**#365 Surviving in hostile and inhomogeneous environments**

*Authors: Rodrigo Rocha, Wagner Figueiredo, Samir Suweis and Amos Maritan*In this work we study the survival probability of a single-species in the context of hostile and inhomogeneous environments. Here we use random growth rates with negative mean to model hostile and inhomogeneous environments i.e. population dynamics (modeled using a Fisher equation) in this environment is characterized by negative average growth rate ($\bar{a}$), except in some random spatially distributed patches that support life ($a_i>0$). In particular, we are interested in the phase diagram of the survival probability and the critical size problem, i.e. the minimum size of the (local) habitat required for surviving in the long time limit. In Fisher's formalism the spatio-temporal evolution of the population density is described by a reaction-diffusion equation with a logistic nonlinear birth-death process. We propose a measure for the critical size as being proportional to the participation ratio (PR) of the eigenvector corresponding to the largest eigenvalue of the linearized Fisher dynamics. We numerically obtain the (extinction-survival) phase diagram and the critical size distribution for two network topologies, namely, the linear chain and the Peano basin fractal. We show that both topologies share the same qualitative features, but the fractal topology requires higher spatial fluctuations to guarantee species survival, which leads to a slightly larger critical size than the linear chain. In addition, we perform a finite-size scaling and we obtain the associated critical exponents.

**#367 Asymmetric organization in social-ecological systems: a proof of concept model**

*Authors: Abril Cid, J. Mario Siqueiros, Luis Bojórquez-Tapia and David Manuel-Navarrete*Social systems associated to fisheries are structurally coupled with the marine ecosystems where fish thrive. This means that each system acts and reacts to the other’s dynamics while each keeps functioning as a whole. Social-ecological coupling exhibits an asymmetric organization because social-institutional-technological subsystems (SITS) are not determined by emergent functional interaction in the same way that bio-physical systems (BS) are. Human agents shape social organization by reflexive processes that are relatively autonomous from functional dynamics. Formalizing this autonomy and asymmetric coupling caused by human reflexive agency poses analytical challenges to modeling social-ecological dynamics. We developed a proof of concept hybrid model to tackle these analytical challenges and applied it to sport fishing systems, where the asymmetry between the BS and SITS is particularly evident. Our hybrid model proposes that asymmetric coupling is mediated via “transducers”; culturally and semantically built objects to structurally couple biophysical and social processes. Transducers regulate: a) social interactions around exchanges with the BS, and b) interactions between BS and SITS. Sport fishing permits are an example of the instantiation of a transducer that mediates couplings between target species population states and fishing effort. This transducer emerges as one of many meaning-symbolic elements deliberately created to regulate social-ecological relations. Our model formalizes BS dynamics with a system model of the behavior of target species’ populations. SITS dynamics are formalized with a game theory implementation on social networks that describes the cooperative patterns that emerge from the relations among actors involved in sport fishing and information from the BS introduced by the transducer. We discuss the relevance of our model and the concept of transducers for the analysis of asymmetric organization in social-ecological systems.

**#368 Stochastic household models of soil-transmitted helminthiasis**

*Author: Alex Bishop*Soil-transmitted helminthiasis (intestinal worms) is a collection of neglected tropical diseases affecting more than 1 billion people worldwide.

These infections cause severe morbidity in school-aged children in whom the majority of worms are harboured.

De-worming treatments are available through mass drug administration (MDA); however recent research has concluded that treatment of children alone is not sufficient to break the cycle of transmission in a high transmission setting.

We develop a stochastic transmission model focusing on the household structure in the population. Using Markov Chain Monte Carlo (MCMC) and Approximate Bayesian Computation (ABC) methods we fit the model to a cross-sectional study of worm burdens in rural Nigeria and evaluate the effectiveness of alternative MDA strategies that prioritise the treatment of `wormy` households.

We discuss how the approach taken enables the utilisation of efficient MCMC methods up to a certain model complexity after which computational considerations necessitate the use of likelihood-free inference methods such as ABC.

**#371 Understanding the LBT phenomena through an integrated LGN model**

*Author: Kuntal Ghosh*The lateral geniculate nucleus (LGN), strategically interposed between the retina and the visual cortex is in the focus of intensive investigation as a relay centre for many years. Yet, it has received surprisingly less attention compared to V1 or even retinal ganglion neurons towards developing either neurophysiologically or psychophysically validated models. This is the reality in spite of the baffling riddle that there exists a massive projection from layer V1 of visual cortex back to the LGN which far surpasses the forward projection both from retina to LGN and from LGN to visual cortex in terms of fiber number, thus posing a serious challenge to the relay center theory of the LGN (Sherman S, Guillery R, 1996, Functional organization of thalamocortical relays. J Neurophysiol 76: 1367-1395). Understanding Lightness, Brightness, Transparency (LBT) has also, similarly remained an intriguing question to the Vision community for years (Kingdom FAA, 2011, Lightness, brightness and transparency: A quarter century of new ideas, captivating demonstrations and unrelenting controversy. Vis Res 51: 652-673). Here we have attempted to explain LBT in terms of an integrated LGN model that draws its inspiration from the blackboard metaphor of Mumford (Mumford D, 1991, On the computational architecture of the neocortex. Biol Cybern 65: 135-145). This model, based on the concept of Extended Classical Receptive Field (ECRF) of the Magnocellular and Parvocellular LGN neurons, involves computation of Marr’s zero-crossings (Marr D, Hildreth E, 1980, Theory of edge detection. Proc R Soc Lond B 207: 187-217) on one hand and a newly defined concept termed as Directional Weber Contrast (DWC) on the other. Such computation towards LBT perception, it is proposed, is performed in V1 on a vision-at-a-glance output through the faster Magnocellular channel, based on which a feedback mechanism works at LGN to provide a vision-with-scrutiny through the slower Parvocellular channel.

**#384 A theory for the command & control of an IoE environment**

*Author: Francesco Rago*With the increasing complexity of systems the need of control grows in parallel. The control grows with the increase of technology power. The social acceptance can exist only if precise and public rules are used. Control should become the norm and privacy is modulated by the individual only if personal capabilities are partially or totally actualized.

To allow the modeling of a community of individuals (Big) data are necessary, and the attribute Big is a consequence of communities complexity.

A solution to the control problem of communities in IoE context can be suggested by the formulation of entropy optimal control. We have used the Entropy formulation of Optimal Control based on Jaynes' Principle of Maximum Entropy, as modified by Saridis. Using the state equations and cost ,we define the differential entropy, for some control u(x,t) and the probability density of selecting u(x,t) is the probability density of agents capabilities. The Capability Models follow Yu Zhang’s approach.

Entropy was used as the measure of the energy associated with the assumption of irreversibility of the process. In this way the optimal control problem was recast as an entropy minimization problem.

The proposed solution express the need of reducing the maximum entropy of the system which is generated when we implement improvements of the quality of community life. We studied an application to an Internet of Everything and the contribution of this paper is that Optimal Control, which minimizes the maximum entropy produced by human intervention, may reduce the decay of communities phenomena with a way to improve human life realized by satisfying personal capabilities.

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