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Fachtagung Modellierung, 2018

作者： Kuryazov, Dilshod Winter, Andreas Reussner, Ralf University of Oldenburg Software Engineering Group Uhlhornsweg 84 Oldenburg26111 Germany Karlsruhe Institute of Technology Institute for Program Structures and Data Organization Postfach 6980 KarlsruheD-76128 Germany

ISBN: (纸本)9783885796749

Model-Driven software Development is a key field in software development activities which is well-suited to design and develop large-scale software systems. Developing and maintaining large-scale model-driven software systems entail a need for collaborative modeling by a large number of software designers and developers. As long as software models are constantly changed during development and maintenance, collaborative modeling requires frequently sharing of model changes between collaborators. Thereby, a solid change representation support for model changes plays an essential role for collaborative modeling systems. This paper focuses on the problem of model change representation for collaborative modeling. It introduces a meta-model generic, operation-based and textual difference language to represent model changes in collaborative modeling. This paper also demonstrates a collaborative modeling application Kotelett. © 2018 Gesellschaft fur Informatik (GI).

关键词： software design

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Improving Quality of Life Based on Electronic Patient Reported Outcomes in Patients with CLL and MDS: The Mypal Study

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Blood 2024年 144卷 2358-2358页

作者： Thomas Chatzikonstantinou Maria Vasilopoulou Dimitrios Kyrou Georgios Karakatsoulis Panagiotis Argyropaidas Sotiria Besikli-Dimou Panos Bonotis Maria Chatzimina Jana Didi Christos Demosthenous Tina Garani-Papadatos Stefan Hoffman Christine Kakalou Eleni Kazantzaki Stephan Kiefer Lefteris Koumakis Eva Hellstrom Lindberg Julie Ling Christos Maramis Marcel Meyerheim Lydia Scarfo Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki Greece Institute of Computer Science Foundation for Research and Technology-Hellas Heraklion Greece Center for Molecular Medicine Central European Institute of Technology Masaryk University Brno Czech Republic Hematology Department - BMT Unit G Papanicolaou Hospital Thessaloniki Greece Department of Public Health Policy School of Public Health University of West Attica Athens Greece Promotion Software GmbH Berlin Germany Department of Hematology University of Crete School of Medicine Heraklion Greece Fraunhofer Institute For Biomedical Engineering (IBMT) Sulzbach Germany Center for Hematology and Regenerative Medicine (HERM) Department of Medicine Huddinge Karolinska Institutet Stockholm Sweden European Association for Palliative Care Vilvoorde Belgium Department of Pediatric Oncology and Hematology Faculty of Medicine Saarland University Homburg Germany Strategic Research Program on CLL IRCCS Ospedale San Raffaele Milano Italy Università Vita-Salute San Raffaele Milan Italy

Introduction: Palliative care is a critical component of care for patients with chronic hematological malignancies. Leveraging eHealth approaches can potentially facilitate the delivery of palliative care, leading to improved quality of life (QoL). Methods: The MyPal study (NCT04370457) is a randomized controlled clinical trial assessing an eHealth intervention on QoL of patients. The study was conducted according to the Declaration of Helsinki. The ethical aspects of the study, given the vulnerability of the patients, were addressed with diligence and approvals were obtained from the appropriate Research Ethics Committees. Patients who were receiving or had previously received treatment for CLL or MDS were randomly assigned (1:1) to access the MyPal platform versus standard of care. The MyPal platform included a smartphone application used to report QoL status (ePRO questionnaires) and symptoms. The primary endpoint was QoL at 12 months, assessed by the European Organization for Research and Treatment of Cancer (EORTC) quality of life QLQ-C30 General Questionnaire and the Euroqol 5-dimension (Euroqol EQ-5D-3L). Secondary endpoints included physical and emotional functioning, measured by the Integrated Palliative Care Outcome Scale (IPOS), satisfaction with care, measured by the EORTC Patient Satisfaction with Cancer Care questionnaire PATSAT-C33 (PATSAT-C33), and overall survival (OS). Additionally, the Edmonton Symptom Assessment System (ESAS), the Brief Pain Inventory (BPI), and the Emotion Thermometers (ET) QoL questionnaires were assessed only in the intervention group. Results: A total of 171 patients (97 and 74 in the control and intervention arms, respectively) who answered multiple questionnaires were included in the analysis. The intervention group reported a significant decrease in pain (β2=-0.48 (-0.77, -0.19), p<0.001) compared to the control group (β1=0.3 (0.09, 0.5), p=0.01) as measured by the EORTC QLQ-C30. Communication and pain as measured by IPOS

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Bitcoin’s future carbon footprint

arXiv

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arXiv 2020年

作者： Qin, Shize Klaaßen, Lena Gallersdörfer, Ulrich Stoll, Christian Zhang, Da Institute of Energy Environment and Economy Tsinghua University Beijing100084 China TUM School of Management Technical University of Munich Munich Germany TUM Software Engineering for Business Information Systems Department of Informatics Technical University of Munich Munich Germany MIT Center for Energy and Environmental Policy Research Massachusetts Institute of Technology CambridgeMA United States TUM Center for Energy Markets TUM School of Management Technical University of Munich Munich Germany MIT Joint Program on the Science and Policy of Global Change CambridgeMA02139 United States

The carbon footprint of Bitcoin has drawn wide attention, but Bitcoin’s long-term impact on the climate remains uncertain. Here we present a framework to overcome uncertainties in previous estimates and project Bitcoin’s electricity consumption and carbon footprint in the long term. If we assume Bitcoin’s market capitalization grows in line with the one of gold, we find that the annual electricity consumption of Bitcoin may increase from 50 to 400 TWh between 2020 and 2100. The future carbon footprint of Bitcoin strongly depends on the decarbonization pathway of the electricity sector. If the electricity sector achieves carbon neutrality by 2050, Bitcoin’s carbon footprint has peaked already. However, in the business-as-usual scenario, emissions sum up to 2 gigatons until 2100, an amount comparable to 6% of global emissions in 2018. Therefore, we also discuss policy instruments to reduce Bitcoin’s future carbon footprint. Copyright © 2020, The Authors. All rights reserved.

关键词： Carbon footprint

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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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A Collection of software engineering Challenges for Big Data System Development

A Collection of Software Engineering Challenges for Big Data...

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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Oliver Hummel Holger Eichelberger Andreas Giloj Dominik Werle Klaus Schmid Faculty of Computer Science Mannheim University of Applied Sciences Software Systems Engineering University of Hildesheim Institute for Program Structures and Data Organization Karlsruhe Institute of Technology

ISBN: (纸本)9781538673843

In recent years, the development of systems for processing and analyzing large amounts of data (so-called Big Data) has become an important sub-discipline of software engineering. However, to date there exits no comprehensive summary of the specific idiosyncrasies and challenges that the development of Big Data systems imposes on software engineers. With this paper, we aim to provide a first step towards filling this gap based on our collective experience from industry and academic projects as well as from consulting and initial literature reviews. The main contribution of our work is a concise summary of 26 challenges in engineering Big Data systems, collected and consolidated by means of a systematic identification process. The aim is to make practitioners more aware of common challenges and to offer researchers a solid baseline for identifying novel software engineering research directions.

关键词： Big Data software software engineering Computer architecture Data analysis Data privacy Systematics

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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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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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10 quick tips for making your software outlive your job

arXiv

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arXiv 2025年

作者： Littauer, Richard Wilson, Greg Ainali, Jan AlOmar, Eman Abdullah Arabas, Sylwester Saibene, Yanina Bellini Bubendorfer, Kris Champion, Kaylea Dillon, Clare Helske, Jouni Huybrechts, Pieter Katz, Daniel S. Liao, Chang Lippert, David Liu, Fang Marshall, Pierre McCloy, Daniel R. McInerney, Ian Mkaouer, Mohamed Wiem Ojha, Priyanka Treude, Christoph White, Ethan P. CURIOSS SustainOSS GNOME Foundation Te Herenga Waka Victoria University of Wellington Te Whanganui-a-Tara Aotearoa Wellington New Zealand Third Bit TorontoON Canada Open By Default Italy Department of Systems and Enterprises Stevens Institute of Technology HobokenNJ United States AGH University of Krakow Poland rOpenSci LP Santa Rosa Argentina Te Herenga Waka Victoria University of Wellington Wellington New Zealand Computing and Software Systems University of Washington Bothell BothellWA United States CURIOSS Lero University of Galway Ireland INVEST Research Flagship Centre University of Turku Finland Brussels Belgium University of Illinois Urbana-ChampaignIL United States Pacific Northwest National Laboratory RichlandWA United States Open Source Program Office George Washington University WashingtonDC United States Georgia Institute of Technology GA United States Oxford University United Kingdom Institute for Learning & Brain Sciences University of Washington SeattleWA United States Department of Mechanical Engineering Imperial College London London United Kingdom Department of Computer Science University of Michigan-Flint FlintMI United States Singapore Management University Singapore Department of Wildlife Ecology and Conservation University of Florida GainesvilleFL United States

Loss of key personnel has always been a risk for research software projects. Key members of the team may have to step away due to illness or burnout, to care for a family member, from a loss of financial support, or because their career is going in a new direction. Today, though, political and financial changes are putting large numbers of researchers out of work simultaneously, potentially leaving large amounts of research software abandoned. This article presents ten tips to help researchers ensure that the software they have built will continue to be usable after they have left their present job—whether in the course of voluntary career moves or researcher mobility, but particularly in cases of involuntary departure due to political or institutional changes. © 2025, CC BY-SA.

关键词： Human engineering

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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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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...

引用

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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