We use stochastic closure theory and generalized Green-Kubo relations to show that the velocity structure functions have two distinct scaling regimes connected by a passover. Initially, after a brief ballistic (Batchelor) scaling, the structure functions exhibit a Lagrangian scaling regime with no intermittency, and then pass over to a regime with Eulerian scaling, with intermittency. This transition time for the passover region is controlled by the second structure function, through a generalization of Green-Kubo-Obukhov relations. The ultimate time interval of decay seems to be controlled by the scaling of free eddies, analogous to the scaling in the buffer layer of boundary layer turbulence [B. Birnir, L. Angheluta, J. Kaminsky, and X. Chen, Spectral link of the Generalized Townsend-Perry constants in turbulent boundary layers, Phys. Rev. Res. 3, 043054 (2021)]. The dip observed in the log-derivatives of the structure function [L. Biferale, E. Bodenschatz, M. Cencini, A. S. Lanotte, N. T. Ouellette, F. Toschi, and H. Xu, Lagrangian structure functions in turbulence: A quantitative comparison between experiment and direct numerical simulation, Phys. Fluids 20, 065103 (2008)], with respect to the second structure function S2, is caused only by the time scales probed by S2. It seems better to take the log-derivative with respect to t, instead of S2, to fully understand the different scaling regimes of Lagrangian turbulence.

We prove the existence of unique global weak solutions to equations describing the sediment flow in the evolution of fluvial land surfaces, with constant water depth. These equations describe the so-called transport-limited situation, where all the sediment can be transported away given enough water. This is in distinction to the detachment-limited situation where we must wait for rock to weather (to sediment) before it can be transported away. Earlier work shows that these equations describe the optimal transport of sediment and the evolution of the surfaces in optimal transport theory. The existence theory is also extended to include diffusion in the water and the land surfaces.

Angiogenesis is a multiscale process by which a primary blood vessel issues secondary vessel sprouts that reach regions lacking oxygen. Angiogenesis can be a natural process of organ growth and development or a pathological one induced by a cancerous tumor. A mean-field approximation for a stochastic model of angiogenesis consists of a partial differential equation (PDE) for the density of active vessel tips. Addition of Gaussian and jump noise terms to this equation produces a stochastic PDE that defines an infinite-dimensional Lévy process and is the basis of a statistical theory of angiogenesis. The associated functional equation has been solved and the invariant measure obtained. The results of this theory are compared to direct numerical simulations of the underlying angiogenesis model. The invariant measure and the moments are functions of a Korteweg–de Vries-like soliton, which approximates the deterministic density of active vessel tips.

Because of its responsiveness to changes in the marine environment, it has been suggested by Rose in 2005 that the capelin, a small pelagic fish that is key to the ecology and fisheries of the North Atlantic, could be seen as a "canary in the coalmine" to detect signals of changes in the Arctic and sub-Arctic Ocean. We describe the historical data that make possible a quantitative assessment of the geographical shift capelin migration-paths and spawning grounds undergo, with increasing temperature, and the time it takes to make these shifts long-lasting. Then we introduce recent data that make these quantitative measurements more accurate and predictive. Direct measurements made in the fall expeditions of Iceland's Marine and Freshwater Research Institute along the East Coast of Greenland, and the Copernicus database of the European Union, are used to examine the evolution of the returning Atlantic water (from Svalbard) that is forming a warmer and saltier boundary current under the colder and fresher East Greenland polar current. The returning Atlantic water has a temperature range (1 to 4 degrees Centigrade) suitable for feeding migrations of the capelin. This current is reaching further north along the coast of North East Greenland and we use simulated data from Copernicus to monitor this evolution. We calibrate the Copernicus data with the direct measurements made by the Marine and Freshwater Research Institute, in Iceland. A trend emerges, both in the direct measurements and in Copernicus data, showing that the returning Atlantic water boundary current may reach Greenland's major Northeastern glacier streams, draining the bulk of the Greenland Glacier in the relatively near future We use the capelin data to predict when this may happen.

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Earthquakes and Volcanic Eruptions Driven by Magma SolitonsAuthorsCadenLiniDBjornBirnirSee all authors Caden LiniDCorresponding Author• Submitting AuthorThe Harker SchooliDhttps://orcid.org/0000-0003-0249-0771view email addressThe email was not providedcopy email addressBjorn BirnirUC Santa Barbaraview email addressThe email was not providedcopy email address

We propose a first minimal theory for boundary layer turbulence that captures very well the profile of the mean-square velocity fluctuations in the streamwise direction and give a quantitative prediction of the Townsend-Perry constants. Our theory is based on connecting all moments of velocity fluctuations as a function of the distance to the wall with the turbulent energy spectrum. A similar spectral theory was proposed in G. Gioia and P. Chakraborty [Phys. Rev. Lett. 96, 044502 (2006)] to explain the friction factor and the von Karman law in G. Gioia, N. Guttenberg, N. Goldenfeld, and P. Chakraborty [Phys. Rev. Lett. 105, 184501 (2010)]. We generalized it by including fluctuations in the wall-shear stress and the streamwise velocity. The theoretical predictions for the mean velocity and mean-square fluctuations reproduce the shape of the velocity profiles in the buffer and inertial layer obtained from wind tunnel experiments.

• Birnir, B., (2020). The Build-Up of Aerosols Carrying the SARS-CoV-2 Coronavirus, in Poorly Ventilated, Confined Spaces [online] Available at: https://www.medrxiv.org/ content/10.1101/2020.08.11.20173195v2 [Accessed 15 July 2021]. • Birnir, B. (2020). Ventilation and the SARS-CoV-2 Coronavirus, Analysis of outbreaks in a restaurant and on a bus in China, and at a Call Center in South Korea. [online] Available at: https://www.medrxiv.org/ content/10.1101/2020.09.11.20192997v2 [Accessed 15 July 2021] • Warhaft, Z., (2000). Passive Scalars in Turbulent Flows. Annual Review of Fluid Mechanics, 32(1), pp.203-240. Research Objectives

Abstract In a previous paper [10] a model of the distribution of respiratory droplets and aerosols by Lagrangian turbulent air-flow was developed. It is used to show how the SARS-CoV-2 Coronavirus can be spread by the breathing of single infected person. The model shows that the concentration of viruses in the cloud, exhaled by one person, can increase to infectious levels within a certain amount of time, in a confined space where the air re-circulates. In [10] the model was used to analyze the air-flow and SARS-CoV-2 Coronavirus build-up in a restaurant in Guangzhou, China [19, 18]. In this paper, we add the analysis of two more cases, an outbreak among lay-Buddhists, on a bus [26], traveling to a ceremony in Zhejiang province, China, and an outbreak in a Call Center in Seoul, Korea [20]. The analysis and comparison of these three cases, leads to the conclusion that the SARS-CoV-2 Coronavirus attacks in two steps: The first step is a linear spread between individuals with a couple of days delay. The second step is an exponential spread effected by the air-conditioning system affecting a much larger number of people. Thus in the second step, the ventilation becomes the super-spreader.

We compare the predictions of stochastic closure theory (SCT) with experimental measurements of homogeneous turbulence made in the Variable Density Turbulence Tunnel (VDTT) at the Max Planck Institute for Dynamics and Self-Organization in Gottingen. While the general form of SCT contains infinitely many free parameters, the data permit us to reduce the number to seven, only three of which are active over the entire inertial range. Of these three, one parameter characterizes the variance of the mean field noise in SCT and another characterizes the rate in the large deviations of the mean. The third parameter is the decay exponent of the Fourier variables in the Fourier expansion of the noise, which characterizes the smoothness of the turbulent velocity. SCT compares favorably with velocity structure functions measured in the experiment. We considered even-order structure functions ranging in order from two to eight as well as the third-order structure functions at five Taylor-Reynolds numbers (Rl) between 110 and 1450. The comparisons highlight several advantages of the SCT, which include explicit predictions for the structure functions at any scale and for any Reynolds number. We observed that finite-Rl corrections, for instance, are important even at the highest Reynolds numbers produced in the experiments. SCT gives us the correct basis function to express all the moments of the velocity differences in turbulence in Fourier space. The SCT produces the coefficients of the series and so determines the statistical quantities that characterize the small scales in turbulence. It also characterizes the random force acting on the fluid in the stochastic Navier-Stokes equation, as described in the paper.

A model of the distribution of respiratory droplets and aerosols by Lagrangian turbulent air-flow is developed and used to show how the SARS-CoV-2 Coronavirus can be dispersed by the breathing of an infected person. It is shown that the concentration of viruses in the exhaled cloud can increase to infectious levels with time (grow linearly), in a confined space where the air re-circulates. The model is used to analyze the air-flow and SARS-CoV-2 Coronavirus build-up in a restaurant in Guangzhou, China [32, 30]. It is concluded that the outbreak of Covid-19 pandemic in the restaurant in January 2020, is due to the build-up of the airborne droplets and aerosols carrying the SARS-CoV-2 Coronavirus and would not have been prevented by standard ventilation. A comparison with standard models for aerosol concentration shows that, in the absence of ventilation, the decay of the aerosol concentration is also controlled by the decay time of the virions in aerosols. This decay time is very long, with low relative humidity, and a steady state is not achieved in the time-frame of the contagion. Instead the concentration exhibits a polynomial increase and reaches infectious levels in a relatively short time, explaining the outbreak in the restaurant in Guangzhou.

In a previous paper [10] a model of the distribution of respiratory droplets and aerosols by Lagrangian turbulent air-flow was developed. It is used to show how the SARS-CoV-2 Coronavirus can be spread by the breathing of single infected person. The model shows that the concentration of viruses in the cloud, exhaled by one person, can increase to infectious levels within a certain amount of time, in a confined space where the air re-circulates. In [10] the model was used to analyze the air-flow and SARS-CoV-2 Coronavirus build-up in a restaurant in Guangzhou, China [19,18]. In this paper, we add the analysis of two more cases, an outbreak among lay-Buddhists, on a bus [26], traveling to a ceremony in Zhejiang province, China, and an outbreak in a Call Center in Seoul, Korea [20]. The analysis and comparison of these three cases, leads to the conclusion that the SARS-CoV-2 Coronavirus attacks in two steps: The first step is a linear spread between individuals with a couple of days delay. The second step is an exponential spread effected by the air-conditioning system affecting a much larger number of people. Thus in the second step, the ventilation becomes the super-spreader.

We compare the stochastic closure theory (SCT) to the Townsend-Perry constants as estimated from measurements in the Flow Physic Facility (FPF) at the University of New Hampshire. First, we explain the derivation of the Townsend-Perry constants, which were originally formulated by Meneveau and Marusic, in analogy with a Gaussian distribution. However, this was not supported by the data. Instead, the data show a sub-Gaussian relation that was explained by Birnir and Chen. We show herein how the SCT can be used to compute the constants, which explains their sub-Gaussian relations. We then compare the SCT theory predictions, including Reynolds-number-dependent corrections, with the data, showing good agreement.

We demonstrate how mechanistic modeling can be used to predict whether and how biological responses to chemicals at (sub)organismal levels in model species (i.e., what we typically measure) translate into impacts on ecosystem service delivery (i.e., what we care about). We consider a hypothetical case study of two species of trout, brown trout (Salmo trutta; BT) and greenback cutthroat trout (Oncorhynchus clarkii stomias; GCT). These hypothetical populations live in a high-altitude river system and are exposed to human-derived estrogen (17α‑ethinyl estradiol, EE2), which is the bioactive estrogen in many contraceptives. We use the individual-based model inSTREAM to explore how seasonally varying concentrations of EE2 could influence male spawning and sperm quality. Resulting impacts on trout recruitment and the consequences of such for anglers and for the continued viability of populations of GCT (the state fish of Colorado) are explored. inSTREAM incorporates seasonally varying river flow and temperature, fishing pressure, the influence of EE2 on species-specific demography, and inter-specific competition. The model facilitates quantitative exploration of the relative importance of endocrine disruption and inter-species competition on trout population dynamics. Simulations predicted constant EE2 loading to have more impacts on GCT than BT. However, increasing removal of BT by anglers can enhance the persistence of GCT and offset some of the negative effects of EE2. We demonstrate how models that quantitatively link impacts of chemicals and other stressors on individual survival, growth, and reproduction to consequences for populations and ecosystem service delivery, can be coupled with ecosystem service valuation. The approach facilitates interpretation of toxicity data in an ecological context and gives beneficiaries of ecosystem services a more explicit role in management decisions. Although challenges remain, this type of approach may be particularly helpful for site-specific risk assessments and those in which tradeoffs and synergies among ecosystem services need to be considered.

The stochastic closure theory (SCT) of turbulence \cite{BB211} and \cite{BB312} has been extended to Homogeneous Turbulence \cite{KBSB18}, Boundary Turbulence \cite{BC16,KKB18} and Lagrangian turbulence. These extensions give excellent agreement with experimental data. We explain how the theory puts the Kolmogorov-Obukhov '62 scaling theory, with the She-Leveque intermittency corrections \cite{SL94} on a firm theoretical foundation and permits one to compute the scaling coefficients, in addition to the scaling exponents, and their dependence on the (Taylor) Reynolds (T-R) number.For Homogeneous Turbulence, SCT is compared to data obtained in the Variable Density Turbulence Tunnel (VDTT) \cite{BBNSX14}, at the Max Planck Institute for Dynamics and Self-Organization in Gottingen. Given a mean flow in homogeneous turbulence the comparison with the data reduces the number of parameters in SCT to just three, one characterizing the variance of the mean-field noise and another characterizing the rate in the large deviations of the mean. The third parameter is the decay exponent of the Fourier variables in the Fourier expansion of the noise. This characterizes the smoothness of the turbulent velocity. For Boundary Turbulence, we compare the predictions of SCT to the generalized Townsend-Perry constants \cite{MM13} as estimated from measurements in the Flow Physic Facility (FPF) \cite{VK13} at the University of New Hampshire. Following \cite{KKB18} we show how the SCT can be used to compute the constants, explaining the sub-Gaussian relations of the constants. We then compare the SCT theory predictions with the data \cite{ZK14}, showing good agreement. Finally, we discuss the extension of SCT to Lagrangian Turbulence. Very surprisingly the comparison of SCT and the data also gives information about the smoothness of the turbulent velocity as T-R becomes very large, see \cite{KBSB18}.

Assessing and managing risks of anthropogenic activities to ecological systems is necessary to ensure sustained delivery of ecosystem services for future generations. Ecological models provide a means of quantitatively linking measured risk assessment endpoints with protection goals, by integrating potential chemical effects with species life history, ecological interactions, environmental drivers and other potential stressors. Here we demonstrate how an ecosystem modeling approach can be used to quantify insecticide-induced impacts on ecosystem services provided by a lake from toxicity data for organism-level endpoints. We used a publicly available aquatic ecosystem model AQUATOX that integrates environmental fate of chemicals and their impacts on food webs in aquatic environments. By simulating a range of exposure patterns, we illustrated how exposure to a hypothetical insecticide could affect aquatic species populations (e.g., recreational fish abundance) and environmental properties (e.g., water clarity) that would in turn affect delivery of ecosystem services. Different results were observed for different species of fish, thus the decision to manage the use of the insecticide for ecosystem services derived by anglers depends upon the favored species of fish. In our hypothetical shallow reservoir, water clarity was mostly driven by changes in food web dynamics, specifically the presence of zooplankton. In contrast to the complex response by fishing value, water clarity increased with reduced insecticide use, which produced a monotonic increase in value by waders and swimmers. Our study clearly showed the importance of considering nonlinear ecosystem feedbacks where the presence of insecticide changed the modeled food-web dynamics in unexpected ways. Our study highlights one of the main advantages of using ecological models for risk assessment, namely the ability to generalize to meaningful levels of organization and to facilitate quantitative comparisons among alternative scenarios and associated trade-offs among them while explicitly accounting for different groups of beneficiaries.

We explore the design parameter space of short (5–25 period), n-doped, Ga/(Al,Ga)As semiconductor superlattices (SSLs) in the sequential resonant tunneling regime. We consider SSLs at cool (77 K) and warm (295 K) temperatures, simulating the electronic response to variations in (a) the number of SSL periods, (b) the contact conductivity, and (c) the strength of disorder (aperiodicities). Our analysis shows that the chaotic dynamical phases exist on a number of sub-manifolds of codimension zero within the design parameter space. This result provides an encouraging guide towards the experimental observation of high-frequency intrinsic dynamical chaos in shorter SSLs.

Quantifying the action of antibiotics on biofilms is essential to devise therapies against chronic infections. Biofilms are bacterial communities attached to moist surfaces, sheltered from external aggressions by a polymeric matrix. Coupling a dynamic energy budget based description of cell metabolism to surrounding concentration fields, we are able to approximate survival curves measured for different antibiotics. We reproduce numerically stratified distributions of cell types within the biofilm and introduce ways to incorporate different resistance mechanisms. Qualitative predictions follow that are in agreement with experimental observations, such as higher survival rates of cells close to the substratum when employing antibiotics targeting active cells or enhanced polymer production when antibiotics are administered. The current computational model enables validation and hypothesis testing when developing therapies.