Abrupt State Changes Research Articles

From Slow Shifts to Fast Flips: Unraveling problem-based learning group function dynamics

PurposeProblem-Based Learning (PBL) relies on self-directed learning in small groups in the presence of a tutor. While the effectiveness of PBL is often attributed to the dynamics of group function, change in group function over time and factors influencing group function development are less understood. This study aims to explore the development of PBL group function over time to better understand the factors that give rise to high-functioning groups.MethodWe examined time-function graphs of group function and conducted semi-structured focus group discussions in 2023 with medical students enrolled in a PBL curriculum. Students reflected on their experiences in four different PBL groups, creating time-function graphs to characterize development of group function over 8–12-week periods. We analyzed graphs and transcripts in a staged approach using qualitative description and direct content analysis, sensitized by two frameworks: Tuckman’s Stages of Group Development and the Dimensions of PBL Group Function.ResultsThree archetypes of PBL group function development were identified: Slow Shifters, Fast Flippers, and Coasters. (1) Slow Shifters were characterized by a complex and extended pattern of growth consistent with Tuckman’s model, typically occurring amongst inexperienced groups, or groups faced with a novel task. (2) Fast Flippers were characterized by abrupt state changes in group function arising from internal or external disruptions. (3) Coasters were characterized by plateaus, where maintenance of group function was a frequently cited challenge. Abrupt changes and plateaus occurred more among mature groups and groups with significant PBL experience.ConclusionsPBL group function varies over time in 3 different patterns. Classic Tuckman’s stages are apparent among inexperienced groups, or groups facing novel tasks, whereas experienced groups often face abrupt change or plateaus. PBL educators and students should consider the need for novelty and disruption in more experienced groups to incite growth.

Application of early warning signs to physiological contexts: a comparison of multivariate indices in patients on long-term hemodialysis.

Early warnings signs (EWSs) can anticipate abrupt changes in system state, known as "critical transitions," by detecting dynamic variations, including increases in variance, autocorrelation (AC), and cross-correlation. Numerous EWSs have been proposed; yet no consensus on which perform best exists. Here, we compared 15 multivariate EWSs in time series of 763 hemodialyzed patients, previously shown to present relevant critical transition dynamics. We calculated five EWSs based on AC, six on variance, one on cross-correlation, and three on AC and variance. We assessed their pairwise correlations, trends before death, and mortality predictive power, alone and in combination. Variance-based EWSs showed stronger correlations (r = 0.663 ± 0.222 vs. 0.170 ± 0.205 for AC-based indices) and a steeper increase before death. Two variance-based EWSs yielded HR95 > 9 (HR95 standing for a scale-invariant metric of hazard ratio), but combining them did not improve the area under the receiver-operating curve (AUC) much compared to using them alone (AUC = 0.798 vs. 0.796 and 0.791). Nevertheless, the AUC reached 0.825 when combining 13 indices. While some indicators did not perform overly well alone, their addition to the best performing EWSs increased the predictive power, suggesting that indices combination captures a broader range of dynamic changes occurring within the system. It is unclear whether this added benefit reflects measurement error of a unified phenomenon or heterogeneity in the nature of signals preceding critical transitions. Finally, the modest predictive performance and weak correlations among some indices call into question their validity, at least in this context.

Abrupt change in tropical Pacific climate mean state during the Little Ice Age

The mean state of the tropical Pacific ocean-atmosphere climate, in particular its east-west asymmetry, has profound consequences for regional climates and for the El Niño/Southern Oscillation variability. Here we present a new high-resolution paleohydrological record using the stable-hydrogen-isotopic composition of terrestrial-lipid biomarkers (δDwax) from a 1,400-year-old lake sedimentary sequence from northern Philippines. Results show a dramatic and abrupt increase in δDwax values around 1630 AD with sustained high values until around 1900 AD. We interpret this change as a shift to significantly drier conditions in the western tropical Pacific during the second half of the Little Ice Age as a result of a change in tropical Pacific mean state tied to zonal sea surface temperature (SST) gradients. Our findings highlight the prominent role of abrupt shifts in zonal SST gradients on multidecadal to multicentennial timescales in shaping the tropical Pacific hydrology of the last millennium, and demonstrate that a marked transition in the tropical Pacific mean state can occur within a period of a few decades.

Identifying the true drivers of abrupt changes in ecosystem state with a focus on time lags: Extreme precipitation can determine water quality in shallow lakes

A better understanding of abrupt ecosystem changes is needed to improve prediction of future ecosystem states under climate change. Chronological analysis based on long-term monitoring data is an effective way to estimate the frequency and magnitude of abrupt ecosystem changes. In this study, we used abrupt-change detection to differentiate changes of algal community composition in two Japanese lakes and to identify the causes of long-term ecological transitions. Additionally, we focused on finding statistically significant relationships between abrupt changes to aid with factor analysis. To estimate the strengths of driver–response relationships underlying abrupt algal transitions, the timing of the algal transitions was compared to that of abrupt changes in climate and basin characteristics to identify any synchronicities between them. The timing of abrupt algal changes in the two study lakes corresponded most closely to that of heavy runoff events during the past 30–40 years. This strongly suggests that changes in the frequency of extreme events (e.g., heavy rain, prolonged drought) have a greater effect on lake chemistry and community composition than do shifts in the means of climate and basin factors. Our analysis of synchronicity (with a focus on time lags) could provide an easy method to identify better adaptative strategies for future climate change.

On the initial value problem for semiclassical gravity without and with quantum state collapses

Semiclassical gravity is the theory in which the classical Einstein tensor of a spacetime is coupled to quantum matter fields propagating on the spacetime via the expectation value of their renormalized stress-energy tensor in a quantum state. We explore two issues, taking the Klein Gordon equation as our model quantum field theory. The first is the provision of a suitable initial value formulation for the theory. Towards this, we address the question, for given initial data consisting of the classical metric and its first three 'time' derivatives off the surface together with a choice of initial quantum state, of what is an appropriate 'surface Hadamard' condition such that, for initial data for which it is satisfied it is reasonable to conjecture that there will be a Cauchy development whose quantum state is Hadamard. This requires dealing with the fact that, given two points on an initial surface, the spacetime geodesic between them does not, in general, lie on that surface. So the (squared) geodesic distance that occurs in the Hadamard subtraction differs from that intrinsic to the initial surface. We handle this complication by expanding the former as a suitable 3-dimensional covariant Taylor expansion in the latter. Moreover the renormalized expectation value of the stress-energy tensor in the initial surface depends explicitly on the fourth, 'time', derivative of the metric, which is not part of the initial data, but which we argue is given, implicitly, by the semiclassical Einstein equations on the initial surface. (The rôle played by those equations also entails that the surface Hadamard condition subsumes the constraints.) We also introduce the notion of physical solutions, which, inspired by a 1993 proposal of Parker and Simon, we define to be solutions which are smooth in ħ at ħ = 0. We conjecture that for these solutions the second and third time derivatives of the metric will be determined once the first and second time derivatives are specified. We point out that a simpler treatment of the initial value problem can be had if we adopt yet more of the spirit of Parker and Simon and content ourselves with solutions to order ħ which are Hadamard to order ħ. A further simplification occurs if we consider semiclassical gravity to order ħ 0. This resembles classical general relativity in that it is free from the complications of higher derivative terms and does not require any Hadamard condition. But it can still incorporate nontrivial quantum features such as superpositions of classical-like quantum states of the matter fields. Our second issue concerns the prospects for combining semiclassical gravity with theories of spontaneous quantum state collapse. We will focus our attention on proposals involving abrupt changes in the quantum field state which occur on certain (random, non-intersecting) Cauchy surfaces according to some — yet to be developed — generally covariant objective collapse model but that, in between such collapse surfaces, we have a physical semiclassical solution (or a solution of order O(ħ) or a solution of order O(ħ 0)). On each collapse surface, the semiclassical gravity equations will necessarily be violated and, as Page and Geilker pointed out in 1981, there will therefore necessarily be a discontinuity in the expectation value of the renormalized stress-energy tensor. Nevertheless, we argue, based on our conjecture about the well-posedness of the initial value problem for physical solutions, that, with a suitable rule for the jump in the metric and/or the extrinsic curvature, the time evolution will still be uniquely determined. We tentatively argue that a natural jump rule would be one in which the metric itself and the transverse traceless part of the extrinsic curvature will be continuous and the jump will be confined to the remaining parts of the extrinsic curvature. We aid and complement our discussion by studying our two issues also in the simpler cases of a semiclassical scalars model and semiclassical electrodynamics.

Fault detection of a Li-ion battery using SVM based machine learning and unscented Kalman filter

The focal aim of the current paper is to present a unique support vector machine (SVM) based fault diagnosis technique for a Lithium ion battery. This paper basically objectifies the derivative free estimator paradigm for the assessment of State of Charge (SOC) for a Lithium-ion (Li-ion) battery. The estimation error has been taken as the references for support vector machine (SVM) based learning for fault detection. The model of Li-ion battery which is instrumented here is basically the lumped model. One of the primary drawbacks of this type of battery is the estimation of the state of charge (SOC) perfectly because of the electrochemical topographies. These features alters with alteration of physical constraints which causes the increase of process and measurement noise levels while updating the online battery model. These drawbacks also create some fault condition in battery management system. The short circuit current due to abrupt change in state of charge cause short circuit fault which is not desirable. Unscented Kalman Filter obviates the noise level by minimizing estimation error and fault can be diagnosed accurately using machine learning with great accuracy and less number of false alarms. In one word the main novelty of this paper is to detect fault of state of charge of Li-ion battery using SVM based machine learning where the vector machine is trained by the error value deduced by the UKF. It is proven from the simulation studies that machine learning can detect fault faster with higher range coverage than other statistical methods.

Likelihood-Ratio-Based Recovery for Seismic Reflectivity Series

This article proposes a likelihood-ratio-based method for recovering sparse reflectivity series from noisy seismic signals. Unlike the existing methods, the proposed method is specifically developed to be adaptive to the variance of the observational noise. This enhances the robustness of the reflectivity series recovering, as shown experimentally. The proposed method is briefly summarized as follows: First, the convolutional model of seismic signals is reformulated as a discrete-time linear system. In this linear system, the nonzero reflectivity impulses are treated as abrupt state changes with unknown magnitudes occurring at unknown times. Second, a Kalman filter is applied to the linear system without considering the changes. Third, the likelihood ratio as a function of both the magnitudes and occurring times is evaluated by using the Kalman filtering innovations. Fourth, the magnitudes and occurring times of the changes are estimated by alternately fixing one parameter and maximizing the ratio with the other parameter. Finally, the reflectivity series are reconstructed from the estimated magnitudes and occurring times of the changes. Experimental results verified the reliability of the reflectivity series recovery from noisy seismic traces.

Dispersive shock waves in lattices: A dimension reduction approach

Dispersive shock waves (DSWs), which connect states of different amplitude via a modulated wave train, form generically in nonlinear dispersive media subjected to abrupt changes in state. The primary tool for the analytical study of DSWs is Whitham’s modulation theory. While this framework has been successfully employed in many space-continuous settings to describe DSWs, the Whitham modulation equations are cumbersome in most spatially discrete systems. In this article, we illustrate the relevance of the reduction of the DSW dynamics to a planar ODE in a broad class of lattice examples. Solutions of this low-dimensional ODE accurately describe the orbits of the DSW in self-similar coordinates and the local averages in a manner consistent with the modulation equations. We use data-driven and quasi-continuum approaches within the context of a discrete system of conservation laws to demonstrate how the underlying low dimensional structure of DSWs can be identified and analyzed. The connection of these results to Whitham modulation theory is also discussed.

Synchrony of biomarker variability indicates a critical transition: Application to mortality prediction in hemodialysis

SummaryCritical transition theory suggests that complex systems should experience increased temporal variability just before abrupt state changes. We tested this hypothesis in 763 patients on long-term hemodialysis, using 11 biomarkers collected every two weeks and all-cause mortality as a proxy for critical transitions. We find that variability—measured by coefficients of variation (CVs)—increases before death for all 11 clinical biomarkers, and is strikingly synchronized across all biomarkers: the first axis of a principal component analysis on all CVs explains 49% of the variance. This axis then generates powerful predictions of mortality (HR95 = 9.7, p < 0.0001, where HR95 is a scale-invariant metric of hazard ratio; AUC up to 0.82) and starts to increase markedly ∼3 months prior to death. Our results provide an early warning sign of physiological collapse and, more broadly, a quantification of joint system dynamics that opens questions of how system modularity may break down before critical transitions.

Automation of a Hybrid Control for Electrohydraulic Servo-Actuators with Residual Dynamics

In this paper, a hybrid position/force controller for electrohydraulic servo-actuators is designed in the presence of residual dynamics. The purpose is to apply a cycle of movement that is mostly used in industrial fatigue test applications, which consists in imposing a specified force on a flexible load after a movement to some position. This cycle can be repeated several times with different magnitudes and frequencies of force and position trajectories. Some damages could occur at switching times, especially in the presence of some residual dynamics. To avoid any damages at switching times, a force trajectory generator is designed. Then, our contribution defines a method to automatically generate the switching signal in order to commute between two controllers with any abrupt change of state. To show the effectiveness of the proposed approach, several simulation tests are carried out on the electrohydraulic system.

Changes in kelp forest communities off Washington, USA, during and after the 2014-2016 marine heatwave and sea star wasting syndrome

Canopy-forming kelps are foundation species in many coastal ecosystems, but kelp-forest communities are subject to abrupt state changes caused by environmental drivers and trophic dynamics. We examined changes in kelp communities at 5 sites along the Olympic Coast of Washington State, USA, during and following the recent perturbations of anomalous warm-water events and sea star wasting syndrome (SSWS). Anomalously warm water in 2013 and 2014 corresponded with a loss of approximately 50% of Macrocystis pyrifera and Nereocystis luetkeana canopy. However, the canopy quickly recovered, and stipe density increased after 2015. Purple sea urchins Strongylocentrotus purpuratus increased in density 164-fold, largely at one site, but this increase was first observed in 2017 and peaked in 2019, after the warm period. Sea stars did not show recovery from SSWS, with several species including Pycnopodia helianthoides continuing to decline. The majority of variation in assemblage structure occurred at the site level for kelps, macroinvertebrates, and fishes, while year explained most of the variability for juvenile rockfishes Sebastes spp. We did not see strong top-down effects of urchins on kelp, suggesting that top-down impacts were not dominant regionally during this period. In contrast, we found evidence for a bottom-up influence of kelp habitat on juvenile rockfishes, as rockfish recruits occurred with higher probability where kelp stipe density was higher. Our analyses highlight the importance of spatial variation in structuring changes in kelp forest communities associated with disturbance and suggest that it is essential to ensure the protection of a diversity of kelp forests.

Identifying the True Drivers of Abrupt Changes in Ecosystem State with a Focus on Time Lags: Extreme Precipitation Can Determine Water Quality in Shallow Lakes

Identifying the True Drivers of Abrupt Changes in Ecosystem State with a Focus on Time Lags: Extreme Precipitation Can Determine Water Quality in Shallow Lakes

Noise-to-state stability criteria of switching stochastic nonlinear systems with synchronous and asynchronous impulses and its application to singular systems

In this paper, the problem of noise-to-state stability of switching stochastic nonlinear systems with synchronous and asynchronous impulses is addressed. Compared with the existing results, the phenomenon of synchronous and asynchronous impulses is taken into account at the same time. Then, synchronous and asynchronous impulsive sequences are properly constrained in terms of eventually uniform boundedness, which are substantially broader than the previously defined impulsive signals. In addition, multiple Lyapunov functions with the time-varying infinitesimal operator are employed to derive some noise-to-state stability criteria. Furthermore, the obtained result is applied to a general class of singular systems, where inherent state jumps raised by switching behavior and abrupt state changes induced by external factors are considered simultaneously, and some computable stability conditions are given.

Complex networks of interacting stochastic tipping elements: Cooperativity of phase separation in the large-system limit.

Tipping elements in the Earth system have received increased scientific attention over recent years due to their nonlinear behavior and the risks of abrupt state changes. While being stable over a large range of parameters, a tipping element undergoes a drastic shift in its state upon an additional small parameter change when close to its tipping point. Recently, the focus of research broadened towards emergent behavior in networks of tipping elements, like global tipping cascades triggered by local perturbations. Here, we analyze the response to the perturbation of a single node in a system that initially resides in an unstable equilibrium. The evolution is described in terms of coupled nonlinear equations for the cumulants of the distribution of the elements. We show that drift terms acting on individual elements and offsets in the coupling strength are subdominant in the limit of large networks, and we derive an analytical prediction for the evolution of the expectation (i.e., the first cumulant). It behaves like a single aggregated tipping element characterized by a dimensionless parameter that accounts for the network size, its overall connectivity, and the average coupling strength. The resulting predictions are in excellent agreement with numerical data for Erdös-Rényi, Barabási-Albert, and Watts-Strogatz networks of different size and with different coupling parameters.

An Equation Free Algorithm Accurately Simulates Macroscale Shocks Arising From Heterogeneous Microscale Systems

Scientists and engineers often create accurate, trustworthy, computational simulation schemes—but all too often these are too computationally expensive to execute over the time or spatial domain of interest. The equation-free approach is to marry such trusted simulations to a framework for numerical macroscale reduction—the patch dynamics scheme. This article extends the patch scheme to scenarios in which the trusted simulation resolves abrupt state changes on the microscale that appear as shocks on the macroscale. Accurate simulation for problems in these scenarios requires capturing the shock within a novel patch, and also modifying the patch coupling rules in the vicinity in order to maintain accuracy. With these two extensions to the patch scheme, straightforward arguments derive consistency conditions that match the usual order of accuracy for patch schemes. The new scheme is successfully tested to simulate a heterogeneous microscale partial differential equation. This technique will empower scientists and engineers to accurately and efficiently simulate, over large spatial domains, multiscale multiphysics systems that have rapid transition layers on the microscale.

Multisensor decentralized nonlinear fusion using adaptive cubature information filter

In nonlinear multisensor system, abrupt state changes and unknown variance of measurement noise are very common, which challenges the majority of the previously developed models for precisely known multisensor fusion techniques. In terms of this issue, an adaptive cubature information filter (CIF) is proposed by embedding strong tracking filter (STF) and variational Bayesian (VB) method, and it is extended to multi-sensor fusion under the decentralized fusion framework with feedback. Specifically, the new algorithms use an equivalent description of STF, which avoid the problem of solving Jacobian matrix during determining strong trace fading factor and solve the interdependent problem of combination of STF and VB. Meanwhile, A simple and efficient method for evaluating global fading factor is developed by introducing a parameter variable named fading vector. The analysis shows that compared with the traditional information filter, this filter can effectively reduce the data transmission from the local sensor to the fusion center and decrease the computational burden of the fusion center. Therefore, it can quickly return to the normal error range and has higher estimation accuracy in response to abrupt state changes. Finally, the performance of the developed algorithms is evaluated through a target tracking problem.

Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise.

Estimating the latent dynamics underlying biological processes is a central problem in computational biology. State-space models with Gaussian statistics are widely used for estimation of such latent dynamics and have been successfully utilized in the analysis of biological data. Gaussian statistics, however, fail to capture several key features of the dynamics of biological processes (e.g., brain dynamics) such as abrupt state changes and exogenous processes that affect the states in a structured fashion. Although Gaussian mixture process noise models have been considered as an alternative to capture such effects, data-driven inference of their parameters is not well-established in the literature. The objective of this paper is to develop efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and to utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting. We develop an algorithm based on Expectation-Maximization to estimate the process noise parameters from state-space observations. We apply our algorithm to simulated and experimentally-recorded MEG data from auditory experiments in the cocktail party paradigm to estimate the underlying dynamic Temporal Response Functions (TRFs). Our simulation results show that the richer representation of the process noise as a Gaussian mixture significantly improves state estimation and capturing the heterogeneity of the TRF dynamics. Application to MEG data reveals improvements over existing TRF estimation techniques, and provides a reliable alternative to current approaches for probing neural dynamics in a cocktail party scenario, as well as attention decoding in emerging applications such as smart hearing aids. Our proposed methodology provides a framework for efficient inference of Gaussian mixture process noise models, with application to a wide range of biological data with underlying heterogeneous and latent dynamics.

LRS retention fail based on joule heating effect in InGaZnO resistive-switching random access memory

This study reports the low-resistance state retention fail of InGaZnO resistive-switching random access memory (ReRAM) under constant DC bias stress conditions by Joule heating effect. There were the abrupt state changes of InGaZnO ReRAM devices with high voltage stress over 0.6 V because of thermal energy in conducting filament. In addition, SPICE simulation was conducted with verilog-A to verify this retention fail mechanism. We believe these results are potentially useful to the analysis on the retention fail properties of ReRAM devices as well as the system-level simulations with reliability-awareness.

Quantum-behaved particle swarm optimization-based active noise control system with timing varying path

Active noise control systems can effectively suppress the impact of low-frequency noise and they have been applied in many fields. Recently, the evolutionary computation algorithm-based active noise control system has attracted considerable attention. To improve the noise reduction performance of the evolutionary computation algorithm-based active noise control system and solve the problem that the system cannot converge again when the path abruptly changes in steady state, we propose the path abruptly change-quantum-behaved particle swarm optimization algorithm. We apply quantum-behaved particle swarm optimization, a global optimization algorithm, to the active noise control system to improve noise reduction performance. In addition, the scheme of detecting the abrupt path change in steady state and performing re-convergence processing is designed to effectively address the problem that the system cannot regain convergence after a path change in steady state. The simulation study demonstrates that the proposed algorithm can efficiently improve noise reduction performance, accurately detect the path change, and re-converge to new global optimization.

Extended Object Tracking Using Random Matrix With Skewness

For extended object tracking, the random matrix approach is a computationally efficient framework that is capable of estimating the kinematic state, and extension of the object jointly, and thus is gaining momentum in recent years. Existing random matrix approaches have an underlying assumption that scatter centers are symmetrically distributed around the centroid. In many real scenarios, however, they are often distributed on particular portions of the object since these parts reflect more radar energy, and measurement distributions over an object are skewed. To effectively describe such a phenomenon, this paper proposes a new measurement model using a skew normal distribution. Based on the proposed model, a variational Bayesian approach is derived to recursively estimate the kinematic state, and the extension through convergent iterations. The resultant algorithm inherits the simplicity of the random matrix approach. To cope with the possible abrupt change of kinematic state, extension, and measurement distribution over an object (especially the skewness) when a target maneuvers, a multiple model approach is presented in the information theoretic interacting multiple model framework. Effectiveness of the proposed algorithms is evaluated using simulated data, and real experimental data. Results show that the proposed algorithms outperform the existing random matrix methods when measurement distributions are skewed.