Structure-preserving model reduction for marginally stable LTI systems.
Abstract not provided.
Peng, Liqian↗
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A structured data model frame for human factors application - 175
Building a Human Factors analysis and Verification and Validation case for new control rooms and even for control room modifications is a highly labor intensive project. Any optimization that can be made in the process translates into savings in the overall control room project schedule and budget. Several support tools from word, to excel, mind maps and databases have been traditionally used by human factors practitioners to conduct the analysis and document its results. While the use of these tools has had satisfactory results, they don't provide in our experience strong enough means for configuration control management, to avoid duplicities, streamline the analysis processes and allow collaborative team efforts. This paper documents how the established practices (methods, processes and tools) were questioned, which were the sources for comparison and how the results were translated into requirements for the new tool constituting the data frame model for Human Factors Engineering applications. The exercise has allowed us to evaluate the efficiency of our current practices in comparison to previous ones while improving our compliance with the quality assurance standards. (authors)
42 ENGINEERING↗
Atomic-Layer-Deposited Aluminum Oxide Thin Films Probed with X-ray Scattering and Compared to Molecular Dynamics and Density Functional Theory Models
A better understanding of amorphous aluminum oxide’s structure and electronic properties is obtained through combined experimental and computational approaches. Grazing incidence X-ray scattering measurements were carried out on aluminum oxide thin films grown using thermal atomic layer deposition. The corresponding pair distribution functions (PDFs) showed structures similar to previously reported PDFs of solid-state amorphous alumina and molten alumina. Structural models based on crystalline alumina polymorphs (PDFgui) and amorphous alumina (molecular dynamics, MD) were examined for structural comparisons to the experimental PDF data. Smaller MD models were optimized and verified against larger models to allow for quantum chemical electronic structure calculations. The electronic structure of the amorphous alumina models yields additional insight into the band structure and electronic defects present in amorphous alumina that are not present in crystalline samples.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Crystallographic and Computational Analyses of AUUCU Repeating RNA That Causes Spinocerebellar Ataxia Type 10 (SCA10)
Spinocerebellar ataxia type 10 (SCA10) is caused by a pentanucleotide repeat expansion of r(AUUCU) within intron 9 of the ATXN10 pre-mRNA. The RNA causes disease by a gain-of-function mechanism in which it inactivates proteins involved in RNA biogenesis. Spectroscopic studies showed that r(AUUCU) repeats form a hairpin structure; however, there were no high-resolution structural models prior to this work. Herein, we report the first crystal structure of model r(AUUCU) repeats refined to 2.8 Å and analysis of the structure via molecular dynamics simulations. The r(AUUCU) tracts adopt an overall A-form geometry in which 3 × 3 nucleotide 5'UCU3'/3'UCU5' internal loops are closed by AU pairs. Helical parameters of the refined structure as well as the corresponding electron density map on the crystallographic model reflect dynamic features of the internal loop. The computational analyses captured dynamic motion of the loop closing pairs, which can form single-stranded conformations with relatively low energies. Overall, the results presented here suggest the possibility for r(AUUCU) repeats to form metastable A-from structures, which can rearrange into single-stranded conformations and attract proteins such as heterogeneous nuclear ribonucleoprotein K (hnRNP K). Here, the information presented here may aid in the rational design of therapeutics targeting this RNA.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
How structural differences influence cross-model consistency: An electric sector case study
Multiple models are often employed to describe a range of possible outcomes for one or more scenarios, yielding insights into causal relationships and their uncertainties. Electric sector capacity expansion scenarios are a common topic of such efforts due to the economic influence of the electric sector, but model results typically span a broad solution space despite efforts to harmonize input assumptions, making decision implications difficult to discern. This study investigates the relationship between input harmonization and cross-model scenario consistency under disparate electric sector scenarios. We compare cross-model consistency between two state-of-the-art electric sector capacity expansion models (GCAM-USA and ReEDS) for six electric sector scenarios comprising alternate assumptions about fossil fuel resource availability, technology innovation, and long-term economy-wide transitions under four harmonization configurations varying model representations of electricity demand, fuel prices, renewable resources, and capacity retirements. These comparisons reveal that cross-model consistency can vary across scenarios under a given harmonization configuration, suggesting that harmonization efforts must often be scenario-specific if comparable cross-model consistency is desired. Model structural differences can hinder consistency, and the impact of these differences can depend on the scenario. Ultimately, thorough harmonization can reveal insights into cross-model consistency, which can be used to tighten uncertainty bounds and improve the decision-making implications of multi-model activities.
Cohen, Stuart↗
A Chain Model of a Zigzag Contact of Lateral Graphene-Like Heterostructures
A simple structural model is proposed for the zigzag interface formed by contacting two-dimensional graphene-like compounds AB and CD (both free and formed on a metal). For the graphene–hexagonal boron nitride system, analytical expressions for the electron spectrum, density of states, and atom occupation numbers at the interface are obtained. The results of calculating the densities of states and occupation numbers within two alternative approximations are in good agreement.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
The {sup 13}C-pocket structure in AGB models: constraints from zirconium isotope abundances in single mainstream SiC grains
We present postprocess asymptotic giant branch (AGB) nucleosynthesis models with different {sup 13}C-pocket internal structures to better explain zirconium isotope measurements in mainstream presolar SiC grains by Nicolussi et al. and Barzyk et al. We show that higher-than-solar {sup 92}Zr/{sup 94}Zr ratios can be predicted by adopting a {sup 13}C-pocket with a flat {sup 13}C profile, instead of the previous decreasing-with-depth {sup 13}C profile. The improved agreement between grain data for zirconium isotopes and AGB models provides additional support for a recent proposal of a flat {sup 13}C profile based on barium isotopes in mainstream SiC grains by Liu et al.
79 ASTRONOMY AND ASTROPHYSICS↗
Gaussian approximation potential for amorphous Si : H
Hydrogenation of amorphous silicon (a–Si : H) is critical for reducing defect densities, passivating midgap states and surfaces, and improving photoconductivity in silicon-based electro-optical devices. Modeling the atomic-scale structure of this material is critical to understanding these processes, which in turn is needed to describe c–Si/a–Si : H heterojunctions that are at the heart of modern solar cells with world-record efficiency. Density functional theory (DFT) studies achieve the required high accuracy but are limited to moderate system sizes of 100 atoms or so by their high computational cost. Simulations of amorphous materials have been hindered by this high cost because large structural models are required to capture the medium-range order that is characteristic of such materials. Empirical potential models are much faster, but their accuracy is not sufficient to correctly describe the frustrated local structure. Data-driven, machine-learned interatomic potentials have broken this impasse and have been highly successful in describing a variety of amorphous materials in their elemental phase. Here, we extend the Gaussian approximation potential (GAP) for silicon by incorporating the interaction with hydrogen, thereby significantly improving the degree of realism with which amorphous silicon can be modeled. We show that our Si : H GAP enables the simulation of hydrogenated silicon with an accuracy very close to DFT but with computational expense and run times reduced by several orders of magnitude for large structures. Here, we demonstrate the capabilities of the Si : H GAP by creating models of hydrogenated liquid and amorphous silicon and showing that their energies, forces, and stresses are in excellent agreement with DFT results, and their structure as captured by bond and angle distributions are in agreement with both DFT and experiments.
36 MATERIALS SCIENCE↗
Thermo-Mechanical Modeling and Evaluation of the Cracking Response of Additively Manufactured Monolithic SiC Lattice Structures Subjected to Laser Heating
Increasing operating temperatures of solar receivers is paramount to the efficiency of concentrated solar thermal (CST) and solar power (CSP) systems. Owing to its high temperature stability combined with excellent thermal and optical properties, SiC has been the material of choice for application in high-temperature solar receivers. The state-of-the-art SiC volumetric concentrating solar air receivers such as honeycomb design have been demonstrated in field tests to achieve exit air temperatures approaching 800oC. However, successful application of CST systems for decarbonization requires significant increase in temperature capability of SiC receiver technology. Supported by an award from the Solar Technology Office (SETO), US Department of Energy (DOE), GE Research in collaboration with Heliogen Holdings Inc, is engaged in the development of ultra-High Operating Temperature SiC-matrix Solar Thermal Air Receiver (HOTSSTAR) enabled by additive manufacturing. The program objective is to design and to demonstrate a techno-economically viable SiC air receiver technology to achieve exit air temperature of 1100oC for CST applications. This report summarizes the learnings of a computational study on additively manufactured SiC lattice structures subjected to laser heating. The results from this study aim to help guide the design of a SiC receiver through a better understanding of lattice structure geometric parameters and their implications on the cracking response of the structure. In the study, a heat flux was applied to the surface of a 2”-diameter cylindrical lattice structure to simulate a 4kW CO2 laser. The resulting temperature distribution was applied to a structural model to approximate the stress distribution within the lattice structure and a Weibull analysis was performed to gain insight into the probability of failure and to evaluate the cracking response of the structure. With this approach, the influence of lattice density on temperature, stress, and probability of failure was explored for two different lattice beam spacings. A discussion on modeling assumptions, a comparison with experimental results, and an evaluation of the lattice cracking response is provided.
14 SOLAR ENERGY↗
Molecular envelope and atomic model of an anti-terminated glyQS T-box regulator in complex with tRNAGly
A T-box regulator or riboswitch actively monitors the levels of charged/uncharged tRNA and participates in amino acid homeostasis by regulating genes involved in their utilization or biosynthesis. It has an aptamer domain for cognate tRNA recognition and an expression platform to sense the charge state and modulate gene expression. These two conserved domains are connected by a variable linker that harbors additional secondary structural elements, such as Stem III. The structural basis for specific tRNA binding is known, but the structural basis for charge sensing and the role of other elements remains elusive. To gain new structural insights on the T-box mechanism, a molecular envelope was calculated from small angle X-ray scattering data for the Bacillus subtilis glyQS T-box riboswitch in complex with an uncharged tRNAGly. A structural model of an antiterminated glyQS T-box in complex with its cognate tRNAGly was derived based on the molecular envelope. It shows the location and relative orientation of various secondary structural elements. The model was validated by comparing the envelopes of the wild-type complex and two variants. The structural model suggests that in addition to a possible regulatory role, Stem III could aid in preferential stabilization of the T-box anti-terminated state allowing read-through of regulated genes.
59 BASIC BIOLOGICAL SCIENCES↗
Reply to “Structural and magnetic behavior of the cubic oxyfluoride SrFeO{sub 2}F studied by neutron diffraction”
In this article we comment on the results published by Thompson et al. (, J. Solid State Chem. 219 (2014) 173–178) on the crystal structure of SrFeO{sub 2}F, who claim the compound to crystallize in the cubic space group Pm-3m. We give a more detailed explanation of the determination of our previously reported structural model with Imma symmetry (Clemens et al., J. Solid State Chem. 206 (2013) 158–169), with addition of variable temperature XRD measurements with high counting time to provide unambiguous evidence for the Imma model being correct for our sample. - Graphical abstract: The crystal structure of SrFeO{sub 2}F is discussed with regards to previous reports. - Highlights: • SrFeO{sub 2}F was synthesized by polymer based fluorination of SrFeO{sub 3}. • Evaluation of the diffraction data shows a pseudocubic cell metric. • Superstructure reflections at low d-spacings indicate deviation from cubic symmetry. • The phase transition temperature from orthorhombic to cubic was determined using variable temperature X-ray diffraction. • Results published by Thompson et al. are critically discussed with respect to those observations.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
RCSB Protein Data Bank: supporting research and education worldwide through explorations of experimentally determined and computationally predicted atomic level 3D biostructures
The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules ( e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.
59 BASIC BIOLOGICAL SCIENCES↗
Electric Double-Layer Structure in Primitive Model Electrolytes. Comparing Molecular Dynamics with Local-Density Approximations
We evaluate the accuracy of local-density approximations (LDAs) using explicit molecular dynamics simulations of binary electrolytes comprised of equisized ions in an implicit solvent. The Bikerman LDA, which considers ions to occupy a lattice, poorly captures excluded volume interactions between primitive model ions. Instead, LDAs based on the Carnahan–Starling (CS) hard-sphere equation of state capture simulated values of ideal and excess chemical potential profiles extremely well, as is the relationship between surface charge density and electrostatic potential. Excellent agreement between the EDL capacitances predicted by CS-LDAs and computed in molecular simulations is found even in systems where ion correlations drive strong density and free charge oscillations within the EDL, despite the inability of LDAs to capture the oscillations in the detailed EDL profiles.
25 ENERGY STORAGE↗
Predicted structural proteome of Sphagnum divinum and proteome-scale annotation
Sphagnum-dominated peatlands store a substantial amount of terrestrial carbon. The genus is undersampled and under-studied. No experimental crystal structure from any Sphagnum species exists in the Protein Data Bank and fewer than 200 Sphagnum-related genes have structural models available in the AlphaFold Protein Structure Database. Tools and resources are needed to help bridge these gaps, and to enable the analysis of other structural proteomes now made possible by accurate structure prediction. We present the predicted structural proteome (25,134 primary transcripts) of Sphagnum divinum computed using AlphaFold, structural alignment results of all high-confidence models against an annotated nonredundant crystallographic database of over 90,000 structures, a structure-based classification of putative Enzyme Commission (EC) numbers across this proteome, and the computational method to perform this proteome-scale structure-based annotation.
59 BASIC BIOLOGICAL SCIENCES↗
PDBx/mmCIF Ecosystem: Foundational Semantic Tools for Structural Biology
PDBx/mmCIF, Protein Data Bank Exchange (PDBx) macromolecular Crystallographic Information Framework (mmCIF), has become the data standard for structural biology. With its early roots in the domain of small-molecule crystallography, PDBx/mmCIF provides an extensible data representation that is used for deposition, archiving, remediation, and public dissemination of experimentally determined three-dimensional (3D) structures of biological macromolecules by the Worldwide Protein Data Bank (wwPDB, wwpdb.org). Extensions of PDBx/mmCIF are similarly used for computed structure models by ModelArchive (modelarchive.org), integrative/hybrid structures by PDB-Dev (pdb-dev.wwpdb.org), small angle scattering data by Small Angle Scattering Biological Data Bank SASBDB (sasbdb.org), and for models computed generated with the AlphaFold 2.0 deep learning software suite (alphafold.ebi.ac.uk). Community-driven development of PDBx/mmCIF spans three decades, involving contributions from researchers, software and methods developers in structural sciences, data repository providers, scientific publishers, and professional societies. Having a semantically rich and extensible data framework for representing a wide range of structural biology experimental and computational results, combined with expertly curated 3D biostructure data sets in public repositories, accelerates the pace of scientific discovery. Herein, we describe the architecture of the PDBx/mmCIF data standard, tools used to maintain representations of the data standard, governance, and processes by which data content standards are extended, plus community tools/software libraries available for processing and checking the integrity of PDBx/mmCIF data. Use cases exemplify how the members of the Worldwide Protein Data Bank have used PDBx/mmCIF as the foundation for its pipeline for delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data to many millions of users worldwide.
59 BASIC BIOLOGICAL SCIENCES↗
Global and adaptive K-nearest neighbor graphs in a spectral target detector based on Schroedinger Eigenmaps
The Schroedinger Eigenmaps (SE) embedding has been previously introduced and applied to spectral target detection problems in hyperspectral imagery (HSI). The proposed SEbased detection approach combines the spectral and spatial connectivity of target-like pixels into the Schroedinger operator by using a “knowledge propagation” scheme. Likewise, it has been noted the impact that the local data structure modeling has over the SE-based detector. This local data structure is modeled by using an adjacency graph, i.e, set of vertices and edges, that in most of the applications is a k nearest neighbor (knn) graph. In this paper, the impact of k in the graph-building process is analyzed and assessed from a target detection point of view. Here, two ways to estimate the parameter k in the graph construction are considered and their assessment is performed by using two HSI data sets and the Receiver Optimal Curve (ROC) as a detection performance metric
97 MATHEMATICS AND COMPUTING↗
MFANS 2024 - Extensions of SysML Using Category Theory for Structured Analysis
Models provide understanding and physical modeling is as important in a Cyber Physical System as cyber modeling. By using category theory, which includes syntax for describing the system and semantics for using the model, we can provide a foundation to unify modeling.
97 MATHEMATICS AND COMPUTING↗
Knowledge Graph for End-to-End Traceability of an Integrated Human-Earth System Model
Integrated human-Earth system models inform energy-water-land system dynamics and policies, yet their results are difficult to trace through input-data, model structure, scenario configurations, and solved outputs. Because this information is siloed across disconnected artifacts, process-based IAMs have historically lacked a unified, queryable representation. Such lack of traceability prevents researchers from systematically isolating the multi-sector drivers of complex outcomes (such as tracing water-scarcity results back to distant energy-system dynamics) or conducting holistic uncertainty attribution across hundreds of interacting parameters. To address this concern, our work documents the software engineering process of a knowledge graph that unifies these four layers for the Global Change Analysis Model (GCAM-USA_Reference scenario, GCAM v9.1). The graph was built as a relational property graph in DuckDB from the run’s own artifacts: the input-preparation dependency map (gcamdata chunk map), the model’s XML input files, the run configuration, and the results database (BaseX), successfully mapping the model’s declared structure. The resulting graph comprises 204,321 nodes and 1,687,814 edges across 16 node types and 15 edge types, with approximately 16.3 million time-series values stored separately to maintain structural efficiency. To ensure representation fidelity, every edge carries an epistemic-status annotation recording the warrant for the relationship (structural, provenance, dependency, or model-derived), and a machine-readable provenance ledger classifying the origin of every schema element. Evaluation against a fixed five-benchmark suite with locked baselines reports zero structural orphans, zero dangling edge endpoints, and 100% of output-producing technologies traceable to raw input files. Two interactive interfaces present the graph, including a serverless browser application built on DuckDB-Wasm. By establishing the first end-to-end provenance framework for an IAM, this work enables researchers and scientists to systematically audit complex policy scenarios, debug model structures, and trace policy-relevant outputs to their data origins in real time.
Artifical Intelligence↗