Collective Behavior Research Articles

Some aspects of bidomain modeling with volume conductors

The concept of dynamic multiscaling has changed our approach to multi-neuronal cable theory. Previously, computational neuroscientists considered individual neurons as neural masses or compartmental models, but now, a distributed representation of single neurons as ionic cable structures is most likely to lead to a greater understanding of how the distribution of ionic channels and synaptic input along the dendrites of a few neurons can offset the collective behavior of a large ensemble of neurons and, therefore, provide a measure of the dynamical brain. This change in perspective forms the basis of volume conductor-bidomain modeling, a new method that captures multiscalar electrophysiology.

Combining Computational Fluid Dynamics and Experimental Data to Understand Fish Schooling Behavior.

Understanding the flow physics behind fish schooling poses significant challenges due to the difficulties in directly measuring hydrodynamic performance and the three-dimensional, chaotic, and complex flow structures generated by collective moving organisms. Numerous previous simulations and experiments have utilized computational, mechanical, or robotic models to represent live fish. And existing studies of live fish schools have contributed significantly to dissecting the complexities of fish schooling. But the scarcity of combined approaches that include both computational and experimental studies, ideally of the same fish schools, has limited our ability to understand the physical factors that are involved in fish collective behavior. This underscores the necessity of developing new approaches to working directly with live fish schools. An integrated method that combines experiments on live fish schools with computational fluid dynamic (CFD) simulations represents an innovative method of studying the hydrodynamics of fish schooling. CFD techniques can deliver accurate performance measurements and high-fidelity flow characteristics for comprehensive analysis. Concurrently, experimental approaches can capture the precise locomotor kinematics of fish and offer additional flow information through particle image velocimetry (PIV) measurements, potentially enhancing the accuracy and efficiency of CFD studies via advanced data assimilation techniques. The flow patterns observed in PIV experiments with fish schools and the complex hydrodynamic interactions revealed by integrated analyses highlight the complexity of fish schooling, prompting a reevaluation of the classic Weihs model of school dynamics. The synergy between CFD models and experimental data grants us comprehensive insights into the flow dynamics of fish schools, facilitating the evaluation of their functional significance and enabling comparative studies of schooling behavior. In addition, we consider the challenges in developing integrated analytical methods and suggest promising directions for future research.

Order–disorder phase transitions in front of the exit during human crowd evacuations

Human crowds often exhibit collective behaviors through self-organization, such as orderly queueing and disordered congestion at exits. Understanding the dynamic mechanisms behind these behaviors is crucial for pedestrian traffic management and the handling of mass crowds. Although current models and experiments have provided profound insights into ordered and disordered pedestrian groups separately, the transition mechanism from order to disorder within pedestrian crowds in front of exits remains unclear. In this study, a pedestrian queueing evacuation experiment was presented to demonstrate the phase transition process of pedestrian movement states as urgency levels in the environment change. We discovered the migration pattern of the phase transition point under evacuation control and identified different propagation modes of pedestrian velocity waves. Furthermore, a pedestrian motion model based on experimental data and observed phenomena was established. This model not only replicates the observed phenomena and regularities from the experiments but also reveals the quantitative phase transition mechanism of pedestrian movement under the influence of a single factor (i.e., urgency level or queueing ratio). Our findings hold significant implications for various domains, including pedestrian management, traffic control, and pedestrian dynamics.

Motor coordination induces social identity-A novel paradigm for the investigation of the group performance-identity link.

Joint action theorizing implies that any coordinated behaviour that induces co-representation with a partner should increase social identification, especially when the associated actions require a high degree of coordination and are experienced as being performed effectively. The current research provides a first test of this new theoretical prediction for complementary (rather than synchronous) joint actions. In each of two pre-registered experiments establishing a novel paradigm, participants performed a digital joystick task with a joint performance goal with three different partners. The task varied in coordination requirements across partners. In Experiment 1, results showed that when task segments were discrete between partners, they identified less as a group than when they had to coordinate their behaviour. Surprisingly, although constant coordination increased co-representation relative to intermittent coordination, it did not correspondingly increase social identification. However, performance correlated positively with identification; as performance was worse when participants had to coordinate, this may explain the results. Experiment 2 showed that performance is causally linked to identification when coordination is necessary. Taken together, our results suggest that experiencing effective coordination leads to greater social identification. In general, paradigms capable of examining the perceptual and motor aspects of collective behaviour may offer a new perspective on social identification in general and the performance-identification link in particular.

Time-Delay Effects on the Collective Resonant Behavior in Two Coupled Fractional Oscillators with Frequency Fluctuations

In this study, we propose coupled time-delayed fractional oscillators with dichotomous fluctuating frequencies and investigate the collective resonant behavior. Firstly, we obtain the condition of complete synchronization between the average behavior of the two oscillators. Subsequently, we derive the precise analytical expression of the output amplitude gain. Based on the analytical results, we observe the collective resonant behavior of the coupled time-delayed system and further study its dependence on various system parameters. The observed results underscore that the coupling strength, fractional order, and time delay play significant roles in controlling the collective resonant behavior by facilitating the occurrence and optimizing the intensity. Finally, numerical simulations are also conducted and verify the accuracy of the analytical results.

Self-reverting vortices in chiral active matter

There is currently a strong interest in the collective behavior of chiral active particles that can propel and rotate themselves. In the presence of alignment interactions for many chiral particles, chiral self-propulsion can induce vortex patterns in the velocity fields. However, these emerging patterns are non-permanent, and do not induce global vorticity. Here we combine theoretical arguments and computer simulations to predict a so-far unknown class of collective behavior. We show that, for chiral active particles, vortices with significant dynamical coherence emerge spontaneously. They originate from the interplay between attraction interactions and chirality in the absence of alignment interactions. Depending on parameters, the vortices can either feature a constant vorticity or a vorticity that oscillates periodically in time, resulting in self-reverting vortices. Our results may guide future experiments to realize customized collective phenomena such as spontaneously rotating gears and patterns with a self-reverting order.

Body and Brain Quality-Diversity in Robot Swarms

In biological societies, complex interactions between the behavior and morphology of evolving organisms and their environment have given rise to a wide range of complex and diverse social structures. Similarly, in artificial counterparts such as swarm-robotics systems, collective behaviors emerge via the interconnected dynamics of robot morphology (sensory-motor configuration), behavior (controller), and environment (task). Various studies have demonstrated morphological and behavioral diversity enables biological groups to exhibit adaptive, robust, and resilient collective behavior across changing environments. However, in artificial (swarm robotic) systems there is little research on the impact of changing environments on morphological and behavioral (body-brain) diversity in emergent collective behavior, and the benefits of such diversity. This study uses evolutionary collective robotics as an experimental platform to investigate the impact of increasing task environment complexity (collective behavior task difficulty) on the evolution and benefits of morphological and behavioral diversity in robotic swarms. Results indicate that body-brain evolution using coupled behavior and morphology diversity maintenance yields higher behavioral and morphological diversity, which is beneficial for collective behavior task performance across task environments. Results also indicate that such behavioral and morphological diversity maintenance coupled with body-brain evolution produces neuro-morpho complexity that does not increase concomitantly with task complexity.

Combinatorial entropy determines the early stages of nucleation

AbstractBiomolecular phase separation, a complex phenomenon within living systems, has garnered significant interest due to its diverse roles in cellular organization and function. Despite its importance, studying phase separation dynamics experimentally, particularly in the early stages, poses challenges. Our study investigates the dynamics of biomolecular phase separation using a graph‐based simulation module, particularly emphasizing its early stages. Through a simplified model, we dissect the influences of various factors on collective behavior, highlighting the crucial role of combinatorial entropy in percolation dynamics. This study offers valuable insights into the fundamental principles governing biomolecular phase separation, with implications for understanding cellular processes and disease mechanisms.

Reduced Variance Random Batch Methods for Nonlocal PDEs

Random Batch Methods (RBM) for mean-field interacting particle systems enable the reduction of the quadratic computational cost associated with particle interactions to a near-linear cost. The essence of these algorithms lies in the random partitioning of the particle ensemble into smaller batches at each time step. The interaction of each particle within these batches is then evolved until the subsequent time step. This approach effectively decreases the computational cost by an order of magnitude while increasing the amount of fluctuations due to the random partitioning. In this work, we propose a variance reduction technique for RBM applied to nonlocal PDEs of Fokker-Planck type based on a control variate strategy. The core idea is to construct a surrogate model that can be computed on the full set of particles at a linear cost while maintaining enough correlations with the original particle dynamics. Examples from models of collective behavior in opinion spreading and swarming dynamics demonstrate the great potential of the present approach.

Mapping of Long-Wavelength Phonon Contribution in the Thermal Transport of Alloyed Semiconductor Superlattices.

The mapping of long-wavelength phonons is important to understand and manipulate the thermal transport in multilayered structures, but it remains a long-standing challenge due to the collective behaviors of phonons. In this study, an experimental demonstration of mapping the long-wavelength phonons in an alloyed Al0.1Ga0.9As/Al0.9Ga0.1As superlattice system is reported. Multiple strategies to filter out the short- to mid-wavelength phonons are used. The phonon mean-free-path-dependent thermal transport properties directly demonstrate both the suppression effect of the ErAs nanoislands and the contribution of long-wavelength phonons. The contribution from phonons with mean free path longer than 1 μm is clearly demonstrated. A model based on the Boltzmann transport equation is proposed to calculate and describe the thermal transport properties, which depicts a clear physical picture of the transport mechanisms. This method can be extended to map different wavelength phonons and become a universal strategy to explore their thermal transport in various application scenarios.

Magnetic Microrobot Swarms with Polymeric Hands Catching Bacteria and Microplastics in Water.

The forefront of micro- and nanorobot research involves the development of smart swimming micromachines emulating the complexity of natural systems, such as the swarming and collective behaviors typically observed in animals and microorganisms, for efficient task execution. This study introduces magnetically controlled microrobots that possess polymeric sequestrant "hands" decorating a magnetic core. Under the influence of external magnetic fields, the functionalized magnetic beads dynamically self-assemble from individual microparticles into well-defined rotating planes of diverse dimensions, allowing modulation of their propulsion speed, and exhibiting a collective motion. These mobile microrobotic swarms can actively capture free-swimming bacteria and dispersed microplastics "on-the-fly", thereby cleaning aquatic environments. Unlike conventional methods, these microrobots can be collected from the complex media and can release the captured contaminants in a second vessel in a controllable manner, that is, using ultrasound, offering a sustainable solution for repeated use in decontamination processes. Additionally, the residual water is subjected to UV irradiation to eliminate any remaining bacteria, providing a comprehensive cleaning solution. In summary, this study shows a swarming microrobot design for water decontamination processes.

3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking

AbstractMarkerless methods for animal posture tracking have been rapidly developing recently, but frameworks and benchmarks for tracking large animal groups in 3D are still lacking. To overcome this gap in the literature, we present 3D-MuPPET, a framework to estimate and track 3D poses of up to 10 pigeons at interactive speed using multiple camera views. We train a pose estimator to infer 2D keypoints and bounding boxes of multiple pigeons, then triangulate the keypoints to 3D. For identity matching of individuals in all views, we first dynamically match 2D detections to global identities in the first frame, then use a 2D tracker to maintain IDs across views in subsequent frames. We achieve comparable accuracy to a state of the art 3D pose estimator in terms of median error and Percentage of Correct Keypoints. Additionally, we benchmark the inference speed of 3D-MuPPET, with up to 9.45 fps in 2D and 1.89 fps in 3D, and perform quantitative tracking evaluation, which yields encouraging results. Finally, we showcase two novel applications for 3D-MuPPET. First, we train a model with data of single pigeons and achieve comparable results in 2D and 3D posture estimation for up to 5 pigeons. Second, we show that 3D-MuPPET also works in outdoors without additional annotations from natural environments. Both use cases simplify the domain shift to new species and environments, largely reducing annotation effort needed for 3D posture tracking. To the best of our knowledge we are the first to present a framework for 2D/3D animal posture and trajectory tracking that works in both indoor and outdoor environments for up to 10 individuals. We hope that the framework can open up new opportunities in studying animal collective behaviour and encourages further developments in 3D multi-animal posture tracking.

Exploring non-linear effects of environmental factors on the volume of pedestrians of different ages using street view images and computer vision technology

Creating pedestrian-friendly neighborhoods and encouraging walking activities not only improves urban liveliness but also delivers health and environmental benefits. Previous research has largely focused on individual walking behavior, which often exhibits stronger associations with personal traits than with built environment characteristics. Pedestrian volume, a significant indicator of urban vitality and collective walking behavior, may have a stronger relationship with environmental characteristics. Moreover, prior studies often hypothesize a linear relationship between built environmental determinants and walking behavior. The exploration of potential non-linear influences may help policy makers and urban planners by identifying minimum, maximum, and optimal values of built environmental variables conducive for walking. Furthermore, studies focusing on the collective walking of older pedestrians remain scarce in the context of aging societies.In this study, we utilize street view imagery and advanced computer vision algorithms to estimate citywide pedestrian volumes and their corresponding age classifications (older adults vs. all). Additionally, we assessed both micro and macro built environmental factors. The possible non-linear impact is examined using Gradient Boosting Decision Tree (GBDT). Our findings reveal disparities in the influence of environmental determinants on the volume of older pedestrians versus that of all pedestrians. Also, the significance of environmental elements exhibits variations across different spatial resolutions. Further, both eye-level vegetation and building level show an inverted U-shaped influence on the pedestrian count.

Extracting macroscopic quantities in crowd behaviour with deep learning

Abnormal behaviours in crowded populations can pose significant threats to public safety, with the occurrence of such anomalies often corresponding to changes in macroscopic quantities of the complex system. Therefore, the automatic extraction and prediction of macroscopic quantities in pedestrian collective behaviour becomes significant. In this study, we generated pedestrian evacuation data through simulation, and calculated the average kinetic energy, entropy and order parameter of the system based on principles of statistical physics. These macroscopic quantities can characterize the changes in crowd behaviour patterns over time and can also assist in detecting abnormalities. Subsequently, we designed deep convolutional neural networks(CNNs) to estimate these macroscopic quantities directly from frame-by-frame image data. In the end, a convolutional auto-encoder(CAE) model is trained to learn the underlying physics unsupervisedly. Successful results indicate that deep learning methods can directly extract macroscopic information from crowd dynamics, aiding in analysing collective behaviour.

Mechanochemical dynamics of collective cells and hierarchical topological defects in multicellular lumens.

Collective cell dynamics is essential for tissue morphogenesis and various biological functions. However, it remains incompletely understood how mechanical forces and chemical signaling are integrated to direct collective cell behaviors underlying tissue morphogenesis. Here, we propose a three-dimensional (3D) mechanochemical theory accounting for biochemical reaction-diffusion and cellular mechanotransduction to investigate the dynamics of multicellular lumens. We show that the interplay between biochemical signaling and mechanics can trigger either pitchfork or Hopf bifurcation to induce diverse static mechanochemical patterns or generate oscillations with multiple modes both involving marked mechanical deformations in lumens. We uncover the crucial role of mechanochemical feedback in emerging morphodynamics and identify the evolution and morphogenetic functions of hierarchical topological defects including cell-level hexatic defects and tissue-level orientational defects. Our theory captures the common mechanochemical traits of collective dynamics observed in experiments and could provide a mechanistic context for understanding morphological symmetry breaking in 3D lumen-like tissues.

Asteroid mining vs the carbon bubble: ethical considerations for space resource extraction

PurposeThe purpose of this paper is to consider the ethical and environmental implications of allowing space resource extraction to disrupt existing fuel economies, including how companies can be held accountable for ensuring the responsible use of their space assets. It will also briefly consider how such assets should be taxed, and the cost/benefit analyses required to justify the considerable expense of supporting this emerging space industry.Design/methodology/approachThis paper adopts theoretical bioethics methodologies to explore issues of normative ethics and the formulation of moral rules to govern individual, collective and institutional behaviour. Specifically, it considers social justice and social contract theory, consequentialist and deontological accounts of ethical evaluation. It also draws on sociological and organisational literature to discuss Dowling and Pfeffer’s (1975) and Suchman’s (1995) theories of pragmatic, cognitive and moral legitimacy as they may be applied to off-world mining regulations and the handling of space assets.FindingsThe findings of this conceptual paper indicate there is both a growing appetite for tighter resource extraction regulations to address climate change and wealth concentration globally, and an opportunity to establish and legitimise new ethical norms for commercial activity in space that can avoid some of the challenges currently facing fossil fuel divestment movements on Earth.Originality/valueBy adopting methodologies from theoretical bioethics, sociology and business studies, including applying a legitimacy lens to the issue of off-world mining, this paper synthesises existing knowledges from these fields and brings them to the new context of the future space resource economy.

Direct Observation of Polaritonic Chemistry by Nuclear Magnetic Resonance Spectroscopy

AbstractPolaritonic chemistry is emerging as a powerful approach to modifying the properties and reactivity of molecules and materials. However, probing how the electronics and dynamics of molecular systems change under strong coupling has been challenging due to the narrow range of spectroscopic techniques that can be applied in situ. Here we develop microfluidic optical cavities for vibrational strong coupling (VSC) that are compatible with nuclear magnetic resonance (NMR) spectroscopy using standard liquid NMR tubes. VSC is shown to influence the equilibrium between two conformations of a molecular balance sensitive to London dispersion forces, revealing an apparent change in the equilibrium constant under VSC. In all compounds studied, VSC does not induce detectable changes in chemical shifts, J‐couplings, or spin‐lattice relaxation times. This unexpected finding indicates that VSC does not substantially affect molecular electron density distributions, and in turn has profound implications for the possible mechanisms at play in polaritonic chemistry under VSC and suggests that the emergence of collective behavior is critical.

Direct Observation of Polaritonic Chemistry by Nuclear Magnetic Resonance Spectroscopy.

Polaritonic chemistry is emerging as a powerful approach to modifying the properties and reactivity of molecules and materials. However, probing how the electronics and dynamics of molecular systems change under strong coupling has been challenging due to the narrow range of spectroscopic techniques that can be applied in situ. Here we develop microfluidic optical cavities for vibrational strong coupling (VSC) that are compatible with nuclear magnetic resonance (NMR) spectroscopy using standard liquid NMR tubes. VSC is shown to influence the equilibrium between two conformations of a molecular balance sensitive to London dispersion forces, revealing an apparent change in the equilibrium constant under VSC. In all compounds studied, VSC does not induce detectable changes in chemical shifts, J-couplings, or spin-lattice relaxation times. This unexpected finding indicates that VSC does not substantially affect molecular electron density distributions, and in turn has profound implications for the possible mechanisms at play in polaritonic chemistry under VSC and suggests that the emergence of collective behavior is critical.

Collective behaviour: Stingless bees are self-organised nest builders

A new study compiles compelling evidence that stingless bees construct their brood combs in a self-organised manner in which local modification of a structure stimulates further modifications, a process known as stigmergy.

The α-element enrichment of gas in distant galaxies

Context. The chemical evolution of distant galaxies cannot be assessed from observations of individual stars, in contrast to the case of nearby galaxies. On the other hand, the study of the interstellar medium (ISM) offers an alternative way to reveal important properties of the chemical evolution of distant galaxies. The chemical enrichment of the ISM is produced by all the previous generations of stars and it is possible to precisely determine the metal abundances in the neutral ISM in galaxies. The chemical abundance patterns in the neutral ISM are determined by the gas metallicity, presence of dust (the depletion of metals into dust grains), and possible deviations due to specific nucleosynthesis, for example, α-element enhancements. Aims. We aim to derive the metallicities, dust depletion, and α-element enhancements in the neutral ISM of gas-rich mostly-metal-poor distant galaxies (Damped Lyman-α absorbers, DLAs). Furthermore, we aim to constrain the distribution of α-element enhancements with metallicity in these galaxies. Methods. We collected a literature sample of column density measurements of O, Mg, Si, S, Ti, Cr, Fe, Ni, Zn, P, and Mn in the neutral ISM of DLAs at redshifts of 0.60 < z < 3.40. We used this sample to define a golden sample of DLAs with constrained observations of Ti and at least one other α-element. By studying the abundance patterns, we determined the amount of dust depletion, solely based on the observed relative abundances of the α-elements. We then used the abundances of Fe-peak elements to determine the overall metallicity of each system, after correcting for dust depletion. In addition, we studied the deviations from the basic (linear) abundance patterns. We divided our sample into two groups of galaxies based on the widths of their absorption lines (Δv90 above or below 100 km s−1), which may be considered as a proxy for their dynamical mass. We characterised the distribution of the α-element enhancements as a function of metallicity for the galaxy population as a whole, by fitting a piecewise function (plateau, decline, plateau) to the data. Results. We observed systematic deviations from the basic abundance patterns for O, Mg, Si, S, Ti, and Mn, which we interpreted as α-element enhancements and a Mn underabundance. The distribution of the α-element enhancements with metallicity is different in the high-Δv90 and low-Δv90 groups of galaxies. We constrained the metallicity of the α-element knee for the high-Δv90 and low-Δv90 groups of galaxies to be −1.02±0.15 dex and −1.84±0.11 dex, respectively. The average α-element enhancement at the high-plateau is [α/Fe]=0.38±0.07 dex. On the other hand, Mn shows an underabundance in all DLAs in the golden sample of −0.36±0.07 dex, on average. Conclusions. We have constrained, for the first time, the distribution of the α-element enhancement with metallicity in the neutral ISM in distant galaxies. Less massive galaxies show an α-element knee at lower metallicities than more massive galaxies. This can be explained by a lower star formation rate in less massive galaxies. If this collective behaviour can be interpreted in the same way as it is for individual systems, this would suggest that more massive and metal-rich systems evolve to higher metallicities before the contribution of SN-Ia to [α/Fe] levels out that of core-collapse SNe. This finding may plausibly be supported by different SFRs in galaxies of different masses. Overall, our results offer important clues to the study of chemical evolution in distant galaxies.