scholarly journals MotionGlyphs: Visual Abstraction of Spatio‐Temporal Networks in Collective Animal Behavior

2020 ◽  
Vol 39 (3) ◽  
pp. 63-75
Author(s):  
E. Cakmak ◽  
H. Schäfer ◽  
J. Buchmüller ◽  
J. Fuchs ◽  
T. Schreck ◽  
...  
Keyword(s):  
Animal Behavior ◽  
Temporal Networks ◽  
Collective Animal Behavior ◽  
Spatio Temporal

scholarly journals Spatio-temporal clustering benchmark for collective animal behavior

2021 ◽  
Author(s):  
Eren Cakmak ◽  
Manuel Plank ◽  
Daniel S. Calovi ◽  
Alex Jordan ◽  
Daniel Keim
Keyword(s):  
Animal Behavior ◽  
Temporal Clustering ◽  
Collective Animal Behavior ◽  
Spatio Temporal

scholarly journals An Algorithm for Global Optimization Inspired by Collective Animal Behavior

2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Erik Cuevas ◽  
Mauricio González ◽  
Daniel Zaldivar ◽  
Marco Pérez-Cisneros ◽  
Guillermo García
Keyword(s):  
Global Optimization ◽  
Animal Behavior ◽  
Food Source ◽  
Collective Motion ◽  
Global Optimum ◽  
Migration Routes ◽  
Collective Behaviors ◽  
Biological Laws ◽  
Harvesting Efficiency ◽  
Collective Animal Behavior

A metaheuristic algorithm for global optimization called the collective animal behavior (CAB) is introduced. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central locations, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of animals which interact with each other based on the biological laws of collective motion. The proposed method has been compared to other well-known optimization algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.

scholarly journals Probabilistic models of individual and collective animal behavior

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0193049 ◽  
Author(s):  
Katarína Bod’ová ◽  
Gabriel J. Mitchell ◽  
Roy Harpaz ◽  
Elad Schneidman ◽  
Gašper Tkačik
Keyword(s):  
Animal Behavior ◽  
Probabilistic Models ◽  
Collective Animal Behavior

Synchronization for Nonlinear Complex Spatio-Temporal Networks with Multiple Time-Invariant Delays and Multiple Time-Varying Delays

2018 ◽  
Vol 50 (2) ◽  
pp. 1051-1064 ◽  
Author(s):  
Chengdong Yang ◽  
Tingwen Huang ◽  
Kejia Yi ◽  
Ancai Zhang ◽  
Xiangyong Chen ◽  
...  
Keyword(s):  
Multiple Time ◽  
Time Varying ◽  
Temporal Networks ◽  
Spatio Temporal ◽  
Time Invariant

Spatio-temporal Networks

2013 ◽  
Author(s):  
Betsy George ◽  
Sangho Kim
Keyword(s):  
Temporal Networks ◽  
Spatio Temporal

scholarly journals The Role of Clustering in the Adoption of Organic Dairy: A Longitudinal Networks Analysis between 2002 and 2015

2019 ◽  
Vol 11 (6) ◽  
pp. 1514 ◽  
Author(s):  
Juan Sánchez Herrera ◽  
Carolyn Dimitri
Keyword(s):  
Relative Size ◽  
Dairy Farm ◽  
Organic Production ◽  
Market Development ◽  
Temporal Networks ◽  
Organic Dairy ◽  
The Usa ◽  
Networks Analysis ◽  
Spatio Temporal

This paper uses network analysis to study the geo-localization decisions of new organic dairy farm operations in the USA between 2002 and 2015. Given a dataset of organic dairy certifications we simulated spatio-temporal networks based on the location of existing and new organic dairy farming operations. The simulations were performed with different probabilities of connecting with existing or incoming organic farmer operations, to overcome the lack of data describing actual connections between farmers. Calculated network statistics on the simulated networks included the average degree, average shortest path, closeness (centrality), clustering coefficients, and the relative size of the largest cluster, to demonstrate how the networks evolved over time. The findings revealed that new organic dairy operations cluster around existing ones, reflecting the role of networks in the conversion into organic production. The contributions of this paper are twofold. First, we contribute to the literature on clustering, information sharing, and market development in the agri-food industry by analyzing the potential implications of social networking in the development of a relatively new agriculture market. Second, we add to the literature on empirical social networks by using a new dataset with information on actors not previously studied analytically.

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