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3D bioprinting of in vitro porous hepatoma models:establishment,evaluation,and anticancer drug testing

Bio-Design and Manufacturing 2024年第2期7卷 137-152页

作者： Xiaoyuan Wang Zixian Liu Qianqian Duan Boye Zhang Yanyan Cao Zhizhong Shen Meng Li Yanfeng Xi Jianming Wang Shengbo Sang Shanxi Key Laboratory of Micro Nano Sensors and Artificial Intelligence Perception College of Electronic Information and Optical EngineeringTaiyuan University of TechnologyTaiyuan 030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan 030024China Department of Pathology Shanxi Provincial Cancer HospitalTaiyuan 030024China General Hospital of Taiyuan Iron and Steel Company Taiyuan 030809China

Traditional tumor models do not tend to accurately simulate tumor growth in vitro or enable personalized treatment and are particularly unable to discover more beneficial targeted *** address this,this study describes the use of threedimensional(3D)bioprinting technology to construct a 3D model with human hepatocarcinoma SMMC-7721 cells(3DP-7721)by combining gelatin methacrylate(GelMA)and poly(ethylene oxide)(PEO)as two immiscible aqueous phases to form a bioink and innovatively applying fluorescent carbon quantum dots for long-term tracking of *** GelMA(10%,mass fraction)and PEO(1.6%,mass fraction)hydrogel with 3:1 volume ratio offered distinct pore-forming characteristics,satisfactorymechanical properties,and biocompatibility for the creation of the 3DP-7721 *** analysis and quantitative real-time fluorescence polymerase chain reaction(PCR)were used to evaluate the biological properties of the *** with the two-dimensional culture cell model(2D-7721)and the 3D mixed culture cell model(3DM-7721),3DP-7721 significantly improved the proliferation of cells and expression of tumor-related proteins and ***,we evaluated the differences between the three culture models and the effectiveness of antitumor drugs in the three models and discovered that the efficacy of antitumor drugs varied because of significant differences in resistance proteins and genes between the three *** addition,the comparison of tumor formation in the three models found that the cells cultured by the 3DP-7721 model had strong tumorigenicity in nude *** evaluation of the levels of biochemical indicators related to the formation of solid tumors showed that the 3DP-7721 model group exhibited pathological characteristics of malignant tumors,the generated solid tumors were similar to actual tumors,and the deterioration was *** research therefore acts as a foundation for the application of 3DP-7721 models in drug devel

关键词： 3D bioprinting Hepatoma tumor models Drug screening Antitumor drug development

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User-assisted Caching Strategy Based on Reverse Auction and Recommendation in Railway Station 5

User-assisted Caching Strategy Based on Reverse Auction and ...

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5th IEEE International Conference on Electronics Technology, ICET 2022

作者： Sun, Chenghao Wu, Hao Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety School of Electronics and Information Engineering Beijing China Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Frontiers Science Center for Smart High-speed Railway System Beijing China

ISBN: (纸本)9781665485081

In the field of high-speed railway communication, solving the growing demand for multimedia services of passengers has always been a problem. To provide users with stable video services, caching technology has received more and more attention. In this paper, we refine the details when users assist the on-board relay to cache videos in railway stations. Based on this scenario, we improve the existing asynchronous allocation strategy and propose a user-assisted caching strategy named sequential allocation algorithm with recommendation for caching videos. The experimental results show that the algorithm is superior to synchronous and asynchronous allocation algorithm in social revenue and user participation ratio. © 2022 IEEE.

关键词： Multimedia services

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TL-KDNET: A Malicious Traffic Detection Model Integrating Transfer Learning and Knowledge Distillation 21

TL-KDNET: A Malicious Traffic Detection Model Integrating Tr...

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21st International Computer Conference on Wavelet Active Media Technology and information processing, ICCWAMTIP 2024

作者： Yu, Kun Huang, YuanYuan Zhang, Linshan Lv, Jun Lu, Jiazhong School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China Co. Ltd. China

ISBN: (纸本)9798331519254

With the evolution of cyberattacks, traditional methods for malicious traffic detection face increasingly complex threats. To address this challenge, we propose a novel model, TL-KDNet, which integrates transfer learning, domain adaptation, and knowledge distillation techniques to enhance the accuracy and generalization capabilities of malicious traffic detection. In our model, KullbackLeibler (KL) divergence serves as a core module to quantify the differences in probability distributions between the source and target domains, thereby effectively facilitating knowledge transfer from known source domains to unknown target domains. Additionally, by leveraging Maximum Mean Discrepancy (MMD) and a single-sample distillation loss function, we achieve alignment of domain features and transfer of knowledge. Experimental results demonstrate that TL-KDNet improves detection accuracy by 4% on public datasets compared to existing methods. © 2024 IEEE.

关键词： Domain Knowledge

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LOGNET: A Knowledge Distillation-Based Model for Log Anomaly Detection 21

LOGNET: A Knowledge Distillation-Based Model for Log Anomaly...

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21st International Computer Conference on Wavelet Active Media Technology and information processing, ICCWAMTIP 2024

作者： Yu, Kun Huang, Yuanyuan Zhang, Linshan Lv, Jun Lu, Jiazhong School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China Co. Ltd. China

ISBN: (纸本)9798331519254

Due to the diverse nature of attack forms, supervised log anomaly detection models require a large volume of labeled data, making the annotation process time-consuming and labor-intensive. Conversely, unsupervised log anomaly detection models often struggle to effectively capture log features and accurately identify anomalous logs. To address these challenges, this study constructs a collaborative learning framework and proposes the LogNet model. This framework enables mutual learning between two distinct models, enhancing their performance synergistically. In the LogNet model, log data is first converted into directed graphs, leveraging the strengths of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Through collaborative learning, the two models guide each other, boosting their detection capabilities. The effectiveness of LogNet is validated on five public datasets, with experimental results demonstrating that LogNet significantly outperforms traditional models in malicious log detection, achieving up to a 4 % improvement in accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Thermal analysis and package optimization for the multi-wavelength laser bar module

Thermal analysis and package optimization for the multi-wave...

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2022 International Symposium on Semiconductor and Electronic Technology, ISSET 2022

作者： Sun, Wanjun Yang, Lijiao Xie, Jianan Zhang, Xiaxia Sha, Yang Mi, Shuai Lin, Tao College of Automation and Information Engineering Xi’an University of Technology Shaanxi Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Shaanxi Xi’an710048 China

To meet the demand of the laser application market for semiconductor laser function diversification, this paper proposes a handheld multi-wavelength laser stack packaging module. The 808 nm, 915 nm, and 980 nm semiconductor laser bars are selected for the integrated assembly of the multi-wavelength module. However, with the continuous increase of the optical power density of the integrated packaging module, thermal management has become a key factor affecting the performance and life of the device. In this paper, the finite element analysis method is used to analyze the influence of the arrangement of three laser stripe chips in the stacked packaging structure on the heat dissipation of the multi-wavelength module. The simulation results show that the heat dissipation efficiency of the multi-wavelength module can be improved by 13.74 % by changing the arrangement of chips. A back heat dissipation packaging structure with higher heat dissipation efficiency is proposed, and the influence of chip arrangement and working mode on the thermal characteristics of the device in the new packaging structure is simulated and analyzed. According to the simulation results, a chip arrangement that can make the heat dissipation effect of the multi-wavelength module achieve the best is determined. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Semiconductor lasers

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Self-Supervised Learning with Min-Max Distance Approach for Few-Shot Steel Surface Defects Recognition 43

Self-Supervised Learning with Min-Max Distance Approach for ...

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43rd Chinese control Conference, CCC 2024

作者： Wang, Tianlei Chen, Xinyao Li, Zeliang Zhou, Zhencai Zhou, Zhiheng Wuyi University School of Electronic and Information Engineering Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China Wuyi University School of Mechanical and Automation Engineering Jiangmen China Guangdong Province Kejie Machinery Automation Company Technical Department Jiangmen China

ISBN: (纸本)9789887581581

Surface defects in steel are crucial for quality inspection in the manufacturing of industrial metal products. While deep learning methods have been extensively studied in this field, the lack of large-scale annotated defect samples imposes significant labeling pressure, making training fully supervised deep models challenging. In response to this issue, we employ self-supervised learning for few-shot recognition, effectively learning representations from limited unlabeled data and achieving defect recognition with a small amount of labeled samples. In particular, we construct Other encoder with multi-view information and Base encoder with single view information based on the distillation self-supervised learning framework through the multi-crops mechanism, and the Other encoders and Base encoders perform information share. Our main contribution is that, based on this framework, we propose a new regularization method to solve the problem of instances deviating from the center of self-supervised clustering by minimizing the maximum instance distance (MMD). In testing, fine-tuning the MMD pre-trained model with a small number of labeled data achieves superior recognition performance on the steel surface defect dataset NEU, significantly outperforming other testing methods. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Self-supervised learning

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection 30

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Cheng, Long He, Haoyang Gu, Yan Liu, Qingzhi Zhao, Zhiming Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Wageningen University and Research Information Technology Group Netherlands University of Amsterdam Research Group of Multi-Scale Networked Systems Netherlands

ISBN: (纸本)9798331515966

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of 53.18% and costs by 61.98% compared to basic techniques, and achieving 20.26% and 25.71% improvements over the latest advanced methods, respectively. © 2024 IEEE.

关键词： Deep reinforcement learning

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Extended NNs-Based Adaptive Event-Triggered Asymptotic control for Nonlinear systems Against Actuator Faults

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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International Symposium on Autonomous systems (ISAS)

作者： Shanlin Liu Yingwei Zhang Xudong Zhao Hesheng Wang College of Information Science and Engineering Northeastern University Shenyang China State Laboratory of Synthesis Automation of Process Industry Northeastern University Shenyang China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of IntelligentControl and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent.

关键词： Actuators Backstepping Fault tolerant systems process control Artificial neural networks control systems Approximation error

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Resilient Distributed Quadrotor UAVs Game: Addressing FDI and Physical Attacks

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IEEE Transactions on Vehicular Technology 2025年

作者： Geng, Mei-Jie Ding, Hua-Feng Yao, Xiang-Yu Lv, Maolong China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan430074 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Air Force Engineering University College of Air Traffic Control and Navigation Xi'an710038 China

Distributed quadrotor unmanned aerial vehicles (UAVs) are increasingly deployed in critical applications, including reconnaissance, disaster response, and transportation, where system resilience is paramount. However, their complex dynamics and reliance on cyber-physical systems make them highly vulnerable to adversities such as uncertain dynamics, time-varying input disturbances, false data injection (FDI), and physical attacks. This paper proposes a hierarchical framework consisting of a game layer and an execution layer to enhance the resilience of a noncooperative game involving distributed UAVs. In the game layer, a novel FDI observer mitigates communication and computational FDI attacks, facilitating convergence to infinitesimal ranges around the Nash equilibrium (NE) point. The execution layer addresses uncertain dynamics and physical attacks, ensuring that UAVs can track a narrow neighborhood of the NE even in the presence of actuator failures. Extensive simulations validate the algorithm's effectiveness and robustness against mixed attack scenarios, underscoring its potential for real-world UAV applications. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Polarized Modulation for RIS-Aided Vehicular Communication Networks

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IEEE Internet of Things Journal 2025年第12期12卷 18616-18627页

作者： Shang, Pingping Xie, Longhui Li, Jun Feng, Li Hai, Han Xu, Binbin Li, Jie Yu, Ling Hubei University of Technology School of Electrical and Electronic Engineering Wuhan430068 China Hubei University of Technology Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan430068 China Guangzhou University Research Center of Intelligent Communication Engineering School of Electronics and Communication Engineering Guangzhou510006 China Key Laboratory of On-Chip Communication and Sensor Chip of Guangdong Higher Education Institutes Guangzhou510006 China Donghua University School of Information Science and Technology Shanghai201620 China Maritime Communication Research Institute Wuhan430068 China Guangdong Provincial Hospital of Chinese Medicine Guangzhou510120 China State Key Laboratory of Traditional Chinese Medicine Syndrome Guangzhou510120 China

The reconfigurable intelligent surface (RIS) stands as an emergent technique poised to revolutionize future vehicular communication. In order to facilitate the application of RIS in vehicular communication, this paper proposes two effective RIS-aided schemes tailored for polarization modulation (PM) aiming to achieve remarkable spectral efficiency. Specifically, Scheme I proposes a novel index modulation (IM) aided PM-RIS framework that innovatively encodes information across three distinct dimensions, the indices of dual-polarized antennas (DP-AEs), PM symbols and the reflecting surface indices of the RIS. Scheme II employs a deep neural network method to analyze the channel state information (CSI) for transmit antenna selection, that enhances the reliability and interference immunity in dynamic conditions while reducing computational complexity. Furthermore, a stepwise estimation (SE) method is introduced at the detection stage, which significantly reduces the complexity associated with information decoding. The simulation results highlight the superiority of the proposed system. Compared with traditional PM systems, it demonstrates enhanced performance in terms of spectral efficiency, channel stability, and bit error rate (BER). © 2014 IEEE.

关键词： Signal modulation

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