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Introducing macromolecular micelles into a biocompatible hyaluronic acid (HA) hydrogel is a promising strategy to improve its mechanical properties for biomedical applications. However, it is still unclear whether the solvent nature has an influence on the structure and property of HA gels especially when they are used for those cases containing binary solvents because reversible hydrophobic association within micelles could be weakened or even dissociated by organic solvents. In this work, we demonstrated that a binary solvent consisting of water and low-toxic dimethyl sulfoxide (DMSO), a commonly used cryoprotectant agent in biomedicine, can enhance the mechanical properties of hydrophobic-associated methacrylated hyaluronate (MeHA) gels crosslinked by diacrylated PEO99-PPO65-PEO99 (F127DA) macromolecular micelles, namely FH gels. The resulting FH hydro/organo-gels showed a crystalline structure due to polymer/solvent interactions. The FH gels showed a low swelling degree and the maximum strength (10.12 MPa), modulus (106.8 kPa) and toughness (1540 J m-2) in DMSO with a volume fraction of around 0.6. Moreover, the FH gels displayed a rapid recoverability under cyclic loading-unloading stress particularly in the presence of DMSO within the network due to their dual-dynamic dissipation networks. Such novel hydrophobic associated polysaccharide gels with tunable mechanical properties in binary solvents would be attractive in a cryopreservation system for cell-based applications.

Modular robot network architectures are experiencing growing popularity. The problem of automatically reconfiguring a set of modular robots into a given target shape presents a real challenge to distributed computing.Many works on the subject restrict the nature of the constructed target forms. The bolder approaches focus on reducing the number of overall required movements. In this work, we propose a distributed asynchronous self-reconfiguration algorithm allowing to distribute the effort made by each robot to reach the final shape. This makes it possible to extend the life of the network of micro-robots.We compare our TBSR algorithm with C2SR self-reconfiguration algorithm using VisibleSim simulator. The obtained results show that globally TBSR outperforms C2SR except for rare cases. The TBSR algorithm allows reducing the number of required moves up to 17%. Besides, the ability of TBSR to balance the number of moves over the modular robots makes that the maximum number of moves per robot is reduced up to 40%.

Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population.In this study we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine the mathematical modelling with structural information from non-invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values.We demonstrate, along the example of diffusion-weighted magnetic resonance imaging (MRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e. the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.

The architecture of coordination mechanisms is central to the performance and behaviour of (self-)integrated systems across natural, socio-technical and cyber–physical domains. Multi-scale coordination schemes are prevalent in large-scale systems with bounded performance requirements and limited resource constraints. However, theories to formalise how coordination can be implemented across multi-scale systems are often domain-specific, lacking generic, reusable principles. In these systems, feedback among system entities is a key component to coordination. Building on theories of hierarchies and complexity, in previous work we formalised Multi-Scale Abstraction Feedbacks (MSAF) as a design pattern to describe the architecture of feedback across system scales, highlighting the role played by micro-entities and macro-entities, as well as their interconnections. Focusing on exogenous coordination, this paper refines the MSAF pattern, describing a feedback cycle across scales as one where information flows bottom-up and top-down through five actions: state information communication, state information abstraction, information processing, control information communication, and adaptation from control information. Abstracted state information at each scale is processed with control input from the scale above and provides control input to the scale below. Using the example of distributed task allocation through exogenous coordination, NetLogo simulations are implemented to analyse the impact that different exogenous coordination strategies, and their internal timing configurations, have on resource consumption and on convergence performance. The experimental insights and refinement of the MSAF pattern contribute to a general theory of multi-scale feedback and adaptation. This architectural pattern and associated analysis and evaluation tools are still developing, but offer a concrete basis for further expansion, improvement, and implementation, while addressing questions that are at the core of the behaviour of multi-scale systems.

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Geometry, calculus and in particular integrals, are too often seen by young students as technical tools with no link to the reality. This fact generates into the students a loss of interest with a consequent removal of motivation in the study of such topics and more widely in pursuing scientific curricula. With this note we put to the fore a simple example of practical interest where the above concepts prove central; our aim is thus to motivate students and to reverse the dropout trend by proposing an introduction to the theory starting from practical applications. More precisely, we will show how using a mixture of geometry, calculus and integrals one can easily share a watermelon into regular slices with equal volume., corrected version

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Problems to solve nowadays have never been so complex and are continuously increasing in complexity. In this context Systems of Systems (SoS) may be a solution but the study of such systems is far from over. An SoS is a complex system characterized by the particular nature of its components: the latter, which are systems, tend to be managerially and operationally independent as well as geographically distributed. This specific characterization led to re-think research fields of classic systems engineering, such as definition, taxonomy, modeling, architecting, etc. SoS architecting focuses on the way independent components of an SoS can be dynamically structured and can autonomously and efficiently modify their interactions in order to fulfill the goal of the SoS and to cope with the high dynamics of the environment. This paper contributes to the multi-agent and SoS research fields by proposing a new generic SoS model called SApHESIA which considers the SoS main characteristics found in the literature and extends the notion of environment and interactions between component systems. It also proposes a new SoS architecting procedure based on the Adaptive Multi-Agent System (AMAS) approach that advocates full cooperation between all the components of the SoS through the concept of criticality. This criticality is a measure, local to each component, expressing its difficulties to reach its local goals. In this procedure, the SoS architecture evolves to self-adapt to the dynamics of the environment in which it is plunged, while considering the respective local goals of its components. An instantiation of SApHESIA to two distinct cases studies from different domains (logistics and exploratory missions) is done to obtain two SoSs. These two SoS are then coupled to form a new SoS at an upper level. Evaluations of these SoSs show that their cooperation make each of them more efficient.

Autonomic systems manage resources to achieve high-level goals with minimum human intervention, by continuously adapting based on a model of themselves and of their environment. Adaptation requires managing runtime uncertainties produced by changes within a dynamic environment, by modeling unexpected situations that were not considered at design time. This challenge becomes even more critical in a self-integration context where autonomic systems, traditionally engineered systems, and human organizations, influence each other in a shared environment. In this paper, we propose a cognitive control system that aims to manage runtime uncertainty in self-integrating autonomic systems. The cognitive control system autonomously extends the existing modeling language, by generating knowledge to represent unexpected situations as they are encountered at runtime, and reasons on the resulting model, in order to determine the required adaptation knowledge. As proof-of-concept validation, we present a scenario supported by an implementation of our proposal, illustrating the capabilities of the cognitive control system in the smart home domain. The obtained results set the basis for further studying the modeling language for describing the adaptation knowledge, with the objective of enriching the collaboration capabilities of autonomic systems in a self-integration context as future work.

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Controlled self-assembly of polymeric systems behave according to their composition and physical-chemical properties. Inclusion of a guest molecule may influence interactions and response to the media and to other surfaces and bodies, for example, cells. Hence, loading the polymeric system with curcumin (CUR), a natural and anticancer drug, confer interesting features to the carrier material. Micelle formation was achieved, in aqueous solution of carbohydrate-based block copolymer consisting in maltoheptaose-block-polyisoprene (MH-b-PI3.8kDa), by adding a large amount of water (a selective solvent for maltoheptaose, MH) into a solution of well-dissolved MH-b-PI3.8kDa (THF/H2O 9:1 (% w/w)]. Morphology and size (ca. 89 nm) of these glyco-nanoparticles were characterized using light scattering (static and dynamic modes – SLS and DLS) complemented by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM) and atomic force microscopy (AFM). The lyophilization efficiency was confirmed by measuring the relation between final and initial diameters (Sf/Si). CUR was successfully loaded into the nanoparticles with an entrapment efficiency of ca. 70%. CUR-charged nanoparticles showed stability into simulated gastric and intestinal fluids. Such micelles served as drug delivery system to carry CUR and presented a great role in wiping out unhealthy cells from many sorts.

Theta-nested gamma oscillations have been reported in many areas of the brain and are believed to represent a fundamental mechanism to transfer information across spatial and temporal scales. In a series of recent experiments in vitro it has been possible to replicate with an optogenetic theta frequency stimulation several features of cross-frequency coupling among theta and gamma rhythms observed in behaving animals. In order to reproduce the main findings of these experiments we have considered a new class of neural mass models able to reproduce exactly the macroscopic dynamics of spiking neural networks. In this framework, we have examined two set-ups able to support collective gamma oscillations: the pyramidal interneuronal network gamma (PING) and the interneuronal network gamma (ING). In both set-ups we observe the emergence of theta-nested gamma oscillations by driving the system with a sinusoidal theta-forcing in proximity of a Hopf bifurcation. These mixed rhythms display always phase amplitude coupling. However 2 different types of nested oscillations can be identified: one characterized by a perfect phase locking between theta and gamma rhythms, corresponding to an overall periodic behaviour; another one where the locking is imperfect and the dynamics is quasi-periodic or even chaotic. From our analysis it emerges that the locked states are more frequent in the ING set-up. In agreement with the experiments, we find theta-nested gamma oscillations for forcing frequencies in the range [1:10] Hz, whose amplitudes grow proportionally to the forcing one and which are clearly modulated by the theta phase. At variance with experimental findings, the gamma-power peak does not shift to higher frequencies by increasing the theta frequency. This effect can be obtained, in or model, only by incrementing, at the same time, also the noise or the forcing amplitude., 28 pages, 14 figures

This small essay is a tribute to John W. Mohr. It is meant as a complement to the essays by Robin Wagner-Pacifici and Ronald Breiger, and by Paul DiMaggio focused on their personal experiences of work and friendship with John. Our contribution, in contrast, presents a computationally produced map of the sociocultural space of John, molded by his numerous projects and initiatives in his crucial role as an editor, thinker, and boundary spanner. Our map utilizes a combination of online publication data and a survey of John’s social alters across the world. This map, we hope, illuminates the broad engagement of John into diverse research areas and scholarly communities, which definitely stimulated both his bright mind and the dwellers of these communities. We also believe this visualization highlights the meaningful social and cultural connections inspired by John and molding the socio-semantic network analysis of today.

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We present a simple three-dimensional model to describe the autonomous expansion of a substrate which grows driven by the local mean curvature of its surface. The model aims to reproduce the nest construction process in arboreal Nasutitermes termites, whose cooperation may similarly be mediated by the shape of the structure they are walking on, for example focusing the building activity of termites where local mean curvature is high. We adopt a phase-field model where the nest is described by one continuous scalar field and its growth is governed by a single nonlinear equation with one adjustable parameter d. When d is large enough the equation is linearly unstable and fairly reproduces a growth process where the initial walls expand, branch and merge, while progressively invading all the available space, which is consistent with the intricate structures of real nests. Interestingly, the linear problem associated to our growth equation is analogous to the buckling of a thin elastic plate under symmetric in-plane compression which is also known to produce rich pattern through non linear and secondary instabilities. We validated our model by collecting nests of two species of arboreal Nasutitermes from the field and imaging their structure with a micro-CT scanner. We found a strong resemblance between real and simulated nests, characterised by the emergence of a characteristic length-scale and by the abundance of saddle-shaped surfaces with zero-mean curvature which validates the choice of the driving mechanism of our growth model., 24 pages 6 figures + 9 pages supplementary material

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Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Self-adaptation of the learning structure is a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can significantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces a self-adaptive learning approach based on holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864,000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning., Cluster Computing, 23 (1), ISSN:1386-7857, ISSN:1573-7543

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The counter-intuitive phenomenon of coherence resonance describes a non-monotonic behavior of the regularity of noise-induced oscillations in the excitable regime, leading to an optimal response in terms of regularity of the excited oscillations for an intermediate noise intensity. We study this phenomenon in populations of FitzHugh-Nagumo (FHN) neurons with different coupling architectures. For networks of FHN systems in excitable regime, coherence resonance has been previously analyzed numerically. Here we focus on an analytical approach studying the mean-field limits of the locally and globally coupled populations. The mean-field limit refers to the averaged behavior of a complex network as the number of elements goes to infinity. We derive a mean-field limit approximating the locally coupled FHN network with low noise intensities. Further, we apply mean-field approach to the globally coupled FHN network. We compare the results of the mean-field and network frameworks for coherence resonance and find a good agreement in the globally coupled case, where the correspondence between the two approaches is sufficiently good to capture the emergence of anticoherence resonance. Finally, we study the effects of the coupling strength and noise intensity on coherence resonance for both the network and the mean-field model., Comment: 31 pages, 11 figures

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Recent experiments demonstrate substantial volatility of excitatory connectivity in the absence of any learning. This challenges the hypothesis that stable synaptic connections are necessary for long-term maintenance of acquired information. Here we measure ongoing synaptic volatility and use theoretical modeling to study its consequences on cortical dynamics. We show that in the balanced cortex, patterns of neural activity are primarily determined by inhibitory connectivity, despite the fact that most synapses and neurons are excitatory. Similarly, we show that the inhibitory network is more effective in storing memory patterns than the excitatory one. As a result, network activity is robust to ongoing volatility of excitatory synapses, as long as this volatility does not disrupt the balance between excitation and inhibition. We thus hypothesize that inhibitory connectivity, rather than excitatory, controls the maintenance and loss of information over long periods of time in the volatile cortex.

Continuous-in-scale multifractal cascades has long been an attractive choice for mathematically modeling turbulent and turbulent-like geophysical fields. These fields are usually anisotropic as they are subject to both stratification and rotation, thereby questioning the isotropy assumption often made to model them. The self-affine and generalized scale invariance approaches to scaling are used here to introduce anisotropy in such models. These anisotropic simulations have (1) unresolved large-scale features and (2) statistics that deviate from the desired power-law scaling mainly in the small scales. The former issue is solved via nesting, whereas the latter is attempted to be overcome using singularity correction methods. While earlier studies have proposed isotropic correction methods, here they have been generalized to correct anisotropic simulations. These singularity corrections seem to improve the small-scale statistical properties of mildly anisotropic simulations; nesting, on the other hand, appears to enhance statistics over almost all scales even for strongly anisotropic simulations. Both the correction and nesting techniques lead to a reduction in computational time and memory usage suggesting that nested singularity-corrected cascades offer a better framework for quantitatively modeling the atmosphere, ocean, solid earth, and associated fields.

Communities and societies are known to achieve key benefits, such as trade growth, risk mitigation and resource sustainability [1], by following and observing conventionally-agreed and mutable rules (e.g., norms and laws). We focus on "democratic" societies-where citizens can contribute to changes in these governing regulations (directly or indirectly), which then apply equally to all [2], [3]. However, the unrestricted self-interested, or power-seeking behavior of some members may progressively deform such society; for example the iron law of oligarchy [4]. Consequently, scholars of socio-political sciences have identified "defense mechanisms" that help protect societies against such phenomena [3], [5]. These include comprehensible1 transparency, responsible engagement, and the perceptible impact of pro-social contributions.

Efficient transportation is crucial for urban mobility, cell function and the survival of animal groups. From humans driving on the highway, to ants running on a trail, the main challenge faced by all collective systems is how to prevent traffic jams in crowded environments. Here, we show that ants, despite their behavioral simplicity, have managed the tour de force of avoiding the formation of traffic jams at high density. At the macroscopic level, we demonstrated that ant traffic is best described by a two-phase flow function. At low densities there is a clear linear relationship between ant density and the flow, while at large density, the flow remains constant and no congestion occurs. From a microscopic perspective, the individual tracking of ants under varying densities revealed that ants adjust their speed and avoid time consuming interactions at large densities. Our results point to strategies by which ant colonies solve the main challenge of transportation by self-regulating their behavior., eLife digest Humans and ants are among the few species that engage in two-way traffic. Maintaining a smooth and efficient traffic flow while avoiding collisions is challenging for humans. Yet ants seem to be masters of traffic management. They can efficiently move back and forth between their nests and food without overtaking or passing each other, forming a steady stream of traffic. Few studies have looked at how ants maintain such a smooth flow even as the number of ants on a path increases. Now, Poissonnier, Motsch et al. have designed an experiment to investigate whether ants can maintain their steady stream of traffic when their path to food gets more crowded. This involved manipulating the density of ants using a combination of different sized colonies (ranging from 400 to 25,600 Argentine ants) and changing the width of the bridge connecting the ants to their source of food. The experiment was repeated 170 times, and data was collected on traffic flow, speed of the ants, and number of collisions. For pedestrians and car traffic, the flow of movement will slow down if occupancy levels reach over 40%. Whereas in ants, the flow of traffic showed no signs of declining even when bridge occupancy reached 80%. The experiments revealed that ants do this by adjusting their behavior to their circumstances. They speed up at intermediate densities, avoid collisions at large densities, and avoid entering overcrowded trails. Studying ant traffic management has been a source of inspiration for scientists working with large groups of interacting particles in many fields. This includes molecular biology, statistical physics, and telecommunications. It may also have relevance for managing human traffic, particularly as scientists develop autonomous vehicles that might be programmed to work together cooperatively as ants do.

International audience; We show that nonlinear continuum elasticity can be effective in modeling plastic flows in crystals if it is viewed as a Landau theory with an infinite number of equivalent energy wells whose configuration is dictated by the symmetry group GLð2; ZÞ. Quasistatic loading can be then handled by athermal dynamics, while lattice-based discretization can play the role of regularization. As a proof of principle, we study dislocation nucleation in a homogeneously sheared 2D crystal and show that the global tensorial invariance of the elastic energy foments the development of complexity in the configuration of collectively nucleating defects. A crucial role in this process is played by the unstable higher symmetry crystallographic phases,typically thought to be unrelated to plastic flow.

The term ‘digital humanities’ may be understood in three different ways: as ‘digitized humanities’, by dealing essentially with the constitution, management, and processing of digitized archives; as ‘numerical humanities’, by putting the emphasis on mathematical abstraction and the development of numerical and formal models; and as ‘humanities of the digital’, by focusing on the study of computer-mediated interactions and online communities. Discussing their methods and actors, we show how these three potential acceptations cover markedly distinct epistemological endeavors and, eventually, non-overlapping scientific communities.

In this work we suggest modeling the dynamics of power grids in terms of a two-layer network, and we use the Italian high-voltage power grid as a proof-of-principle example. The first layer in our model represents the power grid consisting of generators and consumers, while the second layer represents a dynamic communication network that serves as a controller of the first layer. In particular, the dynamics of the power grid is modeled by the Kuramoto model with inertia, while the communication layer provides a control signal ${P}_{i}^{c}$ for each generator to improve frequency synchronization within the power grid. We propose different realizations of the communication layer topology and different ways to calculate the control signal. Then we conduct a systematic survey of the two-layer system against a multitude of different realistic perturbation scenarios, such as disconnecting generators, increasing demand of consumers, or generators with stochastic power output. When using a control topology that allows all generators to exchange information, we find that a control scheme aimed to minimize the frequency difference between adjacent nodes operates very efficiently even against the worst scenarios with the strongest perturbations.

The phase separation observed at low temperature (below circa 600 K) in the U1-yCeyO2-x system and for values of y between roughly 0.34 and 0.5 purportedly involves fluorite structures only. However, for y values above 0.5, an oxygen-deficient C-type bixbyite is also reported. In this work, the phase separation in U0.54Ce0.46O2-x has been reexamined using X-ray and neutron diffraction. Below a critical temperature, the existence of two fluorite related structures in the miscibility gap is confirmed: a stoichiometric U0.54Ce0.46O2 phase and an oxygen-deficient U0.54Ce0.46O2-x phase. Although the former is indeed a fluorite, we show that the other end-member phase has a C-type bixbyite structure. This would suggest that the oxygen-deficient phase can be described as a bixbyite over the entire cerium composition range.

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We study a network of spiking neurons with heterogeneous excitabilities connected via inhibitory delayed pulses. For globally coupled systems the increase of the inhibitory coupling reduces the number of firing neurons by following a Winner Takes All mechanism. For sufficiently large transmission delay we observe the emergence of collective oscillations in the system beyond a critical coupling value. Heterogeneity promotes neural inactivation and asynchronous dynamics and its effect can be counteracted by considering longer time delays. In sparse networks, inhibition has the counterintuitive effect of promoting neural reactivation of silent neurons for sufficiently large coupling. In this regime, current fluctuations are on one side responsible for neural firing of sub-threshold neurons and on the other side for their desynchronization. Therefore, collective oscillations are present only in a limited range of coupling values, which remains finite in the thermodynamic limit. Out of this range the dynamics is asynchronous and for very large inhibition neurons display a bursting behaviour alternating periods of silence with periods where they fire freely in absence of any inhibition., 13 pages, 13 Figures, submitted to Physical review E

Recent work has identified persistent cluster states which were shown to be assembled and held together by hydrodynamic interactions alone [Driscoll \textit{et al.} (2017) Nature Physics, 13(4), 375]. These states were seen in systems of colloidal microrollers; microrollers are colloidal particles which rotate about an axis parallel to the floor and generate strong, slowly decaying, advective flows. To understand these bound states, we study a simple, yet rich, model system of two microrollers. Here we show that pairs of microrollers can exhibit hydrodynamic bound states whose nature depends on a dimensionless number, denoted $B$, that compares the relative strength of gravitational forces and external torques. Using a dynamical system framework, we characterize these various states in phase space and analyze the bifurcations of the system as $B$ varies. In particular, we show that there is a critical value, $B^*$, above which active flows can beat gravity and lead to stable motile orbiting, or "leapfrog", trajectories, reminiscent of the self-assembled motile structures, called "critters", observed by Driscoll \textit{et al}. We identify the conditions for the emergence of these trajectories and study their basin of attraction. This work shows that a wide variety of stable bound states can be obtained with only two particles. Our results aid in understanding the mechanisms that lead to spontaneous self-assembly in hydrodynamic systems, such as microroller suspensions, as well as how to optimize these systems for particle transport.

This article analyzes the evolution of the staff composition of the European Commission from 1980 to 2013 using the theory of representative bureaucracy. The article first demonstrates how the Commission formulates guidelines which aim at offering fair levels of representation to each member state. However, comparing recruitment targets and actual staffing figures reveals very heterogeneous staff levels. Some member states enjoy unexpectedly high levels of representation whereas others present very low levels. The latter are particularly intriguing and open the door to the formulation of a “rejection hypothesis.” This hypothesis challenges one of the foundations of the theory of representative bureaucracy and leads us to suggest that the theory be enhanced in order to take into account its context of implementation in terms of consolidated or contested statehood, which in turn may explain the phenomena of rejected offers of bureaucratic representation.

Urban systems are composed of complex couplings of several components, and more particularly between the built environment and transportation networks. Their interaction is involved in the emergence of the urban form. We propose in this chapter to introduce an approach to urban morphology grasping both aspects and their interaction. We first define complementary measures and study their empirical values and their spatial correlations on European territorial systems. The behavior of indicators and correlations suggest underlying non-stationary and multi-scalar processes. We then introduce a generative model of urban growth at a mesoscopic scale. Given a fixed exogenous growth rate, population is distributed following a preferential attachment depending on a potential controlled by the local urban form (density, distance to network) and network measures (centralities and generalized accessibilities), and then diffused in space to capture urban sprawl. Network growth is included through a multi-modeling paradigm: implemented heuristics include biological network generation and gravity potential breakdown. The model is calibrated both at the first (measures) and second (correlations) order, the later capturing indirectly relations between networks and territories.

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International audience; The control of self-organized structures such as Turing patterns is a challenge for chemical engineers. We propose a reaction-diffusion model that includes the reactive role of the solvent and admits Turing patterns of adaptable wavelength. Changing the concentration of the solute is sufficient to tune the wavelength. Particles dynamics simulations are successfully performed, illustrating wavelength control down to the submicrometric scale.

Table of Contents1. Architecture and Research Rachel Armstrong 1.1 Experimental Architecture Reader 1.2 Architecture as Discipline 1.3 Neoliberalism as Worldview 1.4 Ways of Knowing 1.5 Research 1.6 Nature of Architectural Research 1.7 Evaluation 1.8 Impact 1.9 Outreach 1.10 Research Environment 1.11 Research Context 1.12 Economic Importance of Architectural Research 1.13 Principles of Architectural Research: A "wicked" discipline 1.13.1 Personal Exploration 1.13.2 Academic Environment 1.13.3 Research and Education 1.13.4 Professional Practice 2. Experimental Architecture Rachel Armstrong 2.1 Designing Change 2.2 Experiments versus "Wicked" Experiments 2.3 Introducing Experimental Architecture 2.4 Ethics 2.5 Research Principles of Experimental Architecture 2.5.1 The Role of Apparatuses 2.5.2 Sorting, Ordering and Valuing 2.5.3 Laboratory Environments 2.5.4 Monstering 2.5.5 Question-making 2.6 Towards an Ecological Architecture 2.6.1 The Houme 3. Architectural Experiments 3.1 Short Experiments 3.1.1 Harvested Sunlight: An experiment with a novel photoautotrophic biomaterial Simon Park and Victoria Geaney 3.1.2 SuperTree Claudia Pasquero and Marco Poletto 3.1.3 Co-Occupancy Joyce Hwang 3.1.4 PolyBrick 3.0: DNA glaze and digital ceramics Jenny Sabin 3.1.5 Graphene Architecture: Integrated intelligence in soft responsive skins Areti Markopoulou 3.1.6 High-Resolution Architecture Designed/Built by Humans/AI/Robots Alisa Andrasek 3.1.7 Tempietto on Mylar T+E+A+M 3.018 Nightly Catie Newell 3.2 Long Experiments 3.2.1 Evolving, Growing, and Gardening Cyber-physical Systems Susan Stepney and colleagues 3.2.2 About the Architecture of the Human Mind: A mathematical experiment Françoise Chatelin 3.2.3 Nomadic Hamlet 2.0: Testing place and video space for audiences Esther M. Armstrong and Dick Straker 3.2.4 Exaptive Design: Radical co-authorship as method Simone Ferracina 3.2.5 The Cloud Chamber Andrew Ballantyne 3.2.6 The Third Thing Rolf Hughes 4. Afterword Rachel Armstrong; International audience

International audience; The competition between a cancer and the immune system are modelled at cell scale in the framework of thermostatted kinetic theory. Cell activation and learning are reproduced by the increase of cell activity during interactions. The fluctuations of system activity are controlled by a thermostat which reproduces the regulation of the learning process and memory loss through cell death. An algorithm, including spatial description and inspired from the direct simulation Monte Carlo (DSMC) method, is used to simulate stochastic trajectories for cell numbers and activities. We focus on the decisive role played by the thermostat. For inefficient thermalization, the divergence of the number of cancer cells is obtained in spite of favored production of immune system cells. Conversely, when the activity fluctuations are controlled, the development of cancer is contained even for weakened immune defenses. These results may be correlated to unexpected clinical observations in the case of different cancers, such as carcinoma, lymphoma, and melanoma.

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The activity of a sparse network of leaky integrate-and-fire neurons is carefully revisited with reference to a regime of a bona-fide asynchronous dynamics. The study is preceded by a finite-size scaling analysis, carried out to identify a setup where collective synchronization is negligible. The comparison between quenched and annealed networks reveals the emergence of substantial differences when the coupling strength is increased, via a scenario somehow reminiscent of a phase transition. For sufficiently strong synaptic coupling, quenched networks exhibit a highly bursting neural activity, well reproduced by a self-consistent approach, based on the assumption that the input synaptic current is the superposition of independent renewal processes. The distribution of interspike intervals turns out to be relatively long-tailed; a crucial feature required for the self-sustainment of the bursting activity in a regime where neurons operate on average (much) below threshold. A semi-quantitative analogy with Ornstein-Uhlenbeck processes helps validating this interpretation. Finally, an alternative explanation in terms of Poisson processes is offered under the additional assumption of mutual correlations among excitatory and inhibitory spikes., Comment: 12 pages, 12 figures

The Digital Transformation (DX) is a broad term describing the changes and innovations brought about by the introduction of information and communication technologies into all aspects of society. One such innovation is to empower bottom-up, self-governing socio-technical systems for a range of applications. Such systems can be based on Ostrom’s design principles for self-governing institutions for sustainable common-pool resource management. However, two of these principles, both focussing on self-determination, are vulnerable to distortion: either from within, as a narrow clique take control and run the system in their own, rather than the collective, interest; or from without, as an external authority constrains opportunities for self-organisation. In this chapter, we propose that one approach to maintaining ‘good’, ‘democratic’ self-governance is to appeal to the transparent and inclusive knowledge management processes that were critical to the successful and sustained period of classical Athenian democracy, and reproduce those in computational form. We review a number of emerging technologies which could provide the building blocks for democratic self-governance in socio-technical systems. However, the reproduction of analogue social processes in digital form is not seamless and not without impact on, or consequences for, society, and we also consider a number of open issues which could disrupt this proposal. We conclude with the observation that ‘democracy’ is not an end-state, and emphasise that self-governing socio-technical systems need responsible design and deployment of technologies that allow for continuous re-design and self-organisation.

Scientific reports 8(1), 11077 (2018). doi:10.1038/s41598-018-29405-7, Published by Nature Publishing Group, London

We report a transition from asynchronous to oscillatory behaviour in balanced inhibitory networks for class I and II neurons with instantaneous synapses. Collective oscillations emerge for sufficiently connected networks. Their origin is understood in terms of a recently developed mean-field model, whose stable solution is a focus. Microscopic irregular firings, due to balance, trigger sustained oscillations by exciting the relaxation dynamics towards the macroscopic focus. The same mechanism induces in balanced excitatory-inhibitory networks quasi-periodic collective oscillations., Accepted for publication in Physical Review Letters, 4 pages, 4 figures, supplemental material available on the APS web site

Ferromagnetic Shape Memory Alloys (FSMAs) exhibit large strains by the magnetic-field-induced martensite reorientation. But, due to the high-frequency field-induced cyclic frictional martensite twin boundary motion in FSMAs, the dissipation heat can cause a large temperature rise. Thus, the output strain amplitude of FSMAs would decrease significantly if the temperature increases to be high enough to trigger the Martensite-Austenite phase transformation. Such thermal effects on the dynamic responses of FSMAs are unclear in literature because most existing dynamic experiments were performed only for a short-time period (a few seconds) to avoid the temperature rise. In this paper, systematic long-time experiments (>100 s) on a Ni-Mn-Ga single crystal are conducted at various levels of magnetic field frequency, initial compressive stress and ambient airflow velocity. It is found that, during the long-time actuation, the specimen temperature increases and then saturates at a certain level (stable temperature) while the strain oscillation evolves to a stable cycle; both the stable temperature and the stable strain amplitude depend on the frequency, the stress level and the heat exchange condition (i.e., ambient airflow velocity). Particularly, when the specimen temperature reaches a critical level to partially transform the martensite to the austenite, the output strain amplitude reduces suddenly because of less martensite reorientation. Changing the ambient heat-exchange condition (by the airflow) can modify the specimen temperature evolution to avoid the phase transformation, but it also changes the behaviors of the martensite reorientation that is sensitive to temperature. Eventually, the output strain amplitude depends on the airflow velocity non-monotonically, i.e., there exists a critical heat exchange condition to achieve the maximum stable strain amplitude. Based on the systematic experiments and a simplified one-dimensional heat-transfer model, the critical condition can be determined. The new experimental phenomena of the thermal effects can be well understood and described by the heat-transfer model. Further, instead of avoiding the temperature rise and the phase transformation, we propose to take advantage of the interaction between the temperature-induced phase transformation and the magnetic-field-induced martensite reorientation to develop a special “isothermal” FSMA actuator with a tunable output strain amplitude and a constant working temperature. This paper provides systematic experimental data and theoretical analysis for understanding the thermo-magneto-mechanical coupling in FSMAs and developing reliable high-frequency long-time running FSMA-actuators.

The interactions between a tumor and the immune system are modeled at cell scale in the framework of thermostatted kinetic theory. Cell activation and learning are reproduced by the increase of cell activity during interactions. The second moment of the activity of the whole system is controlled by a thermostat which reproduces the regulation of the learning process and memory loss through cell death. An algorithm inspired from the direct simulation Monte Carlo (DSMC) method is used to simulate stochastic trajectories for the numbers of cells and to study the sensitivity of the dynamics to various parameters. The nonintuitive role played by the thermostat is pointed out. For inefficient thermalization, the divergence of the number of cancer cells is obtained in spite of favored production of immune system cells. Conversely, when the activity fluctuations are controlled, the development of cancer is contained even for weakened immune defenses. These results may be correlated to unexpected clinical observations in the case of different cancers, such as carcinoma, lymphoma, and melanoma.

We extensively explore networks of weakly unbalanced, leaky integrate-and-fire (LIF) neurons for different coupling strength, connectivity, and by varying the degree of refractoriness, as well as the delay in the spike transmission. We find that the neural network does not only exhibit a microscopic (single-neuron) stochastic-like evolution, but also a collective irregular dynamics (CID). Our analysis is based on the computation of a suitable order parameter, typically used to characterize synchronization phenomena and on a detailed scaling analysis (i.e. simulations of different network sizes). As a result, we can conclude that CID is a true thermodynamic phase, intrinsically different from the standard asynchronous regime., 12 pages, 13 figures

Complex organisms, such as multi-cellular ones, have neither emerged spontaneously, nor evolved directly, from a disorganised mass of quarks. Stable intermediary sub-systems, like atoms and uni-cel...

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