Thermal Resistance across Interfaces Comprising Dimensionally Mismatched Carbon Nanotube-Graphene Junctions in 3D Carbon Nanomaterials

In the present study, reverse nonequilibrium molecular dynamics is employed to study thermal resistance across interfaces comprising dimensionally mismatched junctions of single layer graphene floors with (6,6) single-walled carbon nanotube (SWCNT) pillars in 3D carbon nanomaterials. Results obtained from unit cell analysis indicate the presence of notable interfacial thermal resistance in the out-of-plane direction (along the longitudinal axis of the SWCNTs) but negligible resistance in the in-plane direction along the graphene floor. The interfacial thermal resistance in the out-of-plane direction is understood to be due to the change in dimensionality as well as phonon spectra mismatch as the phonons propagate from SWCNTs to the graphene sheet and then back again to the SWCNTs. The thermal conductivity of the unit cells was observed to increase nearly linearly with an increase in cell size, that is, pillar height as well as interpillar distance, and approaches a plateau as the pillar height and the interpillar distance approach the critical lengths for ballistic thermal transport in SWCNT and single layer graphene. The results indicate that the thermal transport characteristics of these SWCNT-graphene hybrid structures can be tuned by controlling the SWCNT-graphene junction characteristics as well as the unit cell dimensions

Thermal Transport in 3D Pillared SWCNT−Graphene Nanostructures

We present results of a molecular dynamics study using adaptive intermolecular reactive empirical bond order interatomic potential to analyze thermal transport in three-dimensional pillared singlewalled carbon nanotube (SWCNT)-graphene superstructures comprised of unit cells with graphene floors and SWCNT pillars. The results indicate that in-plane as well as out-of-plane thermal conductivity in these superstructures can be tuned by varying the interpillar distance and/or the pillar height. The simulations also provide information on thermal interfacial resistance at the graphene-SWCNT junctions in both the in-plane and out-of-plane directions. Among the superstructures analyzed, the highest effective (based on the unit cell cross-sectional area) in-plane thermal conductivity was 40 W/(m K) with an out-of-plane thermal conductivity of 1.0 W/(m K) for unit cells with an interpillar distance D x 5 3.3 nm and pillar height D z 5 1.2 nm, while the highest out-of-plane thermal conductivity was 6.8 W/(m K) with an in-plane thermal conductivity of 6.4 W/(m K) with D x 5 2.1 nm and D z 5 4.2 nm

Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data

Breast cancer screening, primarily conducted through mammography, is often supplemented with ultrasound for women with dense breast tissue. However, existing deep learning models analyze each modality independently, missing opportunities to integrate information across imaging modalities and time. In this study, we present Multi-modal Transformer (MMT), a neural network that utilizes mammography and ultrasound synergistically, to identify patients who currently have cancer and estimate the risk of future cancer for patients who are currently cancer-free. MMT aggregates multi-modal data through self-attention and tracks temporal tissue changes by comparing current exams to prior imaging. Trained on 1.3 million exams, MMT achieves an AUROC of 0.943 in detecting existing cancers, surpassing strong uni-modal baselines. For 5-year risk prediction, MMT attains an AUROC of 0.826, outperforming prior mammography-based risk models. Our research highlights the value of multi-modal and longitudinal imaging in cancer diagnosis and risk stratification.Comment: ML4H 2023 Findings Trac

Latent class models for analyzing multilevel nested data: model misspecification, model selection, sample sizes, and covariates

The fundamental assumption in any latent variable model is that observations are independent of one another, given the latent status. However, this assumption is often inadequate when observations are nested within higher-level units because such nested data structures induce dependencies in data. The nonparametric version of the multilevel latent class model (MLCM) is an extension of latent class models in which the dependencies in data are accounted for by discrete random effects in the model. To date, this modeling framework has been used in a wide variety of empirical applications. Nonetheless, several unresolved issues relating to the application of the MLCM require further investigation. This dissertation consists of four independent manuscripts addressing the issues of model misspecification, model selection, sample size requirements, and covariate effects in MLCM. The first manuscript (Chapter 2) evaluates the adverse impact of ignoring higher-level nesting structures to provide a rationale for the use of nonparametric MLCMs. The second manuscript (Chapter 3) investigates the relative performance of diverse information criteria (IC) in identifying the optimal number of latent classes. The third manuscript (Chapter 4) systematically examines the minimum sample size requirement for the nonparamtric MLCM. The fourth manuscript (Chapter 5) explores the performance of several approaches to evaluating the effects of covariate in situations in which level-1 and level-2 covariates are simultaneously included to predict the latent class membership at each level. A summary and discussion of the manuscript's contributions and practical recommendations regarding the application of the MLCM in empirical data analyses are presented at the end of each manuscript.L'indépendance des observations est une hypothèse fondamentale des modèles de variable latente. Cependant, ce postulat échoue lorsque les observations sont imbriquées dans des unités de haut niveau puisque ces structures de données imbriquées induisent une dépendance des données amassées. La version non paramétrique d'un modèle de structure latente multiniveau (MLCM) est une extension des LCM dans laquelle la dépendance des données est comptabilisée par les effets aléatoires discrets dans le modèle. Même si ce cadre de modélisation peut être utilisé dans plusieurs contextes, il reste que son application est encore quelque peu problématique. Cette dissertation comprend quatre manuscrits indépendants ; ceux-ci portent sur les différents enjeux reliés aux erreurs de spécification et de sélection, à la taille d'échantillon requise, et aux effets des covariables. Le premier manuscrit (chapitre 2) évalue les effets négatifs qui surviennent lorsqu'on ignore les structures imbriquées de haut niveau. En tenant compte de ces effets, on peut mieux justifier l'utilisation d'un modèle non paramétrique MLCM. Le deuxième manuscrit (chapitre 3) explore la performance relative des critères d'information (IC). Le troisième manuscrit (chapitre 4) examine systématiquement la taille minimale requise pour l'échantillon dans la modélisation des MLCM. Le quatrième manuscrit (chapitre 5) étudie la performance de diverses approches utilisées pour évaluer les effets simultanés des covariables sur deux niveaux. Finalement, à la fin de chaque manuscrit et dans la section de synthèse, on offre une discussion approfondie sur les contributions et des recommandations pratiques quant à l'utilisation des MLCM