Compressive mechanical response of graphene foams and their thermal resistance with copper interfaces

We report compressive mechanical response of graphene foams (GFs) and the thermal resistance ($R_{TIM}$) between copper (Cu) and GFs, where GFs were prepared by the chemical vapor deposition (CVD) method. We observe that Young's modulus ($E_{GF}$) and compressive strength ($\sigma_{GF}$) of GFs have a power law dependence on increasing density ($\rho_{GF}$) of GFs. The maximum efficiency of absorbed energy ($\eta_{max}$) for all GFs during the compression is larger than ~0.39. We also find that a GF with a higher $\rho_{GF}$ shows a larger $\eta_{max}$. In addition, we observe that the measured $R_{TIM}$ of Cu/GFs at room temperature with a contact pressure of 0.25 MP applied increases from ~50 to ~90 $mm^2K/W$ when $\rho_{GF}$ increases from 4.7 to 31.9 $mg/cm^3$

Sampling is Matter: Point-guided 3D Human Mesh Reconstruction

This paper presents a simple yet powerful method for 3D human mesh reconstruction from a single RGB image. Most recently, the non-local interactions of the whole mesh vertices have been effectively estimated in the transformer while the relationship between body parts also has begun to be handled via the graph model. Even though those approaches have shown the remarkable progress in 3D human mesh reconstruction, it is still difficult to directly infer the relationship between features, which are encoded from the 2D input image, and 3D coordinates of each vertex. To resolve this problem, we propose to design a simple feature sampling scheme. The key idea is to sample features in the embedded space by following the guide of points, which are estimated as projection results of 3D mesh vertices (i.e., ground truth). This helps the model to concentrate more on vertex-relevant features in the 2D space, thus leading to the reconstruction of the natural human pose. Furthermore, we apply progressive attention masking to precisely estimate local interactions between vertices even under severe occlusions. Experimental results on benchmark datasets show that the proposed method efficiently improves the performance of 3D human mesh reconstruction. The code and model are publicly available at: https://github.com/DCVL-3D/PointHMR_release.Comment: Accepted by CVPR 202

Electrical and thermal conductivities of reduced graphene oxide/polystyrene composites

The author reports an experimental study of electrical and thermal transport in reduced graphene oxide (RGO)/polystyrene (PS) composites. The electrical conductivity (sigma) of RGO/PS composites with different RGO concentrations at room temperature shows a percolation behavior with the percolation threshold of similar to 0.25 vol. %. Their temperature-dependent electrical conductivity follows Efros-Shklovskii variable range hopping conduction in the temperature range of 30-300K. The thermal conductivity (kappa) of composites is enhanced by similar to 90% as the concentration is increased from 0 to 10 vol. %. The thermal conductivity of composites approximately linearly increases with increasing temperature from 150 to 300 K. Composites with a higher concentration show a stronger temperature dependence in the thermal conductivity. (C) 2014 AIP Publishing LLC

Stratifying non-small cell lung cancer patients using an inverse of the treatment decision rules: validation using electronic health records with application to an administrative database

To validate a stratification method using an inverse of treatment decision rules that can classify non-small cell lung cancer (NSCLC) patients in real-world treatment records. (1) To validate the index classifier against the TNM 7th edition, we analyzed electronic health records of NSCLC patients diagnosed from 2011 to 2015 in a tertiary referral hospital in Seoul, Korea. Predictive accuracy, stage-specific sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and c-statistic were measured. (2) To apply the index classifier in an administrative database, we analyzed NSCLC patients in Korean National Health Insurance Database, 2002–2013. Differential survival rates among the classes were examined with the log-rank test, and class-specific survival rates were compared with the reference survival rates. (1) In the validation study (N = 1375), the overall accuracy was 93.8% (95% CI: 92.5–95.0%). Stage-specific c-statistic was the highest for stage I (0.97, 95% CI: 0.96–0.98) and the lowest for stage III (0.82, 95% CI: 0.77–0.87). (2) In the application study (N = 71,593), the index classifier showed a tendency for differentiating survival probabilities among classes. Compared to the reference TNM survival rates, the index classification under-estimated the survival probability for stages IA, IIIB, and IV, and over-estimated it for stages IIA and IIB. The inverse of the treatment decision rules has a potential to supplement a routinely collected database with information encoded in the treatment decision rules to classify NSCLC patients. It requires further validation and replication in multiple clinical settings