Academic literature on the topic 'Network behavior mechanisms'
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Journal articles on the topic "Network behavior mechanisms"
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El Manira, Abdeljabbar, and Peter Wallén. "Mechanisms of Modulation of a Neural Network." Physiology 15, no. 4 (August 2000): 186–91. http://dx.doi.org/10.1152/physiologyonline.2000.15.4.186.
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Neural networks form the basis for the generation and control of various patterns of behavior. Such networks are subjected to modulatory systems that influence their operation and, thereby, the behavior. In the lamprey locomotor network, analysis on the ion channel, synaptic, and cellular levels has given new insights into the organization of such modulatory systems.
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Beranek, Ladislav, and Radim Remes. "Distribution of Node Characteristics in Evolving Tripartite Network." Entropy 22, no. 3 (February 25, 2020): 263. http://dx.doi.org/10.3390/e22030263.
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Many real-world networks have a natural tripartite structure. Investigating the structure and the behavior of actors in these networks is useful to gain a deeper understanding of their behavior and dynamics. In our paper, we describe an evolving tripartite network using a network model with preferential growth mechanisms and different rules for changing the strength of nodes and the weights of edges. We analyze the characteristics of the strength distribution and behavior of selected nodes and selected actors in this tripartite network. The distributions of these analyzed characteristics follow the power-law under different modeled conditions. Performed simulations have confirmed all these results. Despite its simplicity, the model expresses well the basic properties of the modeled network. It can provide further insights into the behavior of systems with more complex behaviors, such as the multi-actor e-commerce system that we have used as a real basis for the validation of our model.
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BADHAM, JENNIFER, FRANK KEE, and RUTH F. HUNTER. "Simulating network intervention strategies: Implications for adoption of behaviour." Network Science 6, no. 2 (May 16, 2018): 265–80. http://dx.doi.org/10.1017/nws.2018.4.
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AbstractThis study uses simulation over real and artificial networks to compare the eventual adoption outcomes of network interventions, operationalized as idealized contagion processes with different sets of seeds. While the performance depends on the details of both the network and behaviour adoption mechanisms, interventions with seeds that are central to the network are more effective than random selection in the majority of simulations, with faster or more complete adoption throughout the network. These results provide additional theoretical justification for utilizing relevant network information in the design of public health behavior interventions.
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Jayashree, Devasagayam, V. Uma Rani, and K. Soma Sundaram. "Trust Based Misbehavior Detection in Wireless Sensor Networks." Applied Mechanics and Materials 622 (August 2014): 191–98. http://dx.doi.org/10.4028/www.scientific.net/amm.622.191.
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Due to emerging technology Wireless Sensor Network (WSN), it is necessary to monitor the behavior of sensor nodes and establish the secure communication in network. Security is a challenging task in wireless environment. Several encryption mechanisms are available to prevent outsider attacks, but no mechanism available for insider attacks. A trust model is a collection of rules used to establish co-operation or collaboration among nodes as well as monitoring misbehavior of wireless sensor networks. Trust model is necessary to enhance secure localization, communication or routing, aggregation, collaboration among nodes. In this paper, proposed a behavior based distributed trust model for wireless sensor network to effectively deal with self-ish or malicious nodes. Here, take multidimensional trust attributes derived from communications and networks to evaluate the overall trust of sensor nodes. It monitors the behavior of nodes and establishes secure communication among networks.
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Papan, Jozef, Pavel Segec, Oleksandra Yeremenko, Ivana Bridova, and Michal Hodon. "Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure." Sensors 20, no. 12 (June 17, 2020): 3428. http://dx.doi.org/10.3390/s20123428.
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The sprawling nature of Internet of Things (IoT) sensors require the comprehensive management and reliability of the entire network. Modern Internet Protocol (IP) networks demand specific qualitative and quantitative parameters that need to be met. One of these requirements is the minimal packet loss in the network. After a node or link failure within the network, the process of network convergence will begin. This process may take an unpredictable time, mostly depending on the size and the structure of the affected network segment and the routing protocol used within the network. The categories of proposed solutions for these problems are known as Fast ReRoute (FRR) mechanisms. The majority of current Fast ReRoute mechanisms use precomputation of alternative backup paths in advance. This paper presents an Enhanced Multicast Repair (EM-REP) FRR mechanism that uses multicast technology to create an alternate backup path and does not require pre-calculation. This principle creates a unique reactive behavior in the Fast ReRoute area. The enhanced M-REP FRR mechanism can find an alternative path in the event of multiple links or nodes failing at different times and places in the network. This unique behavior can be applied in the IoT sensors area, especially in network architecture that guarantees reliability of data transfer.
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Trojanowski, Nicholas F., Olivia Padovan-Merhar, David M. Raizen, and Christopher Fang-Yen. "Neural and genetic degeneracy underlies Caenorhabditis elegans feeding behavior." Journal of Neurophysiology 112, no. 4 (August 15, 2014): 951–61. http://dx.doi.org/10.1152/jn.00150.2014.
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Degenerate networks, in which structurally distinct elements can perform the same function or yield the same output, are ubiquitous in biology. Degeneracy contributes to the robustness and adaptability of networks in varied environmental and evolutionary contexts. However, how degenerate neural networks regulate behavior in vivo is poorly understood, especially at the genetic level. Here, we identify degenerate neural and genetic mechanisms that underlie excitation of the pharynx (feeding organ) in the nematode Caenorhabditis elegans using cell-specific optogenetic excitation and inhibition. We show that the pharyngeal neurons MC, M2, M4, and I1 form multiple direct and indirect excitatory pathways in a robust network for control of pharyngeal pumping. I1 excites pumping via MC and M2 in a state-dependent manner. We identify nicotinic and muscarinic receptors through which the pharyngeal network regulates feeding rate. These results identify two different mechanisms by which degeneracy is manifest in a neural circuit in vivo.
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Jones, Jeff. "Characteristics of Pattern Formation and Evolution in Approximations of Physarum Transport Networks." Artificial Life 16, no. 2 (April 2010): 127–53. http://dx.doi.org/10.1162/artl.2010.16.2.16202.
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Most studies of pattern formation place particular emphasis on its role in the development of complex multicellular body plans. In simpler organisms, however, pattern formation is intrinsic to growth and behavior. Inspired by one such organism, the true slime mold Physarum polycephalum, we present examples of complex emergent pattern formation and evolution formed by a population of simple particle-like agents. Using simple local behaviors based on chemotaxis, the mobile agent population spontaneously forms complex and dynamic transport networks. By adjusting simple model parameters, maps of characteristic patterning are obtained. Certain areas of the parameter mapping yield particularly complex long term behaviors, including the circular contraction of network lacunae and bifurcation of network paths to maintain network connectivity. We demonstrate the formation of irregular spots and labyrinthine and reticulated patterns by chemoattraction. Other Turing-like patterning schemes were obtained by using chemorepulsion behaviors, including the self-organization of regular periodic arrays of spots, and striped patterns. We show that complex pattern types can be produced without resorting to the hierarchical coupling of reaction-diffusion mechanisms. We also present network behaviors arising from simple pre-patterning cues, giving simple examples of how the emergent pattern formation processes evolve into networks with functional and quasi-physical properties including tensionlike effects, network minimization behavior, and repair to network damage. The results are interpreted in relation to classical theories of biological pattern formation in natural systems, and we suggest mechanisms by which emergent pattern formation processes may be used as a method for spatially represented unconventional computation.
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Palenciano, Ana F., Carlos González-García, Juan E. Arco, and María Ruz. "Transient and Sustained Control Mechanisms Supporting Novel Instructed Behavior." Cerebral Cortex 29, no. 9 (October 25, 2018): 3948–60. http://dx.doi.org/10.1093/cercor/bhy273.
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Abstract The success of humans in novel environments is partially supported by our ability to implement new task procedures via instructions. This complex skill has been associated with the activity of control-related brain areas. Current models link fronto-parietal and cingulo-opercular networks with transient and sustained modes of cognitive control, based on observations during repetitive task settings or rest. The current study extends this dual model to novel instructed tasks. We employed a mixed design and an instruction-following task to extract phasic and tonic brain signals associated with the encoding and implementation of novel verbal rules. We also performed a representation similarity analysis to capture consistency in task-set encoding within trial epochs. Our findings show that both networks are involved while following novel instructions: transiently, during the implementation of the instruction, and in a sustained fashion, across novel trials blocks. Moreover, the multivariate results showed that task representations in the cingulo-opercular network were more stable than in the fronto-parietal one. Our data extend the dual model of cognitive control to novel demanding situations, highlighting the high flexibility of control-related regions in adopting different temporal profiles.
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Bechtel, William. "Resituating cognitive mechanisms within heterarchical networks controlling physiology and behavior." Theory & Psychology 29, no. 5 (October 2019): 620–39. http://dx.doi.org/10.1177/0959354319873725.
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Cognitive science has traditionally focused on mechanisms involved in high-level reasoning and problem-solving processes. Such mechanisms are often treated as autonomous from but controlling underlying physiological processes. I offer a different perspective on cognition which starts with the basic production mechanisms through which organisms construct and repair themselves and navigate their environments and then I develop a framework for conceptualizing how cognitive control mechanisms form a heterarchical network that regulates production mechanisms. Many of these control mechanisms perform cognitive tasks such as evaluating circumstances and making decisions. Cognitive control mechanisms are present in individual cells, but in metazoans, intracellular control is supplemented by a nervous system in which a multitude of neural control mechanisms are organized heterarchically. On this perspective, high-level cognitive mechanisms are not autonomous, but are elements in larger heterarchical networks. This has implications for future directions in cognitive science research.
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Wiedemann, Urs Achim, and Anita Lüthi. "Timing of Network Synchronization By Refractory Mechanisms." Journal of Neurophysiology 90, no. 6 (December 2003): 3902–11. http://dx.doi.org/10.1152/jn.00284.2003.
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Even without active pacemaker mechanisms, temporally patterned synchronization of neural network activity can emerge spontaneously and is involved in neural development and information processing. Generation of spontaneous synchronization is thought to arise as an alternating sequence between a state of elevated excitation followed by a period of quiescence associated with neuronal and/or synaptic refractoriness. However, the cellular factors controlling recruitment and timing of synchronized events have remained difficult to specify, although the specific temporal pattern of spontaneous rhythmogenesis determines its impact on developmental processes. We studied spontaneous synchronization in a model of 600–1,000 integrate-and-fire neurons interconnected with a probability of 5–30%. One-third of neurons generated spontaneous discharges and provided a background of intrinsic activity to the network. The heterogeneity and random coupling of these neurons maintained this background activity asynchronous. Refractoriness was modeled either by use-dependent synaptic depression or by cellular afterhyperpolarization. In both cases, the recruitment of neurons into spontaneous synchronized discharges was determined by the interplay of refractory mechanisms with stochastic fluctuations in background activity. Subgroups of easily recruitable neurons served as amplifiers of these fluctuations, thereby initiating a cascade-like recruitment of neurons (“avalanche effect”). In contrast, timing depended on the precise implementation of neuronal refractoriness and synaptic connectivity. With synaptic depression, neuronal synchronization always occurred stochastically, whereas with cellular afterhyperpolarization, stochastic turned into periodic behavior with increasing synaptic strength. These results associate the type of refractory mechanism with the temporal statistics and the mechanism of synchronization, thereby providing a framework for differentiating between cellular mechanisms of spontaneous rhythmogenesis.
Dissertations / Theses on the topic "Network behavior mechanisms"
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Bland, James Travis. "Front-Line Participatory Behavior in the Era of Networks." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/30002.
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In recent years, the network concept has become a central component of administrative scholarship. One cannot ignore the increased use of networks as both an explicit policy choice and a condition of public funding. This trend suggests that the network concept now represents an approach to governance. Regardless, active participation in these multi-organizational, multi-governmental, and multi-sectoral relationships has outpaced empirical description and theoretical explanation. Making the important case that network management has become a critical activity in public administration, researchers have neglected the relationship between front-line participatory behavior and the use of the network approach. As a result, the vocabulary and the imagery needed to describe and theorize about the specific front-line participatory behaviors that accompany the use of the network approach does not exist. Due to the limitations of past research, there is little understanding of the front-line participatory behaviors that could help make this happen. This study refers to these types of behaviors as network behaviors. Relying on surveys and elite interviews with participants from thirteen social welfare networks throughout the state of Virginia, this study addresses two primary research questions: What are the front-line participatory behaviors that accompany the use of the network approach? And, how do these behaviors differ along with variations in the network approach? Through examining 14 hypotheses, the study relates a framework of four degrees (variations) of the network approach (cooperation, coordination, consolidation, and collaboration) to three categories of behavior (knowledge management, communicative behavior, and commitment/identity). The findings support the underlying rationale for this study that variations in the network approach may shape front-line participatory behavior differently, and vice versa. Ultimately, by exploring this relationship and integrating the literature on networks with the literature on front-line work, this study may serve as the foundation for future efforts to establish a theory or rationale for developing and choosing among variations in the network approach.Ph. D.
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Blakeslee, Jennifer E. "Exploring Support Network Structure, Content, and Stability as Youth Transition from Foster Care." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/620.
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Many older youth in foster care lack adequate resources and ongoing support in their social networks as they transition into young adulthood, while other youth in these circumstances experience stable social networks providing comprehensive support. Systematically measuring the supportive personal and service-oriented relationships in youth networks expands the scope of inquiry in this area by identifying patterns of social network structure, member composition, and relational qualities that are associated with more or less support provision through formal and informal relationships. These can also be measured over time to observe changes in network form and content and assess network stability. This exploratory study (1) describes the support networks for a small sample of youth with foster care experience who are enrolled in post-secondary education and training programs, (2) assesses changes in these networks over time, and (3) demonstrates the reliability and validity of this methodology for broader use with populations of transition-age foster youth. Findings show that family (biological and foster) and friends are the most prevalent informal supports, relationship ties to parent figures are strongest and provide the most stable and multi-dimensional support, and ties with formal service providers are not as strong, but provide more informational support. The stability of a network ties over time is associated with the breadth of support provided, and network-based social support is associated with post-secondary enrollment at follow-up. Support network profiles are described and interpreted in terms of bonding and bridging social capital. Discussion includes implications for future support network research and guidelines for pre-transition assessment of youth networks in practice.
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Yu, Xiaoyang. "Dynamic acoustic emission for the characterization of the nonlinear behavior of complex materials." Thesis, Le Mans, 2020. http://www.theses.fr/2020LEMA1019.
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L’émission acoustique (EA) est reconnue pour être une technique efficace de surveillance de la santé des structures permettant de détecter la création et la propagation de microfissures dans les matériaux structuraux tels que le béton ou les composites lorsqu'ils sont soumis à des contraintes quasi-statiques. Sur la base de méthodes de traitement de signaux adéquates, différentes études ont établi des liens entre les salves d’EA et les micro-dommages créés. D'autres travaux ont montré qu'il est possible de corréler le temps de relaxation des composites et l'énergie des mécanismes d'endommagement mesurée durant la charge quasi-statique en utilisant les salves d’EA enregistrées. Cette thèse propose d'utiliser un protocole expérimental original pour détecter la relaxation non-linéaire d'échantillons de bétons à l'état intact et endommagés. Ce protocole est basé sur l'utilisation de l’EA pour capter passivement la relaxation non-linéaire d'échantillons de bétons au lieu du signal de faible amplitude habituellement utilisé dans les expériences de dynamique lente. Les résultats montrent que les méthodes de détection passives et actives conduisent à des temps de relaxation équivalents. De plus, le capteur d’EA révèle l’existence d’une ‘période de silence’ pendant les premières minutes de la relaxation non-linéaire après laquelle les salves d’EA commencent à être détectées. De plus, les caractéristiques des salves d’EA enregistrées pendant la relaxation passive ont montré une nette ressemblance avec celles obtenues lors de l'endommagement des mêmes échantillons, où des mécanismes de cisaillement et de compression sont impliqués. Enfin, nous notons qu’en plus de l'utilisation d'une approche de reconnaissance des formes non- supervisée pour la classification des salves d’EA, ce travail propose une nouvelle approche de classification des signaux d’EA basé sur l’image de la représentation en ondelettes continue (CWT) et le réseau de neurones convolutifs (CNN). Les résultats liés aux données d’EA dynamiques non-linéaires et quasi-statiques montrent que les deux approches de traitement du signal ont une grande précision de classification, ce qui représente un intérêt certain pour le développement de méthodes d’EA dynamiques en présence de microfissuresAcoustic emission (AE) is well known to be an efficient structural health monitoring technique to detect the creation and propagation of micro-cracks within structural materials such as concrete or composites when submitted to quasi-static stresses. Based on adequate signal processing methods, different research studies have established links between the detected AE hits and the created micro-damages. Other works have shown that it is possible to correlate the relaxation time in composites and the energy of the damage mechanisms measured during the quasi-static loading using the recorded AE hits. This thesis proposes to use an original experimental protocol to probe the nonlinear relaxation of concrete samples at the intact and damaged states. This protocol is based on the use of AE to passively probe the nonlinear relaxation of concrete samples instead of the weak amplitude signal usually used in slow dynamics experiments. Results show that passive and active probing methods lead to equivalent relaxation times. Furthermore, AE probing reveals the existence of a ‘silence period’ during the first minutes of the nonlinear relaxation after which AE hits start to be detected. In addition, the characteristics of AE hits recorded during the passive relaxation showed a clear resemblance to those obtained during the damaging of the same samples, where shear and compression mechanisms are involved. For the clustering of the AE hits, in addition to use of an unsupervised pattern recognition approach to cluster the detected AE hits, this work proposes a novel ‘image- based AE classification’ approach based on continuous wavelet transform (CWT) and convolutional neural network (CNN). Results related to the nonlinear dynamic and quasi-static AE data show that both signal processing approaches have high classification accuracy, which represents a great interest in the development of dynamic AE methods in the presence of micro-cracks
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Gerdzhikov, Petko. "Behaviour of Port-knocking authentication mechanism." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-60929.
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Port-knocking is a security mechanism used in computer systems to hide available network services. Its operation relies on a drop policy firewall setting in order to make impossible for port-scanning attacks to occur. This project researches the impact of implementing such a software solution. Furthermore, it looks into the behavior of three chosen implementations and make conclusions on the benefits and disadvantages that they bring. In addition, the surrounding implications related to both user and administrator are explored. This thesis includes tests on the resource consumption of the implementations as well as records of the added delay of using the mechanism when initiating a SSH session. There has not been such research performed in this field and the results of it could be beneficial to those who are involved in computer science and network security in particular. Finally, the product of this study state that port-knocking is overlooked and has great benefits in preventing zero-day exploits and hacker tools relying on exposed network services.
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Ibarra, Olivares Rebeca. "Social mechanisms of tax behaviour." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:2893069a-a2bf-46ff-a769-e9ec4ec58b48.
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The main goal of this thesis is to provide a sociologically informed analysis of tax avoidance and tax evasion in contemporary Mexico and Sweden, focusing particular attention on the explanatory role of social networks, social interactions, and positive feedback mechanisms. Two major data sources are used: (1) A panel dataset that includes all persons, 16 years or older, who resided in Stockholm County during at least one of the years 1990 to 2003 (N=1,967,993). The dataset includes detailed information on the socio-demographic characteristics, kinship networks, and criminal offences of these individuals; (2) A random sample of 36,949 firms that appeared in the Mexican Federal Register of Taxpayers for the year 2002. The records of the Mexican Federal Administrative Fiscal Tribunal provided data on all types of tax claims appealed before them during the 2002-2008 period. A variety of approaches and techniques are used such as agent-based simulation models, discrete time event history models, random effect logit models, and hierarchical linear models. These models are used to test different hypotheses related to the role of social networks, social interactions, and positive feedback mechanisms in explaining tax behaviour. There are five major empirical findings. (1) Networks seem to matter for individuals' tax behaviour because exposure to tax crimes of family members appears to increase a person’s likelihood of committing a tax crime. (2) Positive feedback mechanisms appear relevant because if a person commits a tax crime, it seems to increase the likelihood that the person will commit more tax crimes in the future. (3) Positive feedback mechanisms are also important for explaining corporate tax behaviour because a firm that has engaged in legal tax avoidance in the past appears to be more likely to engage in tax avoidance in the future. (4) Network effects are important in the corporate world because exposure to the tax avoidance of other firms increase the propensity of a firm to engage in tax avoidance. (5) Substitution effects between tax evasion and tax avoidance are likely to exist because when tax evasion becomes more prevalent in a firm’s environment, their likelihood of engaging in legal tax avoidance is lowered. The results underscore the importance of a sociological perspective on tax behaviour that takes into account social interactions and positive feedback mechanisms. In order to understand microscopic as well as macroscopic tax evasion patterns, the results presented in this thesis suggest that much more attention must be given to mechanisms through which taxation crimes breed more taxation crimes.
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Hu, Qiang. "Effects of nano structure on the deformation behavior of polymeric networks." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/10179.
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Shen, Xi. "Emergent behaviour of neural network models with learning mechanisms coupled with astrocyte and vascular dynamics." Thesis, Imperial College London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.438196.
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Evans, Dominic Andrew. "A midbrain mechanism for computing escape decisions in the mouse." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274871.
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Animals face frequent threats from predators and must generate appropriate behavioural responses to ensure their survival. To achieve this, they process sensory cues to correctly identify the presence and imminence of a predatory threat, and transform this information into defensive actions. However, despite much research in identifying the circuits that may be responsible for such transformations, little is known about how this occurs mechanistically. We focus on how escape behaviour in the mouse is generated from visual predatory threats, and use a combination of behavioural, neurophysiological and anatomical methods to identify the relevant neurons and understand how they perform this computation. In this work, we developed an innate decision making paradigm in which a mouse detects and assesses sensory stimuli of varying threat evidence during exploration, choosing whether to escape to a shelter, or not. The performance data in this task were best formalised with a drift-diffusion model of decision making, providing a framework to understand innate behavioural tasks in terms of evidence accumulation and boundaries. Next, we performed calcium imaging in freely-moving mice to probe for neural correlates of decision elements and flight behaviour in brain areas that we show to be necessary for the flight responses: we found that VGluT2 neurons in the deeper medial superior colliculus (dmSC) increase their activity during a repeated threatening stimulus, while VGluT2 neurons of the dorsolateral periaqueductal gray (dPAG) are silent until just before the initiation of escape, and are maximally active during escape. These results suggest that the dmSC accumulates evidence of threat which dPAG neurons threshold. This interpretation is supported by optogenetic activation of mSC-VGluT2 neurons in vivo, which recapitulates the statistics of escape probability evoked with a visual stimulus, while activation of VGluT2 neurons in the dPAG evokes an all-or-nothing escape response. Finally, using channelrhodopsin-2-assisted circuit mapping and monosynaptic viral tracing, we reveal that over half of dPAG-VGluT2 neurons receive monosynaptic connections from mSC-VGluT2 neurons with a low probability of release, allowing this synapse to act as a high-pass filter and providing a mechanism for the computation of an escape decision. These findings advance our understanding of how defensive behaviours are generated at circuit and single-cell level, and of how neurons process information in a circuit critical for implementing basic behaviours.
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MacNamara, Kailey. "Behavioral and Neural Mechanisms of Social Heterogeneity in Attention Deficit/Hyperactivity Disorder." FIU Digital Commons, 2017. http://digitalcommons.fiu.edu/etd/3390.
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Attention-deficit/hyperactivity disorder (ADHD) is one of the most common child-onset neurodevelopment disorders, affecting 5% of children in the United States (American Psychiatric Association, 2013). Treatment matching in ADHD is difficult and unsatisfactory; the same general treatment algorithm is recommended for all children. It is therefore important to consider the development of specialized treatment programs based on a variety of behavioral and neurological biomarkers. Unfortunately, due to its multi-faceted classification, the heterogeneity of this behavioral disorder is under-investigated (Costa Dias et al., 2015). Scientific research in this area is especially limited as the severity of ADHD goes undiagnosed, children tend to have difficulties remaining still in MRI scanners, and the hyperactivity-impulsivity that is associated with ADHD may cause further challenges when trying to remain motionless in the scanner. Furthermore, tasks such as Facial Emotion Perception Task (FEPT) and Theory of Mind (ToM) have not been used to analyze social and behavioral deficits in children with ADHD. More research needs to be allocated to helping uncover the neural substrates underlying the inattention and hyperactivity traits of this disorder. For this reason, we acquired functional magnetic resonance imaging (fMRI) data from five children with ADHD performing the FEPT and ToM tasks. The results showed the children have an easier and quick time correctly identifying happy emotional states, as compared to the fearful, angry, and neutral conditions. Results from the FEPT task also revealed that the participants were thinking and reasoning more (i.e., taking longer to deduce an ending) when identifying emotions than identifying animals. The ToM task showed that the default mode network (DMN) may not be fully suppressed when the children are choosing the correct cartoon ending, and therefore the children may be having lapses in attention. These findings may assist the current hypothesis that the default mode network has reduced network homogeneity in people with ADHD. Overall, the findings presented in this thesis provide a good diving board into discovering the reason(s) for the social cognition and emotion recognition impairments associated with ADHD, but further research is needed in order to one day pinpoint and ultimately correct the regions(s) of dysfunction.
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de, Freitas Edison Pignaton, Tales Heimfarth, Armando Morado Ferreira, Flávio Rech Wagner, Carlos Eduardo Pereira, and Tony Larsson. "An agent framework to support sensor networks’ setup and adaptation." Högskolan i Halmstad, Inbyggda system (CERES), 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14691.
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Sensor networks are being used in several emerging applications not even imagined some years ago due to advances in sensing, computing, and communication techniques. However, these advances also pose various challenges that must be faced. One important challenge is related to the autonomous capability needed to setup and adapt the networks, which decentralizes the control of the network, saving communication and energy resources. Middleware technology helps in addressing this kind of problem, but there is still a need for additional solutions, particularly considering dynamic changes in users' requirements and operation conditions. This paper presents an agent-based framework acting as an integral part of a middleware to support autonomous setup and adaptation of sensor networks. It adds interoperability among heterogeneous nodes in the network, by means of autonomous behavior and reasoning. These features also address the needs for system setup and adaptations in the network, reducing the communication overhead and decentralizing the decision making mechanism. Additionally, preliminary results are also presented.
Books on the topic "Network behavior mechanisms"
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Baumüller, Martin. Managing cultural diversity: An empirical examination of cultural networks and organizational structures as governance mechanisms in multinational corporations. Bern: P. Lang, 2007.
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The collegial phenomenon: The social mechanisms of cooperation among peers in a corporate law partnership. Oxford: Oxford University Press, 2001.
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Lazega, Emmanuel. The collegial phenomenon: The social mechanisms of co-operation among peers in a corporate law partnership. Oxford: Oxford University Press, 2001.
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Tulane University. Dept. of Mathematics, ed. Mathematical foundations of information flow: Clifford lectures on information flow in physics, geometry and logic and computation, March 12-15, 2008, Tulane University, New Orleans, Louisiana. Providence, R.I: American Mathematical Society, 2012.
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ZnO bao mo zhi bei ji qi guang, dian xing neng yan jiu. Shanghai Shi: Shanghai da xue chu ban she, 2010.
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McClurg, Scott D., Casey A. Klofstad, and Anand Edward Sokhey. Discussion Networks. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.21.
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While political network research is often a holistic enterprise, the network paradigm can also be used to study individual behavior. Specifically, rather than focusing on full network structures, a well-established area of research considers individuals’ “core” networks, their perceptions of interpersonal connections, and the consequences of said micro-social environments for myriad political outcomes and processes. This chapter examines this research tradition, tracing the history of its use in the study of political behavior. It begins with discussion of network research, paying specific attention to “egocentric” network name generator techniques. It then outlines several challenges to this research paradigm: (1) the difficulty of making causal inferences, (2) debates over concept and measurement, and (3) questions about mechanisms of influence. The chapter concludes by reviewing advances in the field that have developed from these challenges and points toward next steps in this research agenda, focused on the connected citizenry.
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Grant, Seth G. N. Synaptic Mechanisms of Psychotic Disorders. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0017.
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Synapses are the hallmark of the neuroanatomy of the brain. The million billion synapses of the human brain connect the nerve cells into the networks that underpin all behavior. The molecular anatomy of synapses is also remarkably complicated with ~2000 proteins in the synapse proteome. The proteins are physically organized into a hierarchy of molecular machines that control synapse biology. These proteins integrate and compute the information in patterns of nerve cell activity. Mutations in hundreds of genes that encode synaptic proteins contribute to over one hundred brain diseases, including common mental disorders. The synapse proteome is of fundamental importance to mental illness.
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Selverston, Allen. Rhythms and oscillations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0021.
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The study of identifiable neurons, a common feature of invertebrate nervous systems, has made it possible to construct a detailed cell-to-cell connectivity map using electrophysiological methods that can inspire the design of biomimetic systems. This chapter describes how the analysis of the neural circuitry in the lobster stomatogastric ganglion (STG) has provided some general principles underlying oscillatory and rhythmic behavior in all animals. The rhythmic and oscillatory patterns produced by the two STG central pattern generating (CPG) circuits are a result of two cooperative mechanisms, intrinsically bursting pacemaker neurons and synaptic network properties. Also covered are the major neuromodulatory and neural control mechanisms. The chapter discusses how a deep knowledge of the stomatogastric circuitry has led to the development of electronic neurons for biomimetic devices that can be used for experimental and prosthetic applications The chapter concludes with a section on new techniques that may help with unraveling oscillatory circuits in the brain.
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Theorell, Töres, Chantal Brisson, Michel Vézina, Alain Milot, and Mahée Gilbert-Ouimet. Psychosocial factors in the prevention of cardiovascular disease. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199656653.003.0018.
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The chapter starts with a theoretical sociological, psychological, and physiological framework for the relationships between psychosocial factors and coronary heart disease (CHD). This is followed by a review of the scientific evidence supporting such an association. Individual behaviours and coping mechanisms as well as environmental conditions of relevance for CHD are described. In particular, type A and D behaviour, depressive states, covert coping, social support and social network, socioeconomic conditions, as well as theoretical work environment models of relevance for CHD (job strain, effort-reward imbalance, organizational justice and leadership) are discussed. The remaining part of the chapter surveys the results from controlled studies of the effects of psychosocial interventions. There is accumulating evidence from such controlled studies that risk factors for CHD can be favourably influenced.
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Theorell, Töres, Chantal Brisson, Michel Vézina, Alain Milot, and Mahée Gilbert-Ouimet. Psychosocial factors in the prevention of cardiovascular disease. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199656653.003.0018_update_001.
Full textAbstract:
The chapter starts with a theoretical sociological, psychological, and physiological framework for the relationships between psychosocial factors and coronary heart disease (CHD). This is followed by a review of the scientific evidence supporting such an association. Individual behaviours and coping mechanisms as well as environmental conditions of relevance for CHD are described. In particular, type A and D behaviour, depressive states, covert coping, social support and social network, socioeconomic conditions, as well as theoretical work environment models of relevance for CHD (job strain, effort-reward imbalance, organizational justice and leadership) are discussed. The remaining part of the chapter surveys the results from controlled studies of the effects of psychosocial interventions. There is accumulating evidence from such controlled studies that risk factors for CHD can be favourably influenced.
Book chapters on the topic "Network behavior mechanisms"
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Ullah, Ihsan, Grégory Bonnet, Guillaume Doyen, and Dominique Gaïti. "Modeling User Behavior in P2P Live Video Streaming Systems through a Bayesian Network." In Mechanisms for Autonomous Management of Networks and Services, 2–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13986-4_2.
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Zhang, Hai-Tao, Zhaomeng Cheng, Ming-Can Fan, and Yue Wu. "Collective Behavior Coordination with Predictive Mechanisms." In Complex Systems and Networks, 283–311. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47824-0_11.
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Davis, W. Jackson. "Neural Mechanisms of Behavioral Plasticity in an Invertebrate Model System." In Model Neural Networks and Behavior, 263–82. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4757-5858-0_14.
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Lin, Shun-Chieh, Shian-Shyong Tseng, and Yao-Tsung Lin. "A New Mechanism of Mining Network Behavior." In Advances in Knowledge Discovery and Data Mining, 218–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47887-6_21.
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Miller, John P., and Allen I. Selverston. "Neural Mechanisms for the Production of the Lobster Pyloric Motor Pattern." In Model Neural Networks and Behavior, 37–48. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4757-5858-0_3.
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Abrams, Thomas W. "Cellular Studies of an Associative Mechanism for Classical Conditioning in Aplysia." In Model Neural Networks and Behavior, 213–35. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4757-5858-0_12.
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Salinas, Emilio, and Nicholas M. Bentley. "Gain Modulation as a Mechanism for Switching Reference Frames, Tasks, and Targets." In Coherent Behavior in Neuronal Networks, 121–42. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0389-1_7.
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Vicente, Raul, Leonardo L. Gollo, Claudio R. Mirasso, Ingo Fischer, and Gordon Pipa. "Far in Space and Yet in Synchrony: Neuronal Mechanisms for Zero-Lag Long-Range Synchronization." In Coherent Behavior in Neuronal Networks, 143–67. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0389-1_8.
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Liang, Xun. "Social Network Propagation Mechanism and Online User Behavior Analysis." In Social Computing with Artificial Intelligence, 179–230. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7760-4_8.
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Fagernes, Siri, and Alva L. Couch. "On the Combined Behavior of Autonomous Resource Management Agents." In Mechanisms for Autonomous Management of Networks and Services, 38–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13986-4_5.
Full textConference papers on the topic "Network behavior mechanisms"
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Taveira, Danilo Michalczuk, and Otto Carlos Muniz Bandeira Duarte. "A Monitor Tool for Anti-Spam Mechanisms and Spammers Behavior." In NOMS 2008 - 2008 IEEE Network Operations and Management Symposium Workshop. IEEE, 2008. http://dx.doi.org/10.1109/nomsw.2007.19.
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Philippsen, Anja, and Yukie Nagai. "Understanding the cognitive mechanisms underlying autistic behavior: a recurrent neural network study." In 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE, 2018. http://dx.doi.org/10.1109/devlrn.2018.8761038.
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Morovati, Vahid, Mohammad Ali Saadat, and Roozbeh Dargazany. "Modelling Stress Softening and Necking Phenomena in Double Network Hydrogels." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-12253.
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Abstract Double network (DN) gels are three-dimensional polymer matrices formed by interpenetrating networks. In contrast to the conventional single-network gels, DN gels have significant toughness, which makes them a promising material for different biomedical and biological applications. However, DN gels show complicated inelastic behavior including the Mullins effect and necking instability. Despite extensive efforts on modelling different aspects of the damage process in gels, the micro-mechanical modelling of the mechanisms that lead to necking in DN gels remains to be a challenging task. Here, a constitutive model is proposed to understand and describe the mechanical behavior of DN gels based on statistical micro-mechanics of interpenetrating polymer networks. DN gels behavior is divided into three parts including pre-necking, necking, and hardening. The first network is dominant in the response of the gel in the pre-necking stage. The breakage of the first network to smaller network fractions (clusters) induces the stress softening observed in this stage. The interaction of both networks and the second network are also considered as main contributors to the response of gel in necking and hardening stages, respectively. The contribution of clusters decreases during the necking as the second network starts hardening. The numerical results of the proposed model are validated and compared by uni-axial cyclic tensile experimental data of DN gels.
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Eichaker, Lauren, Rakshit Ramachandra, and John Bolte. "Analysis of Injury Mechanisms Within Lead-Vehicle Stopped Impacts: Implications for Autonomous Vehicle Behavior Design." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23611.
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Abstract Lead vehicle stopped crashes are a top contributor to traffic and health care expenditures out of NHTSA’s 37 pre-crash scenarios. It is important to better understand how these crashes occur, so that evolving autonomous vehicle technologies may be tailored towards injury mitigation in crash-imminent scenarios. Additionally, as autonomous vehicle technologies increase in prevalence and usage, out of position seating and distracted driving behavior may also increase. In order to analyze injury patterns in real-world lead vehicle stopped crashes, the public portal of Crash Injury Research Engineering Network (CIREN) was surveyed for lead vehicle stopped impacts. The review found that, of all the body regions, the thorax and lower extremity body regions frequently sustained AIS 3+ injuries (P < 0.01). Additionally, the upper extremity frequently sustained AID 3+ injuries in some scenarios. Steering wheel contact (often times through a deployed air bag) was the source of 62% of the thorax injuries and the knee bolster was the source of 76% of the lower extremity injuries. Truck impacts, and complicated crashes accounted for over 50% of the cases in the cohort. Automated vehicle behaviors have the potential to augment passive and active safety systems to potentially decrease the occurrence of AIS 3+ injuries by improving a vehicle’s response to lead vehicle stopped, crash imminent scenarios.
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Nadini, Matthieu, Alessandro Rizzo, and Maurizio Porfiri. "Contagion Processes Over Temporal Networks With Time-Varying Backbones." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9054.
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Abstract Predicting the diffusion of real-world contagion processes requires a simplified description of human-to-human interactions. Temporal networks offer a powerful means to develop such a mathematically-transparent description. Through temporal networks, one may analytically study the co-evolution of the contagion process and the network topology, as well as incorporate realistic feedback-loop mechanisms related to individual behavioral changes to the contagion. Despite considerable progress, the state-of-the-art does not allow for studying general time-varying networks, where links between individuals dynamically switch to reflect the complexity of social behavior. Here, we tackle this problem by considering a temporal network, in which reducible, associated with node-specific properties, and irreducible links, describing dyadic social ties, simultaneously vary over time. We develop a general mean field theory for the Susceptible-Infected-Susceptible model and conduct an extensive numerical campaign to elucidate the role of network parameters on the average degree of the temporal network and the epidemic threshold. Specifically, we describe how the interplay between reducible and irreducible links influences the disease dynamics, offering insights towards the analysis of complex dynamical networks across science and engineering.
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Ashtekar, Nikhil A., and David A. Jack. "Parametric Study of Thermal and Electrical Behavior of 3-D CNT Thin Film Networks." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-39176.
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Thin films composed of single-walled carbon nanotubes (SWCNTs) have been proposed as a possible multifunctional material for aerospace applications, but before these materials can experience industrial acceptance the underlying mechanisms dictating their performance must be understood. Physics-based computational tools must be developed that allow studies in the final part performance, to aid in industrial acceptance. This works presents a full 3D computational modeling approach to study the electrical and thermal behavior of a neat CNT network. The model is based on physics based stochastic distributions for the SWCNT length, diameter, and chirality, SWCNT orientation in a network, and the separation distance between two adjacent overlapping tubes. Previous models did not allow for the physically relevant distributions for nanotube geometry to serve as inputs, and results presented in the present work indicate the sensitivity of the bulk network conductivity to small deviations in the stochastic inputs. The uniqueness of this model lies in its three dimensional nature as previous attempts to predict the behavior of SWCNT thin films assume the film to be a 2D network of CNTs and results show that this is insufficient to accurately predict the thermal and electrical conductive properties. The 3D model is validated against experimental results available in the literature, and comparisons are made between the 3D and 2D network models.
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Erol, Anil, Saad Ahmed, Paris von Lockette, and Zoubeida Ounaies. "Analysis of Microstructure-Based Network Models for the Nonlinear Electrostriction Modeling of Electro-Active Polymers." In ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/smasis2017-3979.
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Relaxor ferroelectric polymers are a unique branch of electro-active polymers (EAPs) that generate high electromechanical strain with relatively low hysteresis and high nonlinearity. Polyvinylidene fluoride-based EAPs possess these qualities due to the semicrystalline nature of their microstructure. The interactions of electric dipoles within the microstructure of the material generate large strains under an external electric field, and the reduced crystalline domain sizes yield a relaxor effect by exhibiting low hysteresis and hyperelastic properties. This phenomenon has been partially modeled by previous works, but micro-electro-mechanisms for electrostriction in the microstructure have been largely ignored. This study focuses on the effects of various microstructural frameworks on the nonlinear dielectric behavior of dipole-based, semicrystalline EAPs. The Helmholtz free energy function of a microscopic representative volume element (RVE) is composed of an electrostatic energy and an elastic energy. The dipole-dipole interaction energy is prescribed for the electrostatic forces observed among the crystalline regions, and the elastic component attributed to the relaxation of the amorphous phase is modeled by the hyperelastic eight-chain model, which is microstructure-based. The RVE of the system is modeled by a central dipole surrounded by dipoles whose relative spatial locations are determined by a probability distribution function (PDF). The hyperelastic amorphous phase constitutes the volume separating the central and surrounding dipoles. The free energy of the RVE is implemented into a continuum description of the equilibrium of the system to obtain electromechanical relations. Additionally, this electromechanical response data is applied to a 1D structural mechanics model for simulating the large deformation of a multi-layered beam. The effects of microstructure on electrostrictive coupling are explored by varying the centers and deviations of dipole locations within the PDF. Discrete microstructural arrangements representing 3-chain network averaging schemes may be studied alongside more continuous ellipsoidal or random models of dipole spatial arrangements. The simulation results of the PDF-based networks are in good agreement with experimental data. The results indicate that the electrostrictive behavior of EAPs is strongly dependent on (1) the relative dipole spatial locations and (2) the extent of the regions containing dipoles, which represent crystalline domains. The model finds that adding extra crystalline domains in the network averaging schemes generates a better characteristic behavior due to a broader averaging of spatial orientations. These results offer a gateway to predicting microstructurally-dependent dipole-based behavior that can lead to the predictive theoretical tailoring of microstructures for desired electromechanical properties.
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Moco-Daijardin, Audrey. "WORKING CONDITIONS: WHEN THE RACE FOR PERFORMANCE TURNS SELF-MEDICATION INTO DOPING BEHAVIOR." In International Psychological Applications Conference and Trends. inScience Press, 2021. http://dx.doi.org/10.36315/2021inpact098.
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"The objective of this research was to better understand the link between self-medication and doping behaviors at work (Hautefeuille,2008). More precisely, we wanted to explore how individuals at work practiced self-medication (Fainzang,2012) and how contextual and individual factors could influence their consumption behaviors at workplace. We know that doping behavior could emerge when there is an objective to be achieved or an obstacle faced by the individual (Laure, 2000). To verify this assumption, we have tried to answer this question: “how self-medication could be diverted to doping purposes?”. To study this more closely, we built a methodology based on two tools. First, 17 interviews with workers were done to identify and evaluate the elements and mechanisms derived from working conditions and consumption behaviors, which may explain the consumption behavior at work and, moreover, the doping. Our results allowed us to see that the self-diagnosis phase play an important role in the consumption behavior of individuals. These initial results helped us to create an online questionnaire to which 219 participants responded. For this second tool, we applied a network analysis, using JASP 10.02 software to help us to visualize and understand the link between self-medication and doping behavior. The results of our research indicated that contextual and individual factors had an impact on the psychological state of individuals at work, leading them to develop consumption at work. We have also seen that the practice of self-medication also contributes to the establishment of consumption behavior at work."
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Mayalu, Michaëlle N., and H. Harry Asada. "Integrated Mechanistic-Empirical Modeling of Cellular Response Based on Intracellular Signaling Dynamics." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3806.
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A hybrid modeling framework integrating a highly specific mechanistic model with highly abstract empirical model is presented. With the growing interest in the scientific and medical community for identification of therapeutic targets in treatment of disease, it is necessary to develop predictive models that can describe cellular behavior in response to environmental cues. Intracellular signaling pathways form complex networks that regulate cellular response in both health and disease. Mechanistic (or white-box) models of biochemical networks are often unable to explain comprehensive cellular response due to lack of knowledge and/or intractable complexity (especially in events distal from the cell membrane). Empirical (or black-box) models may provide a less than accurate representation of cellular response due to data deficiency and/or loss of mechanistic detail. In the proposed framework, we use a mechanistic model to capture early signaling events and apply the resulting generated internal signals (along with external inputs) to a downstream empirical sub-model. The key construct in the approach is the treatment of a cell’s biochemical network as an encoder that creates a functional internal representation of external environmental cues. The signals derived from this representation are then used to inform downstream behaviors. Using this idea, we are able to create a comprehensive framework that describes important mechanisms with sufficient detail, while representing complex or unknown mechanisms in a more abstract form. The model is verified using published biological data describing T-Cells in immune response.
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Nguyen, Thao D., Reese E. Jones, and Brad L. Boyce. "Modeling the Anisotropic Finite-Deformation Viscoelastic Behavior of Soft Fiber-Reinforced Tissues." In ASME 2007 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2007. http://dx.doi.org/10.1115/sbc2007-176919.
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This paper presents a constitutive model for the anisotropic, finite-deformation viscoelastic behavior of soft fiber-reinforced tissues. Soft fiber-reinforced tissues, such as the cornea, tendons, and blood vessels, have a unique combination of mechanical properties that enables them to perform important structural, protective, and energy-absorbing functions. Because of their fiber-reinforced microstructure, these tissues are extraordinarily stiff and strong for their weight. Many are also flexible and tough. The toughness of these tissues arises from the ability of both the soft fiber and matrix phases to dissipate energy through large viscoelastic deformations. The viscoelastic behavior of the matrix of soft tissues can arise from fluid flow through a swollen polymer network and/or the diffusive motion of polymer segments within the network. The time-dependent behavior of the fiber reinforcements, which themselves can be composite structures, stems from the viscoelastic nature of the fiber material and/or the dissipative mechanisms of the fiber/matrix interface. To model the distinct time-dependent behavior of both fiber and matrix constituents, the tissue is represented as a continuum mixture consisting of a variety of fiber families embedded in an isotropic matrix. Both phases are required to deform with the continuum deformation gradient. However, the model attributes a different viscous stretch measure and free energy density to the matrix and fiber phases. Separate viscous flow rules are specified for the matrix phase and the individual fiber families. The flow rules for the fiber families are combined to give an anisotropic effective viscous flow rule for the fiber phase. An attractive feature of model is that key parameters can be related to the material properties (i.e., moduli, viscosities, volume fraction) of the fiber and matrix phases. Also, the anisotropy exhibited by both the elastic and viscous response of the composite arises directly from the fiber arrangement.
Reports on the topic "Network behavior mechanisms"
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Schulz, Jan, Daniel Mayerhoffer, and Anna Gebhard. A Network-Based Explanation of Perceived Inequality. Otto-Friedrich-Universität, 2021. http://dx.doi.org/10.20378/irb-49393.
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Across income groups and countries, the public perception of economic inequality and many other macroeconomic variables such as inflation or unemployment rates is spectacularly wrong. These misperceptions have far-reaching consequences, as it is perceived inequality, not actual inequality informing redistributive preferences. The prevalence of this phenomenon is independent of social class and welfare regime, which suggests the existence of a common mechanism behind public perceptions. We propose a network-based explanation of perceived inequality building on recent advances in random geometric graph theory. The literature has identified several stylised facts on how individual perceptions respond to actual inequality and how these biases vary systematically along the income distribution. Our generating mechanism can replicate all of them simultaneously. It also produces social networks that exhibit salient features of real-world networks; namely, they cannot be statistically distinguished from small-world networks, testifying to the robustness of our approach. Our results, therefore, suggest that homophilic segregation is a promising candidate to explain inequality perceptions with strong implications for theories of consumption behaviour.