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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

The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 86-96页

作者： Qianyun Yun Wen-Biao Han yi-Yang Guo He wang Minghui Du Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Shanghai Astronomical Observatory Chinese Academy of Sciences School of Physics and Astronomy Shanghai Jiao Tong University School of Astronomy and Space Science University of Chinese Academy of Sciences Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou) University of Chinese Academy of Sciences Lanzhou Center for Theoretical Physics Key Laboratory of Theoretical Physics of Gansu Province Key Laboratory of Quantum Theory and Applications of MoE Lanzhou University Institute of Theoretical Physics & Research Center of Gravitation Lanzhou University International Centre for Theoretical Physics Asia-Pacific (ICTP-AP) University of Chinese Academy of Sciences (UCAS) Center for Gravitational Wave Experiment National Microgravity Laboratory Institute of Mechanics Chinese Academy of Sciences

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals(EMRIs). This task is particularly challenging because EMRI signals are complex, lengthy, and faint. In this work, we introduce a2-layer convolutional neural network(CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate(TPR) of 96.9% at a 1% false positive rate(FPR) for signal-to-noise ratio(SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as the mass, spin of the supermassive black hole(SMBH) and the initial eccentricity of the orbit can also be inferred directly by employing a neural network. The mass and spin of the SMBH can be determined at 99% and 92% respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter *** model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation.

关键词： gravitational wave EMRIs deep-learing

Prediction and optimization of flue pressure in sintering process based on SHAP

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 346-359页

作者： Mingyu wang Jue Tang Mansheng chu Quan Shi Zhen Zhang School of Metallurgy Northeastern UniversityShenyang 110819China Liaoning Low-Carbon Steelmaking Technology Engineering Research Center Northeastern UniversityShenyang 110819China Engineering Research Center of Frontier Technologies for Low-Carbon Steelmaking(Ministry of Education) Shenyang 110819China

Sinter is the core raw material for blast *** pressure,which is an important state parameter,affects sinter *** this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment ***,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction *** effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment *** operating parameter combination was then *** flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.

关键词： sintering process flue pressure shapley additive explanation prediction optimization

A simple method of synthesizing multi-wall carbon nanotubes/SnO core/shell nanostructure

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Rare Metals 2007年第S1期26卷 191-194页

作者： SUN Xue1),chu yi2),wang Dawei3),DU Jinhong3),ZHANG Baoyou2),and wang Fuping1)1) School of Science,Harbin Institute of Technology,Harbin 150001,China 2) School of Materials Science and Engineering,Harbin Institute of Technology,Harbin 150001,China 3) Shenyang National Laboratory for Materials Science,Institute of Metal Research,Chinese Academy of Sciences,Shenyang 110016,China Harbin Inst Technol Sch Sci Harbin 150001 Peoples R China Harbin Inst Technol Sch Mat Sci & Engn Harbin 150001 Peoples R China Chinese Acad Sci Inst Met Res Shenyang Natl Lab Mat Sci Shenyang 110016 Peoples R China

Multi-wall carbon nanotubes(MWNTs)/SnO core/shell nanosturcture was synthesized by a simple solution-based method,in which SnCl2 was solved in distilled water containing dispersed MWNTs,then stired,filtered and washed in ambient atmosphere,finally dryed in air at 90 ℃ for 6 *** MWNTs/SnO core/shell nanostructure was characterized by X-ray diffraction(XRD),transmission electron microscopy(TEM).It is found that surface defects of MWNTs are necessary for the deposition of *** mechanism of the SnO nanocoating formation through the hydrolysis of SnCl2 in water(4SnCl_2+2H_2O-Sn_4(OH)2Cl_6+2HCl) was presented.

关键词： precipitate isothermal aging thermodynamic calculation phase diagram high nitrogen austenitic stainless steel

Modifying metabolic and immune hallmarks of cancer by a copper complex

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Science China Chemistry 2025年

作者： Chengyan chu Yunhua Zhang Chengyuan Qian Xiuzhi Yang yicun Shang Zijian Guo Xiaoyong wang State Key Laboratory of Pharmaceutical Biotechnology School of Life Sciences Nanjing University State Key Laboratory of Coordination Chemistry School of Chemistry and Chemical Engineering Nanjing University

Metabolic adaptation of cancer cells is a key tactic for maintaining their tumorigenicity and viability in the tumor immune microenvironment （TIME）.Concurrent modulation on the metabolic pathway and immune system could change the living condition of cancer cells and potentiate the efficacy of anticancer drugs.A copper complex （DDCu） was designed to influence the energy metabolism of cancer cells and reshape the *** restrained the production and transportation of lactate by inhibiting the expression of lactate dehydrogenase and monocarboxylate transporter 4,thereby altering the metabolic pathway of cancer cells and improving the acidic tumor ***,DDCu was reduced to Cu+by cellular glutathione to react with lipoylated proteins of the tricarboxylic acid cycle and destabilize the Fe–S cluster proteins,leading to the aggregation of dihydrolipoamide S-acetyltransferase and cuproptosis in cancer ***,DDCu promoted the polarization of macrophages from the M2 to M1 phenotype,activated CD4+and CD8+T cells,and reversed the *** a result,DDCu inhibited the tumor growth through a synergy between metabolic regulation and immunomodulation.

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Decreased levels of phosphorylated synuclein in plasma are correlated with poststroke cognitive impairment

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Neural Regeneration Research 2025年第9期20卷 2598-2610页

作者： yi wang Yuning Li Yakun Gu Wei Ma Yuying Guan Mengyuan Guo Qianqian Shao Xunming Ji Jia Liu Department of Clinical Laboratory Beijing Bo’ai HospitalChina Rehabilitation Research CenterCapital Medical UniversityBeijingChina Beijing Institute of Brain Disorders Laboratory of Brain DisordersMinistry of Science and TechnologyCollaborative Innovation Center for Brain DisordersBeijing Advanced Innovation Center for Big Data-based Precision MedicineCapital Medical UniversityBeijingChina Department of Neurosurgery Xuanwu HospitalCapital Medical UniversityBeijingChina

Poststro ke cognitive impairment is a major secondary effect of ischemic stroke in many patients;however,few options are available for the early diagnosis and treatment of this *** aims of this study were to(1)determine the specific relationship between hypoxic andα-synuclein during the occur of poststroke cognitive impairment and(2)assess whether the serum phosphorylatedα-synuclein level can be used as a biomarker for poststro ke cognitive *** found that the phosphorylatedα-synuclein level was significantly increased and showed pathological aggregation around the cerebral infa rct area in a mouse model of ischemic *** addition,neuronalα-synuclein phosphorylation and aggregation were observed in the brain tissue of mice subjected to chronic hypoxia,suggesting that hypoxia is the underlying cause ofα-synuclein-mediated pathology in the brains of mice with ischemic *** phosphorylatedα-synuclein levels in patients with ischemic stroke were significantly lower than those in healt hy subjects,and were positively correlated with cognition levels in patients with ischemic ***,a decrease in serum high-density lipoprotein levels in stroke patie nts was significantly correlated with a decrease in phosphorylatedα-synuclein *** ischemic stroke mice did not show significant cognitive impairment or disrupted lipid metabolism 14 days after injury,some of them exhibited decreased cognitive function and reduced phosphorylatedα-synuclein *** together,our results suggest that serum phosphorylatedα-synuclein is a potential biomarker for poststroke cognitive impairment.

关键词： biomarker high-density lipoprotein ischemic stroke phosphorylatedα-synuclein poststroke cognitive impairment

Application of the improved dung beetle optimizer,muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area,China

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Atmospheric and Oceanic Science Letters 2025年第1期18卷 18-23页

作者： Jiarui Cai Bo Sun Huijun wang yi Zheng Siyu Zhou Huixin Li Yanyan Huang Peishu Zong Collaborative Innovation Center on forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjingChina Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiChina China Meteorological Administration Key Laboratory of Transportation MeteorologyNanjingChina Jiangsu Meteorological Observatory NanjingChina

Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical *** learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many *** this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,*** two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction *** addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.

关键词： Groundwater depth Multi-head attention Improved dung beetle optimizer CNN-LSTM CNN-GRU Ningxia

Crystallization Modulation and Holistic Passivation Enables Efficient Two‑Terminal Perovskite/CuIn(Ga)Se_(2)Tandem Solar Cells

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Nano-Micro Letters 2025年第1期17卷 205-215页

作者： Cong Geng Kuanxiang Zhang Changhua wang chung Hsien Wu Jiwen Jiang Fei Long Liyuan Han Qifeng Han yi‑Bing Cheng Yong Peng State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070People’s Republic of China Triumph Photovoltaic Materials Co. Ltd.No.1001 Yannan AvenueHigh‑Tech Development DistrictBengbuAnhui 233000People’s Republic of China College of Materials Science and Engineering Guilin University of TechnologyGuilin 541004People’s Republic of China State Key Laboratory of Metal Matrix Composites Shanghai Jiao Tong UniversityShanghai 200240People’s Republic of China

Two-terminal(2-T)perovskite(PVK)/CuIn(Ga)Se_(2)(CIGS)tandem solar cells(TSCs)have been considered as an ideal tandem cell because of their best bandgap matching regarding to Shockley–Queisser(S–Q)***,the nature of the irregular rough morphology of commercial CIGS prevents people from improving tandem device *** this paper,D-homoserine lactone hydrochloride is proven to improve coverage of PVK materials on irregular rough CIGS surfaces and also passivate bulk defects by modulating the growth of PVK *** addition,the minority carriers near the PVK/C60 interface and the incompletely passivated trap states caused interface recombination.A surface reconstruction with 2-thiopheneethylammonium iodide and N,N-dimethylformamide assisted passivates the defect sites located at the surface and grain ***,LiF is used to create this field effect,repelling hole carriers away from the PVK and C60 interface and thus reducing *** a result,a 2-T PVK/CIGS tandem yielded a power conversion efficiency of 24.6%(0.16 cm^(2)),one of the highest results for 2-T PVK/CIGS TSCs to our *** validation underscores the potential of our methodology in achieving superior performance in PVK/CIGS tandem solar cells.

关键词： PVK/CIGS TSCs Irregular rough surfaces Modulating the growth PVK/C60 interface recombination

Repetitive transcranial magnetic stimulation in Alzheimer’s disease:effects on neural and synaptic rehabilitation

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Neural Regeneration Research 2025年第2期20卷 326-342页

作者： yi Ji Chaoyi Yang Xuerui Pang yibing Yan Yue Wu Zhi Geng Wenjie Hu Panpan Hu Xingqi Wu Kai wang Department of Neurology The First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders HefeiAnhui ProvinceChina Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health HefeiAnhui ProvinceChina Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefeiAnhui ProvinceChina Department of Psychology and Sleep Medicine The Second Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina

Alzheimer’s disease is a neurodegenerative disease resulting from deficits in synaptic transmission and *** Alzheimer’s disease brain tends to be hyperexcitable and hypersynchronized,thereby causing neurodegeneration and ultimately disrupting the operational abilities in daily life,leaving patients *** transcranial magnetic stimulation is a cost-effective,neuro-modulatory technique used for multiple neurological *** the past two decades,it has been widely used to predict cognitive decline;identify pathophysiological markers;promote neuroplasticity;and assess brain excitability,plasticity,and *** has also been applied to patients with dementia,because it can yield facilitatory effects on cognition and promote brain recovery after a neurological ***,its therapeutic effectiveness at the molecular and synaptic levels has not been elucidated because of a limited number of *** study aimed to characterize the neurobiological changes following repetitive transcranial magnetic stimulation treatment,evaluate its effects on synaptic plasticity,and identify the associated *** review essentially focuses on changes in the pathology,amyloidogenesis,and clearance pathways,given that amyloid deposition is a major hypothesis in the pathogenesis of Alzheimer’s *** mechanisms associated with repetitive transcranial magnetic stimulation procedures and different pathways mediating gene transcription,which are closely related to the neural regeneration process,are also ***,we discuss the outcomes of animal studies in which neuroplasticity is modulated and assessed at the structural and functional levels by using repetitive transcranial magnetic stimulation,with the aim to highlight future directions for better clinical translations.

关键词： Alzheimer’s disease amyloid deposition apoptotic mechanisms biomarker neural regeneration neurodegeneration repetitive transcranial magnetic stimulation synaptic plasticity