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1. Data-Driven Topology Optimization With Multiclass Microstructures Using Latent Variable Gaussian Process

   https://doi.org/10.1115/1.4048628  

   Wang, Liwei; Tao, Siyu; Zhu, Ping; Chen, Wei (March 2021, Journal of Mechanical Design)

   null (Ed.)

   Abstract The data-driven approach is emerging as a promising method for the topological design of multiscale structures with greater efficiency. However, existing data-driven methods mostly focus on a single class of microstructures without considering multiple classes to accommodate spatially varying desired properties. The key challenge is the lack of an inherent ordering or “distance” measure between different classes of microstructures in meeting a range of properties. To overcome this hurdle, we extend the newly developed latent-variable Gaussian process (LVGP) models to create multi-response LVGP (MR-LVGP) models for the microstructure libraries of metamaterials, taking both qualitative microstructure concepts and quantitative microstructure design variables as mixed-variable inputs. The MR-LVGP model embeds the mixed variables into a continuous design space based on their collective effects on the responses, providing substantial insights into the interplay between different geometrical classes and material parameters of microstructures. With this model, we can easily obtain a continuous and differentiable transition between different microstructure concepts that can render gradient information for multiscale topology optimization. We demonstrate its benefits through multiscale topology optimization with aperiodic microstructures. Design examples reveal that considering multiclass microstructures can lead to improved performance due to the consistent load-transfer paths for micro- and macro-structures. 

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2. Collaborative System Design of Mixed Reality Communication for Medical Training

   Rebol, Manuel Pietroszek (January 2023, Proceedings of the Annual Hawaii International Conference on System Sciences)

   We present the design of a mixed reality (MR) telehealth training system that aims to close the gap between in-person and distance training and re-training for medical procedures. Our system uses real-time volumetric capture as a means for communicating and relating spatial information between the non-colocated trainee and instructor. The system's design is based on a requirements elicitation study performed in situ, at a medical school simulation training center. The focus is on the lightweight real-time transmission of volumetric data - meaning the use of consumer hardware, easy and quick deployment, and low-demand computations. We evaluate the MR system design by analyzing the workload for the users during medical training. We compare in-person, video, and MR training workloads. The results indicate that the overall workload for central line placement training with MR does not increase significantly compared to video communication. Our work shows that, when designed strategically together with domain experts, an MR communication system can be used effectively for complex medical procedural training without increasing the overall workload for users significantly. Moreover, MR systems offer new opportunities for teaching due to spatial information, hand tracking, and augmented communication. 

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3. Collaborative System Design of Mixed Reality Communication for Medical Training

   Rebol, Manuel (January 2023, Proceedings of the 56th Hawaii International Conference on System Sciences)

   Abstract We present the design of a mixed reality (MR) telehealth training system that aims to close the gap between in-person and distance training and re-training for medical procedures. Our system uses real-time volumetric capture as a means for communicating and relating spatial information between the non-colocated trainee and instructor. The system's design is based on a requirements elicitation study performed in situ, at a medical school simulation training center. The focus is on the lightweight real-time transmission of volumetric data - meaning the use of consumer hardware, easy and quick deployment, and low-demand computations. We evaluate the MR system design by analyzing the workload for the users during medical training. We compare in-person, video, and MR training workloads. The results indicate that the overall workload for central line placement training with MR does not increase significantly compared to video communication. Our work shows that, when designed strategically together with domain experts, an MR communication system can be used effectively for complex medical procedural training without increasing the overall workload for users significantly. Moreover, MR systems offer new opportunities for teaching due to spatial information, hand tracking, and augmented communication. 

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4. An nnU-Net Model to Enhance Segmentation of Cardiac Cine DENSE-MRI Using Phase Information

   https://doi.org/10.1109/ICHI61247.2024.00106  

   Jahromi, Mohammad Naqizadeh; Marques, Augusto Delavald; Ahmed, Mehlil; Liu, Zhan-Qiu; Hannum, Ariel J; Ennis, Daniel B; Perotti, Luigi E; Wu, Dazhong (June 2024, IEEE)

   This study explores the application of deep learning to the segmentation of DENSE cardiovascular magnetic resonance (CMR) images, which is an important step in the analysis of cardiac deformation and may help in the diagnosis of heart conditions. A self-adapting method based on the nnU-Net framework is introduced to enhance the accuracy of DENSE-MR image segmentation, with a particular focus on the left ventricle myocardium (LVM) and left ventricle cavity (LVC), by leveraging the phase information in the cine DENSE-MR images. Two models are built and compared: 1) ModelM, which uses only the magnitude of the DENSE-MR images; and 2) ModelMP, which incorporates magnitude and phase images. DENSE-MR images from 10 human volunteers processed using the DENSE-Analysis MATLAB toolbox were included in this study. The two models were trained using a 2D UNet-based architecture with a loss function combining the Dice similarity coefficient (DSC) and cross-entropy. The findings show the effectiveness of leveraging the phase information with ModelMP resulting in a higher DSC and improved image segmentation, especially in challenging cases, e.g., at early systole and with basal and apical slices. 

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5. Modeling Resilient Modulus of Unsaturated Subgrade Soils under Concurrent Changes in Water Content and Temperature

   https://doi.org/10.1061/9780784484036.037  

   Abdollahi, Masood; Vahedifard, Farshid (March 2022, Proc. Geo-Congress 2022: Site and Soil Characterization, Computational Geotechnics, Risk, and Lessons Learned, Geotechnical Special Publication No. 333)

   Seasonal variations and climatic events cause fluctuations of water content and temperature in shallow unsaturated soils. Such fluctuations can alter the resilient modulus (MR) of subgrade, which is an important parameter in the design and evaluation of pavements. This paper presents a new model to determine MR of unsaturated subgrade soils under concurrent changes in water content and temperature. The proposed analytical model offers the following two new features distinguishing it from alternative models: (1) the model separately accounts for two different soil water retention mechanisms, namely capillary and adsorption, which enables it to predict MR over a wide range of suctions, and (2) it explicitly incorporates the effect of temperature in the calculation of MR through employing temperature-dependent expressions for matric suction and the soil water retention curve (SWRC). The proposed model showed high accuracy when validated against experimentally measured MR values for several different soils reported in the literature. The presented model is simple and can readily be employed in practice to determine MR of subgrade soils under concurrent variations of water content and temperature. 

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