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Proceedings of the Winter Simulation Conference

作者： Parisa Niloofar Hossein Haghbin Sanja Lazarova-Molnar Mærsk Mc-Kinney Møller Institute University of Southern Denmark Odense DENMARK Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr IRAN Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe GERMANY

Simulation is an advantageous solution to calculate reliability of complex systems when analytical methods are not able to deliver results in time or when we do not have the resources to apply analytical methods. Proxel-based simulation has shown to be very efficient in reliability analysis of complex systems. In this paper, we present a package called ftaproxim for proxel-based simulation of complex systems using fault trees, developed in the R programming language. ftaproxim can calculate and plot instantaneous unavailabilities of repairable events and the system as a whole.

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Multi-Agent Economic Modeling Automation Technologies 9

Multi-Agent Economic Modeling Automation Technologies

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Zhukova, Aleksandra A. Pilnik, Nikolai P. Kamenev, Ivan G. Iusup-Akhunov, Batyrzhan B. Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Mathematical Modelling in Economic Social and Ecological Systems Vavilov st. 44 building 2 Moscow119333 Russia Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Mathematical Modelling of Economic Systems Vavilov st. 44 building 2 Moscow119333 Russia Hse University Faculty of Economic Sciences Department of Applied Economics Pokrovsky Bulvar 11 Moscow109028 Russia Fsbeihe Bauman Moscow State Technical University Department of Fundamental Sciences-11 2nd Baumanskaya street 5-1 Moscow05005 Russia The Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology 9 Institutskiy per. Moscow Region Dolgoprudny141701 Russia

ISBN: (纸本)9798350311402

Complex economic models often describe the agent's decisions, i.e. choice of some controlled variables in accordance with criteria set by agents. Unlike purely statistical ones, agent-based models allow to describe processes with different modes, in which the agent's behavior changes quali-tatively when the environment (including the actions of other agents) change. The practical use of mathematical models of complex systems with a number of interacting agents is limited by complexity of their construction and possible errors when dealing with multitude of expressions. Computer tools, such as the 'Ecomod' system discussed in the article, can substantially reduce the time spent on model creation and processing, and help to coordinate the work of several researchers on the model. © 2023 IEEE.

关键词： Autonomous agents

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A Novel Framework Predicting Anxiety in Chronic Disease Using Boosting Algorithm and Feature Selection Techniques 1

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Future of Information and Communication Conference, FICC 2024

作者： Qarmiche, N. Otmani, N. Tachfouti, N. Amara, B. Akasbi, N. Berrady, R. El Fakir, S. Laboratory of Artificial Intelligence Data Science and Emerging Systems National School of Applied Sciences Fez Sidi Mohamed Ben Abdellah University Fez Morocco Biostatistics and Informatics Unit Department of Epidemiology Clinical Research and Community Health Faculty of Medicine and Pharmacy of Fez Sidi Mohamed Ben Abdellah University Fez Morocco Department of Epidemiology Clinical Research and Community Health Faculty of Medicine and Pharmacy of Fez Sidi Mohamed Ben Abdellah University Fez Morocco Pneumology Department Hassan II University Hospital Fez Morocco Rheumatology Department Hassan II University Hospital Fez Morocco Department of Gastro Hepato-Enterology - CHU Hassan II Fez Sidi Mohammed Ben Abdellah University Fez Morocco

ISBN: (数字)9783031540530

ISBN: (纸本)9783031540523

Introduction: Chronic diseases represent a major public health challenge and contribute to a substantial number of deaths. Individuals living with chronic illnesses frequently experience anxiety due to the long-term impact on their overall well-being. The development of a predictive model to accurately identify anxiety in these patients is of great importance for healthcare providers. This study aimed to create a predictive model focused on anxiety among patients with chronic diseases within the Moroccan population. Methods and materials: In this study, a comparative analysis was performed using five different machine learning algorithms. The researchers utilized a cross-sectional dataset comprising 938 patients with chronic diseases. These patients were under monitoring at the Hassan II University Hospital Center in Fez, Morocco, from October 2019 to December 2020. Anxiety levels were evaluated using the validated Moroccan version of the Hospital Anxiety and Depression Scale (HADS). Results: To assess the models’ performance, several metrics were calculated, including accuracy, AUC, precision, recall, and F1-measure. The CatBoost algorithm achieved the highest accuracy of 0.7 in the evaluation. It also obtained an AUC of 0.68, a precision of 0.7, a recall of 0.55, and an F1-measure of 0.6. Based on its accuracy performance, the CatBoost algorithm is considered the optimal model in this study. Conclusion: This study created a CatBoost model to predict anxiety in patients with chronic diseases, offering valuable insights for anxiety prevention. Early psychological support and intervention are beneficial for high-risk patients, aiding in anxiety management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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Exploring digital interventions for business resilience during crisis – a systematic literature review

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Procedia Computer science 2025年 256卷 641-648页

作者： Michael D. Dzandu Sergio De Cesare Richard Evans Yinshan Tang School of Applied Management Westminster Business School University of Westminster 35 Marylebone Road London NW1 5LS UK Faculty of Computer Science at Dalhousie University 6050 University Avenue Halifax B3H 4R2 Canada Business Informatics Systems and Accounting Henley Business School University of Reading Reading RG6 6UD UK

Enterprises responded to the COVID-19 outbreak by deploying various digital technology interventions to achieve business resilience. However, little is known about the nature of enterprise digital technology resilience that emerged during the pandemic and how these can inform businesses to build resilience for future pandemics. The purpose of this study therefore is to explore digital technology interventions and enterprise resilience during the pandemic to inform the development of a pandemic resilience framework for future pandemics. A systematic literature review is being conducted on twenty-three relevant articles sourced from the Scopus database on digital technology intervention and resilience during the COVID-19 pandemic. The analysis of the relevant articles using VOSviewer visualisation revealed five key thematic areas namely information systems, digital health, digital intervention and social media; COVID-19 and wellbeing, and digital resilience. The nature, scope and type of digital technology interventions were mainly data resources, information systems and online platforms. Generally, businesses faced technical, motivational and governance challenges deploying digital technology intervention to emerge resilient from the pandemic. The study concluded that the need for future pandemic preparedness is now, and the lessons learned from businesses in using digital interventions during the recent pandemic are useful. The implications of the study for businesses, policymaking and theorizing in the area of digital intervention and resilience have been highlighted.

关键词： Enterprises business digital technologies interventions resilience pandemic

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Continual Learning of a Time Series Model Using a Mixture of HMMs with Application to the IoT Fuel Sensor Verification

Continual Learning of a Time Series Model Using a Mixture of...

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Federated Conference on Computer science and Information systems (FedCSIS)

作者： Przemysław Głomb Michał Cholewa Paweł Foszner Jakub Bularz Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland AIUT Sp. z o.o. ul Gliwice Poland

This paper presents an application of a mixture of Hidden Markov Models (HMMs) as a tool for verification of IoT fuel sensors. The IoT fuel sensors report the level of fuel in tanks of a petrol station, and are a key component for monitoring system reliability (billing), safety (fuel/oil leak detection) and security (theft prevention). We propose an algorithm for learning a mixture of HMMs based on a continual learning principle, i.e. it adapts the model while monitoring a sensor over time, signalling unexpected or anomalous sensor reports. We have tested the proposed approach on a real-life data of 15 fuel tanks being monitored with the FuelPrime system, where it has shown a very good performance (average area under ROC curve of 0.94) of detecting anomalies in the sensor data. Additionally we show that the proposed method can be used for trend monitoring and present qualitative analysis of the short and long term learning performance. The proposed method has promising performance score, the resulting model has a high degree of explainability, limited memory and computation requirements and can be easily generalized to other domains of sensor verification.

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Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis

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Energy and AI 2021年第3期5卷 53-66页

作者： Meisam Booshehri Lukas Emele Simon Flügel Hannah Förster Johannes Frey Ulrich Frey Martin Glauer Janna Hastings Christian Hofmann Carsten Hoyer-Klick Ludwig Hülk Anna Kleinau Kevin Knosala Leander Kotzur Patrick Kuckertz Till Mossakowski Christoph Muschner Fabian Neuhaus Michaja Pehl Martin Robinius Vera Sehn Mirjam Stappel Institute for Applied Ecology BerlinGermany Institute for Applied Informatics(InfAI)/Leipzig University Germany Department of Energy Systems Analysis Institute of Networked Energy SystemsGerman Aerospace Center(DLR)StuttgartGermany Faculty of Computer Science Otto-von-Guericke-Universität MagdeburgGermany Reiner Lemoine Institut BerlinGermany Institute of Techno-economic Systems Analysis(IEK-3) Forschungszentrum JülichJülichGermany Institute of Energy Economics and Rational Energy Use University of StuttgartGermany Potsdam Institute for Climate Impact Research PotsdamGermany Fraunhofer Institute for Energy Economics and Energy System Technology KasselGermany

Heterogeneous data,different definitions and incompatible models are a huge problem in many domains,with no exception for the field of energy systems ***,it is hard to re-use results,compare model results or couple models at *** provide a precisely defined vocabulary to build a common and shared conceptu-alisation of the energy ***,we present the Open Energy Ontology(OEO)developed for the domain of energy systems *** the OEO provides several benefits for the ***,it enables consistent annotation of large amounts of data from various research *** example is the Open Energy Platform(OEP).Adding such annotations makes data semantically searchable,exchangeable,re-usable and ***,computational model coupling becomes much *** advantages of using an ontology such as the OEO are demonstrated with three use cases:data representation,data annotation and interface *** also describe how the ontology can be used for linked open data(LOD).

关键词： Collaborative ontology development Linked open data Metadata annotation Energy systems analysis

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Digital Twins for Energy-Efficient Manufacturing

Digital Twins for Energy-Efficient Manufacturing

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Annual IEEE systems Conference

作者： Nader Mohamed Sanja Lazarova-Molnar Jameela Al-Jaroodi Department of Computer Science and Information Systems Pennsylvania Western University California PA USA Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany Faculty of Engineering University of Southern Denmark Odense Denmark Department of Engineering Robert Morris University Pittsburgh PA USA

Some manufacturing industries consume massive energy for their manufacturing and logistics processes. The costs of manufacturing can increase significantly as the energy cost increases. Such increase amplifies the final cost of the created products on the consumers. This makes finding different approaches that utilize innovative technologies to improve energy efficiency in manufacturing extremely important. This paper investigates how digital twins can be utilized to enable improved energy efficiency in manufacturing. Different applications of digital twins for energy-efficient manufacturing are investigated. In addition, the paper discusses some challenges of utilizing digital twins for this purpose. A framework for utilizing digital twins for energy-efficient manufacturing is also discussed.

关键词： Manufacturing industries Costs Manufacturing processes Buildings Energy efficiency Raw materials Manufacturing

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AI-Enhanced Tele-Rehabilitation: Predictive Modeling for Fall Risk and Treatment Efficacy in Balance Disorders 24

AI-Enhanced Tele-Rehabilitation: Predictive Modeling for Fal...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Karapintzou, Efterpi Tsakanikas, Vassilios Kikidis, Dimitrios Nikitas, Christos Nairn, Brooke Pavlou, Marousa Bamiou, Doris-Eva Exarchos, Themis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece Dept. of Otorhinolaryngology Head and Neck Surgery National and Kapodistrian University of Athens Hippocrateion General Hospital Athens Greece Ear Institute Faculty of Brain Sciences University College London London United Kingdom University College London Hospitals Dept. of Neuro-Otology London United Kingdom Centre for Human and Applied Physiological Sciences Faculty of Life Sciences and Medicine School of Basic and Medical Biosciences King's College London London United Kingdom Ionian University Dept. of Informatics Corfu Greece Biomedical Research Institute Foundation for Research and Technology Hellas FORTH Ioannina Greece

ISBN: (纸本)9798331518622

This study focuses on the development of artificial intelligence models to enhance telerehabilitation practices. We utilized diverse datasets to create clinically relevant models for predicting two critical outcomes: fall risk and treatment effectiveness. By applying various machine learning techniques, including K-Nearest Neighbors, Random Forest, Decision Tree, Support Vector Machine, and XGBoost, our models demonstrated high accuracy, sensitivity, and specificity. Notably, the Random Forest model achieved an accuracy of 0.97 in predicting fall risk and 0.96 in assessing treatment effectiveness. These models equip clinicians with powerful tools for data-driven decision-making, ultimately improving patient outcomes in rehabilitation settings. © 2024 IEEE.

关键词： Prediction models

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RAFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition

arXiv

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arXiv 2024年

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333031 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Skoda Auto University Na Karmeli 1457 Mlada Boleslav293 01 Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jadavpur University Kolkata32 India

Deep Convolutional Neural Networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing of region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pair aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channel-wise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed Region Attention network for Food items and Agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracies of RAFA-Net are 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net’s generalization capability. © 2024, CC BY.

关键词： Plant diseases

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Existence of solutions for a semilinear parabolic system with singular initial data

arXiv

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arXiv 2024年

作者： Fujishima, Yohei Ishige, Kazuhiro Kawakami, Tatsuki Department of Mathematical and Systems Engineering Faculty of Engineering Shizuoka University 3-5-1 Johoku Shizuoka Hamamatsu432-8561 Japan Graduate School of Mathematical Sciences The University of Tokyo Meguro-ku 3-8-1 Komaba Tokyo153-8914 Japan Applied Mathematics and Informatics Course Faculty of Advanced Science and Technology Ryukoku University 1-5 Yokotani Seta Oe-cho Shiga Otsu520-2194 Japan

Let (u,v) be a solution to the Cauchy problem for a semilinear parabolic system (Equation presented) where N ≥ 1, T > 0, D1 > 0, D2 > 0, 0 1, and (µ,ν) is a pair of nonnegative Radon measures or locally integrable nonnegative functions in RN. In this paper we establish sharp sufficient conditions on the initial data for the existence of solutions to problem (P) using uniformly local Morrey spaces and uniformly local weak Zygmund type spaces. Copyright © 2024, The Authors. All rights reserved.

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