Thermal conductance and thermoelectric figure of merit of C60_{60}-based single-molecule junctions: electrons, phonons, and photons

Motivated by recent experiments, we present here an ab initio study of the impact of the phonon transport on the thermal conductance and thermoelectric figure of merit of C$_{60}$-based single-molecule junctions. To be precise, we combine density functional theory with nonequilibrium Green's function techniques to compute these two quantities in junctions with either a C$_{60}$ monomer or a C$_{60}$ dimer connected to gold electrodes, taking into account the contributions of both electrons and phonons. Our results show that for C$_{60}$ monomer junctions phonon transport plays a minor role in the thermal conductance and, in turn, in the figure of merit, which can reach relatively high values on the order of 0.1, depending on the contact geometry. At the contrary, phonons completely dominate the thermal conductance in C$_{60}$ dimer junctions and strongly reduce the figure of merit as compared to monomer junctions. Thus, claims that by stacking C$_{60}$ molecules one could achieve high thermoelectric performance, which have been made without considering the phonon contribution, are not justified. Moreover, we analyze the relevance of near-field thermal radiation for the figure of merit of these junctions within the framework of fluctuational electrodynamics. We conclude that photon tunneling can be another detrimental factor for the thermoelectric performance, which has been overlooked so far in the field of molecular electronics. Our study illustrates the crucial roles that phonon transport and photon tunneling can play when critically assessing the performance of molecular junctions as potential nanoscale thermoelectric devices

Experimental Characterization and Micromechanical Modeling of Woven Carbon/Copper Composites

The results of an extensive experimental characterization and a preliminary analytical modeling effort for the elastoplastic mechanical behavior of 8-harness satin weave carbon/copper (C/Cu) composites are presented. Previous experimental and modeling investigations of woven composites are discussed, as is the evolution of, and motivation for, the continuing research on C/Cu composites. Experimental results of monotonic and cyclic tension, compression, and Iosipescu shear tests, and combined tension-compression tests, are presented. With regard to the test results, emphasis is placed on the effect of strain gauge size and placement, the effect of alloying the copper matrix to improve fiber-matrix bonding, yield surface characterization, and failure mechanisms. The analytical methodology used in this investigation consists of an extension of the three-dimensional generalized method of cells (GMC-3D) micromechanics model, developed by Aboudi (1994), to include inhomogeneity and plasticity effects on the subcell level. The extension of the model allows prediction of the elastoplastic mechanical response of woven composites, as represented by a true repeating unit cell for the woven composite. The model is used to examine the effects of refining the representative geometry of the composite, altering the composite overall fiber volume fraction, changing the size and placement of the strain gauge with respect to the composite's reinforcement weave, and including porosity within the infiltrated fiber yarns on the in-plane elastoplastic tensile, compressive, and shear response of 8-harness satin C/Cu. The model predictions are also compared with the appropriate monotonic experimental results

Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning

Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD) shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate this challenge, we propose Noise2Recon, a model-agnostic, consistency training method for joint MRI reconstruction and denoising that can use both fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised and self-supervised settings. With limited or no labeled training data, Noise2Recon outperforms compressed sensing and deep learning baselines, including supervised networks, augmentation-based training, fine-tuned denoisers, and self-supervised methods, and matches performance of supervised models, which were trained with 14x more fully-sampled scans. Noise2Recon also outperforms all baselines, including state-of-the-art fine-tuning and augmentation techniques, among low-SNR scans and when generalizing to other OOD factors, such as changes in acceleration factors and different datasets. Augmentation extent and loss weighting hyperparameters had negligible impact on Noise2Recon compared to supervised methods, which may indicate increased training stability. Our code is available at https://github.com/ad12/meddlr

A Double-Layered Timber Plate Shell - Computational Methods for Assembly, Prefabrication, and Structural Design

This paper presents a new lightweight construction system for doubly-curved shells, built from two interconnected layers of structural wood veneer plates. The system uses integral through-tenon joints for a fast, precise, and simple assembly, allowing for the construction of a series of differently shaped shells without a costly mould or support structure. Instead, inclined joints cut with a 5-axis CNC milling machine embed the correct location and angle between plates into the shape of the parts. This constrains the relative motions between joined parts to one assembly path. To take advantage of the benefits of such connectors, the constrained assembly paths must be considered in the fundamental design of the system, allowing for the insertion of each plate. This imposes additional constraints in the segmentation process of doubly-curved shells. In order to meet the requirements and resolve the multi-constraint system, we use a global, non-linear optimisation approach. Developed as a close collaboration between architects, computer scientists and structural engineers, the paper includes an experimental analysis of the influence of parametric modifications in the shape of connectors on their load-bearing performance

VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction

Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstruction. However, such models require a large number of fully-sampled ground truth datasets, which are difficult to curate, and are sensitive to distribution drifts. In this work, we propose applying physics-driven data augmentations for consistency training that leverage our domain knowledge of the forward MRI data acquisition process and MRI physics to achieve improved label efficiency and robustness to clinically-relevant distribution drifts. Our approach, termed VORTEX, (1) demonstrates strong improvements over supervised baselines with and without data augmentation in robustness to signal-to-noise ratio change and motion corruption in data-limited regimes; (2) considerably outperforms state-of-the-art purely image-based data augmentation techniques and self-supervised reconstruction methods on both in-distribution and out-of-distribution data; and (3) enables composing heterogeneous image-based and physics-driven data augmentations. Our code is available at https://github.com/ad12/meddlr.Comment: Accepted to MIDL 202

Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein.Comment: 4 pages, 8 Figures, Conference Submissio

Determinants of patient-reported functional mobility in people with Parkinson's disease: A systematic review.

peer reviewed[en] BACKGROUND: Information on determinants of patient-reported functional mobility is lacking but would inform the planning of healthcare, resources and strategies to promote functional mobility in people with Parkinson's disease (PD). RESEARCH QUESTION: To identify the determinants of patient-reported functional mobility of people with PD. METHODS: Eligible: Randomized Controlled Trials, cohort, case-control, or cross-sectional analyses in people PD without date or setting restrictions, published in English, German, or French. Excluded: instruments with under 50 % of items measuring mobility. On August 9th 2023 we last searched Medline, CINAHL and PsychInfo. We assessed risk of bias using the mixed-methods appraisal tool. Results were synthesized by tabulating the determinants by outcomes and study designs. RESULTS: Eleven studies published 2012-2023 were included (most in Swedish outpatient settings). Samples ranged from 9 to 255 participants. Follow-up varied from 1.5 to 36 months with attrition of 15-42 %. Heterogenic study designs complicated results synthesis. However, determinants related to environment seem to associate the strongest with patient-reported functional mobility, although determinants related to body structures and functions were most investigated. We identified disease duration, the ability to drive, caregiving, sex, age, cognitive impairment, postural instability and social participation as determinants of patient-reported functional mobility. DISCUSSION: Methodological quality of the studies was limited. No study reported an a priori power calculation. Three studies controlled for confounders. The included studies lack representativeness of the population of people living with PD. Standardized sets of outcomes could enable more systematic research synthesis. CONCLUSIONS: Future research should focus on activities, participation and environmental factors and improve methodological quality