of a large biomass that has a high protein content and is rich in *** may thus serve as a high-quality forage material to replace alfalfa and improve the meat quality of farmed *** this study,we evaluated the carcass characteristics and meat quality of goats when 0,35%,75%,and 100%of dietary alfalfa was replaced with *** protein content(linear,P<0.0001)and key muscle color values at 24 h after slaughter decreased with increasing ramie *** content of most individual amino acids,non-essential amino acids(NEAA),total amino acids(TAA),branched chain amino acids(BCAA),functional amino acids(FAA),and flavor amino acids(DAA)decreased(P<0.05)with increasing dietary *** diet in which 35%of alfalfa was replaced with ramie yielded meat with the highest amino acid content,whereas the fatty acid profile was unaffected by the inclusion of *** results indicate that ramie could be used as a potential dietary forage resource for goats,and that substituting 35%of alfalfa with ramie,which is equivalent to 126 g/kg DM content,would be optimal in terms of goat meat quality.

关键词： Goat Meat quality Ramie Alfalfa Carcass

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Task Partition-Based Intelligent Offloading for Cache-Assisted Cloud-Edge Cooperation Networks

Task Partition-Based Intelligent Offloading for Cache-Assist...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Chao Fang Haizhen Luo Xiaoping Yang Haofei Xie Fangqing Tan Shu-Ming Tseng Mianxiong Dong Faculty of Information Technology Beijing University of Technology Beijing China Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin China Key Laboratory of Industrial Internet of Things & Networked Control Chongqing University of Posts and Telecommunications Chongqing China Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan

To satisfy the differentiated service requirements of delay-sensitive and computing-intensive tasks, it is urgent to efficiently allocate limited network resources to improve content distribution in cloud-edge environments. In this paper, we proposes a task partition-based intelligent offloading scheme to optimize resource allocation in cache-assisted cloud-edge cooperation environments. Specifically, we formulate the task partition-based optimal computation offloading problem as a latency minimization model in the cache-aided cloud-edge collaboration system. A new deep reinforcement learning (DRL) algorithm is designed to make optimal subtask offloading and resource allocation decisions based on current network state information, improving resource utilization and network delay. Simulation results demonstrate that the proposed model achieves lower-latency content delivery than the existing popular models in cache-enabled cloud-edge cooperation networks, and fast converges.

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Multi-Agent Power and Resource Allocation for D2D Communications: A Deep Reinforcement Learning Approach

Multi-Agent Power and Resource Allocation for D2D Communicat...

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IEEE Conference on Vehicular technology (VTC)

作者： Honglin Xiang Jingyi Peng Zhen Gao Lingjie Li Yang Yang School of Artificial Intelligence Beijing University of Posts & Telecommunications Beijing China China Industrial Control Systems Cyber Emergency Response Team Beijing China National Mobile Communications Research Laboratory Southeast University Nanjing China Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665454698

The explosion in the number of smartphones and wearable devices brings the challenge of high achievable rate (AR) requirement, and D2D communications become the critical technology to solve this challenge. However, the co-channel interference caused by spectrum reusing and low delay requirement restrict D2D communications performance improvements. In this paper, we consider the cases of no time delay constraint and time delay constraint respectively, and design a joint power control and resource allocation scheme based on deep reinforcement learning (DRL) to maximize the AR of cellular users (CUEs) and D2D users (DUEs). Specifically, D2D pairs are considered multiple agents for reusing CUE spectrum, each agent can independently select spectrum resources and power without any prior information to ease interference. Furthermore, a double deep Q-network with priority sampling (Pr-DDQN) distributed algorithm is proposed, which helps agents to learn more dominant features during experience replay. Simulation results indicate that Pr-DDQN algorithm can obtain a higher AR than the present DRL algorithms. In particular, the probability of selecting low power of agents enlarges as the increase of the remaining transmission time, which demonstrates that the agents can successfully learn and perceive the implicit relationship of time delay constraint.

关键词： Deep learning Delay effects Simulation Wearable computers Power control Reinforcement learning Device-to-device communication

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EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments

EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Sw...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Xin Zhou Jiangchao Zhu Hongyu Zhou Chao Xu Fei Gao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China National Engineering Research Center for Industrial Automation (Ningbo Institute) Ningbo China Huzhou Institute Zhejiang University Huzhou China

This paper presents a decentralized and asynchronous systematic solution for multi-robot autonomous navigation in unknown obstacle-rich scenes using merely onboard resources. The planning system is formulated under gradient-based local planning framework, where collision avoidance is achieved by formulating the collision risk as a penalty of a nonlinear optimization problem. In order to improve robustness and escape local minima, we incorporate a lightweight topological trajectory generation method. Then agents generate safe, smooth, and dynamically feasible trajectories in only several milliseconds using an unreliable trajectory sharing network. Relative localization drift among agents is corrected by using agent detection in depth images. Our method is demonstrated in both simulation and real-world experiments. The source code is released for the reference of the community.

关键词： Location awareness Systematics Codes Navigation Conferences Robustness Trajectory

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EVA-Planner: Environmental Adaptive Quadrotor Planning

EVA-Planner: Environmental Adaptive Quadrotor Planning

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Lun Quan Zhiwei Zhang Xingguang Zhong Chao Xu Fei Gao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China National Engineering Research Center for Industrial Automation (Ningbo Institute) Ningbo China

The quadrotor is popularly used in challenging environments due to its superior agility and flexibility. In these scenarios, trajectory planning plays a vital role in generating safe motions to avoid obstacles while ensuring flight smoothness. Although many works on quadrotor planning have been proposed, a research gap exists in incorporating self-adaptation into a planning framework to enable a drone to automatically fly slower in denser environments and increase its speed in a safer area. In this paper, we propose an environmental adaptive planner to adjust the flight aggressiveness effectively based on the obstacle distribution and quadrotor state. Firstly, we design an environmental adaptive safety aware method to assign the priority of the surrounding obstacles according to the environmental risk level and instantaneous motion tendency. Then, we apply it into a multi-layered model predictive contouring control (Multi-MPCC) framework to generate adaptive, safe, and dynamical feasible local trajectories. Extensive simulations and real-world experiments verify the efficiency and robustness of our planning framework. Benchmark comparison also shows superior performances of our method with another advanced environmental adaptive planning algorithm. Moreover, we release our planning framework as open-source ros-packages¹ .

关键词： Adaptation models Trajectory planning Heuristic algorithms Predictive models Prediction algorithms Robustness Planning

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VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation

VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Acc...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Ziming Ding Tiankai Yang Kunyi Zhang Chao Xu Fei Gao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China National Engineering Research Center for Industrial Automation Ningbo Institute Ningbo China

Recently, quadrotors are gaining significant attention in aerial transportation and delivery. In these scenarios, an accurate estimation of the external force is as essential as the six degree-of-freedom (DoF) pose since it is of vital importance for planning and control of the vehicle. To this end, we propose a tightly-coupled Visual-Inertial-Dynamics (VID) system that simultaneously estimates the external force applied to the quadrotor along with the six DoF pose. Our method builds on the state-of-the-art optimization-based Visual-Inertial system [1], with a novel deduction of the dynamics and external force factor extended from VIMO [2]. Utilizing the proposed dynamics and external force factor, our estimator robustly and accurately estimates the external force even when it varies widely. Moreover, since we explicitly consider the influence of the external force, when compared with VIMO [2] and VINS-Mono [1], our method shows comparable and superior pose accuracy, even when the external force ranges from neglectable to significant. The robustness and effectiveness of the proposed method are validated by extensive real-world experiments and application scenario simulation. We will release an open-source package of this method along with datasets with ground truth force measurements for the reference of the community.

关键词： Force measurement Torque Force Dynamics Pose estimation Estimation Transportation

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Mechanistic Insight into Electron Orientation by Tailoring Ni–Cu Atom-Pairs for High-Performance Co2 Electroreduction

SSRN

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SSRN 2023年

作者： Shen, Yi Zhang, Haizhong Chen, Baoliang Zhu, Chao Yang, Jingyi Fang, Qile He, Zhiqiao Sun, Tulai Song, Shuang Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province College of Environment Zhejiang University of Technology Hangzhou310032 China Department of Environmental Science Zhejiang University Hangzhou310058 China Advanced Institute of Natural Sciences Beijing Normal University Zhuhai Campus Zhuhai519087 China Center for Electron Microscopy State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology Zhejiang University of Technology Hangzhou310032 China

Herein, a Ni–Cu atom-pair catalyst with well-defined electron orientation has been tailored for electrocatalytic CO2RR. In particular, the tailored Ni/Cu0.38–covalent triazine framework catalyst exhibited extremely high catalytic activity with high faradaic efficiency (99.82% at −1.1 V vs. reversible hydrogen electrode, RHE) and turnover frequency (5116 h−1 at −1.1 V vs. RHE). To the best of our knowledge, this is a diatomic catalyst with the lowest reported metal concentration and maximum efficiency and selectivity. Density functional theory calculations and experimental results further revealed that the adsorption of CO2 was enhanced, while the electron orientation from the Cu site to Ni site reduced the antibonding orbital coupling between Ni and C atoms in CO2, thereby breaking the protonation energy barrier and providing high catalytic efficiency. Moreover, the enrichment of electrons from the electrolyte as well as the accumulation of *H at Cu sites was essential for improving CO selectivity. © 2023, The Authors. All rights reserved.

关键词： Electrocatalysis

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Large Magnetoresistance in Phosphorus-Sulfur Compounds (Tmps4) Based Temperature Regulated Spin-Caloritronic Devices

SSRN

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SSRN 2022年

作者： Wang, Tongtong Liu, Bin Liu, Sheng Zhu, Sicong Lu, Yan Hubei Province Key Laboratory of Systems Science in Metallurgical Process The State Key Laboratory for Refractories and Metallurgy Collaborative Innovation Center for Advanced Steels International Research Institute for Steel Technology University of Science and Technology Wuhan430081 China Key Laboratory of Metallurgical Equipment and Control Technology Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China

Two-dimensional (2D) transition metal phosphorus-sulfur compounds (TMPS4) have attracted great theoretical and experimental attention because of their broadband modulation and high hole mobility that can be used in nanodevices. Therefore, their Curie temperature and thermal properties deserve further study for applications in electronic devices. The electromagnetic properties and spin transport properties of 2D transition metal phosphorus-sulfur compounds are systematically investigated by density function theory (DFT) and DFT combined with the nonequilibrium Green's function method. We find that TMPS4 materials cover almost all spin electronic behaviors, including half-metals, magnetic and nonmagnetic semiconductors, and metals. The simulation results show that the magnetic tunnel junction constructed based on TMPS4has a perfect thermal spin filtering effect and a negative differential resistance effect. Especially, we obtain up to 1010% thermal colossal magnetoresistance and almost 100% spin polarization. Based on these results, it is proved that TMPS4 can be a substrate material for the development of high performance for spin-caloritronic and spintronic devices. © 2022, The Authors. All rights reserved.

关键词： Transition metals

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Two Feature Fusion Network Based on Efficient Deep Residual Shrinkage Spatial-Temporal Graph Convolution for Emotion Recognition from Gaits

Two Feature Fusion Network Based on Efficient Deep Residual ...

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Chinese control and Decision Conference, CCDC

作者： Xiaoqing Liu Sen Zhang Wendong Xiao School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan China Beijing Engineering Research Center of Industrial Spectrum Imaging Beijing China

ISBN: (纸本)9781665478977

Emotion Recognition plays a key role in human social activities. Most current methods choose facial information for emotion recognition. But facial information is easily limited by distance in large scenes. Gait information can break this limitation, but the amount of publicly available data is small. This paper proposes two feature fusion network based on efficient deep residual shrinkage spatial-temporal graph convolution to achieve automatic emotion recognition from gaits in large scenes. Firstly chebyshev polynomial approximation graph convolution is combined with efficient deep residual shrinkage network to obtain efficient deep residual shrinkage spatial-temporal graph convolution network to extract deep features. Then sixteen temporal affective features extracted from gaits are used as input of efficient deep residual shrinkage network to extract new affective features. Finally the deep features is spliced with new affective features to divided emotion into happy,angry,sad and neutral. We performed a ten-flod cross-validation experiment on the E-gait real datasets. The highest recognition accuracy is 0.87, with an average of 0.85. Experimental results show that the proposed model is effective in dealing with emotion recognition promblem from gait in fewer sample situation.

关键词： Emotion recognition Convolution Chebyshev approximation Feature extraction

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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