Introduction to Complex Systems, Sustainability and Innovation

Thomas, Ciza; Prasad, Rendhir R.; Mathew, Minu

doi:10.5772/66453

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…So we explored the emergence of brain regions in terms of negative degree distribution. Emergent property explained that individuals may work differently from the whole in a complex system 44 . We found that the negative degree distribution of some brain regions significantly differed from the degree distribution of the whole brain.…”

Section: Effect Of Topology On the Balancementioning

confidence: 99%

Topological impact of negative links on the stability of resting-state brain network

Saberi

Khosrowabadi

Khatibi

et al. 2021

Sci Rep

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Stability is a physical attribute that stands opposite the change. However, it is still unclear how the arrangement of links called topology affects network stability. In this study, we tackled this issue in the resting-state brain network using structural balance. Structural balance theory employs the quality of triadic associations between signed links to determine the network stability. In this study, we showed that negative links of the resting-state network make hubs to reduce balance-energy and push the network into a more stable state compared to null-networks with trivial topologies. In this regard, we created a global measure entitled ‘tendency to make hub’ to assess the hubness of the network. Besides, we revealed nodal degrees of negative links have an exponential distribution that confirms the existence of negative hubs. Our findings indicate that the arrangement of negative links plays an important role in the balance (stability) of the resting-state brain network.

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Section: Effect Of Topology On the Balancementioning

confidence: 99%

Topological impact of negative links on the stability of resting-state brain network

Saberi

Khosrowabadi

Khatibi

et al. 2021

Sci Rep

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show abstract

“…Interdependence in complex systems, which arises from intricate connections existing between components can cause considerable systemic weakness too [3] [4]. Representing the complex systems using complex networks gives effective tools to study the emergent behaviour of these systems [5] [6]. When studying from the perspective of complex networks, it has been revealed that complex networks show similarities in their structure.…”

Section: Complex Systemmentioning

confidence: 99%

Ontology-Based Emotional Decision-Making in Self-Evolving Defensive Agents

Prasad¹,

Thomas

2022

IEEE Access

Self Cite

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Responding instantaneously in an unprecedented situation is inevitable to mitigate zero day attacks in the cyber world. This situation has strong resemblance with the fight for survival in biological world. Both being complex systems, the biological world and the cyber world have structural similarities. Emotions are the key factor used by biological beings to take intuitive decisions, which are key to survival in the natural world. Inspired from the idea of emotional decision making in natural world and the structural similarity shown by biological and cyber worlds, this paper proposes an ontological mechanism to implement artificial emotion-based decision making for autonomous agents in cyber security scenario. Emotions are modelled as manifestation of the goals of agents. Stimuli and response are connected through emotions using the ontological structure. The mechanism is proven useful in eliciting appropriate response for the stimuli using ontological reasoning through SPARQL queries.

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“…The unpredictable characteristic of chaos means that the objective is to stay on the sustainable state represented by the attractor [38]. Based on these senses, it is concluded that the phenomenon of chaos represents sustainability [39]. In fact, the theory of "edge of chaos " [40] shows that the system develops towards sustainability in a perpetual exploration of emergent order moving from chaos into order and back again.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Analysis of a Three-Dimensional Carbon Price Dynamic System

2018

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The carbon market is the least-cost tool to reduce carbon emissions. This study explores the evolution of the carbon price in the carbon market from a dynamic system perspective. A three-dimensional carbon price dynamic system is established to quantify the interactions among the carbon price, energy price, and economic growth. The system built in this study presents various dynamic characteristics including chaotic attractors and stable equilibria. Specifically, the existence of chaos in the system is verified by Lyapunov exponents spectrum and bifurcation diagram. In contrast, the system tends to be stable in the case of China after identifying the system parameters through the genetic algorithm. Furthermore, evolutionary trends of the carbon price are analyzed when the system parameters are perturbed. The results show that the carbon price is positively correlated with energy price as well as energy price policy. Besides, the level of the carbon price is negatively correlated with government control in the short term and positively correlated in the long term. This study can help analyze trends in the carbon price in the mid-term to long-term.

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Introduction to Complex Systems, Sustainability and Innovation

Cited by 6 publications

References 23 publications

Topological impact of negative links on the stability of resting-state brain network

Topological impact of negative links on the stability of resting-state brain network

Ontology-Based Emotional Decision-Making in Self-Evolving Defensive Agents

Evolutionary Analysis of a Three-Dimensional Carbon Price Dynamic System

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