The role of feedback mechanisms in biological network models-A tutorial

2011 ◽  
Vol 13 (5) ◽  
pp. 597-610 ◽  
Author(s):  
Nicole Radde
Keyword(s):  
Biological Network ◽  
Network Models ◽  
Feedback Mechanisms

Towards demand-driven services? The role of feedback mechanisms in agribusiness-based advisory services for smallholder farmers

2019 ◽  
Vol 30 (3) ◽  
pp. 206-220
Author(s):  
Verena Bitzer ◽  
Anne Rappoldt ◽  
Laurens Van Veldhuizen ◽  
Remco Mur
Keyword(s):  
Smallholder Farmers ◽  
Feedback Mechanisms ◽  
Advisory Services

scholarly journals HOW INDUSTRY 4.0 RESHAPES THE WORLD: RECOMMENDATIONS BASED ON COMPLEX GRAPH NETWORK ANALYSIS

2021 ◽  
Vol 1 ◽  
pp. 1755-1764
Author(s):  
Rongyan Zhou ◽  
Julie Stal-Le Cardinal
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Network Models ◽  
Global Supply Chain ◽  
Great Opportunity ◽  
Strategic Research ◽  
Complex Graph ◽  
The World ◽  
Tremendous Challenge

Abstract Industry 4.0 is a great opportunity and a tremendous challenge for every role of society. Our study combines complex network and qualitative methods to analyze the Industry 4.0 macroeconomic issues and global supply chain, which enriches the qualitative analysis and machine learning in macroscopic and strategic research. Unsupervised complex graph network models are used to explore how industry 4.0 reshapes the world. Based on the in-degree and out-degree of the weighted and unweighted edges of each node, combined with the grouping results based on unsupervised learning, our study shows that the cooperation groups of Industry 4.0 are different from the previous traditional alliances. Macroeconomics issues also are studied. Finally, strong cohesive groups and recommendations for businessmen and policymakers are proposed.

scholarly journals The dynamics of faculty hiring networks

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Eun Lee ◽  
Aaron Clauset ◽  
Daniel B. Larremore
Keyword(s):  
Network Models ◽  
Structural Mechanism ◽  
Faculty Hiring ◽  
Network Characteristics ◽  
Institutional Prestige ◽  
Structural Mechanisms ◽  
Global And Local ◽  
Global Placement ◽  
New Hire

AbstractFaculty hiring networks—who hires whose graduates as faculty—exhibit steep hierarchies, which can reinforce both social and epistemic inequalities in academia. Understanding the mechanisms driving these patterns would inform efforts to diversify the academy and shed new light on the role of hiring in shaping which scientific discoveries are made. Here, we investigate the degree to which structural mechanisms can explain hierarchy and other network characteristics observed in empirical faculty hiring networks. We study a family of adaptive rewiring network models, which reinforce institutional prestige within the hierarchy in five distinct ways. Each mechanism determines the probability that a new hire comes from a particular institution according to that institution’s prestige score, which is inferred from the hiring network’s existing structure. We find that structural inequalities and centrality patterns in real hiring networks are best reproduced by a mechanism of global placement power, in which a new hire is drawn from a particular institution in proportion to the number of previously drawn hires anywhere. On the other hand, network measures of biased visibility are better recapitulated by a mechanism of local placement power, in which a new hire is drawn from a particular institution in proportion to the number of its previous hires already present at the hiring institution. These contrasting results suggest that the underlying structural mechanism reinforcing hierarchies in faculty hiring networks is a mixture of global and local preference for institutional prestige. Under these dynamics, we show that each institution’s position in the hierarchy is remarkably stable, due to a dynamic competition that overwhelmingly favors more prestigious institutions. These results highlight the reinforcing effects of a prestige-based faculty hiring system, and the importance of understanding its ramifications on diversity and innovation in academia.

scholarly journals Novel Technological Advances in Functional Connectomics in C. elegans

2019 ◽  
Vol 7 (2) ◽  
pp. 8 ◽  
Author(s):  
DiLoreto ◽  
Chute ◽  
Bryce ◽  
Srinivasan
Keyword(s):  
Nervous System ◽  
Computational Modeling ◽  
Activity Patterns ◽  
Sensory Information ◽  
Neuronal Cell ◽  
Network Models ◽  
Connectivity Matrix ◽  
C Elegans ◽  
Technological Advances

The complete structure and connectivity of the Caenorhabditis elegans nervous system (“mind of a worm”) was first published in 1986, representing a critical milestone in the field of connectomics. The reconstruction of the nervous system (connectome) at the level of synapses provided a unique perspective of understanding how behavior can be coded within the nervous system. The following decades have seen the development of technologies that help understand how neural activity patterns are connected to behavior and modulated by sensory input. Investigations on the developmental origins of the connectome highlight the importance of role of neuronal cell lineages in the final connectivity matrix of the nervous system. Computational modeling of neuronal dynamics not only helps reconstruct the biophysical properties of individual neurons but also allows for subsequent reconstruction of whole-organism neuronal network models. Hence, combining experimental datasets with theoretical modeling of neurons generates a better understanding of organismal behavior. This review discusses some recent technological advances used to analyze and perturb whole-organism neuronal function along with developments in computational modeling, which allows for interrogation of both local and global neural circuits, leading to different behaviors. Combining these approaches will shed light into how neural networks process sensory information to generate the appropriate behavioral output, providing a complete understanding of the worm nervous system.

scholarly journals Construction of a Suite of Computable Biological Network Models Focused on Mucociliary Clearance in the Respiratory Tract

2019 ◽  
Vol 10 ◽  
Author(s):  
Hasmik Yepiskoposyan ◽  
Marja Talikka ◽  
Stefano Vavassori ◽  
Florian Martin ◽  
Alain Sewer ◽  
...  
Keyword(s):  
Respiratory Tract ◽  
Mucociliary Clearance ◽  
Biological Network ◽  
Network Models

Long-Range Structural Regularities and Collectivity of Folded Proteins

2009 ◽  
Vol 1227 ◽  
Author(s):  
Canan Atilgan ◽  
Ibrahim Inanc ◽  
Ali Rana Atilgan
Keyword(s):  
Distribution Function ◽  
Network Models ◽  
Coarse Grained ◽  
Large Set ◽  
Network Construction ◽  
Angular Distribution Function ◽  
Collective Motions ◽  
Folded Proteins ◽  
Construction Strategies

AbstractCoarse-grained network models of proteins successfully predict equilibrium properties related to collective modes of motion. In this study, the network construction strategies and their systematic application to proteins are used to explain the role of network models in defining the collective properties of the system. The analysis is based on the radial distribution function, a newly defined angular distribution function and the spectral dimensions of a large set of globular proteins. Our analysis shows that after reaching a certain threshold for cut-off distance, network construction has negligible effect on the collective motions and the fluctuation patterns of the residues.

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