112 research outputs found

    A data-driven framework for structure-property correlation in ordered and disordered cellular metamaterials

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    Cellular solids and micro-lattices are a class of lightweight architected materials that have been established for their unique mechanical, thermal, and acoustic properties. It has been shown that by tuning material architecture, a combination of topology and solid(s) distribution, one can design new material systems, also known as metamaterials, with superior performance compared to conventional monolithic solids. Despite the continuously growing complexity of synthesized microstructures, mainly enabled by developments in additive manufacturing, correlating their morphological characteristics to the resulting material properties has not advanced equally. This work aims to develop a systematic data-driven framework that is capable of identifying all key microstructural characteristics and evaluating their effect on a target material property. The framework relies on integrating virtual structure generation and quantification algorithms with interpretable surrogate models. The effectiveness of the proposed approach is demonstrated by analyzing the effective stiffness of a broad class of two-dimensional (2D) cellular metamaterials with varying topological disorder. The results reveal the complex manner in which well-known stiffness contributors, including nodal connectivity, cooperate with often-overlooked microstructural features such as strut orientation, to determine macroscopic material behavior. We further re-examine Maxwell's criteria regarding the rigidity of frame structures, as they pertain to the effective stiffness of cellular solids and showcase microstructures that violate them. This framework can be used for structure-property correlation in different classes of metamaterials as well as the discovery of novel architectures with tailored combinations of material properties

    Guided ultrasonic wave monitoring techniques to assess bone implant loosening

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    Total ankle replacement (TAR) is the main clinical treatment for end-stage ankle arthritis, replacing the ankle joint with a metallic implant. Component loosening, fracture, and wear are the main reasons for implant failure, requiring revision surgery. A non-invasive guided wave monitoring technique is being developed to ultimately evaluate in-vivo implant device integrity and bone-implant interface conditions (osseointegration). Finite Element (FE) simulations were performed to investigate the feasibility and sensitivity of ultrasonic monitoring of the interface conditions, assessing suitable guide d wave modes and excitation frequencies. A simplified implant geometry was developed for FE modelling in Abaqus/Explicit. Selected guided wave modes (higher-order longitudinal modes sensitive to bone/implant interface changes) were excited at the distal end of the metallic implant component for detection of variations of bone-implant contact conditions. Simulation results showed the feasibility for guided ultrasonic waves to monitor bone implant osseointegration. Guided wave signal amplitude and changes of arrival time of pulses propagating along the metallic implant can indicate the presence of improved osseointegration. The potential for the integration of the bone implant monitoring sensors and other biosensors into secure, blockchain-based, remote patient data management systems will be further investigated

    Predicting coordination variability of selected lower extremity couplings during a cutting movement:an investigation of deep neural networks with the LSTM structure

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    There are still few portable methods for monitoring lower limb joint coordination during the cutting movements (CM). This study aims to obtain the relevant motion biomechanical parameters of the lower limb joints at 90°, 135°, and 180° CM by collecting IMU data of the human lower limbs, and utilizing the Long Short-Term Memory (LSTM) deep neural-network framework to predict the coordination variability of selected lower extremity couplings at the three CM directions. There was a significant (p < 0.001) difference between the three couplings during the swing, especially at 90° vs the other directions. At 135° and 180°, t13-he coordination variability of couplings was significantly greater than at 90° (p < 0.001). It is important to note that the coordination variability of Hip rotation/Knee flexion-extension was significantly higher at 90° than at 180° (p < 0.001). By the LSTM, the CM coordination variability for 90° (CMC = 0.99063, RMSE = 0.02358), 135° (CMC = 0.99018, RMSE = 0.02465) and 180° (CMC = 0.99485, RMSE = 0.01771) were accurately predicted. The predictive model could be used as a reliable tool for predicting the coordination variability of different CM directions in patients or athletes and real-world open scenarios using inertial sensors

    Pathology Steered Stratification Network for Subtype Identification in Alzheimer's Disease

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    Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for Alzheimer's disease at a late stage, urging for early intervention. However, existing statistical inference approaches of AD subtype identification ignore the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles. Integrating systems biology modeling with machine learning, we propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term trajectories that capture individual progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases

    MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation

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    Perception systems in modern autonomous driving vehicles typically take inputs from complementary multi-modal sensors, e.g., LiDAR and cameras. However, in real-world applications, sensor corruptions and failures lead to inferior performances, thus compromising autonomous safety. In this paper, we propose a robust framework, called MetaBEV, to address extreme real-world environments involving overall six sensor corruptions and two extreme sensor-missing situations. In MetaBEV, signals from multiple sensors are first processed by modal-specific encoders. Subsequently, a set of dense BEV queries are initialized, termed meta-BEV. These queries are then processed iteratively by a BEV-Evolving decoder, which selectively aggregates deep features from either LiDAR, cameras, or both modalities. The updated BEV representations are further leveraged for multiple 3D prediction tasks. Additionally, we introduce a new M2oE structure to alleviate the performance drop on distinct tasks in multi-task joint learning. Finally, MetaBEV is evaluated on the nuScenes dataset with 3D object detection and BEV map segmentation tasks. Experiments show MetaBEV outperforms prior arts by a large margin on both full and corrupted modalities. For instance, when the LiDAR signal is missing, MetaBEV improves 35.5% detection NDS and 17.7% segmentation mIoU upon the vanilla BEVFusion model; and when the camera signal is absent, MetaBEV still achieves 69.2% NDS and 53.7% mIoU, which is even higher than previous works that perform on full-modalities. Moreover, MetaBEV performs fairly against previous methods in both canonical perception and multi-task learning settings, refreshing state-of-the-art nuScenes BEV map segmentation with 70.4% mIoU.Comment: Project page: https://chongjiange.github.io/metabev.htm
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