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Research Progress in Solar Flare Prediction Methods

Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： (Sun:) sunspots magnetohydrodynamics (MHD) Sun: activity Sun: flares Sun: magnetic fields

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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Interpretable machine learning for chiral induced symmetry breaking of spin density boosting hydrogen evolution

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Journal of Energy Chemistry 2025年第4期103卷 68-78页

作者： Xin Song Zhonghua Li Li Sheng Yang Liu School of Chemistry and Chemical Engineering Key Laboratory of Microsystems and Microstructures ManufacturingMinistry of EducationHarbin Institute of TechnologyHarbin 150001HeilongjiangChina School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001HeilongjiangChina

The integration of machine learning and electrocatalysis presents nota ble advancements in designing and predicting the performance of chiral materials for hydrogen evolution reactions(HER).This study utilizes theoretical calculations and machine learning techniques to assess the HER performance of both chiral and achiral M-N-SWCNTs(M=In,Bi,and Sb)single-atom catalysts(SACs).The stability preferences of metal atoms are dependent on chirality when interacting with chiral *** HER activity of the right-handed In-N-SWCNT is 5.71 times greater than its achiral counterpart,whereas the left-handed In-N-SWCNT exhibits a 5.12-fold *** calculated hydrogen adsorption free energy for the right-handed In-N-SWCNT reaches as low as-0.02 *** enhancement is attributed to the symmetry breaking in spin density distribution,transitioning from C_(2V)in achiral SACs to C_(2)in chiral SACs,which facilitates active site transfer and enhances local spin ***-handed M-N-SWCNTs exhibit superiorα-electron separation and transport efficiency relative to left-handed variants,owing to the chiral induced spin selectivity(CISS)effect,with spin-upα-electron density reaching 3.43×10^(-3)e/Bohr^(3)at active *** learning provides deeper insights,revealing that the interplay of weak spatial electronic effects and appropriate curvature-chirality effects significantly enhances HER performance.A weaker spatial electronic effect correlates with higher HER activity,larger exchange current density,and higher turnover *** curvature-chirality effect undersco res the influence of intrinsic structures on HER *** findings offer critical insights into the role of chirality in electrocatalysis and propose innovative approaches for optimizing HER through chirality.

关键词： Symmetry breaking Machine learning Spin density CISS DFT Hydrogen evolution reaction

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PriChain: Efficient Privacy-Preserving Fine-Grained Redactable Blockchains in Decentralized Settings

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Chinese Journal of Electronics 2025年第1期34卷 82-97页

作者： Hongchen Guo Weilin Gan Mingyang Zhao Chuan Zhang Tong Wu Liehuang Zhu Jingfeng Xue School of Computer Science and Technology Beijing Institute of Technology School of Cyberspace Science and Technology Beijing Institute of Technology School of Computer and Communication Engineering University of Science and Technology Beijing

Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O（n） communication and storage overhead compared to prior O（n2） schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.

关键词： Resistance Access control Data privacy Dictionaries Blockchains Encryption Complexity theory Usability Protection

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Systematic experimental investigation on pressure build-up characteristics of water-jet injection into a molten LBE pool

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Nuclear science and Techniques 2025年第1期36卷 161-174页

作者： Hao-Ran Huang Zi-Jian Deng Song-Bai Cheng Jia-Yue Chen Sino‑French Institute of Nuclear Engineering and Technology Sun Yat-Sen UniversityZhuhai 519082China College of Nuclear Science and Technology Harbin Engineering UniversityHarbin 150001China

In the scenario of a steam generator tube rupture accident in a lead-cooled fast reactor,secondary circuit subcooled water under high pressure is injected into an ordinary-pressure primary vessel,where a molten lead-based alloy(typically pure lead or lead-bismuth eutectic(LBE))is used as the *** clarify the pressure build-up characteristics under water-jet injection,this study conducted several experiments by injecting pressurized water into a molten LBE pool at Sun Yat-sen *** obtain a further understanding,several new experimental parameters were adopted,including the melt temperature,water subcooling,injection pressure,injection duration,and nozzle *** detailed analyses,it was found that the pressure and temperature during the water-melt interaction exhibited a consistent variation trend with our previous water-droplet injection mode LBE ***,the existence of a steam explosion was confirmed,which typically results in a much stronger pressure *** the non-explosion cases,increasing the injection pressure,melt-pool temperature,nozzle diameter,and water subcooling promoted pressure build-up in the melt ***,a limited enhancement effect was observed when increasing the injection duration,which may be owing to the continually rising pressure in the interaction vessel or the isolation effect of the generated steam *** of whether a steam explosion occurred,the calculated mechanical and kinetic energy conversion efficiencies of the melt were relatively small(not exceeding 4.1%and 0.7%,respectively).Moreover,the range of the conversion efficiency was similar to that of previous water-droplet experiments,although the upper limit of the jet mode was slightly lower.

关键词： Lead-cooled fast reactor Steam generator tube rupture accident Pressure build-up characteristics Experimental study Pressure water-jet injection

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Block strategy and adaptive storage for sparse matrix–vector multiplication on GPU

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Cluster Computing 2025年第5期28卷 1-22页

作者： Zhao, Zhixiang Wu, Yanxia Zhang, Guoyin Yang, Yiqing Liu, Haibo College of Computer Science and Technology Harbin Engineering University 145 Nantong Street Heilongjiang Harbin150001 China

Sparse matrix–vector multiplication (SpMV) is a computationally intensive kernel ubiquitously employed in various linear algebra computations. In numerous high-performance applications, SpMV computations often become a performance bottleneck, making their optimization crucial. However, the irregular structure of sparse matrices can lead to redundant calculations, resulting in unbalanced loads and inefficient bandwidth utilization during SpMV operations on GPUs. In this paper, we introduce a novel approach called block strategy and adaptive storage (BSAS) to address these challenges. Our methodology involves partitioning the original matrix into uniform-distribution blocks based on the block strategy, ensuring maximum similarity in the count of non-zero elements per block. This partitioning balances the load, reduces redundant calculations, and minimizes conversion overhead. Additionally, we dynamically assign storage formats to each block using an adaptive storage format selection method that considers the distribution of non-zero elements within the blocks. Furthermore, we develop optimized kernel implementations for each block, significantly enhancing thread-level parallelism and memory utilization. We evaluated our proposed method against NVIDIA’s cuSPARSE library and three advanced SpMV algorithms: SELLP, Merge-Based, and BalanceCSR, using matrices from the SuiteSparse dataset on NVIDIA RTX 2080 Ti and A100 GPUs. The results indicated that our method outperformed the compared algorithms, achieving average speedups of 1.84×,1.30×,1.94×, and 1.42× over cuSPARSE, SELLP, Merge-Based, and BalanceCSR, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Matrix algebra

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FLAG: frequency-based local and global network for face forgery detection

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Multimedia Tools and Applications 2025年第2期84卷 647-663页

作者： Zhou, Kai Sun, Guanglu Wang, Jun Wang, Jiahui Yu, Linsen School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Department of Information Engineering and Mathematics University of Siena Siena53100 Italy

Deepfake detection aims to mitigate the threat of manipulated content by identifying and exposing forgeries. However, previous methods primarily tend to perform poorly when confronted with cross-dataset scenarios. To address the above issue, we propose an innovative hybrid network called the Frequency-based Local and Global (FLAG) network to explore local and global information with the help of frequency-domain cues for better generalization capability. In consideration of the fact that forged faces often exhibit flaws in the frequency domain, we design a Frequency-based Attention Enhancement Module (FAEM) to enhance the aggregation of CNN and Vision Transformer (ViT). In this design, local features from CNN are attentively enhanced by selected frequency coefficients in FAEM, facilitating generalizable global features learning by the ViT module. The effectiveness of the proposed method is validated via numerous experiments and the generalization performance is improved under cross-dataset scenarios. Especially, the proposed method have obtained an AUC of 99.26% and an ACC of 96.56% using intra-dataset experimental results on FaceForensics++ (C23). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Face recognition

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Iron-induced NaTi2（PO4）3 with regulated electronic structure enables freshwater harvesting based on rocking-chair desalination battery

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science China Chemistry 2025年第5期68卷 2178-2187页

作者： Yuliang Wu Chen Ling Chaolin Li Wenhui Wang School of Civil and Environmental Engineering Harbin Institute of TechnologyShenzhen School of Environment and Resources Taiyuan University of Science and Technology State Key Laboratory of Urban Water Resource and Environment Harbin Institute of Technology

Despite the promising potential of NaTi2（PO4）3as desalination battery electrode,its mediocre seawater desalination capacity and durability fail to meet the requirements of practical ***,the substitution of Ti by Fe in NaTi2（PO4）3(NT2-xFxP),which possesses a stronger electronegativity relative to Ti,can regulate the electronic structure,thus facilitating charge transfer and ion diffusion during the desalination *** kinetic and thermodynamic facilitation of NT2-xFxP is clarified by the electrochemical analyses and DFT ***,the additional redox center and solid-solution reaction mechanism induced by Fe-dopant further contribute to a superior desalination performance of NT2-xFxP in different seawater *** from the multifunctional effect of Fe-dopant,the optimal NT1.7F0.3P delivers a large salt removal capacity(173.6 mg g-1) with an ultrahigh salt removal rate(8.48 mg g-1min-1),and maintains 96.6%of desalination capacity after 500 cycles in natural ***,the assembled NT1.7F0.3P||NaFeHCF rocking-chair desalination battery（RCDB） demonstrates a higher desalination performance than that of reported RCDB systems,and can desalinate natural seawater to freshwater standards under continuous desalination mode for the first time in RCDB *** work provides a facile but effective strategy to modulate the electronic structure of NaTi2（PO4）3for RCDB,and exploits a new perspective for developing scalable RCDB devices for continuous desalinating real seawater to freshwater.

关键词： NASICON-type NaTi2(PO4)3 electronic structure electrodes seawater desalination rocking-chair desalination battery

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Effects of La and Ce on the microstructure,thermal conductivity and strength synergy of the as-extruded Mg-Mn-RE alloys

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Journal of Magnesium and Alloys 2025年第2期13卷 654-667页

作者： Huafeng Liu Taiki Nakata Chao Xu Guangze Tang Danyang Li Xiaojun Wang Guisong Wang Shigeharu Kamado Lin Geng State Key Laboratory of Precision Welding&Joining of Materials and Structures Harbin Institute of TechnologyHarbin150001PR China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin150001PR China Department of Mechanical Engineering Nagaoka University of TechnologyNagaoka940-2188Japan Laboratory for Space Environment and Physical Sciences Harbin Institute of TechnologyHarbin150001PR China School of Physics Harbin Institute of TechnologyHarbin150001PR China

High thermal conductivity and high strength Mg-1.5Mn-2.5Ce alloy with a tensile yield strength of 387.0 MPa,ultimate tensile strength of 395.8 MPa,and thermal conductivity of 142.1 W/(m·K)was successfully fabricated via hot *** effects of La and Ce additions on the microstructure,thermal conductivity,and mechanical properties of the Mg-1.5Mn alloy were *** results indicated that both the as-extruded Mg-1.5Mn-2.5La and Mg-1.5Mn-2.5Ce alloys exhibited a bimodal grain structure,with dynamically precipitated nano-scaleα-Mn *** comparison with La,the addition of Ce enhanced the dynamic precipitation more effectively during hot extrusion,while its influence on promoting the dynamic recrystallization was relatively *** high tensile strength obtained in the as-extruded Mg-1.5Mn-2.5RE alloys can be attributed to the combined influence of the bimodal grain structure(with fine dynamic recrystallized(DRXed)grain size and high proportion of un-dynamic recrystallized(unDRXed)grains),dense nano-scale precipitates,and broken Mg12RE phases,while the remarkable thermal conductivity was due to the precipitation of Mn-rich phases from the Mg matrix.

关键词： Mg-Mn-RE alloys Thermal conductivity Mechanical properties Dynamic precipitation

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Multi-scale feature fusion for facial expression recognition

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Neural Computing and Applications 2025年 1-22页

作者： Guo, Jiatao Peng, Junjie Huang, Yansong Chen, Gan Cai, Zesu Tan, Shuhua School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China National Engineering Laboratory for Logistics Information Technology YTO Express Co. Ltd. Shanghai China

As an important computer vision task that can be used in many areas, facial expression recognition (FER) has been widely studied which much progress has been obtained especially when deep learning (DL) approaches have been introduced in FER. However, existing methods are restricted to extracting localized information or utilizing attention mechanism to focus on the region most relevant to the emotion, possibly resulting in errors in facial expression interpretation. Moreover, the inconsistency of image sizes in the wild FER datasets further increases the difficulty of the FER task. To address these issues, we propose a multi-scale feature fusion (MSFF) for FER. Specifically, our method first extracts features from facial images using multi-scale feature extraction to obtain rich multi-dimensional information. Meanwhile, extensive feature extraction at different scales is achieved by expanding the receptive field of the model, which provides the basis for more detailed analysis of facial expressions. Eventually, features at different scales are fused to enhance their synergistic effect. Subsequently, the adaptive extraction of comprehensive and valuable information from the fused features is performed, optimizing the performance of expression recognition. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on in-the-wild datasets. It is promising for FER in realistic and complex scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature extraction

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