AIChE Journal, EarlyView.Abstract Air separation via selective adsorption using porous adsorbents offers energy‐efficient alternatives to cryogenic distillation for producing high‐purity O2 and N2. Adsorbent efficacy depends on balancing selectivity, durability, and performance consistency across varying conditions. This comprehensive review critically discusses the design and
Tianqi Wang +9 more
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Comparison of Guide to Expression of Uncertainty in Measurement and Monte Carlo Method for Evaluating Gauge Factor Calibration Test Uncertainty of High-Temperature Wire Strain Gauge. [PDF]
Sensors (Basel)Zhao Y, Zhang F, Ai Y, Tian J, Wang Z.
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Quantifying Distributions of Parameters for Cardiac Action Potential Models Using the Hamiltonian Monte Carlo Method. [PDF]
Comput Cardiol (2010), 2021 Nieto Ramos A +3 more
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Quasi-Monte and Data-Driven Monte Carlo Methods for Efficient Human Joint Model Fitting
, 1969 Sara Greenberg +2 more
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The derivation of Particle Monte Carlo methods for plasma modeling from transport equations [PDF]
, 2008 S. Longo
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AIChE Journal, EarlyView.ABSTRACT Flexibility is a crucial characteristic of industrial systems that face increasing volatilities and is therefore essential to ensure feasible operation under uncertainty. Flexibility is often closely tied to the design of a system, and careful consideration must be taken to understand the trade‐off between design cost and operational ...
Jnana Sai Jagana +3 more
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Design and Validation of a Monte Carlo Method for the Implementation of Noninvasive Wearable Devices for HbA1c Estimation Considering the Skin Effect. [PDF]
Micromachines (Basel)Kwon TH, Hossain S, Turja MS, Kim KD.
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Constrained Path Monte Carlo method for spin $ 1/2 $ fermions at unitarity limit [PDF]
, 2017 Faisal Etminan +1 more
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Designing Memristive Materials for Artificial Dynamic Intelligence
Advanced Intelligent Discovery, EarlyView.Key characteristics required of memristors for realizing next‐generation computing, along with modeling approaches employed to analyze their underlying mechanisms. These modeling techniques span from the atomic scale to the array scale and cover temporal scales ranging from picoseconds to microseconds. Hardware architectures inspired by neural networks
Youngmin Kim, Ho Won Jang
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