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Getting the most out of maths: How to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK

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Journal of Theoretical Biology 2023年 557卷 111332页

作者： Dangerfield, Ciara E. David Abrahams, I. Budd, Chris Butchers, Matt Cates, Michael E. Champneys, Alan R. Currie, Christine S.M. Enright, Jessica Gog, Julia R. Goriely, Alain Déirdre Hollingsworth, T. Hoyle, Rebecca B. INI Professional Services Isham, Valerie Jordan, Joanna Kaouri, Maha H. Kavoussanakis, Kostas Leeks, Jane Maini, Philip K. Marr, Christie Merritt, Clare Mollison, Denis Ray, Surajit Thompson, Robin N. Wakefield, Alexandra Wasley, Dawn Isaac Newton Institute to Mathematical Sciences University of Cambridge United Kingdom Joint UNIversities Pandemic and Epidemiological Research (JUNIPER) Consortium United Kingdom Department for Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Mathematics University of Bath United Kingdom Department of Engineering Mathematics University of Bristol United Kingdom School of Mathematical Sciences University of Southampton United Kingdom School of Computing Science University of Glasgow United Kingdom Mathematical Institute University of Oxford United Kingdom Big Data Institute Li Ka Shing Centre for Health Information and Discovery University of Oxford United Kingdom Department of Statistical Science University College London United Kingdom Independent United Kingdom EPCC The University of Edinburgh United Kingdom Department of Actuarial Mathematics and Statistics Heriot-Watt University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Mathematics Institute University of Warwick United Kingdom Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research University of Warwick United Kingdom The Royal Society London United Kingdom International Centre for Mathematical Sciences University of Edinburgh & Heriot-Watt University United Kingdom

In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”. © 2022 The Author(s)

关键词： COVID-19 Knowledge exchange Mathematical modelling Research co-ordination

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Coordination Evaluation Research of Railway Collection and Distribution System in Port Areas Based on Cloud Model

Coordination Evaluation Research of Railway Collection and D...

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作者： Kai Zeng Haihong Xie Liuyi Wu Yongbin Xu Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Institute of Computing Technology China Academy of Railway Sciences

The railway is of great significance for port cargo to achieve collection and distribution. It is necessary to evaluate the coordination of the railway collection and distribution system scientifically in port areas. Based on the research that analyses factors that affecting the coordination of railway collection and distribution system in port areas and constructs a comprehensive evaluation indicator system, this paper innovates a comprehensive evaluation model suitable for studies of the coordination of railway collection and distribution system in port areas by combining the cloud model algorithm with the traditional expert scoring method. Then, this paper will verify the applicability and operability of the model we proposed by representing a case study of the coordination of the railway collection and distribution system in Caofeidian Port Area.

关键词： Railway in port areas collection and distribution system coordination evaluation cloud model

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Context aware 3d unet for brain tumor segmentation

arXiv

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arXiv 2020年

作者： Ahmad, Parvez Qamar, Saqib Shen, Linlin Saeed, Adnan National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China School of Hydropower and Information Technology Huazhong University of Science and Technology Wuhan430074 China

Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contexual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose a modified UNet architecture for brain tumor segmentation. In the proposed architecture, we used densely connected blocks in both encoder and decoder paths to extract multi-contexual information from the concept of feature reusability. The proposed residual inception blocks (RIB) are used to extract local and global information by merging features of different kernel sizes. We validate the proposed architecture on the multimodal brain tumor segmentation challenges (BRATS) 2020 testing dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancement tumor (ET) are 89.12%, 84.74%, and 79.12%, respectively. Our proposed work is in the top ten methods based on the dice scores of the testing dataset. © 2020, CC-BY.

关键词： Tumors

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Cross-modal data fusion and deep learning techniques enabled non-destructive fish vitality recognition during cryogenic waterless live transport

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Biosystems Engineering 2025年 257卷

作者： Yanfei Zhu Wenguan Zhang Yongjun Zhang Xiaoshuan Zhang Mengjie Zhang College of Engineering China Agricultural University Beijing 100083 China School of Information Engineering Shandong Youth University of Political Science Jinan 250103 China New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong Jinan 250103 China Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong Jinan 250103 China Sanya Research Institute of China Agricultural University Sanya 572000 China College of Animal Science and Technology China Agricultural University Beijing 100193 China

Cryogenic waterless live transport (CWLT) is widely regarded as a green, efficient and low-carbon transportation strategy, which can ensure a certain degree of fish survival rates, enhance water resource utilisation and reduce transportation costs. This study focuses on the pearl gentian grouper, integrating multi-modal sensors, deep learning and cross-modal data fusion techniques for non-destructive vitality recognition during the live transport process. Changes in fish surface and physiological characteristics were captured based on multi-modal sensors. A cross-modal vitality recognition network (CmVRNet) was developed based on cross-modal data fusion and multi-input deep learning techniques. The enhanced whale optimisation algorithm based on multi-strategy fusion (EWOAm) was developed to optimise hyperparameters of the model (EWOAm-CmVRNet). The results showed that: (1) Cluster analysis classified physiological status of the grouper into four categories: high vitality (HV), medium vitality (MV), weak vitality (WV) and near death (ND), with significant differences between physiological data of fish with different vitality levels but the same size (P < 0.05); (2) EWOAm demonstrated significant performance, successfully addressing shortcomings of the standard WOA and outperforming other comparison algorithms, achieving a balance between global exploration and local search; (3) EWOAm-CmVRNet showed excellent vitality recognition performance. For large groupers, the model achieved the average precision of 93.74 %, average recall of 93.89 %, average F1 score of 93.81 %, average specificity of 97.88 % and average accuracy of 93.69 %; (4) The EWOAm-CmVRNet model, which is constructed based on cross-modal data, outperformed other single-modal vitality recognition models. This study aims to provide technical support to enhance market competitiveness of live aquatic products.

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Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc Networks

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Computers, Materials & Continua 2019年第9期61卷 1117-1128页

作者： Xuting Duan Jingyi Wei Daxin Tian Jianshan Zhou Haiying Xia Xin Li Kunxian Zheng Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems&SafetyControl Beijing100191China School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Engineering and Design University of SussexBrightonBN19RHUK Research Institute of Highway Ministry Transport Beijing100088China Department of Computer Science and Technology Tsinghua UniversityHaidian DistrictBeijing100088China

In vehicle ad-hoc networks(VANETs),the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes *** by the self-adaptive paradigm of cellular attractors,this paper regards an individual communication as a cell,so that we can apply the revised attractor selection model to induce each connected *** at improving the Quality of Service(QoS),we presented the bio-inspired handover decision-making *** addition,we employ the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)for any vehicle to choose an access *** paper proposes a novel framework where the bio-inspired mechanism is combined with *** a dynamic and random mobility environment,our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global *** experimental results confirm that the proposed method performs better when compared with conventional schemes.

关键词： Revised attraction selection model TOPSIS VANETs QoS

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Multiclass Single Label Model for Web Page Classification

Multiclass Single Label Model for Web Page Classification

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2019 International Conference on Recent Advances in Energy-efficient computing and Communication, ICRAECC 2019

作者： Kag, Aakash Jenila Livingston, L.M. Livingston Merlin, L.M. Agnel Livingston, L.G.X. Mirabel Technologies Big Data Analyst Hyderabad HyderabadTG500032 India VIT Chennai School of Computing Science and Engineering 600127 India Jeppiaar Institute of Technology ECE Dept SriperumbudurTamil Nadu631604 India St. Xaviers Catholic college of Engineering CSE Dept. Nagercoil629003 India

ISBN: (纸本)9781538694824

Web is a huge repository of information and there is a need of categorization of web pages to facilitate better search and retrieval of pages. Web page classification has become a challenging task due to the exponential growth of the World Wide Web and this study augments classification model with a general facility for automatically assigning class label (e.g., sport, news) to web pages based on the output of a Naive Bayes classifier. For the purpose of build classification model, yahoo Open Directory Project (ODP) data set has been used for create training and testing set. In this research work web page classification was done using Uniform Resource Locator (URL) features, Meta data, Meta keywords, Internal Links and text, which gives better result than URLs based method. © 2019 IEEE.

关键词： Websites

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Energy Saving Strategy of Power System Cluster Based on Container Virtualization

Energy Saving Strategy of Power System Cluster Based on Cont...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Ran Zheng Hao Wang Hai Jin Dechao Xu Yong Chen Xiaomeng Li Yufei Rao Zhenan Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research Institute Haidian District Beijing China State Grid Henan Electric Power Company Electric Power Research Institute Zhengzhou China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

With the continuous development of power grids, the scale of supercomputing clusters has also gradually increased to carry a large number of power system simulation calculations, and the problem of high energy consumption has appeared. To solve this problem, we propose a container virtualization-based supercomputing cluster for power system. We analyze the impact of containers on power simulation calculations and compare the energy consumption effects of various container scheduling and migration algorithms on clusters. Experiments show that compared to virtual machines with hypervisor, which consumes massive resources and reduces performances by 28.4%, the performance degradation of container on power simulation calculation is 1.3%, which can be ignored. The energy consumption of load-concentration or resource-and-load-balance container scheduling algorithms is up to 4.0% lower and at least 2.2% lower than other algorithms. In container migration, the method combining autoregressive model with most-correlation and resource-andload-balance algorithms is better than other methods, which not only minimizes energy consumption, but also has lowest number of migrations and SLA violations. Experiments verify the feasibility and advantages of container migration in power system computing clusters.

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Binary neural networks: A survey

arXiv

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arXiv 2020年

作者： Qin, Haotong Gong, Ruihao Liu, Xianglong Bai, Xiao Song, Jingkuan Sebe, Nicu State Key Lab of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China

The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. To address these issues, a variety of algorithms have been proposed, and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error. We also investigate other practical aspects of binary neural networks such as the hardware-friendly design and the training tricks. Then, we give the evaluation and discussions on different tasks, including image classification, object detection and semantic segmentation. Finally, the challenges that may be faced in future research are prospected. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Asymptotic properties of zeros for discrete-time linear systems with backward triangle sampled-and-hold using delta operator

Asymptotic properties of zeros for discrete-time linear syst...

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Chinese Automation Congress (CAC)

作者： Minghui Ou Shan Liang Xiaobin Wu Zhiyong Yang Hehuan Ran School of Big Data and Internet of Things of Chongqing Vocational Institute of Engineering College of Automation of Chongqing University Chongqing China College of Automation of Chongqing University The Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing China State Grid Chongqing Shiqu Electric Power Supply Branch Chongqing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This article focuses on a well-studied theoretical problem that, for rapid sampling, a continuous-time system with minimum-phase properties may become a non-minimum phase discrete-time system in the discretization process. The properties of zeros for discrete-time linear models in the field of digital control when using the special shift operator was considered in this paper. From the former relevant studies one can know that the backward triangle sample-and-hold (BTSH) as an alternative hold method to discretize the continuous-time systems has an advantage over ZOH in stabilizing sampling zeros for linear system with classical forward shift operator. In this article, we deduce the exact sampled-data model of linear system using delta operator to replace forward shift operator in the case of BTSH. Furthermore, the expressions and properties of zeros for the corresponding discrete-time system are derived.

关键词： Control systems Linear systems Discrete-time systems Process control Numerical models Signal reconstruction Internet of Things

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eROSITA clusters dynamical state and their impact on the BCG luminosity

arXiv

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arXiv 2025年

作者： Zenteno, A. Kluge, M. Kharkrang, R. Hernandez-Lang, D. Damke, G. Saro, A. Monteiro-Oliveira, R. Carrasco, E.R. Salvato, M. Comparat, J. Fabricius, M. Snigula, J. Arevalo, P. Cuevas, H. Castellon, J.L. Nilo Ramirez, A. Astudillo, S. Véliz Landriau, M. Myers, A.D. Schlafly, E. Valdes, F. Weaver, B. Mohr, J.J. Grandis, S. Klein, M. Liu, A. Bulbul, E. Zhang, X. Sanders, J.S. Bahar, Y.E. Ghirardini, V. Ramos, M. Balzer, F. Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile Max Planck Institute for Extraterrestrial Physics Giessenbachstrasse 1 Garching85748 Germany Universitè Paris-Saclay CNRS Institut dÁstrophysique Spatiale Orsay91405 France Faculty of Physics Ludwig-Maximilians-Universität München Scheinerstr. 1 Munich81679 Germany INAF - Osservatorio Astronomico di Trieste via G. B. Tiepolo 11 Trieste34143 Italy IFPU - Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 Trieste34131 Italy INFN - National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy Institute of Astronomy and Astrophysics Academia Sinica Taipei10617 Taiwan International Gemini Observatory NSF NOIRLab Casilla 603 La Serena Chile Instituto de Física y Astronomía Universidad de Valparaíso Gran Bretaña 1111 Valparaíso Chile Chile Departamento de Astronomía Universidad de La Serena Av. Raúl Bitrán 1305 La Serena Chile Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Physics & Astronomy University of Wyoming 1000 E. University Dept. 3905 LaramieWY82071 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States 950 N. Cherry Ave TucsonAZ85732 United States Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 Innsbruck6020 Austria

Context. The SRG (Spectrum Roentgen Gamma)/eROSITA first public release contains 12,247 clusters and groups from the first 6 months of operation. We use the offset between the Brightest Cluster Galaxy (BCG) and the X–ray peak (DBCG−X) to classify the cluster dynamical state of 3,946 galaxy clusters and groups. Aims. We aim to investigate the evolution of the merger and relaxed cluster distributions with redshift and mass, and their impact on the BCG. Methods. We use the X–ray peak from the eROSITA survey and the BCG position from the DECaLS DR10 optical data, which includes the DECam eROSITA Survey optical data, to measure the DBCG−X offset. We model the distribution of DBCG−X, in units of r500, as the sum of two Rayleigh distributions representing the cluster’s relaxed and disturbed populations, and explore their evolution with redshift and mass. To explore the impact of the cluster’s dynamical state on the BCG luminosity, we separate the main sample according to the dynamical state. We define clusters as relaxed if DBCG−X500, disturbed if DBCG−X>0.5r500, and as ‘diverse’ if 0.25BCG−X Copyright © 2025, The Authors. All rights reserved.

关键词： Photons

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