Realistic Dynamics Research Articles

Metabolic prioritization of fish in hypoxic waters: an integrative modeling approach

Marine hypoxia has had major consequences for both economically and ecologically critical fish species around the world. As hypoxic regions continue to grow in severity and extent, we must deepen our understanding of mechanisms driving population and community responses to major stressors. It has been shown that food availability and habitat use are the most critical components of impacts on individual fish leading to observed outcomes at higher levels of organization. However, differences within and among species in partitioning available energy for metabolic demands – or metabolic prioritization – in response to stressors are often ignored. Here, I use both a multispecies size spectrum model and a meta-analysis to explore evidence in favor of metabolic prioritization in a community of commercially important fish species in the Baltic Sea. Modeling results suggest that metabolic prioritization is an important component of the individual response to hypoxia, that it interacts with other components to produce realistic community dynamics, and that different species may prioritize differently. It is thus suggested that declines in feeding activity, assimilation efficiency, and successful reproduction – in addition to low food availability and changing habitat use – are all important drivers of the community response to hypoxia. Meta-analysis results also provide evidence that the dominant predator in the study system prioritizes among metabolic demands, and that these priorities may change as oxygen declines. Going forward, experiments and models should explore how differences in priorities within and among communities drive responses to environmental degradation. This will help management efforts to tailor recovery programs to the physiological needs of species within a given system.

Bioen-OSMOSE: A bioenergetic marine ecosystem model with physiological response to temperature and oxygen

1.Marine ecosystem models have been used to project the impacts of climate-induced changes in temperature and oxygen on biodiversity mainly through changes in species spatial distributions and primary production. However, fish populations may also respond to climatic pressures via physiological changes, leading to modifications in their life history that could either mitigate or worsen the consequences of climate change.2.Building on the individual-based multispecies ecosystem model OSMOSE, Bioen-OSMOSE has been developed to account for high trophic levels’ physiological responses to temperature and oxygen in future climate projections. This paper presents an overview of the Bioen-OSMOSE model, mainly detailing the new developments. These consist in the implementation of a bioenergetic sub-model that mechanistically describes somatic growth, sexual maturation and reproduction as they emerge from the energy fluxes sustained by food intake under the hypotheses of a biphasic growth model and plastic maturation age and size represented by a maturation reaction norm. These fluxes depend on temperature and oxygen concentration, thus allowing plastic physiological responses to climate change.3.To illustrate the capabilities of Bioen-OSMOSE to represent realistic ecosystem dynamics, the model is applied to the North Sea ecosystem. The model outputs are confronted with population biomass, catch, maturity ogive, mean size-at-age and diet data of each species of the fish community. The model succeeds in reproducing observations, with good performances for all indicators. A first exploration of current spatial variability in species’ bioenergetic fluxes resulting from temperature, oxygen, and food availability is presented in this paper, highlighting the role of temperature.4.This new model development opens the scope for new fields of research such as the exploration of seasonal or spatial variation in life history in response to biotic and abiotic factors at the individual, population and community levels. Understanding such variability is crucial to improve our knowledge on potential climate change impacts on marine ecosystems and to make more reliable projections under climate change scenarios.

Salmon lice dispersal and population model for management strategy evaluation

Modern marine salmon aquaculture includes management of salmon lice (Lepeophtheirus salmonis) infestations. Moving from a reactive delousing treatment effort to a proactive preventive strategy requires a simulation tool integrating salmon lice spread between farms with a population dynamic model at individual farms. However, such predictions have proven challenging. Here, we propose a mechanistic cohort-based model of salmon lice that explicitly accounts for the development of lice in relation to temperature coupled with hydrodynamic particle simulations of lice infections between farms. The model was validated against observed salmon lice counts and is able to produce realistic patterns of salmon lice epidemic development, but has a limited ability to resolve realistic temporal salmon lice dynamics on a per-farm basis. The model can, however, be used to evaluate general regional and national management strategies e.g. level of treatment threshold. Results shown that decreasing the treatment threshold has no significant impact the total number of treatments indicating that there is no argument against lowering the treatment threshold in a connected farm network which will eventually benefit the wild salmonid populations.

Generalizing the Wells-Riley Infection Probability: A Superstatistical Scheme for Indoor Infection Risk Estimation.

Recent evidence supports that air is the main transmission pathway of the recently identified SARS-CoV-2 coronavirus that causes COVID-19 disease. Estimating the infection risk associated with an indoor space remains an open problem due to insufficient data concerning COVID-19 outbreaks, as well as, methodological challenges arising from cases where environmental (i.e., out-of-host) and immunological (i.e., within-host) heterogeneities cannot be neglected. This work addresses these issues by introducing a generalization of the elementary Wells-Riley infection probability model. To this end, we adopted a superstatistical approach where the exposure rate parameter is gamma-distributed across subvolumes of the indoor space. This enabled us to construct a susceptible (S)-exposed (E)-infected (I) dynamics model where the Tsallis entropic index q quantifies the degree of departure from a well-mixed (i.e., homogeneous) indoor-air-environment state. A cumulative-dose mechanism is employed to describe infection activation in relation to a host's immunological profile. We corroborate that the six-foot rule cannot guarantee the biosafety of susceptible occupants, even for exposure times as short as 15 min. Overall, our work seeks to provide a minimal (in terms of the size of the parameter space) framework for more realistic indoor SEI dynamics explorations while highlighting their Tsallisian entropic origin and the crucial yet elusive role that the innate immune system can play in shaping them. This may be useful for scientists and decision makers interested in probing different indoor biosafety protocols more thoroughly and comprehensively, thus motivating the use of nonadditive entropies in the emerging field of indoor space epidemiology.

Mass transfer and size control of partially miscible fluid drops in a flow focusing microfluidic device

We conducted laboratory experiments and numerical simulations to investigate the formation and evolution of drops formed by partially miscible two-phase fluid, n-butanol (the continuous phase) and water (the dispersed phase), in a flow focusing microfluidic system. We carefully calibrated the numerical model to obtain good agreement with experimental data in drop velocity and mass transfer, demonstrating the model's capability to capture realistic drop dynamics. Our detailed investigation of the numerical results allowed us to determine the mechanism of drop formation and obtain a relevant criterion in terms of the disperse-to-continuous flow ratio beyond which the tubing patterns would occur. Additionally, we found that the mass transfer between the two phases, specifically at the drop interface, strongly depends on the local distribution of dissolved concentration of the dispersed phase. To enhance mass transfer, we conducted numerical simulations on alternating curved channels, which allows for the lateral advection of the dispersed phase concentration in the continuous phase at the curved section. We found that this lateral movement enhances mass transfer at the drop interface. Through detailed investigation of numerical results, we addressed mechanisms of mass transfer enhancement in the curved channel. Overall, our findings provide insight into the mechanisms of drop formation and mass transfer in partially miscible two-phase fluids in microfluidic systems, which could be useful in designing and optimizing such systems for various applications.

Vessel-following model for inland waterways based on deep reinforcement learning

With the growth of traffic on inland waterways, autonomous driving technologies for vessels will gain increasing significance to ensure traffic flow and safety. Inspired by car-following models for road traffic, which demonstrated their strength to reduce stop-and-go waves and increase efficiency and safety, we propose a vessel-following model for inland waterways based on deep reinforcement learning (RL). Our model is trained under consideration of realistic vessel dynamics and environmental influences, such as varying stream velocity and river profile, and with a reward function favoring observed following behavior and comfort. Aiming at high generalization capabilities, we propose a training environment that uses stochastic processes to model leading the trajectory and river dynamics. Our model demonstrated safe and comfortable driving in different unseen scenarios, including realistic vessel-following on the Middle Rhine. In comparison with an existing model, our model was able to early anticipate safety–critical situations, resulting in higher safety while maintaining comparable efficiency and comfort. In further experiments, the proposed approach demonstrated its potential to dampen traffic oscillations and reduce congestion by using a sequence of followers.

Hydrodynamic analysis of bioinspired vortical cross-step filtration by computational modelling.

Research on the suspension-feeding apparatus of fishes has led recently to the identification of novel filtration mechanisms involving vortices. Structures inside fish mouths form a series of 'backward-facing steps' by protruding medially into the mouth cavity. In paddlefish and basking shark mouths, porous gill rakers lie inside 'slots' between the protruding branchial arches. Vortical flows inside the slots of physical models have been shown to be important for the filtration process, but the complex flow patterns have not been visualised fully. Here we resolve the three-dimensional hydrodynamics by computational fluid dynamics simulation of a simplified mouth cavity including realistic flow dynamics at the porous layer. We developed and validated a modelling protocol in ANSYS Fluent software that combines a porous media model and permeability direction vector mapping. We found that vortex shape and confinement to the medial side of the gill rakers result from flow resistance by the porous gill raker surfaces. Anteriorly directed vortical flow shears the porous layer in the centre of slots. Flow patterns also indicate that slot entrances should remain unblocked, except for the posterior-most slot. This new modelling approach will enable future design exploration of fish-inspired filters.

Inferring Pyramidal Neuron Morphology using EAP Data.

We report a computational algorithm that uses an inverse modeling scheme to infer neuron position and morphology of cortical pyramidal neurons using spatio-temporal extracellular action potential recordings.. We first develop a generic pyramidal neuron model with stylized morphology and active channels that could mimic the realistic electrophysiological dynamics of pyramidal cells from different cortical layers. The generic stylized single neuron model has adjustable parameters for soma location, and morphology and orientation of the dendrites. The ranges for the parameters were selected to include morphology of the pyramidal neuron types in the rodent primary motor cortex. We then developed a machine learning approach that uses the local field potential simulated from the stylized model for training a convolutional neural network that predicts the parameters of the stylized neuron model. Preliminary results suggest that the proposed methodology can reliably infer the key position and morphology parameters using the simulated spatio-temporal profile of EAP waveforms. We also provide partial support to validate the inference algorithm using in vivo data. Finally, we highlight the issues involved and ongoing work to develop a pipeline to automate the scheme.

Regeneration strategies and forest resilience to changing fire regimes: Insights from a Goldilocks model.

Disturbances are ubiquitous in ecological systems, and species have evolved a range of strategies to resist or rebound following disturbance. Understanding how the presence and complementarity of regeneration traits will affect community responses to disturbance is increasingly urgent as disturbance regimes shift beyond their historical ranges of variability. We define "disturbance niche" as a species' fitness across a range of disturbance sizes and frequencies that can reflect the fundamental or realized niche, i.e., whether the species occurs alone or with other species. We developed a model of intermediate complexity (i.e., a Goldilocks model) to infer the disturbance niche. We parameterized the model for subalpine forests in Yellowstone National Park (USA) adapted to infrequent stand-replacing fires and included the three major tree-regeneration strategies: (1) obligate seeders that rely on ex situ seeding into burned areas (non-serotinous lodgepole pine, Pinus contorta var. latifola), (2) obligate seeders that depend on in situ seedbanks (serotinous lodgepole pine, Pinus contorta var. latifola), and (3) species that can resprout from surviving roots following fire (quaking aspen, Populus tremuloides). Our results showed which regeneration strategies increase or decrease in prevalence as fire rotation declines. Non-serotinous pines were extirpated when fire rotation was below 50 years in a monoculture and 100 years in a mixed forest; serotinous pines were extirpated when fire rotation was below 20 years; and aspen was extirpated when fire rotation fell below 6 years. The fundamental and realized disturbance niches pinpointed the key mechanisms limiting regeneration for each strategy, namely, increasing fire size for non-serotinous pine (ex situ seeders), decreasing fire frequency for serotinous pine (in situ seeders), and interspecific competition for aspen (resprouters). In a mixed forest, the three regeneration strategies were complementary and each dominated at different combinations of fire size and frequency. Consequently, diversity of regeneration strategies enhanced forest resilience to declining fire rotations. Despite its simplicity, our Goldilocks model produced realistic dynamics and could be readily adapted to other disturbance-prone ecosystems to explore the generality of these results. The disturbance niche is a key concept for anticipating community resilience to changing disturbance regimes. This article is protected by copyright. All rights reserved.

Micromagnetic study of inertial spin waves in ferromagnetic nanodots

Here we report the possibility to excite ultra-short spin waves in ferromagnetic thin-films by using time-harmonic electromagnetic fields with terahertz frequency. Such ultra-fast excitation requires to include inertial effects in the description of magnetization dynamics. In this respect, we consider the inertial Landau-Lifshitz-Gilbert (iLLG) equation and develop analytical theory for exchange-dominated inertial spin waves. The theory predicts a finite limit for inertial spin wave propagation velocity, as well as spin wave spatial decay and lifetime as function of material parameters. Then, guided by the theory, we perform numerical micromagnetic simulations that demonstrate the excitation of ultra-short inertial spin waves (20 nm long) propagating at finite speed in a confined magnetic nanodot. The results are in agreement with the theory and provide the order of magnitude of quantities observable in realistic ultra-fast dynamics experiments.

A new phase model of the spatiotemporal relationships between three circadian oscillators in the brainstem

Analysis of ex vivo Per2 bioluminescent rhythm previously recorded in the mouse dorsal vagal complex reveals a characteristic phase relationship between three distinct circadian oscillators. These signals represent core clock gene expression in the area postrema (AP), the nucleus of the solitary tract (NTS) and the ependymal cells surrounding the 4th ventricle (4Vep). Initially, the data suggests a consistent phasing in which the AP peaks first, followed shortly by the NTS, with the 4Vep peaking 8–9 h later. Wavelet analysis reveals that this pattern is not consistently maintained throughout a recording, however, the phase dynamics strongly imply that oscillator interactions are present. A simple phase model of the three oscillators is developed and it suggests that realistic phase dynamics occur between three model oscillators with coupling close to a synchronisation transition. The coupling topology suggests that the AP bidirectionally communicates phase information to the NTS and the 4Vep to synchronise the three structures. A comparison of the model with previous experimental manipulations demonstrates its feasibility to explain DVC circadian phasing. Finally, we show that simulating steadily decaying coupling improves the model’s ability to capture experimental phase dynamics.

Project Management Methodology in Regional Self-Government Units

Project implementation is one of the key activities in the process of ensuring development. In public institutions, the challenges in project management are particularly evident. Organizational inflexibility and an inability to adequately evaluate work are particularly emphasized, often creating problems during project implementation. These challenges become even greater if the financing of the project is planned with EU grants or other financial instruments that require great precision and thus exceptional project management skills. This document will present an effective project management model, as well as programs and portfolios in regional self-government units. A methodology has been developed to encourage the transformation of public systems from rigidly functional to project systems. The methodology was tested in Primorje-Gorski Kotar County, Republic of Croatia. An analysis of the quality of implementation of twenty projects has been carried out: an analysis of the final results of ten projects in which the methodology has been applied and of ten projects in which the methodology has not been applied. After conducting empirical research and analysis, the quality of the proposed model was proven at all levels of governance within the public sector. By applying this methodology, significant advances can be made in the quality of realized projects while ensuring the realistic dynamics of this realization and rational financial costs.

International trade and technological competition in markets with dynamic increasing returns

We build a simple international trade model to study how the interaction between imperfect market selection and technological learning jointly shapes trade patterns as well as firm and industry dynamics. The model features two countries populated by firms heterogeneous in their productivity and size. Market selection is driven by a finite pairwise Pólya urn process, while a geometric random walk characterizes the idiosyncratic cumulative firm learning process. We show that the model is able to jointly reproduce a wide ensemble of stylized facts concerning intra-industry trade, industry and firm dynamics as well as realistic dynamics of export and productivity leadership at the country level. A series of sensitivity exercises on the degree of trade openness and the tightness of the market selection process allow us to draw interesting policy insights on concentration, volatility and the dynamics of international leadership in both export shares and aggregate productivity.

Ultrafast generation of atomistic biomolecular trajectories for methods development.

Validation of new methods requires testing data of high (protein-like) complexity, that also can be exhaustively sampled to obtain a precise reference. In practice, however, these criteria are often mutually exclusive. The expense of integrating the equations of motion for molecular dynamics (MD) simulations, for example, typically makes high-quality sampling of complex systems infeasible. Simpler and faster models such as low dimensional potentials can be sampled extensively, but likely do not capture sufficient complexity to challenge cutting-edge new methods. Building on precedents in the literature, we demonstrate a general synthetic dynamics framework for efficiently propagating dynamics in a low dimensional latent space and projecting back to the full-dimensional coordinate space. This framework can efficiently produce extremely long trajectories, and enables rapid testing of enhanced sampling and analysis algorithms on highly non-trivial models. We demonstrate synthetic MD (synMD) trajectory generation based on a fine-grained Markov state model, stratified along slow coordinates to enforce realistic long-time dynamics. Each discrete state is mapped to an atomistic configuration, leading to reasonably realistic MD-mimicking trajectories. We show generation of atomistic synMD trajectories for the fast-folding miniprotein Trp-cage at a rate of over 8 seconds/day on a single core of a standard workstation CPU. We also demonstrate the use of synMD as a dynamics engine for the popular WESTPA implementation of the weighted ensemble enhanced sampling strategy. Although we currently use MSM generators, our implementation provides a generic interface for implementing different generators, such as have been proposed based on machine-learning approaches.

Evaluation of drag coefficient for a quadrotor model

This paper focuses on the quadrotor drag coefficient model and its estimation from flight tests. Precise assessment of such a model permits the use of a quadrotor as a sensor for wind estimation purposes without the need for additional onboard sensors. Firstly, the drag coefficient has been estimated in a controlled environment via wind generator and motion capture system. Later, the evolution of the coefficient is observed for various mass and fuselage shapes. Finally, an estimation method is proposed, based on the least-squares optimization, that evaluates the drag of the quadrotor directly from outdoor flight data. The latter leads the methodology towards easier adoption in other researchers’ systems without the need for complex and expensive flight testing facilities. The accuracy of the proposed method is presented both in simulation, based on a realistic flight dynamics model, and also for real outdoor flights.

The effect of model errors in ensemble sequential assimilation of geomagnetic field

Earth’s magnetic field is generated in the fluid outer core through the dynamo process. Over the last decade, data assimilation has been used to retrieve the core dynamics and predict the evolution of the geomagnetic field. The presence of model errors in the geomagnetic data assimilation is inevitable because current numerical geodynamo models are still far from realistic core dynamics. In this paper, we investigate the effect of model errors in geomagnetic data assimilation based on ensemble Kalman filter (EnKF). We construct two dynamo models with different control parameters but exhibiting similar force balance and magnetic morphology at the core surface. We then use one dynamo model to generate synthetic observations and the other as the forward model in EnKF. Our test experiments show that the EnKF approach with the pre-setting model errors can nevertheless recover large-scale core surface flow and make a rough short-term (5-year) prediction. However, the data assimilation in the presence of model errors cannot keep improving the core state even though new observations are available. Motivated by the planned Macau Science Satellite-1, which is expected to provide improved internal geomagnetic field model, we also perform a test experiment using synthetic observations up to spherical harmonic degree <inline-formula><tex-math id="M1">\begin{document}$\ell=18$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="1-lijinfeng_M1.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="1-lijinfeng_M1.png"/></alternatives></inline-formula>. Our results indicate that high-resolution observations are crucial in reconstructing small scale flow.

A reduced-order closed-loop hybrid dynamic model for design and development of lower limb prostheses.

This manuscript presents a simplified dynamic human-prosthesis model and simulation framework for the purpose of designing and developing lower limb prosthesis hardware and controllers. The objective was to provide an offline design tool to verify the closed-loop behavior of the prosthesis with the human, in order to avoid relying solely on limiting kinematic and kinetic reference trajectories of (able-bodied) subjects and associated static or inverse dynamic analyses, while not having to resort to complete neuromusculoskeletal models of the human that require extensive optimizations to run. The presented approach employs a reduced-order model that includes only the prosthetic limb and trunk in a multi-body dynamic model. External forces are applied to the trunk during stance phase of the intact leg to represent its presence. Walking is realized by employing the well-known spring-loaded inverted pendulum model, which is shown to generate realistic dynamics on the prosthesis while maintaining a stable and modifiable gait. This simple approach is inspired from the rationale that the human is adaptive, and from the desire to facilitate modifications or inclusions of additional user actions. The presented framework is validated with two use cases, featuring a commercial and research knee prosthesis in combination with a passive ankle prosthesis, performing a continuous sequence of standing still, walking at different velocities and stopping.

An Evaluation Framework for Urban Pluvial Flooding Based on Open-Access Data

Identifying the location and estimating the magnitude of urban pluvial flooding events is essential to assess their impacts, particularly in areas where data are unavailable. The present work focused on developing and exemplifying a tool to evaluate urban pluvial flooding based on open-access information. The tool has three separate submodules: (1) sewer network generation and design; (2) hydrodynamic model development; (3) urban pluvial flood evaluation. Application of the first two modules in two catchments and comparison of these results with real data indicated that the tool was able to generate systems with realistic layouts and hydraulic properties. Hydrodynamic models derived from this data were able to simulate realistic flow dynamics. The third module was evaluated for one of the study cases. The results of this indicated that the current approach could be used to identify flood areas and associated flood depths during different rainfall scenarios. The outcomes of this study could be used in a wide variety of contexts. For example, it could provide information in areas with data scarcity or uncertainty or serve as a tool for prospective planning, design, and decision making.

Bifurcations in a diffusive resource-consumer model with distributed memory

Spatial memory is significant in modeling animal movement. For a diffusive consumer-resource model, a memory-based diffusion of consumer can result in richer and more realistic dynamics. In fact, memory-based diffusion is related to the resource distributions in past times because the memory decays over time. We originally propose a consumer-resource model with distributed memory, and then investigate the influence of the weak memory kernel on the stability of the positive constant steady state. When the memory-based diffusion coefficient is negative, the mean delay does not affect the stability of the positive constant steady state; however, when the memory-based diffusion coefficient is positive, the mean delay can lead to the spatially inhomogeneous periodic oscillation patterns. The direction and stability of Turing bifurcation induced by the memory-based diffusion coefficient are calculated by using the methods of Crandall and Rabinowitz, and the direction and stability of Hopf bifurcation induced by the mean delay are determined by the normal form theory.

Universal Learning Waveform Selection Strategies for Adaptive Target Tracking

Online selection of optimal waveforms for target tracking with active sensors has long been a problem of interest. Many conventional solutions utilize an estimation-theoretic interpretation, in which a waveform-specific Cramér–Rao lower bound on measurement error is used to select the optimal waveform for each tracking step. However, this approach is only valid in the high SNR regime, and requires a rather restrictive set of assumptions regarding the target motion and measurement models. Furthermore, due to computational concerns, many traditional approaches are limited to near-term, or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">myopic</i> , optimization, even though radar scenes exhibit strong temporal correlation. More recently, reinforcement learning has been proposed for waveform selection, in which the problem is framed as a Markov decision process, allowing for long-term planning. However, a major limitation of reinforcement learning is that the memory length of the underlying Markov process is often unknown for realistic target and channel dynamics, and a more general framework is desirable. This work develops a universal sequential waveform selection scheme which asymptotically achieves Bellman optimality in any radar scene, which can be modeled as a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$U{\text{th}}$</tex-math></inline-formula> -order Markov process for a finite, but unknown, integer <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$U$</tex-math></inline-formula> . Our approach is based on well-established tools from the field of universal source coding, where a stationary source is parsed into variable length phrases in order to build a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">context-tree</i> , which is used as a probabilistic model for the scene’s behavior. We show that an algorithm based on a multialphabet version of the context-tree weighting method can be used to optimally solve a broad class of waveform-agile tracking problems while making minimal assumptions about the environment’s behavior.