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Enhancing graph representation learning via type-aware decoupling and node influence allocation

Applied Intelligence 2025年第6期55卷 1-14页

作者： Zhu, Guochang Hu, Jun Liu, Li Zhang, Qinghua Wang, Guoyin School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongwen Road Chongqing Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongwen Road Chongqing Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing400065 China

The traditional graph representation methods can fit the information of graph with low-dimensional vectors, but they cannot interpret their composition, resulting in insufficient security. Graph decoupling, as a method of graph representation, can analyze the latent factors composing the graph representation vectors. However, in current graph decoupling methods, the number of factors is a hyperparameter, and enforce uniform decoupling vector dimensions which leads to information loss or redundancy. To address these issues, we propose a type-aware graph decoupling based on influence called Variational Graph Decoupling Auto-Encoder (VGDAE). It uses node labels as interpretable and objectively existing natural semantics for decoupling and allocates embedding space based on node influence, addressing the issues of manually setting the number of factors in traditional graph decoupling and the mismatch between node information size and embedding space. On the Cora, Citeseer, and fb-CMU datasets, VGDAE shows the impact of different node classes as decoupling targets on classification tasks. Furthermore, we perform visualization of the representations, VGDAE exhibits performance improvements of 2% in classification tasks and 12% in clustering tasks when compared with baseline models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Graph embeddings

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NUMERICAL SIMULATION AND MULTI-OBJECTIVE OPTIMIZATION OF ULTRA-LOW NITROGEN BURNERS FOR ETHYLENE CRACKING FURNACES

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Environment Protection Engineering 2025年第1期51卷 37-63页

作者： Hu, Guihua Chen, Kunpeng Chen, Mimi Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai200237 China CIBFINTECH No. 2977 Chuansha Road Pudong New Area Shanghai200131 China

Coupled numerical simulation and multi-objective optimization for ultra-low nitrogen burners of ethylene cracking furnaces using a fast response surrogate model for combustion are proposed. Firstly, based on simplified reaction mechanisms involving 29 species and 164 reactions, a computational fluid dynamics (CFD)-coupled model for turbulent combustion is established. Secondly, a multi-objective optimization scheme based on a strong generalization-based surrogate model is developed for ultra-low nitrogen burners of ethylene cracking furnaces. The results show that a set of optimal operating parameters for the cracking furnace, i.e., the excess air coefficient of 1.07, the fuel gas flow rate of 0.192 kg/s, and the air preheating temperature of 380 K, is obtained. The optimal NOx emission concentration decreased from 75.38 mg/m3 of the original scheme to 71.2 mg/m3, i.e., a decrease of 5.55%. The thermal efficiency of the firebox increased from 43.82% of the original scheme to 44.49%, i.e., an increase of 1.53%, which provides theoretical guidance for energy conservation and emission reduction of cracking furnaces. © 2025 Wroclaw University of science and Technology. All rights reserved.

关键词： Multiobjective optimization

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PFed-NS: an Adaptive Personalized Federated Learning Scheme through Neural Network Segmentation

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IEEE Transactions on Computers 2025年第6期74卷 1936-1948页

作者： Liu, Yuchen He, Ligang Zheng, Zhigao Ren, Shenyuan University of Warwick Department of Computer Science United Kingdom Wuhan University School of Computer Science Wuhan430072 China China Hubei Luojia Laboratory Wuhan430072 China Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing100044 China

Federated Learning (FL) is typically deployed in a client-server architecture, which makes the Edge-Cloud architecture an ideal backbone for FL. A significant challenge in this setup arises from the diverse data feature distributions across different edge locations (i.e., non-IID data). In response, Personalized Federated Learning (PFL) approaches have been developed. Network segmentation-based PFL is an important approach to achieving PFL, in which the training network is divided into a global segment for server aggregation and a local segment maintained client-side. Existing methods determine the segmentation before the training, and the segmentation remains fixed throughout the PFL training. However, our investigation reveals that model representations vary as PFL progresses and the fixed segmentation may not deliver best performance across various training settings. To address this, we propose PFed-NS, a PFL framework based on adaptive network segmentation. This adaptive segmentation technique is composed of two elements: a mechanism for assessing divergence of clients' probability density functions constructed from network layers' outputs, and a model for dynamically establishing divergence thresholds, beyond which server aggregation is deemed detrimental. Further optimization strategies are proposed to reduce the computation and communication costs incurred by divergence modeling. Moreover, we propose a divergence-based BN strategy to optimize BN performance for network segmentation-based PFL. Extensive experiments have been conducted to compare PFed-NS against recent PFL models. The results demonstrate its superiority in enhancing model accuracy and accelerating convergence. © 1968-2012 IEEE.

关键词： Probability density function

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Improved quantum image weighted average filtering algorithm

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Quantum Information Processing 2025年第5期24卷 1-24页

作者： Yuan, Suzhen Li, Xianli Yinxia, Shu Qing, Xianrong Deng, Jermiah D. College of Opto Electronic Engineering Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China School of Computing University of Otago Dunedin Dunedin9054 New Zealand School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China

Average filtering plays a vital role in image smoothing tasks. However, existing quantum image weighted average filtering methods suffer from high circuit complexity. Therefore, this paper proposes an improved quantum color image weighted average filtering algorithm and its corresponding quantum circuit. First, we improve the quantum circuit to prepare classical color images into a quantum state. Then, an improved quantum divider is developed, and a weighted average filter is constructed using basic quantum image processing modules. Next, to enhance the universality of the filter, a quantum comparator with lower circuit complexity is used to design a noise detection module for distinguishing noise from real signals. Finally, a quantum circuit for color image weighted average filtering is designed, and simulations are conducted on the IBM Quantum Experience (IBM Q) platform to verify the feasibility of our algorithm. The analysis shows that compared with existing methods, this method significantly reduces the circuit complexity and has better filtering performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image enhancement

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DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed Problems in Malicious Traffic Detection

DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed P...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Guoliang Zhang, Ruiqi Sun, Zhe Xing, Lingkai Zhang, Yu College of Cyber Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Key Laboratory of Data and Intelligent System Security Ministry of Education Tianjin China

ISBN: (纸本)9798350368741

In recent years, deep learning based malicious traffic detection (MTD) systems have demonstrated remarkable success. However, their effectiveness tend to decrease because most malicious traffic datasets are suffered from noisy-labeled and long-tailed problems. While numerous approaches have been developed to address these two problems individually, they become inefficient when confronting the combined challenge of noisy long-tailed data, as they typically tackle only a single adverse factor at a time. This paper proposes a two-stage method called Distribution-aware sample Selection and Dynamic Instance-based Relabeling (DSDIR), which simultaneously addresses the impacts of noisy-labeled and long-tailed problems. In the first stage, a noise-independent clean sample selection method is designed to obtain a clean dataset, which converts negative effects of the long-tailed problem into positive ones. In the second stage, dynamic instance-based relabeling is designed to train a model and improve the dataset's quality simultaneously. Eventually, DSDIR not only produces a balanced and noise-tolerant model but also obtains a clean dataset. Experimental results demonstrate that in the high noise condition of 60% and 80%, the accuracy rate of DSDIR is 5% higher than the state-of-the-art methods. Our code is available at https://***/nku-ligl/DSDIR. © 2025 IEEE.

关键词： deep learning distribution estimation dynamic relabeling malicious traffic detection noisy long-tailed problem

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Effect of Mn alloying on the hydrogen-assisted cracking behavior in multiphase/duplex stainless steel

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材料科学技术（英文版） 2025年第4期207卷 126-141页

作者： Menghao Liu Cuiwei Du Xiaogang Li Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China Key Laboratory for Corrosion and Protection of the Ministry of Education Institute of Advanced Materials & TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China

There remains debate on whether Mn is beneficial or detrimental to hydrogen embrittlement in stainless *** this work,a series of stainless steels were designed to study the change of hydrogen embrit-tlement sensitivity,crack propagation,and hydrogen trapping behaviors upon Mn *** results suggest that adding 4 wt.％Mn increased hydrogen embrittlement susceptibility,whereas adding 8 wt.％Mn decreased hydrogen embrittlement *** banded α'-martensite through austenitic grain is the main reason for the increased hydrogen embrittlement sensitivity when adding 4 wt.％Mn,by ad-sorbing hydrogen,promoting crack initiation,and accelerating crack propagation.

关键词： Hydrogen-assisted cracking Multiphase stainless steel Manganese

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Reconfigurable Three-Dimensional Thermal Dome

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Engineering 2025年第3期46卷 236-244页

作者： Yuhong Zhou Fubao Yang Liujun Xu Pengfei Zhuang Dong Wang Xiaoping Ouyang Ying Li Jiping Huang State Key Laboratory of Surface Physics&Key Laboratory of Micro-and Nano-Photonic Structures(Ministry of Education) Department of PhysicsFudan UniversityShanghai 200438China Graduate School of China Academy of Engineering Physics Beijing 100193China International Joint Innovation Center The Electromagnetics Academy at Zhejiang UniversityZhejiang UniversityHaining 314400China State Key Laboratory of Extreme Photonics and Instrumentation&Key Laboratory of Advanced Micro-Nano Electronic Devices and Smart Systems and Applications of Zhejiang Province Zhejiang UniversityHangzhou 310027China School of Materials Science and Engineering Xiangtan UniversityXiangtan 411105China

Thermal metamaterial represents a groundbreaking approach to control heat conduction,and,as a crucial component,thermal invisibility is of utmost importance for heat *** the flourishing development of thermal invisibility schemes,they still face two limitations in practical ***,objects are typically completely enclosed in traditional cloaks,making them difficult to use and unsuitable for objects with heat ***,although some theoretical proposals have been put forth to change the thermal conductivity of materials to achieve dynamic invisibility,their designs are complex and rigid,making them unsuitable for large-scale use in real threedimensional(3D)***,we propose a concept of a thermal dome to achieve 3D *** scheme includes an open functional area,greatly enhancing its usability and *** features a reconfigurable structure,constructed with simple isotropic natural materials,making it suitable for dynamic *** performance of our reconfigurable thermal dome has been confirmed through simulations and experiments,consistent with the *** introduction of this concept can greatly advance the development of thermal invisibility technology from theory to engineering and provide inspiration for other physical domains,such as direct current electric fields and magnetic fields.

关键词： Thermal domes Reconfigurable metamaterials Three-dimensional invisibility

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A High-Sensitivity Strain Sensor With Femtosecond Fiber Bragg Grating for Pipeline Deformation Monitoring

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IEEE Sensors Journal 2025年第9期25卷 14830-14841页

作者： Gong, Yongjian Guo, Yongxing Xiong, Li Liu, Chang Zhou, Wanhuan Hu, Zhongchun Li, Xiudong Zhang, Yu Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan430081 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Civil and Environmental Engineering China China Academy of Safety Science and Technology Beijing100012 China China Railway 14th Bureau Group Corporation Ltd. Jinan250000 China

Structural health monitoring (SHM) of hydraulic pipelines plays a crucial role in preventing catastrophic failures, particularly when measuring large strains with high sensitivity. This study presents a novel high-sensitivity strain sensor utilizing femtosecond fiber Bragg grating (FS-FBG) technology to overcome the limitations of conventional monitoring techniques in extreme conditions. The sensor incorporates two key components: sensing and measuring members, featuring an innovative skeletonized sensitized shaft design. A pre-stressed FS-FBG is suspended axially within the structure. The relationship between fiber Bragg grating (FBG) wavelength displacement and pipe surface strain was validated using Bernoulli-Euler beam theory and finite element analysis. In order to accurately calibrate the performance of the sensor, a precision pipe deformation device was designed. Experimental results demonstrate that the sensor achieves a sensitivity of 4.19 pm/ μϵ, which is 3.4 times higher than that of the FBG sensor, in the range of 0-1650 μϵ of the pipe surface microstrain, and at the same time, it has excellent linearity, creep-resistant performance, and temperature compensation effect. The results provide a reliable solution for large strain monitoring of hydraulic pipelines under severe conditions. © 2001-2012 IEEE.

关键词： Fiber Bragg gratings

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Ensemble Knowledge Distillation for Federated Semi-Supervised Image Classification

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Tsinghua science and Technology 2025年第1期30卷 112-123页

作者： Ertong Shang Hui Liu Jingyang Zhang Runqi Zhao Junzhao Du School of Computer Science and Technology Xidian UniversityXi’an 710126China Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of EducationXi’an 710126China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi’an 710126China

Federated learning is an emerging privacy-preserving distributed learning paradigm,in which many clients collaboratively train a shared global model under the orchestration of a remote *** current works on federated learning have focused on fully supervised learning settings,assuming that all the data are annotated with ground-truth ***,this work considers a more realistic and challenging setting,Federated Semi-Supervised Learning(FSSL),where clients have a large amount of unlabeled data and only the server hosts a small number of labeled *** to reasonably utilize the server-side labeled data and the client-side unlabeled data is the core challenge in this *** this paper,we propose a new FSSL algorithm for image classification based on consistency regularization and ensemble knowledge distillation,called *** algorithm uses the global model as the teacher in consistency regularization methods to enhance both the accuracy and stability of client-side unsupervised learning on unlabeled ***,we introduce an additional ensemble knowledge distillation loss to mitigate model overfitting during server-side retraining on labeled *** experiments on several image classification datasets show that our EKDFSSL outperforms current baseline methods.

关键词： federated learning semi-supervised learning federated semi-supervised learning knowledge distillation

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Natural polyphenols as novel interventions for aging and age-related diseases: Exploring efficacy, mechanisms of action and implications for future research

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Chinese Herbal Medicines 2025年第2期17卷 279-291页

作者： Wenze Wu Yan Mi Qingqi Meng Ning Li Wei Li Pu Wang Yue Hou Liaoning Key Laboratory of Bioresource Research and Development National Frontiers Science Center for Industrial Intelligence and Systems OptimizationKey Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationCollege of Life and Health SciencesNortheastern University Key Laboratory for TCM Material Basis Study and Innovative Drug Development of Shenyang City School of Traditional Chinese Materia MedicaShenyang Pharmaceutical University Faculty of Pharmaceutical Sciences Toho University College of Pharmacy Shenzhen Technology University

Natural polyphenols are a group of components widely found in traditional Chinese medicines and have been demonstrated to delay or prevent the development of aging and age-related diseases in recent years. As far as we know, the studies of natural polyphenols in aging and aging-related diseases have never been extensively reviewed. In the present paper, we reviewed recent advances of natural polyphenols in aging and common age-related diseases and the current technological methods to improve the bioavailability of natural polyphenols. The results showed that natural polyphenols have the potential to prevent or treat aging and common age-related diseases through multiple ***, structural modifications, and matrix processing could provide strong technical support for the development of natural polyphenols to prevent or treat aging and age-related diseases. In conclusion, natural polyphenols have important potential in the prevention and treatment of aging and age-related diseases.

关键词： aging cancer cardiovascular disease natural polyphenols neurodegenerative diseases

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