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This work presents a systematic study of the relationship between structural stochasticity and the crush energy absorption capability of lattice structures, with controlled stiffness and weight. We develop a Voronoi tessellation-based approach to generate multiple series of lattice structures with either equal weight or equal stiffness, smoothly transitioning from periodic to stochastic configurations for crush energy absorption analysis. The generated lattice series fall into two categories, originating from periodic honeycomb and diamond lattice structures. A new stochasticity metric is proposed for quantifying the structural stochasticity and is compared with the state-of-the-art stochasticity metrics to ensure a consistent measurement. The crush energy absorption properties are obtained using explicit finite element analysis and we observe similar stochasticity-property trends in simulations using both elastic-plastic and hyperelastic materials. We report a new observation that an intermediate level of stochasticity between periodic and high randomness leads to the best crush energy absorption performance. Our analysis reveals that this optimal performance arises from enhanced activation of deformation hinges, promoting efficient energy absorption. 

A series of perovskite oxides (Ln = La, Pr, Nd, Gd; A = Ba, Sr) was investigated to understand the effects of A-site cation size on oxygen vacancy formation. Quasirandom mixed structures were generated using Alloy Theoretic Automated Toolkit (ATAT), followed by density functional theory (DFT) calculations. While mixing the orthorhombic structures with the hexagonal AMnO3 structures leads to lattices and global symmetries closer to cubic, the average volume generally increases with the average ionic size, and the local bond and angles exhibit more variations due to A-site mixing. DFT calculations and a statistical model were combined to predict oxygen reduction abilities. Thermogravimetric analysis (TGA) provided experimental validation of these predictions by measuring changes in oxygen non-stoichiometry under controlled conditions. Both indicated that larger A-site ionic size differences lead to greater, consistent with the larger variation in local structures, and enhanced redox capabilities. This combined computational-experimental approach highlights the importance of local structure variation, instead of average properties, in A-site cation engineering to optimize perovskite oxides for different devices relying on oxygen vacancy redox activity. 

This work presents a systematic study of the relationship between structural stochasticity and the crush energy absorption capability of lattice structures, with controlled stiffness and weight. We develop a Voronoi tessellation-based approach to generate multiple series of lattice structures with either equal weight or equal stiffness, smoothly transitioning from periodic to stochastic configurations for crush energy absorption analysis. The generated lattice series fall into two categories, originating from periodic honeycomb and diamond lattice structures. A new stochasticity metric is proposed for quantifying the structural stochasticity and is compared with the state-of-the-art stochasticity metrics to ensure a consistent measurement. The crush energy absorption properties are obtained using explicit finite element analysis and we observe similar stochasticity-property trends in simulations using both elastic-plastic and hyperelastic materials. We report a new observation that an intermediate level of stochasticity between periodic and high randomness leads to the best crush energy absorption performance. Our analysis reveals that this optimal performance arises from enhanced activation of deformation hinges, promoting efficient energy absorption. 

Abstract Although the existing causal inference literature focuses on the forward-looking perspective by estimating effects of causes, the backward-looking perspective can provide insights into causes of effects. In backward-looking causal inference, the probability of necessity measures the probability that a certain event is caused by the treatment given the observed treatment and outcome. Most existing results focus on binary outcomes. Motivated by applications with ordinal outcomes, we propose a general definition of the probability of necessity. However, identifying the probability of necessity is challenging because it involves the joint distribution of the potential outcomes. We propose a novel assumption of monotonic incremental treatment effect to identify the probability of necessity with ordinal outcomes. We also discuss the testable implications of this key identification assumption. When it fails, we derive explicit formulas of the sharp large-sample bounds on the probability of necessity. 

This paper studies a cloud datacenter (DC) consisting of two types of tasks with different priority levels. While non-priority tasks generally request the use of a single virtual machine (VM), priority tasks may utilize multiple available VMs to accelerate processing. We focus on determining whether to accept or reject non-priority tasks to maximize overall system benefits. By formulating the problem as a stochastic dynamic program, it is verified that the best approach for handling nonpriority tasks adheres to a control-limit framework. Both experimental outcomes and numerical evaluations highlight the efficacy of the proposed method, leading to the identification of the optimal threshold. The key contribution of this paper is the development of a stochastic dynamic program for DC resource management and the explicit derivation of an optimal control-limit policy. Both value iteration and linear programming methods are utilized to solve optimization problems. These results offer essential understanding for assessing the performance of various DC models, optimizing both rewards and resources efficiently. 

Abstract For efficient roll-to-roll (R2R) production of flexible electronic components, a precise R2R transfer peeling process is essential, requiring accurate modeling and control. This paper introduces a novel approach to confining the dynamics of a nonlinear R2R mechanical peeling system within a convex set known as a norm-bounded linear differential inclusion (NLDI). This method utilizes constraints on uncertain system variables to create a tighter NLDI representation compared to other convexification techniques. Moreover, it offers drastically reduced computational cost compared to previous methods applied to convexify the R2R peeling system. The NLDI is employed to generate an H∞-optimal controller for the R2R peeling system, and both simulations and experiments demonstrate better dynamic performance compared to other controllers for R2R transfer. 

Abstract This study presents an eco-friendly mechanochemical synthesis of cesium lead bromide (CsPbBr₃), eliminating the need of organic solvents and high temperatures. The synthesized CsPbBr₃powder is used to fabricate poly(methyl methacrylate) (PMMA)-CsPbBr₃films and CsPbBr₃nanocrystals (NCs). The photoluminescence (PL) peaks of the emission light are centered at 541 nm, 538 nm, and 514 nm for the CsPbBr₃powder, PMMA-CsPbBr₃films, and CsPbBr₃NCs, respectively, correlating with crystal sizes of 0.96, 0.56, and 0.12μm, respectively. The PL lifetime analysis reveals decay times ( ${\tau }_1,{\tau }_2$) of (4.18, 20.08), (5.7, 46.99), and (5.81, 23.14) in the units (ns, ns) for the CsPbBr₃powder, PMMA-CsPbBr₃films, and CsPbBr₃NCs, respectively. The PL quantum yield of the CsPbBr₃NCs in toluene is 61.3%. Thermal activation energies for thermal quenching are 217.48 meV (films) and 178.15 meV (powder), indicating improved thermal stability with the PMMA encapsulation. The analysis of the PL intensity decay from water diffusion in the PMMA-CsPbBr₃films yields 1.70 × 10⁻¹²m²s⁻¹for the diffusion coefficient of water, comparable to that for water diffusion in pure PMMA. This work demonstrates a scalable, sustainable strategy for CsPbBr₃synthesis and stability enhancement for optoelectronic applications.