检索结果-内蒙古大学图书馆

An optimal control problem for a spatiotemporal SIR model

学校读者我要写书评

暂无评论

International Journal of Dynamics and Control 2018年第1期6卷 384-397页

作者： El-Alami Laaroussi, Adil Rachik, Mostafa Elhia, Mohamed Laboratory of Analysis Modeling and Simulation Department of Mathematics and Computer Science Faculty of Sciences Ben M’Sik Hassan II University Mohammedia Sidi Othman BP 7955 Casablanca Morocco Modelling Laboratory for economics and management Department of Mathematics and Computer Science Faculty of Law Economics and Social Sciences Beausite Ain Sebaa BP 2634 Casablanca Morocco

In this paper, an SIR spatiotemporal epidemic model is formulated as a system of parabolic partial differential equations with no-flux boundary conditions. Immunity is forced through vaccine distribution considered a control variable. Our principal objective is to characterize an optimal control that minimizes the number of infected individuals and the costs associated with vaccination over a finite space and time domain. The existence of solutions to the state system and the existence of an optimal control is proved. An optimal control characterization in terms of state and adjoint functions is provided. Furthermore, a second condition of optimality is given. The optimality systems are solved based on an iterative discrete scheme that converges following an appropriate test similar the one related to the forward–backward sweep method. Numerical results are provided to illustrate the effectiveness of our approach for several scenarios. © 2016, Springer-Verlag Berlin Heidelberg.

关键词： Numerical methods

Modelling investment plans at asset portfolio level using optimum plan rationalisation approaches

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2020年第3期53卷 143-148页

作者： Andrew Hunt Prof. Simon Taylor Dr Meirion Morgan Robert Lange The Woodhouse Partnership Ltd RG20 4SW UK Modelling & Simulation Group in the Department of Computer Science Brunel University London UB8 3PH UK Visiting Lecturer and Consultant Cardiff University UK CF10 3AT Decision Support Tools Ltd RG20 4SW UK

A new approach to modelling asset portfolio capital investment and operational plans using a rationalisation process of multiple asset life cycle cost model’s is presented. It overcomes the traditional limitations of stocastic models which do not explicitly consider deliverability issues such as scrarce resources, access in combination with an organisations objectives and finamcial imapcts. The approach builds on earlier work including the Strategic Asset Lifecycle Value Optimisation (SALVO) research and development project and rational decision making. It uses individual life cycle cost models for each asset class and programme-level constraints and multi-criteria to create an optimal plan with transparent cost/benefit/risk justification. Alternative scenarios can then be developed from this idealised plan using significance cooling and hill climbing algorithms. These explore the real-world constraints and competing objectives that might prevent such a plan from being delivered. In this way, a realistic and deliverable plan is produced, with fully quantified business impact of any constraints or programme changes that are required. Using the idealised plan as a benchmark, the rationalised variants and scenarios may be compared and a budget and resource-committed programme then selected. The approach is found to be equally applicable for capital investment, operations and maintenance activities, including the combined operational expenditures (OpEx) and capital (CapEx) plans at enterprise portfolio level. Further, its application is applicable for any industry and regulated or non-regulated asset managing organisations.

关键词： Deterioration Modelling Maintenance Strategies Asset Life cycle Management simulation Optimisation in Maintenance Maintenance within Asset Management Data-driven Decision-making Decision Making Framework

Lecture Notes in Networks and Systems

学校读者我要写书评

暂无评论

Preface

Lecture Notes in Networks and Systems 2023年 729 LNNS卷 v-vi页

作者： Vasant, Pandian Weber, Gerhard-Wilhelm Arefin, Mohammad Shamsul Rodriguez-Aguilar, Roman Panchenko, Vladimir Munapo, Elias Thomas, J. Joshua Faculty of Electrical and Electronics Engineering Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science Chittagong University of Engineering and Technology Chittagong Bangladesh Laboratory of Non-traditional Energy Systems Department of Theoretical and Applied Mechanics Federal Scientific Agroengineering Center VIM Russian University of Transport Moscow Russia Department of Computer Science UOW Malaysia KDU Penang University College George Town Malaysia Faculty of Engineering Management Poznań University of Technology Poznan Poland School of Economics and Decision Sciences North West University Mmabatho South Africa Facultad de Ciencias Económicas y Empresariales School of Economic and Business Sciences Universidad Panamericana Mexico City Mexico

Gradient descent in materia through homodyne gradient extraction

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Boon, Marcus N. Cassola, Lorenzo Euler, Hans-Christian Ruiz Chen, Tao van de Ven, Bram Ibarra, Unai Alegre Bobbert, Peter A. van der Wiel, Wilfred G. NanoElectronics Group MESA+ Institute for Nanotechnology BRAINS Center for Brain-Inspired Nano Systems University of Twente PO Box 217 Enschede7500 AE Netherlands Exzellenzcluster Science of Intelligence Technische Universität Berlin Marchstr. 23 Berlin10587 Germany Department for Electrical Engineering and Computer Science Modeling of Cognitive Processes Technische Universität Berlin Berlin Germany Molecular Materials and Nanosystems Eindhoven Institute for Renewable Energy Systems Department of Applied Physics Eindhoven University of Technology PO Box 513 Eindhoven5600 MB Netherlands Institute of Physics University of Münster Münster48149 Germany

Deep learning, a multi-layered neural network approach inspired by the brain, has revolutionized machine learning [1]. One of its key enablers has been backpropagation [2], an algorithm that computes the gradient of a loss function with respect to the weights and biases in the neural network model, in combination with its use in gradient descent [3]. However, the implementation of deep learning in digital computers is intrinsically energy hungry, with energy consumption becoming prohibitively high for many applications [4]. This has stimulated the development of specialized hardware, ranging from neuromorphic CMOS integrated circuits [5] [6, 7] and integrated photonic tensor cores [8] to unconventional, material-based computing systems [9] [10] [11] [12] [13] [14] [15] [16]. The learning process in these material systems, realized, e.g., by artificial evolution [13] [14], equilibrium propagation [17] or surrogate modelling [18] [19], is a complicated and time-consuming process. Here, we demonstrate a simple yet efficient and accurate gradient extraction method, based on the principle of homodyne detection, for performing gradient descent on a loss function directly in a physical system without the need of an analytical description. By perturbing the parameters that need to be optimized using sinusoidal waveforms with distinct frequencies, we effectively obtain the gradient information in a highly robust and scalable manner. We illustrate the method in dopant network processing units [14], but argue that it is applicable in a wide range of physical systems. Homodyne gradient extraction can in principle be fully implemented in materia, facilitating the development of autonomously learning material systems. © 2021, CC BY-NC-SA.

关键词： CMOS integrated circuits

Structure-Based Experimental Datasets for Benchmarking of Protein simulation Force Fields

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Cavender, Chapin E. Case, David A. Chen, Julian C.-H. Chong, Lillian T. Keedy, Daniel A. Lindorff-Larsen, Kresten Mobley, David L. Ollila, O.H. Samuli Oostenbrink, Chris Robustelli, Paul Voelz, Vincent A. Wall, Michael E. Wych, David C. Gilson, Michael K. Skaggs School of Pharmacy and Pharmaceutical Sciences UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Dept. of Chemistry & Chemical Biology Rutgers University PiscatawayNJ08854 United States Bioscience Division Los Alamos National Laboratory Los Alamos NM87545 United States Department of Chemistry and Biochemistry The University of Toledo ToledoOH43606 United States University of Pittsburgh 219 Parkman Avenue PittsburghPA15260 United States Structural Biology Initiative CUNY Advanced Science Research Center New YorkNY10031 United States Department of Chemistry and Biochemistry City College of New York New YorkNY10031 United States PhD Programs in Biochemistry Biology and Chemistry CUNY Graduate Center New YorkNY10016 United States Linderstrøm-Lang Centre for Protein Science Department of Biology University of Copenhagen Copenhagen NDK-2200 Denmark Pharmaceutical Sciences Department University of California Irvine IrvineCA United States University of Helsinki Institute of Biotechnology Helsinki Finland Institute for Molecular Modeling and Simulation University of Natural Resources and Life Sciences Muthgasse 11 Vienna1190 Austria Dartmouth College Department of Chemistry HanoverNH03755 United States Department of Chemistry Temple University PhiladelphiaPA United States Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States The Center for Nonlinear Studies Los Alamos National Laboratory Los AlamosNM87545 United States

This review article provides an overview of structurally oriented, experimental datasets that can be used to benchmark protein force fields, focusing on data generated by nuclear magnetic resonance (NMR) spectroscopy and room temperature (RT) protein crystallography. We discuss why these observables are useful for assessing force field accuracy, how they can be calculated from simulation trajectories, and statistical issues that arise when comparing simulations with experiment. The target audience for this article is computational researchers and trainees who develop, benchmark, or use protein force fields for molecular simulations. © 2023, CC BY.

关键词： Proteins

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

学校读者我要写书评

暂无评论

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

Adjustment submersible cages for optimal fish farming: improved spatiotemporal Lotka–Volterra model for n species in partitioned area

学校读者我要写书评

暂无评论

International Journal of Dynamics and Control 2018年第3期6卷 919-928页

作者： Bouaine, Amine Rachik, Mostafa Laboratory of Analysis Modeling and Simulation Faculty of sciences Ben M’Sik Department of Mathematics and Computer Science Hassan II University BP 7955 Sidi Othman Casablanca Morocco Department of Industrial Engineering École Nationale Supérieure de Mines de Rabat Rabat Morocco

The first goal of this research is to set improved spatiotemporal discrete Lotlka–Volterra model including n different species which occupy three dimensional area Ω and obey to any complex marine food chain. The area Ω is partitioned into R subregions, each subregion includes at least two species, and each species can occupy more than one subregion. The next concern is to increase production of special species which survive in a target subregion, taking into account the financial cost and the severe consequences on the environment. So, we propose two different approaches: optimal farming intra-subregion and optimal farming inter-subregions. One the one hand, we develop optimal program of farming intra-subregion in order to rise the growth rate of the concerned species. On the other hand, we aim to protect the concerned species by optimal adjustment of grids constituting the submersible cage in order to control the local interaction between the target subregion and its neighboring subregions. To the best of our knowledge, it’s the first time when permeability of submersible cages will be modeled and introduced in predator–prey models. To end, adequate numerical illustrations are implemented and discussed to prove the effectiveness of each approach. © 2017, Springer-Verlag Berlin Heidelberg.

关键词： Fish

Contributions from Pilot Projects in Quantum Technology Education as Support Action to Quantum Flagship

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Faletic, Sergej Bitzenbauer, Philipp Bondani, Maria Chiofalo, Marilu Goorney, Simon R. Krijtenburg-Lewerissa, Kim Mishina, Oxana Müller, Rainer Pospiech, Gesche Ercan, Ilke Malgieri, Massimiliano Merzel, Avraham Michelini, Marisa Onorato, Pasquale Pol, Henk Santi, Lorenzo Seskir, Zeki Can Sherson, Jacob Kirsten E Stadermann, H. Stefanel, Alberto Surer, Elif Tóth, Kristof Malo, Jorge Yago Zabello, Olgas University of Ljubljana Faculty of Mathematics and Physics Jadranska ulica 19 Ljubljana1000 Slovenia Friedrich-Alexander-Universität Erlangen-Nürnberg Department of Physics Physics Education Research Staudstr. 7 Erlangen91058 Germany CNR-Institute for Photonics and Nanotechnology via Valleggio 11 Como Italy Department of Physics "Enrico Fermi" University of Pisa INFN-Pisa Italy Department of Management School of Business and Social Sciences Aarhus University Denmark Niels Bohr Institute Copenhagen University Denmark Freudenthal Institute Utrecht University P.O. Box 85170 Utrecht3508 AD Netherlands Area Science Park Basovizza TriesteI-34149 Italy Physikdidaktik TU Braunschweig Bienroder Weg 82 Braunschweig38106 Germany TU Dresden Department of Physics Research Group Physics Education Helmholtzstr. 10 Dresden01069 Germany Department of Microelectronics Mekelweg 4 Delft2628 CD Netherlands Department of Physics University of Pavia Via Bassi 6 Pavia27100 Italy The Seymour Fox School of Education The Hebrew University of Jerusalem Mount Scopus Jerusalem9190501 Israel PUnità di Ricerca in Didattica della Fisica Dipartimento di Scienze Matematiche Informatiche e Fisiche University of Udine via delle Scienze 206 Udine33100 Italy Physical Science Communication Laboratory Department of Physics University of Trento TN Povo38123 Italy ELAN Department of Teacher Development University of Twente P.O.Box 217 EnschedeNL-7500 AE Netherlands Karlsruhe Germany Department of Modeling and Simulation Graduate School of Informatics Middle East Technical University Ankara06800 Turkey Physics Education PhD Programme Eötvös Loránd University Budapest Hungary Department of Physics 'Enrico Fermi' INFN University of Pisa Largo B. Pontecorvo 3 PisaI-56127 Italy Offenburg University of Applied Sciences Badstraße 24 Offenburg77652 Germany

The GIREP community on teaching and learning quantum physics and the Education section of the Quantum flagship project of the European Union (QTEdu) have brought together different stakeholders in the field of teaching quantum physics on all levels, including outreach. The goal of QTEdu is to pave the way for the training of the future quantum workforce. To this end, it is necessary to understand the needs of the quantum technology (QT) field, make the general public aware of the existence and importance of QT, and introduce quantum physics already in high school, so that high school students can choose QT as their field of study and career. Finally, new university courses need to be established to support emerging specific profiles such as a "quantum engineer". In this symposium, four QTEdu pilot projects were brought together to demonstrate how their complementary approaches have worked towards realising the above goals. © 2023, CC BY.

关键词： Economic and social effects

PDG Pressure Estimation in Offshore Oil Well: Extended Kalman Filter vs. Artificial Neural Networks

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2019年第1期52卷 508-513页

作者： Andressa Apio Jônathan W.V. Dambros Fabio C. Diehl Marcelo Farenzena Jorge O. Trierweiler Group of Intensification Modeling Simulation Control and Optimization of Processes (GIMSCOP) Chemical Engineering Department Federal University of Rio Grande do Sul (UFRGS) R. Eng Luiz Englert s/n Campus Central Porto Alegre RS Brazil Department of Computer Science University of Kaiserslautern 67663 Kaiserslautern Deutschland Research and Development Center Petrobras 21941915 Rio de Janeiro Brazil

The Permanent Downhole Gauge (PDG) pressure measurement is of great importance for offshore oil well modeling and control since it is measured close to the bottom hole. The PDG is installed in a remote undersea environment, which makes expensive the maintenance in case of fault. For this reason, PDG measurements are frequently unavailable. To overcome this limitation, the PDG pressure can be estimated using other available measurements. The estimation is not a simple task since, depending on process operational conditions, the multiphase flow might present limit cycles. In this work, Artificial Neural Network (ANN) and Extended Kalman Filter (EKF) are proposed as potential techniques for the PDG pressure estimation. The comparison of the results shows that ANN returns precise estimation for a short-time window after the failure, but fails when a different process operating condition is applied, while EKF returns good estimation in all the cases.

关键词： Modelling System Identification Data Analytics Machine Learning Inferential sensing State Estimation Sensor Development