It has been experimentally shown that Brownian motion and active forward drift controlled by quorum sensing is sufficient to produce clustering behavior in orientable Janus particles. This paper explores the group formation and cohesion of Janus particles using both an agent-based computer model and a nonlocal advection-diffusion partial differential equation (PDE) model. Our PDE model can recreate the behavior from both physical experimentation and computer simulations. Additionally, the PDE model highlights an annular structure which was not apparent in prior work.

Understanding the dynamics of excitation patterns in neural fields is an important topic in neuroscience. Neural field equations are mathematical models that describe the excitation dynamics of interacting neurons to perform the theoretical analysis. Although many analyses of neural field equations focus on the effect of neuronal interactions on the flat surface, the geometric constraint of the dynamics is also an attractive topic when modeling organs such as the brain. This paper reports pattern dynamics in a neural field equation defined on spheroids as model curved surfaces. We treat spot solutions as localized patterns and discuss how the geometric properties of the curved surface change their properties. To analyze spot patterns on spheroids with small flattening, we first construct exact stationary spot solutions on the spherical surface and reveal their stability. We then extend the analysis to show the existence and stability of stationary spot solutions in the spheroidal case. One of our theoretical results is the derivation of a stability criterion for stationary spot solutions localized at poles on oblate spheroids. The criterion determines whether a spot solution remains at a pole or moves away. Finally, we conduct numerical simulations to discuss the dynamics of spot solutions with the insight of our theoretical predictions. Our results show that the dynamics of spot solutions depend on the curved surface and the coordination of neural interactions.

We study a simple two-dimensional swarmalator model that incorporates higher-order phase interactions, uncovering a diverse range of collective states. The latter include spatially coherent and gas-like configurations, neither of which appear in models with only pairwise interactions. Additionally, we discover bistability between various states, a phenomenon that arises directly from the inclusion of higher-order interactions. By analyzing several of these emergent states analytically, both for identical and nonidentical populations of swarmalators, we gain deeper insights into their underlying mechanisms and stability conditions. Our findings broaden the understanding of swarmalator dynamics and open new avenues for exploring complex collective behaviors in systems governed by higher-order interactions.

Interactions between solitary waves have been pivotal to understanding nonlinear phenomena across various disciplines. The dynamics of rarefaction solitary waves holds great potential, yet their fundamental characteristics and interactions remain only partially understood through experimental means in mechanical metamaterials. Previous studies highlighted their existence and proposed applications, such as waveguides, impact mitigation, and energy harvesting. Challenges, including energy dissipation and a lack of precise measurement techniques, have hindered deeper exploration, most notably of solitonic collisions. In this work, we provide a definitive platform for examining pure rarefaction solitons propagating through a strain-softening mechanical lattice, addressing these challenges. Employing a theoretical framework based on the Boussinesq approximation and multiple-scale analysis, we predict soliton behavior, including phase shifts resulting from head-on collisions. These theoretical insights are corroborated through numerical simulations and systematic experiments designed to generate and measure pure rarefaction solitons with high precision. Both symmetric and asymmetric collisions are examined, revealing practically elastic interaction behaviors and amplitude-dependent phase shifts. Furthermore, collision dynamics, such as speed and phase shifts during rarefaction soliton collisions, from the experimental results show agreement with theoretical and numerical models. These results validate our experimental platform and findings, underscoring the potential of mechanical rarefaction solitons as robust, controllable wave packets. This suggests a robust paradigm for exploring nonlinear wave interactions in mechanical systems, opening new application avenues in mechanical metamaterials, such as wave-based computing and advanced signal processing.

We discuss the Simple Equations Method (SEsM) for obtaining exact solutions of nonlinear partial differential equations. We show that the Jacobi Elliptic Function Expansion Method, F-Expansion method, Modified Simple Equation method, Trial Function Method, General Projective Riccati Equations Method and the First Integral Method are specific cases of SEsM.

Fields offer a versatile approach for describing complex systems composed of interacting and dynamic components. In particular, some of these dynamical and stochastic systems may exhibit goal-directed behaviors aimed at achieving specific objectives, which we refer to as $\textit{intelligent fields}$. However, due to their inherent complexity, it remains challenging to develop a formal theoretical description of such systems and to effectively translate these descriptions into practical applications. In this paper, we propose three fundamental principles -- complete configuration, locality, and purposefulness -- to establish a theoretical framework for understanding intelligent fields. Moreover, we explore methodologies for designing such fields from the perspective of artificial intelligence applications. This initial investigation aims to lay the groundwork for future theoretical developments and practical advances in understanding and harnessing the potential of such objective-driven dynamical stochastic fields.

The aim of this monograph is twofold: to explain various nonautonomous integrable systems (discrete Painlev\'e all the way up to the elliptic level, as well as generalizations \`a la Garnier) using an interpretation of difference and differential equations as sheaves on noncommutative projective surfaces, and to develop the theory of such surfaces enough to allow one to apply the usual GIT construction of moduli spaces of sheaves. This requires a fairly extensive development of the theory of birationally ruled noncommutative projective surfaces, both showing that the analogues of Cremona transformations work and understanding effective, nef, and ample divisor classes. This combines arXiv:1307.4032, arXiv:1307.4033, arXiv:1907.11301, as well as those portions of arXiv:1607.08876 needed to make things self-contained. Some additional results appear, most notably a proof that the resulting discrete actions on moduli spaces of equations are algebraically integrable.

Let $(m_1, m_2)$ be a pair of positive integers. Denote by $\mathbb{P}^1$ the complex projective line, and by $\mathbb{P}^1_{m_1,m_2}$ the orbifold complex projective line obtained from $\mathbb{P}^1$ by adding $\mathbb{Z}_{m_1}$ and $\mathbb{Z}_{m_2}$ orbifold points. In this paper we introduce a matrix linear difference equation, prove existence and uniqueness of its formal Puiseux-series solutions, and use them to give conjectural formulas for $k$-point ($k\ge2$) functions of Gromov--Witten invariants of $\mathbb{P}^1_{m_1,m_2}$. Explicit expressions of the unique solutions are also obtained. We carry out concrete computations of the first few invariants by using the conjectural formulas. For the case when one of $m_1,m_2$ equals 1, we prove validity of the conjectural formulas with $k\ge3$.

Fast and modular modeling of multi-legged robots (MLRs) is essential for resilient control, particularly under significant morphological changes caused by mechanical damage. Conventional fixed-structure models, often developed with simplifying assumptions for nominal gaits, lack the flexibility to adapt to such scenarios. To address this, we propose a fast modular whole-body modeling framework using Boltzmann-Hamel equations and screw theory, in which each leg's dynamics is modeled independently and assembled based on the current robot morphology. This singularity-free, closed-form formulation enables efficient design of model-based controllers and damage identification algorithms. Its modularity allows autonomous adaptation to various damage configurations without manual re-derivation or retraining of neural networks. We validate the proposed framework using a custom simulation engine that integrates contact dynamics, a gait generator, and local leg control. Comparative simulations against hardware tests on a hexapod robot with multiple leg damage confirm the model's accuracy and adaptability. Additionally, runtime analyses reveal that the proposed model is approximately three times faster than real-time, making it suitable for real-time applications in damage identification and recovery.

Understanding nonlinear social contagion dynamics on dynamical networks, such as opinion formation, is crucial for gaining new insights into consensus and polarization. Similar to threshold-dependent complex contagions, the nonlinearity in adoption rates poses challenges for mean-field approximations. To address this theoretical gap, we focus on nonlinear binary-opinion dynamics on dynamical networks and analytically derive local configurations, specifically the distribution of opinions within any given focal individual's neighborhood. This exact local configuration of opinions, combined with network degree distributions, allows us to obtain exact solutions for consensus times and evolutionary trajectories. Our counterintuitive results reveal that neither biased assimilation (i.e., nonlinear adoption rates) nor preferences in local network rewiring -- such as in-group bias (preferring like-minded individuals) and the Matthew effect (preferring social hubs) -- can significantly slow down consensus. Among these three social factors, we find that biased assimilation is the most influential in accelerating consensus. Furthermore, our analytical method efficiently and precisely predicts the evolutionary trajectories of adoption curves arising from nonlinear contagion dynamics. Our work paves the way for enabling analytical predictions for general nonlinear contagion dynamics beyond opinion formation.

A simple closed-form formula for the period of a pendulum with finite amplitude is proposed. It reproduces the exact analytical forms both in the small and large amplitude limits, while in the mid-amplitude range maintains average error of 0.06% and maximum error of 0.17%. The accuracy should be sufficient for typical engineering applications. Its unique simplicity should be useful in a theoretical development that requires trackable mathematical framework or in an introductory physics course that aims to discuss a finite amplitude pendulum. A simple and formally exact solution of angular displacement for the full range of amplitudes is illustrated.