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Study on the deformation and failure mechanism of tertiary water-rich soft rock tunnels based on the moisture migration-fracture coupling model

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Tunnelling and Underground Space Technology 2025年 163卷

作者： Huang, Kang Huang, Shuling Ding, Xiuli Zhang, Yuting Liu, Dengxue Dai, Zhangjun Key Laboratory of Geotechnical Mechanics and Engineering of the Ministry of Water Resources Changjiang River Scientific Research Institute Wuhan430010 China State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil Mechanics Chinese Academy of Sciences Wuhan430071 China

In Tertiary mudstone formations, the surrounding rock of soft rock tunnels is highly susceptible to large deformations and failures due to moisture migration, presenting significant safety risks during both construction and operation. To address this issue, this study develops a moisture migration-fracture coupling model based on the Finite-Discrete Element Method (FDEM) to simulate and analyze the effects of moisture diffusion on the deformation and failure mechanisms of tunnel surrounding rock. Through numerical simulations considering factors such as moisture diffusion range, gravel layer thickness, and angle, the study investigates the impact of these parameters on the spatiotemporal distribution of moisture content, deformation characteristics, and crack propagation in the surrounding rock. The results indicate that moisture diffusion significantly enhances both deformation and crack formation, with the most significant damage occurring at the arch waist. As the moisture diffusion range and gravel layer thickness increase, the arch foot and arch shoulder are more prone to damage. Moreover, an increase in the gravel layer angle heightens the susceptibility of the arch foot and arch shoulder to failure. This study reveals the deformation and failure mechanisms of expansive rock layers under moisture migration, providing theoretical support for stability assessment and support system design in soft rock tunnels, with significant engineering applications. © 2025

关键词： Shale

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Advanced defense against GAN-based facial manipulation: A multi-domain and multi-dimensional feature fusion approach

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Displays 2025年 89卷

作者： Liu, Yunqi Ouyang, Xue Cui, Xiaohui Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430000 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430000 China

Powerful facial image manipulation offered by encoder-based GAN inversion techniques raises concerns about potential misuse in identity fraud and misinformation. This study introduces the Multi-Domain and Multi-Dimensional Feature Fusion (MDFusion) method, a novel approach that counters encoder-based GAN inversion by generating adversarial samples. Firstly, MDFusion transforms the luminance channel of the target image into spatial, frequency, and spatial-frequency hybrid domains. Secondly, we use the specifically adapted Feature Pyramid Network (FPN) to extract and fuse high-dimensional and low-dimensional features that enhance the robustness of adversarial noise generation. Then, we embed adversarial noise into the spatial-frequency hybrid domain to produce effective adversarial samples. Finally, the adversarial samples are guided by our designed hybrid training loss to achieve a balance between imperceptibility and effectiveness. Tests were conducted on five encoder-based GAN inversion models using ASR, LPIPS, and FID metrics. These tests demonstrated the superiority of MDFusion over 13 baseline methods, highlighting its robust defense and generalization abilities. The implementation code is available at https://***/LuckAlex/MDFusion. © 2025

关键词： Image coding

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UAV Image Stitching via Global Optimal Seamline Detection and Local Alignment with Seamline Constraint

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Li, Ruixiang Pan, Jun Pi, Yingdong Wang, Mi Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China

The goal of image stitching is to generate high-quality panoramic images with minimal computational cost. However, variations in viewpoint or scene depth can cause parallax effects in UAV images, complicating precise alignment and leading to artifacts such as ghosting, blurring, and misalignment. While advanced seamline detection algorithms reduce ghosting and blurring, structural distortions and misalignments near the seamline often remain, negatively affecting stitching quality. Moreover, these algorithms typically face challenges in balancing computational efficiency with alignment accuracy. In this paper, we propose a robust and flexible UAV image stitching method based on global optimal seamline detection and local alignment with seamline constraint. Our approach ensures precise alignment while maintaining processing efficiency. First, a global transformation-based alignment algorithm is used to pre-align the images to a common coordinate system. Then, an efficient weighted fast sweeping (WFS) algorithm is proposed to detect the globally optimal seamline, minimizing artifacts in overlapping regions caused by alignment errors and dynamic objects. Finally, an optical flow-guided local alignment method with seamline constraint is developed to correct residual misalignments along the seamline, reducing global structural distortion. Extensive experiments on a range of challenging datasets demonstrate that the proposed method outperforms existing approaches, producing more natural-looking stitching results. © 2025 IEEE.

关键词： Optical flows

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Influence of hydrofoil characteristic thickness on the cavitation erosion risk distribution of clearance vortex cavitation and its mechanisms

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Journal of Hydrodynamics 2025年第1期37卷 50-62页

作者： Huang, Jia-le Cheng, Huai-yu Cao, Yan-tao Ji, Bin State Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan P.R. China National Key Laboratory on Ship Vibration & Noise China Ship Scientific Research Center Wuxi China Taihu Laboratory of Deepsea Technological Science Wuxi China

To clarify the influence of the hydrofoil characteristic thickness on the distribution characteristics and mechanisms of clearance cavitation erosion risk, a large eddy simulation (LES) is conducted to study the clearance cavitating flow around NACA0012 and NACA0024 hydrofoils under identical conditions. The study predicts cavitation erosion risk using three methods: The erosive power method (EPM), the improved gray level method (IGLM) and the energy conservation method (ECM). The numerical results are in good agreement with the experiment data and the ECM is applied due to its simplicity in parameter adjustment and low sensitivity. The results indicate that the characteristic thickness significantly influences the flow field, leading to variations in the position and intensity of cavitation collapse, ultimately resulting in notable differences in cavitation erosion risk distribution. The high cavitation erosion risk region on the clearance surface of NACA0012 is concentrated around the midsection, while it is concentrated in the upstream region for the NACA0024, with a lower frequency of extreme events. Tip separation vortex (TSV) cavitation is the main cause of the differences in cavitation erosion risk distribution. On the clearance surface of the NACA0012, TSV cavitation primarily collapses in the central region, whereas for the NACA0024 hydrofoil, TSV cavitation occurs only in the upstream region of the clearance surface and exhibits more stability. The differences in vorticity distribution near the clearance surface partially influence the distribution of TSV cavitation, thereby affecting the characteristics of cavitation erosion risk distribution. The larger characteristic thickness of the NACA0024 reduces the effects of the stretching term and the baroclinic torque term, weakening the effect of vorticity on TSV cavitation, resulting in more stable patterns of the TSV cavitation.

关键词： Cavitation cavitation erosion clearance vortex cavitating flows energy conservation method (ECM) numerical simulation

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LSDK-LegalSum: improving legal judgment summarization using logical structure and domain knowledge

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Journal of King Saud University - Computer and Information Sciences 2025年第1期37卷 1-15页

作者： Gao, Wei Yu, Shuai Qin, Yongbin Yang, Caiwei Huang, Ruizhang Chen, Yanping Lin, Chuan Engineering Research Center of Text Computing & CognitiveIntelligence Ministry of Education Guizhou University Guizhou Huaxi Guiyang 550025 China State Key Laboratory of Public Big Data Guizhou University Guizhou Huaxi Guiyang 550025 China College of Computer Science and Technology Guizhou University Guizhou Huaxi Guiyang 550025 China

Legal judgment summarization enhances the efficiency and accuracy of processing and retrieving similar cases by distilling key information from legal judgment documents. Legal judgments, which record the trial process and outcome, tend to have lengthy texts. However, most existing methods for legal judgment summarization struggle to effectively handle long texts and focus only on the serialized text information, neglecting the auxiliary role of the document’s logical structure in generating summaries. Additionally, legal judgment documents contain extensive domain knowledge formed by professional legal terminology, which is crucial for accurately understanding and analyzing the content of the documents. We propose a method to improve the quality of legal judgment summaries by utilizing logical structures and domain knowledge (LSDK-LegalSum). Specifically, we first use a multi-label document classification approach to divide the legal judgment into four parts: ‘Type’, ‘Claim’, ‘Fact’ and ‘Result’, then generate a summary of the ‘Type’ part, and combine it with the ‘Claim’ part to create a new summary, and so forth until the final summary is produced. Moreover, the case descriptions in legal judgments cause substantial textual noise;therefore, we employ a text-denoising method that integrates domain knowledge to further enhance the performance of the summaries. Extensive experiments on the CAIL2020 legal summarization dataset have shown that our method achieves improvements of 8.37, 16.64, and 13.07 percentage points in Rouge-1, Rouge-2, and Rouge-L scores respectively over the state-of-the-art models. This demonstrates that our method not only produces concise and semantically complete summaries but also ensures that the content is logically rigorous and well-organized, meeting practical needs more effectively. © The Author(s) 2025.

关键词： Abstractive summarization Domain knowledge Legal judgment summarization Logical structure Low-source summarization

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On the Cycle Structure of Some Nonlinear Feedback Shift Registers

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Chinese Journal of Electronics 2025年第4期23卷 801-804页

作者： Zhongxiao Wang Hong Xu Wenfeng Qi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute Zhengzhou China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In this paper, we partly determine the cycle structure of two types of Nonlinear feedback shift registers (NFSRs). Based on these results, the cycle structure of a class of NFSRs with symmetric feedback functions can be completely characterized. Furthermore, an alternative proof of Kjeldsen's results is presented. Compared with the original proof based on abstract algebra theory, ours is straightforward and easy to understand.

关键词： Abstract algebra Shift registers Cryptography

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Time-independent Spiking Neuron via Membrane Potential Estimation for Efficient Spiking Neural Networks

Time-independent Spiking Neuron via Membrane Potential Estim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hanqi Chen Lixing Yu Shaojie Zhan Penghui Yao Jiankun Shao Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Yunnan University China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial neural networks (ANNs). This highlights the need to parallelize SNN computations effectively to leverage available hardware parallelism. To address this, we propose Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method for spiking neurons that enhances computational efficiency by enabling parallel processing while preserving the intrinsic dynamic characteristics of SNNs. Our approach exhibits promise for enhancing computational efficiency, particularly under conditions of elevated neuron density. Empirical experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy and efficiency on neuromorphic datasets. Codes are available at https://***/chrazqee/MPE-PSN.

关键词： Neuromorphics Computational modeling Neurons Membrane potentials Estimation Spiking neural networks Parallel processing Signal processing Computational efficiency Speech processing

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The Distributionally Robust Optimization Model of Sparse Principal Component Analysis

arXiv

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arXiv 2025年

作者： Wang, Lei Liu, Xin Chen, Xiaojun Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

We consider sparse principal component analysis (PCA) under a stochastic setting where the underlying probability distribution of the random parameter is uncertain. This problem is formulated as a distributionally robust optimization (DRO) model based on a constructive approach to capturing uncertainty in the covariance matrix, which constitutes a nonsmooth constrained min-max optimization problem. We further prove that the inner maximization problem admits a closed-form solution, reformulating the original DRO model into an equivalent minimization problem on the Stiefel manifold. This transformation leads to a Riemannian optimization problem with intricate nonsmooth terms, a challenging formulation beyond the reach of existing algorithms. To address this issue, we devise an efficient smoothing manifold proximal gradient algorithm. We prove the Riemannian gradient consistency and global convergence of our algorithm to a stationary point of the nonsmooth minimization problem. Moreover, we establish the iteration complexity of our algorithm. Finally, numerical experiments are conducted to validate the effectiveness and scalability of our algorithm, as well as to highlight the necessity and rationality of adopting the DRO model for sparse PCA. Copyright © 2025, The Authors. All rights reserved.

关键词： Optimization algorithms

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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Blockchain-Assisted Privacy-Preserving and Synchronized key Agreement for VDTNs

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Wang, Chenhao Ming, Yang Liu, Hang Feng, Jie Yang, Mengmeng Xiang, Yang Chang'an University School of Information Engineering Xi'an710064 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Commonwealth Scientific and Industrial Research Organization Australia Swinburne University of Technology Department of Computing Technologies VIC3122 Australia

With the continuous development of digitization evolutions, vehicular digital twin networks (VDTNs) facilitate traffic data and optimization results to be exchanged between the vehicle and digital twin as well as shared among a group of digital twins. However, the data exchange and group sharing processes take place in real-time over public communication channels, which suffer from various security and privacy threats. key agreement technologies are promising to enable secure data communications for entities, but the existing key agreement schemes generally fail to fulfill the requirements of synchronization, privacy, and entity management for VDTNs. Therefore, we propose a blockchain-assisted privacy-preserving and synchronized key agreement scheme for VDTNs. In the proposed scheme, the anonymous vehicle and digital twin can negotiate a secret session key in the case of synchronization to achieve secure data exchange. Meanwhile, digital twins are capable of utilizing synchronized state information to dynamically establish a common group encryption key but hold individual decryption keys, which guarantee the security of group sharing. Additionally, the proposed scheme is able to protect identity privacy and manage vehicles and digital twins with the assistance of blockchain and smart contract. The security analysis demonstrates that the proposed scheme provides security and privacy assurances for VDTNs. The performance evaluation indicates that it has excellent expressions in terms of efficiency, practicality, and smart contract consumption. © 2004-2012 IEEE.

关键词： Differential privacy

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