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1
Coveney, Peter V., and Philip W. Fowler. "Modelling biological complexity: a physical scientist's perspective." Journal of The Royal Society Interface 2, no. 4 (2005): 267–80. http://dx.doi.org/10.1098/rsif.2005.0045.
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We discuss the modern approaches of complexity and self-organization to understanding dynamical systems and how these concepts can inform current interest in systems biology. From the perspective of a physical scientist, it is especially interesting to examine how the differing weights given to philosophies of science in the physical and biological sciences impact the application of the study of complexity. We briefly describe how the dynamics of the heart and circadian rhythms, canonical examples of systems biology, are modelled by sets of nonlinear coupled differential equations, which have to be solved numerically. A major difficulty with this approach is that all the parameters within these equations are not usually known. Coupled models that include biomolecular detail could help solve this problem. Coupling models across large ranges of length- and time-scales is central to describing complex systems and therefore to biology. Such coupling may be performed in at least two different ways, which we refer to as hierarchical and hybrid multiscale modelling. While limited progress has been made in the former case, the latter is only beginning to be addressed systematically. These modelling methods are expected to bring numerous benefits to biology, for example, the properties of a system could be studied over a wider range of length- and time-scales, a key aim of systems biology. Multiscale models couple behaviour at the molecular biological level to that at the cellular level, thereby providing a route for calculating many unknown parameters as well as investigating the effects at, for example, the cellular level, of small changes at the biomolecular level, such as a genetic mutation or the presence of a drug. The modelling and simulation of biomolecular systems is itself very computationally intensive; we describe a recently developed hybrid continuum-molecular model, HybridMD, and its associated molecular insertion algorithm, which point the way towards the integration of molecular and more coarse-grained representations of matter. The scope of such integrative approaches to complex systems research is circumscribed by the computational resources available. Computational grids should provide a step jump in the scale of these resources; we describe the tools that RealityGrid, a major UK e-Science project, has developed together with our experience of deploying complex models on nascent grids. We also discuss the prospects for mathematical approaches to reducing the dimensionality of complex networks in the search for universal systems-level properties, illustrating our approach with a description of the origin of life according to the RNA world view.
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Ayadi, Ali, Cecilia Zanni-Merk, François de Bertrand de Beuvron, and Saoussen Krichen. "BNO: An ontology for describing the behaviour of complex biomolecular networks." Procedia Computer Science 112 (2017): 524–33. http://dx.doi.org/10.1016/j.procs.2017.08.159.
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Roukos, Dimitrios H. "Networks medicine: from reductionism to evidence of complex dynamic biomolecular interactions." Pharmacogenomics 12, no. 5 (2011): 695–98. http://dx.doi.org/10.2217/pgs.11.28.
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Xie, Jiang, Dongfang Lu, Jiaxin Li, et al. "Kernel differential subgraph reveals dynamic changes in biomolecular networks." Journal of Bioinformatics and Computational Biology 16, no. 01 (2018): 1750027. http://dx.doi.org/10.1142/s0219720017500275.
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Many major diseases, including various types of cancer, are increasingly threatening human health. However, the mechanisms of the dynamic processes underlying these diseases remain ambiguous. From the holistic perspective of systems science, complex biological networks can reveal biological phenomena. Changes among networks in different states influence the direction of living organisms. The identification of the kernel differential subgraph (KDS) that leads to drastic changes is critical. The existing studies contribute to the identification of a KDS in networks with the same nodes; however, networks in different states involve the disappearance of some nodes or the appearance of some new nodes. In this paper, we propose a new topology-based KDS (TKDS) method to explore the core module from gene regulatory networks with different nodes in this process. For the common nodes, the TKDS method considers the differential value (D-value) of the topological change. For the different nodes, TKDS identifies the most similar gene pairs and computes the D-value. Hence, TKDS discovers the essential KDS, which considers the relationships between the same nodes as well as different nodes. After applying this method to non-small cell lung cancer (NSCLC), we identified 30 genes that are most likely related to NSCLC and extracted the KDSs in both the cancer and normal states. Two significance functional modules were revealed, and gene ontology (GO) analyses and literature mining indicated that the KDSs are essential to the processes in NSCLC. In addition, compared with existing methods, TKDS provides a unique perspective in identifying particular genes and KDSs related to NSCLC. Moreover, TKDS has the potential to predict other critical disease-related genes and modules.
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Ayadi, Ali, François de Bertrand de Beuvron, Cecilia Zanni-Merk, and Saoussen Krichen. "CBNSimulator: a simulator tool for understanding the behaviour of complex biomolecular networks using discrete time simulation." Procedia Computer Science 112 (2017): 514–23. http://dx.doi.org/10.1016/j.procs.2017.08.157.
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Irigoyen, Eloy, Antonio Javier Barragán, Mikel Larrea, and José Manuel Andújar. "About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks." Complexity 2018 (July 8, 2018): 1–12. http://dx.doi.org/10.1155/2018/3671428.
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This work presents a straightforward methodology based on neural networks (NN) which allows to obtain relevant dynamic information of unknown nonlinear systems. It provides an approach for cases in which the complex task of analyzing the dynamic behaviour of nonlinear systems makes it excessively challenging to obtain an accurate mathematical model. After reviewing the suitability of multilayer perceptrons (MLPs) as universal approximators to replace a mathematical model, the first part of this work presents a system representation using a model formulated with state variables which can be exported to a NN structure. Considering the linearization of the NN model in a mesh of operating points, the second part of this work presents the study of equilibrium states in such points by calculating the Jacobian matrix of the system through the NN model. The results analyzed in three case studies provide representative examples of the strengths of the proposed method. Conclusively, it is feasible to study the system behaviour based on MLPs, which enables the analysis of the local stability of the equilibrium points, as well as the system dynamics in its environment, therefore obtaining valuable information of the system dynamic behaviour.
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Bryden, John, Sebastian Funk, Nicholas Geard, Seth Bullock, and Vincent A. A. Jansen. "Stability in flux: community structure in dynamic networks." Journal of The Royal Society Interface 8, no. 60 (2010): 1031–40. http://dx.doi.org/10.1098/rsif.2010.0524.
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The structure of many biological, social and technological systems can usefully be described in terms of complex networks. Although often portrayed as fixed in time, such networks are inherently dynamic, as the edges that join nodes are cut and rewired, and nodes themselves update their states. Understanding the structure of these networks requires us to understand the dynamic processes that create, maintain and modify them. Here, we build upon existing models of coevolving networks to characterize how dynamic behaviour at the level of individual nodes generates stable aggregate behaviours. We focus particularly on the dynamics of groups of nodes formed endogenously by nodes that share similar properties (represented as node state) and demonstrate that, under certain conditions, network modularity based on state compares well with network modularity based on topology. We show that if nodes rewire their edges based on fixed node states, the network modularity reaches a stable equilibrium which we quantify analytically. Furthermore, if node state is not fixed, but can be adopted from neighbouring nodes, the distribution of group sizes reaches a dynamic equilibrium, which remains stable even as the composition and identity of the groups change. These results show that dynamic networks can maintain the stable community structure that has been observed in many social and biological systems.
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Kim, Youngchan, Federico Bertagna, Edeline M. D’Souza, et al. "Quantum Biology: An Update and Perspective." Quantum Reports 3, no. 1 (2021): 80–126. http://dx.doi.org/10.3390/quantum3010006.
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Understanding the rules of life is one of the most important scientific endeavours and has revolutionised both biology and biotechnology. Remarkable advances in observation techniques allow us to investigate a broad range of complex and dynamic biological processes in which living systems could exploit quantum behaviour to enhance and regulate biological functions. Recent evidence suggests that these non-trivial quantum mechanical effects may play a crucial role in maintaining the non-equilibrium state of biomolecular systems. Quantum biology is the study of such quantum aspects of living systems. In this review, we summarise the latest progress in quantum biology, including the areas of enzyme-catalysed reactions, photosynthesis, spin-dependent reactions, DNA, fluorescent proteins, and ion channels. Many of these results are expected to be fundamental building blocks towards understanding the rules of life.
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Kiss, Istvan Z., Luc Berthouze, Timothy J. Taylor, and Péter L. Simon. "Modelling approaches for simple dynamic networks and applications to disease transmission models." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 468, no. 2141 (2012): 1332–55. http://dx.doi.org/10.1098/rspa.2011.0349.
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In this paper a random link activation–deletion (RLAD) model is proposed that gives rise to a stochastically evolving network. This dynamic network is then coupled to a simple susceptible-infectious-suceptible ( SIS ) dynamics on the network, and the resulting spectrum of model behaviour is explored via simulation and a novel pairwise model for dynamic networks. First, the dynamic network model is systematically analysed by considering link-type independent and dependent network dynamics coupled with globally constrained link creation. This is done rigorously with some analytical results and we highlight where such analysis can be performed and how these simpler models provide a benchmark to test and validate full simulations. The pairwise model is used to study the interplay between SIS -type dynamics on the network and link-type-dependent activation–deletion. Assumptions of the pairwise model are identified and their implications interpreted in a way that complements our current understanding. Furthermore, we also discuss how the strong assumptions of the closure relations can lead to disagreement between the simulation and pairwise model. Unlike on a static network, the resulting spectrum of behaviour is more complex with the prevalence of infections exhibiting not only a single steady state, but also bistability and oscillations.
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Vernon, Matthew C., and Matt J. Keeling. "Representing the UK's cattle herd as static and dynamic networks." Proceedings of the Royal Society B: Biological Sciences 276, no. 1656 (2008): 469–76. http://dx.doi.org/10.1098/rspb.2008.1009.
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Network models are increasingly being used to understand the spread of diseases through sparsely connected populations, with particular interest in the impact of animal movements upon the dynamics of infectious diseases. Detailed data collected by the UK government on the movement of cattle may be represented as a network, where animal holdings are nodes, and an edge is drawn between nodes where a movement of animals has occurred. These network representations may vary from a simple static representation, to a more complex, fully dynamic one where daily movements are explicitly captured. Using stochastic disease simulations, a wide range of network representations of the UK cattle herd are compared. We find that the simpler static network representations are often deficient when compared with a fully dynamic representation, and should therefore be used only with caution in epidemiological modelling. In particular, due to temporal structures within the dynamic network, static networks consistently fail to capture the predicted epidemic behaviour associated with dynamic networks even when parameterized to match early growth rates.
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Yusof, Zakariah, Norhaliza Abdul Wahab, Syahira Ibrahim, Shafishuhaza Sahlan, and Mashitah Che Razali. "Modeling of submerged membrane filtration processes using recurrent artificial neural networks." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 1 (2020): 155. http://dx.doi.org/10.11591/ijai.v9.i1.pp155-163.
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<span lang="EN-US">The modeling of membrane filtration processes is a challenging task because it involves many interactions from both biological and physical operational behavior. Membrane fouling behaviour in filtration processes is complex and hard to understand, and to derive a robust model is almost not possible. Therefore, it is the aim of this paper to study the potential of time series neural network based dynamic model for a submerged membrane filtration process. The developed model that represent the dynamic behavior of filtration process is later used in control design of the membrane filtration processes. In order to obtain the dynamic behaviour of permeate flux and transmembrane pressure (TMP), a random step was applied to the suction pump. A recurrent neural network (RNN) structure was employed to perform as the dynamic models of a filtration process, based on nonlinear auto-regressive with exogenous input (NARX) model structure. These models are compared with the linear auto-regressive with exogenous input (ARX) model. The performance of the models were evaluated in terms of %<em>R<sup>2</sup></em>, mean square error (MSE,) and a mean absolute deviation (MAD). For filtration control performance, a proportional integral derivative (PID) controller was implemented. The results showed that the RNN-NARX structure able to model the dynamic behavior of the filtration process under normal conditions in short range of the filtration process. The developed model can also be a reliable assistant for two different control strategies development in filtration processes.</span>
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Gilbert, David, Monika Heiner, Yasoda Jayaweera, and Christian Rohr. "Towards dynamic genome-scale models." Briefings in Bioinformatics 20, no. 4 (2017): 1167–80. http://dx.doi.org/10.1093/bib/bbx096.
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Abstract The analysis of the dynamic behaviour of genome-scale models of metabolism (GEMs) currently presents considerable challenges because of the difficulties of simulating such large and complex networks. Bacterial GEMs can comprise about 5000 reactions and metabolites, and encode a huge variety of growth conditions; such models cannot be used without sophisticated tool support. This article is intended to aid modellers, both specialist and non-specialist in computerized methods, to identify and apply a suitable combination of tools for the dynamic behaviour analysis of large-scale metabolic designs. We describe a methodology and related workflow based on publicly available tools to profile and analyse whole-genome-scale biochemical models. We use an efficient approximative stochastic simulation method to overcome problems associated with the dynamic simulation of GEMs. In addition, we apply simulative model checking using temporal logic property libraries, clustering and data analysis, over time series of reaction rates and metabolite concentrations. We extend this to consider the evolution of reaction-oriented properties of subnets over time, including dead subnets and functional subsystems. This enables the generation of abstract views of the behaviour of these models, which can be large—up to whole genome in size—and therefore impractical to analyse informally by eye. We demonstrate our methodology by applying it to a reduced model of the whole-genome metabolism of Escherichia coli K-12 under different growth conditions. The overall context of our work is in the area of model-based design methods for metabolic engineering and synthetic biology.
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Martelot, Erwan Le, and Peter J. Bentley. "Novel Visualisation and Analysis of Natural and Complex Systems Using Systemic Computation." Information Visualization 10, no. 1 (2010): 1–31. http://dx.doi.org/10.1057/ivs.2010.8.
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The study, analysis and understanding of natural processes are difficult tasks considering the complex nature of such processes. In this respect, the visual analysis of such systems can be of great help in the understanding of their behaviour. The increasing power of modern computers enables novel possible uses of computer graphics for such tasks. Previous work introduced systemic computation, a new model of computation and corresponding computer architecture aiming at enabling a clear formalism of natural and complex systems and providing tools for their analysis. Here, we present an online visualisation of dynamic systems based on this novel paradigm. The observation is done at a high level of abstraction, focussing on information flow, interactions and emergent behaviour, and enabling the identification of similarities and differences between models of complex systems. This visualisation framework is then applied to two biological networks: a bistable gene network and a MAPK signalling cascade.
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Gilbertson, Marie L. J., Nicholas M. Fountain-Jones, and Meggan E. Craft. "Incorporating genomic methods into contact networks to reveal new insights into animal behaviour and infectious disease dynamics." Behaviour 155, no. 7-9 (2018): 759–91. http://dx.doi.org/10.1163/1568539x-00003471.
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Abstract Utilization of contact networks has provided opportunities for assessing the dynamic interplay between pathogen transmission and host behaviour. Genomic techniques have, in their own right, provided new insight into complex questions in disease ecology, and the increasing accessibility of genomic approaches means more researchers may seek out these tools. The integration of network and genomic approaches provides opportunities to examine the interaction between behaviour and pathogen transmission in new ways and with greater resolution. While a number of studies have begun to incorporate both contact network and genomic approaches, a great deal of work has yet to be done to better integrate these techniques. In this review, we give a broad overview of how network and genomic approaches have each been used to address questions regarding the interaction of social behaviour and infectious disease, and then discuss current work and future horizons for the merging of these techniques.
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Paciolla, Mariarita, Daniel J. Arismendi-Arrieta, and Angel J. Moreno. "Coarsening Kinetics of Complex Macromolecular Architectures in Bad Solvent." Polymers 12, no. 3 (2020): 531. http://dx.doi.org/10.3390/polym12030531.
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This study reports a general scenario for the out-of-equilibrium features of collapsing polymeric architectures. We use molecular dynamics simulations to characterize the coarsening kinetics, in bad solvent, for several macromolecular systems with an increasing degree of structural complexity. In particular, we focus on: flexible and semiflexible polymer chains, star polymers with 3 and 12 arms, and microgels with both ordered and disordered networks. Starting from a powerful analogy with critical phenomena, we construct a density field representation that removes fast fluctuations and provides a consistent characterization of the domain growth. Our results indicate that the coarsening kinetics presents a scaling behaviour that is independent of the solvent quality parameter, in analogy to the time–temperature superposition principle. Interestingly, the domain growth in time follows a power-law behaviour that is approximately independent of the architecture for all the flexible systems; while it is steeper for the semiflexible chains. Nevertheless, the fractal nature of the dense regions emerging during the collapse exhibits the same scaling behaviour for all the macromolecules. This suggests that the faster growing length scale in the semiflexible chains originates just from a faster mass diffusion along the chain contour, induced by the local stiffness. The decay of the dynamic correlations displays scaling behavior with the growing length scale of the system, which is a characteristic signature in coarsening phenomena.
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Zhang, Zhaoqi, Panpan Qi, and Wei Wang. "Dynamic Malware Analysis with Feature Engineering and Feature Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1210–17. http://dx.doi.org/10.1609/aaai.v34i01.5474.
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Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation techniques and newly released (“zero-day”) malware. However, existing works typically only consider the API name while ignoring the arguments, or require complex feature engineering operations and expert knowledge to process the arguments. In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection. Specifically, the feature representation approach utilizes a feature hashing trick to encode the API call arguments associated with the API name. The deep neural network architecture applies multiple Gated-CNNs (convolutional neural networks) to transform the extracted features of each API call. The outputs are further processed through bidirectional LSTM (long-short term memory networks) to learn the sequential correlation among API calls. Experiments show that our solution outperforms baselines significantly on a large real dataset. Valuable insights about feature engineering and architecture design are derived from the ablation study.
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Smith, Jeffrey C., Ana P. L. Abdala, Ilya A. Rybak, and Julian F. R. Paton. "Structural and functional architecture of respiratory networks in the mammalian brainstem." Philosophical Transactions of the Royal Society B: Biological Sciences 364, no. 1529 (2009): 2577–87. http://dx.doi.org/10.1098/rstb.2009.0081.
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Neural circuits controlling breathing in mammals are organized within serially arrayed and functionally interacting brainstem compartments extending from the pons to the lower medulla. The core circuit components that constitute the neural machinery for generating respiratory rhythm and shaping inspiratory and expiratory motor patterns are distributed among three adjacent structural compartments in the ventrolateral medulla: the Bötzinger complex (BötC), pre-Bötzinger complex (pre-BötC) and rostral ventral respiratory group (rVRG). The respiratory rhythm and inspiratory–expiratory patterns emerge from dynamic interactions between: (i) excitatory neuron populations in the pre-BötC and rVRG active during inspiration that form inspiratory motor output; (ii) inhibitory neuron populations in the pre-BötC that provide inspiratory inhibition within the network; and (iii) inhibitory populations in the BötC active during expiration that generate expiratory inhibition. Network interactions within these compartments along with intrinsic rhythmogenic properties of pre-BötC neurons form a hierarchy of multiple oscillatory mechanisms. The functional expression of these mechanisms is controlled by multiple drives from more rostral brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behaviour of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple hierarchical levels, which allows flexible, state-dependent expression of different rhythmogenic mechanisms under different physiological and metabolic conditions and enables a wide repertoire of respiratory behaviours.
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Zieliński, Tomasz. "Financial Networks as a Source of Systemic Instability." e-Finanse 12, no. 3 (2016): 59–68. http://dx.doi.org/10.1515/fiqf-2016-0002.
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Abstract Systemic risk is a fundamental constituent of contemporary financial systems. For the past decades a growing number of abrupt upsets in financial systems could be observed. Due to previous experiences, politicians and regulators prefer to identify the offenders outside the system or to blame one of the entities inside the system. However, nowadays many disasters in anthropogenic systems cannot be perceived that way. They are often results of inappropriate interactions rather than external or internal impulses. This requires a paradigm shift in thinking about systemic risk. A component-oriented perspective should be nowadays replaced with a network-oriented view. Closer insight into the concept of systemic risk can refer to the model of the system composed of a huge number of interconnected components. In such a system, systemic risk is usually considered to have a ‘cascading’, ‘domino’ or ‘contagion’ effect, resulting from strong connections. An initial failure could have disastrous effects and cause extreme damage as the number of network nodes goes to infinity. Strongly interconnected, complex dynamic systems cannot be understood by the simple sum of their components’ properties, in contrast to loosely coupled systems. What makes the behaviour of complex financial systems particularly unpredictable is that systemic failures may occur even if everybody involved is highly skilled, highly motivated and behaving properly.
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Coelho, João Paulo, and José Boaventura-Cunha. "Long Term Solar Radiation Forecast Using Computational Intelligence Methods." Applied Computational Intelligence and Soft Computing 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/729316.
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The point prediction quality is closely related to the model that explains the dynamic of the observed process. Sometimes the model can be obtained by simple algebraic equations but, in the majority of the physical systems, the relevant reality is too hard to model with simple ordinary differential or difference equations. This is the case of systems with nonlinear or nonstationary behaviour which require more complex models. The discrete time-series problem, obtained by sampling the solar radiation, can be framed in this type of situation. By observing the collected data it is possible to distinguish multiple regimes. Additionally, due to atmospheric disturbances such as clouds, the temporal structure between samples is complex and is best described by nonlinear models. This paper reports the solar radiation prediction by using hybrid model that combines support vector regression paradigm and Markov chains. The hybrid model performance is compared with the one obtained by using other methods like autoregressive (AR) filters, Markov AR models, and artificial neural networks. The results obtained suggests an increasing prediction performance of the hybrid model regarding both the prediction error and dynamic behaviour.
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Monk, Nick, and Neil Lawrence. "Unravelling Nature's Networks: From Microarray and Proteomic Analysis to Systems Biology: University of Sheffield, 21–22 July 2003." Biochemist 25, no. 6 (2003): 40–41. http://dx.doi.org/10.1042/bio02506040.
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The robust and adaptable behaviours of cells and tissues depend on the operation of complex regulatory biochemical networks. The elucidation of the structure and functioning of such networks poses many daunting challenges. Recently developed experimental techniques, such as large-scale profiling of gene expression and protein interactions, provide unprecedented amounts of information on the molecular composition of cells. The size (and often variable quality) of the resulting data sets necessitates the use of sophisticated computational schemes for the analysis, mining and integration of the data. In all but the simplest cases, the complexity of the networks is such that it is impossible to provide an intuitive picture of the principles governing their dynamic behaviour without synthesizing the experimental data into a coherent mathematical model of the underlying system.
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Riesco, Adrián, Beatriz Santos-Buitrago, Javier De Las Rivas, Merrill Knapp, Gustavo Santos-García, and Carolyn Talcott. "Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search." BioMed Research International 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/1809513.
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In biological systems, pathways define complex interaction networks where multiple molecular elements are involved in a series of controlled reactions producing responses to specific biomolecular signals. These biosystems are dynamic and there is a need for mathematical and computational methods able to analyze the symbolic elements and the interactions between them and produce adequate readouts of such systems. In this work, we use rewriting logic to analyze the cellular signaling of epidermal growth factor (EGF) and its cell surface receptor (EGFR) in order to induce cellular proliferation. Signaling is initiated by binding the ligand protein EGF to the membrane-bound receptor EGFR so as to trigger a reactions path which have several linked elements through the cell from the membrane till the nucleus. We present two different types of search for analyzing the EGF/proliferation system with the help of Pathway Logic tool, which provides a knowledge-based development environment to carry out the modeling of the signaling. The first one is a standard (forward) search. The second one is a novel approach based onnarrowing, which allows us to trace backwards the causes of a given final state. The analysis allows the identification of critical elements that have to be activated to provoke proliferation.
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Brim, Luboš, Samuel Pastva, David Šafránek, and Eva Šmijáková. "Parallel One-Step Control of Parametrised Boolean Networks." Mathematics 9, no. 5 (2021): 560. http://dx.doi.org/10.3390/math9050560.
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Boolean network (BN) is a simple model widely used to study complex dynamic behaviour of biological systems. Nonetheless, it might be difficult to gather enough data to precisely capture the behavior of a biological system into a set of Boolean functions. These issues can be dealt with to some extent using parametrised Boolean networks (ParBNs), as this model allows leaving some update functions unspecified. In our work, we attack the control problem for ParBNs with asynchronous semantics. While there is an extensive work on controlling BNs without parameters, the problem of control for ParBNs has not been in fact addressed yet. The goal of control is to ensure the stabilisation of a system in a given state using as few interventions as possible. There are many ways to control BN dynamics. Here, we consider the one-step approach in which the system is instantaneously perturbed out of its actual state. A naïve approach to handle control of ParBNs is using parameter scan and solve the control problem for each parameter valuation separately using known techniques for non-parametrised BNs. This approach is however highly inefficient as the parameter space of ParBNs grows doubly exponentially in the worst case. We propose a novel semi-symbolic algorithm for the one-step control problem of ParBNs, that builds on symbolic data structures to avoid scanning individual parameters. We evaluate the performance of our approach on real biological models.
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Soltani, Reza. "Design and Modeling of Nano-Robots Control in Medicine." Studies in Engineering and Technology 5, no. 1 (2018): 89. http://dx.doi.org/10.11114/set.v5i1.3406.
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This study aimed to present a new model to develop and expand nanotechnology in particular in the field of medicine. The subject under study focus on the control design of nano-robots for bio-molecular assembly manipulation, and use of evolutionary factors as a suitable method to gain the adaptive properties for proposed model is needed. Moreover, the study use of neural networks as the most practical method for the optimization problem of robot motion using a sensor based system. Thus, the study proposes a useful method within advanced graphics simulation for nano-assembly automation with its focus on an applied model for nano-medicine. Therefore, the study results should provide a great impact for effective design of control instrumentation, helping in the development of nanotechnology. The presented nano-robot model is required to survive and interact with a complex environment. Furthermore the nano-robot has to consider a pre-defined set of tasks both in a competitive scenario and in a collective environment. Nano-robot in a three-dimensional environment monitors organ inlets’ nutritional levels, and assembling new biomolecules into that have to be delivered to the organ inlets with higher priority during each moment of our dynamic simulation. The nano-robot must avoid fuzzy obstacles, and must with proper time and manner react in real time for an environment requiring continuous control. In order to achieve the most pre-programmed set of behaviors the nano-robot uses a local perception through simulated sensors to effectively interact with the surrounding environment. The development of new concepts on nano-mechatronics and automation theory is focused on the problem of molecular machine systems. Finally a novel adaptive optimal method is described and the model validation through the application of nano-robot control design for nano-medicine confirmed.
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Beber, Moritz Emanuel, Christoph Fretter, Shubham Jain, Nikolaus Sonnenschein, Matthias Müller-Hannemann, and Marc-Thorsten Hütt. "Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks." Journal of The Royal Society Interface 9, no. 77 (2012): 3426–35. http://dx.doi.org/10.1098/rsif.2012.0490.
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Few-node subgraphs are the smallest collective units in a network that can be investigated. They are beyond the scale of individual nodes but more local than, for example, communities. When statistically over- or under-represented, they are called network motifs. Network motifs have been interpreted as building blocks that shape the dynamic behaviour of networks. It is this promise of potentially explaining emergent properties of complex systems with relatively simple structures that led to an interest in network motifs in an ever-growing number of studies and across disciplines. Here, we discuss artefacts in the analysis of network motifs arising from discrepancies between the network under investigation and the pool of random graphs serving as a null model. Our aim was to provide a clear and accessible catalogue of such incongruities and their effect on the motif signature. As a case study, we explore the metabolic network of Escherichia coli and show that only by excluding ever more artefacts from the motif signature a strong and plausible correlation with the essentiality profile of metabolic reactions emerges.
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Flis, Anna, Aurora Piñas Fernández, Tomasz Zielinski, et al. "Defining the robust behaviour of the plant clock gene circuit with absolute RNA timeseries and open infrastructure." Open Biology 5, no. 10 (2015): 150042. http://dx.doi.org/10.1098/rsob.150042.
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Our understanding of the complex, transcriptional feedback loops in the circadian clock mechanism has depended upon quantitative, timeseries data from disparate sources. We measure clock gene RNA profiles in Arabidopsis thaliana seedlings, grown with or without exogenous sucrose, or in soil-grown plants and in wild-type and mutant backgrounds. The RNA profiles were strikingly robust across the experimental conditions, so current mathematical models are likely to be broadly applicable in leaf tissue. In addition to providing reference data, unexpected behaviours included co-expression of PRR9 and ELF4 , and regulation of PRR5 by GI . Absolute RNA quantification revealed low levels of PRR9 transcripts (peak approx. 50 copies cell −1 ) compared with other clock genes, and threefold higher levels of LHY RNA (more than 1500 copies cell −1 ) than of its close relative CCA1 . The data are disseminated from BioDare, an online repository for focused timeseries data, which is expected to benefit mechanistic modelling. One data subset successfully constrained clock gene expression in a complex model, using publicly available software on parallel computers, without expert tuning or programming. We outline the empirical and mathematical justification for data aggregation in understanding highly interconnected, dynamic networks such as the clock, and the observed design constraints on the resources required to make this approach widely accessible.
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Nair, Manjusha, Jinesh Manchan Kannimoola, Bharat Jayaraman, Bipin Nair, and Shyam Diwakar. "Temporal constrained objects for modelling neuronal dynamics." PeerJ Computer Science 4 (July 23, 2018): e159. http://dx.doi.org/10.7717/peerj-cs.159.
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Background Several new programming languages and technologies have emerged in the past few decades in order to ease the task of modelling complex systems. Modelling the dynamics of complex systems requires various levels of abstractions and reductive measures in representing the underlying behaviour. This also often requires making a trade-off between how realistic a model should be in order to address the scientific questions of interest and the computational tractability of the model. Methods In this paper, we propose a novel programming paradigm, called temporal constrained objects, which facilitates a principled approach to modelling complex dynamical systems. Temporal constrained objects are an extension of constrained objects with a focus on the analysis and prediction of the dynamic behaviour of a system. The structural aspects of a neuronal system are represented using objects, as in object-oriented languages, while the dynamic behaviour of neurons and synapses are modelled using declarative temporal constraints. Computation in this paradigm is a process of constraint satisfaction within a time-based simulation. Results We identified the feasibility and practicality in automatically mapping different kinds of neuron and synapse models to the constraints of temporal constrained objects. Simple neuronal networks were modelled by composing circuit components, implicitly satisfying the internal constraints of each component and interface constraints of the composition. Simulations show that temporal constrained objects provide significant conciseness in the formulation of these models. The underlying computational engine employed here automatically finds the solutions to the problems stated, reducing the code for modelling and simulation control. All examples reported in this paper have been programmed and successfully tested using the prototype language called TCOB. The code along with the programming environment are available at http://github.com/compneuro/TCOB_Neuron. Discussion Temporal constrained objects provide powerful capabilities for modelling the structural and dynamic aspects of neural systems. Capabilities of the constraint programming paradigm, such as declarative specification, the ability to express partial information and non-directionality, and capabilities of the object-oriented paradigm especially aggregation and inheritance, make this paradigm the right candidate for complex systems and computational modelling studies. With the advent of multi-core parallel computer architectures and techniques or parallel constraint-solving, the paradigm of temporal constrained objects lends itself to highly efficient execution which is necessary for modelling and simulation of large brain circuits.
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Shang, Wen-Long, Yanyan Chen, Chengcheng Song, and Washington Y. Ochieng. "Robustness Analysis of Urban Road Networks from Topological and Operational Perspectives." Mathematical Problems in Engineering 2020 (August 14, 2020): 1–12. http://dx.doi.org/10.1155/2020/5875803.
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This study comprehensively analyses the robustness of urban road networks through topological indices based on the complex network theory and operational indices based on traffic assignment theory: User Equilibrium (UE), System Optimum (SO), and Price of Anarchy (POA). Analysing topological indices may pin down the most important nodes for URNs from the perspective of connectivity, while more sophisticated operational indices are helpful to examine the importance of nodes for URNs by taking into account link capacity, travel demand, and drivers’ behaviour. The previous way is calculated in a static way, which reduces the computation times and increases the efficiency for quick assessment of the robustness of URNs, while the latter is in a dynamic way, namely, calculating is based on removal of individual nodes, although this way is more likely to capture realistic meanings but consumes huge amount of time. The efforts made in this study try to find the relationship between topological and operational indices so as to assist the assessment of robustness of URNs to local disruptions. Seven realistic urban road networks such as Sioux Falls and Anaheim are used as network examples, and results show that different indices reflect robustness characteristics of urban road networks from different ways, and rank correlations between any two indices are poor although small network such as Sioux Falls have better correlations than others.
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Damasceno, Daniela A., R. K. N. D. Nimal Rajapakse, and Euclides Mesquita. "Atomistic Modelling of Size-Dependent Mechanical Properties and Fracture of Pristine and Defective Cove-Edged Graphene Nanoribbons." Nanomaterials 10, no. 7 (2020): 1422. http://dx.doi.org/10.3390/nano10071422.
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Cove-edged graphene nanoribbons (CGNR) are a class of nanoribbons with asymmetric edges composed of alternating hexagons and have remarkable electronic properties. Although CGNRs have attractive size-dependent electronic properties their mechanical properties have not been well understood. In practical applications, the mechanical properties such as tensile strength, ductility and fracture toughness play an important role, especially during device fabrication and operation. This work aims to fill a gap in the understanding of the mechanical behaviour of CGNRs by studying the edge and size effects on the mechanical response by using molecular dynamic simulations. Pristine graphene structures are rarely found in applications. Therefore, this study also examines the effects of topological defects on the mechanical behaviour of CGNR. Ductility and fracture patterns of CGNR with divacancy and topological defects are studied. The results reveal that the CGNR become stronger and slightly more ductile as the width increases in contrast to normal zigzag GNR. Furthermore, the mechanical response of defective CGNRs show complex dependency on the defect configuration and distribution, while the direction of the fracture propagation has a complex dependency on the defect configuration and position. The results also confirm the possibility of topological design of graphene to tailor properties through the manipulation of defect types, orientation, and density and defect networks.
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Li, Qianmu, Jun Hou, Shunmei Meng, and Huaqiu Long. "GLIDE: A Game Theory and Data-Driven Mimicking Linkage Intrusion Detection for Edge Computing Networks." Complexity 2020 (March 30, 2020): 1–18. http://dx.doi.org/10.1155/2020/7136160.
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The real-time and high-continuity requirements of the edge computing network gain more and more attention because of its active defence problem, that is, a data-driven complex problem. Due to the dual constraints of the hybrid feature of edge computing networks and the uncertainty of new attack features, implementing active defence measures such as detection, evasion, trap, and control is essential for the security protection of edge computing networks with high real-time and continuity requirements. The basic idea of safe active defence is to make the defence gain more significant than the attack loss. To encounter the new attacks with uncertain features introduced by the ubiquitous transmission network in the edge computing network, this paper investigates the attack behaviour and presents an attack-defence mechanism based on game theory. Based on the idea of dynamic intrusion detection, we utilize the game theory in the field of edge computing network and suggest a data-driven mimicry intrusion detection game model-based technique called GLIDE. The game income of participants and utility computing methods under different deployment strategies are analysed in detail. According to the proof analysis of the Nash equilibrium condition in the model, the contradictory dynamic game relationship is described. Therefore, the optimal deployment strategy of the multiredundancy edge computing terminal intrusion detection service in the edge computing network is obtained by solving the game balance point. The detection probability of the edge computing network for network attacks is improved, and the cost of intrusion detection of the edge computing network is reduced.
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Ammal, R. Ananthalakshmi, Sajimon PC, and Vinodchandra SS. "Termite inspired algorithm for traffic engineering in hybrid software defined networks." PeerJ Computer Science 6 (August 17, 2020): e283. http://dx.doi.org/10.7717/peerj-cs.283.
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In the era of Internet of Things and 5G networks, handling real time network traffic with the required Quality of Services and optimal utilization of network resources is a challenging task. Traffic Engineering provides mechanisms to guide network traffic to improve utilization of network resources and meet requirements of the network Quality of Service (QoS). Traditional networks use IP based and Multi-Protocol Label Switching (MPLS) based Traffic Engineering mechanisms. Software Defined Networking (SDN) have characteristics useful for solving traffic scheduling and management. Currently the traditional networks are not going to be replaced fully by SDN enabled resources and hence traffic engineering solutions for Hybrid IP/SDN setups have to be explored. In this paper we propose a new Termite Inspired Optimization algorithm for dynamic path allocation and better utilization of network links using hybrid SDN setup. The proposed bioinspired algorithm based on Termite behaviour implemented in the SDN Controller supports elastic bandwidth demands from applications, by avoiding congestion, handling traffic priority and link availability. Testing in both simulated and physical test bed demonstrate the performance of the algorithm with the support of SDN. In cases of link failures, the algorithm in the SDN Controller performs failure recovery gracefully. The algorithm also performs very well in congestion avoidance. The SDN based algorithm can be implemented in the existing traditional WAN as a hybrid setup and is a less complex, better alternative to the traditional MPLS Traffic Engineering setup.
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Dragos, Valentina. "From Finding to Explaining." International Journal of Knowledge and Systems Science 7, no. 1 (2016): 40–55. http://dx.doi.org/10.4018/ijkss.2016010103.
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Supporting anomaly analysis in the maritime field is a challenging problem because of the dynamic nature of the task: the definition of abnormal or suspicious behaviour is subject to change and depends on user interests. This paper provides a novel approach to support anomaly analysis in the maritime domain through the exploration of large collections of interpretation reports. Based on observables or more sophisticated patterns, the approach provides information retrieval strategies going from basic facts retrieval that guide short-term corrective actions to more complex networks of related concepts that help domain experts to understand or to explain abnormal vessel behaviours. Semantic integration is used to link various information sources, by using a commonly adopted standard. The paper seeks to explore different aspects of using information retrieval to support the analysis and interpretation of abnormal vessel behaviours for maritime surveillance.
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Caramidaru, Ibrian, and Andreea Ionica. "In Search of Sustainable Social Impact: A System Dynamics Approach to Managing Nonprofit Organizations Operating in Multi-Project Contexts." MATEC Web of Conferences 343 (2021): 07007. http://dx.doi.org/10.1051/matecconf/202134307007.
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Nonprofit organizations are typically seen as institutional settings that contribute to finding grassroots solutions to various social problems. But in their own turn, these entities exhibit by design manyfold frailties given by factors such as - precarious funding sustainability, balancing the multiple and, at times, divergent interests of stakeholders, finding a suitable manner to assess managerial performance. The aim of this paper consist in employing a system dynamics approach to modelling the managerial behaviour of nonprofit entities delivering their output through project networks. The system dynamics concepts of causal loops, stocks and flows dependencies are used to depict the complex relationships between projects, funding sources and social outcomes. This approach leads to identifying the systemic threatening to nonprofit sustainability and the dynamic nature of managerial decisions in the context of the interactions between nonprofit organizations, their beneficiaries and funding agencies.
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Mackinnon, Lachlan, Liz Bacon, Gabriella Cortellessa, and Amedeo Cesta. "Using Emotional Intelligence in Training Crisis Managers." International Journal of Distance Education Technologies 11, no. 2 (2013): 66–95. http://dx.doi.org/10.4018/jdet.2013040104.
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Multi-agency crisis management represents one of the most complex of real-world situations, requiring rapid negotiation and decision-making under extreme pressure. However, the training offered to strategic planners, called Gold Commanders, does not place them under any such pressure. It takes the form of paper-based, table-top exercises, or expensive, real-world, limited-scope simulations. The Pandora project has developed a rich multimedia training environment for Gold Commanders, based on a crisis scenario, timeline-based, event network, with which the trainees and their trainer interact dynamically. Pandora uses the emotional intelligence of the trainees, through a behavioural modelling component, to support group dynamic and decision-making. It applies systemic emotional intelligence, based on inferred user state and rule-based affective inputs, to impact the stress levels of the trainees. Pandora can impose variable stress on trainees, to impact their decision-making, and model their behaviour and performance under stress, potentially resulting in more effective and realisable strategies.
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CARTLING, BO. "GENERATION OF ASSOCIATIVE PROCESSES IN A NEURAL NETWORK WITH REALISTIC FEATURES OF ARCHITECTURE AND UNITS." International Journal of Neural Systems 05, no. 03 (1994): 181–94. http://dx.doi.org/10.1142/s0129065794000207.
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A recent neural network model of cortical associative memory incorporating neuronal adaptation by a simplified description of its underlying ionic mechanisms is extended towards more realistic network units and architecture. Excitatory units correspond to groups of adapting pyramidal neurons and inhibitory units to groups of nonadapting interneurons. The network architecture is formed from pairs of one pyramidal and one interneuron unit each with inhibitory connections within and excitatory connections between pairs. The degree of adaptability of the pyramidal units controls the character of the network dynamics. An intermediate adaptability generates limit cycles of transitions between stored patterns and regulates oscillation frequencies in the range of theta rhythms observed in the brain. In particular, neuronal adaptation can impose a direction of transitions between overlapping patterns also in a symmetrically connected network. The model permits a detailed analysis of the transition mechanisms. Temporal sequences of patterns thus formed may constitute parts of associative processes, such as recall of stored sequences or search of pattern subspaces. As a special case, neuronal adaptation can accomplish pattern segmentation by which overlapping patterns are temporally resolved. The type of limit cycles produced by neuronal adaptation may also be of significance for central pattern generators, also for networks involving motor neurons. The applied learning rule of Hebbian type is compared to a modified version also common in neural network modelling. It is also shown that the dependence of the network dynamic behaviour on neuronal adaptability, from fixed point attractors at weak adaptability towards more complex dynamics of limit cycles and chaos at strong adaptability, agrees with that recently observed in a more abstract version of the model. The present description of neuronal adaptation is compared to models based on dynamic firing thresholds.
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Alexandris, Christina K. "Registering the Impact of Words in Spoken Political and Journalistic Texts." Human Language, Rights, and Security 1, no. 1 (2021): 26–48. http://dx.doi.org/10.22363/2713-0614-2021-1-1-26-48.
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Words in spoken political and journalistic texts may inspire, infuriate or even become mottos. Often, the entire spoken interaction may be forgotten, yet individual words may remain associated with the Speaker and/or the group represented by the Speaker or even the individual word or words themselves obtain a dynamic of their own, outshining the original Speaker. In the current-state-of affairs, connected with the impact of international news networks and social media, the impact of words in spoken political and journalistic texts is directly linked to its impact to a diverse international audience. The impact or controversy of a word and related topic may be registered by the reaction it generates. Special focus is placed in the registration and evaluation of words and their related topics in spoken political and journalistic discussions and interviews. Although as text types, spoken political and journalistic texts pose challenges for their evaluation, processing and translation, the presented approaches allow the registration of complex and implied information, indications of Speakers attitude and intentions and can contribute to evaluating the behaviour of Speakers-Participants. This registration also allows the identification of words generating positive, negative or diverse reactions, their relation to Cognitive Bias and their impact to a national and international audience within a context of international news networks and social media.
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van Olmen, Josefien, Bruno Marchal, Button Ricarte, Wim Van Damme, and Sara Van Belle. "The Need for a Dynamic Approach to Health SystemCentered Innovations Comment on "What Health System Challenges Should Responsible Innovation in Health Address? Insights From an International Scoping Review"." International Journal of Health Policy and Management 8, no. 7 (2019): 444–46. http://dx.doi.org/10.15171/ijhpm.2019.25.
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Lehoux and colleagues plea for a health systems perspective to evaluate innovations. Since many innovations and their scale-up strategies emerge from processes that are not (centrally) steered, we plea for any assessment with a dynamic, instead of a sequential, approach. We provide further guidance on how to adopt such dynamic approach, in order to better un-derstand and steer innovations for better health systems. A systems-level challenge is constituted by interactions and feedback loops between different actors and components of the health system. It is therefore essential to explore both the entry-point of innovation and the interactions with other components. If innovation is regarded as an injection of resources and opportunities into a health system, this system needs to have the capacity to transform these into desired outputs, the ‘absorption capacity.’ The highly organic diffusion of innovation in complex adapative systems cannot be easily controlled, but the system behaviours can be analysed, with occurance of phenomena such as path dependence, feedback loops, scale-free networks, emergent behaviour and phase transitions. This helps to anticipate unintended consequences, and to engage key actors in ongoing problem-solving and adaptation. By adopting a prospective approach, responsible innovation could set in motion prospective policy evaluations, which on the basis of iterative learning would allow decisionmakers to continuously adapt their policies and programmes. Priority-setting for innovation is an essentially political process that is geared towards consensus-building and grounded in values.
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Farsoni, Saverio, Silvio Simani, and Paolo Castaldi. "Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis." Applied Sciences 11, no. 11 (2021): 5035. http://dx.doi.org/10.3390/app11115035.
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The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. Therefore, this work investigates two fault diagnosis solutions that exploit the direct estimation of the faults by means of data-driven approaches. In this way, the diagnostic residuals are represented by the reconstructed faults affecting the monitored process. The proposed methodologies are based on fuzzy systems and neural networks used to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the considered prototypes are integrated with auto-regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. These residual generators are estimated from the input and output measurements acquired from a high-fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. The robustness and the reliability features of the developed solutions are validated in the presence of model-reality mismatch and modelling error effects featured by the wind turbine simulator. Moreover, a hardware-in-the-loop tool is implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches. The achieved results have demonstrated the effectiveness of the developed schemes also with respect to more complex model-based and data-driven fault diagnosis methodologies.
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Casadei, Roberto, Gianluca Aguzzi, and Mirko Viroli. "A Programming Approach to Collective Autonomy." Journal of Sensor and Actuator Networks 10, no. 2 (2021): 27. http://dx.doi.org/10.3390/jsan10020027.
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Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.
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Manic, G., C. Printemps, M. Zug, and C. Lemoine. "Development and commissioning of decision support tools for sewerage management." Water Science and Technology 53, no. 4-5 (2006): 293–302. http://dx.doi.org/10.2166/wst.2006.134.
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Managing sewerage systems is a highly complex task due to the dynamic nature of the facilities. Their performance strongly depends on the know-how applied by the operators. In order to define optimal operational settings, two decision support tools based on mathematical models have been developed. Moreover, easy-to-use interfaces have been created as well, aiding operators who presumably do not have the necessary skills to use modelling software. The two developed programs simulate the behaviour of both wastewater treatment plants (WWTP) and sewer network systems, respectively. They have essentially the same structure, including raw data management and statistical analysis, a simulation layer using the application programming interface of the applied software and a layer responsible for the representation of the obtained results. Four user modes are provided in the two software including the simulation of historical data using the applied and novel operational settings, as well as modes concerning prediction of possible operation periods and updates. Concerning the WWTP software, it was successfully installed in Nantes (France) in June 2004. Moreover, the one managing sewer networks has been deployed in Saint-Malo (France) in January 2005. This paper presents the structure of the developed software and the first results obtained during the commissioning phase.
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Essenfelder, Arthur H., Francesca Larosa, Paolo Mazzoli, et al. "Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management." Atmosphere 11, no. 12 (2020): 1305. http://dx.doi.org/10.3390/atmos11121305.
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This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets.
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Bentley, Katie, and Shilpa Chakravartula. "The temporal basis of angiogenesis." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1720 (2017): 20150522. http://dx.doi.org/10.1098/rstb.2015.0522.
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The process of new blood vessel growth (angiogenesis) is highly dynamic, involving complex coordination of multiple cell types. Though the process must carefully unfold over time to generate functional, well-adapted branching networks, we seldom hear about the time-based properties of angiogenesis, despite timing being central to other areas of biology. Here, we present a novel, time-based formulation of endothelial cell behaviour during angiogenesis and discuss a flurry of our recent, integrated in silico/in vivo studies, put in context to the wider literature, which demonstrate that tissue conditions can locally adapt the timing of collective cell behaviours/decisions to grow different vascular network architectures. A growing array of seemingly unrelated ‘temporal regulators’ have recently been uncovered, including tissue derived factors (e.g. semaphorins or the high levels of VEGF found in cancer) and cellular processes (e.g. asymmetric cell division or filopodia extension) that act to alter the speed of cellular decisions to migrate. We will argue that ‘temporal adaptation’ provides a novel account of organ/disease-specific vascular morphology and reveals ‘timing’ as a new target for therapeutics. We therefore propose and explain a conceptual shift towards a ‘temporal adaptation’ perspective in vascular biology, and indeed other areas of biology where timing remains elusive. This article is part of the themed issue ‘Systems morphodynamics: understanding the development of tissue hardware’.
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Abdul Wahab, Norhaliza, Nurazizah Mahmod, and Ramon Vilanova. "Permeate Flux Control in SMBR System by Using Neural Network Internal Model Control." Processes 8, no. 12 (2020): 1672. http://dx.doi.org/10.3390/pr8121672.
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This paper presents a design of a data-driven-based neural network internal model control for a submerged membrane bioreactor (SMBR) with hollow fiber for microfiltration. The experiment design is performed for measurement of physical parameters from an actuator input (permeate pump voltage), which gives the information (outputs) of permeate flux and trans-membrane pressure (TMP). The palm oil mill effluent is used as an influent preparation to depict fouling phenomenon in the membrane filtration process. From the experiment, membrane fouling potential is observed from flux decline pattern, with a rapid increment of TMP (above 200 mbar). Membrane fouling is a complex process and the available models in literature are not designed for control system (filtration performance). Therefore, this work proposes an aeration fouling control strategy to measure the filtration performance. The artificial neural networks (Feed-Forward Neural Network—FFNN, Radial Basis Function Neural Network—RBFNN and Nonlinear Autoregressive Exogenous Neural Network—NARXNN) are used to model dynamic behaviour of flux and TMP. In this case, only flux is used in closed loop control application, whereby the TMP effect is used for monitoring. The simulation results show that reliable prediction of membrane fouling potential is obtained. It can be observed that almost all the artificial neural network (ANN) models have similar shape with the actual data set, with the highest accuracy of more than 90% for both RBFNN and NARXN. The RBFNN is preferable due to simple structure of the network. In the control system, the RBFNN IMC depicts the highest closed loop performance with only 3.75 s (settling time) for setpoint changes when compared with other controllers. In addition, it showed fast performance in disturbance rejection with less overshoot. In conclusion, among the different neural network tested configurations the one based on radial basis function provides the best performance with respect to prediction as well as control performance.
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Lewis, Elaine, Simone Volet, Catherine Baudains, and Caroline Mansfield. "Education for Sustainability at a Montessori Primary School: From Silos to Systems Thinking." Australian Journal of Environmental Education 28, no. 2 (2012): 162–64. http://dx.doi.org/10.1017/aee.2013.8.
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AbstractThis research investigated Education for Sustainability (EfS) at an independent Montessori primary school, located in the Perth metropolitan area of Western Australia. A longitudinal case study involving analysis of data from a 20-year period was conducted to determine the effectiveness of EfS. Historical information about EfS at the school from 1990 to 2005 was examined, with the main focus of the study being on the impact of the Australian Sustainable Schools Initiative (AuSSI) between 2005 and 2009. AuSSI promotes a whole school, whole systems thinking approach to EfS.Three school-based issues in EfS were studied. First, the research aimed to determine what elements of EfS were in operation in the school prior to involvement in AuSSI. Second, student outcomes including engagement with whole systems thinking, attitudes and values, knowledge and understandings, and skills and behaviours related to EfS, were investigated during the first 5 years of participation in AuSSI. Third, teacher perceptions of the EfS program, including engagement with whole systems thinking, were examined during this same time period.A case study approach was employed to enable in-depth investigation of EfS in the life of the school prior to, during and post implementation of AuSSI. This approach facilitated revelation of participants’ lived experiences, their perceptions and understandings of EfS, as well as detailed information about student outcomes in EfS. Case study methodology was also compatible with the culture and processes of the participating school and provided an opportunity for utilising a whole systems thinking approach. Data was gathered from a range of sources, through surveys, interviews, observation and document analysis over a 5-year period. The total participants included 11 teachers and 75 students.The research identified particular antecedents of EfS in the Montessori method of education that existed in the school prior to AuSSI, including the whole child approach, together with the Montessori learning environment, curriculum and values. Following participation in AuSSI, student attitudes and values, knowledge and understandings, and skills and behaviours related to EfS were enhanced for all year levels. However, after 3 years, when specific EfS actions and projects ceased, student EfS outcomes were limited. Furthermore, students’ thinking and behaviour indicated a ‘silo’, rather than whole systems thinking approach to EfS. Teachers perceived the EfS program as highly effective in the initial 3 years after joining AuSSI. Key elements that enhanced EfS included EfS staff champions who had access to EfS networks, leadership support, and active school community involvement in all EfS processes. However, after 3 years of being an AuSSI school, the culmination of reduced leadership support for EfS, lack of staff training, vague designation of staff with EfS responsibilities and inadequate community involvement, resulted in cessation of the EfS program. Teacher perceptions on whole systems thinking revealed alignment between Montessori philosophy, EfS and whole system thinking was more in theory than in practice.Through an in-depth longitudinal case study of a school this research highlighted the importance of whole school EfS professional learning, embedding EfS and whole systems thinking across the curriculum at all year levels, whole school support, and the usefulness of a sustainability continuum that recognises the complex, dynamic interplay of issues involved in a school's EfS journey. It is strongly recommended that improvements to pre-service teacher education in EfS are implemented, and a review of the AuSSI toolkit is conducted to refine EfS evaluation processes and to target the specific EfS needs of teachers at different stages of schooling, as well as to enhance understanding and implementation of the whole systems thinking approach. Finally, EfS professional learning for all school staff in all schools is warranted to enhance depth of EfS engagement.
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De la Fuente, Ildefonso M., Luis Martínez, Jose Carrasco-Pujante, Maria Fedetz, José I. López, and Iker Malaina. "Self-Organization and Information Processing: From Basic Enzymatic Activities to Complex Adaptive Cellular Behavior." Frontiers in Genetics 12 (May 21, 2021). http://dx.doi.org/10.3389/fgene.2021.644615.
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One of the main aims of current biology is to understand the origin of the molecular organization that underlies the complex dynamic architecture of cellular life. Here, we present an overview of the main sources of biomolecular order and complexity spanning from the most elementary levels of molecular activity to the emergence of cellular systemic behaviors. First, we have addressed the dissipative self-organization, the principal source of molecular order in the cell. Intensive studies over the last four decades have demonstrated that self-organization is central to understand enzyme activity under cellular conditions, functional coordination between enzymatic reactions, the emergence of dissipative metabolic networks (DMN), and molecular rhythms. The second fundamental source of order is molecular information processing. Studies on effective connectivity based on transfer entropy (TE) have made possible the quantification in bits of biomolecular information flows in DMN. This information processing enables efficient self-regulatory control of metabolism. As a consequence of both main sources of order, systemic functional structures emerge in the cell; in fact, quantitative analyses with DMN have revealed that the basic units of life display a global enzymatic structure that seems to be an essential characteristic of the systemic functional metabolism. This global metabolic structure has been verified experimentally in both prokaryotic and eukaryotic cells. Here, we also discuss how the study of systemic DMN, using Artificial Intelligence and advanced tools of Statistic Mechanics, has shown the emergence of Hopfield-like dynamics characterized by exhibiting associative memory. We have recently confirmed this thesis by testing associative conditioning behavior in individual amoeba cells. In these Pavlovian-like experiments, several hundreds of cells could learn new systemic migratory behaviors and remember them over long periods relative to their cell cycle, forgetting them later. Such associative process seems to correspond to an epigenetic memory. The cellular capacity of learning new adaptive systemic behaviors represents a fundamental evolutionary mechanism for cell adaptation.
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Valdez, Lucas D., Louis Shekhtman, Cristian E. La Rocca, et al. "Cascading failures in complex networks." Journal of Complex Networks 8, no. 2 (2020). http://dx.doi.org/10.1093/comnet/cnaa013.
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Abstract Cascading failure is a potentially devastating process that spreads on real-world complex networks and can impact the integrity of wide-ranging infrastructures, natural systems and societal cohesiveness. One of the essential features that create complex network vulnerability to failure propagation is the dependency among their components, exposing entire systems to significant risks from destabilizing hazards such as human attacks, natural disasters or internal breakdowns. Developing realistic models for cascading failures as well as strategies to halt and mitigate the failure propagation can point to new approaches to restoring and strengthening real-world networks. In this review, we summarize recent progress on models developed based on physics and complex network science to understand the mechanisms, dynamics and overall impact of cascading failures. We present models for cascading failures in single networks and interdependent networks and explain how different dynamic propagation mechanisms can lead to an abrupt collapse and a rich dynamic behaviour. Finally, we close the review with novel emerging strategies for containing cascades of failures and discuss open questions that remain to be addressed.
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Maity, Indrajit, Nathaniel Wagner, Rakesh Mukherjee, et al. "A chemically fueled non-enzymatic bistable network." Nature Communications 10, no. 1 (2019). http://dx.doi.org/10.1038/s41467-019-12645-0.
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Abstract One of the grand challenges in contemporary systems chemistry research is to mimic life-like functions using simple synthetic molecular networks. This is particularly true for systems that are out of chemical equilibrium and show complex dynamic behaviour, such as multi-stability, oscillations and chaos. We report here on thiodepsipeptide-based non-enzymatic networks propelled by reversible replication processes out of equilibrium, displaying bistability. Accordingly, we present quantitative analyses of the bistable behaviour, featuring a phase transition from the simple equilibration processes taking place in reversible dynamic chemistry into the bistable region. This behaviour is observed only when the system is continuously fueled by a reducing agent that keeps it far from equilibrium, and only when operating within a specifically defined parameter space. We propose that the development of biomimetic bistable systems will pave the way towards the study of more elaborate functions, such as information transfer and signalling.
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Rosell-Tarragó, Gemma, and Albert Díaz-Guilera. "Quasi-symmetries in complex networks: a dynamical model approach." Journal of Complex Networks 9, no. 3 (2021). http://dx.doi.org/10.1093/comnet/cnab025.
Full textAbstract:
Abstract The existence of symmetries in complex networks has a significant effect on network dynamic behaviour. Nevertheless, beyond topological symmetry, one should consider the fact that real-world networks are exposed to fluctuations or errors, as well as mistaken insertions or removals. Therefore, the resulting approximate symmetries remain hidden to standard symmetry analysis—fully accomplished by discrete algebra software. There have been a number of attempts to deal with approximate symmetries. In the present work we provide an alternative notion of these weaker symmetries, which we call ‘quasi-symmetries’. Differently from other definitions, quasi-symmetries remain free to impose any invariance of a particular network property and they are obtained from the phase differences at the steady-state configuration of an oscillatory dynamical model: the Kuramoto–Sakaguchi model. The analysis of quasi-symmetries unveils otherwise hidden real-world networks attributes. On the one hand, we provide a benchmark to determine whether a network has a more complex pattern than that of a random network with regard to quasi-symmetries, namely, if it is structured into separate quasi-symmetric groups of nodes. On the other hand, we define the ‘dual-network’, a weighted network (and its corresponding binnarized counterpart) that effectively encodes all the information of quasi-symmetries in the original network. The latter is a powerful instrument for obtaining worthwhile insights about node centrality (obtaining the nodes that are unique from that act as imitators with respect to the others) and community detection (quasi-symmetric groups of nodes).
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"Behaviour Based Data Dispatcher." International Journal of Recent Technology and Engineering 8, no. 4 (2019): 12940–44. http://dx.doi.org/10.35940/ijrte.d8428.118419.
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Human life is a complex social structure. It is not possible for the humans to navigate without reading the other persons. They do it by identifying the faces. The state of response can be decided based on the mood of the opposite person. Whereas a person’s mood can be figured out by observing his emotion (Facial Gesture). The aim of the project is to construct a “Facial emotion Recognition” model using DCNN (Deep convolutional neural network) in real time. The model is constructed using DCNN as it is proven that DCNN work with greater accuracy than CNN (convolutional neural network). The facial expression of humans is very dynamic in nature it changes in split seconds whether it may be Happy, Sad, Angry, Fear, Surprise, Disgust and Neutral etc. This project is to predict the emotion of the person in real time. Our brains have neural networks which are responsible for all kinds of thinking (decision making, understanding). This model tries to develop these decisions making and classification skills by training the machine. It can classify and predict the multiple faces and different emotions at the very same time. In order to obtain higher accuracy, we take the models which are trained over thousands of datasets.
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Joubert, Damien, Alexandre Marcireau, Nic Ralph, Andrew Jolley, André van Schaik, and Gregory Cohen. "Event Camera Simulator Improvements via Characterized Parameters." Frontiers in Neuroscience 15 (July 27, 2021). http://dx.doi.org/10.3389/fnins.2021.702765.
Full textAbstract:
It has been more than two decades since the first neuromorphic Dynamic Vision Sensor (DVS) sensor was invented, and many subsequent prototypes have been built with a wide spectrum of applications in mind. Competing against state-of-the-art neural networks in terms of accuracy is difficult, although there are clear opportunities to outperform conventional approaches in terms of power consumption and processing speed. As neuromorphic sensors generate sparse data at the focal plane itself, they are inherently energy-efficient, data-driven, and fast. In this work, we present an extended DVS pixel simulator for neuromorphic benchmarks which simplifies the latency and the noise models. In addition, to more closely model the behaviour of a real pixel, the readout circuitry is modelled, as this can strongly affect the time precision of events in complex scenes. Using a dynamic variant of the MNIST dataset as a benchmarking task, we use this simulator to explore how the latency of the sensor allows it to outperform conventional sensors in terms of sensing speed.
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Bentham Science Publisher, Bentham Science Publisher. "A Pill to Find Them All: IoT Device Behavior Fingerprinting using Capsule Networks." International Journal of Sensors, Wireless Communications and Control 11 (February 3, 2021). http://dx.doi.org/10.2174/2210327911666210203222153.
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Aim and Background: The application of novel deep learning technique of capsule networks for device behavior fingerprinting. Device behavior fingerprinting emerged as an important means to characterize the network behavior of connected devices due to the dynamic nature of smart systems. The study of device behavior fingerprinting strategies gave us an insight into the strengths and weaknesses of different machine learning techniques. It also led us to some research questions that we incorporated in the proposed framework. Firstly, we explored the means to improve the efficiency of passive device fingerprinting techniques. Secondly, we needed to address the privacy concerns that arise from creation and maintenance of device fingerprints Objective: To our best knowledge, this is the first time that device fingerprints had been generated in the form of images. The use of device fingerprints in image form best utilized the object recognition capabilities of capsule networks. Method: We designed a novel method to classify and save the network behaviour of IoT devices that are connected to a network. The proposed model was based on a two-fold innovation of generation of unique images based on packet parameters of device transmissions, and the design of a model that could carry out efficient and accurate classification of device vendors based on their network behavior. Results and Conclusion: The generation of unique images offered a big advantage of saving the memory of the system. While a packet capture file may take around 150kb or more, the generated images were as small as the order of 2kb. For a smart system made up of thousands of devices, the order of memory space saved would become significant. Furthermore, since the algorithm of image generation could be customized by the network administrators, the images cannot be reverse- engineered by potential attackers, thereby assuring a secure way to save device behaviour fingerprints. The developed model was compiled over 500 epochs that roughly translated to 100 minutes and gave the accuracy of the order of 92%.This was the first time that device network behaviour has been translated into an image and tested through classification using capsule networks. The translation of captured packet flows to black and white images not only saved on memory space but also provided safeguard against reverse engineering by potential attackers. There is a vast scope to further use of this strategy to develop more complex device fingerprinting methods.