934 resultados para Complex adaptive systems


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We investigate the theoretical and numerical computation of rare transitions in simple geophysical turbulent models. We consider the barotropic quasi-geostrophic and two-dimensional Navier–Stokes equations in regimes where bistability between two coexisting large-scale attractors exist. By means of large deviations and instanton theory with the use of an Onsager–Machlup path integral formalism for the transition probability, we show how one can directly compute the most probable transition path between two coexisting attractors analytically in an equilibrium (Langevin) framework and numerically otherWe adapt a class of numerical optimization algorithms known as minimum action methods to simple geophysical turbulent models. We show that by numerically minimizing an appropriate action functional in a large deviation limit, one can predict the most likely transition path for a rare transition between two states. By considering examples where theoretical predictions can be made, we show that the minimum action method successfully predicts the most likely transition path. Finally, we discuss the application and extension of such numerical optimization schemes to the computation of rare transitions observed in direct numerical simulations and experiments and to other, more complex, turbulent systems.

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How do local homeland security organizations respond to catastrophic events such as hurricanes and acts of terrorism? Among the most important aspects of this response are these organizations ability to adapt to the uncertain nature of these "focusing events" (Birkland 1997). They are often behind the curve, seeing response as a linear process, when in fact it is a complex, multifaceted process that requires understanding the interactions between the fiscal pressures facing local governments, the institutional pressures of working within a new regulatory framework and the political pressures of bringing together different levels of government with different perspectives and agendas. ^ This dissertation has focused on tracing the factors affecting the individuals and institutions planning, preparing, responding and recovering from natural and man-made disasters. Using social network analysis, my study analyzes the interactions between the individuals and institutions that respond to these "focusing events." In practice, it is the combination of budgetary, institutional, and political pressures or constraints interacting with each other which resembles a Complex Adaptive System (CAS). ^ To investigate this system, my study evaluates the evolution of two separate sets of organizations composed of first responders (Fire Chiefs, Emergency Management Coordinators) and community volunteers organized in the state of Florida over the last fifteen years. Using a social network analysis approach, my dissertation analyzes the interactions between Citizen Corps Councils (CCCs) and Community Emergency Response Teams (CERTs) in the state of Florida from 1996–2011. It is the pattern of interconnections that occur over time that are the focus of this study. ^ The social network analysis revealed an increase in the amount and density of connections between these organizations over the last fifteen years. The analysis also exposed the underlying patterns in these connections; that as the networks became more complex they also became more decentralized though not in any uniform manner. The present study brings to light a story of how communities have adapted to the ever changing circumstances that are sine qua non of natural and man-made disasters.^

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Background: Biologists often need to assess whether unfamiliar datasets warrant the time investment required for more detailed exploration. Basing such assessments on brief descriptions provided by data publishers is unwieldy for large datasets that contain insights dependent on specific scientific questions. Alternatively, using complex software systems for a preliminary analysis may be deemed as too time consuming in itself, especially for unfamiliar data types and formats. This may lead to wasted analysis time and discarding of potentially useful data. Results: We present an exploration of design opportunities that the Google Maps interface offers to biomedical data visualization. In particular, we focus on synergies between visualization techniques and Google Maps that facilitate the development of biological visualizations which have both low-overhead and sufficient expressivity to support the exploration of data at multiple scales. The methods we explore rely on displaying pre-rendered visualizations of biological data in browsers, with sparse yet powerful interactions, by using the Google Maps API. We structure our discussion around five visualizations: a gene co-regulation visualization, a heatmap viewer, a genome browser, a protein interaction network, and a planar visualization of white matter in the brain. Feedback from collaborative work with domain experts suggests that our Google Maps visualizations offer multiple, scale-dependent perspectives and can be particularly helpful for unfamiliar datasets due to their accessibility. We also find that users, particularly those less experienced with computer use, are attracted by the familiarity of the Google Maps API. Our five implementations introduce design elements that can benefit visualization developers. Conclusions: We describe a low-overhead approach that lets biologists access readily analyzed views of unfamiliar scientific datasets. We rely on pre-computed visualizations prepared by data experts, accompanied by sparse and intuitive interactions, and distributed via the familiar Google Maps framework. Our contributions are an evaluation demonstrating the validity and opportunities of this approach, a set of design guidelines benefiting those wanting to create such visualizations, and five concrete example visualizations.

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Rodents are often involved at several stages of trophic dynamics. Consequently they often play crucial roles in the structure and function of many complex ecological systems. This study sought to address the lack of baseline data concerning rodents in tropical areas, and south Florida in particular. Live trapping took place in the four major habitat types of the Long Pine Key area of Everglades National Park over the course of one year. I compared population structures and abundance of murid rodents in the four habitat types, and tested multiple weather variables for their effectiveness as predictors of rodent abundance. I found the Long Pine Key area to be depauperate in terms of species diversity. Each of the four species of rodent encountered favored a particular habitat type. The density of the understory vegetation and the avoidance of avian predators in particular appear to be the most important factors in the distribution and abundance of rodents in the Long Pine Key area of Everglades National Park.

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Coral reef maps at various spatial scales and extents are needed for mapping, monitoring, modelling, and management of these environments. High spatial resolution satellite imagery, pixel <10 m, integrated with field survey data and processed with various mapping approaches, can provide these maps. These approaches have been accurately applied to single reefs (10-100 km**2), covering one high spatial resolution scene from which a single thematic layer (e.g. benthic community) is mapped. This article demonstrates how a hierarchical mapping approach can be applied to coral reefs from individual reef to reef-system scales (10-1000 km**2) using object-based image classification of high spatial resolution images guided by ecological and geomorphological principles. The approach is demonstrated for three individual reefs (10-35 km**2) in Australia, Fiji, and Palau; and for three complex reef systems (300-600 km**2) one in the Solomon Islands and two in Fiji. Archived high spatial resolution images were pre-processed and mosaics were created for the reef systems. Georeferenced benthic photo transect surveys were used to acquire cover information. Field and image data were integrated using an object-based image analysis approach that resulted in a hierarchically structured classification. Objects were assigned class labels based on the dominant benthic cover type, or location-relevant ecological and geomorphological principles, or a combination thereof. This generated a hierarchical sequence of reef maps with an increasing complexity in benthic thematic information that included: 'reef', 'reef type', 'geomorphic zone', and 'benthic community'. The overall accuracy of the 'geomorphic zone' classification for each of the six study sites was 76-82% using 6-10 mapping categories. For 'benthic community' classification, the overall accuracy was 52-75% with individual reefs having 14-17 categories and reef systems 20-30 categories. We show that an object-based classification of high spatial resolution imagery, guided by field data and ecological and geomorphological principles, can produce consistent, accurate benthic maps at four hierarchical spatial scales for coral reefs of various sizes and complexities.

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Peer reviewed

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In Marxist frameworks “distributive justice” depends on extracting value through a centralized state. Many new social movements—peer to peer economy, maker activism, community agriculture, queer ecology, etc.—take the opposite approach, keeping value in its unalienated form and allowing it to freely circulate from the bottom up. Unlike Marxism, there is no general theory for bottom-up, unalienated value circulation. This paper examines the concept of “generative justice” through an historical contrast between Marx’s writings and the indigenous cultures that he drew upon. Marx erroneously concluded that while indigenous cultures had unalienated forms of production, only centralized value extraction could allow the productivity needed for a high quality of life. To the contrary, indigenous cultures now provide a robust model for the “gift economy” that underpins open source technological production, agroecology, and restorative approaches to civil rights. Expanding Marx’s concept of unalienated labor value to include unalienated ecological (nonhuman) value, as well as the domain of freedom in speech, sexual orientation, spirituality and other forms of “expressive” value, we arrive at an historically informed perspective for generative justice. 

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Cette thèse est une réflexion originale sur le droit international dans la crise, car elle aborde le droit international grâce à une théorie peu connue en droit, celle des systèmes dynamiques complexes. L’étude du cas particulier de la gestion normative de la crise par le Conseil de sécurité illustre les intérêts de l’utilisation d’une théorie novatrice en droit. La théorie des systèmes dynamiques complexes offre les outils d’une réflexion sur le droit, fondée sur les interactions du droit avec le contexte particulier dans lequel il intervient, la crise, et ses acteurs. La mise en lumière de ces interactions favorise une lecture critique du droit international dans la crise et permet de renouveler l’ontologie de ce droit. Les dynamiques complexes appréhendées et définies par cette théorie sont utiles lorsqu’il s’agit d’expliquer les atouts et les limites de l’action normative du Conseil. Par ailleurs en renouvellant l’ontologie du droit, la théorie des systèmes dynamiques complexes facilite une lecture prospective de l’action normative du Conseil et insiste sur le rôle institutionnel du droit.

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Understanding the dynamics of blood cells is a crucial element to discover biological mechanisms, to develop new efficient drugs, design sophisticated microfluidic devices, for diagnostics. In this work, we focus on the dynamics of red blood cells in microvascular flow. Microvascular blood flow resistance has a strong impact on cardiovascular function and tissue perfusion. The flow resistance in microcirculation is governed by flow behavior of blood through a complex network of vessels, where the distribution of red blood cells across vessel cross-sections may be significantly distorted at vessel bifurcations and junctions. We investigate the development of blood flow and its resistance starting from a dispersed configuration of red blood cells in simulations for different hematocrits, flow rates, vessel diameters, and aggregation interactions between red blood cells. Initially dispersed red blood cells migrate toward the vessel center leading to the formation of a cell-free layer near the wall and to a decrease of the flow resistance. The development of cell-free layer appears to be nearly universal when scaled with a characteristic shear rate of the flow, which allows an estimation of the length of a vessel required for full flow development, $l_c \approx 25D$, with vessel diameter $D$. Thus, the potential effect of red blood cell dispersion at vessel bifurcations and junctions on the flow resistance may be significant in vessels which are shorter or comparable to the length $l_c$. The presence of aggregation interactions between red blood cells lead in general to a reduction of blood flow resistance. The development of the cell-free layer thickness looks similar for both cases with and without aggregation interactions. Although, attractive interactions result in a larger cell-free layer plateau values. However, because the aggregation forces are short-ranged at high enough shear rates ($\bar{\dot{\gamma}} \gtrsim 50~\text{s}^{-1}$) aggregation of red blood cells does not bring a significant change to the blood flow properties. Also, we develop a simple theoretical model which is able to describe the converged cell-free-layer thickness with respect to flow rate assuming steady-state flow. The model is based on the balance between a lift force on red blood cells due to cell-wall hydrodynamic interactions and shear-induced effective pressure due to cell-cell interactions in flow. We expect that these results can also be used to better understand the flow behavior of other suspensions of deformable particles such as vesicles, capsules, and cells. Finally, we investigate segregation phenomena in blood as a two-component suspension under Poiseuille flow, consisting of red blood cells and target cells. The spatial distribution of particles in blood flow is very important. For example, in case of nanoparticle drug delivery, the particles need to come closer to microvessel walls, in order to adhere and bring the drug to a target position within the microvasculature. Here we consider that segregation can be described as a competition between shear-induced diffusion and the lift force that pushes every soft particle in a flow away from the wall. In order to investigate the segregation, on one hand, we have 2D DPD simulations of red blood cells and target cell of different sizes, on the other hand the Fokker-Planck equation for steady state. For the equation we measure force profile, particle distribution and diffusion constant across the channel. We compare simulation results with those from the Fokker-Planck equation and find a very good correspondence between the two approaches. Moreover, we investigate the diffusion behavior of target particles for different hematocrit values and shear rates. Our simulation results indicate that diffusion constant increases with increasing hematocrit and depends linearly on shear rate. The third part of the study describes development of a simulation model of complex vascular geometries. The development of the model is important to reproduce vascular systems of small pieces of tissues which might be gotten from MRI or microscope images. The simulation model of the complex vascular systems might be divided into three parts: modeling the geometry, developing in- and outflow boundary conditions, and simulation domain decomposition for an efficient computation. We have found that for the in- and outflow boundary conditions it is better to use the SDPD fluid than DPD one because of the density fluctuations along the channel of the latter. During the flow in a straight channel, it is difficult to control the density of the DPD fluid. However, the SDPD fluid has not that shortcoming even in more complex channels with many branches and in- and outflows because the force acting on particles is calculated also depending on the local density of the fluid.

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Unstructured mesh based codes for the modelling of continuum physics phenomena have evolved to provide the facility to model complex interacting systems. Such codes have the potential to provide a high performance on parallel platforms for a small investment in programming. The critical parameters for success are to minimise changes to the code to allow for maintenance while providing high parallel efficiency, scalability to large numbers of processors and portability to a wide range of platforms. The paradigm of domain decomposition with message passing has for some time been demonstrated to provide a high level of efficiency, scalability and portability across shared and distributed memory systems without the need to re-author the code into a new language. This paper addresses these issues in the parallelisation of a complex three dimensional unstructured mesh Finite Volume multiphysics code and discusses the implications of automating the parallelisation process.

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Unstructured mesh codes for modelling continuum physics phenomena have evolved to provide the facility to model complex interacting systems. Parallelisation of such codes using single Program Multi Data (SPMD) domain decomposition techniques implemented with message passing has been demonstrated to provide high parallel efficiency, scalability to large numbers of processors P and portability across a wide range of parallel platforms. High efficiency, especially for large P requires that load balance is achieved in each parallel loop. For a code in which loops span a variety of mesh entity types, for example, elements, faces and vertices, some compromise is required between load balance for each entity type and the quantity of inter-processor communication required to satisfy data dependence between processors.

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In this dissertation, there are developed different analytical strategies to discover and characterize mammalian brain peptides using small amount of tissues. The magnocellular neurons of rat supraoptic nucleus in tissue and cell culture served as the main model to study neuropeptides, in addition to hippocampal neurons and mouse embryonic pituitaries. The neuropeptidomcis studies described here use different extraction methods on tissue or cell culture combined with mass spectrometry (MS) techniques, matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI). These strategies lead to the identification of multiple peptides from the rat/mouse brain in tissue and cell cultures, including novel compounds One of the goals in this dissertation was to optimize sample preparations on samples isolated from well-defined brain regions for mass spectrometric analysis. Here, the neuropeptidomics study of the SON resulted in the identification of 85 peptides, including 20 unique peptides from known prohormones. This study includes mass spectrometric analysis even from individually isolated magnocellular neuroendocrine cells, where vasopressin and several other peptides are detected. At the same time, it was shown that the same approach could be applied to analyze peptides isolated from a similar hypothalamic region, the suprachiasmatic nucleus (SCN). Although there were some overlaps regarding the detection of the peptides in the two brain nuclei, different peptides were detected specific to each nucleus. Among other peptides, provasopressin fragments were specifically detected in the SON while angiotensin I, somatostatin-14, neurokinin B, galanin, and vasoactive-intestinal peptide (VIP) were detected in the SCN only. Lists of peptides were generated from both brain regions for comparison of the peptidome of SON and SCN nuclei. Moving from analysis of magnocellular neurons in tissue to cell culture, the direct peptidomics of the magnocellular and hippocampal neurons led to the detection of 10 peaks that were assigned to previously characterized peptides and 17 peaks that remain unassigned. Peptides from the vasopressin prohormone and secretogranin-2 are attributed to magnocellular neurons, whereas neurokinin A, peptide J, and neurokinin B are attributed to cultured hippocampal neurons. This approach enabled the elucidation of cell-specific prohormone processing and the discovery of cell-cell signaling peptides. The peptides with roles in the development of the pituitary were analyzed using transgenic mice. Hes1 KO is a genetically modified mouse that lives only e18.5 (embryonic days). Anterior pituitaries of Hes1 null mice exhibit hypoplasia due to increased cell death and reduced proliferation and in the intermediate lobe, the cells differentiate abnormally into somatotropes instead of melanotropes. These previous findings demonstrate that Hes1 has multiple roles in pituitary development, cell differentiation, and cell fate. AVP was detected in all samples. Interestingly, somatostatin [92-100] and provasopressin [151-168] were detected in the mutant but not in the wild type or heterozygous pituitaries while somatostatin-14 was detected only in the heterozygous pituitary. In addition, the putative peptide corresponding to m/z 1330.2 and POMC [205-222] are detected in the mutant and heterozygous pituitaries, but not in the wild type. These results indicate that Hes1 influences the processing of different prohormones having possible roles during development and opens new directions for further developmental studies. This research demonstrates the robust capabilities of MS, which ensures the unbiased direct analysis of peptides extracted from complex biological systems and allows addressing important questions to understand cell-cell signaling in the brain.

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The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artificial Immune Systems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifically for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based modelling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.

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The microorganisms play very important roles in maintaining ecosystems, which explains the enormous interest in understanding the relationship between these organisms as well as between them and the environment. It is estimated that the total number of prokaryotic cells on Earth is between 4 and 6 x 1030, constituting an enormous biological and genetic pool to be explored. Although currently only 1% of all this wealth can be cultivated by standard laboratory techniques, metagenomic tools allow access to the genomic potential of environmental samples in a independent culture manner, and in combination with third generation sequencing technologies, the samples coverage become even greater. Soils, in particular, are the major reservoirs of this diversity, and many important environments around us, as the Brazilian biomes Caatinga and Atlantic Forest, are poorly studied. Thus, the genetic material from environmental soil samples of Caatinga and Atlantic Forest biomes were extracted by direct techniques, pyrosequenced, and the sequences generated were analyzed by bioinformatics programs (MEGAN MG-RAST and WEBCarma). Taxonomic comparative profiles of the samples showed that the phyla Proteobacteria, Actinobacteria, Acidobacteria and Planctomycetes were the most representative. In addition, fungi of the phylum Ascomycota were identified predominantly in the soil sample from the Atlantic Forest. Metabolic profiles showed that despite the existence of environmental differences, sequences from both samples were similarly placed in the various functional subsystems, indicating no specific habitat functions. This work, a pioneer in taxonomic and metabolic comparative analysis of soil samples from Brazilian biomes, contributes to the knowledge of these complex environmental systems, so far little explored

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Part 19: Knowledge Management in Networks