Computational modeling of neural plasticity for self-organization of neural networks.
Chrol-Cannon, Joseph; Jin, Yaochu
2014-11-01
Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Complex Systems and Self-organization Modelling
Bertelle, Cyrille; Kadri-Dahmani, Hakima
2009-01-01
The concern of this book is the use of emergent computing and self-organization modelling within various applications of complex systems. The authors focus their attention both on the innovative concepts and implementations in order to model self-organizations, but also on the relevant applicative domains in which they can be used efficiently. This book is the outcome of a workshop meeting within ESM 2006 (Eurosis), held in Toulouse, France in October 2006.
Huang, Ping-Tzan; Jong, Tai-Lang; Li, Chien-Ming; Chen, Wei-Ling; Lin, Chia-Hung
2017-08-01
Blood leakage and blood loss are serious complications during hemodialysis. From the hemodialysis survey reports, these life-threatening events occur to attract nephrology nurses and patients themselves. When the venous needle and blood line are disconnected, it takes only a few minutes for an adult patient to lose over 40% of his / her blood, which is a sufficient amount of blood loss to cause the patient to die. Therefore, we propose integrating a flexible sensor and self-organizing algorithm to design a cloud computing-based warning device for blood leakage detection. The flexible sensor is fabricated via a screen-printing technique using metallic materials on a soft substrate in an array configuration. The self-organizing algorithm constructs a virtual direct current grid-based alarm unit in an embedded system. This warning device is employed to identify blood leakage levels via a wireless network and cloud computing. It has been validated experimentally, and the experimental results suggest specifications for its commercial designs. The proposed model can also be implemented in an embedded system.
Evolutionary Cell Computing: From Protocells to Self-Organized Computing
Colombano, Silvano; New, Michael H.; Pohorille, Andrew; Scargle, Jeffrey; Stassinopoulos, Dimitris; Pearson, Mark; Warren, James
2000-01-01
On the path from inanimate to animate matter, a key step was the self-organization of molecules into protocells - the earliest ancestors of contemporary cells. Studies of the properties of protocells and the mechanisms by which they maintained themselves and reproduced are an important part of astrobiology. These studies also have the potential to greatly impact research in nanotechnology and computer science. Previous studies of protocells have focussed on self-replication. In these systems, Darwinian evolution occurs through a series of small alterations to functional molecules whose identities are stored. Protocells, however, may have been incapable of such storage. We hypothesize that under such conditions, the replication of functions and their interrelationships, rather than the precise identities of the functional molecules, is sufficient for survival and evolution. This process is called non-genomic evolution. Recent breakthroughs in experimental protein chemistry have opened the gates for experimental tests of non-genomic evolution. On the basis of these achievements, we have developed a stochastic model for examining the evolutionary potential of non-genomic systems. In this model, the formation and destruction (hydrolysis) of bonds joining amino acids in proteins occur through catalyzed, albeit possibly inefficient, pathways. Each protein can act as a substrate for polymerization or hydrolysis, or as a catalyst of these chemical reactions. When a protein is hydrolyzed to form two new proteins, or two proteins are joined into a single protein, the catalytic abilities of the product proteins are related to the catalytic abilities of the reactants. We will demonstrate that the catalytic capabilities of such a system can increase. Its evolutionary potential is dependent upon the competition between the formation of bond-forming and bond-cutting catalysts. The degree to which hydrolysis preferentially affects bonds in less efficient, and therefore less well
Self-organized computation with unreliable, memristive nanodevices
International Nuclear Information System (INIS)
Snider, G S
2007-01-01
Nanodevices have terrible properties for building Boolean logic systems: high defect rates, high variability, high death rates, drift, and (for the most part) only two terminals. Economical assembly requires that they be dynamical. We argue that strategies aimed at mitigating these limitations, such as defect avoidance/reconfiguration, or applying coding theory to circuit design, present severe scalability and reliability challenges. We instead propose to mitigate device shortcomings and exploit their dynamical character by building self-organizing, self-healing networks that implement massively parallel computations. The key idea is to exploit memristive nanodevice behavior to cheaply implement adaptive, recurrent networks, useful for complex pattern recognition problems. Pulse-based communication allows the designer to make trade-offs between power consumption and processing speed. Self-organization sidesteps the scalability issues of characterization, compilation and configuration. Network dynamics supplies a graceful response to device death. We present simulation results of such a network-a self-organized spatial filter array-that demonstrate its performance as a function of defects and device variation
Self-organized quantum rings : Physical characterization and theoretical modeling
Fomin, V.M.; Gladilin, V.N.; Devreese, J.T.; Koenraad, P.M.; Fomin, V.M.
2014-01-01
An adequate modeling of the self-organized quantum rings is possible only on the basis of the modern characterization of those nanostructures.We discuss an atomic-scale analysis of the indium distribution of self-organized InGaAs quantum rings (QRs). The analysis of the shape, size and composition
Modelling the self-organization and collapse of complex networks
Indian Academy of Sciences (India)
Modelling the self-organization and collapse of complex networks. Sanjay Jain Department of Physics and Astrophysics, University of Delhi Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore Santa Fe Institute, Santa Fe, New Mexico.
Self-Organizing Map Models of Language Acquisition
Directory of Open Access Journals (Sweden)
Ping eLi
2013-11-01
Full Text Available Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic PDP architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development.
Modeling self-organization of novel organic materials
Sayar, Mehmet
In this thesis, the structural organization of oligomeric multi-block molecules is analyzed by computational analysis of coarse-grained models. These molecules form nanostructures with different dimensionalities, and the nanostructured nature of these materials leads to novel structural properties at different length scales. Previously, a number of oligomeric triblock rodcoil molecules have been shown to self-organize into mushroom shaped noncentrosymmetric nanostructures. Interestingly, thin films of these molecules contain polar domains and a finite macroscopic polarization. However, the fully polarized state is not the equilibrium state. In the first chapter, by solving a model with dipolar and Ising-like short range interactions, we show that polar domains are stable in films composed of aggregates as opposed to isolated molecules. Unlike classical molecular systems, these nanoaggregates have large intralayer spacings (a ≈ 6 nm), leading to a reduction in the repulsive dipolar interactions that oppose polar order within layers. This enables the formation of a striped pattern with polar domains of alternating directions. The energies of the possible structures at zero temperature are computed exactly and results of Monte Carlo simulations are provided at non-zero temperatures. In the second chapter, the macroscopic polarization of such nanostructured films is analyzed in the presence of a short range surface interaction. The surface interaction leads to a periodic domain structure where the balance between the up and down domains is broken, and therefore films of finite thickness have a net macroscopic polarization. The polarization per unit volume is a function of film thickness and strength of the surface interaction. Finally, in chapter three, self-organization of organic molecules into a network of one dimensional objects is analyzed. Multi-block organic dendron rodcoil molecules were found to self-organize into supramolecular nanoribbons (threads) and
Self-organizing map models of language acquisition
Li, Ping; Zhao, Xiaowei
2013-01-01
Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic parallel distributed processing architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper, we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development. We suggest future directions in which these models can be extended, to better connect with behavioral and neural data, and to make clear predictions in testing relevant psycholinguistic theories. PMID:24312061
A self-organized criticality model for plasma transport
International Nuclear Information System (INIS)
Carreras, B.A.; Newman, D.; Lynch, V.E.
1996-01-01
Many models of natural phenomena manifest the basic hypothesis of self-organized criticality (SOC). The SOC concept brings together the self-similarity on space and time scales that is common to many of these phenomena. The application of the SOC modelling concept to the plasma dynamics near marginal stability opens new possibilities of understanding issues such as Bohm scaling, profile consistency, broad band fluctuation spectra with universal characteristics and fast time scales. A model realization of self-organized criticality for plasma transport in a magnetic confinement device is presented. The model is based on subcritical resistive pressure-gradient-driven turbulence. Three-dimensional nonlinear calculations based on this model show the existence of transport under subcritical conditions. This model that includes fluctuation dynamics leads to results very similar to the running sandpile paradigm
Directory of Open Access Journals (Sweden)
Samreen Laghari
Full Text Available Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT implies an inherent difficulty in modeling problems.It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS. The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC framework to model a Complex communication network problem.We use Exploratory Agent-based Modeling (EABM, as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy.The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.
Laghari, Samreen; Niazi, Muaz A
2016-01-01
Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.
Self-organized Criticality Model for Ocean Internal Waves
International Nuclear Information System (INIS)
Wang Gang; Hou Yijun; Lin Min; Qiao Fangli
2009-01-01
In this paper, we present a simple spring-block model for ocean internal waves based on the self-organized criticality (SOC). The oscillations of the water blocks in the model display power-law behavior with an exponent of -2 in the frequency domain, which is similar to the current and sea water temperature spectra in the actual ocean and the universal Garrett and Munk deep ocean internal wave model [Geophysical Fluid Dynamics 2 (1972) 225; J. Geophys. Res. 80 (1975) 291]. The influence of the ratio of the driving force to the spring coefficient to SOC behaviors in the model is also discussed. (general)
Turbulence and Self-Organization Modeling Astrophysical Objects
Marov, Mikhail Ya
2013-01-01
This book focuses on the development of continuum models of natural turbulent media. It provides a theoretical approach to the solutions of different problems related to the formation, structure and evolution of astrophysical and geophysical objects. A stochastic modeling approach is used in the mathematical treatment of these problems, which reflects self-organization processes in open dissipative systems. The authors also consider examples of ordering for various objects in space throughout their evolutionary processes. This volume is aimed at graduate students and researchers in the fields of mechanics, astrophysics, geophysics, planetary and space science.
Mobility Model for Self-Organizing and Cooperative MSN and MANET Systems
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Andrzej Sikora
2012-03-01
Full Text Available Self-organization mechanisms are used for building scalable systems consisting of a huge number of subsystems. In computer networks, self-organizing is especially important in ad hoc networking. A self-organizing ad hoc network is a collection of wireless devices that collaborate with each other to form a network system that adapts to achieve a goal or goals. Such network is often built from mobile devices that may spontaneously create a network and dynamically adapted to changes in an unknown environment. Mobility pattern is a critical element that influences the performance characteristics of mobile sensor networks (MSN and mobile ad hoc networks (MANET. In this paper, we survey main directions to mobility modeling. We describe a novel algorithm for calculating mobility patterns for mobile devices that is based on a cluster formation and an artificial potential function. Finally, we present the simulation results of its application to a rescue mission planning.
Computational Genetic Regulatory Networks Evolvable, Self-organizing Systems
Knabe, Johannes F
2013-01-01
Genetic Regulatory Networks (GRNs) in biological organisms are primary engines for cells to enact their engagements with environments, via incessant, continually active coupling. In differentiated multicellular organisms, tremendous complexity has arisen in the course of evolution of life on earth. Engineering and science have so far achieved no working system that can compare with this complexity, depth and scope of organization. Abstracting the dynamics of genetic regulatory control to a computational framework in which artificial GRNs in artificial simulated cells differentiate while connected in a changing topology, it is possible to apply Darwinian evolution in silico to study the capacity of such developmental/differentiated GRNs to evolve. In this volume an evolutionary GRN paradigm is investigated for its evolvability and robustness in models of biological clocks, in simple differentiated multicellularity, and in evolving artificial developing 'organisms' which grow and express an ontogeny starting fr...
Self-Organized Criticality Theory Model of Thermal Sandpile
International Nuclear Information System (INIS)
Peng Xiao-Dong; Qu Hong-Peng; Xu Jian-Qiang; Han Zui-Jiao
2015-01-01
A self-organized criticality model of a thermal sandpile is formulated for the first time to simulate the dynamic process with interaction between avalanche events on the fast time scale and diffusive transports on the slow time scale. The main characteristics of the model are that both particle and energy avalanches of sand grains are considered simultaneously. Properties of intermittent transport and improved confinement are analyzed in detail. The results imply that the intermittent phenomenon such as blobs in the low confinement mode as well as edge localized modes in the high confinement mode observed in tokamak experiments are not only determined by the edge plasma physics, but also affected by the core plasma dynamics. (paper)
Dynamical quenching and annealing in self-organization multiagent models
Burgos, E.; Ceva, Horacio; Perazzo, R. P.
2001-07-01
We study the dynamics of a generalized minority game (GMG) and of the bar attendance model (BAM) in which a number of agents self-organize to match an attendance that is fixed externally as a control parameter. We compare the usual dynamics used for the minority game with one for the BAM that makes a better use of the available information. We study the asymptotic states reached in both frameworks. We show that states that can be assimilated to either thermodynamic equilibrium or quenched configurations can appear in both models, but with different settings. We discuss the relevance of the parameter G that measures the value of the prize for winning in units of the fine for losing. We also provide an annealing protocol by which the quenched configurations of the GMG can progressively be modified to reach an asymptotic equilibrium state that coincides with the one obtained with the BAM.
Modeling financial markets by self-organized criticality
Biondo, Alessio Emanuele; Pluchino, Alessandro; Rapisarda, Andrea
2015-10-01
We present a financial market model, characterized by self-organized criticality, that is able to generate endogenously a realistic price dynamics and to reproduce well-known stylized facts. We consider a community of heterogeneous traders, composed by chartists and fundamentalists, and focus on the role of informative pressure on market participants, showing how the spreading of information, based on a realistic imitative behavior, drives contagion and causes market fragility. In this model imitation is not intended as a change in the agent's group of origin, but is referred only to the price formation process. We introduce in the community also a variable number of random traders in order to study their possible beneficial role in stabilizing the market, as found in other studies. Finally, we also suggest some counterintuitive policy strategies able to dampen fluctuations by means of a partial reduction of information.
LSOT: A Lightweight Self-Organized Trust Model in VANETs
Directory of Open Access Journals (Sweden)
Zhiquan Liu
2016-01-01
Full Text Available With the advances in automobile industry and wireless communication technology, Vehicular Ad hoc Networks (VANETs have attracted the attention of a large number of researchers. Trust management plays an important role in VANETs. However, it is still at the preliminary stage and the existing trust models cannot entirely conform to the characteristics of VANETs. This work proposes a novel Lightweight Self-Organized Trust (LSOT model which contains trust certificate-based and recommendation-based trust evaluations. Both the supernodes and trusted third parties are not needed in our model. In addition, we comprehensively consider three factor weights to ease the collusion attack in trust certificate-based trust evaluation, and we utilize the testing interaction method to build and maintain the trust network and propose a maximum local trust (MLT algorithm to identify trustworthy recommenders in recommendation-based trust evaluation. Furthermore, a fully distributed VANET scenario is deployed based on the famous Advogato dataset and a series of simulations and analysis are conducted. The results illustrate that our LSOT model significantly outperforms the excellent experience-based trust (EBT and Lightweight Cross-domain Trust (LCT models in terms of evaluation performance and robustness against the collusion attack.
A self-organized internal models architecture for coding sensory-motor schemes
Directory of Open Access Journals (Sweden)
Esaú eEscobar Juárez
2016-04-01
Full Text Available Cognitive robotics research draws inspiration from theories and models on cognition, as conceived by neuroscience or cognitive psychology, to investigate biologically plausible computational models in artificial agents. In this field, the theoretical framework of Grounded Cognition provides epistemological and methodological grounds for the computational modeling of cognition. It has been stressed in the literature that textit{simulation}, textit{prediction}, and textit{multi-modal integration} are key aspects of cognition and that computational architectures capable of putting them into play in a biologically plausible way are a necessity.Research in this direction has brought extensive empirical evidencesuggesting that textit{Internal Models} are suitable mechanisms forsensory-motor integration. However, current Internal Models architectures show several drawbacks, mainly due to the lack of a unified substrate allowing for a true sensory-motor integration space, enabling flexible and scalable ways to model cognition under the embodiment hypothesis constraints.We propose the Self-Organized Internal ModelsArchitecture (SOIMA, a computational cognitive architecture coded by means of a network of self-organized maps, implementing coupled internal models that allow modeling multi-modal sensory-motor schemes. Our approach addresses integrally the issues of current implementations of Internal Models.We discuss the design and features of the architecture, and provide empirical results on a humanoid robot that demonstrate the benefits and potentialities of the SOIMA concept for studying cognition in artificial agents.
Self-Organized Criticality in an Anisotropic Earthquake Model
Li, Bin-Quan; Wang, Sheng-Jun
2018-03-01
We have made an extensive numerical study of a modified model proposed by Olami, Feder, and Christensen to describe earthquake behavior. Two situations were considered in this paper. One situation is that the energy of the unstable site is redistributed to its nearest neighbors randomly not averagely and keeps itself to zero. The other situation is that the energy of the unstable site is redistributed to its nearest neighbors randomly and keeps some energy for itself instead of reset to zero. Different boundary conditions were considered as well. By analyzing the distribution of earthquake sizes, we found that self-organized criticality can be excited only in the conservative case or the approximate conservative case in the above situations. Some evidence indicated that the critical exponent of both above situations and the original OFC model tend to the same result in the conservative case. The only difference is that the avalanche size in the original model is bigger. This result may be closer to the real world, after all, every crust plate size is different. Supported by National Natural Science Foundation of China under Grant Nos. 11675096 and 11305098, the Fundamental Research Funds for the Central Universities under Grant No. GK201702001, FPALAB-SNNU under Grant No. 16QNGG007, and Interdisciplinary Incubation Project of SNU under Grant No. 5
Self-Organizing Units in an Interdisciplinary Course for Pervasive Computing Design
McNair, Lisa; Newswander, Chad; Coupey, Eloise; Dorsa, Ed; Martin, Tom; Paretti, Marie
2009-01-01
We conducted a case study of a design course that focused on bringing together students from engineering, industrial design, and marketing to use pervasive computing technologies to design, coordinate, and build a “smart” dorm room for disabled individuals. The class was loosely structured to encourage innovation, critical thinking and interdisciplinarity. In this environment, teams were created, disassembled, and re-created in a self-organizing fashion. With few norms, teams were expected to...
Carreón, Gustavo; Gershenson, Carlos; Pineda, Luis A
2017-01-01
The equal headway instability-the fact that a configuration with regular time intervals between vehicles tends to be volatile-is a common regulation problem in public transportation systems. An unsatisfactory regulation results in low efficiency and possible collapses of the service. Computational simulations have shown that self-organizing methods can regulate the headway adaptively beyond the theoretical optimum. In this work, we develop a computer simulation for metro systems fed with real data from the Mexico City Metro to test the current regulatory method with a novel self-organizing approach. The current model considers overall system's data such as minimum and maximum waiting times at stations, while the self-organizing method regulates the headway in a decentralized manner using local information such as the passenger's inflow and the positions of neighboring trains. The simulation shows that the self-organizing method improves the performance over the current one as it adapts to environmental changes at the timescale they occur. The correlation between the simulation of the current model and empirical observations carried out in the Mexico City Metro provides a base to calculate the expected performance of the self-organizing method in case it is implemented in the real system. We also performed a pilot study at the Balderas station to regulate the alighting and boarding of passengers through guide signs on platforms. The analysis of empirical data shows a delay reduction of the waiting time of trains at stations. Finally, we provide recommendations to improve public transportation systems.
Self-organized critical model for protein folding
Moret, M. A.
2011-09-01
The major factor that drives a protein toward collapse and folding is the hydrophobic effect. At the folding process a hydrophobic core is shielded by the solvent-accessible surface area of the protein. We study the fractal behavior of 5526 protein structures present in the Brookhaven Protein Data Bank. Power laws of protein mass, volume and solvent-accessible surface area are measured independently. The present findings indicate that self-organized criticality is an alternative explanation for the protein folding. Also we note that the protein packing is an independent and constant value because the self-similar behavior of the volumes and protein masses have the same fractal dimension. This power law guarantees that a protein is a complex system. From the analyzed data, q-Gaussian distributions seem to fit well this class of systems.
Effects of Some Neurobiological Factors in a Self-organized Critical Model Based on Neural Networks
International Nuclear Information System (INIS)
Zhou Liming; Zhang Yingyue; Chen Tianlun
2005-01-01
Based on an integrate-and-fire mechanism, we investigate the effect of changing the efficacy of the synapse, the transmitting time-delayed, and the relative refractoryperiod on the self-organized criticality in our neural network model.
[Self-organization in the ontogeny of multicellular organisms: a computer simulation].
Markov, M A; Markov, A V
2011-01-01
The progress in understanding the patterns of evolution of ontogeny is hindered by the fact that many features of ontogeny are counterintuitive (as well as the features of other processes related to self-organization, self-assembly, and spontaneous increase in complexity). The basic principle of ontogeny of multicellular organisms is that it is the process of self-assembly of ordered multicellular structures by means of coordinated behavior of many individual modules (cells), each of which follows the same set of"rules" encoded in the genome. These rules are based on the genetic regulatory networks. We hypothesize that many specific features of ontogeny that seem nontrivial or enigmatic are, in fact, the inevitable consequences of this basic principle. If so, they do not need special explanations. In order to verify this hypothesis, we developed the computer program "Evo-Devo" based on the above principle. The program is designed to model the self-assembly of ordered multicellular structures from an aggregation of dividing cells that originate from a single original cell (zygote). Each cell follows a set of rules of behavior ("genotype") that can be specified arbitrarily by the experimenter, and is the same for all cells in the embryo (each cell is programmed in exactly the same way as all other cells). It is not allowed to specify rules for groups of cells or for the whole embryo: only local rules that should be followed at the level of a single cell are possible. The analysis of phenotypic implementation of different genotypes revealed several features which are present in the ontogeny of real organisms and are regularly reproduced in the model. These include: inherent stochasticity; inescapable necessity of development of stabilizing adaptations based on negative feedback in order to decrease this stochasticity; equifinality (noise resistance) resulting from these adaptations; the ability of ontogeny to respond to major perturbations by generating new
Liu, Lei; Hong, Xiaobin; Wu, Jian; Lin, Jintong
As Grid computing continues to gain popularity in the industry and research community, it also attracts more attention from the customer level. The large number of users and high frequency of job requests in the consumer market make it challenging. Clearly, all the current Client/Server(C/S)-based architecture will become unfeasible for supporting large-scale Grid applications due to its poor scalability and poor fault-tolerance. In this paper, based on our previous works [1, 2], a novel self-organized architecture to realize a highly scalable and flexible platform for Grids is proposed. Experimental results show that this architecture is suitable and efficient for consumer-oriented Grids.
Mechanisms of self-organization and finite size effects in a minimal agent based model
International Nuclear Information System (INIS)
Alfi, V; Cristelli, M; Pietronero, L; Zaccaria, A
2009-01-01
We present a detailed analysis of the self-organization phenomenon in which the stylized facts originate from finite size effects with respect to the number of agents considered and disappear in the limit of an infinite population. By introducing the possibility that agents can enter or leave the market depending on the behavior of the price, it is possible to show that the system self-organizes in a regime with a finite number of agents which corresponds to the stylized facts. The mechanism for entering or leaving the market is based on the idea that a too stable market is unappealing for traders, while the presence of price movements attracts agents to enter and speculate on the market. We show that this mechanism is also compatible with the idea that agents are scared by a noisy and risky market at shorter timescales. We also show that the mechanism for self-organization is robust with respect to variations of the exit/entry rules and that the attempt to trigger the system to self-organize in a region without stylized facts leads to an unrealistic dynamics. We study the self-organization in a specific agent based model but we believe that the basic ideas should be of general validity
A self-organizing algorithm for modeling protein loops.
Directory of Open Access Journals (Sweden)
Pu Liu
2009-08-01
Full Text Available Protein loops, the flexible short segments connecting two stable secondary structural units in proteins, play a critical role in protein structure and function. Constructing chemically sensible conformations of protein loops that seamlessly bridge the gap between the anchor points without introducing any steric collisions remains an open challenge. A variety of algorithms have been developed to tackle the loop closure problem, ranging from inverse kinematics to knowledge-based approaches that utilize pre-existing fragments extracted from known protein structures. However, many of these approaches focus on the generation of conformations that mainly satisfy the fixed end point condition, leaving the steric constraints to be resolved in subsequent post-processing steps. In the present work, we describe a simple solution that simultaneously satisfies not only the end point and steric conditions, but also chirality and planarity constraints. Starting from random initial atomic coordinates, each individual conformation is generated independently by using a simple alternating scheme of pairwise distance adjustments of randomly chosen atoms, followed by fast geometric matching of the conformationally rigid components of the constituent amino acids. The method is conceptually simple, numerically stable and computationally efficient. Very importantly, additional constraints, such as those derived from NMR experiments, hydrogen bonds or salt bridges, can be incorporated into the algorithm in a straightforward and inexpensive way, making the method ideal for solving more complex multi-loop problems. The remarkable performance and robustness of the algorithm are demonstrated on a set of protein loops of length 4, 8, and 12 that have been used in previous studies.
Caridakis, G; Karpouzis, K; Drosopoulos, A; Kollias, S
2012-12-01
Modeling and recognizing spatiotemporal, as opposed to static input, is a challenging task since it incorporates input dynamics as part of the problem. The vast majority of existing methods tackle the problem as an extension of the static counterpart, using dynamics, such as input derivatives, at feature level and adopting artificial intelligence and machine learning techniques originally designed for solving problems that do not specifically address the temporal aspect. The proposed approach deals with temporal and spatial aspects of the spatiotemporal domain in a discriminative as well as coupling manner. Self Organizing Maps (SOM) model the spatial aspect of the problem and Markov models its temporal counterpart. Incorporation of adjacency, both in training and classification, enhances the overall architecture with robustness and adaptability. The proposed scheme is validated both theoretically, through an error propagation study, and experimentally, on the recognition of individual signs, performed by different, native Greek Sign Language users. Results illustrate the architecture's superiority when compared to Hidden Markov Model techniques and variations both in terms of classification performance and computational cost. Copyright © 2012 Elsevier Ltd. All rights reserved.
Investigation on Self-Organization Processes in DC Generators by Synergetic Modeling
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Ion Voncilă
2014-09-01
Full Text Available In this paper is suggested a new mathematical model, based on which it can be justified the self-excitation DC generators, either shunt or series excitation, by self-organization phenomena that appear to overcome threshold values (self-excitation in these generators is an avalanche process, a positive feedback, considered at first glance uncontrollable.
Investigation on Self-Organization Processes in DC Generators by Synergetic Modeling
Ion Voncilă; Mădălin Costin; Răzvan Buhosu
2014-01-01
In this paper is suggested a new mathematical model, based on which it can be justified the self-excitation DC generators, either shunt or series excitation, by self-organization phenomena that appear to overcome threshold values (self-excitation in these generators is an avalanche process, a positive feedback, considered at first glance uncontrollable).
Exploring the patterns and evolution of self-organized urban street networks through modeling
Rui, Yikang; Ban, Yifang; Wang, Jiechen; Haas, Jan
2013-03-01
As one of the most important subsystems in cities, urban street networks have recently been well studied by using the approach of complex networks. This paper proposes a growing model for self-organized urban street networks. The model involves a competition among new centers with different values of attraction radius and a local optimal principle of both geometrical and topological factors. We find that with the model growth, the local optimization in the connection process and appropriate probability for the loop construction well reflect the evolution strategy in real-world cities. Moreover, different values of attraction radius in centers competition process lead to morphological change in patterns including urban network, polycentric and monocentric structures. The model succeeds in reproducing a large diversity of road network patterns by varying parameters. The similarity between the properties of our model and empirical results implies that a simple universal growth mechanism exists in self-organized cities.
A Model of Self-Organizing Head-Centered Visual Responses in Primate Parietal Areas
Mender, Bedeho M. W.; Stringer, Simon M.
2013-01-01
We present a hypothesis for how head-centered visual representations in primate parietal areas could self-organize through visually-guided learning, and test this hypothesis using a neural network model. The model consists of a competitive output layer of neurons that receives afferent synaptic connections from a population of input neurons with eye position gain modulated retinal receptive fields. The synaptic connections in the model are trained with an associative trace learning rule which has the effect of encouraging output neurons to learn to respond to subsets of input patterns that tend to occur close together in time. This network architecture and synaptic learning rule is hypothesized to promote the development of head-centered output neurons during periods of time when the head remains fixed while the eyes move. This hypothesis is demonstrated to be feasible, and each of the core model components described is tested and found to be individually necessary for successful self-organization. PMID:24349064
Crossover to self-organized criticality in an inertial sandpile model
Head, DA; Rodgers, GJ
1996-01-01
We introduce a one-dimensional sandpile model which incorporates particle inertia. The inertial dynamics are governed by a new parameter which, as it passes through a threshold value, alters the toppling dynamics in such a way that the system no longer evolves to a self-organized critical state. A range of mean-field theories based on a kinetic equation approach is presented which confirm the numerical findings. We conclude by considering the physical applications of this model, particularly ...
Democracy versus dictatorship in self-organized models of financial markets
D'Hulst, R.; Rodgers, G. J.
2000-06-01
Models to mimic the transmission of information in financial markets are introduced. As an attempt to generate the demand process, we distinguish between dictatorship associations, where groups of agents rely on one of them to make decision, and democratic associations, where each agent takes part in the group decision. In the dictatorship model, agents segregate into two distinct populations, while the democratic model is driven towards a critical state where groups of agents of all sizes exist. Hence, both models display a level of organization, but only the democratic model is self-organized. We show that the dictatorship model generates less-volatile markets than the democratic model.
Organized versus self-organized criticality in the abelian sandpile model
Fey-den Boer, AC Anne; Redig, FHJ Frank
2005-01-01
We define stabilizability of an infinite volume height configuration and of a probability measure on height configurations. We show that for high enough densities, a probability measure cannot be stabilized. We also show that in some sense the thermodynamic limit of the uniform measures on the recurrent configurations of the abelian sandpile model (ASM) is a maximal element of the set of stabilizable measures. In that sense the self-organized critical behavior of the ASM can be understood in ...
International Nuclear Information System (INIS)
Hayashi, T.; Sato, T.
1999-01-01
The primary purpose of this paper is to extract a grand view of self-organization through an extensive computer simulation of plasmas. The assertion is made that self-organization is governed by three key processes, i.e. the existence of an open complex system, the existence of information (energy) sources and the existence of entropy generation and expulsion processes. We find that self-organization takes place in an intermittent fashion when energy is supplied continuously from outside. In contrast, when the system state is suddenly changed into a non-equilibrium state externally, the system evolves stepwise and reaches a minimum energy state. We also find that the entropy production rate is maximized whenever a new ordered structure is created and that if the entropy generated during the self-organizing process is expelled from the system, then the self-organized structure becomes more prominent and clear. (author)
A Data-Driven, Integrated Flare Model Based on Self-Organized Criticality
Dimitropoulou, M.; Isliker, H.; Vlahos, L.; Georgoulis, M.
2013-09-01
We interpret solar flares as events originating in solar active regions having reached the self-organized critical state, by alternatively using two versions of an "integrated flare model" - one static and one dynamic. In both versions the initial conditions are derived from observations aiming to investigate whether well-known scaling laws observed in the distribution functions of characteristic flare parameters are reproduced after the self-organized critical state has been reached. In the static model, we first apply a nonlinear force-free extrapolation that reconstructs the three-dimensional magnetic fields from two-dimensional vector magnetograms. We then locate magnetic discontinuities exceeding a threshold in the Laplacian of the magnetic field. These discontinuities are relaxed in local diffusion events, implemented in the form of cellular-automaton evolution rules. Subsequent loading and relaxation steps lead the system to self-organized criticality, after which the statistical properties of the simulated events are examined. In the dynamic version we deploy an enhanced driving mechanism, which utilizes the observed evolution of active regions, making use of sequential vector magnetograms. We first apply the static cellular automaton model to consecutive solar vector magnetograms until the self-organized critical state is reached. We then evolve the magnetic field inbetween these processed snapshots through spline interpolation, acting as a natural driver in the dynamic model. The identification of magnetically unstable sites as well as their relaxation follow the same rules as in the static model after each interpolation step. Subsequent interpolation/driving and relaxation steps cover all transitions until the end of the sequence. Physical requirements, such as the divergence-free condition for the magnetic field vector, are approximately satisfied in both versions of the model. We obtain robust power laws in the distribution functions of the modelled
Dynamic data-driven integrated flare model based on self-organized criticality
Dimitropoulou, M.; Isliker, H.; Vlahos, L.; Georgoulis, M. K.
2013-05-01
Context. We interpret solar flares as events originating in active regions that have reached the self-organized critical state. We describe them with a dynamic integrated flare model whose initial conditions and driving mechanism are derived from observations. Aims: We investigate whether well-known scaling laws observed in the distribution functions of characteristic flare parameters are reproduced after the self-organized critical state has been reached. Methods: To investigate whether the distribution functions of total energy, peak energy, and event duration follow the expected scaling laws, we first applied the previously reported static cellular automaton model to a time series of seven solar vector magnetograms of the NOAA active region 8210 recorded by the Imaging Vector Magnetograph on May 1 1998 between 18:59 UT and 23:16 UT until the self-organized critical state was reached. We then evolved the magnetic field between these processed snapshots through spline interpolation, mimicking a natural driver in our dynamic model. We identified magnetic discontinuities that exceeded a threshold in the Laplacian of the magnetic field after each interpolation step. These discontinuities were relaxed in local diffusion events, implemented in the form of cellular automaton evolution rules. Subsequent interpolation and relaxation steps covered all transitions until the end of the processed magnetograms' sequence. We additionally advanced each magnetic configuration that has reached the self-organized critical state (SOC configuration) by the static model until 50 more flares were triggered, applied the dynamic model again to the new sequence, and repeated the same process sufficiently often to generate adequate statistics. Physical requirements, such as the divergence-free condition for the magnetic field, were approximately imposed. Results: We obtain robust power laws in the distribution functions of the modeled flaring events with scaling indices that agree well
Self-organization of domain growth in the Ising model with impurities
DEFF Research Database (Denmark)
Andersen, Jørgen Vitting; Mouritsen, Ole G.
1992-01-01
We have studied avalanchelike rearrangements of domain patterns in the two-dimensional Ising model with static impurities, which is quenched to low temperatures. When breaking the up-down symmetry of the spins by a small applied field, the mere fluctuation of a single spin eventually results...... in a cascade of spin flips at the domain boundaries. We have analyzed the lifetime and size distribution functions for the avalanches and related the results to the general phenomena of self-organized criticality and to recent experiments on cellular magnetic domain patterns in magnetic garnet films. Our...... results suggest that the self-organized state in this system appears to be subcritical, in agreement with a recent theory....
Influence of Selective Edge Removal and Refractory Period in a Self-Organized Critical Neuron Model
International Nuclear Information System (INIS)
Lin Min; Gang, Zhao; Chen Tianlun
2009-01-01
A simple model for a set of integrate-and-fire neurons based on the weighted network is introduced. By considering the neurobiological phenomenon in brain development and the difference of the synaptic strength, we construct weighted networks develop with link additions and followed by selective edge removal. The network exhibits the small-world and scale-free properties with high network efficiency. The model displays an avalanche activity on a power-law distribution. We investigate the effect of selective edge removal and the neuron refractory period on the self-organized criticality of the system. (condensed matter: structural, mechanical, and thermal properties)
Gaffney, E. A.; Lee, S. S.
2013-01-01
© The authors 2013. Turing morphogen models have been extensively explored in the context of large-scale self-organization in multicellular biological systems. However, reconciling the detailed biology of morphogen dynamics, while accounting
International Nuclear Information System (INIS)
Christensen, K.; Olami, Z.
1992-01-01
We present a two-dimensional continuous cellular automaton that is equivalent to a driven spring-block model. Both the conservation and the anisotropy in the model are controllable quantities. Above a critical level of conservation, the model exhibits self-organized criticality. The self-organization of this system and hence the critical exponents depend on the conservation and the boundary conditions. In the critical isotropic nonconservative phase, the exponents change continuously as a function of conservation. Furthermore, the exponents vary continuously when changing the boundary conditions smoothly. Consequently, there is no universality of the critical exponents. We discuss the relevance of this for earthquakes. Introducing anisotropy changes the scaling of the distribution function, but not the power-law exponent. We explore the phase diagram of this model. We find that at low conservation levels a localization transition occurs. We see two additional phase transitions. The first is seen when moving from the conservative into the nonconservative model. The second appears when passing from the anisotropic two-dimensional system to the purely one-dimensional system
Directory of Open Access Journals (Sweden)
Johannes Bill
Full Text Available During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input.
Bill, Johannes; Buesing, Lars; Habenschuss, Stefan; Nessler, Bernhard; Maass, Wolfgang; Legenstein, Robert
2015-01-01
During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input. PMID:26284370
Self-organization of hot plasmas the canonical profile transport model
Dnestrovskij, Yu N
2015-01-01
In this monograph the author presents the Canonical Profile Transport Model or CPTM as a rather general mathematical framework to simulate plasma discharges.The description of hot plasmas in a magnetic fusion device is a very challenging task and many plasma properties still lack a physical explanation. One important property is plasma self-organization.It is very well known from experiments that the radial profile of the plasma pressure and temperature remains rather unaffected by changes of the deposited power or plasma density. The attractiveness of the CPTM is that it includes the effect o
Modeling of Instabilities and Self-organization at the Frictional Interface
Mortazavi, Vahid
frictional surface to exhibit "self-protection" and "self-healing" properties. Hence, this research is dealing with the fundamental concepts that allow the possibility of the development of a new generation of tribosystem and materials that reinforce such properties. In chapter 2, we investigate instabilities due to the temperature-dependency of the coefficient of friction. The temperature-dependency of the coefficient of friction can have a significant effect on the frictional sliding stability, by leading to the formation of "hot" and "cold" spots on the contacting surfaces. We formulate a stability criterion and perform a case study of a brake disk. In chapter 3, we study frictional running-in. Running-in is a transient period on the onset of the frictional sliding, in which friction and wear decrease to their stationary values. In this research, running-in is interpreted as friction-induced self-organization process. We introduce a theoretical model of running-in and investigate rough profile evolution assuming that its kinetics is driven by two opposite processes or events, i.e., smoothening which is typical for the deformation-driven friction and wear, and roughening which is typical for the adhesion-driven friction and wear. In chapter 4, we investigate the possibility of the so-called Turing-type pattern formation during friction. Turing or reaction-diffusion systems describe variations of spatial concentrations of chemical components with time due to local chemical reactions coupled with diffusion. During friction, the patterns can form at the sliding interface due to the mass transfer (diffusion), heat transfer, various tribochemical reactions, and wear. In chapter 5, we investigate how interfacial patterns including propagating trains of stick and slip zones form due to dynamic sliding instabilities. These can be categorized as self-organized patterns. We treat stick and slip as two phases at the interface, and study the effects related to phase transitions. Our
Directory of Open Access Journals (Sweden)
Ferdinando Giacco
2008-01-01
Full Text Available In this paper we employ the Kohonen’s Self Organizing Map (SOM as a strategy for an unsupervised analysis of ASTER multispectral (MS images. In order to obtain an accurate clusterization we introduce as input for the network, in addition to spectral data, some texture measures extracted from IKONOS images, which gives a contribution to the classification of manmade structures. After clustering of SOM outcomes, we associated each cluster with a major land cover and compared them with prior knowledge of the scene analyzed.
Self-organized criticality in asymmetric exclusion model with noise for freeway traffic
Nagatani, Takashi
1995-02-01
The one-dimensional asymmetric simple-exclusion model with open boundaries for parallel update is extended to take into account temporary stopping of particles. The model presents the traffic flow on a highway with temporary deceleration of cars. Introducing temporary stopping into the asymmetric simple-exclusion model drives the system asymptotically into a steady state exhibiting a self-organized criticality. In the self-organized critical state, start-stop waves (or traffic jams) appear with various sizes (or lifetimes). The typical interval between consecutive jams scales as ≃ Lv with v = 0.51 ± 0.05 where L is the system size. It is shown that the cumulative jam-interval distribution Ns( L) satisfies the finite-size scaling form ( Ns( L) ≃ L- vf( s/ Lv). Also, the typical lifetime ≃ Lv‧ with v‧ = 0.52 ± 0.05. The cumulative distribution Nm( L) of lifetimes satisfies the finite-size scaling form Nm( L)≃ L-1g( m/ Lv‧).
Self-organized dynamics in local load-sharing fiber bundle models.
Biswas, Soumyajyoti; Chakrabarti, Bikas K
2013-10-01
We study the dynamics of a local load-sharing fiber bundle model in two dimensions under an external load (which increases with time at a fixed slow rate) applied at a single point. Due to the local load-sharing nature, the redistributed load remains localized along the boundary of the broken patch. The system then goes to a self-organized state with a stationary average value of load per fiber along the (increasing) boundary of the broken patch (damaged region) and a scale-free distribution of avalanche sizes and other related quantities are observed. In particular, when the load redistribution is only among nearest surviving fiber(s), the numerical estimates of the exponent values are comparable with those of the Manna model. When the load redistribution is uniform along the patch boundary, the model shows a simple mean-field limit of this self-organizing critical behavior, for which we give analytical estimates of the saturation load per fiber values and avalanche size distribution exponent. These are in good agreement with numerical simulation results.
Studies on Manfred Eigen's model for the self-organization of information processing.
Ebeling, W; Feistel, R
2018-05-01
In 1971, Manfred Eigen extended the principles of Darwinian evolution to chemical processes, from catalytic networks to the emergence of information processing at the molecular level, leading to the emergence of life. In this paper, we investigate some very general characteristics of this scenario, such as the valuation process of phenotypic traits in a high-dimensional fitness landscape, the effect of spatial compartmentation on the valuation, and the self-organized transition from structural to symbolic genetic information of replicating chain molecules. In the first part, we perform an analysis of typical dynamical properties of continuous dynamical models of evolutionary processes. In particular, we study the mapping of genotype to continuous phenotype spaces following the ideas of Wright and Conrad. We investigate typical features of a Schrödinger-like dynamics, the consequences of the high dimensionality, the leading role of saddle points, and Conrad's extra-dimensional bypass. In the last part, we discuss in brief the valuation of compartment models and the self-organized emergence of molecular symbols at the beginning of life.
On modeling complex interplay in small-scale self-organized socio-hydrological systems
Muneepeerakul, Rachata
2017-04-01
Successful and sustainable socio-hydrological systems, as in any coupled natural human-systems, require effective governance, which depends on the existence of proper infrastructure (both hard and soft). Recent work has addressed systems in which resource users and the organization responsible for maintaining the infrastructure are separate entities. However, many socio-hydrological systems, especially in developing countries, are small and without such formal division of labor; rather, such division of labor typically arises from self-organization within the population. In this work, we modify and mathematically operationalize a conceptual framework by developing a system of differential equations that capture the strategic behavior within such a self-organized population, its interplay with infrastructure characteristics and hydrological dynamics, and feedbacks between these elements. The model yields a number of insightful conditions related to long-term sustainability and collapse of the socio-hydrological system in the form of relationships between biophysical and social factors. These relationships encapsulate nonlinear interactions of these factors. The modeling framework is grounded in a solid conceptual foundation upon which additional modifications and realism can be built for potential reconciliation between socio-hydrology with other related fields and further applications.
A continuum self organized critically model of turbulent heat transport in tokamaks
Energy Technology Data Exchange (ETDEWEB)
Tangri, V; Das, A; Kaw, P; Singh, R [Institute for Plasma Research, Gandhinagar (India)
2003-09-01
Based on the now well known and experimentally observed critical gradient length (R/L{sub Te} = RT/{nabla}T) in tokamaks, we present a continuum one dimensional model for explaining self organized heat transport in tokamaks. Key parameters of this model include a novel hysteresis parameter which ensures that the switch of heat transport coefficient {chi} upwards and downwards takes place at two different values of R/L{sub Te}. Extensive numerical simulations of this model reproduce many features of present day tokamaks such as submarginal temperature profiles, intermittent transport events, 1/f scaling of the frequency spectra, propagating fronts, etc. This model utilises a minimal set of phenomenological parameters, which may be determined from experiments and/or simulations. Analytical and physical understanding of the observed features has also been attempted. (author)
A novel self-organizing E-Learner community model with award and exchange mechanisms.
Yang, Fan; Shen, Rui-min; Han, Peng
2004-11-01
How to share experience and resources among learners is becoming one of the hottest topics in the field of E-Learning collaborative techniques. An intuitive way to achieve this objective is to group learners which can help each other into the same community and help them learn collaboratively. In this paper, we proposed a novel community self-organization model based on multi-agent mechanism, which can automatically group learners with similar preferences and capabilities. In particular, we proposed award and exchange schemas with evaluation and preference track records to raise the performance of this algorithm. The description of learner capability, the matchmaking process, the definition of evaluation and preference track records, the rules of award and exchange schemas and the self-organization algorithm are all discussed in this paper. Meanwhile, a prototype has been built to verify the validity and efficiency of the algorithm. Experiments based on real learner data showed that this mechanism can organize learner communities properly and efficiently; and that it has sustainable improved efficiency and scalability.
Interconnected growing self-organizing maps for auditory and semantic acquisition modeling
Directory of Open Access Journals (Sweden)
Mengxue eCao
2014-03-01
Full Text Available Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic--semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners; a reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1 I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2 clear auditory and semantic boundaries can be found in the network representation; (3 cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4 reinforcing-by-link training leads to well-perceived auditory--semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model.
Predicting spiral wave patterns from cell properties in a model of biological self-organization.
Geberth, Daniel; Hütt, Marc-Thorsten
2008-09-01
In many biological systems, biological variability (i.e., systematic differences between the system components) can be expected to outrank statistical fluctuations in the shaping of self-organized patterns. In principle, the distribution of single-element properties should thus allow predicting features of such patterns. For a mathematical model of a paradigmatic and well-studied pattern formation process, spiral waves of cAMP signaling in colonies of the slime mold Dictyostelium discoideum, we explore this possibility and observe a pronounced anticorrelation between spiral waves and cell properties (namely, the firing rate) and particularly a clustering of spiral wave tips in regions devoid of spontaneously firing (pacemaker) cells. Furthermore, we observe local inhomogeneities in the distribution of spiral chiralities, again induced by the pacemaker distribution. We show that these findings can be explained by a simple geometrical model of spiral wave generation.
Itzá Balam, Reymundo; Iturrarán-Viveros, Ursula; Parra, Jorge O.
2018-03-01
Two main stages of seismic modeling are geological model building and numerical computation of seismic response for the model. The quality of the computed seismic response is partly related to the type of model that is built. Therefore, the model building approaches become as important as seismic forward numerical methods. For this purpose, three petrophysical facies (sands, shales and limestones) are extracted from reflection seismic data and some seismic attributes via the clustering method called Self-Organizing Maps (SOM), which, in this context, serves as a geological model building tool. This model with all its properties is the input to the Optimal Implicit Staggered Finite Difference (OISFD) algorithm to create synthetic seismograms for poroelastic, poroacoustic and elastic media. The results show a good agreement between observed and 2-D synthetic seismograms. This demonstrates that the SOM classification method enables us to extract facies from seismic data and allows us to integrate the lithology at the borehole scale with the 2-D seismic data.
Modeling of self-organization of two-dimensional ordered structures
Energy Technology Data Exchange (ETDEWEB)
Egorov, V V; Garmay, Y P; Shaldzhyan, A A; Vasin, A V; Kiselev, O I [Research Institute of Influenza of the Ministry of Health and Social Development of the Russian Federation, Prof. Popova st. 15/17, St-Petersburg (Russian Federation); Lebedev, D V [Department of Molecular and Radiation Biophysics Petersburg Nuclear Physics Institute of the Russian Academy of Science, Orlova Roscha, Gatchina, Leningrad Region (Russian Federation); Grudinina, N A, E-mail: toizeg@gmail.com [Institute of Experimental Medicine, North-Western Branch of the Russian Academy of Medical Science, 12, Akademika Pavlova st., St-Petersburg (Russian Federation)
2011-04-01
The problem of the search of biostructures capable to self-organization is quite urgent considering the prospects of application of nanostructured biomaterials as components of composite materials in transplantology and optics as well as 'scaffolds' for the synthesis of nanostructured materials based on inorganic particles. The given study focuses on modeling of the growth of structures using the cellular automata with a set of states of the two values (0 and 1), with the value corresponding to the state is determined by the contribution of 'the closest neighbor' (by the probability of induction of the state of the nextgeneration in the direction of the interaction) and the geometry of the field isdetermined by the vector of the direction of the particle and the direction of the interaction.
Leader-based and self-organized communication: modelling group-mass recruitment in ants.
Collignon, Bertrand; Deneubourg, Jean Louis; Detrain, Claire
2012-11-21
For collective decisions to be made, the information acquired by experienced individuals about resources' location has to be shared with naïve individuals through recruitment. Here, we investigate the properties of collective responses arising from a leader-based recruitment and a self-organized communication by chemical trails. We develop a generalized model based on biological data drawn from Tetramorium caespitum ant species of which collective foraging relies on the coupling of group leading and trail recruitment. We show that for leader-based recruitment, small groups of recruits have to be guided in a very efficient way to allow a collective exploitation of food while large group requires less attention from their leader. In the case of self-organized recruitment through a chemical trail, a critical value of trail amount has to be laid per forager in order to launch collective food exploitation. Thereafter, ants can maintain collective foraging by emitting signal intensity below this threshold. Finally, we demonstrate how the coupling of both recruitment mechanisms may benefit to collectively foraging species. These theoretical results are then compared with experimental data from recruitment by T. caespitum ant colonies performing group-mass recruitment towards a single food source. We evidence the key role of leaders as initiators and catalysts of recruitment before this leader-based process is overtaken by self-organised communication through trails. This model brings new insights as well as a theoretical background to empirical studies about cooperative foraging in group-living species. Copyright © 2012 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
E. A. Tatokchin
2017-01-01
Full Text Available Development of the modern educational technologies caused by broad introduction of comput-er testing and development of distant forms of education does necessary revision of methods of an examination of pupils. In work it was shown, need transition to mathematical criteria, exami-nations of knowledge which are deprived of subjectivity. In article the review of the problems arising at realization of this task and are offered approaches for its decision. The greatest atten-tion is paid to discussion of a problem of objective transformation of rated estimates of the ex-pert on to the scale estimates of the student. In general, the discussion this question is was con-cluded that the solution to this problem lies in the creation of specialized intellectual systems. The basis for constructing intelligent system laid the mathematical model of self-organizing nonequilibrium dissipative system, which is a group of students. This article assumes that the dissipative system is provided by the constant influx of new test items of the expert and non-equilibrium – individual psychological characteristics of students in the group. As a result, the system must self-organize themselves into stable patterns. This patern will allow for, relying on large amounts of data, get a statistically significant assessment of student. To justify the pro-posed approach in the work presents the data of the statistical analysis of the results of testing a large sample of students (> 90. Conclusions from this statistical analysis allowed to develop intelligent system statistically significant examination of student performance. It is based on data clustering algorithm (k-mean for the three key parameters. It is shown that this approach allows you to create of the dynamics and objective expertise evaluation.
Explaining the “how” of self-esteem development : The self-organizing self-esteem model
de Ruiter, Naomi M.P.; van Geert, Paul L.C.; Kunnen, E. Saskia
2017-01-01
The current article proposes a theoretical model of self-esteem called the Self-Organizing Self-Esteem (SOSE) model. The model provides an integrative framework for conceptualizing and understanding the intrinsic dynamics of self-esteem and the role of the context across 3 levels of development: The
Janson, Natalia B; Marsden, Christopher J
2017-12-05
It is well known that architecturally the brain is a neural network, i.e. a collection of many relatively simple units coupled flexibly. However, it has been unclear how the possession of this architecture enables higher-level cognitive functions, which are unique to the brain. Here, we consider the brain from the viewpoint of dynamical systems theory and hypothesize that the unique feature of the brain, the self-organized plasticity of its architecture, could represent the means of enabling the self-organized plasticity of its velocity vector field. We propose that, conceptually, the principle of cognition could amount to the existence of appropriate rules governing self-organization of the velocity field of a dynamical system with an appropriate account of stimuli. To support this hypothesis, we propose a simple non-neuromorphic mathematical model with a plastic self-organized velocity field, which has no prototype in physical world. This system is shown to be capable of basic cognition, which is illustrated numerically and with musical data. Our conceptual model could provide an additional insight into the working principles of the brain. Moreover, hardware implementations of plastic velocity fields self-organizing according to various rules could pave the way to creating artificial intelligence of a novel type.
Inducing self-organized criticality in a network toy model by neighborhood assortativity.
Allen-Perkins, Alfonso; Galeano, Javier; Pastor, Juan Manuel
2016-11-01
Complex networks are a recent type of framework used to study complex systems with many interacting elements, such as self-organized criticality (SOC). The network nodes' tendency to link to other nodes of similar type is characterized by assortative mixing. Real networks exhibit assortative mixing by vertex degree, however, typical random network models, such as the Erdős-Rényi or the Barabási-Albert model, show no assortative arrangements. In this paper we introduce the notion of neighborhood assortativity as the tendency of a node to belong to a community (its neighborhood) showing an average property similar to its own. Imposing neighborhood assortative mixing by degree in a network toy model, SOC dynamics can be found. These dynamics are driven only by the network topology. The long-range correlations resulting from criticality have been characterized by means of fluctuation analysis and show an anticorrelation in the node's activity. The model contains only one parameter and its statistics plots for different values of the parameter can be collapsed into a single curve. The simplicity of the model allows us to perform numerical simulations and also to study analytically the statistics for a specific value of the parameter, making use of the Markov chains.
Isliker, H.; Pisokas, Th.; Strintzi, D.; Vlahos, L.
2010-08-01
A new self-organized criticality (SOC) model is introduced in the form of a cellular automaton (CA) for ion temperature gradient (ITG) mode driven turbulence in fusion plasmas. Main characteristics of the model are that it is constructed in terms of the actual physical variable, the ion temperature, and that the temporal evolution of the CA, which necessarily is in the form of rules, mimics actual physical processes as they are considered to be active in the system, i.e., a heating process and a local diffusive process that sets on if a threshold in the normalized ITG R /LT is exceeded. The model reaches the SOC state and yields ion temperature profiles of exponential shape, which exhibit very high stiffness, in that they basically are independent of the loading pattern applied. This implies that there is anomalous heat transport present in the system, despite the fact that diffusion at the local level is imposed to be of a normal kind. The distributions of the heat fluxes in the system and of the heat out-fluxes are of power-law shape. The basic properties of the model are in good qualitative agreement with experimental results.
International Nuclear Information System (INIS)
Isliker, H.; Pisokas, Th.; Vlahos, L.; Strintzi, D.
2010-01-01
A new self-organized criticality (SOC) model is introduced in the form of a cellular automaton (CA) for ion temperature gradient (ITG) mode driven turbulence in fusion plasmas. Main characteristics of the model are that it is constructed in terms of the actual physical variable, the ion temperature, and that the temporal evolution of the CA, which necessarily is in the form of rules, mimics actual physical processes as they are considered to be active in the system, i.e., a heating process and a local diffusive process that sets on if a threshold in the normalized ITG R/L T is exceeded. The model reaches the SOC state and yields ion temperature profiles of exponential shape, which exhibit very high stiffness, in that they basically are independent of the loading pattern applied. This implies that there is anomalous heat transport present in the system, despite the fact that diffusion at the local level is imposed to be of a normal kind. The distributions of the heat fluxes in the system and of the heat out-fluxes are of power-law shape. The basic properties of the model are in good qualitative agreement with experimental results.
International Nuclear Information System (INIS)
Lin Min; Wang Gang; Chen Tianlun
2007-01-01
A modified evolution model of self-organized criticality on generalized Barabasi-Albert (GBA) scale-free networks is investigated. In our model, we find that spatial and temporal correlations exhibit critical behaviors. More importantly, these critical behaviors change with the parameter b, which weights the distance in comparison with the degree in the GBA network evolution.
Fort, H; Viola, S
2004-03-01
We analyze, both analytically and numerically, the self-organization of a system of "selfish" adaptive agents playing an arbitrary iterated pairwise game (defined by a 2 x 2 payoff matrix). Examples of possible games to play are the prisoner's dilemma (PD) game, the chicken game, the hero game, etc. The agents have no memory, use strategies not based on direct reciprocity nor "tags" and are chosen at random, i.e., geographical vicinity is neglected. They can play two possible strategies: cooperate (C) or defect (D). The players measure their success by comparing their utilities with an estimate for the expected benefits and update their strategy following a simple rule. Two versions of the model are studied: (1) the deterministic version (the agents are either in definite states C or D) and (2) the stochastic version (the agents have a probability c of playing C). Using a general master equation we compute the equilibrium states into which the system self-organizes, characterized by their average probability of cooperation c(eq). Depending on the payoff matrix, we show that c(eq) can take five different values. We also consider the mixing of agents using two different payoff matrices and show that any value of c(eq) can be reached by tuning the proportions of agents using each payoff matrix. In particular, this can be used as a way to simulate the effect of a fraction d of "antisocial" individuals--incapable of realizing any value to cooperation--on the cooperative regime hold by a population of neutral or "normal" agents.
Self-Organization in 2D Traffic Flow Model with Jam-Avoiding Drive
Nagatani, Takashi
1995-04-01
A stochastic cellular automaton (CA) model is presented to investigate the traffic jam by self-organization in the two-dimensional (2D) traffic flow. The CA model is the extended version of the 2D asymmetric exclusion model to take into account jam-avoiding drive. Each site contains either a car moving to the up, a car moving to the right, or is empty. A up car can shift right with probability p ja if it is blocked ahead by other cars. It is shown that the three phases (the low-density phase, the intermediate-density phase and the high-density phase) appear in the traffic flow. The intermediate-density phase is characterized by the right moving of up cars. The jamming transition to the high-density jamming phase occurs with higher density of cars than that without jam-avoiding drive. The jamming transition point p 2c increases with the shifting probability p ja. In the deterministic limit of p ja=1, it is found that a new jamming transition occurs from the low-density synchronized-shifting phase to the high-density moving phase with increasing density of cars. In the synchronized-shifting phase, all up cars do not move to the up but shift to the right by synchronizing with the move of right cars. We show that the jam-avoiding drive has an important effect on the dynamical jamming transition.
Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.
Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei
2016-02-01
A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.
Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models.
Mahony, Shaun; McInerney, James O; Smith, Terry J; Golden, Aaron
2004-03-05
Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.
Proykova, Ana
2009-04-01
Essential contributions have been made in the field of finite-size systems of ingredients interacting with potentials of various ranges. Theoretical simulations have revealed peculiar size effects on stability, ground state structure, phases, and phase transformation of systems confined in space and time. Models developed in the field of pure physics (atomic and molecular clusters) have been extended and successfully transferred to finite-size systems that seem very different—small-scale financial markets, autoimmune reactions, and social group reactions to advertisements. The models show that small-scale markets diverge unexpectedly fast as a result of small fluctuations; autoimmune reactions are sequences of two discontinuous phase transitions; and social groups possess critical behavior (social percolation) under the influence of an external field (advertisement). Some predicted size-dependent properties have been experimentally observed. These findings lead to the hypothesis that restrictions on an object's size determine the object's total internal (configuration) and external (environmental) interactions. Since phases are emergent phenomena produced by self-organization of a large number of particles, the occurrence of a phase in a system containing a small number of ingredients is remarkable.
Faults self-organized by repeated earthquakes in a quasi-static antiplane crack model
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D. Sornette
1996-01-01
Full Text Available We study a 2D quasi-static discrete crack anti-plane model of a tectonic plate with long range elastic forces and quenched disorder. The plate is driven at its border and the load is transferred to all elements through elastic forces. This model can be considered as belonging to the class of self-organized models which may exhibit spontaneous criticality, with four additional ingredients compared to sandpile models, namely quenched disorder, boundary driving, long range forces and fast time crack rules. In this 'crack' model, as in the 'dislocation' version previously studied, we find that the occurrence of repeated earthquakes organizes the activity on well-defined fault-like structures. In contrast with the 'dislocation' model, after a transient, the time evolution becomes periodic with run-aways ending each cycle. This stems from the 'crack' stress transfer rule preventing criticality to organize in favour of cyclic behaviour. For sufficiently large disorder and weak stress drop, these large events are preceded by a complex spacetime history of foreshock activity, characterized by a Gutenberg-Richter power law distribution with universal exponent B = 1±0.05. This is similar to a power law distribution of small nucleating droplets before the nucleation of the macroscopic phase in a first-order phase transition. For large disorder and large stress drop, and for certain specific initial disorder configurations, the stress field becomes frustrated in fast time: out-of-plane deformations (thrust and normal faulting and/or a genuine dynamics must be introduced to resolve this frustration.
Ultrametricity and memory in a solvable model of self-organized criticality
International Nuclear Information System (INIS)
Boettcher, S.; Paczuski, M.
1996-01-01
Slowly driven dissipative systems may evolve to a critical state where long periods of apparent equilibrium are punctuated by intermittent avalanches of activity. We present a self-organized critical model of punctuated equilibrium behavior in the context of biological evolution, and solve it in the limit that the number of independent traits for each species diverges. We derive an exact equation of motion for the avalanche dynamics from the microscopic rules. In the continuum limit, avalanches propagate via a diffusion equation with a nonlocal, history dependent potential representing memory. This nonlocal potential gives rise to a non-Gaussian (fat) tail for the subdiffusive spreading of activity. The probability for the activity to spread beyond a distance r in time s decays as √(24/π)s -3/2 x 1/3 exp[-3/4x 1/3 ] for x=r 4 /s>1. The potential represents a hierarchy of time scales that is dynamically generated by the ultrametric structure of avalanches, which can be quantified in terms of open-quote open-quote backward close-quote close-quote avalanches. In addition, a number of other correlation functions characterizing the punctuated equilibrium dynamics are determined exactly
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Haifeng Zhao
2014-01-01
Full Text Available Subway emergency may lead to passengers’ panic, especially under self-organizing circumstance, which will spread rapidly and have an adverse impact on the society. This paper builds an improved SIRS model of passengers’ panic spread in subway emergency with consideration of passengers’ density, the characteristic of subway car with the confined space, and passengers’ psychological factors. The spread of passengers’ panic is simulated by use of Matlab, which draws the rules of how group panic spreads dynamically. The trend of stable point of the infection ratio is analyzed by changing different parameters, which help to draw a conclusion that immunization rate, spontaneous immune loss rate, and passenger number have a great influence on the final infected ratio. Finally, we propose an integrated control strategy and find the peak of passengers’ panic and the final infected ratio is greatly improved through the numerical simulation. The research plays a vital role in helping the government and subway administration to master the panic spread mechanism and reduce the panic spread by improving measures and also provides certain reference significance for rail system construction, emergency contingency plans, and the construction and implementation of emergency response system.
Calcium metabolism in the rat: A temporal self-organized model
International Nuclear Information System (INIS)
Staub, J.F.; Tracqui, P.; Brezillon, P.; Milhaud, G.; Perault-Staub, A.M.
1988-01-01
Based on consideration of rat plasma Ca and 45 Ca concentrations, the authors analyze the circadian behavior of Ca metabolism of the rat as the temporal expression of a self-organized system. They present a self-oscillatory model M for rat Ca metabolism based on a compartmental formalism, which includes a second-order autocatalytic process. M describes the entire mass of Ca as made up of eight compartments and predicts a distinction between (1) the amount of Ca deposited in zones of rapid bone growth and reutilized during bone maturation and (2) the amount of Ca in mature bone subdivided into four compartments. Two of these compartments, largely self-oscillating, may represent Ca-P associations at bone liquid/solid interface and are subject to osteoblast-osteocyte control. The other two compartments can be thought of as made up of a large expanding pool of hydroxyapatite (HA) crystals, which are largely unavailable as such, and a small pool of more available HA crystals. Bone Ca influx and rhythmic efflux play a major role in the regulation of Ca in extracellular fluid but must be dissociated from bone accretion and resorption. Application to Ca deficiency was analyzed. Conceptual consequences of the connection of Ca metabolism to a self-regulated system are discussed
International Nuclear Information System (INIS)
Creutz, M.
1993-03-01
Self organized criticality refers to the tendency of highly dissipative systems to drive themselves to a critical state. This has been proposed to explain why observed physics often displays a wide disparity of length and time scales. The phenomenon can be studied in simple cellular automaton models
Self-Organized Transport System
2009-09-28
This report presents the findings of the simulation model for a self-organized transport system where traffic lights communicate with neighboring traffic lights and make decisions locally to adapt to traffic conditions in real time. The model is insp...
Sakaguchi, Hidetsugu; Kadowaki, Shuntaro
2017-07-01
We study slowly pulling block-spring models in random media. Second-order phase transitions exist in a model pulled by a constant force in the case of velocity-strengthening friction. If external forces are slowly increased, nearly critical states are self-organized. Slips of various sizes occur, and the probability distributions of slip size roughly obey power laws. The exponent is close to that in the quenched Edwards-Wilkinson model. Furthermore, the slip-size distributions are investigated in cases of Coulomb friction, velocity-weakening friction, and two-dimensional block-spring models.
Self Organization in Compensated Semiconductors
Berezin, Alexander A.
2004-03-01
In partially compensated semiconductor (PCS) Fermi level is pinned to donor sub-band. Due to positional randomness and almost isoenergetic hoppings, donor-spanned electronic subsystem in PCS forms fluid-like highly mobile collective state. This makes PCS playground for pattern formation, self-organization, complexity emergence, electronic neural networks, and perhaps even for origins of life, bioevolution and consciousness. Through effects of impact and/or Auger ionization of donor sites, whole PCS may collapse (spinodal decomposition) into microblocks potentially capable of replication and protobiological activity (DNA analogue). Electronic screening effects may act in RNA fashion by introducing additional length scale(s) to system. Spontaneous quantum computing on charged/neutral sites becomes potential generator of informationally loaded microstructures akin to "Carl Sagan Effect" (hidden messages in Pi in his "Contact") or informational self-organization of "Library of Babel" of J.L. Borges. Even general relativity effects at Planck scale (R.Penrose) may affect the dynamics through (e.g.) isotopic variations of atomic mass and local density (A.A.Berezin, 1992). Thus, PCS can serve as toy model (experimental and computational) at interface of physics and life sciences.
Research of G3-PLC net self-organization processes in the NS-3 modeling framework
Pospelova, Irina; Chebotayev, Pavel; Klimenko, Aleksey; Myakochin, Yuri; Polyakov, Igor; Shelupanov, Alexander; Zykov, Dmitriy
2017-11-01
When modern infocommunication networks are designed, the combination of several data transfer channels is widely used. It is necessary for the purposes of improvement in quality and robustness of communication. Communication systems based on more than one data transfer channel are named heterogeneous communication systems. For the design of a heterogeneous network, the most optimal solution is the use of mesh technology. Mesh technology ensures message delivery to the destination under conditions of unpredictable interference environment situation in each of two channels. Therewith, one of the high-priority problems is the choice of a routing protocol when the mesh networks are designed. An important design stage for any computer network is modeling. Modeling allows us to design a few different variants of design solutions and also to compute all necessary functional specifications for each of these solutions. As a result, it allows us to reduce costs for the physical realization of a network. In this article the research of dynamic routing in the NS3 simulation modeling framework is presented. The article contains an evaluation of simulation modeling applicability in solving the problem of heterogeneous networks design. Results of modeling may be afterwards used for physical realization of this kind of networks.
Gaffney, E. A.
2013-10-01
© The authors 2013. Turing morphogen models have been extensively explored in the context of large-scale self-organization in multicellular biological systems. However, reconciling the detailed biology of morphogen dynamics, while accounting for time delays associated with gene expression, reveals aberrant behaviours that are not consistent with early developmental self-organization, especially the requirement for exquisite temporal control. Attempts to reconcile the interpretation of Turing\\'s ideas with an increasing understanding of the mechanisms driving zebrafish pigmentation suggests that one should reconsider Turing\\'s model in terms of pigment cells rather than morphogens (Nakamasu et al., 2009, PNAS, 106, 8429-8434; Yamaguchi et al., 2007, PNAS, 104, 4790-4793). Here the dynamics of pigment cells is subject to response delays implicit in the cell cycle and apoptosis. Hence we explore simulations of fish skin patterning, focussing on the dynamical influence of gene expression delays in morphogen-based Turing models and response delays for cell-based Turing models. We find that reconciling the mechanisms driving the behaviour of Turing systems with observations of fish skin patterning remains a fundamental challenge.
Gaffney, E A; Lee, S Seirin
2015-03-01
Turing morphogen models have been extensively explored in the context of large-scale self-organization in multicellular biological systems. However, reconciling the detailed biology of morphogen dynamics, while accounting for time delays associated with gene expression, reveals aberrant behaviours that are not consistent with early developmental self-organization, especially the requirement for exquisite temporal control. Attempts to reconcile the interpretation of Turing's ideas with an increasing understanding of the mechanisms driving zebrafish pigmentation suggests that one should reconsider Turing's model in terms of pigment cells rather than morphogens (Nakamasu et al., 2009, PNAS, 106: , 8429-8434; Yamaguchi et al., 2007, PNAS, 104: , 4790-4793). Here the dynamics of pigment cells is subject to response delays implicit in the cell cycle and apoptosis. Hence we explore simulations of fish skin patterning, focussing on the dynamical influence of gene expression delays in morphogen-based Turing models and response delays for cell-based Turing models. We find that reconciling the mechanisms driving the behaviour of Turing systems with observations of fish skin patterning remains a fundamental challenge. © The Authors 2013. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.
Farhang, Nastaran; Safari, Hossein; Wheatland, Michael S.
2018-05-01
Solar flares are an abrupt release of magnetic energy in the Sun’s atmosphere due to reconnection of the coronal magnetic field. This occurs in response to turbulent flows at the photosphere that twist the coronal field. Similar to earthquakes, solar flares represent the behavior of a complex system, and expectedly their energy distribution follows a power law. We present a statistical model based on the principle of minimum energy in a coronal loop undergoing magnetic reconnection, which is described as an avalanche process. We show that the distribution of peaks for the flaring events in this self-organized critical system is scale-free. The obtained power-law index of 1.84 ± 0.02 for the peaks is in good agreement with satellite observations of soft X-ray flares. The principle of minimum energy can be applied for general avalanche models to describe many other phenomena.
Self-Organized Criticality in a Simple Neuron Model Based on Scale-Free Networks
International Nuclear Information System (INIS)
Lin Min; Wang Gang; Chen Tianlun
2006-01-01
A simple model for a set of interacting idealized neurons in scale-free networks is introduced. The basic elements of the model are endowed with the main features of a neuron function. We find that our model displays power-law behavior of avalanche sizes and generates long-range temporal correlation. More importantly, we find different dynamical behavior for nodes with different connectivity in the scale-free networks.
Murata, Satoshi
2012-01-01
It is man’s ongoing hope that a machine could somehow adapt to its environment by reorganizing itself. This is what the notion of self-organizing robots is based on. The theme of this book is to examine the feasibility of creating such robots within the limitations of current mechanical engineering. The topics comprise the following aspects of such a pursuit: the philosophy of design of self-organizing mechanical systems; self-organization in biological systems; the history of self-organizing mechanical systems; a case study of a self-assembling/self-repairing system as an autonomous distributed system; a self-organizing robot that can create its own shape and robotic motion; implementation and instrumentation of self-organizing robots; and the future of self-organizing robots. All topics are illustrated with many up-to-date examples, including those from the authors’ own work. The book does not require advanced knowledge of mathematics to be understood, and will be of great benefit to students in the rob...
Self-organization of critical behavior in controlled general queueing models
International Nuclear Information System (INIS)
Blanchard, Ph.; Hongler, M.-O.
2004-01-01
We consider general queueing models of the (G/G/1) type with service times controlled by the busy period. For feedback control mechanisms driving the system to very high traffic load, it is shown the busy period probability density exhibits a generic -((3)/(2)) power law which is a typical mean field behavior of SOC models
Self-organization of critical behavior in controlled general queueing models
Blanchard, Ph.; Hongler, M.-O.
2004-03-01
We consider general queueing models of the (G/G/1) type with service times controlled by the busy period. For feedback control mechanisms driving the system to very high traffic load, it is shown the busy period probability density exhibits a generic - {3}/{2} power law which is a typical mean field behavior of SOC models.
Self-Organizing Maps for Fast LES Combustion Modeling, Phase I
National Aeronautics and Space Administration — Tremendous advances have been made in the development of large and accurate detailed reaction chemistry models for hydrocarbon fuels. Comparable progress has also...
Modeling Directional Selectivity Using Self-Organizing Delay-Aadaptation Maps
Tversky, Mr. Tal; Miikkulainen, Dr. Risto
2002-01-01
Using a delay adaptation learning rule, we model the activity-dependent development of directionally selective cells in the primary visual cortex. Based on input stimuli, a learning rule shifts delays to create synchronous arrival of spikes at cortical cells. As a result, delays become tuned creating a smooth cortical map of direction selectivity. This result demonstrates how delay adaption can serve as a powerful abstraction for modeling temporal learning in the brain.
Self-Organized Societies: On the Sakoda Model of Social Interactions
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Pablo Medina
2017-01-01
Full Text Available We characterize the behavior and the social structures appearing from a model of general social interaction proposed by Sakoda. The model consists of two interacting populations in a two-dimensional periodic lattice with empty sites. It contemplates a set of simple rules that combine attitudes, ranges of interactions, and movement decisions. We analyze the evolution of the 45 different interaction rules via a Potts-like energy function which drives the system irreversibly to an equilibrium or a steady state. We discuss the robustness of the social structures, dynamical behaviors, and the existence of spatial long range order in terms of the social interactions and the equilibrium energy.
Self-organized aerial displays of thousands of starlings : a model
Hildenbrandt, H.; Carere, C.; Hemelrijk, C. K.
2010-01-01
Through combining theoretical models and empirical data, complexity science has increased our understanding of social behavior of animals, in particular of social insects, primates, and fish. What are missing are studies of collective behavior of huge swarms of birds. Recently detailed empirical
Evolution of self-organized division of labor in a response threshold model
Duarte, Ana; Pen, Ido; Keller, Laurent; Weissing, Franz J.
Division of labor in social insects is determinant to their ecological success. Recent models emphasize that division of labor is an emergent property of the interactions among nestmates obeying to simple behavioral rules. However, the role of evolution in shaping these rules has been largely
Self-organizing sensing and actuation for automatic control
Cheng, George Shu-Xing
2017-07-04
A Self-Organizing Process Control Architecture is introduced with a Sensing Layer, Control Layer, Actuation Layer, Process Layer, as well as Self-Organizing Sensors (SOS) and Self-Organizing Actuators (SOA). A Self-Organizing Sensor for a process variable with one or multiple input variables is disclosed. An artificial neural network (ANN) based dynamic modeling mechanism as part of the Self-Organizing Sensor is described. As a case example, a Self-Organizing Soft-Sensor for CFB Boiler Bed Height is presented. Also provided is a method to develop a Self-Organizing Sensor.
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Antonov N.V.
2016-01-01
Full Text Available We study effects of the random fluid motion on a system in a self-organized critical state. The latter is described by the continuous stochastic model proposed by Hwa and Kardar [Phys. Rev. Lett. 62: 1813 (1989]. The advecting velocity field is Gaussian, not correlated in time, with the pair correlation function of the form ∝ δ(t − t′/k⊥d-1+ξ , where k⊥ = |k⊥| and k⊥ is the component of the wave vector, perpendicular to a certain preferred direction – the d-dimensional generalization of the ensemble introduced by Avellaneda and Majda [Commun. Math. Phys. 131: 381 (1990]. Using the field theoretic renormalization group we show that, depending on the relation between the exponent ξ and the spatial dimension d, the system reveals different types of large-scale, long-time scaling behaviour, associated with the three possible fixed points of the renormalization group equations. They correspond to ordinary diffusion, to passively advected scalar field (the nonlinearity of the Hwa–Kardar model is irrelevant and to the “pure” Hwa–Kardar model (the advection is irrelevant. For the special case ξ = 2(4 − d/3 both the nonlinearity and the advection are important. The corresponding critical exponents are found exactly for all these cases.
Friedel, Michael J.
2011-01-01
Few studies attempt to model the range of possible post-fire hydrologic and geomorphic hazards because of the sparseness of data and the coupled, nonlinear, spatial, and temporal relationships among landscape variables. In this study, a type of unsupervised artificial neural network, called a self-organized map (SOM), is trained using data from 540 burned basins in the western United States. The sparsely populated data set includes variables from independent numerical landscape categories (climate, land surface form, geologic texture, and post-fire condition), independent landscape classes (bedrock geology and state), and dependent initiation processes (runoff, landslide, and runoff and landslide combination) and responses (debris flows, floods, and no events). Pattern analysis of the SOM-based component planes is used to identify and interpret relations among the variables. Application of the Davies-Bouldin criteria following k-means clustering of the SOM neurons identified eight conceptual regional models for focusing future research and empirical model development. A split-sample validation on 60 independent basins (not included in the training) indicates that simultaneous predictions of initiation process and response types are at least 78% accurate. As climate shifts from wet to dry conditions, forecasts across the burned landscape reveal a decreasing trend in the total number of debris flow, flood, and runoff events with considerable variability among individual basins. These findings suggest the SOM may be useful in forecasting real-time post-fire hazards, and long-term post-recovery processes and effects of climate change scenarios.
Instantons in Self-Organizing Logic Gates
Bearden, Sean R. B.; Manukian, Haik; Traversa, Fabio L.; Di Ventra, Massimiliano
2018-03-01
Self-organizing logic is a recently suggested framework that allows the solution of Boolean truth tables "in reverse"; i.e., it is able to satisfy the logical proposition of gates regardless to which terminal(s) the truth value is assigned ("terminal-agnostic logic"). It can be realized if time nonlocality (memory) is present. A practical realization of self-organizing logic gates (SOLGs) can be done by combining circuit elements with and without memory. By employing one such realization, we show, numerically, that SOLGs exploit elementary instantons to reach equilibrium points. Instantons are classical trajectories of the nonlinear equations of motion describing SOLGs and connect topologically distinct critical points in the phase space. By linear analysis at those points, we show that these instantons connect the initial critical point of the dynamics, with at least one unstable direction, directly to the final fixed point. We also show that the memory content of these gates affects only the relaxation time to reach the logically consistent solution. Finally, we demonstrate, by solving the corresponding stochastic differential equations, that, since instantons connect critical points, noise and perturbations may change the instanton trajectory in the phase space but not the initial and final critical points. Therefore, even for extremely large noise levels, the gates self-organize to the correct solution. Our work provides a physical understanding of, and can serve as an inspiration for, models of bidirectional logic gates that are emerging as important tools in physics-inspired, unconventional computing.
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Hossein Ghayoumi Zadeh
2018-04-01
Conclusion: These days, the cases of diabetes with hypertension are constantly increasing, and one of the main adverse effects of this disease is related to eyes. In this respect, the diagnosis of retinopathy, which is the same as identification of exudates, microanurysm and bleeding, is of particular importance. The results show that the proposed model is able to detect lesions in diabetic retinopathy images and classify them with an acceptable accuracy. In addition, the results suggest that this method has an acceptable performance compared to other methods.
Layer features of the lattice gas model for self-organized criticality
International Nuclear Information System (INIS)
Pesheva, N.C.; Brankov, J.G.
1995-06-01
A layer-by-layer description of the asymmetric lattice gas model for 1/f-noise suggested by Jensen [Phys. Rev. Lett. 64, 3103 (1990)] is presented. The power spectra of the lattice layers in the direction perpendicular to the particle flux is studied in order to understand how the white noise at the input boundary evolves, on the average, into 1/f-noise for the system. The effects of high boundary drive and uniform driving force on the power spectrum of the total number of diffusing particles are considered. In the case of nearest-neighbor particle interactions, high statistics simulation results show that the power spectra of single lattice layers are characterized by different β x exponents such that β x → 1.9 as one approaches the outer boundary. (author). 10 refs, 6 figs
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Tom Froese
Full Text Available Teotihuacan was the first urban civilization of Mesoamerica and one of the largest of the ancient world. Following a tradition in archaeology to equate social complexity with centralized hierarchy, it is widely believed that the city's origin and growth was controlled by a lineage of powerful individuals. However, much data is indicative of a government of co-rulers, and artistic traditions expressed an egalitarian ideology. Yet this alternative keeps being marginalized because the problems of collective action make it difficult to conceive how such a coalition could have functioned in principle. We therefore devised a mathematical model of the city's hypothetical network of representatives as a formal proof of concept that widespread cooperation was realizable in a fully distributed manner. In the model, decisions become self-organized into globally optimal configurations even though local representatives behave and modify their relations in a rational and selfish manner. This self-optimization crucially depends on occasional communal interruptions of normal activity, and it is impeded when sections of the network are too independent. We relate these insights to theories about community-wide rituals at Teotihuacan and the city's eventual disintegration.
Froese, Tom; Gershenson, Carlos; Manzanilla, Linda R
2014-01-01
Teotihuacan was the first urban civilization of Mesoamerica and one of the largest of the ancient world. Following a tradition in archaeology to equate social complexity with centralized hierarchy, it is widely believed that the city's origin and growth was controlled by a lineage of powerful individuals. However, much data is indicative of a government of co-rulers, and artistic traditions expressed an egalitarian ideology. Yet this alternative keeps being marginalized because the problems of collective action make it difficult to conceive how such a coalition could have functioned in principle. We therefore devised a mathematical model of the city's hypothetical network of representatives as a formal proof of concept that widespread cooperation was realizable in a fully distributed manner. In the model, decisions become self-organized into globally optimal configurations even though local representatives behave and modify their relations in a rational and selfish manner. This self-optimization crucially depends on occasional communal interruptions of normal activity, and it is impeded when sections of the network are too independent. We relate these insights to theories about community-wide rituals at Teotihuacan and the city's eventual disintegration.
Liang, Wei; Yu, Xuchao; Zhang, Laibin; Lu, Wenqing
2018-05-01
In oil transmission station, the operating condition (OC) of an oil pump unit sometimes switches accordingly, which will lead to changes in operating parameters. If not taking the switching of OCs into consideration while performing a state evaluation on the pump unit, the accuracy of evaluation would be largely influenced. Hence, in this paper, a self-organization Comprehensive Real-Time State Evaluation Model (self-organization CRTSEM) is proposed based on OC classification and recognition. However, the underlying model CRTSEM is built through incorporating the advantages of Gaussian Mixture Model (GMM) and Fuzzy Comprehensive Evaluation Model (FCEM) first. That is to say, independent state models are established for every state characteristic parameter according to their distribution types (i.e. the Gaussian distribution and logistic regression distribution). Meanwhile, Analytic Hierarchy Process (AHP) is utilized to calculate the weights of state characteristic parameters. Then, the OC classification is determined by the types of oil delivery tasks, and CRTSEMs of different standard OCs are built to constitute the CRTSEM matrix. On the other side, the OC recognition is realized by a self-organization model that is established on the basis of Back Propagation (BP) model. After the self-organization CRTSEM is derived through integration, real-time monitoring data can be inputted for OC recognition. At the end, the current state of the pump unit can be evaluated by using the right CRTSEM. The case study manifests that the proposed self-organization CRTSEM can provide reasonable and accurate state evaluation results for the pump unit. Besides, the assumption that the switching of OCs will influence the results of state evaluation is also verified.
International Nuclear Information System (INIS)
Boyer, D; Lopez-Corona, O
2009-01-01
We introduce a model of travelling agents (e.g., frugivorous animals) who feed on randomly located vegetation patches and disperse their seeds, thus modifying the spatial distribution of the resources in the long term. It is assumed that the survival probability of a seed increases with the distance to its parent patch and decreases with the size of the colonized patch. In turn, the foraging agents use a deterministic strategy with memory that makes them visit the largest possible patches accessible within minimal travelling distances. The combination of these interactions produce complex spatio-temporal patterns. If the patches have a small initial size, the vegetation total mass (biomass) increases with time and reaches a maximum corresponding to a self-organized critical state with power-law-distributed patch sizes and Levy-like movement patterns for the foragers. However, this state collapses as the biomass sharply decreases to reach a noisy stationary regime characterized by corrections to scaling. In systems with low plant competition, the efficiency of the foraging rules leads to the formation of heterogeneous vegetation patterns with 1/f α frequency spectra, and contributes, rather counter-intuitively, to lower the biomass levels.
Kaplan, Bernhard A.; Lansner, Anders
2014-01-01
Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian–Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures. PMID
Leader Election and Shape Formation with Self-Organizing Programmable Matter
Daymude, Joshua J.; Derakhshandeh, Zahra; Gmyr, Robert; Strothmann, Thim; Bazzi, Rida; Richa, Andréa W.; Scheideler, Christian
2015-01-01
We consider programmable matter consisting of simple computational elements, called particles, that can establish and release bonds and can actively move in a self-organized way, and we investigate the feasibility of solving fundamental problems relevant for programmable matter. As a suitable model for such self-organizing particle systems, we will use a generalization of the geometric amoebot model first proposed in SPAA 2014. Based on the geometric model, we present efficient local-control ...
Wheeler, K. I.; Levia, D. F.; Hudson, J. E.
2017-09-01
In autumn, the dissolved organic matter (DOM) contribution of leaf litter leachate to streams in forested watersheds changes as trees undergo resorption, senescence, and leaf abscission. Despite its biogeochemical importance, little work has investigated how leaf litter leachate DOM changes throughout autumn and how any changes might differ interspecifically and intraspecifically. Since climate change is expected to cause vegetation migration, it is necessary to learn how changes in forest composition could affect DOM inputs via leaf litter leachate. We examined changes in leaf litter leachate fluorescent DOM (FDOM) from American beech (Fagus grandifolia Ehrh.) leaves in Maryland, Rhode Island, Vermont, and North Carolina and from yellow poplar (Liriodendron tulipifera L.) leaves from Maryland. FDOM in leachate samples was characterized by excitation-emission matrices (EEMs). A six-component parallel factor analysis (PARAFAC) model was created to identify components that accounted for the majority of the variation in the data set. Self-organizing maps (SOM) compared the PARAFAC component proportions of leachate samples. Phenophase and species exerted much stronger influence on the determination of a sample's SOM placement than geographic origin. As expected, FDOM from all trees transitioned from more protein-like components to more humic-like components with senescence. Percent greenness of sampled leaves and the proportion of tyrosine-like component 1 were found to be significantly different between the two genetic beech clusters, suggesting differences in photosynthesis and resorption. Our results highlight the need to account for interspecific and intraspecific variations in leaf litter leachate FDOM throughout autumn when examining the influence of allochthonous inputs to streams.
DEFF Research Database (Denmark)
Marchetti, Nicola; Prasad, Neeli R.; Johansson, Johan
2010-01-01
In this paper, a general overview of Self-Organizing Networks (SON), and the rationale and state-of-the-art of wireless SON are first presented. The technical and business requirements are then briefly treated, and the research challenges within the field of SON are highlighted. Thereafter, the r...
Growing hierarchical probabilistic self-organizing graphs.
López-Rubio, Ezequiel; Palomo, Esteban José
2011-07-01
Since the introduction of the growing hierarchical self-organizing map, much work has been done on self-organizing neural models with a dynamic structure. These models allow adjusting the layers of the model to the features of the input dataset. Here we propose a new self-organizing model which is based on a probabilistic mixture of multivariate Gaussian components. The learning rule is derived from the stochastic approximation framework, and a probabilistic criterion is used to control the growth of the model. Moreover, the model is able to adapt to the topology of each layer, so that a hierarchy of dynamic graphs is built. This overcomes the limitations of the self-organizing maps with a fixed topology, and gives rise to a faithful visualization method for high-dimensional data.
Directory of Open Access Journals (Sweden)
Kwang Baek Kim
2015-01-01
Full Text Available Accurate measures of liver fat content are essential for investigating hepatic steatosis. For a noninvasive inexpensive ultrasonographic analysis, it is necessary to validate the quantitative assessment of liver fat content so that fully automated reliable computer-aided software can assist medical practitioners without any operator subjectivity. In this study, we attempt to quantify the hepatorenal index difference between the liver and the kidney with respect to the multiple severity status of hepatic steatosis. In order to do this, a series of carefully designed image processing techniques, including fuzzy stretching and edge tracking, are applied to extract regions of interest. Then, an unsupervised neural learning algorithm, the self-organizing map, is designed to establish characteristic clusters from the image, and the distribution of the hepatorenal index values with respect to the different levels of the fatty liver status is experimentally verified to estimate the differences in the distribution of the hepatorenal index. Such findings will be useful in building reliable computer-aided diagnostic software if combined with a good set of other characteristic feature sets and powerful machine learning classifiers in the future.
Directory of Open Access Journals (Sweden)
Dimitrios V Vavoulis
Full Text Available Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm, often in combination with a local search method (such as gradient descent in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a
Self-organizing representations
Energy Technology Data Exchange (ETDEWEB)
Kohonen, T.
1983-01-01
A property which is commonplace in the brain but which has always been ignored in learning machines is the spatial order of the processing units. This order is clearly highly significant and in nature it develops gradually during the lifetime of the organism. It then serves as the basis for perceptual and cognitive processes, and memory, too. The spatial order in biological organisms is often believed to be genetically determined. It is therefore intriguing to learn that a meaningful and optimal spatial order is formed in an extremely simple self-organizing process whereby certain feature maps are formed automatically. 8 references.
Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective
Directory of Open Access Journals (Sweden)
Zhicong Zhang
2018-01-01
Full Text Available We study an online multisource multisink queueing network control problem characterized with self-organizing network structure and self-organizing job routing. We decompose the self-organizing queueing network control problem into a series of interrelated Markov Decision Processes and construct a control decision model for them based on the coupled reinforcement learning (RL architecture. To maximize the mean time averaged weighted throughput of the jobs through the network, we propose a reinforcement learning algorithm with time averaged reward to deal with the control decision model and obtain a control policy integrating the jobs routing selection strategy and the jobs sequencing strategy. Computational experiments verify the learning ability and the effectiveness of the proposed reinforcement learning algorithm applied in the investigated self-organizing network control problem.
Self-organized modularization in evolutionary algorithms.
Dauscher, Peter; Uthmann, Thomas
2005-01-01
The principle of modularization has proven to be extremely successful in the field of technical applications and particularly for Software Engineering purposes. The question to be answered within the present article is whether mechanisms can also be identified within the framework of Evolutionary Computation that cause a modularization of solutions. We will concentrate on processes, where modularization results only from the typical evolutionary operators, i.e. selection and variation by recombination and mutation (and not, e.g., from special modularization operators). This is what we call Self-Organized Modularization. Based on a combination of two formalizations by Radcliffe and Altenberg, some quantitative measures of modularity are introduced. Particularly, we distinguish Built-in Modularity as an inherent property of a genotype and Effective Modularity, which depends on the rest of the population. These measures can easily be applied to a wide range of present Evolutionary Computation models. It will be shown, both theoretically and by simulation, that under certain conditions, Effective Modularity (as defined within this paper) can be a selection factor. This causes Self-Organized Modularization to take place. The experimental observations emphasize the importance of Effective Modularity in comparison with Built-in Modularity. Although the experimental results have been obtained using a minimalist toy model, they can lead to a number of consequences for existing models as well as for future approaches. Furthermore, the results suggest a complex self-amplification of highly modular equivalence classes in the case of respected relations. Since the well-known Holland schemata are just the equivalence classes of respected relations in most Simple Genetic Algorithms, this observation emphasizes the role of schemata as Building Blocks (in comparison with arbitrary subsets of the search space).
Directory of Open Access Journals (Sweden)
Feifan Zhang
2017-06-01
Full Text Available The formation of self-organized patterns in predator-prey models has been a very hot topic recently. The dynamics of these models, bifurcations and pattern formations are so complex that studies are urgently needed. In this research, we transformed a continuous predator-prey model with Lesie-Gower functional response into a discrete model. Fixed points and stability analyses were studied. Around the stable fixed point, bifurcation analyses including: flip, Neimark-Sacker and Turing bifurcation were done and bifurcation conditions were obtained. Based on these bifurcation conditions, parameters values were selected to carry out numerical simulations on pattern formation. The simulation results showed that Neimark-Sacker bifurcation induced spots, spirals and transitional patterns from spots to spirals. Turing bifurcation induced labyrinth patterns and spirals coupled with mosaic patterns, while flip bifurcation induced many irregular complex patterns. Compared with former studies on continuous predator-prey model with Lesie-Gower functional response, our research on the discrete model demonstrated more complex dynamics and varieties of self-organized patterns.
Mei, Zhixiong; Wu, Hao; Li, Shiyun
2018-06-01
The Conversion of Land Use and its Effects at Small regional extent (CLUE-S), which is a widely used model for land-use simulation, utilizes logistic regression to estimate the relationships between land use and its drivers, and thus, predict land-use change probabilities. However, logistic regression disregards possible spatial autocorrelation and self-organization in land-use data. Autologistic regression can depict spatial autocorrelation but cannot address self-organization, while logistic regression by considering only self-organization (NElogistic regression) fails to capture spatial autocorrelation. Therefore, this study developed a regression (NE-autologistic regression) method, which incorporated both spatial autocorrelation and self-organization, to improve CLUE-S. The Zengcheng District of Guangzhou, China was selected as the study area. The land-use data of 2001, 2005, and 2009, as well as 10 typical driving factors, were used to validate the proposed regression method and the improved CLUE-S model. Then, three future land-use scenarios in 2020: the natural growth scenario, ecological protection scenario, and economic development scenario, were simulated using the improved model. Validation results showed that NE-autologistic regression performed better than logistic regression, autologistic regression, and NE-logistic regression in predicting land-use change probabilities. The spatial allocation accuracy and kappa values of NE-autologistic-CLUE-S were higher than those of logistic-CLUE-S, autologistic-CLUE-S, and NE-logistic-CLUE-S for the simulations of two periods, 2001-2009 and 2005-2009, which proved that the improved CLUE-S model achieved the best simulation and was thereby effective to a certain extent. The scenario simulation results indicated that under all three scenarios, traffic land and residential/industrial land would increase, whereas arable land and unused land would decrease during 2009-2020. Apparent differences also existed in the
Directory of Open Access Journals (Sweden)
A. V. Shapovalov
2018-02-01
Full Text Available This review deals with ideas and approaches to nonlinear phenomena, based on different branches of physics and related to biological systems, that focus on how small impacts can significantly change the state of the system at large spatial scales. This problem is very extensive, and it cannot be fully resolved in this paper. Instead, some selected physical effects are briefly reviewed. We consider sine-Gordon solitons and nonlinear Schrodinger solitons in some models of DNA as examples of self-organization at the molecular level, as well as examine features of their formation and dynamics under the influence of external influences. In addition, the formation of patterns in the generalized Fisher–KPP model is viewed as a simple example of self-organization in a system with nonlocal interaction at the cellular level. Symmetries of model equations are employed to analyze the considered nonlinear phenomena. In this context the possible relations between phenomena considered and released activity effect, which is assessed differently in the literature, are discussed.
Self-organization phenomena in plasma physics
International Nuclear Information System (INIS)
Sanduloviciu, M.; Popescu, S.
2001-01-01
The self-assembling in nature and laboratory of structures in systems away from thermodynamic equilibrium is one of the problems that mostly fascinates the scientists working in all branches of science. In this context a substantial progress has been obtained by investigating the appearance of spatial and spatiotemporal patterns in plasma. These experiments revealed the presence of a scenario of self-organization able to suggest an answer to the central problem of the 'Science of Complexity', why matter transits spontaneously from a disordered into an ordered state? Based on this scenario of self-organization we present arguments proving the possibility to explain the challenging problems of nonequilibrium physics in general. These problems refer to: (i) genuine origin of phase transitions observed in gaseous conductors and semiconductors; (ii) the elucidation of the role played by self-organization in the simulation of oscillations; (iii) the physical basis of anomalous transport of matter and energy with special reference to the possibilities of improving the economical performance of fusion devices; (iv) the possibility to use self-confined gaseous space charged configurations as an alternative to the magnetically confined plasma used at present in fusion devices. In other branches of sciences, as for instance in Biology, the self-organization scenario reveals a new insight into a mechanism able to explain the appearance of the simplest possible space charge configuration able to evolve, under suitable conditions, into prebiotic structures. Referring to phenomena observed in nature, the same self-organization scenario suggests plausible answers to the appearance of ball lightening but also to the origin of the flickering phenomena observed in the light emission of the Sun and stars. For theory the described self-organization scenario offers a new physical basis for many problems of nonlinear science not solved yet and also a new model for the so-called 'self
Directory of Open Access Journals (Sweden)
Praveen K Pilly
Full Text Available Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous
Baoying Wang
2013-01-01
In this study, the characteristics of supply chain system are analyzed based on the Self-organization theory from the angle of view of supply chain system. The mathematical models when the system fulfilling social responsibility including self-organization evolution model and self-organization function model are developed to discuss the formation and function of self-organization in supply chain system and coordination. Some basic conditions and tactics about self-organization establishment a...
Computational Modeling | Bioenergy | NREL
cell walls and are the source of biofuels and biomaterials. Our modeling investigates their properties . Quantum Mechanical Models NREL studies chemical and electronic properties and processes to reduce barriers Computational Modeling Computational Modeling NREL uses computational modeling to increase the
Self-organized criticality paradigm
International Nuclear Information System (INIS)
Duran, I.; Stoeckel, J.; Hron, M.; Horacek, J.; Jakubka, K.; Kryska, L.
2000-01-01
According to the paradigm of the Self-Organized Criticality (SOC), the anomalous transport in tokamaks is caused by fast transient processes - avalanches. One of the manifestations of these phenomena should be 1/f decay of electrostatic fluctuations power spectra in a certain frequency range. In this paper, the frequency spectra of floating potential, density and fluctuation-induced flux, measured by poloidal and radial arrays of Langmuir probes on the CASTOR tokamak, are presented. The floating potential and the fluctuation-induced flux decay from 30 kHz up to 100 kHz as f -1 . The plasma density decays as f -1 in a more narrow band, 20 to 40 kHz. The possible limitation of SOC behavior for frequencies higher than 100 kHz due to intermittency is stressed. For this reason the Probability Distribution Functions (PDFs) of floating potential fluctuations were computed at different time scales using wavelet transform. A clear departure of the computed PDFs from Gaussianity, which is a classical signature of intermittency, is observed at time scales under 10 μs (100 kHz). (author)
Self-organized critical pinball machine
DEFF Research Database (Denmark)
Flyvbjerg, H.
2004-01-01
The nature of self-organized criticality (SOC) is pin-pointed with a simple mechanical model: a pinball machine. Its phase space is fully parameterized by two integer variables, one describing the state of an on-going game, the other describing the state of the machine. This is the simplest...
Self-organized Segregation on the Grid
Omidvar, Hamed; Franceschetti, Massimo
2018-02-01
We consider an agent-based model with exponentially distributed waiting times in which two types of agents interact locally over a graph, and based on this interaction and on the value of a common intolerance threshold τ , decide whether to change their types. This is equivalent to a zero-temperature ising model with Glauber dynamics, an asynchronous cellular automaton with extended Moore neighborhoods, or a Schelling model of self-organized segregation in an open system, and has applications in the analysis of social and biological networks, and spin glasses systems. Some rigorous results were recently obtained in the theoretical computer science literature, and this work provides several extensions. We enlarge the intolerance interval leading to the expected formation of large segregated regions of agents of a single type from the known size ɛ >0 to size ≈ 0.134. Namely, we show that for 0.433sites can be observed within any sufficiently large region of the occupied percolation cluster. The exponential bounds that we provide also imply that complete segregation, where agents of a single type cover the whole grid, does not occur with high probability for p=1/2 and the range of intolerance considered.
International Nuclear Information System (INIS)
Helmstetter, Agnes; Hergarten, Stefan; Sornette, Didier
2004-01-01
Following Hergarten and Neugebauer [Phys. Rev. Lett. 88, 238501, 2002] who discovered aftershocks and foreshocks in the Olami-Feder-Christensen (OFC) discrete block-spring earthquake model, we investigate to what degree the simple toppling mechanism of this model is sufficient to account for the clustering of real seismicity in time and space. We find that synthetic catalogs generated by the OFC model share many properties of real seismicity at a qualitative level: Omori's law (aftershocks) and inverse Omori's law (foreshocks), increase of the number of aftershocks and of the aftershock zone size with the mainshock magnitude. There are, however, significant quantitative differences. The number of aftershocks per mainshock in the OFC model is smaller than in real seismicity, especially for large mainshocks. We find that foreshocks in the OFC catalogs can be in large part described by a simple model of triggered seismicity, such as the epidemic-type aftershock sequence (ETAS) model. But the properties of foreshocks in the OFC model depend on the mainshock magnitude, in qualitative agreement with the critical earthquake model and in disagreement with real seismicity and with the ETAS model
Cho, Jeongho; Principe, Jose C.; Erdogmus, Deniz; Motter, Mark A.
2005-01-01
The next generation of aircraft will have dynamics that vary considerably over the operating regime. A single controller will have difficulty to meet the design specifications. In this paper, a SOM-based local linear modeling scheme of an unmanned aerial vehicle (UAV) is developed to design a set of inverse controllers. The SOM selects the operating regime depending only on the embedded output space information and avoids normalization of the input data. Each local linear model is associated with a linear controller, which is easy to design. Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. The proposed multiple modeling and control strategy has been successfully tested in a simulator that models the LoFLYTE UAV.
Self-Organized Criticality and Mass Extinction in Evolutionary Algorithms
DEFF Research Database (Denmark)
Krink, Thiemo; Thomsen, Rene
2001-01-01
The gaps in the fossil record gave rise to the hypothesis that evolution proceeded in long periods of stasis, which alternated with occasional, rapid changes that yielded evolutionary progress. One mechanism that could cause these punctuated bursts is the re-colonbation of changing and deserted...... at a critical state between chaos and order, known as self-organized criticality (SOC). Based on this background, we used SOC to control the size of spatial extinction zones in a diffusion model. The SOC selection process was easy to implement and implied only negligible computational costs. Our results show...
Origin and evolution of the self-organizing cytoskeleton in the network of eukaryotic organelles.
Jékely, Gáspár
2014-09-02
The eukaryotic cytoskeleton evolved from prokaryotic cytomotive filaments. Prokaryotic filament systems show bewildering structural and dynamic complexity and, in many aspects, prefigure the self-organizing properties of the eukaryotic cytoskeleton. Here, the dynamic properties of the prokaryotic and eukaryotic cytoskeleton are compared, and how these relate to function and evolution of organellar networks is discussed. The evolution of new aspects of filament dynamics in eukaryotes, including severing and branching, and the advent of molecular motors converted the eukaryotic cytoskeleton into a self-organizing "active gel," the dynamics of which can only be described with computational models. Advances in modeling and comparative genomics hold promise of a better understanding of the evolution of the self-organizing cytoskeleton in early eukaryotes, and its role in the evolution of novel eukaryotic functions, such as amoeboid motility, mitosis, and ciliary swimming. Copyright © 2014 Cold Spring Harbor Laboratory Press; all rights reserved.
International Nuclear Information System (INIS)
Mitani, Akira; Tsubota, Makoto
2006-01-01
The energy spectrum of decaying quantum turbulence at T=0 obeys Kolmogorov's law. In addition to this, recent studies revealed that the vortex-length distribution (VLD), meaning the size distribution of the vortices, in decaying Kolmogorov quantum turbulence also obeys a power law. This power-law VLD suggests that the decaying turbulence has scale-free structure in real space. Unfortunately, however, there has been no practical study that answers the following important question: why can quantum turbulence acquire a scale-free VLD? We propose here a model to study the origin of the power law of the VLD from a generic point of view. The nature of quantized vortices allows one to describe the decay of quantum turbulence with a simple model that is similar to the Barabasi-Albert model, which explains the scale-invariance structure of large networks. We show here that such a model can reproduce the power law of the VLD well
Self-organizing networks for extracting jet features
International Nuclear Information System (INIS)
Loennblad, L.; Peterson, C.; Pi, H.; Roegnvaldsson, T.
1991-01-01
Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model discrimination and separation of b.c. and light quarks. (orig.)
Parallel computing in enterprise modeling.
Energy Technology Data Exchange (ETDEWEB)
Goldsby, Michael E.; Armstrong, Robert C.; Shneider, Max S.; Vanderveen, Keith; Ray, Jaideep; Heath, Zach; Allan, Benjamin A.
2008-08-01
This report presents the results of our efforts to apply high-performance computing to entity-based simulations with a multi-use plugin for parallel computing. We use the term 'Entity-based simulation' to describe a class of simulation which includes both discrete event simulation and agent based simulation. What simulations of this class share, and what differs from more traditional models, is that the result sought is emergent from a large number of contributing entities. Logistic, economic and social simulations are members of this class where things or people are organized or self-organize to produce a solution. Entity-based problems never have an a priori ergodic principle that will greatly simplify calculations. Because the results of entity-based simulations can only be realized at scale, scalable computing is de rigueur for large problems. Having said that, the absence of a spatial organizing principal makes the decomposition of the problem onto processors problematic. In addition, practitioners in this domain commonly use the Java programming language which presents its own problems in a high-performance setting. The plugin we have developed, called the Parallel Particle Data Model, overcomes both of these obstacles and is now being used by two Sandia frameworks: the Decision Analysis Center, and the Seldon social simulation facility. While the ability to engage U.S.-sized problems is now available to the Decision Analysis Center, this plugin is central to the success of Seldon. Because Seldon relies on computationally intensive cognitive sub-models, this work is necessary to achieve the scale necessary for realistic results. With the recent upheavals in the financial markets, and the inscrutability of terrorist activity, this simulation domain will likely need a capability with ever greater fidelity. High-performance computing will play an important part in enabling that greater fidelity.
Mender, Bedeho M W; Stringer, Simon M
2015-01-01
We propose and examine a model for how perisaccadic visual receptive field dynamics, observed in a range of primate brain areas such as LIP, FEF, SC, V3, V3A, V2, and V1, may develop through a biologically plausible process of unsupervised visually guided learning. These dynamics are associated with remapping, which is the phenomenon where receptive fields anticipate the consequences of saccadic eye movements. We find that a neural network model using a local associative synaptic learning rule, when exposed to visual scenes in conjunction with saccades, can account for a range of associated phenomena. In particular, our model demonstrates predictive and pre-saccadic remapping, responsiveness shifts around the time of saccades, and remapping from multiple directions.
Self-organization through decoupling
Directory of Open Access Journals (Sweden)
Romar Correa
2000-01-01
Full Text Available In one line of research, the transition from Fordism to flexible specialisation is explained by the infeasibility of a mode of regulation that relied on central controls. According to another explanation, which we favour, the disintegration of vertically integrated production is unpredictable. The concept of self-organization is often recommended to model the transition from hierarchical organizational forms to flatter structures. Formally, a conditionally stable nonlinear system of differential equations is examined. In the first thesis, the characteristic roots with positive real parts play the role of ‘order’ parameters which can become unstable modes. The rest of the variables refer to stable modes. The strategy is to show that the stable modes can be expressed in terms of the unstable modes so that the former can be eliminated from the system. On the other hand, we provide a theorem showing that a coupled set of differential equations can become uncoupled and vice versa as an argument in favour of the second thesis. The path of evolution can turn both ways.
Directory of Open Access Journals (Sweden)
Loet Leydesdorff
2010-01-01
Full Text Available Mutual information among three or more dimensions (μ* = –Q has been considered as interaction information. However, Krippendorff [1,2] has shown that this measure cannot be interpreted as a unique property of the interactions and has proposed an alternative measure of interaction information based on iterative approximation of maximum entropies. Q can then be considered as a measure of the difference between interaction information and redundancy generated in a model entertained by an observer. I argue that this provides us with a measure of the imprint of a second-order observing system—a model entertained by the system itself—on the underlying information processing. The second-order system communicates meaning hyper-incursively; an observation instantiates this meaning-processing within the information processing. The net results may add to or reduce the prevailing uncertainty. The model is tested empirically for the case where textual organization can be expected to contain intellectual organization in terms of distributions of title words, author names, and cited references.
Guastello, Stephen J; Craven, Joanna; Zygowicz, Karen M; Bock, Benjamin R
2005-07-01
The process by which an initially leaderless group differentiates into one containing leadership and secondary role structures was examined using the swallowtail catastrophe model and principles of selforganization. The objectives were to identify the control variables in the process of leadership emergence in creative problem solving groups and production groups. In the first of two experiments, groups of university students (total N = 114) played a creative problem solving game. Participants later rated each other on leadership behavior, styles, and variables related to the process of conversation. A performance quality measure was included also. Control parameters in the swallowtail catastrophe model were identified through a combination of factor analysis and nonlinear regression. Leaders displayed a broad spectrum of behaviors in the general categories of Controlling the Conversation and Creativity in their role-play. In the second experiment, groups of university students (total N = 197) engaged in a laboratory work experiment that had a substantial production goal component. The same system of ratings and modeling strategy was used along with a work production measure. Leaders in the production task emerged to the extent that they exhibited control over both the creative and production aspects of the task, they could keep tension low, and the externally imposed production goals were realistic.
Self-organized Learning Environments
DEFF Research Database (Denmark)
Dalsgaard, Christian; Mathiasen, Helle
2007-01-01
system actively. The two groups used the system in their own way to support their specific activities and ways of working. The paper concludes that self-organized learning environments can strengthen the development of students’ academic as well as social qualifications. Further, the paper identifies......The purpose of the paper is to discuss the potentials of using a conference system in support of a project based university course. We use the concept of a self-organized learning environment to describe the shape of the course. In the paper we argue that educational technology, such as conference...... systems, has a potential to support students’ development of self-organized learning environments and facilitate self-governed activities in higher education. The paper is based on an empirical study of two project groups’ use of a conference system. The study showed that the students used the conference...
International Nuclear Information System (INIS)
Freitas Colaco, Daniel; Alexandria, Auzuir R. de; Cortez, Paulo Cesar; Frota, Joao Batista B.; Nunes de Lima, Jose Nunes de; Albuquerque, Victor Hugo C. de
2010-01-01
This work has the objective of developing, analysing and applying a new tool for management the status of break disconnectors in high voltage substations from digital images. This tool uses a non-supervised kind of artificial neural network using the Kohonen learning algorithm, known as a self-organizing maps. In order to develop the proposed tool, C/C++ programming language, provided with easily used interfaces, is used. In order to obtain the results, three environments are considered: one for laboratory simulation and two pilot projects installed in the Fortaleza II/CHESF substation. These pilots are used for 230 kV EV-2000 type and 500 kV semi-pantographic type break disconnector management tests. The results prove the developed system's efficiency, because it is able to detect 100% of open and closed identification situations. However, the neural network utilised for management break disconnectors has become suitable for installation in high voltage substations in order to support the maintenance team in safely handling these disconnectors.
Energy Technology Data Exchange (ETDEWEB)
Freitas Colaco, Daniel, E-mail: colaco@deti.ufc.b [Universidade Federal do Ceara (UFC), Centro de Tecnologia (CT), Departamento de Engenharia de Teleinformatica - DETI, Campus do PICI S/N, Bloco 723, 60455-970 Fortaleza, Ceara (Brazil); Alexandria, Auzuir R. de, E-mail: auzuir@ifce.edu.b [Instituto Federal de Educacao, Ciencia e Tecnologia do Ceara (IFCE), Area da industria, Nucleo de Simulacao Computacional-N5IMCO, Campus Fortaleza, Av. Treze de Maio, 2081, 60040-531 Fortaleza, Ceara (Brazil); Cortez, Paulo Cesar, E-mail: cortez@deti.ufc.b [Universidade Federal do Ceara (UFC), Centro de Tecnologia (CT), Departamento de Engenharia de Teleinformatica - DETI, Campus do PICI S/N, Bloco 723, 60455-970 Fortaleza, Ceara (Brazil); Frota, Joao Batista B., E-mail: jb@ifce.edu.b [Instituto Federal de Educacao, Ciencia e Tecnologia do Ceara (IFCE), Area da industria, Nucleo de Simulacao Computacional-N5IMCO, Campus Fortaleza, Av. Treze de Maio, 2081, 60040-531 Fortaleza, Ceara (Brazil); Nunes de Lima, Jose Nunes de, E-mail: josenl@chesf.gov.b [Companhia Hidro Eletrica do Sao Francisco (CHESF), Rua Delmiro Gouveia, 333, 50761-901 Recife, Pernambuco (Brazil); Albuquerque, Victor Hugo C. de, E-mail: victor.albuquerque@fe.up.p [Universidade de Fortaleza (UNIFOR), Centro de Ciencias Tecnologicas (CCT), Nucleo de Pesquisas Tecnologicas - NPT, Av. Washington Soares, 1321, Sala NPT/CCT, CEP 60.811-905, Edson Queiroz (Brazil); Universidade Federal da Paraiba (UFPB), Departamento de Engenharia Mecanica (DEM), Cidade Universitaria, S/N, 58059-900 Joao Pessoa, Paraiba (Brazil)
2010-11-15
This work has the objective of developing, analysing and applying a new tool for management the status of break disconnectors in high voltage substations from digital images. This tool uses a non-supervised kind of artificial neural network using the Kohonen learning algorithm, known as a self-organizing maps. In order to develop the proposed tool, C/C++ programming language, provided with easily used interfaces, is used. In order to obtain the results, three environments are considered: one for laboratory simulation and two pilot projects installed in the Fortaleza II/CHESF substation. These pilots are used for 230 kV EV-2000 type and 500 kV semi-pantographic type break disconnector management tests. The results prove the developed system's efficiency, because it is able to detect 100% of open and closed identification situations. However, the neural network utilised for management break disconnectors has become suitable for installation in high voltage substations in order to support the maintenance team in safely handling these disconnectors.
Relativistic fluid theories - Self organization
International Nuclear Information System (INIS)
Mahajan, S.M.; Hazeltine, R.D.; Yoshida, Z.
2003-01-01
Developments in two distinct but related subjects are reviewed: 1) Formulation and investigation of closed fluid theories which transcend the limitations of standard magnetohydrodynamics (MHD), in particular, theories which are valid in the long mean free path limit and in which pressure anisotropy, heat flow, and arbitrarily strong sheared flows are treated consistently, and 2) Exploitation of the two-fluid theories to derive new plasma configurations in which the flow-field is a co-determinant of the overall dynamics; some of these states belong to the category of self-organized relaxed states. Physical processes which may provide a route to self-organization and complexity are also explored. (author)
Directory of Open Access Journals (Sweden)
Iaroslav Ispolatov
Full Text Available The generation of two non-identical membrane compartments via exchange of vesicles is considered to require two types of vesicles specified by distinct cytosolic coats that selectively recruit cargo, and two membrane-bound SNARE pairs that specify fusion and differ in their affinities for each type of vesicles. The mammalian Golgi complex is composed of 6-8 non-identical cisternae that undergo gradual maturation and replacement yet features only two SNARE pairs. We present a model that explains how distinct composition of Golgi cisternae can be generated with two and even a single SNARE pair and one vesicle coat. A decay of active SNARE concentration in aging cisternae provides the seed for a cis[Formula: see text]trans SNARE gradient that generates the predominantly retrograde vesicle flux which further enhances the gradient. This flux in turn yields the observed inhomogeneous steady-state distribution of Golgi enzymes, which compete with each other and with the SNAREs for incorporation into transport vesicles. We show analytically that the steady state SNARE concentration decays exponentially with the cisterna number. Numerical solutions of rate equations reproduce the experimentally observed SNARE gradients, overlapping enzyme peaks in cis, medial and trans and the reported change in vesicle nature across the Golgi: Vesicles originating from younger cisternae mostly contain Golgi enzymes and SNAREs enriched in these cisternae and extensively recycle through the Endoplasmic Reticulum (ER, while the other subpopulation of vesicles contains Golgi proteins prevalent in older cisternae and hardly reaches the ER.
Plasticity: modeling & computation
National Research Council Canada - National Science Library
Borja, Ronaldo Israel
2013-01-01
.... "Plasticity Modeling & Computation" is a textbook written specifically for students who want to learn the theoretical, mathematical, and computational aspects of inelastic deformation in solids...
Self-organized critical neural networks
International Nuclear Information System (INIS)
Bornholdt, Stefan; Roehl, Torsten
2003-01-01
A mechanism for self-organization of the degree of connectivity in model neural networks is studied. Network connectivity is regulated locally on the basis of an order parameter of the global dynamics, which is estimated from an observable at the single synapse level. This principle is studied in a two-dimensional neural network with randomly wired asymmetric weights. In this class of networks, network connectivity is closely related to a phase transition between ordered and disordered dynamics. A slow topology change is imposed on the network through a local rewiring rule motivated by activity-dependent synaptic development: Neighbor neurons whose activity is correlated, on average develop a new connection while uncorrelated neighbors tend to disconnect. As a result, robust self-organization of the network towards the order disorder transition occurs. Convergence is independent of initial conditions, robust against thermal noise, and does not require fine tuning of parameters
PREFACE: Self-organized nanostructures
Rousset, Sylvie; Ortega, Enrique
2006-04-01
In order to fabricate ordered arrays of nanostructures, two different strategies might be considered. The `top-down' approach consists of pushing the limit of lithography techniques down to the nanometre scale. However, beyond 10 nm lithography techniques will inevitably face major intrinsic limitations. An alternative method for elaborating ultimate-size nanostructures is based on the reverse `bottom-up' approach, i.e. building up nanostructures (and eventually assemble them to form functional circuits) from individual atoms or molecules. Scanning probe microscopies, including scanning tunnelling microscopy (STM) invented in 1982, have made it possible to create (and visualize) individual structures atom by atom. However, such individual atomic manipulation is not suitable for industrial applications. Self-assembly or self-organization of nanostructures on solid surfaces is a bottom-up approach that allows one to fabricate and assemble nanostructure arrays in a one-step process. For applications, such as high density magnetic storage, self-assembly appears to be the simplest alternative to lithography for massive, parallel fabrication of nanostructure arrays with regular sizes and spacings. These are also necessary for investigating the physical properties of individual nanostructures by means of averaging techniques, i.e. all those using light or particle beams. The state-of-the-art and the current developments in the field of self-organization and physical properties of assembled nanostructures are reviewed in this issue of Journal of Physics: Condensed Matter. The papers have been selected from among the invited and oral presentations of the recent summer workshop held in Cargese (Corsica, France, 17-23 July 2005). All authors are world-renowned in the field. The workshop has been funded by the Marie Curie Actions: Marie Curie Conferences and Training Courses series named `NanosciencesTech' supported by the VI Framework Programme of the European Community, by
Self-organizing of critical state in granulated superconductors
International Nuclear Information System (INIS)
Ginzburg, S.L.; Savitskaya, N.E.
2000-01-01
Critical state in granulated superconductors was studied on the basis of two mathematical models - the system of differential equations for calibration and invariant difference of phases and a simplified model describing the system of associated images and equivalent to the standard models to study self-organizing criticality. The critical state of granulated superconductors in all studied cases was shown to be self-organized. Besides, it is shown that the applied models are practically equivalent ones, that is they both show similar critical behavior and lead to coincidence of noncritical phenomena. For the first time one showed that the occurrence of self-organized critically within the system of nonlinear differential equations and its equivalence to self-organized critically in the standard models [ru
Macromolecular target prediction by self-organizing feature maps.
Schneider, Gisbert; Schneider, Petra
2017-03-01
Rational drug discovery would greatly benefit from a more nuanced appreciation of the activity of pharmacologically active compounds against a diverse panel of macromolecular targets. Already, computational target-prediction models assist medicinal chemists in library screening, de novo molecular design, optimization of active chemical agents, drug re-purposing, in the spotting of potential undesired off-target activities, and in the 'de-orphaning' of phenotypic screening hits. The self-organizing map (SOM) algorithm has been employed successfully for these and other purposes. Areas covered: The authors recapitulate contemporary artificial neural network methods for macromolecular target prediction, and present the basic SOM algorithm at a conceptual level. Specifically, they highlight consensus target-scoring by the employment of multiple SOMs, and discuss the opportunities and limitations of this technique. Expert opinion: Self-organizing feature maps represent a straightforward approach to ligand clustering and classification. Some of the appeal lies in their conceptual simplicity and broad applicability domain. Despite known algorithmic shortcomings, this computational target prediction concept has been proven to work in prospective settings with high success rates. It represents a prototypic technique for future advances in the in silico identification of the modes of action and macromolecular targets of bioactive molecules.
Computational neurogenetic modeling
Benuskova, Lubica
2010-01-01
Computational Neurogenetic Modeling is a student text, introducing the scope and problems of a new scientific discipline - Computational Neurogenetic Modeling (CNGM). CNGM is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This new area brings together knowledge from various scientific disciplines, such as computer and information science, neuroscience and cognitive science, genetics and molecular biol
Complexity in plasma: From self-organization to geodynamo
International Nuclear Information System (INIS)
Sato, T.
1996-01-01
A central theme of open-quote open-quote Complexity close-quote close-quote is the question of the creation of ordered structure in nature (self-organization). The assertion is made that self-organization is governed by three key processes, i.e., energy pumping, entropy expulsion and nonlinearity. Extensive efforts have been done to confirm this assertion through computer simulations of plasmas. A system exhibits markedly different features in self-organization, depending on whether the energy pumping is instantaneous or continuous, or whether the produced entropy is expulsed or reserved. The nonlinearity acts to bring a nonequilibrium state into a bifurcation, thus resulting in a new structure along with an anomalous entropy production. As a practical application of our grand view of self-organization a preferential generation of a dipole magnetic field is successfully demonstrated. copyright 1996 American Institute of Physics
Self-Organization in Embedded Real-Time Systems
Brinkschulte, Uwe; Rettberg, Achim
2013-01-01
This book describes the emerging field of self-organizing, multicore, distributed and real-time embedded systems. Self-organization of both hardware and software can be a key technique to handle the growing complexity of modern computing systems. Distributed systems running hundreds of tasks on dozens of processors, each equipped with multiple cores, requires self-organization principles to ensure efficient and reliable operation. This book addresses various, so-called Self-X features such as self-configuration, self-optimization, self-adaptation, self-healing and self-protection. Presents open components for embedded real-time adaptive and self-organizing applications; Describes innovative techniques in: scheduling, memory management, quality of service, communications supporting organic real-time applications; Covers multi-/many-core embedded systems supporting real-time adaptive systems and power-aware, adaptive hardware and software systems; Includes case studies of open embedded real-time self-organizi...
Wheeler, K. I.; Levia, D. F., Jr.; Hudson, J. E.
2017-12-01
As trees undergo autumnal processes such as resorption, senescence, and leaf abscission, the dissolved organic matter (DOM) contribution of leaf litter leachate to streams changes. However, little research has investigated how the fluorescent DOM (FDOM) changes throughout the autumn and how this differs inter- and intraspecifically. Two of the major impacts of global climate change on forested ecosystems include altering phenology and causing forest community species and subspecies composition restructuring. We examined changes in FDOM in leachate from American beech (Fagus grandifolia Ehrh.) leaves in Maryland, Rhode Island, Vermont, and North Carolina and yellow poplar (Liriodendron tulipifera L.) leaves from Maryland throughout three different phenophases: green, senescing, and freshly abscissed. Beech leaves from Maryland and Rhode Island have previously been identified as belonging to the same distinct genetic cluster and beech trees from Vermont and the study site in North Carolina from the other. FDOM in samples was characterized using excitation-emission matrices (EEMs) and a six-component parallel factor analysis (PARAFAC) model was created to identify components. Self-organizing maps (SOMs) were used to visualize variation and patterns in the PARAFAC component proportions of the leachate samples. Phenophase and species had the greatest influence on determining where a sample mapped on the SOM when compared to genetic clusters and geographic origin. Throughout senescence, FDOM from all the trees transitioned from more protein-like components to more humic-like ones. Percent greenness of the sampled leaves and the proportion of the tyrosine-like component 1 were found to significantly differ between the two genetic beech clusters. This suggests possible differences in photosynthesis and resorption between the two genetic clusters of beech. The use of SOMs to visualize differences in patterns of senescence between the different species and genetic
Deliberative Self-Organizing Traffic Lights with Elementary Cellular Automata
Directory of Open Access Journals (Sweden)
Jorge L. Zapotecatl
2017-01-01
Full Text Available Self-organizing traffic lights have shown considerable improvements compared to traditional methods in computer simulations. Self-organizing methods, however, use sophisticated sensors, increasing their cost and limiting their deployment. We propose a novel approach using simple sensors to achieve self-organizing traffic light coordination. The proposed approach involves placing a computer and a presence sensor at the beginning of each block; each such sensor detects a single vehicle. Each computer builds a virtual environment simulating vehicle movement to predict arrivals and departures at the downstream intersection. At each intersection, a computer receives information across a data network from the computers of the neighboring blocks and runs a self-organizing method to control traffic lights. Our simulations showed a superior performance for our approach compared with a traditional method (a green wave and a similar performance (close to optimal compared with a self-organizing method using sophisticated sensors but at a lower cost. Moreover, the developed sensing approach exhibited greater robustness against sensor failures.
On micro-scale self-organization in a plasma
International Nuclear Information System (INIS)
Maluckov, A.; Jovanovic, M.S.; Skoric, M.M.; Sato, T.
1998-01-01
We concentrate on a nonlinear saturation of a stimulated Raman backscattering in an open convective weakly confined model in the context of micro-kinetic scale self-organization in plasmas. The results have led to an assertion that a long-time nonlinear saturation in an open SRBS model with phenomenological effects of anomalous dissipation, plasma heating and subsequent entropy expulsion, reveals a generic interrelation of self-organization at wave-fluid (macro) and particle-kinetic (micro) levels. (author)
International Nuclear Information System (INIS)
Bonacorsi, D.
2007-01-01
The CMS experiment at LHC has developed a baseline Computing Model addressing the needs of a computing system capable to operate in the first years of LHC running. It is focused on a data model with heavy streaming at the raw data level based on trigger, and on the achievement of the maximum flexibility in the use of distributed computing resources. The CMS distributed Computing Model includes a Tier-0 centre at CERN, a CMS Analysis Facility at CERN, several Tier-1 centres located at large regional computing centres, and many Tier-2 centres worldwide. The workflows have been identified, along with a baseline architecture for the data management infrastructure. This model is also being tested in Grid Service Challenges of increasing complexity, coordinated with the Worldwide LHC Computing Grid community
Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille
2017-12-01
Multiagent systems (MAS) provide a useful tool for exploring the complex dynamics and behavior of financial markets and now MAS approach has been widely implemented and documented in the empirical literature. This paper introduces the implementation of an innovative multi-scale mathematical model for a computational agent-based financial market. The paper develops a method to quantify the degree of self-organization which emerges in the system and shows that the capacity of self-organization is maximized when the agent behaviors are heterogeneous. Numerical results are presented and analyzed, showing how the global market behavior emerges from specific individual behavior interactions.
Self-organization, Networks, Future
Directory of Open Access Journals (Sweden)
T. S. Akhromeyeva
2013-01-01
Full Text Available This paper presents an analytical review of a conference on the great scientist, a brilliant professor, an outstanding educator Sergei Kapitsa, held in November 2012. In the focus of this forum were problems of self-organization and a paradigm of network structures. The use of networks in the context of national defense, economics, management of mass consciousness was discussed. The analysis of neural networks in technical systems, the structure of the brain, as well as in the space of knowledge, information, and behavioral strategies plays an important role. One of the conference purposes was to an online organize community in Russia and to identify the most promising directions in this field. Some of them are presented in this paper.
Self-organizing magnetohydrodynamic plasma
International Nuclear Information System (INIS)
Sato, T.; Horiuchi, R.; Watanabe, K.; Hayashi, T.; Kusano, K.
1990-09-01
In a resistive magnetohydrodynamic (MHD) plasma, both the magnetic energy and the magnetic helicity dissipate with the resistive time scale. When sufficiently large free magnetic energy does exist, however, an ideal current driven instability is excited whereby magnetic reconnection is driven at a converging point of induced plasma flows which does exist in a bounded compressible plasma. At a reconnection point excess free energy (entropy) is rapidly dissipated by ohmic heating and lost by radiation, while magnetic helicity is completely conserved. The magnetic topology is largely changed by reconnection and a new ordered structure with the same helicity is created. It is discussed that magnetic reconnection plays a key role in the MHD self-organization process. (author)
Computational models of neuromodulation.
Fellous, J M; Linster, C
1998-05-15
Computational modeling of neural substrates provides an excellent theoretical framework for the understanding of the computational roles of neuromodulation. In this review, we illustrate, with a large number of modeling studies, the specific computations performed by neuromodulation in the context of various neural models of invertebrate and vertebrate preparations. We base our characterization of neuromodulations on their computational and functional roles rather than on anatomical or chemical criteria. We review the main framework in which neuromodulation has been studied theoretically (central pattern generation and oscillations, sensory processing, memory and information integration). Finally, we present a detailed mathematical overview of how neuromodulation has been implemented at the single cell and network levels in modeling studies. Overall, neuromodulation is found to increase and control computational complexity.
Complexity in plasma. A grand view of self-organization
International Nuclear Information System (INIS)
Sato, Tetsuya.
1994-11-01
The central theme of the Complexity is the inquest of the creation of ordered structure in nature. Extensive computer simulations on plasmas have revealed that self-organization is governed by the three key processes, i.e. energy pumping, entropy expulsion and nonlinearity. A system exhibits characteristically different self-organization, depending on whether the energy pumping is instantaneous or continuous, or whether the produced entropy is expulsed or reserved. The nonlinearity acts to bring a nonequilibrium state into a bifurcation, thus resulting in a new structure along with an anomalous entropy production. (author)
Hierarchical organization versus self-organization
Busseniers, Evo
2014-01-01
In this paper we try to define the difference between hierarchical organization and self-organization. Organization is defined as a structure with a function. So we can define the difference between hierarchical organization and self-organization both on the structure as on the function. In the next two chapters these two definitions are given. For the structure we will use some existing definitions in graph theory, for the function we will use existing theory on (self-)organization. In the t...
9th Workshop on Self-Organizing Maps
Príncipe, José; Zegers, Pablo
2013-01-01
Self-organizing maps (SOMs) were developed by Teuvo Kohonen in the early eighties. Since then more than 10,000 works have been based on SOMs. SOMs are unsupervised neural networks useful for clustering and visualization purposes. Many SOM applications have been developed in engineering and science, and other fields. This book contains refereed papers presented at the 9th Workshop on Self-Organizing Maps (WSOM 2012) held at the Universidad de Chile, Santiago, Chile, on December 12-14, 2012. The workshop brought together researchers and practitioners in the field of self-organizing systems. Among the book chapters there are excellent examples of the use of SOMs in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis, and time series analysis. Other chapters present the latest theoretical work on SOMs as well as Learning Vector Quantization (LVQ) methods.
Self-organized service negotiation for collaborative decision making.
Zhang, Bo; Huang, Zhenhua; Zheng, Ziming
2014-01-01
This paper proposes a self-organized service negotiation method for CDM in intelligent and automatic manners. It mainly includes three phases: semantic-based capacity evaluation for the CDM sponsor, trust computation of the CDM organization, and negotiation selection of the decision-making service provider (DMSP). In the first phase, the CDM sponsor produces the formal semantic description of the complex decision task for DMSP and computes the capacity evaluation values according to participator instructions from different DMSPs. In the second phase, a novel trust computation approach is presented to compute the subjective belief value, the objective reputation value, and the recommended trust value. And in the third phase, based on the capacity evaluation and trust computation, a negotiation mechanism is given to efficiently implement the service selection. The simulation experiment results show that our self-organized service negotiation method is feasible and effective for CDM.
Enin, S. S.; Omelchenko, E. Y.; Fomin, N. V.; Beliy, A. V.
2018-03-01
The paper has a description of a computer model of an overhead crane system. The designed overhead crane system consists of hoisting, trolley and crane mechanisms as well as a payload two-axis system. With the help of the differential equation of specified mechanisms movement derived through Lagrange equation of the II kind, it is possible to build an overhead crane computer model. The computer model was obtained using Matlab software. Transients of coordinate, linear speed and motor torque of trolley and crane mechanism systems were simulated. In addition, transients of payload swaying were obtained with respect to the vertical axis. A trajectory of the trolley mechanism with simultaneous operation with the crane mechanism is represented in the paper as well as a two-axis trajectory of payload. The designed computer model of an overhead crane is a great means for studying positioning control and anti-sway control systems.
Computer Modeling and Simulation
Energy Technology Data Exchange (ETDEWEB)
Pronskikh, V. S. [Fermilab
2014-05-09
Verification and validation of computer codes and models used in simulation are two aspects of the scientific practice of high importance and have recently been discussed by philosophers of science. While verification is predominantly associated with the correctness of the way a model is represented by a computer code or algorithm, validation more often refers to model’s relation to the real world and its intended use. It has been argued that because complex simulations are generally not transparent to a practitioner, the Duhem problem can arise for verification and validation due to their entanglement; such an entanglement makes it impossible to distinguish whether a coding error or model’s general inadequacy to its target should be blamed in the case of the model failure. I argue that in order to disentangle verification and validation, a clear distinction between computer modeling (construction of mathematical computer models of elementary processes) and simulation (construction of models of composite objects and processes by means of numerical experimenting with them) needs to be made. Holding on to that distinction, I propose to relate verification (based on theoretical strategies such as inferences) to modeling and validation, which shares the common epistemology with experimentation, to simulation. To explain reasons of their intermittent entanglement I propose a weberian ideal-typical model of modeling and simulation as roles in practice. I suggest an approach to alleviate the Duhem problem for verification and validation generally applicable in practice and based on differences in epistemic strategies and scopes
A self-organized system of smart preys and predators
Energy Technology Data Exchange (ETDEWEB)
Rozenfeld, Alejandro F. [Instituto de Investigaciones Fisicoquimicas Teoricas y Aplicadas (INIFTA), Facultad de Ciencias Exactas, UNLP, CONICET, Suc. 4, C.C. 16 (1900) La Plata (Argentina); Albano, Ezequiel V. [Instituto de Investigaciones Fisicoquimicas Teoricas y Aplicadas (INIFTA), Facultad de Ciencias Exactas, UNLP, CONICET, Suc. 4, C.C. 16 (1900) La Plata (Argentina)]. E-mail: ealbano@inifta.unlp.edu.ar
2004-11-22
Based on the fact that, a standard prey-predator model (SPPM), exhibits irreversible phase transitions, belonging to the universality class of directed percolation (DP), between prey-predator coexistence and predator extinction [Phys. Lett. A 280 (2001) 45], a self-organized prey-predator model (SOPPM) is formulated and studied by means of extensive Monte Carlo simulations. The SOPPM is achieved defining the parameters of the SPPM as functions of the density of species. It is shown that the SOPPM self-organizes into an active state close the absorbing phase of the SPPM, and consequently their avalanche exponents also belong to the universality class of DP.
International Nuclear Information System (INIS)
Grandi, C; Bonacorsi, D; Colling, D; Fisk, I; Girone, M
2014-01-01
The CMS Computing Model was developed and documented in 2004. Since then the model has evolved to be more flexible and to take advantage of new techniques, but many of the original concepts remain and are in active use. In this presentation we will discuss the changes planned for the restart of the LHC program in 2015. We will discuss the changes planning in the use and definition of the computing tiers that were defined with the MONARC project. We will present how we intend to use new services and infrastructure to provide more efficient and transparent access to the data. We will discuss the computing plans to make better use of the computing capacity by scheduling more of the processor nodes, making better use of the disk storage, and more intelligent use of the networking.
Computational Intelligence, Cyber Security and Computational Models
Anitha, R; Lekshmi, R; Kumar, M; Bonato, Anthony; Graña, Manuel
2014-01-01
This book contains cutting-edge research material presented by researchers, engineers, developers, and practitioners from academia and industry at the International Conference on Computational Intelligence, Cyber Security and Computational Models (ICC3) organized by PSG College of Technology, Coimbatore, India during December 19–21, 2013. The materials in the book include theory and applications for design, analysis, and modeling of computational intelligence and security. The book will be useful material for students, researchers, professionals, and academicians. It will help in understanding current research trends and findings and future scope of research in computational intelligence, cyber security, and computational models.
Feedback, Lineages and Self-Organizing Morphogenesis.
Directory of Open Access Journals (Sweden)
Sameeran Kunche
2016-03-01
Full Text Available Feedback regulation of cell lineage progression plays an important role in tissue size homeostasis, but whether such feedback also plays an important role in tissue morphogenesis has yet to be explored. Here we use mathematical modeling to show that a particular feedback architecture in which both positive and negative diffusible signals act on stem and/or progenitor cells leads to the appearance of bistable or bi-modal growth behaviors, ultrasensitivity to external growth cues, local growth-driven budding, self-sustaining elongation, and the triggering of self-organization in the form of lamellar fingers. Such behaviors arise not through regulation of cell cycle speeds, but through the control of stem or progenitor self-renewal. Even though the spatial patterns that arise in this setting are the result of interactions between diffusible factors with antagonistic effects, morphogenesis is not the consequence of Turing-type instabilities.
Feedback, Lineages and Self-Organizing Morphogenesis
Calof, Anne L.; Lowengrub, John S.; Lander, Arthur D.
2016-01-01
Feedback regulation of cell lineage progression plays an important role in tissue size homeostasis, but whether such feedback also plays an important role in tissue morphogenesis has yet to be explored. Here we use mathematical modeling to show that a particular feedback architecture in which both positive and negative diffusible signals act on stem and/or progenitor cells leads to the appearance of bistable or bi-modal growth behaviors, ultrasensitivity to external growth cues, local growth-driven budding, self-sustaining elongation, and the triggering of self-organization in the form of lamellar fingers. Such behaviors arise not through regulation of cell cycle speeds, but through the control of stem or progenitor self-renewal. Even though the spatial patterns that arise in this setting are the result of interactions between diffusible factors with antagonistic effects, morphogenesis is not the consequence of Turing-type instabilities. PMID:26989903
Computationally Modeling Interpersonal Trust
Directory of Open Access Journals (Sweden)
Jin Joo eLee
2013-12-01
Full Text Available We present a computational model capable of predicting—above human accuracy—the degree of trust a person has toward their novel partner by observing the trust-related nonverbal cues expressed in their social interaction. We summarize our prior work, in which we identify nonverbal cues that signal untrustworthy behavior and also demonstrate the human mind’s readiness to interpret those cues to assess the trustworthiness of a social robot. We demonstrate that domain knowledge gained from our prior work using human-subjects experiments, when incorporated into the feature engineering process, permits a computational model to outperform both human predictions and a baseline model built in naivete' of this domain knowledge. We then present the construction of hidden Markov models to incorporate temporal relationships among the trust-related nonverbal cues. By interpreting the resulting learned structure, we observe that models built to emulate different levels of trust exhibit different sequences of nonverbal cues. From this observation, we derived sequence-based temporal features that further improve the accuracy of our computational model. Our multi-step research process presented in this paper combines the strength of experimental manipulation and machine learning to not only design a computational trust model but also to further our understanding of the dynamics of interpersonal trust.
Order out of Randomness: Self-Organization Processes in Astrophysics
Aschwanden, Markus J.; Scholkmann, Felix; Béthune, William; Schmutz, Werner; Abramenko, Valentina; Cheung, Mark C. M.; Müller, Daniel; Benz, Arnold; Chernov, Guennadi; Kritsuk, Alexei G.; Scargle, Jeffrey D.; Melatos, Andrew; Wagoner, Robert V.; Trimble, Virginia; Green, William H.
2018-03-01
Self-organization is a property of dissipative nonlinear processes that are governed by a global driving force and a local positive feedback mechanism, which creates regular geometric and/or temporal patterns, and decreases the entropy locally, in contrast to random processes. Here we investigate for the first time a comprehensive number of (17) self-organization processes that operate in planetary physics, solar physics, stellar physics, galactic physics, and cosmology. Self-organizing systems create spontaneous " order out of randomness", during the evolution from an initially disordered system to an ordered quasi-stationary system, mostly by quasi-periodic limit-cycle dynamics, but also by harmonic (mechanical or gyromagnetic) resonances. The global driving force can be due to gravity, electromagnetic forces, mechanical forces (e.g., rotation or differential rotation), thermal pressure, or acceleration of nonthermal particles, while the positive feedback mechanism is often an instability, such as the magneto-rotational (Balbus-Hawley) instability, the convective (Rayleigh-Bénard) instability, turbulence, vortex attraction, magnetic reconnection, plasma condensation, or a loss-cone instability. Physical models of astrophysical self-organization processes require hydrodynamic, magneto-hydrodynamic (MHD), plasma, or N-body simulations. Analytical formulations of self-organizing systems generally involve coupled differential equations with limit-cycle solutions of the Lotka-Volterra or Hopf-bifurcation type.
Measuring the Complexity of Self-Organizing Traffic Lights
Directory of Open Access Journals (Sweden)
Darío Zubillaga
2014-04-01
Full Text Available We apply measures of complexity, emergence, and self-organization to an urban traffic model for comparing a traditional traffic-light coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only is traffic a non-stationary problem, requiring controllers to adapt constantly; controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures and extending Ashby’s law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system.
Self-Organization during Friction in Complex Surface Engineered Tribosystems
Directory of Open Access Journals (Sweden)
Ben D. Beake
2010-02-01
Full Text Available Self-organization during friction in complex surface engineered tribosystems is investigated. The probability of self-organization in these complex tribosystems is studied on the basis of the theoretical concepts of irreversible thermodynamics. It is shown that a higher number of interrelated processes within the system result in an increased probability of self-organization. The results of this thermodynamic model are confirmed by the investigation of the wear performance of a novel Ti0.2Al0.55Cr0.2Si0.03Y0.02N/Ti0.25Al0.65Cr0.1N (PVD coating with complex nano-multilayered structure under extreme tribological conditions of dry high-speed end milling of hardened H13 tool steel.
Miyazaki, Yumi; Tsumiyama, Ken; Yamane, Takashi; Ito, Mitsuhiro; Shiozawa, Shunichi
2013-04-18
We have developed a systems biology concept to explain the origin of systemic autoimmunity. From our studies of systemic lupus erythematosus (SLE) we have concluded that this disease is the inevitable consequence of over-stimulating the host's immune system by repeated exposure to antigen to levels that surpass a critical threshold, which we term the system's "self-organized criticality". We observed that overstimulation of CD4 T cells in mice led to the development of autoantibody-inducing CD4 T cells (aiCD4 T) capable of generating various autoantibodies and pathological lesions identical to those observed in SLE. We show here that this is accompanied by the significant expansion of a novel population of effector T cells characterized by expression of programmed death-1 (PD-1)-positive, CD27(low), CD127(low), CCR7(low) and CD44(high)CD62L(low) markers, as well as increased production of IL-2 and IL-6. In addition, repeated immunization caused the expansion of CD8 T cells into fully-matured cytotoxic T lymphocytes (CTL) that express Ly6C(high)CD122(high) effector and memory markers. Thus, overstimulation with antigen leads to the expansion of a novel effector CD4 T cell population that expresses an unusual memory marker, PD-1, and that may contribute to the pathogenesis of SLE.
Keeley, Fred W; Bellingham, Catherine M; Woodhouse, Kimberley A
2002-02-28
Elastin is the major extracellular matrix protein of large arteries such as the aorta, imparting characteristics of extensibility and elastic recoil. Once laid down in tissues, polymeric elastin is not subject to turnover, but is able to sustain its mechanical resilience through thousands of millions of cycles of extension and recoil. Elastin consists of ca. 36 domains with alternating hydrophobic and cross-linking characteristics. It has been suggested that these hydrophobic domains, predominantly containing glycine, proline, leucine and valine, often occurring in tandemly repeated sequences, are responsible for the ability of elastin to align monomeric chains for covalent cross-linking. We have shown that small, recombinantly expressed polypeptides based on sequences of human elastin contain sufficient information to self-organize into fibrillar structures and promote the formation of lysine-derived cross-links. These cross-linked polypeptides can also be fabricated into membrane structures that have solubility and mechanical properties reminiscent of native insoluble elastin. Understanding the basis of the self-organizational ability of elastin-based polypeptides may provide important clues for the general design of self-assembling biomaterials.
Unsupervised learning via self-organization a dynamic approach
Kyan, Matthew; Jarrah, Kambiz; Guan, Ling
2014-01-01
To aid in intelligent data mining, this book introduces a new family of unsupervised algorithms that have a basis in self-organization, yet are free from many of the constraints typical of other well known self-organizing architectures. It then moves through a series of pertinent real world applications with regards to the processing of multimedia data from its role in generic image processing techniques such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management, and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data.
Anomalous relaxation and self-organization in nonequilibrium processes
International Nuclear Information System (INIS)
Fatkullin, Ibrahim; Kladko, Konstantin; Mitkov, Igor; Bishop, A. R.
2001-01-01
We study thermal relaxation in ordered arrays of coupled nonlinear elements with external driving. We find that our model exhibits dynamic self-organization manifested in a universal stretched-exponential form of relaxation. We identify two types of self-organization, cooperative and anticooperative, which lead to fast and slow relaxation, respectively. We give a qualitative explanation for the behavior of the stretched exponent in different parameter ranges. We emphasize that this is a system exhibiting stretched-exponential relaxation without explicit disorder or frustration
5G heterogeneous networks self-organizing and optimization
Rong, Bo; Kadoch, Michel; Sun, Songlin; Li, Wenjing
2016-01-01
This SpringerBrief provides state-of-the-art technical reviews on self-organizing and optimization in 5G systems. It covers the latest research results from physical-layer channel modeling to software defined network (SDN) architecture. This book focuses on the cutting-edge wireless technologies such as heterogeneous networks (HetNets), self-organizing network (SON), smart low power node (LPN), 3D-MIMO, and more. It will help researchers from both the academic and industrial worlds to better understand the technical momentum of 5G key technologies.
Atmospheric Convective Organization: Self-Organized Criticality or Homeostasis?
Yano, Jun-Ichi
2015-04-01
Atmospheric convection has a tendency organized on a hierarchy of scales ranging from the mesoscale to the planetary scales, with the latter especially manifested by the Madden-Julian oscillation. The present talk examines two major possible mechanisms of self-organization identified in wider literature from a phenomenological thermodynamic point of view by analysing a planetary-scale cloud-resolving model simulation. The first mechanism is self-organized criticality. A saturation tendency of precipitation rate with the increasing column-integrated water, reminiscence of critical phenomena, indicates self-organized criticality. The second is a self-regulation mechanism that is known as homeostasis in biology. A thermodynamic argument suggests that such self-regulation maintains the column-integrated water below a threshold by increasing the precipitation rate. Previous analyses of both observational data as well as cloud-resolving model (CRM) experiments give mixed results. A satellite data analysis suggests self-organized criticality. Some observational data as well as CRM experiments support homeostasis. Other analyses point to a combination of these two interpretations. In this study, a CRM experiment over a planetary-scale domain with a constant sea-surface temperature is analyzed. This analysis shows that the relation between the column-integrated total water and precipitation suggests self-organized criticality, whereas the one between the column-integrated water vapor and precipitation suggests homeostasis. The concurrent presence of these two mechanisms are further elaborated by detailed statistical and budget analyses. These statistics are scale invariant, reflecting a spatial scaling of precipitation processes. These self-organization mechanisms are most likely be best theoretically understood by the energy cycle of the convective systems consisting of the kinetic energy and the cloud-work function. The author has already investigated the behavior of this
Self-organized criticality as a paradigm for transport processes in magnetically confined plasma
International Nuclear Information System (INIS)
Karreras, B.A.; N'yuman, D.; Linch, V.E.
1996-01-01
Many models of natural events prove the basic hypotheses of self-organized critically. The concept on self-organized criticality combines self similarity on a spatial and time scale, characteristic of many such events. Application of the self-organized criticality concept to plasma dynamics close to the stability limit opens new possibilities for comprehension of such events as the Bom scaling, profile selfconsistency, wide band fluctuation spectra with universal characteristics and small time scales. Refs. 51, figs. 17
Chaos Modelling with Computers
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 5. Chaos Modelling with Computers Unpredicatable Behaviour of Deterministic Systems. Balakrishnan Ramasamy T S K V Iyer. General Article Volume 1 Issue 5 May 1996 pp 29-39 ...
Self-Organization and Annealed Disorder in a Fracturing Process
DEFF Research Database (Denmark)
Caldarelli, Guido; Di Tolla, Francesco; Petri, Alberto
1996-01-01
We show that a vectorial model for inhomogeneous elastic media self-organizes under external stress. An onset of crack avalanches of every duration and length scale compatible with the lattice size is observed. The behavior is driven by the introduction of annealed disorder, i.e., by lowering...... condition for reproducing the algebraic distribution of the energy released during cracks formation....
International Nuclear Information System (INIS)
Max, G
2011-01-01
Traffic models in computer networks can be described as a complicated system. These systems show non-linear features and to simulate behaviours of these systems are also difficult. Before implementing network equipments users wants to know capability of their computer network. They do not want the servers to be overloaded during temporary traffic peaks when more requests arrive than the server is designed for. As a starting point for our study a non-linear system model of network traffic is established to exam behaviour of the network planned. The paper presents setting up a non-linear simulation model that helps us to observe dataflow problems of the networks. This simple model captures the relationship between the competing traffic and the input and output dataflow. In this paper, we also focus on measuring the bottleneck of the network, which was defined as the difference between the link capacity and the competing traffic volume on the link that limits end-to-end throughput. We validate the model using measurements on a working network. The results show that the initial model estimates well main behaviours and critical parameters of the network. Based on this study, we propose to develop a new algorithm, which experimentally determines and predict the available parameters of the network modelled.
Frank, M; Pacheco, Andreu
1998-01-01
This document is a first attempt to describe the LHCb computing model. The CPU power needed to process data for the event filter and reconstruction is estimated to be 2.2 \\Theta 106 MIPS. This will be installed at the experiment and will be reused during non data-taking periods for reprocessing. The maximal I/O of these activities is estimated to be around 40 MB/s.We have studied three basic models concerning the placement of the CPU resources for the other computing activities, Monte Carlo-simulation (1:4 \\Theta 106 MIPS) and physics analysis (0:5 \\Theta 106 MIPS): CPU resources may either be located at the physicist's homelab, national computer centres (Regional Centres) or at CERN.The CPU resources foreseen for analysis are sufficient to allow 100 concurrent analyses. It is assumed that physicists will work in physics groups that produce analysis data at an average rate of 4.2 MB/s or 11 TB per month. However, producing these group analysis data requires reading capabilities of 660 MB/s. It is further assu...
Energy Technology Data Exchange (ETDEWEB)
Kopper, Claudio, E-mail: claudio.kopper@nikhef.nl [NIKHEF, Science Park 105, 1098 XG Amsterdam (Netherlands)
2013-10-11
Completed in 2008, Antares is now the largest water Cherenkov neutrino telescope in the Northern Hemisphere. Its main goal is to detect neutrinos from galactic and extra-galactic sources. Due to the high background rate of atmospheric muons and the high level of bioluminescence, several on-line and off-line filtering algorithms have to be applied to the raw data taken by the instrument. To be able to handle this data stream, a dedicated computing infrastructure has been set up. The paper covers the main aspects of the current official Antares computing model. This includes an overview of on-line and off-line data handling and storage. In addition, the current usage of the “IceTray” software framework for Antares data processing is highlighted. Finally, an overview of the data storage formats used for high-level analysis is given.
Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan
2018-02-01
Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.
Self-organized topology of recurrence-based complex networks
International Nuclear Information System (INIS)
Yang, Hui; Liu, Gang
2013-01-01
With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., “what is the self-organizing geometry of a recurrence network?” and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks
Guided self-organization inception
2014-01-01
Is it possible to guide the process of self-organisation towards specific patterns and outcomes? Wouldn’t this be self-contradictory? After all, a self-organising process assumes a transition into a more organised form, or towards a more structured functionality, in the absence of centralised control. Then how can we place the guiding elements so that they do not override rich choices potentially discoverable by an uncontrolled process? This book presents different approaches to resolving this paradox. In doing so, the presented studies address a broad range of phenomena, ranging from autopoietic systems to morphological computation, and from small-world networks to information cascades in swarms. A large variety of methods is employed, from spontaneous symmetry breaking to information dynamics to evolutionary algorithms, creating a rich spectrum reflecting this emerging field. Demonstrating several foundational theories and frameworks, as well as innovative practical implementations, Guided S...
Non-Taylor magnetohydrodynamic self-organization
International Nuclear Information System (INIS)
Zhu, Shao-ping; Horiuchi, Ritoku; Sato, Tetsuya.
1994-10-01
A self-organization process in a plasma with a finite pressure is investigated by means of a three-dimensional magnetohydrodynamic simulation. It is demonstrated that a non-Taylor finite β self-organized state is realized in which a perpendicular component of the electric current is generated and the force-free(parallel) current decreases until they reach to almost the same level. The self-organized state is described by an MHD force-balance relation, namely, j perpendicular = B x ∇p/B·B and j parallel = μB where μ is not a constant, and the pressure structure resembles the structure of the toroidal magnetic field intensity. Unless an anomalous perpendicular thermal conduction arises, the plasma cannot relax to a Taylor state but to a non-Taylor (non-force-free) self-organized state. This state becomes more prominent for a weaker resistivity condition. The non-Taylor state has a rather universal property, for example, independence of the initial β value. Another remarkable finding is that the Taylor's conjecture of helicity conservation is, in a strict sense, not valid. The helicity dissipation occurs and its rate slows down critically in accordance with the stepwise relaxation of the magnetic energy. It is confirmed that the driven magnetic reconnection caused by the nonlinearly excited plasma kink flows plays the leading role in all of these key features of the non-Taylor self-organization. (author)
How nature works the science of self-organized criticality
Bak, Per
1996-01-01
This is an acclaimed book intended for the general reader who is interested in science. The author is a physicist who is well-known for his development of the property called "self-organized criticality", a property or phenomenon that lies at the heart of large dynamical systems. It can be used to analyse systems that are complicated, and which are part of the new science of complexity. It is a unifying concept that can be used to study phenomena in fields as diverse as economics, astronomy, the earth sciences, and physics. The author discusses his discovery of self-organized criticality; its relation to the world of classical physics; computer simulations and experiments which aid scientists' understanding of the property; and the relation of the subject to popular areas such as fractal geometry and power laws; cellular automata, and a wide range of practical applications.
Ignatova, Zoya; Zimmermann, Karl-Heinz
2008-01-01
In this excellent text, the reader is given a comprehensive introduction to the field of DNA computing. The book emphasizes computational methods to tackle central problems of DNA computing, such as controlling living cells, building patterns, and generating nanomachines.
Self-organization via active exploration in robotic applications
Ogmen, H.; Prakash, R. V.
1992-01-01
We describe a neural network based robotic system. Unlike traditional robotic systems, our approach focussed on non-stationary problems. We indicate that self-organization capability is necessary for any system to operate successfully in a non-stationary environment. We suggest that self-organization should be based on an active exploration process. We investigated neural architectures having novelty sensitivity, selective attention, reinforcement learning, habit formation, flexible criteria categorization properties and analyzed the resulting behavior (consisting of an intelligent initiation of exploration) by computer simulations. While various computer vision researchers acknowledged recently the importance of active processes (Swain and Stricker, 1991), the proposed approaches within the new framework still suffer from a lack of self-organization (Aloimonos and Bandyopadhyay, 1987; Bajcsy, 1988). A self-organizing, neural network based robot (MAVIN) has been recently proposed (Baloch and Waxman, 1991). This robot has the capability of position, size rotation invariant pattern categorization, recognition and pavlovian conditioning. Our robot does not have initially invariant processing properties. The reason for this is the emphasis we put on active exploration. We maintain the point of view that such invariant properties emerge from an internalization of exploratory sensory-motor activity. Rather than coding the equilibria of such mental capabilities, we are seeking to capture its dynamics to understand on the one hand how the emergence of such invariances is possible and on the other hand the dynamics that lead to these invariances. The second point is crucial for an adaptive robot to acquire new invariances in non-stationary environments, as demonstrated by the inverting glass experiments of Helmholtz. We will introduce Pavlovian conditioning circuits in our future work for the precise objective of achieving the generation, coordination, and internalization
Self-Organized Criticality and $1/f$ Noise in Traffic
Paczuski, Maya; Nagel, Kai
1996-01-01
Phantom traffic jams may emerge ``out of nowhere'' from small fluctuations rather than being triggered by large, exceptional events. We show how phantom jams arise in a model of single lane highway traffic, which mimics human driving behavior. Surprisingly, the optimal state of highest efficiency, with the largest throughput, is a critical state with traffic jams of all sizes. We demonstrate that open systems self-organize to the most efficient state. In the model we study, this critical stat...
International Nuclear Information System (INIS)
Lin Min; Chen Tianlun
2005-01-01
A lattice model for a set of pulse-coupled integrate-and-fire neurons with small world structure is introduced. We find that our model displays the power-law behavior accompanied with the large-scale synchronized activities among the units. And the different connectivity topologies lead to different behaviors in models of integrate-and-fire neurons.
Singularity spectrum of self-organized criticality
International Nuclear Information System (INIS)
Canessa, E.
1992-10-01
I introduce a simple continuous probability theory based on the Ginzburg-Landau equation that provides for the first time a common analytical basis to relate and describe the main features of two seemingly different phenomena of condensed-matter physics, namely self-organized criticality and multifractality. Numerical support is given by a comparison with reported simulation data. Within the theory the origin of self-organized critical phenomena is analysed in terms of a nonlinear singularity spectrum different form the typical convex shape due to multifractal measures. (author). 29 refs, 5 figs
Plasticity modeling & computation
Borja, Ronaldo I
2013-01-01
There have been many excellent books written on the subject of plastic deformation in solids, but rarely can one find a textbook on this subject. “Plasticity Modeling & Computation” is a textbook written specifically for students who want to learn the theoretical, mathematical, and computational aspects of inelastic deformation in solids. It adopts a simple narrative style that is not mathematically overbearing, and has been written to emulate a professor giving a lecture on this subject inside a classroom. Each section is written to provide a balance between the relevant equations and the explanations behind them. Where relevant, sections end with one or more exercises designed to reinforce the understanding of the “lecture.” Color figures enhance the presentation and make the book very pleasant to read. For professors planning to use this textbook for their classes, the contents are sufficient for Parts A and B that can be taught in sequence over a period of two semesters or quarters.
Natural hazards and self-organized criticality
International Nuclear Information System (INIS)
Krenn, R.
2012-01-01
Several natural hazards exhibit power-law behavior on their frequency-size distributions. Self-organized criticality has become a promising candidate that could offer a more in-depth understanding of the origin of temporal and spatial scaling in dissipative nonequilibrium systems. The outcomes of this thesis are presented in three scientific papers followed by a concluding summary and an appendix.In paper (A) we present a semi-phenomenological approach to explain the complex scaling behavior of the Drossel-Schwabl forest-fire model (DS-FFM) in two dimensions. We derive the scaling exponent solely from the scaling exponent of the clusters' accessible perimeter. Furthermore, the unusual transition to an exponential decay is explained both qualitatively and quantitatively. The exponential decay itself could be reproduced at least qualitatively. In paper (B) we extend the DS-FFM towards anthropogenic ignition factors. The main outcomes are an increase of the scaling exponent with decreasing lightning probability as well as a splitting of the partial frequency-size distributions of lightning induced and man made fires. Lightning is identified as the dominant mechanism in the regime of the largest fires. The results could be validated through an analysis of the Canadian Large Fire Database.In paper (C) we obtain an almost complete theory of the Olami-Feder-Christensen (OFC) model's complex spatio-temporal behavior. Synchronization pushes the system towards a critical state and generates the Gutenberg-Richter law. Desynchronization prevents the system from becoming overcritical and generates foreshocks and aftershocks. Our approach also provides a simple explanation of Omori's law. Beyond this, it explains the phenomena of foreshock migration and aftershock diffusion and the occurrence of large earthquakes without any foreshocks. A novel integer algorithm for the numerics is presented in appendix (A).(author) [de
Self-organized criticality in fragmenting
DEFF Research Database (Denmark)
Oddershede, L.; Dimon, P.; Bohr, J.
1993-01-01
The measured mass distributions of fragments from 26 fractured objects of gypsum, soap, stearic paraffin, and potato show evidence of obeying scaling laws; this suggests the possibility of self-organized criticality in fragmenting. The probability of finding a fragment scales inversely to a power...
Functional self-organization in complex systems
Energy Technology Data Exchange (ETDEWEB)
Fontana, W. (Los Alamos National Lab., NM (USA) Santa Fe Inst., NM (USA))
1990-01-01
A novel approach to functional self-organization is presented. It consists of a universe generated by a formal language that defines objects (=programs), their meaning (=functions), and their interactions (=composition). Results obtained so far are briefly discussed. 17 refs., 5 figs.
Models of optical quantum computing
Directory of Open Access Journals (Sweden)
Krovi Hari
2017-03-01
Full Text Available I review some work on models of quantum computing, optical implementations of these models, as well as the associated computational power. In particular, we discuss the circuit model and cluster state implementations using quantum optics with various encodings such as dual rail encoding, Gottesman-Kitaev-Preskill encoding, and coherent state encoding. Then we discuss intermediate models of optical computing such as boson sampling and its variants. Finally, we review some recent work in optical implementations of adiabatic quantum computing and analog optical computing. We also provide a brief description of the relevant aspects from complexity theory needed to understand the results surveyed.
Quantum self-organization and nuclear collectivities
Otsuka, T.; Tsunoda, Y.; Togashi, T.; Shimizu, N.; Abe, T.
2018-02-01
The quantum self-organization is introduced as one of the major underlying mechanisms of the quantum many-body systems. In the case of atomic nuclei as an example, two types of the motion of nucleons, single-particle states and collective modes, dominate the structure of the nucleus. The outcome of the collective mode is determined basically by the balance between the effect of the mode-driving force (e.g., quadrupole force for the ellipsoidal deformation) and the resistance power against it. The single-particle energies are one of the sources to produce such resistance power: a coherent collective motion is more hindered by larger gaps between relevant single particle states. Thus, the single-particle state and the collective mode are “enemies” each other. However, the nuclear forces are demonstrated to be rich enough so as to enhance relevant collective mode by reducing the resistance power by changing singleparticle energies for each eigenstate through monopole interactions. This will be verified with the concrete example taken from Zr isotopes. Thus, when the quantum self-organization occurs, single-particle energies can be self-organized, being enhanced by (i) two quantum liquids, e.g., protons and neutrons, (ii) two major force components, e.g., quadrupole interaction (to drive collective mode) and monopole interaction (to control resistance). In other words, atomic nuclei are not necessarily like simple rigid vases containing almost free nucleons, in contrast to the naïve Fermi liquid picture. Type II shell evolution is considered to be a simple visible case involving excitations across a (sub)magic gap. The quantum self-organization becomes more important in heavier nuclei where the number of active orbits and the number of active nucleons are larger. The quantum self-organization is a general phenomenon, and is expected to be found in other quantum systems.
Self-organizing periodicity in development: organ positioning in plants.
Bhatia, Neha; Heisler, Marcus G
2018-02-08
Periodic patterns during development often occur spontaneously through a process of self-organization. While reaction-diffusion mechanisms are often invoked, other types of mechanisms that involve cell-cell interactions and mechanical buckling have also been identified. Phyllotaxis, or the positioning of plant organs, has emerged as an excellent model system to study the self-organization of periodic patterns. At the macro scale, the regular spacing of organs on the growing plant shoot gives rise to the typical spiral and whorled arrangements of plant organs found in nature. In turn, this spacing relies on complex patterns of cell polarity that involve feedback between a signaling molecule - the plant hormone auxin - and its polar, cell-to-cell transport. Here, we review recent progress in understanding phyllotaxis and plant cell polarity and highlight the development of new tools that can help address the remaining gaps in our understanding. © 2018. Published by The Company of Biologists Ltd.
Zhang, WenJun
2007-07-01
Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance
Directory of Open Access Journals (Sweden)
Rodolfo Franco Puttini
2010-01-01
Full Text Available A complexidade do processo saúde-doença tem ensejado a proposição de uma diversidade de modelos explicativos. Fazemos uma breve revisão dessas propostas, confrontando três perspectivas: o modelo oriundo da Medicina do século XIX, a lógica da História Natural da Doença e o debate epidemiológico no contexto da Medicina Social latino-americana. Tomando-se como referência teórica a ideia de causalidade circular presente na teoria da auto-organização, propomos que os fatores causais privilegiados em cada um dos modelos explicativos acima não seriam conflitantes. Uma noção-chave para se pensar o processo de autoorganização biopsicossocial é o "efeito baldwiniano", que descreve uma relação dialética ou coevolutiva entre processos naturais e socioculturais.The complexity of the health-disease process has elicited the postulation of a diversity of explanatory models. We make a brief review of the proposals, starting with the biomedical model derived from the 19th century medicine. This model influenced the approach on the natural history of disease, and the debate on epidemiologic models in the context of the Latin-American Social Medicine. Broadening the spectrum of the discussion, we introduce the idea of circular causality, proposed by theories of self-organizing systems. We argue that, in a transdisciplinary perspective, these explanatory models are not conflicting. A key notion to understand these classes of concomitant explanatory models is the "Baldwin Effect", describing a dialectic or coevolutionary relation between nature, social organization and culture.
Exploitation of Self Organization in UAV Swarms for Optimization in Combat Environments
National Research Council Canada - National Science Library
Nowak, Dustin J
2008-01-01
...) swarms using autonomous self-organized cooperative control. This development required the design of a new abstract UAV swarm control model which flows from an abstract Markov structure, a Partially Observable Markov Decision Process...
Self-Organizing Neural Circuits for Sensory-Guided Motor Control
National Research Council Canada - National Science Library
Grossberg, Stephen
1999-01-01
The reported projects developed mathematical models to explain how self-organizing neural circuits that operate under continuous or intermittent sensory guidance achieve flexible and accurate control of human movement...
Self-organization in metal complexes
International Nuclear Information System (INIS)
Radecka-Paryzek, W.
1999-01-01
Inorganic self-organization involves the spontaneous generation of well-defined supramolecular architectures from metal ions and organic ligands. The basic concept of supramolecular chemistry is a molecular recognition. When the substrate are metal ions, recognition is expressed in the stability and selectivity of metal ion complexation by organic ligands and depends on the geometry of the ligand and on their binding sites that it contains. The combination of the geometric features of the ligand units and the coordination geometries of the metal ions provides very efficient tool for the synthesis of novel, intriguing and highly sophisticated species such as catenanes, box structures, double and triple helicates with a variety of interesting properties. The article will focus on the examples of inorganic self-organization involving the templating as a first step for the assembly of supramolecular structures of high complexity. (author)
Information Driven Ecohydrologic Self-Organization
Directory of Open Access Journals (Sweden)
Benjamin L. Ruddell
2010-09-01
Full Text Available Variability plays an important role in the self-organized interaction between vegetation and its environment, yet the principles that characterize the role of the variability in these interactions remain elusive. To address this problem, we study the dependence between a number of variables measured at flux towers by quantifying the information flow between the different variables along with the associated time lag. By examining this network of feedback loops for seven ecosystems in different climate regions, we find that: (1 the feedback tends to maximize information production in the entire system, and the latter increases with increasing variability within the whole system; and (2 variables that participate in feedback exhibit moderated variability. Self-organization arises as a tradeoff where the ability of the total system to maximize information production through feedback is limited by moderate variability of the participating variables. This relationship between variability and information production leads to the emergence of ordered organization.
A physicist's model of computation
International Nuclear Information System (INIS)
Fredkin, E.
1991-01-01
An attempt is presented to make a statement about what a computer is and how it works from the perspective of physics. The single observation that computation can be a reversible process allows for the same kind of insight into computing as was obtained by Carnot's discovery that heat engines could be modelled as reversible processes. It allows us to bring computation into the realm of physics, where the power of physics allows us to ask and answer questions that seemed intractable from the viewpoint of computer science. Strangely enough, this effort makes it clear why computers get cheaper every year. (author) 14 refs., 4 figs
Computational modeling in biomechanics
Mofrad, Mohammad
2010-01-01
This book provides a glimpse of the diverse and important roles that modern computational technology is playing in various areas of biomechanics. It includes unique chapters on ab initio quantum mechanical, molecular dynamic and scale coupling methods..
Workplace Accidents and Self-Organized Criticality
Mauro, John C.; Diehl, Brett; Marcellin, Richard F.; Vaughn, Daniel J.
2018-01-01
The occurrence of workplace accidents is described within the context of self-organized criticality, a theory from statistical physics that governs a wide range of phenomena across physics, biology, geosciences, economics, and the social sciences. Workplace accident data from the U.S. Bureau of Labor Statistics reveal a power-law relationship between the number of accidents and their severity as measured by the number of days lost from work. This power-law scaling is indicative of workplace a...
Self-organization in circular shear layers
DEFF Research Database (Denmark)
Bergeron, K.; Coutsias, E.A.; Lynov, Jens-Peter
1996-01-01
Experiments on forced circular shear layers performed in both magnetized plasmas and in rotating fluids reveal qualitatively similar self-organization processes leading to the formation of patterns of coherent vortical structures with varying complexity. In this paper results are presented from...... both weakly nonlinear analysis and full numerical simulations that closely reproduce the experimental observations. Varying the Reynolds number leads to bifurcation sequences accompanied by topological changes in the distribution of the coherent structures as well as clear transitions in the total...
Self-organized criticality in neural networks
Makarenkov, Vladimir I.; Kirillov, A. B.
1991-08-01
Possible mechanisms of creating different types of persistent states for informational processing are regarded. It is presented two origins of criticalities - self-organized and phase transition. A comparative analyses of their behavior is given. It is demonstrated that despite a likeness there are important differences. These differences can play a significant role to explain the physical issue of such highest functions of the brain as a short-term memory and attention. 1.
Computational Methods for Modeling Aptamers and Designing Riboswitches
Directory of Open Access Journals (Sweden)
Sha Gong
2017-11-01
Full Text Available Riboswitches, which are located within certain noncoding RNA region perform functions as genetic “switches”, regulating when and where genes are expressed in response to certain ligands. Understanding the numerous functions of riboswitches requires computation models to predict structures and structural changes of the aptamer domains. Although aptamers often form a complex structure, computational approaches, such as RNAComposer and Rosetta, have already been applied to model the tertiary (three-dimensional (3D structure for several aptamers. As structural changes in aptamers must be achieved within the certain time window for effective regulation, kinetics is another key point for understanding aptamer function in riboswitch-mediated gene regulation. The coarse-grained self-organized polymer (SOP model using Langevin dynamics simulation has been successfully developed to investigate folding kinetics of aptamers, while their co-transcriptional folding kinetics can be modeled by the helix-based computational method and BarMap approach. Based on the known aptamers, the web server Riboswitch Calculator and other theoretical methods provide a new tool to design synthetic riboswitches. This review will represent an overview of these computational methods for modeling structure and kinetics of riboswitch aptamers and for designing riboswitches.
Mathematical Modeling and Computational Thinking
Sanford, John F.; Naidu, Jaideep T.
2017-01-01
The paper argues that mathematical modeling is the essence of computational thinking. Learning a computer language is a valuable assistance in learning logical thinking but of less assistance when learning problem-solving skills. The paper is third in a series and presents some examples of mathematical modeling using spreadsheets at an advanced…
COMPUTATIONAL MODELS FOR SUSTAINABLE DEVELOPMENT
Monendra Grover; Rajesh Kumar; Tapan Kumar Mondal; S. Rajkumar
2011-01-01
Genetic erosion is a serious problem and computational models have been developed to prevent it. The computational modeling in this field not only includes (terrestrial) reserve design, but also decision modeling for related problems such as habitat restoration, marine reserve design, and nonreserve approaches to conservation management. Models have been formulated for evaluating tradeoffs between socioeconomic, biophysical, and spatial criteria in establishing marine reserves. The percolatio...
Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji
2016-04-01
Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the downscaling technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.
Computer-Aided Modeling Framework
DEFF Research Database (Denmark)
Fedorova, Marina; Sin, Gürkan; Gani, Rafiqul
Models are playing important roles in design and analysis of chemicals based products and the processes that manufacture them. Computer-aided methods and tools have the potential to reduce the number of experiments, which can be expensive and time consuming, and there is a benefit of working...... development and application. The proposed work is a part of the project for development of methods and tools that will allow systematic generation, analysis and solution of models for various objectives. It will use the computer-aided modeling framework that is based on a modeling methodology, which combines....... In this contribution, the concept of template-based modeling is presented and application is highlighted for the specific case of catalytic membrane fixed bed models. The modeling template is integrated in a generic computer-aided modeling framework. Furthermore, modeling templates enable the idea of model reuse...
International Nuclear Information System (INIS)
Krommes, J.A.; Ottaviani, M.
2000-01-01
Numerical measurements and analytical studies are performed on a stochastic model with features relevant to plasma confinement. Although the model lacks crucial features of self-organized criticality (SOC) and its transport can be computed by standard techniques, it nevertheless exhibits intermittency and algebraic time correlations. This suggests that SOC need not be the explanation for observed long-time tails in experimental fluctuation data. Arguments based on the renormalized spectral balance equation, and simulation of a standard nonlinear paradigm, predict a range of Hurst exponents in reasonable agreement with the observations without invoking submarginal dynamics
Concept and Feasibility Study of Self-Organized Electrochemical Devices
National Research Council Canada - National Science Library
Moorehead, William
2002-01-01
.... In this work, using attractive and repulsive London-van der Waals forces, a self-organized, interpenetrating, separator-free rechargeable lithium ion battery called a self-organized battery system (SBS) is proposed...
Self-Organization Activities of College Students: Challenges and Opportunities
Shmurygina, Natalia; Bazhenova, Natalia; Bazhenov, Ruslan; Nikolaeva, Natalia; Tcytcarev, Andrey
2016-01-01
The article provides the analysis of self-organization activities of college students related to their participation in youth associations activities. The purpose of research is to disclose a degree of students' activities demonstration based on self-organization processes, assessment of existing self-organization practices of the youth,…
Anomalous relaxation and self-organization in non-equilibrium processes
Fatkullin, Ibrahim; Kladko, Konstantin; Mitkov, Igor; Bishop, A. R.
2000-01-01
We study thermal relaxation in ordered arrays of coupled nonlinear elements with external driving. We find, that our model exhibits dynamic self-organization manifested in a universal stretched-exponential form of relaxation. We identify two types of self-organization, cooperative and anti-cooperative, which lead to fast and slow relaxation, respectively. We give a qualitative explanation for the behavior of the stretched exponent in different parameter ranges. We emphasize that this is a sys...
Chersi, Fabian; Ferro, Marcello; Pezzulo, Giovanni; Pirrelli, Vito
2014-07-01
A growing body of evidence in cognitive psychology and neuroscience suggests a deep interconnection between sensory-motor and language systems in the brain. Based on recent neurophysiological findings on the anatomo-functional organization of the fronto-parietal network, we present a computational model showing that language processing may have reused or co-developed organizing principles, functionality, and learning mechanisms typical of premotor circuit. The proposed model combines principles of Hebbian topological self-organization and prediction learning. Trained on sequences of either motor or linguistic units, the network develops independent neuronal chains, formed by dedicated nodes encoding only context-specific stimuli. Moreover, neurons responding to the same stimulus or class of stimuli tend to cluster together to form topologically connected areas similar to those observed in the brain cortex. Simulations support a unitary explanatory framework reconciling neurophysiological motor data with established behavioral evidence on lexical acquisition, access, and recall. Copyright © 2014 Cognitive Science Society, Inc.
Supervised self-organization of homogeneous swarms using ergodic projections of Markov chains.
Chattopadhyay, Ishanu; Ray, Asok
2009-12-01
This paper formulates a self-organization algorithm to address the problem of global behavior supervision in engineered swarms of arbitrarily large population sizes. The swarms considered in this paper are assumed to be homogeneous collections of independent identical finite-state agents, each of which is modeled by an irreducible finite Markov chain. The proposed algorithm computes the necessary perturbations in the local agents' behavior, which guarantees convergence to the desired observed state of the swarm. The ergodicity property of the swarm, which is induced as a result of the irreducibility of the agent models, implies that while the local behavior of the agents converges to the desired behavior only in the time average, the overall swarm behavior converges to the specification and stays there at all times. A simulation example illustrates the underlying concept.
RM-SORN: a reward-modulated self-organizing recurrent neural network.
Aswolinskiy, Witali; Pipa, Gordon
2015-01-01
Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-modulated Spike-Timing-Dependent Plasticity (STDP). IP enables the network to explore possible output sequences and STDP, modulated by reward, reinforces the creation of the rewarded output sequences. The model is tested on tasks for prediction, recall, non-linear computation, pattern recognition, and sequence generation. It achieves performance comparable to networks trained with supervised learning, while using simple, biologically motivated plasticity rules, and rewarding strategies. The results confirm the importance of investigating the interaction of several plasticity rules in the context of reward-modulated learning and whether reward-modulated self-organization can explain the amazing capabilities of the brain.
Do earthquakes exhibit self-organized criticality?
International Nuclear Information System (INIS)
Yang Xiaosong; Ma Jin; Du Shuming
2004-01-01
If earthquakes are phenomena of self-organized criticality (SOC), statistical characteristics of the earthquake time series should be invariant after the sequence of events in an earthquake catalog are randomly rearranged. In this Letter we argue that earthquakes are unlikely phenomena of SOC because our analysis of the Southern California Earthquake Catalog shows that the first-return-time probability P M (T) is apparently changed after the time series is rearranged. This suggests that the SOC theory should not be used to oppose the efforts of earthquake prediction
Firm Size, a Self-Organized Critical Phenomenon: Evidence from the Dynamical Systems Theory
Chandra, Akhilesh
This research draws upon a recent innovation in the dynamical systems literature called the theory of self -organized criticality (SOC) (Bak, Tang, and Wiesenfeld 1988) to develop a computational model of a firm's size by relating its internal and the external sub-systems. As a holistic paradigm, the theory of SOC implies that a firm as a composite system of many degrees of freedom naturally evolves to a critical state in which a minor event starts a chain reaction that can affect either a part or the system as a whole. Thus, the global features of a firm cannot be understood by analyzing its individual parts separately. The causal framework builds upon a constant capital resource to support a volume of production at the existing level of efficiency. The critical size is defined as the production level at which the average product of a firm's factors of production attains its maximum value. The non -linearity is inferred by a change in the nature of relations at the border of criticality, between size and the two performance variables, viz., the operating efficiency and the financial efficiency. The effect of breaching the critical size is examined on the stock price reactions. Consistent with the theory of SOC, it is hypothesized that the temporal response of a firm breaching the level of critical size should behave as a flicker noise (1/f) process. The flicker noise is characterized by correlations extended over a wide range of time scales, indicating some sort of cooperative effect among a firm's degrees of freedom. It is further hypothesized that a firm's size evolves to a spatial structure with scale-invariant, self-similar (fractal) properties. The system is said to be self-organized inasmuch as it naturally evolves to the state of criticality without any detailed specifications of the initial conditions. In this respect, the critical state is an attractor of the firm's dynamics. Another set of hypotheses examines the relations between the size and the
International Nuclear Information System (INIS)
Potter, J.M.
1985-01-01
The mathematical background for a multiport-network-solving program is described. A method for accurately numerically modeling an arbitrary, continuous, multiport transmission line is discussed. A modification to the transmission-line equations to accommodate multiple rf drives is presented. An improved model for the radio-frequency quadrupole (RFQ) accelerator that corrects previous errors is given. This model permits treating the RFQ as a true eight-port network for simplicity in interpreting the field distribution and ensures that all modes propagate at the same velocity in the high-frequency limit. The flexibility of the multiport model is illustrated by simple modifications to otherwise two-dimensional systems that permit modeling them as linear chains of multiport networks
Computer Based Modelling and Simulation
Indian Academy of Sciences (India)
GENERAL I ARTICLE. Computer Based ... universities, and later did system analysis, ... sonal computers (PC) and low cost software packages and tools. They can serve as useful learning experience through student projects. Models are .... Let us consider a numerical example: to calculate the velocity of a trainer aircraft ...
On self-organized criticality in nonconserving systems
International Nuclear Information System (INIS)
Socolar, J.E.S.; Grinstein, G.; Jayaprakash, C.
1993-01-01
Two models with nonconserving dynamics and slow continuous deterministic driving, a stick-slip model (SSM) of earthquake dynamics and a toy forest-fire model (FFM), have recently been argued to show numerical evidence of self-organized criticality (generic, scale-invariant steady states). To determine whether the observed criticality is indeed generic, we study these models as a function of a parameter γ which was implicitly tuned to a special value, γ=1, in their original definitions. In both cases, the maximum Lyapunov exponent vanishes at γ=1. We find that the FFM does not exhibit self-organized criticality for any γ, including γ=1; nor does the SSM with periodic boundary conditions. Both models show evidence of macroscopic periodic oscillations in time for some range of γ values. We suggest that such oscillations may provide a mechanism for the generation of scale-invariant structure in nonconserving systems, and, in particular, that they underlie the criticality previously observed in the SSM with open boundary conditions
Computational Modeling of Space Physiology
Lewandowski, Beth E.; Griffin, Devon W.
2016-01-01
The Digital Astronaut Project (DAP), within NASAs Human Research Program, develops and implements computational modeling for use in the mitigation of human health and performance risks associated with long duration spaceflight. Over the past decade, DAP developed models to provide insights into space flight related changes to the central nervous system, cardiovascular system and the musculoskeletal system. Examples of the models and their applications include biomechanical models applied to advanced exercise device development, bone fracture risk quantification for mission planning, accident investigation, bone health standards development, and occupant protection. The International Space Station (ISS), in its role as a testing ground for long duration spaceflight, has been an important platform for obtaining human spaceflight data. DAP has used preflight, in-flight and post-flight data from short and long duration astronauts for computational model development and validation. Examples include preflight and post-flight bone mineral density data, muscle cross-sectional area, and muscle strength measurements. Results from computational modeling supplement space physiology research by informing experimental design. Using these computational models, DAP personnel can easily identify both important factors associated with a phenomenon and areas where data are lacking. This presentation will provide examples of DAP computational models, the data used in model development and validation, and applications of the model.
Self-organization in irradiated materials
International Nuclear Information System (INIS)
Gerasimenko, N.N.; Dzhamanbalin, K.K.; Medetov, N.A.
2003-01-01
Full text: By the present time a great deal of experimental material concerning self-organization in irradiated materials is stored. It means that in different materials (single crystal and amorphous semiconductor, metals, polymers) during one process of irradiation with accelerated particles or energetic quanta the structure previously disordered can be reordered to the previous or different order. These processes are considered separately from the processes of radiation-stimulated ordering when the renewal of the structure occurs as the result of extra irradiation, sometimes accompanied with another influence (heating, lighting, application of mechanical tensions). The processes of reordering are divided into two basic classes: the reconstruction of crystalline structure (1) and the formation of space-ordered system (2). The processes of ordering are considered with the use of synergetic approach and are analyzed conformably to the concrete conditions of new order appearance process realization in order to reveal the self-organization factor's role. The concrete experimental results of investigating of the radiation ordering processes are analyzed for different materials: semiconductor, metals, inorganic dielectrics, polymers. The ordering processes are examined from the point of their possible use in the technology of creating nano-dimensional structures general and quantum-dimensional ones in particular
Computational modelling in fluid mechanics
International Nuclear Information System (INIS)
Hauguel, A.
1985-01-01
The modelling of the greatest part of environmental or industrial flow problems gives very similar types of equations. The considerable increase in computing capacity over the last ten years consequently allowed numerical models of growing complexity to be processed. The varied group of computer codes presented are now a complementary tool of experimental facilities to achieve studies in the field of fluid mechanics. Several codes applied in the nuclear field (reactors, cooling towers, exchangers, plumes...) are presented among others [fr
Self-organizing magnetic beads for biomedical applications
International Nuclear Information System (INIS)
Gusenbauer, Markus; Kovacs, Alexander; Reichel, Franz; Exl, Lukas; Bance, Simon; Özelt, Harald; Schrefl, Thomas
2012-01-01
In the field of biomedicine magnetic beads are used for drug delivery and to treat hyperthermia. Here we propose to use self-organized bead structures to isolate circulating tumor cells using lab-on-chip technologies. Typically blood flows past microposts functionalized with antibodies for circulating tumor cells. Creating these microposts with interacting magnetic beads makes it possible to tune the geometry in size, position and shape. We developed a simulation tool that combines micromagnetics and discrete particle dynamics, in order to design micropost arrays made of interacting beads. The simulation takes into account the viscous drag of the blood flow, magnetostatic interactions between the magnetic beads and gradient forces from external aligned magnets. We developed a particle–particle particle–mesh method for effective computation of the magnetic force and torque acting on the particles. - Highlights: ► We propose to use self-organized bead structures to isolate circulating tumor cells. ► Flexible ways are important to get a high probability of catching cancer cells. ► The beads make it possible to tune the geometry in size position and shape.
Classification of perovskites with supervised self-organizing maps
International Nuclear Information System (INIS)
Kuzmanovski, Igor; Dimitrovska-Lazova, Sandra; Aleksovska, Slobotka
2007-01-01
In this work supervised self-organizing maps were used for structural classification of perovskites. For this purpose, structural data for total number of 286 perovskites, belonging to ABO 3 and/or A 2 BB'O 6 types, were collected from literature: 130 of these are cubic, 85 orthorhombic and 71 monoclinic. For classification purposes, the effective ionic radii of the cations, electronegativities of the cations in B-position, as well as, the oxidation states of these cations, were used as input variables. The parameters of the developed models, as well as, the most suitable variables for classification purposes were selected using genetic algorithms. Two-third of all the compounds were used in the training phase. During the optimization process the performances of the models were checked using cross-validation leave-1/10-out. The performances of obtained solutions were checked using the test set composed of the remaining one-third of the compounds. The obtained models for classification of these three classes of perovskite compounds show very good results. Namely, the classification of the compounds in the test set resulted in small number of discrepancies (4.2-6.4%) between the actual crystallographic class and the one predicted by the models. All these results are strong arguments for the validity of supervised self-organizing maps for performing such types of classification. Therefore, the proposed procedure could be successfully used for crystallographic classification of perovskites in one of these three classes
Chaos Modelling with Computers
Indian Academy of Sciences (India)
Chaos is one of the major scientific discoveries of our times. In fact many scientists ... But there are other natural phenomena that are not predictable though ... characteristics of chaos. ... The position and velocity are all that are needed to determine the motion of a .... a system of equations that modelled the earth's weather ...
Patient-Specific Computational Modeling
Peña, Estefanía
2012-01-01
This book addresses patient-specific modeling. It integrates computational modeling, experimental procedures, imagine clinical segmentation and mesh generation with the finite element method (FEM) to solve problems in computational biomedicine and bioengineering. Specific areas of interest include cardiovascular problems, ocular and muscular systems and soft tissue modeling. Patient-specific modeling has been the subject of serious research over the last seven years and interest in the area is continually growing and this area is expected to further develop in the near future.
Computer model for ductile fracture
International Nuclear Information System (INIS)
Moran, B.; Reaugh, J. E.
1979-01-01
A computer model is described for predicting ductile fracture initiation and propagation. The computer fracture model is calibrated by simple and notched round-bar tension tests and a precracked compact tension test. The model is used to predict fracture initiation and propagation in a Charpy specimen and compare the results with experiments. The calibrated model provides a correlation between Charpy V-notch (CVN) fracture energy and any measure of fracture toughness, such as J/sub Ic/. A second simpler empirical correlation was obtained using the energy to initiate fracture in the Charpy specimen rather than total energy CVN, and compared the results with the empirical correlation of Rolfe and Novak
Trust Models in Ubiquitous Computing
DEFF Research Database (Denmark)
Nielsen, Mogens; Krukow, Karl; Sassone, Vladimiro
2008-01-01
We recapture some of the arguments for trust-based technologies in ubiquitous computing, followed by a brief survey of some of the models of trust that have been introduced in this respect. Based on this, we argue for the need of more formal and foundational trust models.......We recapture some of the arguments for trust-based technologies in ubiquitous computing, followed by a brief survey of some of the models of trust that have been introduced in this respect. Based on this, we argue for the need of more formal and foundational trust models....
Structures in plasmas and their self-organizations
International Nuclear Information System (INIS)
Yoshida, Zensho
1989-01-01
This paper is a concise review of the physics of structures. The progress of the structure theory was motivated by the appearances of many different ordered structures that are self-organized through spontaneous dynamics. For typical examples in plasma physics, cited are the MHD equilibria (Taylor relaxed state), the ion acoustic solitons, and the van Kampen modes of continuous-spectrum Langmuir waves. A static theory for the intrinsic structures is developed to clarify the basic difference between the classical orders and the self-organized structures. In linear models, an intrinsic structure is characterized by a singular spectrum of a certain eigenvalue problem. The Taylor relaxed state is characterized by the continuum of the point spectra of the rotational operator. The general MHD equilibrium is related to a nonlinear eigenvalue problem. The soliton is a nonlinear eigenfunction of the Helmholtz-type Bohm equation. The variational expression of an intrinsic structure is characterized by restrictive functionals, which in a dynamical theory, is related to selective conservations. The Taylor relaxed state is obtained by minimizing the magnetic-field energy with conserving the magnetic helicity. This selective dissipation occurs in the fluctuations of kink modes. The soliton is self-organized by the dissipation of the Hamiltonian with keeping the energy approximately constant. The principle of the selective dissipation is logically a generalization of the ergodic hypothesis for the classical order and could be proved in a rigorous way by analyzing the attractor of the dynamical systems, just as the proof the ergodic theorem is obtained by the time-asymptotic analysis of a class of semigroups. (J.P.N.) 85 refs
High-resolution Self-Organizing Maps for advanced visualization and dimension reduction.
Saraswati, Ayu; Nguyen, Van Tuc; Hagenbuchner, Markus; Tsoi, Ah Chung
2018-05-04
Kohonen's Self Organizing feature Map (SOM) provides an effective way to project high dimensional input features onto a low dimensional display space while preserving the topological relationships among the input features. Recent advances in algorithms that take advantages of modern computing hardware introduced the concept of high resolution SOMs (HRSOMs). This paper investigates the capabilities and applicability of the HRSOM as a visualization tool for cluster analysis and its suitabilities to serve as a pre-processor in ensemble learning models. The evaluation is conducted on a number of established benchmarks and real-world learning problems, namely, the policeman benchmark, two web spam detection problems, a network intrusion detection problem, and a malware detection problem. It is found that the visualization resulted from an HRSOM provides new insights concerning these learning problems. It is furthermore shown empirically that broad benefits from the use of HRSOMs in both clustering and classification problems can be expected. Copyright © 2018 Elsevier Ltd. All rights reserved.
Trust models in ubiquitous computing.
Krukow, Karl; Nielsen, Mogens; Sassone, Vladimiro
2008-10-28
We recapture some of the arguments for trust-based technologies in ubiquitous computing, followed by a brief survey of some of the models of trust that have been introduced in this respect. Based on this, we argue for the need of more formal and foundational trust models.
Ch. 33 Modeling: Computational Thermodynamics
International Nuclear Information System (INIS)
Besmann, Theodore M.
2012-01-01
This chapter considers methods and techniques for computational modeling for nuclear materials with a focus on fuels. The basic concepts for chemical thermodynamics are described and various current models for complex crystalline and liquid phases are illustrated. Also included are descriptions of available databases for use in chemical thermodynamic studies and commercial codes for performing complex equilibrium calculations.
Computer Based Modelling and Simulation
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 6; Issue 3. Computer Based Modelling and Simulation - Modelling Deterministic Systems. N K Srinivasan. General Article Volume 6 Issue 3 March 2001 pp 46-54. Fulltext. Click here to view fulltext PDF. Permanent link:
Control of self-organizing nonlinear systems
Klapp, Sabine; Hövel, Philipp
2016-01-01
The book summarizes the state-of-the-art of research on control of self-organizing nonlinear systems with contributions from leading international experts in the field. The first focus concerns recent methodological developments including control of networks and of noisy and time-delayed systems. As a second focus, the book features emerging concepts of application including control of quantum systems, soft condensed matter, and biological systems. Special topics reflecting the active research in the field are the analysis and control of chimera states in classical networks and in quantum systems, the mathematical treatment of multiscale systems, the control of colloidal and quantum transport, the control of epidemics and of neural network dynamics.
Self-organizing physical fields and gravity
International Nuclear Information System (INIS)
Pestov, I.B.
2009-01-01
It is shown that the Theory of Self-Organizing Physical Fields provides the adequate and consistent consideration of the gravitational phenomena. The general conclusion lies in the fact that the essence of gravidynamics is the new field concept of time and the general covariant law of energy conservation which in particular means that dark energy is simply the energy of the gravitational field. From the natural geometrical laws of gravidynamics the dynamical equations of the gravitational field are derived. Two exact solutions of these equations are obtained. One of them represents a shock gravitational wave and the other represents the Universe filled up with the gravitational energy only. These solutions are compared with the Schwarzschild and Friedmann solutions in the Einstein general theory of relativity
Self-organized criticality and urban development
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Michael Batty
1999-01-01
Full Text Available Urban society is undergoing as profound a spatial transformation as that associated with the emergence of the industrial city two centuries ago. To describe and measure this transition, we introduce a new theory based on the concept that large-scale, complex systems composed of many interacting elements, show a surprising degree of resilience to change, holding themselves at critical levels for long periods until conditions emerge which move the system, often abruptly, to a new threshold. This theory is called ‘self-organized criticality’; it is consistent with systems in which global patterns emerge from local action which is the hallmark of self-organization, and it is consistent with developments in system dynamics and their morphology which find expression in fractal geometry and weak chaos theory. We illustrate the theory using a unique space–time series of urban development for Buffalo, Western New York, which contains the locations of over one quarter of a million sites coded by their year of construction and dating back to 1773, some 60 years before the city began to develop. We measure the emergence and growth of the city using urban density functions from which measures of fractal dimension are used to construct growth paths of the way the city has grown to fill its region. These phase portraits suggest the existence of transitions between the frontier, the settled agricultural region, the centralized industrial city and the decentralized postindustrial city, and our analysis reveals that Buffalo has maintained itself at a critical threshold since the emergence of the automobile city some 70 years ago. Our implied speculation is: how long will this kind of urban form be maintained in the face of seemingly unstoppable technological change?
Autonomous Data Collection Using a Self-Organizing Map.
Faigl, Jan; Hollinger, Geoffrey A
2018-05-01
The self-organizing map (SOM) is an unsupervised learning technique providing a transformation of a high-dimensional input space into a lower dimensional output space. In this paper, we utilize the SOM for the traveling salesman problem (TSP) to develop a solution to autonomous data collection. Autonomous data collection requires gathering data from predeployed sensors by moving within a limited communication radius. We propose a new growing SOM that adapts the number of neurons during learning, which also allows our approach to apply in cases where some sensors can be ignored due to a lower priority. Based on a comparison with available combinatorial heuristic algorithms for relevant variants of the TSP, the proposed approach demonstrates improved results, while also being less computationally demanding. Moreover, the proposed learning procedure can be extended to cases where particular sensors have varying communication radii, and it can also be extended to multivehicle planning.
Computer Modelling of Dynamic Processes
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B. Rybakin
2000-10-01
Full Text Available Results of numerical modeling of dynamic problems are summed in the article up. These problems are characteristic for various areas of human activity, in particular for problem solving in ecology. The following problems are considered in the present work: computer modeling of dynamic effects on elastic-plastic bodies, calculation and determination of performances of gas streams in gas cleaning equipment, modeling of biogas formation processes.
Computational models of complex systems
Dabbaghian, Vahid
2014-01-01
Computational and mathematical models provide us with the opportunities to investigate the complexities of real world problems. They allow us to apply our best analytical methods to define problems in a clearly mathematical manner and exhaustively test our solutions before committing expensive resources. This is made possible by assuming parameter(s) in a bounded environment, allowing for controllable experimentation, not always possible in live scenarios. For example, simulation of computational models allows the testing of theories in a manner that is both fundamentally deductive and experimental in nature. The main ingredients for such research ideas come from multiple disciplines and the importance of interdisciplinary research is well recognized by the scientific community. This book provides a window to the novel endeavours of the research communities to present their works by highlighting the value of computational modelling as a research tool when investigating complex systems. We hope that the reader...
Optimality and self-organization in river deltas
Tejedor, A.; Longjas, A.; Edmonds, D. A.; Zaliapin, I. V.; Georgiou, T. T.; Rinaldo, A.; Foufoula-Georgiou, E.
2017-12-01
Deltas are nourished by channel networks, whose connectivity constrains, if not drives, the evolution, functionality and resilience of these systems. Understanding the coevolution of deltaic channels and their flux organization is crucial for guiding maintenance strategies of these highly stressed systems from a range of anthropogenic activities. However, in contrast to tributary channel networks, to date, no theory has been proposed to explain how deltas self-organize to distribute water and sediment to the delta top and the shoreline. Here, we hypothesize the existence of an optimality principle underlying the self-organized partition of fluxes in delta channel networks. Specifically, we hypothesize that deltas distribute water and sediment fluxes on a given delta topology such as to maximize the diversity of flux delivery to the shoreline. By introducing the concept of nonlocal Entropy Rate (nER) and analyzing ten field deltas in diverse environments, we present evidence that supports our hypothesis, suggesting that delta networks achieve dynamically accessible maxima of their nER. Furthermore, by analyzing six simulated deltas using the Delf3D model and following their topologic and flux re-organization before and after major avulsions, we further study the evolution of nER and confirm our hypothesis. We discuss how optimal flux distributions in terms of nER, when interpreted in terms of resilience, are configurations that reflect an increased ability to withstand perturbations.
The Self-Organized Archive: SPASE, PDS and Archive Cooperatives
King, T. A.; Hughes, J. S.; Roberts, D. A.; Walker, R. J.; Joy, S. P.
2005-05-01
Information systems with high quality metadata enable uses and services which often go beyond the original purpose. There are two types of metadata: annotations which are items that comment on or describe the content of a resource and identification attributes which describe the external properties of the resource itself. For example, annotations may indicate which columns are present in a table of data, whereas an identification attribute would indicate source of the table, such as the observatory, instrument, organization, and data type. When the identification attributes are collected and used as the basis of a search engine, a user can constrain on an attribute, the archive can then self-organize around the constraint, presenting the user with a particular view of the archive. In an archive cooperative where each participating data system or archive may have its own metadata standards, providing a multi-system search engine requires that individual archive metadata be mapped to a broad based standard. To explore how cooperative archives can form a larger self-organized archive we will show how the Space Physics Archive Search and Extract (SPASE) data model will allow different systems to create a cooperative and will use Planetary Data System (PDS) plus existing space physics activities as a demonstration.
Weighted Evolving Networks with Self-organized Communities
International Nuclear Information System (INIS)
Xie Zhou; Wang Xiaofan; Li Xiang
2008-01-01
In order to describe the self-organization of communities in the evolution of weighted networks, we propose a new evolving model for weighted community-structured networks with the preferential mechanisms functioned in different levels according to community sizes and node strengths, respectively. Theoretical analyses and numerical simulations show that our model captures power-law distributions of community sizes, node strengths, and link weights, with tunable exponents of ν ≥ 1, γ > 2, and α > 2, respectively, sharing large clustering coefficients and scaling clustering spectra, and covering the range from disassortative networks to assortative networks. Finally, we apply our new model to the scientific co-authorship networks with both their weighted and unweighted datasets to verify its effectiveness
Self-Organization during Friction of Slide Bearing Antifriction Materials
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Iosif S. Gershman
2015-12-01
Full Text Available This article discusses the peculiarities of self-organization behavior and formation of dissipative structures during friction of antifriction alloys for slide bearings against a steel counterbody. It shows that during self-organization, the moment of friction in a tribosystem may be decreasing with the load growth and in the bifurcations of the coefficient of friction with respect to load. Self-organization and the formation of dissipative structures lead to an increase in the seizure load.
MACHINE LEARNING FOR THE SELF-ORGANIZATION OF DISTRIBUTED SYSTEMS IN ECONOMIC APPLICATIONS
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Jerzy Balicki
2017-03-01
Full Text Available In this paper, an application of machine learning to the problem of self-organization of distributed systems has been discussed with regard to economic applications, with particular emphasis on supervised neural network learning to predict stock investments and some ratings of companies. In addition, genetic programming can play an important role in the preparation and testing of several financial information systems. For this reason, machine learning applications have been discussed because some software applications can be automatically constructed by genetic programming. To obtain a competitive advantage, machine learning can be used for the management of self-organizing cloud computing systems performing calculations for business. Also the use of selected economic self-organizing distributed systems has been described, including some testing methods of predicting borrower reliability. Finally, some conclusions and directions for further research have been proposed.
Climate Modeling Computing Needs Assessment
Petraska, K. E.; McCabe, J. D.
2011-12-01
This paper discusses early findings of an assessment of computing needs for NASA science, engineering and flight communities. The purpose of this assessment is to document a comprehensive set of computing needs that will allow us to better evaluate whether our computing assets are adequately structured to meet evolving demand. The early results are interesting, already pointing out improvements we can make today to get more out of the computing capacity we have, as well as potential game changing innovations for the future in how we apply information technology to science computing. Our objective is to learn how to leverage our resources in the best way possible to do more science for less money. Our approach in this assessment is threefold: Development of use case studies for science workflows; Creating a taxonomy and structure for describing science computing requirements; and characterizing agency computing, analysis, and visualization resources. As projects evolve, science data sets increase in a number of ways: in size, scope, timelines, complexity, and fidelity. Generating, processing, moving, and analyzing these data sets places distinct and discernable requirements on underlying computing, analysis, storage, and visualization systems. The initial focus group for this assessment is the Earth Science modeling community within NASA's Science Mission Directorate (SMD). As the assessment evolves, this focus will expand to other science communities across the agency. We will discuss our use cases, our framework for requirements and our characterizations, as well as our interview process, what we learned and how we plan to improve our materials after using them in the first round of interviews in the Earth Science Modeling community. We will describe our plans for how to expand this assessment, first into the Earth Science data analysis and remote sensing communities, and then throughout the full community of science, engineering and flight at NASA.
Computer Profiling Based Model for Investigation
Neeraj Choudhary; Nikhil Kumar Singh; Parmalik Singh
2011-01-01
Computer profiling is used for computer forensic analysis, and proposes and elaborates on a novel model for use in computer profiling, the computer profiling object model. The computer profiling object model is an information model which models a computer as objects with various attributes and inter-relationships. These together provide the information necessary for a human investigator or an automated reasoning engine to make judgments as to the probable usage and evidentiary value of a comp...
Getting computer models to communicate
International Nuclear Information System (INIS)
Caremoli, Ch.; Erhard, P.
1999-01-01
Today's computers have the processing power to deliver detailed and global simulations of complex industrial processes such as the operation of a nuclear reactor core. So should we be producing new, global numerical models to take full advantage of this new-found power? If so, it would be a long-term job. There is, however, another solution; to couple the existing validated numerical models together so that they work as one. (authors)
Computational Modeling in Liver Surgery
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Bruno Christ
2017-11-01
Full Text Available The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.
Cosmic logic: a computational model
International Nuclear Information System (INIS)
Vanchurin, Vitaly
2016-01-01
We initiate a formal study of logical inferences in context of the measure problem in cosmology or what we call cosmic logic. We describe a simple computational model of cosmic logic suitable for analysis of, for example, discretized cosmological systems. The construction is based on a particular model of computation, developed by Alan Turing, with cosmic observers (CO), cosmic measures (CM) and cosmic symmetries (CS) described by Turing machines. CO machines always start with a blank tape and CM machines take CO's Turing number (also known as description number or Gödel number) as input and output the corresponding probability. Similarly, CS machines take CO's Turing number as input, but output either one if the CO machines are in the same equivalence class or zero otherwise. We argue that CS machines are more fundamental than CM machines and, thus, should be used as building blocks in constructing CM machines. We prove the non-computability of a CS machine which discriminates between two classes of CO machines: mortal that halts in finite time and immortal that runs forever. In context of eternal inflation this result implies that it is impossible to construct CM machines to compute probabilities on the set of all CO machines using cut-off prescriptions. The cut-off measures can still be used if the set is reduced to include only machines which halt after a finite and predetermined number of steps
The role of hierarchy in self-organizing systems
Ollfen, van W.; Romme, A.G.L.
1995-01-01
This paper discusses the role of hierarchy in human systems. Two kinds of self-organizing processes are distinguished: conservative and dissipative self-organization. The former leads to rather stable, specialistic systems, whereas the latter leads to continuously changing generalistic systems. When
Enabling Self-Organization in Embedded Systems with Reconfigurable Hardware
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Christophe Bobda
2009-01-01
Full Text Available We present a methodology based on self-organization to manage resources in networked embedded systems based on reconfigurable hardware. Two points are detailed in this paper, the monitoring system used to analyse the system and the Local Marketplaces Global Symbiosis (LMGS concept defined for self-organization of dynamically reconfigurable nodes.
Self-organized plasmonic metasurfaces for all-optical modulation
Della Valle, G.; Polli, D.; Biagioni, P.; Martella, C.; Giordano, M. C.; Finazzi, M.; Longhi, S.; Duò, L.; Cerullo, G.; Buatier de Mongeot, F.
2015-06-01
We experimentally demonstrate a self-organized metasurface with a polarization dependent transmittance that can be dynamically controlled by optical means. The configuration consists of tightly packed plasmonic nanowires with a large dispersion of width and height produced by the defocused ion-beam sputtering of a thin gold film supported on a silica glass. Our results are quantitatively interpreted according to a theoretical model based on the thermomodulational nonlinearity of gold and a finite-element numerical analysis of the absorption and scattering cross-sections of the nanowires. We found that the polarization sensitivity of the metasurface can be strongly enhanced by pumping with ultrashort laser pulses, leading to potential applications in ultrafast all-optical modulation and switching of light.
Self-organized architectures from assorted DNA-framed nanoparticles
Liu, Wenyan; Halverson, Jonathan; Tian, Ye; Tkachenko, Alexei V.; Gang, Oleg
2016-09-01
The science of self-assembly has undergone a radical shift from asking questions about why individual components self-organize into ordered structures, to manipulating the resultant order. However, the quest for far-reaching nanomanufacturing requires addressing an even more challenging question: how to form nanoparticle (NP) structures with designed architectures without explicitly prescribing particle positions. Here we report an assembly concept in which building instructions are embedded into NPs via DNA frames. The integration of NPs and DNA origami frames enables the fabrication of NPs with designed anisotropic and selective interactions. Using a pre-defined set of different DNA-framed NPs, we show it is possible to design diverse planar architectures, which include periodic structures and shaped meso-objects that spontaneously emerge on mixing of the different topological types of NP. Even objects of non-trivial shapes, such as a nanoscale model of Leonardo da Vinci's Vitruvian Man, can be self-assembled successfully.
SORN: a self-organizing recurrent neural network
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Andreea Lazar
2009-10-01
Full Text Available Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing circuits that learn appropriate representations for time-varying sensory stimuli. However, it has been difficult to mimic these abilities in artificial neural network models. Here we introduce SORN, a self-organizing recurrent network. It combines three distinct forms of local plasticity to learn spatio-temporal patterns in its input while maintaining its dynamics in a healthy regime suitable for learning. The SORN learns to encode information in the form of trajectories through its high-dimensional state space reminiscent of recent biological findings on cortical coding. All three forms of plasticity are shown to be essential for the network's success.
Minimal models of multidimensional computations.
Directory of Open Access Journals (Sweden)
Jeffrey D Fitzgerald
2011-03-01
Full Text Available The multidimensional computations performed by many biological systems are often characterized with limited information about the correlations between inputs and outputs. Given this limitation, our approach is to construct the maximum noise entropy response function of the system, leading to a closed-form and minimally biased model consistent with a given set of constraints on the input/output moments; the result is equivalent to conditional random field models from machine learning. For systems with binary outputs, such as neurons encoding sensory stimuli, the maximum noise entropy models are logistic functions whose arguments depend on the constraints. A constraint on the average output turns the binary maximum noise entropy models into minimum mutual information models, allowing for the calculation of the information content of the constraints and an information theoretic characterization of the system's computations. We use this approach to analyze the nonlinear input/output functions in macaque retina and thalamus; although these systems have been previously shown to be responsive to two input dimensions, the functional form of the response function in this reduced space had not been unambiguously identified. A second order model based on the logistic function is found to be both necessary and sufficient to accurately describe the neural responses to naturalistic stimuli, accounting for an average of 93% of the mutual information with a small number of parameters. Thus, despite the fact that the stimulus is highly non-Gaussian, the vast majority of the information in the neural responses is related to first and second order correlations. Our results suggest a principled and unbiased way to model multidimensional computations and determine the statistics of the inputs that are being encoded in the outputs.
Computational Models of Rock Failure
May, Dave A.; Spiegelman, Marc
2017-04-01
Practitioners in computational geodynamics, as per many other branches of applied science, typically do not analyse the underlying PDE's being solved in order to establish the existence or uniqueness of solutions. Rather, such proofs are left to the mathematicians, and all too frequently these results lag far behind (in time) the applied research being conducted, are often unintelligible to the non-specialist, are buried in journals applied scientists simply do not read, or simply have not been proven. As practitioners, we are by definition pragmatic. Thus, rather than first analysing our PDE's, we first attempt to find approximate solutions by throwing all our computational methods and machinery at the given problem and hoping for the best. Typically this approach leads to a satisfactory outcome. Usually it is only if the numerical solutions "look odd" that we start delving deeper into the math. In this presentation I summarise our findings in relation to using pressure dependent (Drucker-Prager type) flow laws in a simplified model of continental extension in which the material is assumed to be an incompressible, highly viscous fluid. Such assumptions represent the current mainstream adopted in computational studies of mantle and lithosphere deformation within our community. In short, we conclude that for the parameter range of cohesion and friction angle relevant to studying rocks, the incompressibility constraint combined with a Drucker-Prager flow law can result in problems which have no solution. This is proven by a 1D analytic model and convincingly demonstrated by 2D numerical simulations. To date, we do not have a robust "fix" for this fundamental problem. The intent of this submission is to highlight the importance of simple analytic models, highlight some of the dangers / risks of interpreting numerical solutions without understanding the properties of the PDE we solved, and lastly to stimulate discussions to develop an improved computational model of
Visualizing the topical structure of the medical sciences: a self-organizing map approach.
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André Skupin
Full Text Available We implement a high-resolution visualization of the medical knowledge domain using the self-organizing map (SOM method, based on a corpus of over two million publications. While self-organizing maps have been used for document visualization for some time, (1 little is known about how to deal with truly large document collections in conjunction with a large number of SOM neurons, (2 post-training geometric and semiotic transformations of the SOM tend to be limited, and (3 no user studies have been conducted with domain experts to validate the utility and readability of the resulting visualizations. Our study makes key contributions to all of these issues.Documents extracted from Medline and Scopus are analyzed on the basis of indexer-assigned MeSH terms. Initial dimensionality is reduced to include only the top 10% most frequent terms and the resulting document vectors are then used to train a large SOM consisting of over 75,000 neurons. The resulting two-dimensional model of the high-dimensional input space is then transformed into a large-format map by using geographic information system (GIS techniques and cartographic design principles. This map is then annotated and evaluated by ten experts stemming from the biomedical and other domains.Study results demonstrate that it is possible to transform a very large document corpus into a map that is visually engaging and conceptually stimulating to subject experts from both inside and outside of the particular knowledge domain. The challenges of dealing with a truly large corpus come to the fore and require embracing parallelization and use of supercomputing resources to solve otherwise intractable computational tasks. Among the envisaged future efforts are the creation of a highly interactive interface and the elaboration of the notion of this map of medicine acting as a base map, onto which other knowledge artifacts could be overlaid.
On the self-organizing process of large scale shear flows
Energy Technology Data Exchange (ETDEWEB)
Newton, Andrew P. L. [Department of Applied Maths, University of Sheffield, Sheffield, Yorkshire S3 7RH (United Kingdom); Kim, Eun-jin [School of Mathematics and Statistics, University of Sheffield, Sheffield, Yorkshire S3 7RH (United Kingdom); Liu, Han-Li [High Altitude Observatory, National Centre for Atmospheric Research, P. O. BOX 3000, Boulder, Colorado 80303-3000 (United States)
2013-09-15
Self organization is invoked as a paradigm to explore the processes governing the evolution of shear flows. By examining the probability density function (PDF) of the local flow gradient (shear), we show that shear flows reach a quasi-equilibrium state as its growth of shear is balanced by shear relaxation. Specifically, the PDFs of the local shear are calculated numerically and analytically in reduced 1D and 0D models, where the PDFs are shown to converge to a bimodal distribution in the case of finite correlated temporal forcing. This bimodal PDF is then shown to be reproduced in nonlinear simulation of 2D hydrodynamic turbulence. Furthermore, the bimodal PDF is demonstrated to result from a self-organizing shear flow with linear profile. Similar bimodal structure and linear profile of the shear flow are observed in gulf stream, suggesting self-organization.
Self-organization in a diversity induced thermodynamics.
Scirè, Alessandro; Annovazzi-Lodi, Valerio
2017-01-01
In this work we show how global self-organized patterns can come out of a disordered ensemble of point oscillators, as a result of a deterministic, and not of a random, cooperative process. The resulting system dynamics has many characteristics of classical thermodynamics. To this end, a modified Kuramoto model is introduced, by including Euclidean degrees of freedom and particle polarity. The standard deviation of the frequency distribution is the disorder parameter, diversity, acting as temperature, which is both a source of motion and of disorder. For zero and low diversity, robust static phase-synchronized patterns (crystals) appear, and the problem reverts to a generic dissipative many-body problem. From small to moderate diversity crystals display vibrations followed by structure disintegration in a competition of smaller dynamic patterns, internally synchronized, each of which is capable to manage its internal diversity. In this process a huge variety of self-organized dynamic shapes is formed. Such patterns can be seen again as (more complex) oscillators, where the same description can be applied in turn, renormalizing the problem to a bigger scale, opening the possibility of pattern evolution. The interaction functions are kept local because our idea is to build a system able to produce global patterns when its constituents only interact at the bond scale. By further increasing the oscillator diversity, the dynamics becomes erratic, dynamic patterns show short lifetime, and finally disappear for high diversity. Results are neither qualitatively dependent on the specific choice of the interaction functions nor on the shape of the probability function assumed for the frequencies. The system shows a phase transition and a critical behaviour for a specific value of diversity.
Self-organized complex space charge configurations at the origin of flicker noise
International Nuclear Information System (INIS)
Popescu, S.; Lozneanu, E.; Sanduloviciu, M.
2003-01-01
Based on experimental results obtained from a plasma diode we explain the fluctuations of the voltage supported by a non-linear gaseous conductor by the dynamical behavior of spatiotemporal patterns, in the form of moving double layers, formed after self-organization. Such phenomena appear when the system is subjected to an external constraint that creates and maintains a local gradient of electron kinetic energy. The described phenomenology suggests a plausible explanation for the appearance of flicker noise also in other physical systems, as for example semiconductors and, implicitly, offers a new model for the so-called self-organized criticality concept
Physics of far-from-equilibrium systems and self-organization
International Nuclear Information System (INIS)
Nicolis, G.
1993-01-01
The status of self-organization phenomena from the stand point of the physical sciences are analyzed. Non linear dynamics and the presence of constraints maintaining the system far from equilibrium are shown to be the basic mechanism involved in the emergence of these phenomena. Some particularly representative experiments are first presented: thermal conversion, chemical reactions (Benard problem), biological systems, and their explanation through order, disorder, non-linearity, irreversibility, stability, bifurcation, symmetry breaking, etc., concepts. Then it is shown how the self-organization paradigm allows to model problems outside the traditional realm of the physical sciences. 29 figs., 27 refs
Business model elements impacting cloud computing adoption
DEFF Research Database (Denmark)
Bogataj, Kristina; Pucihar, Andreja; Sudzina, Frantisek
The paper presents a proposed research framework for identification of business model elements impacting Cloud Computing Adoption. We provide a definition of main Cloud Computing characteristics, discuss previous findings on factors impacting Cloud Computing Adoption, and investigate technology a...
Computational Modeling in Tissue Engineering
2013-01-01
One of the major challenges in tissue engineering is the translation of biological knowledge on complex cell and tissue behavior into a predictive and robust engineering process. Mastering this complexity is an essential step towards clinical applications of tissue engineering. This volume discusses computational modeling tools that allow studying the biological complexity in a more quantitative way. More specifically, computational tools can help in: (i) quantifying and optimizing the tissue engineering product, e.g. by adapting scaffold design to optimize micro-environmental signals or by adapting selection criteria to improve homogeneity of the selected cell population; (ii) quantifying and optimizing the tissue engineering process, e.g. by adapting bioreactor design to improve quality and quantity of the final product; and (iii) assessing the influence of the in vivo environment on the behavior of the tissue engineering product, e.g. by investigating vascular ingrowth. The book presents examples of each...
A strategy for tissue self-organization that is robust to cellular heterogeneity and plasticity.
Cerchiari, Alec E; Garbe, James C; Jee, Noel Y; Todhunter, Michael E; Broaders, Kyle E; Peehl, Donna M; Desai, Tejal A; LaBarge, Mark A; Thomson, Matthew; Gartner, Zev J
2015-02-17
Developing tissues contain motile populations of cells that can self-organize into spatially ordered tissues based on differences in their interfacial surface energies. However, it is unclear how self-organization by this mechanism remains robust when interfacial energies become heterogeneous in either time or space. The ducts and acini of the human mammary gland are prototypical heterogeneous and dynamic tissues comprising two concentrically arranged cell types. To investigate the consequences of cellular heterogeneity and plasticity on cell positioning in the mammary gland, we reconstituted its self-organization from aggregates of primary cells in vitro. We find that self-organization is dominated by the interfacial energy of the tissue-ECM boundary, rather than by differential homo- and heterotypic energies of cell-cell interaction. Surprisingly, interactions with the tissue-ECM boundary are binary, in that only one cell type interacts appreciably with the boundary. Using mathematical modeling and cell-type-specific knockdown of key regulators of cell-cell cohesion, we show that this strategy of self-organization is robust to severe perturbations affecting cell-cell contact formation. We also find that this mechanism of self-organization is conserved in the human prostate. Therefore, a binary interfacial interaction with the tissue boundary provides a flexible and generalizable strategy for forming and maintaining the structure of two-component tissues that exhibit abundant heterogeneity and plasticity. Our model also predicts that mutations affecting binary cell-ECM interactions are catastrophic and could contribute to loss of tissue architecture in diseases such as breast cancer.
Nakajima, Kohei; Haruna, Taichi
2011-09-01
In this paper, we propose a new class of cellular automata based on the modification of its state space. It is introduced to model a computation which is exposed to an environment. We formalized the computation as extension and projection processes of its state space and resulting misidentifications of the state. This is motivated to embed the role of an environment into the system itself, which naturally induces self-organized internal perturbations rather than the usual external perturbations. Implementing this structure into the elementary cellular automata, we characterized its effect by means of input entropy and power spectral analysis. As a result, the cellular automata with this structure showed robust class IV behavior and a 1/f power spectrum in a wide range of rule space comparative to the notion of the edge of chaos. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
Aliouane, Leila; Ouadfeul, Sid-Ali; Rabhi, Abdessalem; Rouina, Fouzi; Benaissa, Zahia; Boudella, Amar
2013-04-01
The main goal of this work is to realize a comparison between two lithofacies segmentation techniques of reservoir interval. The first one is based on the Kohonen's Self-Organizing Map neural network machine. The second technique is based on the Walsh transform decomposition. Application to real well-logs data of two boreholes located in the Algerian Sahara shows that the Self-organizing map is able to provide more lithological details that the obtained lithofacies model given by the Walsh decomposition. Keywords: Comparison, Lithofacies, SOM, Walsh References: 1)Aliouane, L., Ouadfeul, S., Boudella, A., 2011, Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network, Arabian Journal of geosciences, doi: 10.1007/s12517-011-0459-4 2) Aliouane, L., Ouadfeul, S., Djarfour, N., Boudella, A., 2012, Petrophysical Parameters Estimation from Well-Logs Data Using Multilayer Perceptron and Radial Basis Function Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 730-736, doi : 10.1007/978-3-642-34500-5_86 3)Ouadfeul, S. and Aliouane., L., 2011, Multifractal analysis revisited by the continuous wavelet transform applied in lithofacies segmentation from well-logs data, International journal of applied physics and mathematics, Vol01 N01. 4) Ouadfeul, S., Aliouane, L., 2012, Lithofacies Classification Using the Multilayer Perceptron and the Self-organizing Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 737-744, doi : 10.1007/978-3-642-34500-5_87 5) Weisstein, Eric W. "Fast Walsh Transform." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/FastWalshTransform.html
The concept of self-organizing systems. Why bother?
Elverfeldt, Kirsten v.; Embleton-Hamann, Christine; Slaymaker, Olav
2016-04-01
Complexity theory and the concept of self-organizing systems provide a rather challenging conceptual framework for explaining earth systems change. Self-organization - understood as the aggregate processes internal to an environmental system that lead to a distinctive spatial or temporal organization - reduces the possibility of implicating a specific process as being causal, and it poses some restrictions on the idea that external drivers cause a system to change. The concept of self-organizing systems suggests that many phenomena result from an orchestration of different mechanisms, so that no causal role can be assigned to an individual factor or process. The idea that system change can be due to system-internal processes of self-organization thus proves a huge challenge to earth system research, especially in the context of global environmental change. In order to understand the concept's implications for the Earth Sciences, we need to know the characteristics of self-organizing systems and how to discern self-organizing systems. Within the talk, we aim firstly at characterizing self-organizing systems, and secondly at highlighting the advantages and difficulties of the concept within earth system sciences. The presentation concludes that: - The concept of self-organizing systems proves especially fruitful for small-scale earth surface systems. Beach cusps and patterned ground are only two of several other prime examples of self-organizing earth surface systems. They display characteristics of self-organization like (i) system-wide order from local interactions, (ii) symmetry breaking, (iii) distributed control, (iv) robustness and resilience, (v) nonlinearity and feedbacks, (vi) organizational closure, (vii) adaptation, and (viii) variation and selection. - It is comparatively easy to discern self-organization in small-scale systems, but to adapt the concept to larger scale systems relevant to global environmental change research is more difficult: Self-organizing
Opportunity for Realizing Ideal Computing System using Cloud Computing Model
Sreeramana Aithal; Vaikunth Pai T
2017-01-01
An ideal computing system is a computing system with ideal characteristics. The major components and their performance characteristics of such hypothetical system can be studied as a model with predicted input, output, system and environmental characteristics using the identified objectives of computing which can be used in any platform, any type of computing system, and for application automation, without making modifications in the form of structure, hardware, and software coding by an exte...
The dynamics of marginality and self-organized criticality as a paradigm for turbulent transport
International Nuclear Information System (INIS)
Newman, D.E.; Carreras, B.A.; Diamond, P.H.; Hahm, T.S.
1995-01-01
A general paradigm, based on the concept of self-organized criticality (SOC), for turbulent transport in magnetically confined plasmas has been recently suggested as an explanation for some of the apparent discrepancies between most theoretical models of turbulent transport and experimental observations of the transport in magnetically confined plasmas. This model describes the dynamics of the transport without relying on the underlying local fluctuation mechanisms. Computations based on a cellular automata realization of such a model have found that noise driven SOC systems can maintain average profiles that are linearly stable (submarginal) and yet are able to sustain active transport dynamics. It is also found that the dominant scales in the transport dynamics in the absence of sheared flow are system scales rather than the underlying local fluctuation scales. The addition of sheared flow into the dynamics leads to a large reduction of the system-scale transport events and a commensurate increase in the fluctuation-scale transport events needed to maintain the constant flux. The dynamics of these models and the potential ramifications for transport studies are discussed
Effect of prediction on the self-organization of pedestrian counter flow
International Nuclear Information System (INIS)
Wang Ziyang; Zhao Hui; Ma Jian; Qin Yong; Jia Limin
2012-01-01
Pedestrians may predict the behavior of others and then adjust their movement accordingly to avoid potential conflicts in advance. Motivated by this fact, we propose a predictive control theory-based pedestrian counter flow model, which describes the predictive mechanism underlying pedestrian self-organization phenomena. In this model, a pedestrian will make in-advance-avoid behavior based on the estimation of future moving gain within a given predictive length to reduce potential conflicts. The future gain in the present model is affected by three factors, i.e. the predictive length, the smooth degree of entrance and the influential area of coming pedestrians. Simulation results of the model show that increasing predictive length has a remarkable effect on reducing conflicts, improving pedestrian velocity, smoothing pedestrian movement and stabilizing the self-organized lanes. When enlarging the influential area of coming pedestrians, pedestrians tend to aggregate to the formed self-organized lanes, which makes the lanes wider and the lane number reduced. Interestingly, moderate enlargement (of the influential area) will reduce conflicts significantly, while excessive enlargement will lead to an increase in conflicts. We also discuss the predictive effect toward the smooth degree of entrance. When there are some formed self-organized lanes in the system, the effect is significant, and it will make the lanes more regular and stable, while when the existing lanes are unstable, the effect has little impact on the system. (paper)
International Conference on Computational Intelligence, Cyber Security, and Computational Models
Ramasamy, Vijayalakshmi; Sheen, Shina; Veeramani, C; Bonato, Anthony; Batten, Lynn
2016-01-01
This book aims at promoting high-quality research by researchers and practitioners from academia and industry at the International Conference on Computational Intelligence, Cyber Security, and Computational Models ICC3 2015 organized by PSG College of Technology, Coimbatore, India during December 17 – 19, 2015. This book enriches with innovations in broad areas of research like computational modeling, computational intelligence and cyber security. These emerging inter disciplinary research areas have helped to solve multifaceted problems and gained lot of attention in recent years. This encompasses theory and applications, to provide design, analysis and modeling of the aforementioned key areas.
International Nuclear Information System (INIS)
Xu Jianguo; Xu Xianli; Wang Weiguo
2008-01-01
The article describes the model construction of self-organizing competition artificial neural network, its principle and automatic recognition process of borehole lithology in detail, and then proves the efficiency of the neural network model for automatically recognizing the borehole lithology with some cases. The self-organizing competition artificial neural network has the ability of self- organization, self-adjustment and high permitting errors. Compared with the BP algorithm, it takes less calculation quantity and more rapidly converges. Furthermore, it can automatically confirm the category without the known sample information. Trial results based on contrasting the identification results of the borehole lithology with geological documentations, indicate that self-organizing artificial neural network can be well applied to automatically performing the category of borehole lithology, during the logging data explanation of sandstone-hosted uranium deposits. (authors)
Computer modeling of liquid crystals
International Nuclear Information System (INIS)
Al-Barwani, M.S.
1999-01-01
In this thesis, we investigate several aspects of the behaviour of liquid crystal molecules near interfaces using computer simulation. We briefly discuss experiment, theoretical and computer simulation studies of some of the liquid crystal interfaces. We then describe three essentially independent research topics. The first of these concerns extensive simulations of a liquid crystal formed by long flexible molecules. We examined the bulk behaviour of the model and its structure. Studies of a film of smectic liquid crystal surrounded by vapour were also carried out. Extensive simulations were also done for a long-molecule/short-molecule mixture, studies were then carried out to investigate the liquid-vapour interface of the mixture. Next, we report the results of large scale simulations of soft-spherocylinders of two different lengths. We examined the bulk coexistence of the nematic and isotropic phases of the model. Once the bulk coexistence behaviour was known, properties of the nematic-isotropic interface were investigated. This was done by fitting order parameter and density profiles to appropriate mathematical functions and calculating the biaxial order parameter. We briefly discuss the ordering at the interfaces and make attempts to calculate the surface tension. Finally, in our third project, we study the effects of different surface topographies on creating bistable nematic liquid crystal devices. This was carried out using a model based on the discretisation of the free energy on a lattice. We use simulation to find the lowest energy states and investigate if they are degenerate in energy. We also test our model by studying the Frederiks transition and comparing with analytical and other simulation results. (author)
Self-Organizing Maps on the Cell Broadband Engine Architecture
International Nuclear Information System (INIS)
McConnell, Sabine M
2010-01-01
We present and evaluate novel parallel implementations of Self-Organizing Maps for the Cell Broadband Engine Architecture. Motivated by the interactive nature of the data-mining process, we evaluate the scalability of the implementations on two clusters using different network characteristics and incarnations (PS3 TM console and PowerXCell 8i) of the architecture. Our implementations use varying combinations of the Power Processing Elements (PPEs) and Synergistic Processing Elements (SPEs) found in the Cell architecture. For a single processor, our implementation scaled well with the number of SPEs regardless of the incarnation. When combining multiple PS3 TM consoles, the synchronization over the slower network resulted in poor speedups and demonstrated that the use of such a low-cost cluster may be severely restricted, even without the use of SPEs. When using multiple SPEs for the PowerXCell 8i cluster, the speedup grew linearly with increasing number of SPEs for a given number of processors, and linear up to a maximum with the number of processors for a given number of SPEs. Our implementation achieved a worst-case efficiency of 67% for the maximum number of processing elements involved in the computation, but consistently higher values for smaller numbers of processing elements with speedups of up to 70.
Photoluminescence of self-organized perylene bisimide polymers
Neuteboom, E.E.; Meskers, S.C.J.; Meijer, E.W.; Janssen, R.A.J.
2004-01-01
Three polymers consisting of alternating perylene bisimide chromophores and flexible polytetrahydrofuran segments of different length have been studied using absorption and (time-resolved) photoluminescence spectroscopy. In o-dichlorobenzene, the chromophores self organize to form H-like aggregates.
Self-organizing maps: A tool to ascertain taxonomic relatedness ...
Indian Academy of Sciences (India)
MADHU
what is known as numerical taxonomy (Garrity et al. 2001). ... Curvilinear component analysis; self-organizing maps; principal component analysis. Abbreviations used: ... This tool undergoes unsupervised learning and is particularly useful in ...
Computer models for economic and silvicultural decisions
Rosalie J. Ingram
1989-01-01
Computer systems can help simplify decisionmaking to manage forest ecosystems. We now have computer models to help make forest management decisions by predicting changes associated with a particular management action. Models also help you evaluate alternatives. To be effective, the computer models must be reliable and appropriate for your situation.
Jung, Minju; Hwang, Jungsik; Tani, Jun
2015-01-01
It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns.
Innovative Mechanism of Rural Organization Based on Self-Organization
Wang, Xing jin; Gao, Bing
2011-01-01
The paper analyzes the basic situation for the formation of innovative rural organizations with the form of self-organization; revels the features of self-organization, including the four aspects of openness of rural organization, innovation of rural organization is far away from equilibrium, the non-linear response mechanism of rural organization innovation and the random rise and fall of rural organization innovation. The evolution mechanism of rural organization innovation is reveled accor...
Extending Particle Swarm Optimisers with Self-Organized Criticality
DEFF Research Database (Denmark)
Løvbjerg, Morten; Krink, Thiemo
2002-01-01
Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions.......Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions....
Optical electronics self-organized integration and applications
Yoshimura, Tetsuzo
2012-01-01
IntroductionFrom Electronics to Optical ElectronicsAnalysis Tools for Optical CircuitsSelf-Organized Optical Waveguides: Theoretical AnalysisSelf-Organized Optical Waveguides: Experimental DemonstrationsOptical Waveguide Films with Vertical Mirrors 3-D Optical Circuits with Stacked Waveguide Films Heterogeneous Thin-Film Device IntegrationOptical Switches OE Hardware Built by Optical ElectronicsIntegrated Solar Energy Conversion SystemsFuture Challenges.
International Nuclear Information System (INIS)
Sarkar, Jaya; Basumallick, A; Khan, Gobinda Gopal
2009-01-01
By correlating the experimental evidence obtained from atomic force microscopy, conventional x-ray diffraction, and a surface sensitive modified x-ray diffraction technique with the results of density functional theory based computations, we demonstrate that self-organized nanostripe patterns formed on the electropolished surface of aluminium originate as a consequence of relaxation and reconstruction of the new surfaces exposed and textural changes at the surface caused by the dissolution during polishing.
Emergence or self-organization?: Look to the soil population.
Addiscott, Tom
2011-07-01
EMERGENCE IS NOT WELL DEFINED, BUT ALL EMERGENT SYSTEMS HAVE THE FOLLOWING CHARACTERISTICS: the whole is more than the sum of the parts, they show bottom-up rather top-down organization and, if biological, they involve chemical signaling. Self-organization can be understood in terms of the second and third stages of thermodynamics enabling these stages used as analogs of ecosystem functioning. The second stage system was suggested earlier to provide a useful analog of the behavior of natural and agricultural ecosystems subjected to perturbations, but for this it needs the capacity for self-organization. Considering the hierarchy of the ecosystem suggests that this self-organization is provided by the third stage, whose entropy maximization acts as an analog of that of the soil population when it releases small molecules from much larger molecules in dead plant matter. This it does as vigorously as conditions allow. Through this activity, the soil population confers self-organization at both the ecosystem and the global level. The soil population has been seen as both emergent and self-organizing, supporting the suggestion that the two concepts are are so closely linked as to be virtually interchangeable. If this idea is correct one of the characteristics of a biological emergent system seems to be the ability to confer self-organization on an ecosystem or other entity which may be larger than itself. The beehive and the termite colony are emergent systems which share this ability.
Impact of network topology on self-organized criticality
Hoffmann, Heiko
2018-02-01
The general mechanisms behind self-organized criticality (SOC) are still unknown. Several microscopic and mean-field theory approaches have been suggested, but they do not explain the dependence of the exponents on the underlying network topology of the SOC system. Here, we first report the phenomena that in the Bak-Tang-Wiesenfeld (BTW) model, sites inside an avalanche area largely return to their original state after the passing of an avalanche, forming, effectively, critically arranged clusters of sites. Then, we hypothesize that SOC relies on the formation process of these clusters, and present a model of such formation. For low-dimensional networks, we show theoretically and in simulation that the exponent of the cluster-size distribution is proportional to the ratio of the fractal dimension of the cluster boundary and the dimensionality of the network. For the BTW model, in our simulations, the exponent of the avalanche-area distribution matched approximately our prediction based on this ratio for two-dimensional networks, but deviated for higher dimensions. We hypothesize a transition from cluster formation to the mean-field theory process with increasing dimensionality. This work sheds light onto the mechanisms behind SOC, particularly, the impact of the network topology.
Computer Modeling of the Earliest Cellular Structures and Functions
Pohorille, Andrew; Chipot, Christophe; Schweighofer, Karl
2000-01-01
In the absence of extinct or extant record of protocells (the earliest ancestors of contemporary cells). the most direct way to test our understanding of the origin of cellular life is to construct laboratory models of protocells. Such efforts are currently underway in the NASA Astrobiology Program. They are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures and developing designs for molecules that perform proto-cellular functions. Many of these functions, such as import of nutrients, capture and storage of energy. and response to changes in the environment are carried out by proteins bound to membranestructures at water-membrane interfaces and insert into membranes, (b) how these peptides aggregate to form membrane-spanning structures (eg. channels), and (c) by what mechanisms such aggregates perform essential proto-cellular functions, such as proton transport of protons across cell walls, a key step in cellular bioenergetics. The simulations were performed using the molecular dynamics method, in which Newton's equations of motion for each item in the system are solved iteratively. The problems of interest required simulations on multi-nanosecond time scales, which corresponded to 10(exp 6)-10(exp 8) time steps.
Transformation-invariant visual representations in self-organizing spiking neural networks.
Evans, Benjamin D; Stringer, Simon M
2012-01-01
The ventral visual pathway achieves object and face recognition by building transformation-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transformation-invariant representations has been demonstrated using either of two biologically plausible learning mechanisms, Trace learning and Continuous Transformation (CT) learning. However, it has not previously been investigated how transformation-invariant representations may be learned in a more biologically accurate spiking neural network. A key issue is how the synaptic connection strengths in such a spiking network might self-organize through Spike-Time Dependent Plasticity (STDP) where the change in synaptic strength is dependent on the relative times of the spikes emitted by the presynaptic and postsynaptic neurons rather than simply correlated activity driving changes in synaptic efficacy. Here we present simulations with conductance-based integrate-and-fire (IF) neurons using a STDP learning rule to address these gaps in our understanding. It is demonstrated that with the appropriate selection of model parameters and training regime, the spiking network model can utilize either Trace-like or CT-like learning mechanisms to achieve transform-invariant representations.
Transform-invariant visual representations in self-organizing spiking neural networks
Directory of Open Access Journals (Sweden)
Benjamin eEvans
2012-07-01
Full Text Available The ventral visual pathway achieves object and face recognition by building transform-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transform invariant representations has been demonstrated using either of two biologically plausible learning mechanisms, Trace learning and Continuous Transformation (CT learning. However, it has not previously been investigated how transform invariant representations may be learned in a more biologically accurate spiking neural network. A key issue is how the synaptic connection strengths in such a spiking network might self-organize through Spike-Time Dependent Plasticity (STDP where the change in synaptic strength is dependent on the relative times of the spikes emitted by the pre- and postsynaptic neurons rather than simply correlated activity driving changes in synaptic efficacy. Here we present simulations with conductance-based integrate-and-fire (IF neurons using a STDP learning rule to address these gaps in our understanding. It is demonstrated that with the appropriate selection of model pa- rameters and training regime, the spiking network model can utilize either Trace-like or CT-like learning mechanisms to achieve transform-invariant representations.
Self-organization of spatio-temporal earthquake clusters
Directory of Open Access Journals (Sweden)
S. Hainzl
2000-01-01
Full Text Available Cellular automaton versions of the Burridge-Knopoff model have been shown to reproduce the power law distribution of event sizes; that is, the Gutenberg-Richter law. However, they have failed to reproduce the occurrence of foreshock and aftershock sequences correlated with large earthquakes. We show that in the case of partial stress recovery due to transient creep occurring subsequently to earthquakes in the crust, such spring-block systems self-organize into a statistically stationary state characterized by a power law distribution of fracture sizes as well as by foreshocks and aftershocks accompanying large events. In particular, the increase of foreshock and the decrease of aftershock activity can be described by, aside from a prefactor, the same Omori law. The exponent of the Omori law depends on the relaxation time and on the spatial scale of transient creep. Further investigations concerning the number of aftershocks, the temporal variation of aftershock magnitudes, and the waiting time distribution support the conclusion that this model, even "more realistic" physics in missed, captures in some ways the origin of the size distribution as well as spatio-temporal clustering of earthquakes.
Self-organized criticality in developing neuronal networks.
Directory of Open Access Journals (Sweden)
Christian Tetzlaff
Full Text Available Recently evidence has accumulated that many neural networks exhibit self-organized criticality. In this state, activity is similar across temporal scales and this is beneficial with respect to information flow. If subcritical, activity can die out, if supercritical epileptiform patterns may occur. Little is known about how developing networks will reach and stabilize criticality. Here we monitor the development between 13 and 95 days in vitro (DIV of cortical cell cultures (n = 20 and find four different phases, related to their morphological maturation: An initial low-activity state (≈19 DIV is followed by a supercritical (≈20 DIV and then a subcritical one (≈36 DIV until the network finally reaches stable criticality (≈58 DIV. Using network modeling and mathematical analysis we describe the dynamics of the emergent connectivity in such developing systems. Based on physiological observations, the synaptic development in the model is determined by the drive of the neurons to adjust their connectivity for reaching on average firing rate homeostasis. We predict a specific time course for the maturation of inhibition, with strong onset and delayed pruning, and that total synaptic connectivity should be strongly linked to the relative levels of excitation and inhibition. These results demonstrate that the interplay between activity and connectivity guides developing networks into criticality suggesting that this may be a generic and stable state of many networks in vivo and in vitro.
Self-organizing maps based on limit cycle attractors.
Huang, Di-Wei; Gentili, Rodolphe J; Reggia, James A
2015-03-01
Recent efforts to develop large-scale brain and neurocognitive architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason for this is that most conventional SOMs use a static encoding representation: each input pattern or sequence is effectively represented as a fixed point activation pattern in the map layer, something that is inconsistent with the rhythmic oscillatory activity observed in the brain. Here we develop and study an alternative encoding scheme that instead uses sparsely-coded limit cycles to represent external input patterns/sequences. We establish conditions under which learned limit cycle representations arise reliably and dominate the dynamics in a SOM. These limit cycles tend to be relatively unique for different inputs, robust to perturbations, and fairly insensitive to timing. In spite of the continually changing activity in the map layer when a limit cycle representation is used, map formation continues to occur reliably. In a two-SOM architecture where each SOM represents a different sensory modality, we also show that after learning, limit cycles in one SOM can correctly evoke corresponding limit cycles in the other, and thus there is the potential for multi-SOM systems using limit cycles to work effectively as hetero-associative memories. While the results presented here are only first steps, they establish the viability of SOM models based on limit cycle activity patterns, and suggest that such models merit further study. Copyright © 2014 Elsevier Ltd. All rights reserved.
Spontaneous neuronal activity as a self-organized critical phenomenon
de Arcangelis, L.; Herrmann, H. J.
2013-01-01
Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behaviour. Avalanche activity can be modelled within the self-organized criticality framework, including threshold firing, refractory period and activity-dependent synaptic plasticity. The size and duration distributions confirm that the system acts in a critical state, whose scaling behaviour is very robust. Next, we discuss the temporal organization of neuronal avalanches. This is given by the alternation between states of high and low activity, named up and down states, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homeostatic mechanisms. Finally, we verify if a system with no characteristic response can ever learn in a controlled and reproducible way. Learning in the model occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. Learning is a truly collective process and the learning dynamics exhibits universal features. Even complex rules can be learned provided that the plastic adaptation is sufficiently slow.
Nonlinear dynamics analysis of a self-organizing recurrent neural network: chaos waning.
Eser, Jürgen; Zheng, Pengsheng; Triesch, Jochen
2014-01-01
Self-organization is thought to play an important role in structuring nervous systems. It frequently arises as a consequence of plasticity mechanisms in neural networks: connectivity determines network dynamics which in turn feed back on network structure through various forms of plasticity. Recently, self-organizing recurrent neural network models (SORNs) have been shown to learn non-trivial structure in their inputs and to reproduce the experimentally observed statistics and fluctuations of synaptic connection strengths in cortex and hippocampus. However, the dynamics in these networks and how they change with network evolution are still poorly understood. Here we investigate the degree of chaos in SORNs by studying how the networks' self-organization changes their response to small perturbations. We study the effect of perturbations to the excitatory-to-excitatory weight matrix on connection strengths and on unit activities. We find that the network dynamics, characterized by an estimate of the maximum Lyapunov exponent, becomes less chaotic during its self-organization, developing into a regime where only few perturbations become amplified. We also find that due to the mixing of discrete and (quasi-)continuous variables in SORNs, small perturbations to the synaptic weights may become amplified only after a substantial delay, a phenomenon we propose to call deferred chaos.
Self-Organization and the Self-Assembling Process in Tissue Engineering
Eswaramoorthy, Rajalakshmanan; Hadidi, Pasha; Hu, Jerry C.
2015-01-01
In recent years, the tissue engineering paradigm has shifted to include a new and growing subfield of scaffoldless techniques which generate self-organizing and self-assembling tissues. This review aims to provide a cogent description of this relatively new research area, with special emphasis on applications toward clinical use and research models. Particular emphasis is placed on providing clear definitions of self-organization and the self-assembling process, as delineated from other scaffoldless techniques in tissue engineering and regenerative medicine. Significantly, during formation, self-organizing and self-assembling tissues display biological processes similar to those that occur in vivo. These help lead to the recapitulation of native tissue morphological structure and organization. Notably, functional properties of these tissues also approach native tissue values; some of these engineered tissues are already in clinical trials. This review aims to provide a cohesive summary of work in this field, and to highlight the potential of self-organization and the self-assembling process to provide cogent solutions to current intractable problems in tissue engineering. PMID:23701238
Electronic self-organization in layered transition metal dichalcogenides
Energy Technology Data Exchange (ETDEWEB)
Ritschel, Tobias
2015-10-30
The interplay between different self-organized electronically ordered states and their relation to unconventional electronic properties like superconductivity constitutes one of the most exciting challenges of modern condensed matter physics. In the present thesis this issue is thoroughly investigated for the prototypical layered material 1T-TaS{sub 2} both experimentally and theoretically. At first the static charge density wave order in 1T-TaS{sub 2} is investigated as a function of pressure and temperature by means of X-ray diffraction. These data indeed reveal that the superconductivity in this material coexists with an inhomogeneous charge density wave on a macroscopic scale in real space. This result is fundamentally different from a previously proposed separation of superconducting and insulating regions in real space. Furthermore, the X-ray diffraction data uncover the important role of interlayer correlations in 1T-TaS{sub 2}. Based on the detailed insights into the charge density wave structure obtained by the X-ray diffraction experiments, density functional theory models are deduced in order to describe the electronic structure of 1T-TaS{sub 2} in the second part of this thesis. As opposed to most previous studies, these calculations take the three-dimensional character of the charge density wave into account. Indeed the electronic structure calculations uncover complex orbital textures, which are interwoven with the charge density wave order and cause dramatic differences in the electronic structure depending on the alignment of the orbitals between neighboring layers. Furthermore, it is demonstrated that these orbital-mediated effects provide a route to drive semiconductor-to-metal transitions with technologically pertinent gaps and on ultrafast timescales. These results are particularly relevant for the ongoing development of novel, miniaturized and ultrafast devices based on layered transition metal dichalcogenides. The discovery of orbital textures
Spatial self-organization in a multi-strain host–pathogen system
International Nuclear Information System (INIS)
Liu, Quan-Xing; Van de Koppel, Johan; Wang, Rong-Hua; Jin, Zhen; Alonso, David
2010-01-01
We develop stochastic spatial epidemic models with the competition of two pathogenic strains. The dynamics resulting from different approaches are examined using both non-spatial and spatially explicit models. Our results show that pair approximation, well-mixed ordinary differential equations (ODEs), Gillespie-algorithm-based simulations and spatially explicit models give similar qualitative results. In particular, the temporal evolution of the spatial model can be successfully approximated by pair equations. Simulation results obtained from the spatially explicit model show that, first, mutation plays a major role in multi-strain coexistence, second, mild virulence remarkably decreases the coexistence domain of the parameter space and, third, large-scale self-organized spatial patterns emerge for a wide range of transmission and virulence parameter values, where spatial self-organized clusters reveal a power law behavior within the coexistence domain
Self-Organization in Communication Networks
V. Bala; S. Goyal (Sanjeev)
1997-01-01
textabstractWe develop a dynamic model to study the formation of communication networks. In this model, individuals periodically make decisions concerning the continuation of existing information links and the formation of new information links, with their cohorts. These decisions trade off the
Disciplines, models, and computers: the path to computational quantum chemistry.
Lenhard, Johannes
2014-12-01
Many disciplines and scientific fields have undergone a computational turn in the past several decades. This paper analyzes this sort of turn by investigating the case of computational quantum chemistry. The main claim is that the transformation from quantum to computational quantum chemistry involved changes in three dimensions. First, on the side of instrumentation, small computers and a networked infrastructure took over the lead from centralized mainframe architecture. Second, a new conception of computational modeling became feasible and assumed a crucial role. And third, the field of computa- tional quantum chemistry became organized in a market-like fashion and this market is much bigger than the number of quantum theory experts. These claims will be substantiated by an investigation of the so-called density functional theory (DFT), the arguably pivotal theory in the turn to computational quantum chemistry around 1990.
Computational biomechanics for medicine imaging, modeling and computing
Doyle, Barry; Wittek, Adam; Nielsen, Poul; Miller, Karol
2016-01-01
The Computational Biomechanics for Medicine titles provide an opportunity for specialists in computational biomechanics to present their latest methodologies and advancements. This volume comprises eighteen of the newest approaches and applications of computational biomechanics, from researchers in Australia, New Zealand, USA, UK, Switzerland, Scotland, France and Russia. Some of the interesting topics discussed are: tailored computational models; traumatic brain injury; soft-tissue mechanics; medical image analysis; and clinically-relevant simulations. One of the greatest challenges facing the computational engineering community is to extend the success of computational mechanics to fields outside traditional engineering, in particular to biology, the biomedical sciences, and medicine. We hope the research presented within this book series will contribute to overcoming this grand challenge.
Self-organization of punishment in structured populations
Perc, Matjaž; Szolnoki, Attila
2012-04-01
Cooperation is crucial for the remarkable evolutionary success of the human species. Not surprisingly, some individuals are willing to bear additional costs in order to punish defectors. Current models assume that, once set, the fine and cost of punishment do not change over time. Here we show that relaxing this assumption by allowing players to adapt their sanctioning efforts in dependence on the success of cooperation can explain both the spontaneous emergence of punishment and its ability to deter defectors and those unwilling to punish them with globally negligible investments. By means of phase diagrams and the analysis of emerging spatial patterns, we demonstrate that adaptive punishment promotes public cooperation through the invigoration of spatial reciprocity, the prevention of the emergence of cyclic dominance, or the provision of competitive advantages to those that sanction antisocial behavior. The results presented indicate that the process of self-organization significantly elevates the effectiveness of punishment, and they reveal new mechanisms by means of which this fascinating and widespread social behavior could have evolved.
Surface self-organization in multilayer film coatings
Shuvalov, Gleb M.; Kostyrko, Sergey A.
2017-12-01
It is a recognized fact that during film deposition and subsequent thermal processing the film surface evolves into an undulating profile. Surface roughness affects many important aspects in the engineering application of thin film materials such as wetting, heat transfer, mechanical, electromagnetic and optical properties. To accurately control the morphological surface modifications at the micro- and nanoscale and improve manufacturing techniques, we design a mathematical model of the surface self-organization process in multilayer film materials. In this paper, we consider a solid film coating with an arbitrary number of layers under plane strain conditions. The film surface has a small initial perturbation described by a periodic function. It is assumed that the evolution of the surface relief is governed by surface and volume diffusion. Based on Gibbs thermodynamics and linear theory of elasticity, we present a procedure for constructing a governing equation that gives the amplitude change of the surface perturbation with time. A parametric study of the evolution equation leads to the definition of a critical undulation wavelength that stabilizes the surface. As a numerical result, the influence of geometrical and physical parameters on the morphological stability of an isotropic two-layered film coating is analyzed.
Self-organization of social hierarchy on interaction networks
International Nuclear Information System (INIS)
Fujie, Ryo; Odagaki, Takashi
2011-01-01
In order to examine the effects of interaction network structures on the self-organization of social hierarchy, we introduce the agent-based model: each individual as on a node of a network has its own power and its internal state changes by fighting with its neighbors and relaxation. We adopt three different networks: regular lattice, small-world network and scale-free network. For the regular lattice, we find the emergence of classes distinguished by the internal state. The transition points where each class emerges are determined analytically, and we show that each class is characterized by the local ranking relative to their neighbors. We also find that the antiferromagnetic-like configuration emerges just above the critical point. For the heterogeneous networks, individuals become winners (or losers) in descending order of the number of their links. By using mean-field analysis, we reveal that the transition point is determined by the maximum degree and the degree distribution in its neighbors
Self-organization of muscle cell structure and function.
Directory of Open Access Journals (Sweden)
Anna Grosberg
2011-02-01
Full Text Available The organization of muscle is the product of functional adaptation over several length scales spanning from the sarcomere to the muscle bundle. One possible strategy for solving this multiscale coupling problem is to physically constrain the muscle cells in microenvironments that potentiate the organization of their intracellular space. We hypothesized that boundary conditions in the extracellular space potentiate the organization of cytoskeletal scaffolds for directed sarcomeregenesis. We developed a quantitative model of how the cytoskeleton of neonatal rat ventricular myocytes organizes with respect to geometric cues in the extracellular matrix. Numerical results and in vitro assays to control myocyte shape indicated that distinct cytoskeletal architectures arise from two temporally-ordered, organizational processes: the interaction between actin fibers, premyofibrils and focal adhesions, as well as cooperative alignment and parallel bundling of nascent myofibrils. Our results suggest that a hierarchy of mechanisms regulate the self-organization of the contractile cytoskeleton and that a positive feedback loop is responsible for initiating the break in symmetry, potentiated by extracellular boundary conditions, is required to polarize the contractile cytoskeleton.
Self-organization of muscle cell structure and function.
Grosberg, Anna; Kuo, Po-Ling; Guo, Chin-Lin; Geisse, Nicholas A; Bray, Mark-Anthony; Adams, William J; Sheehy, Sean P; Parker, Kevin Kit
2011-02-01
The organization of muscle is the product of functional adaptation over several length scales spanning from the sarcomere to the muscle bundle. One possible strategy for solving this multiscale coupling problem is to physically constrain the muscle cells in microenvironments that potentiate the organization of their intracellular space. We hypothesized that boundary conditions in the extracellular space potentiate the organization of cytoskeletal scaffolds for directed sarcomeregenesis. We developed a quantitative model of how the cytoskeleton of neonatal rat ventricular myocytes organizes with respect to geometric cues in the extracellular matrix. Numerical results and in vitro assays to control myocyte shape indicated that distinct cytoskeletal architectures arise from two temporally-ordered, organizational processes: the interaction between actin fibers, premyofibrils and focal adhesions, as well as cooperative alignment and parallel bundling of nascent myofibrils. Our results suggest that a hierarchy of mechanisms regulate the self-organization of the contractile cytoskeleton and that a positive feedback loop is responsible for initiating the break in symmetry, potentiated by extracellular boundary conditions, is required to polarize the contractile cytoskeleton.
International Nuclear Information System (INIS)
Tereshko, I.; Abidzina, V.; Tereshko, A.; Glushchenko, V.; Elkin, I.
2007-01-01
The goal of this paper is to study self-organization processes that cause nanostructural evolution in nonlinear crystal media. The subjects of the investigation were nonlinear homogeneous and heterogeneous atom chains. The method of computer simulation was used to investigate the interaction between low-energy ions and crystal lattices. It was based on the conception of three-dimensional lattice as a nonlinear atom chain system. We showed that that in homogeneous atom chains critical energy needed for self-organization processes development is less than for nonlinear atom chain with already embedded clusters. The possibility of nanostructure formation was studied by a molecular dynamics method of nonlinear oscillations in atomic oscillator systems of crystal lattices after their low-energy ion irradiation. (authors)
Information and Self-Organization A Macroscopic Approach to Complex Systems
Haken, Hermann
2006-01-01
This book presents the concepts needed to deal with self-organizing complex systems from a unifying point of view that uses macroscopic data. The various meanings of the concept "information" are discussed and a general formulation of the maximum information (entropy) principle is used. With the aid of results from synergetics, adequate objective constraints for a large class of self-organizing systems are formulated and examples are given from physics, life and computer science. The relationship to chaos theory is examined and it is further shown that, based on possibly scarce and noisy data, unbiased guesses about processes of complex systems can be made and the underlying deterministic and random forces determined. This allows for probabilistic predictions of processes, with applications to numerous fields in science, technology, medicine and economics. The extensions of the third edition are essentially devoted to an introduction to the meaning of information in the quantum context. Indeed, quantum inform...
Self-organization of high intensity laser pulses propagating in gases
International Nuclear Information System (INIS)
Koga, James
2001-01-01
In recent years the development of high intensity short pulse lasers has opened up wide fields of science which had previously been difficult to study. Recent experiments of short pulse lasers propagating in air have shown that these laser pulses can propagate over very long distances (up to 12 km) with little or no distortion of the pulse. Here we present a model of this propagation using a modified version of the self-organized criticality model developed for sandpiles by Bak, Tang, and Weisenfeld. The additions to the sandpile model include the formation of plasma which acts as a threshold diffusion term and self-focusing by the nonlinear index of refraction which acts as a continuous inverse diffusion. Results of this simple model indicate that a strongly self-focusing laser pulse shows self-organized critical behavior. (author)
Computer modeling of the gyrocon
International Nuclear Information System (INIS)
Tallerico, P.J.; Rankin, J.E.
1979-01-01
A gyrocon computer model is discussed in which the electron beam is followed from the gun output to the collector region. The initial beam may be selected either as a uniform circular beam or may be taken from the output of an electron gun simulated by the program of William Herrmannsfeldt. The fully relativistic equations of motion are then integrated numerically to follow the beam successively through a drift tunnel, a cylindrical rf beam deflection cavity, a combination drift space and magnetic bender region, and an output rf cavity. The parameters for each region are variable input data from a control file. The program calculates power losses in the cavity wall, power required by beam loading, power transferred from the beam to the output cavity fields, and electronic and overall efficiency. Space-charge effects are approximated if selected. Graphical displays of beam motions are produced. We discuss the Los Alamos Scientific Laboratory (LASL) prototype design as an example of code usage. The design shows a gyrocon of about two-thirds megawatt output at 450 MHz with up to 86% overall efficiency
The Fermilab central computing facility architectural model
International Nuclear Information System (INIS)
Nicholls, J.
1989-01-01
The goal of the current Central Computing Upgrade at Fermilab is to create a computing environment that maximizes total productivity, particularly for high energy physics analysis. The Computing Department and the Next Computer Acquisition Committee decided upon a model which includes five components: an interactive front-end, a Large-Scale Scientific Computer (LSSC, a mainframe computing engine), a microprocessor farm system, a file server, and workstations. With the exception of the file server, all segments of this model are currently in production: a VAX/VMS cluster interactive front-end, an Amdahl VM Computing engine, ACP farms, and (primarily) VMS workstations. This paper will discuss the implementation of the Fermilab Central Computing Facility Architectural Model. Implications for Code Management in such a heterogeneous environment, including issues such as modularity and centrality, will be considered. Special emphasis will be placed on connectivity and communications between the front-end, LSSC, and workstations, as practiced at Fermilab. (orig.)
The Fermilab Central Computing Facility architectural model
International Nuclear Information System (INIS)
Nicholls, J.
1989-05-01
The goal of the current Central Computing Upgrade at Fermilab is to create a computing environment that maximizes total productivity, particularly for high energy physics analysis. The Computing Department and the Next Computer Acquisition Committee decided upon a model which includes five components: an interactive front end, a Large-Scale Scientific Computer (LSSC, a mainframe computing engine), a microprocessor farm system, a file server, and workstations. With the exception of the file server, all segments of this model are currently in production: a VAX/VMS Cluster interactive front end, an Amdahl VM computing engine, ACP farms, and (primarily) VMS workstations. This presentation will discuss the implementation of the Fermilab Central Computing Facility Architectural Model. Implications for Code Management in such a heterogeneous environment, including issues such as modularity and centrality, will be considered. Special emphasis will be placed on connectivity and communications between the front-end, LSSC, and workstations, as practiced at Fermilab. 2 figs
Interpretation of fingerprint image quality features extracted by self-organizing maps
Danov, Ivan; Olsen, Martin A.; Busch, Christoph
2014-05-01
Accurate prediction of fingerprint quality is of significant importance to any fingerprint-based biometric system. Ensuring high quality samples for both probe and reference can substantially improve the system's performance by lowering false non-matches, thus allowing finer adjustment of the decision threshold of the biometric system. Furthermore, the increasing usage of biometrics in mobile contexts demands development of lightweight methods for operational environment. A novel two-tier computationally efficient approach was recently proposed based on modelling block-wise fingerprint image data using Self-Organizing Map (SOM) to extract specific ridge pattern features, which are then used as an input to a Random Forests (RF) classifier trained to predict the quality score of a propagated sample. This paper conducts an investigative comparative analysis on a publicly available dataset for the improvement of the two-tier approach by proposing additionally three feature interpretation methods, based respectively on SOM, Generative Topographic Mapping and RF. The analysis shows that two of the proposed methods produce promising results on the given dataset.
Directory of Open Access Journals (Sweden)
Sheng-Jun Wang
2011-06-01
Full Text Available Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. They are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and ﬁnally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality. We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. It was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We ﬁnd that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and self-organized criticality, which are not present in the respective random networks. The underlying mechanism is that each dense module cannot sustain activity on its own, but displays self-organized criticality in the presence of weak perturbations. The hierarchical modular networks provide the coupling among subsystems with self-organized criticality. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivityof critical state and predictability and timing of oscillations for efficient
Self-organization of the critical state in Josephson lattices and granulated superconductors
International Nuclear Information System (INIS)
Ginzburg, S.L.
1994-01-01
A number of models of a Josephson medium and granulated superconductors are studied. It is shown that an important parameter is the quantity V∼j c a 3 /Φ 0 , where j c is the Josephson-current density, a is the granule size, and Φ 0 is the quantum of flux. In the limit V>>1 the continuum approximation is inapplicable. In this case the Josephson medium is transformed into a system in which pinning is realized on elementary loops that incorporate Josephson junctions. Here, nonlinear properties of these junctions obtain. The equations obtained for the currents of the Josephson lattice are identical to the standard formulation in the problem of self-organized criticality, while in granulated superconductors a problem of self-organized criticality with a different symmetry arises-a problem not of sites, but of loop. From the point of view of the critical state in granulated superconductors the concept of self-organized criticality radically changes the entire customary picture. The usual equations of the critical state describe only the average values of the magnetic field in the hydrodynamic approximation. However, it follows from the concept of self-organized criticality that the critical state has an extremely complicated structure, much more complicated than that which follows from the equation of the critical state. In particular, the fluctuations of various quantities in the critical state are much stronger than the ordinary statistical fluctuations, since there are large-scale fluctuations of the currents and fields, with a power-law (scaling) behavior that extends up to scales of the order of the size of the system, as in a turbulent medium. On the other hand, the basic equations in it reflect all the features of pinning - hysteresis and threshold behavior. Therefore, the self-organization of the critical state of a superconductor is a natural realization of this extremely general problem. 15 refs., 4 figs
A self-organized learning strategy for object recognition by an embedded line of attraction
Seow, Ming-Jung; Alex, Ann T.; Asari, Vijayan K.
2012-04-01
For humans, a picture is worth a thousand words, but to a machine, it is just a seemingly random array of numbers. Although machines are very fast and efficient, they are vastly inferior to humans for everyday information processing. Algorithms that mimic the way the human brain computes and learns may be the solution. In this paper we present a theoretical model based on the observation that images of similar visual perceptions reside in a complex manifold in an image space. The perceived features are often highly structured and hidden in a complex set of relationships or high-dimensional abstractions. To model the pattern manifold, we present a novel learning algorithm using a recurrent neural network. The brain memorizes information using a dynamical system made of interconnected neurons. Retrieval of information is accomplished in an associative sense. It starts from an arbitrary state that might be an encoded representation of a visual image and converges to another state that is stable. The stable state is what the brain remembers. In designing a recurrent neural network, it is usually of prime importance to guarantee the convergence in the dynamics of the network. We propose to modify this picture: if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented with an unknown encoded representation of a visual image belonging to a different category. That is, the identification of an instability mode is an indication that a presented pattern is far away from any stored pattern and therefore cannot be associated with current memories. These properties can be used to circumvent the plasticity-stability dilemma by using the fluctuating mode as an indicator to create new states. We capture this behavior using a novel neural architecture and learning algorithm, in which the system performs self-organization utilizing a stability mode and an instability mode for the dynamical system. Based
Self-organization in the tornado: the new approach in the tornado description
Bystrai, G. P.; Lykov, I. A
2012-01-01
For the mathematical modeling of highly non-equilibrium and nonlinear processes in a tornado in this paper a new approach based on nonlinear equations of momentum transfer with function of sources and sinks is suggested. In constructing the model thermodynamic description is used, which is not entered before and allows discovering new principles of self-organization in a tornado. This approach gives fairly consistent physical results. This is an attempt to answer some fundamental questions co...
Biomechanical factors contributing to self-organization in seagrass landscapes
Fonseca, M.S.; Koehl, M.A.R.; Kopp, B.S.
2007-01-01
Field observations have revealed that when water flow is consistently from one direction, seagrass shoots align in rows perpendicular to the primary axis of flow direction. In this study, live Zostera marina shoots were arranged either randomly or in rows perpendicular to the flow direction and tested in a seawater flume under unidirectional flow and waves to determine if shoot arrangement: a) influenced flow-induced force on individual shoots, b) differentially altered water flow through the canopy, and c) influenced light interception by the canopy. In addition, blade breaking strength was compared with flow-induced force to determine if changes in shoot arrangement might reduce the potential for damage to shoots. Under unidirectional flow, both current velocity in the canopy and force on shoots were significantly decreased when shoots were arranged in rows as compared to randomly. However, force on shoots was nearly constant with downstream distance, arising from the trade-off of shoot bending and in-canopy flow reduction. The coefficient of drag was higher for randomly-arranged shoots at low velocities (rows tended to intercept slightly more light than those arranged randomly. Effects of shoot arrangement under waves were less clear, potentially because we did not achieve the proper plant size?row spacing ratio. At this point, we may only suggest that water motion, as opposed to light capture, is the dominant physical mechanism responsible for these shoot arrangements. Following a computation of the Environmental Stress Factor, we concluded that even photosynthetically active blades may be damaged or broken under frequently encountered storm conditions, irrespective of shoot arrangement. We hypothesize that when flow is generally from one direction, seagrass bed patterns over multiple scales of consideration may arise as a cumulative effect of individual shoot self-organization driven by reduced force and drag on the shoots and somewhat improved light capture.
Quantum vertex model for reversible classical computing.
Chamon, C; Mucciolo, E R; Ruckenstein, A E; Yang, Z-C
2017-05-12
Mappings of classical computation onto statistical mechanics models have led to remarkable successes in addressing some complex computational problems. However, such mappings display thermodynamic phase transitions that may prevent reaching solution even for easy problems known to be solvable in polynomial time. Here we map universal reversible classical computations onto a planar vertex model that exhibits no bulk classical thermodynamic phase transition, independent of the computational circuit. Within our approach the solution of the computation is encoded in the ground state of the vertex model and its complexity is reflected in the dynamics of the relaxation of the system to its ground state. We use thermal annealing with and without 'learning' to explore typical computational problems. We also construct a mapping of the vertex model into the Chimera architecture of the D-Wave machine, initiating an approach to reversible classical computation based on state-of-the-art implementations of quantum annealing.
Modeling Computer Virus and Its Dynamics
Directory of Open Access Journals (Sweden)
Mei Peng
2013-01-01
Full Text Available Based on that the computer will be infected by infected computer and exposed computer, and some of the computers which are in suscepitible status and exposed status can get immunity by antivirus ability, a novel coumputer virus model is established. The dynamic behaviors of this model are investigated. First, the basic reproduction number R0, which is a threshold of the computer virus spreading in internet, is determined. Second, this model has a virus-free equilibrium P0, which means that the infected part of the computer disappears, and the virus dies out, and P0 is a globally asymptotically stable equilibrium if R01 then this model has only one viral equilibrium P*, which means that the computer persists at a constant endemic level, and P* is also globally asymptotically stable. Finally, some numerical examples are given to demonstrate the analytical results.
Thought analysis on self-organization theories of MHD plasma
International Nuclear Information System (INIS)
Kondoh, Yoshiomi; Sato, Tetsuya.
1992-08-01
A thought analysis on the self-organization theories of dissipative MHD plasma is presented to lead to three groups of theories that lead to the same relaxed state of ∇ x B = λB, in order to find an essential physical picture embedded in the self-organization phenomena due to nonlinear and dissipative processes. The self-organized relaxed state due to the dissipation by the Ohm loss is shown to be formulated generally as the state such that yields the minimum dissipation rate of global auto-and/or cross-correlations between two quantities in j, B, and A for their own instantaneous values of the global correlations. (author)
Self-organization of physical fields and spin
International Nuclear Information System (INIS)
Pestov, I.B.
2008-01-01
The subject of the present investigation is the laws of intrinsic self-organization of fundamental physical fields. In the framework of the Theory of Self-Organization the geometrical and physical nature of spin phenomena is uncovered. The key points are spin symmetry (the fundamental realization of the concept of geometrical internal symmetry) and the spinning field (space of defining representation of spin symmetry). It is shown that the essence of spin is the bipolar structure of spin symmetry induced by the gravitational potentials. The bipolar structure provides natural violation of spin symmetry and leads to spinstatics (theory of spinning field outside the time) and spindynamics. The equations of spinstatics and spindynamics are derived. It is shown that Sommerfeld's formula can be derived from the equations of spindynamics and hence the correspondence principle is valid. This means that the Theory of Self-Organization provides the new understanding of spin phenomena
Self-Organized Construction with Continuous Building Material
DEFF Research Database (Denmark)
Heinrich, Mary Katherine; Wahby, Mostafa; Divband Soorati, Mohammad
2016-01-01
Self-organized construction with continuous, structured building material, as opposed to modular units, offers new challenges to the robot-based construction process and lends the opportunity for increased flexibility in constructed artifact properties, such as shape and deformation. As an example...... investigation, we look at continuous filaments organized into braided structures, within the context of bio-hybrids constructing architectural artifacts. We report the result of an early swarm robot experiment. The robots successfully constructed a braid in a self-organized process. The construction process can...... be extended by using different materials and by embedding sensors during the self-organized construction directly into the braided structure. In future work, we plan to apply dedicated braiding robot hardware and to construct sophisticated 3-d structures with local variability in patterns of filament...
The IceCube Computing Infrastructure Model
CERN. Geneva
2012-01-01
Besides the big LHC experiments a number of mid-size experiments is coming online which need to define new computing models to meet the demands on processing and storage requirements of those experiments. We present the hybrid computing model of IceCube which leverages GRID models with a more flexible direct user model as an example of a possible solution. In IceCube a central datacenter at UW-Madison servers as Tier-0 with a single Tier-1 datacenter at DESY Zeuthen. We describe the setup of the IceCube computing infrastructure and report on our experience in successfully provisioning the IceCube computing needs.
Self-Organized Fission Control for Flocking System
Directory of Open Access Journals (Sweden)
Mingyong Liu
2015-01-01
Full Text Available This paper studies the self-organized fission control problem for flocking system. Motivated by the fission behavior of biological flocks, information coupling degree (ICD is firstly designed to represent the interaction intensity between individuals. Then, from the information transfer perspective, a “maximum-ICD” based pairwise interaction rule is proposed to realize the directional information propagation within the flock. Together with the “separation/alignment/cohesion” rules, a self-organized fission control algorithm is established that achieves the spontaneous splitting of flocking system under conflict external stimuli. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed algorithm.
TWO CHANNELS OF SELF-ORGANIZATION OF IONIZED GASEOUS MEDIA
Directory of Open Access Journals (Sweden)
Benedict Oprescu
2013-12-01
Full Text Available The appearance is pointed out, experimentally, of a complex electric charge structure, within an ionized gas, relatively homogeneous at first, under the influence of a number of external constraints. Two different mechanisms of self-organization are presented: the former implying, essentially, long-range interactions, and the latter implying, essentially, short-range quantum interactions. The phenomenological scenarios are presented, which underlie the two mechanisms of self-organization, as well as the broader theoretical frame, currently accepted, concerning the generation of complexity in the material media that are far from the state of thermodynamic equilibrium.
Self-Organization in Coupled Map Scale-Free Networks
International Nuclear Information System (INIS)
Xiao-Ming, Liang; Zong-Hua, Liu; Hua-Ping, Lü
2008-01-01
We study the self-organization of phase synchronization in coupled map scale-free networks with chaotic logistic map at each node and find that a variety of ordered spatiotemporal patterns emerge spontaneously in a regime of coupling strength. These ordered behaviours will change with the increase of the average links and are robust to both the system size and parameter mismatch. A heuristic theory is given to explain the mechanism of self-organization and to figure out the regime of coupling for the ordered spatiotemporal patterns
Computational nanophotonics modeling and applications
Musa, Sarhan M
2013-01-01
This reference offers tools for engineers, scientists, biologists, and others working with the computational techniques of nanophotonics. It introduces the key concepts of computational methods in a manner that is easily digestible for newcomers to the field. The book also examines future applications of nanophotonics in the technical industry and covers new developments and interdisciplinary research in engineering, science, and medicine. It provides an overview of the key computational nanophotonics and describes the technologies with an emphasis on how they work and their key benefits.
Pervasive Computing and Prosopopoietic Modelling
DEFF Research Database (Denmark)
Michelsen, Anders Ib
2011-01-01
the mid-20th century of a paradoxical distinction/complicity between the technical organisation of computed function and the human Being, in the sense of creative action upon such function. This paradoxical distinction/complicity promotes a chiastic (Merleau-Ponty) relationship of extension of one......This article treats the philosophical underpinnings of the notions of ubiquity and pervasive computing from a historical perspective. The current focus on these notions reflects the ever increasing impact of new media and the underlying complexity of computed function in the broad sense of ICT...... that have spread vertiginiously since Mark Weiser coined the term ‘pervasive’, e.g., digitalised sensoring, monitoring, effectuation, intelligence, and display. Whereas Weiser’s original perspective may seem fulfilled since computing is everywhere, in his and Seely Brown’s (1997) terms, ‘invisible...
A distance weighted-based approach for self-organized aggregation in robot swarms
Khaldi, Belkacem
2017-12-14
In this paper, a Distance-Weighted K Nearest Neighboring (DW-KNN) topology is proposed to study self-organized aggregation as an emergent swarming behavior within robot swarms. A virtual physics approach is applied among the proposed neighborhood topology to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach is used as a key factor to identify the K-Nearest neighbors taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbors is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach showing various self-organized aggregations performed by a swarm of N foot-bot robots.
Climate Ocean Modeling on Parallel Computers
Wang, P.; Cheng, B. N.; Chao, Y.
1998-01-01
Ocean modeling plays an important role in both understanding the current climatic conditions and predicting future climate change. However, modeling the ocean circulation at various spatial and temporal scales is a very challenging computational task.
Computational Intelligence. Mortality Models for the Actuary
Willemse, W.J.
2001-01-01
This thesis applies computational intelligence to the field of actuarial (insurance) science. In particular, this thesis deals with life insurance where mortality modelling is important. Actuaries use ancient models (mortality laws) from the nineteenth century, for example Gompertz' and Makeham's
Applications of computer modeling to fusion research
International Nuclear Information System (INIS)
Dawson, J.M.
1989-01-01
Progress achieved during this report period is presented on the following topics: Development and application of gyrokinetic particle codes to tokamak transport, development of techniques to take advantage of parallel computers; model dynamo and bootstrap current drive; and in general maintain our broad-based program in basic plasma physics and computer modeling
Large Scale Computations in Air Pollution Modelling
DEFF Research Database (Denmark)
Zlatev, Z.; Brandt, J.; Builtjes, P. J. H.
Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998......Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998...
Computer Aided Continuous Time Stochastic Process Modelling
DEFF Research Database (Denmark)
Kristensen, N.R.; Madsen, Henrik; Jørgensen, Sten Bay
2001-01-01
A grey-box approach to process modelling that combines deterministic and stochastic modelling is advocated for identification of models for model-based control of batch and semi-batch processes. A computer-aided tool designed for supporting decision-making within the corresponding modelling cycle...
On the nature and shape of tubulin trails: implications on microtubule self-organization.
Glade, Nicolas
2012-06-01
Microtubules, major elements of the cell skeleton are, most of the time, well organized in vivo, but they can also show self-organizing behaviors in time and/or space in purified solutions in vitro. Theoretical studies and models based on the concepts of collective dynamics in complex systems, reaction-diffusion processes and emergent phenomena were proposed to explain some of these behaviors. In the particular case of microtubule spatial self-organization, it has been advanced that microtubules could behave like ants, self-organizing by 'talking to each other' by way of hypothetic (because never observed) concentrated chemical trails of tubulin that are expected to be released by their disassembling ends. Deterministic models based on this idea yielded indeed like-looking spatio-temporal self-organizing behaviors. Nevertheless the question remains of whether microscopic tubulin trails produced by individual or bundles of several microtubules are intense enough to allow microtubule self-organization at a macroscopic level. In the present work, by simulating the diffusion of tubulin in microtubule solutions at the microscopic scale, we measure the shape and intensity of tubulin trails and discuss about the assumption of microtubule self-organization due to the production of chemical trails by disassembling microtubules. We show that the tubulin trails produced by individual microtubules or small microtubule arrays are very weak and not elongated even at very high reactive rates. Although the variations of concentration due to such trails are not significant compared to natural fluctuations of the concentration of tubuline in the chemical environment, the study shows that heterogeneities of biochemical composition can form due to microtubule disassembly. They could become significant when produced by numerous microtubule ends located in the same place. Their possible formation could play a role in certain conditions of reaction. In particular, it gives a mesoscopic
Comparative investigation of two different self-organizing map ...
African Journals Online (AJOL)
Purpose: To demonstrate the ability and investigate the performance of two different wavelength selection approaches based on self-organizing map (SOM) technique in partial least-squares (PLS) regression for analysis of pharmaceutical binary mixtures with strongly overlapping spectra. Methods: Two different variable ...
Eco-evolutionary feedbacks in self-organized ecosystems
de Jager, M.
2015-01-01
Spatial patterns in natural systems may appear amazingly complex. Yet, they can often be explained by a few simple rules. In self-organized ecosystems, complex spatial patterns at the ecosystem scale arise as the consequence of actions of and interactions between organisms at a local scale.
Self-organized criticality in a network of interacting neurons
Cowan, J.D.; Neuman, J.; Kiewiet, B.; van Drongelen, W.
2013-01-01
This paper contains an analysis of a simple neural network that exhibits self-organized criticality. Such criticality follows from the combination of a simple neural network with an excitatory feedback loop that generates bistability, in combination with an anti-Hebbian synapse in its input pathway.
Self-organization as a possible route to fusion energy
International Nuclear Information System (INIS)
Sanduloviciu, M.; Lozneanu, E.; Popescu, S.
2000-01-01
The generation of a ball lightning-like complex structure by sudden injection of matter and energy proves the presence of a cascading self-organization scenario in an experimental device containing a collisional plasma. Based on these results, we suggest the possibility to replicate, under controlled laboratory conditions, the ball lightning-like structures with potential fusion applications. (author)
Gaining insight in domestic violence with emergent self organizing maps
Poelmans, J.; Elzinga, P.; Viaene, S.; van Hulle, M.M.; Dedene, G.
2009-01-01
Topographic maps are an appealing exploratory instrument for discovering new knowledge from databases. During the past years, new types of Self Organizing Maps (SOM) were introduced in the literature, including the recent Emergent SOM. The ESOM tool is used here to analyze a large set of police
10th Workshop on Self-Organizing Maps
Schleif, Frank-Michael; Kaden, Marika; Lange, Mandy
2014-01-01
The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first WSOM-workshop 1997 in Helsinki this workshop focuses on newest results in the field of supervised and unsupervised vector quantization like self-organizing maps for data mining and data classification. This 10th WSOM brought together more than 50 researchers, experts and practitioners in the beautiful small town Mittweida in Saxony (Germany) nearby the mountains Erzgebirge to discuss new developments in the field of unsupervised self-organizing vector quantization systems and learning vector quantization approaches for classification. The book contains the accepted papers of the workshop after a careful review process as well as summaries of the invited talks. Among these book chapters there are excellent examples of the use of self-organizing maps in agriculture, ...
Hierarchical self-organization of non-cooperating individuals.
Directory of Open Access Journals (Sweden)
Tamás Nepusz
Full Text Available Hierarchy is one of the most conspicuous features of numerous natural, technological and social systems. The underlying structures are typically complex and their most relevant organizational principle is the ordering of the ties among the units they are made of according to a network displaying hierarchical features. In spite of the abundant presence of hierarchy no quantitative theoretical interpretation of the origins of a multi-level, knowledge-based social network exists. Here we introduce an approach which is capable of reproducing the emergence of a multi-levelled network structure based on the plausible assumption that the individuals (representing the nodes of the network can make the right estimate about the state of their changing environment to a varying degree. Our model accounts for a fundamental feature of knowledge-based organizations: the less capable individuals tend to follow those who are better at solving the problems they all face. We find that relatively simple rules lead to hierarchical self-organization and the specific structures we obtain possess the two, perhaps most important features of complex systems: a simultaneous presence of adaptability and stability. In addition, the performance (success score of the emerging networks is significantly higher than the average expected score of the individuals without letting them copy the decisions of the others. The results of our calculations are in agreement with a related experiment and can be useful from the point of designing the optimal conditions for constructing a given complex social structure as well as understanding the hierarchical organization of such biological structures of major importance as the regulatory pathways or the dynamics of neural networks.
25 Years of Self-organized Criticality: Concepts and Controversies
Watkins, Nicholas W.; Pruessner, Gunnar; Chapman, Sandra C.; Crosby, Norma B.; Jensen, Henrik J.
2016-01-01
Introduced by the late Per Bak and his colleagues, self-organized criticality (SOC) has been one of the most stimulating concepts to come out of statistical mechanics and condensed matter theory in the last few decades, and has played a significant role in the development of complexity science. SOC, and more generally fractals and power laws, have attracted much comment, ranging from the very positive to the polemical. The other papers (Aschwanden et al. in Space Sci. Rev., 2014, this issue; McAteer et al. in Space Sci. Rev., 2015, this issue; Sharma et al. in Space Sci. Rev. 2015, in preparation) in this special issue showcase the considerable body of observations in solar, magnetospheric and fusion plasma inspired by the SOC idea, and expose the fertile role the new paradigm has played in approaches to modeling and understanding multiscale plasma instabilities. This very broad impact, and the necessary process of adapting a scientific hypothesis to the conditions of a given physical system, has meant that SOC as studied in these fields has sometimes differed significantly from the definition originally given by its creators. In Bak's own field of theoretical physics there are significant observational and theoretical open questions, even 25 years on (Pruessner 2012). One aim of the present review is to address the dichotomy between the great reception SOC has received in some areas, and its shortcomings, as they became manifest in the controversies it triggered. Our article tries to clear up what we think are misunderstandings of SOC in fields more remote from its origins in statistical mechanics, condensed matter and dynamical systems by revisiting Bak, Tang and Wiesenfeld's original papers.
On the self-organized critical state of Vesuvio volcano
Luongo, G.; Mazzarella, A.; Palumbo, A.
1996-01-01
The catalogue of volcanic earthquakes recorded at Vesuvio (1972-1993) is shown to be complete for events with magnitude enclosed between 1.8 and 3.0. Such a result is converted in significant fractal laws (power laws) relating the distribution of earthquakes to the distribution of energy release, seismic moment, size of fractured zone and linear dimension of faults. The application of the Cantor dust model to time sequence of Vesuvio seismic and eruptive events allows the determination of significant time-clustering fractal structures. In particular, the Vesuvio eruptive activity shows a double-regime process with a stronger clustering on short-time scales than on long-time scales. The complexity of the Vesuvio system does not depend on the number of geological, geophysical and geochemical factors that govern it, but mainly on the number of their interconnections, on the intensity of such linkages and on the feed-back processes. So, all the identified fractal features are taken as evidence that the Vesuvio system is in a self-organized critical state i.e., in a marginally stable state in which a small perturbation can start a chain reaction that can lead to catastrophe. After the catatrophe, the system regulates itself and begins a new cycle, not necessarily periodic, that will end with a successive catastrophe. The variations of the fractal dimension and of the specific scale ranges, in which the fractal behaviour is found to hold, serve as possible volcanic predictors reflecting changes of the same volcanic process.
Computer Based Modelling and Simulation
Indian Academy of Sciences (India)
where x increases from zero to N, the saturation value. Box 1. Matrix Meth- ... such as Laplace transforms and non-linear differential equa- tions with .... atomic bomb project in the. US in the early ... his work on game theory and computers.
Cornacchia, Loreta; van de Koppel, Johan; van der Wal, Daphne; Wharton, Geraldene; Puijalon, Sara; Bouma, Tjeerd J
2018-04-01
Spatial heterogeneity plays a crucial role in the coexistence of species. Despite recognition of the importance of self-organization in creating environmental heterogeneity in otherwise uniform landscapes, the effects of such self-organized pattern formation in promoting coexistence through facilitation are still unknown. In this study, we investigated the effects of pattern formation on species interactions and community spatial structure in ecosystems with limited underlying environmental heterogeneity, using self-organized patchiness of the aquatic macrophyte Callitriche platycarpa in streams as a model system. Our theoretical model predicted that pattern formation in aquatic vegetation - due to feedback interactions between plant growth, water flow and sedimentation processes - could promote species coexistence, by creating heterogeneous flow conditions inside and around the plant patches. The spatial plant patterns predicted by our model agreed with field observations at the reach scale in naturally vegetated rivers, where we found a significant spatial aggregation of two macrophyte species around C. platycarpa. Field transplantation experiments showed that C. platycarpa had a positive effect on the growth of both beneficiary species, and the intensity of this facilitative effect was correlated with the heterogeneous hydrodynamic conditions created within and around C. platycarpa patches. Our results emphasize the importance of self-organized patchiness in promoting species coexistence by creating a landscape of facilitation, where new niches and facilitative effects arise in different locations. Understanding the interplay between competition and facilitation is therefore essential for successful management of biodiversity in many ecosystems. © 2018 The Authors Ecology published by Wiley Periodicals, Inc. on behalf of Ecological Society of America.
Self-organization principles result in robust control of flexible manufacturing systems
DEFF Research Database (Denmark)
Nature shows us in our daily life how robust, flexible and optimal self-organized modular constructions work in complex physical, chemical and biological systems, which successfully adapt to new and unexpected situations. A promising strategy is therefore to use such self-organization and pattern...... problems with several autonomous robots and several targets are considered as model of flexible manufacturing systems. Each manufacturing target has to be served in a given time interval by one and only one robot and the total working costs have to be minimized (or total winnings maximized). A specifically...... constructed dynamical system approach (coupled selection equations) is used which is based on pattern formation principles and results in fault resistant and robust behaviour. An important feature is that this type of control also guarantees feasiblitiy of the assignment solutions. In previous work...
Self-Organized Criticality in Astrophysics The Statistics of Nonlinear Processes in the Universe
Aschwanden, Markus
2011-01-01
The concept of ‘self-organized criticality’ (SOC) has been applied to a variety of problems, ranging from population growth and traffic jams to earthquakes, landslides and forest fires. The technique is now being applied to a wide range of phenomena in astrophysics, such as planetary magnetospheres, solar flares, cataclysmic variable stars, accretion disks, black holes and gamma-ray bursts, and also to phenomena in galactic physics and cosmology. Self-organized Criticality in Astrophysics introduces the concept of SOC and shows that, due to its universality and ubiquity, it is a law of nature. The theoretical framework and specific physical models are described, together with a range of applications in various aspects of astrophyics. The mathematical techniques, including the statistics of random processes, time series analysis, time scale and waiting time distributions, are presented and the results are applied to specific observations of astrophysical phenomena.
Song, Bo; Liu, Guanqing; Xu, Rui; Yin, Shouchun; Wang, Zhiqiang; Zhang, Xi
2008-04-15
This article discusses the relationship between the molecular structure of bolaamphiphiles bearing mesogenic groups and their interfacial self-organized morphology. On the basis of the molecular structures of bolaamphiphiles, we designed and synthesized a series of molecules with different hydrophobic alkyl chain lengths, hydrophilic headgroups, mesogenic groups, and connectors between the alkyl chains and the mesogenic group. Through investigating their interfacial self-organization behavior, some experiential rules are summarized: (1) An appropriate alkyl chain length is necessary to form stable surface micelles; (2) different categories of headgroups have a great effect on the interfacial self-organized morphology; (3) different types of mesogenic groups have little effect on the structure of the interfacial assembly when it is changed from biphenyl to azobenzene or stilbene; (4) the orientation of the ester linker between the mesogenic group and alkyl chain can greatly influence the interfacial self-organization behavior. It is anticipated that this line of research may be helpful for the molecular engineering of bolaamphiphiles to form tailor-made morphologies.
Computer-Aided Modelling Methods and Tools
DEFF Research Database (Denmark)
Cameron, Ian; Gani, Rafiqul
2011-01-01
The development of models for a range of applications requires methods and tools. In many cases a reference model is required that allows the generation of application specific models that are fit for purpose. There are a range of computer aided modelling tools available that help to define the m...
A Categorisation of Cloud Computing Business Models
Chang, Victor; Bacigalupo, David; Wills, Gary; De Roure, David
2010-01-01
This paper reviews current cloud computing business models and presents proposals on how organisations can achieve sustainability by adopting appropriate models. We classify cloud computing business models into eight types: (1) Service Provider and Service Orientation; (2) Support and Services Contracts; (3) In-House Private Clouds; (4) All-In-One Enterprise Cloud; (5) One-Stop Resources and Services; (6) Government funding; (7) Venture Capitals; and (8) Entertainment and Social Networking. U...
A computational model of selection by consequences.
McDowell, J J
2004-01-01
Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of computational experiments that arranged reinforcement according to random-interval (RI) schedules. The quantitative features of the model were varied o...
Creation of 'Ukrytie' objects computer model
International Nuclear Information System (INIS)
Mazur, A.B.; Kotlyarov, V.T.; Ermolenko, A.I.; Podbereznyj, S.S.; Postil, S.D.; Shaptala, D.V.
1999-01-01
A partial computer model of the 'Ukrytie' object was created with the use of geoinformation technologies. The computer model makes it possible to carry out information support of the works related to the 'Ukrytie' object stabilization and its conversion into ecologically safe system for analyzing, forecasting and controlling the processes occurring in the 'Ukrytie' object. Elements and structures of the 'Ukryttia' object were designed and input into the model
Computational models in physics teaching: a framework
Directory of Open Access Journals (Sweden)
Marco Antonio Moreira
2012-08-01
Full Text Available The purpose of the present paper is to present a theoretical framework to promote and assist meaningful physics learning through computational models. Our proposal is based on the use of a tool, the AVM diagram, to design educational activities involving modeling and computer simulations. The idea is to provide a starting point for the construction and implementation of didactical approaches grounded in a coherent epistemological view about scientific modeling.
Introducing Seismic Tomography with Computational Modeling
Neves, R.; Neves, M. L.; Teodoro, V.
2011-12-01
Learning seismic tomography principles and techniques involves advanced physical and computational knowledge. In depth learning of such computational skills is a difficult cognitive process that requires a strong background in physics, mathematics and computer programming. The corresponding learning environments and pedagogic methodologies should then involve sets of computational modelling activities with computer software systems which allow students the possibility to improve their mathematical or programming knowledge and simultaneously focus on the learning of seismic wave propagation and inverse theory. To reduce the level of cognitive opacity associated with mathematical or programming knowledge, several computer modelling systems have already been developed (Neves & Teodoro, 2010). Among such systems, Modellus is particularly well suited to achieve this goal because it is a domain general environment for explorative and expressive modelling with the following main advantages: 1) an easy and intuitive creation of mathematical models using just standard mathematical notation; 2) the simultaneous exploration of images, tables, graphs and object animations; 3) the attribution of mathematical properties expressed in the models to animated objects; and finally 4) the computation and display of mathematical quantities obtained from the analysis of images and graphs. Here we describe virtual simulations and educational exercises which enable students an easy grasp of the fundamental of seismic tomography. The simulations make the lecture more interactive and allow students the possibility to overcome their lack of advanced mathematical or programming knowledge and focus on the learning of seismological concepts and processes taking advantage of basic scientific computation methods and tools.
Uncertainty in biology a computational modeling approach
Gomez-Cabrero, David
2016-01-01
Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies. Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process. This book wants to address four main issues related to the building and validation of computational models of biomedical processes: Modeling establishment under uncertainty Model selection and parameter fitting Sensitivity analysis and model adaptation Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples. This book is intended for graduate stude...
Ranked retrieval of Computational Biology models.
Henkel, Ron; Endler, Lukas; Peters, Andre; Le Novère, Nicolas; Waltemath, Dagmar
2010-08-11
The study of biological systems demands computational support. If targeting a biological problem, the reuse of existing computational models can save time and effort. Deciding for potentially suitable models, however, becomes more challenging with the increasing number of computational models available, and even more when considering the models' growing complexity. Firstly, among a set of potential model candidates it is difficult to decide for the model that best suits ones needs. Secondly, it is hard to grasp the nature of an unknown model listed in a search result set, and to judge how well it fits for the particular problem one has in mind. Here we present an improved search approach for computational models of biological processes. It is based on existing retrieval and ranking methods from Information Retrieval. The approach incorporates annotations suggested by MIRIAM, and additional meta-information. It is now part of the search engine of BioModels Database, a standard repository for computational models. The introduced concept and implementation are, to our knowledge, the first application of Information Retrieval techniques on model search in Computational Systems Biology. Using the example of BioModels Database, it was shown that the approach is feasible and extends the current possibilities to search for relevant models. The advantages of our system over existing solutions are that we incorporate a rich set of meta-information, and that we provide the user with a relevance ranking of the models found for a query. Better search capabilities in model databases are expected to have a positive effect on the reuse of existing models.
Directory of Open Access Journals (Sweden)
Khuat Thanh Tung
2016-11-01
Full Text Available Optical Character Recognition plays an important role in data storage and data mining when the number of documents stored as images is increasing. It is expected to find the ways to convert images of typewritten or printed text into machine-encoded text effectively in order to support for the process of information handling effectively. In this paper, therefore, the techniques which are being used to convert image into editable text in the computer such as principal component analysis, multilayer perceptron network, self-organizing maps, and improved multilayer neural network using principal component analysis are experimented. The obtained results indicated the effectiveness and feasibility of the proposed methods.
Portraits of self-organization in fish schools interacting with robots
Aureli, M.; Fiorilli, F.; Porfiri, M.
2012-05-01
In this paper, we propose an enabling computational and theoretical framework for the analysis of experimental instances of collective behavior in response to external stimuli. In particular, this work addresses the characterization of aggregation and interaction phenomena in robot-animal groups through the exemplary analysis of fish schooling in the vicinity of a biomimetic robot. We adapt global observables from statistical mechanics to capture the main features of the shoal collective motion and its response to the robot from experimental observations. We investigate the shoal behavior by using a diffusion mapping analysis performed on these global observables that also informs the definition of relevant portraits of self-organization.
Fluid forces enhance the performance of an aspirant leader in self-organized living groups.
Directory of Open Access Journals (Sweden)
Alessandro De Rosis
Full Text Available In this paper, the performance of an individual aiming at guiding a self-organized group is numerically investigated. A collective behavioural model is adopted, accounting for the mutual repulsion, attraction and orientation experienced by the individuals. Moreover, these represent a set of solid particles which are supposed to be immersed in a fictitious viscous fluid. In particular, the lattice Boltzmann and Immersed boundary methods are used to predict the fluid dynamics, whereas the effect of the hydrodynamic forces on particles is accounted for by solving the equation of the solid motion through the time discontinuous Galerkin scheme. Numerical simulations are carried out by involving the individuals in a dichotomous process. On the one hand, an aspirant leader (AL additional individual is added to the system. AL is forced to move along a prescribed direction which intersects the group. On the other hand, these tend to depart from an obstacle represented by a rotating lamina which is placed in the fluid domain. A numerical campaign is carried out by varying the fluid viscosity and, as a consequence, the hydrodynamic field. Moreover, scenarios characterized by different values of the size of the group are investigated. In order to estimate the AL's performance, a proper parameter is introduced, depending on the number of individuals following AL. Present findings show that the sole collective behavioural equations are insufficient to predict the AL's performance, since the motion is drastically affected by the presence of the surrounding fluid. With respect to the existing literature, the proposed numerical model is enriched by accounting for the presence of the encompassing fluid, thus computing the hydrodynamic forces arising when the individuals move.
Computational challenges in modeling gene regulatory events.
Pataskar, Abhijeet; Tiwari, Vijay K
2016-10-19
Cellular transcriptional programs driven by genetic and epigenetic mechanisms could be better understood by integrating "omics" data and subsequently modeling the gene-regulatory events. Toward this end, computational biology should keep pace with evolving experimental procedures and data availability. This article gives an exemplified account of the current computational challenges in molecular biology.
Self-organization and forcing templates in coastal barrier response to storms
Lazarus, E.
2015-12-01
When a storm event pushes water up and over a coastal barrier, cross-shore flow transports sediment from the barrier face to the back-barrier environment. This natural physical process is called "overwash", and "washover" is the sedimentary deposit it forms. Overwash and washover support critical coastal habitats, and enable barriers to maintain their height and width relative to rising sea level. On developed barrier coasts, overwash constitutes a natural hazard, which sea-level rise will exacerbate. Overwash is also a prerequisite for barrier breaching and coastal flooding. Predicting occurrence and characteristics of overwash and washover has significant societal value. Hazard models typically assume that pre-storm barrier morphology determines how the barrier changes during a storm. However, classic work has documented the absence of a relationship between pre/post-storm topography in some cases, and has also identified rhythmic patterns in washover alongshore. Previous explanations for these spatial patterns have looked to forcing templates, forms that get imprinted in the barrier shape. An alternative explanation is that washover patterns self-organize, emerging from feedbacks between water flow and sediment transport. Self-organization and forcing templates are often framed as mutually exclusive, but patterns likely form across a continuum of conditions. Here, I use data from a new physical experiment to suggest that spatial patterns in washover can self-organize within the limit of a forcing template of some critical "strength", beyond which pre/post-storm morphologies are highly correlated. Quantifying spatial patterns in washover deposits opens exciting questions regarding coastal morphodynamic response to storms. Measurement of relative template strength over extended spatial (and temporal) scales has the potential to improve hazard assessment and prediction, particularly where template strength is low and self-organization dominates barrier change.
Notions of similarity for computational biology models
Waltemath, Dagmar
2016-03-21
Computational models used in biology are rapidly increasing in complexity, size, and numbers. To build such large models, researchers need to rely on software tools for model retrieval, model combination, and version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of similarity may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here, we introduce a general notion of quantitative model similarities, survey the use of existing model comparison methods in model building and management, and discuss potential applications of model comparison. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on different model aspects. Potentially relevant aspects of a model comprise its references to biological entities, network structure, mathematical equations and parameters, and dynamic behaviour. Future similarity measures could combine these model aspects in flexible, problem-specific ways in order to mimic users\\' intuition about model similarity, and to support complex model searches in databases.
Notions of similarity for computational biology models
Waltemath, Dagmar; Henkel, Ron; Hoehndorf, Robert; Kacprowski, Tim; Knuepfer, Christian; Liebermeister, Wolfram
2016-01-01
Computational models used in biology are rapidly increasing in complexity, size, and numbers. To build such large models, researchers need to rely on software tools for model retrieval, model combination, and version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of similarity may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here, we introduce a general notion of quantitative model similarities, survey the use of existing model comparison methods in model building and management, and discuss potential applications of model comparison. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on different model aspects. Potentially relevant aspects of a model comprise its references to biological entities, network structure, mathematical equations and parameters, and dynamic behaviour. Future similarity measures could combine these model aspects in flexible, problem-specific ways in order to mimic users' intuition about model similarity, and to support complex model searches in databases.
Directory of Open Access Journals (Sweden)
Heiko eHamann
2016-04-01
Full Text Available Hybrid societies are self-organizing, collective systems composed of different components, for example, natural and artificial parts (bio-hybrid or human beings interacting with and through technical systems (socio-technical. Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A~stronger collaboration between these disciplines could allow for re-use of methods and create significant synergies. We identify three main areas of challenges in the design of self-organizing hybrid societies. First, we identify the formalization challenge. There is an urgent need for a generic model that allows a description and comparison of collective hybrid societies. Second, we identify the system design challenge. Starting from the formal specification of the system, we need to develop an integrated design process. Third, we identify the challenge of interdisciplinarity. Current research on self-organizing hybrid societies stretches over many different fields and hence requires the re-use and synthesis of methods at intersections between disciplines. We then conclude by presenting our perspective for future approaches with high potential in this area.
Discerning Thermodynamic Basis of Self-Organization in Critical Zone Structure and Function
Richardson, M.; Kumar, P.
2017-12-01
Self-organization characterizes the spontaneous emergence of order. Self-organization in the Critical Zone, the region of Earth's skin from below the groundwater table to the top of the vegetation canopy, involves the interaction of biotic and abiotic processes occurring through a hierarchy of temporal and spatial scales. The self-organization is sustained through input of energy and material in an open system framework, and the resulting formations are called dissipative structures. Why do these local states of organization form and how are they thermodynamically favorable? We hypothesize that structure formation is linked to energy conversion and matter throughput rates across driving gradients. Furthermore, we predict that structures in the Critical Zone evolve based on local availability of nutrients, water, and energy. By considering ecosystems as open thermodynamic systems, we model and study the throughput signatures on short times scales to determine origins and characteristics of ecosystem structure. This diagnostic approach allows us to use fluxes of matter and energy to understand the thermodynamic drivers of the system. By classifying the fluxes and dynamics in a system, we can identify patterns to determine the thermodynamic drivers for organized states. Additionally, studying the partitioning of nutrients, water, and energy throughout ecosystems through dissipative structures will help identify reasons for structure shapes and how these shapes impact major Critical Zone functions.
Predictive Models and Computational Embryology
EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...
Self-organized lattice of ordered quantum dot molecules
International Nuclear Information System (INIS)
Lippen, T. von; Noetzel, R.; Hamhuis, G.J.; Wolter, J.H.
2004-01-01
Ordered groups of InAs quantum dots (QDs), lateral QD molecules, are created by self-organized anisotropic strain engineering of a (In,Ga)As/GaAs superlattice (SL) template on GaAs (311)B in molecular-beam epitaxy. During stacking, the SL template self-organizes into a two-dimensionally ordered strain modulated network on a mesoscopic length scale. InAs QDs preferentially grow on top of the nodes of the network due to local strain recognition. The QDs form a lattice of separated groups of closely spaced ordered QDs whose number can be controlled by the GaAs separation layer thickness on top of the SL template. The QD groups exhibit excellent optical properties up to room temperature
SOUNET: Self-Organized Underwater Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Hee-won Kim
2017-02-01
Full Text Available In this paper, we propose an underwater wireless sensor network (UWSN named SOUNET where sensor nodes form and maintain a tree-topological network for data gathering in a self-organized manner. After network topology discovery via packet flooding, the sensor nodes consistently update their parent node to ensure the best connectivity by referring to the timevarying neighbor tables. Such a persistent and self-adaptive method leads to high network connectivity without any centralized control, even when sensor nodes are added or unexpectedly lost. Furthermore, malfunctions that frequently happen in self-organized networks such as node isolation and closed loop are resolved in a simple way. Simulation results show that SOUNET outperforms other conventional schemes in terms of network connectivity, packet delivery ratio (PDR, and energy consumption throughout the network. In addition, we performed an experiment at the Gyeongcheon Lake in Korea using commercial underwater modems to verify that SOUNET works well in a real environment.
SOUNET: Self-Organized Underwater Wireless Sensor Network.
Kim, Hee-Won; Cho, Ho-Shin
2017-02-02
In this paper, we propose an underwater wireless sensor network (UWSN) named SOUNET where sensor nodes form and maintain a tree-topological network for data gathering in a self-organized manner. After network topology discovery via packet flooding, the sensor nodes consistently update their parent node to ensure the best connectivity by referring to the timevarying neighbor tables. Such a persistent and self-adaptive method leads to high network connectivity without any centralized control, even when sensor nodes are added or unexpectedly lost. Furthermore, malfunctions that frequently happen in self-organized networks such as node isolation and closed loop are resolved in a simple way. Simulation results show that SOUNET outperforms other conventional schemes in terms of network connectivity, packet delivery ratio (PDR), and energy consumption throughout the network. In addition, we performed an experiment at the Gyeongcheon Lake in Korea using commercial underwater modems to verify that SOUNET works well in a real environment.
Energy driven self-organization in nanoscale metallic liquid films.
Krishna, H; Shirato, N; Favazza, C; Kalyanaraman, R
2009-10-01
Nanometre thick metallic liquid films on inert substrates can spontaneously dewet and self-organize into complex nanomorphologies and nanostructures with well-defined length scales. Nanosecond pulses of an ultraviolet laser can capture the dewetting evolution and ensuing nanomorphologies, as well as introduce dramatic changes to dewetting length scales due to the nanoscopic nature of film heating. Here, we show theoretically that the self-organization principle, based on equating the rate of transfer of thermodynamic free energy to rate of loss in liquid flow, accurately describes the spontaneous dewetting. Experimental measurements of laser dewetting of Ag and Co liquid films on SiO(2) substrates confirm this principle. This energy transfer approach could be useful for analyzing the behavior of nanomaterials and chemical processes in which spontaneous changes are important.
11th Workshop on Self-Organizing Maps
Mendenhall, Michael; O'Driscoll, Patrick
2016-01-01
This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization. The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and ...
Energy sources, self-organization, and the origin of life.
Boiteau, Laurent; Pascal, Robert
2011-02-01
The emergence and early developments of life are considered from the point of view that contingent events that inevitably marked evolution were accompanied by deterministic driving forces governing the selection between different alternatives. Accordingly, potential energy sources are considered for their propensity to induce self-organization within the scope of the chemical approach to the origin of life. Requirements in terms of quality of energy locate thermal or photochemical activation in the atmosphere as highly likely processes for the formation of activated low-molecular weight organic compounds prone to induce biomolecular self-organization through their ability to deliver quanta of energy matching the needs of early biochemical pathways or the reproduction of self-replicating entities. These lines of reasoning suggest the existence of a direct connection between the free energy content of intermediates of early pathways and the quanta of energy delivered by available sources of energy.
Self-Organized Criticality of Rainfall in Central China
Directory of Open Access Journals (Sweden)
Zhiliang Wang
2012-01-01
Full Text Available Rainfall is a complexity dynamics process. In this paper, our objective is to find the evidence of self-organized criticality (SOC for rain datasets in China by employing the theory and method of SOC. For this reason, we analyzed the long-term rain records of five meteorological stations in Henan, a central province of China. Three concepts, that is, rain duration, drought duration, accumulated rain amount, are proposed to characterize these rain events processes. We investigate their dynamics property by using scale invariant and found that the long-term rain processes in central China indeed exhibit the feature of self-organized criticality. The proposed theory and method may be suitable to analyze other datasets from different climate zones in China.
Sierra toolkit computational mesh conceptual model
International Nuclear Information System (INIS)
Baur, David G.; Edwards, Harold Carter; Cochran, William K.; Williams, Alan B.; Sjaardema, Gregory D.
2010-01-01
The Sierra Toolkit computational mesh is a software library intended to support massively parallel multi-physics computations on dynamically changing unstructured meshes. This domain of intended use is inherently complex due to distributed memory parallelism, parallel scalability, heterogeneity of physics, heterogeneous discretization of an unstructured mesh, and runtime adaptation of the mesh. Management of this inherent complexity begins with a conceptual analysis and modeling of this domain of intended use; i.e., development of a domain model. The Sierra Toolkit computational mesh software library is designed and implemented based upon this domain model. Software developers using, maintaining, or extending the Sierra Toolkit computational mesh library must be familiar with the concepts/domain model presented in this report.
Computer simulations of the random barrier model
DEFF Research Database (Denmark)
Schrøder, Thomas; Dyre, Jeppe
2002-01-01
A brief review of experimental facts regarding ac electronic and ionic conduction in disordered solids is given followed by a discussion of what is perhaps the simplest realistic model, the random barrier model (symmetric hopping model). Results from large scale computer simulations are presented...
A method of computer aided design with self-generative models in NX Siemens environment
Grabowik, C.; Kalinowski, K.; Kempa, W.; Paprocka, I.
2015-11-01
Currently in CAD/CAE/CAM systems it is possible to create 3D design virtual models which are able to capture certain amount of knowledge. These models are especially useful in an automation of routine design tasks. These models are known as self-generative or auto generative and they can behave in an intelligent way. The main difference between the auto generative and fully parametric models consists in the auto generative models ability to self-organizing. In this case design model self-organizing means that aside from the possibility of making of automatic changes of model quantitative features these models possess knowledge how these changes should be made. Moreover they are able to change quality features according to specific knowledge. In spite of undoubted good points of self-generative models they are not so often used in design constructional process which is mainly caused by usually great complexity of these models. This complexity makes the process of self-generative time and labour consuming. It also needs a quite great investment outlays. The creation process of self-generative model consists of the three stages it is knowledge and information acquisition, model type selection and model implementation. In this paper methods of the computer aided design with self-generative models in NX Siemens CAD/CAE/CAM software are presented. There are the five methods of self-generative models preparation in NX with: parametric relations model, part families, GRIP language application, knowledge fusion and OPEN API mechanism. In the paper examples of each type of the self-generative model are presented. These methods make the constructional design process much faster. It is suggested to prepare this kind of self-generative models when there is a need of design variants creation. The conducted research on assessing the usefulness of elaborated models showed that they are highly recommended in case of routine tasks automation. But it is still difficult to distinguish
Self-organization analysis for a nonlocal convective Fisher equation
Energy Technology Data Exchange (ETDEWEB)
Cunha, J.A.R. da [Instituto de Fisica, Universidade de Brasilia, 70919-970 Brasilia DF (Brazil); International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil); Penna, A.L.A. [Instituto de Fisica, Universidade de Brasilia, 70919-970 Brasilia DF (Brazil); International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil)], E-mail: penna.andre@gmail.com; Vainstein, M.H. [Instituto de Fisica, Universidade de Brasilia, 70919-970 Brasilia DF (Brazil); International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil); Morgado, R. [International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil); Departamento de Matematica, Universidade de Brasilia, 70910-900 Brasilia DF (Brazil); Oliveira, F.A. [Instituto de Fisica, Universidade de Brasilia, 70919-970 Brasilia DF (Brazil); International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil)
2009-02-02
Using both an analytical method and a numerical approach we have investigated pattern formation for a nonlocal convective Fisher equation with constant and spatial velocity fields. We analyze the limits of the influence function due to nonlocal interaction and we obtain the phase diagram of critical velocities v{sub c} as function of the width {mu} of the influence function, which characterize the self-organization of a finite system.
General fluid theories, variational principles and self-organization
International Nuclear Information System (INIS)
Mahajan, S.M.
2002-01-01
This paper reports two distinct but related advances: (1) The development and application of fluid theories that transcend conventional magnetohydrodynamics (MHD), in particular, theories that are valid in the long-mean-free-path limit and in which pressure anisotropy, heat flow, and arbitrarily strong sheared flows are treated consistently. (2) The discovery of new pressure-confining plasma configurations that are self-organized relaxed states. (author)
Structures formation through self-organized accretion on cosmic strings
International Nuclear Information System (INIS)
Murdzek, R.
2009-01-01
In this paper, we shall show that the formation of structures through accretion by a cosmic string is driven by a natural feed-back mechanism: a part of the energy radiated by accretions creates a pressure on the accretion disk itself. This phenomenon leads to a nonlinear evolution of the accretion process. Thus, the formation of structures results as a consequence of a self-organized growth of the accreting central object.
Self-organized vortex multiplets in swirling flow
DEFF Research Database (Denmark)
Okulov, Valery; Naumov, Igor; Sørensen, Jens Nørkær
2008-01-01
The possibility of double vortex multiplet formation at the center of an intensively swirling cocurrent flow generated in a cylindrical container by its rotating lid is reported for the first time. The boundary of the transition to unsteady flow regimes, which arise as a result of the equilibrium...... rotation of self-organized vortex multiplets (triplet, double triplet, double doublet, and quadruplet), has been experimentally determined for cylinders with the aspect (height to radius) ratios in a wider interval than that studied previously....
Architectural Patterns for Self-Organizing Systems-of-Systems
2011-05-01
show that they are necessary for self-organization to occur. Common Purpose Abraham Maslow proposed a theory on human motivation based on a hierarchy...http://www.hole-in-the-wall.com/abouthiwel.html (accessed October 28, 2010). 21. Maslow , Abraham . 1943. A theory of human motivation. In Psychological...in-the-wall Education Ltd. http://www.hole- in-the-wall.com/abouthiwel.html (accessed October 28, 2010). 22. Maslow , Abraham . 1943. A theory of human
Risk-based fault detection using Self-Organizing Map
International Nuclear Information System (INIS)
Yu, Hongyang; Khan, Faisal; Garaniya, Vikram
2015-01-01
The complexity of modern systems is increasing rapidly and the dominating relationships among system variables have become highly non-linear. This results in difficulty in the identification of a system's operating states. In turn, this difficulty affects the sensitivity of fault detection and imposes a challenge on ensuring the safety of operation. In recent years, Self-Organizing Maps has gained popularity in system monitoring as a robust non-linear dimensionality reduction tool. Self-Organizing Map is able to capture non-linear variations of the system. Therefore, it is sensitive to the change of a system's states leading to early detection of fault. In this paper, a new approach based on Self-Organizing Map is proposed to detect and assess the risk of fault. In addition, probabilistic analysis is applied to characterize the risk of fault into different levels according to the hazard potential to enable a refined monitoring of the system. The proposed approach is applied on two experimental systems. The results from both systems have shown high sensitivity of the proposed approach in detecting and identifying the root cause of faults. The refined monitoring facilitates the determination of the risk of fault and early deployment of remedial actions and safety measures to minimize the potential impact of fault. - Highlights: • A new approach based on Self-Organizing Map is proposed to detect faults. • Integration of fault detection with risk assessment methodology. • Fault risk characterization into different levels to enable focused system monitoring
Self-organization at the frictional interface for green tribology.
Nosonovsky, Michael
2010-10-28
Despite the fact that self-organization during friction has received relatively little attention from tribologists so far, it has the potential for the creation of self-healing and self-lubricating materials, which are important for green or environment-friendly tribology. The principles of the thermodynamics of irreversible processes and of the nonlinear theory of dynamical systems are used to investigate the formation of spatial and temporal structures during friction. The transition to the self-organized state with low friction and wear occurs through destabilization of steady-state (stationary) sliding. The criterion for destabilization is formulated and several examples are discussed: the formation of a protective film, microtopography evolution and slip waves. The pattern formation may involve self-organized criticality and reaction-diffusion systems. A special self-healing mechanism may be embedded into the material by coupling the corresponding required forces. The analysis provides the structure-property relationship, which can be applied for the design optimization of composite self-lubricating and self-healing materials for various ecologically friendly applications and green tribology.
Innovative Mechanism of Rural Organization Based on Self-Organization
Institute of Scientific and Technical Information of China (English)
2011-01-01
The paper analyzes the basic situation of the formation of innovative rural organizations with the form of self-organization;reveals the features of self-organization,including the four aspects of openness of rural organization,innovation of rural organization far away from equilibrium,the non-linear response mechanism of rural organization innovation and the random rise and fall of rural organization innovation.The evolution mechanism of rural organization innovation is revealed according to the growth stage,the ideal stage,the decline and the fall stage.The paper probes into the basic restriction mechanism of the self-organization evaluation of rural organization from three aspects,including target recognition,path dependence and knowledge sharing.The basic measures on cultivating the innovative mechanism of rural organization are put forward.Firstly,constructing the dissipative structure of rural organization innovation;secondly,cultivating the dynamic study capability of rural organization innovation system;thirdly,selecting the step-by-step evolution strategy of rural organization innovation system.
Self-organization of polymerizable bolaamphiphiles bearing diacetylene mesogenic group.
Yin, Shouchun; Song, Bo; Liu, Guanqing; Wang, Zhiqiang; Zhang, Xi
2007-05-22
We report herein the synthesis of a series of polymerizable bolaamphiphiles containing a diacetylene group and mesogenic unit and their self-organization behaviors in bulk and at interface. The polymerizable bolaamphiphiles are noted as DPDA-n, where n refers to the spacer length of alkyl chain. DPDA-10 with suitable spacer length can self-organize into stable cylindrical micellar nanostructures, and these nanostructures have preferred orientation regionally when adsorbed at the mica/water interface. It is confirmed that the micellar nanostructure of DPDA-10 can be polymerized both in the bulk solution and in the film by UV irradiation. The emission property of DPDA-10 after UV irradiation has been significantly enhanced in comparison to that before polymerization, which may be due to the extension of the conjugated system arising from the transformation of the diacetylene group into polydiacetylene upon polymerization. In addition, the self-organization of DPDA-n is dependent on the spacer length. DPDA-7 with a short spacer length forms an irregular flat sheet structure with many defects; DPDA-15 with a long spacer length forms rodlike micellar structures. Thus, this work may provide a new approach for designing and fabricating organic functional nanostructured materials.
International Nuclear Information System (INIS)
Zuccaro, S.; Raker, Th.; Niedernostheide, F.-J.; Kuhn, T.; Purwins, H.-G.
2003-01-01
Physical processes in thin ZnS:Mn films and their relation to the formation of dynamical patterns in the electroluminescence of AC driven films are investigated. The technique of photo-depolarization-spectroscopy is used to investigate defect states in these films and it is shown that specific features in the spectra correlate with the observed self-organized patterns. Furthermore, the time dependence of the dissipative current is measured at the same samples and compared with current waveforms obtained from numerical simulations of a drift-diffusion model. The results are used to discuss the origin of the self-organized processes in ZnS:Mn-films
Intelligent self-organization methods for wireless ad hoc sensor networks based on limited resources
Hortos, William S.
2006-05-01
A wireless ad hoc sensor network (WSN) is a configuration for area surveillance that affords rapid, flexible deployment in arbitrary threat environments. There is no infrastructure support and sensor nodes communicate with each other only when they are in transmission range. To a greater degree than the terminals found in mobile ad hoc networks (MANETs) for communications, sensor nodes are resource-constrained, with limited computational processing, bandwidth, memory, and power, and are typically unattended once in operation. Consequently, the level of information exchange among nodes, to support any complex adaptive algorithms to establish network connectivity and optimize throughput, not only deplete those limited resources and creates high overhead in narrowband communications, but also increase network vulnerability to eavesdropping by malicious nodes. Cooperation among nodes, critical to the mission of sensor networks, can thus be disrupted by the inappropriate choice of the method for self-organization. Recent published contributions to the self-configuration of ad hoc sensor networks, e.g., self-organizing mapping and swarm intelligence techniques, have been based on the adaptive control of the cross-layer interactions found in MANET protocols to achieve one or more performance objectives: connectivity, intrusion resistance, power control, throughput, and delay. However, few studies have examined the performance of these algorithms when implemented with the limited resources of WSNs. In this paper, self-organization algorithms for the initiation, operation and maintenance of a network topology from a collection of wireless sensor nodes are proposed that improve the performance metrics significant to WSNs. The intelligent algorithm approach emphasizes low computational complexity, energy efficiency and robust adaptation to change, allowing distributed implementation with the actual limited resources of the cooperative nodes of the network. Extensions of the
Computational Modeling of Culture's Consequences
Hofstede, G.J.; Jonker, C.M.; Verwaart, T.
2010-01-01
This paper presents an approach to formalize the influence of culture on the decision functions of agents in social simulations. The key components are (a) a definition of the domain of study in the form of a decision model, (b) knowledge acquisition based on a dimensional theory of culture,
Computational aspects of premixing modelling
Energy Technology Data Exchange (ETDEWEB)
Fletcher, D.F. [Sydney Univ., NSW (Australia). Dept. of Chemical Engineering; Witt, P.J.
1998-01-01
In the steam explosion research field there is currently considerable effort being devoted to the modelling of premixing. Practically all models are based on the multiphase flow equations which treat the mixture as an interpenetrating continuum. Solution of these equations is non-trivial and a wide range of solution procedures are in use. This paper addresses some numerical aspects of this problem. In particular, we examine the effect of the differencing scheme for the convective terms and show that use of hybrid differencing can cause qualitatively wrong solutions in some situations. Calculations are performed for the Oxford tests, the BNL tests, a MAGICO test and to investigate various sensitivities of the solution. In addition, we show that use of a staggered grid can result in a significant error which leads to poor predictions of `melt` front motion. A correction is given which leads to excellent convergence to the analytic solution. Finally, we discuss the issues facing premixing model developers and highlight the fact that model validation is hampered more by the complexity of the process than by numerical issues. (author)
Computational modeling of concrete flow
DEFF Research Database (Denmark)
Roussel, Nicolas; Geiker, Mette Rica; Dufour, Frederic
2007-01-01
particle flow, and numerical techniques allowing the modeling of particles suspended in a fluid. The general concept behind each family of techniques is described. Pros and cons for each technique are given along with examples and references to applications to fresh cementitious materials....
International Nuclear Information System (INIS)
Kawazura, Y.; Yoshida, Z.
2012-01-01
Two different types of self-organizing and sustaining ordered motion in fluids or plasmas--one is a Benard convection (or streamer) and the other is a zonal flow--have been compared by introducing a thermodynamic phenomenological model and evaluating the corresponding entropy production rates (EP). These two systems have different topologies in their equivalent circuits: the Benard convection is modeled by parallel connection of linear and nonlinear conductances, while the zonal flow is modeled by series connection. The ''power supply'' that drives the systems is also a determinant of operating modes. When the energy flux is a control parameter (as in usual plasma experiments), the driver is modeled by a constant-current power supply, and when the temperature difference between two separate boundaries is controlled (as in usual computational studies), the driver is modeled by a constant-voltage power supply. The parallel (series)-connection system tends to minimize (maximize) the total EP when a constant-current power supply drives the system. This minimum/maximum relation flips when a constant-voltage power supply is connected.
Computer Modeling of Direct Metal Laser Sintering
Cross, Matthew
2014-01-01
A computational approach to modeling direct metal laser sintering (DMLS) additive manufacturing process is presented. The primary application of the model is for determining the temperature history of parts fabricated using DMLS to evaluate residual stresses found in finished pieces and to assess manufacturing process strategies to reduce part slumping. The model utilizes MSC SINDA as a heat transfer solver with imbedded FORTRAN computer code to direct laser motion, apply laser heating as a boundary condition, and simulate the addition of metal powder layers during part fabrication. Model results are compared to available data collected during in situ DMLS part manufacture.
Visual and Computational Modelling of Minority Games
Directory of Open Access Journals (Sweden)
Robertas Damaševičius
2017-02-01
Full Text Available The paper analyses the Minority Game and focuses on analysis and computational modelling of several variants (variable payoff, coalition-based and ternary voting of Minority Game using UAREI (User-Action-Rule-Entities-Interface model. UAREI is a model for formal specification of software gamification, and the UAREI visual modelling language is a language used for graphical representation of game mechanics. The URAEI model also provides the embedded executable modelling framework to evaluate how the rules of the game will work for the players in practice. We demonstrate flexibility of UAREI model for modelling different variants of Minority Game rules for game design.
Model to Implement Virtual Computing Labs via Cloud Computing Services
Directory of Open Access Journals (Sweden)
Washington Luna Encalada
2017-07-01
Full Text Available In recent years, we have seen a significant number of new technological ideas appearing in literature discussing the future of education. For example, E-learning, cloud computing, social networking, virtual laboratories, virtual realities, virtual worlds, massive open online courses (MOOCs, and bring your own device (BYOD are all new concepts of immersive and global education that have emerged in educational literature. One of the greatest challenges presented to e-learning solutions is the reproduction of the benefits of an educational institution’s physical laboratory. For a university without a computing lab, to obtain hands-on IT training with software, operating systems, networks, servers, storage, and cloud computing similar to that which could be received on a university campus computing lab, it is necessary to use a combination of technological tools. Such teaching tools must promote the transmission of knowledge, encourage interaction and collaboration, and ensure students obtain valuable hands-on experience. That, in turn, allows the universities to focus more on teaching and research activities than on the implementation and configuration of complex physical systems. In this article, we present a model for implementing ecosystems which allow universities to teach practical Information Technology (IT skills. The model utilizes what is called a “social cloud”, which utilizes all cloud computing services, such as Software as a Service (SaaS, Platform as a Service (PaaS, and Infrastructure as a Service (IaaS. Additionally, it integrates the cloud learning aspects of a MOOC and several aspects of social networking and support. Social clouds have striking benefits such as centrality, ease of use, scalability, and ubiquity, providing a superior learning environment when compared to that of a simple physical lab. The proposed model allows students to foster all the educational pillars such as learning to know, learning to be, learning
Computational modeling of epiphany learning.
Chen, Wei James; Krajbich, Ian
2017-05-02
Models of reinforcement learning (RL) are prevalent in the decision-making literature, but not all behavior seems to conform to the gradual convergence that is a central feature of RL. In some cases learning seems to happen all at once. Limited prior research on these "epiphanies" has shown evidence of sudden changes in behavior, but it remains unclear how such epiphanies occur. We propose a sequential-sampling model of epiphany learning (EL) and test it using an eye-tracking experiment. In the experiment, subjects repeatedly play a strategic game that has an optimal strategy. Subjects can learn over time from feedback but are also allowed to commit to a strategy at any time, eliminating all other options and opportunities to learn. We find that the EL model is consistent with the choices, eye movements, and pupillary responses of subjects who commit to the optimal strategy (correct epiphany) but not always of those who commit to a suboptimal strategy or who do not commit at all. Our findings suggest that EL is driven by a latent evidence accumulation process that can be revealed with eye-tracking data.
Self-organizing weights for Internet AS-graphs and surprisingly simple routing metrics
DEFF Research Database (Denmark)
Scholz, Jan Carsten; Greiner, Martin
2011-01-01
The transport capacity of Internet-like communication networks and hence their efficiency may be improved by a factor of 5–10 through the use of highly optimized routing metrics, as demonstrated previously. The numerical determination of such routing metrics can be computationally demanding...... to an extent that prohibits both investigation of and application to very large networks. In an attempt to find a numerically less expensive way of constructing a metric with a comparable performance increase, we propose a local, self-organizing iteration scheme and find two surprisingly simple and efficient...... metrics. The new metrics have negligible computational cost and result in an approximately 5-fold performance increase, providing distinguished competitiveness with the computationally costly counterparts. They are applicable to very large networks and easy to implement in today's Internet routing...
Self-organized criticality as a paradigm for transport in magnetically confined plasmas
International Nuclear Information System (INIS)
Carreras, B.A.; Newman, D.; Lynch, V.E.; Diamond, P.H.
1996-01-01
Many models of natural phenomena manifest the basic hypothesis of self-organized criticality (SOC) [P. Bak, C. Tang, and K. Weisenfeld, Phys. Rev. Lett., 1987, vol. 59, p. 381]. The SOC concept brings together the self-similarity on space and time scales that are common to many of these phenomena. The application of the SOC modeling concept to the plasma dynamics near marginal stability opens new possibilities of understanding issues such as Bohm scaling, profile consistency, broad-band fluctuation spectra with universal characteristics, and fast time scales. In this paper, we review the SOC concept and its possible applications to the study of transport in magnetically confined plasmas
Computational models of airway branching morphogenesis.
Varner, Victor D; Nelson, Celeste M
2017-07-01
The bronchial network of the mammalian lung consists of millions of dichotomous branches arranged in a highly complex, space-filling tree. Recent computational models of branching morphogenesis in the lung have helped uncover the biological mechanisms that construct this ramified architecture. In this review, we focus on three different theoretical approaches - geometric modeling, reaction-diffusion modeling, and continuum mechanical modeling - and discuss how, taken together, these models have identified the geometric principles necessary to build an efficient bronchial network, as well as the patterning mechanisms that specify airway geometry in the developing embryo. We emphasize models that are integrated with biological experiments and suggest how recent progress in computational modeling has advanced our understanding of airway branching morphogenesis. Copyright © 2016 Elsevier Ltd. All rights reserved.
Computational multiscale modeling of intergranular cracking
International Nuclear Information System (INIS)
Simonovski, Igor; Cizelj, Leon
2011-01-01
A novel computational approach for simulation of intergranular cracks in a polycrystalline aggregate is proposed in this paper. The computational model includes a topological model of the experimentally determined microstructure of a 400 μm diameter stainless steel wire and automatic finite element discretization of the grains and grain boundaries. The microstructure was spatially characterized by X-ray diffraction contrast tomography and contains 362 grains and some 1600 grain boundaries. Available constitutive models currently include isotropic elasticity for the grain interior and cohesive behavior with damage for the grain boundaries. The experimentally determined lattice orientations are employed to distinguish between resistant low energy and susceptible high energy grain boundaries in the model. The feasibility and performance of the proposed computational approach is demonstrated by simulating the onset and propagation of intergranular cracking. The preliminary numerical results are outlined and discussed.
Modeling multimodal human-computer interaction
Obrenovic, Z.; Starcevic, D.
2004-01-01
Incorporating the well-known Unified Modeling Language into a generic modeling framework makes research on multimodal human-computer interaction accessible to a wide range off software engineers. Multimodal interaction is part of everyday human discourse: We speak, move, gesture, and shift our gaze
A Computational Model of Selection by Consequences
McDowell, J. J.
2004-01-01
Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of…
Generating Computational Models for Serious Gaming
Westera, Wim
2018-01-01
Many serious games include computational models that simulate dynamic systems. These models promote enhanced interaction and responsiveness. Under the social web paradigm more and more usable game authoring tools become available that enable prosumers to create their own games, but the inclusion of
Security Management Model in Cloud Computing Environment
Ahmadpanah, Seyed Hossein
2016-01-01
In the cloud computing environment, cloud virtual machine (VM) will be more and more the number of virtual machine security and management faced giant Challenge. In order to address security issues cloud computing virtualization environment, this paper presents a virtual machine based on efficient and dynamic deployment VM security management model state migration and scheduling, study of which virtual machine security architecture, based on AHP (Analytic Hierarchy Process) virtual machine de...
Ewe: a computer model for ultrasonic inspection
International Nuclear Information System (INIS)
Douglas, S.R.; Chaplin, K.R.
1991-11-01
The computer program EWE simulates the propagation of elastic waves in solids and liquids. It has been applied to ultrasonic testing to study the echoes generated by cracks and other types of defects. A discussion of the elastic wave equations is given, including the first-order formulation, shear and compression waves, surface waves and boundaries, numerical method of solution, models for cracks and slot defects, input wave generation, returning echo construction, and general computer issues
Light reflection models for computer graphics.
Greenberg, D P
1989-04-14
During the past 20 years, computer graphic techniques for simulating the reflection of light have progressed so that today images of photorealistic quality can be produced. Early algorithms considered direct lighting only, but global illumination phenomena with indirect lighting, surface interreflections, and shadows can now be modeled with ray tracing, radiosity, and Monte Carlo simulations. This article describes the historical development of computer graphic algorithms for light reflection and pictorially illustrates what will be commonly available in the near future.
Finite difference computing with exponential decay models
Langtangen, Hans Petter
2016-01-01
This text provides a very simple, initial introduction to the complete scientific computing pipeline: models, discretization, algorithms, programming, verification, and visualization. The pedagogical strategy is to use one case study – an ordinary differential equation describing exponential decay processes – to illustrate fundamental concepts in mathematics and computer science. The book is easy to read and only requires a command of one-variable calculus and some very basic knowledge about computer programming. Contrary to similar texts on numerical methods and programming, this text has a much stronger focus on implementation and teaches testing and software engineering in particular. .
Do's and Don'ts of Computer Models for Planning
Hammond, John S., III
1974-01-01
Concentrates on the managerial issues involved in computer planning models. Describes what computer planning models are and the process by which managers can increase the likelihood of computer planning models being successful in their organizations. (Author/DN)
Quantum Vertex Model for Reversible Classical Computing
Chamon, Claudio; Mucciolo, Eduardo; Ruckenstein, Andrei; Yang, Zhicheng
We present a planar vertex model that encodes the result of a universal reversible classical computation in its ground state. The approach involves Boolean variables (spins) placed on links of a two-dimensional lattice, with vertices representing logic gates. Large short-ranged interactions between at most two spins implement the operation of each gate. The lattice is anisotropic with one direction corresponding to computational time, and with transverse boundaries storing the computation's input and output. The model displays no finite temperature phase transitions, including no glass transitions, independent of circuit. The computational complexity is encoded in the scaling of the relaxation rate into the ground state with the system size. We use thermal annealing and a novel and more efficient heuristic \\x9Dannealing with learning to study various computational problems. To explore faster relaxation routes, we construct an explicit mapping of the vertex model into the Chimera architecture of the D-Wave machine, initiating a novel approach to reversible classical computation based on quantum annealing.
Self-organizing adaptive map: autonomous learning of curves and surfaces from point samples.
Piastra, Marco
2013-05-01
Competitive Hebbian Learning (CHL) (Martinetz, 1993) is a simple and elegant method for estimating the topology of a manifold from point samples. The method has been adopted in a number of self-organizing networks described in the literature and has given rise to related studies in the fields of geometry and computational topology. Recent results from these fields have shown that a faithful reconstruction can be obtained using the CHL method only for curves and surfaces. Within these limitations, these findings constitute a basis for defining a CHL-based, growing self-organizing network that produces a faithful reconstruction of an input manifold. The SOAM (Self-Organizing Adaptive Map) algorithm adapts its local structure autonomously in such a way that it can match the features of the manifold being learned. The adaptation process is driven by the defects arising when the network structure is inadequate, which cause a growth in the density of units. Regions of the network undergo a phase transition and change their behavior whenever a simple, local condition of topological regularity is met. The phase transition is eventually completed across the entire structure and the adaptation process terminates. In specific conditions, the structure thus obtained is homeomorphic to the input manifold. During the adaptation process, the network also has the capability to focus on the acquisition of input point samples in critical regions, with a substantial increase in efficiency. The behavior of the network has been assessed experimentally with typical data sets for surface reconstruction, including suboptimal conditions, e.g. with undersampling and noise. Copyright © 2012 Elsevier Ltd. All rights reserved.
Computational disease modeling – fact or fiction?
Directory of Open Access Journals (Sweden)
Stephan Klaas
2009-06-01
Full Text Available Abstract Background Biomedical research is changing due to the rapid accumulation of experimental data at an unprecedented scale, revealing increasing degrees of complexity of biological processes. Life Sciences are facing a transition from a descriptive to a mechanistic approach that reveals principles of cells, cellular networks, organs, and their interactions across several spatial and temporal scales. There are two conceptual traditions in biological computational-modeling. The bottom-up approach emphasizes complex intracellular molecular models and is well represented within the systems biology community. On the other hand, the physics-inspired top-down modeling strategy identifies and selects features of (presumably essential relevance to the phenomena of interest and combines available data in models of modest complexity. Results The workshop, "ESF Exploratory Workshop on Computational disease Modeling", examined the challenges that computational modeling faces in contributing to the understanding and treatment of complex multi-factorial diseases. Participants at the meeting agreed on two general conclusions. First, we identified the critical importance of developing analytical tools for dealing with model and parameter uncertainty. Second, the development of predictive hierarchical models spanning several scales beyond intracellular molecular networks was identified as a major objective. This contrasts with the current focus within the systems biology community on complex molecular modeling. Conclusion During the workshop it became obvious that diverse scientific modeling cultures (from computational neuroscience, theory, data-driven machine-learning approaches, agent-based modeling, network modeling and stochastic-molecular simulations would benefit from intense cross-talk on shared theoretical issues in order to make progress on clinically relevant problems.
Self-Organization in Integrated Conservation and Development Initiatives
Directory of Open Access Journals (Sweden)
Cristiana Simão Seixas
2007-11-01
Full Text Available This paper uses a cooking metaphor to explore key elements (i.e., ingredients for a great meal that contribute to self-organization processes in the context of successful community-based conservation (CBC or integrated conservation and development projects (ICDP. We pose two major questions: (1 What are the key factors that drive peoples' and/or organizations' willingness to take responsibilities and to act? (2 What contributes to community self-organization? In other words, how conservation-development projects originate, evolve, survive or disappear? In order to address these questions we examine trigger events and catalytic elements in several cases among the Equator Prize finalists and short-listed nominees, from both the 2002 and 2004 awards. The Prize recognizes efforts in integrating biodiversity conservation and poverty reduction. We use secondary data in our analysis, including data from several technical reports and scientific papers written about the Equator Prize finalists and short-listed nominees. We observed common ingredients in most projects including: (1 involvement and commitment of key players (including communities, (2 funding, (3 strong leadership, (4 capacity building, (5 partnership with supportive organizations and government, and (6 economic incentives (including alternative livelihood options. We also observed that CBC and ICDP initiatives opportunistically evolve in a multi-level world, in which local communities establish linkages with people and organizations at different political levels, across different geographical scales and for different purposes. We conclude that there is no right 'recipe' to promote community self-organization but often a mix of some of these six ingredients need to come together for 'success' and that one or two ingredients are not sufficient to ensure success. Also the existence of these six ingredients does not guarantee a great meal - the 'chef's' creativity also is critical. That is
Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille
This paper introduces the implementation of a computational agent-based financial market model in which the system is described on both microscopic and macroscopic levels. This artificial financial market model is used to study the system response when a shock occurs. Indeed, when a market experiences perturbations, financial systems behavior can exhibit two different properties: resilience and robustness. Through simulations and different scenarios of market shocks, these system properties are studied. The results notably show that the emergence of collective herding behavior when market shock occurs leads to a temporary disruption of the system self-organization. Numerical simulations highlight that the market can absorb strong mono-shocks but can also be led to rupture by low but repeated perturbations.
Self-organized template formation for quantum dot ordering
International Nuclear Information System (INIS)
Noetzel, Richard; Mano, Takaaki; Wolter, Joachim H.
2004-01-01
Ordered arrays of quantum dots (QDs) are created by self-organized anisotropic strain engineering of (In,Ga)As/GaAs quantum wire (QWR) superlattice (SL) templates on exactly oriented GaAs (100) substrates by molecular beam epitaxy (MBE). The well-defined one-dimensional arrays of (In,Ga)As QDs formed on top of these templates due to local strain recognition are of excellent structural and optical quality up to room temperature. The QD arrays thus allow for fundamental studies and device operation principles based on single- and multiple carrier- and photon-, and coherent quantum interference effects
Theoretical and applied aspects of the self-organizing maps
Cottrell , Marie; Olteanu , Madalina; Rossi , Fabrice; Villa-Vialaneix , Nathalie
2016-01-01
International audience; The Self-Organizing Map (SOM) is widely used, easy to implement , has nice properties for data mining by providing both clustering and visual representation. It acts as an extension of the k-means algorithm that preserves as much as possible the topological structure of the data. However, since its conception, the mathematical study of the SOM remains difficult and has be done only in very special cases. In WSOM 2005, Jean-Claude Fort presented the state of the art, th...
Self-organized critical behavior in pinned flux lattices
International Nuclear Information System (INIS)
Pla, O.; Nori, F.
1991-01-01
We study the response of pinned fluxed lattices, under small perturbations in the driving force, below and close to the pinning-depinning transition. For driving Lorentz forces below F c (the depinning force at which the whole flux lattice slides), the system has instabilities against small force increases, with a power-law distribution characteristic of self-organized criticality. Specifically, D(d)∼d -1,3 , where d is the displacement of a flux line after a very small force increase. We also study the initial stages of the motion of the lattice once the driving force overcomes the pinning forces
Simple lecture demonstrations of instability and self-organization
International Nuclear Information System (INIS)
Mayer, V V; Varaksina, E I; Saranin, V A
2014-01-01
A dielectric liquid layer with an electric field created inside it is proposed as a means for demonstrating the phenomenon of self-organization. The field is produced by the distributed charge transferred by a corona discharge from the tip to the liquid surface. The theory of the phenomenon is presented. An analogy with the Rayleigh – Taylor instability is drawn and a comparison with the Benard instability is given. The practicality of the method for both natural sciences and the humanities is discussed. (methodological notes)
Study on self organized criticality of China power grid blackouts
Energy Technology Data Exchange (ETDEWEB)
Zhao, Xingyong; Zhang, Xiubin; He, Bin [Department of Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240 (China)
2009-03-15
Based on the complex system theory and the concept of self organized criticality (SOC) theory, the mechanism of China power grid blackout is studied by analyzing the blackout data in the China power system from 1981 to 2002. The probability distribution functions of various measures of blackout size have a power tail. The analysis of scaled window variance and rescaled range statistics of the time series show moderate long time correlations. The blackout data seem consistent with SOC; the results obtained show that SOC dynamics may play an important role in the dynamics of power systems blackouts. It would be possible to propose novel approaches for understanding and controlling power systems blackouts. (author)
Study on self organized criticality of China power grid blackouts
Energy Technology Data Exchange (ETDEWEB)
Zhao Xingyong [Department of Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240 (China)], E-mail: zhaoxingyong@sjtu.edu.cn; Zhang Xiubin; He Bin [Department of Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240 (China)
2009-03-15
Based on the complex system theory and the concept of self organized criticality (SOC) theory, the mechanism of China power grid blackout is studied by analyzing the blackout data in the China power system from 1981 to 2002. The probability distribution functions of various measures of blackout size have a power tail. The analysis of scaled window variance and rescaled range statistics of the time series show moderate long time correlations. The blackout data seem consistent with SOC; the results obtained show that SOC dynamics may play an important role in the dynamics of power systems blackouts. It would be possible to propose novel approaches for understanding and controlling power systems blackouts.
Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network.
Del Papa, Bruno; Priesemann, Viola; Triesch, Jochen
2017-01-01
Many experiments have suggested that the brain operates close to a critical state, based on signatures of criticality such as power-law distributed neuronal avalanches. In neural network models, criticality is a dynamical state that maximizes information processing capacities, e.g. sensitivity to input, dynamical range and storage capacity, which makes it a favorable candidate state for brain function. Although models that self-organize towards a critical state have been proposed, the relation between criticality signatures and learning is still unclear. Here, we investigate signatures of criticality in a self-organizing recurrent neural network (SORN). Investigating criticality in the SORN is of particular interest because it has not been developed to show criticality. Instead, the SORN has been shown to exhibit spatio-temporal pattern learning through a combination of neural plasticity mechanisms and it reproduces a number of biological findings on neural variability and the statistics and fluctuations of synaptic efficacies. We show that, after a transient, the SORN spontaneously self-organizes into a dynamical state that shows criticality signatures comparable to those found in experiments. The plasticity mechanisms are necessary to attain that dynamical state, but not to maintain it. Furthermore, onset of external input transiently changes the slope of the avalanche distributions - matching recent experimental findings. Interestingly, the membrane noise level necessary for the occurrence of the criticality signatures reduces the model's performance in simple learning tasks. Overall, our work shows that the biologically inspired plasticity and homeostasis mechanisms responsible for the SORN's spatio-temporal learning abilities can give rise to criticality signatures in its activity when driven by random input, but these break down under the structured input of short repeating sequences.
Towards The Deep Model : Understanding Visual Recognition Through Computational Models
Wang, Panqu
2017-01-01
Understanding how visual recognition is achieved in the human brain is one of the most fundamental questions in vision research. In this thesis I seek to tackle this problem from a neurocomputational modeling perspective. More specifically, I build machine learning-based models to simulate and explain cognitive phenomena related to human visual recognition, and I improve computational models using brain-inspired principles to excel at computer vision tasks.I first describe how a neurocomputat...
Hybrid computer modelling in plasma physics
International Nuclear Information System (INIS)
Hromadka, J; Ibehej, T; Hrach, R
2016-01-01
Our contribution is devoted to development of hybrid modelling techniques. We investigate sheath structures in the vicinity of solids immersed in low temperature argon plasma of different pressures by means of particle and fluid computer models. We discuss the differences in results obtained by these methods and try to propose a way to improve the results of fluid models in the low pressure area. There is a possibility to employ Chapman-Enskog method to find appropriate closure relations of fluid equations in a case when particle distribution function is not Maxwellian. We try to follow this way to enhance fluid model and to use it in hybrid plasma model further. (paper)
Time series modeling, computation, and inference
Prado, Raquel
2010-01-01
The authors systematically develop a state-of-the-art analysis and modeling of time series. … this book is well organized and well written. The authors present various statistical models for engineers to solve problems in time series analysis. Readers no doubt will learn state-of-the-art techniques from this book.-Hsun-Hsien Chang, Computing Reviews, March 2012My favorite chapters were on dynamic linear models and vector AR and vector ARMA models.-William Seaver, Technometrics, August 2011… a very modern entry to the field of time-series modelling, with a rich reference list of the current lit
Biomedical Imaging and Computational Modeling in Biomechanics
Iacoviello, Daniela
2013-01-01
This book collects the state-of-art and new trends in image analysis and biomechanics. It covers a wide field of scientific and cultural topics, ranging from remodeling of bone tissue under the mechanical stimulus up to optimizing the performance of sports equipment, through the patient-specific modeling in orthopedics, microtomography and its application in oral and implant research, computational modeling in the field of hip prostheses, image based model development and analysis of the human knee joint, kinematics of the hip joint, micro-scale analysis of compositional and mechanical properties of dentin, automated techniques for cervical cell image analysis, and iomedical imaging and computational modeling in cardiovascular disease. The book will be of interest to researchers, Ph.D students, and graduate students with multidisciplinary interests related to image analysis and understanding, medical imaging, biomechanics, simulation and modeling, experimental analysis.
Computational algebraic geometry of epidemic models
Rodríguez Vega, Martín.
2014-06-01
Computational Algebraic Geometry is applied to the analysis of various epidemic models for Schistosomiasis and Dengue, both, for the case without control measures and for the case where control measures are applied. The models were analyzed using the mathematical software Maple. Explicitly the analysis is performed using Groebner basis, Hilbert dimension and Hilbert polynomials. These computational tools are included automatically in Maple. Each of these models is represented by a system of ordinary differential equations, and for each model the basic reproductive number (R0) is calculated. The effects of the control measures are observed by the changes in the algebraic structure of R0, the changes in Groebner basis, the changes in Hilbert dimension, and the changes in Hilbert polynomials. It is hoped that the results obtained in this paper become of importance for designing control measures against the epidemic diseases described. For future researches it is proposed the use of algebraic epidemiology to analyze models for airborne and waterborne diseases.
Computer modeling of commercial refrigerated warehouse facilities
International Nuclear Information System (INIS)
Nicoulin, C.V.; Jacobs, P.C.; Tory, S.
1997-01-01
The use of computer models to simulate the energy performance of large commercial refrigeration systems typically found in food processing facilities is an area of engineering practice that has seen little development to date. Current techniques employed in predicting energy consumption by such systems have focused on temperature bin methods of analysis. Existing simulation tools such as DOE2 are designed to model commercial buildings and grocery store refrigeration systems. The HVAC and Refrigeration system performance models in these simulations tools model equipment common to commercial buildings and groceries, and respond to energy-efficiency measures likely to be applied to these building types. The applicability of traditional building energy simulation tools to model refrigerated warehouse performance and analyze energy-saving options is limited. The paper will present the results of modeling work undertaken to evaluate energy savings resulting from incentives offered by a California utility to its Refrigerated Warehouse Program participants. The TRNSYS general-purpose transient simulation model was used to predict facility performance and estimate program savings. Custom TRNSYS components were developed to address modeling issues specific to refrigerated warehouse systems, including warehouse loading door infiltration calculations, an evaporator model, single-state and multi-stage compressor models, evaporative condenser models, and defrost energy requirements. The main focus of the paper will be on the modeling approach. The results from the computer simulations, along with overall program impact evaluation results, will also be presented
Use of the self-organizing feature map to diagnose abnormal engineering change
Lu, Ruei-Shan; Wu, Zhi-Ting; Peng, Kuo-Wei; Yu, Tai-Yi
2015-07-01
This study established identification manners with self-organizing feature map (SOM) to achieve the goal of monitoring Engineering Change (EC) based on historical data of a company that specializes in computers and peripherals. The product life cycle of this company is 3-6 months. The historical data were divided into three parts, each covering four months. The first part, comprising 2,343 records from January to April (the training period), comprise the Control Group. The second and third parts comprise Experimental Groups (EG) 1 and 2, respectively. For EG 1 and 2, the successful rate of recognizing information on abnormal ECs was approximately 96% and 95%, respectively. This paper shows the importance and screening procedures of abnormal engineering change for a particular company specializing in computers and peripherals.
Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis †
Directory of Open Access Journals (Sweden)
Carlos Dafonte
2018-05-01
Full Text Available Analyzing huge amounts of data becomes essential in the era of Big Data, where databases are populated with hundreds of Gigabytes that must be processed to extract knowledge. Hence, classical algorithms must be adapted towards distributed computing methodologies that leverage the underlying computational power of these platforms. Here, a parallel, scalable, and optimized design for self-organized maps (SOM is proposed in order to analyze massive data gathered by the spectrophotometric sensor of the European Space Agency (ESA Gaia spacecraft, although it could be extrapolated to other domains. The performance comparison between the sequential implementation and the distributed ones based on Apache Hadoop and Apache Spark is an important part of the work, as well as the detailed analysis of the proposed optimizations. Finally, a domain-specific visualization tool to explore astronomical SOMs is presented.
Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis †.
Dafonte, Carlos; Garabato, Daniel; Álvarez, Marco A; Manteiga, Minia
2018-05-03
Analyzing huge amounts of data becomes essential in the era of Big Data, where databases are populated with hundreds of Gigabytes that must be processed to extract knowledge. Hence, classical algorithms must be adapted towards distributed computing methodologies that leverage the underlying computational power of these platforms. Here, a parallel, scalable, and optimized design for self-organized maps (SOM) is proposed in order to analyze massive data gathered by the spectrophotometric sensor of the European Space Agency (ESA) Gaia spacecraft, although it could be extrapolated to other domains. The performance comparison between the sequential implementation and the distributed ones based on Apache Hadoop and Apache Spark is an important part of the work, as well as the detailed analysis of the proposed optimizations. Finally, a domain-specific visualization tool to explore astronomical SOMs is presented.
FPGA implementation of self organizing map with digital phase locked loops.
Hikawa, Hiroomi
2005-01-01
The self-organizing map (SOM) has found applicability in a wide range of application areas. Recently new SOM hardware with phase modulated pulse signal and digital phase-locked loops (DPLLs) has been proposed (Hikawa, 2005). The system uses the DPLL as a computing element since the operation of the DPLL is very similar to that of SOM's computation. The system also uses square waveform phase to hold the value of the each input vector element. This paper discuss the hardware implementation of the DPLL SOM architecture. For effective hardware implementation, some components are redesigned to reduce the circuit size. The proposed SOM architecture is described in VHDL and implemented on field programmable gate array (FPGA). Its feasibility is verified by experiments. Results show that the proposed SOM implemented on the FPGA has a good quantization capability, and its circuit size very small.
Comparison between genetic algorithm and self organizing map to detect botnet network traffic
Yugandhara Prabhakar, Shinde; Parganiha, Pratishtha; Madhu Viswanatham, V.; Nirmala, M.
2017-11-01
In Cyber Security world the botnet attacks are increasing. To detect botnet is a challenging task. Botnet is a group of computers connected in a coordinated fashion to do malicious activities. Many techniques have been developed and used to detect and prevent botnet traffic and the attacks. In this paper, a comparative study is done on Genetic Algorithm (GA) and Self Organizing Map (SOM) to detect the botnet network traffic. Both are soft computing techniques and used in this paper as data analytics system. GA is based on natural evolution process and SOM is an Artificial Neural Network type, uses unsupervised learning techniques. SOM uses neurons and classifies the data according to the neurons. Sample of KDD99 dataset is used as input to GA and SOM.
Self-organized criticality, long-time correlations, and the standard transport paradigm
International Nuclear Information System (INIS)
Krommes, J.A.
2000-01-01
Some aspects of low-frequency, long-wavelength fluctuations are considered. A stochastic model is used to show that power-law time correlations need not arise from self-organized criticality. A formula for the frequency spectrum of uncorrelated, overlapping avalanches is shown to be a special case of the spectral balance equation of renormalized statistical turbulence theory. It is argued that there need be no contradiction between the presence of long-time correlations and the existence of local transport coefficients
Effects of Vertex Activity and Self-organized Criticality Behavior on a Weighted Evolving Network
International Nuclear Information System (INIS)
Zhang Guiqing; Yang Qiuying; Chen Tianlun
2008-01-01
Effects of vertex activity have been analyzed on a weighted evolving network. The network is characterized by the probability distribution of vertex strength, each edge weight and evolution of the strength of vertices with different vertex activities. The model exhibits self-organized criticality behavior. The probability distribution of avalanche size for different network sizes is also shown. In addition, there is a power law relation between the size and the duration of an avalanche and the average of avalanche size has been studied for different vertex activities
Qiu, T.; Wu, X. L.; Mei, Y. F.; Chu, P. K.; Siu, G. G.
2005-09-01
Unique silver dendritic nanostructures, with stems, branches, and leaves, were synthesized with self-organization via a simple electroless metal deposition method in a conventional autoclave containing aqueous HF and AgNO3 solution. Their growth mechanisms are discussed in detail on the basis of a self-assembled localized microscopic electrochemical cell model. A process of diffusion-limited aggregation is suggested for the formation of the silver dendritic nanostructures. This nanostructured material is of great potential to be building blocks for assembling mini-functional devices of the next generation.
Micro- and macro-scale self-organization in a dissipative plasma
International Nuclear Information System (INIS)
Skoric, M.M.; Sato, T.; Maluckov, A.; Jovanovic, M.S.
1998-10-01
We study a nonlinear three-wave interaction in an open dissipative model of stimulated Raman backscattering in a plasma. A hybrid kinetic-fluid scheme is proposed to include anomalous kinetic dissipation due to electron trapping and plasma wave breaking. We simulate a finite plasma with open boundaries and vary a transport parameter to examine a route to spatio-temporal complexity. An interplay between self-organization at micro (kinetic) and macro (wave/fluid) scales is revealed through quasi-periodic and intermittent evolution of dynamical variables, dissipative structures and related entropy rates. An evidence that entropy rate extrema correspond to structural transitions is found. (author)
Autonomous distributed self-organizing and self-healing hardware architecture - The eDNA concept
DEFF Research Database (Denmark)
Boesen, Michael Reibel; Madsen, Jan; Keymeulen, Didier
2011-01-01
This paper presents the current state of the autonomous distributed self-organizing and self-healing electronic DNA (eDNA) hardware architecture (patent pending). In its current prototype state, the eDNA architecture is capable of responding to multiple injected faults by autonomously reconfiguring...... itself to accommodate the fault and keep the application running. This paper will also disclose advanced features currently available in the simulation model only. These features are future work and will soon be implemented in hardware. Finally we will describe step-by-step how an application...
Elements of automata theory and the theory of Markov chains. [Self-organizing control systems
Energy Technology Data Exchange (ETDEWEB)
Lind, M
1975-03-01
Selected topics from automata theory and the theory of Markov chains are treated. In particular, finite-memory automata are discussed in detail, and the results are used to formulate an automation model of a class of continuous systems. Stochastic automata are introduced as a natural generalization of the deterministic automaton. Markov chains are shown to be closely related to stochastic automata. Results from Markov chain theory are thereby directly applicable to analysis of stochastic automata. This report provides the theoretical foundation for the investigation in Riso Report No. 315 of a class of self-organizing control systems. (25 figures) (auth)
Plasticity of ductile metallic glasses: a self-organized critical state.
Sun, B A; Yu, H B; Jiao, W; Bai, H Y; Zhao, D Q; Wang, W H
2010-07-16
We report a close correlation between the dynamic behavior of serrated flow and the plasticity in metallic glasses (MGs) and show that the plastic deformation of ductile MGs can evolve into a self-organized critical state characterized by the power-law distribution of shear avalanches. A stick-slip model considering the interaction of multiple shear bands is presented to reveal complex scale-free intermittent shear-band motions in ductile MGs and quantitatively reproduce the experimental observations. Our studies have implications for understanding the precise plastic deformation mechanism of MGs.
Multicellular Self-Organization of P. aeruginosa due to Interactions with Secreted Trails.
Gelimson, Anatolij; Zhao, Kun; Lee, Calvin K; Kranz, W Till; Wong, Gerard C L; Golestanian, Ramin
2016-10-21
Guided movement in response to slowly diffusing polymeric trails provides a unique mechanism for self-organization of some microorganisms. To elucidate how this signaling route leads to microcolony formation, we experimentally probe the trajectory and orientation of Pseudomonas aeruginosa that propel themselves on a surface using type IV pili motility appendages, which preferentially attach to deposited exopolysaccharides. We construct a stochastic model by analyzing single-bacterium trajectories and show that the resulting theoretical prediction for the many-body behavior of the bacteria is in quantitative agreement with our experimental characterization of how cells explore the surface via a power-law strategy.
Physical Modeling of microtubule force generation and self-organization
Tanase, C.
2004-01-01
Biological systems are complex heterogeneous and far from equilibrium systems. The fundamental questions posed by the physics of such systems are what the force generation mechanisms are, and how energy is processed and distributed among the components inside them. In answering these questions we
Self-organizing map classifier for stressed speech recognition
Partila, Pavol; Tovarek, Jaromir; Voznak, Miroslav
2016-05-01
This paper presents a method for detecting speech under stress using Self-Organizing Maps. Most people who are exposed to stressful situations can not adequately respond to stimuli. Army, police, and fire department occupy the largest part of the environment that are typical of an increased number of stressful situations. The role of men in action is controlled by the control center. Control commands should be adapted to the psychological state of a man in action. It is known that the psychological changes of the human body are also reflected physiologically, which consequently means the stress effected speech. Therefore, it is clear that the speech stress recognizing system is required in the security forces. One of the possible classifiers, which are popular for its flexibility, is a self-organizing map. It is one type of the artificial neural networks. Flexibility means independence classifier on the character of the input data. This feature is suitable for speech processing. Human Stress can be seen as a kind of emotional state. Mel-frequency cepstral coefficients, LPC coefficients, and prosody features were selected for input data. These coefficients were selected for their sensitivity to emotional changes. The calculation of the parameters was performed on speech recordings, which can be divided into two classes, namely the stress state recordings and normal state recordings. The benefit of the experiment is a method using SOM classifier for stress speech detection. Results showed the advantage of this method, which is input data flexibility.
SELF-ORGANIZATION OF LEAD SULFIDE QUANTUM DOTS INTO SUPERSTRUCTURES
Directory of Open Access Journals (Sweden)
Elena V. Ushakova
2014-11-01
Full Text Available The method of X-ray structural analysis (X-ray scattering at small angles is used to show that the structures obtained by self-organization on a substrate of lead sulfide (PbS quantum dots are ordered arrays. Self-organization of quantum dots occurs at slow evaporation of solvent from a cuvette. The cuvette is a thin layer of mica with teflon ring on it. The positions of peaks in SAXS pattern are used to calculate crystal lattice of obtained ordered structures. Such structures have a primitive orthorhombic crystal lattice. Calculated lattice parameters are: a = 21,1 (nm; b = 36,2 (nm; c = 62,5 (nm. Dimensions of structures are tens of micrometers. The spectral properties of PbS QDs superstructures and kinetic parameters of their luminescence are investigated. Absorption band of superstructures is broadened as compared to the absorption band of the quantum dots in solution; the luminescence band is slightly shifted to the red region of the spectrum, while its bandwidth is not changed much. Luminescence lifetime of obtained structures has been significantly decreased in comparison with the isolated quantum dots in solution, but remained the same for the lead sulfide quantum dots close-packed ensembles. Such superstructures can be used to produce solar cells with improved characteristics.
Self-organization in cathode boundary layer discharges in xenon
International Nuclear Information System (INIS)
Takano, Nobuhiko; Schoenbach, Karl H
2006-01-01
Self-organization of direct current xenon microdischarges in cathode boundary layer configuration has been studied for pressures in the range 30-140 Torr and for currents in the range 50 μA-1 mA. Side-on and end-on observations of the discharge have provided information on the structure and spatial arrangement of the plasma filaments. The regularly spaced filaments, which appear in the normal glow mode when the current is lowered, have a length which is determined by the cathode fall. It varies, dependent on pressure and current, between 50 and 70 μm. The minimum diameter is approximately 80 μm, as determined from the radiative emission in the visible. The filaments are sources of extensive excimer emission. Measurements of the cathode fall length have allowed us to determine the secondary emission coefficient for the discharge in the normal glow mode and to estimate the cathode fall voltage at the transition from normal glow mode to filamentary mode. It was found that the cathode fall voltage at this transition decreases, indicating the onset of additional electron gain processes at the cathode. The regular arrangement of the filaments, self-organization, is assumed to be due to Coulomb interactions between the positively charged cathode fall channels and positive space charges on the surface of the surrounding dielectric spacer. Calculations based on these assumptions showed good agreement with experimentally observed filament patterns
Obtaining parton distribution functions from self-organizing maps
International Nuclear Information System (INIS)
Honkanen, H.; Liuti, S.; Loitiere, Y.C.; Brogan, D.; Reynolds, P.
2007-01-01
We present an alternative algorithm to global fitting procedures to construct Parton Distribution Functions parametrizations. The proposed algorithm uses Self-Organizing Maps which at variance with the standard Neural Networks, are based on competitive-learning. Self-Organizing Maps generate a non-uniform projection from a high dimensional data space onto a low dimensional one (usually 1 or 2 dimensions) by clustering similar PDF representations together. The SOMs are trained on progressively narrower selections of data samples. The selection criterion is that of convergence towards a neighborhood of the experimental data. All available data sets on deep inelastic scattering in the kinematical region of 0.001 ≤ x ≤ 0.75, and 1 ≤ Q 2 ≤ 100 GeV 2 , with a cut on the final state invariant mass, W 2 ≥ 10 GeV 2 were implemented. The proposed fitting procedure, at variance with standard neural network approaches, allows for an increased control of the systematic bias by enabling the user to directly control the data selection procedure at various stages of the process. (author)
Informational temperature concept and the nature of self-organization
International Nuclear Information System (INIS)
Lin, Shu-Kun
1996-01-01
Self-organization phenomena are spontaneous processes. Their behavior should be governed by the second law of thermodynamics. The dissipative structure theory of the Prigogine school of thermodynamics claims that open-quotes order out of chaosclose quotes through open-quotes self-organizationclose quotes and challenges the validity of the second law of thermodynamics. Unfortunately this theory is questionable. Therefore we have to reconsider the related fundamental theoretical problems. Informational entropy (S) and information (I) are related by S = S max - I, where S max is the maximum informational entropy. This conforms with the broadly accepted definition that entropy is the information loss. As informational entropy concept has been proved to be useful, it will be convenient to define an informational temperature, T I . This can be related to energy E and the informational entropy S. Information registration is a process of ΔI > 0, or ΔS 0). Therefore, T I is negative, and has the opposite sign of the conventional thermodynamic temperature, T. This concept is useful for clarifying the concepts of open-quotes orderclose quotes and open-quotes disorderclose quotes of static structures and characterizing many typical information loss processes of self-organization
Applied Mathematics, Modelling and Computational Science
Kotsireas, Ilias; Makarov, Roman; Melnik, Roderick; Shodiev, Hasan
2015-01-01
The Applied Mathematics, Modelling, and Computational Science (AMMCS) conference aims to promote interdisciplinary research and collaboration. The contributions in this volume cover the latest research in mathematical and computational sciences, modeling, and simulation as well as their applications in natural and social sciences, engineering and technology, industry, and finance. The 2013 conference, the second in a series of AMMCS meetings, was held August 26–30 and organized in cooperation with AIMS and SIAM, with support from the Fields Institute in Toronto, and Wilfrid Laurier University. There were many young scientists at AMMCS-2013, both as presenters and as organizers. This proceedings contains refereed papers contributed by the participants of the AMMCS-2013 after the conference. This volume is suitable for researchers and graduate students, mathematicians and engineers, industrialists, and anyone who would like to delve into the interdisciplinary research of applied and computational mathematics ...
Description of mathematical models and computer programs
International Nuclear Information System (INIS)
1977-01-01
The paper gives a description of mathematical models and computer programs for analysing possible strategies for spent fuel management, with emphasis on economic analysis. The computer programs developed, describe the material flows, facility construction schedules, capital investment schedules and operating costs for the facilities used in managing the spent fuel. The computer programs use a combination of simulation and optimization procedures for the economic analyses. Many of the fuel cycle steps (such as spent fuel discharges, storage at the reactor, and transport to the RFCC) are described in physical and economic terms through simulation modeling, while others (such as reprocessing plant size and commissioning schedules, interim storage facility commissioning schedules etc.) are subjected to economic optimization procedures to determine the approximate lowest-cost plans from among the available feasible alternatives
Modeling inputs to computer models used in risk assessment
International Nuclear Information System (INIS)
Iman, R.L.
1987-01-01
Computer models for various risk assessment applications are closely scrutinized both from the standpoint of questioning the correctness of the underlying mathematical model with respect to the process it is attempting to model and from the standpoint of verifying that the computer model correctly implements the underlying mathematical model. A process that receives less scrutiny, but is nonetheless of equal importance, concerns the individual and joint modeling of the inputs. This modeling effort clearly has a great impact on the credibility of results. Model characteristics are reviewed in this paper that have a direct bearing on the model input process and reasons are given for using probabilities-based modeling with the inputs. The authors also present ways to model distributions for individual inputs and multivariate input structures when dependence and other constraints may be present
Mechanical coupling limits the density and quality of self-organized carbon nanotube growth
Bedewy, Mostafa; Hart, A. John
2013-03-01
Aligned carbon nanotube (CNT) structures are promising for many applications; however, as-grown CNT "forests" synthesized by chemical vapor deposition (CVD) are typically low-density and mostly comprise tortuous defective CNTs. Here, we present evidence that the density and alignment of self-organized CNT growth is limited by mechanical coupling among CNTs in contact, in combination with their diameter-dependent growth rates. This study is enabled by comprehensive X-ray characterization of the spatially and temporally-varying internal morphology of CNT forests. Based on this data, we model the time evolution and diameter-dependent scaling of the ensuing mechanical forces on catalyst nanoparticles during CNT growth, which arise from the mismatch between the collective lengthening rate of the forest and the diameter-dependent growth rates of individual CNTs. In addition to enabling self-organization of CNTs into forests, time-varying forces between CNTs in contact dictate the hierarchical tortuous morphology of CNT forests, and may be sufficient to influence the structural quality of CNTs. These forces reach a maximum that is coincident with the maximum density observed in our growth process, and are proportional to CNT diameter. Therefore, we propose that improved manufacturing strategies for self-organized CNTs should consider both chemical and mechanical effects. This may be especially necessary to achieve high density CNT forests with low defect density, such as for improved thermal interfaces and high-permeability membranes.Aligned carbon nanotube (CNT) structures are promising for many applications; however, as-grown CNT "forests" synthesized by chemical vapor deposition (CVD) are typically low-density and mostly comprise tortuous defective CNTs. Here, we present evidence that the density and alignment of self-organized CNT growth is limited by mechanical coupling among CNTs in contact, in combination with their diameter-dependent growth rates. This study is
Integrating interactive computational modeling in biology curricula.
Directory of Open Access Journals (Sweden)
Tomáš Helikar
2015-03-01
Full Text Available While the use of computer tools to simulate complex processes such as computer circuits is normal practice in fields like engineering, the majority of life sciences/biological sciences courses continue to rely on the traditional textbook and memorization approach. To address this issue, we explored the use of the Cell Collective platform as a novel, interactive, and evolving pedagogical tool to foster student engagement, creativity, and higher-level thinking. Cell Collective is a Web-based platform used to create and simulate dynamical models of various biological processes. Students can create models of cells, diseases, or pathways themselves or explore existing models. This technology was implemented in both undergraduate and graduate courses as a pilot study to determine the feasibility of such software at the university level. First, a new (In Silico Biology class was developed to enable students to learn biology by "building and breaking it" via computer models and their simulations. This class and technology also provide a non-intimidating way to incorporate mathematical and computational concepts into a class with students who have a limited mathematical background. Second, we used the technology to mediate the use of simulations and modeling modules as a learning tool for traditional biological concepts, such as T cell differentiation or cell cycle regulation, in existing biology courses. Results of this pilot application suggest that there is promise in the use of computational modeling and software tools such as Cell Collective to provide new teaching methods in biology and contribute to the implementation of the "Vision and Change" call to action in undergraduate biology education by providing a hands-on approach to biology.
Integrating interactive computational modeling in biology curricula.
Helikar, Tomáš; Cutucache, Christine E; Dahlquist, Lauren M; Herek, Tyler A; Larson, Joshua J; Rogers, Jim A
2015-03-01
While the use of computer tools to simulate complex processes such as computer circuits is normal practice in fields like engineering, the majority of life sciences/biological sciences courses continue to rely on the traditional textbook and memorization approach. To address this issue, we explored the use of the Cell Collective platform as a novel, interactive, and evolving pedagogical tool to foster student engagement, creativity, and higher-level thinking. Cell Collective is a Web-based platform used to create and simulate dynamical models of various biological processes. Students can create models of cells, diseases, or pathways themselves or explore existing models. This technology was implemented in both undergraduate and graduate courses as a pilot study to determine the feasibility of such software at the university level. First, a new (In Silico Biology) class was developed to enable students to learn biology by "building and breaking it" via computer models and their simulations. This class and technology also provide a non-intimidating way to incorporate mathematical and computational concepts into a class with students who have a limited mathematical background. Second, we used the technology to mediate the use of simulations and modeling modules as a learning tool for traditional biological concepts, such as T cell differentiation or cell cycle regulation, in existing biology courses. Results of this pilot application suggest that there is promise in the use of computational modeling and software tools such as Cell Collective to provide new teaching methods in biology and contribute to the implementation of the "Vision and Change" call to action in undergraduate biology education by providing a hands-on approach to biology.
Energy Technology Data Exchange (ETDEWEB)
Hasegawa, A [Bell Labs., Murray Hill, NJ (USA)
1982-02-01
Theoretical treatments of turbulence in fluids and plasmas often assume that the turbulence is isotropic and homogeneous. It is also often considered that turbulence produces uniformly distributed chaos, even when starting with a coherent initial condition. Recently, however, phenomena which do not obey these classic concepts have emerged. For example, in two-dimensional Navier-Stokes turbulence, an organized flow or structure is found to appear even from a chaotic initial condition. The author attempts to review some of the recent developments of a phenomenon called self-organization in the field of hydrodynamics and plasma physics.
Formation of self-organized anode patterns in arc discharge simulations
International Nuclear Information System (INIS)
Trelles, Juan Pablo
2013-01-01
Pattern formation and self-organization are phenomena commonly observed experimentally in diverse types of plasma systems, including atmospheric-pressure electric arc discharges. However, numerical simulations reproducing anode pattern formation in arc discharges have proven exceedingly elusive. Time-dependent three-dimensional thermodynamic non-equilibrium simulations reveal the spontaneous formation of self-organized patterns of anode attachment spots in the free-burning arc, a canonical thermal plasma flow established by a constant dc current between an axi-symmetric electrode configuration in the absence of external forcing. The number of spots, their size and distribution within the pattern depend on the applied total current and on the resolution of the spatial discretization, whereas the main properties of the plasma flow, such as maximum temperatures, velocity and voltage drop, depend only on the former. The sensibility of the solution to the spatial discretization stresses the computational requirements for comprehensive arc discharge simulations. The obtained anode patterns qualitatively agree with experimental observations and confirm that the spots originate at the fringes of the arc–anode attachment. The results imply that heavy-species–electron energy equilibration, in addition to thermal instability, has a dominant role in the formation of anode spots in arc discharges. (paper)
Phase transitions and self-organized criticality in networks of stochastic spiking neurons.
Brochini, Ludmila; de Andrade Costa, Ariadne; Abadi, Miguel; Roque, Antônio C; Stolfi, Jorge; Kinouchi, Osame
2016-11-07
Phase transitions and critical behavior are crucial issues both in theoretical and experimental neuroscience. We report analytic and computational results about phase transitions and self-organized criticality (SOC) in networks with general stochastic neurons. The stochastic neuron has a firing probability given by a smooth monotonic function Φ(V) of the membrane potential V, rather than a sharp firing threshold. We find that such networks can operate in several dynamic regimes (phases) depending on the average synaptic weight and the shape of the firing function Φ. In particular, we encounter both continuous and discontinuous phase transitions to absorbing states. At the continuous transition critical boundary, neuronal avalanches occur whose distributions of size and duration are given by power laws, as observed in biological neural networks. We also propose and test a new mechanism to produce SOC: the use of dynamic neuronal gains - a form of short-term plasticity probably located at the axon initial segment (AIS) - instead of depressing synapses at the dendrites (as previously studied in the literature). The new self-organization mechanism produces a slightly supercritical state, that we called SOSC, in accord to some intuitions of Alan Turing.
Self-Organization of Motor-Propelled Cytoskeletal Filaments at Topographically Defined Borders
Directory of Open Access Journals (Sweden)
Alf Månsson
2012-01-01
Full Text Available Self-organization phenomena are of critical importance in living organisms and of great interest to exploit in nanotechnology. Here we describe in vitro self-organization of molecular motor-propelled actin filaments, manifested as a tendency of the filaments to accumulate in high density close to topographically defined edges on nano- and microstructured surfaces. We hypothesized that this “edge-tracing” effect either (1 results from increased motor density along the guiding edges or (2 is a direct consequence of the asymmetric constraints on stochastic changes in filament sliding direction imposed by the edges. The latter hypothesis is well captured by a model explicitly defining the constraints of motility on structured surfaces in combination with Monte-Carlo simulations [cf. Nitta et al. (2006] of filament sliding. In support of hypothesis 2 we found that the model reproduced the edge tracing effect without the need to assume increased motor density at the edges. We then used model simulations to elucidate mechanistic details. The results are discussed in relation to nanotechnological applications and future experiments to test model predictions.
Energy Technology Data Exchange (ETDEWEB)
Hansmann, Ralf; Bernasconi, Petra; Smieszek, Timo; Loukopoulos, Peter; Scholz, Roland W [Chair of Environmental Sciences: Natural and Social Science Interface, Swiss Federal Institute of Technology, Zurich (ETH Zuerich), Universitaetsstrasse 22, ETH Zentrum CHN J76.3, CH-8092 Zurich (Switzerland)
2006-06-15
Much previous research on recycling behavior has drawn heavily on models of personal and perceived social norms, as well as of personal attitudes, to explain recycling behavior. Although such models have received empirical support, the issue concerning discrepancies between norms, personal attitudes and an individual's behavior is yet to be resolved. Using battery recycling in Switzerland as a case in point, the present questionnaire-based research examines via regression analyses the relationship between self-reported recycling behavior and socio-demographic variables, attitudes towards ecologically positive waste disposal, trust in waste disposal authorities, specific knowledge concerning recycling, justifications for not participating in the recycling scheme, self-organization of recycling behavior, and level of battery consumption. It was found that recycling knowledge, self-organization of recycling, and disagreement with justifications for non-recycling were positively related to recycling behavior, while attitudes towards ecological waste disposal and trust in waste disposal authorities were not directly related to respondents' self-reported battery recycling behavior. On the basis of these results, with reference to Sykes and Matza's Neutralization theory [Sykes GM, Matza D. Techniques of neutralization: a theory of delinquency. Am Sociol Rev 1957:22(6):664-70] a contextualized model of recycling behavior is proposed. This model is able to account for inconsistencies between personal attitudes and perceived social norms, and has practical implications for the design of public intervention strategies for enhancing participation in the recycling. (author)
Computer Modelling of Photochemical Smog Formation
Huebert, Barry J.
1974-01-01
Discusses a computer program that has been used in environmental chemistry courses as an example of modelling as a vehicle for teaching chemical dynamics, and as a demonstration of some of the factors which affect the production of smog. (Author/GS)
A Computational Model of Fraction Arithmetic
Braithwaite, David W.; Pyke, Aryn A.; Siegler, Robert S.
2017-01-01
Many children fail to master fraction arithmetic even after years of instruction, a failure that hinders their learning of more advanced mathematics as well as their occupational success. To test hypotheses about why children have so many difficulties in this area, we created a computational model of fraction arithmetic learning and presented it…
Model Checking - Automated Verification of Computational Systems
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 14; Issue 7. Model Checking - Automated Verification of Computational Systems. Madhavan Mukund. General Article Volume 14 Issue 7 July 2009 pp 667-681. Fulltext. Click here to view fulltext PDF. Permanent link:
Computational Modeling of Complex Protein Activity Networks
Schivo, Stefano; Leijten, Jeroen; Karperien, Marcel; Post, Janine N.; Prignet, Claude
2017-01-01
Because of the numerous entities interacting, the complexity of the networks that regulate cell fate makes it impossible to analyze and understand them using the human brain alone. Computational modeling is a powerful method to unravel complex systems. We recently described the development of a
Computer Modeling of Platinum Reforming Reactors | Momoh ...
African Journals Online (AJOL)
This paper, instead of using a theoretical approach has considered a computer model as means of assessing the reformate composition for three-stage fixed bed reactors in platforming unit. This is done by identifying many possible hydrocarbon transformation reactions that are peculiar to the process unit, identify the ...
Particle modeling of plasmas computational plasma physics
International Nuclear Information System (INIS)
Dawson, J.M.
1991-01-01
Recently, through the development of supercomputers, a powerful new method for exploring plasmas has emerged; it is computer modeling of plasmas. Such modeling can duplicate many of the complex processes that go on in a plasma and allow scientists to understand what the important processes are. It helps scientists gain an intuition about this complex state of matter. It allows scientists and engineers to explore new ideas on how to use plasma before building costly experiments; it allows them to determine if they are on the right track. It can duplicate the operation of devices and thus reduce the need to build complex and expensive devices for research and development. This is an exciting new endeavor that is in its infancy, but which can play an important role in the scientific and technological competitiveness of the US. There are a wide range of plasma models that are in use. There are particle models, fluid models, hybrid particle fluid models. These can come in many forms, such as explicit models, implicit models, reduced dimensional models, electrostatic models, magnetostatic models, electromagnetic models, and almost an endless variety of other models. Here the author will only discuss particle models. He will give a few examples of the use of such models; these will be taken from work done by the Plasma Modeling Group at UCLA because he is most familiar with work. However, it only gives a small view of the wide range of work being done around the US, or for that matter around the world
Reproducibility in Computational Neuroscience Models and Simulations
McDougal, Robert A.; Bulanova, Anna S.; Lytton, William W.
2016-01-01
Objective Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. Methods Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. Results Building on these standard practices, model sharing sites and tools have been developed that fit into several categories: 1. standardized neural simulators, 2. shared computational resources, 3. declarative model descriptors, ontologies and standardized annotations; 4. model sharing repositories and sharing standards. Conclusion A number of complementary innovations have been proposed to enhance sharing, transparency and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. Significance Model management will become increasingly important as multiscale models become larger, more detailed and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment. PMID:27046845
Applied modelling and computing in social science
Povh, Janez
2015-01-01
In social science outstanding results are yielded by advanced simulation methods, based on state of the art software technologies and an appropriate combination of qualitative and quantitative methods. This book presents examples of successful applications of modelling and computing in social science: business and logistic process simulation and optimization, deeper knowledge extractions from big data, better understanding and predicting of social behaviour and modelling health and environment changes.
Validation of a phytoremediation computer model
Energy Technology Data Exchange (ETDEWEB)
Corapcioglu, M Y; Sung, K; Rhykerd, R L; Munster, C; Drew, M [Texas A and M Univ., College Station, TX (United States)
1999-01-01
The use of plants to stimulate remediation of contaminated soil is an effective, low-cost cleanup method which can be applied to many different sites. A phytoremediation computer model has been developed to simulate how recalcitrant hydrocarbons interact with plant roots in unsaturated soil. A study was conducted to provide data to validate and calibrate the model. During the study, lysimeters were constructed and filled with soil contaminated with 10 [mg kg[sub -1
Automating sensitivity analysis of computer models using computer calculus
International Nuclear Information System (INIS)
Oblow, E.M.; Pin, F.G.
1986-01-01
An automated procedure for performing sensitivity analysis has been developed. The procedure uses a new FORTRAN compiler with computer calculus capabilities to generate the derivatives needed to set up sensitivity equations. The new compiler is called GRESS - Gradient Enhanced Software System. Application of the automated procedure with direct and adjoint sensitivity theory for the analysis of non-linear, iterative systems of equations is discussed. Calculational efficiency consideration and techniques for adjoint sensitivity analysis are emphasized. The new approach is found to preserve the traditional advantages of adjoint theory while removing the tedious human effort previously needed to apply this theoretical methodology. Conclusions are drawn about the applicability of the automated procedure in numerical analysis and large-scale modelling sensitivity studies
Automating sensitivity analysis of computer models using computer calculus
International Nuclear Information System (INIS)
Oblow, E.M.; Pin, F.G.
1985-01-01
An automated procedure for performing sensitivity analyses has been developed. The procedure uses a new FORTRAN compiler with computer calculus capabilities to generate the derivatives needed to set up sensitivity equations. The new compiler is called GRESS - Gradient Enhanced Software System. Application of the automated procedure with ''direct'' and ''adjoint'' sensitivity theory for the analysis of non-linear, iterative systems of equations is discussed. Calculational efficiency consideration and techniques for adjoint sensitivity analysis are emphasized. The new approach is found to preserve the traditional advantages of adjoint theory while removing the tedious human effort previously needed to apply this theoretical methodology. Conclusions are drawn about the applicability of the automated procedure in numerical analysis and large-scale modelling sensitivity studies. 24 refs., 2 figs
Self-organized phenomena of pedestrian counterflow through a wide bottleneck in a channel
Dong, Li-Yun; Lan, Dong-Kai; Li, Xiang
2016-09-01
The pedestrian counterflow through a bottleneck in a channel shows a variety of flow patterns due to self-organization. In order to reveal the underlying mechanism, a cellular automaton model was proposed by incorporating the floor field and the view field which reflects the global information of the studied area and local interactions with others. The presented model can well reproduce typical collective behaviors, such as lane formation. Numerical simulations were performed in the case of a wide bottleneck and typical flow patterns at different density ranges were identified as rarefied flow, laminar flow, interrupted bidirectional flow, oscillatory flow, intermittent flow, and choked flow. The effects of several parameters, such as the size of view field and the width of opening, on the bottleneck flow are also analyzed in detail. The view field plays a vital role in reproducing self-organized phenomena of pedestrian. Numerical results showed that the presented model can capture key characteristics of bottleneck flows. Project supported by the National Basic Research Program of China (Grant No. 2012CB725404) and the National Natural Science Foundation of China (Grant Nos. 11172164 and 11572184).
Lifelong learning of human actions with deep neural network self-organization.
Parisi, German I; Tani, Jun; Weber, Cornelius; Wermter, Stefan
2017-12-01
Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather learn a batch of training data with a predefined number of action classes and samples. Thus, there is the need to develop learning systems with the ability to incrementally process available perceptual cues and to adapt their responses over time. We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences. The architecture comprises growing self-organizing networks equipped with recurrent neurons for processing time-varying patterns. We use a set of hierarchically arranged recurrent networks for the unsupervised learning of action representations with increasingly large spatiotemporal receptive fields. Lifelong learning is achieved in terms of prediction-driven neural dynamics in which the growth and the adaptation of the recurrent networks are driven by their capability to reconstruct temporally ordered input sequences. Experimental results on a classification task using two action benchmark datasets show that our model is competitive with state-of-the-art methods for batch learning also when a significant number of sample labels are missing or corrupted during training sessions. Additional experiments show the ability of our model to adapt to non-stationary input avoiding catastrophic interference. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.
International Nuclear Information System (INIS)
Tsytovich, V N
2015-01-01
We review research aimed at understanding the phenomena occurring in a complex plasma under microgravity conditions. Some aspects of the work already performed are considered that have not previously been given sufficient attention but which are potentially crucial for future work. These aspects, in particular, include the observation of compact dust structures that are estimated to be capable of confining all components of a dust plasma in a bounded spatial volume; experimental evidence of the nonlinear screening of dust particles; and experimental evidence of the excitation of collective electric fields. In theoretical terms, novel collective attraction processes between likely charged dust particles are discussed and all schemes of the shadowy attraction between dust particles used earlier, including in attempts to interpret observations, are reviewed and evaluated. Dust structures are considered from the standpoint of the current self-organization theory. It is emphasized that phase transitions between states of self-organized systems differ significantly from those in homogeneous states and that the phase diagrams should be constructed in terms of the parameters of a self-organized structure and cannot be constructed in terms of the temperature and density or similar parameters of homogeneous structures. Using the existing theoretical approaches to modeling self-organized structures in dust plasmas, the parameter distribution of a structure is recalculated for a simpler model that includes the quasineutrality condition and neglects diffusion. These calculations indicate that under microgravity conditions, any self-organized structure can contain a limited number of dust particles and is finite in size. The maximum possible number of particles in a structure determines the characteristic inter-grain distance in dust crystals that can be created under microgravity conditions. Crystallization criteria for the structures are examined and the quasispherical
Grid computing in large pharmaceutical molecular modeling.
Claus, Brian L; Johnson, Stephen R
2008-07-01
Most major pharmaceutical companies have employed grid computing to expand their compute resources with the intention of minimizing additional financial expenditure. Historically, one of the issues restricting widespread utilization of the grid resources in molecular modeling is the limited set of suitable applications amenable to coarse-grained parallelization. Recent advances in grid infrastructure technology coupled with advances in application research and redesign will enable fine-grained parallel problems, such as quantum mechanics and molecular dynamics, which were previously inaccessible to the grid environment. This will enable new science as well as increase resource flexibility to load balance and schedule existing workloads.
Attacker Modelling in Ubiquitous Computing Systems
DEFF Research Database (Denmark)
Papini, Davide
in with our everyday life. This future is visible to everyone nowadays: terms like smartphone, cloud, sensor, network etc. are widely known and used in our everyday life. But what about the security of such systems. Ubiquitous computing devices can be limited in terms of energy, computing power and memory...... attacker remain somehow undened and still under extensive investigation. This Thesis explores the nature of the ubiquitous attacker with a focus on how she interacts with the physical world and it denes a model that captures the abilities of the attacker. Furthermore a quantitative implementation...
Climate models on massively parallel computers
International Nuclear Information System (INIS)
Vitart, F.; Rouvillois, P.
1993-01-01
First results got on massively parallel computers (Multiple Instruction Multiple Data and Simple Instruction Multiple Data) allow to consider building of coupled models with high resolutions. This would make possible simulation of thermoaline circulation and other interaction phenomena between atmosphere and ocean. The increasing of computers powers, and then the improvement of resolution will go us to revise our approximations. Then hydrostatic approximation (in ocean circulation) will not be valid when the grid mesh will be of a dimension lower than a few kilometers: We shall have to find other models. The expert appraisement got in numerical analysis at the Center of Limeil-Valenton (CEL-V) will be used again to imagine global models taking in account atmosphere, ocean, ice floe and biosphere, allowing climate simulation until a regional scale
Rough – Granular Computing knowledge discovery models
Directory of Open Access Journals (Sweden)
Mohammed M. Eissa
2016-11-01
Full Text Available Medical domain has become one of the most important areas of research in order to richness huge amounts of medical information about the symptoms of diseases and how to distinguish between them to diagnose it correctly. Knowledge discovery models play vital role in refinement and mining of medical indicators to help medical experts to settle treatment decisions. This paper introduces four hybrid Rough – Granular Computing knowledge discovery models based on Rough Sets Theory, Artificial Neural Networks, Genetic Algorithm and Rough Mereology Theory. A comparative analysis of various knowledge discovery models that use different knowledge discovery techniques for data pre-processing, reduction, and data mining supports medical experts to extract the main medical indicators, to reduce the misdiagnosis rates and to improve decision-making for medical diagnosis and treatment. The proposed models utilized two medical datasets: Coronary Heart Disease dataset and Hepatitis C Virus dataset. The main purpose of this paper was to explore and evaluate the proposed models based on Granular Computing methodology for knowledge extraction according to different evaluation criteria for classification of medical datasets. Another purpose is to make enhancement in the frame of KDD processes for supervised learning using Granular Computing methodology.
40 CFR 194.23 - Models and computer codes.
2010-07-01
... 40 Protection of Environment 24 2010-07-01 2010-07-01 false Models and computer codes. 194.23... General Requirements § 194.23 Models and computer codes. (a) Any compliance application shall include: (1... obtain stable solutions; (iv) Computer models accurately implement the numerical models; i.e., computer...
Invariant visual object and face recognition: neural and computational bases, and a model, VisNet
Directory of Open Access Journals (Sweden)
Edmund T eRolls
2012-06-01
Full Text Available Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy modelin which invariant representations can be built by self-organizing learning based on the temporal and spatialstatistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associativesynaptic learning rule with a short term memory trace, and/or it can use spatialcontinuity in Continuous Spatial Transformation learning which does not require a temporal trace. The model of visual processing in theventral cortical stream can build representations of objects that are invariant withrespect to translation, view, size, and also lighting. The modelhas been extended to provide an account of invariant representations in the dorsal visualsystem of the global motion produced by objects such as looming, rotation, and objectbased movement. The model has been extended to incorporate top-down feedback connectionsto model the control of attention by biased competition in for example spatial and objectsearch tasks. The model has also been extended to account for how the visual system canselect single objects in complex visual scenes, and how multiple objects can berepresented in a scene. The model has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.
Is there a self-organization principle of river deltas?
Tejedor, Alejandro; Longjas, Anthony; Foufoula-Georgiou, Efi
2017-04-01
River deltas are known to possess a complex topological and flux-partitioning structure which has recently been quantified using spectral graph theory [Tejedor et al., 2015a,b]. By analysis of real and simulated deltas it has also been shown that there is promise in formalizing relationships between this topo-dynamic delta structure and the underlying delta forming processes [e.g., Tejedor et al., 2016]. The question we pose here is whether there exists a first order organizational principle behind the self-organization of river deltas and whether this principle can be unraveled from the co-evolving topo-dynamic structure encoded in the delta planform. To answer this question, we introduce a new metric, the nonlocal Entropy Rate (nER) that captures the information content of a delta network in terms of the degree of uncertainty in delivering fluxes from any point of the network to the shoreline. We hypothesize that if the "guiding principle" of undisturbed deltas is to efficiently and robustly build land by increasing the diversity of their flux pathways over the delta plane, then they would exhibit maximum nonlocal Entropy Rate at states at which geometry and flux dynamics are at equilibrium. At the same time, their nER would be non-optimal at transient states, such as before and after major avulsions during which topology and dynamics adjust to each other to reach a new equilibrium state. We will present our results for field and simulated deltas, which confirm this hypothesis and open up new ways of thinking about self-organization, complexity and robustness in river deltas. One particular connection of interest might have important implications since entropy rate and resilience are related by the fluctuation theorem [Demetrius and Manke, 2005], and therefore our results suggest that deltas might in fact self-organize to maximize their resilience to structural and dynamic perturbations. References: Tejedor, A., A. Longjas, I. Zaliapin, and E. Foufoula
Computational Aerodynamic Modeling of Small Quadcopter Vehicles
Yoon, Seokkwan; Ventura Diaz, Patricia; Boyd, D. Douglas; Chan, William M.; Theodore, Colin R.
2017-01-01
High-fidelity computational simulations have been performed which focus on rotor-fuselage and rotor-rotor aerodynamic interactions of small quad-rotor vehicle systems. The three-dimensional unsteady Navier-Stokes equations are solved on overset grids using high-order accurate schemes, dual-time stepping, low Mach number preconditioning, and hybrid turbulence modeling. Computational results for isolated rotors are shown to compare well with available experimental data. Computational results in hover reveal the differences between a conventional configuration where the rotors are mounted above the fuselage and an unconventional configuration where the rotors are mounted below the fuselage. Complex flow physics in forward flight is investigated. The goal of this work is to demonstrate that understanding of interactional aerodynamics can be an important factor in design decisions regarding rotor and fuselage placement for next-generation multi-rotor drones.
Self-organized criticality revisited: non-local transport by turbulent amplification
DEFF Research Database (Denmark)
Milovanov, Alexander V.; Rasmussen, Jens Juul
2015-01-01
We revise the applications of self-organized criticality (SOC) as a paradigmatic model for tokamak plasma turbulence. The work, presented here, is built around the idea that some systems do not develop a pure critical state associable with SOC, since their dynamical evolution involves as a compet......We revise the applications of self-organized criticality (SOC) as a paradigmatic model for tokamak plasma turbulence. The work, presented here, is built around the idea that some systems do not develop a pure critical state associable with SOC, since their dynamical evolution involves...... as a competing key factor an inverse cascade of the energy in reciprocal space. Then relaxation of slowly increasing stresses will give rise to intermittent bursts of transport in real space and outstanding transport events beyond the range of applicability of the 'conventional' SOC. Also, we are concerned...... with the causes and origins of non-local transport in magnetized plasma, and show that this type of transport occurs naturally in self-consistent strong turbulence via a complexity coupling to the inverse cascade. We expect these coupling phenomena to occur in the parameter range of strong nonlinearity and time...
Self-organized control in cooperative robots using a pattern formation principle
International Nuclear Information System (INIS)
Starke, Jens; Ellsaesser, Carmen; Fukuda, Toshio
2011-01-01
Self-organized modular approaches proved in nature to be robust and optimal and are a promising strategy to control future concepts of flexible and modular manufacturing processes. We show how this can be applied to a model of flexible manufacturing based on time-dependent robot-target assignment problems where robot teams have to serve manufacturing targets such that an objective function is optimized. Feasibility of the self-organized solutions can be guaranteed even for unpredictable situations like sudden changes in the demands or breakdowns of robots. As example an uncrewed space mission is visualized in a simulation where robots build a space station. - Highlights: → Adapting a pattern formation principle to control cooperative robots in a robust way. → Flexible manufacturing systems are modelled by time-dependent assignment problems. → Coupled selection equations guarantee feasibility of solutions. → Solution structure (permutations) is not destroyed by inhomogeneous growth rates. → Example of an uncrewed space mission shows effectivity and robustness.
Self-organized amniogenesis by human pluripotent stem cells in a biomimetic implantation-like niche
Shao, Yue; Taniguchi, Kenichiro; Gurdziel, Katherine; Townshend, Ryan F.; Xue, Xufeng; Yong, Koh Meng Aw; Sang, Jianming; Spence, Jason R.; Gumucio, Deborah L.; Fu, Jianping
2017-04-01
Amniogenesis--the development of amnion--is a critical developmental milestone for early human embryogenesis and successful pregnancy. However, human amniogenesis is poorly understood due to limited accessibility to peri-implantation embryos and a lack of in vitro models. Here we report an efficient biomaterial system to generate human amnion-like tissue in vitro through self-organized development of human pluripotent stem cells (hPSCs) in a bioengineered niche mimicking the in vivo implantation environment. We show that biophysical niche factors act as a switch to toggle hPSC self-renewal versus amniogenesis under self-renewal-permissive biochemical conditions. We identify a unique molecular signature of hPSC-derived amnion-like cells and show that endogenously activated BMP-SMAD signalling is required for the amnion-like tissue development by hPSCs. This study unveils the self-organizing and mechanosensitive nature of human amniogenesis and establishes the first hPSC-based model for investigating peri-implantation human amnion development, thereby helping advance human embryology and reproductive medicine.
Self-Organization in Aggregating Robot Swarms: A DW-KNN Topological Approach
Khaldi, Belkacem
2018-02-02
In certain swarm applications, where the inter-agent distance is not the only factor in the collective behaviours of the swarm, additional properties such as density could have a crucial effect. In this paper, we propose applying a Distance-Weighted K-Nearest Neighbouring (DW-KNN) topology to the behaviour of robot swarms performing self-organized aggregation, in combination with a virtual physics approach to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach, which is used to evaluate the robot density in the swarm, is applied as the key factor for identifying the K-nearest neighbours taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbours is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach, showing various self-organized aggregations performed by a swarm of N foot-bot robots. Also, we compared the aggregation quality of DW-KNN aggregation approach to that of the conventional KNN approach and found better performance.
Self-organization of mesoscopic silver wires by electrochemical deposition
Directory of Open Access Journals (Sweden)
Sheng Zhong
2014-08-01
Full Text Available Long, straight mesoscale silver wires have been fabricated from AgNO3 electrolyte via electrodeposition without the help of templates, additives, and surfactants. Although the wire growth speed is very fast due to growth under non-equilibrium conditions, the wire morphology is regular and uniform in diameter. Structural studies reveal that the wires are single-crystalline, with the [112] direction as the growth direction. A possible growth mechanism is suggested. Auger depth profile measurements show that the wires are stable against oxidation under ambient conditions. This unique system provides a convenient way for the study of self-organization in electrochemical environments as well as for the fabrication of highly-ordered, single-crystalline metal nanowires.
Filamentary structures that self-organize due to adhesion
Sengab, A.; Picu, R. C.
2018-03-01
We study the self-organization of random collections of elastic filaments that interact adhesively. The evolution from an initial fully random quasi-two-dimensional state is controlled by filament elasticity, adhesion and interfilament friction, and excluded volume. Three outcomes are possible: the system may remain locked in the initial state, may organize into isolated fiber bundles, or may form a stable, connected network of bundles. The range of system parameters leading to each of these states is identified. The network of bundles is subisostatic and is stabilized by prestressed triangular features forming at bundle-to-bundle nodes, similar to the situation in foams. Interfiber friction promotes locking and expands the parametric range of nonevolving systems.
Experimental investigation of multiple self-organized structures in plasma
International Nuclear Information System (INIS)
Ivan, L. M.; Gaman, C.; Aflori, M.; Mihai-Plugaru, M.; Dimitriu, D.G.; Lozneanu, E.; Sanduloviciu, M.
2005-01-01
Complex space charge configuration emerges by self-organization in front of an electrode immersed in plasma when its potential is increased at a certain critical value. Consisting from a nucleus protected from the surrounding plasma by an electrical double layer, the complexity reveals an internal structure and behaviour which remind us primitive organisms. Thus the complexity is not static but stationary open system in which continuous decay is constantly compensated by substance and energy from the surrounding plasma. Endowed with a special kind of memory the complexity can work as an intelligent multifunctional system and consequently it is also able to perform innovations after selective interaction with an environment in evolution. Additionally, the complexity is able to replicate by division. (authors)
Magnetic reconnection and self-organized plasma systems
International Nuclear Information System (INIS)
Yamada, Masaaki; Ji, Hantao
2000-01-01
In this paper the recent results from the Magnetic Reconnection Experiment (MRX) at PPPL are discussed along with their relationship to observations from solar flares, the magnetosphere, and current carrying pinch discharges such as tokamaks, reversed field pinches, spheromaks and field reversed configurations. It is found that the reconnection speed decreases as the angle of merging field lines decreases, consistent with the well-established observation in the dayside magnetosphere. This observation can also provide a qualitative interpretation of a generally observed trend in pinch plasmas, namely that magnetic field diffuses (or reconnects) faster when magnetic shear is larger. A recently conceived research project, SPIRIT (Self-organized Plasma with Induction, Reconnection, and Injection Techniques), will also be discussed. (author)
Clustering analysis of malware behavior using Self Organizing Map
DEFF Research Database (Denmark)
Pirscoveanu, Radu-Stefan; Stevanovic, Matija; Pedersen, Jens Myrup
2016-01-01
For the time being, malware behavioral classification is performed by means of Anti-Virus (AV) generated labels. The paper investigates the inconsistencies associated with current practices by evaluating the identified differences between current vendors. In this paper we rely on Self Organizing...... Map, an unsupervised machine learning algorithm, for generating clusters that capture the similarities between malware behavior. A data set of approximately 270,000 samples was used to generate the behavioral profile of malicious types in order to compare the outcome of the proposed clustering...... approach with the labels collected from 57 Antivirus vendors using VirusTotal. Upon evaluating the results, the paper concludes on shortcomings of relying on AV vendors for labeling malware samples. In order to solve the problem, a cluster-based classification is proposed, which should provide more...
Dicyanovinyl sexithiophenes: self-organization and photovoltaic properties
Energy Technology Data Exchange (ETDEWEB)
Levichkova, Marieta; Wynands, David; Levin, Alexandr; Leo, Karl; Riede, Moritz [Institut fuer Angewandte Photophysik, TU Dresden (Germany); Walzer, Karsten; Hildebrandt, Dirk [Heliatek GmbH, Dresden (Germany); Baeuerle, Peter [Institut fuer Organische Chemie II und Neue Materialien, Universitaet Ulm (Germany); Rentenberger, Rosina [Institut fuer Physik, TU Ilmenau (Germany)
2010-07-01
Recently, vacuum deposited films consisting of conjugated dicyanovinyl-capped (DCV) oligothiophenes have shown significant potential as photoactive layers in small molecule solar cells. Here, we study the structural and optical properties of films of two DCV-derivatives both comprising six thiophene rings (DCV6Ts) but having different side groups. For both derivatives, neat DCV6T and mixed DCV6T:C{sub 60} films are compared using UV-VIS absorption and photoluminescence spectroscopy, X-ray diffraction (XRD), and atomic force microscopy. It is shown that the modification of the molecular structure results in a structured and red shifted absorption band, which indicates better molecular arrangement in the solid state. The improved self-organization at room temperature deposition is confirmed by XRD. Furthermore, the nanomorphology of the mixed DCV6T:C{sub 60} films is optimized using substrate heating. Bulk heterojunction solar cells with power conversion efficiencies exceeding 4% are presented.
Characterization of Suicidal Behaviour with Self-Organizing Maps
Directory of Open Access Journals (Sweden)
José M. Leiva-Murillo
2013-01-01
Full Text Available The study of the variables involved in suicidal behavior is important from a social, medical, and economical point of view. Given the high number of potential variables of interest, a large population of subjects must be analysed in order to get conclusive results. In this paper, we describe a method based on self-organizing maps (SOMs for finding the most relevant variables even when their relation to suicidal behavior is strongly nonlinear. We have applied the method to a cohort with more than 8,000 subjects and 600 variables and discovered four groups of variables involved in suicidal behavior. According to the results, there are four main groups of risk factors that characterize the population of suicide attempters: mental disorders, alcoholism, impulsivity, and childhood abuse. The identification of specific subpopulations of suicide attempters is consistent with current medical knowledge and may provide a new avenue of research to improve the management of suicidal cases.
Self-Organizing Maps for Fingerprint Image Quality Assessment
DEFF Research Database (Denmark)
Olsen, Martin Aastrup; Tabassi, Elham; Makarov, Anton
2013-01-01
Fingerprint quality assessment is a crucial task which needs to be conducted accurately in various phases in the biometric enrolment and recognition processes. Neglecting quality measurement will adversely impact accuracy and efficiency of biometric recognition systems (e.g. verification and iden......Fingerprint quality assessment is a crucial task which needs to be conducted accurately in various phases in the biometric enrolment and recognition processes. Neglecting quality measurement will adversely impact accuracy and efficiency of biometric recognition systems (e.g. verification...... machine learning techniques. We train a self-organizing map (SOM) to cluster blocks of fingerprint images based on their spatial information content. The output of the SOM is a high-level representation of the finger image, which forms the input to a Random Forest trained to learn the relationship between...
Self-organization of progress across the century of physics
Perc, Matjaž
2013-04-01
We make use of information provided in the titles and abstracts of over half a million publications that were published by the American Physical Society during the past 119 years. By identifying all unique words and phrases and determining their monthly usage patterns, we obtain quantifiable insights into the trends of physics discovery from the end of the 19th century to today. We show that the magnitudes of upward and downward trends yield heavy-tailed distributions, and that their emergence is due to the Matthew effect. This indicates that both the rise and fall of scientific paradigms is driven by robust principles of self-organization. Data also confirm that periods of war decelerate scientific progress, and that the later is very much subject to globalisation.
Self-organized internal architectures of chiral micro-particles
International Nuclear Information System (INIS)
Provenzano, Clementina; Mazzulla, Alfredo; Desiderio, Giovanni; Pagliusi, Pasquale; De Santo, Maria P.; Cipparrone, Gabriella; Perrotta, Ida
2014-01-01
The internal architecture of polymeric self-assembled chiral micro-particles is studied by exploring the effect of the chirality, of the particle sizes, and of the interface/surface properties in the ordering of the helicoidal planes. The experimental investigations, performed by means of different microscopy techniques, show that the polymeric beads, resulting from light induced polymerization of cholesteric liquid crystal droplets, preserve both the spherical shape and the internal self-organized structures. The method used to create the micro-particles with controlled internal chiral architectures presents great flexibility providing several advantages connected to the acquired optical and photonics capabilities and allowing to envisage novel strategies for the development of chiral colloidal systems and materials