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Self-Awareness for Autonomous Systems

PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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Impact of zoning strategies for building performance simulation 24

Impact of zoning strategies for building performance simulat...

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24th EG-ICE International Workshop on Intelligent computing in Engineering 2017

作者： Bres, Aurelien Judex, Florian Suter, Georg De Wilde, Pieter Energy Department AIT Austrian Institute of Technology Austria Center for Geometry and Computational Design Design Computing Group Vienna University of Technology Austria School of Architecture Design and Environment University of Plymouth United Kingdom

Building performance simulation (BPS) can provide detailed information on the thermal behaviour of buildings. Zoning is a key step in input data preparation for BPS. A simulation zone is typically composed of one or more rooms. Large spaces, such as open office spaces, may have multiple zones. Simulation zoning can be distinguished from HVAC zoning, which is a property of the HVAC system. An appropriate simulation zoning may depend on the simulation problem, and results from a trade-off between accuracy and simplicity. In current simulation practice, zoning is usually carried out based on the user's experience. This does not ensure consistency across modelled buildings and practitioners. This paper demonstrates how systematic bottom-up strategies can be applied to aggregate rooms into simulation zones. The impact of such strategies is investigated by applying them to several floors of residential buildings. Building performance simulation is carried out with either conceptual or detailed HVAC modeling. In the second case, variations in both simulation zoning and HVAC zoning are investigated. Simulation results are compared to those obtained with detailed one-zone-per-room models, to assess the deviations due to simulation zoning. A coarser simulation zoning is shown to cause significant deviations in certain cases. A fine simulation zoning may allow control inefficiencies due to coarse HVAC zoning to be quantified.

关键词： Zoning

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"So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

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International Journal of Information Management 2023年 71卷

作者： Dwivedi, Yogesh K. Kshetri, Nir Hughes, Laurie Slade, Emma Louise Jeyaraj, Anand Kar, Arpan Kumar Baabdullah, Abdullah M. Koohang, Alex Raghavan, Vishnupriya Ahuja, Manju Albanna, Hanaa Albashrawi, Mousa Ahmad Al-Busaidi, Adil S. Balakrishnan, Janarthanan Barlette, Yves Basu, Sriparna Bose, Indranil Brooks, Laurence Buhalis, Dimitrios Carter, Lemuria Chowdhury, Soumyadeb Crick, Tom Cunningham, Scott W. Davies, Gareth H. Davison, Robert M. Dé, Rahul Dennehy, Denis Duan, Yanqing Dubey, Rameshwar Dwivedi, Rohita Edwards, John S. Flavián, Carlos Gauld, Robin Grover, Varun Hu, Mei-Chih Janssen, Marijn Jones, Paul Junglas, Iris Khorana, Sangeeta Kraus, Sascha Larsen, Kai R. Latreille, Paul Laumer, Sven Malik, F. Tegwen Mardani, Abbas Mariani, Marcello Mithas, Sunil Mogaji, Emmanuel Nord, Jeretta Horn O'Connor, Siobhan Okumus, Fevzi Pagani, Margherita Pandey, Neeraj Papagiannidis, Savvas Pappas, Ilias O. Pathak, Nishith Pries-Heje, Jan Raman, Ramakrishnan Rana, Nripendra P. Rehm, Sven-Volker Ribeiro-Navarrete, Samuel Richter, Alexander Rowe, Frantz Sarker, Suprateek Stahl, Bernd Carsten Tiwari, Manoj Kumar van der Aalst, Wil Venkatesh, Viswanath Viglia, Giampaolo Wade, Michael Walton, Paul Wirtz, Jochen Wright, Ryan Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Bay Campus Fabian Bay Wales Swansea United Kingdom Maharashtra Pune India Bryan School of Business and Economics University of North Carolina at Greensboro United States University of Bristol Business School University of Bristol BS8 1SD United Kingdom Professor of Information Systems Raj Soin College of Business Wright State University 3640 Colonel Glenn Highway DaytonOH45435 United States School of Artificial Intelligence Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Studies Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia School of Computing Middle Georgia State University MaconGA United States Client Advisory and Transformation Stackroute NIIT Limited India Department of Information Systems Analytics and Operations College of Business University of Louisville United States Northumbria University London United Kingdom IRC-FDE KFUPM Saudi Arabia ISOM KFUPM Business School KFUPM Saudi Arabia Innovation and Technology Transfer Center Sultan Qaboos University Oman Department of Business Communication Sultan Qaboos University Oman Department of Management Studies National Institute of Technology Tiruchirappalli India Montpellier France FORE School of Management New Delhi India Indian Institute of Management Ahmedabad Vastrapur Ahmedabad380015 India Information School University of Sheffield United Kingdom Bournemouth University Business School Poole United Kingdom School of Information Systems and Technology Management University of New South Wales Sydney Australia Information Operations and Management Sciences Department TBS Business School 1 Place Alphonse Jourdain Toulouse31068 France Department of Education & Childhood Studies Swansea Univers

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics;digital transformation of organisations and societies;and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI;examining biases of generative AI attributable to training datasets and processes;exploring business and societal contexts best suited for generative AI implementation;determining optimal combinations of human and generative AI for various tasks;identifying ways to assess accuracy of text produced by generative AI;and uncovering the ethical and legal issues in using generative AI across different contexts. © 2023 The Authors

关键词： Marketing

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A method for automated generation of HVAC distribution subsystems for building performance simulation 15

A method for automated generation of HVAC distribution subsy...

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15th International Conference of the International Building Performance Simulation Association, Building Simulation 2017

作者： Bres, Aurelien Judex, Florian Suter, Georg de Wilde, Pieter AIT Austrian Institute of Technology Energy Department Vienna Austria Design Computing Group Center for Geometry and Computational Design Vienna University of Technology Vienna Austria School of Architecture Design and Environment University of Plymouth Plymouth United Kingdom

ISBN: (纸本)9781510870673

Building performance simulation (BPS) may provide valuable support to the planning of more energy efficient buildings, but the effort needed to create a complete model can hinder this potential. In particular, an idealized representation of HVAC systems is often used instead of a more explicit and insightful model, because of the difficulty of creating the latter. While automated methods are available for the translation of building geometry for BPS, or for the sizing of delivery components, there is no generally accepted way of determining the HVAC distribution subsystems. This paper presents a method allowing models of these subsystems to be automatically created for the purpose of energy simulation. Based on an input consisting of a zoned building model and sized delivery components, networks of potential distribution components are determined and components corresponding to subsets of these networks are created and sized. The output is a component-based model of delivery and distribution HVAC systems, to be completed with generation components and translated into an input for a building performance simulation engine. The method is tested in a case study where its results are compared to an existing heating system for an office building. © 2017 Building Simulation Conference Proceedings. All rights reserved.

关键词： Office buildings

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Author Correction: Proton acceleration in thermonuclear nova explosions revealed by gamma rays

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Nature Astronomy 2022年 760页

作者： V. A. Acciari J. Becerra González E. Colombo R. J. García López J. Herrera A. López-Oramas C. Maggio V. Moreno M. Nievas Rosillo J. Otero-Santos G. Vanzo M. Vazquez Acosta S. Ansoldi B. De Lotto A. Donini F. Longo D. Miceli M. Palatiello M. Persic L. A. Antonelli C. Bigongiari S. Covino V. D’Elia F. Dazzi A. Lamastra F. Leone S. Lombardi L. Maraschi L. Nava S. Paiano C. Righi F. G. Saturni A. Stamerra F. Tavecchio A. Tutone A. Biland S. Fukami M. Artero O. Blanch S. M. Colak M. Delfino J. Delgado E. Do Souto Espiñeira L. Fariña Alonso L. Jouvin D. Kerszberg M. Martínez A. Moralejo E. Moretti C. Nigro C. Priyadarshi J. Rico K. Asano D. Hadasch M. Hütten T. Inada Y. Iwamura Y. Kobayashi D. Mazin K. Noda Y. Ohtani T. Saito S. Sakurai J. Sitarek M. Strzys M. Takahashi R. Takeishi M. Teshima I. Vovk D. Baack H. Bökenkamp D. Elsaesser A. Fattorini L. Linhoff S. Mender W. Rhode K. Schmidt A. Babić Ž. Bošnjak S. Cikota A. Baquero J. A. Barrio J. L. Contreras M. V. Fonseca J. Hoang M. López-Moya A. Mas Aguilar T. Miener D. Morcuende P. Peñil U. Barres de Almeida B. Machado de Oliveira Fraga I. Batković M. Bernardos G. Busetto A. De Angelis M. Doro R. López-Coto M. Mallamaci M. Mariotti E. Prandini A. Spolon W. Bednarek P. Gliwny K. Ishio D. Sobczynska L. Bellizzi S. Menchiari J. M. Miranda R. Paoletti S. Truzzi S. Ventura C. Wunderlich E. Bernardini W. Bhattacharyya M. Garczarczyk K. Satalecka A. Arbet Engels A. Berti J. Besenrieder G. Ceribella Y. Chai G. D’Amico C. Fruck J. G. Green D. Green A. Hahn L. Heckmann R. Mirzoyan D. Paneque M. Pihet T. Schweizer D. Strom J. van Scherpenberg M. Will G. Bonnoli R. Carosi P. Da Vela P. G. Prada Moroni A. Rugliancich M. Cerruti E. Molina J. M. Paredes M. Ribó A. Chilingarian J. Cortina C. Delgado Mendez T. Hassan I. Jiménez Martínez A. Del Popolo D. Depaoli F. Di Pierro C. F. Vigorito L. Di Venere N. Giglietto F. Giordano S. Loporchio D. Dominis Prester M. Manganaro S. Mićanović L. Pavletić T. Terzić D. Dorner K. Mannheim B. Schleicher V. Fallah Ramazani J. Jormanainen E. Lindfors Instituto de Astrofísica de Canarias and Dpto. de Astrofísica Universidad de La Laguna La Laguna Tenerife Spain Departament de Física and CERES-IEEC Universitat Autònoma de Barcelona Bellaterra Spain Università di Udine and INFN Trieste Udine Italy International Center for Relativistic Astrophysics (ICRA) Rome Italy Dipartimento di Fisica Università di Trieste Trieste Italy Laboratoire d’Annecy de Physique des Particules (LAPP) CNRS-IN2P3 Annecy Cedex France INAF Trieste and Dept. of Physics and Astronomy University of Bologna Bologna Italy National Institute for Astrophysics (INAF) Rome Italy ETH Zürich Zürich Switzerland Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology (BIST) Bellaterra Barcelona Spain Port d’Informació Científica (PIC) Bellaterra Barcelona Spain Japanese MAGIC Group: Institute for Cosmic Ray Research (ICRR) The University of Tokyo Kashiwa Chiba Japan Max-Planck-Institut für Physik München Germany Technische Universität Dortmund Dortmund Germany Croatian MAGIC Group: University of Zagreb Faculty of Electrical Engineering and Computing (FER) Zagreb Croatia IPARCOS Institute and EMFTEL Department Universidad Complutense de Madrid Madrid Spain Department of Astronomy University of California Berkeley Berkeley CA USA Centro Brasileiro de Pesquisas Físicas (CBPF) Rio de Janeiro RJ Brazil Università di Padova and INFN Padova Italy University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz Poland Università di Siena and INFN Pisa Siena Italy Deutsches Elektronen-Synchrotron (DESY) Zeuthen Germany Department for Physics and Technology University of Bergen Bergen Norway Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada Spain Università di Pisa and INFN Pisa Pisa Italy University of Innsbruck Innsbruck Austria Universitat de Barcelona ICCUB IEEC-UB Barcelona Spain Armenian MAGIC Group: A. Alikhanyan National Science Labo

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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

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Nature methods 2023年第6期20卷 824-835页

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

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

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Publisher Correction: The Scales Project, a cross-national dataset on the interpretation of thermal perception scales

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Scientific data 2020年第1期7卷 11页

作者： Marcel Schweiker Amar Abdul-Zahra Maíra André Farah Al-Atrash Hanan Al-Khatri Rea Risky Alprianti Hayder Alsaad Rucha Amin Eleni Ampatzi Alpha Yacob Arsano Montazami Azadeh Elie Azar Bannazadeh Bahareh Amina Batagarawa Susanne Becker Carolina Buonocore Bin Cao Joon-Ho Choi Chungyoon Chun Hein Daanen Siti Aisyah Damiati Lyrian Daniel Renata De Vecchi Shivraj Dhaka Samuel Domínguez-Amarillo Edyta Dudkiewicz Lakshmi Prabha Edappilly Jesica Fernández-Agüera Mireille Folkerts Arjan Frijns Gabriel Gaona Vishal Garg Stephanie Gauthier Shahla Ghaffari Jabbari Djamila Harimi Runa T Hellwig Gesche M Huebner Quan Jin Mina Jowkar Renate Kania Jungsoo Kim Nelson King Boris Kingma M Donny Koerniawan Jakub Kolarik Shailendra Kumar Alison Kwok Roberto Lamberts Marta Laska M C Jeffrey Lee Yoonhee Lee Vanessa Lindermayr Mohammadbagher Mahaki Udochukwu Marcel-Okafor Laura Marín-Restrepo Anna Marquardsen Francesco Martellotta Jyotirmay Mathur Gráinne McGill Isabel Mino-Rodriguez Di Mou Bassam Moujalled Mia Nakajima Edward Ng Marcellinus Okafor Mark Olweny Wanlu Ouyang Ana Ligia Papst de Abreu Alexis Pérez-Fargallo Indrika Rajapaksha Greici Ramos Saif Rashid Christoph F Reinhart Ma Isabel Rivera Mazyar Salmanzadeh Karin Schakib-Ekbatan Stefano Schiavon Salman Shooshtarian Masanori Shukuya Veronica Soebarto Mohammad Tahsildoost Federico Tartarini Despoina Teli Priyam Tewari Samar Thapa Maureen Trebilcock Jörg Trojan Ruqayyatu B Tukur Conrad Voelker Yeung Yam Liu Yang Gabriela Zapata-Lancaster Yongchao Zhai Yingxin Zhu Zahra Sadat Zomorodian Building Science Group Karlsruhe Institute of Technology Englerstr. 7 76131 Karlsruhe Germany. marcel.schweiker@kit.edu. WIN-Kolleg Thermal comfort and pain Heidelberg Academy of Sciences and Humanities Karlstr. 4 69117 Heidelberg Germany. marcel.schweiker@kit.edu. Mechanical Engineering Department University of Technology Iraq Al sinaea 10066 Baghdad Iraq. CTC/Department of Civil Engineering Federal University of Santa Catarina Rua João Pio Duarte Silva s/n. Córrego Grande 88.040-901 Florianópolis SC Brazil. School of Architecture and Built Environment German Jordanian University Darat Othman Bdeir 11180 Amman Jordan. Civil and Architectural Engineering Department Sultan Qaboos University Al-Khod P.O. Box: 33 PC: 123 Muscat Oman. Laboratory of Building Technology Department of Architecture Institut Teknologi Bandung Ganesha 10 40132 Bandung Indonesia. Department of Building Physics Bauhaus-University Weimar Coudraystr. 11A 99423 Weimar Germany. Energy & Climate Change Group Faculty of Engineering & Physical Sciences University of Southampton University Road SO17 1BJ Southampton United Kingdom. Welsh School of Architecture Cardiff University King Edward VII Avenue CF10 3NB Cardiff United Kingdom. Department of Architecture Massachusetts Institute of Technology Massachusetts Ave 02139 Cambridge United States of America. Faculty of Engineering Environment and Computing BNE Research Centre Coventry University Gulson road CV1 2JH Coventry United Kingdom. Industrial and Systems Engineering Khalifa University Masdar Campus Building 1B 54224 Abu Dhabi United Arab Emirates. Architecture University of Tehran Kish International Campus Niyayesh St. Mirmohanna Blvd 79416-55665 Kish Iran. Department of Architecture Ahmadu Bello University Zaria Nigeria Samaru Zaria Nigeria. WIN-Kolleg Thermal comfort and pain Heidelberg Academy of Sciences and Humanities Karlstr. 4 69117 Heidelberg Germany. Department of Cognitive and Clinical Neurosci

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Heat transfer performance of buried extremely long ground-coupled heat exchangers with concentric pipes

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Energy Procedia 2017年 143卷 106-111页

作者： Guosheng Jia John C. Chai Cong Zhou Min Zhao Zeyu Tao Lianying Zhang Xiaohu Yang Xiangzhao Meng Liwen Jin Group of Building Environment and Sustainability Technology Shaanxi Engineering Research Center of Building Environment and Energy School of Human Settlements and Civil Engineering Xi’an Jiaotong University Xi’an 710049 China Department of Engineering and Technology School of Computing and Engineering University of Huddersfield Huddersfield HD1 3DH UK Shanghai CCI Energy Investment Co. Ltd. Shanghai 200030 China China Northwest Architecture Design and Research Institute Co. Ltd. Xi’an 710016 China

This study focuses on the heat transfer between the vertical concentric tubes buried underground and the medium-deep rock-soil geothermal energy. The numerical simulation based on an in-house FORTRAN computation program was performed to analyze the mechanism of heat exchange underground. The program was shown to predict the test results of outlet water temperature and heat gain very well under the corresponding operating conditions when engineering data of an actual geothermal well were obtained to validate the numerical scheme. The effects of inlet water temperature and flow rate on the heat exchanger performance were also analyzed in detail. The comparison indicated that the present numerical scheme is able to predict the heat transfer performance of extremely long ground-coupled heat exchanger subject to various geothermal conditions.

关键词： Heat transfer performance Concentric tube heat exchanger FORTRAN program Geothermal

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

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Nature methods 2024年第10期21卷 1959页

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