A Markov approach for increasing precision in the assessment of data-intensive behavioral interventions.

Health interventions using real-time sensing technology are characterized by intensive longitudinal data, which has the potential to enable nuanced evaluations of individuals' responses to treatment. Existing analytic tools were not developed to capitalize on this opportunity as they typically focus on first-order findings such as changes in the level and/or slope of outcome variables over different intervention phases. This paper introduces an exploratory, Markov-based empirical transition method that offers a more comprehensive assessment of behavioral responses when intensive longitudinal data are available. The procedure projects a univariate time-series into discrete states and empirically determines the probability of transitioning from one state to another. State transition probabilities are summarized separately in phase-specific transition matrices. Comparing transition matrices illuminates intricate, quantifiable differences in behavior between intervention phases. Statistical significance is estimated via bootstrapping techniques. This paper introduces the methodology via three case studies from a secondhand smoke reduction trial utilizing real-time air particle sensors. Analysis enabled the identification of complex phenomena such as avoidance and escape behavior in response to punitive contingencies for tobacco use. Additionally, the largest changes in behavior dynamics were associated with the introduction of behavioral feedback. The Markov approach's ability to elucidate subtle behavioral details has not typically been feasible with standard methodologies, mainly due to historical limitations associated with infrequent repeated measures. These results suggest that the evaluation of intervention effects in data-intensive single-case designs can be enhanced, providing rich information that can ultimately be used to develop interventions uniquely tailored to specific individuals.

Interactions and scattering of quantum vortices in a polariton fluid.

Quantum vortices, the quantized version of classical vortices, play a prominent role in superfluid and superconductor phase transitions. However, their exploration at a particle level in open quantum systems has gained considerable attention only recently. Here we study vortex pair interactions in a resonant polariton fluid created in a solid-state microcavity. By tracking the vortices on picosecond time scales, we reveal the role of nonlinearity, as well as of density and phase gradients, in driving their rotational dynamics. Such effects are also responsible for the split of composite spin-vortex molecules into elementary half-vortices, when seeding opposite vorticity between the two spinorial components. Remarkably, we also observe that vortices placed in close proximity experience a pull-push scenario leading to unusual scattering-like events that can be described by a tunable effective potential. Understanding vortex interactions can be useful in quantum hydrodynamics and in the development of vortex-based lattices, gyroscopes, and logic devices.

Computational model for behavior shaping as an adaptive health intervention strategy.

Adaptive behavioral interventions that automatically adjust in real-time to participants' changing behavior, environmental contexts, and individual history are becoming more feasible as the use of real-time sensing technology expands. This development is expected to improve shortcomings associated with traditional behavioral interventions, such as the reliance on imprecise intervention procedures and limited/short-lived effects. JITAI adaptation strategies often lack a theoretical foundation. Increasing the theoretical fidelity of a trial has been shown to increase effectiveness. This research explores the use of shaping, a well-known process from behavioral theory for engendering or maintaining a target behavior, as a JITAI adaptation strategy. A computational model of behavior dynamics and operant conditioning was modified to incorporate the construct of behavior shaping by adding the ability to vary, over time, the range of behaviors that were reinforced when emitted. Digital experiments were performed with this updated model for a range of parameters in order to identify the behavior shaping features that optimally generated target behavior. Narrowing the range of reinforced behaviors continuously in time led to better outcomes compared with a discrete narrowing of the reinforcement window. Rapid narrowing followed by more moderate decreases in window size was more effective in generating target behavior than the inverse scenario. The computational shaping model represents an effective tool for investigating JITAI adaptation strategies. Model parameters must now be translated from the digital domain to real-world experiments so that model findings can be validated.

Multifrequency synthesis using two coupled nonlinear oscillator arrays.

We illustrate a scheme that exploits the theory of symmetry-breaking bifurcations for generating a spatio-temporal pattern in which one of two interconnected arrays, each with N Van der Pol oscillators, oscillates at N times the frequency of the other. A bifurcation analysis demonstrates that this type of frequency generation cannot be realized without the mutual interaction between the two arrays. It is also demonstrated that the mechanism for generating these frequencies between the two arrays is different from that of a master-slave interaction, a synchronization effect, or that of subharmonic and ultraharmonic solutions generated by forced systems. This kind of frequency generation scheme can find applications in the developed field of nonlinear antenna and radar systems.