From continuous to discontinuous transitions in social diffusion

Models of social diffusion reflect processes of how new products, ideas or behaviors are adopted in a population. These models typically lead to a continuous or a discontinuous phase transition of the number of adopters as a function of a control parameter. We explore a simple model of social adoption where the agents can be in two states, either adopters or non-adopters, and can switch between these two states interacting with other agents through a network. The probability of an agent to switch from non-adopter to adopter depends on the number of adopters in her network neighborhood, the adoption threshold $T$ and the adoption coefficient $a$, two parameters defining a Hill function. In contrast, the transition from adopter to non-adopter is spontaneous at a certain rate $\mu$. In a mean-field approach, we derive the governing ordinary differential equations and show that the nature of the transition between the global non-adoption and global adoption regimes depends mostly on the balance between the probability to adopt with one and two adopters. The transition changes from continuous, via a transcritical bifurcation, to discontinuous, via a combination of a saddle-node and a transcritical bifurcation, through a supercritical pitchfork bifurcation. We characterize the full parameter space. Finally, we compare our analytical results with Montecarlo simulations on annealed and quenched degree regular networks, showing a better agreement for the annealed case. Our results show how a simple model is able to capture two seemingly very different types of transitions, i.e., continuous and discontinuous and thus unifies underlying dynamics for different systems. Furthermore the form of the adoption probability used here is based on empirical measurements.Comment: 7 pages, 3 figure

Update rules and interevent time distributions: Slow ordering vs. no ordering in the Voter Model

We introduce a general methodology of update rules accounting for arbitrary interevent time distributions in simulations of interacting agents. In particular we consider update rules that depend on the state of the agent, so that the update becomes part of the dynamical model. As an illustration we consider the voter model in fully-connected, random and scale free networks with an update probability inversely proportional to the persistence, that is, the time since the last event. We find that in the thermodynamic limit, at variance with standard updates, the system orders slowly. The approach to the absorbing state is characterized by a power law decay of the density of interfaces, observing that the mean time to reach the absorbing state might be not well defined.Comment: 5pages, 4 figure

Extinction-induced community reorganization in bipartite networks

We study how the community structure of bipartite mutualistic networks changes in a dynamic context. First, we consider a real mutualistic network and introduce extinction events according to several scenarios. We model extinctions as node or interaction removals. For node removal, we consider random, directed and sequential extinctions; for interaction removal, we consider random extinctions. The bipartite network reorganizes showing an increase of the effective modularity and a fast decrease of the persistence of the species in the original communities with increasing number of extinction events. Second, we compare extinctions in a real mutualistic network with the growth of a bipartite network model. The modularity reaches a stationary value and nodes remain in the same community after joining the network. Our results show that perturbations and disruptive events affect the connectivity pattern of mutualistic networks at the mesoscale level. The increase of the effective modularity observed in some scenarios could provide some protection to the remaining ecosystem

Flexible model of network embedding

There has lately been increased interest in describing complex systems not merely as single networks but rather as collections of networks that are coupled to one another. We introduce an analytically tractable model that enables one to connect two layers in a multilayer network by controlling the locality of coupling. In particular we introduce a tractable model for embedding one network (A) into another (B), focusing on the case where network A has many more nodes than network B. In our model, nodes in network A are assigned, or embedded, to the nodes in network B using an assignment rule where the extent of node localization is controlled by a single parameter. We start by mapping an unassigned `source' node in network A to a randomly chosen `target' node in network B. We then assign the neighbors of the source node to the neighborhood of the target node using a random walk starting at the target node and with a per-step stopping probability $q$. By varying the parameter $q$, we are able to produce a range of embeddings from local ($q = 1$) to global ($q \to 0$). The simplicity of the model allows us to calculate key quantities, making it a useful starting point for more realistic models

From mechanisms to data-inspired modeling of collective social phenomena

Tesis realizada en la Universitat de les Illes Balears.Statistical physics is at the core of the study of complex systems. A complex system is one composed by simple entities which interact and through their interactions global emergent phenomena appear. These phenomena are impossible to derive given the study of the isolated units, as they arise from the interaction of those particles. Statistical physics creates specifically the link between microscopic mechanisms and global behavior. It has been successful in the traditional study of physics for example in describing phase transitions. But its success is not restricted to physics and it has been applied also in other fields such as biology, medicine, or computer science. Social phenomena are also being studied using this framework, as the book emph{Micromotives and Macrobehavior} by T. Schelling. This framework aims at explaining global regularities, such as the sudden appearance of fashions, or the adoption of one of two apparently equivalent technological innovations, or the sudden massive spread of a fad starting from the microscopic interactions of the entities in the system. In society the basic entities of the system are humans and as such they are very complex and their specific dynamics may be very difficult to describe. Nevertheless statistical physics teaches us that in many cases the specific details of the interaction are not important in order to qualitatively describe the behavior of the system. Symmetries, dimensionality and conservation laws are usually sufficient to know the behavior of the system. This concept is called universality and motivates the study of social phenomena using minimal models which isolate mechanisms (not individuals) and describe their consequences at the global level. The so called emph{Big Data} era has also clearly influenced the development of research here reproduced. In social phenomena this refers to the fast growing amount of data produced and stored, shaping the digital trace of virtually all individuals, organizations and other entities in (the developed) society. In this field computer scientist have the lead, as they are able to produce the tools that can properly handle this vast amount of data. Nevertheless the typical focus of those scientists is in extracting information from the data or creating informatics tools that can reproduce the data in an automated way (data-driven modeling, machine learning, Bayesian inference methods, pattern recognition).As physicists what we have to offer is different, namely modeling from a theoretical perspective. The framework of Big Data offers the physicist the opportunity to test, compare and refine model results in order to devise the mechanisms in society responsible for a large class of social phenomena (diffusion of opinions or cultural traits, spreading of infectious diseases, traffic allocation problems among others). And why are models interesting or useful? On one side from a model one gains universal knowledge, that can be applied anywhere inside the frame of the model. On the other side a validated model lets the researcher investigate situations and apply measures which may be unfeasible in the real world, but can be reproduced with the use of computer simulations. Therefore they are useful for predicting unobserved situations or forecasting. This thesis is an instance of the abstract journey that many physicists have began. It is a journey that brings the traveler from a pure modeling framework that is sometimes flavored with a motivation coming from results of data analysis, toward bringing together information from the data and the theoretical mechanisms in a systematic way, both for having better informed models and for contrasting their results with real world data. Just modeling social systems from a Statistical physics perspective obliges the researcher to be between disciplines, but the addition of big data opens an extra dimension, which makes much more challenging the research. This thesis exemplifies just partly this journey and from a particular viewpoint, which is the one gained through the research and interactions with other scientists (mainly my advisors) I have developed in the last four years. So we will begin by abstract modeling unrelated to particular data (chapter~ref{ch1}), investigating the consequences of having states on the edges of a network. Typically social dynamics in the Statistical Physics framework had been studied by using individual based models, where agents are represented by nodes on a network and where the links between them represent their social relations.Then the nodes usually are endowed with variables which encode their social option or state and evolve following certain microscopic rules that depend on their network environment. In this first work we change the focus in order to evaluate the consequences of several types of relation (states on the links of the social network) competing in a society under a majority rule. We find results that were not to be expected when using the node states-paradigm on the same network. In the next step we have as a starting point empirical results that show that human timing of interactions is highly heterogeneous. As usually this characteristic had not been taken into account, we develop a framework to add this characteristic in individual based models and show that implementing it may change the qualitative behavior of the studied models and not only changing the timescales. In the third step we go almost to the core of the data world, as we study hospital dynamics in the US, in particular hospital transfers and their characteristics referring to spreading processes. The last stop in the journey is the most complete of all, as it brings together data analysis of electoral data; bibliography research on social, political and physical sciences; model development both analytically and through simulations; naturally bringing real data into the model framework; and contrastation of the model results against real data. This effort is rewarded by a model that reproduces statistical regularities found in election data. The model is not just a model for elections, but an opinion dynamics model, giving us insights into the way opinions and hopefully cultural traits or even innovations diffuse in society. Furthermore it triggers further theoretical questions on the role of heterogeneities on diffusion processes. As a summary, this thesis follows from an effort of bringing together several disciplines, methodologies and points of view, and trying to accommodate the different inputs coming from them together in a unifying framework.Peer Reviewe

Is the Voter Model a model for voters?

The voter model has been studied extensively as a paradigmatic opinion dynamics' model. However, its ability for modeling real opinion dynamics has not been addressed. We introduce a noisy voter model (accounting for social influence) with agents' recurrent mobility (as a proxy for social context), where the spatial and population diversity are taken as inputs to the model. We show that the dynamics can be described as a noisy diffusive process that contains the proper anysotropic coupling topology given by population and mobility heterogeneity. The model captures statistical features of the US presidential elections as the stationary vote-share fluctuations across counties, and the long-range spatial correlations that decay logarithmically with the distance. Furthermore, it recovers the behavior of these properties when a real-space renormalization is performed by coarse-graining the geographical scale from county level through congressional districts and up to states. Finally, we analyze the role of the mobility range and the randomness in decision making which are consistent with the empirical observations.Comment: 13 pages, 13 figure

Efectos de los compost sobre las propiedades del suelo : evaluación comparativa de compost con separación en origen y sin separación en orgien

El objetivo global de este trabajo es la evaluación comparativa de los efectos, a corto y medio plazo, que se producen en las propiedades físicas, químicas y biológicas de un suelo típico semiárido (calcisol), tras la aplicación de 2 diferentes compost de RSU: uno procedente de una planta en la que los residuos han sido recogidos de forma separada (Barcelona), y otra, en la que no ha existido separación en la recogida (Murcia). Los objetivos específicos son: 1. Determinar los cambios, a corto y medio plazo, en los contenidos en carbono orgánico, macro y micronutrientes y propiedades físicas del suelo, con la adición de ambos tipos de residuo. 2. Establecer la efectividad, en cuanto al tiempo de permanencia, de los cambios producidos. 3. Evaluar el efecto en la contaminación del suelo por metales pesados, en función de la dosis y tipo de residuo. 4. Comparar los efectos positivos y negativos de cada tipo de residuo, para determinar la eficiencia de la separación en origen. 34Escuela Técnica Superior de Ingeniería IndustrialUniversidad Politécnica de Cartagen

Characteriation of Mediterranean Aleppo pine forest using low-density ALS data

Los espacios forestales son una fuente de servicios, tanto ambientales como económicos, de gran importancia para la sociedad. La caracterización de estos ambientes ha requerido tradicionalmente de un laborioso trabajo de campo. La aplicación de técnicas de teledetección ha proporcionado una visión más amplia a escala espacial y temporal, a la par que ha generado una reducción de los costes. La utilización de sensores óptico-pasivo multiespectrales y de sensores radar posibilita la estimación de parámetros forestales, si bien el desarrollo de sensores LiDAR, como el caso de los escáneres láser aeroportados (ALS), ha mejorado la caracterización tridimensional de la estructura de los bosques. La disponibilidad pública de dos coberturas LiDAR, generadas en el marco del Plan Nacional de Ortofotografía Aérea (PNOA), ha abierto nuevas líneas de investigación que permiten proporcionar información útil para la gestión forestal. La presente tesis utiliza datos LiDAR aeroportados de baja densidad para estimar diversas variables forestales, con ayuda de trabajo de campo, en masas forestales de Pino carrasco (Pinus halepensis Miller) en Aragón. La investigación aborda dos cuestiones relevantes como son la exploración de las metodologías más adecuadas para estimar variables forestales considerando escalas locales y regionales, teniendo en cuenta las posibles fuentes de error en el modelado; y, además, analiza la potencialidad de los datos LiDAR del PNOA para el desarrollo de aplicaciones forestales que valoricen las áreas forestales como recursos socio-económicos. La tesis se ha desarrollado según la modalidad de compendio de publicaciones, incluyendo cuatro trabajos que dan respuesta a los objetivos planteados. En primer lugar, se realiza un análisis comparativo de distintos modelos de regresión, paramétricos y no paramétricos, para estimar la pérdida de biomasa y las emisiones de CO2 en un incendio, mediante la utilización de datos LiDAR-PNOA y datos ópticos del satélite Landsat 8. En segundo lugar, se explora la idoneidad de distintos métodos de selección de variables para estimar biomasa total en masas de Pino carrasco utilizando datos LiDAR de baja densidad. En tercer lugar, se cuantificó y cartografió la biomasa residual forestal en el conjunto de masas de Pino carrasco de Aragón y se evaluó el efecto de diversas características de la tecnología LiDAR y de las variables ambientales en la precisión de los modelos. Finalmente, se analiza la transferibilidad temporal de modelos para estimar a escala regional siete variables forestales, utilizando datos LiDAR-PNOA multi-temporales. A este respecto, se compararon dos enfoques que permiten analizar la transferibilidad temporal: en primer lugar, el método directo ajusta un modelo para un determinado punto en el tiempo y estima las variables forestales para otra fecha; por otra parte, el método indirecto ajusta dos modelos diferentes para cada momento en el tiempo, estimando las variables forestales en dos fechas distintas. Los resultados obtenidos y las conclusiones derivadas de la investigación indican que la técnica basada en coeficientes de correlación de Spearman y el método de selección por todos los subconjuntos constituyen los métodos de selección de métricas LiDAR más apropiados para la modelización. El análisis de métodos de regresión para la estimación de variables forestales indicó que su idoneidad variaba de acuerdo con el tamaño y complejidad de la muestra. El método de regresión linear multivariante arrojó mejores resultados que los métodos no-paramétricos en el caso de muestras pequeñas. Por el contrario, el método Support Vector Machine produjo los mejores resultados con muestras grandes. El incremento de la densidad de puntos y de los valores de penetración de los pulsos LiDAR en el dosel, así como la presencia de ángulos de escaneo pequeños, incrementó la exactitud de los modelos. De forma similar, el incremento de la pendiente y la presencia de arbustos en el sotobosque implican una reducción en la exactitud de los modelos. En la estimación de variables forestales utilizando datos LiDAR multi-temporales, aunque la utilización del enfoque indirecto arrojó generalmente una mayor precisión en los modelos, se obtuvieron resultados similares con el enfoque directo, el cual constituye una alternativa óptima para reducir el tiempo de modelado y los costes de realización de trabajo de campo. La fusión de datos LiDAR y datos óptico-pasivos ha evidenciado la conveniencia de los métodos aplicados para cuantificar las emisiones de CO2 a la atmósfera generadas por un incendio. Esta metodología constituye una alternativa adecuada cuando no existen datos multi-temporales LiDAR. La estimación de variables de inventario forestal, así como de diversas fracciones de biomasa, como la biomasa total y la biomasa residual forestal, proporciona información valiosa para caracterizar las masas forestales mediterráneas de Pino carrasco y mejorar la gestión forestalForest ecosystems provide environmental and economic services of great importance to the society. The characterization of these environments has been traditionally accomplished with intense field work. In comparison, the application of remote sensing tools provides a greater overview over large spatial and temporal scales while minimizing costs. Although optical data and Synthetic Aperture Radar (SAR) allow estimating forest stand variables, the development of LiDAR sensors such as Airborne Laser Scanner (ALS) have improved three-dimensional characterization of forest structure. The availability of two ALS public data coverages for the Spanish territory, provided by the National Plan for Aerial Ortophotography (PNOA), opens new research opportunities to generate useful information for forest management. This PhD Thesis used low-density ALS-PNOA data to estimate different forest variables, with support in fieldwork, in the Aleppo pine (Pinus halepensis Miller) forests of Aragón region. The addressed research is relevant mainly for two reasons: first, the examination of suitable methodologies and error sources in forest stand variables prediction at local (small area) and regional scales (large area), and second, the application of ALS data to the characterization of forest areas as a socio-economic reservoir. This PhD Thesis is a compendium of four scientific papers, which sequentially answer the objectives established. Firstly, a comparative analysis of different parametric and non-parametric models was performed to estimate biomass losses and CO2 emissions using low-density ALS and Landsat 8 data in a burnt Aleppo pine forest. Secondly, we assess the suitability of variable selection methods when estimating total biomass in Aleppo pine forest stands using low-density ALS data. In the third manuscript, the quantification and mapping of forest residual biomass in Aleppo pine forest of Aragón region and the assessment of the effect of ALS and environmental variables in model accuracy were accomplished. Finally, the temporal transferability of seven forest stands attributes modelling using multi-temporal ALS-PNOA data in Aleppo pine forest at regional scale was explored. In this case, the temporal transferability was assessed comparing two methodologies; the direct and indirect approach. The first one fits a model for one point in time and estimates the forest variable for another point in time. The indirect approach adjusts two models in different points in time to estimate the forest variables in two different dates. The results derived from this research indicated that Spearman’s rank and All Subset Selection are the most appropriate methods in the ALS metrics selection step commonly applied in modelling. The suitability of the regression methods depends on the sample size and complexity. Thus, multivariate linear regression outperformed non-parametric methods with small samples while support vector machine was the most accurate method with larger samples. Model accuracy increased with higher point density and canopy pulse penetration, while decreasing with wider scan angles. Furthermore, the presence of steep slopes and shrub reduced model performance. In the case of forest stand variables prediction using multi-temporal ALS data, although the indirect approach produced generally a higher precision, the direct approach provided similar results, constituting a suitable alternative to reduce modelling time and fieldwork costs. The fusion of ALS and passive optical data have evidenced the suitability of this information for quantifying wildfire CO2 emissions to atmosphere, constituting a good alternative when multi-temporal ALS data is not available. The estimation of forest inventory variables as well as different biomass fractions, such as total biomass and forest residual biomass, provided valuable information to characterize Mediterranean Aleppo pine forests and improve forest management.<br /

Reactions to synthetic membranes dialyzers: Is there an increase in incidence?

Background: Reactions to dialyzers used in dialysis have been reported more frequently in recent years. Evidence, however, shows that the reaction rate has remained stable for years. Summary: One explanation for the apparent increase in publication frequency could be the lack of knowledge that dialyzer reactions may well occur with biocompatible membranes. Studies showed that the cause of these reactions is very diverse and varied, involving multiple materials. However, polyvinylpyrrolidone continues to be the main suspect, but without conclusive results. There are no differences between the different fibers, and although polysulfone is the most described, it is also the most used. Key Messages: The change to cellulose triacetate continues to be the most appropriate form of treatment. The classification of these reactions into type A and B complicates the diagnosis, and its true usefulness is in doubtThe research presented in this article is supported by the grants from the Spanish Ministry of Economy and Competitiveness and European Regional Development Funds (ERDF/FEDER) through ISCIII/FIS grants PI16/01298, PI17/01495, CIBERDEM and REDINREN RD016/0019 and through the Madrid Renal Society (SOMANE) grant