112 research outputs found
RESEARCH ON THE INFLUENCE OF CULTIVATION AND INNOVATION OF CERAMIC ART AND DESIGN EDUCATION ON ALLEVIATING AUDIENCE’S PSYCHOLOGICAL ANXIETY
RESEARCH ON THE INFLUENCE OF CULTIVATION AND INNOVATION OF CERAMIC ART AND DESIGN EDUCATION ON ALLEVIATING AUDIENCE’S PSYCHOLOGICAL ANXIETY
A data-driven framework for structure-property correlation in ordered and disordered cellular metamaterials
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
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
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
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
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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