Academic literature on the topic 'New Weighted Complex Network'
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Journal articles on the topic "New Weighted Complex Network"
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WANG, XUTAO, HONGTAO LU, and GUANRONG CHEN. "THE MODELLING OF WEIGHTED COMPLEX NETWORKS." International Journal of Modern Physics B 21, no. 16 (June 20, 2007): 2813–20. http://dx.doi.org/10.1142/s0217979207037399.
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In order to further explore the mechanism responsible for weighted complex networks, we introduce a new model that incorporates the network topology and the weights' dynamical evolutions. Our model can capture the details of weight dynamics caused not only by the addition of a new node with new links and new links between old nodes, but also the deletion of old links. We calculate analytically the distributions of both degree and strength and found that all these distributions show scale-free behavior, as confirmed in many real networks. Thus our model characterizes the real weighted complex networks more precisely.
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Nguyen, Quang, Ngoc-Kim-Khanh Nguyen, Davide Cassi, and Michele Bellingeri. "New Betweenness Centrality Node Attack Strategies for Real-World Complex Weighted Networks." Complexity 2021 (October 15, 2021): 1–17. http://dx.doi.org/10.1155/2021/1677445.
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In this work, we introduce a new node attack strategy removing nodes with the highest conditional weighted betweenness centrality (CondWBet), which combines the weighted structure of the network and the node’s conditional betweenness. We compare its efficacy with well-known attack strategies from literature over five real-world complex weighted networks. We use the network weighted efficiency (WEFF) like a measure encompassing the weighted structure of the network, in addition to the commonly used binary-topological measure, i.e., the largest connected cluster (LCC). We find that if the measure is WEFF, the CondWBet strategy is the best to decrease WEFF in 3 out of 5 cases. Further, CondWBet is the most effective strategy to reduce WEFF at the beginning of the removal process, whereas the Strength that removes nodes with the highest sum of the link weights first shows the highest efficacy in the final phase of the removal process when the network is broken into many small clusters. These last outcomes would suggest that a better attacking in weighted networks strategy could be a combination of the CondWBet and Strength strategies.
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TASGIN, MURSEL, and HALUK O. BINGOL. "GOSSIP ON WEIGHTED NETWORKS." Advances in Complex Systems 15, supp01 (June 2012): 1250061. http://dx.doi.org/10.1142/s0219525912500610.
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In this work, we analyze gossip spreading on weighted networks. We try to define a new metric to classify weighted complex networks using our model. The model proposed here is based on the gossip spreading model introduced by Lind et al. on unweighted networks. The new metric is based on gossip spreading activity in the network, which is correlated with both topology and relative edge weights in the network. The model gives more insight about the weight distribution and correlation of topology with edge weights in a network. It also measures how suitable a weighted network is for gossip spreading. We analyze gossip spreading on real weighted networks of human interactions. Six co-occurrence and seven social pattern networks are investigated. Gossip propagation is found to be a good parameter to distinguish co-occurrence and social pattern networks. As a comparison some miscellaneous networks of comparable sizes and computer generated networks based on ER, BA and WS models are also investigated. They are found to be quite different from the human interaction networks.
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Xing, Yingying, Jian Lu, and Shendi Chen. "Weighted Complex Network Analysis of Shanghai Rail Transit System." Discrete Dynamics in Nature and Society 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1290138.
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With increasing passenger flows and construction scale, Shanghai rail transit system (RTS) has entered a new era of networking operation. In addition, the structure and properties of the RTS network have great implications for urban traffic planning, design, and management. Thus, it is necessary to acquire their network properties and impacts. In this paper, the Shanghai RTS, as well as passenger flows, will be investigated by using complex network theory. Both the topological and dynamic properties of the RTS network are analyzed and the largest connected cluster is introduced to assess the reliability and robustness of the RTS network. Simulation results show that the distribution of nodes strength exhibits a power-law behavior and Shanghai RTS network shows a strong weighted rich-club effect. This study also indicates that the intentional attacks are more detrimental to the RTS network than to the random weighted network, but the random attacks can cause slightly more damage to the random weighted network than to the RTS network. Our results provide a richer view of complex weighted networks in real world and possibilities of risk analysis and policy decisions for the RTS operation department.
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Wang, Yu, Jinli Guo, and Han Liu. "A New Evaluation Method of Node Importance in Directed Weighted Complex Networks." Journal of Systems Science and Information 5, no. 4 (September 17, 2017): 367–75. http://dx.doi.org/10.21078/jssi-2017-367-09.
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AbstractCurrent researches on node importance evaluation mainly focus on undirected and unweighted networks, which fail to reflect the real world in a comprehensive and objective way. Based on directed weighted complex network models, the paper introduces the concept of in-weight intensity of nodes and thereby presents a new method to identify key nodes by using an importance evaluation matrix. The method not only considers the direction and weight of edges, but also takes into account the position importance of nodes and the importance contributions of adjacent nodes. Finally, the paper applies the algorithm to a microblog-forwarding network composed of 34 users, then compares the evaluation results with traditional methods. The experiment shows that the method proposed can effectively evaluate the node importance in directed weighted networks.
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DAI, MEIFENG, and DANPING ZHANG. "A WEIGHTED EVOLVING NETWORK WITH AGING-NODE-DELETING AND LOCAL REARRANGEMENTS OF WEIGHTS." International Journal of Modern Physics C 25, no. 02 (February 2014): 1350093. http://dx.doi.org/10.1142/s0129183113500939.
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In previous study of complex network, researchers generally considered the increase of the un-weighted network by the method of adding new nodes and new links. However, most of real networks are weighted and characterized by capacities or strength instead of a binary state (present or absent), and their nodes and links experience both increase and deletion. Barrat, Barthlemy and Vespignani, Phys. Rev. Lett.92, 228701 (2004) presented an evolutionary model (BBV model) to investigate weighted networks. We present a weighted evolution network model based on BBV model, which not only considers to add a new node and m links, but also to remove an old node and corresponding links with probability at each time step. By using rate equation and mean-field method, we study the network's properties: The weight, strength and their distributions. We find that the relationship between weight and strength is nonlinear. In addition, we theoretically prove that the weight distribution and the strength distribution follow a power-law distribution, respectively.
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Wu, Huijun, Hao Wang, and Linyuan Lü. "Individual T1-weighted/T2-weighted ratio brain networks: Small-worldness, hubs and modular organization." International Journal of Modern Physics C 29, no. 05 (May 2018): 1840007. http://dx.doi.org/10.1142/s0129183118400077.
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Applying network science to investigate the complex systems has become a hot topic. In neuroscience, understanding the architectures of complex brain networks was a vital issue. An enormous amount of evidence had supported the brain was cost/efficiency trade-off with small-worldness, hubness and modular organization through the functional MRI and structural MRI investigations. However, the T1-weighted/T2-weighted (T1w/T2w) ratio brain networks were mostly unexplored. Here, we utilized a KL divergence-based method to construct large-scale individual T1w/T2w ratio brain networks and investigated the underlying topological attributes of these networks. Our results supported that the T1w/T2w ratio brain networks were comprised of small-worldness, an exponentially truncated power–law degree distribution, frontal-parietal hubs and modular organization. Besides, there were significant positive correlations between the network metrics and fluid intelligence. Thus, the T1w/T2w ratio brain networks open a new avenue to understand the human brain and are a necessary supplement for future MRI studies.
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An, Xin-lei, and Li Zhang. "A New Complex Network Model with Multiweights and Its Synchronization Control." Advances in Mathematical Physics 2020 (January 13, 2020): 1–12. http://dx.doi.org/10.1155/2020/2872795.
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Based on the weighted complex network model, this paper establishes a multiweight complex network model, which possesses several different weights on the one edge. According to the method of network split, the complex network with multiweights is split into several different complex networks with single weight. Some new static characteristics, such as node weight, node degree, node weight strength, node weight distribution, edge weight distribution, and diversity of weight distribution are defined. Then, by using Lyapunov stability theory, the adaptive feedback synchronization controller is designed, and the complete synchronization of the new complex network model is investigated. Two numerical examples of a triweight network model with the same and diverse structure are given to demonstrate the effectiveness of the control strategies. The synchronization design can achieve good results in the same and diverse structure network models with multiweights, which enrich complex network and control theory, so has certain theoretical and practical significance.
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Luo, Yutai, Baocheng Sha, and Tao Xu. "A Recommended Method Based on the Weighted RippleNet Network Mode." Journal of Physics: Conference Series 2025, no. 1 (September 1, 2021): 012011. http://dx.doi.org/10.1088/1742-6596/2025/1/012011.
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Abstract User preferences were modeled by the RippleNet network and successfully applied in the recommender systems, but the weight of the entity was not considered. This paper proposes a RippleNet model incorporating the influence of complex network nodes. After the construction of complex networks based on knowledge Graphs, we build the maximum subnet model and calculate the influence of nodes in the graph network. We added it to the RippleNet as the weight of entities. The experimental results showed that new method increased the AUC and ACC values of RippleNet to 92.0% and 84.6%, solve the problem that entity influence was not considered in the RippleNet network, and made the recommended results more in line with users’ expectations.
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Alodjants, A. P., A. Yu. Bazhenov, and M. M. Nikitina. "Phase Transitions in Quantum Complex Networks." Journal of Physics: Conference Series 2249, no. 1 (April 1, 2022): 012014. http://dx.doi.org/10.1088/1742-6596/2249/1/012014.
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Abstract In this work we examine a superradiant (SR) and/or ferromagnetic (FM) - paramagnetic (PM) phase transitions problem in quantum materials which may be established by Barabási-Albert (BA) scale-free network that possesses power law degree distribution and specific degree correlations. We represent quantum material by means of Dicke-Ising model, that describes the interaction between a spin-1/2 (two-level) system and external classical (magnetic) and quantized (transverse) fields. To describe PM-FM and SR phase transitions we introduce three order parameters: the total (topologically) weighted as well as un-weighted z-spin components, and the normalized transverse field amplitude, which correspond to the spontaneous magnetization in z- and x-directions, respectively. We have shown that SR state occurs as a result of the interaction between the ordering of the spins in the z− and x-directions and depends on assortativity or disassortativity of the network medium. We have shown that non-trivial topological behavior associated with large fluctuations of network parameters inherent to assortative networks reduces of PM-FM phase transition temperature, while dissasortative networks exhibit high temperature phase transitions. Our findings demonstrate new opportunities to design of quantum materials which may be implemented for current quantum technologies at relatively high temperatures.
Dissertations / Theses on the topic "New Weighted Complex Network"
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McAndrew, Thomas Charles. "Weighted Networks: Applications from Power grid construction to crowd control." ScholarWorks @ UVM, 2017. http://scholarworks.uvm.edu/graddis/668.
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Since their discovery in the 1950's by Erdos and Renyi, network theory (the study of objects and their associations) has blossomed into a full-fledged branch of mathematics.
Due to the network's flexibility, diverse scientific problems can be reformulated as networks and studied using a common set of tools.
I define a network G = (V,E) composed of two parts: (i) the set of objects V, called nodes, and (ii) set of relationships (associations) E, called links, that connect objects in V.
We can extend the classic network of nodes and links by describing the intensity of these associations with weights.
More formally, weighted networks augment the classic network with a function f(e) from links to the real line, uncovering powerful ways to model real-world applications.
This thesis studies new ways to construct robust micro powergrids, mine people's perceptions of causality on a social network, and proposes a new way to analyze crowdsourcing all in the context of the weighted network model.
The current state of Earth's ecosystem and intensifying climate calls on scientists to find new ways to harvest clean affordable energy.
A microgrid, or neighborhood-scale powergrid built using renewable energy sources attached to personal homes, suggest one way to ameliorate this energy crisis.
We can study the stability (robustness) of such a small-scale system with weighted networks.
A novel use of weighted networks and percolation theory guides the safe and efficient construction of power lines (links, E) connecting a small set of houses (nodes, V) to one another and weights each power line by the distance between houses.
This new look at the robustness of microgrid structures calls into question the efficacy of the traditional utility.
The next study uses the twitter social network to compare and contrast causal language from everyday conversation.
Collecting a set of 1 million tweets, we find a set of words (unigrams), parts of speech, named entities, and sentiment signal the use of informal causal language.
Breaking a problem difficult for a computer to solve into many parts and distributing these tasks to a group of humans to solve is called Crowdsourcing.
My final project asks volunteers to 'reply' to questions asked of them and 'supply' novel questions for others to answer.
I model this 'reply and supply' framework as a dynamic weighted network, proposing new theories about this network's behavior and how to steer it toward worthy goals.
This thesis demonstrates novel uses of, enhances the current scientific literature on, and presents novel methodology for, weighted networks.
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Sekgoka, Chaka Patrick. "Modeling cross-border financial flows using a network theoretic approach." Thesis, University of Pretoria, 2021. http://hdl.handle.net/2263/78773.
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Criminal networks exploit vulnerabilities in the global financial system, using it as a conduit to launder criminal proceeds. Law enforcement agencies, financial institutions, and regulatory organizations often scrutinize voluminous financial records for suspicious activities and criminal conduct as part of anti-money laundering investigations. However, such studies are narrowly focused on incidents and triggered by tip-offs rather than data mining insights.
This research models cross-border financial flows using a network theoretic approach and proposes a symmetric-key encryption algorithm to preserve information privacy in multi-dimensional data sets. The newly developed tools will enable regulatory organizations, financial institutions, and law enforcement agencies to identify suspicious activity and criminal conduct in cross-border financial transactions.
Anti-money laundering, which comprises laws, regulations, and procedures to combat money laundering, requires financial institutions to verify and identify their customers in various circumstances and monitor suspicious activity transactions. Instituting anti-money laundering laws and regulations in a country carries the benefit of creating a data-rich environment, thereby facilitating non-classical analytical strategies and tools.
Graph theory offers an elegant way of representing cross-border payments/receipts between resident and non-resident parties (nodes), with links representing the parties' transactions. The network representations provide potent data mining tools, facilitating a better understanding of transactional patterns that may constitute suspicious transactions and criminal conduct.
Using network science to analyze large and complex data sets to detect anomalies in the data set is fast becoming more important and exciting than merely learning about its structure. This research leverages advanced technology to construct and visualize the cross-border financial flows' network structure, using a directed and dual-weighted bipartite graph.
Furthermore, the develops a centrality measure for the proposed cross-border financial flows network using a method based on matrix multiplication to answer the question, "Which resident/non-resident nodes are the most important in the cross-border financial flows network?" The answer to this question provides data mining insights about the network structure.
The proposed network structure, centrality measure, and characterization using degree distributions can enable financial institutions and regulatory organizations to identify dominant nodes in complex multi-dimensional data sets. Most importantly, the results showed that the research provides transaction monitoring capabilities that allow the setting of customer segmentation criteria, complementing the built-in transaction-specific triggers methods for detecting suspicious activity transactions.Thesis (PhD)--University of Pretoria, 2021.
Banking Sector Education and Training Authority (BANKSETA)
UP Postgraduate Bursary
Industrial and Systems Engineering
PhD
Unrestricted
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Betts, Loren Cole. "A new theoretical and experimental approach to nonlinear vector network and complex signal analysis." Thesis, University of Leeds, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522925.
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Rui, Yikang. "Urban Growth Modeling Based on Land-use Changes and Road Network Expansion." Doctoral thesis, KTH, Geodesi och geoinformatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-122182.
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A city is considered as a complex system. It consists of numerous interactivesub-systems and is affected by diverse factors including governmental landpolicies, population growth, transportation infrastructure, and market behavior.Land use and transportation systems are considered as the two most importantsubsystems determining urban form and structure in the long term. Meanwhile,urban growth is one of the most important topics in urban studies, and its maindriving forces are population growth and transportation development. Modelingand simulation are believed to be powerful tools to explore the mechanisms ofurban evolution and provide planning support in growth management. The overall objective of the thesis is to analyze and model urban growth basedon the simulation of land-use changes and the modeling of road networkexpansion. Since most previous urban growth models apply fixed transportnetworks, the evolution of road networks was particularly modeled. Besides,urban growth modeling is an interdisciplinary field, so this thesis made bigefforts to integrate knowledge and methods from other scientific and technicalareas to advance geographical information science, especially the aspects ofnetwork analysis and modeling. A multi-agent system was applied to model urban growth in Toronto whenpopulation growth is considered as being the main driving factor of urbangrowth. Agents were adopted to simulate different types of interactiveindividuals who promote urban expansion. The multi-agent model with spatiotemporalallocation criterions was shown effectiveness in simulation. Then, anurban growth model for long-term simulation was developed by integratingland-use development with procedural road network modeling. The dynamicidealized traffic flow estimated by the space syntax metric was not only used forselecting major roads, but also for calculating accessibility in land-usesimulation. The model was applied in the city centre of Stockholm andconfirmed the reciprocal influence between land use and street network duringthe long-term growth. To further study network growth modeling, a novel weighted network model,involving nonlinear growth and neighboring connections, was built from theperspective of promising complex networks. Both mathematical analysis andnumerical simulation were examined in the evolution process, and the effects ofneighboring connections were particular investigated to study the preferentialattachment mechanisms in the evolution. Since road network is a weightedplanar graph, the growth model for urban street networks was subsequentlymodeled. It succeeded in reproducing diverse patterns and each pattern wasexamined by a series of measures. The similarity between the properties of derived patterns and empirical studies implies that there is a universal growthmechanism in the evolution of urban morphology. To better understand the complicated relationship between land use and roadnetwork, centrality indices from different aspects were fully analyzed in a casestudy over Stockholm. The correlation coefficients between different land-usetypes and road network centralities suggest that various centrality indices,reflecting human activities in different ways, can capture land development andconsequently influence urban structure. The strength of this thesis lies in its interdisciplinary approaches to analyze andmodel urban growth. The integration of ‘bottom-up’ land-use simulation androad network growth model in urban growth simulation is the major contribution.The road network growth model in terms of complex network science is anothercontribution to advance spatial network modeling within the field of GIScience.The works in this thesis vary from a novel theoretical weighted network modelto the particular models of land use, urban street network and hybrid urbangrowth, and to the specific applications and statistical analysis in real cases.These models help to improve our understanding of urban growth phenomenaand urban morphological evolution through long-term simulations. Thesimulation results can further support urban planning and growth management.The study of hybrid models integrating methods and techniques frommultidisciplinary fields has attracted a lot attention and still needs constantefforts in near future.QC 20130514
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Zemzami, Maria. "Variations sur PSO : approches parallèles, jeux de voisinages et applications Application d’un modèle parallèle de la méthode PSO au problème de transport d’électricité A modified Particle Swarm Optimization algorithm linking dynamic neighborhood topology to parallel computation An evolutionary hybrid algorithm for complex optimization problems Interoperability optimization using a modified PSO algorithm A comparative study of three new parallel models based on the PSO algorithm Optimization in collaborative information systems for an enhanced interoperability network." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMIR11.
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Reconnue depuis de nombreuses années comme une méthode efficace pour la résolution de problèmes difficiles, la méta-heuristique d’optimisation par essaim de particules PSO (Particle Swarm Optimization) présente toutefois des inconvénients dont les plus étudiés sont le temps de calcul élevé et la convergence prématurée. Cette thèse met en exergue quelques variantes de la méthode PSO visant à échapper à ces deux inconvénients de la méthode. Ces variantes combinent deux approches : la parallélisation de la méthode de calcul et l’organisation de voisinages appropriés pour les particules. L’évaluation de la performance des modèles proposés a été effectuée sur la base d'une expérimentation sur une série de fonctions tests. A la lumière de l’analyse des résultats expérimentaux obtenus, nous observons que les différents modèles proposés donnent des résultats meilleurs que ceux du PSO classique en termes de qualité de la solution et du temps de calcul. Un modèle basé PSO a été retenu et développé en vue d'une expérimentation sur le problème du transport d’électricité. Une variante hybride de ce modèle avec la méthode du recuit simulé SA (Simulated Annealing) a été considérée et expérimentée sur la problématique des réseaux de collaborationKnown for many years as a stochastic metaheuristic effective in the resolution of difficult optimization problems, the Particle Swarm Optimization (PSO) method, however, shows some drawbacks, the most studied: high running time and premature convergence. In this thesis we consider some variants of the PSO method to escape these two disadvantages. These variants combine two approaches: the parallelization of the calculation and the organization of appropriate neighborhoods for the particles. To prove the performance of the proposed models, we performed an experiment on a series of test functions. By analyzing the obtained experimental results, we observe that the proposed models based on the PSO algorithm performed much better than basic PSO in terms of computing time and solution quality. A model based on the PSO algorithm was selected and developed for an experiment on the problem of electricity transmission. A hybrid variant of this model with Simulated Annealing (SA) algorithm has been considered and tested on the problem of collaborative networks
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Supriya, Supriya. "Brain Signal Analysis and Classification by Developing New Complex Network Techniques." Thesis, 2020. https://vuir.vu.edu.au/40551/.
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Brain signal analysis has a crucial role in the investigation of the neuronal activity for diagnosis of brain diseases and disorders. The electroencephalogram (EEG) is the most efficient biomarker for the analysis of brain signal that assists in the diagnosis of brain disorder medication and also plays an essential role in all the neurosurgery related to the brain. EEG findings illustrate the meticulous condition, and clinical content of the brain dysfunctions, and has an undisputed importance role in the detection of epilepsy condition and sleep disorders and dysfunctions allied to alcohol. The clinicians visually study the EEG recording to determine the manifestation of abnormalities in the brain. The visual EEG assessment is tiresome, fallible, and also high-priced. In this dissertation, a number of frameworks have been developed for the analysis and classification of EEG signals by addressing three different domains named: Epilepsy, Sleep staging, and Alcohol Use Disorder.
Epilepsy is a non-contagious chronic disease of the brain that affects around 65 million people worldwide. The sudden onset tendency of the epileptic attacks vulnerable their sufferers to injuries. It is also challenging for the clinical staff to detect the epileptic-seizure activity early enough for determining the semiology associated with the seizure onset. For that reason, automated techniques that can accurately detect the epilepsy from EEG are of great importance to epileptic patients and especially to those patients who are resistive to therapies and medications. In this dissertation, four different techniques (named Weighted Visibility Network, Weighted Horizontal Visibility Network, Weighted Complex Network, and New Weighted Complex Network) have been developed for the automated identification of epileptic activity from the EEG signals. Most of the developed schemes attained 100% classification outcomes in their experimental evaluation for the identification of seizure activity from non-seizure activity.
A sleep disorder can increase the menace of seizure incidence or severity, cognitive tasks impairments, mood deviation, diminution in the functionality of the immune system and other brain anomalies such as insomnia, sleep apnoea, etc. Hence, sleep staging is essential to discriminate among distinct sleep stages for the diagnosis of sleep and its disorders. EEG provides vital and inimitable information regarding the sleeping brain. The study of EEG has documented deformities in sleep patterns. This research has developed an innovative graph- theory based framework named weighted visibility network for sleep staging from EEG signals. The developed framework in this thesis, outperforms with 97.93% overall classification accuracy for categorizing distinct sleep states
Alcoholism causes memory issues as well as motor skill defects by affecting the different portions of the brain. Excessive use of alcohol can cause sudden cardiac death and cardiomyopathy. Also, alcohol use disorder leads to respiratory infections, Vision impairment, liver damage, and cancer, etc. Research study demonstrates the use of EEG for diagnosis the patient with a high menace of developmental impediments with alcohol. In this current Ph.D. project, I developed a weighted graph-based technique that analyses EEG to distinguish between alcoholic subject and non-alcoholic person. The promising classification outcome demonstrates the effectiveness of the proposed technique.
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Bhattacharyya, Moitrayee. "Probing Ligand Induced Perturbations In Protien Structure Networks : Physico-Chemical Insights From MD Simulations And Graph Theory." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2341.
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The fidelity of biological processes and reactions, inspite of the widespread diversity, is programmed by highly specific physico-chemical principles. This underlines our basic understanding of different interesting phenomena of biological relevance, ranging from enzyme specificity to allosteric communication, from selection of fold to structural organization / states of oligomerization, from half-sites-reactivity to reshuffling of the conformational free energy landscape, encompassing the dogma of sequence-structure dynamics-function of macromolecules. The role of striking an optimal balance between rigidity and flexibility in macromolecular 3D structural organisation is yet another concept that needs attention from the functional perspective. Needless to say that the variety of protein structures and conformations naturally leads to the diversity of their function and consequently many other biological functions in general. Classical models of allostery like the ‘MWC model’ or the ‘KNF model’ and the more recently proposed ‘population shift model’ have advanced our understanding of the underlying principles of long range signal transfer in macromolecules. Extensive studies have also reported the importance of the fold selection and 3D structural organisation in the context of macromolecular function. Also ligand induced conformational changes in macromolecules, both subtle and drastic, forms the basis for controlling several biological processes in an ordered manner by re-organizing the free energy landscape.
The above mentioned biological phenomena have been observed from several different biochemical and biophysical approaches. Although these processes may often seem independent of each other and are associated with regulation of specialized functions in macromolecules, it is worthwhile to investigate if they share any commonality or interdependence at the detailed atomic level of the 3D structural organisation. So the nagging question is, do these diverse biological processes have a unifying theme, when probed at a level that takes into account even subtle re-orchestrations of the interactions and energetics at the protein/nucleic acid side-chain level. This is a complex problem to address and here we have made attempts to examine this problem using computational tools. Two methods have been extensively applied: Molecular Dynamics (MD) simulations and network theory and related parameters. Network theory has been extensively used in the past in several studies, ranging from analysis of social networks to systems level networks in biology (e.g., metabolic networks) and have also found applications in the varied fields of physics, economics, cartography and psychology. More recently, this concept has been applied to study the intricate details of the structural organisation in proteins, providing a local view of molecular interactions from a global perspective. On the other hand, MD simulations capture the dynamics of interactions and the conformational space associated with a given state (e.g., different ligand-bound states) of the macromolecule. The unison of these two methods enables the detection and investigation of the energetic and geometric re-arrangements of the 3D structural organisation of macromolecule/macromolecular complexes from a dynamical or ensemble perspective and this has been one of the thrust areas of the current study. So we not only correlate structure and functions in terms of subtle changes in interactions but also bring in conformational dynamics into the picture by studying such changes along the MD ensemble.
The focus was to identify the subtle rearrangements of interactions between non-covalently interacting partners in proteins and the interacting nucleic acids. We propose that these rearrangements in interactions between residues (amino acids in proteins, nucleic acids in RNA/DNA) form the common basis for different biological phenomena which regulates several apparently unrelated processes in biology. Broadly, the major goal of this work is to elucidate the physico-chemical principles underlying some of the important biological phenomena, such as allosteric communication, ligand induced modulation of rigidity/flexibility, half-sites-reactivity and so on, in molecular details. We have investigated several proteins, protein-RNA/DNA complexes to formulate general methodologies to address these questions from a molecular perspective. In the process we have also specifically illuminated upon the mechanistic aspects of the aminoacylation reaction by aminoacyl-tRNA synthetases like tryptophanyl and pyrrolysyl tRNA synthetase, structural details related to an enzyme catalyzed reaction that influences the process of quorum sensing in bacteria. Further, we have also examined the ‘dynamic allosterism’ that manipulates the activity of MutS, a prominent component of the DNA bp ‘mismatch repair’ machinery. Additionally, our protein structure network (PSN) based studies on a dataset of Rossmann fold containing proteins have provided insights into the structural signatures that drive the adoption of a fold from a repertoire of diverse sequences. Ligand induced percolations distant from the active sites, which may be of functional relevance have also been probed, in the context of the S1A family of serine proteases. In the course of our investigation, we have borrowed several concepts of network parameters from social network analysis and have developed new concepts.
The Introduction (Chapter-1) summarizes the relevant literature and lays down a suitable background for the subsequent chapters in the thesis. The major questions addressed and the main goal of this thesis are described to set an appropriate stage for the detailed discussions. The methodologies involved are discussed in Chapter-2. Chapter-3 deals with a protein, LuxS that is involved in the bacterial quorum sensing; the first part of the chapter describes the application of network analysis on the static structures of several LuxS proteins from different organisms and the second part of this chapter describes the application of a dynamic network approach to analyze the MD trajectories of H.pylori LuxS. Chapter-4 focuses on the investigation of human tryptophanyl-tRNA synthetase (hTrpRS), with an emphasis to identify ligand induced subtle conformational changes in terms of the alternation of rigidity/flexibility at different sites and the re-organisation of the free energy landscape. Chapter-5 presents a novel application of a quantum clustering (QC) technique, popular in the fields of pattern recognition, to objectively cluster the conformations, sampled by molecular dynamics simulations performed on different ligand bound structures of the protein. The protein structure network (PSN) in the earlier studies were constituted on the basis of geometric interactions. In Chapters 6 and 7, we describe the networks (proteins+nucleic acids) using interaction energy as edges, thus incorporating the detailed chemistry in terms of an energy-weighted complex network. Chapter-6 describes an application of the energy weighted network formalism to probe allosteric communication in D.hafniense pyrrolysyl-tRNA synthetase. The methodology developed for in-depth study of ligand induced changes in DhPylRS has been adopted to the protein MutS, the first ‘check-point protein’ for DNA base pair (bp) mismatch repair. In Chapter-7, we describe the network analysis and the biological insights derived from this study (the work is done in collaboration with Prof. David Beveridge and Dr. Susan Pieniazek). Chapter-8 describes the application of a network approach to capture the ligand-induced subtle global changes in protein structures, using a dataset of high resolution structures from the S1A family of serine proteases. Chapter-9 deals with probing the structural rationale behind diverse sequences adopting the same fold with the NAD(P)-binding Rossmann fold as a case study. Future directions are discussed in the final chapter of the thesis (Chapter-10).
Books on the topic "New Weighted Complex Network"
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Cernison, Matteo. Social Media Activism. NL Amsterdam: Amsterdam University Press, 2019. http://dx.doi.org/10.5117/9789462980068.
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This book focuses on the referendums against water privatization in Italy and explores how activists took to social media, ultimately convincing twenty-seven million citizens to vote. Investigating the relationship between social movements and internet-related activism during complex campaigns, this book examines how a technological evolution — the increased relevance of social media platforms — affected in very different ways organizations with divergent characteristics, promoting at the same time decentralized communication practices, and new ways of coordinating dispersed communities of people. Matteo Cernison combines and adapts a wide set of methods, from social network analysis to digital ethnography, in order to explore in detail how digital activism and face-to-face initiatives interact and overlap. He argues that the geographical scale of actions, the role played by external media professionals, and the activists’ perceptions of digital technologies are key elements that contribute in a significant way to shape the very different communication practices often described as online activism.
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Verloo, Nanke, and Luca Bertolini, eds. Seeing the City. NL Amsterdam: Amsterdam University Press, 2020. http://dx.doi.org/10.5117/9789463728942.
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The city is a complex object. Some researchers look at its shape, others at its people, animals, ecology, policy, infrastructures, buildings, history, art, or technical networks. Some researchers analyse processes of in- or exclusion, gentrification, or social mobility; others biological evolution, traffic flows, or spatial development. Many combine these topics or add still more topics beyond this list. Some projects cross the boundaries of research and practice and engage in action research, while others pursue knowledge for the sake of curiosity. This volume embraces this variety of perspectives and provides an essential collection of methodologies for studying the city from multiple, interdisciplinary, and transdisciplinary perspectives. We start by recognizing that the complexity of the urban environment cannot be understood from a single vantage point. We therefore offer multiple methodologies in order to gather and analyse data about the city, and provide ways to connect and integrate these approaches. The contributors form a talented network of urban scholars and practitioners at the forefront of their fields. They offer hands-on methodological techniques and skills for data collection and analysis. Furthermore, they reveal honest and insightful reflections from behind the scenes. All methodologies are illustrated with examples drawn from the authors own research applying them in the city of Amsterdam. In this way, the volume also offers a rich collection of Amsterdam-based research and outcomes that may inform local urban practitioners and policy makers. Altogether, the volume offers indispensable tools for and aims to educate a new generation of interdisciplinary and transdisciplinary-minded urban scholars and practitioners.
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Bianconi, Ginestra. Complex Systems as Multilayer Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0001.
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Chapter 1 constitutes Part I of the book: ‘Single and Multilayer Networks’. This chapter introduces multilayer networks as an important new development of Network Science that allows a more comprehensive understanding of Complex Systems. It identifies the main motivations driving the research activity in this field of multilayer networks and emphasizes the benefits of taking a multilayer network perpective to characterize network data. The main advantages of a multilayer network approach with respect to the more traditional single layer characterization of complex networks are broadly discussed, focusing on the information gain resulting from the analysis of multilayer networks, the non-reducibility of a multilayer network to a large single network and the rich interplay between structure and function in multilayer networks.
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Henry, Adam Douglas. Network Segregation and Policy Learning. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.23.
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Learning is an important concept in the study of public policy and covers a range of actions where evidence is used to shape and improve decisions, including using science to inform responses to problems; adjusting policy based on successes and failures; and forming new beliefs about salient issues, their causes, and appropriate solutions. Network concepts are central to theoretical treatments of learning. Three assumptions are often made about networks and their role in learning processes: (1) most policy networks exhibit segregation, in the sense that network ties tend to exist among actors with shared traits, such as belief systems or institutional affiliations; (2) segregated networks inhibit policy learning; and (3) network segregation is a result of homophily. This chapter reviews the rich literature underlying each of these propositions and shows that the relationships between networks and learning are more complex than often assumed.
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Bianconi, Ginestra. Structural Correlations of Multiplex Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0007.
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Multiplex networks encode relevant information in their correlated structure, including interlayer degree correlation, link overlap, weight–topology correlations in weighted multiplex networks and activity of the nodes. Interlayer degree correlations among a pair of layers indicates for instance whether or not the hub nodes of one layer are also hub nodes in the other. Link overlap indicates that a finite fraction of nodes are connected in more than one layer. Weight–topology correlations of weighted complex networks reveal that the weight of the links is not random, but often correlated with the link overlap. Finally, the nodes of a multiplex network might be connected only in a subset of the network, leading to a heterogeneity node activity. This chapter identifies the main multiplex network measures for characterizing these correlations, and evaluates their significance using statistical and information theory methods and novel multiplex network measures, including multilinks and multidegrees.
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Thomson, C. Claire. Mapping Messiness: The Informational Film Archive and Actor-Network Theory. Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474424134.003.0004.
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This chapter offers Actor-Network Theory (ANT) as a toolkit for analysing the often messy and complex networks and relationships involved in the production and distribution of useful cinema. Stressing that ANT is employed in the book as a way of thinking rather than as an explicit framework, the chapter briefly outlines the key principles of ANT and relates them to documentary and informational filmmaking. In particular, the chapter discusses the potential of ANT for rendering visible or audible the many non-human actors in any instance of filmmaking, and for revealing how facts are constructed in documentary and related genres. The institutions, individuals, networks, technologies and other actors involved in mid-twentieth-century Danish informational filmmaking are then mapped. The chapter concludes with a discussion of the role of the archive and the researcher in the network of any given film, explaining how contemporary archival practices, especially digital technologies, are creating new dispositifs for historical informational film.
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Kaup, Monika. New Ecological Realisms. Edinburgh University Press, 2021. http://dx.doi.org/10.3366/edinburgh/9781474483094.001.0001.
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What is the singular reality of humanistic objects of study? New Ecological Realism argues that our contemporary moment after the exhaustion of postmodernism presents an unprecedented opportunity to pursue this question. It proposes that the answer is found in a new concept of the real that hinges on, instead of denying, context, organization and form. New Ecological Realism showcases a context-based concept of the real, arguing that new realisms of complex and embedded wholes, actor-networks, and ecologies, rather than old realisms of isolated parts and things, represent the most promising escape from the impasses of constructivism and positivism. To achieve this, this study devotes equal attention to literature and theory. By pairing post-apocalyptic novels by Margaret Atwood, José Saramago, Octavia Butler, and Cormac McCarthy with new realist theories, this study shows that, just as new realist theories can illuminate post-apocalyptic fiction, post-apocalyptic fiction also embeds new theories of the real. Reassessing the recent revival of interest in ontology in contemporary theory, this study brings together four contemporary theories that formulate context-based realisms: Bruno Latour’s actor-network theory; Chilean neurophenomenologists Humberto Maturana and Francisco Varela’s theories of autopoiesis and enactivism; German philosopher Markus Gabriel’s new ontology of fields of sense; French philosopher Jean-Luc Marion’s phenomenology of givenness and American philosopher Alphonso Lingis’s writings on passionate identification. Their shared emphasis on interconnectedness over individuation has gone unnoticed because these theories have never been considered together before.
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Namatame, Akira, and Takanori Komatsu. Modeling of Desirable Socioeconomic Networks. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.15.
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This chapter discusses the issues of designing desirable socioeconomic networks. Such networks permeate our lives. Evidence of this has generated increasing interest in dynamic processes in complex networks, especially the interplay between processes and the influences of network structure on performance and robustness. Performance optimization and robustness are important issues of socioeconomic networks. Diffusion is the process by which new products are invented and successfully introduced into a society (good diffusion) or infectious diseases spread (bad diffusion). Many studies shed light on how network topology interacts with the structure of social networked systems such as financial institutions to determine systemwide crises. In this context, entire classes of optimization problems range from maximizing the diffusion of innovations to minimizing risk distributions and cascade failures. The structure of interconnections influences network performance.
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Beaty, Roger E., and Rex E. Jung. Interacting Brain Networks Underlying Creative Cognition and Artistic Performance. Edited by Kalina Christoff and Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.10.
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Cognitive neuroscience research has begun to address the potential interaction of brain networks supporting creativity by employing new methods in brain network science. Network methods offer a significant advance compared to individual region of interest studies due to their ability to account for the complex and dynamic interactions among discrete brain regions. As this chapter demonstrates, several recent studies have reported a remarkably similar pattern of brain network connectivity across a range of creative tasks and domains. In general, such work suggests that creative thought may involve dynamic interactions, primarily between the default and control networks, providing key insights into the roles of spontaneous and controlled processes in creative cognition. The chapter summarizes this emerging body of research and proposes a framework designed to account for the joint influence of controlled and spontaneous thought processes in creativity.
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Nolte, David D. Flight of the Swallows. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805847.003.0001.
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The science of modern dynamics takes the simple idea of Galileo’s parabolic trajectory and generalizes it into abstract hyperspaces of multiple dimensions. This chapter introduces the new way that physicists and mathematicians visualize dynamical systems, taking a global view of complex behavior and finding that the laws of physics capture the orbits of planets around suns (and the paths of light around black holes) as easily as the evolution of new species or the rise and fall of economies. This new visualization uses phase space to capture the global behavior of complex systems. The path across life, the universe and so many hyperdimensional worlds is being captured by new disciplines within new sciences like chaos theory, entanglement, network science, econophysics and evolutionary dynamics.
Book chapters on the topic "New Weighted Complex Network"
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Romero, Juan, Jorge Finke, and Andrés Salazar. "Fitness-Weighted Preferential Attachment with Varying Number of New Connections." In Complex Networks and Their Applications VIII, 612–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36687-2_51.
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Zhang, Hanyong, Qingfang Meng, Mingmin Liu, and Yang Li. "A New Epileptic Seizure Detection Method Based on Fusion Feature of Weighted Complex Network." In Advances in Neural Networks – ISNN 2018, 834–41. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92537-0_94.
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Rajeh, Stephany, Marinette Savonnet, Eric Leclercq, and Hocine Cherifi. "Modularity-Based Backbone Extraction in Weighted Complex Networks." In Network Science, 67–79. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97240-0_6.
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Zhou, Zhi, Xiaojun Zou, Xueqiang Lv, and Junfeng Hu. "Research on Weighted Complex Network Based Keywords Extraction." In Lecture Notes in Computer Science, 442–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45185-0_47.
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Davis, Michael, Weiru Liu, Paul Miller, Ruth F. Hunter, and Frank Kee. "Agwan: A Generative Model for Labelled, Weighted Graphs." In New Frontiers in Mining Complex Patterns, 181–200. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08407-7_12.
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Gao, Zhong-Ke, Ning-De Jin, and Wen-Xu Wang. "Directed Weighted Complex Network for Characterizing Gas-Liquid Slug Flow." In Nonlinear Analysis of Gas-Water/Oil-Water Two-Phase Flow in Complex Networks, 73–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38373-1_8.
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Jian, Zhou, Zhai Qun, and Tao Jianping. "A Network Security Risk Fuzzy Clustering Assessment Model Based on Weighted Complex Network." In Computing and Intelligent Systems, 143–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24010-2_20.
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Kim, Kibae, and Jörn Altmann. "A Complex Network Analysis of the Weighted Graph of the Web2.0 Service Network." In Advances in Intelligent and Soft Computing, 79–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25321-8_7.
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Ghoshal, Gourab. "Some New Applications of Network Growth Models." In Dynamics On and Of Complex Networks, 217–36. Boston, MA: Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4751-3_13.
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Miyauchi, Minami, Masatoshi Seki, Akira Watanabe, and Arata Miyauchi. "Interpretation of optical flow through complex neural network." In New Trends in Neural Computation, 645–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56798-4_215.
Full textConference papers on the topic "New Weighted Complex Network"
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Haley, Brandon M., Andy Dong, and Irem Y. Tumer. "Creating Faultable Network Models of Complex Engineered Systems." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34407.
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This paper presents a new methodology for modeling complex engineered systems using complex networks for failure analysis. Many existing network-based modeling approaches for complex engineered systems “abstract away” the functional details to focus on the topological configuration of the system and thus do not provide adequate insight into system behavior. To model failures more adequately, we present two types of network representations of a complex engineered system: a uni-partite architectural network and a weighted bi-partite behavioral network. Whereas the architectural network describes physical inter-connectivity, the behavioral network represents the interaction between functions and variables in mathematical models of the system and its constituent components. The levels of abstraction for nodes in both network types affords the evaluation of failures involving morphology or behavior, respectively. The approach is shown with respect to a drivetrain model. Architectural and behavioral networks are compared with respect to the types of faults that can be described. We conclude with considerations that should be employed when modeling complex engineered systems as networks for the purpose of failure analysis.
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Wang, Zhongsheng, and Yan Jun Liang. "A New Scheme of Synchronization-Based Topology Identification for a Class of Weighted General Complex Dynamical Networks with Time-Varying Coupling Delay." In 2009 Fifth International Conference on Natural Computation. IEEE, 2009. http://dx.doi.org/10.1109/icnc.2009.75.
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Zhang, Jianpeng, Yutong Xie, Pingping Zhang, Hao Chen, Yong Xia, and Chunhua Shen. "Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/593.
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Automated segmentation of liver tumors in contrast-enhanced abdominal computed tomography (CT) scans is essential in assisting medical professionals to evaluate tumor development and make fast therapeutic schedule. Although deep convolutional neural networks (DCNNs) have contributed many breakthroughs in image segmentation, this task remains challenging, since 2D DCNNs are incapable of exploring the inter-slice information and 3D DCNNs are too complex to be trained with the available small dataset. In this paper, we propose the light-weight hybrid convolutional network (LW-HCN) to segment the liver and its tumors in CT volumes. Instead of combining a 2D and a 3D networks for coarse-to-fine segmentation, LW-HCN has a encoder-decoder structure, in which 2D convolutions used at the bottom of the encoder decreases the complexity and 3D convolutions used in other layers explore both spatial and temporal information. To further reduce the complexity, we design the depthwise and spatiotemporal separate (DSTS) factorization for 3D convolutions, which not only reduces parameters dramatically but also improves the performance. We evaluated the proposed LW-HCN model against several recent methods on the LiTS and 3D-IRCADb datasets and achieved, respectively, the Dice per case of 73.0% and 94.1% for tumor segmentation, setting a new state of the art.
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Liu, Dehao, Pranav Pusarla, and Yan Wang. "Multi-Fidelity Physics-Constrained Neural Networks With Minimax Architecture for Materials Modeling." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-91219.
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Abstract Data sparsity is still the main challenge to apply machine learning models to solve complex scientific and engineering problems. The root cause is the “curse of dimensionality” in training these models. Training algorithms need to explore and exploit in a very high dimensional parameter space to search the optimal parameters for complex models. In this work, a new scheme of multi-fidelity physics-constrained neural networks with minimax architecture is proposed to improve the data efficiency of training neural networks by incorporating physical knowledge as constraints and sampling data with various fidelities. In this new framework, fully-connected neural networks with two levels of fidelities are combined to improve the prediction accuracy. The low-fidelity neural network is used to approximate the low-fidelity data, whereas the high-fidelity neural network is adopted to approximate the correlation function between the low-fidelity and high-fidelity data. To systematically search the optimal weights of various losses for reducing the training time, the Dual-Dimer algorithm is adopted to search high-order saddle points of the minimax optimization problem. The proposed framework is demonstrated with two-dimensional heat transfer, phase transition, and dendritic growth problems, which are fundamental in materials modeling. With the same set of training data, the prediction error of the multi-fidelity physics-constrained neural network with minimax architecture can be two orders of magnitude lower than that of the multi-fidelity neural network with minimax architecture.
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Li, Jianmin, and Krishna C. Gupta. "Mathematical Programming Neural Networks (MPNN) for Mechanism Design." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/dac-3755.
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Abstract The prevalent Mathematical Programming Neural Network (MPNN) models are surveyed, and MPNN models have been developed and applied to the unconstrained optimization of mechanisms. Algorithms which require Hessian inversion and those which build up a variable approach matrix, are investigated. Based upon a comprehensive investigation of the Augmented Lagrange Multiplier (ALM) method, new algorithms have been developed from the combination of ideas from MPNN and ALM methods and applied to the constrained optimization of mechanisms. A relationship between the weighted least square minimization of design equation error residuals and the mini-max norm of the structure error for function generating mechanisms is developed and employed in the optimization process; as a result, the computational difficulties arising from the direct usage of the complex structural error function have been avoided. The paper presents relevant theory as well as some numerical experience for four MPNN algorithms.
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Zeng, Ming, Wenkang Xu, Chunyu Zhao, Qi Li, and Jingjing Han. "Weighted Complex Network Based on Visibility Angle Measurement." In 2020 39th Chinese Control Conference (CCC). IEEE, 2020. http://dx.doi.org/10.23919/ccc50068.2020.9189168.
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Liang, Yin. "Chinese keyword extraction based on weighted complex network." In 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2017. http://dx.doi.org/10.1109/iske.2017.8258737.
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Hui-Jia Li, Chi Zhang, and Xiang-Sun Zhang. "A study of inflammation immunization strategy in weighted complex network." In 11th International Symposium on Operations Research and its Applications in Engineering, Technology and Management 2013 (ISORA 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.2281.
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Yang, Ting, Dinghua Zhang, Bing Chen, and Shan Li. "Analysis of Mixed Production Line Based on Complex Weighted Network." In 2010 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2010. http://dx.doi.org/10.1109/icicta.2010.463.
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Garci´a, Jaime, Jose´ Posada, Pedro Villalba, and Marco Sanjuan. "Soft Sensor Design for Biodiesel Concentration in a Transesterification Reactor." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-68697.
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Biofuels production is facing new challenges every day, related to better process control and quality monitoring. It is very important for the sustainability of these processes to implement strategies and alternatives in order to achieve a continuous production process and to control significant variables involved in the reaction. One of the most difficult variables to measure is the actual Biodiesel concentration inside the reactor. Neural networks have become a useful strategy to give solutions to complex problems; its application is growing faster at industries due to the inherent nonlinear behavior of the processes, modeled easily by this computational tool. The capacity of mapping a complex behavior trough input and output process data, without a complicated and hardly to obtain mathematical model, makes neural networks an attractive strategy to be implemented in most industries, in a soft sensor or a process model scheme. This investigation addresses the need to predict the concentrations of esters (biodiesel) when different triglycerides are reacting with alcohol. Concentration was estimated using an approach that uses a soft sensor that captures the dynamics of these variables through off line laboratory experiments. The soft sensor is actually a Random Activation Weight Neural Net (RAWN), which is a back propagation neural network with a fast training algorithm that does not need any iteration. Also, to reduce the complexity of the soft sensor an optimization procedure was carried out to determine the optimum number of neurons in the hidden layer. In this research Biodiesel was produced by transesterification of palm oil with ethanol and KOH as catalyst. During transesterification reaction the estimation of concentrations is determined by laboratory analysis at off line stages, these variables are very important to control the continuous process of a biodiesel plant.
Reports on the topic "New Weighted Complex Network"
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Patel, Reena. Complex network analysis for early detection of failure mechanisms in resilient bio-structures. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41042.
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Bio-structures owe their remarkable mechanical properties to their hierarchical geometrical arrangement as well as heterogeneous material properties. This dissertation presents an integrated, interdisciplinary approach that employs computational mechanics combined with flow network analysis to gain fundamental insights into the failure mechanisms of high performance, light-weight, structured composites by examining the stress flow patterns formed in the nascent stages of loading for the rostrum of the paddlefish. The data required for the flow network analysis was generated from the finite element analysis of the rostrum. The flow network was weighted based on the parameter of interest, which is stress in the current study. The changing kinematics of the structural system was provided as input to the algorithm that computes the minimum-cut of the flow network. The proposed approach was verified using two classical problems three- and four-point bending of a simply-supported concrete beam. The current study also addresses the methodology used to prepare data in an appropriate format for a seamless transition from finite element binary database files to the abstract mathematical domain needed for the network flow analysis. A robust, platform-independent procedure was developed that efficiently handles the large datasets produced by the finite element simulations. Results from computational mechanics using Abaqus and complex network analysis are presented.
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Nechaev, V., Володимир Миколайович Соловйов, and A. Nagibas. Complex economic systems structural organization modelling. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1118.
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One of the well-known results of the theory of management is the fact, that multi-stage hierarchical organization of management is unstable. Hence, the ideas expressed in a number of works by Don Tapscott on advantages of network organization of businesses over vertically integrated ones is clear. While studying the basic tendencies of business organization in the conditions of globalization, computerization and internetization of the society and the results of the financial activities of the well-known companies, the authors arrive at the conclusion, that such companies, as IBM, Boeing, Mercedes-Benz and some others companies have not been engaged in their traditional business for a long time. Their partner networks performs this function instead of them. The companies themselves perform the function of system integrators. The Tapscott’s idea finds its confirmation within the framework of a new powerful direction of the development of the modern interdisciplinary science – the theory of the complex networks (CN) [2]. CN-s are multifractal objects, the loss of multifractality being the indicator of the system transition from more complex state into more simple state. We tested the multifractal properties of the data using the wavelet transform modulus maxima approach in order to analyze scaling properties of our company. Comparative analysis of the singularity spectrumf(®), namely, the difference between maximum and minimum values of ® (∆ = ®max ¡ ®min) shows that IBM company is considerably more fractal in comparison with Apple Computer. Really, for it the value of ∆ is equal to 0.3, while for the vertically integrated company Apple it only makes 0.06 – 5 times less. The comparison of other companies shows that this dependence is of general character. Taking into consideration the fact that network organization of business has become dominant in the last 5-10 years, we carried out research for the selected companies in the earliest possible period of time which was determined by the availability of data in the Internet, or by historically later beginning of stock trade of computer companies. A singularity spectrum of the first group of companies turned out to be considerably narrower, or shifted toward the smaller values of ® in the pre-network period. The latter means that dynamic series were antipersistant. That is, these companies‘ management was rigidly controlled while the impact of market mechanisms was minimized. In the second group of companies if even the situation did changed it did not change for the better. In addition, we discuss applications to the construction of portfolios of stock that have a stable ratio of risk to return.
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Hovakimyan, Naira, Hunmin Kim, Wenbin Wan, and Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-016.
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Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on the one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging because AVs operate in dynamic environments with unforeseen challenges. This project aimed to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatiotemporal data fusion, machine learning, and robust adaptive control. By leveraging data shared over a cloud network available to all entities, vehicles proactively adapted to new environments on the proactive level, thus coping with large-scale environmental changes. On the reactive level, control-barrier-function-based robust adaptive control with machine learning improved the performance around nominal models, providing performance and control certificates. The proposed research shaped a robust foundation for autonomous driving on cloud-connected highways of the future.
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Соловйов, В. М., and О. С. Лук’янчук. Фолксономія соціально-економічних об’єктів в складних мережах засобами CorrRank. Брама-Україна, 2014. http://dx.doi.org/10.31812/0564/1307.
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The financial and economic crisis 2007-2009 shown that economic institutions are closely linked and the behavior of complex systems is difficult predictable. There is an urgent need to develop new quantitative methods that adequately describe the dynamic changes in complex systems during normal conditions and during the crisis. There is a need for methods that describe the topology of the interaction between economic institutions, using the tools developed in the theory of networks. The paper used a method of investigation of nonlinear dynamics, as the random matrices theory, which when combined with network methods are adequate means for the study of complex systems. The given technique we have implemented the study in the real time series of global stock markets.
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Chen, Junping, Zach Adam, and Arie Admon. The Role of FtsH11 Protease in Chloroplast Biogenesis and Maintenance at Elevated Temperatures in Model and Crop Plants. United States Department of Agriculture, May 2013. http://dx.doi.org/10.32747/2013.7699845.bard.
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specific objectives of this proposal were to: 1) determine the location, topology, and oligomerization of FtsH11 protease; 2) identify the substrate/s of FtsH11 and the downstream components involved in maintaining thermostability of chloroplasts; 3) identify new elements involved in FtsH11 protease regulatory network related to HT adaptation processes in chloroplast; 4) Study the role of FtsH11 homologs from crop species in HT tolerance. Background to the topic: HT-tolerant varieties that maintain high photosynthetic efficiency at HT, and cope better with daily and seasonal temperature fluctuations are in great need to alleviate the effect of global warming on food production. Photosynthesis is a very complex process requiring accurate coordination of many complex systems and constant adjustments to the changing environments. Proteolytic activities mediated by various proteases in chloroplast are essential part of this process and critical for maintaining normal chloroplast functions under HT. However, little is known about mechanisms that contribute to adaptation of photosynthetic processes to HT. Our study has shown that a chloroplast-targeted Arabidopsis FtsH11 protease plays an essential and specific role in maintaining thermostability of thylakoids and normal photosynthesis at moderate HT. We hypothesized that FtsH11 homologs recently identified in other plant species might have roles similarly to that of AtFtsH1. Thus, dissecting the underlying mechanisms of FtsH11 in the adaptation mechanisms in chloroplasts to HT stress and other elements involved will aid our effort to produce more agricultural products in less favorable environments. Major conclusions, solutions, achievements - Identified the chloroplast inner envelope membrane localization of FtsH11. - Revealed a specific association of FtsH11 with the a and b subunits of CPN60. - Identified the involvement of ARC6, a protein coordinates chloroplast division machineries in plants, in FtsH11 mediated HT adaptation process in chloroplast. -Reveal possible association of a polyribonucleotide nucleotidyltransferase (cpPNPase), coded by At3G03710, with FtsH11 mediated HT adaptation process in chloroplast. - Mapped 4 additional loci in FtsH11 mediated HT adaptation network in chloroplast. - Demonstrated importance of the proteolytic activity of FtsH11 for thermotolerance, in addition to the ATPase activity. - Demonstrated a conserved role of plant FtsH11 proteases in chloroplast biogenesis and in maintaining structural and functional thermostability of chloroplast at elevated temperatures. Implications, both scientific and agricultural:Three different components interacting with FtsH11 were identified during the course of this study. At present, it is not known whether these proteins are directly involved in FtsH11mediated thermotolerance network in chloroplast and/or how these elements are interrelated. Studies aiming to connect the dot among biological functions of these networks are underway in both labs. Nevertheless, in bacteria where it was first studied, FtsH functions in heat shock response by regulating transcription level of σ32, a heat chock factor regulates HSPsexpression. FtsH also involves in control of biosynthesis of membrane components and quality control of membrane proteins etc. In plants, both Arc 6 and CPN60 identified in this study are essential in chloroplast division and developments as mutation of either one impairs chloroplast division in Arabidopsis. The facts that we have found the specific association of both α and β CPN60 with FtsH11 protein biochemically, the suppression/ enhancement of ftsh11 thermosensitive phenotype by arc6 /pnp allele genetically, implicate inter-connection of these networks via FtsH11 mediated network(s) in regulating the dynamic adaptation processes of chloroplast to temperature increases at transcriptional, translational and post-translational levels. The conserved role of FtsH11 proteases in maintaining thermostability of chloroplast at HT demonstrated here provides a foundation for improving crop photosynthetic performance at high temperatures.
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Davies, Will. Improving the engagement of UK armed forces overseas. Royal Institute of International Affairs, January 2022. http://dx.doi.org/10.55317/9781784135010.
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The UK government’s Integrated Review of security, defence, development and foreign policy, published in March 2021 alongside a supporting defence command paper, set a new course for UK national security and highlighted opportunities for an innovative approach to international engagement activity. The Integrated Review focused principally on the state threats posed by China’s increasing power and by competitors – including Russia – armed with nuclear, conventional and hybrid capabilities. It also stressed the continuing risks to global security and resilience due to conflict and instability in weakened and failed states. These threats have the potential to increase poverty and inequality, violent extremism, climate degradation and the forced displacement of people, while presenting authoritarian competitors with opportunities to enhance their geopolitical influence. There are moral, security and economic motives to foster durable peace in conflict-prone and weakened regions through a peacebuilding approach that promotes good governance, addresses the root causes of conflict and prevents violence, while denying opportunities to state competitors. The recent withdrawal from Afghanistan serves to emphasize the complexities and potential pitfalls associated with intervention operations in complex, unstable regions. Success in the future will require the full, sustained and coordinated integration of national, allied and regional levers of power underpinned by a sophisticated understanding of the operating environment. The UK armed forces, with their considerable resources and global network, will contribute to this effort through ‘persistent engagement’. This is a new approach to overseas operations below the threshold of conflict, designed as a pre-emptive complement to warfighting. To achieve this, the UK Ministry of Defence (MOD) must develop a capability that can operate effectively in weak, unstable and complex regions prone to violent conflict and crises, not least in the regions on the eastern and southern flanks of the Euro-Atlantic area. The first step must be the development of a cohort of military personnel with enhanced, tailored levels of knowledge, skills and experience. Engagement roles must be filled by operators with specialist knowledge, skills and experience forged beyond the mainstream discipline of combat and warfighting. Only then will individuals develop a genuinely sophisticated understanding of complex, politically driven and sensitive operating environments and be able to infuse the design and delivery of international activities with practical wisdom and insight. Engagement personnel need to be equipped with: An inherent understanding of the human and political dimensions of conflict, the underlying drivers such as inequality and scarcity, and the exacerbating factors such as climate change and migration; - A grounding in social sciences and conflict modelling in order to understand complex human terrain; - Regional expertise enabled by language skills, cultural intelligence and human networks; - Familiarity with a diverse range of partners, allies and local actors and their approaches; - Expertise in building partner capacity and applying defence capabilities to deliver stability and peace; - A grasp of emerging artificial intelligence technology as a tool to understand human terrain; - Reach and insight developed through ‘knowledge networks’ of external experts in academia, think-tanks and NGOs. Successful change will be dependent on strong and overt advocacy by the MOD’s senior leadership and a revised set of personnel policies and procedures for this cohort’s selection, education, training, career management, incentivization, sustainability and support.
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Raychev, Nikolay. Can human thoughts be encoded, decoded and manipulated to achieve symbiosis of the brain and the machine. Web of Open Science, October 2020. http://dx.doi.org/10.37686/nsrl.v1i2.76.
Full textAbstract:
This article discusses the current state of neurointerface technologies, not limited to deep electrode approaches. There are new heuristic ideas for creating a fast and broadband channel from the brain to artificial intelligence. One of the ideas is not to decipher the natural codes of nerve cells, but to create conditions for the development of a new language for communication between the human brain and artificial intelligence tools. Theoretically, this is possible if the brain "feels" that by changing the activity of nerve cells that communicate with the computer, it is possible to "achieve" the necessary actions for the body in the external environment, for example, to take a cup of coffee or turn on your favorite music. At the same time, an artificial neural network that analyzes the flow of nerve impulses must also be directed at the brain, trying to guess the body's needs at the moment with a minimum number of movements. The most important obstacle to further progress is the problem of biocompatibility, which has not yet been resolved. This is even more important than the number of electrodes and the power of the processors on the chip. When you insert a foreign object into your brain, it tries to isolate itself from it. This is a multidisciplinary topic not only for doctors and psychophysiologists, but also for engineers, programmers, mathematicians. Of course, the problem is complex and it will be possible to overcome it only with joint efforts.
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Ivanova, Iryna, and Elena Afanasieva. MODEL OF INTERACTION BETWEEN ADVERTISING, PR AND JOURNALISM. Ivan Franko National University of Lviv, February 2021. http://dx.doi.org/10.30970/vjo.2021.49.11060.
Full textAbstract:
The article is an overview of the journalism – PR – advertising relationship at the terminological, empirical-analytical and practical levels. It traces the state of the discussion of these correlations in the post-soviet media such as Ukraine. The study describes that domesticating the importance of the appropriate partnership between the three communication technologies. The thesis is that journalism, advertising and PR create a mutual connection that takes place in an atmosphere of PR and advertising permissiveness and deepens with the development of digitalization, Social network development. The present research is based on a comprehensive approach. The inductive and deductive methods are adopted to discuss theoretical materials, and the interdisciplinary research method is used to detect PR-specific features as a philosophy of a new journalism project. The interpretive approach, usually employed to analyze media text as a complex synthetic structure, was also taken into consideration. The analytical method application identified the modern means of substantiating the ideological, esthetical and informative value of brand journalism and spin doctor. The innovative character of modern media as a behavioral strategy in the advertising and PR industry consists in the fact that it is a form of creative production and behavior rather than adapting a specific communication situation. The article examines the main directions of contemporary interactions between PR, advertising and journalism as a media content creation. In this context, it is asserted that advertising, journalism and PR activities can contribute to the creation of media content. At some point, good media content is achieved not only as a result of this competition but also from the correlation between PR, advertising and journalism.
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Eshed-Williams, Leor, and Daniel Zilberman. Genetic and cellular networks regulating cell fate at the shoot apical meristem. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7699862.bard.
Full textAbstract:
The shoot apical meristem establishes plant architecture by continuously producing new lateral organs such as leaves, axillary meristems and flowers throughout the plant life cycle. This unique capacity is achieved by a group of self-renewing pluripotent stem cells that give rise to founder cells, which can differentiate into multiple cell and tissue types in response to environmental and developmental cues. Cell fate specification at the shoot apical meristem is programmed primarily by transcription factors acting in a complex gene regulatory network. In this project we proposed to provide significant understanding of meristem maintenance and cell fate specification by studying four transcription factors acting at the meristem. Our original aim was to identify the direct target genes of WUS, STM, KNAT6 and CNA transcription factor in a genome wide scale and the manner by which they regulate their targets. Our goal was to integrate this data into a regulatory model of cell fate specification in the SAM and to identify key genes within the model for further study. We have generated transgenic plants carrying the four TF with two different tags and preformed chromatin Immunoprecipitation (ChIP) assay to identify the TF direct target genes. Due to unforeseen obstacles we have been delayed in achieving this aim but hope to accomplish it soon. Using the GR inducible system, genetic approach and transcriptome analysis [mRNA-seq] we provided a new look at meristem activity and its regulation of morphogenesis and phyllotaxy and propose a coherent framework for the role of many factors acting in meristem development and maintenance. We provided evidence for 3 different mechanisms for the regulation of WUS expression, DNA methylation, a second receptor pathway - the ERECTA receptor and the CNA TF that negatively regulates WUS expression in its own domain, the Organizing Center. We found that once the WUS expression level surpasses a certain threshold it alters cell identity at the periphery of the inflorescence meristem from floral meristem to carpel fate [FM]. When WUS expression highly elevated in the FM, the meristem turn into indeterminate. We showed that WUS activate cytokinine, inhibit auxin response and represses the genes required for root identity fate and that gradual increase in WUCHEL activity leads to gradual meristem enlargement that affect phyllotaxis. We also propose a model in which the direction of WUS domain expansion laterally or upward affects meristem structure differently. We preformed mRNA-seq on meristems with different size and structure followed by k-means clustering and identified groups of genes that are expressed in specific domains at the meristem. We will integrate this data with the ChIP-seq of the 4 TF to add another layer to the genetic network regulating meristem activity.
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Ori, Naomi, and Mark Estelle. Role of GOBLET and Auxin in Controlling Organ Development and Patterning. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7697122.bard.
Full textAbstract:
The size and shape of plant leaves are extremely diverse within and among species, and are also sensitive to growth conditions. Compound leaves, such as those of tomato, maintain morphogenetic activity during early stages of their development, enabling them to elaborate lateral appendages such as leaflets. The aim of the research project was to understand the interaction between the plant hormone auxin, the putative auxin response inhibitor ENTIRE (E, SlIAA9) and the NAM/CUC transcription factor GOBLET (GOB) in compound-leaf development in tomato (Solanum lycopersicum). The specific aims of the project were: 1. Investigation of the role of GOB in compound-leaf development. 2. Characterization of E function in auxin signaling. 3. Characterization of the role of auxin in compound-leaf development. 4. Investigation of the genetic and molecular interaction between E and GOB. 5. Investigate the role of these factors in fruit development. There were no major changes in these objectives. GOB was shown to mark and promote the boundaries between the leaf and initiating leaflets. Its accurate distribution was found to be required for proper leaflet initiation and separation. E was found to interact with the TIR1 and AFB6 proteins in an auxin-dependant manner, indicating that these are functional auxin receptors that mediate E degradation in the presence of auxin. This was further supported by the stabilization of E by a mutation in domain II of the protein, which is thought to mediate its auxin-dependant degradation. Over expression of this stabilized form in tomato leaves and characterization of the e mutant phenotype and the E expression domain indicated that E acts between initiating leaflets to inhibit auxin response and lamina growth. Generation and analysis of tomato plants expressing the auxin response reporter DR5::VENUS, and analysis of the effect of auxin microapplication or overexpression of an auxin biosynthesis gene, indicated that auxin marks the sites of leaflet initiation and promotes lamina growth. Investigation of the molecular and genetic interaction between auxin, GOB and E revealed a complex network of mutual regulation that is utilized to precisely pattern the leaf margin in a manner that enables the combination of tight control and flexibility. E, auxin and GOB were shown to affect fruit development and fruit set, and in an extension of the project are currently utilized to identify new players that affect these processes. The research project yielded enhanced understanding of the mechanisms of compound leaf patterning and provided tools that will enable the manipulation of leaf shape and fruit set.