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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

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Galactic transient sources with the Cherenkov Telescope Array Observatory

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Monthly Notices of the Royal Astronomical Society 2025年第1期540卷 205–238页

作者： Abe, K Abe, S Abhir, J Abhishek, A Acero, F Acharyya, A Adam, R Aguasca-Cabot, A Agudo, I Aguirre-Santaella, A Alfaro, J Alfaro, R Alvarez-Crespo, N Alves Batista, R Amans, J-P Amato, E Ambrosi, G Ambrosino, F Angüner, E O Antonelli, L A Aramo, C Arcaro, C Arnesen, T T H Asano, K Ascasibar, Y Aschersleben, J Ashkar, H Augusto Stuani, L Baack, D Backes, M Balazs, C Balbo, M Baquero Larriva, A Barbosa Martins, V Barres de Almeida, U Barrio, J A Barrios-Jiménez, L Batković, I Batzofin, R Baxter, J Becerra González, J Becker Tjus, J Belmont, R Benbow, W Bernete, J Bernlöhr, K Berti, A Bertucci, B Beshley, V Bhattacharjee, P Bhattacharyya, S Bigongiari, C Bissaldi, E Blanch, O Blazek, J Bocchino, F Boisson, C Bolmont, J Bonnoli, G Bonollo, A Bordas, P Bosnjak, Z Bottacini, E Böttcher, M Bradascio, F Bronzini, E Brown, A M Brunelli, G Bulgarelli, A Burelli, I Burger-Scheidlin, C Burmistrov, L Burton, M Buscemi, M Cailleux, J Campoy-Ordaz, A Cantlay, B K Capasso, G Caproni, A Capuzzo-Dolcetta, R Caraveo, P Caroff, S Carosi, A Carosi, R Carquin, E Carrasco, M-S Cascone, E Cassol, F Castaldini, L Castrejon, N Castro-Tirado, A J Cerasole, D Ceribella, G Cerruti, M Chadwick, P M Chaty, S Chen, A W Chernyakova, M Chiavassa, A Chudoba, J Chytka, L Cicciari, G M Cifuentes, A Coimbra Araujo, C H Colapietro, M Conforti, V Contreras, J L Cortina, J Costa, A Costantini, H Cotter, G Cristofari, P Cuevas, O Curtis-Ginsberg, Z D’Aì, A D’Amico, G D’Ammando, F Dai, S Dazzi, F de Bony de Lavergne, M De Caprio, V De Cesare, G De Frondat Laadim, F de Gouveia Dal Pino, E M De Lotto, B De Lucia, M de Martino, D de Menezes, R de Naurois, M de Ona Wilhelmi, E de Souza, V del Peral, L del Valle, M V Delgado, C Dell’aiera, M Della Valle, M della Volpe, D Depaoli, D Devin, J Di Girolamo, T Di Piano, A Di Pierro, F Di Tria, R Di Venere, L Diebold, S Dignam, C Dima, R Dinesh, A Djuvsland, J Dominik, R M Dominis Prester, D Donini, A Dorner, D Dörner, J Doro, M Dubos, C Ducci, L Dwarkadas, V V Ebr, J Eckner, C Egberts, K Einecke, S Elsässer, D Emery Department of Physics Tokai University 4-1-1 Kita-Kaname Hiratsuka Kanagawa 259-1292 Japan Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Kashiwa Chiba 277-8582 Japan ETH Zürich Institute for Particle Physics and Astrophysics Otto-Stern-Weg 5 CH-8093 Zürich Switzerland INFN and Università degli Studi di Siena Dipartimento di Scienze Fisiche della Terra e dell’Ambiente (DSFTA) Sezione di Fisica Via Roma 56 I-53100 Siena Italy Université Paris-Saclay Université Paris Cité CEA CNRS AIM F-91191 Gif-sur-Yvette Cedex France FSLAC IRL 2009 CNRS/IAC E-38205 La Laguna Tenerife Spain University of Alabama Tuscaloosa Department of Physics and Astronomy Gallalee Hall Box 870324 Tuscaloosa AL 35487-0324 USA Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Laboratoire Lagrange UF 06304 Nice France Laboratoire Leprince-Ringuet CNRS/IN2P3 École Polytechnique Institut Polytechnique de Paris F-91120 Palaiseau France Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Martí i Franquès 1 E-08028 Barcelona Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n E-18008 Granada Spain Institute for Computational Cosmology and Department of Physics Durham University South Road Durham DH1 3LE UK Pontificia Universidad Católica de Chile Av. Libertador Bernardo O’Higgins 340 8331150 Santiago Chile Universidad Nacional Autónoma de México Delegación Coyoacán 04510 Ciudad de México Mexico IPARCOS-UCM Instituto de Física de Partículas y del Cosmos and EMFTEL Department Universidad Complutense de Madrid E-28040 Madrid Spain Instituto de Física Teórica UAM/CSIC and Departamento de Física Teórica Universidad Autónoma de Madrid c/ Nicolás Cabrera 13-15 Campus de Cantoblanco UAM E-28049 Madrid Spain LUTH GEPI and LERMA Observatoire de Paris Université PSL Université Paris Cité CNRS 5 place Jules Janssen F-92190 Meudon France INAF – Osservatorio

A wide variety of Galactic sources show transient emission at soft and hard X-ray energies: low- and high-mass X-ray binaries containing compact objects, isolated neutron stars exhibiting extreme variability as magnetars as well as pulsar-wind nebulae. Although most of them can show emission up to MeV and/or GeV energies, many have not yet been detected in the TeV domain by Imaging Atmospheric Cherenkov Telescopes. In this paper, we explore the feasibility of detecting new Galactic transients with the Cherenkov Telescope Array Observatory (CTAO) and the prospects for studying them with Target of Opportunity observations. We show that CTAO will likely detect new sources in the TeV regime, such as the massive microquasars in the Cygnus region, low-mass X-ray binaries with low-viewing angle, flaring emission from the Crab pulsar-wind nebula or other novae explosions, among others. Since some of these sources could also exhibit emission at larger time-scales, we additionally test their detectability at longer exposures. We finally discuss the multiwavelength synergies with other instruments and large astronomical facilities.

关键词： binaries: general stars: magnetars novae cataclysmic variables pulsars: general gamma-rays: general

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An intra-body communication research platform based on virtual instrument 13

An intra-body communication research platform based on virtu...

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13th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2017

作者： Lin, Shi Gao, Yueming Zhang, Hengfei Željka, Lučev Vasić Vai, Mang-I Du, Min Cifrek, Mario Pun, Sio-Hang College of Physics and Information Engineering Fuzhou University Fuzhou350116 China Key Lab of Medical Instrumentation and Pharmaceutical Technology of Fujian Province Fuzhou350116 China Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia State Key Laboratory of Analog and Mixed Signal VLSI University of Macau 999078 China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau 999078 China Key Lab of Eco-industrial Green Technology of Fujian Province Nanping China

ISBN: (纸本)9781509050345

An intra-body communication (IBC) research platform based on virtual instrument was established for the hardware design of IBC transceiver. Firstly, the feasibility of constructing IBC research platform using optic-coupling circuit was verified via comparison of three kinds of measurement methods based on virtual instrument. Then, ASK, FSK and BPSK modulation-demodulation were realized via LabVIEW programming, and user interface of the platform was also designed. The platform could achieve signal transmission or recover under different coupling type, different channel distance and different modulation-demodulation method, etc., by means of dynamic adjusting of platform parameters. Experiment results of capacitive-coupling under the platform indicated the authenticity and feasibility of signal transmission. Finally, a full-floating IBC transceiver was designed aided by the parameters provided by the research platform under the scene of capacitive-coupling, FSK modulation and 180 cm channel distance. Moreover, Bit Error Rate (BER) of the transceiver was less than 1e-5, which meant that it is effective and convenient for the design of IBC transceiver aided by virtual instrument platform. © 2017 IEEE.

关键词： User interfaces

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Developments of two 4 × 10 Gb/s VCSEL array drivers in 65 nm CMOS for HEP experiments

Developments of two 4 × 10 Gb/s VCSEL array drivers in 65 n...

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作者： Guo, D. Gong, D. Xiang, A.C. Moreira, P. Kulis, S. Chen, J. Hou, S. Liu, C. Liu, T. Prosser, A. He, H. Sun, Q. Wang, J. Yang, D. Ye, J. Zhou, W. Department of Physics Southern Methodist University DallasTX75275 United States CERN Geneva 231211 Switzerland Department of Electrical Engineering University of Houston HoustonTX77004 United States Institute of Physics Academia Sinica Nangang Taipei11529 Taiwan Real-Time Systems Engineering Department Fermi National Laboratory BataviaIL60510 United States Shenzhen Polytechnic Shzenzhen Guangdong518055 China Department of Physics Central China Normal University Wuhan Hubei430079 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei Anhui230026 China

This paper presents the designs and test results of two radiation tolerant 4 × 10 Gb/s vertical cavity surface emitting laser (VCSEL) array drivers VLAD and lpVLAD, both fabricated in a 1.2V 65 nm CMOS technology. VLAD adopts a power efficient bandwidth-boost technology, and lpVLAD employs a novel high-efficiency output structure to achieve an ultra-lowpower consumption of 2.2 mW/Gb/ch with 2mA bias current and 6mA modulation current. Both drivers are optically tested passing 10 Gb/s eye mask with all channels active under the radiation of a total dose up to 350 Mrad(SiO2). © 2017 IOP Publishing Ltd and Sissa Medialab srl.

关键词： Timing circuits

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An information infrastructure framework for smart grids leveraging SDN and cloud

An information infrastructure framework for smart grids leve...

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Clemson University Power Systems Conference, PSC 2016

作者： Narayanan, Rajesh Xu, Ke Wang, Kuang-Ching Kumar Venayagamoorthy, G. Dell Research Santa ClaraCA United States Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Clemson University SC United States

ISBN: (纸本)9781509006878

Smart grids are recognized as the next-generation power grid that uses digital information and communications technology to create an advanced, automated, and efficient energy network among a wealth of electronic appliances, meters, and energy producing and distributing devices. As more complex and distributed interactions among these different components are envisioned, an open, scalable, and manageable framework is needed to support the communication and computing needs. This paper proposes an innovative method to derive scalable information infrastructure for smart grids. The framework well aligns with the emerging micro-grid operational model of the smart grid. A prototype framework is being developed consisting of CloudLab [1], GENI [2] wired and wireless networks, and real-time power system platform at the RTPIS Lab [3]. © 2016 IEEE.

关键词： Smart power grids

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Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data 37

Application of pattern spectra and convolutional neural netw...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Aschersleben, J. Peletier, R.F. Vecchi, M. Wilkinson, M.H.F. Abdalla, H. Abe, H. Abe, S. Abusleme, A. Acero, F. Acharyya, A. Acín Portella, V. Ackley, K. Adam, R. Adams, C. Adhikari, S.S. Aguado-Ruesga, I. Agudo, I. Aguilera, R. Aguirre-Santaella, A. Aharonian, F. Alberdi, A. Alfaro, R. Alfaro, J. Alispach, C. Aloisio, R. Alves Batista, R. Amans, J.-P. Amati, L. Amato, E. Ambrogi, L. Ambrosi, G. Ambrosio, M. Ammendola, R. Anderson, J. Anduze, M. Angüner, E.O. Antonelli, L.A. Antonuccio, V. Antoranz, P. Anutarawiramkul, R. Aragunde Gutierrez, J. Aramo, C. Araudo, A. Araya, M. Arbet-Engels, A. Arcaro, C. Arendt, V. Armand, C. Armstrong, T. Arqueros, F. Arrabito, L. Arsioli, B. Artero, M. Asano, K. Ascasíbar, Y. Ashley, M. Attinà, P. Aubert, P. Singh, C.B. Baack, D. Babic, A. Backes, M. Baena, V. Bajtlik, S. Baktash, A. Balazs, C. Balbo, M. Ballester, O. Ballet, J. Balmaverde, B. Bamba, A. Bandiera, R. Baquero Larriva, A. Barai, P. Barbier, C. Barbosa Martins, V. Barcelo, M. Barkov, M. Barnard, M. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Bastieri, D. Batista, P.I. Batkovic, I. Bauer, C. Bautista-González, R. Baxter, J. Becciani, U. Becerra González, J. Becherini, Y. Beck, G. Becker Tjus, J. Bednarek, W. Belfiore, A. Bellizzi, L. Belmont, R. Benbow, W. Berge, D. Bernardini, E. Bernardos, M.I. Bernlöhr, K. Berti, A. Berton, M. Bertucci, B. Beshley, V. Bhatt, N. Bhattacharyya, S. Bhattacharyya, W. Bhattacharyya, S. Bi, B. Bicknell, G. Biederbeck, N. Bigongiari, C. Biland, A. Bird, R. Bissaldi, E. Biteau, J. Bitossi, M. Blanch, O. Blank, M. Blazek, J. Bobin, J. Boccato, C. Bocchino, F. Boehm, C. Bohacova, M. Boisson, C. Boix, J. Bolle, J.-P. Bolmont, J. Bonanno, G. Bonavolontà, C. Bonneau Arbeletche, L. Bonnoli, G. Bordas, P. Borkowski, J. Bórquez, S. Bose, R. Bose, D. Bosnjak, Z. Bottacini, E. Böttcher, M. Botticella, M.T. Boutonnet, C. Bouyjou, F. Bozhilov, V. Bozzo, E. Brahimi, L. Braiding, C. Brau-Nogué, S. Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen PO Box 407 GroningenNL-9700 AK Netherlands Centre for Space Research North-West University Potchefstroom2520 South Africa Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan Pontificia Universidad Católica de Chile Av. Libertador Bernardo O'Higgins 340 Santiago Chile AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité CEA Paris-Saclay IRFU DAp Bat 709 Orme des Merisiers Gif-sur-Yvette91191 France Centre for Advanced Instrumentation Dept. of Physics Durham University South Road DurhamDH1 3LE United Kingdom Port d'Informació Científica Edifici D Carrer de l'Albareda Bellaterrra Cerdanyola del Vallès08193 Spain School of Physics and Astronomy Monash University MelbourneVIC3800 Australia Laboratoire Leprince-Ringuet École Polytechnique UMR 7638 CNRS/IN2P3 Institut Polytechnique de Paris Palaiseau91128 France Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States University of Oslo Department of Physics Sem Saelandsvei 24 - PO Box 1048 Blindern OsloN-0316 Norway EMFTEL Department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés08193 Spain Instituto de Física Teórica UAM/CSIC Departamento de Física Teórica Universidad Autónoma de Madrid c/Nicolás Cabrera 13-15 Campus de Cantoblanco UAM Madrid28049 Spain Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Universidad Nacional Autónoma de México Delegación Coyoacán Ciudad de México04510 Mexico University of Geneva Département de Physique Nucléaire et Corpusculaire 24 rue d

The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be the major global instrument for very-high-energy astronomy over the next decade, offering 5 − 10 × better flux sensitivity than current generation gamma-ray telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides images that can be used as training data for convolutional neural networks (CNNs) to determine the energy of the initial gamma rays. Compared to other state-of-the-art algorithms, analyses based on CNNs promise to further enhance the performance to be achieved by CTA. Pattern spectra are commonly used tools for image classification and provide the distributions of the shapes and sizes of various objects comprising an image. The use of relatively shallow CNNs on pattern spectra would automatically select relevant combinations of features within an image, taking advantage of the 2D nature of pattern spectra. In this work, we generate pattern spectra from simulated gamma-ray events instead of using the raw images themselves in order to train our CNN for energy reconstruction. This is different from other relevant learning and feature selection methods that have been tried in the past. Thereby, we aim to obtain a significantly faster and less computationally intensive algorithm, with minimal loss of performance. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Gamma rays

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Non-Intrusive energy disaggregation using non-negative matrix factorization with sum-to-k constraint

arXiv

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arXiv 2017年

作者： Rahimpour, Alireza Qi, Hairong Fugate, David Kuruganti, Teja Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Computing and Computational Sciences Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

Energy disaggregation or Non-Intrusive Load Monitoring (NILM) addresses the issue of extracting device-level energy consumption information by monitoring the aggregated signal at one single measurement point without installing meters on each individual device. Energy disaggregation can be formulated as a source separation problem where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework. In this paper an approach based on Sum-to-k constrained Non-negative Matrix Factorization (S2KNMF) is proposed. By imposing the sum-to-k constraint and the non-negative constraint, S2K-NMF is able to effectively extract perceptually meaningful sources from complex mixtures. The strength of the proposed algorithm is demonstrated through two sets of experiments: Energy disaggregation in a residential smart home;and HVAC components energy monitoring in an industrial building testbed maintained at the Oak Ridge National laboratory (ORNL). Extensive experimental results demonstrate the superior performance of S2K-NMF as compared to state-of-theart decomposition-based disaggregation algorithms. The source code and our collected data (HVORUT) for studying NILM for HVAC units can be found at: Https://***/aicip/nonintrusive-load-monitoring. Copyright © 2017, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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Modeling of minibands for Si/Sc quantum dot superlattice solar cells 11

Modeling of minibands for Si/Sc quantum dot superlattice sol...

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11th Annual TechConnect World Innovation Conference and Expo, Held Jointly with the 20th Annual Nanotech Conference and Expo, and the 2017 National SBIR/STTR Conference

作者： Tsai, Yi-Chia Lee, Ming-Yi Li, Yiming Samukawa, Seiji Parallel and Scientific Computing Laboratory National Chiao Tung University Hsinchu300 Taiwan Institute of Communications Engineering National Chiao Tung University Hsinchu300 Taiwan Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan Institute of Fluid Science Tohoku University Aoba-ku Sendai Miyagi9808577 Japan WPI-Advanced Institute for Materials Research Tohoku University Sendai Miyagi9808577 Japan

ISBN: (纸本)9780997511796

The silicon (Si)/silicon carbide (SiC) quantum dot superlattice solar cells (SiC-QDSL) with aluminum oxide (A12O3-QDSL) passivation approaches a high short-circuit current (Jsc) of 4.77 mA/cm2 under AMI.5 and one sun illumination due to the quantum enhancement on effective bandgap and minimum transition energy. This value matches well with experimental measurement of 4.75 mA/cm2. To further optimize the conversion efficiency through the geometry of Al2O3-QDSL, we introduce several parameters to define a complete QDSL configuration. A high conversion efficiency of 17.4% is optimized by using the QD geometry from experiment and applying hexagonal QDSL formation with an inter-dot spacing of 0.3 nm.

关键词： Solar cells

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The MAPLE package TDDS for computing Thomas decompositions of systems of nonlinear PDEs

arXiv

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arXiv 2018年

作者： Gerdt, Vladimir P. Lange-Hegermann, Markus Robertz, Daniel Laboratory of Information Technologies Joint Institute for Nuclear Research 6 Joliot-Curie St Dubna141980 Russia Russian Federation and Peoples Friendship University of Russia RUDN University 6 Miklukho-Maklaya St Moscow117198 Russia Department of Electrical Engineering and Computer Science Ostwestfalen-Lippe University of Applied Sciences Liebigstrabe 87 Lemgo32657 Germany School of Computing Electronics and Mathematics Plymouth University 2-5 Kirkby Place Drake Circus PlymouthPL48AA United Kingdom

We present the Maple package TDDS (Thomas Decomposition of Differential Systems). Given a polynomially nonlinear differential system, which in addition to equationsmay contain inequations, this package computes a decomposition of it into a finite set of differentially triangular and algebraically simple subsystems whose subsets of equations are involutive. Usually the decomposed system is substantially easier to investigate and solve both analytically and numerically. The distinctive property of a Thomas decomposition is disjointness of the solution sets of the output subsystems. Thereby, a solution of a well-posed initial problem belongs to one and only one output subsystem. The Thomas decomposition is fully algorithmic. It allows to perform important elements of algebraic analysis of an input differential system such as: Verifying consistency, i.e., the existence of solutions;detecting the arbitrariness in the general analytic solution;given an additional equation, checking whether this equation is satisfied by all common solutions of the input system;eliminating a part of dependent variables from the system if such elimination is possible;revealing hidden constraints on dependent variables, etc. Examples illustrating the use of the package are given. Copyright © 2018, The Authors. All rights reserved.

关键词： Differential equations

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Predictive modeling of exercise response in CVD patients under rehabilitation

Predictive modeling of exercise response in CVD patients und...

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Joint Conference of the European Medical and Biological engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical engineering and Medical Physics, NBC 2107

作者： Filos, Dimitris Triantafyllidis, A. Manolios, V. Livitckaia, K. Claes, J. Buys, R. Cornelissen, V. Kouidi, E. Maglaveras, N. Chouvarda, I. Centre for Research and Technology Hellas Institute of Applied Biosciences Thessaloniki Greece Lab of Computing Medical Informatics & Biomedical lmaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Katholieke Universiteit Leuven Research Group for Cardiovascular and Respiratory Rehabilitation Belgium Laboratory of Sports Medicine Aristotle University of Thessaloniki Thermi Greece Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL United States

ISBN: (纸本)9789811051210

Exercise-based rehabilitation plays a key role for patients with cardiovascular disease (CVD) in improving their well-being and reducing their symptoms. Monitoring and assessing the exercise response at an individual level is critical toward achieving better health outcomes. 15 exercise sessions performed by 5 CVD patients and 9 sessions from 3 regularly active individuals were monitored, and heart rate (HR) data were acquired. A model based on the HR dynamics during exercising at different intensities was built, and simulations were performed to assess performance in different scenarios of exercise selection. Our results show that the application of simple rules in exercise selection, which consider both the HR and the beneficial HR zones of individuals, can lead to beneficial execution of exercise programs (%time spent in beneficial HR zones: 60.6±27.5 for CVD patients). Personalized guidance during exercise has the potential to significantly contribute in the beneficial execution of exercise-based cardiac rehabilitation programs. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Patient rehabilitation

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