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27th Annual Conference of the International Group for Lean Construction, IGLC 2019

作者： Pikas, Ergo Koskela, Lauri Oehmen, Josef Dave, Bhargav Department of Technology Management and Economics Technical University of Denmark Denmark University of Huddersfield Department of Architecture and 3D Design United Kingdom Department of Computer Science Aalto University Finland

Building project delivery is beset with many long-standing problems. Often, these problems, resulting in failures of facilities and cost-time overruns, are directly related to poor design and design management practices. This motivated the definition of the main aim to develop an initial framing for the design process support systems, incorporating ideas from the human error and performance management domains, and on checklists. In this conceptual paper, a literature review method is used. It is suggested that cognitive systems engineering could be used to conceptualize the designers work and to incorporate checklists into the design process. Then, key aspects and elements for the development of design process support systems are addressed. © 27th Annual Conference of the International Group for Lean Construction, IGLC 2019. All rights reserved.

关键词： Design

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Maintenance engineering for urban utility tunnel using 3D simulation

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International Journal of Performability Engineering 2020年第7期16卷 1118-1129页

作者： Han, Rui Shao, Dan Lu, Xian Creative Centre for ArtSciArch Jilin Jianzhu University Changchun130118 China Department of Computer Science and Technology Changchun University Changchun130022 China Department of Architecture University of Ferrara Via Savonarola Ferrara44121 Italy

With the development and popularity of BIM (building information model), the application of 3D information models in the municipal engineering entire life cycle is gradually becoming the focus in the field of the operation and maintenance of engineering. This paper deeply analyzes the application prospect of the operation and maintenance system for an urban utility tunnel, while discussing the system's operation structure and major function. Through a practical engineering case, the research team uses the core technique to realize "separation" and "interlinkage" between component block and parameter information, and it shows the technique route from the BIM completion model to the visual tunnel operation and maintenance system completely. The research results provide a reliable basis for the construction and the application of intelligent public municipal construction management system, which is designed based on the interdisciplinary concept of art design and architecture science. © 2020 Totem Publisher, Inc. All rights reserved.

关键词： Maintenance

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Assessment of PM2.5-associated mortality burden among the elderly: Insights into demographic, socio-economic, and geographic factors

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Air Quality, Atmosphere & Health 2024年第7期17卷 1535-1545页

作者： Sharma, Ayushi Hsiao, Hsi-yu Liu, Jia-Yi Lung, Shih-Chun Candice Su, Huey-Jen Shen, Ching-Fen Chen, Nai-Tzu Wu, Pei-Chih Lin, Cheng-Yu Liang, Sheng-Fu Cheng, Tain-Junn Chan, Ta-Chien Tsay, Yaw-Shyan Chung, Hsin-Ying Wang, Yu-Chun Department of Environmental Engineering College of Engineering Chung Yuan Christian University Taoyuan Taiwan Department of Civil Engineering College of Engineering Chung Yuan Christian University Taoyuan Taiwan Research Center for Environmental Changes Academia Sinica Taipei City Taiwan Department of Environmental and Occupational Health National Cheng Kung University Tainan City Taiwan National Cheng Kung University Hospital Pediatrics Office Tainan City Taiwan Department of Green Energy and Environmental Resources Chang Jung Christian University Tainan City Taiwan Sleep Medicine Center College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering & Institute of Medical Informatics National Cheng Kung University Tainan City Taiwan Department of Neurology and Occupational Medicine Chi Mei Medical Center Tainan City Taiwan School of Medicine College of Medicine National Sun Yat-Sen University Kaohsiung City Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taipei City Taiwan Department of Architecture National Cheng Kung University Tainan City Taiwan Department of Plant Industry National Pingtung University of Science and Technology Tainan City Taiwan

Since long-term exposure to PM2.5 is a growing concern in Taiwan, therefore, our study aimed to investigate the association between different PM2.5 concentrations and cause-specific mortalities, as well as explore the influence of demographic, socio-economic, and geographic determinants on mortality. Utilizing a Distributed Lag Non-Linear Model (DLNM), we estimated the PM2.5-attributable mortality burden among the elderly population (above 65 years) in Taiwan from 2005 to 2018. Our analysis considered various thresholds defined by the World Health Organization (WHO) to assess the impact of PM2.5 concentrations, ranging from standard Air Quality Guidelines (AQG) levels (0–15 μg/m3) to higher interim targets (up to 80 μg/m3). Our study reveals that achieving AQG levels (< 15 μg/m3) in Taiwan could potentially prevent 5571 elderly deaths, including 889 deaths from CVDs, 934 deaths from RTIs, and 680 deaths from chronic diseases. We observed a rapid increase in attributable deaths at lower PM2.5 concentrations, with a subsequent marginal increase at higher levels of exposure. We also identified several factors that modify the effects of PM2.5 exposures among elderly. For example, we found that higher elderly population ratios and high-income households were associated with an elevated risk of PM2.5-related deaths. In contrast, higher social welfare expenditure, better availability of medical practitioners per square kilometer, and higher forest-to-land ratios were associated with positive effects, indicating their potential in reducing mortality risk. This study fills a knowledge gap by examining the health consequences of varying PM2.5 levels and explored the complex interplay between demographic, socio-economic, and geographic factors in relation to PM2.5-related mortality.

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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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Thermodynamic Analysis of a Jet Engine Using a Liquid Propellant

Research Square

引用

Research Square 2022年

作者： Alliche, Mounir Rebhi, Redha Inc, Mustafa Kidar, Ali Menni, Younes Ameur, Houari Lorenzini, Giulio Alsharif, Sameer Althobaiti, Ahmed Aly, Ayman A. Department of Mechanical Engineering Faculty of Technology University Yahia Fares of Medea Medea26000 Algeria Department of Computer Engineering Biruni University Istanbul Turkey Department of Mathematics Science Faculty Firat University Elazig23119 Turkey Department of Medical Research China Medical University Hospital China Medical University Taichung40402 Taiwan Faculty of Exact Sciences and Informatics Ziane Achour University of Djelfa Algeria Faculty of Technology Ziane Achour University of Djelfa Algeria Unit of Research on Materials and Renewable Energies Department of Physics Faculty of Sciences Abou Bekr Belkaid University P.O. Box 119 Tlemcen13000 Algeria Department of Technology University Centre of Naama - Salhi Ahmed P.O. Box 66 Naama 45000 Algeria Department of Engineering and Architecture University of Parma Parco Area Delle Scienze 181/A Parma43124 Italy Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Mechanical Engineering College of Engineering Taif University Taif21944 Saudi Arabia

Combustion-chamber is a critical component of the propulsion engine, which is widely used in the space industry and aeronautics. The goal of this article is to perform a numerical-analysis on the combustion process using a liquid-type propellant. The steps that must be followed until total combustion is achieved are emphasized. It concerns the fuel feeding phase, its injection and the combustion operation. The amount of combustion products and the energy generated are evaluated. It has been shown that the liquid propellant may present an efficient alternative fuel than the kerosene. In addition, the temperature of combustion does not exceed a certain limit to avoid structural problems in the chamber. The parametric survey allowed determining the range of the most influence factors, including the pressure, mixture richness, velocity and flow rates of injection for the fuel and oxidizer. The number and type of injectors revealed a considerable influence on the velocity and flow rates of injection. To maximize thrust force and system propulsion, a careful selection of chamber material and ignition methods is required. A thorough inspection on the issues of walls cooling showed the necessary survey of maximum temperatures that may be reached during the combustion. Finally, an investigation of the thermal exchange through the walls will be very interesting. © 2022, CC BY.

关键词： Surveys

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Collaborating with a robot biases human spatial attention

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iScience 2025年第7期28卷

作者： Giulia Scorza Azzarà Joshua Zonca Francesco Rea Joo-Hyun Song Alessandra Sciutti Robotics Brain and Cognitive Sciences Unit Italian Institute of Technology 16152 Genoa GE Italy Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genoa 16145 Genoa GE Italy Cognitive Architecture for Collaborative Technologies (CONTACT) Unit Italian Institute of Technology 16152 Genoa GE Italy Department of Cognitive & Psychological Sciences Brown University Providence RI 02912 USA

Advancements in collaborative technologies have increased the accessibility of human-robot interaction (HRI), yet a gap remains in understanding how HRI affects human systems. This work investigates how HRI influences human visual processing, specifically through the near-hand effect, a phenomenon where the proximity of one’s hand enhances spatial attention. Research has shown that this attentional priority can extend to another person’s hand during collaborative tasks, integrating it into an individual’s body schema. A critical question arises: can a robot’s anthropomorphic hand similarly bias human attentional priority? Our findings revealed that collaborative HRI facilitates target detection near the robot’s hand, an effect absent before the interaction. Additionally, we examined social and kinematic metrics that enhance this attentional shift by fostering joint body schema formation. These results highlight that HRI can shape human visual processing and body schema integration, offering insight into the interplay between our perceptual and cognitive systems and robotic collaborators.

关键词： Classification Description Robotics Social sciences

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Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors

arXiv

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arXiv 2023年

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Transportation Systems Engineering Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023, CC BY-NC-SA.

关键词： Travel time

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A preliminary approach to composition classification of ultra-high energy cosmic rays 3rd

A preliminary approach to composition classification of ultr...

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3rd International Conference on Applied Physics, System Science and computers, APSAC 2018

作者： Guillén, Alberto Todero, Carlos Martínez, José Carlos Herrera, Luis Javier Computer Architecture and Technology Department University of Granada Granada Spain University of São Paulo Escola de Engenharia de Lorena São Paulo Brazil

ISBN: (纸本)9783030215064

The study of cosmic rays remains as one of the most challenging research fields in Physics. There are several reasons why it is a hot topic as it can reveal information on Galaxy life-cycle and the mystery of how these rays arrive to the Earth with such high energy values (beyond 1020 eV). The work presented in this paper tries to answer a first question regarding to the type of primary particle that hits the atmosphere. Starting from a subset of known variables, we will try to perform a classification using Deep Learning models. We also compare two different approaches: classical classification and continuous output (like in regression). The results obtained are quite encouraging as we were able to obtain high classification accuracy even with a very reduced subset of variables. © Springer Nature Switzerland AG 2019.

关键词： Cosmic rays

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Conversation to Automation in Banking Through Chatbot Using Artificial Machine Intelligence Language

Conversation to Automation in Banking Through Chatbot Using ...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Sasha Fathima Suhel Vinod Kumar Shukla Sonali Vyas Ved Prakash Mishra Bachelor of Science Information Technology Amity University Dubai UAE Department of Engineering and Architecture Amity University Dubai UAE Department of Computer Application University of Petroleum and Energy Studies Dehradun India

ISBN: (数字)9781728170169

ISBN: (纸本)9781728170176

Artificial Machine Intelligence is a very complicated topic. It involves creating machines that are capable of simulating knowledge. This paper examines some of the latest AI patterns and activities and then provides alternative theory of change in some of the popular and widely accepted postulates of today. Based on basic A.I. (Artificial Intelligence) structuring and working for this, System-Chatbots are made (or chatter bots). The paper shows that A.I is ever improving. As of now there isn't enough information on A.I. however this paper provides a new concept which addresses machine intelligence and sheds light on the potential of intelligent systems. The rise of chatbots in the finance sector is the latest disruptive force that has changed the way customers interact. In the banking industry, the introduction of Artificial Intelligence has driven chatbots and changed the face of the interaction between bank and customers. The banking sector plays an important role in development into any country. It also explores the existing usability of chatbot to assess whether it can fulfill customers ever-changing needs.

关键词： Business Machine learning Online banking Natural language processing

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OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings

arXiv

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arXiv 2021年

作者： Huang, Yunyou Wang, Nana Tang, Suqin Ma, Li Hao, Tianshu Jiang, Zihan Zhang, Fan Kang, Guoxin Miao, Xiuxia Guan, Xianglong Zhang, Ruchang Zhang, Zhifei Zhan, Jianfeng Guangxi Key Lab of Multi-Source Information Mining & Security School of Computer Science and Engineering School of Software Guangxi Normal University Guilin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Guilin Medical University Guilin China Department of Physiology and Pathophysiology Capital Medical University Beijing China University of Chinese Academy of Sciences China International Open Benchmark Council

This paper quantitatively reveals the state-of-the-art and state-of-the-practice AI systems only achieve acceptable performance on the stringent conditions that all categories of subjects are known, which we call closed clinical settings, but fail to work in real-world clinical settings. Compared to the diagnosis task in the closed setting, real-world clinical settings pose severe challenges, and we must treat them differently. We build a clinical AI benchmark named Clinical AIBench to set up real-world clinical settings to facilitate researches. We propose an open, dynamic machine learning framework and develop an AI system named OpenClinicalAI to diagnose diseases in real-world clinical settings. The first versions of Clinical AIBench and OpenClinicalAI target Alzheimer’s disease. In the real-world clinical setting, OpenClinicalAI significantly outperforms the state-of-the-art AI system. In addition, OpenClinicalAI develops personalized diagnosis strategies to avoid unnecessary testing and seamlessly collaborates with clinicians. It is promising to be embedded in the current medical systems to improve medical services. © 2021, CC BY.

关键词： Diagnosis

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