Additive Noise Effects on the Stabilization of Fractional-Space Diffusion Equation Solutions

This paper considers a class of stochastic fractional-space diffusion equations with polynomials. We establish a limiting equation that specifies the critical dynamics in a rigorous way. After this, we use the limiting equation, which is an ordinary differential equation, to approximate the solution of the stochastic fractional-space diffusion equation. This equation has never been studied before using a combination of additive noise and fractional-space, therefore we generalize some previously obtained results as special cases. Furthermore, we use Fisher’s and Ginzburg–Landau equations to illustrate our results. Finally, we look at how additive noise affects the stabilization of the solutions.

Unusual Surface Latent Heat Flux Variations and Their Critical Dynamics Revealed before Strong Earthquakes

We focus on the possible thermal channel of the well-known Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) mechanism to identify the behavior of thermal anomalies during and prior to strong seismic events. For this, we investigate the variation of Surface Latent Heat Flux (SLHF) as resulting from satellite observables. We demonstrate a spatio-temporal variation in the SLHF before and after a set of strong seismic events occurred in Kathmandu, Nepal, and Kumamoto, Japan, having magnitudes of 7.8, 7.3, and 7.0, respectively. Before the studied earthquake cases, significant enhancements in the SLHF were identified near the epicenters. Additionally, in order to check whether critical dynamics, as the signature of a complex phenomenon such as earthquake preparation, are reflected in the SLHF data, we performed a criticality analysis using the natural time analysis method. The approach to criticality was detected within one week before each mainshock.

Nanoscale Neuromorphic Networks and Criticality: A Perspective

Numerous studies suggest critical dynamics may play a role in information processing and task performance in biological systems. However, studying critical dynamics in these systems can be challenging due to many confounding biological variables that limit access to the physical processes underpinning critical dynamics. Here we offer a perspective on the use of abiotic, neuromorphic nanowire networks as a means to investigate critical dynamics in complex adaptive systems. Neuromorphic nanowire networks are composed of metallic nanowires and possess metal-insulator-metal junctions. These networks self-assemble into a highly interconnected, variable-density structure and exhibit nonlinear electrical switching properties and information processing capabilities. We highlight key dynamical characteristics observed in neuromorphic nanowire networks, including persistent fluctuations in conductivity with power law distributions, hysteresis, chaotic attractor dynamics, and avalanche criticality. We posit that neuromorphic nanowire networks can function effectively as tunable abiotic physical systems for studying critical dynamics and leveraging criticality for computation.

Criticality-Driven Evolution of Adaptable Morphologies of Voxel-Based Soft-Robots

The paradigm of voxel-based soft robots has allowed to shift the complexity from the control algorithm to the robot morphology itself. The bodies of voxel-based soft robots are extremely versatile and more adaptable than the one of traditional robots, since they consist of many simple components that can be freely assembled. Nonetheless, it is still not clear which are the factors responsible for the adaptability of the morphology, which we define as the ability to cope with tasks requiring different skills. In this work, we propose a task-agnostic approach for automatically designing adaptable soft robotic morphologies in simulation, based on the concept of criticality. Criticality is a property belonging to dynamical systems close to a phase transition between the ordered and the chaotic regime. Our hypotheses are that 1) morphologies can be optimized for exhibiting critical dynamics and 2) robots with those morphologies are not worse, on a set of different tasks, than robots with handcrafted morphologies. We introduce a measure of criticality in the context of voxel-based soft robots which is based on the concept of avalanche analysis, often used to assess criticality in biological and artificial neural networks. We let the robot morphologies evolve toward criticality by measuring how close is their avalanche distribution to a power law distribution. We then validate the impact of this approach on the actual adaptability by measuring the resulting robots performance on three different tasks designed to require different skills. The validation results confirm that criticality is indeed a good indicator for the adaptability of a soft robotic morphology, and therefore a promising approach for guiding the design of more adaptive voxel-based soft robots.

Faded Critical Dynamics in Adult Moyamoya Disease Revealed by EEG and fMRI

Criticality is considered a dynamic signature of healthy brain activity that can be measured on the short-term timescale with neural avalanches and long-term timescale with long-range temporal correlation (LRTC). It is unclear how the brain dynamics change in adult moyamoya disease (MMD). We used BOLD-fMRI for LRTC analysis from 16 hemorrhagic ( H MMD ) and 34 ischemic ( I MMD ) patients and 25 healthy controls. Afterwards, they were examined by EEG recordings in the eyes-closed (EC), eyes-open (EO), and working memory (WM) states. The EEG data of 11 H MMD and 13 I MMD patients and 21 healthy controls were in good quality for analysis. Regarding the 4 metrics of neural avalanches (e.g., size ( α ), duration ( β ), κ value, and branching parameter ( σ )), both MMD subtypes exhibited subcritical states in the EC state. When switching to the WM state, H MMD remained inactive, while I MMD surpassed controls and became supercritical ( p < 0.05 ). Regarding LRTC, the amplitude envelope in the EC state was more analogous to random noise in the MMD patients than in controls. During state transitions, LRTC decreased sharply in the controls but remained chaotic in the MMD individuals ( p < 0.05 ). The spatial LRTC reduction distribution based on both EEG and fMRI in the EC state implied that, compared with controls, the two MMD subtypes might exhibit mutually independent but partially overlapping patterns. The regions showing decreased LRTC in both EEG and fMRI were the left supplemental motor area of H MMD and right pre-/postcentral gyrus and right inferior temporal gyrus of I MMD . This study not only sheds light on the decayed critical dynamics of MMD in both the resting and task states for the first time but also proposes several EEG and fMRI features to identify its two subtypes.

Approximate Solutions of the Space-Fractional Diffusion Equations with Additive Noise

In this article we take into account a class of stochastic space diffusion equations with polynomials forced by additive noise. We derive rigorously limiting equations which de…ne the critical dynamics. Also, we approximate solutions of stochastic fractional space di¤usion equations with polynomial term by limiting equations, which are ordinary di¤er-ential equations. Moreover, we address the e¤ect of the noise on the solution’s stabilization. Finally, we apply our results to Fisher’s equation and Ginzburg–Landau models.