Magnetic hysteresis mechanism in Lu0.9Sr0.1Cr0.5Fe0.5O3 studied by Monte Carlo simulations.

In this work we report magnetic properties of the orthorhombic perovskite Lu0.9Sr0.1Cr0.5Fe0.5O3 synthesized by a wet chemical method. As in LuCr0.5Fe0.5O3 the compound with Sr shows the magnetization reversal phenomenon, but the magnetic order and the compensation temperature occur at higher temperatures. Interestingly, in M vs H curves a hysteresis loop is observed when Cr4+ and Cr3+ ions coexist as a consequence of the aliovalent substitution of Lu3+ by Sr2+ in the B sites of the perovskite. To explain this behavior, we performed numerical simulations with a magnetic model for Lu1-xSrxCr0.5Fe0.5O3 perovskites with x=0 and x=0.1. We found that the ferromagnetic coupling of Fe3+ and Cr4+ through superexchange interactions (according the empiric Goodenough-Kanamori-Anderson rules, GKA) increases the magnetization at high fields and that the presence of ferromagnetic clusters explains the hysteretic behavior found in simulations.

Magnetization reversal study of Ni4-xZnxNb2O9compounds using Monte Carlo simulations.

The magnetization reversal (MR) of the layered Ni4-xZnxNb2O9ferrimagnetic compounds, withx=0,0.25,0.50and 0.75, is studied in this work using Monte Carlo (MC) simulations and mean field (MF) calculations. First, we analyze the parent compound to set the parameters of our simulations; testing together MC simulations, MF calculations, and MR experiments reported by Bollettaet al(2022J. Appl. Phys.132153901). Then using two different approaches we fit the MR curves of the series of compounds finding a quite good agreement between MC simulations and the experiments. According to these results, Zn substitutions change the relative contribution to the magnetization of the different layers. Here we present two possible hypotheses to explain this effect; one involving a heterogeneous distribution of Zn2+among the layers, and the other related to distortions of the NiO6octahedra.

Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis.

Evidence of critical dynamics has been found recently in both experiments and models of large-scale brain dynamics. The understanding of the nature and features of such a critical regime is hampered by the relatively small size of the available connectome, which prevents, among other things, the determination of its associated universality class. To circumvent that, here we study a neural model defined on a class of small-world networks that share some topological features with the human connectome. We find that varying the topological parameters can give rise to a scale-invariant behavior either belonging to the mean-field percolation universality class or having nonuniversal critical exponents. In addition, we find certain regions of the topological parameter space where the system presents a discontinuous, i.e., noncritical, dynamical phase transition into a percolated state. Overall, these results shed light on the interplay of dynamical and topological roots of the complex brain dynamics.

Short-ranged memory model with preferential growth.

In this work we introduce a variant of the Yule-Simon model for preferential growth by incorporating a finite kernel to model the effects of bounded memory. We characterize the properties of the model combining analytical arguments with extensive numerical simulations. In particular, we analyze the lifetime and popularity distributions by mapping the model dynamics to corresponding Markov chains and branching processes, respectively. These distributions follow power laws with well-defined exponents that are within the range of the empirical data reported in ecologies. Interestingly, by varying the innovation rate, this simple out-of-equilibrium model exhibits many of the characteristics of a continuous phase transition and, around the critical point, it generates time series with power-law popularity, lifetime and interevent time distributions, and nontrivial temporal correlations, such as a bursty dynamics in analogy with the activity of solar flares. Our results suggest that an appropriate balance between innovation and oblivion rates could provide an explanatory framework for many of the properties commonly observed in many complex systems.

Mitochondrial network complexity emerges from fission/fusion dynamics.

Mitochondrial networks exhibit a variety of complex behaviors, including coordinated cell-wide oscillations of energy states as well as a phase transition (depolarization) in response to oxidative stress. Since functional and structural properties are often interwinded, here we characterized the structure of mitochondrial networks in mouse embryonic fibroblasts using network tools and percolation theory. Subsequently we perturbed the system either by promoting the fusion of mitochondrial segments or by inducing mitochondrial fission. Quantitative analysis of mitochondrial clusters revealed that structural parameters of healthy mitochondria laid in between the extremes of highly fragmented and completely fusioned networks. We confirmed our results by contrasting our empirical findings with the predictions of a recently described computational model of mitochondrial network emergence based on fission-fusion kinetics. Altogether these results offer not only an objective methodology to parametrize the complexity of this organelle but also support the idea that mitochondrial networks behave as critical systems and undergo structural phase transitions.

Structure constrained by metadata in networks of chess players.

Chess is an emblematic sport that stands out because of its age, popularity and complexity. It has served to study human behavior from the perspective of a wide number of disciplines, from cognitive skills such as memory and learning, to aspects like innovation and decision-making. Given that an extensive documentation of chess games played throughout history is available, it is possible to perform detailed and statistically significant studies about this sport. Here we use one of the most extensive chess databases in the world to construct two networks of chess players. One of the networks includes games that were played over-the-board and the other contains games played on the Internet. We study the main topological characteristics of the networks, such as degree distribution and correlations, transitivity and community structure. We complement the structural analysis by incorporating players' level of play as node metadata. Although both networks are topologically different, we show that in both cases players gather in communities according to their expertise and that an emergent rich-club structure, composed by the top-rated players, is also present.

A Study of Memory Effects in a Chess Database.

A series of recent works studying a database of chronologically sorted chess games-containing 1.4 million games played by humans between 1998 and 2007- have shown that the popularity distribution of chess game-lines follows a Zipf's law, and that time series inferred from the sequences of those game-lines exhibit long-range memory effects. The presence of Zipf's law together with long-range memory effects was observed in several systems, however, the simultaneous emergence of these two phenomena were always studied separately up to now. In this work, by making use of a variant of the Yule-Simon preferential growth model, introduced by Cattuto et al., we provide an explanation for the simultaneous emergence of Zipf's law and long-range correlations memory effects in a chess database. We find that Cattuto's Model (CM) is able to reproduce both, Zipf's law and the long-range correlations, including size-dependent scaling of the Hurst exponent for the corresponding time series. CM allows an explanation for the simultaneous emergence of these two phenomena via a preferential growth dynamics, including a memory kernel, in the popularity distribution of chess game-lines. This mechanism results in an aging process in the chess game-line choice as the database grows. Moreover, we find burstiness in the activity of subsets of the most active players, although the aggregated activity of the pool of players displays inter-event times without burstiness. We show that CM is not able to produce time series with bursty behavior providing evidence that burstiness is not required for the explanation of the long-range correlation effects in the chess database. Our results provide further evidence favoring the hypothesis that long-range correlations effects are a consequence of the aging of game-lines and not burstiness, and shed light on the mechanism that operates in the simultaneous emergence of Zipf's law and long-range correlations in a community of chess players.

Magnetization reversal in mixed ferrite-chromite perovskites with non magnetic cation on the A-site.

In this work, we have performed Monte Carlo simulations in a classical model for RFe1-x Cr x O3 with R = Y and Lu, comparing the numerical simulations with experiments and mean field calculations. In the analyzed compounds, the antisymmetric exchange or Dzyaloshinskii-Moriya (DM) interaction induced a weak ferromagnetism due to a canting of the antiferromagnetically ordered spins. This model is able to reproduce the magnetization reversal (MR) observed experimentally in a field cooling process for intermediate x values and the dependence with x of the critical temperatures. We also analyzed the conditions for the existence of MR in terms of the strength of DM interactions between Fe(3+) and Cr(3+) ions with the x values variations.

Finite-temperature phase diagram of ultrathin magnetic films without external fields.

We analyze the finite-temperature phase diagram of ultrathin magnetic films by introducing a mean-field theory, valid in the low-anisotropy regime, i.e., close to the spin reorientation transition. The theoretical results are compared with Monte Carlo simulations carried out on a microscopic Heisenberg model. Connections between the finite-temperature behavior and the ground-state properties of the system are established. Several properties of the stripe pattern, such as the presence of canted states, the stripe width variation phenomenon, and the associated magnetization profiles, are also analyzed.

Smart random walkers: the cost of knowing the path.

In this work we study the problem of targeting signals in networks using entropy information measurements to quantify the cost of targeting. We introduce a penalization rule that imposes a restriction on the long paths and therefore focuses the signal to the target. By this scheme we go continuously from fully random walkers to walkers biased to the target. We found that the optimal degree of penalization is mainly determined by the topology of the network. By analyzing several examples, we have found that a small amount of penalization reduces considerably the typical walk length, and from this we conclude that a network can be efficiently navigated with restricted information.

Emergent self-organized complex network topology out of stability constraints.

Although most networks in nature exhibit complex topologies, the origins of such complexity remain unclear. We propose a general evolutionary mechanism based on global stability. This mechanism is incorporated into a model of a growing network of interacting agents in which each new agent's membership in the network is determined by the agent's effect on the network's global stability. It is shown that out of this stability constraint complex topological properties emerge in a self-organized manner, offering an explanation for their observed ubiquity in biological networks.