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On To A Centralized Sponsorship of Blockchain Based IoT Operational Infrastructure

Journal of Physics: Conference Series 2021年第4期1964卷

作者： M S Josephine K Balasubadra S Kevin Andrews K lakshmi Narayanan Department of Computer Applications Dr.MGR Educational and Research Institute Chennai Tamil Nadu India Department of Information Technology R.M.D Engineering College Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli Tamil Nadu India

With the rapidly developed IT technology, large-scale networks can be designed to link many heterogeneous devices into the Internet. The majority of current infrastructures are clustered, easier to maintain, but cannot efficiently provide immutable and verifiable service between multiple parties. For large-scale infrastructures like decentralization, reliability, trackability, and immutability, Blockchain technology offers several desirable functions. This article introduces an infrastructure based on blockchain that facilitates unchanging and controllable services. However, the need for storage capacity presents a threat to resource-controlled architecture when adding blockchain technologies to the infrastructure. The paper attempts to tackle the storage dilemma with a centralized storage side chain. In fact, the proposed architecture has a distributed storage structure where most of the blockchain is kept at the cloud, although these current blocks are kept in the real network integration node. The framework architecture allows the local, blockchain and server infrastructures to be conveniently merged to create a distributed blockchain storage between two connectors, the database and the blockchain. The extended version blockchain connection builds block of the network info, and addresses network/cloud overlay sync problems in blockchain. In a practical case of professional IoT, we also have a test case to demonstrate the reliability of the general architecture processing blockchain.

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Analysis on Sports data Match Result Prediction Using Machine learning libraries

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Journal of Physics: Conference Series 2021年第4期1964卷

作者： S Kevin Andrews K lakshmi Narayanan K Balasubadra M S Josephine Department of Computer Applications Dr.MGR Educational and Research Institute Chennai Tamil Nadu India Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli Tamil Nadu India Department of Information Technology R.M.D Engineering College Kavaraipettai Tamil Nadu India

Sports research and betting have been powered by quick exposure to the Internet and machine learning popularity. Football is considered the most popular game in 200 countries and contrasts to other sports. It is considered much more diverse and complicated, making soccer an enticing area to do research. A variety of methodologies and methods are used for the production of prediction systems. We expect the outcome of a match between the Premier league and a home side. The projections are based on numerous significant evidences from the Premier league's previous seasons. These essential characteristics would possibly decide the result of a match. We use three different algorithms to predict the machine learning techniques and then choose from those three the best algorithm for predicting the label.

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Optimization of Neural Network Parameters Based on a Genetic Algorithm for Prediction of Time Series

Optimization of Neural Network Parameters Based on a Genetic...

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International Multi-Conference on Industrial engineering and Modern Technologies (FarEastCon)

作者： V.S. Tormozov A.l. Zolkin K.A. Vasilenko Senior lecturer of the Software engineering for computers and computer-based systems sub-faculty Pacific National University Khabarovsk Tikhookeanskaya St. 136 Ph.D. in Engineering Science Povolzhskiy State University of Telecommunications and Informatics Samara Highest category lecturer Service and design college of the Vladivostok State University of Economics and Service Service and design college of the Vladivostok State University of Economics and Service Vladivostok

ISBN: (数字)9781728169514

ISBN: (纸本)9781728169521

The article proposes a method that allows to solve the complex combinatorial problem of structural optimization of an artificial neural network with a large dimension of the space of optimization parameters using the stochastic mutation function with a constant coefficient of variation. A study on assessment of applicability of this approach to solve the problem is given. This method is proposed to be used for the forecasting of time series values.

关键词： Genetic algorithms Sociology Neurons Biological cells Artificial neural networks Time series analysis Optimization

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dECIdABIlITY FOR STURMIAN WORdS

arXiv

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arXiv 2021年

作者： Hieronymi, Philipp Ma, dun Oei, Reed Schaeffer, luke Schulz, Chris Shallit, Jeffrey Mathematisches Institut Universität Bonn Endenicher Allee 60 BonnD-53115 Germany Department of Computer Science and Engineering University of California 9500 Gilman Drive La Jolla San DiegoCA92093-0404 United States Department of Mathematics University of Illinois at Urbana-Champaign 1409 West Green Street UrbanaIL61801 United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Pure Mathematics 200 University Avenue West WaterlooONN2L 3G1 Canada School of Computer Science University of Waterloo WaterlooONN2L 3G1 Canada

We show that the first-order theory of Sturmian words over Presburger arithmetic is decidable. Using a general adder recognizing addition in Ostrowski numeration systems by Baranwal, Schaeffer and Shallit, we prove that the first-order expansions of Presburger arithmetic by a single Sturmian word are uniformly ω-automatic, and then deduce the decidability of the theory of the class of such structures. Using an implementation of this decision algorithm called Pecan, we automatically reprove classical theorems about Sturmian words in seconds, and are able to obtain new results about antisquares and antipalindromes in characteristic Sturmian words. © 2021, CC BY.

关键词： Computability and decidability

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Selective electrocatalytic hydrogenation of bio-oil to oxygenated chemicals via suppression of deoxygenation

Research Square

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Research Square 2021年

作者： Peng, Tao Zhuang, Taotao Yan, Yu Qian, Jin dick, Graham de Bueren, Jean Behaghel Hung, Sung-Fu Wang, Ziyun Wicks, Joshua de Arquer, F. Pelayo Garcia Abed, Jehad Wang, Ning Rasouli, Armin lee, Geonhui Wang, Miao He, daping Wang, Zhe liang, Zhixiu Song, liang Wang, Xue Chen, Bin Ozden, Adnan lum, Yanwei leow, Wan Ru luo, Mingchuan Meira, déboa Ip, Alexander luterbacher, Jeremy Zhao, Wei Sargent, Edward University of Toronto Canada Lawrence Berkeley National Laboratory United States Ecole Polytechnique Fédérale de Lausanne Switzerland Shenzhen University China Hubei Engineering Research Center of RF-Microwave Technology and Application Wuhan University of Technology China Wuhan University of Technology China Brookhaven National Laboratory United States Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S 3G4 Canada Canadian Light Source Canada EPFL Switzerland

Catalytic hydrogenation of bio-oil provides an avenue to produce renewable chemicals. To this end, electrocatalytic hydrogenation is especially interesting when powered using low-carbon electricity;however, it has to date lacked the needed selectivity: when hydrogenating bio-oil to oxygenated hydrocarbons, for example, it reduces the desired oxygenated groups (-OH and -OCH3). Here we report that Rh and Au modulate electronic structure of Pt and steer intermediate energetics to favor the hydrogenation while suppressing deoxygenation using computational studies and in-situ spectroscopies. PtRhAu catalysts achieve a record 47% faradaic efficiency (FE) and a partial current density (Jp) of 28 mA·cm-2 toward oxygenated 2-methoxycyclohexanol from lignin-derived guaiacol under room temperature and ambient pressure, representing 1.5x FE and 3.5x Jp increases compared to the best prior reports. We further demonstrate an integrated lignin biorefinery where wood-derived lignin oils are selectively hydrogenated and funneled to the oxygenated 2-methoxy-4-propylcyclohexanol using PtRhAu catalysts. © 2021, CC BY.

关键词： lignin

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Unconventional Resistivity Scaling in Topological Semimetal CoSi

arXiv

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arXiv 2022年

作者： lien, Shang-Wei Garate, Ion Bajpai, Utkarsh Huang, Cheng-Yi Hsu, Chuang-Han Tu, Yi-Hsin lanzillo, Nicholas A. Bansil, Arun Chang, Tay-Rong liang, Gengchiau lin, Hsin Chen, Ching-Tzu Department of Physics National Cheng Kung University Tainan701 Taiwan Département de Physique Institut Quantique and Regroupement Québécois sur les Matériaux de Pointe Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada IBM Research 257 Fuller Road AlbanyNY12203 United States Department of Physics Northeastern University BostonMA02115 United States Institute of Physics Academia Sinica Taipei11529 Taiwan Tainan701 Taiwan Physics Division National Center for Theoretical Sciences Hsinchu Taiwan Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Singapore Singapore IBM T.J. Watson Research Center Yorktown HeightsNY10598 United States

Nontrivial band topologies in semimetals lead to robust surface states that can contribute dominantly to the total conduction. This may result in reduced resistivity with decreasing feature size contrary to conventional metals, which may highly impact the semiconductor industry. Here we study the resistivity scaling of a representative topological semimetal CoSi using realistic band structures and Green’s function methods. We show that there exists a critical thickness !! dividing different scaling trends. Above !!, when the defect density is low such that surface conduction dominates, resistivity reduces with decreasing thickness;when the defect density is high such that bulk conduction dominates, resistivity increases as in conventional metals. Below !!, the persistent remnants of the surface states give rise to decreasing resistivity down to the ultrathin limit, unlike in topological insulators. The observed CoSi scaling can apply to broad classes of topological semimetals, providing guidelines for materials screening and engineering. Our study shows that topological semimetals bear the potential of overcoming the resistivity scaling challenges in back-end-of-line interconnect applications. © 2022, CC BY.

关键词： Topology

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Smart interface development for Sensor data Analytics in Internet of Robotic things

Smart interface development for Sensor Data Analytics in Int...

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Intelligent Computing and Control Systems (ICCS), International Conference on

作者： Namrata Mahakalkar Rahul Pethe Kiran M. Kimmatkar Honey durga Tiwari Computer Science Engineering Priyadarshini Institute of Engineering and Technology Nagpur Maharashtra India Electrical Engineering Priyadarshini J. L. College of Engineering Nagpur Maharashtra India Electronics and Communication Engineering Priyadarshini Institute of Engineering and Technology Nagpur Maharashtra India Department of Electronics and communication Narsimha Reddy Engineering College Secunderabad Telangana State India

ISBN: (数字)9781538681138

ISBN: (纸本)9781538681145

In recent years, the number of transducers connected to a common entity has increased significantly. In order to develop collaborative networks of robotic elements, the data from each sensor is shared through the network. As the number of sensors increases the data handled by the network also increases significantly. This causes serious network congestion issues even for a small network. In this paper, a framework for smart sensor interface is presented to optimize the data sharing and diagnostics operations. different functions, operating conditions and architecture for cloud connectivity of sensor interface is presented. The implementation scheme and verification experiment are presented to show the application of the proposed design in multiple related sensors. The partial experiment results show that the proposed scheme can provide valuable insight to detect any deviation from normal behavior. Based on this information smart sensor and remote application can be used to take appropriate action. The partial results show that the proposed scheme can be employed for sensor data analytics in collaborative robotic networks.

关键词： Robot sensing systems Intelligent sensors Cloud computing Calibration Monitoring Transducers Temperature sensors

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia langlotz, Curtis P. Rueckert, daniel Mzurikwao, deogratias Fotiadis, dimitrios I. Zhussupov, doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWl Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, lameck M. Fromont, lauren A. Maier-Hein, lena Alberich, leonor Cerdá Rittner, leticia Phiri, lighton Marrakchi-Kacem, linda donoso-Bach, lluís Martí-Bonmatí, luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina linguraru, Marius George Wenzel, Markus de Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos lazrak, Noussair díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter lambin, Philippe Brown, Pieta Abolmaesumi, Purang dou, Qi lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Robust joint registration of multiple stains and MRI for multimodal 3d histology reconstruction: Application to the Allen human brain atlas

arXiv

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arXiv 2021年

作者： Casamitjana díaz, Adrià lorenzi, Marco Ferraris, Sebastiano Peter, loïc Modat, Marc Stevens, Allison Fischl, Bruce Vercauteren, Tom Iglesias, Juan Eugenio Center for Medical Image Computing University College London United Kingdom Université Côte dÁzur Inria Epione Team Sophia Antipolis06902 France School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Program in Health Sciences and Technology Massachusetts Institute of Technology United States

Joint registration of a stack of 2d histological sections to recover 3d structure ("3d histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (`2 norm, which can be minimised in closed form) and laplacian (`1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3d reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely

关键词： Brain

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Corrigendum to “Novel solar PV/Thermal collector design for the enhancement of thermal and electrical performances” [Renew. Enegry 146 (2020) 610-627]

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Renewable Energy 2021年 168卷 1399-1401页

作者： Oussama Rejeb leon Gaillard Stéphanie Giroux-Julien Chaouki Ghenai Abdelmajid Jemni Maamar Bettayeb Christophe Menezo Université Savoie Mont Blanc LOCIE UMR CNRS/USMB 5271 FédESol FR3344 INES/Solar Academy Campus Scientifique Savoie Technolac - Bâtiment Hélios Avenue du Lac Léman F-73376 Le Bourget-du-Lac France Sustainable Energy Development Research Group Research Institute for Sciences and Engineering (RISE) University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Université de Lyon CETHIL UMR Claude Bernard Lyon 1/CNRS/INSA 5008 Campus Lyon Tech La Doua F-69621 Cedex France Department of Sustainable and Renewable Energy Engineering College of Engineering University of Sharjah Sharjah United Arab Emirates Université de Monastir Ecole Nationale d'Ingénieurs de Monastir (ENIM) Laboratoire d'Etudes des Systèmes Thermiques et Energétiques (LESTE) LR99ES31 5000 Monastir Tunisia Department of Electrical Engineering University of Sharjah United Arab Emirates

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