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arXiv 2021年

作者： Sakai, Yusuke Itoh, Yousuke Jung, Piljong Kokeyama, Keiko Kozakai, Chihiro Nakahira, Katsuko T. Oshino, Shoichi Shikano, Yutaka Takahashi, Hirotaka Uchiyama, Takashi Ueshima, Gen Washimi, Tatsuki Yamamoto, Takahiro Yokozawa, Takaaki Research Center for Space Science Advanced Research Laboratories Tokyo City University Setagaya-ku Tokyo158-0082 Japan Graduate School of Science Osaka Metropolitan University Sumiyoshi-ku Osaka Osaka558-8585 Japan Osaka Metropolitan University Sumiyoshi-ku Osaka Osaka558-8585 Japan National Institute for Mathematical Sciences Daejeon34047 Korea Republic of School of Physics and Astronomy Cardiff University The Parade CardiffCF24 3AA United Kingdom Gravitational Wave Science Project Kamioka branch National Astronomical Observatory of Japan Gifu Hida506-1205 Japan Department of Information and Management Systems Engineering Nagaoka University of Technology Nagaoka Niigata940-2188 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo Gifu Hida506-1205 Japan Graduate School of Science and Technology Gunma University Gunma Maebashi371-8510 Japan Institute for Quantum Studies Chapman University OrangeCA92866 United States JST PRESTO Saitama Saitama332-0012 Japan Institute for Cosmic Ray Research The University of Tokyo Kashiwa City Chiba277-8582 Japan Earthquake Research Institute The University of Tokyo Bunkyo-ku Tokyo113-0032 Japan

In the data obtained by laser interferometric gravitational wave detectors, transient noise with non-stationary and non-Gaussian features occurs at a high rate. This often results in problems such as detector instability and the hiding and/or imitation of gravitational-wave signals. This transient noise has various characteristics in the time-frequency representation, which is considered to be associated with environmental and instrumental origins. Classification of transient noise can offer clues for exploring its origin and improving the performance of the detector. One approach for accomplishing this is supervised learning. However, in general, supervised learning requires annotation of the training data, and there are issues with ensuring objectivity in the classification and its corresponding new classes. By contrast, unsupervised learning can reduce the annotation work for the training data and ensure objectivity in the classification and its corresponding new classes. In this study, we propose an unsupervised learning architecture for the classification of transient noise that combines a variational autoencoder and invariant information clustering. To evaluate the effectiveness of the proposed architecture, we used the dataset (time-frequency two-dimensional spectrogram images and labels) of the Laser Interferometer Gravitational-wave Observatory (LIGO) first observation run prepared by the Gravity Spy project. The classes provided by our proposed unsupervised learning architecture were consistent with the labels annotated by the Gravity Spy project, which manifests the potential for the existence of unrevealed classes. © 2021, CC BY.

关键词： Classification (of information)

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The value and broader impacts of agricultural and environmental scientific meetings

Agricultural & Environmental Letters

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Agricultural & Environmental Letters 2025年第1期10卷

作者： Aaron Lee M. Daigh Samira H. Daroub Peter M. Kyveryga Mark E. Sorrells James A. Ippolito Endy Kailer Shannon L. Osborne Felix B. Fritschi Wade E. Thomason Ronald F. Turco Michael A. Grusak Carrie A. M. Laboski Seth C. Murray Kimberly A. Garland-Campbell Joann K. Whalen Kristen S. Veum Nathan Ehresman Zoe Brindley James M. Cudahy Agronomy & Horticulture University of Nebraska–Lincoln Lincoln Nebraska USA Biological Systems Engineering University of Nebraska–Lincoln Lincoln Nebraska USA Environmental Agricultural and Occupational Health University of Nebraska Medical Center Omaha Nebraska USA Soil Water and Ecosystem Sciences University of Florida Gainesville Florida USA Everglades Research and Education Center University of Florida Belle Glade Florida USA John Deere Science Agronomy Johnston Iowa USA Department of Agronomy Iowa State University Ames Iowa USA Plant Breeding and Genetics School of Integrative Plant Science Cornell University Ithaca New York USA School of Environment and Natural Resources The Ohio State University Columbus Ohio USA Department of Agronomy Kansas State University Manhattan Kansas USA USDA-ARS North Central Agricultural Research Laboratory Brookings South Dakota USA Division of Plant Science and Technology University of Missouri Columbia Missouri USA Department of Plant & Soil Sciences Oklahoma State University Stillwater Oklahoma USA Department of Agronomy Purdue University West Lafayette Indiana USA USDA-ARS Office of National Programs Beltsville Maryland USA USDA-ARS The Pasture Systems & Watershed Management Research University Park Pennsylvania USA Department of Soil and Crop Sciences Texas A&M University College Station Texas USA USDA-ARS Wheat Health Genetics and Quality Research Pullman Washington USA Department of Natural Resource Sciences McGill University Montreal Quebec Canada USDA-ARS Cropping Systems and Water Quality Research Columbia Missouri USA Meetings Team Alliance of Crop Soil & Environmental Science Societies Madison Wisconsin USA

The socioeconomic value of content presented at the ASA-CSSA-SSSA (where ASA-CSSA-SSSA is American Society of Agronomy–Crop Science Society of America–Soil Science Society of America) Annual Meetings from 2014 to 2023 is estimated at $64.2 billion and is presented in this commentary as a thought exercise, highlighting the potential scale of research dissemination in scientific meetings. Scientific meetings are instrumental for propelling the quality and advancement of research via fostering timely feedback, knowledge dissemination, fresh perspectives, stimulation for networking and new collaborations, preparing scientists for public engagement, and addressing contemporary challenges of cultural accessibility and opportunity. Additionally, the broader impacts include near-term benefits to agricultural and environmental scientists that can transform careers and perspectives on the world, especially for students and early career members. The benefits from these impacts on scientists are then anticipated to propagate into broader and longer term positive impacts on humanity worldwide. In this commentary, we offer the above as a provocation to spark peer discussion on evaluating scientific meetings’ contributions, alongside a working list of broader impacts to inspire philosophical and methodological innovations for quantifying their value. Scientific meetings enhance the scientific enterprise. $64.2 billion socioeconomic value of works are presented at the ASA-CSSA-SSSA Annual Meetings (2014–2023). Quantified and unquantified broader impacts are identified and discussed.

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A subgeometric convergence formula for finite-level M/G/1-type Markov chains: via a block-decomposition-friendly solution for the Poisson equation of deviation matrix

arXiv

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arXiv 2018年

作者： Masuyama, Hiroyuki Katsumata, Yosuke Kimura, Tatsuaki Graduate School of Management Tokyo Metropolitan University Tokyo192-0397 Japan Department of Systems Science Graduate School of Informatics Kyoto University Kyoto606-8501 Japan Department of Information and Communications Technology Graduate School of Engineering Osaka University Suita565-0871 Japan

This paper studies the subgeometric convergence of the stationary distribution in taking the infinite-level limit of a finite-level M/G/1-type Markov chain, that is, in letting the upper boundary level go to infinity. This study is performed through the fundamental deviation matrix, which is a block-decomposition-friendly solution for the Poisson equation of the deviation matrix. The fundamental deviation matrix yields a difference formula for the respective stationary distributions of the finite-level chain and the corresponding infinite-level chain. The difference formula plays a crucial role in deriving the main result of this paper: a subgeometric convergence formula for the infinite-level limit of the stationary distribution of the finite-level chain. Copyright © 2018, The Authors. All rights reserved.

关键词： Matrix algebra

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Health co-benefits of post-COVID-19 low-carbon recovery in Chinese cities

Nature Cities

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Nature Cities 2024年 695-705页

作者： Chenxi Lu Felix Creutzig Jiawei Hu Yingjian Huang Huibin Mo Ying Yu Yun Li Da Huo Xuanren Song Xiaoting Huang Zhu Liu Yun Sun Kai Liu Shaohui Zhang Karyn Morrissey Jinpyo Hong Zhu Deng Zhuanjia Du Mercator Research Institute on Global Commons and Climate Change Berlin Germany Sustainability Economics of Human Settlements Technical University Berlin Berlin Germany Belfer Center for Science and International Affairs Harvard Kennedy School Harvard University Cambridge MA USA Alibaba Cloud Hangzhou China Guangdong Key Laboratory for Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University Guangzhou China Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Institute of Environment and Ecology Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Data-Driven EnviroLab Department of Public Policy University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Environmental Sciences and Engineering Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill NC USA Faculty of Engineering Science Department of Architecture KU Leuven Leuven Belgium Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China Department of Civil & Mineral Engineering University of Toronto Toronto Ontario Canada School of Environmental Science and Engineering Tianjin University Tianjin China Department of Geography King’s College London London UK Guangdong Holdings Limited Guangzhou China International Institute for Applied Systems Analysis (IIASA) Laxenburg Austria Division for Climate and Energy Policy Department of Technology Management and Economics Technical University of Denmark Lyngby Denmark School of Engineering Brown University Providence RI USA Department of Geography University of Hong Kong Hong Kong SAR China Department of Political Science Columbia University New York NY USA

Post-pandemic green recovery is pivotal in achieving global sustainable development goals by simultaneously revitalizing economies and reducing greenhouse gas emissions, air pollution and improving public welfare. However, subnational and city-level understanding of green recovery, its efficacy and its alignment with public health is poorly understood. Here we focus on post-COVID-19 low-carbon recovery—economic growth combined with reduced carbon emissions—and explore health co-benefits in Chinese cities. A novel near-real-time daily carbon emission dataset of 48 cities in China is developed, coupled with detailed health and economic municipal statistics and models. We find that, on average, six low-carbon-recovery cities, mainly megacities, saved 1.2 times as many lives per 100,000 population compared with the 42 other cities, and their annual monetary avoided premature deaths per 100,000 population was 1.5 times more than the 42 other cities. The accumulated monetary health co-benefits for low-carbon-recovery cities were US$ 4.2 billion (95% confidence interval, 2.1–6.3) during the post-COVID-19 period. We show that government spending on electric vehicles increases the likelihood of achieving low-carbon recovery in Chinese cities. Our results underscore the significant health co-benefits of low-carbon recovery, pointing to synergies between advancing local welfare and global environmental objectives.

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Optimization and reliability analysis tool based on multi-dimensional wiener processes for big data on cloud computing 23

Optimization and reliability analysis tool based on multi-di...

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23rd ISSAT International Conference on Reliability and Quality in Design 2017

作者： Tamura, Yoshinobu Yamada, Shigeru Department of Industrial and Management Systems Engineering Faculty of Knowledge Engineering Tokyo City University 1-28-1 Tamazutsumi Setagaya-ku Tokyo158-8557 Japan Department of Social Management Engineering Graduate School of Engineering Tottori University Minami 4-101 Koyama Tottori-shi680-8552 Japan

ISBN: (纸本)9780991057641

This paper focuses on a big data on cloud computing environment by using open source software such as OpenStack and Eucalyptus because of the unification management of data and low cost. We discuss a new approach to software dependability assessment based on stochastic differential equation modeling in order to consider the interesting aspect of the numbers of components, cloud applications, and users. Moreover, we consider the determination of an optimum software maintenance time minimizing the total expected software cost. In particular, we develop the three-dimensional AIR application for reliability and cost optimization analysis based on the proposed method. Then, we show numerical performance of the developed AIR application to evaluate the method of software reliability assessment for the big data on cloud computing.

关键词： Cloud computing

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Predicting Spring Phenology in Deciduous Broadleaf Forests: Neon Phenology Forecasting Community Challenge

SSRN

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SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Ross, Arun A. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chen, Min Goldstein, Benjamin Guo, Jessica Hao, Dalei Jones, Chris Kelly-Fair, Mira Liu, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Computer Science and Engineering Michigan State University 428 South Shaw Lane East LansingMI48824 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Instit 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Tech 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building Universi

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative’s National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate what factors impact current predictive skill of phenology forecasts. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge

SSRN

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SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chenp, Min Goldstein, Benjamin Guo, Jessica S. Haoq, Dalei Jones, Chris Kelly-Fair, Mira Liup, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Ross, Arun A. Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Institute State University 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Polytechnic Institute State University 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building University of California Berkeley BerkeleyCA94720 United States Department of For

Accurate phenology models are important to predict how global climate change will continue to alter the timing of plant phenological events, such as spring greenup in deciduous broadleaf forests. While there is merit in long-term predictions, investigating how models can predict near-term (1– 35 days) canopy greenness throughout the spring allows us to validate performance and understanding now. The Ecological Forecasting Initiative’s NEON Forecasting Challenge, is an open challenge to the community to predict daily greenness values, measured through digital images collected by the PhenoCam Network at National Ecological Observatory Network (NEON) sites. For the first round of the challenge, which is presented here, teams were tasked to forecast phenology at eight deciduous broadleaf sites. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts, which future rounds will continue to investigate. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Privacy preserving fuzzy modeling for secure multiparty computation

Privacy preserving fuzzy modeling for secure multiparty comp...

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2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017

作者： Miyajima, Hirofumi Shigei, Noritaka Miyajima, Hiromi Miyanishi, Yohtaro Kitagami, Shinji Shiratori, Norio Graduate School of Science and Engineering Kagoshima University 1-21-40 Korimoto Kagoshima890-0065 Japan Information Systems Engineering and Management Tokyo Japan Tokyo Japan Waseda University GITS Tokyo Japan

ISBN: (纸本)9789881404732

Many studies on privacy preserving of machine learning and data mining have been done in various methods by use of randomization techniques, cryptographic algorithms, anonymization methods, etc. Data encryption is one of typical approaches. However, its system requires both encryption and decryption for requests of client or user, so its complexity of computation is very high. Therefore, studies on secure computation using shared data are made to avoid secure risks being abused or leaked and to reduce computing cost. The secure multiparty computation (SMC) is one of these methods. So far, some studies have been done with SMC, but complex calculation processing such as machine learning has never proposed yet. In the previous paper, we proposed BP learning method for SMC on cloud computing system. In this paper, we propose learning method(Fuzzy modeling) of fuzzy inference system for SMC and prove the validity of it. Further, the performance of the proposed method is shown in numerical simulations.

关键词： Cloud computing

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Research on burst gravitational waves with the Hilbert-Huang transform 14

Research on burst gravitational waves with the Hilbert-Huang...

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14th Marcel Grossman Meeting On Recent Developments in Theoretical and Experimental General Relativity, Astrophysics and Relativistic Field Theories

作者： Kaneyama, Masato Oohara, Ken-Ichi Wakamatsu, Takashi Takahashi, Hirotaka Camp, Jordan B. Department of Physics Osaka City University Osaka558-8585 Japan Graduate School of Science and Technology Niigata University Niigata950-2181 Japan Department of Information and Management Systems Engineering Nagaoka Uniersity of Technology Nagaoka Niigata940-2188 Japan Earthquake Research Institute University of Tokyo Bunkyo-Ku Tokyo113-0032 Japan Laboratory for Gravitational Physics NASA Goddard Space Flight Center GreenbeltMD20771 United States

ISBN: (纸本)9789813226593

The reconstruction of waveform is one of the most important issue of the parameter estimation of source in the GW astronomy. In this paper, we investigate a method of the waveform reconstruction of burst gravitational waves from a single detector's data using Hilbert-Huang Transform. Moreover, we demonstrate the wave reconstruction to confirm the effectiveness of our proposed method using the simulated data. As the results, we found that the reconstructed waveforms were obtained with good accuracy from only one detector data. Copyright © 2018 by the *** rights reserved.

关键词： Hilbert-Huang transform

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The Influence of the Dispersing Agent Nature on the Interaction of DNA Nucleotides With the Surface of YSZ Nanoparticles and the Homogeneity of Lyophilites Based on Them

SSRN

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SSRN 2022年

作者： Doroshkevich, Aleksander S. Doroshkevich, Viktor S. Zelenyak, Tatyana Yu. Doroshkevich, Nelya V. Lyubchyk, Andriy I. Lyubchyk, Sergiy I. Lyubchyk, Svitlana B. Tatarinova, Alisa A. Stanculescu, Anca Chicea, Dan Mita, Carmen Cornei, Nicoleta Mardare, Diana Oksengendler, Boris L. Nikiforova, Nadejda N. Mirzayev, Matlab N. Popov, Evgeni P. Donkov, Aleksandr A. Teofilović, Vesna Jasinska, Bozena Khiem, Le Hong Joint Institute for Nuclear Research Dubna Russia Donetsk Institute for Physics and Engineering named after O.O. Galkin Kiev Ukraine Donetsk National University Ukraine Donetsk National Vasyl Stus University Vinnitsa Ukraine Nanotechcenter LLC Krzhizhanovsky str. 3 Kyiv03680 Ukraine Research Centre in Industrial Engineering Management and Sustainability Lusófona University Campo Grande 376 Lisboa1749-024 Portugal REQUIMTE Faculdade de Ciências e Tecnologia Universidade Nova de Lisboa Quina de Torre Caparica2829-516 Portugal Optical Processes in Nanostructured Materials Laboratory National Institute of Materials Physics 405A Atomistilor Street P.O. Box MG-7 Magurele077125 Romania Research Center for Complex Physical Systems Faculty of Sciences "Lucian Blaga" University of Sibiu Dr. Ion Ratiu str. no. 5–7 Sibiu550012 Romania Faculty of Chemistry "Alexandru Ioan Cuza" University of Iasi Bld. Carol I No. 11 Iasi700506 Romania Faculty of Physics "Alexandru Ioan Cuza" University of Iasi Bld. Carol I No. 11 Iasi700506 Romania Ion-plasma and laser technologies Institute after U. Arifov Tashkent Uzbekistan Institute of Radiation Problems Azerbaijan National Academy of Sciences BakuАZ1143 Azerbaijan Institute of Solid-State Physics Bulgarian Academy of Sciences Sofia1784 Bulgaria Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia1784 Bulgaria Laboratory of Physics INN Vinča University of Belgrade M. Petrovica Alasa 12-14 Belgrade Serbia Institute of Physics Maria Curie-Sklodowska University Lublin Poland Graduate University of Science and Technology Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Cau Giay Hanoi Viet Nam Institute of Physics 10 Dao Tan Ba Dinh Hanoi Viet Nam

A variational analysis of dispersion medium (electrolytes and surfactants) was carried out for the best nucleotide interactions with the presence of nanoparticles and the ability to formation of homogeneous coatings upon drying / lyophilization. Using UV-spectroscopy and transmission electron microscopy, the processes of immobilization of nucleotides on the surface of nanoparticles of the composition ZrO2-3mol% Y2O3 (YSZ) in the presence of various dispersing agents were investigated. Promising dispersing agents were established, the optimal composition of suspensions based on DNA nucleotides and YSZ nanoparticles, which is potentially suitable for adaptive technologies of bionanoelectronics, was *** aim of the work is to obtain and study functional media for molecular electronics based on heterojunctions biological molecule – wide-gap dielectric. © 2022, The Authors. All rights reserved.

关键词： Electric fields

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