Paper List
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A Unified Variational Principle for Branching Transport Networks: Wave Impedance, Viscous Flow, and Tissue Metabolism
This paper solves the core problem of predicting the empirically observed branching exponent (α≈2.7) in mammalian arterial trees, which neither Murray...
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Household Bubbling Strategies for Epidemic Control and Social Connectivity
This paper addresses the core challenge of designing household merging (social bubble) strategies that effectively control epidemic risk while maximiz...
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Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening
This paper addresses the core challenge of bridging the gap between scalable chemical structure screening and biologically informative but resource-in...
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A mechanical bifurcation constrains the evolution of cell sheet folding in the family Volvocaceae
This paper addresses the core problem of why there is an evolutionary gap in species with intermediate cell numbers (e.g., 256 cells) in Volvocaceae, ...
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Bayesian Inference in Epidemic Modelling: A Beginner’s Guide Illustrated with the SIR Model
This guide addresses the core challenge of estimating uncertain epidemiological parameters (like transmission and recovery rates) from noisy, real-wor...
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Geometric framework for biological evolution
This paper addresses the fundamental challenge of developing a coordinate-independent, geometric description of evolutionary dynamics that bridges gen...
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A multiscale discrete-to-continuum framework for structured population models
This paper addresses the core challenge of systematically deriving uniformly valid continuum approximations from discrete structured population models...
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Whole slide and microscopy image analysis with QuPath and OMERO
使QuPath能够直接分析存储在OMERO服务器中的图像而无需下载整个数据集,克服了大规模研究的本地存储限制。
A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
Department of Computer Science and Technology, Capital University of Economics and Business, Beijing 100070, China.
30秒速读
IN SHORT: This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by proposing a mechanism of rapid subgroup merging driven by topological network dynamics.
核心创新
- Theory Introduces a topological coordination theory based on time-varying directed interaction networks, identifying a single dominant Strongly Connected Component (SCC) as the driver of group velocity.
- Methodology Proposes the 'velocity inheritance' mechanism, where a trailing subgroup aligns with and inherits the velocity of the leading subgroup's dominant SCC during merging events.
- Biology Provides a unified, mechanistic explanation for multiple empirical features of animal groups, including broad neighbor-distance distributions, directional asymmetry, and narrow-front/wide-rear geometry.
主要结论
- Large moving groups form not by slow accumulation but through rapid merging of pre-existing subgroups under high-density conditions, driven by topological network structure.
- The long-term interaction network of any coordinated group contains a single dominant SCC that dictates the collective velocity (speed and direction) for the entire group.
- Repeated subgroup merging, governed by velocity inheritance, predicts that larger groups move more slowly than the mean speed of the original constituent subgroups—a testable hypothesis for existing 3D tracking datasets.
摘要: Large animal groups—bird flocks, fish schools, insect swarms—are often assumed to form by gradual aggregation of sparsely distributed individuals. Using a mathematically precise framework based on time-varying directed interaction networks, we show that this widely held view is incomplete. The theory demonstrates that large moving groups do not arise by slow accumulation; instead, they emerge through the rapid merging of multiple pre-existing subgroups that are simultaneously activated under high-density conditions. The key mechanism is topological: the long-term interaction structure of any moving group contains a single dominant strongly connected component (SCC). This dominant SCC determines the collective velocity—both speed and direction—of the entire group. When two subgroups encounter one another, the trailing subgroup aligns with—and ultimately inherits—the velocity of the dominant SCC of the leading subgroup. Repeated merging events naturally generate large groups whose speed is predicted to be lower than the mean speed of the original subgroups. The same dynamics explain several universal empirical features: broad neighbour-distance distributions, directional asymmetry in neighbour selection, and the characteristic narrow-front, wide-rear geometry of real flocks. The framework yields testable predictions for STARFLAG-style 3D datasets, offering a unified explanation for the formation, maintenance, and geometry of coordinated animal groups.