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In recent times, a considerable effort has been dedicated to identify certain conditions -- the so-called swampland conjectures -- with an eye on identifying effective theories which have no consistent UV-completions in string theory. In this paper, we examine the anti-de Sitter vacua corresponding to solutions which arise from purely non-perturbative contributions to the superpotential and show that these solutions satisfy the (axionic) weak gravity conjecture and the AdS-moduli scale separation conjecture. We also sketch out their advantages over other constructions., Comment: 13 pages, 1 figure

Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generallydecreasedrather thanincreasedprior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple time scales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of time scale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics.

The structure of large-scale hypersonic boundary layer separation and reattachment is studied numerically using a flat plate/compression corner geometry. Apart from verifying the large scale separation characteristics in hypersonic flow, a detailed discussion of secondary separation and fragmentation into multiple vortices embedded within the main recirculation region is presented. The unique relation between the second minimum in shear stress and the scaled angle is highlighted in the context of the reverse flow singularity of Smith (Proc. R. Soc. Lond. A, vol. A420, 1988, pp. 21–52) and it appears that for a small wall temperature ratio, such a singularity is unlikely. It is shown that the size of the separation can be estimated in terms of Burggraf’s expression based on asymptotic theory.

This article reviews the general features of the multiscale structures in particle-fluid systems and the characterization, modeling, and simulation methods for these systems. The discussion focuses on the effects of mesoscale behavior, especially those present in process industries for materials and energy transformation and utilization. When there is substantial multiscale heterogeneity in these systems, local non-equilibrium and anisotropy generally lead to a lack of scale separation. Accurate and efficient simulation methods based on first principles and applied across different scales are highly desirable to reveal and quantify the complexities of these systems. Meanwhile, precisely designed experiments and exhaustive nonintrusive measurements are necessary to validate and expand the numerical findings. With this knowledge, rational mesoscale models can be established to provide multiscale simulation methods that do not need to fully reproduce the micro- and mesoscale details of the systems but can still take into account their effects on macroscales. Such multiscale methods are attractive for industrial applications but substantial effort in physical modeling and numerical implementation is still required before their widespread implementation. (C) 2019 Elsevier Ltd. All rights reserved.

Liquid-phase enantioseparations have been fruitfully applied in several fields of science. Various applications along with technical and theoretical advancements contributed to increase significantly the knowledge in this area. Nowadays, chromatographic techniques, in particular HPLC on chiral stationary phase, are considered as mature technologies. In the last thirty years, CE has been also recognized as one of the most versatile technique for analytical scale separation of enantiomers. Despite the huge number of papers published in these fields, understanding mechanistic details of the stereoselective interaction between selector and selectand is still an open issue, in particular for high-molecular weight chiral selectors like polysaccharide derivatives. With the ever growing improvement of computer facilities, hardware and software, computational techniques have become a basic tool in enantioseparation science. In this field, molecular docking and dynamics simulations proved to be extremely adaptable to model and visualize at molecular level the spatial proximity of interacting molecules in order to predict retention, selectivity, enantiomer elution order, and profile noncovalent interactions patterns underlying the recognition process. On this basis, topics and trends in using docking and molecular dynamics as theoretical complement of experimental LC and CE chiral separations are described herein. The basic concepts of these computational strategies and seminal studies performed over time are presented, with a specific focus on literature published between 2015 and November 2018. A systematic compilation of all published literature has not been attempted.

We describe a local iterative corrector scheme that significantly improves the accuracy of the multiscale finite element method (MsFEM). Our technique is based on the definition of a local corrector problem for each multiscale basis function that is driven by the residual of the previous multiscale solution. Each corrector problem results in a local corrector solution that improves the accuracy of the corresponding multiscale basis function at element interfaces. We cast the strategy of residual-driven correction in an iterative scheme that is straightforward to implement and, due to the locality of corrector problems, well-suited for parallel computing. We show that the iterative scheme converges to the best possible fine-mesh solution. Finally, we illustrate the effectiveness of our approach with multiscale benchmarks characterized by missing scale separation, including the microCT-based stress analysis of a vertebra with trabecular microstructure.

We propose a stochastic model reduction strategy for deterministic and stochastic slow-fast systems with a moderate time scale separation. The stochastic model reduction strategy improves the appro...

In [SIAM J. Sci. Comput., 36 (2014), pp. A693--A713] the authors present a new coarse propagator for the parareal method applied to oscillatory PDEs that exhibit time-scale separation and show, und...

Recent advances in statistical and machine learning have opened the possibility of forecasting the behaviour of chaotic systems using recurrent neural networks. In this article we investigate the applicability of such a framework to geophysical flows, known to involve multiple scales in length, time and energy and to feature intermittency. We show that both multiscale dynamics and intermittency introduce severe limitations to the applicability of recurrent neural networks, both for short-term forecasts as well as for the reconstruction of the underlying attractor. We suggest that possible strategies to overcome such limitations should be based on separating the smooth large-scale dynamics from the intermittent/small-scale features. We test these ideas on global sea-level pressure data for the past 40 years, a proxy of the atmospheric circulation dynamics. Better short- and long-term forecasts of sea-level pressure data can be obtained with an optimal choice of spatial coarse graining and time filtering.

Up-to-date, there is no known example of a classical de Sitter solution of string theory, despite several good candidates. We consider here two newly discovered 10d supergravity de Sitter solutions, and analyse in great detail whether they can be promoted to classical string backgrounds. To that end, we identify five requirements to be met, and develop the necessary 10d tools to test the solutions. Eventually, they both fail to verify simultaneously all requirements, in spite of positive partial results. The explicit values obtained offer a clear illustration of the situation, and the analysis highlights various subtleties. We finally discuss the relation to the problem of scale separation., 9 pages; v2: few additions on scale separation; v3: small additions, published version

A study of the combined effect of outer-layer large-scale superposition, amplitude modulation, and distortions on the near-wall dynamics of turbulent wall-bounded flows is presented. A novel approach to detect the amplitude modulation is introduced that more clearly reveals this phenomenon compared to existing techniques. The study also shows that scale separation by the empirical mode decomposition approach and by conventional spectral filtering yields similar conclusions about the interaction mechanisms.

Stimulus perception is assumed to involve the (fast) detection of sensory inputs and their (slower) integration. The capacity of the brain to quickly adapt, at all times, to unexpected stimuli suggests that the interplay between the slow and fast processes happens at short timescales. We hypothesised that, even during resting-state, the flow of information across the brain regions should evolve quickly, but not homogeneously in time. Applying edge-wise connectivity to high temporal-resolution Magnetoencephalography (MEG) signals, we estimate the persistence of the information in functional interactions across the brain. The characterization of brain network edges according to their information storage capability naturally partitions the brain into two anatomically distinct networks. The short storage network (SSN) is localized in the regions that typically belong to the dorsal and ventral streams during perceptive tasks, while the long storage network (LSN) hinges on classically associative areas. Finally, we show that only a subset of the brain regions, which we name the multi-storage core (MSC), belong to both networks. The MSC is hypothesized to play a role in the communication between the (otherwise) segregated subnetworks.

Recent advances in statistical and machine learning have opened the possibility to forecast the behavior of chaotic systems using recurrent neural networks. In this article we investigate the applicability of such a framework to geophysical flows, known to involve multiple scales in length, time and energy and to feature intermittency. We show that both multiscale dynamics and intermittency introduce severe limitations on the applicability of recurrent neural networks, both for short-term forecasts, as well as for the reconstruction of the underlying attractor. We suggest that possible strategies to overcome such limitations should be based on separating the smooth large-scale dynamics from the intermittent/small-scale features. We test these ideas on global sea-level pressure data for the past 40 years, a proxy of the atmospheric circulation dynamics. Better short and long term forecasts of sea-level pressure data can be obtained with an optimal choice of spatial coarse grain and time filtering.

The aim of this work is to compare two existing multilevel computational approaches coming from two different families of multiscale methods in a nonlinear solid mechanics framework. A locally adaptive multigrid method and a numerical homogenization technique are considered. Both classes of methods aim to enrich a global model representing the structure’s behavior with more sophisticated local models depicting fine localized phenomena. It is clearly shown that even being developed with different vocations, such approaches reveal several common features. The main conceptual difference relying on the scale separation condition has finally a limited influence on the algorithmic aspects. Hence, this comparison enables to highlight a unified framework for multiscale coupling methods.

A widespread framework for understanding frictional rupture, such as earthquakes along geological faults, invokes an analogy to ordinary cracks. A distinct feature of ordinary cracks is that their near edge fields are characterized by a square root singularity, which is intimately related to the existence of strict dissipation-related lengthscale separation and edge-localized energy balance. Yet, the interrelations between the singularity order, lengthscale separation and edge-localized energy balance in frictional rupture are not fully understood, even in physical situations in which the conventional square root singularity remains approximately valid. Here we develop a macroscopic theory that shows that the generic rate-dependent nature of friction leads to deviations from the conventional singularity, and that even if this deviation is small, significant non-edge-localized rupture-related dissipation emerges. The physical origin of the latter, which is predicted to vanish identically in the crack analogy, is the breakdown of scale separation that leads an accumulated spatially-extended dissipation, involving macroscopic scales. The non-edge-localized rupture-related dissipation is also predicted to be position dependent. The theoretical predictions are quantitatively supported by available numerical results, and their possible implications for earthquake physics are discussed. Ordinary cracks in bulk materials feature square root singular deformation fields near their edge. Here, the authors show that rupture fronts propagating along frictional interfaces, while resembling ordinary cracks in some respects, feature edge sigularity that differs from the conventional square root one.

Large scale separation of liquid mixtures into their various components is one of the major operations in the process industries. Distillation remains one of the predominant separation techniques that is use for these separations. It accounts for about 90% of product recovery and purification in the process industries. The strength of the distillation technique lies on its simplicity, low capital investment, and low risk as compared to other traditional separation techniques. In the present study, the synergistic effects of multiple packings in binary distillation column operations was investigated. The investigation was done using Aspen HYSYS process simulation software. Specifically, the study was done to establish a packing order that will be required to achieve a reduction in energy consumption and the overall cost of separation processes that involve distillation operation. The packed distillation was simulated using Berl saddles, Intalox saddles, and Pall rings as the column internals. Single, dual and triple packing arrangements were evaluated. The multiple packing arrangement investigated were Berl/Intalox, Berl/Pall, Pall/Intalox, and Berl/Intalox/Pall. Binary mixtures of pentane and hexane was used as feed to the column. The results of this simulation revealed that the use of multiple packing arrangement had a significant effect on the energy consumption, the height of packing and hence the cost of equipment fabrication. For instance, the estimated cost of packings (USD) are 524.89 for Berl, 406.85 for Pall, 303.77 for Intallox, 427.09 for Berl/Intalox, 473.06 for Berl/Pall, 323.51 for Pall/Intalox, and 388.60 for Berl/Intalox/Pall. These results clearly revealed that multiple packing arrangements can be used to reduce the cost of column fabrication without compromising the performance of the equipment.

In this note we examine certain classes of solutions of IIA theory without sources, of the form AdS$_2\times {\cal M}^{(1)}\times \dots \times {\cal M}^{(n)}$, where ${\cal M}^{(i)}$ are Riemannian spaces. We show that large hierarchies of curvatures can be obtained between the different factors, however the absolute value of the scalar curvature of AdS$_2$ must be of the same order or larger than the absolute values of the scalar curvatures of all the other factors., Comment: 20 pages, one additional ref. in replaced version, 2nd replaced version with minor corrections

Recent advances in statistical learning have opened the possibility to forecast the behavior of chaotic systems using recurrent neural networks. In this letter we investigate the applicability of this framework to geophysical flows, known to be intermittent and turbulent. We show that both turbulence and intermittency introduce severe limitations on the applicability of recurrent neural networks, both for short term forecasts as well as for the reconstruction of the underlying attractor. We test these ideas on global sea-level pressure data for the past 40 years, issued from the NCEP reanalysis datase, a proxy of the atmospheric circulation dynamics. The performance of recurrent neural network in predicting both short and long term behaviors rapidly drops when the systems are perturbed with noise. However, we found that a good predictability is partially recovered when scale separation is performed via a moving average filter. We suggest that possible strategies to overcome limitations should be based on separating the smooth large-scale dynamics, from the intermittent/turbulent features.

We scrutinize the use of machine learning, based on reservoir computing, to build data-driven effective models of multiscale chaotic systems. We show that, for a wide scale separation, machine learning generates effective models akin to those obtained using multiscale asymptotic techniques and, remarkably, remains effective in predictability also when the scale separation is reduced. We also show that predictability can be improved by hybridizing the reservoir with an imperfect model., 12 pages with 10 figures. Accepted in Physical Review E

Accepted; International audience; A Coarse Mesh Condensation Multiscale Method (CMCM) is proposed to solve large heterogeneous linear structures without scale separation assumption. The technique aims to approximate the full field solution in heterogeneous structures by performing parallel calculations on subdomains. In the linear case, treated in this paper, direct linear relationships can be established between a reduced number of parameters describing Dirichlet boundary conditions on the subdomains boundaries and the degrees of freedom of a coarse mesh. The problem associated with the coarse mesh can be solved in one iteration and allows reconstructing the fine mesh solution in all subdomains. The accuracy of the method is analyzed through benchmarks involving subdomains crossed by the interfaces. Appplications to large industrial finite element applications are presented, including one involving around 1.3 billion degrees of freedom.

In this work, we study two seemingly unrelated aspects of core genetic nonlinear dynamical control of the competence phenotype in Bacillus subtilis, a common Gram-positive bacterium living in the soil. We focus on hitherto unchartered aspects of the dynamics by exploring the effect of time-scale separation between transcription and translation and, as well, the effect of intrinsic molecular stochasticity. We consider these aspects of regulatory control as two possible evolutionary handles. Hence, using theory and computations, we study how the onset of oscillations breaks the excitability-based competence phenotype in two topologically close evolutionary-competing circuits: the canonical “wild-type” regulation circuit selected by Evolution and the corresponding indirect-feedback inverted circuit that failed to be selected by Evolution, as was shown elsewhere, due to dynamical reasons. Relying on in-silico perturbation of the living state, we show that the canonical core genetic regulation of excitability-based competence is more robust against switching to phenotype-breaking oscillations than the inverted feedback organism. We show how this is due to time-scale separation and stochasticity.

This paper presents the application of the recently developed Generic Flight Controller design approach by the authors for a delta canard fighter aircraft referred to Aero Data Model in Research Environment (ADMIRE). The generic flight controller developed for high performance fixed wing aircrafts uses the good features of nonlinear dynamic inversion with time scale separation, control allocation and integrator backstepping with the major advantage that the whole design cycle can be carried out quickly. The results obtained for ADMIRE using generic flight controller results have been compared with those obtained from the ADMIRE controller taken from literature. The quick adoption of generic flight controller to ADMIRE has been demonstrated along with encouraging results.

This paper concerns the large-scale separation flow control over an airfoil using a synthetic jet (SJ). The flow is simulated by solving unsteady Reynolds averaged Navier-Stokes (URANS) equations w...

In this paper, we investigate the effect of insufficient time-scale separation between inner and the outer loops in networked, cascaded systems. First, a qualitative model is developed where the stability of a cascaded system is analyzed in terms of the gains acting at the outer and inner loops. Next, interconnected cascaded systems are considered, which represent networks congested by data sent by multiple clients. Then, the effect of the number of clients for the performance is examined through the scalable stability notion, which guarantees positively lower-bounded convergence rates with respect to the number of clients.

This paper presents a general multiscale framework towards the computation of the emergent effective elastodynamics of heterogeneous materials, to be applied for the analysis of acoustic metamaterials and phononic crystals. The generality of the framework is exemplified by two key characteristics. First, the underlying formalism relies on the Floquet–Bloch theorem to derive a robust definition of scales and scale separation. Second, unlike most homogenization approaches that rely on a classical volume average, a generalized homogenization operator is defined with respect to a family of particular projection functions. This yields a generalized macro-scale continuum, instead of the classical Cauchy continuum. This enables (in a micromorphic sense) to homogenize the rich dispersive behavior resulting from both Bragg scattering and local resonance. For an arbitrary unit cell, the homogenization projection functions are constructed using the Floquet–Bloch eigenvectors obtained in the desired frequency regime at select high symmetry points, which effectively resolves the emergent phenomena dominating that regime. Furthermore, a generalized Hill–Mandel condition is proposed that ensures power consistency between the homogenized and full-scale model. A high-order spatio-temporal gradient expansion is used to localize the multiscale problem leading to a series of recursive unit cell problems giving the appropriate micro-mechanical corrections. The developed multiscale method is validated against standard numerical Bloch analysis of the dispersion spectra of example unit cells encompassing multiple high-order branches generated by local resonance and/or Bragg scattering.

The long-term behavior and safety performance of hydraulic concrete structures are influenced by chemo-mechanical deterioration due to leaching and frost. The single-scale experimental investigations on the certain deterioration phenomenon of concrete structure submitted to leaching or frost have been performed. In this work, the multi-scale numerical simulation approach is introduced to analyze the deterioration phenomena due to leaching or/and frost in hydraulic concrete and the structural responses to the above deterioration phenomena. The three-dimensional (3D) multi-scale modeling and homogenization methods of hydraulic concrete submitted to leaching or/and frost are developed. The effect analysis of leaching or/and frost on the mechanical properties of hydraulic concrete is implemented. First, according to the multi-scale and multi-phase characteristics of hydraulic concrete as composite material, the scale separation standard of hydraulic concrete is given. The hydration model and random aggregate model are introduced to reconstruct the representative volume element model of hydraulic concrete with different scales, namely micro-scale (cement paste), meso-scale (mortar), macro-scale (concrete). The numerical homogenization method is presented to determine the mechanical properties of the hydraulic concrete. Second, the methods are developed to implement the 3D multi-scale simulation for random leaching process and thermo-mechanical coupling frost damage process of hydraulic concrete. Regarding seepage leaching as the leading role, a numerical method investigating the combined effect of leaching and frost on the mechanical properties of hydraulic concrete is proposed. Finally, the water-saturated hydraulic concrete is taken as an example. The deterioration phenomena due to leaching or/and frost in selected concrete are analyzed. The structural responses to the above deterioration phenomena are identified. It is indicated that the proposed multi-scale progressive analysis approach can capture the potential deterioration phenomena of hydraulic concrete. The variation mechanism of concrete structural behavior can be given a deeper insight. It can be seen that the nonlinear superimposed effect of leaching and frost can easily cause serious damage of water-saturated hydraulic concrete.

International audience; First order asymptotic homogenization allows to determine the effective behaviour of a porous medium by starting from the pore scale description, when there is a large separation between the pore scale and the macroscopic scale. When the scale ratio is â ˘ AIJsmall but not too small,â ˘ A ˙ I additional terms need to be taken into account, which can be obtained by exploiting higher order equations in the asymptotic homogenization procedure. The aim of the present study is to derive second order models to describe solute transport in a macroscopically homogeneous porous medium at low scale separation. The three following macroscopic transport regimes are successively considered: pure diffusion with fluid at rest, predominant diffusion with fluid in motion and advection-diffusion. The results show that while the transport regime remains of diffusive type when the fluid is at rest, low scale separation induces modification of apparent transport regime when fluid is in motion. Indeed, predominant diffusion and advection-diffusion lead to the apparent regimes of advection-diffusion and of dispersion, respectively.

Comparative judgment (CJ) is an alternative method for assessing competences based on Thurstone’s law of comparative judgment. Assessors are asked to compare pairs of students work (representations) and judge which one is better on a certain competence. These judgments are analyzed using the Bradly–Terry–Luce model resulting in logit estimates for the representations. In this context, the Scale Separation Reliability (SSR), coming from Rasch modeling, is typically used as reliability measure. But, to the knowledge of the authors, it has never been systematically investigated if the meaning of the SSR can be transferred from Rasch to CJ. As the meaning of the reliability is an important question for both assessment theory and practice, the current study looks into this. A meta-analysis is performed on 26 CJ assessments. For every assessment, split-halves are performed based on assessor. The rank orders of the whole assessment and the halves are correlated and compared with SSR values using Bland–Altman plots. The correlation between the halves of an assessment was compared with the SSR of the whole assessment showing that the SSR is a good measure for split-half reliability. Comparing the SSR of one of the halves with the correlation between the two respective halves showed that the SSR can also be interpreted as an interrater correlation. Regarding SSR as expressing a correlation with the truth, the results are mixed.

Hemp and cannabis industry is undergoing a renewed interest due to legalization of marijuana (a topic that all countries are discussing, especially in recent years) and the growing importance of therapeutic properties of cannabinoids. Together with an increment in the production of hemp and recreational cannabis, there has been an increasing demand for accurate potency testing of products (i.e. quantification of main cannabinoids present in the plant in terms of weight percentage) prior commercialization. This translates in an urgent need of reliable analytical methods to characterize cannabis and hemp samples. Cannabis and hemp preparations are commercialized under various forms (e.g., flowers, oils, candies or even baked goods) usually containing a large number of often very similar compounds making their separation very challenging. Strictly connected to this, another emerging topic concerns the need for the developing of large scale separation techniques for the purification of cannabinoids from complex matrices and for the preparation of analytical-grade standards (including the chiral ones). This paper reviews the most recent achievements in both these aspects. Cutting-edge applications and novel opportunities in potency testing by high performance liquid chromatography (HPLC) with UV detection (which is becoming the golden standard, according to several pharmacopeias, for this kind of measurements) are discussed. The focus has been given to the very important topic of enantio-discrimination of chiral cannabinoids, for which supercritical fluid chromatography (SFC) appears to be particularly suitable. The last part of the work covers the purification of cannabinoids through preparative chromatography. In this regard, particular attention has been given to the most innovative multi-column techniques allowing for the continuous purification of target molecules. The most recent advancements and future challenges in this field are discussed.