Ground motion selection for simulation-based seismic hazard and structural reliability assessment

This paper examines four methods by which ground motions can be selected for dynamic seismic response analyses of engineered systems when the underlying seismic hazard is quantified via ground motion simulation rather than empirical ground motion prediction equations. Even with simulation-based seismic hazard, a ground motion selection process is still required in order to extract a small number of time series from the much larger set developed as part of the hazard calculation. Four specific methods are presented for ground motion selection from simulation-based seismic hazard analyses, and pros and cons of each are discussed via a simple and reproducible illustrative example. One of the four methods (method 1 ‘direct analysis’) provides a ‘benchmark’ result (i.e. using all simulated ground motions), enabling the consistency of the other three more efficient selection methods to be addressed. Method 2 (‘stratified sampling’) is a relatively simple way to achieve a significant reduction in the number of ground motions required through selecting subsets of ground motions binned based on an intensity measure, IM. Method 3 (‘simple multiple stripes’) has the benefit of being consistent with conventional seismic assessment practice using as-recorded ground motions, but both methods 2 and 3 are strongly dependent on the efficiency of the conditioning IM to predict the seismic responses of interest. Method 4 (‘GCIM-based selection’) is consistent with ‘advanced’ selection methods used for as-recorded ground motions, and selects subsets of ground motions based on multiple IMs, thus overcoming this limitation in methods 2 and 3

High Dielectric Constants in BaTiO3 Due to Phonon Mode Softening Induced by Lattice Strains: First Principles Calculations

High-dielectric-constant materials attract much attention due to their broad applications in modern electronics. Barium titanate (BTO) is an established material possessing an ultrahigh dielectric constant; however, a complete understanding of the responsible underlying physical mechanism remains elusive. Here a set of density-functional-theory calculations for the static dielectric tensors of barium titanate under strain has been performed. The dielectric constant increases to ≈7300 under strain. The analysis of the computed vibrational modes shows that transverse vibrational mode softening (the appearance of low-frequency modes) is responsible for this significant increase as driven by the relationship between lattice contribution for the static dielectric constant (k) and vibrational frequency (ω), i.e., urn:x-wiley:27511200:media:apxr202300001:apxr202300001-math-0001. The relevant vibrational mode indicates a large counter-displacement of Ti ions and O anions, which greatly enhances electrical dipoles to screen the electric field. The calculations not only interpreted experimental data on the high dielectric constants of BTO, where the lattice deformation due to the strains from the grain nanostructure plays an important role, but also pointed to exploring high-throughput calculations to facilitate the discovery of the advanced dielectric materials. Moreover, the calculations can prove useful for doped BTO, for which local strains fields can be achieved using defect engineering

Quantum-Classical Multiple Kernel Learning

As quantum computers become increasingly practical, so does the prospect of using quantum computation to improve upon traditional algorithms. Kernel methods in machine learning is one area where such improvements could be realized in the near future. Paired with kernel methods like support-vector machines, small and noisy quantum computers can evaluate classically-hard quantum kernels that capture unique notions of similarity in data. Taking inspiration from techniques in classical machine learning, this work investigates simulated quantum kernels in the context of multiple kernel learning (MKL). We consider pairwise combinations of several classical-classical, quantum-quantum, and quantum-classical kernels in an empirical investigation of their classification performance with support-vector machines. We also introduce a novel approach, which we call QCC-net (quantum-classical-convex neural network), for optimizing the weights of base kernels together with any kernel parameters. We show this approach to be effective for enhancing various performance metrics in an MKL setting. Looking at data with an increasing number of features (up to 13 dimensions), we find parameter training to be important for successfully weighting kernels in some combinations. Using the optimal kernel weights as indicators of relative utility, we find growing contributions from trainable quantum kernels in quantum-classical kernel combinations as the number of features increases. We observe the opposite trend for combinations containing simpler, non-parametric quantum kernels.Comment: 15 pages, Supplementary Information on page 15, 6 main figures, 1 supplementary figur

Systems-Based Design of Bi-Ligand Inhibitors of Oxidoreductases: Filling the Chemical Proteomic Toolbox

Genomics-driven growth in the number of enzymes of unknown function has created a need for better strategies to characterize them. Since enzyme inhibitors have traditionally served this purpose, we present here an efficient systems-based inhibitor design strategy, enabled by bioinformatic and NMR structural developments. First, we parse the oxidoreductase gene family into structural subfamilies termed pharmacofamilies, which share pharmacophore features in their cofactor binding sites. Then we identify a ligand for this site and use NMR-based binding site mapping (NMR SOLVE) to determine where to extend a combinatorial library, such that diversity elements are directed into the adjacent substrate site. The cofactor mimic is reused in the library in a manner that parallels the reuse of cofactor domains in the oxidoreductase gene family. A library designed in this manner yielded specific inhibitors for multiple oxidoreductases

Rodrigues Formula for the Nonsymmetric Multivariable Laguerre Polynomial

Extending a method developed by Takamura and Takano, we present the Rodrigues formula for the nonsymmetric multivariable Laguerre polynomials which form the orthogonal basis for the $B_{N}$-type Calogero model with distinguishable particles. Our construction makes it possible for the first time to algebraically generate all the nonsymmetric multivariable Laguerre polynomials with different parities for each variable.Comment: 6 pages, LaTe

Bringing pupils into the ORBYTS of research

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