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3D bioprinting of a dermal scaffold for full-thickness skin tissue regeneration

Bio-Design and Manufacturing 2025年第1期8卷 68-84,I0039-I0041页

作者： Lu Han Zixian Liu Meng Li Zhizhong Shen Jianming Wang Shengbo Sang Shanxi Key Laboratory of Micro Nano Sensors&Artificial Intelligence Perception College of Electronic Information and Optical EngineeringTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan 030024China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China General Hospital of TISCO Taiyuan 030809China

Dermal substitutes have provided a template for the regeneration and reconstruction of the ***,the healed skin tissue often exhibits abnormal morphology and functionality,including scarring and *** this study,a composite bioink composed of methacrylated gelatin(GelMA)and chitosan oligosaccharide(COS)was proposed for printing a dermal scaffold using digital light processing(DLP)*** GelMA/COS bioink exhibited suitable porosity,swelling,degradation rate,and mechanical *** inclusion of COS demonstrated antibacterial effects against both Gram positive and Gram-negative bacteria,while simultaneously fostering the proliferation of human dermal fibroblasts(HDFs).Additionally,the application of COS could effectively reduce the expression levels of fibrosis-related genes,such as collagen I,collagen III,and fibronectin *** three-dimensionally printed cell-laden dermal scaffold exhibited excellent shape fidelity and high cellular viability,facilitating the extension of HDFs along the scaffold and the simultaneous secretion of extracellular matrix ***,the HDF-laden dermal scaffold transplanted into full-thickness skin defect sites in nude mice was shown to accelerate wound closure,reduce inflammation,and improve wound ***,the DLP-printed dermal scaffold provides an appealing approach for effectively treating full-thickness skin defects in clinical settings.

关键词： 3D printing Dermal scaffold Photo-cross-linking Skin tissue regeneration

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SPAC: Sampling-based Progressive Attribute Compression for Dense Point Clouds

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IEEE Transactions on Image Processing 2025年 34卷 2939-2953页

作者： Mao, Xiaolong Yuan, Hui Guo, Tian Jiang, Shiqi Hamzaoui, Raouf Kwong, Sam Shandong University School of Software Jinan 250100 China Shandong University School of Control Science and Engineering 250061 China Shandong University School of Control Science and Engineering Jinan 250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan 250061 China De Montfort University School of Engineering and Sustainable Development Leicester LE1 9BH United Kingdom Lingnan University School of Data Science Hong Kong

We propose an end-to-end attribute compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module with geometry assistance, and a global hyperprior entropy model. The frequency sampling module uses a Hamming window and the Fast Fourier Transform to extract high-frequency components of the point cloud. The difference between the original point cloud and the sampled point cloud is divided into multiple sub-point clouds. These sub-point clouds are then partitioned using an octree, providing a structured input for feature extraction. The feature extraction module integrates adaptive convolutional layers and uses offset-attention to capture both local and global features. Then, a geometry-assisted attribute feature refinement module is used to refine the extracted attribute features. Finally, a global hyperprior model is introduced for entropy encoding. This model propagates hyperprior parameters from the deepest (base) layer to the other layers, further enhancing the encoding efficiency. At the decoder, a mirrored network is used to progressively restore features and reconstruct the color attribute through transposed convolutional layers. The proposed method encodes base layer information at a low bitrate and progressively adds enhancement layer information to improve reconstruction accuracy. Compared to the best anchor of the latest geometry-based point cloud compression (G-PCC) standard that was proposed by the Moving Picture Experts Group (MPEG), the proposed method can achieve an average Bjøntegaard delta bitrate of -24.58% for the Y component (resp. -21.23% for YUV components) on the MPEG Category Solid dataset and -22.48% for the Y component (resp. -17.19% for YUV components) on the MPEG Category Dense dataset. This is the first instance that a learning-based attribute codec outperforms the G-PCC standard on these datasets by following the common test conditions specified by MPEG. ©

关键词： attribute compression augmented reality immersive communication metaverse point cloud compression point cloud sampling scalable coding

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Distributed target tracking via UWSNs in the presence of multipath interference

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Signal Processing 2025年 236卷

作者： Tang, Miaoyi Liu, Meiqin Zhang, Senlin Zheng, Ronghao Dong, Shanling Liu, Zhunga College of Electrical Engineering Zhejiang University Hangzhou310027 China National Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China National Key Laboratory of Human–Machine Hybrid Augmented Intelligence Xi'an Jiaotong University Xi'an710049 China School of Automation Northwestern Polytechnical University Xi'an710072 China

This article addresses the practical challenge of robust target tracking in a distributed network of underwater acoustic sensors operating under multipath interference. In underwater environments, multipath effects can cause received signals to interfere at the transducer, leading to the degradation of acoustic echoes. Consequently, this degradation introduces autocorrelated biases into the original measurements, thereby reducing tracking accuracy. To tackle this issue, we adopt a state-augmentation approach combined with Gaussian filtering to develop a novel distributed filter for a class of nonlinear time-varying systems. By augmenting both the target states and multipath-induced biases, the proposed method effectively handles the nonlinearities and interdependencies between state variables and multipath autocorrelation during the estimation process. We refer to the proposed method as DUKF-Mp and provide theoretical analysis to investigate the stability by verifying its stochastic boundedness. Numerical simulations validate the proposed method, showing that DUKF-Mp outperforms existing approaches in tracking accuracy and maintains robustness even under high levels of multipath interference. © 2025

关键词： Underwater acoustics

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Edge-End Cooperative Network Resource Allocation With Time Synchronization Awareness for Federated Learning-Based Distributed Energy Regulation

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IEEE Transactions on Smart Grid 2025年第3期16卷 2671-2682页

作者： Wang, Zhao Shu, Yiling Liao, Haijun Zhou, Zhenyu Lv, Lei Zhang, Le Mumtaz, Shahid Guizani, Mohsen North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China China Electric Power Research Institute Beijing100192 China State Grid Sichuan Information and Communication Company Chengdu610095 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi Yongin17104 Korea Republic of Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

Distributed energy regulation is essential for carbon reduction and neutralization of smart park, and relies heavily on distributed energy regulation model training based on federated learning (FL). Network resource allocation is vital to reduction of model training loss and delay. However, time synchronization offset as well as adversarial competition in resource allocation cause low training precision and high training delay. Therefore, we investigate the edge-end cooperative network resource allocation problem for low-delay and high-precision FL, considering the long-term time synchronization offset constraint. We propose a penalty dueling deep Q network (PDDQN)-based edge-end cooperative network resource allocation algorithm with time synchronization awareness, named TARGET, to solve the formulated problem. TARGET achieves joint optimization of adversarial routing selection and device scheduling through the combination of PDDQN and DQN. TARGET provides model training precision guarantee by considering both long-term and short-term constraints of time synchronization offset. Simulation results indicate TARGET achieves superior performance in global loss function, time synchronization offset, and distributed energy regulation. © 2010-2012 IEEE.

关键词： Training Synchronization Logic gates Resource management Regulation Relays Routing Delays Optimization Data models

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Intelligent Construction and Analysis of a Complex Network for Determining Risk-Influencing Factors in Ship Self-Sinking Accidents

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ASCE-ASME Journal of Risk and Uncertainty in Engineering systems, Part A: Civil Engineering 2025年第3期11卷

作者： Ma, Jun Wang, Liguang Xu, Luhui Zhao, Jiong Lin, Rui School of Computer Science Xijing Univ. Xi'An Key Laboratory of Human-Machine Integration and Control Technology for Intelligence Xi'an710123 China School of Computer Science Xijing Univ. Xi'an710123 China Xi'An Senke Construction Engineering Co. Xi'an710043 China

One of the most effective methods for analyzing risk-influencing factors (RIFs) in maritime accidents is the complex network approach. However, there is limited research specifically focused on RIFs in ship self-sinking accidents. Moreover, challenges remain in the accurate ontology construction of RIFs based on the data set, intelligent extraction of RIF propagation paths, RIF importance ranking, and robustness analysis of complex networks. To address these issues, an intelligent method is proposed to construct a ship self-sinking accident RIF complex network (SSARIFCN) based on 204 accident reports, yielding 247 nodes and 1,872 edges derived from 488 RIF propagation paths. This approach significantly improves the objectivity and accuracy of RIF identification while enhancing the intelligence level of the information extraction process. The three-weight LeaderRank algorithm is applied for node importance ranking and network robustness analysis, outperforming eight traditional ranking models and five attack algorithms, enhancing the accuracy of key RIF identification. Based on this algorithm, the top five RIFs for ship self-sinking accidents were identified, concluding that "unfit for maritime navigation"is a frequently overlooked key RIF. The experiment validates the method's effectiveness in identifying key RIFs, controlling RIF propagation, and enhancing accident prevention and navigational safety. © 2025 American Society of Civil Engineers.

关键词： Risk assessment

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Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution

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IEEE Transactions on Multimedia 2025年

作者： Fu, Congrui Yuan, Hui Jiang, Shiqi Zhang, Guanghui Shen, Liquan Hamzaoui, Raouf Center of Experimental Teaching China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China Shandong University School of software Shandong Jinan China Shandong University School of Computer Science and Technology China Shanghai University School of Communication and Information Engineering Shanghai China De Montfort University School of Engineering and Sustainable Development LeicesterLE1 9BH United Kingdom

By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and facilitate more efficient information dissemination. We propose a convolutional neural network (CNN) for space-time video super-resolution, namely GIRNet. Our method combines long-term global information and short-term local information from the video to better extract complete and accurate spatial-temporal information. To generate highly accurate features and thus improve performance, the proposed network integrates a feature-level temporal interpolation module with deformable convolutions and a global spatial-temporal information-based residual convolutional long short-term memory (convLSTM) module. In the feature-level temporal interpolation module, we leverage deformable convolution, which adapts to deformations and scale variations of objects across different scene locations. This provides a more efficient solution than conventional convolution for extracting features from moving objects. Our network effectively uses forward and backward feature information to determine inter-frame offsets, leading to the direct generation of interpolated frame features. In the global spatial-temporal information-based residual convLSTM module, the first convLSTM is used to derive global spatial-temporal information from the input features, and the second convLSTM uses the previously computed global spatial-temporal information feature as its initial cell state. This second convLSTM adopts residual connections to preserve spatial information, thereby enhancing the output features. Experiments on the Vimeo90K dataset show that the proposed method outperforms open source state-of-the-art techniques in peak signal-to-noise-ratio (by 1.45 dB, 1.14 dB, and 0.2 dB over STARnet, TMNet, and 3DAttGAN, respectively), structural similarity index(by 0.027, 0.023, and 0.006 over STARnet, TMNet, and 3DAttGAN, respectiv

关键词： Long short-term memory

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Secure Consensus of Multi-agent systems via Fully Distributed Dynamic Edge-based Event Triggered Under Denial-of-service Attacks

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International Journal of control, Automation and systems 2025年第5期23卷 1337-1355页

作者： Li, Lei Liu, Lei Yan, Dongmei Liang, Chengqing School of Mathematics Hohai University Nanjing 211100 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai 200240 China School of Modern Posts Nanjing University of Posts and Telecommunications Nanjing 210003 China College of Artificial Intelligence and Automation Hohai University Changzhou 213200 China Key Laboratory of Maritime Intelligent Cyberspace Technology Hohai University Ministry of Education Nanjing 210000 China

This paper addresses the issue of ensuring secure consensus in linear multi-agent systems under Denial-of-Service attacks. At the outset, to address the issue of partially observable agent states, a state observer is engineered to offer state estimations for the agents. Using the capabilities of this observer, a novel fully distributed dynamic adaptive edge-based event triggered control mechanism is introduced, and a timing variable is utilized to ensure that the inter-event intervals for all communication links in the network are strictly positive, thereby excluding Zeno behavior. Following that, by leveraging the piecewise Lyapunov function approach, the necessary conditions are meticulously crafted to ensure that the system reaches a secure consensus, thereby establishing a quantitative link between control gains, the duration of attacks, and their frequencies. Ultimately, the effectiveness of the developed event-triggered control strategy is validated through a simulation example. © ICROS, KIEE and Springer 2025.

关键词： Denial-of-service attacks edge-based event triggered control multi-agent systems observer positive minimum interevent time secure consensus

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PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport

arXiv

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arXiv 2025年

作者： Guo, Tian Yuan, Hui Liu, Qi Su, Honglei Hamzaoui, Raouf Kwong, Sam School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Ji’nan250061 China School of Electronic Information Qingdao University Qingdao266071 China School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom Department of Computing and Decision Science Lingnan University Hong Kong

Point cloud compression significantly reduces data volume but sacrifices reconstruction quality, highlighting the need for advanced quality enhancement techniques. Most existing approaches focus primarily on point-to-point fidelity, often neglecting the importance of perceptual quality as interpreted by the human visual system. To address this issue, we propose a generative adversarial network for point cloud quality enhancement (PCE-GAN), grounded in optimal transport theory, with the goal of simultaneously optimizing both data fidelity and perceptual quality. The generator consists of a local feature extraction (LFE) unit, a global spatial correlation (GSC) unit and a feature squeeze unit. The LFE unit uses dynamic graph construction and a graph attention mechanism to efficiently extract local features, placing greater emphasis on points with severe distortion. The GSC unit uses the geometry information of neighboring patches to construct an extended local neighborhood and introduces a transformer-style structure to capture long-range global correlations. The discriminator computes the deviation between the probability distributions of the enhanced point cloud and the original point cloud, guiding the generator to achieve high quality reconstruction. Experimental results show that the proposed method achieves state-of-the-art performance. Specifically, when applying PCE-GAN to the latest geometry-based point cloud compression (G-PCC) test model, it achieves an average BD-rate of -19.2% compared with the PredLift coding configuration and -18.3% compared with the RAHT coding configuration. Subjective comparisons show a significant improvement in texture clarity and color transitions, revealing finer details and more natural color gradients. Copyright © 2025, The Authors. All rights reserved.

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Polytope Volume Monitoring Problem: Formulation and Solution via Parametric Linear Program Based control Barrier Function

arXiv

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arXiv 2025年

作者： Wu, Shizhen Dong, Jinyang Fang, Xu Sun, Ning Fang, Yongchun Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300353 China The Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China

Motivated by the latest research on feasible space monitoring of multiple control barrier functions (CBFs) as well as polytopic collision avoidance, this paper studies the Polytope Volume Monitoring (PVM) problem, whose goal is to design a control law for inputs of nonlinear systems to prevent the volume of some state-dependent polytope from decreasing to zero. Recent studies have explored the idea of applying Chebyshev ball method in optimization theory to solve the case study of PVM;however, the underlying difficulties caused by nonsmoothness have not been addressed. This paper continues the study on this topic, where our main contribution is to establish the relationship between nonsmooth CBF and parametric optimization theory through directional derivatives for the first time, so as to solve PVM problems more conveniently. In detail, inspired by Chebyshev ball approach, a parametric linear program (PLP) based nonsmooth barrier function candidate is established for PVM, and then, sufficient conditions for it to be a nonsmooth CBF are proposed, based on which a quadratic program (QP) based safety filter with guaranteed feasibility is proposed to address PVM problems. Finally, a numerical simulation example is given to show the efficiency of the proposed safety filter. © 2025, CC BY.

关键词： Chebyshev filters

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Safe RL-based Adaptive Cooperative Game control of Wing Deformation and Flight State Tracking for Morphing Hypersonic Vehicles

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Wang, Jun-Wei Wang, Feng Wu, Huai-Ning Liu, Zhou-Yang University of Science and Technology Beijing School of Intelligence Science and Technology Beijing100083 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Hangzhou International Innovation Institute Hangzhou311115 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing100074 China

This study addresses the issue of adaptive cooperative game control (ACGC) design for wing deformation and flight state tracking of morphing hypersonic vehicles (MHVs) with variable wing shapes, input constraints and state constraints. To access a better flight control performance, the wing deformation parameter is extended in this work as an auxiliary control input. An oriented-coordinated-control nonlinear differential equation model is first established to describe the complicated longitudinal flight state tracking error dynamics of the MHV. Then, a control barrier function (CBF) is introduced and incorporated into the value function as a risk penalty term of state security constraint. Based on the differential game (DG) theory, the optimal coordinated control problem for the MHV with input constraints and state constraints is treated as a constrained cooperative DG that is further formulated as a Hamilton-Jacobi-Bellman (HJB) equation with barrier and hyperbolic functions. A safe RL algorithm is proposed to solve the above HJB equation and an online Pareto joint control strategy is achieved thereby. Hereafter the stability analysis of the closed-loop state tracking error system is done by the Lyapunov technique. Meanwhile, both state and input constraints are met during the the parameter learning regulation process. At last, a simulation study is performed to support the proposed safe RL-ACGC algorithm. © 2025 IEEE.

关键词： Hyperbolic functions

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