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

The transdiagnostic role of event-related rumination on internalizing and externalizing symptoms during the pandemic: a two-wave longitudinal study

BackgroundRumination is a well-established transdiagnostic vulnerability. However, few studies have explored the transdiagnostic role of event-related rumination. Moreover, there is a paucity of longitudinal studies clarifying the temporal precedence of event-related rumination. Therefore, this study aimed to longitudinally examine the mediating paths of event-related rumination between perceived stress and diverse symptomatic dimensions.MethodsA representative sample of Korean adults (N = 316) was recruited online and they completed a package of self-reported measures twice over a one-year period. Using prospective two-wave data collected during the pandemic, longitudinal indirect effects were examined using the hypothesized path model.ResultsAs expected, intrusive rumination acted as a transdiagnostic mediator in both internalizing and externalizing psychopathology and was positively associated with all subsequent symptom dimensions, except mania. Meanwhile, the beneficial role of deliberate rumination was less-transdiagnostic.ConclusionThese initial findings suggest that event-related rumination could be considered a transdiagnostic mediator and a target for prevention and intervention to maintain mental health during and after the pandemic

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

NarSha: Pioneering The Korean Microsatellite Constellation for Spaceborne Methane Monitoring

Methane (CH4) is the second most abundant anthropogenic greenhouse gas contributing the global warming. Its global warming potential is estimated to be about 80 times greater than that of carbon dioxide (CO2) over the last 20 years. To achieve a global net zero in carbon emissions, it is important to monitor and manage point sources of methane emissions worldwide. We introduce the first Korean spaceborne methane monitoring platform development project, termed NarSha. Collaborating with Nara Space Technology, the Climate Laboratory of Seoul National University, and the Korea Astronomy and Space Science Institute, the NarSha project aims to develop and launch the standard microsatellite by 2026. The microsatellite system, named the Korean methane monitoring microsatellite (K3M), is designed to be compatible with the 16U CubeSat standard and is equipped with two optical payloads. The primary payload is a hyperspectral imager operating in the short-wave infrared (SWIR) range, with a spectral resolution finer than 1 nm within the weak methane absorption band (1625-1670 nm) and ground sampling distance (GSD) of 30 meter at an altitude of 500 km. The secondary payload, VIS/NIR camera, is integrated with the hyperspectral imager to identify clouds within its scene. Both payloads have a swath greater than 10 km at 500 km altitude, enabling a local-level monitoring. The agile and precise attitude control system can improve a SNR during the mission. Furthermore, the on-board processing capability and high-speed communication facilitate the delivery of large volumes of raw data essential for the detection and quantification of methane plumes. This proposed system will be operated as LEO constellation to obtain a global methane point source data with high spatial and temporal resolution. This data will significantly contribute to the tracking and quantifying of global methane emissions and establishing a strategy for global warming mitigation. In this study, we introduce the NarSha project and outlines the design of microsatellite systems and the constellation for spaceborne methane monitoring