676 research outputs found
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 -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
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