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Sketches are fundamental in design and crucial representations for ideation, problem-solving, and communication. In the realm of architecture, sketches encapsulate the evolution of ideas from conceptualization to construction. Hand-drawn sketches, characterized by their open-ended, ambiguous nature and rapid production, are indispensable in bridging the gap between abstract concepts and tangible designs, guiding the progression from early design stages to final product realization. However, despite their significant potential and pivotal role in the design process, hand sketches have often been overlooked and swiftly abandoned in the ongoing discourse surrounding traditional versus digital design methodologies, particularly with the widespread integration of computer technologies. This study endeavours to unlock the wealth of information embedded within hand sketches, from initial design concepts to intricate manufacturing details, using a hybrid digital form-finding tool. By employing swarm algorithms in the quest for form, it is anticipated that the boundaries of conceptual ideas delineated by hand sketches will be expanded. This is facilitated by an algorithm developed in Processing using the Java coding language, complemented by an intuitive interface. The research commences with a comprehensive literature review encompassing biomimetics, sketching techniques, and tools for transitioning hand sketches into digital realms. Subsequently, a thorough elucidation of the algorithm, crafted within the Processing environment, is provided. The tool's efficacy is assessed through experimentation involving adjustments of various parameters on identical sketches and application to seven architectural sketches representing built designs, with subsequent interpretation of the outcomes. It is posited that the adaptability of the algorithm's core logic, coupled with the development-friendly environment of Processing, holds immense potential for empowering designers to steer sketches in desired directions through tailored enhancements. [ABSTRACT FROM AUTHOR]

How to analyze flocking behaviors of a multi-agent system with local interaction functions is a challenging problem in theory. Motsch and Tadmor in 2011 also stressed the significance to assume that the interaction function is rapidly decaying or cut-off at a finite distance (cf. Motsch and Tadmor in J. Stat. Phys. 2011). In this paper, we study the flocking behavior of a Cucker–Smale type model with compactly supported interaction functions. Using properties of a connected stochastic matrix, together with an elaborate analysis on perturbations of a linearized system, we obtain a sufficient condition imposed only on model parameters and initial data to guarantee flocking. Moreover, it is shown that the system achieves flocking at an exponential rate. [ABSTRACT FROM AUTHOR]

One of the main challenges in swarm robotics is to achieve robust and scalable flocking, such that large numbers of robots can move together in a coordinated and cohesive manner while avoiding obstacles or threats. Flocking models in swarm robotic systems typically use reactive behaviors, such as cohesion, alignment, and avoidance. The use of potential fields has enabled the derivation of reactive control laws using obstacles and neighboring robots as sources of force for flocking. However, reactive behaviors, especially when a multitude of them are simultaneously active, as in the case of flocking, are prone to cause collisions or inefficient motion within the flock due to its short-sighted approach. Approaches that aimed to generate smoother and optimum flocking, such as the use of model predictive control, would either require centralized coordination, or distributed coordination which requires low-latency and high-bandwidth communication requirements within the swarm as well as high computational resources. In this paper, we present a predictive search model that can generate smooth and safe flocking of robotic swarms in the presence of obstacles by taking into account the predicted states of other robots in a computationally efficient way. We tested the proposed model in environments with static and dynamic obstacles and compared its performance with a potential field flocking model in simulation. The results show that the predictive search model can generate smoother and faster flocking in swarm robotic systems in the presence of static and dynamic obstacles. Furthermore, we tested the predictive search model with different numbers of robots in environments with static obstacles in simulations and demonstrated that it is scalable to large swarm sizes. The performance of the predictive search model is also validated on a swarm of six quadcopters indoors in the presence of static and dynamic obstacles. [ABSTRACT FROM AUTHOR]

Several simulation models have demonstrated how flocking behavior emerges from the interaction among individuals that react to the relative orientation of their neighbors based on simple rules. However, the precise nature of these rules and the relationship between the characteristics of the rules and the efficacy of the resulting collective behavior are unknown. In this article, we analyze the effect of the strength with which individuals react to the orientation of neighbors located in different sectors of their visual fields and the benefit that could be obtained by using control rules that are more elaborate than those normally used. Our results demonstrate that considering only neighbors located on the frontal side of the visual field permits an increase in the aggregation level of the swarm. Using more complex rules and/or additional sensory information does not lead to better performance. [ABSTRACT FROM AUTHOR]

Featured Application: Non-deep-learning approach to recognize gestures from an imprecise tracking system such as passive RFID. In this paper, we present a novel non-deep-learning-based approach for real-time object tracking and activity recognition within smart homes, aiming to minimize human intervention and dataset requirements. Our method utilizes discreet, easily concealable sensors and passive RFID technology to track objects in real-time, enabling precise activity recognition without the need for extensive datasets typically associated with deep learning techniques. Central to our approach is AtomGID, an algorithm tailored to extract highly generalizable spatial features from RFID data. Notably, AtomGID's adaptability extends beyond RFID to other imprecise tracking technologies like Bluetooth beacons and radars. We validate AtomGID through simulation and real-world RFID data collection within a functioning smart home environment. To enhance recognition accuracy, we employ a clustering adaptation of the flocking algorithm, leveraging previously published Activities of Daily Living (ADLs) data. Our classifier achieves a robust classification rate ranging from 85% to 93%, underscoring the efficacy of our approach in accurately identifying activities. By prioritizing non-deep-learning techniques and harnessing the strengths of passive RFID technology, our method offers a pragmatic and scalable solution for activity recognition in smart homes, significantly reducing dataset dependencies and human intervention requirements. [ABSTRACT FROM AUTHOR]

In this work, we propose a minimalistic swarm flocking approach for multirotor unmanned aerial vehicles (UAVs). Our approach allows the swarm to achieve cohesively and aligned flocking (collective motion), in a random direction, without externally provided directional information exchange (alignment control). The method relies on minimalistic sensory requirements as it uses only the relative range and bearing of swarm agents in local proximity obtained through onboard sensors on the UAV. Thus, our method is able to stabilize and control the flock of a general shape above a steep terrain without any explicit communication between swarm members. To implement proximal control in a three-dimensional manner, the Lennard-Jones potential function is used to maintain cohesiveness and avoid collisions between robots. The performance of the proposed approach was tested in real-world conditions by experiments with a team of nine UAVs. Experiments also present the usage of our approach on UAVs that are independent of external positioning systems such as the Global Navigation Satellite System (GNSS). Relying only on a relative visual localization through the ultraviolet direction and ranging (UVDAR) system, previously proposed by our group, the experiments verify that our system can be applied in GNSS-denied environments. The degree achieved of alignment and cohesiveness was evaluated using the metrics of order and steady-state value. [ABSTRACT FROM AUTHOR]

The concept of swarm comes from the biological world. Drones gather in groups of 100 or even 1000 to fly like a flock of birds, called swarms. Swarm systems satisfy several assumptions such as decentralized controls, local information and simple platforms. Swarm systems have attractive properties such as resilience, scalability, and ease of development and implementation. Swarm techniques can perform simple tasks such as moving in a coordinated direction. The flocking behavior of a group of animals that converge by local interactions toward the same heading is an example of simple consensus for decentralized dynamic systems. However, the notion of decentralized control based on local information suffers from taking into account the overall behavior of the group. For example, in a complex environment, a swarm will adapt to the presence of obstacles and congestion reactively, whereas we would like more anticipatory control. The objective of this paper is to propose a solution based on Mean Field Game (MFG) concepts to integrate macro-level knowledge at the micro-level in decentralized flocking. [ABSTRACT FROM AUTHOR]

In this paper, we study the flocking behavior of a locally interacting multi‐agent system with a unit speed constraint. Using the spectral gap of a connected stochastic matrix, together with an elaborate estimation on perturbations of a linearized system, we obtain a sufficient condition imposed only on initial data and model parameters to guarantee flocking and further show that the system achieves a consensus at an exponential rate. [ABSTRACT FROM AUTHOR]

This article investigates the flocking behavior of the Cucker–Smale (CS) model on infinite graphs, considering both standard and cut-off interactions. We introduce the concept of connected infinite graphs with a central vertex group and then derive sufficient conditions for the CS model to produce flocking behavior. For standard interaction, we find that the CS model will exhibit flocking behavior exponentially when the connected infinite graph is equipped with a central vertex group. However, for cut-off interaction, we need the time-varying graph induced by interparticle distance to have a fixed central vertex group and the coupling strength to be above a certain threshold to produce the flocking behavior. Our theoretical analysis shows that if a connected infinite graph has a central vertex group, the second eigenvalue of the corresponding Laplacian is positive, which is crucial for the proof of flocking behavior. The consistent convergence towards flocking may well reveal the advantages and necessities of having a central vertex group in an infinite-particle complex system with sufficient intelligence. [ABSTRACT FROM AUTHOR]

This article presents studies on the evaluation of the impact of surface modification of cotton, viscose, and polyester fabrics using three techniques (flocking, layer by layer, and screen printing) with materials with electrically conductive properties on their structural, biophysical, and conductive properties. Each tested fabric is characterized by specific biophysical properties. which can be disturbed by various modification methods, therefore, the following tests were carried out in the article: optical microscopy, micro-computed tomography, guarded perspiration heating plate, air permeability, sorption and electrical conductivity tester. The use of screen printing increased the thermal resistance of the cotton woven fabric by 119%, the polyester woven fabric by 156%, and the viscose fabric by 261%. The smallest changes in thermal resistance compared to unmodified textiles were observed in layer by layer modified fabrics and are as follows: −15% (cotton woven fabric), +77% (PES woven fabric), and +80% (viscose woven fabric). [ABSTRACT FROM AUTHOR]

In this paper, a control method is proposed for the flocking of multi-agent systems in the presence of obstacles. One of the main contributions of this work is the introduction of a safety distance parameter that ensures agents do not enter this safety distance during the flocking process. To achieve this, a fuzzy logic-based gradient of the potential function is designed. Furthermore, it is demonstrated that no consensus term is necessary in the control signal when all agents are informed about the desired path. Additionally, stability analysis is conducted for the proposed algorithm in free space, which allows the extraction of the ultimate bound of the tracking error. Finally, the effectiveness of the proposed algorithm is demonstrated through simulations conducted in free space, space with obstacles, and in the presence of measurement noise. The results obtained from these simulations are compared with the existing methods in the literature. [ABSTRACT FROM AUTHOR]

We show that the locations where finite- and infinite-time clustering occur for the 1D Euler-alignment system can be determined using only the initial data. Our present work provides the first results on the structure of the finite-time singularity set and asymptotic clusters associated with a weak solution. In many cases, the eventual size of the cluster can be read off directly from the flux associated with a scalar balance law formulation of the system. [ABSTRACT FROM AUTHOR]

This study proposes a method for uniformly revolving swarm robots to entrap multiple targets, which is based on a gene regulatory network, an adaptive decision mechanism, and an improved Vicsek-model. Using the gene regulatory network method, the robots can generate entrapping patterns according to the environmental input, including the positions of the targets and obstacles. Next, an adaptive decision mechanism is proposed, allowing each robot to choose the most well-adapted capture point on the pattern, based on its environment. The robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly, without colliding with other robots or obstacles. The proposed decision mechanism, combined with the improved Vicsek-model, can form a uniform entrapment shape and create a revolving effect around targets while entrapping them. This study also enables swarm robots, with an adaptive pattern formation, to entrap multiple targets in complex environments. Swarm robots can be deployed in the military field of unmanned aerial vehicles’ (UAVs) entrapping multiple targets. Simulation experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.

We consider the compressible Euler system with a family of nonlinear velocity alignments. The system is a nonlinear extension of the Euler-alignment system in collective dynamics. We show the asymptotic emergent phenomena of the system: alignment and flocking. Different types of nonlinearity and nonlocal communication protocols are investigated, resulting in a variety of different asymptotic behaviors. [ABSTRACT FROM AUTHOR]

We study analytical properties of a semi-discrete discontinuous Galerkin (DG) scheme for the kinetic Cucker–Smale (CS) equation. The kinetic CS equation appears in the mean-field limit of the particle CS model and it corresponds to the dissipative Vlasov type equation approximating the large particle CS system. For this proposed DG scheme, we show that it exhibits analytical properties such as the conservation of mass, L 2 -stability and convergence to the sufficiently regular solution, as the mesh-size tends to zero. In particular, we verify that the convergence rate of the DG numerical solution to the sufficiently regular kinetic solution is dependent on the Sobolev regularity of the kinetic soluiton. We also present several numerical simulations for low-dimensional cases. [ABSTRACT FROM AUTHOR]

Wings in birds vary tremendously in size and shape across species. Ecological needs are thought to shape variation across species in wing morphology over evolutionary times. For instance, demands for energetic efficiency in species that fly long distances have produced long and narrow wings with a high aspect ratio. Demand for maneuverability in cluttered habitats or to escape predators more easily have favored wings with large areas relative to body size producing a low wing loading. The association between ecological needs and wing morphology is complex, however, and could involve other ecological variables. Here, I draw attention to two ecological factors that have received little attention, namely, life history and sociality. Species at the slow end of the life history continuum emphasize adult survival over reproduction and are considered risk averse. I predicted that such species would benefit from low wing loading and low aspect ratio to increase maneuverability and the ability to escape predators. More solitary species cannot rely on others in their groups to decrease predation risk. I predicted that such species should also benefit from low wing loading and low aspect ratio. Using data from the literature on wing loading and aspect ratio in a large number of species, I examined these predictions in a phylogenetic framework including several potentially confounding ecological variables that have been previously linked to wing morphology such as habitat, lifestyle, and migration. As predicted, more solitary species tended to have low wing loading and low aspect ratio. Low wing loading was also more common in species at the slow end of the life history continuum, but aspect ratio was higher than predicted. Overall, the results support the idea that life history and sociality can predict variation across species in wing morphology and highlight the effect of predation risk on the evolution of wing morphology in birds. [ABSTRACT FROM AUTHOR]

In this paper, we study the pattern formation phenomenon in the Cucker-Smale model with short-range communication weight under the interaction with introduced driving force. We show that the multi-cluster flocking and multi-cluster alignment emerge, and each flock achieves a line-shaped motion pattern for a given class of admissible initial configurations if the coupling strength and driving force strengths satisfy certain conditions. We also show the emergence of the global flocking, the global alignment, and the line-shaped motion pattern in the Cucker-Smale model with short-range communication weight if the coupling strength has a lower bound. [ABSTRACT FROM AUTHOR]

The rate of convergence is a vital factor in determining the outcome of the mission execution of unmanned aerial vehicle (UAV) swarms. However, the difficulty of developing a rapid convergence strategy increases dramatically with the growth of swarm scale. In the present work, a novel fractional-order flocking algorithm (FOFA) is proposed for large-scale UAV swarms. First, based on the interaction rules of repulsion, attraction and alignment among swarm individuals, fractional calculus is introduced to replace traditional integer-order velocity updating, which enables UAVs to utilize historical information during flight. Subsequently, the convergence of the algorithm is theoretically analyzed. Some sufficient convergence conditions for the FOFA are presented by exploiting graph theory. Finally, the simulation results validate that our proposed FOFA performs much better than traditional flocking algorithms in terms of convergence rate. Meanwhile, the relationships between the fractional order of the FOFA and the convergence time of the UAV swarm are discussed. We find that under certain conditions, the fractional order is strongly correlated with the convergence rate of the UAV swarm; that is, a small fractional order (more consideration of historical information) leads to better performance. Moreover, the fractional order can be used as an important parameter to control the convergence rate of a large-scale UAV swarm. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Moving animal groups consist of many distinct individuals but can operate and function as one unit when performing different tasks. Effectively evading unexpected predator attacks is one primary task for many moving groups. The current explanation for predator evasion responses in moving animal groups require the individuals in the groups to interact via (velocity) alignment. However, experiments have shown that some animals do not use alignment. This suggests that another explanation for the predator evasion capacity in at least these species is needed. Here we establish that effective collective predator evasion does not require alignment, it can be induced via attraction and repulsion alone. We also show that speed differences between individuals that have directly observed the predator and those that have not influence evasion success and the speed of the collective evasion process, but are not required to induce the phenomenon. Our work here adds collective predator evasion to a number of phenomena previously thought to require alignment interactions that have recently been shown to emerge from attraction and repulsion alone. Based on our findings we suggest experiments and make predictions that may lead to a deeper understanding of not only collective predator evasion but also collective motion in general. [ABSTRACT FROM AUTHOR]

To guide the movement of a robotic swarm in a corridor‐like environment, a curve virtual tube with no obstacle inside was designed in our previous work. This article generalizes the controller design to the condition that all robots have self‐localization drifts and precise relative navigation, where the flocking algorithm is introduced to reduce the negative impact of the self‐localization drift. Similar to the "many wrongs principle," it is shown that the cohesion behavior and the velocity alignment behavior are able to reduce the influence of the position measurement drift and the velocity measurement error, respectively. For convenience in practical use, a modified vector field controller with five control terms is put forward. Finally, the effectiveness of the proposed method is validated by numerical simulations and real experiments. [ABSTRACT FROM AUTHOR]

Over evolutionary times, the eye has acquired several adaptations to improve feeding efficiency and reduce predation pressure. Eye size, in particular, represents a target for selection, as it affects light capture and image resolution. Previous research on variation in eye size across species has focused on ecological factors related to light availability and foraging needs. Larger eyes are also thought to allow species to detect distant predators more easily, but this conjecture has not been examined across species. I predicted that risk-averse species or those exposed to high predation risk benefit from relatively larger eyes to enhance predator detection. To test the prediction, I performed a comparative analysis involving 660 species of birds while controlling for phylogeny, body size and other known ecological correlates of eye size. The results show that species at the slow end of the life history continuum, which emphasize survival over reproduction and are expected to be risk-averse, have evolved relatively larger eyes. In addition, solitary species, which cannot rely on others in their groups to decrease predation risk, are also characterized by relatively larger eyes. The results indicate that predation risk, through its association with life history and sociality, is an important ecological factor in the evolution of eye size across species.

Abstract Background Song performed in flocks by European starlings (Sturnus vulgaris), referred to here as gregarious song, is a non-sexual, social behavior performed by adult birds. Gregarious song is thought to be an intrinsically reinforced behavior facilitated by a low-stress, positive affective state that increases social cohesion within a flock. The medial preoptic area (mPOA) is a region known to have a role in the production of gregarious song. However, the neurochemical systems that potentially act within this region to regulate song remain largely unexplored. In this study, we used RNA sequencing to characterize patterns of gene expression in the mPOA of male and female starlings singing gregarious song to identify possibly novel neurotransmitter, neuromodulator, and hormonal pathways that may be involved in the production of gregarious song. Results Differential gene expression analysis and rank rank hypergeometric analysis indicated that dopaminergic, cholinergic, and GABAergic systems were associated with the production of gregarious song, with multiple receptor genes (e.g., DRD2, DRD5, CHRM4, GABRD) upregulated in the mPOA of starlings who sang at high rates. Additionally, co-expression network analyses identified co-expressing gene clusters of glutamate signaling-related genes associated with song. One of these clusters contained five glutamate receptor genes and two glutamate scaffolding genes and was significantly enriched for genetic pathways involved in neurodevelopmental disorders associated with social deficits in humans. Two of these genes, GRIN1 and SHANK2, were positively correlated with performance of gregarious song. Conclusions This work provides new insights into the role of the mPOA in non-sexual, gregarious song in starlings and highlights candidate genes that may play a role in gregarious social interactions across vertebrates. The provided data will also allow other researchers to compare across species to identify conserved systems that regulate social behavior.

In this paper, we present a novel non-deep-learning-based approach for real-time object tracking and activity recognition within smart homes, aiming to minimize human intervention and dataset requirements. Our method utilizes discreet, easily concealable sensors and passive RFID technology to track objects in real-time, enabling precise activity recognition without the need for extensive datasets typically associated with deep learning techniques. Central to our approach is AtomGID, an algorithm tailored to extract highly generalizable spatial features from RFID data. Notably, AtomGID’s adaptability extends beyond RFID to other imprecise tracking technologies like Bluetooth beacons and radars. We validate AtomGID through simulation and real-world RFID data collection within a functioning smart home environment. To enhance recognition accuracy, we employ a clustering adaptation of the flocking algorithm, leveraging previously published Activities of Daily Living (ADLs) data. Our classifier achieves a robust classification rate ranging from 85% to 93%, underscoring the efficacy of our approach in accurately identifying activities. By prioritizing non-deep-learning techniques and harnessing the strengths of passive RFID technology, our method offers a pragmatic and scalable solution for activity recognition in smart homes, significantly reducing dataset dependencies and human intervention requirements.

Abstract The rate of convergence is a vital factor in determining the outcome of the mission execution of unmanned aerial vehicle (UAV) swarms. However, the difficulty of developing a rapid convergence strategy increases dramatically with the growth of swarm scale. In the present work, a novel fractional-order flocking algorithm (FOFA) is proposed for large-scale UAV swarms. First, based on the interaction rules of repulsion, attraction and alignment among swarm individuals, fractional calculus is introduced to replace traditional integer-order velocity updating, which enables UAVs to utilize historical information during flight. Subsequently, the convergence of the algorithm is theoretically analyzed. Some sufficient convergence conditions for the FOFA are presented by exploiting graph theory. Finally, the simulation results validate that our proposed FOFA performs much better than traditional flocking algorithms in terms of convergence rate. Meanwhile, the relationships between the fractional order of the FOFA and the convergence time of the UAV swarm are discussed. We find that under certain conditions, the fractional order is strongly correlated with the convergence rate of the UAV swarm; that is, a small fractional order (more consideration of historical information) leads to better performance. Moreover, the fractional order can be used as an important parameter to control the convergence rate of a large-scale UAV swarm.

AbstractThe problem of flocking and formation of a group of nonholonomic wheeled mobile robot is addressed in this paper. Based on the cascade structure of the kinematic model, two kinematic controllers are proposed to achieve formation and flocking behaviors. The proposed control laws present a P and PI-like structure plus a feedforward term and do not require complex calculations. The interaction between the robots is modeled using graph theory. The performance of the proposed control laws is validated by experimental tests on a group of four differential-drive wheeled mobile robots.

We study stochastic flocking dynamics of the inertial spin (IS) model with state‐dependent noises. The IS model was considered to describe the collective behaviors of starling flocks moving with constant speed. Unlike mechanical flocking models extensively studied in the literature, this model incorporates an internal dynamic observable, namely spin (internal angular momentum) in addition to mechanical observables (position and velocity), and it describes how spin interacts with mechanical observables. In previous works, emergent dynamics of the deterministic counterparts for the IS model and its mean‐field limit have been investigated under some specific setting in which network topology is multiplicatively separable. In this work, we present sufficient frameworks for stochastic flocking dynamics of the IS model, which state‐dependent noises vanish at the equilibria of the deterministic IS model. The proposed frameworks are in terms of coupling strength, friction, and inertial coefficients, and our asymptotic convergence results for sample paths are given in both an almost sure and an expectation sense. We have also conducted several numerical experiments to verify our analytical results and to explore what can be studied further in future work [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For biological groups, the behaviors of individuals will have an impact on the alignment efficiency of the collective movement. Motivated by Vicsek's pioneering research on self-organized particles and other related works about flocking behaviors, we propose two mathematical models based on the local information of individuals to include more realistic details in the interaction mechanism between individuals and the rest of the group during the flocking process. The local information of the individual refers to the local consistency, representing the degree of alignment with its neighbors. These two models are the self-introspection model, where the process of orientation adjustment of one individual is ruled by the degree of local consistency with the neighborhood, and the credit evaluation model, where the average orientation of the neighborhoods is weighed using the local consistency of the interacting individuals. Different metrics are calculated to analyze the effects of the model parameters and flocking parameters on groups. Simulation calculations indicate that the two improved models have certain advantages in terms of alignment efficiency for the group. Finally, the optimal model parameters are determined, and the effects of random noise on groups with a single behavior and mixed behaviors are analyzed. The results confirm that individuals with mixed behaviors still possess robustness against noise. This research would contribute to the further interdisciplinary cooperation that involves biology, ethology, and multi-agent complex systems. [ABSTRACT FROM AUTHOR]

Simple Summary: The eye in birds has evolved through time to facilitate foraging and detect potential threats, such as predators. The size of the eye affects how much light is captured and image resolution. Eye size varies across species, and this variation is typically associated with the availability of light in the environment and the foraging needs of each species. Larger eyes could also allow species to detect distant predators more easily, but this has not been tested across species. I predicted that species that are averse to risk and those exposed to high predation risk benefit from larger eyes to enhance the detection of predators. I performed an analysis using 660 different species of birds with various foraging needs and life histories. Controlling for relatedness among species and known correlates of eye size in birds, I found that species that emphasize survival over reproduction, which are expected to be risk-averse, have larger eyes relative to their body size. In addition, species that live alone and cannot rely on others in their groups to decrease the risk of predation also have larger eyes. The results show that the risk of predation through its association with life history and sociality can shape variations in eye size across species of birds. Over evolutionary times, the eye has acquired several adaptations to improve feeding efficiency and reduce predation pressure. Eye size, in particular, represents a target for selection, as it affects light capture and image resolution. Previous research on variation in eye size across species has focused on ecological factors related to light availability and foraging needs. Larger eyes are also thought to allow species to detect distant predators more easily, but this conjecture has not been examined across species. I predicted that risk-averse species or those exposed to high predation risk benefit from relatively larger eyes to enhance predator detection. To test the prediction, I performed a comparative analysis involving 660 species of birds while controlling for phylogeny, body size and other known ecological correlates of eye size. The results show that species at the slow end of the life history continuum, which emphasize survival over reproduction and are expected to be risk-averse, have evolved relatively larger eyes. In addition, solitary species, which cannot rely on others in their groups to decrease predation risk, are also characterized by relatively larger eyes. The results indicate that predation risk, through its association with life history and sociality, is an important ecological factor in the evolution of eye size across species. [ABSTRACT FROM AUTHOR]

The comprehension of how local interactions arise in global collective behavior is of utmost importance in both biological and physical research. Traditional agent-based models often rely on static rules that fail to capture the dynamic strategies of the biological world. Reinforcement learning (RL) has been proposed as a solution, but most previous methods adopt handcrafted reward functions that implicitly or explicitly encourage the emergence of swarming behaviors. In this study, we propose a minimal predator–prey coevolution framework based on mixed cooperative–competitive multiagent RL, and adopt a reward function that is solely based on the fundamental survival pressure, that is, prey receive a reward of −1 if caught by predators while predators receive a reward of +1. Surprisingly, our analysis of this approach reveals an unexpectedly rich diversity of emergent behaviors for both prey and predators, including flocking and swirling behaviors for prey, as well as dispersion tactics, confusion, and marginal predation phenomena for predators. Overall, our study provides novel insights into the collective behavior of organisms and highlights the potential applications in swarm robotics. [ABSTRACT FROM AUTHOR]

Background: Song performed in flocks by European starlings (Sturnus vulgaris), referred to here as gregarious song, is a non-sexual, social behavior performed by adult birds. Gregarious song is thought to be an intrinsically reinforced behavior facilitated by a low-stress, positive affective state that increases social cohesion within a flock. The medial preoptic area (mPOA) is a region known to have a role in the production of gregarious song. However, the neurochemical systems that potentially act within this region to regulate song remain largely unexplored. In this study, we used RNA sequencing to characterize patterns of gene expression in the mPOA of male and female starlings singing gregarious song to identify possibly novel neurotransmitter, neuromodulator, and hormonal pathways that may be involved in the production of gregarious song. Results: Differential gene expression analysis and rank rank hypergeometric analysis indicated that dopaminergic, cholinergic, and GABAergic systems were associated with the production of gregarious song, with multiple receptor genes (e.g., DRD2, DRD5, CHRM4, GABRD) upregulated in the mPOA of starlings who sang at high rates. Additionally, co-expression network analyses identified co-expressing gene clusters of glutamate signaling-related genes associated with song. One of these clusters contained five glutamate receptor genes and two glutamate scaffolding genes and was significantly enriched for genetic pathways involved in neurodevelopmental disorders associated with social deficits in humans. Two of these genes, GRIN1 and SHANK2, were positively correlated with performance of gregarious song. Conclusions: This work provides new insights into the role of the mPOA in non-sexual, gregarious song in starlings and highlights candidate genes that may play a role in gregarious social interactions across vertebrates. The provided data will also allow other researchers to compare across species to identify conserved systems that regulate social behavior. [ABSTRACT FROM AUTHOR]

In this paper, a new method for flocking quadcopters is introduced. For this purpose, the idea of two-level control has been used, where the high-level controller is the same as the flocking algorithm and acts as the path designer of quadcopters. Moreover, tracking of the generated desired path is performed by a low-level controller. The main focus of this paper is on the high-level controller. A novel leaderless flocking algorithm is introduced, where new potential functions are generated using fuzzy logic to achieve a proper lattice. The introduced potential functions have a minimum value in the lattice positions. Therefore, the control signal minimizes its value by using the gradient-descent method to reach the desired situation. A safety radius is defined for every agent such that using the proposed flocking algorithm, the quadcopters do not enter each other's safety region. The stability and convergence of the structural and transitional dynamics of the system are shown. Finally, the proposed method is evaluated through simulations of five quadcopters. The results show that The proposed method provides better performance in creating a lattice, and maintaining obstacle as compared with recently published methods in literature.

This article presents studies on the evaluation of the impact of surface modification of cotton, viscose, and polyester fabrics using three techniques (flocking, layer by layer, and screen printing) with materials with electrically conductive properties on their structural, biophysical, and conductive properties. Each tested fabric is characterized by specific biophysical properties. which can be disturbed by various modification methods, therefore, the following tests were carried out in the article: optical microscopy, micro-computed tomography, guarded perspiration heating plate, air permeability, sorption and electrical conductivity tester. The use of screen printing increased the thermal resistance of the cotton woven fabric by 119%, the polyester woven fabric by 156%, and the viscose fabric by 261%. The smallest changes in thermal resistance compared to unmodified textiles were observed in layer by layer modified fabrics and are as follows: −15% (cotton woven fabric), +77% (PES woven fabric), and +80% (viscose woven fabric).

Group living has long been viewed as an adaptation to reduce predation risk. Earlier comparative analyses provided support for the hypothesis but typically ignored variation in group size at the local scale and included proxies of predation risk rather than more direct estimates. Here, we related variation in group size at the scale of a study site in various species with the diversity and abundance of local predators. If larger groups provide protection against predators, we expected larger groups to evolve in species facing locally more diverse and abundant predators. We examined this hypothesis in one avian family, the Paridae, which are small arboreal birds that include some of the better studied species in ecology. From the literature, we gathered 275 flock size estimates from 34 species. In a phylogenetic framework and controlling for the potential confounding effect of latitude, we found that flock size increased with predation risk but only in flocks that included more than one species. We suggest that competition sets an upper limit to the size of flocks including conspecifics only. Joining flocks with other species, thus, allows individuals to increase flock size in response to higher predation risk without a substantial increase in competition. Overall, our results based on more direct estimates of predation risk provide further comparative evidence for an association between predation and the evolution of flocking in birds. [ABSTRACT FROM AUTHOR]

We study the swarming behavior of hydrodynamic alignment. Alignment reflects steering toward a weighted average heading. We consider the class of so-called p-alignment hydrodynamics, based on 2p-Laplacians and weighted by a general family of symmetric communication kernels. The main new aspect here is the long-time emergence behavior for a general class of pressure tensors without a closure assumption, beyond the mere requirement that they form an energy dissipative process. We refer to such pressure laws as "entropic", and prove the flocking of p-alignment hydrodynamics, driven by singular kernels with a general class of entropic pressure tensors. These results indicate the rigidity of alignment in driving long-time flocking behavior despite the lack of thermodynamic closure. [ABSTRACT FROM AUTHOR]

In this note we continue our study of unidirectional solutions to hydrodynamic Euler alignment systems with strongly singular communication kernels ϕ (x): = |x|(n+α) for α ∊ (0, 2). Here, we consider the critical case α = 1 and establish a couple of global existence results of smooth solutions, together with a description of their long time dynamics. The first one is obtained via Schauder-type estimates under a null initial entropy condition and the other is a small data result. We extend the notion of weak solution and prove the existence of global Leray--Hopf solutions for α ∊ [1, 2). Furthermore, we give anisotropic Onsager-type criteria for the validity of the natural energy law of the system. Finally, we provide quantitative estimates that show how far the density of the limiting flock is from a uniform distribution, depending solely on the size of the initial entropy. [ABSTRACT FROM AUTHOR]

The physics of active biological matter, such as bacterial colonies and bird flocks, exhibiting interesting self-organizing dynamical behavior has gained considerable importance in recent years. Current theoretical advances use techniques from hydrodynamics, kinetic theory, and non-equilibrium statistical physics. However, for biological agents, these approaches do not seem to recognize explicitly their critical feature: namely, the role of survival-driven purpose and the attendant pursuit of maximum utility. Here, we propose a game-theoretic framework, statistical teleodynamics, that demonstrates that the bird-like agents self-organize dynamically into flocks to approach a stable arbitrage equilibriumof equal effective utilities. This is essentially the invisible handmechanism of Adam Smith's in an ecological context. What we demonstrate is for ideal systems, similar to the ideal gas or Ising model in thermodynamics. The next steps would involve examining and learning how real swarms behave compared to their ideal versions. Our theory is not limited to just birds flocking but can be adapted for the self-organizing dynamics of other active matter systems. [ABSTRACT FROM AUTHOR]

In this paper, we reconsider the stochastic kinetic Cucker-Smale model with multiplicative Brownian noise, in which we remove the positive lower bound assumption on the communication. In this case, the communication weight may vanish at the far field and the system is no longer controlled by a linear dissipative system. Then, we apply proper a priori estimates to prove the emergence of conditional flocking in strong sense, which naturally involves classical results for the deterministic model when noise intensity tends to zero. Moreover, we show that unconditional strong flocking occurs when communication weight decays slowly at the far field. Finally, these time asymptotical results imply uniform stability and mean-field limit. In particular, strong stability and mean-field limit in the expectation sense are established for the one-dimensional model via Lyapunov functional approach. [ABSTRACT FROM AUTHOR]