scholarly journals Catching Critical Transition in Engineered Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jin Huang ◽  
Tianchuang Meng ◽  
Yangdong Deng ◽  
Fanling Huang
Keyword(s):  
Early Warning ◽  
Multiple Scales ◽  
Stable State ◽  
Critical Threshold ◽  
Supporting Evidence ◽  
Critical Transition ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Critical Transitions ◽  
Engineered Systems

A variety of engineered systems can encounter critical transitions where the system suddenly shifts from one stable state to another at a critical threshold. The critical transition has aroused vital concerns for its potentially disastrous impacts. We validate an often taken-for-granted hypothesis that the failure of engineered systems can be attributed to the respective critical transitions and show how early warning signals are closely associated with critical transitions. We demonstrate that it is feasible to use early warning signals to predict system failures. Our findings open a new path to forecast failures of engineered systems with a generic method and provide supporting evidence for the universal existence of critical transition in dynamical systems at multiple scales.

scholarly journals Several Indicators of Critical Transitions for Complex Diseases Based on Stochastic Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Gang Wang ◽  
Yuanyuan Li ◽  
Xiufen Zou
Keyword(s):  
Early Warning ◽  
Self Assembly ◽  
Disease Onset ◽  
Complex Diseases ◽  
Critical Threshold ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Critical Transitions ◽  
Development And Differentiation ◽  
Quantitative Changes

Many complex diseases (chronic disease onset, development and differentiation, self-assembly, etc.) are reminiscent of phase transitions in a dynamical system: quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. Understanding such nonlinear behaviors is critical to dissect the multiple genetic/environmental factors that together shape the genetic and physiological landscape underlying basic biological functions and to identify the key driving molecules. Based on stochastic differential equation (SDE) model, we theoretically derive three statistical indicators, that is, coefficient of variation (CV), transformed Pearson’s correlation coefficient (TPC), and transformed probability distribution (TPD), to identify critical transitions and detect the early-warning signals of the phase transition in complex diseases. To verify the effectiveness of these early-warning indexes, we use high-throughput data for three complex diseases, including influenza caused by either H3N2 or H1N1 and acute lung injury, to extract the dynamical network biomarkers (DNBs) responsible for catastrophic transition into the disease state from predisease state. The numerical results indicate that the derived indicators provide a data-based quantitative analysis for early-warning signals for critical transitions in complex diseases or other dynamical systems.

Seizures Start without Common Signatures of Critical Transition

2016 ◽  
Vol 26 (08) ◽  
pp. 1650053 ◽  
Author(s):  
Piotr Milanowski ◽  
Piotr Suffczynski
Keyword(s):  
Ocean Circulation ◽  
Early Warning ◽  
Human Subjects ◽  
Epileptic Seizures ◽  
Critical Transition ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Human Epilepsy ◽  
Critical Transitions ◽  
Eeg Recordings

Complex dynamical systems may exhibit sudden autonomous changes from one state to another. Such changes that occur rapidly in comparison to the regular dynamics have been termed critical transitions. Examples of such phenomena can be found in many complex systems: changes in climate and ocean circulation, changes in wildlife populations, financial crashes, as well as in medical conditions like asthma attacks and depression. It has been recognized that critical transitions, even if they arise in completely different contexts and situations, share several common attributes and also generic early-warning signals that indicate that a critical transition is approaching. In the present study, we review briefly the general characteristics that have been observed in systems prior to critical transitions and apply these general indicators to nearly 300 epileptic seizures collected from human subjects using invasive EEG. Only in about 8% of the patients was evidence of critical transitions found. In the remaining majority of cases no early warning signals that behaved consistently prior to seizures were observed. These results do not rule out the possibility of critical transitions to seizure but point to limited relevance of their early warning signals in the context of human epilepsy observed using intracranial EEG recordings.

scholarly journals Anticipating Critical Transitions in Psychological Systems using Early Warning Signals: Theoretical and Practical Considerations

2020 ◽  
Author(s):  
Fabian Dablander ◽  
Anton Pichler ◽  
Arta Cika ◽  
Andrea Bacilieri
Keyword(s):  
Early Warning ◽  
Stable State ◽  
Critical Slowing Down ◽  
Future Research ◽  
Frequency Noise ◽  
Alternative Stable State ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Slowing Down ◽  
Critical Transitions

Many real-world systems can exhibit sudden shifts from one stable state to another, and the theory of dynamical systems points to the existence of generic early warning signals that precede such shifts. Recently, psychologists have begun to conceptualize mental disorders such as depression as an alternative stable state, and suggested that early warning signals based on the phenomenon of critical slowing down might be useful for predicting sudden transitions into depression or other psychiatric disorders. Harnessing the potential of early warning signals requires us to understand their limitations as well as the factors influencing their performance in practice. In this paper, we (a) review limitations of early warning signals based on critical slowing down to better understand when they can and cannot occur, and (b) study the conditions under which early warning signals may anticipate critical transitions in online-monitoring settings by simulating from a bistable dynamical system, varying crucial features such as sampling frequency, noise intensity, and speed of approaching the tipping point. We find that, in sharp contrast to their reputation of being generic or model-agnostic, whether early warning signals occur or not strongly depends on the specifics of the system. We also find that they are very sensitive to noise, potentially limiting their utility in practical applications. We discuss the implications of our findings and provide suggestions and recommendations for future research.

scholarly journals Variance and Rate-of-Change as Early Warning Signals for a Critical Transition in an Aquatic Ecosystem State: A Test Case From Tasmania, Australia

2018 ◽  
Vol 123 (2) ◽  
pp. 495-508 ◽  
Author(s):  
Kristen K. Beck ◽  
Michael-Shawn Fletcher ◽  
Patricia S. Gadd ◽  
Henk Heijnis ◽  
Krystyna M. Saunders ◽  
...  
Keyword(s):  
Early Warning ◽  
Aquatic Ecosystem ◽  
Rate Of Change ◽  
Test Case ◽  
Critical Transition ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Ecosystem State

scholarly journals Detecting and distinguishing tipping points using spectral early warning signals

2020 ◽  
Vol 17 (170) ◽  
pp. 20200482
Author(s):  
T. M. Bury ◽  
C. T. Bauch ◽  
M. Anand
Keyword(s):  
Complex Systems ◽  
Early Warning ◽  
Dynamical Regime ◽  
Tipping Points ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Density Dependent ◽  
Critical Transitions ◽  
Fold Bifurcation ◽  
Characteristic Signal

Theory and observation tell us that many complex systems exhibit tipping points—thresholds involving an abrupt and irreversible transition to a contrasting dynamical regime. Such events are commonly referred to as critical transitions. Current research seeks to develop early warning signals (EWS) of critical transitions that could help prevent undesirable events such as ecosystem collapse. However, conventional EWS do not indicate the type of transition, since they are based on the generic phenomena of critical slowing down. For instance, they may fail to distinguish the onset of oscillations (e.g. Hopf bifurcation) from a transition to a distant attractor (e.g. Fold bifurcation). Moreover, conventional EWS are less reliable in systems with density-dependent noise. Other EWS based on the power spectrum (spectral EWS) have been proposed, but they rely upon spectral reddening, which does not occur prior to critical transitions with an oscillatory component. Here, we use Ornstein–Uhlenbeck theory to derive analytic approximations for EWS prior to each type of local bifurcation, thereby creating new spectral EWS that provide greater sensitivity to transition proximity; higher robustness to density-dependent noise and bifurcation type; and clues to the type of approaching transition. We demonstrate the advantage of applying these spectral EWS in concert with conventional EWS using a population model, and show that they provide a characteristic signal prior to two different Hopf bifurcations in data from a predator–prey chemostat experiment. The ability to better infer and differentiate the nature of upcoming transitions in complex systems will help humanity manage critical transitions in the Anthropocene Era.

scholarly journals Performance of early warning signals for disease emergence: a case study on COVID-19 data

2021 ◽  
Author(s):  
Daniele Proverbio ◽  
Françoise Kemp ◽  
Stefano Magni ◽  
Jorge Gonçalves
Keyword(s):  
Early Warning ◽  
Disease Emergence ◽  
Warning Signals ◽  
Active Monitoring ◽  
Prevalence Data ◽  
Early Warning Signals ◽  
Epidemic Dynamics ◽  
Critical Transitions ◽  
Parameter Values

AbstractThe sudden emergence of infectious diseases pose threats to societies worldwide and it is notably difficult to detect. In the past few years, several early warning signals (EWS) were introduced, to alert to impending critical transitions and extend the set of indicators for risk assessment. While they were originally thought to be generic, recent works demonstrated their sensitivity to some dynamical characteristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data is so far limited. Hence, validating their performance remains a challenge. In this study, we analyse the performance of common EWS such as increasing variance and autocorrelation in detecting the emergence of COVID-19 outbreaks in various countries, based on prevalence data. We show that EWS are successful in detecting disease emergence provided that some basic assumptions are satisfied: a slow forcing through the transitions and not fat-tailed noise. We also show cases where EWS fail, thus providing a verification analysis of their potential and limitations. Overall, this suggests that EWS can be useful for active monitoring of epidemic dynamics, but that their performance is sensitive to surveillance procedures. Our results thus represent a further step towards the application of EWS indicators for informing public health policies.

scholarly journals Early warning signals from the periphery

2021 ◽  
Author(s):  
Manfred Füllsack ◽  
Daniel Reisinger ◽  
Marie Kapeller ◽  
Georg Jäger
Keyword(s):  
Time Series ◽  
Social Interactions ◽  
Early Warning ◽  
Focus Attention ◽  
Warning Signals ◽  
Feedback Effects ◽  
Early Warning Signals ◽  
Agent Based ◽  
Critical Transitions ◽  
Micro Level

AbstractStudies on the possibility of predicting critical transitions with statistical methods known as early warning signals (EWS) are often conducted on data generated with equation-based models (EBMs). These models base on difference or differential equations, which aggregate a system’s components in a mathematical term and therefore do not allow for a detailed analysis of interactions on micro-level. As an alternative, we suggest a simple, but highly flexible agent-based model (ABM), which, when applying EWS-analysis, gives reason to (a) consider social interaction, in particular negative feedback effects, as an essential trigger of critical transitions, and (b) to differentiate social interactions, for example in network representations, into a core and a periphery of agents and focus attention on the periphery. Results are tested against time series from a networked version of the Ising-model, which is often used as example for generating hysteretic critical transitions.

scholarly journals Data-driven identification of reliable sensor species to predict regime shifts in ecological networks

2020 ◽  
Vol 7 (8) ◽  
pp. 200896 ◽  
Author(s):  
Amin Ghadami ◽  
Shiyang Chen ◽  
Bogdan I. Epureanu
Keyword(s):  
Indicator Species ◽  
Early Warning ◽  
Regime Shifts ◽  
Data Driven ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Critical Transitions ◽  
Spatially Distributed ◽  
Data Driven Approach ◽  
Interacting Components

Signals of critical slowing down are useful for predicting impending transitions in ecosystems. However, in a system with complex interacting components not all components provide the same quality of information to detect system-wide transitions. Identifying the best indicator species in complex ecosystems is a challenging task when a model of the system is not available. In this paper, we propose a data-driven approach to rank the elements of a spatially distributed ecosystem based on their reliability in providing early-warning signals of critical transitions. The proposed method is rooted in experimental modal analysis techniques traditionally used to identify structural dynamical systems. We show that one could use natural system fluctuations and the system responses to small perturbations to reveal the slowest direction of the system dynamics and identify indicator regions that are best suited for detecting abrupt transitions in a network of interacting components. The approach is applied to several ecosystems to demonstrate how it successfully ranks regions based on their reliability to provide early-warning signals of regime shifts. The significance of identifying the indicator species and the challenges associated with ranking nodes in networks of interacting components are also discussed.

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