Quantifying control circuit regulation in the human brain.

As a field, control systems engineering has developed quantitative methods to characterize the regulation of systems or processes, whose functioning is ubiquitous within synthetic systems. In this context, a control circuit is objectively ″well regulated″ when discrepancy between desired and achieved output trajectories is minimized, and ″robust″ to the degree that it is able to regulate well in response to a wide range of stimuli. Most psychiatric disorders are assumed to reflect dysregulation of brain circuits. Yet, probing circuit regulation requires fundamentally different analytic strategies than the correlations relied upon for analyses of connectivity and their resultant networks. Here, we demonstrate how well-established methods for system identification in control systems engineering may be applied to functional magnetic resonance imaging (fMRI) data to extract generative computational models of human brain circuits. These provide two quantitative measures of direct relevance for psychiatric disorders: a circuit′s sensitivity to external perturbation and its dysregulation.

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control-systems engineering is a multidisciplinary subject that applies automatic-control theory to design systems with desired behaviors in control environments [...]

Inelastic Analysis of Mdof Systems Damaged by Earthquakes, Posteriorly Subjected to Wind Load

This paper deals with the analysis of the inelastic response of buildings originally damaged by earthquakes and subjected to earthquake aftershock and wind loading. The overall aim is to establish the effect of wind actions on structural stability. To that end, one four-story bare frame benchmarked by the European Laboratory for Structural Assessment, is subject to various levels of winds and earthquake joint load while monitoring changes on the ductility demand. In this paper is shown that the combined action of strong winds and earthquakes, however its low probability of occurrence, would cause a decrease of strength reduction factors and considerably increase the ductility demand of damaged infrastructure hence inducing additional risks that would otherwise remain unquantified. The paper examines the non-linear performance of Multi-degree of freedom systems subject to various levels of winds and earthquake load and deals with the estimation of strength reduction factors. This is a relatively unexplored area of research which builds on past developments whereby inelastic performance of buildings has been discussed. It also links to various other paths of development such as structural reliability, forensic and control systems engineering. Doi: 10.28991/cej-2021-03091675 Full Text: PDF

Personalization in digital interventions for behavior change (Preprint)

UNSTRUCTURED Effective behavior change interventions may require ongoing personalized support for users. Rapid developments in digital technology and artificial intelligence are giving rise to more advanced types of personalized interventions that can analyze large amounts of data to provide real-time, contextualized support. Despite growing research attention, there is still a lack of consensus in the literature about what is considered a personalized system, and how to design such system. This paper provides a definition of personalization and proposes a set of building blocks to design and implement personalized behavior change interventions, drawing on concepts from control systems engineering. We also discuss existing challenges in evaluating the net effects of personalized interventions and outline future directions in this field.

Personalized models of physical activity responses to text message micro-interventions: A proof-of-concept application of control systems engineering methods

Objectives: The conceptual models underlying physical activity interventions have been based largely on differences between more and less active people. Yet physical activity is a dynamic behavior, and such models are not sensitive to factors that regulate behavior at a momentary level or how people respond to individual attempts at intervening. We demonstrate how a control systems engineering approach can be applied to develop personalized models of behavioral responses to an intensive text message-based intervention. Design & Method: To establish proof-of-concept for this approach, 10 adults wore activity monitors for 16 weeks and received five text messages daily at random times. Message content was randomly selected from three types of messages designed to target (1) social-cognitive processes associated with increasing physical activity, (2) social-cognitive processes associated with reducing sedentary behavior, or (3) general facts unrelated to either physical activity or sedentary behavior. A dynamical systems model was estimated for each participant to examine the magnitude and timing of responses to each type of text message. Results: Models revealed heterogeneous responses to different message types that varied between people and between weekdays and weekends. Conclusions: This proof-of-concept demonstration suggests that parameters from this model can be used to develop personalized algorithms for intervention delivery. More generally, these results demonstrate the potential utility of control systems engineering models for optimizing physical activity interventions.