Self-organized Behavior Research Articles

Experimental study on mechanical response and crack evolution law of coal and sandstone under different stress environments.

In order to study the mechanical response and crack evolution law of different lithologic rock bodies under different stress environments in deep stress mines, based on the deviator stress theory, the actual triaxial disturbance unloading rock test system was used to simulate the stress occurrence environment of the original rock. The mechanical characteristics of different σ2 coal rock masses were studied, and the crack evolution law of coal and sandstone under different stress environments was analyzed. The results show that the increase of σ2 inhibits the deformation in the σ2 direction of coal and sandstone, promotes the expansion and deformation in the σ3 direction, and enhances its peak strength and elastic modulus. The development characteristics of internal cracks in rock mass are directly related to the stress environment, and the increase of σ2 promotes the increase of the proportion of coal RA value, weakens the proportion of sandstone RA value, aggravates the development of coal internal shear cracks, and inhibits the development of internal shear cracks in sandstone. The larger σ2, the greater the initial AE ringing count of coal and sandstone, and the greater the AE cumulative energy when the rock mass is finally damaged. At the same time, due to the self-organizing behavior in the process of crystal failure in sandstone, the cumulative energy curve of sandstone fluctuates in a step-like manner. The ringing count and cumulative energy increase suddenly, which can predict the imminent instability and failure of the rock, and the research results can provide an experimental basis for the sudden instability of deep high-stress roadways.

The sperm hook as a functional adaptation for migration and self-organized behavior.

In most murine species, spermatozoa exhibit a falciform apical hook at the head end. The function of the sperm hook is not yet clearly understood. In this study, we investigate the role of the sperm hook in the migration of spermatozoa through the female reproductive tract in Mus musculus (C57BL/6), using a deep tissue imaging custom-built two-photon microscope. Through live reproductive tract imaging, we found evidence indicating that the sperm hook aids in the attachment of spermatozoa to the epithelium and facilitates interactions between spermatozoa and the epithelium during migration in the uterus and oviduct. We also observed synchronized sperm beating, which resulted from the spontaneous unidirectional rearrangement of spermatozoa in the uterus. Based on live imaging of spermatozoa-epithelium interaction dynamics, we propose that the sperm hook plays a crucial role in successful migration through the female reproductive tract by providing anchor-like mechanical support and facilitating interactions between spermatozoa and the female reproductive tract in the house mouse.

Simulation of a Bio-Inspired Flocking-Based Aggregation Behaviour in Swarm Robotics.

This paper presents a biologically inspired flocking-based aggregation behaviour of a swarm of mobile robots. Aggregation behaviour is essential to many swarm systems, such as swarm robotics systems, in order to accomplish complex tasks that are impossible for a single agent. In this work, we developed a robot controller using Reynolds' flocking rules to coordinate the movements of multiple e-puck robots during the aggregation process. To improve aggregation behaviour among these robots and address the scalability issues in current flocking-based aggregation approaches, we proposed using a K-means algorithm to identify clusters of agents. Using the developed controller, we simulated the aggregation behaviour among the swarm of robots. Five experiments were conducted using Webots simulation software. The performance of the developed system was evaluated under a variety of environments and conditions, such as various obstacles, agent failure, different numbers of robots, and arena sizes. The results of the experiments demonstrated that the proposed algorithm is robust and scalable. Moreover, we compared our proposed algorithm with another implementation of the flocking-based self-organizing aggregation behaviour based on Reynolds' rules in a swarm of e-puck robots. Our algorithm outperformed this method in terms of cohesion performance and aggregation completion time.

Influence of Surface Roughness on the Self-Organization Behaviors of Shear Bands in TA7 Titanium Alloy Cylinders

Influence of Surface Roughness on the Self-Organization Behaviors of Shear Bands in TA7 Titanium Alloy Cylinders

Unraveling Complexity: Exploring Agricultural Systems as Complex Adaptive System with a Focus on the Konya Closed Basin

The study of complex adaptive systems (CAS) has garnered significant attention across interdisciplinary research, particularly within the realms of social and natural sciences. This paper delves into the multifaceted nature of CAS, exploring its definitions, components, and properties, drawing from various scholarly perspectives. It examines the agricultural sector as a prime example of a CAS, highlighting the interactions, adaptations, and emergent behaviors within farming communities. Using the Konya Closed Basin in Türkiye as a case study, the paper elucidates how agricultural landscapes exemplify the complexities inherent in CAS, underscoring the intricate interplay between human activities, environmental dynamics, and socio-economic factors. By recognizing these interactions, decision-makers can create more efficient and resilient strategies for managing water resources, enhancing crop production, and reducing the effects of climate variability. This comprehensive approach emphasizes the role of adaptation, self-organization, and emergent behaviors in promoting sustainable farming practices and maintaining ecological balance over the long term. Through this analysis, the paper contributes to a deeper understanding of CAS and its implications for sustainable agricultural development and resource management.

Unveiling Hidden Hyperuniformity: Radial Turing Pattern Formation of Marangoni-Driven SiO2 Nanoparticles on Liquid Metal Surface.

Mastering the self-organization of nanoparticle morphologies is pivotal in soft matter physics and film growth. Silicon dioxide (SiO2) nanoparticles are an archetypical model of nanomotor in soft matter. Here, the emphasis is on the self-organizing behavior of SiO2 nanoparticles under extreme conditions. It is unveiled that manipulating the states of the metal substrate profoundly dictates the motion characteristics of SiO2 nanoparticles. This manipulation triggers the emergence of intricate morphologies and distinctive patterns. Employing a reaction-diffusion model, the fundamental roles played by Brownian motion and Marangoni-driven motion in shaping fractal structures and radial Turing patterns are demonstrated, respectively. Notably, these radial Turing patterns showcase hyperuniform order, challenging conventional notions of film morphology. These discoveries pave the way for crafting non-equilibrium morphological materials, poised with the potential for self-healing, adaptability, and innovative applications.

The influence of individuality on performance traits in mussels: Assessments based on defensive behavior and physiology

Cultured Mytilus coruscus fall-offs regularly led to the reduction of harvest yields of suspension-cultured mussels. High temperature and marine pollutants affect mussel physiological health, which increases the risk of mussel fall-offs and results in yield declines. In this study, we classified mussels into proactive and reactive groups based on their self-organizing behavior after air exposure stress and investigated the differences in defense performance of different group mussels. Proactive mussels were found to have a significantly higher byssus number, adhesion area, and gene expression in the mussel foot protein mfp5 and the byssus collagen preCOL-P. Exposure of mussels to high temperature and different concentrations of BDE-47 revealed a decrease in byssus performance and byssus gene expression in both proactive and reactive groups, but the decreased level of expression of the proactive mussel foot proteins mfp5 and mfp6 was significantly less than that of the reactive mussels. Our study found that proactive mussels have more and better byssuses, better adhesion and defense ability, and greater adaptability under higher temperature and pollutant-exposed conditions. The performance of byssus is correlated with the individuality of stress coping, providing a new direction for future mussel selection breeding and aquaculture.

Dynamics of the osmotic lysis of mineral protocells and its avoidance at the origins of life.

The osmotic rupture of a cell, its osmotic lysis or cytolysis, is a phenomenon that active biological cell volume regulation mechanisms have evolved in the cell membrane to avoid. How then, at the origin of life, did the first protocells survive prior to such active processes? The pores of alkaline hydrothermal vents in the oceans form natural nanoreactors in which osmosis across a mineral membrane plays a fundamental role. Here, we discuss the dynamics of lysis and its avoidance in an abiotic system without any active mechanisms, reliant upon self-organized behaviour, similar to the first self-organized mineral membranes within which complex chemistry may have begun to evolve into metabolism. We show that such mineral nanoreactors could function as protocells without exploding because their self-organized dynamics have a large regime in parameter space where osmotic lysis does not occur and homeostasis is possible. The beginnings of Darwinian evolution in proto-biochemistry must have involved the survival of protocells that remained within such a safe regime.

Understanding pedestrian crowd dynamics on ramps: An empirical study triggered by Seoul Halloween crowd crush

Pedestrian safety on ramps demands greater attention compared to flat surfaces, as exemplified by a tragic crowd crush accident that claimed the lives of over 150 individuals on a densely populated ramp in South Korea in 2022. Understanding the dynamics of pedestrian movement on ramps is crucial for effectively managing mass events and daily pedestrian transportation. This paper presents an empirical study investigating bidirectional pedestrian flows on a simulated ramp with varying inclinations. Flow ratios, indicating the group size of major and minor flows in both directions, were examined. Results show that bidirectional pedestrian streams exhibit self-organizing behavior, characterized by the emergence of distinct lane formation phenomenon in both level and ramp walking scenarios. The shortest overall time required for pedestrians to exit the ramp was observed at a 3° inclination. As the ramp inclination increases, the disparities between upward and downward directions become more pronounced. Specifically, the uphill flow consistently exhibits a lower average speed compared to the downhill flow at the same inclination. In general, an increase in inclination is associated with a decrease in forward speed, an increase in lateral speed, and a greater utilization of handrails. Handrail plays a particularly important role in assisting pedestrians down very steep ramps. Furthermore, we found that the influence of flow ratio on walking parameters undergoes a change from level walking to ramp walking scenarios. Notably, for the same flow ratio, the results can vary significantly depending on whether the major flow is uphill or downhill. Our findings provide valuable insights into crowd management on ramps and hold significant implications for the safer design of ramps in subway stations, underpasses, and overpasses.

Synergistic control for enhancing frequency stability in grid-integrated network with decentralized renewable energy resources, energy storage, and electric vehicles

The integration of Decentralized Energy Resources (DERs), Energy Storage Systems (ESS), and Electric Vehicles (EVs) into grid-connected networks presents a transformative paradigm in modern power systems. This article introduces a synergistic control framework tailored for optimizing frequency stability within grid-integrated distributed networks. The proposed approach leverages advanced control algorithms to orchestrate the dynamic interaction between DERs, ESS, and EVs, ensuring seamless operation and enhanced grid resilience. The effectiveness of the proposed control strategy is demonstrated in mitigating frequency deviations, thereby contributing to a more stable and reliable grid infrastructure. This article introduces a novel approach utilizing the Selfish Herd Optimization (SHO) algorithm to address this critical issue. Inspired by the self-preservation behavior observed in animal herding, the SHO algorithm is adapted to coordinate the operation of DERs with loads within a distributed network. By dynamically adjusting their output based on local frequency measurements, DERs collectively exhibit a self-organizing behavior, resulting in improved frequency stability. The SHO based PID controller is also proven to be far more effective at controlling frequency than the traditional PI controller. The findings underscore the potential of bio-inspired algorithms in enhancing the resilience of grid-integrated DER systems, offering a promising avenue for future grid control methodologies.

The Analysis on the Construction Direction of the Coordinated Action System of Natural Disaster Volunteers in China

From the perspective of collaborative governance, it is found that volunteers have become an indispensable force in the process of responding to disasters. However, due to the late start but rapid development of emergency volunteer service in China, the current emergency management system lacks a coordination mechanism to ensure the orderly participation of emergency volunteers in disaster rescue, and there are problems such as the lack of the concept of polycentric governance, the disorder of self-organized behavior, the lack of coordination within the system, the excessive emphasis on the gratuitous nature of public welfare, and the lag in the construction of laws and regulations.

Exotic swarming dynamics of high-dimensional swarmalators.

Swarmalators are oscillators that can swarm as well as sync via a dynamic balance between their spatial proximity and phase similarity. Swarmalator models employed so far in the literature comprise only one-dimensional phase variables to represent the intrinsic dynamics of the natural collectives. Nevertheless, the latter can indeed be represented more realistically by high-dimensional phase variables. For instance, the alignment of velocity vectors in a school of fish or a flock of birds can be more realistically set up in three-dimensional space, while the alignment of opinion formation in population dynamics could be multidimensional, in general. We present a generalized D-dimensional swarmalator model, which more accurately captures self-organizing behaviors of a plethora of real-world collectives by self-adaptation of high-dimensional spatial and phase variables. For a more sensible visualization and interpretation of the results, we restrict our simulations to three-dimensional spatial and phase variables. Our model provides a framework for modeling complicated processes such as flocking, schooling of fish, cell sorting during embryonic development, residential segregation, and opinion dynamics in social groups. We demonstrate its versatility by capturing the maneuvers of a school of fish, qualitatively and quantitatively, by a suitable extension of the original model to incorporate appropriate features besides a gallery of its intrinsic self-organizations for various interactions. We expect the proposed high-dimensional swarmalator model to be potentially useful in describing swarming systems and programmable and reconfigurable collectives in a wide range of disciplines, including the physics of active matter, developmental biology, sociology, and engineering.

Topology-guided polar ordering of collective cell migration.

The ability of epithelial monolayers to self-organize into a dynamic polarized state, where cells migrate in a uniform direction, is essential for tissue regeneration, development, and tumor progression. However, the mechanisms governing long-range polar ordering of motility direction in biological tissues remain unclear. Here, we investigate the self-organizing behavior of quiescent epithelial monolayers that transit to a dynamic state with long-range polar order upon growth factor exposure. We demonstrate that the heightened self-propelled activity of monolayer cells leads to formation of vortex-antivortex pairs that undergo sequential annihilation, ultimately driving the spread of long-range polar order throughout the system. A computational model, which treats the monolayer as an active elastic solid, accurately replicates this behavior, and weakening of cell-to-cell interactions impedes vortex-antivortex annihilation and polar ordering. Our findings uncover a mechanism in epithelia, where elastic solid material characteristics, activated self-propulsion, and topology-mediated guidance converge to fuel a highly efficient polar self-ordering activity.

A general framework and decentralised algorithms for collective computational processes

Recent research on collective adaptive systems and macro-programming has shown the importance of programming abstractions for expressing the self-organising behaviour of ensembles, large and dynamic sets of collaborating devices. These generally leverage the interplay between the execution model and the program logic to steer the global-level emergent behaviour of the system. One notable example is the aggregate process abstraction: in an asynchronous round-based computational model, it allows to specify how aggregate-level computations are spawned, take form or spread on a domain of devices, and ultimately quit. Previous presentations of aggregate processes, however, are given in the formal framework of the field calculus, requiring knowledge of its syntax and articulated semantics. To provide a more accessible and language-agnostic presentation of such an abstraction, in this paper we introduce a general formal framework of collective computational processes (CCP). Specifically, as key contribution, we model and describe the programming interface (spawn construct) and dynamics of CCPs on event structures. Furthermore, we also propose novel algorithms for efficient propagation and termination of CCPs, based on statistics on the information speed and a notion of progressive wave-like closure. Crucially, thanks to our theoretical framework, we can provide optimality guarantees for the proposed algorithms, whose performance, superior to the state of the art, is assessed by simulation. Finally, to show applicability of CCPs, we provide a case study of situated service discovery in peer-to-peer networks.

Order parameter dynamics in complex systems: From models to data

Collective ordering behaviors are typical macroscopic manifestations embedded in complex systems and can be ubiquitously observed across various physical backgrounds. Elements in complex systems may self-organize via mutual or external couplings to achieve diverse spatiotemporal coordinations. The order parameter, as a powerful quantity in describing the transition to collective states, may emerge spontaneously from large numbers of degrees of freedom through competitions. In this minireview, we extensively discussed the collective dynamics of complex systems from the viewpoint of order-parameter dynamics. A synergetic theory is adopted as the foundation of order-parameter dynamics, and it focuses on the self-organization and collective behaviors of complex systems. At the onset of macroscopic transitions, slow modes are distinguished from fast modes and act as order parameters, whose evolution can be established in terms of the slaving principle. We explore order-parameter dynamics in both model-based and data-based scenarios. For situations where microscopic dynamics modeling is available, as prototype examples, synchronization of coupled phase oscillators, chimera states, and neuron network dynamics are analytically studied, and the order-parameter dynamics is constructed in terms of reduction procedures such as the Ott–Antonsen ansatz, the Lorentz ansatz, and so on. For complicated systems highly challenging to be well modeled, we proposed the eigen-microstate approach (EMP) to reconstruct the macroscopic order-parameter dynamics, where the spatiotemporal evolution brought by big data can be well decomposed into eigenmodes, and the macroscopic collective behavior can be traced by Bose–Einstein condensation-like transitions and the emergence of dominant eigenmodes. The EMP is successfully applied to some typical examples, such as phase transitions in the Ising model, climate dynamics in earth systems, fluctuation patterns in stock markets, and collective motion in living systems.

Differential effects of ocean warming and BDE-47 on mussels with various personalities

Marine warming and polybrominated diphenyl ethers (PBDEs) pollution are two of the most concerning environmental problems in recent years. However, the impact of their co-occurrence on marine bivalves and the tolerance of bivalves with different traits remain unknown. In this study, thick shell mussels Mytilus coruscus were divided into two personalities according to individual feeding and byssus growth. The reliability of the classification was validated by respiration, self-organization, and post-stress behavior. Then, the survival rate, hemolymph immunity, and digestive glands oxidase activity of classified mussels were evaluated after 21 days of compound exposure to warming and BDE-47. The results showed that mussels could be divided into proactive and reactive types consistently. Compared to reactive mussels, proactive mussels exhibited some traits, such as faster food recovery, more byssus growth, higher metabolic rate, and more efficient clustering. Both single or combined warming and BDE-47 exposure impacted the individual survival, hemolymph, and antioxidase of mussels. Notably, the negative impacts of BDE-47 were exacerbated by warming. Moreover, proactive mussels displayed better adaptability with higher survival rates along with less damage to hemolymph immunity and antioxidant ability compared to reactive ones when facing environmental challenges. This study highlights potential risks associated with the coexistence of marine warming and PBDEs pollution while demonstrating differential fitness among individuals with distinct personalities.

Analysis of the Impact of Structuralized Industrial Upgrading and Industry Chain Resilience

This paper integrates several theoretical perspectives, such as economics, sociology, environmental science, organizational ecology, global value chain theory and complexity theory, to explore in depth the interrelationship between structured industrial upgrading and the resilience of the industrial chain. From the economic dimension, industrial upgrading not only promotes the development of the industry chain to a higher level, but also improves the resilience of the whole industry chain by reducing the dependence on limited resources through the introduction of new technologies and the enhancement of efficiency. The consideration of the social dimension emphasizes the importance of paying attention to social responsibility and social participation in the upgrading process, which helps establish a stronger social foundation and improves the resilience of the whole industrial chain. The environmental dimension perspective reveals the positive impact of the environmental friendliness of industrial chain upgrading on resilience, which enhances the overall stability of the industrial chain by reducing environmental shocks. Organizational ecology theory, on the other hand, highlights the roles and interactions at the organizational level, and factors such as organizational evolution, adaptation, and resource allocation have far-reaching impacts on the resilience of the industry chain. The global value chain theory provides a global perspective, emphasizing the importance of division of labor synergy and diversification in each link of the chain. Finally, complexity theory profoundly analyzes the complex mechanism of industry chain evolution from the perspectives of self-organizing behavior, path dependence, and nonlinear effects, enabling a better understanding of the dynamic evolution process of the industry chain. The synthesis of these theoretical perspectives provides a comprehensive understanding of the relationship between industrial structuralization and upgrading and industry chain resilience, and provides theoretical support for the development of more comprehensive and targeted policies and strategies in the future. In practice, an in-depth analysis of how various factors interact will help promote the sustainable development and adaptive upgrading of the industrial chain.

Developing Explainable Deep Model for Discovering Novel Control Mechanism of Neuro-Dynamics.

Human brain is a complex system composed of many components that interact with each other. A well-designed computational model, usually in the format of partial differential equations (PDEs), is vital to understand the working mechanisms that can explain dynamic and self-organized behaviors. However, the model formulation and parameters are often tuned empirically based on the predefined domain-specific knowledge, which lags behind the emerging paradigm of discovering novel mechanisms from the unprecedented amount of spatiotemporal data. To address this limitation, we sought to link the power of deep neural networks and physics principles of complex systems, which allows us to design explainable deep models for uncovering the mechanistic role of how human brain (the most sophisticated complex system) maintains controllable functions while interacting with external stimulations. In the spirit of optimal control, we present a unified framework to design an explainable deep model that describes the dynamic behaviors of underlying neurobiological processes, allowing us to understand the latent control mechanism at a system level. We have uncovered the pathophysiological mechanism of Alzheimer's disease to the extent of controllability of disease progression, where the dissected system-level understanding enables higher prediction accuracy for disease progression and better explainability for disease etiology than conventional (black box) deep models.

Synthesis, Supramolecular Self-Organization, and Thermal Behavior of the Double Three-Dimensional Pseudo-Polymer Complex [Au{S2CN(CH2)6}2]4[Ag5Cl9] Comprising the New Type Silver(I) Anion

Synthesis, Supramolecular Self-Organization, and Thermal Behavior of the Double Three-Dimensional Pseudo-Polymer Complex [Au{S2CN(CH2)6}2]4[Ag5Cl9] Comprising the New Type Silver(I) Anion

Innovation of teaching mode of economic management informationization in colleges and universities based on the RDMIE model

Abstract The accelerated application of emerging technologies in the financial field in the era of informationization has put forward brand-new challenges and requirements for the development of disciplines as well as the teaching mode of economics and management majors in colleges and universities. In this paper, the teaching resource base of economics and management majors is first constructed on the basis of the RNMIE model combined with cloud computing and other technologies. Then the theory of self-organizing behavior is introduced to build a three-dimensional informatization teaching platform for economics and management majors. On this basis, combined with the online learning teaching resources analysis model, the personalized recommendation method of hybrid recommendation is used to recommend matching resources for economics and management students. The learning satisfaction assessment model is constructed with the students’ learning satisfaction assessment system and structural equation model. Simulation tests and example analyses were conducted to recommend resources and ensure student satisfaction in the three-dimensional computer-based teaching platform for economics and management in colleges and universities. It is found that when the number of nearest neighbors increases from 5 to 50, the MAE of personalized recommendation of economic and management teaching resources based on hybrid recommendation decreases by 30.91%, and the direct utility path coefficient of teaching effect on students’ learning satisfaction is 0.62. The teaching resources of economics and management constructed by combining the RNMIE model can help students realize the diversified choices of teaching resources and also provide a new reference for the innovative informationized teaching mode of economics and management majors.