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Journal of Systems Science & Complexity 2024年第3期37卷 1251-1270页

作者： peng Yanjin WANG lei School of Statistics and Data Science KLMDASRLEBPS and LPMCNankai UniversityTianjin 300071China

In this paper,the authors propose a two-stage online debiased lasso estimation and statistical inference method for high-dimensional quantile regression(QR)models in the presence of streaming data.In the first stage,the authors modify the QR score function based on kernel smoothing and obtain the online lasso smoothed QR estimator through iterative algorithms.The estimation process only involves the current data batch and specific historical summary statistics,which perfectly accommodates to the special structure of streaming data.In the second stage,an online debiasing procedure is carried out to eliminate biases caused by the lasso penalty as well as the accumulative approximation error so that the asymptotic normality of the resulting estimator can be established.The authors conduct extensive numerical experiments to evaluate the performance of the proposed method.These experiments demonstrate the effectiveness of the proposed method and support the theoretical results.An application to the Beijing PM2.5 Dataset is also presented.

关键词： Adaptive tuning asymptotic normality debiased lasso online updating quantile regres-sion

Deep Learning and Tensor-Based Multiple Clustering Approaches for Cyber-Physical-Social Applications

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Computers, Materials & Continua 2024年第3期78卷 4109-4128页

作者： Hongjun Zhang Hao Zhang yu lei Hao Ye peng Li Desheng Shi School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210023China China Communications Services Co. Ltd.Beijing100071China China Communications Services Hexin Science&Technology Co. Ltd.Hefei230031China

The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contr

关键词： Network platform tensor-based clustering weight learning multi-linear euclidean

Boosted Electrocatalytic Glucose Oxidation Reaction on Noble-Metal-Free MoO_(3)-Decorated Carbon Nanotubes

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Transactions of Tianjin University 2024年第1期30卷 63-73页

作者： yu-Long Men Ning Dou Yiyi Zhao Yan Huang lei Zhang peng Liu Department of Chemical Engineering School of Chemistry and Chemical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Thyroid Breast and Vascular Surgery Shanghai Fourth People’s Hospital Affi liated to Tongji University School of MedicineShanghai 200081China Department of Vascular Surgery Changhai HospitalNaval Medical UniversityShanghai 200433China

Electrocatalytic glucose oxidation reaction(GOR)has attracted much attention owing to its crucial role in biofuel cell fabrication.Herein,we load MoO_(3)nanoparticles on carbon nanotubes(CNTs)and use a discharge process to prepare a noblemetal-free MC-60 catalyst containing MoO_(3),Mo_(2)C,and a Mo_(2)C–MoO_(3)interface.In the GOR,MC-60 shows activity as high as 745μA/(mmol/L cm^(2)),considerably higher than those of the Pt/CNT(270μA/(mmol/L cm^(2)))and Au/CNT catalysts(110μA/(mmol/L cm^(2))).In the GOR,the response minimum on MC-60 is as low as 8μmol/L,with a steady-state response time of only 3 s.Moreover,MC-60 has superior stability and anti-interference ability to impurities in the GOR.The better performance of MC-60 in the GOR is attributed to the abundant Mo sites bonding to C and O atoms at the MoO_(3)–Mo_(2)C interface.These Mo sites create active sites for promoting glucose adsorption and oxidation,enhancing MC-60 performance in the GOR.Thus,these results help to fabricate more effi cient noble-metal-free catalysts for the fabrication of glucose-based biofuel cells.

关键词： Interface eff ect Electrocatalysis Molybdenum oxide Glucose Oxidation reaction

Inducing mitochondriopathy-like damages by transformable nucleopeptide nanoparticles for targeted therapy of bladder cancer

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National Science Review 2024年第4期11卷 216-227页

作者： Da-Yong Hou Ni-yuan Zhang Lu Wang Mei-yu Lv Xiang-peng Li peng Zhang yue-Ze Wang lei Shen Xiu-Hai Wu Bo Fu peng-yu Guo Zi-Qi Wang Dong-Bing Cheng Hao Wang Wanhai Xu NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics Heilongjiang Key Laboratory of Scientific Research in Urology Harbin Medical University Department of Urology Harbin Medical University Cancer Hospital CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety CAS Center for Excellence in NanoscienceNational Center for Nanoscience and Technology(NCNST) School of Chemistry Chemical Engineering & Life Science Hubei Key Laboratory of Nanomedicine for Neurodegenerative Diseases Wuhan University of Technology

Mitochondriopathy inspired adenosine triphosphate(ATP) depletions have been recognized as a powerful way for controlling tumor growth. Nevertheless, selective sequestration or exhaustion of ATP under complex biological environments remains a prodigious challenge. Harnessing the advantages of in vivo self-assembled nanomaterials, we designed an Intracellular ATP Sequestration(IAS) system to specifically construct nanofibrous nanostructures on the surface of tumor nuclei with exposed ATP binding sites,leading to highly efficient suppression of bladder cancer by induction of mitochondriopathy-like damages.Briefly, the reported transformable nucleopeptide(NLS-FF-T) self-assembled into nuclear-targeted nanoparticles with ATP binding sites encapsulated inside under aqueous conditions. By interaction with KPNA2, the NLS-FF-T transformed into a nanofibrous-based ATP trapper on the surface of tumor nuclei,which prevented the production of intracellular energy. As a result, multiple bladder tumor cell lines(T24,EJ and RT-112) revealed that the half-maximal inhibitory concentration(IC50) of NLS-FF-T was reduced by approximately 4-fold when compared to NLS-T. Following intravenous administration, NLS-FF-T was found to be dose-dependently accumulated at the tumor site of T24 xenograft mice. More significantly, this IAS system exhibited an extremely antitumor efficacy according to the deterioration of T24 tumors and simultaneously prolonged the overall survival of T24 orthotopic xenograft mice. Together, our findings clearly demonstrated the therapeutic advantages of intracellular ATP sequestration-induced mitochondriopathy-like damages, which provides a potential treatment strategy for malignancies.

关键词： self-assembly nucleopeptide nanomaterial bladder cancer mitochondriopathy

Development and validation of a machine learning-based early prediction model for massive intraoperative bleeding in patients with primary hepatic malignancies

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World Journal of Gastrointestinal Oncology 2024年第1期16卷 90-101页

作者： Jin Li yu-Ming Jia Zhi-lei Zhang Cheng-yu Liu Zhan-Wu Jiang Zhi-Wei Hao Li peng Department of Hepatological Surgery The Fourth Hospital of Hebei Medical UniversityShijiazhuang 050011Hebei ProvinceChina Department of General Surgery II Baoding First Central HospitalBaoding 071000Hebei ProvinceChina

BACKGROUND Surgical resection remains the primary treatment for hepatic malignancies,and intraoperative bleeding is associated with a significantly increased risk of death.Therefore,accurate prediction of intraoperative bleeding risk in patients with hepatic malignancies is essential to preventing bleeding in advance and providing safer and more effective treatment.AIM To develop a predictive model for intraoperative bleeding in primary hepatic malignancy patients for improving surgical planning and outcomes.METHODS The retrospective analysis enrolled patients diagnosed with primary hepatic malignancies who underwent surgery at the Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University between 2010 and 2020.Logistic regression analysis was performed to identify potential risk factors for intraoperative bleeding.A prediction model was developed using Python programming language,and its accuracy was evaluated using receiver operating characteristic(ROC)curve analysis.RESULTS Among 406 primary liver cancer patients,16.0%(65/406)suffered massive intraoperative bleeding.Logistic regression analysis identified four variables as associated with intraoperative bleeding in these patients:ascites[odds ratio(OR):22.839;P<0.05],history of alcohol consumption(OR:2.950;P<0.015),TNM staging(OR:2.441;P<0.001),and albumin-bilirubin score(OR:2.361;P<0.001).These variables were used to construct the prediction model.The 406 patients were randomly assigned to a training set(70%)and a prediction set(30%).The area under the ROC curve values for the model’s ability to predict intraoperative bleeding were 0.844 in the training set and 0.80 in the prediction set.CONCLUSION The developed and validated model predicts significant intraoperative blood loss in primary hepatic malignancies using four preoperative clinical factors by considering four preoperative clinical factors:ascites,history of alcohol consumption,TNM staging,and albumin-bilirubin score.Consequently,this model holds promise for enhancing individ

关键词： Primary liver cancer Intraoperative bleeding Machine learning Model

Near-zero-adhesion-enabled intact wafer-scale resist-transfer printing for high-fidelity nanofabrication on arbitrary substrates

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International Journal of Extreme Manufacturing 2024年第1期6卷 313-326页

作者： Zhiwen Shu Bo Feng peng Liu lei Chen Huikang Liang Yiqin Chen Jianwu yu Huigao Duan College of Mechanical and Vehicle Engineering National Engineering Research Centre for High Efficiency GrindingHunan UniversityChangsha 410082People’s Republic of China Greater Bay Area Institute for Innovation Hunan UniversityGuangzhou 511300People’s Republic of China School of Mechanical Engineering Hunan University of Science and TechnologyXiangtan 411201People’s Republic of China Shenzhen Research Institute Hunan UniversityShenzhen 518000People’s Republic of China

There is an urgent need for novel processes that can integrate different functional nanostructures onto specific substrates,so as to meet the fast-growing need for broad applications in nanoelectronics,nanophotonics,and fexible optoelectronics.Existing direct-lithography methods are difficult to use on fexible,nonplanar,and biocompatible surfaces.Therefore,this fabrication is usually accomplished by nanotransfer printing.However,large-scale integration of multiscale nanostructures with unconventional substrates remains challenging because fabrication yields and quality are often limited by the resolution,uniformity,adhesivity,and integrity of the nanostructures formed by direct transfer.Here,we proposed a resist-based transfer strategy enabled by near-zero adhesion,which was achieved by molecular modification to attain a critical surface energy interval.This approach enabled the intact transfer of wafer-scale,ultrathin-resist nanofilms onto arbitrary substrates with mitigated cracking and wrinkling,thereby facilitating the in situ fabrication of nanostructures for functional devices.Applying this approach,fabrication of three-dimensional-stacked multilayer structures with enhanced functionalities,nanoplasmonic structures with~10 nm resolution,and MoS2-based devices with excellent performance was demonstrated on specific substrates.These results collectively demonstrated the high stability,reliability,and throughput of our strategy for optical and electronic device applications.

关键词： resist-based transfer printing near-zero adhesion critical surface energy wafer-scale nanofabrication in situ fabrication optoelectronic devices

Artificial Intelligence Meets Flexible Sensors:Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses

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Nano-Micro Letters 2024年第1期16卷 235-273页

作者： Tianming Sun Bin Feng Jinpeng Huo yu Xiao Wengan Wang Jin peng Zehua Li Chengjie Du Wenxian Wang Guisheng Zou lei Liu Department of Mechanical Engineering State Key Laboratory of Tribology in Advanced EquipmentKey Laboratory for Advanced Manufacturing by Materials Processing TechnologyMinistry of Education of PR ChinaTsinghua UniversityBeijing 100084People’s Republic of China College of Materials Science and Engineering Shanxi ProvinceTaiyuan University of TechnologyTaiyuan 030024People’s Republic of China

The recent wave of the artificial intelligence(AI)revolution has aroused unprecedented interest in the intelligentialize of human society.As an essential component that bridges the physical world and digital signals,flexible sensors are evolving from a single sensing element to a smarter system,which is capable of highly efficient acquisition,analysis,and even perception of vast,multifaceted data.While challenging from a manual perspective,the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm(machine learning)and the framework(artificial synapses)level.This review presents the recent progress of the emerging AI-driven,intelligent flexible sensing systems.The basic concept of machine learning and artificial synapses are introduced.The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed,which significantly advances the applications such as flexible sensory systems,soft/humanoid robotics,and human activity monitoring.As two of the most profound innovations in the twenty-first century,the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.

关键词： Flexible electronics Wearable electronics Neuromorphic Memristor Deep learning

Mutual aid instead of mutual restraint:interactive probing for topological charge and phase of a vortex beam of large aberrations

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Photonics Research 2024年第1期12卷 172-182页

作者： SHENGYANG WU BENLI yu lei ZHANG Key Laboratory of Opto-electronic Information Acquisition and Manipulation Ministry of EducationAnhui UniversityHefei 230601China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui UniversityHefei 230601China

An imperfect propagation environment or optical system would introduce wavefront aberrations to vortex beams.The phase aberrations and orbital angular momentum in a vortex beam are proved to be mutually restrictive in parameter measurement.Aberrations make traditional topological charge(TC)probing methods ineffective while the phase singularity makes phase retrieval difficult due to the aliasing between the wrapped phase jump and the vortex phase jump.An interactive probing method is proposed to make measurements of the aberrated phase and orbital angular momentum in a vortex beam assist rather than hinder each other.The phase unwrapping is liberated from the phase singularity by an annular shearing interference technique while the TC value is determined by a Moirétechnique immune to aberrations.Simulation and experimental results proving the method effective are presented.It is of great significance to judge the characteristics of vortex beams passing through nonideal environments and optical systems.

关键词： vortex topological beam

Numerical study on local scour characteristics around submarine pipelines in the Yellow River Delta silty sandy soil under waves and currents

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Deep Underground Science and Engineering 2024年第2期3卷 182-196页

作者： peng yu Ruigeng Hu Jike Zhang Qi Yang Jieru Zhao lei Cao Chenghao Zhu Key Laboratory of Geological Safety of Coastal Urban Underground Space Ministry of Natural ResourcesQingdaoChina Qingdao Geo-Engineering Surveying Institute(Qingdao Geological Exploration Development Bureau) QingdaoChina Sinopec Petroleum Engineering Corporation DongyingChina State Key Laboratory of Hydroscience and Engineering Tsinghua UniversityBeijingChina Department Civil and Environmental Engineering Politecnico di MilanoMilanItaly College of Environmental Science and Engineering Ocean University of ChinaQingdaoChina

Due to their high reliability and cost-efficiency,submarine pipelines are widely used in offshore oil and gas resource engineering.Due to the interaction of waves,currents,seabed,and pipeline structures,the soil around submarine pipelines is prone to local scour,severely affecting their operational safety.With the Yellow River Delta as the research area and based on the renormalized group(RNG)k-εturbulence model and Stokes fifth-order wave theory,this study solves the Navier-Stokes(N-S)equation using the finite difference method.The volume of fluid(VOF)method is used to describe the fluid-free surface,and a threedimensional numerical model of currents and waves-submarine pipeline-silty sandy seabed is established.The rationality of the numerical model is verified using a self-built waveflow flume.On this basis,in this study,the local scour development and characteristics of submarine pipelines in the Yellow River Delta silty sandy seabed in the prototype environment are explored and the influence of the presence of pipelines on hydrodynamic features such as surrounding flow field,shear stress,and turbulence intensity is analyzed.The results indicate that(1)local scour around submarine pipelines can be divided into three stages:rapid scour,slow scour,and stable scour.The maximum scour depth occurs directly below the pipeline,and the shape of the scour pits is asymmetric.(2)As the water depth decreases and the pipeline suspension height increases,the scour becomes more intense.(3)When currents go through a pipeline,a clear stagnation point is formed in front of the pipeline,and the flow velocity is positively correlated with the depth of scour.This study can provide a valuable reference for the protection of submarine pipelines in this area.

关键词： local scour numerical simulation submarine pipelines Yellow River Delta

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Feature-Grounded Single-Stage Text-to-Image Generation

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Tsinghua Science and Technology 2024年第2期29卷 469-480页

作者： yuan Zhou peng Wang lei Xiang Haofeng Zhang School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Recently,Generative Adversarial Networks(GANs)have become the mainstream text-to-image(T2I)framework.However,a standard normal distribution noise of inputs cannot provide sufficient information to synthesize an image that approaches the ground-truth image distribution.Moreover,the multistage generation strategy results in complex T2I applications.Therefore,this study proposes a novel feature-grounded single-stage T2I model,which considers the“real”distribution learned from training images as one input and introduces a worst-case-optimized similarity measure into the loss function to enhance the model's generation capacity.Experimental results on two benchmark datasets demonstrate the competitive performance of the proposed model in terms of the Frechet inception distance and inception score compared to those of some classical and state-of-the-art models,showing the improved similarities among the generated image,text,and ground truth.

关键词： text-to-image(T2I) feature-grounded single-stage generation Generative Adversarial Network(GAN)