Molecular Mechanisms by which Tetrahydrofuran Affects CO₂ Hydrate Growth: Implications for Carbon Storage

Gas hydrates have attracted siginifcant fundamental and applied interests due to their important role in various technological and enviromental processes. More recently, gas hydrates have shown potential applications for greenhouse gas capture and storage. To facilitate the latter application, introducing chemical additives into clathrate hydrates could help to enhance hydrate formation/growth rates, provided the gas storage capacity is not reduced. Employing equilibrium molecular dynamics, we study the impact of tetrahydrofuran (THF) on the kinetics of carbon dioxide (CO₂) hydrate growth/dissociation and on the CO₂ storage capacity of hydrates. Our simulations reproduce experimental data for CO₂ and CO₂+THF hydrates at selected operating conditions. The simulated results confirm that THF in stoichiometric concentration does reduce CO₂ storage capacity. This is not only due to the shortage of CO₂ trapping in sII hydrate 5^{12} cages, but also because of the favored THF occupancy in hydrate cages due to preferential THF−water hydrogen bonds. An analysis of the dynamical properties for CO₂ and THF at the hydrate-liquid interface reveals that THF can expedite CO₂ diffusion yielding a shift in the conditions conducive to CO₂ hydrate growth and stability to lower pressures and higher temperatures compared to systems without THF. These simulation results augment literature experimental observations, as they provide needed insights into the molecular mechanisms that can be adjusted to achieve optimal CO₂ storage in hydrates

Screen test for cadmium and nickel plates as developed and used within the Aerospace Corporation

A new procedure described here was recently developed to quantify loading uniformity of nickel and cadmium plates and to screen finished electrodes prior to cell assembly. The technique utilizes the initial solubility rates of the active material in a standard chemical deloading solution at fixed conditions. The method can provide a reproducible indication of plate loading uniformity in situations where high surface loading limits the free flow of deloading solution into the internal porosity of the sinter plate. A preliminary study indicates that 'good' cell performance is associated with higher deloading rates

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis

The widespread use of multi-sensor technology and the emergence of big datasets has highlighted the limitations of standard flat-view matrix models and the necessity to move towards more versatile data analysis tools. We show that higher-order tensors (i.e., multiway arrays) enable such a fundamental paradigm shift towards models that are essentially polynomial and whose uniqueness, unlike the matrix methods, is guaranteed under verymild and natural conditions. Benefiting fromthe power ofmultilinear algebra as theirmathematical backbone, data analysis techniques using tensor decompositions are shown to have great flexibility in the choice of constraints that match data properties, and to find more general latent components in the data than matrix-based methods. A comprehensive introduction to tensor decompositions is provided from a signal processing perspective, starting from the algebraic foundations, via basic Canonical Polyadic and Tucker models, through to advanced cause-effect and multi-view data analysis schemes. We show that tensor decompositions enable natural generalizations of some commonly used signal processing paradigms, such as canonical correlation and subspace techniques, signal separation, linear regression, feature extraction and classification. We also cover computational aspects, and point out how ideas from compressed sensing and scientific computing may be used for addressing the otherwise unmanageable storage and manipulation problems associated with big datasets. The concepts are supported by illustrative real world case studies illuminating the benefits of the tensor framework, as efficient and promising tools for modern signal processing, data analysis and machine learning applications; these benefits also extend to vector/matrix data through tensorization. Keywords: ICA, NMF, CPD, Tucker decomposition, HOSVD, tensor networks, Tensor Train

Pediatric Automatic Sleep Staging: A comparative study of state-of-the-art deep learning methods.

Despite the tremendous progress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overnight polysomnography (PSG). To answer the question, in this work, we conduct a large-scale comparative study on the state-of-the-art deep learning methods for pediatric automatic sleep staging. Six different deep neural networks with diverging features are adopted to evaluate a sample of more than 1,200 children across a wide spectrum of obstructive sleep apnea (OSA) severity. Our experimental results show that the individual performance of automated pediatric sleep stagers when evaluated on new subjects is equivalent to the expert-level one reported on adults. Combining the six stagers into ensemble models further boosts the staging accuracy, reaching an overall accuracy of 88.8%, a Cohens kappa of 0.852, and a macro F1-score of 85.8%. At the same time, the ensemble models lead to reduced predictive uncertainty. The results also show that the studied algorithms and their ensembles are robust to concept drift when the training and test data were recorded seven months apart and after clinical intervention. However, we show that the improvements in the staging performance are not necessarily clinically significant although the ensemble models lead to more favorable clinical measures than the six standalone models. Detailed analyses further demonstrate "almost perfect" agreement between the automatic stagers to one another and their similar patterns on the staging errors, suggesting little room for improvement

Exchange Bias Effect in Au-Fe3O4 Nanocomposites

We report exchange bias (EB) effect in the Au-Fe3O4 composite nanoparticle system, where one or more Fe3O4 nanoparticles are attached to an Au seed particle forming dimer and cluster morphologies, with the clusters showing much stronger EB in comparison with the dimers. The EB effect develops due to the presence of stress in the Au-Fe3O4 interface which leads to the generation of highly disordered, anisotropic surface spins in the Fe3O4 particle. The EB effect is lost with the removal of the interfacial stress. Our atomistic Monte-Carlo studies are in excellent agreement with the experimental results. These results show a new path towards tuning EB in nanostructures, namely controllably creating interfacial stress, and open up the possibility of tuning the anisotropic properties of biocompatible nanoparticles via a controllable exchange coupling mechanism.Comment: 28 pages, 6 figures, submitted to Nanotechnolog