scholarly journals Percolation of temporal hierarchical mobility networks during COVID-19

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
Haoyu He ◽  
Hengfang Deng ◽  
Qi Wang ◽  
Jianxi Gao
Keyword(s):  
Infectious Disease ◽  
Large Scale ◽  
Data Science ◽  
Multiple Scales ◽  
Traditional Approach ◽  
Connected Components ◽  
Metropolitan Statistical Area ◽  
Long Distance ◽  
Hierarchical Networks ◽  
Block Groups

Percolation theory is essential for understanding disease transmission patterns on the temporal mobility networks. However, the traditional approach of the percolation process can be inefficient when analysing a large-scale, dynamic network for an extended period. Not only is it time-consuming but it is also hard to identify the connected components. Recent studies demonstrate that spatial containers restrict mobility behaviour, described by a hierarchical topology of mobility networks. Here, we leverage crowd-sourced, large-scale human mobility data to construct temporal hierarchical networks composed of over 175 000 block groups in the USA. Each daily network contains mobility between block groups within a Metropolitan Statistical Area (MSA), and long-distance travels across the MSAs. We examine percolation on both levels and demonstrate the changes of network metrics and the connected components under the influence of COVID-19. The research reveals the presence of functional subunits even with high thresholds of mobility. Finally, we locate a set of recurrent critical links that divide components resulting in the separation of core MSAs. Our findings provide novel insights into understanding the dynamical community structure of mobility networks during disruptions and could contribute to more effective infectious disease control at multiple scales. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

scholarly journals Infectious Disease Research in the Era of Big Data

2020 ◽  
Vol 3 (1) ◽  
pp. 43-59
Author(s):  
Peter M. Kasson
Keyword(s):  
Infectious Disease ◽  
Large Scale ◽  
Data Science ◽  
Research Progress ◽  
Biomedical Data ◽  
Disease Research ◽  
Research Areas ◽  
Large Scale Data ◽  
Infectious Disease Research ◽  
Unique Nature

Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.

scholarly journals EAWNet: An Edge Attention-Wise Objector for Real-Time Visual Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhichao Zhang ◽  
Hui Chen ◽  
Xiaoqing Yin ◽  
Jinsheng Deng
Keyword(s):  
Internet Of Things ◽  
Object Detection ◽  
Real Time ◽  
High Performance ◽  
Large Scale ◽  
Multiple Scales ◽  
Hot Spot ◽  
Training Data ◽  
Long Distance ◽  
Size Limitation

With the upgrading of the high-performance image processing platform and visual internet of things sensors, VIOT is widely used in intelligent transportation, autopilot, military reconnaissance, public safety, and other fields. However, the outdoor visual internet of things system is very sensitive to the weather and unbalanced scale of latent object. The performance of supervised learning is often limited by the disturbance of abnormal data. It is difficult to collect all classes from limited historical instances. Therefore, in terms of the anomaly detection images, fast and accurate artificial intelligence-based object detection technology has become a research hot spot in the field of intelligent vision internet of things. To this end, we propose an efficient and accurate deep learning framework for real-time and dense object detection in VIOT named the Edge Attention-wise Convolutional Neural Network (EAWNet) with three main features. First, it can identify remote aerial and daily scenery objects fast and accurately in terms of an unbalanced category. Second, edge prior and rotated anchor are adopted to enhance the efficiency of detection in edge computing internet. Third, our EAWNet network uses an edge sensing object structure, makes full use of an attention mechanism to dynamically screen different kinds of objects, and performs target recognition on multiple scales. The edge recovery effect and target detection performance for long-distance aerial objects were significantly improved. We explore the efficiency of various architectures and fine tune the training process using various backbone and data enhancement strategies to increase the variety of the training data and overcome the size limitation of input images. Extensive experiments and comprehensive evaluation on COCO and large-scale DOTA datasets proved the effectiveness of this framework that achieved the most advanced performance in real-time VIOT object detection.

scholarly journals Long-Distance Travel Behaviours Accelerate and Aggravate the Large-Scale Spatial Spreading of Infectious Diseases

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Zhijing Xu ◽  
Zhenghu Zu ◽  
Tao Zheng ◽  
Wendou Zhang ◽  
Qing Xu ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Large Scale ◽  
Connected Components ◽  
Contact Network ◽  
Model Parameters ◽  
Long Distance ◽  
Wide Range ◽  
Distance Travel ◽  
Long Distance Travel ◽  
Spatial Spreading

The study analyses the role of long-distance travel behaviours on the large-scale spatial spreading of directly transmitted infectious diseases, focusing on two different travel types in terms of the travellers travelling to a specific group or not. For this purpose, we have formulated and analysed a metapopulation model in which the individuals in each subpopulation are organised into a scale-free contact network. The long-distance travellers between the subpopulations will temporarily change the network structure of the destination subpopulation through the “merging effects (MEs),” which indicates that the travellers will be regarded as either connected components or isolated nodes in the contact network. The results show that the presence of the MEs has constantly accelerated the transmission of the diseases and aggravated the outbreaks compared to the scenario in which the diversity of the long-distance travel types is arbitrarily discarded. Sensitivity analyses show that these results are relatively constant regarding a wide range variation of several model parameters. Our study has highlighted several important causes which could significantly affect the spatiotemporal disease dynamics neglected by the present studies.

scholarly journals Data science approaches to confronting the COVID-19 pandemic: a narrative review

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
Qingpeng Zhang ◽  
Jianxi Gao ◽  
Joseph T. Wu ◽  
Zhidong Cao ◽  
Daniel Dajun Zeng
Keyword(s):  
Infectious Disease ◽  
Disease Surveillance ◽  
Large Scale ◽  
Data Science ◽  
Drug Repurposing ◽  
Epidemiological Studies ◽  
Contact Tracing ◽  
Social Media Analytics ◽  
Computer Scientists ◽  
Disease Epidemics

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale ‘big data’ generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

Going the Distance

2020 ◽  
Author(s):  
Ron Harris
Keyword(s):  
Large Scale ◽  
Western Europe ◽  
Family Firms ◽  
Global Trade ◽  
Long Distance ◽  
Business Corporation ◽  
Modern Economy ◽  
Key Factor ◽  
Silk Route ◽  
Passive Investors

Before the seventeenth century, trade across Eurasia was mostly conducted in short segments along the Silk Route and Indian Ocean. Business was organized in family firms, merchant networks, and state-owned enterprises, and dominated by Chinese, Indian, and Arabic traders. However, around 1600 the first two joint-stock corporations, the English and Dutch East India Companies, were established. This book tells the story of overland and maritime trade without Europeans, of European Cape Route trade without corporations, and of how new, large-scale, and impersonal organizations arose in Europe to control long-distance trade for more than three centuries. It shows that by 1700, the scene and methods for global trade had dramatically changed: Dutch and English merchants shepherded goods directly from China and India to northwestern Europe. To understand this transformation, the book compares the organizational forms used in four major regions: China, India, the Middle East, and Western Europe. The English and Dutch were the last to leap into Eurasian trade, and they innovated in order to compete. They raised capital from passive investors through impersonal stock markets and their joint-stock corporations deployed more capital, ships, and agents to deliver goods from their origins to consumers. The book explores the history behind a cornerstone of the modern economy, and how this organizational revolution contributed to the formation of global trade and the creation of the business corporation as a key factor in Europe's economic rise.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Future Power Train Solutions for Long-Haul Trucks

2021 ◽  
Vol 13 (4) ◽  
pp. 2225
Author(s):  
Ralf Peters ◽  
Janos Lucian Breuer ◽  
Maximilian Decker ◽  
Thomas Grube ◽  
Martin Robinius ◽  
...  
Keyword(s):  
Fuel Cells ◽  
Co2 Emissions ◽  
Value Chain ◽  
Large Scale ◽  
New Technology ◽  
Co2 Reduction ◽  
Road Transport ◽  
Transport Sector ◽  
Long Distance ◽  
Production Of Hydrogen

Achieving the CO2 reduction targets for 2050 requires extensive measures being undertaken in all sectors. In contrast to energy generation, the transport sector has not yet been able to achieve a substantive reduction in CO2 emissions. Measures for the ever more pressing reduction in CO2 emissions from transportation include the increased use of electric vehicles powered by batteries or fuel cells. The use of fuel cells requires the production of hydrogen and the establishment of a corresponding hydrogen production system and associated infrastructure. Synthetic fuels made using carbon dioxide and sustainably-produced hydrogen can be used in the existing infrastructure and will reach the extant vehicle fleet in the medium term. All three options require a major expansion of the generation capacities for renewable electricity. Moreover, various options for road freight transport with light duty vehicles (LDVs) and heavy duty vehicles (HDVs) are analyzed and compared. In addition to efficiency throughout the entire value chain, well-to-wheel efficiency and also other aspects play an important role in this comparison. These include: (a) the possibility of large-scale energy storage in the sense of so-called ‘sector coupling’, which is offered only by hydrogen and synthetic energy sources; (b) the use of the existing fueling station infrastructure and the applicability of the new technology on the existing fleet; (c) fulfilling the power and range requirements of the long-distance road transport.

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