Results 111 to 120 of about 52,951 (307)
Advanced Science, EarlyView.An integrated transfer learning framework integrates CALPHAD simulations, diffusion‐multiple experiments, and literature data to predict long‐term microstructural stability and short‐term mechanical properties of Ni‐based powder metallurgy superalloys. Based on these model predictions, a high‐performance, low‐density alloy, USTB‐PM750, is designed from
Zixin Li +8 more
wiley +1 more source
Advanced Science, EarlyView.This study systematically reveals a complex interactive network involving plants, microbes, and insects, elucidating the ecological and molecular mechanisms by which cotton enhances its resistance to aphids through the active recruitment of the beneficial soil bacterium Delftia tsuruhatensis.
Hui Xue +11 more
wiley +1 more source
Advanced Science, EarlyView.Biological rhythms coordinate physiology, from genes to behavior. Study of circadian rhythms in brain tissue is constrained by limited throughput and spatial and temporal information quality. A new platform for high‐throughput, long‐term multiplexed fluorescent live imaging of circadian rhythms in brain slices is introduced.
Marco Ferrari +3 more
wiley +1 more source
Combining multivariate density forecasts using predictive criteria [PDF]
This paper combines multivariate density forecasts of output growth, inflation and interest rates from a suite of models. An out-of-sample weighting scheme based on the predictive likelihood as proposed by Eklund and Karlsson (2005) and Andersson and ...
Hugo Gerard, Kristoffer Nimark
core +3 more sources
Efficient estimation of parameters in marginals in semiparametric multivariate models [PDF]
Recent literature on semiparametric copula models focused on the situation when the marginals are specified nonparametrically and the copula function is given a parametric form.
Artem Prokhorov, Valentyn Panchenko
core
Advanced Science, EarlyView.Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang +2 more
wiley +1 more source
Multivariate Nonparametric Volatility Density Estimation
, 2009 We consider a continuous-time stochastic volatility model. The model contains a stationary volatility process, the multivariate density of the finite dimensional distributions of which we aim to estimate. We assume that we observe the process at discrete instants in time. The sampling times will be equidistant with vanishing distance.
van Es, Bert, Spreij, Peter
openaire +2 more sources
Nonparametric Density Estimation for Positive Time Series [PDF]
The Gaussian kernel density estimator is known to have substantial problems for bounded random variables with high density at the boundaries. For i.i.d. data several solutions have been put forward to solve this boundary problem. In this paper we propose
Jeroen V.K. Rombouts, Taoufik Bouezmarni
core
Engineering Neuronal Network Connectivity Through Precise and Scalable Electrical Modulation
Advanced Science, EarlyView.This study presents a scalable all‐electrical method for precise neuronal‐circuit reconfiguration based on high‐density microelectrode arrays. By employing biologically inspired plasticity rules, targeted connectivity changes were successfully induced and quantified across diverse neuronal preparations.
Sreedhar S. Kumar +10 more
wiley +1 more source
Multivariate Density Estimation with Missing Data
, 2018 Multivariate density estimation is a popular technique in statistics with wide applications including regression models allowing for heteroskedasticity in conditional variances. The estimation problems become more challenging when observations are missing in one or more variables of the multivariate vector.
Li, Zhen +3 more
openaire +2 more sources