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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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Roadmap on quantum nanotechnologies

arXiv

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arXiv 2021年

作者： Laucht, Arne Hohls, Frank Ubbelohde, Niels Fernando Gonzalez-Zalba, M. Reilly, David J. Stobbe, Søren Schröder, Tim Scarlino, Pasquale Koski, Jonne V. Dzurak, Andrew Yang, Chih-Hwan Yoneda, Jun Kuemmeth, Ferdinand Bluhm, Hendrik Pla, Jarryd Hill, Charles Salfi, Joe Oiwa, Akira Muhonen, Juha T. Verhagen, Ewold LaHaye, M.D. Kim, Hyun Ho Tsen, Adam W. Culcer, Dimitrie Geresdi, Attila Mol, Jan A. Mohan, Varun Jain, Prashant K. Baugh, Jonathan Centre for Quantum Computation and Communication Technology School of Electrical Engineering and Telecommunications UNSW SydneyNSW2052 Australia Physikalisch-Technische Bundesanstalt Braunschweig38116 Germany Quantum Motion Technologies Nexus Discovery Way LeedsLS2 3AA United Kingdom School of Physics University of Sydney SydneyNSW2006 Australia Microsoft Corporation Station Q Sydney University of Sydney SydneyNSW2006 Australia Department of Photonics Engineering DTU Fotonik Technical University of Denmark Building 343 Kgs. LyngbyDK-2800 Denmark Department of Physics Humboldt-Universität zu Berlin Berlin12489 Germany Department of Physics ETH Zürich ZürichCH-8093 Switzerland Niels Bohr Institute University of Copenhagen Copenhagen2100 Denmark JARA-FIT Institute for Quantum Information RWTH Aachen University and Forschungszentrum Jülich Aachen52074 Germany School of Physics University of Melbourne Melbourne Australia Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Institute of Scientific and Industrial Research Osaka University Ibaraki Osaka567-0047 Japan Center for Quantum Information and Quantum Biology Institute for open and Transdisciplinary Research Initiative Osaka University Osaka560-8531 Japan Center for Nanophotonics AMOLF Amsterdam1098 XG Netherlands School of Physics The University of New South Wales Sydney2052 Australia Australian Research Council Centre of Excellence in Future Low-Energy Electronics Technologies UNSW Node The University of New South Wales Sydney2052 Australia QuTech and Kavli Institute of Nanoscience Delft University of Technology Delft2600 GA Netherlands School of Physics and Astronomy Queen Mary University of London E1 4NS United Kingdom Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Chemistry University of Illinois at Urbana-Champaign Urbana

Quantum phenomena are typically observable at length and time scales smaller than those of our everyday experience, often involving individual particles or excitations. The past few decades have seen a revolution in the ability to structure matter at the nanoscale, and experiments at the single particle level have become commonplace. This has opened wide new avenues for exploring and harnessing quantum mechanical effects in condensed matter. These quantum phenomena, in turn, have the potential to revolutionize the way we communicate, compute and probe the nanoscale world. Here, we review developments in key areas of quantum research in light of the nanotechnologies that enable them, with a view to what the future holds. Materials and devices with nanoscale features are used for quantum metrology and sensing, as building blocks for quantum computing, and as sources and detectors for quantum communication. They enable explorations of quantum behaviour and unconventional states in nano- and opto-mechanical systems, low-dimensional systems, molecular devices, nano-plasmonics, quantum electrodynamics, scanning tunnelling microscopy, and more. This rapidly expanding intersection of nanotechnology and quantum science/technology is mutually beneficial to both fields, laying claim to some of the most exciting scientific leaps of the last decade, with more on the horizon. © 2021, CC BY.

关键词： Quantum computers

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Superiorization of preconditioned conjugate gradient algorithms for tomographic image reconstruction

arXiv

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arXiv 2018年

作者： Helou, Elias S. Herman, Gabor T. Lin, Chuan Zibetti, Marcelo V.W. Institute of Mathematical Science and Computation University of São Paulo SP Brazil Department of Computer Science Graduate Center City University of New York NY United States School of Electrical Engineering Southwest Jiaotong University Chengdu China Program in Electrical and Computer Engineering Federal University of Technology PR Brazil

Properties of Superiorized Preconditioned Conjugate Gradient (SupPCG) algorithms in image reconstruction from projections are examined. Least squares (LS) is usually chosen for measuring data-inconsistency in these inverse problems. Preconditioned Conjugate Gradient algorithms are fast methods for finding an LS solution. However, for ill-posed problems, such as image reconstruction, an LS solution may not provide good image quality. This can be taken care of by superiorization. A superiorized algorithm leads to images with the value of a secondary criterion (a merit function such as the total variation) improved as compared to images with similar data-inconsistency obtained by the algorithm without superiorization. Numerical experimentation shows that SupPCG can lead to high-quality reconstructions within a remarkably short time. A theoretical analysis is also provided.15A29, 65F22, 65F10, 94A08 Copyright © 2018, The Authors. All rights reserved.

关键词： Image reconstruction

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Suspended load path tracking control using a tilt-rotor UAV based on zonotopic state estimationI

arXiv

引用

arXiv 2018年

作者： Rego, Brenner S. Raffo, Guilherme V. Graduate Program in Electrical Engineering Federal University of Minas Gerais Belo Horizonte MG31270-901 Brazil Department of Electronics Engineering Federal University of Minas Gerais Belo Horizonte MG31270-901 Brazil

This work addresses the problem of path tracking control of a suspended load using a tilt-rotor UAV. The main challenge in controlling this kind of system arises from the dynamic behavior imposed by the load, which is usually coupled to the UAV by means of a rope, adding unactuated degrees of freedom to the whole system. Furthermore, to perform the load transportation it is often needed the knowledge of the load position to accomplish the task. Since available sensors are commonly embedded in the mobile platform, information on the load position may not be directly available. To solve this problem in this work, initially, the kinematics of the multi-body mechanical system are formulated from the load’s perspective, from which a detailed dynamic model is derived using the Euler-Lagrange approach, yielding a highly coupled, nonlinear state-space representation of the system, affine in the inputs, with the load’s position and orientation directly represented by state variables. A zonotopic state estimator is proposed to solve the problem of estimating the load position and orientation, which is formulated based on sensors located at the aircraft, with different sampling times, and unknown-but-bounded measurement noise. To solve the path tracking problem, a discrete-time mixed H2/H∞ controller with pole-placement constraints is designed with guaranteed time-response properties and robust to unmodeled dynamics, parametric uncertainties, and external disturbances. Results from numerical experiments, performed in a platform based on the Gazebo simulator and on a Computer Aided Design (CAD) model of the system, are presented to corroborate the performance of the zonotopic state estimator along with the designed controller. Copyright © 2018, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Day-To-Day Quantification of Changes in Global Lightning Activity Based on Schumann Resonances

引用

Journal of Geophysical Research: Atmospheres 2023年第11期128卷 e2023JD038557-e2023JD038557页

作者： T. Bozóki G. Sátori E. Williams A. Guha Y. Liu P. Steinbach A. Leal M. Herein M. Atkinson C. D. Beggan E. DiGangi A. Koloskov A. Kulak J. LaPierre D. K. Milling J. Mlynarczyk A. Neska A. Potapov T. Raita R. Rawat R. Said A. K. Sinha Y. Yampolski Institute of Earth Physics and Space Science (EPSS) Sopron Hungary Department of Optics and Quantum Electronics University of Szeged Szeged Hungary Department of Geophysics and Space Science Eötvös Loránd University Budapest Hungary Parsons Laboratory Massachusetts Institute of Technology Cambridge MA USA Department of Physics Tripura University Agartala India ELKH-ELTE Space Research Group Budapest Hungary Department of Physics and Langmuir Laboratory New Mexico Institute of Mining and Technology Socorro NM USA Graduate Program in Electrical Engineering Federal University of Pará Belem Brazil ELKH-ELTE Theoretical Physics Research Group Budapest Hungary Department of Theoretical Physics Institute of Physics and Astronomy Eötvös Loránd University Budapest Hungary HeartMath Institute Boulder Creek CA USA British Geological Survey Edinburgh UK Advanced Environmental Monitoring (AEM) Germantown MD USA Department of Physics University of New Brunswick Fredericton NB Canada State Institution National Antarctic Scientific Center of Ukraine Kyiv Ukraine Institute of Electronics AGH University of Science and Technology Krakow Poland Department of Physics University of Alberta Edmonton AB Canada Institute of Geophysics Polish Academy of Sciences Warsaw Poland Institute of Solar-Terrestrial Physics SB RAS Irkutsk Russia Sodankylä Geophysical Observatory University of Oulu Sodankylä Finland Indian Institute of Geomagnetism Navi Mumbai India Vaisala Louisville CO USA Department of Physics School of Science University of Bahrain Zallaq Bahrain Institute of Radio Astronomy National Academy of Sciences of Ukraine Kharkiv Ukraine

The importance of lightning has long been recognized from the point of view of climate-related phenomena. However, the detailed investigation of lightning on global scales is currently hindered by the incomplete and spatially uneven detection efficiency of ground-based global lightning detection networks and by the restricted spatio-temporal coverage of satellite observations. We are developing different methods for investigating global lightning activity based on Schumann resonance (SR) measurements. SRs are global electromagnetic resonances of the Earth-ionosphere cavity maintained by the vertical component of lightning. Since charge separation in thunderstorms is gravity-driven, charge is typically separated vertically in thunderclouds, so every lightning flash contributes to the measured SR field. This circumstance makes SR measurements very suitable for climate-related investigations. In this study, 19 days of global lightning activity in January 2019 are analyzed based on SR intensity records from 18 SR stations and the results are compared with independent lightning observations provided by ground-based (WWLLN, GLD360, and ENTLN) and satellite-based (GLM, LIS/OTD) global lightning detection. Daily average SR intensity records from different stations exhibit strong similarity in the investigated time interval. The inferred intensity of global lightning activity varies by a factor of 2–3 on the time scale of 3–5 days which we attribute to continental-scale temperature changes related to cold air outbreaks from polar regions. While our results demonstrate that the SR phenomenon is a powerful tool to investigate global lightning, it is also clear that currently available technology limits the detailed quantitative evaluation of lightning activity on continental scales. Daily average Schumann resonance intensity is a quasi-global invariant quantity that shows good agreement with global daily stroke rates and thunder hours Global lightning activity can vary by a fa

关键词： global lightning activity Schumann resonances extremely low frequency lightning and climate day-to-day variability

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

引用

Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Active ISFET sensor readout circuit

Active ISFET sensor readout circuit

引用

International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT)

作者： Luciano Lourenço Furtado da Silva Felipe Silveira Carlos Augusto de Moraes Cruz Thiago Daniel de Oliveira Moura Graduate Program in Electrical Engineering - Department of Electronics and Computation Federal University of Amazonas Manaus AM Brazil Federal Institute of Amazonas Manaus AM Brazil Graduate Program in Electrical Engineering - Department of Electrical Engineering - Federal University of Minas Gerais Belo Horizonte MG Brazil

An improved readout circuit interface for ion-sensitive field-effect transistor (ISFET) is proposed in this work. When compared with conventional topologies found on the literature, the proposed ISFET readout circuit presents at least two advantages. The first concerns the simplicity of the new circuity topology, and the second is the additional gain conferred to the sensor output signal. The performance of the proposed readout circuit integrated with the electrical ISFET model was investigated through BSIM3v3 simulations. The conventional ISFET readout topologies are found not to confer gain to the electrical ISFET output signal, and the sensor output signal has its limit predicted by the Nernst s model. Simulation results shows that the proposed ISFET readout circuit topology grants additional gain to the sensor output signal increasing its sensitivity up to three times the Nernst limit.

关键词： Sensitivity Electrodes Integrated circuit modeling Topology Transistors Logic gates Threshold voltage

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Small P values may not yield robust findings:an example using REST-meta-PD

引用

Science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

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A novel electrical circuit model for ion-sensitive field-effect transistor

A novel electrical circuit model for ion-sensitive field-eff...

引用

International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT)

作者： Luciano Lourenço F. da Silva Jair Fernandes de Souza Marcus Vinicius Pelegrini Carlos Augusto de Moraes Cruz Valtemar Fernandes Cardoso Graduate Program in Electrical Engineering - Department of Electronics and Computation Federal University of Amazonas Manaus AM Brazil Department of Electrical Engineering Federal Institute of Amazonas Manaus AM Brazil Senai Institute of Innovation in Microelectronics Manaus AM Brazil

An alternative electrical model for Ion-Sensitive Field-Effect Transistor (ISFET) sensors is presented in this work. The proposed model is worth to be employed in both DC and transient simulations where the behavior of the ISFET sensor coupled to its readout circuit can therefore be investigated. Whereas, previous models found in the literature could only be employed to perform DC simulations. The electrochemical stage of an ISFET is responsible to emulate the device's sensitivity to ion concentration. The alternative electrical model enables the representation of the ISFET electrochemical stage through a much simpler circuit topology than those found in the literature, without loss of generality. The simulation results employing the proposed ISFET model are compliant with those presented in the literature, asserting thus its effectiveness.

关键词： Integrated circuit modeling Ions Semiconductor device modeling Sensors Logic gates MOSFET Electrodes

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Design a novel small pattern UV LED array curing system

Design a novel small pattern UV LED array curing system

引用

2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Chen, Yi-Tai Sun, Tai-Ping Huang, Po-Yan Shieh, Hsiu-Li Department of Electrical Engineering National Chi Nan University Nantou Taiwan Graduate Program of Optoelectronic Technology National Chi Nan University Nantou Taiwan Department of Electronics Engineering Nan Kai University of Technology Nantou Taiwan Chi Li Technology Co. Ltd Hsinchu Taiwan

ISBN: (纸本)9781509048977

This study designs a small arrayed Ultraviolet Light Emitting Diode (UV LED) curing system, using automatic electric power control circuit to drive 3 modules and 6 modules UV LED in wavelength of 365 nm and 385 nm, which is applicable to all sided adhesive material curing. The power output is controlled stably by the automatic electric power control circuit, so that the electric power is controlled stably, and the electric power is stable at ambient temperature10°C to 40°C, and the stable value is as high as 95%. © 2017 IEEE.

关键词： Power control

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