Articles publicats en revistes (Institut de Recerca en Sistemes Complexos (UBICS))

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Tunable dynamics of flexible magnetic microcrosses: synchronous rotation, breathing and out-of-plane arm overtaking
(Royal Society of Chemistry, 2025-10-13) Tavacoli, Joe; Stikuts, Andris P.; Dass, Mihir; Liedl, Tim; Tierno, Pietro
We combine colloidal self-assembly and soft-lithography techniques to realize flexible magnetic microcrosses that can be manipulated via external, time dependent magnetic fields. The crosses are characterized by a central domain connected via four flexible arms. When subjected to an in-plane, rotating magnetic field, the crosses transit from a synchronous to an asynchronous spinning motion where their average rotation decreases with the driving frequency. In the asynchronous regime and at low field amplitudes, the crosses display a breathing mode, characterized by relative oscillations between the arms, while remaining localized in the two dimensional plane. In contrast, for high field amplitudes, we observe an arm overtaking regime where two opposite filaments surpass the remaining ones forcing the cross to perform a three-dimensional gyroscopic-like rotation. Using slender body theory and balancing the effect of magnetic and elastic interactions, we recover the experimental findings and show that the overtaking regime occurs due to different arm magnetizations. Our engineered microscopic colloidal rotors characterized by multiple flexible filaments may find potential applications for precise lab-on-a-chip operations or as stirrers dispersed within microfluidic or biological channels.
Role of connectivity anisotropies in the dynamics of cultured neuronal networks
(Public Library of Science (PLoS), 2025-11-06) Houben, Akke Mats; García Ojalvo, Jordi; Soriano i Fradera, Jordi
An inherent challenge in designing laboratory-grown, engineered living neuronal networks lies in predicting the dynamic repertoire of the resulting network and its sensitivity to experimental variables. To fill this gap, and inspired by recent experimental studies, we present a numerical model designed to replicate the anisotropies in connectivity introduced through engineering, characterize the emergent collective behavior of the neuronal network, and make predictions. The numerical model is developed to replicate experimental data, and subsequently used to quantify network dynamics in relation to tunable structural and dynamical parameters. These include the strength of imprinted anisotropies, synaptic noise, and average axon lengths. We show that the model successfully captures the behavior of engineered neuronal cultures, revealing a rich repertoire of activity patterns that are highly sensitive to connectivity architecture and noise levels. Specifically, the imprinted anisotropies promote modularity and high clustering coefficients, substantially reducing the pathological-like bursting of standard neuronal cultures, whereas noise and axonal length influence the variability in dynamical states and activity propagation velocities. Moreover, connectivity anisotropies significantly enhance the ability to reconstruct structural connectivity from activity data, an aspect that is important to understand the structure–function relationship in neuronal networks. Our work provides a robust in silico framework to assist experimentalists in the design of in vitro neuronal systems and in anticipating their outcomes. This predictive capability is particularly valuable in developing reliable brain-on-a-chip platforms and in exploring fundamental aspects of neural computation, including input–output relationships and information coding.
Citizen Science initiatives in climate-vulnerable neighbourhoods: a new transdisciplinary approach to tackle sustainability challenges?
(Université Côte d'Azur, 2025-11-01) Bonhoure, Isabelle; Perelló, Josep, 1974-
According to the Spanish State Meteorological Agency (AEMET), the summer of 2025 (June 1-August 31) was exceptionally warm across Spain, with an average temperature of 24.2°C on the mainland. This value is 2.1°C above the seasonal average for the reference period 1991-2020. It was the warmest summer since records began in 1961, surpassing the previous record set in 2022 by 0.1°C . Barcelona and its metropolitan area, located along the Catalan coast, have been particularly affected by rising summer temperatures, a situation exacerbated by the urban heat island effect (Ward et al., 2016; Zhao et al., 2018). On 16 August 2025, a temperature of 38.9°C was recorded at the Fabra Observatory , one of the city's four official meteorological stations. This value exceeded the previous August record of 38.8°C, registered in 2023. Furthermore, according to an international study (Barnes et al., 2025), Barcelona reported the third-highest number of heat-related deaths among European cities during the summer of 2025, surpassed only by Milan and Rome.
Preparation and Mechano-Functional Characterization of PEGylated Fibrin Hydrogels: Impact of Thrombin Concentration
(2024-02-01) López-León, Clara F.; Planet Latorre, Ramon; Soriano i Fradera, Jordi
Three-dimensional (3D) neuronal cultures grown in hydrogels are promising platforms to design brain-like neuronal networks in vitro. However, the optimal properties of such cultures must be tuned to ensure a hydrogel matrix sufficiently porous to promote healthy development but also sufficiently rigid for structural support. Such an optimization is difficult since it implies the exploration of different hydrogel compositions and, at the same time, a functional analysis to validate neuronal culture viability. To advance in this quest, here we present a combination of a rheological protocol and a network-based functional analysis to investigate PEGylated fibrin hydrogel networks with gradually higher stiffness, achieved by increasing the concentration of thrombin. We observed that moderate thrombin concentrations of 10% and 25% in volume shaped healthy networks, although the functional traits depended on the hydrogel stiffness, which was much higher for the latter concentration. Thrombin concentrations of 65% or higher led to networks that did not survive. Our results illustrate the difficulties and limitations in preparing 3D neuronal networks, and stress the importance of combining a mechano-structural characterization of a biomaterial with a functional one.
Modular architecture confers robustness to damage and facilitates recovery in spikingneural networks modeling in vitro neurons
(Frontiers Media, 2025-06-19) Sumi, Takuma; Houben, Akke Mats; Yamamoto, Hideaki; Kato, Hideyuki; Katori, Yuichi; Soriano i Fradera, Jordi; Hirano-Iwata, Ayumi
Impaired brain function is restored following injury through dynamic processes that involve synaptic plasticity. This restoration is supported by the brain’s inherent modular organization, which promotes functional separation and redundancy. However, it remains unclear how modular structure interacts with synaptic plasticity to define damage response and recovery efficiency. In this work, we numerically modeled the response and recovery to damage of a neuronal network in vitro bearing a modular structure. The simulations aimed at capturing experimental observations in cultured neurons with modular traits which were physically disconnected through a focal lesion. The damage reduced the frequency of spontaneous collective activity events in the cultures, which recovered to pre-damage levels within 24 h. We rationalized this recovery in the numerical simulations by considering a plasticity mechanism based on spike-timing-dependent plasticity, a form of synaptic plasticity that modifies synaptic strength based on the relative timing of pre- and postsynaptic spikes. We observed that the in silico numerical model effectively captured the decline and subsequent recovery of spontaneous activity following the injury. The model supports that the combination of modularity and plasticity confers robustness to the damaged neuronal network by preventing the total loss of spontaneous network-wide activity and facilitating recovery. Additionally, by using our model within the reservoir computing framework, we show that information representation in the neuronal network improves with the recovery of network-wide activity.
Trends and drivers of pedestrian mobility in Barcelona: A fine-grained study across its commercial tissue
(Elsevier, 2025-03) Rames, Clément; Rhoads, Daniel; Meseguer Artola, Antoni; Lozano, Sergi; Borge Holthoefer, Javier; Solé Ribalta, Albert
Identifying factors that promote active mobility, especially walking, is essential for designing resilient and livable cities and promoting sustainable urban mobility. In spite of recent advances in this direction, available data often remains too spatially and temporally coarse, which constrains analysis. This paper leverages high resolution data from over 200 pedestrian count sensors, placed along Barcelona’s commercial areas, providing a detailed understanding of how walking volume has evolved over the past five years, how it varies across neighborhoods, and which socioeconomic and urban attributes influence it. We find that while overall pedestrian traffic has increased, a neighborhood-scale analysis reveals a nuanced picture of fluctuations, including increases, declines, and periodic patterns. The use of global regression models allows us to identify seven key urban factors that shape pedestrian mobility. Subsequently moving the analysis to spatially-aware regression models, we identify the spatial non-stationarity of these factors across the city, indicating the presence of distinct behavioral groups within the urban population. The detailed spatial resolution of our findings provides municipal decision-makers with insights for implementing precise interventions and continually evaluating their effects. Moreover, monitoring pedestrian traffic before and after urban initiatives, while adjusting for seasonal, daily, and time-of-day variations, can yield critical insights for developing pedestrian-oriented urban environments.
Multicaloric effects and magnetostructural coupling in the Cr2Ge2Te6 van der Waals crystal
(Elsevier, 2025-04-22) Abadia-Huguet, Aleix; Mendive Tapia, Eduardo; Stern Taulats, Enric; Planes Vila, Antoni; Eggert, Benedikt; Wende, Heiko; Acet, Mehmet; Sturza, Mihai-Ionut; Kohlmann, Holger; Costache, Marius V.; Mañosa, Lluís
Materials with significant coupling between magnetism and their crystal structure are prone to exhibit multicaloric effects, which offer a novel approach to addressing the bottlenecks of ecologic solid-state refrigeration by optimizing the interplay of multiple driving fields. Here we uncover the multicaloric properties of CrGeTe, establishing ferromagnetic van der Waals (vdW) crystals, famous for their spintronics applications, as a previously unrecognized class of multicaloric materials. By combining magnetization measurements with an ab initio disordered local moment theory, we report, for the first time, pronounced barocaloric and multicaloric effects induced by the application of magnetic fields and hydrostatic pressure around CrGeTe’s ferromagnetic phase transition. Our experimental and ab initio analysis quantifies the underlying magnetostructural coupling in this material, which accounts for approximately 25% of the total multicaloric entropy change. Significant multicaloric effects are expected to be found in other vdW ferromagnets with strong magnetostructural coupling.
First Order Alignment Transition in an Interfaced Active Nematic
(American Physical Society, 2024-06-15) Bantysh, Olga; Martinez-Prat, Berta; Nambisan, Jyothishraj; Fernandez de las Nieves, Alberto; Sagués i Mestre, Francesc; Ignés i Mullol, Jordi
We investigate experimentally the dynamic phase transition of a two-dimensional active nematic layer interfaced with a passive liquid crystal. Under a temperature ramp that leads to the transition of the passive liquid into a highly anisotropic lamellar smectic-A phase, and in the presence of a magnetic field, the coupled active nematic reorganizes its flow and orientational patterns from the turbulent into a quasilaminar regime aligned perpendicularly to the field. Remarkably, while the phase transition of the passive fluid is known to be continuous, or second order, our observations reveal intermittent dynamics of the order parameter and the coexistence of aligned and turbulent regions in the active nematic, a signature of discontinuous, or first order, phase transitions, similar to what is known to occur in relation to flocking in dry active matter. Our results suggest that alignment transitions in active systems are intrinsically discontinuous, regardless of the symmetry and momentum-damping mechanisms.
Discounting the Distant Future: What Do Historical Bond Prices Imply about the Long-Term Discount Rate?
(MDPI, 2024-03-01) Farmer, J. Doyne; Geanakoplos, John; Richiardi, Matteo G.; Montero Matellanes, M. Mikel; Perelló, Josep, 1974-; Masoliver, Jaume, 1951-
We present a thorough empirical study on real interest rates by also including risk aversion through the introduction of the market price of risk. From the viewpoint of complex systems science and its multidisciplinary approach, we use the theory of bond pricing to study the long-term discount rate to estimate the rate when taking historical US and UK data, and to further contribute to the discussion about the urgency of climate action in the context of environmental economics and stochastic methods. Century-long historical records of 3-month bonds, 10-year bonds, and inflation allow us to estimate real interest rates for the UK and the US. Real interest rates are negative about a third of the time and the real yield curves are inverted more than a third of the time, sometimes by substantial amounts. This rules out most of the standard bond-pricing models, which are designed for nominal rates that are assumed to be positive. We, therefore, use the Ornstein–Uhlenbeck model, which allows negative rates and gives a good match to inversions of the yield curve. We derive the discount function using the method of Fourier transforms and fit it to the historical data. The estimated long-term discount rate is 1.7% for the UK and 2.2% for the US. The value of 1.4% used by Stern is less than a standard deviation from our estimated long-run return rate for the UK, and less than two standard deviations of the estimated value for the US. All of this once more reinforces the need for immediate and substantial spending to combat climate change.
Integrated information decomposition unveils major structural traits of in silico and in vitro neuronal networks
(American Institute of Physics (AIP), 2024-05-01) Menesse, Gustavo; Houben, Akke Mats; Soriano i Fradera, Jordi; Torres, Joaquín J.
The properties of complex networked systems arise from the interplay between the dynamics of their elements and the underlying topology. Thus, to understand their behavior, it is crucial to convene as much information as possible about their topological organization. However, in large systems, such as neuronal networks, the reconstruction of such topology is usually carried out from the information encoded in the dynamics on the network, such as spike train time series, and by measuring the transfer entropy between system elements. The topological information recovered by these methods does not necessarily capture the connectivity layout, but rather the causal flow of information between elements. New theoretical frameworks, such as Integrated Information Decomposition (Phi-ID), allow one to explore the modes in which information can flow between parts of a system, opening a rich landscape of interactions between network topology, dynamics, and information. Here, we apply Phi-ID on in silico and in vitro data to decompose the usual transfer entropy measure into different modes of information transfer, namely, synergistic, redundant, or unique. We demonstrate that the unique information transfer is the most relevant measure to uncover structural topological details from network activity data, while redundant information only introduces residual information for this application. Although the retrieved network connectivity is still functional, it captures more details of the underlying structural topology by avoiding to take into account emergent high-order interactions and information redundancy between elements, which are important for the functional behavior, but mask the detection of direct simple interactions between elements constituted by the structural network topology.
Exploring Food Addiction Across Several Behavioral Addictions: Analysis of Clinical Relevance
(MDPI, 2025-04-06) Gaspar Pérez, Anahí; Granero, Roser; Fernández Aranda, Fernando; Rosinska, Magda; Artero Martinez, Cristina; Ruiz Torras, Silvia; Gearhardt, Ashley N.; Demetrovics, Zsolt; Guàrdia-Olmos, Joan, 1958-; Jiménez-Murcia, Susana
Background/Objectives: Recently, interest in studying food addiction (FA) in the context of behavioral addictions (BAs) has increased. However, research remains limited to determine the FA prevalence among various BAs. The current study aimed to investigate FA in a clinical sample of patients seeking treatment for gaming disorder, compulsive buying-shopping disorder (CBSD), compulsive sexual behavior disorder, and the comorbid presence of multiple BAs, as well as to determine the sociodemographic characteristics, personality traits, and general psychopathology of this clinical population. In addition, we analyzed whether FA is linked to a higher mean body mass index (BMI). Methods: The sample included 209 patients (135 men and 74 women) attending a specialized behavioral addiction unit. The assessment included a semi-structured clinical interview for the diagnosis of the abovementioned BAs, in addition to self-reported psychometric assessments for FA (using the Yale Food Addiction Scale 2. 0, YFAS-2), CBSD (using the Pathological Buying Screener, PBS), general psychopathology (using the Symptom Checklist-Revised, SCL-90-R), personality traits (using the Temperament and Character Inventory-Revised, TCI-R), emotional regulation (using Difficulties in Emotion Regulation Strategies, DERS), and impulsivity (using Impulsive Behavior Scale, UPPS-P). The comparison between the groups for the clinical profile was performed using logistic regression (categorical variables) and analysis of covariance (ANCOVA), adjusted based on the patients' gender. The sociodemographic profile was based on chi-square tests for categorical variables and analysis of variance (ANOVA) for quantitative measures. Results: The prevalence of FA in the total sample was 22.49%. The highest prevalence of FA was observed in CBSD (31.3%), followed by gaming disorder (24.7%), and the comorbid presence of multiple BAs (14.3%). No group differences (FA+/-) were found in relation to sociodemographic variables, but the comorbidity between FA and any BA was associated more with females as well as having greater general psychopathology, greater emotional dysregulation, higher levels of impulsivity, and a higher mean BMI. Conclusions: The comorbidity between FA and BA is high compared to previous studies (22.49%), and it is also associated with greater severity and dysfunctionality. Emotional distress levels were high, which suggests that the group with this comorbidity may be employing FA behaviors to cope with psychological distress. However, a better understanding of the latent mechanisms that contribute to the progression of this multifaceted comorbid clinical disorder is needed. One aspect that future studies could consider is to explore the existence of FA symptoms early and routinely in patients with BAs.
Modular architecture facilitates noise-driven control of synchrony in neuronal networks
(American Association for the Advancement of Science, 2023-08-25) Yamamoto, Hideaki; Spitzner, F. Paul; Takemuro, Taiki; Buendía, Victor; Murota, Hakuba; Morante, Carla; Konno, Tomohiro; Sato, Shigeo; Hirano-Iwata, Ayumi; Levina, Anna; Priesemann, Viola; Muñoz Pérez, Miguel Ángel; Zierenberg, Johannes; Soriano i Fradera, Jordi
High-level information processing in the mammalian cortex requires both egregated processing in specialized circuits and integration across multiple circuits. One possible way to implement these seemingly opposing demands is by flexibly switching between states with different levels of synchrony. However, the mechanisms behind the control of complex synchronization patterns in neuronal networks remain elusive. Here, we use precision neuroengineering to manipulate and stimulate networks of cortical neurons in vitro, in combination with an in silico model of spiking neurons and a mesoscopic model of stochastically coupled modules to show that (i) a modular architecture enhances the sensitivity of the network to noise delivered as external asynchronous stimulation and that (ii) the persistent depletion of synaptic resources in stimulated neurons is the underlying mechanism for this effect. Together, our results demonstrate that the inherent dynamical state in structured networks of excitable units is determined by both its modular architecture and the properties of the external inputs.
Signatures of criticality in turning avalanches of schooling fish
(American Physical Society, 2023-10-02) Puy, Andreu; Gimeno Rosell, Elisabet; March Pons, David; Miguel López, María del Carmen; Pastor Satorras, Romualdo
Moving animal groups transmit information through propagating waves or behavioral cascades, exhibiting characteristics akin to systems near a critical point from statistical physics. Using data from freely swimming schooling fish in an experimental tank, we investigate spontaneous behavioral cascades involving turning avalanches, where large directional shifts propagate across the group. We analyze several avalanche metrics and provide a detailed picture of the dynamics associated with turning avalanches, employing tools from avalanche behavior in condensed-matter physics and seismology. Our results identify power-law distributions and robust scale-free behavior through data collapses and scaling relationships, confirming a necessary condition for criticality in fish schools. We explore the biological function of turning avalanches and link them to collective decision-making processes in selecting a new movement direction for the school. We report relevant boundary effects arising from interactions with the tank walls and influential roles of boundary individuals. Finally, spatial and temporal correlations in avalanches are explored using the concept of aftershocks from seismology, revealing clustering of avalanche events below a designated timescale and an Omori law with a faster decay rate than observed in earthquakes.
Magnetically driven confined colloids: From enhanced diffusion to bidirectional transport
(Elsevier B.V., 2024-02-01) Ostinato, Mattia; Ortiz-Ambriz, Antonio; Tierno, Pietro
Inspired by previous experimental results, we use numerical simulations to investigate the collective dynamics of paramagnetic colloidal particles confined between two plates closer than twice the particle diameter and driven by an external precessing magnetic field. We show that, when the field is spatially isotropic there is no net particle current and the colloids display enhanced diffusive dynamics with an effective diffusion coefficient which raises up to 60 times that of the undriven case. In contrast, when the field is spatially anisotropic due to a small tilt angle , the particles organize into a robust bidirectional current, flowing along two parallel planes by periodically exchanging their positions. In this regime, we also analyze how the presence of small impurities which can be described as “magnetic holes” affect the particle current breaking the bidirectional flow. Our system provides a general method to transport magnetic colloids in a viscous fluid, without using any field gradient, but based on the fine balance between confinement and magnetic dipolar interactions.
Aligning active particles py package
(Elsevier B.V., 2023-05-10) Rüdiger, Kürsten
The package performs molecular-dynamics-like agent-based simulations for models of aligning self-propelled particles in two dimensions such as e.g. the seminal Vicsek model or variants of it. In one class of the covered models, the microscopic dynamics is determined by certain time discrete interaction rules. Thus, it is no Hamiltonian dynamics and quantities such as energy are not defined. In the other class of considered models (that are generally believed to behave qualitatively the same) Brownian dynamics is considered. However, also there, the forces are not derived from a Hamiltonian. Furthermore, in most cases, the forces depend on the state of all particles and can not be decomposed into a sum of forces that only depend on the states of pairs of particles. Due to the above specified features of the microscopic dynamics of such models, they are not implemented in major molecular dynamics simulation frameworks to the best of the authors knowledge. Models that are covered by this package have been studied with agent-based simulations by dozens of papers. However, no simulation framework of such models seems to be openly available. The program is provided as a Python package. The simulation code is written in C. In the current version, parallelization is not implemented.
Bidirectional zigzag growth from clusters of active colloidal shakers
(American Physical Society, 2024-03-15) Junot, Gaspard; Manzano González, Andrés Javier; Tierno, Pietro
Driven or self-propelling particles moving in viscoelastic fluids recently emerged as a novel class of active systems showing a complex yet rich set of phenomena due to the non-Newtonian nature of the dispersing medium. Here we investigate the one-dimensional growth of clusters made of active colloidal shakers, which are realized by oscillating magnetic rotors dispersed within a viscoelastic fluid and at different concentrations of the dissolved polymer. These magnetic particles when actuated by an oscillating field display a flow profile similar to that of a shaker force dipole, i.e., without any net propulsion. We design a protocol to assemble clusters of colloidal shakers and induce their controlled expansion into elongated zigzag structures. We observe a power law growth of the mean chain length and use theoretical arguments to explain the measured 1/3 exponent. These arguments agree well with both experiments and particle based numerical simulations.
Inferring structure of cortical neuronal networks from activity data: A statistical physics approach
(Oxford University Press, 2025-01-09) Fai Po, H.; Houben, Akke Mats; Haeb, Anna-Christina; Jenkins, D.R.; Hill, E.J.; Parri, H.R; Soriano i Fradera, Jordi; Saad, D.
Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve over time, spontaneously or under stimulation. It requires a method for inferring the structure and composition of a network from neuronal activities. Tracking the evolution of networks and their changing functionality will provide invaluable insight into the occurrence of plasticity and the underlying learning process. We devise a probabilistic method for inferring the effective network structure by integrating techniques from Bayesian statistics, statistical physics, and principled machine learning. The method and resulting algorithm allow one to infer the effective network structure, identify the excitatory and inhibitory type of its constituents, and predict neuronal spiking activity by employing the inferred structure. We validate the method and algorithm’s performance using synthetic data, spontaneous activity of an in silico emulator, and realistic in vitro neuronal networks of modular and homogeneous connectivity, demonstrating excellent structure inference and activity prediction. We also show that our method outperforms commonly used existing methods for inferring neuronal network structure. Inferring the evolving effective structure of neuronal networks will provide new insight into the learning process due to stimulation in general and will facilitate the development of neuron-based circuits with computing capabilities.
Chitinase 3-like 1 is neurotoxic in multiple sclerosis patient-derived cortical neurons
(John Wiley & Sons, 2024-11-25) Pinteac, R.; Soriano i Fradera, Jordi; Matute-Blanch, C.; Lizcano, José Miguel; Duarri, A.; Malhotra, S.; Eixarch, H.; López Comellas, G.; Montalban, X.; Comabella, M.
We are pleased to present our latest findings regarding the neurotoxic role of Chitinase 3-like 1 (CHI3L1) in multiple sclerosis (MS). CHI3L1, a 40 kD glycoprotein, is primarily produced by activated astrocytes and microglia in the central nervous system (CNS), and it has garnered considerable attention due to its implications in inflammation and tissue remodelling.1 It is notably increased in several conditions, including MS, and accumulating evidence supports CHI3L1 as a biomarker in early MS, with elevated cerebrospinal fluid (CSF) levels associated with increased disability risk.2, 3 This association led us to investigate whether CHI3L1 simply reflects glial activation or if it exerts direct neurotoxicity. Our prior work in murine neurons demonstrated CHI3L1's neurotoxic effects,4 prompting us to explore its impact on MS patient-derived human induced pluripotent stem cells (hiPSC). Here, we aim to characterize these effects at both molecular and functional levels, further exploring CHI3L1's potential as a biomarker and therapeutic target for MS.
Multiscale voter model on real networks
(Elsevier Ltd, 2022-12-01) Ortiz Castillo, Elisenda; Serrano Moral, Ma. Ángeles (María Ángeles)
We introduce the Multiscale Voter Model (MVM) to investigate clan influence at multiple scales—family, neighborhood, political party…—in opinion formation on real complex networks. Clans, consisting of similar nodes, are constructed using a coarse-graining procedure on network embeddings that allows us to control for the length scale of interactions. We ran numerical simulations to monitor the evolution of MVM dynamics in real and synthetic networks, and identified a transition between a final stage of full consensus and one with mixed binary opinions. The transition depends on the scale of the clans and on the strength of their influence. We found that enhancing group diversity promotes consensus while strong kinship yields to metastable clusters of same opinion. The segregated domains, which signal opinion polarization, are discernible as spatial patterns in the hyperbolic embeddings of the networks. Our multiscale framework can be easily applied to other dynamical processes affected by scale and group influence.
Enhancement of the Elastocaloric Performance of Natural Rubber by Forced Air Convection
(MDPI, 2024-10-31) Valdés, Emma; Stern Taulats, Enric; Candau, Nicolas; Mañosa, Lluís; Vives i Santa-Eulàlia, Eduard
We study the enhancement of the elastocaloric effect in natural rubber by using forced air convection to favour heat extraction during the elongation stage of a stretching–unstretching cycle. Elastocaloric performance is quantified by means of the adiabatic undercooling that occurs after fast removal of the stress, measured by infrared thermography. To ensure accuracy, spatial averaging on thermal maps of the sample surface is performed since undercooled samples display heterogeneities caused by various factors. The influence of the stretching velocity and the air velocity is analysed. The findings indicate that there is an optimal air velocity that maximises adiabatic undercooling, with stretching velocities needing to be high enough to enhance cooling power. Our experiments allowed the characterisation of the dependence of the Newton heat transfer coefficient on the air convection velocity, which revealed an enhancement up to 600% for air velocities around 4 m/s.

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