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RETRACTED ARTICLE: Nanomaterial heat transfer through a complex shaped solar system considering variable magnetic field

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Applied Nanoscience 2022年第4期13卷 3109-3109页

作者： Ajour, Mohammed N. Rawa, Muhyaddin J. H. Milyani, Ahmad H. Li, Meicheng Selim, Mahmoud M. Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia School of New Energy New Energy Materials and Devices Center North China Electric Power University Beijing China Department of Mathematics Al-Aflaj College of Science and Humanities Studies Prince Sattam Bin Abdulaziz University Al-Aflaj Saudi Arabia Department of Mathematics Suez Faculty of Science Suez University Suez Egypt

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Correction to: Investigating the bioenergy potential of invasive Reed Canary (Phalaris arundinacea) through thermal and kinetic analyses

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Biomass Conversion and Biorefinery 2021年第9期13卷 7687-7687页

作者： Alhumade, Hesham Ahmad, Muhammad Sajjad Mauri, Emanuele Al-Turki, Yusuf Elkamel, Ali Department of Chemical and Materials Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Department of Chemical Engineering Hebei University of Technology Tianjin China Department of Chemical Engineering University of Waterloo Waterloo Canada Department of Engineering Università Campus Bio-Medico Di Roma Rome Italy Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots

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Physical Review Applied 2020年第5期13卷 054005-054005页

作者： J. Darulová S.J. Pauka N. Wiebe K.W. Chan G.C Gardener M.J. Manfra M.C. Cassidy M. Troyer Theoretische Physik ETH Zurich 8093 Zurich Switzerland ARC Centre of Excellence for Engineered Quantum Systems School of Physics The University of Sydney Sydney NSW 2006 Australia Pacific Northwest National Laboratory Richland Washington 99354 USA Department of Physics University of Washington Seattle Washington 98195 USA Microsoft Quantum The University of Sydney Sydney NSW 2006 Australia Birck Nanotechnology Center Purdue University West Lafayette Indiana 47907 USA Microsoft Quantum Purdue Purdue University West Lafayette Indiana 47907 USA Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA School of Materials Engineering and School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Microsoft Quantum Redmond Washington 98052 USA

Defining quantum dots in semiconductor-based heterostructures is an essential step in initializing solid-state qubits. With growing device complexity and increasing number of functional devices required for measurements, a manual approach to finding suitable gate voltages to confine electrons electrostatically is impractical. Here, we implement a two-stage device characterization and dot-tuning process, which first determines whether devices are functional and then attempts to tune the functional devices to the single or double quantum-dot regime. We show that automating well-established manual-tuning procedures and replacing the experimenter’s decisions by supervised machine learning is sufficient to tune double quantum dots in multiple devices without premeasured input or manual intervention. The quality of measurement results and charge states are assessed by four binary classifiers trained with experimental data, reflecting real device behavior. We compare and optimize eight models and different data preprocessing techniques for each of the classifiers to achieve reliable autonomous tuning, an essential step towards scalable quantum systems in quantum-dot-based qubit architectures.

关键词： Machine learning Quantum information with solid state qubits Double quantum dots III-V semiconductors

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Review on the Effect of Student Learning Outcome and Teaching Technology in Omani’s Higher Education Institution’s Academic Accreditation Process

Review on the Effect of Student Learning Outcome and Teachin...

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2018年第七届软件与计算机应用国际会议

作者： Ragad M.Tawafak Muamer N.Mohammed Ruzaini bin Abdullah Arshah Awanis Romli Faculty of Computer Systems and Software Engineering University Malaysia Pahang Malaysia IBM Center of Excellence University Malaysia Pahang Faculty of Computer Systems and Software Engineering University Malaysia Pahang

Academic accreditation standards are defined clearly in Higher Educational Institutions（HEI） plans, management, and delivering results of assessment student with learning outcomes. In recent years, all universities and institutions work hard to increase their approval privileges, skills, achievements through getting the accreditation from HEI. The aim of this integrative review was to examine the current evidence on the impact of technology learning on student learning and academic performance and analysis the main factors of an institution accreditation process. The authors searched several electronic databases for relevant articles, with different learning techniques. The main contribution of this study lies in showing the general criteria’s of accreditation standards that focus on essential technology enhanced learning, student learning outcomes, and faculty technology experience on academic performance of universities. The result from this paper is showing the relations between what have been done and presented before with methods, factors, suggestions to identify these activities.

关键词： Higher Educational Institute （HEI） Student Learning Outcome （SLO） Technology Learning Quality Assurance Accreditation

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Multipath routing protocol based on cross-layer approach for MANET

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2017年第1期11卷 71-83页

作者： Allahham, Alaa Azmi Mohammed, Muamer N. Kadhim, Nassir Sallom Faculty of Computer Systems and Software Engineering University Malaysia Pahang Kuantan Pahang26300 Malaysia IBM Center of Excellence Kuantan Pahang26300 Malaysia

Mobile Ad-hoc Networks (MANETs) involved in many applications, whether smart or traditional and for both civilian and military uses, and that because of their special features, where it does not depend on any infrastructure during its working, as well as the nodes in MANETs have a freedom of movement with the ability to self-configure, in addition, to working as a router or client at the same time. Moreover, MANETs considered as an infrastructure less network, so the cost of this type of networks is less in comparison to other traditional networks. On the other hand, the routing considered one of most important challenges in MANETs due to the perpetual motion and randomness of the nodes that can causing a continuous change of the network topology and thus to all paths between nodes, where finding valid paths between the nodes is the core task of routing protocols. Recently, it has been argued that the traditional layered architecture is ineffective to deal with receiving signal strength related problems. In an effort to improve the performance of MANETs, there has been increased in protocols that rely on cross-layer interaction between different layers. In this paper, a Cross-layer design among Network, MAC and Physical layers based on Threshold Multipath Routing Protocol (CTMRP) is proposed. The CTMRP is designed for decision maker based on threshold value of average paths signal for efficient transmission of the Text, image, audio and video as well as sending the data via multiple paths, which mitigate the negatives effects causes from forcing the nodes to send the data via single. The Route Discovery Delay, Number of RREQ Messages, Number of RREP Messages, End-to-End Delay, Packet Delivery Ratio (PDR), and Throughput were selected as the main performance evaluation metrics. The results show that the proposed algorithm has better performance and lead to increase stability of transmission link.

关键词： Mobile ad hoc networks

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Tunneling and fluctuating electron-hole cooper pairs in double bilayer graphene

arXiv

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arXiv 2019年

作者： Efimkin, Dmitry K. William Burg, G. Tutuc, Emanuel MacDonald, Allan H. Center for Complex Quantum Systems University of Texas at Austin AustinTX78712-1192 United States School of Physics and Astronomy ARC Centre of Excellence in Future Low-Energy Electronics Technologies Monash University Victoria3800 Australia Microelectronics Research Center Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78758 United States

A strong low-temperature enhancement of the tunneling conductance between graphene bilayers has been reported recently, and interpreted as a signature of equilibrium electron-hole pairing, first predicted in bilayers more than forty years ago but previously unobserved. Here we provide a detailed theory of conductance enhanced by fluctuating electron-hole Cooper pairs, which are a precursor to equilibrium pairing, that accounts for specific details of the multi-band double graphene bilayer system which supports several different pairing channels. Above the equilibrium condensation temperature, pairs have finite temporal coherence and do not support dissipationless tunneling. Instead they strongly boost the tunneling conductivity via a fluctuational internal Josephson effect. Our theory makes predictions for the dependence of the zero bias peak in the differential tunneling conductance on temperature and electron-hole density imbalance that capture important aspects of the experimental observations. In our interpretation of the observations, cleaner samples with longer disorder scattering times would condense at temperatures Tc up to ∼ 50K, compared to the record Tc ∼ 1.5K achieved to date in experiment. Copyright © 2019, The Authors. All rights reserved.

关键词： Scanning tunneling microscopy

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Linear electrical generator topologies for direct-drive marine wave energy conversionan overview

Linear electrical generator topologies for direct-drive mari...

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作者： Faiz, Jawad Nematsaberi, Alireza Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Wave energy is one of the most attractive forms of renewable energy. The reasons include its promising availability, predictability, persistence, and power density. This study focuses on all linear generator designs and technologies which have been used so far in direct-drive wave energy converters (DD-WECs). Currently, linear permanent magnet generators (LPMG) have been proposed as the most advantageous generator system developed for DD-WECs. After a brief description of linear generator based wave energy converters, all proposed state-of-the-art of LPMG topologies available in the literature are discussed and compared in terms of flux path, core type, location of PMs, and etc. In addition, other linear generator technologies such as linear switched reluctance and linear superconducting generators, as an alternative to LPMGs, are reviewed. Finally, based on the surveyed quantitative comparisons performed in previous works, eight major concepts are evaluated in terms of economic and operational aspects. © The Institution of engineering and Technology 2017.

关键词： Topology

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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

arXiv

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arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration

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A framework of biomarkers for skeletal aging: a consensus statement by the Aging Biomarker Consortium

Life Medicine

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Life Medicine 2023年第6期2卷 1页

作者： Aging Biomarker Consortium,Jinlong Suo Yibo Gan Yangli Xie Shuqin Xu Jianfang Wang Di Chen Lin Chen Lianfu Deng Shiqing Feng Jingdong Jackie Han Qing Jiang Guanghua Lei Peng Liu Xianghang Luo Xin Ma Jing Qu Chunli Song Peifu Tang Tingting Tang Sijia Wang Xiaochun Wei Chengtie Wu Guozhi Xiao Liu Yang Licheng Zhang Weiqi Zhang Zhenlin Zhang Guang-Hui Liu Changqing Zhang Gang Pei Jian Luo Rui Yue Weiguo Zou Department of Orthopedic Surgery Institute of Microsurgery on Extremities Shanghai Sixth People?s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China State Key Laboratory of Trauma and Chemical Poisoning Department of Spine Surgery Center of Orthopedics Daping Hospital Army Medical University (Third Military Medical University) Chongqing China State Key Laboratory of Trauma and Chemical Poisoning Department of Wound Repair and Rehabilitation Medicine Center of Bone Metabolism and Repair (CBMR) Daping Hospital Army Medical University (Third Military Medical University) Chongqing China Department of Osteoporosis and Bone Diseases Shanghai Clinical Research Center of Bone Disease Shanghai Sixth People?s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Shanghai Key Laboratory of Signaling and Disease Research Frontier Science Center for Stem Cell Research School of Life Sciences and Technology Institute for Regenerative Medicine Shanghai East Hospital Tongji University Shanghai China Research Center for Computer-Aided Drug Discovery Faculty of Pharmaceutical Sciences Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases Department of Orthopaedics Shanghai Institute of Traumatology and Orthopaedics Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Orthopaedics Qilu Hospital of Shandong University Shandong University Centre for Orthopaedics Advanced Medical Research Institute Cheeloo College of Medicine Shandong University Jinan China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Center for Quantitative Biology (CQB) Peking University Beijing China State Key Laboratory of Pharmaceutical Biotechnology Division of Sports Medicine and Adult Reconstructive Surgery Department of Orthopedic Surgery Drum Towe

The skeleton is an important structural and metabolic organ in human body, while aging is the physiological basis for degenerative skeletal diseases. China has the largest aging population in the world and faces great challenges in preventing and managing diseases related to skeletal aging. To address these challenges, the Aging China Biomarkers Consortium (ABC) has reached an expert consensus on biomarkers of skeletal aging by synthesizing the literature and insights from scientists and clinicians. The consensus provides a comprehensive assessment of biomarkers associated with skeletal aging and proposes a systematic framework that categorizes biomarkers into three dimensions, namely, functional, structural, and humoral dimensions. Within each dimension, the ABC recommended clinical and evidential research-based biomarkers for physiological aging and degenerative pathologies of the skeleton. This expert consensus aims to lay the foundation for future studies to assess the prediction, diagnosis, early warning, and treatment of diseases associated with skeletal aging, with the ultimate goal of improving the skeletal health of elderly populations in China and around the world.

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