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Recently, frequent flood disasters in China have seriously threatened economic development and public safety. This paper addresses the need for a dynamic urban flood emergency knowledge management system in emergency management departments and the lack of systematic knowledge among emergency managers regarding urban flood control. A multimodal knowledge graph-based urban flood emergency knowledge management model was constructed to enhance the decision-making capabilities of emergency management departments, improve the efficiency of public emergency evacuation, and reduce losses from urban flood disasters by analyzing the shortcomings of the existing emergency management system. An intelligent and dynamic flood emergency knowledge management model was built. This paper integrates multimodal knowledge graph technology to establish a multimodal emergency knowledge management framework for urban flood control. It develops and simulates the proposed model's application scenarios for urban flood emergency evacuation using the Flocking algorithm on the NetLogo platform. Through simulation experiments, the practicality and effectiveness of the model in real flood disaster situations were examined, particularly in simulating crowd evacuation behavior. The study found that the model significantly improves the accuracy of information and decision-making speed during emergency responses and supports emergency management departments in conducting targeted and personalized emergency decisions. This research provides a scientific basis for emergency management departments to optimize their emergency response strategies to flood disasters and serves as a reference and example for the application of multimodal knowledge graph technology in emergency management. [ABSTRACT FROM AUTHOR]

This document discusses the use of computational simulation in crisis and disaster management. It highlights the advantages of simulation models in examining alternative strategies and predicting the effects of changes in real-world systems. The document also acknowledges the challenges in incorporating realistic human behavior, increasing acceptability of simulation models, and creating multi-scale models. The special issue focuses on current real-world problems and includes papers on topics such as epidemics, seismic crises, population sheltering management, and mass evacuation. Two companion papers on airport evacuation and the Syrian refugee crisis are also mentioned. [Extracted from the article]

This paper introduces network science to museum studies. The spatial structure of the museum and the exhibit display largely determine what visitors see and in which order, thereby shaping their visit experience. Despite the importance of spatial properties in museum studies, few scientific tools have been developed to analyze and compare the results across museums. This paper introduces the six habitually used network science indices and assesses their applicability to museum studies. Network science is an empirical research field that focuses on analyzing the relationships between components in an attempt to understand how individual behaviors can be converted into collective behaviors. By taking the museum and the visitors as the network, this methodology could reveal unknown aspects of museum functions and visitor behavior, which could enhance exhibition knowledge and lead to better methods for creating museum narratives along the routes. [ABSTRACT FROM AUTHOR]

This paper investigates the decision-making behaviors of opinion leaders and netizens in the context of uncertain information dissemination with the aim of effectively managing online public opinion crises triggered by major sudden events. The decision-making behaviors of opinion leaders are categorized into positive and negative guidance, while those of netizens are classified into acceptance and nonacceptance. Using an evolutionary game model, this study introduces random factors to examine their influence on the decisionmaking processes of both groups. A stochastic evolutionary game model is constructed to analyze the behaviors of opinion leaders and netizens in the context of uncertain information dissemination. The evolutionary stability strategies and stochastic evolutionary processes of the model are analyzed based on the theory of Itô stochastic differential equations. The impacts of key variables such as random disturbances, the degree of psychological identification of netizens with opinion leaders, and the intensity of government penalties for those spreading negative information are examined through numerical simulations. The findings indicate that opinion leaders evolve to make stable strategies more rapidly than netizens do; random disturbances slow the evolution of stable strategies for both groups but do not alter their strategic choices; a higher degree of psychological identification increases the likelihood of netizens adopting the views of opinion leaders; and as punitive measures intensify, both opinion leaders and netizens are inclined to choose strategies of positive guidance and acceptance. The results of this study offer theoretical insights and decision-making guidance for future government strategies for managing similar online collective behaviors. [ABSTRACT FROM AUTHOR]

Research on political participation almost unanimously assumes that social pressure by neighbors induces collective behavior. Yet most experimental studies focus on individually based forms of political and civic behavior, such as voting and recycling, in Western industrialized societies. The paper tests the effect of neighborly social pressure on collective action in Tunisia. In a field experiment, I manipulate whether neighbors or community outsiders invite citizens to contribute to a public good (i.e., trash collection). I run the experiment in three neighborhoods of varying socioeconomic composition in Tunis (n = 1199). I do not find evidence to suggest that neighborly social pressure encourages participation in neighborhood cleanups, with low participation rates both for the neighbor and outsider contact conditions. While the effect of social pressure does not significantly vary across communities, overall participation rates do. Residents of the poor neighborhood are most likely to respond in a socially desirable way when asked about their intentions but least likely to participate. The paper also discusses some limitations of the study and outlines avenues for future research. [ABSTRACT FROM AUTHOR]

This research paper presents a review of the use of convolutional neural networks (CNNs) for human crowd analysis. The paper discusses the challenges and limitations of methods and highlights the potential of CNNs in addressing these limitations. This study reveals and provides an in-depth analysis of the different techniques, architectures, and algorithms used in CNNs for human crowd analysis and their respective advantages and limitations. Additionally, the paper discusses the potential applications of CNNs in crowd analysis, including pedestrian detection, crowd counting, and crowd behavior recognition. The review also provides insights into the performance evaluation metrics commonly used in this area and the datasets used for training and testing CNNs. Overall, this review provides a comprehensive overview of the latest developments in the use of CNNs for human crowd analysis, as well as insights into future research directions in this field. [ABSTRACT FROM AUTHOR]

In this paper, we analyze submersions with Poisson fibres. These are submersions whose total space carries a Poisson structure, on which the ambient Poisson structure pulls back, as a Dirac structure, to Poisson structures on each individual fibre. Our "Poisson--Dirac viewpoint" is prompted by natural examples of Poisson submersions with Poisson fibres -- in toric geometry and in Poisson--Lie groups -- whose analysis was not possible using the existing tools in the Poisson literature. The first part of the paper studies the Poisson--Dirac perspective of inducing Poisson structures on submanifolds. This is a rich landscape, in which subtle behaviours abound, as illustrated by a surprising "jumping phenomenon" concerning the complex relation between the induced and the ambient symplectic foliations, which we discovered here. These pathologies, however, are absent from the well-behaved and abundant class of coregular submanifolds, with which we are mostly concerned here. The second part of the paper studies Poisson submersions with Poisson fibres -- the natural Poisson generalization of flat symplectic bundles. These Poisson submersions have coregular Poisson--Dirac fibres, and behave functorially with respect to such submanifolds. We discuss the subtle collective behavior of the Poisson fibres of such Poisson fibrations, and explain their relation to pencils of Poisson structures. The third and final part applies the theory developed to Poisson submersions with Poisson fibres which arise in Lie theory. We also show that such submersions are a convenient setting for the associated bundle construction, and we illustrate this by producing new Poisson structures with a finite number of symplectic leaves. Some of the points in the paper being fairly new, we illustrate the many fine issues that appear with an abundance of (counter-)examples. [ABSTRACT FROM AUTHOR]

Small bubbles in fluids rise to the surface due to Archimede's force. Remarkably, in turbulent flows this process is severely hindered by the presence of vortex filaments, which act as moving potential wells, dynamically trapping light particles and bubbles. Quantifying the statistical weights and roles of vortex filaments in turbulence is, however, still an outstanding experimental and computational challenge due to their small scale, fast chaotic motion, and transient nature. Here we show that, under the influence of a modulated oscillatory forcing, the collective bubble behavior switches from a dynamically localized to a delocalized state. Additionally, we find that by varying the forcing frequency and amplitude, a remarkable resonant phenomenon between light particles and small-scale vortex filaments emerges, likening particle behavior to a forced damped oscillator. We discuss how these externally actuated bubbles can be used as a type of microscopic probe to investigate the space-time statistical properties of the smallest turbulence scales, allowing to quantitatively measure physical characteristics of vortex filaments. We develop a superposition model that is in excellent agreement with the simulation data of the particle dynamics which reveals the fraction of localized/delocalized particles as well as characteristics of the potential landscape induced by vortices in turbulence. Our approach paves the way for innovative ways to accurately measure turbulent properties and to the possibility to control light particles and bubble motions in turbulence with potential applications to oceanography, medical imaging, drug/gene delivery, chemical reactions, wastewater treatment, and industrial mixing. [ABSTRACT FROM AUTHOR]

Leader–follower modalities and other asymmetric interactions that drive the collective motion of organisms are often quantified using information theory metrics like transfer or causation entropy. These metrics are difficult to accurately evaluate without a much larger number of data than is typically available from a time series of animal trajectories collected in the field or from experiments. In this paper, we use a generalized leader–follower model to argue that the time-separated mutual information between two organism positions can serve as an alternative metric for capturing asymmetric correlations that is much less data intensive and more accurately estimated by popular k-nearest neighbor algorithms than transfer entropy. Our model predicts a local maximum of this mutual information at a time separation value corresponding to the fundamental reaction timescale of the follower organism. We confirm this prediction by analyzing time series trajectories recorded for a pair of golden shiner fish circling an annular tank. [ABSTRACT FROM AUTHOR]

The dynamics of synaptic interactions within spiking neuron networks play a fundamental role in shaping emergent collective behavior. This paper studies a finite-size network of quadratic integrate-and-fire neurons interconnected via a general synaptic function that accounts for synaptic dynamics and time delays. Through asymptotic analysis, we transform this integrate-and-fire network into the Kuramoto-Sakaguchi model, whose parameters are explicitly expressed via synaptic function characteristics. This reduction yields analytical conditions on synaptic activation rates and time delays determining whether the synaptic coupling is attractive or repulsive. Our analysis reveals alternating stability regions for synchronous and partially synchronous firing, dependent on slow synaptic activation and time delay. We also demonstrate that the reduced microscopic model predicts the emergence of synchronization, weakly stable cyclops states, and non-stationary regimes remarkably well in the original integrate-and-fire network and its theta neuron counterpart. Our reduction approach promises to open the door to rigorous analysis of rhythmogenesis in networks with synaptic adaptation and plasticity. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of collective behavior often depends on the adequate interaction of individuals through self-organization and the exchange of local information. When facing external threats, communication among individuals requires both rapid and effective information exchange to characterize sudden events. In this paper, we introduce the mechanism of emotions into the modeling of dynamics to study collective avoidance behavior in response to threats. Methods: A scenario involving a hidden dynamic threat is constructed to test the avoidance and survival capabilities of the collective when faced with a lack of effective information. By employing the activation and spread of emotion in modeling, the collective may self-organized and adeptly mitigate risks and enhance their own benefits. Results: Through adjustments to the intensity of emotional activation, spread, and decay, rich behaviors emerge. Relying on the regulation of emotion, the collective exhibits different response strategies and action patterns when facing threats, in which the optimal performance from the macroscopic level is expectable. Discussion: By analyzing these phenomena, it can enhance our understanding of the emotional states of collective in response to threats and the methods of controlling in intelligent collective motion. [ABSTRACT FROM AUTHOR]

This paper proposes the complex dynamics of collective behavior through an interdisciplinary approach that integrates individual cognition with potential fields. Firstly, the interaction between individual cognition and external potential fields in complex social systems is explored, integrating perspectives from physics, cognitive psychology, and social science. Subsequently, a new modeling method for the multidimensional potential field mechanism is proposed, aiming to reduce individual behavioral errors and cognitive dissonance, thereby improving system efficiency and accuracy. The approach uses cooperative control and distributed learning algorithms to simulate collective behavior, allowing individuals to iteratively adapt based on local information and collective intelligence. Simulations highlight the impact of factors such as individual density, noise intensity, communication radius, and negative potential fields on collective dynamics. For instance, in a high-density environment with 180 individuals, increased social friction and competition for resources significantly decrease collective search efficiency. Validation is achieved by comparing simulation results with existing research, showing consistency and improvements over traditional models. In noisy environments, simulations maintain higher accuracy and group cohesion compared to standard methods. Additionally, without communication, the Mean Squared Error (MSE) initially drops rapidly as individuals adapt but stabilizes over time, emphasizing the importance of communication in maintaining collective efficiency. The study concludes that collective behavior emerges from complex nonlinear interactions between individual cognition and potential fields, rather than being merely the sum of individual actions. These insights enhance the understanding of complex system dynamics, providing a foundation for future applications in adaptive urban environments and the design of autonomous robots and AI systems. [ABSTRACT FROM AUTHOR]

Introduction: Exit selection is crucial in indoor emergency evacuation. Domestic and foreign scholars have found that exit choice behavior is influenced by three factors: environmental factors, social interactions, and individual internal factors. Previous studies have shown that in addition to a single environmental factor affecting exit decisions, the influence of other available exit options in the context can ultimately lead to a reversal of exit decisions -The context effect. However, the impact of context effects on exit decisions in emergency situations has not been thoroughly explored. Therefore, this article identifies three basic independent variables: context effects, crowd flows, and gender differences, to study the exit decisions of different gender groups facing different crowd flows, as well as how context effects affect existing exit decisions. Methods: In this paper, we used virtual reality technology to construct an indoor fire scene and designed a total of 15 virtual experiments with different crowd distribution or context effects. 131 participants were divided into two groups, male and female, and their exit decisions were observed under different crowd flows and contextual effects. Results: The research results show that: 1) Both men and women have an innate preference to avoid crowded exits, and the proportion of following crowd evacuation significantly decreases when there are crowded crowds in the scene; 2) The exit decisions of female participants are more influenced by the crowd, while men tend to be more influenced by context effects when evacuating independently; 3) The context effects on exit decisions in emergency situations is statistically significant, and this performance is more significant in the male population. Further analysis reveals that similarity effects have a more significant impact on exit decisions than attraction effects. Discussions: These findings provide deeper insights into the exit choice behavior of the population and may contribute to the design of safe exits in indoor buildings. In addition, this article emphasizes the importance of context effects and provides a foundation for future research. [ABSTRACT FROM AUTHOR]

Many classical examples of models of self-organized dynamics, including the Cucker-- Smale, Motsch--Tadmor, multi-species, and several others, include an alignment force that is based upon density-weighted averaging protocol. Those protocols can be viewed as special cases of "environmental averaging". In this paper we formalize this concept and introduce a unified framework for systematic analysis of alignment models. A series of studies are presented including the mean-field limit in deterministic and stochastic settings, hydrodynamic limits in the monokinetic and Maxwellian regimes, hypocoercivity and global relaxation for dissipative kinetic models, several general alignment results based on chain connectivity and spectral gap analysis. These studies covermany of the known results and reveal new ones, which include asymptotic alignment criteria based on connectivity conditions, new estimates on the spectral gap of the alignment force that do not rely on the upper bound of the macroscopic density, uniform gain of positivity for solutions of the Fokker--Planck-alignment model based on smooth environmental averaging. As a consequence, we establish unconditional relaxation result for global solutions to the Fokker--Planck-alignment model, which presents a substantial improvement over previously known perturbative results. [ABSTRACT FROM AUTHOR]

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 realworld 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. [ABSTRACT FROM AUTHOR]

Facing the exponential demand for massive connectivity and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets. Particularly, the operators have to cope with the continuous prosperity of the Internet of things (IoT) along with the ever-increasing deployment of machine-type devices (MTDs). In this regard, due to its compelling benefits, non-orthogonal multiple access (NOMA) has sparked a significant interest as a sophisticated technology to address the above-mentioned challenges. In this paper, we consider a hybrid NOMA scenario, wherein the MTDs are divided into different groups, each of which is allocated an orthogonal resource block (RB) so that the members of each group share a given RB to simultaneously transmit their signals. Firstly, we model the densely deployed network using a mean field game (MFG) framework while taking into consideration the effect of the collective behavior of devices. Then, in order to reduce the complexity of the proposed technique, we apply the multi-armed bandit (MAB) framework to jointly address the resource allocation and the power control problem. Thereafter, we derive two distributed decision-making algorithms that enable the users to autonomously regulate their transmit power levels and self-organize into coalitions based on brief feedback received from the base station (BS). Simulation results are given to underline the equilibrium properties of the proposed resource allocation algorithms and to reveal the robustness of the proposed learning process. [ABSTRACT FROM AUTHOR]

This conceptual paper presents the analytical theory of agency (ATA), an overlooked philosophical approach to the concept of action, to develop its theoretical basis in routines research in which the constructs of action and agency play crucial roles. It expounds, in the framework of ATA, the ideas of the spectrum of intentionality, kinds of action, and collective agency, which help advance the rigor of action-theoretical concepts in routines research as well as reveal the rationale of the microfoundational approach to routine actions. To uncover the developmental potential of ATA, the article discusses the most crucial conceptual challenges for routines research; it also briefly examines the limitations and future work related to using ATA in the management field. [ABSTRACT FROM AUTHOR]

Social insects are ecologically and evolutionarily the most successful organisms on earth and can achieve robust collective behaviors through local interactions among group members. Colony migration has been considered as a leading example of collective decision making in social insects. In this paper, a piecewise colony migration system with recruitment switching is proposed to explore underlying mechanisms and synergistic effects of colony size and quorum on the outcomes of collective decision. The dynamical behavior of the nonsmooth system is studied, and sufficient conditions for the existence and stability of equilibrium are provided. The theoretical results suggest that large colonies are more likely to emigrate to a new site. More interesting findings include but are not limited to that (a) the system may exhibit oscillation when the colony size is below a critical level and (b) the system may also exhibit a bistable state, i.e., the colony migrates to a new site or the old nest depending on the initial size of recruiters. Bifurcation analysis shows that the variations of colony size and quorum threshold greatly impact the dynamics. The results suggest that it is important to distinguish between two populations of recruiters in modeling. This work may provide important insights on how simple and local interactions achieve the collective migrating activity in social insects. [ABSTRACT FROM AUTHOR]

The escalating process of urbanization has raised concerns about incidents arising from overcrowding, necessitating a deep understanding of large human crowd behavior and the development of effective crowd management strategies. This study employs computational methods to analyze real-world crowd behaviors, emphasizing self-organizing patterns. Notably, the intersection of two streams of individuals triggers the spontaneous emergence of striped patterns, validated through both simulations and live human experiments. Addressing a gap in computational methods for studying these patterns, previous research utilized the pattern-matching technique, employing the Nelder-Mead Simplex algorithm for fitting a two-dimensional sinusoidal function to pedestrian coordinates. This paper advances the pattern-matching procedure by introducing Simulated Annealing as the optimization algorithm and employing a two-dimensional square wave for data fitting. The amalgamation of Simulated Annealing and the square wave significantly enhances pattern fitting quality, validated through statistical hypothesis tests. The study concludes by outlining potential applications of this method across diverse scenarios. [ABSTRACT FROM AUTHOR]

In crowd simulation, representing crowd behavior in complex dynamic environments is one of the biggest challenges. In this paper, we propose new algorithms to make crowds satisfy a given formation while they are moving towards a destination. For this, we apply the Position Based Dynamics (PBD) framework, but introduce a new formation constraint based on a so-called Short Range Destination (SRD). The SRD is a short-term goal to which an agent must move in formation. In addition, a grid structure that we use for neighbor search is also used for congestion control. Depending on the congestion value, the agents in the cell may break the formation and instead exhibit emergent behaviors such as collision avoidance, but must automatically restore the original formation once the situation is resolved. Smooth movement of agents is also achieved by adding special behaviors when they are moving along the path that the user specifies. From several experiments, we show that the proposed scheme is capable of exhibiting natural aggregate behavior of crowds in real time, even for a highly condensed environment. [ABSTRACT FROM AUTHOR]

In this paper, we unite concepts from Husserlian phenomenology, the active inference framework in theoretical biology, and category theory in mathematics to develop a comprehensive framework for understanding social action premised on shared goals. We begin with an overview of Husserlian phenomenology, focusing on aspects of inner time-consciousness, namely, retention, primal impression, and protention. We then review active inference as a formal approach to modeling agent behavior based on variational (approximate Bayesian) inference. Expanding upon Husserl's model of time consciousness, we consider collective goal-directed behavior, emphasizing shared protentions among agents and their connection to the shared generative models of active inference. This integrated framework aims to formalize shared goals in terms of shared protentions, and thereby shed light on the emergence of group intentionality. Building on this foundation, we incorporate mathematical tools from category theory, in particular, sheaf and topos theory, to furnish a mathematical image of individual and group interactions within a stochastic environment. Specifically, we employ morphisms between polynomial representations of individual agent models, allowing predictions not only of their own behaviors but also those of other agents and environmental responses. Sheaf and topos theory facilitates the construction of coherent agent worldviews and provides a way of representing consensus or shared understanding. We explore the emergence of shared protentions, bridging the phenomenology of temporal structure, multi-agent active inference systems, and category theory. Shared protentions are highlighted as pivotal for coordination and achieving common objectives. We conclude by acknowledging the intricacies stemming from stochastic systems and uncertainties in realizing shared goals. [ABSTRACT FROM AUTHOR]

Inspired by the collective behaviors of active systems in nature, the collective behavior of micromotors has attracted more and more attention in recent years. However, little attention has been paid to the collective behavior of the immobilized micromotor, i.e., the micropump. In this paper, a unique pentacene-based micropump is reported, which demonstrates dynamic collective behavior activated by white light irradiation. The light irradiation may generate the photochemical reactions between pentacene and water, leading to the electroosmotic flow. As a result, this micropump is capable of pumping the surrounding solution inward along the substrate surface based on the electroosmosis mechanism. Intriguingly, the inward pumping causes the agglomeration of the tracer particles on the surface of the micropump. In addition, the aggregation can migrate following the change in the light irradiation position between two adjacent micropumps. Based on the aggregating and migrating behaviors of this pentacene-based micropump, we have achieved the conductivity restoration of the cracked circuit. [ABSTRACT FROM AUTHOR]

The traditional social force model (SFM) in crowd simulation experiences difficulty coping with the complexity of the crowd, limited by singular physical formulas and parameters. Recent attempts to combine deep learning with these models focus more on simulating specific states of crowds. This paper introduces an advanced deep social force model, influenced by crowd states. It utilizes deep neural networks to accurately fit crowd trajectory features, enhancing behavior simulation capabilities. Geometrical constraints within the model provide control over varied crowd behaviors, adjustable to simulate different crowd types. Before training, we use the SFM to refine behaviors in real trajectories with excessively small distances, aiming to enhance the general applicability of the model. Comparative experiments affirm the effectiveness of the model, showing comparable performance to both classic physical models and modern learning-based hybrid models in pedestrian simulations, with reduced collisions. In addition, the model has a certain ability to simulate crowds with high density and diverse behaviors. [ABSTRACT FROM AUTHOR]

We consider the car following problem for a set of autonomous vehicles following each other on either an infinite or circular road. The behavior of each car is specified by its "speed regulator", a device that decides to increase or decrease the speed of the car as a function of the head-tail distance to its predecessor and the speed of both cars. A collective behavior emerges that corresponds to previously proposed cellular automata traffic models. We further analyze the traffic patterns of the system in the long term, as governed by the speed regulator and we study under which conditions traffic patterns of maximum flow can or cannot be reach. We show the existence of suboptimal flow conditions that require external coordination mechanisms (that we do not consider in this paper) in order to reach the optimal flow achievable with the given density. In contrast with other approaches, we do not try to reproduce observed or measured traffic patterns. We analyze a deterministic speed regulator in order to decipher the emergent dynamics, and to ponder what maneuvers can be safely performed. Here, we restrict our attention to the car following problem. By comparing our speed regulator with classical models, auch as the Nagel–Schreckenberg and KKW models, we observe that although our regulator is formulated in simple terms, its dynamics share similarities with these models. In particular, the KKW model is designed to reproduce the observed behavior that a trailing car in the synchronization range of the leading car tends to regulate its speed to maintain a constant distance. this same behavior is adopted by our speed regulator, showing that this is a safe way of driving. [ABSTRACT FROM AUTHOR]

This paper focuses on Herbert A. Simon's visionary theory of the Artificial World. The artificial world evolves over time as a result of various actions, including interactions with the external world as well as interactions among its internal components. This paper proposes a mathematical theory of the conceptual framework of the artificial world. This goal requires the development of new mathematical tools, inspired in some way by statistical physics and stochastic game theory. The mathematical theory is applied in particular to the study of the dynamics of organizational learning, where cooperation and competition evolve through decomposition and recombination of organizational structures; the effectiveness of the evolutionary changes depends on the dynamic prevalence of cooperative over selfish behaviors, showing features common to the evolution of all living systems. [ABSTRACT FROM AUTHOR]

Research has recently grown on multi-agent systems (MAS) and their coordination and secure cooperative control, for example in the field of edge-cloud computing. MAS offers robustness and flexibility compared to centralized systems by distributing control across decentralized agents, allowing the system to adapt and scale without overhaul. The collective behavior emerging from agent interactions can solve complex tasks beyond individual capabilities. However, controlling high-order nonlinear MAS with unknown dynamics raises challenges. This paper proposes an enhanced genetic algorithm strategy to enhance secure cooperative control performance. An efficient encoding method, adaptive decoding schemes, and heuristic initialization are introduced. These innovations enable compelling exploration of the solution space and accelerate convergence. Individual enhancement via load balancing, communication avoidance, and iterative refinement intensifies local search. Simulations demonstrate superior performance over conventional algorithms for complex control problems with uncertainty. The proposed method promises robust, efficient, and consistent solutions by adapting to find optimal points and exploiting promising areas in the space. This has implications for securely controlling real-world MAS across domains like robotics, power systems, and autonomous vehicles. [ABSTRACT FROM AUTHOR]

The construction of living systems from the bottom‐up helps to explore the complex processes of life and to understand their working mechanism. Prototissues, constructed using artificial cells as building blocks, mimic life systems at a high‐order tissue level, whilst artificial cells usually mimic living cells at the individual cell level. The 3D biomimetic prototissues demonstrate exceptional performances and collective functions, which reveal the working mechanisms of living tissues and hold promising potential for biomedical applications. This review systematically summarized the research progress of the field of prototissues. The engineering methods for fabricating two types of prototissues are introduced first, followed by the functions of prototissues including collective behaviors and signal communications, as well as their biomedical applications. The challenges and future trends are proposed at the end of the paper. [ABSTRACT FROM AUTHOR]

Maintenance goals have a fundamental albeit understudied role in human motivation. From health to relationships, to personal possessions and the environment, the things people value require regular care. When maintaining a friendship, for instance, one may need to catch‐up regularly, share life updates and offer support, and occasionally make time to meet and spend time together. Distinct from the more commonly studied approach and avoidance goals, maintenance goals focus on sustaining current states over time, a process that has recently begun to attract increasing scholarly attention. This paper explores the construct of maintenance goals; It provides a comprehensive review of key findings and offers a novel perspective on the potential role of maintenance in individual and collective behavior, centering on topics such as well‐being, leadership, and group perceptions across different cultural contexts. By integrating theory with empirical evidence, this review underscores the necessity of incorporating maintenance goals into a broader psychological framework and recognizes their potential to inform interventions aimed at fostering resilience and stability in an ever‐changing world. [ABSTRACT FROM AUTHOR]

In this paper, we construct and analyze a mathematically reasonable and simplest population dynamics model based on Mark Granovetter’s idea for the spread of a matter (rumor, innovation, psychological state, etc.) in a population. The model is described by a one-dimensional difference equation. Individual threshold values with respect to the decision-making on the acceptance of a spreading matter are distributed throughout the population ranging from low (easily accepts it) to high (hardly accepts). Mathematical analysis on our model with some general threshold distributions (uniform; monotonically decreasing/increasing; unimodal) shows that a critical value necessarily exists for the initial frequency of acceptors. Only when the initial frequency of acceptors is beyond the critical, the matter eventually spreads over the population. Further, we give the mathematical results on how the equilibrium acceptor frequency depends on the nature of threshold distribution. [ABSTRACT FROM AUTHOR]

Many Australian students fail to meet an acceptable standard of reading proficiency. This can negatively impact their academic progress, social, and emotional well-being, and increase their risk of developing challenging behaviors. These risks and challenges have been found to compound over the lifetime of the learner. Unfortunately, the proportion of Australian students who fail to meet reading proficiency standards increases as they move through their years of schooling, and reading difficulties disproportionately affect historically marginalized groups. This has raised concerns about the effectiveness of instructional approaches used within the Australian education system, particularly in reading, and prompted discussions of reform. The purpose of this review paper was to examine the contributions of the science of reading and science of behavior to our collective knowledge regarding reading development and effective reading instruction, and how this knowledge is currently being used in the Australian context. We provide a discussion on the current state of reading instruction and achievement in Australia by considering national trends, inequities, and systemic challenges. Implications and recommendations to address inequities in reading outcomes, using both the science of reading and science of behavior, are discussed. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to optimize the thinning of an elliptical cylindrical array (ECA) composed of uniformly stimulated, isotropic antennas with the objective of achieving a directed beam characterized by a significantly reduced relative Sidelobe Level (SLL). The optimization process employs the Novel Particle Swarm Optimization (NPSO) method, which offers a fresh perspective on addressing electromagnetic optimization challenges by its ability to effectively explore solution spaces. By employing the NPSO algorithm, which emulates the collective behavior of swarming particles to search for optimal solutions, this study addresses complex optimization challenges inherent in antenna array design. This study focuses on identifying the optimal combination of ON–OFF components (use the minimum number of ON antenna elements) within the antenna array to produce a radiation pattern exhibiting the greatest decrease in SLL. Additionally, the First Null Beam Width (FNBW) is targeted for optimization without predefined values. The optimization approach also considers the effect of thinned array element spacing on the overall performance metrics. Out of total 36 elements, only 15 elements are switched ON and the remaining of the elements are OFF, so the total reduction or thinning of ECAA is 41.66%. Simulation results demonstrate that the proposed methodology enables a simultaneous reduction of more than half of the antenna array elements while achieving superior SLL minimization. This significant reduction in antenna elements not only contributes to simplifying the array design, but also enhances the array’s beamforming capabilities. [ABSTRACT FROM AUTHOR]

Social behavior in crowds, such as herding or increased interpersonal spacing, is driven by the psychological states of pedestrians. Current macroscopic crowd models assume that these are static, limiting the ability of models to capture the complex interplay between evolving psychology and collective crowd dynamics that defines a "social crowd". This paper introduces a novel approach by explicitly incorporating an "activity" variable into the modeling framework, which represents the evolving psychological states of pedestrians and is linked to crowd dynamics. To demonstrate the role of activity, we model pedestrian egress when this variable captures stress and awareness of contagion. In addition, to highlight the importance of dynamic changes in activity, we examine a scenario in which an unexpected incident necessitates alternative exits. These case studies demonstrate that activity plays a pivotal role in shaping crowd behavior. The proposed modeling approach thus opens avenues for more realistic macroscopic crowd descriptions with practical implications for crowd management. [ABSTRACT FROM AUTHOR]

This special issue presents scientific findings of the latest advancements in synchronization and other collective behaviors within higher-order networks. While the concept of higher-order interactions was introduced long ago, recent research has witnessed a surge in interest surrounding these networks. These developments underscore the significant impact that non-pairwise connections can have on network dynamics, particularly synchronization. Therefore, this issue aims to represent the emerging trends within this field. The collection comprises papers exploring diverse topics, including synchronization, chimera states, spiral waves, stochastic resonance in higher-order networks, higher-order social networks, and real brain networks, and the impacts of link removal on synchronization and hyper-network alignment. The studies can shed light on the complexities of higher-order networks and pave the way for future advancements in the field. [ABSTRACT FROM AUTHOR]

Object detection using deep learning has attracted considerable interest from researchers because of its competency in performing state-of-the-art tasks, including detection, observation, and action recognition. Deep Learning (DL)-based object detection models extract features directly from data, which is more efficient and effective than traditional methods requiring handcrafted features. Further, DL also effectively tackles spatiotemporal challenges by leveraging techniques; hence, researchers can develop better object detection models and implement more efficient strategies for object recognition. Moreover, optimizing these models improves performance and recognizes objects within videos or images. This survey comprises of an overview of related review papers and DL-based Object Detection (OD) algorithms. Object detection algorithms are presented as two classifications, namely Two-stage and One-stage methods, with Convolutional Neural Network (CNN) as the backbone. OD's applications are examined here, and crowd analysis has been extensively studied and researched for potential applications. Most of the papers in object detection rely on Convolutional Neural Networks (CNNs) (28%), whereas crowd analysis papers are distributed as follows: 24% in counting, 25% in categorizing, and 25% in analyzing individual behaviors, and 27% in others. In recent years, deep learning has significantly advanced object detection capabilities to provide effective solutions for various applications, including crowd analysis. • GAI-Driven Crowd behavior analysis: The crowd behavior analysis in the future will integrate GAI with realtime monitoring to analyze extensive data from social media and surveillance, providing detailed insights into crowd sentiments and movements.This integration will enhance crowd management and reduce the risk of violence. • IoT and Edge: Future Trends in Crowd Management: Integration of IoT and edge computing in crowd management enables real-time data collection on crowd behavior, facilitating swift analysis to optimize resource allocation for enhanced safety and efficiency. • The Deepfakes positive impact in Crowd: In crowds, deepfakes such as DeepSync, and Deep-Media can be used positively to offer personalized entertainment experiences like interactive story-telling and virtual performances to foster community engagement and social interaction (Venkata Rao and Venugopal, 2024). • The Deepfakes negative impact in Crowd: In crowds, deepfakes can potentially damage community bonds and escalate social tensions by spreading misinformation and provoking conflicts among crowds. [ABSTRACT FROM AUTHOR]

We consider a time varying delayed Cucker-Smale system in a harmonic potential field and analyze its long time collective behavior. Under appropriate assumptions on the initial data, we show the asymptotic collective behavior when the time varying delay is uniformly bounded by a sufficiently small constant. Our strategies are based on the Lyapunov functional approach, forward-backward estimate and the continuity argument. Finally, some numerical tests are performed to illustrate the theoretical result. [ABSTRACT FROM AUTHOR]

The article focuses on analyzing developments in the academic literature regarding the relationship between monetary policy and herd behavior in the stock market. The main research method used in this article is a systematic review of the literature, including methods of synthesis and bibliographic analysis. Accordingly, the bibliographic method is employed to organise and analyze the standard deviation associated with behavioral economics research. The research offers scholarly publications that include the term "monetary policy and herd behavior" from Dimensions database between the middle of 2014 to the beginning of 2023. The results show that there are two main research trends on the relationship between monetary policy and herd behavior, which are based on theory and market effects. Accordingly, the theoretical background to explain this interaction includes the micro-foundation of economics, behavioral macroeconomics, and the mediation of the stock market cycle. However, like most other micro-factors, crowd psychology has not been studied and appreciated in terms of its influence on economic policy, even though they are purely a crucial components causing asset bubbles are beyond the control and adjustment of monetary policy. Therefore, future studies should have a specific definition, as well as a proper assessment of the role of the crowd in the ability to make investment decisions as well as the market's risk tolerance in order to have specific recommendations for monetary policy. [ABSTRACT FROM AUTHOR]

Anthropologists have recently argued that the divide between nature and culture is not a universal framework suitable for understanding collective behavior but rather a local variation among various ways of composing the experience of the world. Notably, in the case of Philippe Descola’s anthropology, this critique led to a radical reconceptualization of social sciences and the humanities in terms of ontological regimes, which draws upon key aspects of the phenomenological tradition. In this paper, I develop a phenomenological perspective on Descola’s anthropology to clarify whether and how we can assess our engagement with the world beyond the divide between nature and culture. The paper is divided into three sections. In the first section, I present the main claims of Descola’s position, which he calls “relative universalism,” and introduce two critiques of this project: the potential conflation between his ontological framework and aspects of modern naturalism and the risk of reifying cultural determinations as ontological properties. In the second section, I address the first critique by showing how the universalist claim of Descola’s anthropology, according to which collective experience is organized by the duality of planes of physicality and interiority, can be elucidated through Husserl’s account of the embodied experience to avoid a conflation with the naturalist framework. Finally, I contend that anthropology’s idea of a diversity of ontological regimes can be made coherent by analyzing the two layers of the world constitution: the primordial experience of the lived body and the intersubjective process of communalization. [ABSTRACT FROM AUTHOR]

Although the spontaneous collective behavior of swimmers has attracted considerable attention, there has been little physical discussion of the extensibility of the collective system. This paper numerically studies the extensible collective behavior of a self-propelled beam integrated in a stable tandem two-beam system. Like the beams in the two-beam system, the inserting beam is driven by the heaving motions of its leading edge. Different cases are simulated according to different inserting positions and self-propelled motion with different phases. Two types of extensible collective configurations, the regular configuration and the compact configuration, are observed. The swimming speed, input power and propulsion efficiency of the beams are compared. It is found that the extensible collective behavior is conducive to the improvement of the propulsion performance of the whole system. Analyses of the flow-mediated interaction between the beams indicate that the vortex-locking mechanism can be used to predict the formation process and the extensible collective structure of the three-beam system. The vortex-induced hydrodynamic forces experienced by the inserting beam at different inserting positions determine the hydrodynamic behavior of the extensible collective system. It is recommended that the additional individual is inserted from the near front of the two-beam system. • The extensible collective behavior of three self-propelled beams is physically studied. • Both the regular and compact configurations of three-beam system are observed. • Performances of three tandem beams are promoted in the compact configuration. • Formation mechanism of the extensible collective behavior is revealed. [ABSTRACT FROM AUTHOR]

• A self-propelled particle model considering leadership and experience is proposed. • Group queuing and stable movement are achieved under the guidance of leaders. • The optimal leadership and vision intervals are obtained. The coordinated and ordered collective behavior arising from dispersed and local self-organizing interactions among individuals is widely observed in many biological communities. For populations engaging in group foraging or migration, complex societies with multiple hierarchical levels are common. A prevalent scenario involves two dominant levels, one corresponding to leaders and the other composed of followers. In this paper, we explore how a group can be influenced by a set of leaders to organize and move in a coordinated manner towards a preferred direction. Specifically, we balance social interactions among group members through two weighted factors, corresponding to the leadership and experience of leaders. Through computer simulations, we observe a linear relationship between the proportion of leaders guiding group movement and the group size, with larger groups requiring a higher proportion of leaders while maintaining initial density. Additionally, we identify an optimal leadership interval where group movement performance enhances with increasing leadership up to a certain point, beyond which it starts to decline. Similar patterns are observed concerning individual visual field, indicating the existence of an optimal interval for visual angle. Our findings not only contribute to understanding collective motion in natural biological systems but also provide new insights into effective leadership mechanisms in artificial swarm systems. [ABSTRACT FROM AUTHOR]

Collective motion, such as milling, flocking, and collective turning, is a common and captivating phenomenon in nature, which arises in a group of many self-propelled individuals using local interaction mechanisms. Recently, vision-based mechanisms, which establish the relationship between visual inputs and motion decisions, have been applied to model and better understand the emergence of collective motion. However, previous studies often characterize the visual input as a transient Boolean-like sensory stream, which makes it challenging to capture the salient movements of neighbors. This further hinders the onset of the collective response in vision-based mechanisms and increases demands on visual sensing devices in robotic swarms. An explicit and context-related visual cue serving as the sensory input for decision-making in vision-based mechanisms is still lacking. Here, we hypothesize that body orientation change (BOC) is a significant visual cue characterizing the motion salience of neighbors, facilitating the emergence of the collective response. To test our hypothesis, we reveal the significant role of BOC during collective U-turn behaviors in fish schools by reconstructing scenes from the view of individual fish. We find that an individual with the larger BOC often takes on the leading role during U-turns. To further explore this empirical finding, we build a pairwise interaction mechanism on the basis of the BOC. Then, we conduct experiments of collective spin and collective turn with a real-time physics simulator to investigate the dynamics of information transfer in BOC-based interaction and further validate its effectiveness on 50 real miniature swarm robots. The experimental results show that BOC-based interaction not only facilitates the directional information transfer within the group but also leads to scale-free correlation within the swarm. Our study highlights the practicability of interaction governed by the neighbor's body orientation change in swarm robotics and the effect of scale-free correlation in enhancing collective response. Collective motion in nature, such as flocking or turning, arises from local interactions between individuals, but vision-based mechanisms often struggle to capture critical neighbor movements. This study demonstrates that body orientation change (BOC) as a visual cue enhances coordination in fish schools and robotic swarms, improving collective responses. [ABSTRACT FROM AUTHOR]

The global steady state of a system in thermal equilibrium exponentially favors configurations with lesser energy. This principle is a powerful explanation of selforganization because energy is a local property of configurations. For nonequilibrium systems, there is no such property for which an analogous principle holds, hence no common explanation of the diverse forms of self-organization they exhibit. However, a flurry of recent empirical results has shown that a local property of configurations called "rattling" predicts the steady states of some nonequilibrium systems, leading to claims of a far-reaching principle of nonequilibrium self-organization. But for which nonequilibrium systems is rattling accurate, and why? Wedevelop a theory of rattling in terms of Markov processes that gives simple and precise answers to these key questions. Our results show that rattling predicts a broader class of nonequilibrium steady states than has been claimed and for different reasons than have been suggested. Its predictions hold to an extent determined by the relative variance of, and correlation between, the local and global "parts" of a steady state. We show how these quantities characterize the local-global relationships of various random walks on random graphs, spin-glass dynamics, and models of animal collective behavior. Surprisingly, we find that the core idea of rattling is so general as to apply to equilibrium and nonequilibrium systems alike. [ABSTRACT FROM AUTHOR]

Quorum sensing (QS) is a mechanism by which bacteria detect and respond to cell density, regulating collective behaviors. Burkholderia plantarii, the causal agent of rice seedling blight, employs the LuxIR-type QS system, common among Gram-negative bacteria, where LuxI-type synthase produces QS signals recognized by LuxR-type regulators to control gene expression. This study aimed to elucidate the QS mechanism in B. plantarii KACC18965. Through whole-genome analysis and autoinducer assays, the plaI gene, responsible for QS signal production, was identified. Motility assays confirmed that C8-homoserine lactone (C8-HSL) serves as the QS signal. Physiological experiments revealed that the QS-defective mutant exhibited reduced virulence, impaired swarming motility, and delayed biofilm formation compared to the wild type. Additionally, the QS mutant demonstrated weakened antibacterial activity against Escherichia coli and decreased phosphate solubilization. These findings indicate that QS in B. plantarii significantly influences various pathogenicity and survival traits, including motility, biofilm formation, antibacterial activity, and nutrient acquisition, highlighting the critical role of QS in pathogen virulence and adaptability. [ABSTRACT FROM AUTHOR]

This research introduces a novel approach to crisis management by implementing a multi-agent algorithm within a strategic decision system. The proposed system harnesses multiple agents' collective intelligence and adaptive capabilities to enhance decision-making processes during critical situations. The study first investigates the theoretical foundations of crisis management and multi-agent systems, emphasizing the need for an integrated approach that combines strategic decision-making with autonomous agents. Subsequently, the research presents the design and implementation of the multi-agent algorithm, outlining its ability to gather, process, and analyze diverse data sources in real time. The multi-agent algorithm is specifically tailored to adapt to dynamic crisis scenarios, ensuring a resilient decision-making framework. Experimental simulations present the implementation of a panic simulator and prediction of evacuation and intervention routes using multi-agent artificial intelligence algorithms. The results demonstrate the multi-agent algorithm-driven decision system's superiority in response time, resource allocation, and overall crisis mitigation. Furthermore, the research explores the system's scalability and adaptability to different crisis types, illustrating its potential applicability across diverse domains. [ABSTRACT FROM AUTHOR]

As the influence and risk of infectious diseases increase, efforts are being made to predict the number of confirmed infectious disease patients, but research involving the qualitative opinions of social media users is scarce. However, social data can change the psychology and behaviors of crowds through information dissemination, which can affect the spread of infectious diseases. Existing studies have used the number of confirmed cases and spatial data to predict the number of confirmed cases of infectious diseases. However, studies using opinions from social data that affect changes in human behavior in relation to the spread of infectious diseases are inadequate. Therefore, herein, we propose a new approach for sentiment analysis of social data by using opinion mining and to predict the number of confirmed cases of infectious diseases by using machine learning techniques. To build a sentiment dictionary specialized for predicting infectious diseases, we used Word2Vec to expand the existing sentiment dictionary and calculate the daily sentiment polarity by dividing it into positive and negative polarities from collected social data. Thereafter, we developed an algorithm to predict the number of confirmed infectious patients by using both positive and negative polarities with DNN, LSTM and GRU. The method proposed herein showed that the prediction results of the number of confirmed cases obtained using opinion mining were 1.12% and 3% better than those obtained without using opinion mining in LSTM and GRU model, and it is expected that social data will be used from a qualitative perspective for predicting the number of confirmed cases of infectious diseases. [ABSTRACT FROM AUTHOR]

The UAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles (UAVs), which is pivotal in public safety applications, particularly in scenarios involving intrusion monitoring and interception. To address the challenges of data acquisition, real-world deployment, and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks, we propose an innovative swarm intelligence-based UAV pursuit-evasion control framework, namely "Boids Model-based DRL Approach for Pursuit and Escape" (Boids-PE), which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning (DRL). The Boids model, which simulates collective behavior through three fundamental rules, separation, alignment, and cohesion, is adopted in our work. By integrating Boids model with the Apollonian Circles algorithm, significant improvements are achieved in capturing UAVs against simple evasion strategies. To further enhance decision-making precision, we incorporate a DRL algorithm to facilitate more accurate strategic planning. We also leverage self-play training to continuously optimize the performance of pursuit UAVs. During experimental evaluation, we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios, customizing the state space, action space, and reward function models for each scenario. Extensive simulations, supported by the PyBullet physics engine, validate the effectiveness of our proposed method. The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks, providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions. [ABSTRACT FROM AUTHOR]

Ensembles of suspended spinning particles in liquids form a distinct category of active matter systems known as chiral fluids. Recent experimental instances of dense chiral fluids have comprised of spinning colloidal magnets powered by an external rotating magnetic field. These particles interact through both magnetic and hydrodynamic forces, organizing collectively into circulating clusters characterized by unidirectional edge flows. Here, we externally drive the collective behavior of spinning colloids by leveraging diffusiophoretic interactions among the geometrically anisotropic particles. We show that these nanoscale interfacial flows lead to the formation of bound states between spinning colloids that are stabilized through near-field hydrodynamic and chemical interactions. At a collective level, we demonstrate that added diffusiophoretic interactions cause a loss in structural cohesion of the circulating clusters and promote expansion, while preserving global cluster inter-connectivity. The expanded cluster state is characterized by the formation of a dynamic interconnected network promoted by axi-asymmetric interactions around particles with attractive dipolar interactions dominating along the direction of the magnetic moment. This process is observed to be entirely reversible, offering external control over the emergent dynamics in dense chiral fluids, paving the way for new self-organization routes in chiral fluids and broader forms of active matter. Chiral active systems are composed of spinning constituent particles that self-organize into complex structures through hydrodynamic interactions. The authors develop methods to control these self-organized structures by introducing additional chemical interactions between spinning particles. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are computational techniques based on the collective behavior of swarms and the study of organisms acting in communities. These algorithms involve different types of organisms. Finding controller values for nonlinear systems is a challenging task using classical approaches. Hence, using metaheuristics to find the controller values of a twin rotor multi-input multi-output system (TRMS), one of the nonlinear systems studied in the literature, seems to be more appropriate than using classical methods. In this study, different types of metaheuristic algorithms were used to find the PID controller values for a TRMS, including a genetic algorithm (GA), a dragonfly algorithm, a cuckoo algorithm, a particle swarm optimization (PSO) algorithm, and a coronavirus optimization algorithm (COVIDOA). The obtained graphs were analyzed based on certain criteria for the main rotor and tail rotor angles to reach the reference value in the TRMS. The experimental results show that when the rise and settlement times of the TRMS are compared in terms of performance, the GA took 1.5040 s (seconds) and the COVIDOA took 9.59 s to increase the pitch angle to the reference value, with the GA taking 0.7845 s and the COVIDOA taking 2.4950 s to increase the yaw angle to the reference value. For the settling time, the GA took 11.67 s and the COVIDOA took 28.01 s for the pitch angle, while the GA took 14.97 s and the COVIDOA took 26.69 s for the yaw angle. With these values, the GA and COVIDOA emerge as the foremost algorithms in this context. [ABSTRACT FROM AUTHOR]

Hajj is an annual Islamic event attended by millions of pilgrims every year from around the globe. It is considered to be the biggest religious event that includes large human crowds in the world. Managing such crowds and detecting abnormal behaviors is one of the most significant challenges for the host country, particularly the crowds of pilgrims. Most of the current solutions can only handle small-scale crowd management issues, that involve simple and clear abnormal behaviors. Therefore, there is a need to have a human abnormal behavior detection approach that can deal with large-scale crowd situations. This study aims to propose a computer vision-based framework that automatically analyzes video sequences and detects human abnormal behaviors. The Convolutional LSTM Autoencoder is used for analyzing video scenes and extracting valuable spatial and temporal features. The proposed approach has achieved a good loss reduction of 0.176587 in detecting abnormal pilgrims' behavior. The results demonstrate a promising picture of the effectiveness of computer vision technologies to detect abnormal behavior in large-scale crowds. [ABSTRACT FROM AUTHOR]