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A quinolinyl analog of resveratrol improves neuronal damage after ischemic stroke by promoting Parkin-mediated mitophagy

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Chinese Journal of Natural Medicines 2025年第2期23卷 214-224页

作者： Qingqi Meng Yan Mi Libin Xu Yeshu Liu Dong Liang Yongping Wang Yan Wang Yueyang Liu Guoliang Chen Yue Hou Key Laboratory of Bioresource Research and Development of Liaoning Province College of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationKey Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang 110167China State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilin 541004China Shenyang Key Laboratory of Vascular Biology Science and Research CenterDepartment of PharmacologyShenyang Medical CollegeShenyang 110034China Key Laboratory of Structure-Based Drug Design&Discovery of Ministry of Education School of Pharmaceutical EngineeringShenyang Pharmaceutical UniversityShenyang 110016China

Ischemic stroke(IS)is a prevalent neurological disorder often resulting in significant disability or ***,extracted from Polygonum cuspidatum *** Zucc.(commonly known as Japanese knotweed),has been recognized for its potent neuroprotective ***,the neuroprotective efficacy of its derivative,(E)-4-(3,5-dimethoxystyryl)quinoline(RV02),against ischemic stroke remains inadequately *** study aimed to evaluate the protective effects of RV02 on neuronal ischemia-reperfusion injury both in vitro and in *** research utilized an animal model of middle cerebral artery occlusion/reperfusion and SH-SY5Y cells subjected to oxygen-glucose deprivation and reperfusion to simulate ischemic *** findings demonstrate that RV02 attenuates neuronal mitochondrial damage and scavenges reactive oxygen species(ROS)through mitophagy ***,Parkin knockdown was found to abolish RV02's ability to activate mitophagy and neuroprotection in *** results suggest that RV02 shows promise as a neuroprotective agent,with the activation of Parkin-mediated mitophagy potentially serving as the primary mechanism underlying its neuroprotective effects.

关键词： (E)-4-(3 5-dimethoxystyryl)quinoline Resveratrol Ischemic stroke Mitophagy Parkin

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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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Two efficient beamforming methods for hybrid IRS-aided AF relay wireless networks

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Science china(Information Sciences) 2025年第4期68卷 363-379页

作者： Xuehui WANG Qingbo LI Wen ZHU Feng SHU Mengxing HUANG Fuhui ZHOU Riqing CHEN Cunhua PAN Yongpeng WU Jiangzhou WANG School of Mathematics and Statistics Hainan Normal University Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University School of Information and Communication Engineering Hainan University Collaborative Innovation Center of Information Technology Hainan University School of Electronic and Optical Engineering Nanjing University of Science and Technology College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Digital Fujian Institute of Big Data for Agriculture Fujian Agriculture and Forestry University National Mobile Communications Research Laboratory Southeast University Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University School of Engineering University of Kent

Owing to its ability to mitigate the double-fading effect by amplifying the reflected signal, the active intelligent reflecting surface(IRS) has garnered significant attention. In this paper, an amplify-and-forward(AF) relay network assisted by a hybrid IRS consisting of both passive and active units is developed. A signal-to-noise ratio(SNR) maximization problem is formulated, where the AF relay beamforming matrix and the hybrid IRS reflecting coefficient matrices for two-time slots need to be optimized. To address the SNR maximization problem, this paper proposes both a high-performance(HP) method and a low-complexity(LC) method. The HP method is based on the semidefinite relaxation and fractional programming(SDR-FP)algorithm, with rank-1 solutions obtained through Gaussian randomization. For the LC method, the amplification coefficient of each active IRS element is assumed to be equal. The SNR maximization problem is then addressed using the whitening filter,generalized power iteration, and generalized Rayleigh-Ritz(WF-GPI-GRR) approach. Simulation results show that compared with the benchmarks, such as the passive IRS-aided AF relay network, the proposed HP-SDR-FP and WF-GPI-GRR methods achieve significant rate improvements. In particular, the HP-SDR-FP and WF-GPI-GRR methods yield more than a 135.0%rate gain when the transmit power Ps of the source is 10 dBm. Furthermore, the proposed HP-SDR-FP method outperforms the WF-GPI-GRR method in terms of rate performance.

关键词： double-fading intelligent reflecting surface active elements passive elements hybrid IRS AF relay

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SSMamba: Superpixel Segmentation With Mamba

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IEEE Signal Processing Letters 2025年 32卷 1715-1719页

作者： Jia, Xiaohong Li, Yonghui Jiao, Jianjun Zhao, Yao Xia, Zhiwei Lanzhou Jiaotong University School of Electronic and Information Engineering Lanzhou730070 China Beijing Jiaotong University Key Laboratory of Big Data and Artiffcial Intelligence in Transportation Ministry of Education Beijing100044 China Beijing Jiaotong University Institute of Information Science Beijing100044 China

Deep convolutional networks have achieved remarkable success in superpixel segmentation. However, they only focus on local features ignoring global attributes. The visual Mamba demonstrates an exceptional capability to capture long-range dependencies and offers a lower computational cost compared to the Transformer. Building on this inspiration, we propose a novel superpixel segmentation with Mamba, termed SSMamba. In SSMamba, Mamba is integrated into a global-local architecture, enabling efficient interaction between global attributes and local features to produce high-quality superpixels. The designed activation function further enhances the effectiveness of SSMamba. Extensive experiments on four public datasets demonstrate that SSMamba outperforms existing state-of-the-art methods, achieving competitive average values of ASA = 0.9541, BR = 0.8768, BP = 0.2124, UE = 0.0910, and CO = 0.3698. © 1994-2012 IEEE.

关键词： Convolutional neural networks

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An Anti-Clutter Distance Measurement Method for IoT Linear Frequency Modulation Radar in Heavy Rainfall Environment

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IEEE Internet of Things Journal 2025年第11期12卷 17906-17919页

作者： Zhu, Lingzhi Li, Yi Xia, Weijie Chen, Kuiyu Ma, Qun Zhang, Qi Nanjing University of Aeronautics and Astronautics Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing211106 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Nanjing210023 China City University of Macau Faculty of Data Science 999078 China

Linear frequency modulation (LFM) radar which can measure distance of target is the key sensor in autonomous driving system in the context of Internet of Things (IoT). However, huge amount of raindrops in rainfall environment would reflect signal and result in negative influence. In order to improve the detection ability of LFM radar, echo signal model of LFM radar target in rainfall environment is established. Working principle of LFM radar, characteristics of raindrops, influence of rain clutter and comparison with measured data in time-domain and frequency-domain distribution are given in sequence. Secondly, three features are defined and extracted from frequency-domain spectrum and singular-domain spectrum to recognize target echo signal, rain clutter and mixed signal with high precision. When the rainfall rate is 3.33 mm/h, the overall recognition accuracy can still be up to 95.15%. Thirdly, an adaptive ensemble empirical mode decomposition (EEMD) algorithm is proposed to suppress rain clutter in mixed signal. Simulation results prove the effectiveness of EEMD in improving ranging accuracy in rainfall environment. At last, outfield experiments in normal environment and rainfall environment are conducted respectively. Measured distance obtained by proposed method in rainfall environment is still more accurate than that obtained by traditional fast Fourier transform (FFT) method in normal environment, illustrating that the proposed method can ensure the high performance of LFM radar in rainfall environment. Research in this paper has positive influence on improving reliability of LFM radar in inclement environment and can contribute to more stable target information for the whole in-vehicle IoT. © 2014 IEEE.

关键词： Frequency domain analysis

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Secure Multi-way Join Query and Its Bench-Marking with Trusted Execution Environments 2nd

Secure Multi-way Join Query and Its Bench-Marking with Tru...

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2nd International Conference on data Security and Privacy Protection, DSPP 2024

作者： Zhao, Yi Zhao, Sen Lv, Siyi School of Cyber Engineering Xidian University Xi’an China State Key Laboratory of Integrated Service Networks Xidian University Xi’an China Key Laboratory of Data and Intelligent System Security Ministry of Education Nankai University Tianjin China

ISBN: (纸本)9789819785452

Because of server-untrustable model in outsourcing encrypted database system, it’s fatal for data owners to keep their data confidential under complex query operations performed by database servers. Trusted Execution Environments (TEEs) provide a secure space for privacy computation via hardware-based methods to increase the efficiency of database system, and it ensures the availability of computing directly on sensitive data in untrusted outsourcing database servers. However, while various query schemes are designed under the combination of encrypted database and TEEs, designing an available and secure scheme for join queries on multiple data tables with TEEs remains an open problem. Besides, previous works also devote less to bench-marking multi-way joins on TEEs. In our work, we extend binary join query scheme into multi-way forms in encrypted database system based on TEEs. It combines various join algorithms based on hash functions (hash-based, radix-based, etc.) with parallelized optimizing mechanism. Besides, we do bench-marking with TEEBench framework on those multi-way join schemes in three-way forms using both cache-fit and cache-exceed datasets and do different evaluation on two TEEs hardware platforms (Intel SGX and AMD SEV) to show its availability. After that, we also propose a multi-way join scheme with access pattern protection through the combination between our previous scheme and oblivious I/O methods. With the evaluation of its efficiency and memory leakage, we can also verify the feasibility of our access-pattern-protected multi-way join scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Outsourcing

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Network intrusion detection based on feature fusion of attack dimension

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Journal of Supercomputing 2025年第6期81卷 1-33页

作者： Sun, Xiaolong Gu, Zhengyao Zhang, Hao Gu, Jason Liu, Yanhua Dong, Chen Ye, Junwei College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou350116 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3J 1Z1 Canada

Network traffic anomaly detection involves the rapid identification of intrusions within a network through the detection, analysis, and classification of network traffic data. The variety of cyberattacks encompasses diverse attack principles. Employing an indiscriminate feature selection strategy may lead to the neglect of key features highly correlated with specific attack types. This oversight could diminish the recognition rate for that category, thereby impacting the overall performance of the detection model. To address this issue, this paper proposes a network traffic anomaly detection model based on the fusion of attack-dimensional features. Firstly, construct binary classification datasets independently for each attack class and perform individual feature selection to extract positively correlated features for each class. The features are then fused by employing a combination methods. Subsequently, based on the fused sub-datasets, base classifiers are trained. Finally, an ensemble learning approach is introduced to integrate the predictions of individual classifiers, enhancing the robustness of the model. The proposed approach, validated on NSL-KDD and UNSW-NB15 benchmark datasets, outperforms the latest methods in the field by achieving a 2% and 7% increase in precision on weighted averages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

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Device-edge collaborative occluded face recognition method based on cross-domain feature fusion

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Digital Communications and Networks 2025年第2期11卷 482-492页

作者： Puning Zhang Lei Tan Zhigang Yang Fengyi Huang Lijun Sun Haiying Peng School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing Innovation Center of Industrial Big-Data Co.Lt

The lack of facial features caused by wearing masks degrades the performance of facial recognition systems. Traditional occluded face recognition methods cannot integrate the computational resources of the edge layer and the device layer. Besides, previous research fails to consider the facial characteristics including occluded and unoccluded parts. To solve the above problems, we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature fusion. Specifically, the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face recognition. Then, a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain facial. Furthermore, a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load, computational power, bandwidth, and delay tolerance constraints of the edge. This method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition accuracy. The experimental results show that the proposed method achieves an average gain of about 21% in recognition latency, while the accuracy of the face recognition task is basically the same compared to the baseline method.

关键词： Occluded face recognition Cross-domain feature fusion Device-edge collaboration

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DMC-Watermark: A backdoor richer watermark for dual identity verification by dynamic mask covering

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Applied Intelligence 2025年第8期55卷 1-15页

作者： Zhu, Yujia Wang, Ruoxi Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Key Laboratory of Advanced Computing Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

With the increasing use of neural networks, the importance of copyright protection for these models has gained significant attention. Backdoor watermarking is one of the key methods for protecting copyright. However, on the one hand, most existing backdoor watermarks are triggered by visual images, making them easily detectable, and therefore vulnerable to various attacks. On the other hand, it is difficult for these methods to carry information related to the creator’s identity which can easily lead to fraudulent claims of ownership. These factors contribute to the vulnerability and limitations of backdoor watermarking. In this paper, we propose DMC-Watermark, a backdoor richer watermarking method that uses dynamic mask-covered image structures as triggers. Leveraging the semantic preservation of image structure in transformation attacks, we select image structure as triggers. Furthermore, we convert the author-related information into an array of color information and apply it as a mask to the extracted image structures, allowing it to serve as a second layer of verification during the validation phase to resist fraudulent claims of ownership. The final trigger pattern, embedded with author-related image structures, is applied to the selected images in the trigger set, generating a final trigger set that is trained together with clean samples to produce a protected model. The experiments show that the proposed DMC-Watermark performs well in terms of fidelity, invisibility, undetectability, functionality, dual verification and robustness on three different datasets and four representative DNNs, and it has wide applicability and excellent results in high-resolution images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image watermarking

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AMSCF-Net: An adaptive multi-scale cost fusion stereo network for high-quality disparity estimation

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 226卷 76-101页

作者： Tian, Mao Fan, Jiajin Zhang, Qinghua Chen, Chi Yang, Bisheng College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism China

Accurate and complete reconstruction of the high-quality three-dimensional (3D) geometric information from stereo images has been one of the challenging tasks in the fields of photogrammetry and computer vision. Although tremendous efforts have been made in stereo matching by utilizing convolutional neural network (CNN)-based deep learning architectures, the matching ambiguities in the case of occlusions, disparity discontinuities, and poor and repetitive texture areas remain to be explored. In this paper, to address these issues, we propose a novel end-to-end disparity estimation network named AMSCF-Net, which consists of two main components: the context and geometry adaptive attention weight generation (CGA-AWG) subnetwork for yielding robust multi-scale attention weights, and the adaptive multi-scale cost volume fusion (AMS-CVF) subnetwork to enhance the feature representation of the cost volumes for accurate disparity map prediction. Firstly, the multi-scale feature maps are obtained from the stereo images by employing a weight-sharing hierarchical feature extraction subnetwork. Secondly, the CGA-AWG subnetwork is adopted to produce accurate and robust multi-scale attention weights. Thirdly, the adaptive multi-scale cost volume fusion (AMS-CVF) subnetwork is introduced to boost the robustness of the fused cost volume in the poor and repetitive texture areas, while simultaneously preserving the details of the scene's geometric structure in the rich texture regions. Finally, the context and geometry adaptive disparity map prediction (CGA-DMP) subnetwork is applied to further refine the cost volumes for high-accuracy disparity map reconstruction. Three publicly available benchmark datasets, i.e., the KITTI 2015, US3D, and Vaihingen stereo datasets, were used to evaluate the performance of AMSCF-Net. The comprehensive experiments demonstrate that AMSCF-Net achieves state-of-the-art disparity prediction results on all datasets in terms of the ("D1-bg", "D1-fg", "D1-a

关键词： Convolutional neural networks

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