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An efficient quantum proactive incremental learning algorithm

Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 49-57页

作者： Lingxiao Li Jing Li Yanqi Song Sujuan Qin Qiaoyan Wen Fei Gao State Key laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications School of cyberspace security Beijing University of Posts and Telecommunications

In scenarios where a large amount of data needs to be learned, incremental learning can make full use of old knowledge, significantly reduce the computational cost of the overall learning process, and maintain high performance. In this paper, taking the MaxCut problem as our example, we introduce the idea of incremental learning into quantum computing, and propose a Quantum Proactive Incremental Learning algorithm(QPIL). Instead of a one-off training of quantum circuit, QPIL contains a multi-phase training on gradually-increased subgraphs of all vertices, proactively reducing large-scale problems to smaller ones to solve in steps, providing an efficient solution for MaxCut. Specifically, some vertices and corresponding edges are randomly selected for training to obtain optimized parameters of the quantum circuit at first. Then, in each incremental phase, the remaining vertices and corresponding edges are gradually added and the parameters obtained from the previous phase are reused in the parameter initialization of the current phase. We perform experiments on 120 different small-scale graphs, and it shows that QPIL performs superior to prevalent quantum and classical baselines in terms of approximation ratio(AR), time cost, anti-forgetting, and solving stability. In particular, QPIL's AR surpasses 20% of mainstream quantum baselines, and the time cost is less than 1/5 of them. The idea of QPIL is expected to inspire efficient and high-quality solutions in large-scale Max Cut and other combinatorial optimization problems.

关键词： variational quantum algorithm incremental learning multi-phase training MaxCut quantum computing

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Malicious Document Detection Based on GGE Visualization

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Computers, Materials & Continua 2025年第1期82卷 1233-1254页

作者： Youhe Wang Yi Sun Yujie Li Chuanqi Zhou Henan Province Key laboratory of Information security Information Engineering UniversityZhengzhou450000China

With the development of anti-virus technology,malicious documents have gradually become the main pathway of Advanced Persistent Threat(APT)attacks,therefore,the development of effective malicious document classifiers has become particularly ***,detection methods based on document structure and behavioral features encounter challenges in feature engineering,these methods not only have limited accuracy,but also consume large resources,and usually can only detect documents in specific formats,which lacks versatility and *** address such problems,this paper proposes a novel malicious document detection method-visualizing documents as GGE images(Grayscale,Grayscale matrix,Entropy).The GGE method visualizes the original byte sequence of the malicious document as a grayscale image,the information entropy sequence of the document as an entropy image,and at the same time,the grayscale level co-occurrence matrix and the texture and spatial information stored in it are converted into grayscale matrix image,and fuses the three types of images to get the GGE color *** Convolutional Block Attention Module-EfficientNet-B0(CBAM-EfficientNet-B0)model is then used for classification,combining transfer learning and applying the pre-trained model on the ImageNet dataset to the feature extraction process of GGE *** shown in the experimental results,the GGE method has superior performance compared with other methods,which is suitable for detecting malicious documents in different formats,and achieves an accuracy of 99.44%and 97.39%on Portable Document Format(PDF)and office datasets,respectively,and consumes less time during the detection process,which can be effectively applied to the task of detecting malicious documents in real-time.

关键词： Malicious document visualization EfficientNet-B0 convolutional block attention module GGE image

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A Cross-Multi-Domain Trust Assessment Authority Delegation Method Based on Automotive Industry Chain

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Computers, Materials & Continua 2025年第1期82卷 407-426页

作者： Binyong Li Liangming Deng Jie Zhang Xianhui Deng School of Cybersecurity(Xin Gu Industrial College) Chengdu University of Information TechnologyChengdu610225China Advanced Cryptography and System security Key laboratory of Sichuan Province Chengdu610225China SUGON Industrial Control and security Center Chengdu610225China

To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants,this research focuses on addressing the complex supply relationships in the automotive market,improving data sharing and interactions across various platforms,and achieving more detailed integration of data and *** propose a trust evaluation permission delegation method based on the automotive industry *** proposed method combines smart contracts with trust evaluation mechanisms,dynamically calculating the trust value of users based on the historical behavior of the delegated entity,network environment,and other factors to avoid malicious node attacks during the permission delegation *** also introduce strict control over the cross-domain permission granting and revocation mechanisms to manage the delegation path,prevent information leakage caused by malicious node interception,and effectively protect data integrity and *** analysis shows that this method meets the realtime requirements of collaborative interaction in the automotive industry chain and provides a feasible solution to permission delegation issues in the automotive industry chain,offering dynamic flexibility in authorization and scalability compared to most existing solutions.

关键词： Automotive industry chain cross-domain access block chain delegate authority trust assessment

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DDoS Attack Autonomous Detection Model Based on Multi-Strategy Integrate Zebra Optimization Algorithm

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Computers, Materials & Continua 2025年第1期82卷 645-674页

作者： Chunhui Li Xiaoying Wang Qingjie Zhang Jiaye Liang Aijing Zhang School of Information Engineering Institute of Disaster PreventionLangfang065201China Langfang Key laboratory of Network Emergency Protection and Network security Langfang065201China

Previous studies have shown that deep learning is very effective in detecting known ***,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low ***,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 *** hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 *** evaluation accuracy of the new dataset GINKS2023 created in this paper is *** to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.

关键词： Distributed denial of service attack intrusion detection deep learning zebra optimization algorithm multi-strategy integrated zebra optimization algorithm

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E-PRedictor: an approach for early prediction of pull request acceptance

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Science China(Information Sciences) 2025年

作者： Kexing CHEN Lingfeng BAO Xing HU Xin XIA Xiaohu YANG State Key laboratory of Blockchain and Data security Zhejiang University Software Engineering Application Technology Lab

A pull request (PR) is an event in Git where a contributor asks project maintainers to review code he/she wants to merge into a project. The PR mechanism greatly improves the efficiency of distributed software development in the opensource community. Nevertheless, the massive number of PRs in an open-source software (OSS) project increases the workload of developers. To reduce the burden on developers, many previous studies have investigated factors that affect the chance of PRs getting accepted and built prediction models based on these factors. However, most prediction models are built on the data after PRs are submitted for a while (e.g., comments on PRs), making them not useful in practice. Because integrators still need to spend a large amount of effort on inspecting PRs. In this study, we propose an approach named E-PRedictor (earlier PR predictor) to predict whether a PR will be merged when it is created. E-PRedictor combines three dimensions of manual statistic features (i.e., contributor profile, specific pull request, and project profile) and deep semantic features generated by BERT models based on the description and code changes of PRs. To evaluate the performance of E-PRedictor, we collect475192 PRs from 49 popular open-source projects on GitHub. The experiment results show that our proposed approach can effectively predict whether a PR will be merged or not. E-PRedictor outperforms the baseline models (e.g., Random Forest and VDCNN) built on manual features significantly. In terms of F1@Merge, F1@Reject, and AUC (area under the receiver operating characteristic curve), the performance of E-PRedictor is 90.1%, 60.5%, and 85.4%, respectively.

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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

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Computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and Information security Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research Center of Industrial Internet security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of cyberspace Technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

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Electrocatalytic cleavage of a lignin β-O-4 model compound and coupling with nitrogen-containing aromatics using Prussian blue analogue-derived nickel–cobalt spinel

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Journal of Energy Chemistry 2025年第1期100卷 628-636页

作者： Yi Qi Xueying Chen Bowen Liu Xuliang Lin Xueqing Qiu Yanlin Qin Guangdong Provincial Key laboratory of Plant Resources Biorefinery School of Chemical Engineering and Light Industry Guangdong University of Technology Guangdong Provincial laboratory of Chemistry and Fine Chemical Engineering Jieyang Center Guangdong Basic Research Center of Excellence for Ecological security and Green Development Guangdong University of Technology

Electrochemical conversion of lignin for the production of high-value heterocyclic aromatic compounds has great *** demonstrate the targeted synthesis and cation modulation of NiCo2O4spinel nanoboxes,synthesized via cation exchange and calcination *** catalysts exhibit excellent efficacy in the electrocatalytic conversion of lignin model compounds,specifically 2-phenoxy-1-phenylethanol,into nitrogen-containing aromatics,achieving high conversion rates and *** catalysts were synthesized via a cation exchange and calcination oxidation process,using Prussian blue nanocubes as *** porous architecture and polymetallic composition of the NiCo2O4spinel demonstrated superior performance in electrocatalytic oxidative coupling,achieving a 99.2 wt%conversion rate of the 2-phenoxy-1-phenylethanol with selectivities of 37.5 wt%for quinoline derivatives and 31.5 wt%for *** innovations include the development of a sustainable one-pot synthesis method for quinoline derivatives,the elucidation of a multistage reaction pathway involving CAO bond cleavage,hydroxyaldol condensation,and CAN bond formation,and a deeper mechanistic understanding derived from DFT *** work establishes a new strategy for lignin valorization,offering a sustainable route to produce high-value nitrogen-containing aromatics from renewable biomass under mild conditions,without the need for additional reagents.

关键词： Spinel Biomass valorization Electrocatalysis Lignin oxidation Coupling

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Genesis of geothermal water in the hinterland of Guanzhong Basin， China: Insight from hydrochemical and isotopic analysis

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Journal of Earth Science 2025年

作者： Jiangxia Wang Panpan Xu Hui Qian Yongqi Zang Qiming Wang Zhiyuan Ma School of Water and Environment， Chang'an University Key laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education， Chang'an University Key laboratory of Eco-hydrology and Water security in Arid and Semi-arid Regions of Ministry of Water Resources， Chang'an University

The Guanzhong Basin， located in Shaanxi Province， China， has abundant geothermal resources in its hinterland. Focusing on typical thermal reservoir structural units， namely the Xianli fault-step (XL) and Xi'an depression (XA)， this study reveals the genesis of geothermal water by combining hydrochemistry and environmental isotopes. The geothermal waters of XL uniformly correspond to the Na-Cl type with high contents of major ions， while the geothermal water of XA has complex hydrochemical types. The quartz geothermometer (with maximum steam loss) and Na-K-Ca geothermometer are recommended for XL and the quartz geothermometer (with no and maximum steam loss) and Na-K geothermometer for XA. Their temperature ranges are 99-159℃ and 93-146℃， respectively. The circulation depth of geofluids in XL and XA range from 2579 to 4766 m and from 2541 to 4330 m， with mean apparent ages of 21543 and 20345 yr B.P.， respectively. The geothermal water in XL originates from a mixture of sedimentary water and ancient infiltration water， and its formation mode was the vertical heat conduction with cooling process. In contrast， the geothermal water in XA originates from the dual process of cooling and mixing， with the process being dominated by ancient infiltration water and supplementation by modern infiltration water.

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Preparation and applications of calcium ferrite as a functional material:A review

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 292-310页

作者： Xiuli Han Bowen Duan Lei Liu Shilong Fang Weiwei Wang College of Mining Engineering North China University of Science and TechnologyTangshan 063210China Collaborative Innovation Center of Green Development and Ecological Restoration of Mineral Resources of Hebei Province Tangshan 063210China Key laboratory of Mining Development and security Technology of Hebei Province Tangshan 063210China

Calcium ferrite(CF)is recognized as a potential green and efficient functional material because of its advantages of magnetism,electrochemistry,catalysis,and biocompatibility in the fields of materials chemistry,environmental engineering,and ***-fore,the obtained research results need to be systematically summarized,and new perspectives on CF and its composite materials need to be *** on the presented studies of CF and its composite materials,the types and structures of the crystal are *** addition,the current application technologies and theoretical mechanisms with various properties in different fields are ***,the various preparation methods of CF and its composite materials are elaborated in *** importantly,the advantages and disadvantages of the synthesis methods of CF and its composite materials are discussed,and the existing problems and emerging challenges in practical production are ***,the key future research directions of CF and its composite materials have been prospected from the potential application technologies to provide references for its synthesis and efficient utilization.

关键词： calcium ferrite mineral functional materials preparation application perspectives

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Smart Gas Sensors:Recent Developments and Future Prospective

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Nano-Micro Letters 2025年第3期17卷 55-86页

作者： Boyang Zong Shufang Wu Yuehong Yang Qiuju Li Tian Tao Shun Mao College of Environmental Science and Engineering Biomedical Multidisciplinary Innovation Research InstituteShanghai East HospitalState Key Laboratory of Pollution Control and Resource ReuseTongji University1239 Siping RoadShanghai 200092People’s Republic of China Shanghai Institute of Pollution Control and Ecological security Shanghai 200092People’s Republic of China Microbiome Medicine Center Department of Laboratory MedicineZhujiang HospitalSouthern Medical UniversityGuangzhou 510280People’s Republic of China

Gas sensor is an indispensable part of modern society withwide applications in environmental monitoring,healthcare,food industry,public safety,*** the development of sensor technology,wireless communication,smart monitoring terminal,cloud storage/computing technology,and artificial intelligence,smart gas sensors represent the future of gassensing due to their merits of real-time multifunctional monitoring,earlywarning function,and intelligent and automated *** electronicand optoelectronic gas sensors have been developed for high-performancesmart gas *** the development of smart terminals and the maturityof integrated technology,flexible and wearable gas sensors play an increasingrole in gas *** review highlights recent advances of smart gassensors in diverse *** structural components and fundamentalprinciples of electronic and optoelectronic gas sensors are described,andflexible and wearable gas sensor devices are ***,sensorarray with artificial intelligence algorithms and smart gas sensors in“Internet of Things”paradigm are ***,the challengesand perspectives of smart gas sensors are discussed regarding the future need of gas sensors for smart city and healthy living.

关键词： Smart gas sensor Electronic sensor Optoelectronic sensor Flexible and wearable sensor Artificial intelligence

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