arXiv subjects

Yingying Lu

Publications and source records attributed to Yingying Lu.

5 recordsLinked to original sources

arXiv2025

Time-varying treatment effect models in stepped-wedge cluster-randomized trials with multiple interventions

The traditional model specification of stepped-wedge cluster-randomized trials assumes a homogeneous treatment effect across time while adjusting for fixed-time effects. However, when treatment effects vary over time, the constant effect estimator may be biased. In the general setting of stepped-wedge cluster-randomized trials with multiple interventions, we derive the expected value of the constant effect estimator when the true treatment effects depend on exposure time periods. Applying this result to concurrent and factorial stepped wedge designs, we show that the estimator represents a weighted average of exposure-time-specific treatment effects, with weights that are not necessarily uniform across exposure periods. Extensive simulation studies reveal that ignoring time heterogeneity can result in biased estimates and poor coverage of the average treatment effect. In this study, we examine two models designed to accommodate multiple interventions with time-varying treatment effects: (1) a time-varying fixed treatment effect model, which allows treatment effects to vary by exposure time but remain fixed for each time point, and (2) a random treatment effect model, where the time-varying treatment effects are modeled as random deviations from an overall mean. In the simulations considered in this study, concurrent designs generally achieve higher power than factorial designs under a time-varying fixed treatment effect model, though the differences are modest. Finally, we apply the constant effect model and both time-varying treatment effect models to data from the Prognosticating Outcomes and Nudging Decisions in the Electronic Health Record (PONDER) trial. All three models indicate a lack of treatment effect for either intervention, though they differ in the precision of their estimates, likely due to variations in modeling assumptions.

stat.ME↗

arXiv2024

Enhanced torque efficiency in ferromagnetic multilayers by introducing naturally oxidized Cu

Spin-orbit torque (SOT) in the heavy elements with a large spin-orbit coupling (SOC) has been frequently used to manipulate the magnetic states in spintronic devices. Recent theoretical works have predicted that the surface oxidized light elements with a negligible SOC can yield a sizable orbital torque (OT), which plays an important role in switching the magnetization. Here, we report anomalous-Hall-resistance and harmonic-Hall-voltage measurements on perpendicularly magnetized Ta/Cu/[Ni/Co]$_5$/Cu-CuO$_x$ multilayers. Both torque efficiency and spin-Hall angle of these multilayers are largely enhanced by introducing a naturally oxidized Cu-CuO$_x$ layer, where the SOC is negligible. Such an enhancement is mainly due to the collaborative driven of the SOT from the Ta layer and the OT from the Cu/CuO$_x$ interface, and can be tuned by controlling the thickness of Cu-CuO$_x$ layer. Compared to the Cu-CuO$_x$-free multilayers, the maximum torque efficiency and spin-Hall angle were enhanced by a factor of ten, larger than most of the reported values in the other heterostructures.

cond-mat.mtrl-sci↗

arXiv2021

Training Data Set Refinement for the Machine Learning Potential of Li-Si Alloys via Structural Similarity Analysis

Machine learning potential enables molecular dynamics simulations of systems beyond the capability of classical force fields. The traditional approach to develop structural sets for training machine learning potential typically generate a great number of redundant configurations, which will result in unnecessary computational costs. This work investigates the possibility of reducing redundancy in an initial data set containing 6183 configurations for a Li-Si machine learning potential. Starting from the initial data set, we constructed a series of subsets ranging from 25 to 1500 configurations by combining a structural similarity analysis algorithm and the farthest point sampling method. Results show that the machine learning potential trained from a data set containing 400 configurations can achieve an accuracy comparable to the one developed from the initial data set of 6183 configurations in describing potential energies, atomic forces, and structural properties of Li-Si systems. In addition, the redundancy reducing approach also demonstrates advantages over the classic stochastic method for constructing a concise training data set for Li-Si systems.

physics.comp-ph↗

arXiv2016

Maxima and minima of independent and non-identically distributed bivariate Gaussian triangular arrays

In this paper, joint limit distributions of maxima and minima on independent and non-identically distributed bivariate Gaussian triangular arrays is derived as the correlation coefficient of $i$th vector of given $n$th row is the function of $i/n$. Furthermore, second-order expansions of joint distributions of maxima and minima are established if the correlation function satisfies some regular conditions.

math.PR↗

arXiv2014

Stable Lithium Electrodeposition in Liquid and Nanoporous Solid Electrolytes

Rechargeable lithium, sodium, and aluminum metal-based batteries are among the most versatile platform for high-energy, cost effective electrochemical energy storage. Non-uniform metal deposition and dendrite formation on the negative electrode during repeated cycles of charge and discharge are major hurdles to commercialization of energy storage devices based on each of these chemistries. A long held view is that unstable electrodeposition is a consequence of inherent characteristics of these metals and their inability to form uniform electrodeposits on surfaces with inevitable defects. We report on electrodeposition of lithium in simple liquid electrolytes and in nanoporous solids infused with liquid electrolytes. We find that simple liquid electrolytes reinforced with halogenated salt blends exhibit stable long-term cycling at room temperature, often with no signs of deposition instabilities over hundreds of cycles of charge and discharge and thousands of operating hours. We rationalize these observations with the help of surface energy data for the electrolyte/lithium interface and impedance analysis of the interface during different stages of cell operation. Our findings provide support for an important recent theoretical prediction that the surface mobility of lithium is significantly enhanced in the presence of lithium halide salts.

cond-mat.mtrl-sci↗