This paper explores the spatial variations of the statistical scaling features of low to high latitude geomagnetic field fluctuations at Swarm altitude. The data for this study comes from the vector field magnetometer onboard Swarm A satellite, measured at low resolution (1 Hz) for one year (from 9 March 2016, to 9 March 2017). We estimated the structure-function scaling exponents using the p-leaders discrete wavelet multifractal technique, from which we obtained the singularity spectrum related to the magnetic fluctuations in the North-East-Center (NEC) coordinate system. From this estimation, we retain just the maximal fractal subset, associated with the Hurst exponent H. Here we present thresholding for two levels of the Auroral Electrojet index and almost the whole northern and southern hemispheres, the Hurst exponent, the structure-function scaling exponent of order 2, and the multifractal p-exponent width for the geomagnetic fluctuations. The latter quantifies the relevance of the multifractal property. Sometimes, we found negative values of H, suggesting a behavior similar to wave breaking or shocklet-like propagating front. Furthermore, we found some asymmetries in the magnetic field turbulence between the northern and southern hemispheres. These estimations suggest that different turbulent regimes of the geomagnetic field fluctuations exist along the Swarm path.

In the hope of making passenger travel times shorter and more reliable, many cities are introducing dedicated bus lanes (e.g., Bogota, London, Miami). Here we show that chaotic travel times are actually a natural consequence of individual bus function, and hence of public transport systems more generally, i.e., chaotic dynamics emerge even when the route is empty and straight, stops and lights are equidistant and regular, and loading times are negligible. More generally, our findings provide a novel example of chaotic dynamics emerging from a single object following Newton's laws of motion in a regularized one-dimensional system. DOI: http://dx.doi.org/10.1103/PhysRevE.89.062922 ©2014 American Physical Society

Understanding the fundamental interactions in the complex behavior of one car moving in a sequence of traffic lights necessarily implies the inclusion of finite braking and accelerating capabilities. This characteristic is usually not considered in the standard cellular automaton models, where car interactions are the main concern. Therefore, here we develop a model which includes interactions and finite braking and accelerating capabilities, filling the gap between a standard cellular automaton model that considers car interactions but infinite braking and accelerating capabilities and the continuous one car model that includes finite braking and accelerating capabilities but does not consider, as the name indicates, car interactions. The proposed new model bridge these two seemingly different approaches in an effort to investigate how the traffic jams are produced. We found that, in the appropriate limits, we can reproduce the complex behavior of the one car continuous model and the dynamics close to the resonance induced by the interacting cars, forced by the traffic lights. In the processes of introducing car interactions, we observe how the average velocity decreases to finally obtain traffic jams, which are an emergent state in which the traffic lights control the generation of pulses of cars but do not control its average speed. This model is expected to improve our understanding of the complexity that appears in city traffic situations, as the finite braking and accelerating capabilities are necessary to describe the vehicle dynamics, the control strategy of traffic light synchronization, the motion of buses in segregated lights, and the whole urban design.

Collective motion is an innate ability of all living systems, which depends on physiological and psychosocial factors in the case of humans. Such a collective organization is becoming of great interest in collective motion in human crowds. Using a cellular automaton (CA) simulation model, we demonstrate that emergency egress from a two-dimensional corridor with optimal stress leads to less evacuation time and efficient mass evacuations. We study how three types of stress (i.e., mild stress, optimal stress, and anxiety) described in the literature have a significant impact on the collective dynamics. We found that low-stress levels could decrease the evacuation time in an entire occupied room since agents choose alternative routes rather than the shortest path to the exit and display cooperative behavior. Therefore, the combination of mild and optimal stress can lead to efficient evacuations. Also CA simulations may be used to find safer and more efficient ways to conduct mass evacuation procedures.

We propose a method, based on Neural Networks, that detects the nonlinear robust interplanetary solar wind variables, with varying delays, driving the coupled behavior of three geomagnetic indices (Dst, AL, and AU). As opposed to minimizing a prediction error, the method is based on degrading the prediction by distorting the inputs of the trained Neural Networks in order to highlight the most sensible drivers. We show that the z component of the magnetic field, the duskward oriented electric field, and the speed of the particles of the interplanetary medium, at particular time delays, seem to be the most efficient drivers of the three coupled geomagnetic indices. Using only the sensible or robust drivers in the model, we demonstrate that iterated predictions during geomagnetic storm are significantly improved from models that only use one of the outstanding drivers with multiple time delays. The derived robust nonlinear Neural Network model is also a significant improvement over linear approximations, specially when used as iterated predictors.

The interplay between local nonlinear dynamics and spatial coupling gives rise to a rich variety of emergent phenomena in extended systems. We investigate the complex behaviors of a metapopulation model composed of two identical, chaotic logistic maps coupled symmetrically by a migration term. Although coupling is often assumed to increase complexity, our central finding is that migration can serve as a potent mechanism for the suppression and complete elimination of chaos. This control is strikingly non-monotonic: as the migration rate is increased, the system alternates between chaotic and ordered regimes. Through extensive numerical simulations---including bifurcation diagrams and the \textit{complete Lyapunov spectrum} $(\Lambda_1, \Lambda_2)$ computed via QR decomposition---we provide a comprehensive analysis of the system's dynamical landscape. The full spectrum reveals a hyperchaotic sub-regime at very low coupling ($\Lambda_1, \Lambda_2 > 0$) and quantitatively characterizes the transverse stability of the synchronization manifold throughout the synchronized regime. We identify two ``windows of order'' where chaos is suppressed: one at low migration rates ($\epsilon \approx 0.047$--$0.154$), established via a reverse period-doubling cascade preceded by a boundary crisis, and a second at high migration rates ($\epsilon \approx 0.846$--$0.953$), initiated by a Hopf (Neimark--Sacker) bifurcation and ended by a forward period-doubling cascade into chaos. We further demonstrate that these two windows are not independent dynamical accidents but the same underlying mechanism observed at conjugate values of the coupling: the system satisfies an exact time-shift duality $G_{1-\epsilon}^{\,2} = G_\epsilon^{\,2}$, mapping the cascade in one window onto that in the other modulo a one-iteration shift implemented by the parity operator. The duality enforces the sum rule $\epsilon_c^{(1)} + \epsilon_c^{(2)} = 1$ on every pair of corresponding bifurcation thresholds, providing a precision test on numerical estimates. These transitions are fundamentallylinked to the spontaneous breaking and restoration of the system's inherent parity symmetry. We provide a theoretical interpretation through stability analysis of the synchronization manifold, demonstrating that transitions to and from synchronized chaos are well-described as blowout bifurcations. Beyond their intrinsic interest as nonlinear-dynamics phenomena, these results carry a counterintuitive ecological message: strong dispersal is not always protective, since it locks both subpopulations onto a synchronized chaotic attractor whose excursions periodically approach extinction thresholds in unison, whereas the windows of order opened by bifurcations at intermediate coupling shelter the metapopulation in bounded, regular dynamics. Our findings find direct support in recent experimental observations of bacterial metapopulations.