Engineering topics

Lu, Yan

Publications and source records attributed to Lu, Yan.

4 records

DOE OSTI2024

A bioactive supramolecular and covalent polymer scaffold for cartilage repair in a sheep model

Regeneration of hyaline cartilage in human-sized joints remains a clinical challenge, and it is a critical unmet need that would contribute to longer healthspans. Injectable scaffolds for cartilage repair that integrate both bioactivity and sufficiently robust physical properties to withstand joint stresses offer a promising strategy. We report here on a hybrid biomaterial that combines a bioactive peptide amphiphile supramolecular polymer that specifically binds the chondrogenic cytokine transforming growth factor β-1 (TGFβ-1) and crosslinked hyaluronic acid microgels that drive formation of filament bundles, a hierarchical motif common in natural musculoskeletal tissues. The scaffold is an injectable slurry that generates a porous rubbery material when exposed to calcium ions once placed in cartilage defects. The hybrid material was found to support in vitro chondrogenic differentiation of encapsulated stem cells in response to sustained delivery of TGFβ-1. Using a sheep model, we implanted the scaffold in shallow osteochondral defects and found it can remain localized in mechanically active joints. Evaluation of resected joints showed significantly improved repair of hyaline cartilage in osteochondral defects injected with the scaffold relative to defects injected with the growth factor alone, including implantation in the load-bearing femoral condyle. These results demonstrate the potential of the hybrid biomimetic scaffold as a niche to favor cartilage repair in mechanically active joints using a clinically relevant large-animal model.

Science & Technology - Other Topics↗

DOE OSTI2021

Rotational Velocities of Am and Non-chemical-peculiar Stars Based on Kepler and LAMOST DR5

Rotation is a critical physical process operating in the formation of Am stars. There is a strong correlation between low-velocity rotation and chemical peculiarity. However, the existence of many non-chemical-peculiar slow rotators challenges the understanding of Am stars. The purpose of our work is to search for low-velocity rotating non-chemical-peculiar A-type stars and Am stars and to make a comparative analysis. In this paper, we pick out a sample from the LAMOST–Kepler project, including 21 Am stars, 125 non-chemical-peculiar slow rotators, and 53 non-chemical-peculiar fast rotators. We calculate the rotational frequencies through a periodic change of light curves caused by inhomogeneous stellar surfaces and then obtain the rotational velocities. For slow rotators, the age of Am stars is statistically younger than that of non-chemical-peculiar stars in the same temperature bin. In the comparison of the period, the average amplitude, and stellar mass of Am and non-chemical-peculiar stars, we discover that there is no difference in the photometric variability between Am and non-chemical-peculiar stars, which implies similar inhomogeneities on the surfaces. The average amplitude of non-chemical-peculiar stars has a downward trend with the increase of effective temperature and stellar mass, which is consistent with the theoretical prediction caused by weak dynamo-generated magnetic fields in A-type stars. In addition, we confirm four non-chemical-peculiar stars that have flares by checking field-of-view images, pixel images, and pixel-level light curves.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Vegetation Detection Using Deep Learning and Conventional Methods

Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer vision, and machine learning (ML) techniques, is also proposed. The vegetation detection methods were applied to high-resolution airborne color images which consist of RGB and near-infrared (NIR) bands. RGB color images alone were also used with the two deep learning methods to examine their detection performances without the NIR band. The detection performances of the deep learning methods with respect to the object-based detection approach are discussed and sample images from the datasets are used for demonstrations.

58 GEOSCIENCES↗

DOE OSTI2020

Deep Learning for Land Cover Classification Using Only a Few Bands

There is an emerging interest in using hyperspectral data for land cover classification. The motivation behind using hyperspectral data is the notion that increasing the number of narrowband spectral channels would provide richer spectral information and thus help improve the land cover classification performance. Although hyperspectral data with hundreds of channels provide detailed spectral signatures, the curse of dimensionality might lead to degradation in the land cover classification performance. Moreover, in some practical applications, hyperspectral data may not be available due to cost, data storage, or bandwidth issues, and RGB and near infrared (NIR) could be the only image bands available for land cover classification. Light detection and ranging (LiDAR) data is another type of data to assist land cover classification especially if the land covers of interest have different heights. In this paper, we examined the performance of two Convolutional Neural Network (CNN)-based deep learning algorithms for land cover classification using only four bands (RGB+NIR) and five bands (RGB+NIR+LiDAR), where these limited number of image bands were augmented using Extended Multi-attribute Profiles (EMAP). The deep learning algorithms were applied to a well-known dataset used in the 2013 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest. With EMAP augmentation, the two deep learning algorithms were observed to achieve better land cover classification performance using only four bands as compared to that using all 144 hyperspectral bands.

58 GEOSCIENCES↗