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A new computation of the normal forms of Bogdanov-Takens singularities is developed in this paper. In the theoretical model for the nonplanar nonlinear oscillation of a cantilever beam, the computation method is applied to compute the coefficients of the normal forms for the case of one non-semisimple double zero and a pair of pure imaginary eigenvalues.
We demonstrate that the broad surface plasmon resonance (SPR) of a single layer of near-coalescence silver nanoparticles (NPs), embedded in a dielectric matrix can be tailored by irradiation with a single nanosecond laser pulse into a distribution featuring a sharp resonance at 435 nm. Scanning electron microscopy studies reveal the underlying mechanism to be a transformation into a distribution of well-separated spherical particles. Additional exposure to multiple femtosecond laser pulses at 400 nm or 800 nm wavelength induces polarization anisotropy of the SPR, with a peak shift that increases with laser wavelength. The spectral changes are measured in-situ, employing reflection and transmission micro-spectroscopy with a lateral resolution of 4 μm. Spectral maps as a continuous function of local fluence can be readily produced from a single spot. The results open exciting perspectives for dynamically tuning and switching the optical response of NP systems, paving the way for next-generation applications.
We propose a technique for ultrabroadband planar coherent Raman spectroscopy that enables wideband chemically selective mapping of molecular partition functions in the gas-phase within a single-laser-shot. A spectral region spanning 0–4200 cm⁻¹ is excited simultaneously, in principle allowing for coherent planar imaging of most all fundamental Raman-active modes. This unique instantaneous and spatially correlated assessment enables multiplexed studies of transient dynamical systems in a two-dimensional (2D) field. Here, we demonstrate single-laser-shot high temperature diagnostics of H₂, with spatially resolved 2D measurement of transitions of both the pure-rotational H₂ S-branch and the vibrational H₂ Q-branch, analyzing the temperature contour of a reacting fuel-species as it evolves at a flame-front.
Motivated by physical and topological applications, we study representations of the group LB 3 of motions of 3 unlinked oriented circles in R 3 . Our point of view is to regard the three strand braid group B 3 as a subgroup of LB 3 and study the problem of extending B 3 representations. We introduce the notion of a standard extension and characterize B 3 representations admitting such an extension. In particular we show, using a classification result of Tuba and Wenzl [Pacific J. Math. 197, 491–510 (2001)], that every irreducible B 3 representation of dimension at most 5 has a (standard) extension. We show that this result is sharp by exhibiting an irreducible 6-dimensional B 3 representation that has no extensions (standard or otherwise). Here, we obtain complete classifications of (1) irreducible 2-dimensional LB 3 representations, (2) extensions of irreducible 3-dimensional B 3 representations, and (3) irreducible LB 3 representations whose restriction to B 3 has abelian image.
Fluid flow in the transonic regime finds relevance in aerospace engineering, particularly in the design of commercial air transportation vehicles. Computational fluid dynamics models of transonic flow for aerospace applications are computationally expensive to solve because of the high degrees of freedom as well as the coupled nature of the conservation laws. While these issues pose a bottleneck for the use of such models in aerospace design, computational costs can be significantly minimized by constructing special, structure-preserving surrogate models called reduced-order models. In this work, we propose a machine learning method to construct reduced-order models via deep neural networks and we demonstrate its ability to preserve accuracy with a significantly lower computational cost. In addition, our machine learning methodology is physics-informed and constrained through the utilization of an interpretable encoding by way of proper orthogonal decomposition. Application to the inviscid transonic flow past the RAE2822 airfoil under varying freestream Mach numbers and angles of attack, as well as airfoil shape parameters with a deforming mesh, shows that the proposed approach adapts to high-dimensional parameter variation well. Notably, the proposed framework precludes the knowledge of numerical operators utilized in the data generation phase, thereby demonstrating its potential utility in the fast exploration of design space for diverse engineering applications. Comparison against a projection-based nonintrusive model order reduction method demonstrates that the proposed approach produces comparable accuracy and yet is orders of magnitude computationally cheap to evaluate, despite being agnostic to the physics of the problem.
The solid-electrolyte interphase (SEI) layer is a critical constituent of battery technology, which incorporates the use of lithium metals. Since the formation of the SEI is difficult to avoid, the engineering and harnessing of the SEI are absolutely critical to advancing energy storage. One problem is that much fundamental information about SEI properties is lacking due to the difficulty in probing a chemically complex interfacial system. One such property that is currently unknown is the dissolution of the SEI. This process can have significant effects on the stability of the SEI, which is critical to battery performance but is difficult to probe experimentally. Here, we report the use of ab initio computational chemistry simulations to probe the solution state properties of SEI components LiF, Li 2 O, LiOH, and Li 2 CO 3 in order to study their dissolution and other solution-based characteristics. Ab initio molecular dynamics was used to study the solvation structures of the SEI with a combination of radial distribution functions, discrete solvation structure maps, and vibrational density of states, which allows for the determination of free energies. From the change in free energy of dissolution, we determined that LiOH is the most likely component to dissolve in the electrolyte followed by LiF, Li 2 CO 3 , and Li 2 O although none were favored thermodynamically. Furthermore, this indicates that dissolution is not probable, but Li 2 O would make the most stable SEI with regard to dissolution in the electrolyte.
As spectral imaging techniques are becoming more prominent in science, advanced image segmentation algorithms are required to identify appropriate domains in these images. We present a version of image segmentation called manifold projection image segmentation (MPIS) that is generally applicable to a broad range of systems without the need for training because MPIS uses unsupervised machine learning with a few physically motivated hyperparameters. We apply MPIS to nanoscale x-ray absorption near edge structure (XANES) imaging, where XANES spectra are collected with nanometer spatial resolution. We show the superiority of manifold projection over linear transformations, such as the commonly used principal component analysis (PCA). Moreover, MPIS maintains accuracy while reducing computation time and sensitivity to noise compared to the standard nano-XANES imaging analysis procedure. Finally, we demonstrate how multimodal information, such as x-ray fluorescence data and spatial location of pixels, can be incorporated into the MPIS framework. We propose that MPIS is adaptable for any spectral imaging technique, including scanning transmission x-ray microscopy, where the length scale of domains is larger than the resolution of the experiment.
This article describes a method for calculating higher order or nonadiabatic corrections in Born–Oppenheimer theory and its interaction with the translational degrees of freedom. The method uses the Wigner–Weyl correspondence to map nuclear operators into functions on the classical phase space and the Moyal star product to represent operator multiplication on those functions. These are explained in the body of the paper. The result is a power series in κ 2 , where κ = (m/M)1/4 is the usual Born–Oppenheimer parameter. The lowest order term is the usual Born–Oppenheimer approximation, while higher order terms are nonadiabatic corrections. These are needed in calculations of electronic currents, momenta, and densities. The separation of nuclear and electronic degrees of freedom takes place in the context of the exact symmetries (for an isolated molecule) of translations and rotations, and these, especially translations, are explicitly incorporated into our discussion. This article presents an independent derivation of the Moyal expansion in molecular Born–Oppenheimer theory. Here, we show how electronic currents and momenta can be calculated within the framework of Moyal perturbation theory; we derive the transformation laws of the electronic Hamiltonian, the electronic eigenstates, and the derivative couplings under translations; we discuss in detail the rectilinear motion of the molecular center of mass in the Born–Oppenheimer representation; and we show how the elimination of the translational components of the derivative couplings leads to a unitary transformation that has the effect of exactly separating the translational degrees of freedom.
The ensemble optical/ultraviolet (UV) variability of narrow-line Seyfert 1 (NLS1)-type active galactic nuclei (AGNs) is investigated, based on a sample selected from the Sloan Digital Sky Survey (SDSS) Stripe 82 region with multi-epoch photometric scanning data. As a comparison, a control sample of broad-line Seyfert 1 (BLS1)-type AGNs is also incorporated. To quantify properly the intrinsic variation amplitudes and their uncertainties, a novel method of parametric maximum likelihood is introduced that has, as we argued, certain virtues over previously used methods. The majority of NLS1-type AGNs exhibit significant variability on timescales from about 10 days to a few years with, however, smaller amplitudes on average compared to BLS1-type AGNs. About 20 NLS1-type AGNs that show relatively large variations are presented and may deserve future monitoring observations, for instance, reverberation mapping. The averaged structure functions of variability, constructed using the same maximum likelihood method, show remarkable similarity in shape for the two types of AGNs on timescales longer than about 10 days, which can be approximated by a power law or an exponential function. This, along with other similar properties, such as the wavelength-dependent variability, is indicative of a common dominant mechanism responsible for the long-term optical/UV variability of both NLS1- and BLS1-type AGNs. Toward the short timescales, however, there is tentative evidence that the structure function of NLS1-type AGNs continues to decline, whereas that of BLS1-type AGNs flattens with some residual variability on timescales of days. If this can be confirmed, it may suggest that an alternative mechanism, such as X-ray reprocessing, starts to dominate in BLS1-type AGNs, but not in NLS1-type AGNs, on such timescales.
In this paper, we present the results of a high resolution (5'') Combined Array for Research in Millimeter-wave Astronomy and Sunyaev-Zeldovich Array survey of the 3 mm continuum emission from 11 of the brightest (at 1.1 mm) starless cores in the Perseus molecular cloud. We detect 2 of the 11 cores, both of which are composed of single structures, and the median 3{sigma} upper limit for the non-detections is 0.2 M{sub sun} in a {approx}5'' beam. These results are consistent with, and as stringent as, the low detection rate of compact 3 mm continuum emission in dense cores in Perseus reported by Olmi et al. From the non-detection of multiple components in any of the 11 cores, we conclude that starless core mass functions derived from bolometer maps at resolutions range 10''-30'' (e.g., with MAMBO, SCUBA, or Bolocam) are unlikely to be significantly biased by the blending of lower mass cores with small separations. These observations provide additional evidence that the majority of starless cores in Perseus have inner density profiles shallower than r {sup -2}.
We report the detection of color gradients in six massive (stellar mass (M{sub star}) > 10{sup 10} M{sub sun}) and passively evolving (specific star formation rate <10{sup -11} yr{sup -1}) galaxies at redshift 1.3 < z < 2.5 identified in the Hubble Ultra Deep Field using ultra-deep Hubble Space Telescope (HST) Advanced Camera for Surveys and WFC3/IR images. After carefully matching the different point-spread functions, we obtain color maps and multi-band optical/near-IR photometry (BVizYJH) in concentric annuli, from the smallest resolved radial distance ({approx}1.7 kpc) up to several times the H-band effective radius. We find that the inner regions of these galaxies have redder rest-frame UV-optical colors (U - V, U - B, and B - V) than the outer parts. The slopes of the color gradient have no obvious dependence on the redshift and on the stellar mass of the galaxies. They do mildly depend, however, on the overall dust obscuration (E(B - V)) and rest-frame (U - V) color, with more obscured or redder galaxies having steeper color gradients. The z {approx} 2 color gradients are also steeper than those of local early-type ones. The gradient of a single parameter (age, extinction, or metallicity) cannot fully explain the observed color gradients. Fitting the spatially resolved HST seven-band photometry to stellar population synthesis models, we find that, regardless of assumptions on the metallicity gradient, the redder inner regions of the galaxies have slightly higher dust obscuration than the bluer outer regions, implying that dust partly contributes to the observed color gradients, although the magnitude depends on the assumed extinction law. Due to the age-metallicity degeneracy, the derived age gradient depends on the assumptions for the metallicity gradient. We discuss the implications of a number of assumptions for metallicity gradients on the formation and evolution of these galaxies. We find that the evolution of the mass-size relationship from z {approx} 2 to the present cannot be driven by in situ extended star formation, which implies that accretion or merger is mostly responsible for the growth of their stellar mass and size. The lack of a correlation between the strength of the color gradient and the stellar mass argues against the metallicity gradient predicted by the monolithic-collapse scenario, which would require significant major mergers to evolve into the one observed at the present.
The unprecedented rate of warming in the Arctic is driving changes in the structure and composition of tundra vegetation. Increases in deciduous tall shrub cover, height, and density are of particular concern, as these changes alter local surface albedo in ways that could amplify effects on the regional surface energy budget (SEB). Despite this importance, significant uncertainties remain in understanding the interplay between fine-scale vegetation patterns and emergent albedo dynamics across space and time. Here, we address these uncertainties by (1) quantifying spatiotemporal variation in surface shortwave albedo and (2) determining the relative influence of fine-scale vegetation composition, structure, and environmental conditions on albedo across a representative low-Arctic tundra landscape on Alaska’s Seward Peninsula. To do this, we synthesized multi-scale, multi-platform remote sensing observations, including a novel Landsat-derived albedo time series, a fine-scale map of Arctic plant functional type (PFT) fractional cover, and airborne LiDAR estimates of canopy height and topography. We show that there are substantial reductions in winter albedo for pixels dominated by tall, woody PFTs (28.13%) relative to pixels dominated by non-woody vegetation, but almost no change in summer albedo (3% increase). Further, we identified a unimodal trend in the relationship between canopy height and the timing of the springtime transition from high (snowy) to low (leafy) albedo (peak at 5.5 m), possibly because of competing ‘snow-fence’ and ‘protrusion’ snow-shrub interactions. To explore the primary drivers of albedo, we constructed a random forest model and found that canopy height and the fractional cover of woody PFTs were as- or more important predictors of winter albedo than topographic features. These findings provide strong evidence for the impacts of local vegetation characteristics on regional surface albedo, highlighting the need for better quantification of snow-shrub interactions to accurately predict the Arctic’s SEB under future environmental change.
Genome reduction is widely used to improve microbial bioprocessing hosts by reducing the burden of inessential physiology. Rationally identifying genomic regions that are dispensable or even detrimental to bioprocessing is challenged by our inability to map genome sequence to function across complex regulation and physiology. Thus, there is a need for tools that rapidly generate reduced genome strains with improved performance in process-relevant conditions. Here, we report a Cascade-Cas3-enabled method called TRIM3 that generates large deletions by targeting a randomly integrated transposon, enabling facile generation of a genome-reduced mutant library. Mutants with improved performance were isolated following growth-coupled selection and analyzed by long-read DNA sequencing to identify deletions in their genomes. We deploy this system iteratively in the industrial host Cupriavidus necator H16 on fructose and on formate. After two rounds of TRIM3, we isolate a strain containing a total reduction of 1.4 Mb (18.4% of the genome) that grows 25% faster in a bioreactor on fructose and a strain with a total reduction of 0.5 Mb (7.3% of the genome) that grows 14% faster on formate. This work demonstrates a method for random, iterative, growth-selectable genome reduction that represents a new avenue for large-scale genome modifications and the development of improved bioprocessing hosts.
Kinetic simulations of collisionless (or weakly collisional) plasmas using the Vlasov equation are often infeasible due to high-resolution requirements and the exponential scaling of computational cost with respect to dimension. Recently, it has been proposed that matrix product state (MPS) methods, a quantum-inspired but classical algorithm, can be used to solve partial differential equations with exponential speed-up, provided that the solution can be compressed and efficiently represented as a MPS within some tolerable error threshold. Here, in this work, we explore the practicality of MPS methods for solving the Vlasov-Poisson equations for systems with one coordinate in space and one coordinate in velocity, and find that important features of linear and nonlinear dynamics, such as damping or growth rates and saturation amplitudes, can be captured while compressing the solution significantly. Furthermore, by comparing the performance of different mappings of the distribution functions onto the MPS, we develop an intuition of the MPS representation and its behavior in the context of solving the Vlasov-Poisson equations, which will be useful for extending these methods to higher-dimensional problems.
Lysine methylation is a key regulator of histone protein function. Beyond histones, few connections have been made to the enzymes responsible for the deposition of these posttranslational modifications. Here, we debut a high-throughput functional proteomics platform that maps the sequence determinants of lysine methyltransferase (KMT) substrate selectivity without a priori knowledge of a substrate or target proteome. We demonstrate the predictive power of this approach for identifying KMT substrates, generating scaffolds for inhibitor design, and predicting the impact of missense mutations on lysine methylation signaling. By comparing KMT selectivity profiles to available lysine methylome datasets, we reveal a disconnect between preferred KMT substrates and the ability to detect these motifs using standard mass spectrometry pipelines. Collectively, our studies validate the use of this platform for guiding the study of lysine methylation signaling and suggest that substantial gaps exist in proteome-wide curation of lysine methylomes.
We conducted a comprehensive comparative analysis of extracellular vesicles (EVs) from two Acanthamoeba castellanii strains, Neff (environmental) and T4 (clinical). Morphological analysis via transmission electron microscopy revealed slightly larger Neff EVs (average = 194.5 nm) compared to more polydisperse T4 EVs (average = 168.4 nm). Nanoparticle tracking analysis (NTA) and dynamic light scattering validated these differences. Proteomic analysis of the EVs identified 1,352 proteins, with 1,107 common, 161 exclusive in Neff, and 84 exclusively in T4 EVs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) mapping revealed distinct molecular functions and biological processes and notably, the T4 EVs enrichment in serine proteases, aligned with its pathogenicity. Lipidomic analysis revealed a prevalence of unsaturated lipid species in Neff EVs, particularly triacylglycerols, phosphatidylethanolamines (PEs), and phosphatidylserine, while T4 EVs were enriched in diacylglycerols and diacylglyceryl trimethylhomoserine, phosphatidylcholine and less unsaturated PEs, suggesting differences in lipid metabolism and membrane permeability. Metabolomic analysis indicated Neff EVs enrichment in glycerolipid metabolism, glycolysis, and nucleotide synthesis, while T4 EVs, methionine metabolism. Furthermore, RNA-seq of EVs revealed differential transcript between the strains, with Neff EVs enriched in transcripts related to gluconeogenesis and translation, suggesting gene regulation and metabolic shift, while in the T4 EVs transcripts were associated with signal transduction and protein kinase activity, indicating rapid responses to environmental changes. In this novel study, data integration highlighted the differences in enzyme profiles, metabolic processes, and potential origins of EVs in the two strains shedding light on the diversity and complexity of A. castellanii EVs and having implications for understanding host-pathogen interactions and developing targeted interventions for Acanthamoeba-related diseases.
Ultrafast intense photoexcitation of a silicon surface is complementarily studied experimentally and theoretically, with its prompt optical dielectric function obtained by means of time-resolved optical reflection microscopy and the underlying electron-hole plasma dynamics modeled numerically, using a quantum kinetic approach. The corresponding transient surface plasmon-polariton (SPP) dispersion curves of the photo-excited material were simulated as a function of the electron-hole plasma density, using the derived optical dielectric function model, and directly mapped at several laser photon energies, measuring spatial periods of the corresponding SPP-mediated surface relief nanogratings. The unusual spectral dynamics of the surface plasmon resonance, initially increasing with the increase in the electron-hole plasma density but damped at high interband absorption losses induced by the high-density electron-hole plasma through instantaneous bandgap renormalization, was envisioned through the multi-color mapping.
The integrability of the Calogero model can be expressed as zero curvature condition using Dunkl operators. The corresponding flat connections are non-local gauge transformations, which map the Calogero wave functions to symmetrized wave functions of the set of N free particles, i.e. it relates the corresponding scattering matrices to each other. The integrability of the Calogero model implies that any k-particle scattering is reduced to successive pairwise scatterings. The consistency condition of this requirement is expressed by the analog of the Yang–Baxter relation.