Collective Behavior Research Articles

Collapse characteristics of binary granular columns considering inhomogeneous particle size distributions

AbstractDebris avalanches and dry granular flows exhibit similar characteristics. In order to comprehend the fundamental mechanisms and improve the accuracy in predicting disasters such as landslides, debris flows, and rock avalanches, the collapse characteristics of a binary granular column are investigated through a three‐dimensional discrete element model. A novel approach is proposed by incorporating the concept of local granular velocity fluctuation and applying a cluster analysis method. Then, the flow mechanism of the binary granular column is analyzed, by considering the inhomogeneous particle size distribution. The research results show that: (1) The normalized final packing height of the granular column gradually increases when the content of coarse particles exceeds 20% and when the coarse‐fine particle size ratio increases. Conversely, the normalized run‐out distance of the granular column decreases gradually with the increase in coarse particle content and the coarse‐fine particle size ratio. (2) The particles with higher granular velocity fluctuations tend to move together and form clusters, demonstrating dynamical heterogeneity. As the coarse particle content and coarse‐fine particle size ratio increase, there is a greater tendency for particles to assemble into larger‐scale active clusters. This means that a larger number of particles exhibit collective behavior during the collapse process, resulting in increased resistance to shear deformation. Ultimately, this leads to a greater packing height and a reduced run‐out distance when observed from a macroscopic perspective.

Collective flow and fluid behavior in p/d/3He+Au collisions at GeV*

Abstract By varying the intrinsic initial geometry, p/d/3He+Au collisions at the Relativistic Heavy Ion Collider (RHIC) provide a unique opportunity to understand the collective behavior and probe possible sub-nucleon fluctuations in small systems. In this study, we employed the hybrid model under TRENTo initial conditions to study the collective flow and fluid behavior in p/d/3He+Au collisions. With fine-tuned parameters, can describe the and data from the PHENIX and STAR collaborations. However, for certain parameter sets with initial sub-nucleon fluctuations, the hydrodynamic simulations already go beyond their limits with an average Knudsen number clearly larger than unity. Our calculations demonstrate that, for a meaningful evaluation of the fluid behavior in small systems, model simulations must also pay attention to the validity range of hydrodynamics.

Noise-induced phase transition in the Vicsek model through eigen microstate methodology

Abstract This paper presents a comprehensive framework for analyzing phase transitions in collective models like the Vicsek model under various noise types. The Vicsek model, focuses on understanding the collective behaviors of social animals, is known for its discontinuous phase transitions under vector noise. However, its behavior under scalar noise remains less conclusive. Renowned for its efficacy in the analysis of complex systems under both equilibrium and non-equilibrium state, the eigen microstate method is employed here for a quantitative examination of the phase transitions in the Vicsek model under both vector and scalar noise. The study finds that the Vicsek model exhibits discontinuous phase transitions regardless of noise type. Furthermore, the dichotomy method is utilized to identify the critical points for these phase transitions. A significant finding is the observed increase in the critical point for discontinuous phase transitions with the escalation of population density.

Ecological theater, evolutionary play, and collective behavior

Ecological theater, evolutionary play, and collective behavior

The geometry of interpersonal synchrony in human dance

Collective synchronized behavior has powerful social-communicative functions observed across several animal taxa.1,2,3,4,5,6,7 Operationally, synchronized behavior can be explained by individuals responding to shared external cues (e.g., light, sound, or food) as well as by inter-individual adaptation.3,8,9,10,11 We contrasted these accounts in the context of a universal human practice-collective dance-by recording full-body kinematics from dyads of laypersons freely dancing to music in a "silent disco" setting. We orthogonally manipulated musical input (whether participants were dancing to the same, synchronous music) and visual contact (whether participants could see their dancing partner). Using a data-driven method, we decomposed full-body kinematics of 70 participants into 15 principal movement patterns, reminiscent of common dance moves, explaining over 95% of kinematic variance. We find that both music and partners drive synchrony, but through distinct dance moves. This leads to distinct kinds of synchrony that occur in parallel by virtue of a geometric organization: anteroposterior movements such as head bobs synchronize through music, while hand gestures and full-body lateral movements synchronize through visual contact. One specific dance move-vertical bounce-emerged as a supramodal pacesetter of coordination, synchronizing through both music and visual contact, and at the pace of the musical beat. These findings reveal that synchrony in human dance is independently supported by shared musical input and inter-individual adaptation. The independence between these drivers of synchrony hinges on a geometric organization, enabling dancers to synchronize to music and partners simultaneously by allocating distinct synchronies to distinct spatial axes and body parts.

Collective Transport Behavior in a Robotic Swarm with Hierarchical Imitation Learning

Swarm robotics is the study of how a large number of relatively simple physically embodied robots can be designed such that a desired collective behavior emerges from local interactions. Furthermore, reinforcement learning (RL) is a promising approach for training robotic swarm controllers. However, the conventional RL approach suffers from the sparse reward problem in some complex tasks, such as key-to-door tasks. In this study, we applied hierarchical imitation learning to train a robotic swarm to address a key-to-door transport task with sparse rewards. The results demonstrate that the proposed approach outperforms the conventional RL method. Moreover, the proposed method outperforms the conventional hierarchical RL method in its ability to adapt to changes in the training environment.

Applying Social Practice Theory to Foster Mindful Consumption: A Case of Waste Banks in Indonesia

Mindful consumption is an emerging research topic. The literature about mindful consumption is currently more focused on the individual. However, the more significant behavioral impact is collective behavior. So, the purpose of this research is to analyze how mindfulness instills changes in mindful consumption behavior at a collective level. This study applies social practice theory to explain how waste management as a social practice increases collective mindfulness and collective mindful consumption. This study applied a qualitative research design using a case study technique. A series of in-depth interviews were conducted with two waste banks and several households around them. The result of this study is waste management practice will evoke collective mindfulness and collective mindful consumption with the following processes: (1) Agents may reconfigure the elements of material, rules, and skills of waste management practices; (2) When agents perform the waste management practice frequently and consistently, the residents will participate in the practice which means that the new practice recruits new carriers; (3) For practice in the community to evolve, the recruitment of carriers depends on frequent exposure to the practice; and (4) When the residents are mindful, they will transform their product choices into solutions that benefit themselves and the environment. This study confirms that social practice may foster mindful consumption behavior at a collective leve

Extracting the speed of sound in quark–gluon plasma with ultrarelativistic lead–lead collisions at the LHC

Ultrarelativistic nuclear collisions create a strongly interacting state of hot and dense quark–gluon matter that exhibits a remarkable collective flow behavior with minimal viscous dissipation. To gain deeper insights into its intrinsic nature and fundamental degrees of freedom, we determine the speed of sound in an extended volume of quark–gluon plasma using lead–lead (PbPb) collisions at a center-of-mass energy per nucleon pair of 5.02 TeV. The data were recorded by the CMS experiment at the CERN LHC and correspond to an integrated luminosity of 0.607 nb−1. The measurement is performed by studying the multiplicity dependence of the average transverse momentum of charged particles emitted in head-on PbPb collisions. Our findings reveal that the speed of sound in this matter is nearly half the speed of light, with a squared value of 0.241±0.002(stat)±0.016(syst) in natural units. The effective medium temperature, estimated using the mean transverse momentum, is 219±8(syst)MeV . The measured squared speed of sound at this temperature aligns precisely with predictions from lattice quantum chromodynamic (QCD) calculations. This result provides a stringent constraint on the equation of state of the created medium and direct evidence for a deconfined QCD phase being attained in relativistic nuclear collisions.

Assessing the instantaneous social dilemma on social distancing attitudes and vaccine behavior in disease control

In the face of infectious disease outbreaks, the collective behavior of a society can has a profound impact on the course of the epidemic. This study investigates the instantaneous social dilemma presented by individuals' attitudes toward vaccine behavior and its influence on social distancing as a critical component in disease control strategies. The research employs a multifaceted approach, combining modeling techniques and simulation to comprehensively assess the dynamics between social distancing attitudes and vaccine uptake during disease outbreaks. With respect to modeling, we introduce a new vaccination game (VG) where, unlike conventional VG models, a 2-player and 2-strategy payoff structure is aptly embedded in the individual behavior dynamics. Individuals' willingness to adhere to social distancing measures, such as mask-wearing and physical distancing, is strongly associated with their inclination to receive vaccines. The study reveals that a positive attitude towards social distancing tends to align with a higher likelihood of vaccine acceptance, ultimately contributing to more effective disease control. As the COVID-19 pandemic has demonstrated, swift and coordinated public health measures are essential to curbing the spread of infectious diseases. This study underscores the urgency of addressing the instantaneous social dilemma posed by individuals' attitudes. By understanding the intricate relationship between these factors, policymakers, and healthcare professionals can develop tailored strategies to promote both social distancing compliance and vaccine acceptance, thereby enhancing our ability to control and mitigate the impact of disease outbreaks in the future.

Unveiling Multi-Agent Strategies: A Data-Driven Approach for Extracting and Evaluating Team Tactics from Football Event and Freeze-Frame Data

Studying collective behavior in opposing multi-agent teams is crucial across game theory, robotics, and sports analytics. In sports, especially football, team tactics involve intricate strategic spatial and action behaviors displayed as event sequences during possession. Understanding and analyzing these tactics is essential for successful training, strategic planning, and on-field success. While traditional approaches, such as notational and statistical analyses, offer valuable insights into team tactics, they often lack a comprehensive consideration of contextual information, thereby limiting the holistic evaluation of teams’ performances. To bridge this gap and capture the nuanced intricacies of team tactics, we employed advanced methodologies. The sequential pattern mining algorithm PrefixSpan was utilized to extract tactical patterns from possession sequences, enabling a deeper understanding of how teams strategize and adapt during play. Additionally, the neural marked spatio temporal point process (NMSTPP) model was leveraged to model and predict team behaviors, facilitating a fair comparison among teams. The evaluation of team possessions was further enhanced through the innovative holistic possession utilization score metrics, providing a more nuanced assessment of performance. In our experimental exploration, we identified and classified five distinct team tactics, validated the efficacy of the NMSTPP model when integrating StatsBomb 360 data, and conducted a comprehensive analysis of English Premier League teams during the 2022/2023 season. The results were visualized using radar plots and scatter plots with mean shift clustering. Lastly, the potential applications to RoboCup were discussed.

Debugging collective digging in Drosophila melanogaster using an agent-based model

Being a member of a group entails a range of possible costs and benefits; observing when and with whom groups form can help identify how these are weighted. Clustering behaviour in fruit fly (Drosophila melanogaster) third-instar larvae has been shown to provide individuals that cluster with improved foraging opportunities on otherwise inaccessible food. To isolate how environmental and genetic factors affect who clusters and when, we created an on-lattice agent-based model and systematically explored the effects of several variables, such as population density, resource distribution, and the phenotypic composition of the group. Our simple model reproduces most of the key features of larval clustering and demonstrates drastic differences in collective behaviour and fitness in response to changes in environmental variables, in ways that align with the limited empirical data available. This model can improve our understanding of this complex social behaviour and make testable predictions about the behavioural and ecological mechanisms of larval clustering.

A Chemical Mechanism for the Bistable‐to‐Oscillatory Transition in Colloidal Aggregates of Silver Phosphate

We previously reported collective behavior in colloidal aggregates of silver phosphate in H2O2 and under UV light. Diffusiophoretic interactions between aggregates lead to non‐linear behavior such as oscillations and synchronization, in which oscillation frequencies increase with H2O2 concentration. The aggregates transition between schooling and dispersed behaviors with incipient spatiotemporal correlations. We identified a kinetic model that maps the chemical species that are thought to underlie non‐linear phenomena in the colloidal aggregates, i.e. adsorbed oxygen species *OOH and ∗O. We investigate the emergent dynamics for the simplest case, the coupling of two otherwise bistable clusters. Two coupling schemes are proposed and we find that – depending on whether the coupling is excitatory or inhibitory – the clusters may oscillate with zero or π phase shift.

Label-Free Optical Mapping for Large-Area Biomechanical Dynamics of Multicellular Systems.

Mapping cellular activities over large areas is crucial for understanding the collective behaviors of multicellular systems. Biomechanical properties, such as cellular traction force, serve as critical regulators of physiological states and molecular configurations. However, existing technologies for mapping large-area biomechanical dynamics are limited by the small field of view and scanning nature. To address this, we propose a novel platform that utilizes a vast number of optical diffractive elements for mapping large-area biomechanical dynamics. This platform achieves a field-of-view of 10.6 mm X 10.6 mm, a three-orders-of-magnitude improvement over traditional traction force microscopy. Transient mechanical waves generated by monolayer neonatal rat ventricular myocytes were captured with high spatiotemporal resolution (130 fps and 20 µm for temporal and spatial resolution, respectively). Furthermore, its label-free nature allows for long-term observations extended to a week, with minimal disruption of cellular functions. Finally, simultaneous measurements of calcium ions concentrations and biomechanical dynamics are demonstrated.

The network of sports: using network analysis to understand the relationship between sports and socio-physiological factors in contemporary China

This study examines sports and physical activities among Chinese aged 18–65, using network analysis on a significant random sample. It categorizes sports into 11 groups based on public selection, with a community detection algorithm aiding classification. Variables like age, gender, and education were integrated, revealing how life stages, gender disparities, and social class influence activity participation. The mixed graph model shows both positive and negative correlations among activities, highlighting the role of sports as both a social integrator and divider, reflective of broader societal norms and inequalities. The exponential random graph model further illustrates a complex network of demographic-driven participation patterns. The purpose of this investigation is twofold: to advance methodological approaches in the study of sports-related social networks and to explore the broader implications such networks may have on individual and collective behaviors within this field.

Individual and group behavioral responses to nutritional state and context in a social fish

Understanding how animal collectives and societies form and function is a fundamental goal in animal biology. To date, research examining fish shoaling behavior has focused mostly on the general principles and ecological relevance of the phenomeon, while the ways in which physiological state (e.g., nutrition) affects collective behavior remain overlooked. Here, we investigated the shoaling behavior of common minnows (Phoxinus phoxinus) with three different nutritional states (control treatment: fasting for 24 h, fasting treatment: fasting for 7 days, and digestion treatment: 1 h after satiation feeding) across two ecological contexts (i.e., without and with food). No effects of either nutritional state or context were found on swimming speed, but the acceleration was greater in the digestion group than in the control group, with that in the fasting group being intermediate. Similar to change tendency in group length and group width of shoals, both interindividual distance and nearest neighbor distance were also greater in the fasting group than in the digestion group, suggesting that fasting and digestion may exert opposite driving forces on group cohesion. However, neither nutritional state nor context influenced the group area, group speed, group percent time moving, or group polarization. Both the foraging efficiency and the percentage of food items consumed by the fish shoals were greater in the fasting and control groups than in the digestion group. Our study suggested that one week of hunger and the energetically demanding stage of food digestion tend to have opposite influences on group shape, while the social foraging context does not influence the individual and group behavior of fish.

Laboratory realization of relativistic pair-plasma beams

Relativistic electron-positron plasmas are ubiquitous in extreme astrophysical environments such as black-hole and neutron-star magnetospheres, where accretion-powered jets and pulsar winds are expected to be enriched with electron-positron pairs. Their role in the dynamics of such environments is in many cases believed to be fundamental, but their behavior differs significantly from typical electron-ion plasmas due to the matter-antimatter symmetry of the charged components. So far, our experimental inability to produce large yields of positrons in quasi-neutral beams has restricted the understanding of electron-positron pair plasmas to simple numerical and analytical studies, which are rather limited. We present the first experimental results confirming the generation of high-density, quasi-neutral, relativistic electron-positron pair beams using the 440 GeV/c beam at CERN’s Super Proton Synchrotron (SPS) accelerator. Monte Carlo simulations agree well with the experimental data and show that the characteristic scales necessary for collective plasma behavior, such as the Debye length and the collisionless skin depth, are exceeded by the measured size of the produced pair beams. Our work opens up the possibility of directly probing the microphysics of pair plasmas beyond quasi-linear evolution into regimes that are challenging to simulate or measure via astronomical observations.

Student's and Classmates' Prosocial Behavior predict Academic Engagement in Middle School.

Students' academic engagement is greatly informed by a classroom's social climate. However, more research is needed regarding how specific peer behavior, especially prosocial behavior, come to shape academic engagement. The present study investigated whether students' perceptions about their classmates' prosocial behavior were associated with their academic engagement (cognitive, behavioral, affective) across the school year. Indirect effects via increases in students' own prosocial behavior were examined. Participants were 905 middle school students from rural, low-income communities in the Midwestern United States (50% girls, 46% boys; Mage = 12.94 years). Students completed self-report surveys in the fall and spring of the 2022-2023 school year. Results revealed that students' perceptions of their classmates' prosocial behavior were positively associated with students' own prosocial behavior. Students' own prosocial behavior was positively associated with all three dimensions of engagement. The positive indirect effect of classmates' prosocial behavior on engagement through students' own prosocial behavior was significant. The findings highlight the importance of classmates' behavior on individuals' academic engagement and offer insights into classroom-based interventions aimed at improving collective behavior.

The Promising Potential of Cold Atmospheric Plasma Therapies

The outstanding properties and chemistry of cold atmospheric plasma (CAP) are not sufficiently understood due to their relatively complex systems and transient properties. In this paper, we tried to present a detailed review of the applications of CAP in modern medicine, highlighting the biochemistry of this phenomenon. Due to its unique characteristics, CAP has emerged as a promising tool in various medical applications. CAP, as a partially—or fully ionized—gas-retaining state of quasi-neutrality, contains many particles, such as electrons, charged atoms, and molecules displaying collective behaviour caused by Coulomb interactions. CAP can be generated at atmospheric pressure, making it suitable for medical settings. Cold plasma’s anti-microbial properties create an alternative method to antibiotics when treating infections. It also enhances cell proliferation, migration, and differentiation, leading to accelerated tissue regeneration. CAP can also be a powerful tool in anti-tumour therapies, stem cell proliferation, dental applications, and disease treatment, e.g., neurology. It is our belief that this article contributes to the deeper understanding of cold plasma therapy and its potential in medicine. The objective of this study is to demonstrate the potential of this relatively novel approach as a promising treatment modality. By covering a range of various biomedical fields, we hope to provide a comprehensive overview of CAP applications for multiple medical conditions. In order to gain further insight into the subject, we attempted to gather crucial research and evidence from various studies, hopefully creating a compelling argument in favour of CAP therapy. Our aim is to highlight the innovative aspects of CAP therapy where traditional methods may have limitations. Through this article, we intend to provide a convenient reference source for readers engaged in the examination of CAP’s potential in medicine.

The dance of neurons: Exploring nonlinear dynamics in brain networks

The brain is a complex, nonlinear system, exhibiting ever-evolving patterns of activities, whether in the presence or absence of external stimuli or task demands. Nonlinearity can notably obscure the link between structural constraints enforced on the interaction and its dynamical consequences. Suitable nonlinear dynamical models and their analysis serve as essential tools not only for bridging structural and functional understanding of the brain but also for predictably altering the complex dynamical organization of the brain. Here, starting from a large-scale network of threshold Hodgkin–Huxley style neurons, we formulate the average nonlinear dynamics implicitly following from the Wilson–Cowan assumptions. We investigate the influence of biophysical and structural properties on the complexity of neural dynamics at the microscale level and its relationship with the macroscopic Wilson–Cowan model. Incorporating the elements in the model can help identify more realistic regimes of activity and connect the mathematical prediction of increasing nonlinearity to physical manipulations. Our simulations of the temporal profiles reveal dependency on the binary state of interacting subpopulations and the random property of structural network at the transition points, when different synaptic weights are considered. For substantial configurations of stimulus intensity, our model provides further estimates of the neural population’s dynamics, ranging from simple-periodic to aperiodic patterns and phase transition regimes. This reflects the potential contribution of the microscopic nonlinear scheme to the mean-field approximation in studying the collective behaviour of individual neurons with particularly concentrating on the occurrence of critical phenomena. We show that finite-size effects kick the system in a state of irregular modes to evolve differently from predictions of the original Wilson–Cowan reference. Additionally, we report that the complexity and temporal diversity of neural dynamics, especially in terms of limit cycle trajectory, and synchronization can be induced by either small heterogeneity in the degree of various types of local excitatory connectivity or considerable diversity in the external drive to the excitatory pool.

Promoting collective precycling behavior: results from a group intervention with Berlin households in Germany.

To tackle the global waste crisis, there is an urgent need for decisive and joint action at multiple levels. The collective behavior of a community could make a significant contribution. This paper presents the results of a field experiment designed to promote packaging waste prevention - called precycling - in a newly formed community setting, in Berlin, Germany. The aim was to examine the effect of the intervention on precycling and to examine the underlying social identity processes. Over a four-week period, 132 participants from 96 different households digitally received a combination of different interventions, that were theoretically informed by the Social Identity Model of Pro-Environmental Action (SIMPEA). Households were assigned to two intervention groups and a waiting control group. Data was collected before, immediately after and four months after the intervention to assess the impact of the intervention using multilevel models. After the intervention, the overall precycling behavior increased significantly, but not as a result of the different group conditions. In the more comprehensive intervention group, which included social interaction and behavioral experimentation, the community identification was strengthened and the reuse behavior, as a subset of precycling, increased. While a number of social identity processes (collective efficacy beliefs, having a precycling action goal, crisis appraisal, and sufficiency attitudes) were found to positively predict the precycling behavior, surprisingly, the predictive power of social norms and ingroup identification could not be confirmed. Overall, the presented community intervention promoted precycling. However, in this dynamic real-world setting, not all intervention elements worked as expected. The pitfalls and opportunities of this intervention are discussed, and ideas for translating the results into everyday precycling activities are presented.