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Studies in Health Technology and informatics 2024年 316卷 V-VI页

作者： Mantas, John Hasman, Arie Demiris, George Saranto, Kaija Marschollek, Michael Arvanitis, Theodoros Ognjanović, Ivana Benis, Arriel Gallos, Parisis Zoulias, Emmanouil Andrikopoulou, Elisavet School of Health Sciences National and Kapodistrian University of Athens Athens Greece Department of Medical Informatics Amsterdam UMC Location AMC Netherlands Perelman School of Medicine University of Pennsylvania United States University of Eastern Finland Kuopio Campus Finland Peter L. Reichertz Institute for Medical Informatics TU Braunschweig Hannover Medical School Germany Department of Electronic Electrical & Systems Engineering School of Engineering University of Birmingham United Kingdom University of Donja Gorica Montenegro Department of Digital Medical Technologies Holon Institute of Technology Holon Israel Computational Biomedicine Research Lab Department of Digital Systems University of Piraeus Greece Health Informatics Lab National and Kapodistrian University of Athens Greece Faculty of Technology School of Computing University of Portsmouth United Kingdom

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Behavioral Analytics for Distributed Practices in Programming Problem-Solving

Behavioral Analytics for Distributed Practices in Programmin...

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Frontiers in Education (FIE) Conference

作者： Mohammed Alzaid I-Han Hsiao School of computing Informatics & Decision System Engineering Arizona State University Tempe AZ USA

ISBN: (数字)9781728117461

ISBN: (纸本)9781728117478

This Research Full Paper aims to investigate the learning analytics of students' problem solving when working on distributed programming practices. Typical programming practice activities (i.e. assignments) in a lecture-dominant course may violate the principles of distributed retrieval practice. However, there are tradeoffs between managing depth and breadth of the content and classroom disruptions with the modern platforms and technologies. In this work, we investigate students' behavioral analytics in distributed programming practices. A classroom study was conducted in an introductory programming course and the learners' patterns were observed. Results showed that there were three distinct patterns found: affirmative, experimental, and surrendering. Better-performing students demonstrated more affirmative behaviors and fewer surrendering acts; Below-average students showed a lack of persistence in distributed practices. Additionally, the study reconfirmed the value of spacing effects on learning, which is the importance of spending time and to spreading the working sessions to solve diverse quizzes. Ineffective trial-and-error strategy and neglect the power of practices can be two alarming behaviors in distributed programming practices. Finally, predictive models of performance were presented based on the behavioral patterns.

关键词： Programming profession Problem-solving Reflection Knowledge engineering Encoding Testing

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology and Evolution 2025年第6期9卷 1031-1042页

作者： Pringle, Stephen Dallimer, Martin Goddard, Mark A. Le Goff, Léni K. Hart, Emma Langdale, Simon J. Fisher, Jessica C. Abad, Sara-Adela Ancrenaz, Marc Angeoletto, Fabio Auat Cheein, Fernando Austen, Gail E. Bailey, Joseph J. Baldock, Katherine C. R. Banin, Lindsay F. Banks-Leite, Cristina Barau, Aliyu S. Bashyal, Reshu Bates, Adam J. Bicknell, Jake E. Bielby, Jon Bosilj, Petra Bush, Emma R. Butler, Simon J. Carpenter, Dan Clements, Christopher F. Cully, Antoine Davies, Kendi F. Deere, Nicolas J. Dodd, Michael Drinkwater, Rosie Driscoll, Don A. Dutilleux, Guillaume Dyrmann, Mads Edwards, David P. Farhadinia, Mohammad S. Faruk, Aisyah Field, Richard Fletcher, Robert J. Foster, Chris W. Fox, Richard Francksen, Richard M. Franco, Aldina M. A. Gainsbury, Alison M. Gardner, Charlie J. Giorgi, Ioanna Griffiths, Richard A. Hamaza, Salua Hanheide, Marc Hayward, Matt W. Hedblom, Marcus Helgason, Thorunn Heon, Sui P. Hughes, Kevin A. Hunt, Edmund R. Ingram, Daniel J. Jackson-Mills, George Jowett, Kelly Keitt, Timothy H. Kloepper, Laura N. Kramer-Schadt, Stephanie Labisko, Jim Labrosse, Frédéric Lawson, Jenna Lecomte, Nicolas de Lima, Ricardo F. Littlewood, Nick A. Marshall, Harry H. Masala, Giovanni L. Maskell, Lindsay C. Matechou, Eleni Mazzolai, Barbara McConnell, Alistair Melbourne, Brett A. Miriyev, Aslan Nana, Eric Djomo Ossola, Alessandro Papworth, Sarah Parr, Catherine L. Payo-Payo, Ana Perry, Gad Pettorelli, Nathalie Pillay, Rajeev Potts, Simon G. Prendergast-Miller, Miranda T. Qie, Lan Rolley-Parnell, Persie Rossiter, Stephen J. Rowcliffe, Marcus Rumble, Heather Sadler, Jon P. Sandom, Christopher J. Sanyal, Asiem Schrodt, Franziska Sethi, Sarab S. Shabrani, Adi Siddall, Robert Smith, Simón C. Snep, Robbert P. H. Soulsbury, Carl D. Stanley, Margaret C. Stephens, Philip A. Stephenson, P.J. Struebig, Matthew J. Studley, Matthew Svátek, Martin Tang, Gilbert Taylor, Nicholas K. Umbers, Kate D. L. Ward, Robert J. White, Patrick J. C. Whittingham, Mark J. Wich, Serge Williams, Christopher D. Yakubu, Ibrahim B. Yoh, Natalie Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury United Kingdom Centre for Environmental Policy Imperial College London London United Kingdom Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Synthotech Ltd Milner Court Hornbeam Square Harrogate United Kingdom Mechanical Engineering Department University College London London United Kingdom HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brazil Department of Engineering Harper Adams University Newport United Kingdom Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge United Kingdom Centre for Ecology and Hydrology Penicuik United Kingdom Department of Life Sciences Imperial College London Silwood Park Campus Ascot United Kingdom Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire United Kingdom School of Biological and Environmental Sciences Liverpool John Moores University Liverpool United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln Lincoln United Kingdom Royal Botanic Garden Edinburgh Edinburgh United Kingdom School of Biological Sciences University of East Anglia Norwich Research Park Norwich United Kingdom Digital Ecology Limited Bristol United Kingdom School of Biological Sciences University of Bristol Bristol United Kingdom Department of Computing Imperial College London London United Kingdom Department of Ecology and Evolutionary Biology University of Colorado Boulder CO United States Faculty of Science Technology Engineering

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. Robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively. © The Author(s) 2025.

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Low Earth Orbit Satellite Security and Reliability: Issues, Solutions, and the Road Ahead

arXiv

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arXiv 2022年

作者： Yue, Pingyue An, Jianping Zhang, Jiankang Ye, Jia Pan, Gaofeng Wang, Shuai Xiao, Pei Hanzo, Lajos The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China The Department of Computing and Informatics Bournemouth University BournemouthBH12 5BB United Kingdom The school of Electrical Engineering Chongqing University Chongqing400044 China The 5GIC & 6GIC Institute for Communication Systems University of Surrey GU2 7XH United Kingdom The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Low Earth Orbit (LEO) satellites undergo a period of rapid development driven by ever-increasing user demands, reduced costs, and technological progress. Since there is a lack of literature on the security and reliability issues of LEO Satellite Communication systems (SCSs), we aim to fill this knowledge gap. Specifically, we critically appraise the inherent characteristics of LEO SCSs and elaborate on their security and reliability requirements. In light of this, we further discuss their vulnerabilities, including potential security attacks launched against them and reliability risks, followed by outlining the associated lessons learned. Subsequently, we discuss the corresponding security and reliability enhancement solutions, unveil a range of trade-offs, and summarize the lessons gleaned. Furthermore, we shed light on several promising future research directions for enhancing the security and reliability of LEO SCSs, such as integrated sensing and communication, computer vision aided communications, as well as challenges brought about by mega-constellation and commercialization. Finally, we summarize the lessons inferred and crystallize the take-away messages in our design guidelines. Copyright © 2022, The Authors. All rights reserved.

关键词： Satellite communication systems

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Benders subproblem decomposition for bilevel problems with convex follower

arXiv

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arXiv 2019年

作者： Byeon, Geunyeong van Hentenryck, Pascal School of Computing Informatics and Decision Systems Engineering Arizona State University United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States

Bilevel optimization formulates hierarchical decision-making processes that arise in many real-world applications such as in pricing, network design, and infrastructure defense planning. In this paper, we consider a class of bilevel optimization problems where the upper level problem features some integer variables while the lower level problem enjoys strong duality. We propose a dedicated Benders decomposition method for solving this class of bilevel problems, which decomposes the Benders subproblem into two more tractable, sequentially solvable problems that can be interpreted as the upper and the lower level problems. We show that the Benders subproblem decomposition carries over to an interesting extension of bilevel problems, which connects the upper level solution with the lower level dual solution, and discuss some special cases of bilevel problems that allow sequence-independent subproblem decomposition. Several novel schemes for generating numerically stable cuts, finding a good incumbent solution, and accelerating the search tree are discussed. A computational study demonstrates the computational benefits of the proposed method over a state-of-the-art bilevel-tailored branch-and-cut method, a commercial solver, and the standard Benders method on standard test cases and the motivating applications in sequential energy markets. Copyright © 2019, The Authors. All rights reserved.

关键词： Integer programming

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Modulated Model Predictive Torque and Current Control of Squirrel Cage Induction Generator-Based Wind Power Generation System

Modulated Model Predictive Torque and Current Control of Squ...

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IEEE Workshop on Computers in Power Electronics

作者： Venkata Yaramasu Apparao Dekka Jose Rodriguez School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff USA Lakehead University Thunder Bay Canada Faculty of Engineering Universidad Andres Bello Santiago Chile

ISBN: (数字)9781728171609

ISBN: (纸本)9781728171616

This paper investigates novel control methods for the squirrel-cage induction generator based variable speed wind power generation system composed of back-to-back connected two-level voltage source converters. A modulated model predictive torque control method is designed by combining the operating principles of direct torque control, predictive torque control and space vector modulation to effectively control the electromagnetic torque and stator flux control of an induction generator. Similarly, a modulated model predictive current control method is designed for the grid-side converter to control the active and reactive powers injected to the three-phase grid. The proposed control methods employ duty-cycle optimized cost functions for fixed switching frequency operation, fast dynamic response and low steady-state errors during a wide-dynamic range operation. Simulation results are presented with a 750-kW wind power generation system to validate the effectiveness of proposed control methods.

关键词： Torque Rotors Generators Cost function Stator windings Couplings Torque control

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Deep residual dense U-net for resolution enhancement in accelerated MRI acquisition

arXiv

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arXiv 2020年

作者： Ding, Pak Lun Kevin Li, Zhiqiang Zhou, Yuxiang Li, Baoxin School of Computing Informatics Decision Systems Engineering Arizona State University TempeAZ85281 United States Dept. of Neuroradiology Barrow Neurological Institute PhoenixAZ85013 United States Dept. of Radiology Mayo Clinic Arizona PhoenixAZ85054 United States

Typical Magnetic Resonance Imaging (MRI) scan may take 20 to 60 minutes. Reducing MRI scan time is beneficial for both patient experience and cost considerations. Accelerated MRI scan may be achieved by acquiring less amount of k-space data (down-sampling in the k-space). However, this leads to lower resolution and aliasing artifacts for the reconstructed images. There are many existing approaches for attempting to reconstruct high-quality images from down-sampled k-space data, with varying complexity and performance. In recent years, deep-learning approaches have been proposed for this task, and promising results have been reported. Still, the problem remains challenging especially because of the high fidelity requirement in most medical applications employing reconstructed MRI images. In this work, we propose a deep-learning approach, aiming at reconstructing high-quality images from accelerated MRI acquisition. Specifically, we use Convolutional Neural Network (CNN) to learn the differences between the aliased images and the original images, employing a U-Net-like architecture. Further, a micro-architecture termed Residual Dense Block (RDB) is introduced for learning a better feature representation than the plain U-Net. Considering the peculiarity of the down-sampled k-space data, we introduce a new term to the loss function in learning, which effectively employs the given k-space data during training to provide additional regularization on the update of the network weights. To evaluate the proposed approach, we compare it with other state-of-the-art methods. In both visual inspection and evaluation using standard metrics, the proposed approach is able to deliver improved performance, demonstrating its potential for providing an effective solution. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Operations & Supply Chain Management: Principles and Practice

arXiv

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arXiv 2025年

作者： Petropoulos, Fotios Akkermans, Henk Aksin, O. Zeynep Ali, Imran Babai, Mohamed Zied Barbosa-Povoa, Ana Battaïa, Olga Besiou, Maria Boysen, Nils Brammer, Stephen Brandon-Jones, Alistair Briskorn, Dirk Browning, Tyson R. Buijs, Paul Centobelli, Piera Chiarini, Andrea Cousins, Paul Cudney, Elizabeth A. Davies, Andrew Day, Steven J. de Koster, René Dekker, Rommert Denicol, Juliano Despeisse, Mélanie Disney, Stephen M. Dolgui, Alexandre Duong, Linh El-Qallali, Malek Fahimnia, Behnam Fakhredin, Fatemeh Gershwin, Stanley B. Ghamat, Salar Giannikas, Vaggelis Glock, Christoph H. Godsell, Janet Govindan, Kannan Hannibal, Claire Haug, Anders Hernaus, Tomislav Hsuan, Juliana Ivanov, Dmitry Jahre, Marianne Johansson, Björn Kalidoss, Madan Shankar Kanellopoulos, Argyris Kannan, Devika Karul, Elif Katsikopoulos, Konstantinos V. Kilic-Ararat, Ayse Begüm Kolisch, Rainer Koppenberg, Maximilian Kumar, Maneesh Kuo, Yong-Hong Kusiak, Andrew Lewis, Michael A. Lim, Stanley Frederick W.T. Limère, Veronique Liu, Jiyin Maghazei, Omid Marić, Matija Meissner, Joern Meuwissen, Miranda Micheli, Pietro Nanayakkara, Samudaya Özdemir, Bengü Nur Papadopoulos, Thanos Pavelin, Stephen Perera, Srinath Phillips, Wendy Prak, Dennis Pun, Hubert Qazi, Sharfah Ahmad Ramanathan, Usha Reiner, Gerald Reitsma, Ewout Roehrich, Jens K. Sanders, Nada R. Sarkis, Joseph Schmid, Nico André Schmidt, Christoph G. Schroeder, Andreas Selviaridis, Kostas Seuring, Stefan Shi, Chuan Son, Byung-Gak Spring, Martin Squire, Brian van der Valk, Wendy van Donk, Dirk Pieter van Houtum, Geert-Jan Wilhelm, Miriam Wynstra, Finn Zheng, Ting School of Management University of Bath Bath United Kingdom Department of Information Systems & Operations Management Tilburg School of Economics and Management Tilburg University Netherlands College of Administrative Sciences and Economics Koc University Turkey School of Business and Law Central Queensland University Australia Kedge Business School France Center for Management Studies Instituto Superior Técnico University of Lisbon Portugal Kühne Logistics University Hamburg Germany Friedrich-Schiller-Universität Jena Lehrstuhl für Operations Management Germany Bergische Universität Wuppertal Germany Neeley School of Business Texas Christian University Fort WorthTX United States Department of Operations University of Groningen Netherlands Department of Industrial Engineering University of Naples "Federico II" Italy Department of Management University of Verona Italy Management School University of Liverpool United Kingdom John E. Simon School of Business Maryville University of Saint Louis Missouri United States Science Policy Research Unit University of Sussex Business School Brighton United Kingdom School of Management Zhejiang University China Rotterdam School of Management Erasmus University Netherlands Erasmus School of Economics Erasmus University Netherlands Bartlett School of Sustainable Construction The Bartlett Faculty of the Built Environment University College London United Kingdom Department of Industrial and Material Sciences Chalmers University of Technology Sweden Center for Simulation Analytics and Modelling University of Exeter Business School United Kingdom IMT Atlantique LS2N-CNRS Nantes France College of Business and Law University of the West of England United Kingdom Rabdan Academy United Arab Emirates Institute of Transport and Logistics Studies The University of Sydney Business School Australia Kühne Logistics University Germany Department of Mechanical Engineering Massachusetts Institute of Technology Massachusetts United S

Operations and Supply Chain Management (OSCM) has continually evolved, incorporating a broad array of strategies, frameworks, and technologies to address complex challenges across industries. This encyclopedic article provides a comprehensive overview of contemporary strategies, tools, methods, principles, and best practices that define the field’s cutting-edge advancements. It also explores the diverse environments where OSCM principles have been effectively implemented. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. © 2025, CC BY.

关键词： Supply chain management

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Bradycardia Prediction in Preterm Infants Using Nonparametric Kernel Density Estimation

Bradycardia Prediction in Preterm Infants Using Nonparametri...

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Asilomar Conference on Signals, systems & Computers

作者： Subhasish Das Bahman Moraffah Ayan Banerjee Sandeep K.S. Gupta Antonia Papandreou-Suppappola School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe Arizona School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona

ISBN: (数字)9781728143002

ISBN: (纸本)9781728143019

In this paper, we propose a statistical method to predict the onset of bradycardia in preterm infants without any prior knowledge. To model information on the QRS complex R wave, we exploit nonparametric methods to estimate the density. Our proposed method takes advantage of the kernel density estimator in order to provide a statistical guarantee of 95% accuracy. We also demonstrate our results through simulations to support our proposed method using preterm infant electrocardiogram (ECG) signals from a database. We show that the method achieves a 5% false alarm rate in predicting the onset of upcoming bradycardia events.

关键词： Electrocardiography Kernel Heart beat Predictive models Rail to rail inputs Probability density function Databases

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Causal emergence in discrete & continuous dynamical systems

arXiv

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arXiv 2020年

作者： Varley, Thomas F. Complex Networks & Systems School of Informatics Computing & Engineering Indiana University BloomingtonIN47401 United States Psychological & Brain Sciences Indiana University BloomingtonIN47401 United States

Emergence, the phenomena where a system’s micro-scale dynamics facilitate the development of non-trivial, informative higher scales, has become a foundational concept in modern sciences, tying together fields as diverse as physics, biology, economics, and ecology. Despite it’s apparent universality and the considerable interest, historically researchers have struggled to provide a rigorous, formal definition of emergence that is applicable across fields. Recent theoretical work using information theory and network science to formalize emergence in state-transition networks (causal emergence) has provided a promising way forward, however the relationship between this new framework and other well-studied system dynamics is unknown. In this study, we apply causal emergence analysis to two well-described dynamical systems: the 88 unique elementary cellular automata and the continuous Rossler system in periodic, critical, and chaotic regimes. We find that emergence, as well as its component elements (determinism, degeneracy, and effectiveness) vary dramatically in different dynamical regimes in sometimes unexpected ways. We conclude that the causal emergence framework provides a rich new area of research to explore both to theoreticians and natural scientists in many fields. Copyright © 2020, The Authors. All rights reserved.

关键词： Information theory

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