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

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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Laser image refractive index sensor based on the combination of a coreless optical fiber and a digital camera

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Optoelectronics Letters 2025年第2期21卷 65-68页

作者： GUO Jingzhong LAN Feng CHEN Xiang LIU Haifeng Xi'an Institute of Space Radio Technology Key Laboratory of Optoelectronic Sensor and Sensing network Technology College of Electronic Information and Optical Engineering Nankai University

As a laser passes through a scattering medium,the light interacts with the irregular reflections within the medium,resulting in light scattering and the formation of *** this paper,an image sensor based on the combination of a coreless optical fiber and a digital camera is proposed for liquid refractive index sensing *** coreless fiber is used as a sensing unit,and the change in the speckle pattern is measured using the digital correlation method to detect the magnitude of the liquid's refractive *** experimental results indicate that the laser image sensing technique is capable of effectively distinguishing liquid samples with refractive indices ranging from 1.332 8 to1.390 8,with a sensing sensitivity of-1.306 ***,the laser image sensing technique,with its advantages of high experimental reproducibility,simple system design,remote over-control,holds great research significance and potential application in laser communication and sensor integration.

关键词： Digital cameras

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Boosting cationic and anionic redox activity of Li-rich layered oxide cathodes via Li/Ni disordered regulation

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Journal of Energy Chemistry 2025年第1期100卷 533-543页

作者： Zewen Liu Zhen Wu Hao Wang Xudong Zhang Yuanzhen Chen Yongning Liu Shengwu Guo Shenghua Chen Yanli Nan Yan Liu State Key Laboratory for Mechanical Behavior of Materials Xi'an Jiaotong University School of Materials Science and Engineering Xi'an University of Architecture and Technology Hefei Advanced Computing Center Operation Management Corp Ltd network information Center Xi'an Jiaotong University

Lithium-rich layered oxides （LLOs） are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries （LIBs）.However,they suffer from voltage decay and low initial Coulombic efficiency （ICE） due to severe structural degradation caused by irreversible O ***,we introduce a three-in-one strategy of increasing Ni and Mn content,along with Li/Ni disordering and TM–O covalency regulation to boost cationic and anionic redox activity simultaneously and thus enhance the electrochemical activity of *** target material,Li1.2Ni0.168Mn0.558Co0.074O2（L1）,exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g-1and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li1.2Ni0.13Mn0.54Co0.13O2（Ls）(274.4 mA h g-1for initial capacity,73.8%for ICE and voltage decay of 0.84 mV/cycle over 300 cycles at 1C).Theoretical calculations reveal that the density of states （DOS） area near the Fermi energy level for L1 is larger than that of Ls,indicating higher anionic and cationic redox reactivity than ***,L1 exhibits increased O-vacancy formation energy due to higher Li/Ni disordering of 4.76%（quantified by X-ray diffraction Rietveld refinement） and enhanced TM–O covalency,making lattice O release more difficult and thus improving electrochemical *** increased Li/Ni disordering also leads to more Ni2+presence in the Li layer,which acts as a pillar during Li+de-embedding,improving structural *** research not only presents a viable approach to designing low-Co LLOs with enhanced capacity and ICE but also contributes significantly to the fundamental understanding of structural regulation in high-performance LIB cathodes.

关键词： Ice

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DecMamba:Mamba Utilizing Series Decomposition for Multivariate Time Series Forecasting

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Computers, Materials & Continua 2025年第1期82卷 1049-1068页

作者： Jianxin Feng Jianhao Zhang Ge Cao Zhiguo Liu Yuanming Ding Communication and network Key Laboratory Dalian UniversityDalian116622China

Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy *** decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time ***,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between *** problems make it difficult to analyze the intricate temporal variations of real-world time *** this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between *** backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend *** with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.

关键词： Data prediction time series Mamba series decomposition

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Distribution and mapping of temperate savanna in the sandy lands of eastern China

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Science China Earth Sciences 2025年第1期68卷 128-141页

作者： Xiaoya LI Feng WANG Tao DUAN Kaijie YANG Bin YANG Chunmei WANG Xin TIAN Qi LU Institute of Ecological Conservation and Restoration Institute of Desertification Studies Chinese Academy of Forestry Institute of Desertification Institute of Desertification Studies Chinese Academy of Forestry Institute of Great Green Wall Satellite Application centre for Ecology and Environment MEE Institute of Microelectronics Chinese Academy of Sciences Aerospace information Research Institute Chinese Academy of Sciences Institute of Forest Resource information Techniques Chinese Academy of Forestry

Savannas are distributed globally across various *** perform a significant socioeconomic role in regulating net primary productivity and the terrestrial carbon *** to the high spatial heterogeneity induced by the mixed distribution of trees and grasses,current global land-cover classification maps often classify savannas as forests or grasslands Temperate savannas in China cover a vast area,yet their spatial patterns and extent remain poorly *** study developed a novel remote sensing temperate savannas mapping method by integrating very high-resolution （VHR） unmanned aerial vehicle （UAV） and GF-6 satellite imagery based on the vegetation structure characteristics of temperate savannas using the FAO definition of *** and validation datasets were collected from VHR UAV imagery covering the main temperate savannah types with a ground area of 1818 *** fractional woody vegetation cover （FWVC） and fractional herbaceous vegetation cover （FHVC） in temperate savannas were estimated by integrating Classification and Regression Tree and Random Forest *** current distribution of temperate savannas in the Horqin and Otindag Sandy Lands of Eastern China was mapped to a unit area of 0.5 *** root-mean-square error （RMSE） values for the FWVC and FHVC models were 10.69% and 11.67%,and the R2values were 0.66 and 0.63,*** assessment indicated that the new temperate savanna map had high identification accuracy,with a Kappa coefficient of 0.70,overall accuracy of 0.88,and average accuracy of *** savannas in the sandy lands of eastern China were primarily distributed in the northern Horqin Sandy Land the central and eastern regions of the Otindag Sandy Land,and the transition zone from the southern slopes of the Greater Khingan Mountains to the plains,covering a total area of 3.27×10～6 *** main savannah types were elm,oak/maple,and shrub *** savannas were widely distributed in the Otindag Sandy

关键词： Temperate savanna UAV imagery GF-6 Fractional woody vegetation cover Fractional herbaceous vegetation cover Machine learning

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Glia-to-neuron reprogramming to the rescue?

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Neural Regeneration Research 2025年第5期20卷 1395-1396页

作者： Jack W.Hickmott Cindi M.Morshead Department of Surgery University of TorontoTorontoONCanada Donnelly centre for Cellular and Biomolecular Research TorontoONCanada Institute of Biomedical Engineering University of TorontoTorontoONCanada The KITE Research Institute Toronto Rehabilitation InstituteUniversity Health NetworkTorontoONCanada

Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative *** brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be *** exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.

关键词： programming passing proof

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A robust statistical prediction model for late-summer heavy precipitation days in North China

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Science China Earth Sciences 2025年第1期68卷 158-171页

作者： Shunli JIANG Tingting HAN Xin ZHOU Huijun WANG Zhicong YIN Xiaolei SONG Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education Nanjing University for Information Science and Technology Nansen-Zhu International Research centre Institute of Atmospheric Physics Chinese Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

Recently, heavy precipitation(HP) events have occurred frequently in North China(NC), causing devastating economic losses and human fatalities. However, the short-term climate prediction of HP is quite limited. Combining year-to-year increment(DY) method and sliding correlations, we developed a robust seasonal prediction model for late-summer HP days(HPDs) in NC during 1982–2022, utilizing three independent predictors—February sea surface temperature(SST) in the Indian Ocean(SST＿IO), February snow depth over North Asia(SDE＿NA), and May melted snow depth in NC(MSDE＿NC). The SST＿IO anomalies affect NC's precipitation through the Pacific-Japan pattern. The SDE＿NA anomalies are associated with East Asian anomalous anticyclone by southeastern propagation of Rossby wave at Eurasia. The MSDE＿NC anomalies are followed by vertical motion and moisture anomalies in situ and thereby cause precipitation anomalies. This prediction model can well simulate the variations of the HPDs, with a correlation coefficient(CC) of 0.81(0.65) between the observed and predicted HPDs＿DY(HPDs＿anomaly). The percentage with the same sign for 15 extreme HPDs＿anomaly years(PSSE) is 100%.Moreover, in the cross-validation test during 1982–2022, the PSSE for HPDs＿anomaly is as high as 100%, along with a low rootmean-square error of 1.14. For independent hindcasts during 2013–2022, the CC between the observed and predicted HPDs＿DY(HPDs＿anomaly) is 0.93(0.83), together with high Nash-Sutcliffe efficiency(0.82) and agreement index(0.89). Specifically, the predictions are broadly consistent with the observations for 2015, 2016, 2017, 2021, and 2022, reflecting excellent performance of this prediction model of HPDs in NC.

关键词： Heavy precipitation at North China Year-to-year increment approach Robust seasonal prediction Sea surface temperature Snow depth

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IDSSCNN-XgBoost:Improved Dual-Stream Shallow Convolutional Neural network Based on Extreme Gradient Boosting Algorithm for Micro Expression Recognition

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Computers, Materials & Continua 2025年第1期82卷 729-749页

作者： Adnan Ahmad Zhao Li Irfan Tariq Zhengran He School of information Science and Engineering Southeast UniversityNanjing210016China

Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial *** deep neural networks(DNNs)with convolutional structures have been ***,unlike DNNs,shallow convolutional neural networks often outperform deeper models in mitigating overfitting,particularly with small ***,many of these methods rely on a single feature for recognition,resulting in an insufficient ability to extract highly effective *** address this limitation,in this paper,an Improved Dual-stream Shallow Convolutional Neural network based on an Extreme Gradient Boosting Algorithm(IDSSCNN-XgBoost)is introduced for ME *** proposed method utilizes a dual-stream architecture where motion vectors(temporal features)are extracted using Optical Flow TV-L1 and amplify subtle changes(spatial features)via EulerianVideoMagnification(EVM).These features are processed by IDSSCNN,with an attention mechanism applied to refine the extracted effective *** outputs are then fused,concatenated,and classified using the XgBoost *** comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information,supported by the robust classification power of *** proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database(CASMEII),Spontaneous Micro-Expression Database(SMICHS),and Spontaneous Actions and Micro-Movements(SAMM).Experimental results indicate that the proposed model can achieve outstanding results compared to recent *** accuracy results are 79.01%,69.22%,and 68.99%on CASMEII,SMIC-HS,and SAMM,and the F1-score are 75.47%,68.91%,and 63.84%,*** proposed method has the advantage of operational efficiency and less computational time.

关键词： ME recognition dual stream shallow convolutional neural network euler video magnification TV-L1 XgBoost

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Attention Eraser and Quantitative Measures for Automated Bone Age Assessment

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Computers, Materials & Continua 2025年第1期82卷 627-644页

作者： Liuqiang Shu Lei Yu College of Computer and information Science Chongqing Normal UniversityChongqing401331China

Bone age assessment(BAA)aims to determine whether a child’s growth and development are normal concerning their chronological *** predict bone age more accurately based on radiographs,and for the left-hand X-ray images of different races model can have better adaptability,we propose a neural network in parallel with the quantitative features from the left-hand bone measurements for *** this study,a lightweight feature extractor(LFE)is designed to obtain the featuremaps fromradiographs,and amodule called attention erasermodule(AEM)is proposed to capture the fine-grained ***,the dimensional information of the metacarpal parts in the radiographs is measured to enhance the model’s generalization capability across images fromdifferent *** is trained and validated on the RSNA,RHPE,and digital hand atlas datasets,which include images from various racial *** model achieves a mean absolute error(MAE)of 4.42 months on the RSNA dataset and 15.98 months on the RHPE *** to ResNet50,InceptionV3,and several state-of-the-art methods,our proposed method shows statistically significant improvements(p<0.05),with a reduction in MAE by 0.2±0.02 years across different racial ***,t-tests on the features also confirm the statistical significance of our approach(p<0.05).

关键词： Bone age assessment attention eraser quantitative feature metacarpal bones

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Secure beamforming and deployment design for rate-splitting multiple access-based UAV communications

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Science China(information Sciences) 2025年第1期68卷 342-356页

作者： Bing WANG Xiaofeng TAO Shujun HAN Huici WU Kai YANG Zhu HAN National Engineering Research Center of Mobile network Technologies Beijing University of Posts and Telecommunications Peng Cheng Laboratory Department of Electrical and Computer Engineering University of Houston

Rate-splitting multiple access(RSMA) has recently gained attention as a potential robust multiple access(MA)scheme for upcoming wireless networks. Given its ability to efficiently utilize wireless resources and design interference management strategies, it can be applied to unmanned aerial vehicle(UAV) networks to provide convenient services for large-scale access ground users. However, due to the line-of-sight(LoS) broadcast nature of UAV transmission, information is susceptible to eavesdropping in RSMA-based UAV networks. Moreover, the superposition of signals at the receiver in such networks becomes complicated. To cope with the challenge, we propose a two-user multi-input single-output(MISO) RSMA-based UAV secure transmission framework in downlink communication networks. In a passive eavesdropping scenario, our goal is to maximize the sum secrecy rate by optimizing the transmit beamforming and deployment location of the UAV-base station(UAV-BS),while considering quality-of-service(QoS) constraints, maximum transmit power, and flight space limitations. To address the non-convexity of the proposed problem, the optimization problem is first decoupled into two subproblems. Then, the successive convex approximation(SCA) method is employed to solve each subproblem using different propositions. In addition, an alternating optimization(AO)-based location RSMA(L-RSMA) beamforming algorithm is developed to implement joint optimization to obtain the suboptimal solution. Numerical results demonstrate that(1) the proposed L-RSMA scheme yields a28.97% higher sum secrecy rate than the baseline L-space division multiple access(SDMA) scheme;(2) the proposed L-RSMA scheme improves the security performance by 42.61% compared to the L-non-orthogonal multiple access(NOMA) scheme.

关键词： rate-splitting multiple access UAV communication precoding optimization deployment location physical layer security

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