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Polydopamine-assisted loading of bioactive ions onto the multi-scale composite-structured surface of 3D-printed porous Ti-6Al-4 V implant to enhance its osteogenic and antibacterial properties

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Surfaces and Interfaces 2025年 67卷

作者： Ji, Zhenbing Wan, Yi Wang, Hongwei Song, Weiye Zou, Yujin Zhou, Libo Key Laboratory of High Efficiency and Clean Manufacturing School of Mechanical Engineering National Demonstration Center for Experimental Mechanical Engineering Education Shandong University Jinan 250061 China College of Artificial Intelligence and Big Data for Medical Sciences Shandong First Medical University Jinan 250117 China Key Laboratory of Chemical Biology (Ministry of Education) School of Pharmaceutical Sciences Shandong University Jinan 250012 China

3D-printed porous Ti-6Al-4 V implants show promise for bone regeneration due to their interconnected porosity. However, the implants face challenges including residual particle detachment and insufficient osteoinductive/antibacterial functionality. This study developed a hierarchical surface modification strategy. It combined flow acid etching, anodic oxidation, and hydrothermal deposition of polydopamine (PDA)-Mg²⁺-Cu²⁺ coatings. Surface modification eliminated residual particles while creating micro-pits (3.930 μm roughness) and nanotube arrays (40 nm diameter), improving hydrophilicity (28.3° contact angle) and Mg/Cu loading (1.8 wt% Mg, 2.4 wt% Cu). The modified surface synergistically enhanced pre-osteoblasts (MC3T3-E1) proliferation and adhesion. The cells had more pseudopods sticking out. Moreover, the osteogenic gene expression was upregulated more than 2-fold at 14 days with doubled mineralization capacity. In vivo, the treated implants increased bone volume fraction by 1.5 fold. The maximum pushout force was improved to 55 N at 4 weeks. In addition, it significantly reduced the risk of bacterial infection. Bacterial cells demonstrated notable morphological perturbations, concomitant with a pronounced diminution in colony-forming capacity. The inhibition rate of bacterial viability on modified surfaces increased to 70% versus controls. These findings present a promising strategy for the application of 3D-printed porous Ti-6Al-4 V implants in orthopedics and maxillofacial surgery. © 2025

关键词： 3D-printed porous Ti-6Al-4V implants Antibacterial Bioactive coating Multi-scale composite structure Osteogenesis

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A Fusion Tuning Method for Named Entity Recognition 12th

A Fusion Tuning Method for Named Entity Recognition

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12th CCF Conference on Bigdata, Bigdata 2024

作者： Wang, Jitian Chen, Yanping Zou, Anqi Qin, Yongbin Huang, Ruizhang Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819610235

In named entity recognition, the main methods for constructing deep neural networks are fine-tuning and prompt tuning. Fine-tuning is a commonly used paradigm to optimize neural networks by using task-specific objective functions to make them more adapted to solve downstream tasks. In which pre-trained language models are used as external resources to provide contextual representations. Prompt tuning is a recent paradigm in which the downstream tasks are reformulated to look more like those solved during the original PLM training with the help of a textual prompt. It is effective to utilize potential knowledge of PLMs. In this paper, we propose the fusion approach, which sufficiently fuses the semantics obtained by extracting fine-tuning and prompt-tuning. In our approach, the model makes decisions based on the information obtained from the reconstructive fusion of the fine-tuned semantics and the semantics of the pre-trained language model. The representation obtained by fine-tuning and the representation obtained by prompt tuning are reconstructed into a multidimensional semantics using a gate fusion module (GFM). In particularly, our model has two gate components, one gate to extract the semantics of fine-tuning and the other gate to extract the semantics of prompt tuning, and finally the reconstructed semantic representation is obtained. This takes full advantage of the potential knowledge of PLM. Our model is evaluated on the CONLL2003, ACE2005 and GENIA corpora. It achieves performance close to the state-of-the-art in F1 scores. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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Rethinking attention mechanism for enhanced pedestrian attribute recognition

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Neurocomputing 2025年 639卷

作者： Wu, Junyi Huang, Yan Gao, Min Niu, Yuzhen Chen, Yuzhong Wu, Qiang Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fujian Fuzhou350108 China Engineering Research Center of BigData Intelligence Ministry of Education Fujian Fuzhou350108 China Australian Artificial Intelligence Institute University of Technology Sydney SydneyNSW2007 Australia Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fujian Fuzhou350108 China School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia

Pedestrian Attribute Recognition (PAR) plays a crucial role in various computer vision applications, demanding precise and reliable identification of attributes from pedestrian images. Traditional PAR methods, though effective in leveraging attention mechanisms, often suffer from the lack of direct supervision on attention, leading to potential overfitting and misallocation. This paper introduces a novel and model-agnostic approach, Attention-Aware Regularization (AAR), which rethinks the attention mechanism by integrating causal reasoning to provide direct supervision of attention maps. AAR employs perturbation techniques and a unique optimization objective to assess and refine attention quality, encouraging the model to prioritize attribute-specific regions. Our method demonstrates significant improvement in PAR performance by mitigating the effects of incorrect attention and fostering a more effective attention mechanism. Experiments on standard datasets showcase the superiority of our approach over existing methods, setting a new benchmark for attention-driven PAR models. © 2025 The Authors

关键词： Perturbation techniques

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A prediction approach based on long short-term memory networks for dynamic multiobjective optimization

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Expert Systems with Applications 2025年 283卷

作者： Xu, Biao Rang, Gejie Xie, Ruijie Li, Wenji Gong, Dunwei Fan, Zhun Yang, Shengxiang He, Jie College of Engineering Shantou University Shantou515063 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao266100 China Shenzhen institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Dynamic multiobjective optimization problems (DMOPs) present significant challenges to conventional evolutionary optimization methods because of the continuous changes in their Pareto-optimal sets (PSs) and fronts (PFs). Prediction-driven approaches have demonstrated potential in rapidly adapting to these changes. However, many existing methods depend on linear models to forecast the evolving PSs, which may be restrictive. To counteract this limitation, this research presents a novel dynamic multiobjective evolutionary optimization algorithm that incorporates predictions from long short-term memory (LSTM) networks. Initially, in our methodology, the PS for each problem is segmented into multiple clusters, and the centroid of each cluster is identified. These cluster centroids, representing the PSs across various environmental conditions, are then transformed into a series of time series data. The LSTM network models are subsequently trained on this time series data as input samples. Utilizing these refined models, the centroids of the evolving PSs are predicted with improved precision. Moreover, to enhance the performance of the algorithm, an innovative population-generation strategy is also introduced that guarantees a well-converged and diverse starting population. Our proposed algorithm undergoes rigorous testing using benchmark functions, and the outcomes validate its proficiency in tackling DMOPs, showing superior performance compared to existing state-of-the-art algorithms. © 2025 Elsevier Ltd

关键词： Multiobjective optimization

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

arXiv

引用

arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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CMRM: Collaborative Multi-agent Reinforcement Learning for Multi-objective Traffic Signal Control

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IEEE Transactions on Consumer Electronics 2025年

作者： Nie, Lei Qi, Dandan Liu, Bingyi Li, Peng Bao, Haizhou He, Heng Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430065 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

Efficient traffic signal control is a cost-effective way to ease urban traffic congestion. Multi-agent reinforcement learning (MARL) has become a widely adopted method for optimizing traffic signal control (TSC). However, existing MARL-based methods often focus on a single optimization objective, lacking a comprehensive consideration of traffic efficiency, environmental pollution, and traffic safety. Simultaneously, these methods often fail to effectively capture the dynamic and complex interactions among agents in multi-intersection scenarios, which negatively impacts traffic efficiency. In this article, we propose a collaborative MARL-based method for multi-objective TSC, called CMRM. First, we introduce a multi-objective reward mechanism that integrates traffic efficiency, environmental impact, and safety to guide agents toward more comprehensive optimization. Second, we design a cooperation enhancement module (CEM) based on the graph attention mechanism to dynamically capture neighboring agents’ state information. This mitigates the partial observability problem in independent proximal policy optimization (IPPO) and enhances the model’s ability to capture dynamic and complex interactions among agents. Finally, we assess the performance of the proposed CMRM method using SUMO on two real traffic networks. Experimental results demonstrate that our method significantly improves traffic efficiency while reducing environmental pollution and enhancing traffic safety, compared to the best performing baseline, our method reduces CO2 emission by approximately 17.53% and 9.57%, and lowers vehicle collision risks by 44.39% and 42.85% in two different traffic networks. © 1975-2011 IEEE.

关键词： Traffic congestion

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Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach

arXiv

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arXiv 2025年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Liang, Shuang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Information Science and Technology Northeast Normal University Changchun130117 China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism ach

关键词： Multiobjective optimization

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Hot spot temperature optimization of micro-channel heat sinks enabled by designing vortex generators and realizing optimal flow pattern

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International Journal of Heat and Mass Transfer 2025年 247卷

作者： Yu, Lingfeng Wu, Xiaoling Song, Mengxuan Chen, Kai Key Laboratory of Enhanced Heat Transfer and Energy Conservation of the Ministry of Education School of Chemistry and Chemical Engineering South China University of Technology Guangdong Guangzhou510640 China Lab of Applied Biocatalysis School of Food Science and Engineering South China University of Technology Guangdong Guangzhou510640 China School of Energy and Materials Shanghai Key Laboratory of Engineering Materials Application and Evaluation Shanghai Polytechnic University Shanghai 201209 China Shanghai Thermophysical Properties Big Data Professional Technical Service Platform Shanghai Engineering Research Center of Advanced Thermal Functional Materials Shanghai 201209 China

Reduction of hot spot temperature is critical for electronic devices under high heat flux conditions. Micro-channel heat sinks (MCHSs) are commonly used in electronic cooling, with vortex generators introduced to enhance the heat transfer. However, existing convective heat transfer optimization equations are not consistent with the aim of hot spot temperature minimization, which are not efficient for design of vortex generators in MCHSs. To address this issue, the optimization equations with minimizing the hot spot temperature of MCHSs are developed, and a novel method is proposed to efficiently design vortex generators in MCHSs. The heat convection optimization equations aiming at hot spot temperature minimization of MCHSs under fixed power consumption are derived using variational method. The equations enable us to achieve the optimal flow field that guarantees a lower hot spot temperature than those obtained based on equations that aim at entropy generation minimization or entransy dissipation extremum in previous studies. The results reveal that the mechanism of hot spot temperature minimization in the rectangular channel is to induce four longitudinal vortices at the cross-section with specific rotations that drive more fluid to the heating surface. Furthermore, vortex generators composed of the inclined triangular-prism ribs and plates are inserted into the channel, which successfully constructs the optimal flow patterns matching the derived optimization equations. The generated similar longitudinal vortices are evidenced by numerical results. With mass flow rate at 0.1 g/s, the hot spot temperature of the MCHSs with the designed vortex generators is reduced by 32.7 K, and the maximum temperature rising decreases by 48 %, and the performance evaluation criterion increases by 60 % when compared to the channel without vortex generators. Moreover, the designed vortex generator increases the performance evaluation criterion by more than 55 % compared to the one in

关键词： Electronic cooling

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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Modified g-C3N4 photocatalysts for clean energy and environmental applications: a review

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Sustainable Energy and Fuels 2025年第11期9卷 2900-2927页

作者： Tofil, Hafiz Muhammad Ghazi, Rizwana Ghaib, Fadhil A. Dara, Rebwar Nasir Kebaili, Imen Boukhris, Imed Ding, He Rehman, Ziaur Department of Chemistry Quaid-i-Azam University Islamabad45320 Pakistan Department of Petroleum and Mining Engineering Faculty of Engineering Tishk International University Kurdistan Region Erbil44001 Iraq Department of Earth Sciences and Petroleum College of Science Salahaddin University-Erbil Erbil44002 Iraq Department of Petroleum Engineering College of Engineering Knowledge University Erbil Iraq Department of Physics Faculty of Science King Khalid University 9004 Abha Saudi Arabia Key Laboratory of Superlight Materials and Surface Technology Ministry of Education College of Materials Science and Chemical Engineering Harbin Engineering University Harbin150001 China

The exponential growth in human population presents significant challenges to the long-term sustainability of civilization through escalating global energy crises and environmental issues. Governments and researchers are actively investigating green and clean technologies as potential solutions to these problems. Moreover, water pollution has become a critical concern, with industrial effluents and anthropogenic activities contaminating potable water sources, resulting in severe health risks. Simultaneously, CO2 emissions from fossil fuel combustion contribute to global warming, necessitating immediate intervention. In this context, semiconductor-based photocatalysis has emerged as a cost-effective, renewable, clean, and secure technology distinguished by its demonstrable advantages. Graphitic carbon nitride (g-C3N4) is noteworthy among the photocatalysts for its affordability, high thermal and chemical stability, suitable bandgap, and appropriate band positions. Nevertheless, the photocatalytic performance of g-C3N4 is affected by its weak light response and high rate of photoinduced charge recombination. This article outlines the synthesis methods and different approaches for improving g-C3N4-based photocatalysis to broaden its use in renewable energy generation and environmental cleanup. © 2025 The Royal Society of Chemistry.

关键词： River pollution

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