The Impact of Heterogeneity and Awareness in Modeling Epidemic Spreading on Multiplex Networks.

In the real world, dynamic processes involving human beings are not disjoint. To capture the real complexity of such dynamics, we propose a novel model of the coevolution of epidemic and awareness spreading processes on a multiplex network, also introducing a preventive isolation strategy. Our aim is to evaluate and quantify the joint impact of heterogeneity and awareness, under different socioeconomic conditions. Considering, as case study, an emerging public health threat, Zika virus, we introduce a data-driven analysis by exploiting multiple sources and different types of data, ranging from Big Five personality traits to Google Trends, related to different world countries where there is an ongoing epidemic outbreak. Our findings demonstrate how the proposed model allows delaying the epidemic outbreak and increasing the resilience of nodes, especially under critical economic conditions. Simulation results, using data-driven approach on Zika virus, which has a growing scientific research interest, are coherent with the proposed analytic model.This work was partially supported by the following Research Grant: Italian Ministry of University and Research - MIUR “Programma Operativo Nazionale Ricerca e Competitività 2007–2013” within the project “PON-03PE-00132-1” - Servify

Design Tools for Bolted End-Plate Beam-to-Column Joints

Predicting the response of beam-to-column joints is essential to evaluate the response of moment frames. The well-known component method is based on a mechanical modelling of the joint, through joint subdivision into more elementary components subsequently reassembled together to obtain the whole joint characteristics. Significant advantages of the component method are the following: (i) the mechanics-based modelling approach; (ii) the easier general characteristics of components. However, the method is commonly perceived by practicing engineers as being too laborious for practical applications. Within this context, this paper summarizes the results of a theoretical study aiming to develop simplified analysis tools for bolted end-plate beam-to-column joints, based on the Eurocode 3 component method. The accuracy of the component method was first evaluated, by comparing theoretical predictions of the plastic resistance and initial stiffness with corresponding experimental data collected from the available literature. Subsequently, design/analysis charts were developed through a parametric application of the component method by means of automatic calculation tools. They are easy and quick tools to be used in the first phases of the design process, in order to identify joint configurations and geometrical properties satisfying specified joint structural performances. The parametric analysis allowed also identifying further simplified analytical tools, in the form of nondimensional equations for predicting quickly the joint structural properties. With reference to selected geometries, the approximate equations were verified to provide sufficiently accurate predictions of both the stiffness and the resistance of the examined beam-to-column joints

Quantifying the propagation of distress and mental disorders in social networks.

Heterogeneity of human beings leads to think and react differently to social phenomena. Awareness and homophily drive people to weigh interactions in social multiplex networks, influencing a potential contagion effect. To quantify the impact of heterogeneity on spreading dynamics, we propose a model of coevolution of social contagion and awareness, through the introduction of statistical estimators, in a weighted multiplex network. Multiplexity of networked individuals may trigger propagation enough to produce effects among vulnerable subjects experiencing distress, mental disorder, which represent some of the strongest predictors of suicidal behaviours. The exposure to suicide is emotionally harmful, since talking about it may give support or inadvertently promote it. To disclose the complex effect of the overlapping awareness on suicidal ideation spreading among disordered people, we also introduce a data-driven approach by integrating different types of data. Our modelling approach unveils the relationship between distress and mental disorders propagation and suicidal ideation spreading, shedding light on the role of awareness in a social network for suicide prevention. The proposed model is able to quantify the impact of overlapping awareness on suicidal ideation spreading and our findings demonstrate that it plays a dual role on contagion, either reinforcing or delaying the contagion outbreak

Improving QoE in multi-layer social sensing: A cognitive architecture and game theoretic model

This paper proposes a novel cognitive architecture and game-theoretic model for resource sharing among netizens, thus improving their quality of experience (QoE) in multi-layer social sensing environments. The underlying approach is to quantify micro-rewards and inequalities derived from social multi-layer interactions. Specifically, we model our society as a social multi-layer network of individuals or groups of individuals (nodes), where the layers represent multiple channels of interactions (on various services). The weighted edges correspond to the multiple social relationships between nodes participating in diferent services, refecting the importance assigned to each of these edges and are defned based on the concepts of awareness and homophily. Heterogeneity, both interactions-wise on the multiple layers and related to homophily between individuals, on each node and layer of a weighted multiplex network produces a complex multi-scale interplay between nodes in the multi-layer structure. Applying game theory, we quantify the impact of heterogeneity on the evolutionary dynamics of social sensing through a data driven approach based on the propagation of individual-level micro-afrmations and micro-inequalities. The micro-packets of energy continuously exchanged between nodes may impact positively or negatively on their social behaviors, producing peaks of extreme dissatisfaction and in some cases a form of distress. Quantifying the evolutionary dynamics of human behaviors enables the detection of such peaks in the population and enable us design a targeted control mechanism, where social rewards and self-healing help improve the QoE of the netizens

Social Dynamics Modeling of Chrono-nutrition

Gut microbiota and human relationships are strictly connected to each other. What we eat reflects our body-mind connection and synchronizes with people around us. However, how this impacts on gut microbiota and, conversely, how gut bacteria influence our dietary behaviors has not been explored yet. To quantify the complex dynamics of this interplay between gut and human behaviors we explore the ``gut-human behavior axis'' and its evolutionary dynamics in a real-world scenario represented by the social multiplex network. We consider a dual type of similarity, homophily and gut similarity, other than psychological and unconscious biases. We analyze the dynamics of social and gut microbial communities, quantifying the impact of human behaviors on diets and gut microbial composition and, backwards, through a control mechanism. Meal timing mechanisms and ``chrono-nutrition'' play a crucial role in feeding behaviors, along with the quality and quantity of food intake. Considering a population of shift workers, we explore the dynamic interplay between their eating behaviors and gut microbiota, modeling the social dynamics of chrono-nutrition in a multiplex network. Our findings allow us to quantify the relation between human behaviors and gut microbiota through the methodological introduction of gut metabolic modeling and statistical estimators, able to capture their dynamic interplay. Moreover, we find that the timing of gut microbial communities is slower than social interactions and shift-working, and the impact of shift-working on the dynamics of chrono-nutrition is a fluctuation of strategies with a major propensity for defection (e.g. high-fat meals). A deeper understanding of the relation between gut microbiota and the dietary behavioral patterns, by embedding also the related social aspects, allows improving the overall knowledge about metabolic models and their implications for human health, opening the possibility to design promising social therapeutic dietary interventions