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基于同义词词林和《知网》的短语主题提取

厦门大学学报（自然科学版） 2015年第2期54卷 263-269页

作者：曾聪张东站厦门大学信息科学与技术学院福建厦门361005

提出了利用主题词存在与否的基于主题词的短语抽取算法,并在其基础上利用社会知识词簇集合作为分类信息,词的相似度作为距离权重,利用改进K最近邻分类算法(KNN)的分类思想,提出基于《知网》词相似度的短语主题抽取算法.并在其基础上提... 详细信息

提出了利用主题词存在与否的基于主题词的短语抽取算法,并在其基础上利用社会知识词簇集合作为分类信息,词的相似度作为距离权重,利用改进K最近邻分类算法(KNN)的分类思想,提出基于《知网》词相似度的短语主题抽取算法.并在其基础上提出一种根据中文表达习惯的基于加权主题词的短语主题抽取算法.实验结果表明,后两种算法对短语主题抽取效果良好,平均查全率分别达到78.88%和83.39%,平均查准率达99.06%和99.70%.

关键词：短语主题主题抽取同义词词林知网

Role of cancer stem cell ecosystem on breast cancer metastasis and related mouse models

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Zoological Research 2024年第3期45卷 506-517页

作者： Xilei Peng Haonan dong Lixing zhang Suling Liu Fudan University Shanghai Cancer Center&Institutes of Biomedical Sciences/State Key Laboratory of Genetic Engineering/Cancer Institutes/Department of Oncology/Key Laboratory of Breast Cancer in Shanghai/Shanghai Key Laboratory of Medical Epigenetics/Shanghai Key Laboratory of Radiation Oncology/International Co-laboratory of Medical Epigenetics and Metabolism Ministry of Science and Technology/Shanghai Medical College/Fudan UniversityShanghai 200032China

Breast cancer metastasis is responsible for most breast cancer-related deaths and is influenced by many factors within the tumor ecosystem,including tumor cells and *** cancer stem cells(BCSCs)constitute a small population of cancer cells with unique characteristics,including their capacity for self-renewal and *** have shown that BCSCs not only drive tumorigenesis but also play a crucial role in promoting metastasis in breast *** tumor microenvironment(TME),composed of stromal cells,immune cells,blood vessel cells,fibroblasts,and microbes in proximity to cancer cells,is increasingly recognized for its crosstalk with BCSCs and role in BCSC survival,growth,and dissemination,thereby influencing metastatic ***,a thorough understanding of BCSCs and the TME is critical for unraveling the mechanisms underlying breast cancer *** this review,we summarize current knowledge on the roles of BCSCs and the TME in breast cancer metastasis,as well as the underlying regulatory ***,we provide an overview of relevant mouse models used to study breast cancer metastasis,as well as treatment strategies and clinical trials addressing BCSC-TME interactions during ***,this study provides valuable insights for the development of effective therapeutic strategies to reduce breast cancer metastasis.

关键词： Breast cancer Metastasis Cancer stem cell Ecosystem Tumor microenvironment Mouse model

Incorporating adaptive genomic variation into predictive models for invasion risk assessment

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Environmental Science and Ecotechnology 2024年第2期18卷 83-90页

作者： Yiyong Chen Yangchun Gao Xuena Huang Shiguo Li Zhixin zhang Aibin zhan Research Center for Eco-Environmental Sciences Chinese Academy of SciencesBeijing100085China Guangdong Key Laboratory of Animal Conservation and Resource Utilization Guangdong Public Laboratory of Wild Animal Conservation and UtilizationInstitute of ZoologyGuangdong Academy of ScienceGuangzhou510260China University of Chinese Academy of Sciences Chinese Academy of SciencesBeijing100049China CAS Key Laboratory of Tropical Marine Bio-resources and Ecology South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou510275China Global Ocean and Climate Research Center South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou510275China

Global climate change is expected to accelerate biological invasions,necessitating accurate risk forecasting and management ***,current invasion risk assessments often overlook adaptive genomic variation,which plays a significant role in the persistence and expansion of invasive *** we used Molgula manhattensis,a highly invasive ascidian,as a model to assess its invasion risks along Chinese coasts under climate *** population genomics analyses,we identified two genetic clusters,the north and south clusters,based on geographic *** predict invasion risks,we employed the gradient forest and species distribution models to calculate genomic offset and species habitat suitability,*** approaches yielded distinct predictions:the gradient forest model suggested a greater genomic offset to future climatic conditions for the north cluster(i.e.,lower invasion risks),while the species distribution model indicated higher future habitat suitability for the same cluster(i.e,higher invasion risks).By integrating these models,we found that the south cluster exhibited minor genome-niche disruptions in the future,indicating higher invasion *** study highlights the complementary roles of genomic offset and habitat suitability in assessing invasion risks under climate ***,incorporating adaptive genomic variation into predictive models can significantly enhance future invasion risk predictions and enable effective management strategies for biological invasions in the future.

关键词： Invasion risk Climate change Adaptive genomic variation Genomic offset Habitat suitability

A Galaxy Image Augmentation Method Based on Few-shot Learning and Generative Adversarial Networks

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Research in Astronomy and Astrophysics 2024年第3期24卷 180-193页

作者： Yiqi Yao Jinqu zhang Ping Du Shuyu dong School of Artificial Intelligence South China Normal UniversityFoshan 528225China School of Computer Science South China Normal UniversityGuangzhou 510631China Guangdong Construction Vocational Technology Institute Guangzhou 511500China

Galaxy morphology classifications based on machine learning are a typical technique to handle enormous amounts of astronomical observation data,but the key challenge is how to provide enough training data for the machine learning *** this article proposes an image data augmentation method that combines few-shot learning and generative adversarial *** Galaxy10 DECaLs data set is selected for the experiments with consistency,variance,and augmentation effects being *** popular networks,including AlexNet,VGG,and ResNet,are used as examples to study the effectiveness of different augmentation methods on galaxy morphology *** results show that the proposed method can generate galaxy images and can be used for expanding the classification model’s training *** to comparative studies,the best enhancement effect on model performance is obtained by generating a data set that is 0.5–1 time larger than the original data ***,different augmentation strategies have considerably varied effects on different types of ***-GAN achieved the best classification performance on the ResNet network for In-between Round Smooth Galaxies and Unbarred Loose Spiral Galaxies,with F1 Scores of 89.54%and 63.18%,*** comparison reveals that various data augmentation techniques have varied effects on different categories of galaxy morphology and machine learning ***,the best augmentation strategies for each galaxy category are suggested.

关键词： techniques image processing-galaxies structure-galaxies general

Hydrogen storage thermodynamics and kinetics of as-cast Ce-Mg-Ni-based alloy

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Journal of Iron and Steel Research International 2024年第3期31卷 752-766页

作者： Yan Qi Xin zhang Jun Li dong-liang Zhao Shi-hai Guo Yang-huan zhang Department of Functional Material Research Central Iron and Steel Research InstituteBeijing 100081China China Rare Earth New Material(Weishan)Co..Ltd. Jining 277600ShandongChina Collaborative Innovation Center of Integrated Exploitation of Bayan Obo Multi-Metal Resources Inner Mongolia University of Science and TechnologyBaotou 014010Inner MongoliaChina

The reaction kinetics of alloys based on magnesium are known to be greatly improved by the partial substitution of Mg with rare earths and transition metals,particularly *** enhanced superficial hydrogen dissociation rate,the weakened Mg-H bond and the lower activation energy following element replacement are thought to be related to the better *** experimental alloys Ce5Mg_(95-x)Ni_(x)(x=5,10,15)were smelted by the vacuum induction *** phase transformation and structural evolution of experimental alloys before and after reaction with hydrogen were char-acterized by X-ray diffraction,scanning electron microscopy and transmission electron *** cast specimens contain CeMg_(12),Mg and Mg_(2)Ni phases,and the increase in Ni content results in an obvious growth of Mg_(2)Ni *** isothermal and non-isothermal hydrogenation and dehydrogenation kinetics of the experimental specimens were investi-gated using the Sievert apparatus,differential scanning calorimetry and thermal gravimetric *** activation energy may be calculated using the Arrhenius and Kissinger *** experimental alloys have been shown to have good activation properties,with a reversible hydriding and dehydriding capacities of around 5.0 wt.%in the first *** initial dehydrogenation temperature of MgH_(2) decreases from 557.5 to 537.7 K with changing Ni content from 5 to 15 at.%.The dehydrogenation activation energy also reduces from 77.09 to 62.96 kJ/mol,which explains the improved hydrogen storage performance caused by Ni *** can be shown that the impact of Ni on the decomposition enthalpy of MgH_(2) is quite modest,with the absolute enthalpy(ΔHr)only decreasing from 78.48 to 76.15 kJ/mol.

关键词： Mg-based alloy Ni content Hydrogen storage kinetics Thermodynamic property Activation energy

Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction

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Petroleum Science 2024年第4期21卷 2329-2344页

作者： Jiu-Qiang Yang Nian-Tian Lin Kai zhang Yan Cui Chao Fu dong zhang College of Earth Sciences and Engineering Shandong University of Science and Technology Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and Technology Transportation Institute of Inner Mongolia University Key Laboratory of Gas Hydrate Qingdao Institute of Marine Geology Ministry of Natural Resources

Conventional machine learning(CML) methods have been successfully applied for gas reservoir prediction. Their prediction accuracy largely depends on the quality of the sample data; therefore, feature optimization of the input samples is particularly important. Commonly used feature optimization methods increase the interpretability of gas reservoirs; however, their steps are cumbersome, and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently. In contrast to CML methods, deep learning(DL) methods can directly extract the important features of targets from raw data. Therefore, this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM). This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes, considerably simplifying the feature optimization. A CNN was used for feature optimization to highlight sensitive gas reservoir information. APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results. The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction, giving full play to the advantages of DL and CML methods. The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model. In the feature optimization process of multicomponent seismic attribute data, CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods. In the prediction process, the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy. The constructed CNN-APSO-LSSVM mode

关键词： Multicomponent seismic data Deep learning Adaptive particle swarm optimization Convolutional neural network Least squares support vector machine Feature optimization Gas-bearing distribution prediction

Balanced Fracturing and Cold-welding of Magnesium during Ball Milling Assisted by Carbon Coating:Experimental and Molecular Dynamic Simulation

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Journal of Wuhan University of Technology(Materials Science) 2024年第4期39卷 895-903页

作者：韩宗盈 dong Hui DING Guoyang zhang Jiale SONG Xiufang College of Energy Storage Technology Shandong University of Science and TechnologyQingdao 266590China College of Chemical and Biological Engineering Shandong University of Science and TechnologyQingdao 266590China

The lignite-derived carbon from self-protection pyrolysis was employed to balance the fracturing and cold-welding of magnesium during ball *** size analysis indicates that the introduction of lignite-derived carbon can effectively reduce the particle size of Mg while the introduction of graphite does no ***,the effect of lignite-derived carbon on crystallite size reduction of Mg is also better than graphite.A moderate cold-welding phenomenon was observed after ball-milling Mg with the lignite-derived carbon,suggesting less Mg is wasted on the milling vials and *** dynamic simulations reveal that the balanced fracturing and cold-welding of magnesium during ball milling is mainly attributed to the special structure of the lignite-derived carbon:graphitized short-range ordered stacking function as dry lubricant and irregular shape/sharp edge function as milling *** preliminary findings in current study are expected to offer implications for designing efficient Mg-based hydrogen storage materials.

关键词： magnesium lignite-derived carbon cold-welding ball milling molecular dynamic

Tendencies of alloyed engineering in BiOX-based photocatalysts:a state-of-the-art review

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Rare Metals 2024年第4期43卷 1488-1512页

作者： Yan-dong Sun Chao Zeng Xue zhang Zi-Qi zhang Bo Yang Sheng-Qi Guo Tianjin Key Laboratory of Clean Energy and Pollutant Control School of Energy and Environmental EngineeringHebei University of TechnologyTianjin300401China State-Province Joint Engineering Laboratory of Zeolite Membrane Materials Institute of Advanced MaterialsCollege of Chemistry and Chemical EngineeringJiangxi Normal UniversityNanchang330022China Anhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental Remediation School of Physics and Electronic InformationAnhui Province Industrial Generic Technology Research Center for Alumics MaterialsHuaibei Normal UniversityHuaibei235000China

Energy-saving and environmentally friendly photocatalysis has emerged as a popular research area in response to issues with energy scarcity and environmental *** to the unique layer-like structure,BiOX(Cl,Br,I)is frequently used in ***,inherent flaws in BiOX,such as an inappropriate band gap and low carrier separation efficiency,restrict its capacity for *** to the tunable grouping layer,alloying engineering is employed to optimize the intrinsic properties of BiOX and alloyed BiOX becomes a promising photocatalytic *** review describes the structure of BiOX,where tunable halogen layers provide favorable conditions for the implementation of alloying engineering to improve intrinsic *** article compares the effects and mechanisms of alloying engineering on the optimization of the energy band structure and carrier behavior of BiOX,and lists various modification methods used to improve the optimization of the intrinsic properties by alloying engineering,including defect engineering,morphology control as well as the synergy between alloying and other modification methods(bismuth-rich strategies,cation doping,construction of heterojunctions and plasma resonance effects).Subsequently,applications of alloyed BiOX in energy and environmental fields are summarized,including contaminant degradation,antibacterial,CO_(2)reduction,nitrogen fixation and organic ***,we summarize the current challenges and future directions of alloyed *** is expected that this work will provide guidance and assistance for an in-depth study and understanding of the mechanisms of alloying engineering to optimize intrinsic properties and design alloyed BiOX with higher photocatalytic activity.

关键词： Bismuth oxyhalide Photocatalytic Alloyed engineering Microstructural regulation Application

Prediction of dissolved gas content in transformer oil based on multi-information fusion

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High Voltage 2024年第3期9卷 685-699页

作者： Tongliang Yang Yun Fang Chengming zhang Chao Tang dong Hu College of Engineering and Technology Southwest UniversityChongqingChina International R&D Center for New Technologies of Smart Grid and Equipment Southwest UniversityChongqingChina

In order to accurately predict the content and variation trend of dissolved gas in trans-former oil and guide the condition maintenance of power transformers,a combined prediction model based on multi-information fusion is proposed and its effectiveness is *** of all,based on the possibility of pathological and missing historical sample data,a detection and filling method based on variable weight combination samples is ***,the authors propose two *** at the non-linear and non-stationary characteristics of gas content,a univariate decomposition prediction mode HBA-VMD-TCN which based on the Honey Badger algorithm,variational mode decomposition and time convolutional network(TCN)is *** the multi-variate Informer prediction model is established for gas content affected by multiple ***,the cross-entropy theory is used to determine the weight coefficients of the two models,and the multi-information fusion combined prediction model is ***,on the basis of the above,a method to determine the time step and the position information of the transition point adaptively in the process of prediction is proposed to further improve the prediction *** results show that,through a series of simulation experiments of model comparison and transformer anomaly prediction,the accuracy and effectiveness of the combined prediction model are verified.

关键词： content prediction dissolved