Skeleton Network Extraction and Analysis on Bicycle Sharing Networks

Skeleton network extraction has been adopted unevenly in transportation networks whose nodes are always represented as spatial units. In this article, the TPks skeleton network extraction method is proposed and applied to bicycle sharing networks. The method aims to reduce the network size while preserving key topologies and spatial features. The authors quantified the importance of nodes by an improved topology potential algorithm. The spatial clustering allows to detect high traffic concentrations and allocate the nodes of each cluster according to their spatial distribution. Then, the skeleton network is constructed by aggregating the most important indicated skeleton nodes. The authors examine the skeleton network characteristics and different spatial information using the original networks as a benchmark. The results show that the skeleton networks can preserve the topological and spatial information similar to the original networks while reducing their size and complexity.

Download Full-text

HEXACO personality factors and their associations with Facebook use and Facebook network characteristics

10.31234/osf.io/3zvhq ◽

2018 ◽

Author(s):

Riana Brown ◽

Sam G. B. Roberts ◽

Thomas V. Pollet

Keyword(s):

Social Networks ◽

Network Size ◽

Openness To Experience ◽

Personality Factors ◽

Number Of Clusters ◽

Network Characteristics ◽

Online Networks ◽

Facebook Use ◽

Objectively Measured ◽

Size And Structure

Personality factors affect the properties of ‘offline’ social networks, but how they are associated with the structural properties of online networks is still unclear. We investigated how the six HEXACO personality factors (Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness and Openness to Experience) relate to Facebook use and three objectively measured Facebook network characteristics - network size, density, and number of clusters. Participants (n = 107, mean age = 20.6, 66% female) extracted their Facebook networks using the GetNet app, completed the 60-item HEXACO questionnaire and the Facebook Usage Questionnaire. Users high in Openness to Experience spent less time on Facebook. Extraversion was positively associated with network size and the number of network clusters (but not after controlling for size). These findings suggest that personality factors are associated with Facebook use and the size and structure of Facebook networks, and that personality is an important influence on both online and offline sociality.

Download Full-text

Relationships between medical students’ co-regulatory network characteristics and self-regulated learning: a social network study

Perspectives on Medical Education ◽

10.1007/s40037-021-00664-x ◽

2021 ◽

Author(s):

Derk Bransen ◽

Marjan J. B. Govaerts ◽

Dominique M. A. Sluijsmans ◽

Jeroen Donkers ◽

Piet G. C. Van den Bossche ◽

...

Keyword(s):

Social Network ◽

Medical Students ◽

Regulatory Network ◽

Regulatory Networks ◽

Learning Opportunities ◽

Network Size ◽

Tie Strength ◽

Network Characteristics ◽

Self Regulated Learning ◽

Regulated Learning

Abstract Introduction Recent conceptualizations of self-regulated learning acknowledge the importance of co-regulation, i.e., students’ interactions with others in their networks to support self-regulation. Using a social network approach, the aim of this study is to explore relationships between characteristics of medical students’ co-regulatory networks, perceived learning opportunities, and self-regulated learning. Methods The authors surveyed 403 undergraduate medical students during their clinical clerkships (response rate 65.5%). Using multiple regression analysis, structural equation modelling techniques, and analysis of variance, the authors explored relationships between co-regulatory network characteristics (network size, network diversity, and interaction frequency), students’ perceptions of learning opportunities in the workplace setting, and self-reported self-regulated learning. Results Across all clerkships, data showed positive relationships between tie strength and self-regulated learning (β = 0.095, p < 0.05) and between network size and tie strength (β = 0.530, p < 0.001), and a negative relationship between network diversity and tie strength (β = −0.474, p < 0.001). Students’ perceptions of learning opportunities showed positive relationships with both self-regulated learning (β = 0.295, p < 0.001) and co-regulatory network size (β = 0.134, p < 0.01). Characteristics of clerkship contexts influenced both co-regulatory network characteristics (size and tie strength) and relationships between network characteristics, self-regulated learning, and students’ perceptions of learning opportunities. Discussion The present study reinforces the importance of co-regulatory networks for medical students’ self-regulated learning during clinical clerkships. Findings imply that supporting development of strong networks aimed at frequent co-regulatory interactions may enhance medical students’ self-regulated learning in challenging clinical learning environments. Social network approaches offer promising ways of further understanding and conceptualising self- and co-regulated learning in clinical workplaces.

Download Full-text

A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving Information

Sensors ◽

10.3390/s21030708 ◽

2021 ◽

Vol 21 (3) ◽

pp. 708

Author(s):

Wenbo Liu ◽

Fei Yan ◽

Jiyong Zhang ◽

Tao Deng

Keyword(s):

Information Exchange ◽

Information Transfer ◽

Spatial Information ◽

Contextual Information ◽

Great Influence ◽

Light Condition ◽

Lane Detection ◽

Detection Model ◽

Spatial Features ◽

Proposed Model

The quality of detected lane lines has a great influence on the driving decisions of unmanned vehicles. However, during the process of unmanned vehicle driving, the changes in the driving scene cause much trouble for lane detection algorithms. The unclear and occluded lane lines cannot be clearly detected by most existing lane detection models in many complex driving scenes, such as crowded scene, poor light condition, etc. In view of this, we propose a robust lane detection model using vertical spatial features and contextual driving information in complex driving scenes. The more effective use of contextual information and vertical spatial features enables the proposed model more robust detect unclear and occluded lane lines by two designed blocks: feature merging block and information exchange block. The feature merging block can provide increased contextual information to pass to the subsequent network, which enables the network to learn more feature details to help detect unclear lane lines. The information exchange block is a novel block that combines the advantages of spatial convolution and dilated convolution to enhance the process of information transfer between pixels. The addition of spatial information allows the network to better detect occluded lane lines. Experimental results show that our proposed model can detect lane lines more robustly and precisely than state-of-the-art models in a variety of complex driving scenarios.

Download Full-text

A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features

Sensors ◽

10.3390/s21051885 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1885

Author(s):

Qiong Yao ◽

Dan Song ◽

Xiang Xu ◽

Kun Zou

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Spatial Information ◽

Individual Recognition ◽

Vein Pattern ◽

Low Contrast ◽

Feature Extraction Method ◽

Structure Information ◽

Finger Vein ◽

Specific Implementation

Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easily degraded to low-contrast, blur, as well as serious noise disturbance. Therefore, how to extract more efficient and robust features from these low-quality FV images, remains to be addressed. In this paper, a novel feature extraction method of FV images is presented, which combines curvature and radon-like features (RLF). First, an enhanced vein pattern image is obtained by calculating the mean curvature of each pixel in the original FV image. Then, a specific implementation of RLF is developed and performed on the previously obtained vein pattern image, which can effectively aggregate the dispersed spatial information around the vein structures, thus highlight vein patterns and suppress spurious non-boundary responses and noises. Finally, a smoother vein structure image is obtained for subsequent matching and verification. Compared with the existing curvature-based recognition methods, the proposed method can not only preserve the inherent vein patterns, but also eliminate most of the pseudo vein information, so as to restore more smoothing and genuine vein structure information. In order to assess the performance of our proposed RLF-based method, we conducted comprehensive experiments on three public FV databases and a self-built FV database (which contains 37,080 samples that derived from 1030 individuals). The experimental results denoted that RLF-based feature extraction method can obtain more complete and continuous vein patterns, as well as better recognition accuracy.

Download Full-text

Social Networks and Loneliness During the COVID-19 Pandemic

Socius Sociological Research for a Dynamic World ◽

10.1177/2378023120985254 ◽

2021 ◽

Vol 7 ◽

pp. 237802312098525

Author(s):

Balazs Kovacs ◽

Nicholas Caplan ◽

Samuel Grob ◽

Marissa King

Keyword(s):

Social Network ◽

Social Isolation ◽

Close Relationships ◽

Network Size ◽

Global Network ◽

Network Data ◽

Network Characteristics ◽

Face To Face ◽

Social Network Data ◽

Social Network Size

We utilize longitudinal social network data collected pre–COVID-19 in June 2019 and compare them with data collected in the midst of COVID in June 2020. We find significant decreases in network density and global network size following a period of profound social isolation. While there is an overall increase in loneliness during this era, certain social network characteristics of individuals are associated with smaller increases in loneliness. Specifically, we find that people with fewer than five “very close” relationships report increases in loneliness. We further find that face-to-face interactions, as well as the duration and frequency of interactions with very close ties, are associated with smaller increases in loneliness during the pandemic. We also report on factors that do not moderate the effect of social isolation on perceived loneliness, such as gender, age, or overall social network size.

Download Full-text

The Role of Spatio-Temporal Information to Govern the COVID-19 Pandemic: A European Perspective

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030166 ◽

2021 ◽

Vol 10 (3) ◽

pp. 166

Author(s):

Hartmut Müller ◽

Marije Louwsma

Keyword(s):

European Union ◽

Spatial Distribution ◽

Spatial Information ◽

National Level ◽

Temporal Information ◽

The European Union ◽

Member States ◽

Heavy Burden ◽

Spatio Temporal

The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems.

Download Full-text

Spatial odor discrimination in hawkmoth, Manduca sexta (L.)

10.1101/2020.07.08.194126 ◽

2020 ◽

Author(s):

P. Kalyanasundaram ◽

M. A. Willis

Keyword(s):

Spatial Distribution ◽

Manduca Sexta ◽

Flight Control ◽

Spatial Information ◽

Egg Laying ◽

Proboscis Extension Reflex ◽

Flying Insects ◽

Odor Learning ◽

Odor Plumes ◽

Proboscis Extension

AbstractFlying insects track turbulent odor plumes to find mates, food and egg-laying sites. To maintain contact with the plume, insects are thought to adapt their flight control according to the distribution of odor in the plume using the timing of odor onsets and intervals between odor encounters. Although timing cues are important, few studies have addressed whether insects are capable of deriving spatial information about odor distribution from bilateral comparisons between their antennae in flight. The proboscis extension reflex (PER) associative learning protocol, originally developed to study odor learning in honeybees, was modified to show hawkmoths, Manduca sexta, can discriminate between odor stimuli arriving on either antenna. We show moths discriminated the odor arrival side with an accuracy of >70%. The information about spatial distribution of odor stimuli is thus available to moths searching for odor sources, opening the possibility that they use both spatial and temporal odor information.

Download Full-text

Spatial distribution and predictors of domestic violence against women: evidence from analysis of Ethiopian demographic health survey 2016

BMC Women s Health ◽

10.1186/s12905-021-01465-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Elias Seid ◽

Tesfahun Melese ◽

Kassahun Alemu

Keyword(s):

Domestic Violence ◽

Spatial Distribution ◽

Regression Analysis ◽

Violence Against Women ◽

Health Survey ◽

Spatial Clustering ◽

Wife Beating ◽

Moran’S I ◽

Demographic Health Survey ◽

Moran's I

Abstract Background Violence against women particularly that is committed by an intimate partner is becoming a social and public health problem across the world. Studies show that the spatial variation in the distribution of domestic violence was commonly attributed to neighborhood-level predictors. Despite the prominent benefits of spatial techniques, research findings are limited. Therefore, the current study intends to determine the spatial distribution and predictors of domestic violence among women aged 15–49 in Ethiopia. Methods Data from the Ethiopian demographic health survey 2016 were used to determine the spatial distribution of domestic violence in Ethiopia. Spatial auto-correlation statistics (both Global and Local Moran’s I) were used to assess the spatial distribution of domestic violence cases in Ethiopia. Spatial locations of significant clusters were identified by using Kuldorff’s Sat Scan version 9.4 software. Finally, binary logistic regression and a generalized linear mixed model were fitted to identify predictors of domestic violence. Result The study found that spatial clustering of domestic violence cases in Ethiopia with Moran’s I value of 0.26, Z score of 8.26, and P value < 0.01. The Sat Scan analysis identifies the primary most likely cluster in Oromia, SNNP regions, and secondary cluster in the Amhara region. The output from regression analysis identifies low economic status, partner alcohol use, witnessing family violence, marital controlling behaviors, and community acceptance of wife-beating as significant predictors of domestic violence. Conclusion There is spatial clustering of IPV cases in Ethiopia. The output from regression analysis shows that individual, relationship, and community-level predictors were strongly associated with IPV. Based upon our findings, we give the following recommendation: The government should give prior concern for controlling factors such as high alcohol consumption, improper parenting, and community norm that encourage IPV that were responsible for IPV in the identified hot spot areas.

Download Full-text

The Social Networks and Social Support of Siblings of Children with Cancer

Children ◽

10.3390/children9010113 ◽

2022 ◽

Vol 9 (1) ◽

pp. 113

Author(s):

Sarah E. Wawrzynski ◽

Melissa A. Alderfer ◽

Whitney Kvistad ◽

Lauri Linder ◽

Maija Reblin ◽

...

Keyword(s):

Social Networks ◽

Network Size ◽

Support Networks ◽

Support Network ◽

Structured Interviews ◽

Children With Cancer ◽

Network Characteristics ◽

The Social ◽

Need Support ◽

Sources Of Support

Siblings of children with cancer need support to ameliorate the challenges they encounter; however, little is known about what types and sources of support exist for siblings. This study addresses this gap in our understanding of the social networks and sources of support for adolescents with a brother or sister who has cancer. Additionally, we describe how the support siblings receive addresses what they feel are the hardest aspects of being a sibling of a child with cancer. During semi-structured interviews, siblings (ages 12–17) constructed ecomaps describing their support networks. Data were coded for support type (emotional, instrumental, informational, validation, companionship) and support provider (e.g., mother, teacher, friend). Network characteristics and patterns of support were explored. Support network size ranged from 3 to 10 individuals (M = 6 ± 1.9); siblings most frequently reported mothers as sources of support (n = 22, 91.7%), followed by fathers (n = 19, 79.2%), close friends (n = 19, 79.2%) and siblings (with or without cancer) (n = 17, 70.8%). Friends and brothers or sisters most often provided validation and companionship while instrumental and informational supports came from parents. This study provides foundational knowledge about siblings’ support networks, which can be utilized to design interventions that improve support for siblings of children with cancer.

Download Full-text

Mathematical modeling of cement paste microstructure by mosaic pattern. Part II. Application

Journal of Materials Research ◽

10.1557/jmr.1997.0240 ◽

1997 ◽

Vol 12 (7) ◽

pp. 1741-1746 ◽

Cited By ~ 1

Author(s):

Paul D. Tennis ◽

Yunping Xi ◽

Hamlin M. Jennings

Keyword(s):

Mathematical Modeling ◽

Spatial Distribution ◽

Portland Cement ◽

Cement Paste ◽

Pattern Analysis ◽

Mosaic Pattern ◽

Cement Pastes ◽

Multiphase Materials ◽

Spatial Features ◽

Complex Shapes

A model based on mosaic pattern analysis is shown to have the potential to describe the complex shapes and spatial distribution of phases in the microstructures of multiphase materials. Several characteristics of both micrographs of portland cement pastes and images generated using the few parameters of the model are determined and, for the most part, agreement is good. The advantage is that spatial features of the microstructures can be captured by a few parameters.

Download Full-text