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Safe food for infants: An EU-China project to enhance the control of safety risks raised by microbial and chemical hazards all along the infant food chains

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Global Pediatrics 2022年 2卷

作者： Engel, Erwan Rivière, Gilles Kemmer, Diana Deusch, Oliver Fuchsbauer, Norbert Biesterveld, Steven Krystalli, Evangelia Bondoux, Marion Li, Guang Yang, Weikang Hou, Jianbo Liang, Ying Yang, Hua Fang, Weihuan Pettoello-Mantovani, Massimo Flynn, Brian Rantsiou, Kalliopi Van der Burg, Bart Bover-Cid, Sara Zwietering, Marcel H. French National Research Institute for Agriculture Food and Environment (INRAE) UR QuaPA MASS Group Saint-Genès-Champanelle France French Agency for Food Environmental and Occupational Health & Safety (ANSES) Maisons-Alfort France Fraunhofer Institute for Process Engineering and Packaging (IVV) Freising Germany Computomics GmbH Tübingen Germany HiPP-Werk Georg Hipp OHG Pfaffenhofen Germany Friesland Campina Nederland BV Amersfoort Netherlands Yiotis Anonimos Emporiki & Viomixaniki Etaireia Athens Greece INRAE Transfert Clermont-Ferrand France Beingmate (Hangzhou) Food Research Institute Co. Ltd Hangzhou China Yangzhou Fangguang Food Co. Ltd. Yangzhou China Zhejiang Academy of Science & Technology for Inspection & Quarantine (ZAIQ Hangzhou China Jiangsu Academy of Agricultural Sciences (JAAS) Nanjing China Zhejiang Academy of Agricultural Sciences (ZAAS) Hangzhou China Zhejiang University (ZJU) Hangzhou China EPA-UNEPSA Berlin Germany Creme software LTD (Creme Global) Dublin Ireland University of Turin Department of Agricultural Forest and Food Sciences Turin Italy BioDetection Systems B.V. Amsterdam Netherlands Institut de Recerca i Tecnologia Agroalimentàries (IRTA) Food Safety and Functionality Program Monells Spain Wageningen University & Research Food Microbiology Wageningen Netherlands

The EU-project SAFFI targets food for EU's 15 million and China's 45 million children under the age of three. It aims at developing an integrated approach to enhance the identification, assessment, detection and mitigation of health risks raised by microbial and chemical hazards along EU and China infant food chains. SAFFI will benchmark the main risks through an extensive hazard identification system based on multiple data sources and a risk ranking procedure. It will also develop procedures to enhance top-down and bottom-up hazard control by combining management options with a panel of technologies for the detection and mitigation of priority hazards. Furthermore, it will explore unexpected contaminants by predictive toxicology and improve risk-based food safety management of biohazards by omics and predictive microbiology. SAFFI will co-develop with and deliver to stakeholders a decision-support system (DSS) to enhance safety control all along the food chain. This DSS will integrate the databases, procedures and methods described above and will be a framework for a generic DSS dedicated to other food. This overall methodology will also be implemented in a complementary Chinese side of the project, and exemplified for each side, with four case studies that were selected to cover priority hazards, main ingredients, processes and control steps of the infant food chain. Resulting databases, tools and procedures will be shared, cross-validated, concatenated, benchmarked and finally harmonized for further use in the EU and China. This EU-China multi-actor consortium of 20 partners involves academia, food safety authorities, infant food companies, a paediatrics association and technological and data-science SMEs. © 2022

关键词： Baby food Chemical contaminants Decision support system Food safety Foodborne hazards Hazard control Hazard detection Risk assessment

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Constructing a Smart Home Control System with the Internet of Things

Constructing a Smart Home Control System with the Internet o...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Huan-Po Hsu Kun-Ming Yu Wen Ouyang Cheng Jie Xu Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Chung-Hua University Hsinchu Taiwan Faculty of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu China

The development of Internet of Things (IoT) makes the application of smart homes grow rapidly. It is very popular to install smart appliances in the house. However, building a smart control system at home not only costs a lot but also has many limitations. For this reason, this study proposes a smart homes control system to easily integrate IoT, WSN, smart robot and single-board computer to implement smart home applications. We use wireless technology and automatic equipment to avoid excessive communication cable and to make the house more intelligent to keep in-house movement unimpeded and indoor space tidy and to let appliance adjust appropriate environment settings automatically. This system brings intelligence and convenience to home, making living environment more comfortable.

关键词： Robot sensing systems Smart homes Control systems Robot kinematics Wireless sensor networks Internet of Things

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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems 32

End-to-end symmetry preserving inter-atomic potential energy...

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32nd Conference on Neural Information Processing Systems, NeurIPS 2018

作者： Zhang, Linfeng Han, Jiequn Wang, Han Saidi, Wissam A. Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University United States Institute of Applied Physics and Computational Mathematics China CAEP Software Center for High Performance Numerical Simulation China Department of Mechanical Engineering and Materials Science University of Pittsburgh United States Department of Chemistry Department of Physics Princeton University United States Princeton Institute for the Science and Technology of Materials Princeton University United States Department of Mathematics Princeton University United States Beijing Institute of Big Data Research China

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES of a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity. © 2018 Curran Associates Inc..All rights reserved.

关键词： Potential energy

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Transient Thermal Analysis of Welding Fixtures Design 2

Transient Thermal Analysis of Welding Fixtures Design

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2nd International Conference on Eco Engineering Development 2018, ICEED 2018

作者： Anual, M.T. Sahroni, T.R. Akmal, S. Nasution, F.A. Faculty of Manufacturing Engineering Universiti Teknikal Malaysia Melaka Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Faculty of Manufacturing Engineering Innovative Software System and Services Research Group Centre for Advanced Computing Technology Universiti Teknikal Malaysia Melaka Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia

This paper presents the design and analysis of welding fixtures based on transient thermal analysis. Welding fixtures is designed to hold and support the workpiece securely during the welding process. The heat transformation from the workpiece could lead the temperature rises and impacts to the welding fixtures. Therefore, the study introduces the development of welding fixtures as one of a safety measure prevents the welder from injury while handling welding process. The conceptual design of welding fixtures and material were applied to conduct the simulation. Transient thermal analysis was performed on temperature and total heat flux by using computer aided engineering software. The result shows the analysis of equivalent stress, total deformation, factors of safety, fatigue life and fatigue damage. Furthermore, the equivalent stress failure theory used to predict yielding in welding fixture design. The design of welding fixtures and material selection were proposed to hold and support the workpiece securely. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Heat flux

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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems 18

End-to-end symmetry preserving inter-atomic potential energy...

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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Linfeng Zhang Jiequn Han Han Wang Wissam A. Saidi Roberto Car E. Weinan Program in Applied and Computational Mathematics Princeton University Institute of Applied Physics and Computational Mathematics China and CAEP Software Center for High Performance Numerical Simulation China Department of Mechanical Engineering and Materials Science University of Pittsburgh Program in Applied and Computational Mathematics Princeton University and Department of Chemistry and Department of Physics Princeton University and Princeton Institute for the Science and Technology of Materials Princeton University Program in Applied and Computational Mathematics Princeton University and Department of Mathematics Princeton University and Beijing Institute of Big Data Research China

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Multidisciplinary design and aerodynamic assessment of an agile and highly swept aircraft configuration

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CEAS Aeronautical Journal 2016年第4期7卷 677-694页

作者： Liersch, C.M. Huber, K.C. Schütte, A. Zimper, D. Siggel, M. Institute of Aerodynamics and Flow Technology Brunswick Germany Göttingen Germany Program Coordination Defence and Security Research Cologne Germany Simulation and Software Technology Cologne Germany

The characteristics of highly swept aircraft configurations have been studied in a series of consecutive research projects in DLR for more than 15 years. Currently, the investigations focus on the generic SACCON UCAV configuration, which was specified in a common effort together with the NATO STO/AVT-161 task group. This paper is the first one in a series of articles presenting the SACCON-related research work within DLR. First, the article describes the conceptual design studies being performed for this aircraft configuration. At this point the question is raised, whether the simple aerodynamic methods used within conceptual design can be applied to such type of aircraft configurations with sufficient accuracy. Thus, the second part of this article provides a comparison of the aerodynamic characteristics of the SACCON configuration predicted by low- and high-fidelity aerodynamic methods, as well as some results from wind tunnel experiments. © 2016, Deutsches Zentrum für Luft- und Raumfahrt e.V.

关键词： Conceptual design

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DeePCG: Constructing coarse-grained models via deep neural networks

arXiv

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arXiv 2018年

作者： Weinan, E. Zhang, Linfeng Han, Jiequn Wang, Han Car, Roberto Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China Caep Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Program in Applied and Computational Mathematics Princeton Institute for Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China

We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- A nd/or three-body contributions. The scheme, called Deep Coarse-Grained Potential (abbreviated DeePCG), exploits a carefully crafted neural network to construct a many-body coarse-grained potential. The network is trained with full atomistic data in a way that preserves the natural symmetries of the system. The resulting model is very accurate and can be used to sample the configurations of the coarse-grained variables in a much faster way than with the original atomistic model. As an application we consider liquid water and use the oxygen coordinates as the coarse-grained variables, starting from a full atomistic simulation of this system at the ab initio molecular dynamics level. We find that the two-body, three-body, and higher-order oxygen correlation functions produced by the coarse-grained and full atomistic models agree very well with each other, illustrating the effectiveness of the DeePCG model on a rather challenging task. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Recent Advances in Systems and Network Medicine: Meeting Report from the First International Conference in Systems and Network Medicine

Recent Advances in Systems and Network Medicine: Meeting Rep...

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作者： Kurnat-Thoma, Emma Baranova, Ancha Baird, Pat Brodsky, Elia Butte, Atul J. Cheema, Amrita K. Cheng, Feixiong Dutta, Shuchismita Grant, Christina Giordano, James Maitland-Van Der Zee, Anke H. Fridsma, Douglas B. Jarrin, Robert Kann, Maricel G. Keeney, Jonathon Loscalzo, Joseph Madhavan, Guru Maron, Bradley A. Mcbride, Dennis K. Mckean, Maeve Mun, Seong K. Palmer, James C. Patel, Bakul Parakh, Kapil Pariser, Anne R. Pristipino, Christian Radstake, Timothy R.D.J. Rajasimha, Harsha K. Rouse, William B. Rozman, Damjana Saleh, Alif Schmidt, Harald H.H.W. Schultz, Nikolaus Sethi, Tavpritesh Silverman, Edwin K. Skopac, Jessica Svab, Igor Trujillo, Sylvia Valentine, James E. Verma, Dinesh West, Bruce J. Vasudevan, Sona BethesdaMD United States Genetics and Genomics Georgetown University School of Nursing and Health Studies WashingtonDC United States School of Systems Biology George Mason University FairfaxVA United States Global Software Standards Philips Pleasant PrairieWI United States Pine Biotech New OrleansLA United States San FranciscoCA United States Department of Biochemistry Molecular and Cellular Biology Georgetown University Medical Center WashingtonDC United States Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland Clinic ClevelandOH United States Institute for Quantitative Biomedicine Rutgers University The State University of New Jersey PiscatawayNJ United States Rare Disease Institute Division of Genetics and Metabolism Children's National Medical Center WashingtonDC United States Department of Neurology Neuroethics Studies Program-Pellegrino Center for Clinical Bioethics O'Neill-Pellegrino Program in Brain Sciences and Global Law and Policy Georgetown University Medical Center WashingtonDC United States Department of Biochemistry Neuroethics Studies Program-Pellegrino Center for Clinical Bioethics O'Neill-Pellegrino Program in Brain Sciences and Global Law and Policy Georgetown University Medical Center WashingtonDC United States Department of Respiratory Medicine Amsterdam University Medical Centers University of Amsterdam Amsterdam Netherlands American Medical Informatics Association WashingtonDC United States Department of Biological Sciences University of Maryland Baltimore County BaltimoreMD United States Department of Biochemistry and Molecular Medicine George Washington University WashingtonDC United States Department of Medicine Brigham and Women's Hospital Harvard Medical School BostonMA United States National Academy of Engineering The National Academies of Sciences Engineering and Medicine WashingtonDC United States WashingtonDC United States Georgetown University Global Health

The First International Conference in Systems and Network Medicine gathered together 200 global thought leaders, scientists, clinicians, academicians, industry and government experts, medical and graduate students, postdoctoral scholars and policymakers. Held at Georgetown University Conference Center in Washington D.C. on September 11-13, 2019, the event featured a day of pre-conference lectures and hands-on bioinformatic computational workshops followed by two days of deep and diverse scientific talks, panel discussions with eminent thought leaders, and scientific poster presentations. Topics ranged from: Systems and Network Medicine in Clinical Practice;the role of -omics technologies in Health Care;the role of Education and Ethics in Clinical Practice, Systems Thinking, and Rare Diseases;and the role of Artificial Intelligence in Medicine. The conference served as a unique nexus for interdisciplinary discovery and dialogue and fostered formation of new insights and possibilities for health care systems advances. © Emma Kurnat-Thoma et al. 2020 Published by Mary Ann Liebert, Inc. 2020.

关键词： Artificial intelligence

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User behavior and application modeling in decentralized edge cloud infrastructures 14th

User behavior and application modeling in decentralized edge...

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14th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2017

作者： Violos, John de Lira, Vinicius Monteiro Dazzi, Patrizio Altmann, Jörn Al-Athwari, Baseem Schwichtenberg, Antonia Jung, Young-Woo Varvarigou, Theodora Tserpes, Konstantinos School of Electrical and Computer Engineering National Technical University of Athens Zografou15771 Greece Pisa Italy Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul Korea Republic of CAS Software AG Karlsruhe Germany Daejeon Korea Republic of Department of Informatics and Telematcs Harokopio University of Athens Tavros Greece

ISBN: (纸本)9783319680651

Edge computing has emerged as a solution that can accommodate complex application requirements by shifting data and computation to infrastructure elements that are more suitable to manage them given the current circumstances. The BASMATI Knowledge Extractor is a component that facilitates the modeling of the resource utilization by providing tools to analyze application usage together with user behavior. This is particularly relevant in the case of mobile applications where user context and activity are tightly coupled to the application performance. © 2017, Springer International Publishing AG.

关键词： Behavioral research

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