The random cascading origin of abrupt transitions in interdependent systems
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View abstract on PubMed
Summary
This summary is machine-generated.Abrupt phase transitions in superconducting networks arise from internal cascading, not just external changes. A metastable state with a long-lasting resistance plateau acts as an early warning for system collapse.
Area Of Science
- Statistical Physics
- Condensed Matter Physics
- Network Science
Background
- Continuous phase transitions are understood through external global changes.
- The origins of abrupt phase transitions remain unclear.
- Interdependent superconducting networks exhibit complex behaviors.
Purpose Of The Study
- To elucidate the mechanism behind abrupt phase transitions in interdependent superconducting networks.
- To identify the role of internal cascading mechanisms.
- To investigate the properties of a metastable state preceding abrupt transitions.
Main Methods
- Experimental investigation of interdependent superconducting networks.
- Analysis of resistance cascading phenomena.
- Characterization of metastable states and cascading events.
- Measurement of plateau time length, system size, and distance from criticality.
- Calculation of branching factor and critical exponents.
Main Results
- Abrupt phase transitions are driven by an internal random spatial cascading mechanism.
- A unique metastable state, characterized by a long-living resistance cascading plateau, governs the transition.
- Cascading events occur spontaneously at random locations, preceding a global phase shift.
- Plateau duration exhibits scaling laws with system size and proximity to criticality.
- The branching factor, analogous to epidemic spreading, equals one at the critical point and indicates proximity to system collapse.
Conclusions
- Internal cascading mechanisms, particularly the metastable resistance plateau, are key to abrupt phase transitions.
- The branching factor serves as a critical early warning signal for catastrophic system cascades.
- Understanding these mechanisms is crucial for predicting and preventing system collapse in complex networks.
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