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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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Stress measures in SOM learning

Stress measures in SOM learning

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作者： Krbcova, Zuzana Kukal, Jaromir University of Chemistry and Technology in Prague Faculty of Chemical Engineering Technicka 5 Prague 6 166 26 Czech Republic Department of Computing and Control Engineering Technicka 5 Prague 6 166 26 Czech Republic

Various stress measures can be used in generalized version of Sammon’s mapping. Kohonen SOM with iterative or batch learning is a standard tool for data self-organization, too. Our method applies stress functions to pattern relationships in SOM and converts batch learning to discrete optimization task. Due to NP–completeness of SOM learning, optimization heuristics have to be used. Simulated annealing making use of Lévy flights is the recommended heuristics for this task. © 2018, Brno University of Technology. All rights reserved.

关键词： Stresses

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An Algorithm of Object Detection Based on Regression Learning for Remote Sensing Images

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Journal of Physics: Conference Series 2021年第1期1903卷

作者： Jiajia Wang Ran Wu Xiaqiong Yu Beijing Jinghang Research Institute of Computing and Communication Beijing 100076 China Applied Mathematics Research Center of CASIC Beijing 100076 China Beijing Air Traffic Control Engineering Technology Co. Ltd Beijing 100621 China 32021 Troops Beijing 100094 China

Saliency detection method is widely used in the field of object detection. It can greatly improve the efficiency of detection by extracting the region that may contain the objects and reduce the processing of background area. It is very difficult to detect the airports in remote sensing image for its changeable structure and complex background. Considering this situation, this paper proposes a object detection method of saliency model based on regression learning, this method extracts salient regions that may contain airports, and then detects salient regions through feature bag model. Our method uses super-pixel segmentation, minimization of absolute shrinkage and selection operator and hierarchical learning theory based on back-propagation. The salient regions that contain potential objects are extracted and sent to the feature bag model for classification. and it can location the final objects. This paper constructs the airport training dataset and test dataset, and the experimental results fully prove the effectiveness and superiority of this method.

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RBML: A Refined Behavior Modeling Language for Safety-Critical Hybrid Systems

RBML: A Refined Behavior Modeling Language for Safety-Critic...

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Asia-Pacific Conference on Software engineering

作者： Zhangtao Chen Jing Liu Xi Ding Miaomiao Zhang Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China AECC Aero-Engine Control System Institute Wuxi China School of Software Engineering Tongji University Shanghai China

As a widely used modeling language, AADL (Architecture Analysis and Design Language) plays an important role in designing safety-critical systems. It provides abundant components for describing system architecture and supports the early prediction and repetitive analysis of performance-critical attributes. However, the approach used by AADL to describe the system behavior is based mainly on automata theory; thus, encountering the state space explosion problem when modeling and verifying large and complex systems is inevitable. Furthermore, due to the lack of means to describe the behavior details, it is also difficult for AADL to support the accurate analysis and verification of functional and non-functional requirements. In this paper, we propose a language called RBML that supports refined behavior modeling to compensate for the behavior modeling and verification deficiencies of AADL. This new language is based on AADL but extends the ability to detail various behaviors and allows SMT (Satisfiability Modulo Theories) solvers to verify the constructed refined behavior model, thus alleviating the state space explosion problem to some extent. Experiments on Baidu Apollo are presented to demonstrate the feasibility of our proposed approach.

关键词： Analytical models Syntactics Mathematical model Tools Semantics Computational modeling Computer architecture

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Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks

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IFAC-PapersOnLine 2020年第5期53卷 512-517页

作者： Tariku Sinshaw Tamir Gang Xiong Zhishuai Li Hao Tao Zhen Shen Bin Hu Heruye Mulugeta Menkir State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China China Ship Development and Design Center Wuhan 430064 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Debremarkos University Institute of Technology Debremarkos 269 Ethiopia

Traffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. Therefore, an accurate congestion prediction algorithm to limit these misfortunes is fundamental. This paper presents a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks. Five days of traffic information (1,231,200 samples) are utilized to drive the prediction model. The TensorFlow and the Clementine machine learning platforms are used for data preprocessing, training, and testing of the model. The confusion matrix clears that decision tree has better prediction performance and leads the other two methods with accuracy (97%), macro-average precision (95%), macro-average recall (96%), and macro-average F1_score (96%) in the python programming environment. Moreover, performance of the three prediction models is verified in Clementine environment and decision tree outperforms all other models with an accuracy of 97.65%.

关键词： Confusion matrix Data preprocessing Python TensorFlow Clementine environment ITS

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Application of three-dimensional vision perception technology to industrial robots

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Journal of Physics: Conference Series 2021年第1期2082卷

作者： Rui Yang Qinglong Mo Yuhong Li Lin Gan Ruihan Hu Guangdong Institute of Intelligent Manufacturing Guangdong Key Laboratory of Modern Control Technology Guangzhou 510070 China Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 china Guangdong Key Laboratory of Modern Control Technology Guangdong Institute of Intelligent Manufacturing Guangzhou China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery Shihezi University Shihezi China

The three-dimensional vision system can improve the active perception ability of the robot, and then guide its flexible operation. This system has been widely used in industrial production processes, such as disorderly sorting, assembly, flexible welding, and defect detection. In sorting, assembly and other applications, accurate perception in a complex and changeable industrial environment is essential. Moreover, the control and other operations should be completed under the guidance of feedback information based on the collected three-dimensional perception results. Nonetheless, improvements are still required, such as accurate three-dimensional detection and positioning of work-in-progress and autonomous guidance in a complicated industrial context with continuous changes.

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Application of 3D vision intelligent calibration and imaging technology for industrial robots

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Journal of Physics: Conference Series 2021年第1期2082卷

作者： Rui Yang Qinglong Mo Yuhong Li Yizhi Liu Ruihan Hu Guangdong Institute of Intelligent Manufacturing Guangdong Key Laboratory of Modern Control Technology Guangzhou 510070 China Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 china Guangdong Key Laboratory of Modern Control Technology Guangdong Institute of Intelligent Manufacturing Guangzhou China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery Shihezi University Shihezi China

During modern flexible lean manufacturing, flexible operation of irregular and complex workpieces with different specifications and arbitrary placement is an essential ability of industrial robots, while it cannot be met by traditional clamping methods. Vision technology brings flexibility and convenience to industrial robots, but the common two-dimensional technology only involves three degrees of freedom (plane displacement and rotation), which hinders the positioning of arbitrarily placing workpieces (often six degrees of freedom) and disorderly sorting. In addition, for typical visual tasks in industrial environments like defect detection, accurate distinguishing of such defects as pits and scratches is challenging under two-dimensional plane imaging. The introduction of three-dimensional information provides an effective solution to this problem. Thus, in the face of increasingly complex, flexible, intelligent and personalized manufacturing needs, the acquisition and processing of 3D visual information are of much importance.

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Graphene Quantum Dots Enhanced with Gold Nanoparticles for Advanced Antibacterial Applications

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Journal of Drug Delivery Science and Technology 2025年

作者： Mila Milenković Svetlana Jovanović Zoran Marković Gabriele Ciasca Riccardo Di Santo James L. Mead Marija Mojsin Biljana Dojčinović Dušan Milivojević Biljana Todorović Marković Vinča Institute of Nuclear Sciences – National Institute of the Republic of Serbia University of Belgrade P.O. Box 522 11000 Belgrade Serbia Dipartimento di Neuroscienze Sezione di Fisica Università Cattolica del Sacro Cuore & Fondazione Policlinico Gemell Largo Francesco Vito 1 00168 Roma Italy Department of Life Science Health and Health Professions Via del Casale di S. Pio V 44 00165 Roma Italy Division Microrobotics and Control Engineering Department of Computing Science University of Oldenburg D-26129 Oldenburg Germany Institute of Molecular Genetics and Genetic Engineering University of Belgrade Vojvode Stepe 444a 11042 Belgrade 152 Serbia Institute of Chemistry Technology and Metallurgy – National Institute of the Republic of Serbia University of Belgrade P.O. Box 815 11000 Belgrade Serbia

In the era of antibiotic resistance, exploring novel nanomaterials offers a promising avenue for combating infections. This study investigates the antibacterial effects of graphene quantum dots (GQDs) combined with gold nanoparticles, synthesized in situ via gamma irradiation of GQDs with chloroauric acid and isopropyl alcohol at doses of 1, 5, 10, and 20 kGy. The composites were analyzed using several characterization methods, including photoluminescence and infrared spectroscopy, dynamic light scattering, zeta potential measurements, and scanning electron microscopy with energy-dispersive X-ray spectroscopy. Inductively coupled plasma optical emission spectrometry analysis confirmed the presence of gold in all samples, with the highest concentration of 11.14 μg/mL. Hemolytic assays confirmed excellent biocompatibility with hemolysis below 2%, while MTT assays revealed that cell viability exceeded 80% for most samples, confirming their non-toxic nature. The findings showed that these materials have antibacterial activity against the MRSA strain. These results demonstrate that gold nanoparticles significantly contribute to the antibacterial properties of GQDs, indicating their potential for biomedical applications.

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A NOVEL TENSOR-BASED FEATURE EXTRACTION METHOD FOR POLSAR IMAGE CLASSIFICATION

A NOVEL TENSOR-BASED FEATURE EXTRACTION METHOD FOR POLSAR IM...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Xiayuan Huang Xiangli Nie Hong Qiao Bo Zhang State Key Lab of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China State Key Lab of Scientific and Engineering Computing and Institute of Applied Mathematics AMSS Chinese Academy of Sciences Beijing China

Spatial information helps improve the performance of polarimetric synthetic aperture radar (PolSAR) image classification. Some existing methods have combined the spatial information and polarimetric features by the third-order tensor representation for feature extraction. They describe a pixel with the patch centered on this pixel. But they neglect the spatial heterogeneity, which may influence the classification performance. Therefore, we firstly seek k nearest samples based on the polarimetric feature similarity for each pixel to construct the second-order tensor, whose first order denotes the nearest samples and the second order denotes the polarimetric features. Moreover, k nearest samples are searched in a spatial local region rather than the full image, which can exploit the spatial information and reduce the computational burden. Then we employ tensor principal component analysis (TPCA) to extract low-dimensional features. Experimental results demonstrate that the proposed method can improve the classification performance compared with other methods.

关键词： Feature extraction Principal component analysis Visualization Euclidean distance Eigenvalues and eigenfunctions Roads

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Estimation of Grounding Resistance of Transmission Line Towers based on Artificial Neural Networks: A Practical Analysis based on a developed Android App

Estimation of Grounding Resistance of Transmission Line Towe...

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International Symposium on Lightning Protection (XII SIPDA)

作者： Raphael Batista João R. Souza Graduate Program in Automation and Control Engineering IFMG R. Itaguaçu 595 Betim MG Brazil Dept. of Electroeletronics and Computing CEFET-MG Alameda das Perdizes 61 Contagem MG Brazil

This work presents a practical evaluation of an Android app developed for grounding resistance estimation of typical transmission line (TL) towers arrangement. Such arrangements, composed by counterpoise cables, are evaluated for a two-layered soil model by a trained artificial neural network (ANN), which derives in a significative low computational cost for the solution problem. The developed algorithm is evaluated with measurements made in TL towers and compared to the solution provided by a rigorous numerical routine. The results indicate a good accuracy, with median and standard deviation lower than 7%, of the developed app for conditions where the input parameters have values within the range used to training ANN. Values outside the specified range can reach significative errors up to 5 and 17 times, respectively, for median and standard deviation.

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