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Pt/Mo chalcogenide composite deriving from Pt-Mo_(6)S_8 by high temperature shock for enhanced HER performance

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Chinese Chemical Letters 2024年第3期35卷 497-501页

作者： Meng liu Guocheng Lv Hao liu tianming liu Lingchang Kong Libing Liao Engineering Research Center of Ministry of Education for Geological Carbon Storage and Low Carbon Utilization of Resources Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid WastesNational Laboratory of Mineral MaterialsSchool of Materials Science and TechnologyChina University of Geosciences(Beijing)Beijing 100083China School of Science China University of GeosciencesBeijing 100083China

Highly efficient catalysts for electrolysis of water are crucial to the development of hydrogen energy which is helpful to carbon ***,high temperature shock(HTS),with advantage of rapid speed,universality and scalable production,has been a promising method in synthesis of *** this paper,HST was used to treat low Pt loading Mo_(6)S_(8)for enhanced water splitting ***,the optimized MoS_(2)/MoO_(2)/Mo_(6)S_(8)nano-composite with low Pt mass loading(~4%)displays well hydrogen evolution reaction(HER)electrochemical *** overpotential is 124 mV to reach 10 mA/cm^(2)and the corresponding Tafel slope is 88 mV/dec in acidic *** mass activity is 6.2 mA/μg_(Pt)at-124 mV ***,which is almost 2 times relative to 20%Pt/***,it presents distinguished stability even after 2000 *** work will broaden the way of catalysts preparation and the application of hydrogen evolution.

关键词： HTS Low Pt loading Mo_(6)S_8 MoS_(2)/MoO_(2)/Mo_(6)S_8 composite HER

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A Survey of MRI-Based Brain Tumor Segmentation Methods

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Tsinghua Science and Technology 2014年第6期19卷 578-595页

作者： Jin liu Min Li Jianxin Wang Fangxiang Wu tianming liu Yi Pan School of Information Science and Engineering Central South University Department of Mechanical Engineering and Division of Biomedical Engineering University of Saskatchewan Saskatoon SK S7N 5A9Canada Department of Computer Science University of Georgia Boyd 420 Athens GA 30602 USA Department of Computer Science Georgia State University Atlanta GA 30302-3994 USA

Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core,and edema from normal brain tissues of White Matter（WM）, Gray Matter（GM）, and Cerebrospinal Fluid（CSF）. MRIbased brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging（MRI） images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumor are becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for MRI-based brain tumor segmentation methods. Firstly, a brief introduction to brain tumors and imaging modalities of brain tumors is given. Then, the preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced. Moreover, the evaluation and validation of the results of MRI-based brain tumor segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for MRI-based brain tumor segmentation methods.

关键词： brain tumor Magnetic Resonance Imaging(MRI) segmentation

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Urdnet:A Cryo-EM Particle Automatic Picking Method

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Computers, Materials & Continua 2022年第7期72卷 1593-1610页

作者： Jianquan Ouyang Yue Zhang Kun Fang tianming liu Xiangyu Pan School of Computer Science&School of Cyberspace Science Xiangtan UniversityXiangtan411105China Hunan Meteorological Information Center Hunan Meteorological BureauChangshaHunan410118China Department of Computer Science The University of GeorgiaAthensGeorgiaUSA

Cryo-Electron Microscopy(Cryo-EM)images are characterized by the low signal-to-noise ratio,low contrast,serious background noise,more impurities,less data,difficult data labeling,simpler image semantics,and relatively fixed structure,while U-Net obtains low resolution when downsampling rate information to complete object category recognition,obtains highresolution information during upsampling to complete precise segmentation and positioning,fills in the underlying information through skip connection to improve the accuracy of image segmentation,and has advantages in biological image processing like Cryo-EM *** article proposes A U-Net based residual intensive neural network(Urdnet),which combines point-level and pixel-level tags,used to accurately and automatically locate particles from cryo-electron microscopy images,and solve the bottleneck that cryo-EM Single-particle biologicalmacromolecule reconstruction requires tens of thousands of automatically picked *** 80S ribosome,HCN1 channel and TcdA1 toxin subunits,and other public protein datasets have been trained and tested on *** experimental results show that Urdnet could reach the same excellent particle picking performances as the mainstream methods of RELION,DeepPicker,and acquire the 3Dstructure of picked particleswith higher resolution.

关键词： Deep learning convolutional neural network particle picking cryo-electron microscopy single-particle reconstruction

SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission

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Computers, Materials & Continua 2020年第1期62卷 293-307页

作者： Huanrong Tang Aoming Peng Dongming Zhang tianming liu Jianquan Ouyang Key Laboratory of Intelligent Computing and Information Processing Ministry of EducationCollege of Information EngineeringXiangtan UniversityXiangtan411105China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100029China Department of Computer Science the University of GeorgiaAthensGeorgiaUSA

With the improvement of the national economic level,the number of vehicles is still increasing year by *** to the statistics of National Bureau of Statics,the number is approximately up to 327 million in China by the end of 2018,which makes urban traffic pressure continues to rise so that the negative impact of urban traffic order is *** parking-the common problem in the field of transportation security is urgent to be solved and traditional methods to address it are mainly based on ground loop and manual supervision,which may miss detection and cost much *** to the rapidly developing deep learning sweeping the world in recent years,object detection methods relying on background segmentation cannot meet the requirements of complex and various scenes on speed and ***,an improved Single Shot MultiBox Detector(SSD)based on deep learning is proposed in our study,we introduce attention mechanism by spatial transformer module which gives neural networks the ability to actively spatially transform feature maps and add contextual information transmission in specified ***,we found out the best connection layer in the detection model by repeated experiments especially for small objects and increased the precision by 1.5%than the baseline SSD without extra training ***,we designed an illegal parking vehicle detection method by the improved SSD,reaching a high precision up to 97.3%and achieving a speed of 40FPS,superior to most of vehicle detection methods,will make contributions to relieving the negative impact of illegal parking.

关键词： Contextual information transmission illegal parking detection spatial attention mechanism deep learning

Chevrel phase: A review of its crystal structure and electrochemical properties

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Progress in Natural Science:Materials International 2023年第1期33卷 8-15页

作者： Meng liu Guocheng Lv tianming liu Hao liu Lingchang Kong Lintao Tian Wenxiu Rao Yuxin Li Libing Liao Juchen Guo Engineering Research Center of Ministry of Education for Geological Carbon Storage and Low Carbon Utilization of Resources Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes National Laboratory of Mineral Materials School of Materials Science and Technology China University of Geosciences(Beijing) School of Science China University of Geosciences Materials Science and Engineering Program University of California-Riverside Department of Chemical and Environmental Engineering University of California – Riverside

Chevrel phase compounds have attracted increasing attention as electrochemical energy storage materials and electro-catalysts. Benefiting from their unique crystal structure, Chevrel phase compounds can not only function as the host structures for reversible intercalation of a broad range of cations, but also exhibit high catalytic activity in electrochemical reduction reactions. Here we provide an overview of recent progress in the development of Chevrel phase materials including new understanding of structural features, synthetic methods, and electrochemical properties. A brief conclusion and perspectives on the future development of Chevrel phase materials are also provided.

关键词： Chevrel phase Electrochemical properties Energy storage Electro-catalysis

Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior

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Psychoradiology 2021年第1期1卷 23-41页

作者： Xi Jiang Tuo Zhang Shu Zhang Keith MKendrick tianming liu School of Life Science and Technology MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Automation Northwestern Polytechnical UniversityXi’an 710129China Center for Brain and Brain-Inspired Computing Research School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710129China Cortical Architecture Imaging and Discovery Laboratory Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensGA 30605USA

Folding of the cerebral cortex is a prominent characteristic of mammalian *** or deficits in cortical folding are strongly correlated with abnormal brain function,cognition,and ***,a precise mapping between the anatomy and function of the brain is critical to our understanding of the mechanisms of brain structural architecture in both health and *** and sulci,the standard nomenclature for cortical anatomy,serve as building blocks to make up complex folding patterns,providing a window to decipher cortical anatomy and its relation with brain *** efforts have been devoted to this research topic from a variety of disciplines including genetics,cell biology,anatomy,neuroimaging,and neurology,as well as involving computational approaches based on machine learning and artificial intelligence ***,despite increasing progress,our understanding of the functional anatomy of gyro-sulcal patterns is still in its *** this review,we present the current state of this field and provide our perspectives of the methodologies and conclusions concerning functional differentiation between gyri and sulci,as well as the supporting information from genetic,cell biology,and brain structure *** particular,we will further present a proposed framework for attempting to interpret the dynamic mechanisms of the functional interplay between gyri and ***,this review will provide a comprehensive summary of anatomo-functional relationships in the cortical gyro-sulcal system together with a consideration of how these contribute to brain function,cognition,and behavior,as well as to mental disorders.

关键词： cortical folding gyro-sulcal pattern brain anatomo-function

Dynamical analysis,circuit realization,and application in pseudorandom number generators of a fractional-order laser chaotic system

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Chinese Physics B 2021年第12期30卷 254-263页

作者： Chenguang Ma Santo Banerjee Li Xiong tianming liu Xintong Han Jun Mou School of Information Science and Engineering Dalian Polytechnic UniversityDalian 116034China School of Physics and Electromechanical Engineering Hexi UniversityZhangye 734000China Department of Mathematical Sciences Giuseppe Luigi LagrangePolitecnico di TorinoCorso Duca degli Abruzzi 24TorinoItaly

A new five-dimensional fractional-order laser chaotic system(FOLCS)is constructed by incorporating complex variables and fractional calculus into a Lorentz-Haken-type laser *** behavior of the system,circuit realization and application in pseudorandom number generators are *** types of multi-stable states are discovered in the ***,there are two types of state transition phenomena in the system,one is the chaotic state degenerates to a periodical state,and the other is the intermittent chaotic *** addition,the complexity of the system when two parameters change simultaneously is measured by the spectral entropy ***,a digital circuit is design and the chaotic oscillation behaviors of the system are verified on this ***,a pseudo-random sequence generator is designed using the FOLCS,and the statistical characteristics of the generated pseudo-random sequence are tested with the *** study enriches the research on the dynamics and applications of FOLCS.

关键词： fractional-order laser chaotic system SE complexity intermittent chaos NIST test circuit realization

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A Noise Extraction Method for Cryo-EM Single-Particle Denoising

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Journal on Big Data 2022年第1期4卷 61-76页

作者： Huanrong Tang Sihan Wang Jianquan Ouyang tianming liu Key Laboratory of Intelligent Computing Information Processing Ministry of EducationSchool of Computer Science and School of Cyberspace ScienceXiangtan UniversityXiangtan411100China Department of Computer Science The University of GeorgiaGeorgia30301USA

Cryo-Electron Microscopy(cryo-EM)has become a powerful method to study the structure and function of biological ***,in clustering tasks based on the projection angle of particles in cryoEM,the noise considerably affects the clustering *** denoising algorithms are ineffective due to the extremely low signal-to-noise ratio(SNR)of cryo-EM images and the complexity of noise *** noise of a single particle greatly influences the orientation estimation of the subsequent clustering task,and the result of the clustering task directly affects the accuracy of the 3D *** this paper,we propose a construction method of cryo-EM denoising dataset that uses U-Net to extract noise blocks from cryoEM images,superimpose the noise block with the projected pure particles to construct our simulated *** we adopt a supervised generative adversarial network(GAN)with perceptual loss to train on our simulated dataset and denoise the real cryo-EM single *** method can solve the problem of poor denoising performance caused by assuming that the noise of the Gaussian distribution does not conform to the noise distribution of cryo-EM,and it can retain the useful information of particles to a great *** compared traditional image filtering methods and the classic deep learning denoising algorithm DnCNN on the simulated and real *** results show that the method based on deep learning has more advantages than traditional image denoising *** is worth mentioning that our method achieves a competitive peak signal to noise ratio(PSNR)and structural similarity(SSIM).Moreover,visualization results,indicate that our method can retain the structure information and orientation information of particles to a greater extent compared with other state-of-the-art image denoising *** means that our denoising task can provide considerable help for subsequent cryo-EM clustering tasks.

关键词： Cryo-EM noise extraction denoising GAN

CenterPicker:An Automated Cryo-EM Single-Particle Picking Method Based on Center Point Detection

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Journal of Cyber Security 2022年第2期4卷 65-77页

作者： Jianquan Ouyang Jinling Wang Yaowu Wang tianming liu Key Laboratory of Intelligent Computing Information Processing Ministry of EducationComputer Science College of Xiangtan UniversityXiangtanHunan Province411100China Department of Computer Science the University of GeorgiaAthensGeorgiaUSA

Cryo-electron microscopy(cryo-EM)has become one of the mainstream techniques for determining the structures of proteins andmacromolecular complexes,with prospects for development and *** must select hundreds of thousands of particles from micrographs to acquire the database for single-particle cryo-EM ***,existing particle picking methods cannot ensure that the particles are in the center of the bounding box because the signal-to-noise ratio(SNR)of micrographs is extremely low,thereby directly affecting the efficiency and accuracy of 3D *** propose an automated particle-picking method(CenterPicker)based on particle center point detection to automatically select a large number of high-quality particles from low signal-to-noise,low-contrast refrigerated microscopy *** method uses a fully convolutional neural network to generate a keypoint *** heatmap value represents the probability that a micrograph pixel belongs to a particle center *** can process images of any size and can directly predict the center point and size of the *** network implements multiscale feature fusion and introduces an attention mechanism to improve the feature fusion part to obtain more accurate selection *** have conducted a detailed evaluation of CenterPicker on a range of datasets,and results indicate that it excels in single-particle picking tasks.

关键词： Cryo-electron microscope deep learning particle picking object detection