scholarly journals Coauthorship networks: A directed network approach considering the order and number of coauthors

2015 ◽  
Vol 66 (12) ◽  
pp. 2685-2696 ◽  
Author(s):  
Jinseok Kim ◽  
Jana Diesner
Keyword(s):  
Directed Network ◽  
Network Approach ◽  
Coauthorship Networks

scholarly journals Unveiling socioeconomic differences in colombia by means of urban mobility complex networks

Memorias ◽  
2018 ◽  
pp. 80-86
Author(s):  
Laura Lotero ◽  
Rafael Hurtado Heredia ◽  
Patricia Jaramillo Álvarez
Keyword(s):  
Complex Networks ◽  
Social Stratification ◽  
Directed Network ◽  
Socioeconomic Differences ◽  
Urban Mobility ◽  
Topological Properties ◽  
Network Approach ◽  
Structural Differences ◽  
Socioeconomic Strata ◽  
Do So

Social stratification lead to marked differences between people in several aspects of their lives, such as income, education, work, welfare and mobility. Here, we aim to analyze urban mobility by socioeconomic differences of travelers. In order to do so, we represent urban mobility by a complex network approach. We show that the topological properties of the networks allow to characterize mobility flows and to recognize differences in the dynamics of socioeconomic strata. We use data from origin destination surveys made for the two most populated cities in Colombia and we represent it in the form of a weighted and directed network. We found that urban mobility networks have structural differences if analyzed by socioeconomic strata of the population and unveil segregation patterns in the highest and lowest income strata.

scholarly journals Increased gamma and decreased fast ripple connections of epileptic tissue: A high‐frequency directed network approach

Epilepsia ◽  
2019 ◽  
Vol 60 (9) ◽  
pp. 1908-1920 ◽  
Author(s):  
Willemiek J. E. M. Zweiphenning ◽  
Hanneke M. Keijzer ◽  
Eric Diessen ◽  
Maryse A. van ‘t Klooster ◽  
Nicole E. C. Klink ◽  
...  
Keyword(s):  
High Frequency ◽  
Directed Network ◽  
Network Approach

The Collaborative Network Approach: a model for advancing patient-centric research for Castleman disease and other rare diseases

2019 ◽  
Vol 3 (1) ◽  
pp. 97-105
Author(s):  
Mary Zuccato ◽  
Dustin Shilling ◽  
David C. Fajgenbaum
Keyword(s):  
Rare Diseases ◽  
Treatment Options ◽  
Castleman Disease ◽  
High Impact ◽  
Collaborative Network ◽  
Network Approach ◽  
Grant Applications ◽  
Research Questions ◽  
Novel Strategy ◽  
Patient Centric

Abstract There are ∼7000 rare diseases affecting 30 000 000 individuals in the U.S.A. 95% of these rare diseases do not have a single Food and Drug Administration-approved therapy. Relatively, limited progress has been made to develop new or repurpose existing therapies for these disorders, in part because traditional funding models are not as effective when applied to rare diseases. Due to the suboptimal research infrastructure and treatment options for Castleman disease, the Castleman Disease Collaborative Network (CDCN), founded in 2012, spearheaded a novel strategy for advancing biomedical research, the ‘Collaborative Network Approach’. At its heart, the Collaborative Network Approach leverages and integrates the entire community of stakeholders — patients, physicians and researchers — to identify and prioritize high-impact research questions. It then recruits the most qualified researchers to conduct these studies. In parallel, patients are empowered to fight back by supporting research through fundraising and providing their biospecimens and clinical data. This approach democratizes research, allowing the entire community to identify the most clinically relevant and pressing questions; any idea can be translated into a study rather than limiting research to the ideas proposed by researchers in grant applications. Preliminary results from the CDCN and other organizations that have followed its Collaborative Network Approach suggest that this model is generalizable across rare diseases.

Don’t blame the model: Reconsidering the network approach to psychopathology.

2018 ◽  
Vol 125 (4) ◽  
pp. 606-615 ◽  
Author(s):  
Laura F. Bringmann ◽  
Markus I. Eronen
Keyword(s):  
Network Approach

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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