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        <title>Frontiers in Physics | Complex Physical Systems section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/physics/sections/complex-physical-systems</link>
        <description>RSS Feed for Complex Physical Systems section in the Frontiers in Physics journal | New and Recent Articles</description>
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        <pubDate>2026-04-04T01:36:35.42+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1731777</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1731777</link>
        <title><![CDATA[AI needs physics more than physics needs AI]]></title>
        <pubdate>2026-01-27T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Peter V. Coveney</author><author>Roger R. Highfield</author>
        <description><![CDATA[Artificial intelligence (AI) is commonly depicted as transformative. Yet, after more than a decade of hype, its measurable impact remains modest outside a few high-profile scientific and commercial successes. The 2024 Nobel Prizes in Chemistry and Physics recognized AI’s potential, but broader assessments indicate the impact to date is often more promotional than technical. We argue that while current AI may influence physics, physics has significantly more to offer this generation of AI. Current architectures—large language models, reasoning models, and agentic AI–can depend on trillions of meaningless parameters, suffer from distributional bias, lack uncertainty quantification, provide no mechanistic insights, and fail to capture even elementary scientific laws. We review critiques of these limits, highlight opportunities in quantum AI and analogue computing, and lay down a roadmap for the adoption of ‘Big AI’: a synthesis of theory-based rigour with the flexibility of machine learning.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1704910</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1704910</link>
        <title><![CDATA[Architecture of full-analogue photonic AI for non-standard problem solving]]></title>
        <pubdate>2026-01-22T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Aleksandr Raikov</author>
        <description><![CDATA[Some non-standard physical problems are challenging to solve because of the fundamental impossibility of experimental confirmation of theories, resulting from the lack of adequate methods and equipment with the required parameters, such as energy and frequency on the order of Planck. In recent years, generative artificial intelligence (AI) has significantly enhanced the efficiency of solving many standard problems, particularly in fields such as diagnostics, business analytics, pattern recognition, programming, and prediction. There are also attempts to leverage AI to address complex physical issues through indirect approaches, such as simulating a training dataset. This article aims to formulate the basic requirements that an AI system must meet to support the solution of non-standard physical problems that are also complex in their interdisciplinarity, data scarcity, time constraints, energy limitations, and hypothetical goals. This was conducted as a thought experiment by analysing two hypothetical phenomena: the emergence of cosmic strings (CS) and photons during the Planck and Grand Unification epochs. The author’s convergent method, based on thermodynamics and inverse problem-solving in topological space, has ensured the research’s stability and purposefulness. As a result of the article, it is justified that, to significantly improve AI support for the solution of some scientific non-standard problems, it is necessary to use the full-analogue photonic AI, which can be realised on a holographic basis and a Fourier transform approach. The conceptual architecture of a required AI system is represented.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1760758</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1760758</link>
        <title><![CDATA[A new kind of science]]></title>
        <pubdate>2026-01-13T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Alex Hansen</author><author>Sauro Succi</author>
        <description><![CDATA[We discuss whether science is in the process of being transformed from a quest for causality to a quest for correlation in light of the recent development in artificial intelligence. We observe that while a blind trust in the most seductive promises of AI is surely to be avoided, a judicious combination of computer simulation based on physical insight and the machine learning ability to explore ultra-dimensional spaces, holds potential for transformative progress in the way science is going to be pursued in the years to come.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1669813</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1669813</link>
        <title><![CDATA[Exploring the fourth-order Boussinesq water wave equation: soliton analysis, modulation instability, sensitivity behavior, and chaotic analysis]]></title>
        <pubdate>2026-01-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Muhammad Raheel</author><author>Asim Zafar</author><author>Abdulaziz Khalid Alsharidi</author><author>Naif Almusallam</author>
        <description><![CDATA[In this article, we reveal the novel types of exact solitons to the fourth-order nonlinear (1 + 1)-dimensional Boussinesq water wave equation. This model is obtained under the consideration of the smaller water depth and larger wavelength of the waves. The Boussinesq water wave equation is useful in understanding water wave behavior, harbor design, coastal dynamics, wave propagation in shallow seas, ocean wave models, marine environments, etc. For our aim, we used the Sardar sub-equation technique. As a result, new types of exact wave solitons involving trigonometry, hyperbolic trigonometry, and rational functions are gained. Some gained solutions are represented through 2D, 3D, contour, and density plots. In bifurcation analysis, a new planar dynamical system of the governing model is obtained by applying the Galilean transformation, and all possible phase portraits are discussed. Modulation instability is used to obtain the steady-state solutions of the concerned model. Furthermore, the chaotic behavior of the governing model is analyzed. Sensitivity analysis is utilized to determine the sensitivity behavior of the model. The achieved solutions are fruitful in distinct areas of mathematical physics and engineering fields. At the end, the technique is a useful and reliable approach to solving other important nonlinear partial differential equations. This study applies the Sardar sub-equation method to derive new analytical solutions of the fourth-order nonlinear (1 + 1)-dimensional Boussinesq water wave equation. The method demonstrates greater flexibility than traditional approaches in handling nonlinear terms. However, the results depend on specific parameter conditions, and experimental or numerical validation is left for future investigation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1736037</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1736037</link>
        <title><![CDATA[Neural network–based approach for improving the evaluation of antibody–antigen docking poses]]></title>
        <pubdate>2026-01-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alessandro Meta</author><author>Giancarlo Ruocco</author><author>Edoardo Milanetti</author>
        <description><![CDATA[The role of artificial intelligence (AI)–based approaches in computational biology and molecular biophysics has become increasingly central over the past decade; however, many challenges remain unresolved, such as the accurate prediction of protein–protein complexes, the complete solution of which would have a significant impact both on our understanding of cellular mechanisms and on the development of therapeutic and diagnostic strategies. Here, we present a protocol based on multiple minimal neural network (NN)–based approaches, trained on a set of carefully selected physicochemical features, to discriminate docking decoy poses (structurally distant from the experimental complex) from native-like poses (structurally close to the native conformation) within a specific class of biologically relevant protein–protein complexes, namely antibody–antigen systems in which the antigen is a protein. A specific version of the proposed method, trained on a set of antibody–antigen interface descriptors, some of which are derived from graph theory to capture the geometric complexity of intermolecular interactions, was compared with ITScore-PP, the docking score provided by HDOCK. This NN-based approach, demonstrates the ability not only to distinguish native-like poses from decoys, but also, more challengly, to discriminate intermediate poses from native-like ones. Furthermore, it was also able to predict the DockQ score, a widely used metric for assessing docking pose quality, showing a larger absolute Pearson correlation coefficient than ITScore-PP. The ability of our NN-based approach, which relies solely on structural interface features, to identify accurate dockings highlights its potential as a valuable tool for improving the ranking of antibody–antigen docking poses and underscores the importance of sppropriate feature selection in protein-protein interaction modeling.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1633608</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1633608</link>
        <title><![CDATA[Engine sounds reflect a racecar driver’s cognition]]></title>
        <pubdate>2026-01-06T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Jaskeerat Singh</author><author>Yawer H. Shah</author><author>Lucio Tonello</author><author>Glenda Cappello</author><author>Raffaele Giammaria</author><author>Scott Kerick</author><author>Paolo Grigolini</author><author>Bruce J. West</author>
        <description><![CDATA[We analyze the engine noise of racecars to shed light on the interaction between the brains of the drivers and their racecars and also the interaction between the brains of different drivers for the International Automobile Federation (FIA) Formula 4, E4 Championship. Statistical analysis is performed using the same theoretical tools as those adopted in the recent past to study the brain of an orchestra director through the resulting music. The result of this statistical analysis is the evaluation of a scaling parameter that we compare between drivers. We interpret this scaling parameter as a measure of the driver’s ability, with 1 representing maximal adaptability and 0.5 representing random or minimal adaptability (less than 0.5 does not exist for the trajectory model we have). The results obtained show that higher values of the scaling parameter, measured in a single qualifying lap, correspond to better performance in their championship. We also study the training process that allows novice drivers to move from values of the scaling parameter around 0.7 to values very close to 1 as they gain experience. We find that more experienced drivers have a larger scaling parameter and we also explore the effects of competition that can lead to a decrease of the said scaling parameter. This is in line with phenomenology theory, despite being temporary. This work suggests that the study of racecar noise can shed light on the difficult issue of cognition. Having in mind the therapeutic applications of music, we conjecture that this discovery may provide an important contribution to rehabilitation therapy. We also contribute to the emerging field of human-machine interaction by showing how to transmit crucial events to a machine and detect them.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1593392</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1593392</link>
        <title><![CDATA[Commentary: Mini-review on periodic properties of MEMS oscillators]]></title>
        <pubdate>2025-08-26T00:00:00Z</pubdate>
        <category>General Commentary</category>
        <author>Hao Chang</author><author>Jian-Gang Zhang</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1562805</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1562805</link>
        <title><![CDATA[First- and second-order network coherence in N-duplication weighted corona networks]]></title>
        <pubdate>2025-06-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chao Liu</author>
        <description><![CDATA[This paper studies first- and second-order coherence problems for N-duplication weighted corona networks subject to stochastic disturbances. Explicit expressions of the coherence for first-order (and second-order) dynamics, which are determined by the sum of the reciprocal (and square of reciprocal) of each nonzero eigenvalue of the Laplacian matrix, are derived. In particular, for both first- and second-order systems, the analytical formulas of the network coherence are presented from two different perspectives. Based on these formulas, the influence of the duplication N, the weight ω, and the factor networks G1 and G2 on the network coherence of the corona network G1◦G2 is investigated. Some noteworthy topological properties of the N-duplication weighted corona network are also revealed.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1548966</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1548966</link>
        <title><![CDATA[Shear strength, avalanches, and structures of soft cohesive particles under shear]]></title>
        <pubdate>2025-03-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kuniyasu Saitoh</author>
        <description><![CDATA[The physics of granular materials, including rheology and jamming, is strongly influenced by cohesive forces between the constituent grains. Despite significant progress in understanding the mechanical properties of granular materials, it remains unresolved how the range and strength of cohesive interactions influence mechanical failure or avalanches. In this study, we use molecular dynamics simulations to investigate simple shear flows of soft cohesive particles. The particles are coated with thin sticky layers, and both the range and strength of cohesive interactions are determined by the layer thickness. We examine shear strength, force chains, particle displacements, and avalanches, and find that these quantities change drastically even when the thickness of the sticky layers is only 1% of the particle diameter. We also analyze avalanche statistics and find that the avalanche size, maximum stress drop rate, and dimensionless avalanche duration are related by scaling laws. Remarkably, the scaling exponents of the scaling laws are independent of the layer thickness but differ from the predictions of mean-field theory. Furthermore, the power-law exponents for the avalanche size distribution and the distribution of the dimensionless avalanche duration are universal but do not agree with mean-field predictions. We confirm that the exponents estimated from numerical data are mutually consistent. In addition, we show that particle displacements at mechanical failure tend to be localized when the cohesive forces are sufficiently strong.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1480749</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1480749</link>
        <title><![CDATA[Random forest grid fault prediction based on genetic algorithm optimization]]></title>
        <pubdate>2025-03-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kai Liu</author><author>Yingcheng Gu</author><author>Lei Tang</author><author>Yuanhan Du</author><author>Chen Zhang</author><author>Junwu Zhu</author>
        <description><![CDATA[The operation of the power grid is closely related to meteorological disasters. Changes in meteorological conditions may have an impact on the operation and stability of the power system, leading to economic losses. This paper proposes a Random Forest grid fault prediction model based on Genetic Algorithm optimization (GA-RF) to classify the grid fault types, which improves the distribution network fault prediction accuracy by constructing an optimized random forest model. Specifically, the model’s performance is initially enhanced by calculating the Gini index for each feature. The weather attributes with higher Gini indices are subsequently selected as pivotal features to alleviate the detrimental impact of unnecessary attributes on the model. In addition, a genetic algorithm is used to optimize the parameters of the random forest model for early warning of grid fault occurrence. The experimental results demonstrate that the proposed GA-RF in this paper achieves significantly higher accuracy compared to Random Forest (RF), Support Vector Machine (SVM), and Linear Regression (LR). Specifically, it outperforms them by 14.77%, 23.22%, and 13.77% respectively. This method effectively supports the safe and stable operation of the power system.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1568077</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1568077</link>
        <title><![CDATA[Editorial: Nonequilibrium and nonlinear processes in collective dynamical phenomena]]></title>
        <pubdate>2025-02-26T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Sanja Janićević</author><author>Djordje Spasojević</author><author>Branko Kolaric</author><author>Maria Antonietta Ferrara</author><author>Adriano Tiribocchi</author><author>Svetislav Mijatović</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2024.1472564</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2024.1472564</link>
        <title><![CDATA[Pattern phase transition of spin particle lattice system]]></title>
        <pubdate>2024-11-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yue Wu</author><author>Jingnan Yan</author><author>Bowen Xu</author><author>Yili Zheng</author><author>Duxin Chen</author>
        <description><![CDATA[To better understand the pattern phase transition of both physical and biological systems, we investigate a two-dimensional spin particle lattice system using statistical mechanics methods together with XY model governed by Hamiltonian equations of motion. By tweaking the coupling strength and the intensity of the generalization field, we observe phase transitions among four patterns of spin particles, i.e., vortex, ferromagnet, worm and anti-ferromagnet. In addition, we analyze the effect of space boundaries on the formations of vortex and worm. Considering the inherent dynamics of individual particles, we revealed the forming mechanism of such phase transitions, which provides a new perspective for understanding the emergence of phase transition of spin particles systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2024.1452241</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2024.1452241</link>
        <title><![CDATA[Action potentials in vitro: theory and experiment]]></title>
        <pubdate>2024-10-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ziqi Pi</author><author>Giovanni Zocchi</author>
        <description><![CDATA[Action potential generation underlies some of the most consequential dynamical systems on Earth, from brains to hearts. It is therefore interesting to develop synthetic cell-free systems, based on the same molecular mechanisms, which may allow for the exploration of parameter regions and phenomena not attainable, or not apparent, in the live cell. We previously constructed such a synthetic system, based on biological components, which fires action potentials. We call it “Artificial Axon”. The system is minimal in that it relies on a single ion channel species for its dynamics. Here we characterize the Artificial Axon as a dynamical system in time, using a simplified Hodgkin-Huxley model adapted to our experimental context. We construct a phase diagram in parameter space identifying regions corresponding to different temporal behavior, such as Action Potential (AP) trains, single shot APs, or damped oscillations. The main new result is the finding that our system with a single ion channel species, with inactivation, is dynamically equivalent to the system of two channel species without inactivation (the Morris-Lecar system), which exists in nature. We discuss the transitions and bifurcations occurring crossing phase boundaries in the phase diagram, and obtain criteria for the channels’ properties necessary to obtain the desired dynamical behavior. In the second part of the paper we present new experimental results obtained with a system of two AAs connected by excitatory and/or inhibitory electronic “synapses”. We discuss the feasibility of constructing an autonomous oscillator with this system.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2024.1498185</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2024.1498185</link>
        <title><![CDATA[Mini-review on periodic properties of MEMS oscillators]]></title>
        <pubdate>2024-10-23T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Yi Tian</author><author>Yabin Shao</author>
        <description><![CDATA[This paper features a survey of the periodic property of micro-electro-mechanical systems by the homotopy perturbation method, the variational iteration method, the variational theory, He’s frequency formulation, and Taylor series method. Fractal MEMS systems are also introduced, and future prospective is elucidated. The emphasis of this min-review article is put mainly on the developments in last decade, so the references, therefore, are not exhaustive.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2024.1484701</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2024.1484701</link>
        <title><![CDATA[Chatbots and zero sales resistence]]></title>
        <pubdate>2024-10-14T00:00:00Z</pubdate>
        <category>Opinion</category>
        <author>Sauro Succi</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2024.1492485</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2024.1492485</link>
        <title><![CDATA[Editorial: Static and dynamic pattern formation from nano to macroscales]]></title>
        <pubdate>2024-09-18T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Masaki Itatani</author><author>Gábor Holló</author><author>Pawan Kumar</author><author>Muneyuki Matsuo</author><author>Hideki Nabika</author><author>István Lagzi</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2024.1447788</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2024.1447788</link>
        <title><![CDATA[State estimation for Markovian jump Hopfield neural networks with mixed time delays]]></title>
        <pubdate>2024-09-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lili Guo</author><author>Wanhui Huang</author>
        <description><![CDATA[Markovian jump Hopfield NNs (MJHNNs) have received considerable attention due to their potential for application in various areas. This paper deals with the issue of state estimation concerning a category of MJHNNs with discrete and distributed delays. Both time-invariant and time-variant discrete delay cases are taken into account. The objective is to design full-order state estimators such that the filtering error systems exhibit exponential stability in the mean-square sense. Two sufficient conditions on the mean-square exponential stability of MJHNNs are established utilizing augmented Lyapunov–Krasovskii functionals, the Wirtinger–based integral inequality, the Bessel-Legendre inequality, and the convex combination inequality. Then, linear matrix inequalities-based design methods for the required estimators are developed through eliminating nonlinear coupling terms. The feasibility of these linear matrix inequalities can be readily verified via available Matlab software, thus enabling numerically tractable implementation of the proposed design methods. Finally, two numerical examples with simulations are provided to demonstrate the applicability and less conservatism of the proposed stability criteria and estimators. Lastly, two numerical examples are given to demonstrate the applicability and reduced conservatism of the proposed stability criteria and estimator design methods. Future research could explore further refinement of these analysis and design results, and exporing their extention to more complex neural network models.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2024.1429731</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2024.1429731</link>
        <title><![CDATA[Modelling network motifs as higher order interactions: a statistical inference based approach]]></title>
        <pubdate>2024-08-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Anatol E. Wegner</author>
        <description><![CDATA[The prevalent approach to motif analysis seeks to describe the local connectivity structure of networks by identifying subgraph patterns that appear significantly more often in a network then expected under a null model that conserves certain features of the original network. In this article we advocate for an alternative approach based on statistical inference of generative models where nodes are connected not only by edges but also copies of higher order subgraphs. These models naturally lead to the consideration of latent states that correspond to decompositions of networks into higher order interactions in the form of subgraphs that can have the topology of any simply connected motif. Being based on principles of parsimony the method can infer concise sets of motifs from within thousands of candidates allowing for consistent detection of larger motifs. The inferential approach yields not only a set of statistically significant higher order motifs but also an explicit decomposition of the network into these motifs, which opens new possibilities for the systematic study of the topological and dynamical implications of higher order connectivity structures in networks. After briefly reviewing core concepts and methods, we provide example applications to empirical data sets and discuss how the inferential approach addresses current problems in motif analysis and explore how concepts and methods common to motif analysis translate to the inferential framework.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2024.1394983</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2024.1394983</link>
        <title><![CDATA[How fear emotion impacts collective motion in threat environment]]></title>
        <pubdate>2024-08-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yi-Xuan Lü</author><author>Si-Ping Zhang</author><author>Guan-Yu Meng</author><author>Bing-Hui Guo</author><author>Xiao-Long Liang</author><author>Zhi-Xi Wu</author><author>Zi-Gang Huang</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2024.1358766</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2024.1358766</link>
        <title><![CDATA[Theory of Turing pattern formation and its experimental realization in the CIMA reaction system in the presence of materials lowering the diffusivity of activators]]></title>
        <pubdate>2024-07-31T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Amiko Aizawa</author><author>Kouichi Asakura</author>
        <description><![CDATA[In 1952, Alan Turing accomplished a pioneering theoretical study to show that the coupling of nonlinear chemical reactions and diffusion leads to the instability of spatially homogeneous states. The activator and inhibitor are synthesized as intermediates of the reaction system in the Turing model. Turing found that spatially periodic stationary concentration patterns are spontaneously generated when the diffusion coefficient of the activator is lower than that of the inhibitor. The first experimental realization of the Turing pattern was achieved in 1990 in a chlorite–iodide–malonic acid (CIMA) reaction system. Iodide and chlorite anions act as the activator and inhibitor of this reaction system, respectively. Although there is no significant difference in the diffusion coefficient of iodide and chlorite anions, the Turing pattern was generated because starch was added to the gel reactor to enhance the color tone. This formed a complex with iodide to inhibit its diffusion to satisfy the condition for the Turing instability. Several examples were found after this finding. We focused on the high affinity of quaternary alkyl ammonium cations to iodide. The CIMA reaction was performed in an open gel reactor by adding a quaternary alkyl ammonium cationic surfactant. In addition, the polymer gel consists of the quaternary alkyl ammonium group as the side chain was utilized for the open gel reactor. The micelles of the surfactants and the polymer gels trapped iodide in their vicinity as a counter anion to lower the effective diffusivity to satisfy the condition for the Turing instability.]]></description>
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