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3D printed-electrospun PCL/hydroxyapatite/MWCNTs scaffolds for the repair of subchondral bone

Regenerative Biomaterials 2023年第1期10卷 294-305页

作者： Yanyan Cao Lei Sun Zixian Liu Zhizhong Shen Wendan Jia Peiyi Hou Shengbo Sang College of Information Science and Engineering Hebei North UniversityZhangjiakou 075000China Shanxi Key Laboratory of Micro Nano Sensors&Artificial Intelligence Perception College of Information and ComputerTaiyuan University of TechnologyTaiyuan 030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China

Osteochondral defect caused by trauma or osteoarthritis exhibits a major challenge in clinical treatment with limited symptomatic effects at *** regeneration and remodeling of subchondral bone play a positive effect on cartilage regeneration and further promotes the repair of osteochondral *** use of the strengths of each preparation method,the combination of 3D printing and electrospinning is a promising method for designing and constructing multi-scale scaffolds that mimic the complexity and hierarchical structure of subchondral bone at the microscale and nanoscale,*** this study,the 3D printed-electrospun poly(ɛ-caprolactone)/nano-hydroxyapatites/multi-walled carbon nanotubes(PCL/nHA/MWCNTs)scaffolds were successfully constructed by the combination of electrospinning and layer-by-layer 3D *** resulting dual-scale scaffold consisted of a dense layer of disordered nanospun fibers and a porous microscale 3D scaffold layer to support and promote the ingrowth of subchondral ***,the biomimetic PCL/nHA/MWCNTs scaffolds enhanced cell seeding efficiency and allowed for higher cell-cell interactions that supported the adhesion,proliferation,activity,morphology and subsequently improved the osteogenic differentiation of bone marrow mesenchymal stem cells in ***,this study elucidates that the construction of 3D printed-electrospun PCL/nHA/MWCNTs scaffolds provides an alternative strategy for the regeneration of subchondral bone and lays a foundation for subsequent in vivo studies.

关键词： additive manufacturing 3D printing electrospinning subchondral bone

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LBA: Matching Theory Based Latency-Sensitive Binary Offloading in IoT-Fog Networks 16

LBA: Matching Theory Based Latency-Sensitive Binary Offloadi...

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16th International Conference on COMmunication systems and NETworkS, COMSNETS 2024

作者： Soni, Priyanka Deshlahre, Omkar Chand Satpathy, Anurag Addya, Sourav Kanti National Institute of Technology Karnataka Cloud and Smart System Services Lab Department of Computer Science and Engineering India Missouri University of Science and Technology RollaMO United States

ISBN: (纸本)9798350383119

The Internet of Things (IoT) is growing more popular with applications like healthcare services, traffic monitoring, video streaming, smart homes, etc. These applications produce an enormous amount of data, so a realistic option in this instance is to offload computational tasks to their proximity fog nodes (FNs) instead of the remote cloud. However, a negligent offloading strategy may cause anomalous computational traffic load at the FNs, causing congestion that may adversely affect the latency. However, the latency of task flows from IoT devices comprises communications latency at BS and computational latency at FNs. Therefore, designing offloading algorithms to distribute the computational load at FN evenly and efficiently utilize the FN resources is crucial. To solve this problem, we proposed LBA in a fog network with a binary offloading strategy using the matching theory-based approach. We utilize the Analytic Hierarchy Process (AHP) to generate the preference list. Furthermore, the binary offloading technique follows the deferred acceptance algorithm (DAA) to produce a stable assignment, and the complete offloading problem is modeled as a one-to-many matching game. Comprehensive simulations ensure that LBA can accomplish a better-balanced assignment for homogeneous and heterogeneous input concerning all the baseline algorithms. © 2024 IEEE.

关键词： Analytic Hierarchy Process (AHP) Binary offloading Fog computing IoT Load balancing Matching Theory

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C2P-Net: Comprehensive Depth Map to Planar Depth Conversion for Room Layout Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5350-5367页

作者： Zhang, Weidong Zhou, Mengjie Cheng, Jiyu Liu, Ying Zhang, Wei Xi'an University of Posts & Telecommunications School of Communications and Information Engineering China Xi'an Key Laboratory of Image Processing Technology and Applications for Public Security China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China

Room layout estimation seeks to infer the overall spatial configuration of indoor scenes using perspective or panoramic images. As the layout is determined by the dominant indoor planes, this problem inherently requires the reconstruction of these planes. Some studies reconstruct indoor planes from perspective images by learning pixel-level or instance-level plane parameters. However, directly learning these parameters has the problems of susceptibility to occlusions and position dependency. In this paper, we introduce the Comprehensive depth map to Planar depth (C2P) conversion, which reformulates planar depth reconstruction into the prediction of a comprehensive depth map and planar visibility confidence. Based on the parametric representation of planar depth we propose, the C2P conversion is applicable to both panoramic and perspective images. Accordingly, we present an effective framework for room layout estimation that jointly learns the comprehensive depth map and planar visibility confidence. Due to the differentiability of the C2P conversion, our network autonomously learns planar visibility confidence by constraining the estimated plane parameters and reconstructed planar depth map. We further propose a novel approach for 3D layout generation through sequential planar depth map integration. Experimental results demonstrate the superiority of our method across all evaluated panoramic and perspective datasets. © 1979-2012 IEEE.

关键词： Layout Three-dimensional displays Image reconstruction Estimation Cameras Floors Cognition Transforms Training Semantics

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Dynamic Spectrum Anti-Jamming with Distributed Learning and Transfer Learning

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China Communications 2023年第12期20卷 52-65页

作者： Xinyu Zhu Yang Huang Delong Liu Qihui Wu Xiaohu Ge Yuan Liu Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information TechnologyNanjing University of Aeronautics and AstronauticsNanjing 210016China The 723 Institute of CSSC Yangzhou 225001China School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhan 430074China School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China

Physical-layer security issues in wireless systems have attracted great *** this paper,we investigate the spectrum anti-jamming(AJ)problem for data transmissions between *** fast-changing physical-layer jamming attacks in the time/frequency domain,frequency resources have to be configured for devices in advance with unknown jamming patterns(*** time-frequency distribution of the jamming signals)to avoid jamming signals emitted by malicious *** process can be formulated as a Markov decision process and solved by reinforcement learning(RL).Unfortunately,stateof-the-art RL methods may put pressure on the system which has limited computing *** a result,we propose a novel RL,by integrating the asynchronous advantage actor-critic(A3C)approach with the kernel method to learn a flexible frequency pre-configuration ***,in the presence of time-varying jamming patterns,the traditional AJ strategy can not adapt to the dynamic interference *** handle this issue,we design a kernelbased feature transfer learning method to adjust the structure of the policy function *** results reveal that our proposed approach can significantly outperform various baselines,in terms of the average normalized throughput and the convergence speed of policy learning.

关键词： A3C anti-jamming reinforcement learning spectrum transfer learning wireless system

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Stackelberg-Game Computation Offloading Scheme for Parked Vehicle-Assisted VEC and Experiment Analysis

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-12页

作者： Liu, Chunhong Zhao, Mengdi Wang, Huaichen Cheng, Bo Liu, Jialei Yuan, Peiyan Engineering Lab of Intelligence Business & Internet of Things and School of Computer and Information Engineering Henan Normal University Xinxiang China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle and School of Computer Engineering Hubei University of Arts and Science Xiangyang China

Vehicle edge computing (VEC) offers users low-latency and high-reliability services by using computational resources at the network's edge. Nevertheless, because of inadequate infrastructure and limited resources, computation-intensive and delay-sensitive vehicle applications cannot be performed efficiently at the edge. Therefore, several studies have used the idle resources of parked vehicles to assist in computation offloading. In this paper, we propose a parked vehicle-assisted vehicle edge computing architecture considering multi-agent collaboration, including intelligent vehicles and edge servers. Additionally, we propose a framework for a parallel Internet of Vehicles (IoV) utilizing computational experiment. The service provider is assigned the role of owning VEC resources and recruiting parking vehicle resources. The model was constructed by using the resource consumption-service relationship of both offloading parties to ensure service quality. First, a Stackelberg game model was constructed based on the interaction between requesting vehicles and a service provider. The latter was the leader, and the requesting vehicles were the followers. The Nash equilibrium for optimal pricing and offloading allocations was attained and verified, and a distributed gradient-based equilibrium algorithm was designed to solve the Stackelberg game model and obtain the final decision through mutual communication. The method also protects the privacy of participants and respects the willingness of requesting vehicles to offload. Finally, the simulation experiments confirmed that the proposed algorithm can achieve game equilibrium. Furthermore, it outperformed state-of-the-art algorithms in improving the service provider's utility. IEEE

关键词： Vehicles

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FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection 42

FedMoS: Taming Client Drift in Federated Learning with Doubl...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Wang, Xiong Chen, Yuxin Li, Yuqing Liao, Xiaofei Jin, Hai Li, Bo Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Federated learning (FL) enables massive clients to collaboratively train a global model by aggregating their local updates without disclosing raw data. Communication has become one of the main bottlenecks that prolongs the training process, especially under large model variances due to skewed data distributions. Existing efforts mainly focus on either single momentum-based gradient descent, or random client selection for potential variance reduction, yet both often lead to poor model accuracy and system efficiency. In this paper, we propose FedMoS, a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance. Specifically, FedMoS maintains customized momentum buffers on both server and client sides, which track global and local update directions to alleviate the model discrepancy. Taking momentum results as input, we design an adaptive selection scheme to provide a proper client representation during FL aggregation. By optimally calibrating clients' selection probabilities, we can effectively reduce the sampling variance, while ensuring unbiased aggregation. Through a rigid analysis, we show that FedMoS can attain the theoretically optimal O(T - 2/3) convergence rate. Extensive experiments using real-world datasets further validate the superiority of FedMoS, with 58%-87% communication reduction for achieving the same target performance compared to state-of-the-art techniques. © 2023 IEEE.

关键词： Momentum

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Targeted regeneration and upcycling of spent graphite by defect‐driven tin nucleation

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Carbon Energy 2024年第4期6卷 91-103页

作者： Zhiheng Cheng Zhiling Luo Hao Zhang Wuxing Zhang Wang Gao Yang Zhang Long Qie Yonggang Yao Yunhui Huang Kun Kelvin Fu State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhanChina Key Laboratory of Automobile Materials(Jilin University) Ministry of EducationDepartment of Materials Science and EngineeringJilin UniversityChangchunChina Electronic Materials Research Laboratory(Key Lab of Education Ministry) School of Electronic and Information Engineeringand Instrumental Analysis CenterXi'an Jiaotong UniversityXi'anChina Department of Mechanical Engineering University of DelawareNewarkUSA

The recycling of spent batteries has become increasingly important owing to their wide applications,abundant raw material supply,and sustainable *** with the degraded cathode,spent anode graphite often has a relatively intact structure with few defects after long ***,most spent graphite is simply burned or discarded due to its limited value and inferior performance on using conventional recycling methods that are complex,have low efficiency,and fail in performance ***,we propose a fast,efficient,and“intelligent”strategy to regenerate and upcycle spent graphite based on defect‐driven targeted *** Sn as a nanoscale healant,we used rapid heating(~50 ms)to enable dynamic Sn droplets to automatically nucleate around the surface defects on the graphite upon cooling owing to strong binding to the defects(~5.84 eV/atom),thus simultaneously achieving Sn dispersion and graphite *** a result,the regenerated graphite showed enhanced capacity and cycle stability(458.9 mAh g^(−1) at 0.2 A g^(−1) after 100 cycles),superior to those of commercial *** from the self‐adaption of Sn dispersion,spent graphite with different degrees of defects can be regenerated to similar structures and *** analysis indicates that targeted regeneration and upcycling have significantly lower energy consumption(~99%reduction)and near‐zero CO_(2) emission,and yield much higher profit than hydrometallurgy,which opens a new avenue for direct upcycling of spend graphite in an efficient,green,and profitable manner for sustainable battery manufacture.

关键词： battery recycling spent graphite targeted regeneration upcycling graphite

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A merged machine learning model for seasonal climate prediction in China

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science China Earth sciences 2025年第5期68卷 1469-1484页

作者： Danwei QIAN Yanyan HUANG Huijun WANG State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Nansen-Zhu International Research Centre Institute of Atmospheric Physics Chinese Academy of Sciences

Improving the current level of skill in seasonal climate prediction is urgent for achieving sustainable socioeconomic development, and this is especially true in China where meteorological disasters are experienced frequently. In this study, based upon big climate data and traditional statistical prediction experiences, a merged machine learning model(Y-model) was developed to address this, as well as to further explore unknown potential predictors. In Y-model, empirical orthogonal function analysis was firstly applied to reduce the data dimensionality of the target predictand(temperature and precipitation in the four seasons over China). Image recognition techniques were used to automatically identify possible predictors from the big climate data. These predictors, associated with significant circulation anomalies, were recombined into a large ensemble according to different threshold settings for five factors determining the statistical forecast skill. Facebook Prophet was chosen to conduct the independent hindcasts for each season's climate at a lead time of two months. During 2011–2022, the seasonal climate in China was skillfully predicted by Y-model, with an averaged pattern correlation coefficient skill of 0.60 for temperature and 0.24 for precipitation, outperforming CFSv2. Potential predictor analysis for recent extreme events suggested that prior signals from the Indian Ocean and the stratosphere were important for determining the super Mei-yu in 2020, while the prior sea surface temperature over the western Pacific and the soil temperature over West Asia may have contributed to the extreme high temperatures in 2022. Our study provides new insights for seasonal climate prediction in China.

关键词： Machine learning Seasonal climate prediction China’s climate Image recognition techniques Large ensemble

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Vector Bending Sensor Based on Tapered Few-Mode Multi-Core Fiber Enabled by Power Monitoring

Vector Bending Sensor Based on Tapered Few-Mode Multi-Core F...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Liu, Zhuyixiao Jiang, Zijian Wu, Qixuan Zhang, Fengming Du, Haoze Yao, Yucheng Zhao, Luming Tang, Ming National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781957171395

We proposed a vector bending sensor based on tapered few-mode multi-core fiber. The sensor could accomplish the recognition of direction and curvature through only power monitoring. The theoretical curvature sensitivity is higher than 0.14/m-1 within the interval of 2.5 m-1 to 10 m-1 © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Coupled mode theory Curvature Fabry Perot interferometers Few mode fibers Fiber optic sensors Multicore fibers

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Distributed Curvature Sensing using BOCDA with Polarization-maintaining 1+8 Multi-Core Optical Fiber

Distributed Curvature Sensing using BOCDA with Polarization-...

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CLEO: Applications and technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Du, Haoze Chen, Shuyan He, Huan Hua, Xuchen Liu, Zhuyixiao Zhao, Zhiyong Tang, Ming National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

We propose a BOCDA curvature sensor based on polarization-maintaining 1+8 multicore fiber. Thanks to the unique fiber geometry, excellent fiber self-twisting resistance is obtained, which ensures better curvature meas... 详细信息

ISBN: (纸本)9781957171395

关键词： Polarization-maintaining fiber

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