Kirill Glavatskiy

Background: Bipolar disorder is a recurrent and disabling condition, with a critical clinical need to prevent transitions from euthymia or depression (normal or low activation states) to mania (a high activation state). This study investigates how disruptions in sleep–wake and circadian rhythms may trigger these high activation states, to inform more effective relapse prevention strategies. Methods: We developed a computational agent-based model integrating empirical evidence, clinical expertise, and lived experience to simulate how 24-hour sleep–wake behaviors (SWBs) influence manic episodes. Individual characteristics were drawn from the Brain and Mind Youth Cohort ( N = 2,330), and multiple scenarios were simulated to assess how SWB dynamics affect the emergence and course of mania. Results: In the absence of all irregularities, no individuals experienced a manic episode. Removing behavioral feedback loops resulted in a substantial reduction in manic episodes and delayed onset. In contrast, eliminating light–dark entrainment slightly increased the frequency of manic episodes, suggesting that seasonal adaptation plays a stabilizing role. When examining components of SWB separately, removing sleep irregularities alone had only a modest effect on mania rates, whereas reducing activity irregularities led to the largest benefit: a significant drop in mean manic episodes, a delay in onset, and preventing mania in 65% of the simulated agent population. Conclusions: Our findings highlight the value of computational modeling for uncovering causal dynamics in mental health. These specific findings demonstrate how daily irregularities in sleep–wake behavior may be a necessary condition for mania. Targeting behavioral regularity may offer a powerful pathway for prevention and early intervention.

A simple expression for the non-equilibrium distribution function in ultra-fast transient processes is proposed. Postulating its dependence on temporal derivatives of the equilibrium integrals of motion, non-equilibrium analogues of the thermodynamic relationships are derived and the conditions that maximize the non-equilibrium entropy are identified. A rigorous threshold between ``slow" and ``fast" processes is suggested, identifying the range of applicability of classical quasi-equilibrium description. The proposed theory is validated by deriving the known law of inertial heat conduction, which accounts for finite speed of thermal propagation. Finally, a new expression for the non-equilibrium work is derived, revealing two kinds of pressure that emerge in fast non-equilibrium.

Extended irreversible thermodynamics (EIT) has been widely used to overcome the deficiencies of classical irreversible thermodynamics in describing fast transport phenomena. By employing fluxes as additional independent variables and rejecting local equilibrium hypothesis, EIT may provide a thermodynamically consistent framework for high-frequency and non-local processes. Here, we propose an alternative approach to EIT that shares the same objective but does not reject local equilibrium hypothesis. Using the rates of change of the energy density as the additional independent variable, we illustrate this approach for two typical problems of transient heat conduction: the Cattaneo-type flux model with thermodynamic inertia and the two-temperature model of energy transfer in a phonon–electron system.

Bipolar disorder (BD) is highly disabling and often leads to premature mortality. Interventions focus on treating acute episodes, maintaining euthymia or preventing recurrence. A clinical priority is preventing transition from euthymia or depression (normal or low activation states) to mania (a high activation state). Identifying causal mechanisms underlying the temporal dynamics of this transition may enable more effective early intervention and secondary prevention. We demonstrate how agent-based modelling aids evaluating causal hypotheses for the onset and course of mania in youth by quantifying how perturbations in sleep-wake and circadian mechanisms precipitate transitions to high activation (manic) states. The proposed framework can be used as a digital laboratory to interrogate hypotheses about causal mechanisms that give rise to mania and the potential impacts of various behavioural interventions. A longitudinal simulation study of digital agents is performed, using the research evidence base, input from clinicians, and youth with lived experience. This is done using a purposely created digital twin model of youth individuals, which operationalizes the relationships between individual 24-hour cycles of sleep-wake behaviors (SWB) and the onset of manic episodes. A population of 200 digital ‘agents’ with individual characteristics derived from young people at different stages of BD from the longitudinal study of the Brain and Mind Youth Cohort (N=2,330) is studied. Multiple simulation scenarios that explore and quantify how typical features of the 24-hour cycle of SWB and the underlying circadian system impact the temporal dynamics of mania are investigated.

In this article, we consider a variety of different mechanisms through which crises such as COVID-19 can propagate from the micro-economic behaviour of individual agents through to an economy’s aggregate dynamics and subsequently spill over into the global economy. Our central theme is one of changes in the behaviour of heterogeneous agents, agents who differ in terms of some measure of size, wealth, connectivity, or behaviour, in different parts of an economy. These are illustrated through a variety of case studies, from individuals and households with budgetary constraints, to financial markets, to companies composed of thousands of small projects, to companies that implement single multi-billion dollar projects. In each case, we emphasise the role of data or theoretical models and place them in the context of measuring their inter-connectivity and emergent dynamics. Some of these are simple models that need to be ‘dressed’ in socio-economic data to be used for policy-making, and we give an example of how to do this with housing markets, while others are more similar to archaeological evidence; they provide hints about the bigger picture but have yet to be unified with other results. The result is only an outline of what is possible but it shows that we are drawing closer to an integrated set of concepts, principles, and models. In the final section, we emphasise the potential as well as the limitations and what the future of these methods hold for economics.

We propose a non-equilibrium framework for modelling the evolution of cities, which describes intra-urban migration as an irreversible diffusive process. We validate this framework using the actual migration data for the Australian capital cities. With respect to the residential relocation, the population is shown to be composed of two distinct groups, exhibiting different relocation frequencies. In the context of the developed framework, these groups can be interpreted as two components of a binary fluid mixture, each with its own diffusive relaxation time. Using this approach, we obtain long-term predictions of the cities’ spatial structures, which define their equilibrium population distribution.