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Dynamic characteristics of charging effects on the dielectric constant due to E-beam irradiation:a numerical simulation

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Plasma Science and technology 2018年第3期20卷 1-9页

作者： Guobao FENG Wanzhao CUI Lu LIU National Key Lab of Science and Technology on Space Microwave China Academy of Space TechnologyXi'an 710000People's Republic of China School of Electronic and Information Engineering Xi'an Jiaotong UniversityXi'an 710049People's Republic of China School of Computer Science and Engineering Xi'an University of TechnologyXi'an 710048People's Republic of China

A series of synthetic variations of material intrinsic properties always come with charging phenomena due to electron beam *** effects of charging on the dielectric constant will influence the charging dynamic in *** this paper,we propose a numerical simulation for investigating the dynamic characteristics of charging effects on the dielectric constant due to electron beam *** scattering process between electrons and atoms is calculated considering elastic and inelastic collisions via the Rutherford model and the fast secondary electron model,*** charge drift due to E-field,density gradient caused diffusion,charges trap by material defect,free electron and hole neutralization,and variation in the internal dielectric constant are considered when simulating the transport *** dynamics of electron and hole distributions and charging states are demonstrated during E-beam *** a function of material nonlinear susceptibility and primary energy,the dynamics of charging states and dielectric constants are then presented in the charging *** is found that the variation in the internal dielectric constant is more with respect to the depth and irradiation *** with a larger nonlinear susceptibility corresponds a faster charging *** addition,the effective dielectric constant and the surface potential have a linear relationship in the charging ***,with shrinking charging affect range,the situation with a higher energy primary electron comes with less dielectric constant *** proposed numerical simulation mode of the charging process and the results presented in this study offer a comprehensive insight into the complicated charging phenomena in electron irradiation related fields.

关键词： charging effect dielectric constant E-beam irradiation numerical simulation

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Neighborhood linear discriminant analysis

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Pattern Recognition 2022年 123卷

作者： Fa Zhu Junbin Gao Jian Yang Ning Ye College of Information Science and Technology Nanjing Forestry University Nanjing 210037 PR China Discipline of Business Analytics University of Sydney Business School University of Sydney NSW 2006 Australia PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and the Jiangsu Key Lab of Image and Video Understanding for Social Security the School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 PR China

Linear Discriminant Analysis (LDA) assumes that all samples from the same class are independently and identically distributed (i.i.d.). LDA may fail in the cases where the assumption does not hold. Particularly when a class contains several clusters (or subclasses), LDA cannot correctly depict the internal structure as the scatter matrices that LDA relies on are defined at the class level. In order to mitigate the problem, this paper proposes a neighborhood linear discriminant analysis (nLDA) in which the scatter matrices are defined on a neighborhood consisting of reverse nearest neighbors. Thus, the new discriminator does not need an i.i.d. assumption. In addition, the neighborhood can be naturally regarded as the smallest subclass, for which it is easier to be obtained than subclass without resorting to any clustering algorithms. The projected directions are sought to make sure that the within-neighborhood scatter as small as possible and the between-neighborhood scatter as large as possible, simultaneously. The experimental results show that nLDA performs significantly better than previous discriminators, such as LDA, LFDA, ccLDA, LM-NNDA, and l 2 , 1 -RLDA.

关键词： Linear discriminant analysis Reverse nearest neighbors Neighborhood linear discriminant analysis Multimodal class

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Long short term memory model for analysis and forecast of PM2.5 4th

Long short term memory model for analysis and forecast of PM...

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4th International Conference on Cloud Computing and Security, ICCCS 2018

作者： Yan, Leiming Zhou, Min Wu, Yaowen Yan, Luqi Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9783030000059

Atmospheric PM2.5 is a pollutant that has a major impact on the atmospheric environment and human health. Based on LSTM, we construct two prediction models, Stack LSTM and Encoder-Decoder, and evaluate the prediction performance of the model through four years of meteorological data training and testing models in Nanjing, Beijing, and Sanya. In the experiment, using the meteorological factors, contaminant factors, seasonal factors, and the normalized results of PM2.5 as input, the daily average PM2.5 concentration is predicted from 1 to 3 days. Experimental results show that the LSTM model has better performance than Random Forest and Encoder-Decoder. Using Nanjing as an example, comparing the forecast results of Nanjing PM2.5 with the data released by the environmental authorities, it is found that the value of the PM2.5 concentration predicted by the LSTM model is very close to the value of PM2.5 monitored by Nanjing’s environmental authorities. In prediction of PM2.5 for three consecutive days, the Root Mean Square Error (RMSE) of the LSTM model is only 18.96. Under the LSTM model, the prediction result of the three cities are better than other prediction models, which shows that the LSTM model has a good adaptability in predicting the PM2.5 concentration. © Springer Nature Switzerland AG 2018.

关键词： Long short-term memory

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Fast Classification Algorithms via Distributed Accelerated Alternating Direction Method of Multipliers

Fast Classification Algorithms via Distributed Accelerated A...

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IEEE International Conference on Data Mining (ICDM)

作者： Huihui Wang Shunmei Meng Yiming Qiao Jing Zhang Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Technology School of Software Nanjing University of Posts and Telecommunications Nanjing China

Distributed machine learning has gained lots of attention due to the rapid growth of data. In this paper, we focus regularized empirical risk minimization problems, and propose two novel Distributed Accelerated Alternating Direction Method of Multipliers (D-A2DM2) algorithms for distributed classification. Based on the framework of Alternating Direction Method of Multipliers (ADMM), we decentralize the distributed classification problem as a global consensus optimization problem with a series of sub-problems. In D-A2DM2, we exploit ADMM with variance reduction for sub-problem optimization in parallel. Taking global update and local update into consideration respectively, we propose two acceleration mechanisms in the framework of D-A2DM2. In particular, inspired by Nesterov's accelerated gradient descent, we utilize it for global update to further improve time efficiency. Moreover, we also introduce Nesterov's acceleration for local update, and develop the corrected local update and symmetric dual update to accelerate the convergence with only a little change in the computational effort. Theoretically, D-A2DM2 has a linear convergence rate. Empirically, experimental results show that D-A2DM2 converge faster than existing distributed ADMM-based classification, and could be a highly efficient algorithm for practical use.

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二维材料最新研究进展

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物理化学学报 2021年第12期37卷 1-151页

作者：常诚陈伟陈也陈永华陈雨丁峰樊春海范红金范战西龚成宫勇吉何其远洪勋胡晟胡伟达黄维黄元季威李德慧李连忠李强林立凌崇益刘鸣华刘楠刘庄 Kian Ping Loh 马建民缪峰彭海琳邵明飞宋礼苏邵孙硕谭超良唐智勇王定胜王欢王金兰王欣王欣然 Andrew T.S.Wee 魏钟鸣吴宇恩吴忠帅熊杰熊启华徐伟高尹鹏曾海波曾志远翟天佑张晗张辉张其春张铁锐张翔赵立东赵美廷赵伟杰赵运宣周凯歌周兴周喻朱宏伟张华刘忠范 Institute of Science and Technology Austria Am Campus 13400 KlosterneuburgAustria Department of Chemistry National University of Singapore3 Science Drive 3117543Singapore Department of Chemistry The Chinese University of Hong KongShatinNew TerritoriesHong KongChina Key Laboratory of Flexible Electronics and Institute of Advanced Materials Nanjing Tech UniversityNanjing 211816China School of Life Sciences Shanghai UniversityShanghai 200444China Centre for Multidimensional Carbon Materials Institute for Basic ScienceUlsan 44919Korea School of Chemistry and Chemical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore Department of Chemistry City University of Hong KongKowloonHong KongChina Department of Electrical and Computer Engineering and Quantum Technology Center University of MarylandCollege ParkMaryland 20742USA School of Materials Science and Engineering Beihang UniversityBeijing 100191China Department of Materials Science and Engineering City University of Hong KongKowloonHong KongChina Center of Advanced Nanocatalysis(CAN) Hefei National Laboratory for Physical Sciences at the MicroscaleDepartment of Applied ChemistryUniversity of Science and Technology of ChinaHefei 230026China College of Chemistry and Chemical Engineering State Key Laboratory of Physical Chemistry of Solid SurfacesCollaborative Innovation Center of Chemistry for Energy Materials(iChEM)Xiamen UniversityXiamen361005Fujian ProvinceChina State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing100081China Beijing Key Laboratory of Optoelectronic Functional Materials&Micro-Nano Devices Department of PhysicsRenmin University of ChinaBeijing 100872China School of Optical and Electronic Information Wuhan National Laboratory

Research on two-dimensional(2D) materials has been explosively increasing in last seventeen years in varying subjects including condensed matter physics, electronic engineering, materials science, and chemistry since the mechanical exfoliation of graphene in 2004. Starting from graphene, 2D materials now have become a big family with numerous members and diverse categories. The unique structural features and physicochemical properties of 2D materials make them one class of the most appealing candidates for a wide range of potential applications. In particular, we have seen some major breakthroughs made in the field of 2D materials in last five years not only in developing novel synthetic methods and exploring new structures/properties but also in identifying innovative applications and pushing forward commercialisation. In this review, we provide a critical summary on the recent progress made in the field of 2D materials with a particular focus on last five years. After a brief backgroundintroduction, we first discuss the major synthetic methods for 2D materials, including the mechanical exfoliation, liquid exfoliation, vapor phase deposition, and wet-chemical synthesis as well as phase engineering of 2D materials belonging to the field of phase engineering of nanomaterials(PEN). We then introduce the superconducting/optical/magnetic properties and chirality of 2D materials along with newly emerging magic angle 2D superlattices. Following that, the promising applications of 2D materials in electronics, optoelectronics, catalysis, energy storage, solar cells, biomedicine, sensors, environments, etc. are described sequentially. Thereafter, we present the theoretic calculations and simulations of 2D materials. Finally, after concluding the current progress, we provide some personal discussions on the existing challenges and future outlooks in this rapidly developing field.

关键词： Two-dimensional materials Transition metal dichalcogenides Phase engineering of nanomaterials Electronics Optoelectronics Catalysis Energy storage and conversion

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Computational modeling of ketamine-induced changes in gamma-band oscillations: The contribution of parvalbumin and somatostatin interneurons

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PLOS Computational Biology 2025年第6 June期21卷 e1013118页

作者： Rademacher, Jessie Grent-'t-Jong, Tineke Rivolta, Davide Sauer, Andreas Scheller, Bertram Gonzalez-Burgos, Guillermo Metzner, Christoph Uhlhaas, Peter J. Department of Child and Adolescent Psychiatry Charité Universitätsmedizin Berlin Germany Department of Education Psychology and Communication University of Bari Aldo Moro Bari Italy Max Planck Institute for Brain Research Frankfurt am Main Germany for Neuroscience in Cooperation with Max Planck Society Frankfurt am Main Germany SRH University Department of Applied Psychology Heidelberg Germany Department of Anesthesiology and Intensive Care Medicine St. Josefs Hospital Wiesbaden Germany Department of Psychiatry University of Pittsburgh PittsburghPA United States Neural Information Processing Group Institute of Software Engineering and Theoretical Computer Science Technische Universität Berlin Berlin Germany School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom

Ketamine, an NMDA receptor (NMDA-R) antagonist, produces psychotomimetic effects when administered in sub-anesthetic dosages. While previous research suggests that Ketamine alters the excitation/inhibition (E/I)-balance in cortical microcircuits, the precise neural mechanisms by which Ketamine produces these effects are not well understood. We analyzed resting-state MEG data from n = 12 participants who were administered Ketamine to assess changes in gamma-band (30–90 Hz) power and the slope of the aperiodic power spectrum compared to placebo. In addition, correlations of these effects with gene-expression of GABAergic interneurons and NMDA-Rs subunits were analyzed. Finally, we compared Ketamine-induced spectral changes to the effects of systematically changing NMDA-R levels on pyramidal cells, and parvalbumin-, somatostatin- and vasoactive intestinal peptide-expressing interneurons in a computational model of cortical layer-2/3 to identify crucial sites of Ketamine action. Ketamine resulted in a flatter aperiodic slope and increased gamma-band power across brain regions, with pronounced effects in prefrontal and central areas. These effects were correlated with the spatial distribution of parvalbumin and GluN2D gene expression. Computational modeling revealed that reduced NMDA-R activity in parvalbumin or somatostatin interneurons could reproduce increased gamma-band power by increasing pyramidal neuron firing rate, but did not account for changes in the aperiodic slope. The results suggest that parvalbumin and somatostatin interneurons may underlie increased gamma-band power following Ketamine administration in healthy volunteers, while changes in the aperiodic component could not be recreated. These findings have implications for current models of E/I-balance, as well as for understanding the mechanisms underlying the circuit effects of Ketamine. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modifi

关键词： Transcription

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COSINE:a software development model integrating collective intelligence, service and ecosystem

COSINE:a software development model integrating collective i...

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IEEE World Congress on Services (SERVICES)

作者： Hongbing Wang Tianjing Hong Jian Cao Haijun Zhang Changhai Nie Bo Cheng Yangfan He Li Kuang Dunwei Gong Wuhui Chen Yuliang Shi Deyi Huang School of Computer Science and Engineering and Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China Shanghai Jiaotong University Shanghai China College of Computer Science and Technology Harbin Institute of Technology Shenzhen China Nanjing University Nanjing China Wuhan University Wuhan China Research Institute of Network Technology Beijing University of Posts and Telecommunications Beijing China Software College Central South University Changsha China College of information and control engineering China University of mining and technology Xuzhou China College of data science and computer science Zhongshan University Guangzhou China Shandong University Jinan China Landry Company Shenzhen China

ISBN: (数字)9781728182032

ISBN: (纸本)9781728182049

With the development of the internet technology, a large amount of softwares have emerged to meet users' increasing needs. At the mean time, software systems have been faced with a problem that they must adapt to the dynamic network environment. It is obvious that a variety of software development models have been proposed in the past few decades. However, the majority of these methods are gradually unadaptable to new circumstances. In this paper, we proposed a new software development model integrating collective intelligence, service and ecosystem. On the one hand, we have introduced the model in detail. On the other hand, We took a practical example to demonstrate the effectiveness of the proposed model.

关键词： Adaptation models Biological system modeling Ecosystems Collective intelligence software systems

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Correction: Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review

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Archives of Computational Methods in engineering 2022年第5期30卷 3469-3469页

作者： Maaroof, Bestan B. Rashid, Tarik A. Abdulla, Jaza M. Hassan, Bryar A. Alsadoon, Abeer Mohammadi, Mokhtar Khishe, Mohammad Mirjalili, Seyedali Information Technology Department College of Commerce University of Sulaimani Sulaymaniyah Iraq Computer Science and Engineering University of Kurdistan Hewler Erbil Iraq Computer Science Department College of Science Komar University of Science and Technology Sulaymaniyah Iraq Information Technology College of Commerce University of Sulaimani Sulaymaniyah Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Information Technology Department Asia Pacific International College (APIC) Sydney Australia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Marine Electronics and Communication Engineering Imam Khomeini Marine Science University Nowshahr Iran Centre for Artificial Intelligence Research and Optimization Torrens University Adelaide Australia Yonsei Frontier Lab Yonsei University Seoul Korea

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Network Electronic Record Management Based on Linked Dat

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Journal on Big Data 2019年第1期1卷 9-15页

作者： Jian Qi Yongjun Ren Qirun Wang School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210000China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210000China School of Engineering and Technology University of HertfordshireHertfordUK

With the rapid development of e-government,e-commerce and self-media,a large number of electronic network records have been generated on the *** to archive these resources has become a focus of attention in academic *** on the theory of linked data,this paper analyzes the archiving of electronic records,and proposes that the corresponding network electronic records should be collected using the theory of linked *** the resource description framework and corresponding international standards are utilized to convert the network resources,the electronic network records are stored in common databases to solve the problem of network record archiving management.

关键词： Electronic record linked data storage technology RDF

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Identifying the Predictive Capability of Machine Learning Classifiers for Designing Heart Disease Detection System

Identifying the Predictive Capability of Machine Learning Cl...

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International Conference on Wavelet Active Media technology and information Processing (ICWAMTIP)

作者： AMIN UL HAQ JIANPING LI JALALUDDIN KHAN MUHAMMAD HAMMAD MEMON SHADMA PARVEEN MORDECAI FOLARIN RAJI WASIF AKBAR TANVIR AHMAD SANA ULLAH LATIPOVA SHOISTA HAPPY N. MONDAY School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Management Science and Economics University of Electronic Science and Technology of China School of Control and Computer Engineering North China Electric Power University Beijing China School of Information and Software Engineering University of Electronic Science and Technology of China

ISBN: (数字)9781728142425

ISBN: (纸本)9781728142432

The diagnosis of heart diseases through invoice based techniques as well as ordinary medical based methods are not reliable. On other hand, non-invoice based techniques are more effective for heart disease diagnosis. Therefore, we check the capability of various Machine Learning (ML) classifiers and deep learning classifier for heart disease identification in this paper. Six machine-learning classifiers and BPNN were used in order to check which one classifier is more effective for diagnosis the heart disease. The feature selection algorithm Relief was used for selection of important features and on these selected features, classifiers performances were also computed. Ensemble machine learning techniques (boosting, bagging, stacking) were used to further increase the classifiers performance. Furthermore, cross-validation techniques k-folds was also used. Additionally backward propagation neural network (BPNN) was also used for classification purpose because deep learning algorithm not need feature selection algorithms and it automatically select important features for good result. Based on model performance evaluation metrics the SVM (RBF) performed excellently on full features achieved accuracy 86%, and 88% accuracy on selected features as compared other classifiers. Through Ensemble learning techniques, SVM obtained the classification accuracy 92.30%. The BPNN achieved 93% classification accuracy. Thus the performance of deep neural networks learning algorithm is better than traditional machine learning algorithms. As per our experimental results shows that the performance of BPNN based diagnosis system is more effective for heart disease diagnosis.

关键词： Heart Classification algorithms Machine learning Diseases Machine learning algorithms Training Feature extraction

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