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  1. 881

    How can we use social network analysis to better understand Chimpanzee and Gorilla sociality and communication? by Anna Ilona Roberts, Sam George Bradley Roberts

    Published 2025-07-01
    “…This program of research will therefore lead to a new, systematic way of comparing social complexity across species, something that is lacking in current comparative studies of social structure. Considering that hominins were likely characterized by a fission-fusion social structure, comparing the social complexity of such systems with that of more stable groups may yield valuable insights into the evolution of human sociality.…”
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  2. 882

    Community Vitality in Dynamic Temporal Networks by Fu Cai, Li Min, Zou Deqing, Qu Shuyan, Han Lansheng, James J. Park

    Published 2013-12-01
    “…Moreover, CV cannot only be used to quantify the life intensity of a community but also be used to describe the process of community evolution over time. More specifically, the change of community's structure will be found if CVs for different time slices of a community were compared, while the community with big value of CV can be selected if CVs for different communities were compared. …”
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  3. 883
  4. 884

    Evolutionary changes in the complexity of the tectum of nontetrapods: a cladistic approach. by Caio Maximino

    Published 2008-01-01
    “…<h4>Background</h4>The tectum is a structure localized in the roof of the midbrain in vertebrates, and is taken to be highly conserved in evolution. …”
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  5. 885
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  7. 887

    Geography is a stronger predictor of diversification of monogenean parasites (Platyhelminthes) than host relatedness in characin fishes of Middle America. by Fernando Alda, Edgar F Mendoza-Franco, William Hanson-Regan, Ruth G Reina, Eldredge Bermingham, MarkE Torchin

    Published 2025-01-01
    “…We hypothesize that parasite diversity and community structure are more strongly correlated with host species (suggesting cospeciation) than with geographic location (indicative of allopatric speciation). …”
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  8. 888
  9. 889

    Numerical Characteristic of Gear System Attractor in Phase Space based on G-P Algorithm of Correlation Dimension by He Lin, Matthias Rätsch, Sanmin Wang, Guangshen Xu

    Published 2019-07-01
    “…The results show that the more complex of attractor structure generates the larger correlation dimension values, and causing strong vibration to the system, correlation dimension of chaotic attractor is between integer 1 and 2 with fractal characteristic.…”
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  10. 890

    Evolutionary Model and Simulation Research of Collaborative Innovation Network: A Case Study of Artificial Intelligence Industry by Fang Wei, Dai Sheng, Wang Lili

    Published 2018-01-01
    “…There exists coupling evolution between the technological knowledge and the network structure. …”
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  11. 891

    Quantifying hot topic dynamics in scientific literature: An information-theoretical approach. by Artem Chumachenko

    Published 2025-01-01
    “…Understanding the internal structure of scientific discourse is essential for tracking the evolution of research topics and their conceptual interdependencies. …”
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  12. 892

    Experimental Studies on Wave Interactions of Partially Perforated Wall under Obliquely Incident Waves by Jong-In Lee, Young-Taek Kim, Sungwon Shin

    Published 2014-01-01
    “…This study presents wave height distribution in terms of stem wave evolution phenomena on partially perforated wall structures through three-dimensional laboratory experiments. …”
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  13. 893

    Young artists and the development of artistic quarters in Polish cities by Jarosław Działek, Monika Murzyn-Kupisz

    Published 2014-12-01
    “…In contrast, in Katowice the chaotic spatial structure of the heart of this historic industrial region makes the flow of artists more difficult and less likely, as they tend to concentrate in a poorly delineated area of the inner city. …”
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  14. 894
  15. 895

    Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks by Jingjing Ma, Jie Liu, Wenping Ma, Maoguo Gong, Licheng Jiao

    Published 2014-01-01
    “…Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.…”
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  16. 896

    Spatial network characteristics and influencing factors of residential land prices under the background of coordinated development: A case study of the Wuhan metropolitan area in C... by Wenqi Li, Fengjuan Wei

    Published 2025-01-01
    “…Similar aggregation types exhibit a distinct cluster distribution in space. (2) The network structure of residential land prices in the Wuhan metropolitan area increases yearly, but the evolution speed is slow. (3) Compared to OLS and GWR, the MGWR model more accurately measures the impact and spatial variability of variables on residential land prices. …”
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  17. 897

    Characteristics and Mechanisms of Long-lived Concentric Eyewalls in Typhoon Lekima in 2019 by Liu Tao, Duan Yihong, Feng Jianing, Wang Hui

    Published 2021-05-01
    “…Therefore, based on the EnKF analysis fields, evolution characteristics of three-dimensional eyewalls structure in Typhoon Lekima are further analyzed. …”
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  18. 898

    Reliability Analysis in Presence of Random Variables and Fuzzy Variables by Cui Lijie, Lü Zhenzhou, Li Guijie

    Published 2015-01-01
    “…For mixed uncertainties of random variables and fuzzy variables in engineering, three indices, that is, interval reliability index, mean reliability index, and numerical reliability index, are proposed to measure safety of structure. Comparing to the reliability membership function for measuring the safety in case of mixed uncertainties, the proposed indices are more intuitive and easier to represent the safety degree of the engineering structure, and they are more suitable for the reliability design in the case of the mixed uncertainties. …”
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  19. 899
  20. 900

    Contact prediction is hardest for the most informative contacts, but improves with the incorporation of contact potentials. by Jack Holland, Qinxin Pan, Gevorg Grigoryan

    Published 2018-01-01
    “…Thus, the remaining limitations of contact prediction algorithms are most noticeable in conjunction with geometrically restrictive contacts-precisely those that contribute more information in structure prediction. We suggest that to improve prediction rates for such "informative" contacts one could combine co-evolution scores with additional indicators of contact likelihood. …”
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