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Dynamic and Programmable Cellular-Scale Granules Enable Tissue-like Materials
Living tissues are an integrated, multiscale architecture consisting of dense cellular ensembles and extracellular matrices (ECMs). The cells and ECMs cooperate to enable specialized mechanical properties and dynamic responsiveness. However, the mechanical properties of living tissues are difficult to replicate. A particular challenge is identification of a cell-like synthetic component, which is tightly integrated with its matrix and also responsive to external stimuli. Here, we demonstrate that cellular-scale hydrated starch granules, an underexplored component in materials science, can turn conventional hydrogels into tissue-like materials when composites are formed. By using several synchrotron-based X-ray techniques, we reveal the mechanically induced organization and training dynamics of the starch granules in the hydrogel matrix. These dynamic behaviors enable multiple tissue-like properties such as programmability, anisotropy, strain-stiffening, mechanochemistry, and self-healability.
Fast multiplication of random dense matrices with sparse matrices
This work focuses on accelerating the multiplication of a dense random matrix with a (fixed) sparse matrix, which is frequently used in sketching algorithms.
Propulsion Electrification Architecture Selection Process and Cost of Carbon Abatement Analysis for Heavy-Duty Off-Road Material Handler
The heavy-duty off-road industry continues to expand efforts to reduce fuel consumption and CO 2 e (carbon dioxide equivalent) emissions. Many manufacturers are pursuing electrification to decrease fuel consumption and emissions. Future policies will likely require electrification for CO2e savings, as seen in light-duty on-road vehicles. Electrified architectures vary widely in the heavy-duty off-road space, with parallel hybrids in some applications and series hybrids in others. The diverse applications for different types of equipment mean different electrified configurations are required. Companies must also determine the value in pursuing electrified architectures; this work analyzes a range of electrified architectures, from micro hybrids to parallel hybrids to series hybrids to a BEV, looking at the total cost, total CO 2 e, and cost per CO 2 e (cost of carbon abatement, or cost of carbon reduction) using data for the year 2021. This study is focused on a heavy-duty off-road material handler, the Pettibone Cary-Lift 204i. This machine’s specialty application, including events like unloading large oil pipes from a railcar, requires a unique electrified architecture that suits its specific needs. However, the results from this study may be extrapolated to similar machinery to inform fuel savings options across the heavy-duty off-road industry. In this study, a unique electrified architecture is determined for the Cary-Lift. This architecture is informed by multiple rounds of a Pugh matrix decision analysis to select a shortened list of desirable electrified architectures. The shortened list is modeled and simulated to determine CO 2 e, cost, and cost per CO 2 e. A final architecture is determined as a plug-in series hybrid that reduces fuel consumption by 65%, targeting the large fuel and CO 2 e savings that are likely to be required for the future of the heavy-duty off-road industry.
Source identification by non-negative matrix factorization combined with semi-supervised clustering
Machine-learning methods and apparatus are provided to solve blind source separation problems with an unknown number of sources and having a signal propagation model with features such as wave-like propagation, medium-dependent velocity, attenuation, diffusion, and/or advection, between sources and sensors. In exemplary embodiments, multiple trials of non-negative matrix factorization are performed for a fixed number of sources, with selection criteria applied to determine successful trials. A semi-supervised clustering procedure is applied to trial results, and the clustering results are evaluated for robustness using measures for reconstruction quality and cluster separation. The number of sources is determined by comparing these measures for different trial numbers of sources. Source locations and parameters of the signal propagation model can also be determined. Disclosed methods are applicable to a wide range of spatial problems including chemical dispersal, pressure transients, and electromagnetic signals, and also to non-spatial problems such as cancer mutation.
Source identification by non-negative matrix factorization combined with semi-supervised clustering
Machine-learning methods and apparatus are provided to solve blind source separation problems with an unknown number of sources and having a signal propagation model with features such as wave-like propagation, medium-dependent velocity, attenuation, diffusion, and/or advection, between sources and sensors. In exemplary embodiments, multiple trials of non-negative matrix factorization are performed for a fixed number of sources, with selection criteria applied to determine successful trials. A semi-supervised clustering procedure is applied to trial results, and the clustering results are evaluated for robustness using measures for reconstruction quality and cluster separation. The number of sources is determined by comparing these measures for different trial numbers of sources. Source locations and parameters of the signal propagation model can also be determined. Disclosed methods are applicable to a wide range of spatial problems including chemical dispersal, pressure transients, and electromagnetic signals, and also to non-spatial problems such as cancer mutation.
An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM
This work presents a new method for online selection of multiple penalty parameters for the alternating direction method of multipliers (ADMM) algorithm applied to optimization problems with multiple constraints or functions with block matrix components. ADMM is widely used for solving constrained optimization problems in a variety of fields, including signal and image processing. Implementations of ADMM often utilize a single hyperparameter, referred to as the penalty parameter, which needs to be tuned to control the rate of convergence. However, in problems with multiple constraints, ADMM may demonstrate slow convergence regardless of penalty parameter selection due to scale differences between constraints. Accounting for scale differences between constraints to improve convergence in these cases requires introducing a penalty parameter for each constraint. The proposed method is able to adaptively account for differences in scale between constraints, providing robustness with respect to problem transformations and initial selection of penalty parameters. It is also simple to understand and implement. Our numerical experiments demonstrate that the proposed method performs favorably compared to a variety of existing penalty parameter selection methods.
Introduction of the Adding and Doubling Method for Solving Bateman Equations for Nuclear Fuel Depletion
This paper introduces and evaluates the Adding and Doubling Method (ADM) for solving the Bateman equations for depletion systems with varying numbers of nuclides and compares it to the Chebyshev Rational Approximation Method (CRAM), both implemented in the reactor physics analysis application Griffin. ADM, when applied to the Crank-Nicolson Finite Difference method, can produce results comparable in accuracy and precision to CRAM with comparable run times for systems with 35 or 297 nuclides. For systems with more than 300 nuclides, the matrix-matrix operations required by ADM are significantly more costly than the matrix-vector operations required by CRAM, making CRAM the more efficient method for systems with large numbers of nuclides. ADM is an accurate method that maintains other advantages over CRAM in that it does not depend on pre-generated coefficients or require complex number operations. ADM also manages to outperform CRAM by a factor of more than 250 in terms of run time for depletion systems that require multiple Bateman solves while the depletion matrix and time step size remain constant over all depletion intervals.
Interface instabilities in hafnium hydride entrained iron metal matrix composites
The chemical interactions in Fe–HfH 2 metal matrix composites (MMCs) are studied across multiple length scales to elucidate the decomposition of the parent phases and corresponding reaction zone physics during direct current sintering. Fe–HfH 2 composites were synthesized with increasing as-mixed hydride contents of Fe–25% HfH 2 , Fe–40% HfH 2 , Fe–55% HfH 2 , and Fe–70% HfH 2 (all in vol. %) to demonstrate the ability to achieve sintered MMCs with target hydride contents. Samples were probed across multiple length scales through a multi-modal workflow employing x-ray diffraction, scanning electron microscopy and segmentation analysis, and synchrotron techniques including hard x-ray fluorescence mapping and nanoprobe x-ray absorption near-edge structure measurements. Under the selected sintering temperature and pressure conditions, hydrogen evolution is seen to evolve through parallel paths: thermal decomposition from during the transformation of HfH 2 to HfH x<2 and through subsequent reaction with the Fe matrix leading to intermetallic phase formation. Specifically, HfFe and HfFe 2 intermetallic formation accelerates the release of hydrogen with a subsequent HfO 2 phase forming at grain boundaries. For this MMC, the consumption or loss of hydrogen can be considerable in compacts with initial hydride loading of 25%–40% HfH 2 approaching 83% hydrogen loss for the lower volume fraction composites. Increasing the volume fraction of HfH 2 to 70% enhanced the retained hydrogen content to 53% and attributed to the reduced interfacial area intrinsic to the increased HfH 2 loading in this MMC.
Assessing Multi-Dimensional Impacts of Achieving Sustainability Goals by Projecting the Sustainable Agriculture Matrix Into the Future
The concept of sustainability inherently spans multiple spatial scales, sectors, variables, and time horizons. This study links a recently developed method of assessing present-day agricultural sustainability across environmental, economic, and social dimensions with a process-based integrated assessment model, in order to allow forward-looking analysis of sustainability by region and scenario. The sustainable agriculture matrix estimates present-day agricultural sustainability at the national level using 18 indicator variables, of which this study estimates nine to the year 2100, using an enhanced version of the Global Change Analysis Model. Scenarios include a reference scenario, and scenarios that apply the following measures, both individually and in combination, that are thought to improve sustainability: yield intensification, transition toward more plant-based (“flexitarian”) diets, and economy-wide greenhouse gas emissions mitigation. The scenarios illustrate considerable complexity and tradeoffs inherent to efforts to improve agricultural sustainability in all regions globally. For example, yield intensification typically increases nitrogen pollution, flexitarian diets can reduce agricultural output, and greenhouse gas mitigation efforts may either increase deforestation or crowd out crop and livestock production due to consequent bioenergy demands. However, there is considerable inter-regional heterogeneity in the responses, and the importance of such secondary responses also differs by region. The analysis and post-processing methods developed in this study allow quantification and visualization of the absolute and relative magnitude of the tradeoffs between agricultural sustainability indicator variables across regions, time periods, and scenarios.
Signatures of QCD conductivities in heavy-ion collisions
Dissipative processes are pivotal for understanding the hydrodynamic evolution of hot and dense quantum chromodynamics (QCD) matter created in relativistic nuclear collisions. The interplay of multiple conserved charges—net baryon, strangeness, and electric charge—is of particular interest. Here, we simulate the longitudinal hydrodynamic evolution with the three diffusion currents in a hydrodynamic model with a lattice-QCD-based equation of state, NEOS -4 D , and estimate rapidity distributions including diffusive corrections to the phase-space distribution in the presence of multiple charges, which ensure charge conservation at particlization. We determine the response of particle yields at midrapidity to changes in the diagonal and off-diagonal conductivities. Inversely, we find that most components of the conductivity matrix can be constrained experimentally using identified particle multiplicities at different collision energies.
Quantitative Structure Determination from Experimental Four-Dimensional Scanning Transmission Electron Microscopy via the Scattering Matrix
Considerable inroads have recently been made on algorithms to determine the sample potential from four-dimensional scanning transmission electron microscopy data from thick samples where multiple scattering cannot be neglected. This paper further develops the scattering matrix approach to such structure determination. Through simulation, we demonstrate how this approach can be modified to better handle partial spatial coherence, unknown probe defocus, and information from the dark field region. By combining these developments we reconstruct the electrostatic potential of a monolithic SrTiO 3 crystal showing good quantitative agreement with the expected structure.
Scattering Matrix Determination in Crystalline Materials from 4D Scanning Transmission Electron Microscopy at a Single Defocus Value
Recent work has revived interest in the scattering matrix formulation of electron scattering in transmission electron microscopy as a stepping stone toward atomic-resolution structure determination in the presence of multiple scattering. Here, we discuss ways of visualizing the scattering matrix that make its properties clear. Through a simulation-based case study incorporating shot noise, we shown how regularizing on this continuity enables the scattering matrix to be reconstructed from 4D scanning transmission electron microscopy (STEM) measurements from a single defocus value. Intriguingly, for crystalline samples, this process also yields the sample thickness to nanometer accuracy with no a priori knowledge about the sample structure. The reconstruction quality is gauged by using the reconstructed scattering matrix to simulate STEM images at defocus values different from that of the data from which it was reconstructed.
Design of High Efficient Mid-Wavelength Infrared Polarizer on ORMOCHALC Polymer
While an organically modified chalcogenide (ORMOCHALC) can be used to fabricate a polymeric mid-wavelength infrared (MWIR) polarizer with competitive extinction ratio compared to the commercial wire-grid polarizers, which are composed of fragile inorganic materials, there is still a knowledge gap regarding the systematic design process to obtain high transmission efficiency and extinction ratio. As such, a computational parameter study for design optimization is conducted with the geometric parameters of the bilayer grating ORMOCHALC polarizer. The computational study shows that the Fabry–Pérot cavity is the primary mechanism that determines the transmission behaviors and the extinction ratio. A bilayer grating design, guided by the parameter study, is realized through the thermal nanoimprint and metal deposition processes. The extinction ratios measured with the Fourier-transform infrared are 245, 304, and 351 at the wavelength of 3, 4, and 5 μm, respectively. Compared to the state-of-the-art of the polymeric MWIR linear polarizers, the extinction ratio is improved by 1.4 times, and the transmission efficiency is increased by 2.5 times. Theoretical analysis with the multiple-layer model based on the transfer matrix method predicts a matched transmission behavior with the experiment and a full-wave electromagnetic simulation.
Osteocytic oxygen sensing: Distinct impacts of VHL and HIF-2alpha on bone integrity
Skeletal fracture resistance emerges from multiple components of bone structure like microarchitecture, matrix mineralization, and organization. These characteristics are engendered via mechanisms like the hypoxia-inducible factors (HIF) pathway, involving two paralogs, HIF-1α and HIF-2α. Under normoxia, HIF-α is targeted for degradation via von-Hippel Lindau (VHL); hypoxia enables HIF-α stabilization and induction of target genes. We previously showed that osteocytic Vhl deletion or expression of degradation-resistant HIF-2α cDR female mice each produced high bone mass, whereas degradation-resistant osteocytic HIF-1α produced no overt phenotype. We report within that Vhl cKO increased bone strength, while HIF-2α cDR displayed markedly reduced bone strength below Cre-negative controls. This suggests that VHL and HIF-2α drive distinct responses that promote disparate effects on bone strength. Both Vhl deletion or HIF-2α accumulation generated two discrete bone morphologies: an outer lamellar cortex and a woven, poorly mineralized endocortex that imparted dramatically different functional outcomes. Our studies reveal novel influence of osteocytic HIF-2α signaling on collagen matrix organization, mineralization, and bone strength.
Coevolution of phyllosilicate, carbon, sulfide, and apatite in Ryugu's parent body
Abstract We analyzed an asteroid Ryugu sample returned to Earth by JAXA's Hayabusa2 mission using nanoIR, SEM, and TEM microscopy. We identified multiple distinct carbon reservoirs within the phyllosilicate matrix and demonstrate infrared spectral affinities for some of the carbon to insoluble organic matter (IOM). TEM studies of Ryugu samples have allowed us to better understand the interrelationship between the crystallographic orientations of phyllosilicates and the secondary minerals such as carbonate, sulfide, and apatite. Transport of elements provides a unifying theme for understanding these interrelationships.
Multi-site reaction dynamics through multi-fragment density matrix embedding
The practical description of disordered chemical reactions, where the reactions involve multiple species at multiple sites, is presently a challenge using correlated electronic structure methods due to their high computational cost and steep scaling. Here, we describe the gradient theory of multi-fragment density matrix embedding theory, which potentially provides a minimal computational framework to model such processes at the correlated electron level. We present the derivation and implementation of the gradient theory, its validation on model systems and chemical reactions using density matrix embedding, and its application to a molecular dynamics simulation of proton transport in a small water cluster, a simple example of multi-site reaction dynamics.
Ebola and Marburg virus matrix layers are locally ordered assemblies of VP40 dimers
Filoviruses such as Ebola and Marburg virus bud from the host membrane as enveloped virions. This process is achieved by the matrix protein VP40. When expressed alone, VP40 induces budding of filamentous virus-like particles, suggesting that localization to the plasma membrane, oligomerization into a matrix layer, and generation of membrane curvature are intrinsic properties of VP40. There has been no direct information on the structure of VP40 matrix layers within viruses or virus-like particles. We present structures of Ebola and Marburg VP40 matrix layers in intact virus-like particles, and within intact Marburg viruses. VP40 dimers assemble extended chains via C-terminal domain interactions. These chains stack to form 2D matrix lattices below the membrane surface. These lattices form a patchwork assembly across the membrane and suggesting that assembly may begin at multiple points. Our observations define the structure and arrangement of the matrix protein layer that mediates formation of filovirus particles.