Browsing by Author "Li, Gang"
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Item A novel digital lifecycle for Material-Process-Microstructure-Performance relationships of thermoplastic olefins foams manufactured via supercritical fluid assisted foam injection molding(Polymer Engineering and Science, 2024-03-15) Pradeep, Sai Aditya; Deshpande, Amit M.; Lavertu, Pierre‐Yves; Zheng, Ting; Yerra, Veera Aditya; Shimabukuro, Yiro; Li, Gang; Pilla, SrikanthThis research significantly enhances the applicability of thermoplastic olefins (TPOs) in the automotive industry using supercritical N2 as a physical foaming agent, effectively addressing the limitations of traditional chemical agents. It merges experimental results with simulations to establish detailed material-process-microstructure-performance (MP2) relationships, targeting 5–20% weight reductions. This innovative approach labeled digital lifecycle (DLC) helps accurately predict tensile, flexural, and impact properties based on the foam microstructure, along with experimentally demonstrating improved paintability. The study combines process simulations with finite element models to develop a comprehensive digital model for accurately predicting mechanical properties. Our findings demonstrate a strong correlation between simulated and experimental data, with about a 5% error across various weight reduction targets, marking significant improvements over existing analytical models. This research highlights the efficacy of physical foaming agents in TPO enhancement and emphasizes the importance of integrating experimental and simulation methods to capture the underlying foaming mechanism to establish material-process-microstructure-performance (MP2) relationships. Highlights - Establishes a material-process-microstructure-performance (MP2) for TPO foams - Sustainably produces TPO foams using supercritical (ScF) N2 with 20% lightweighting - Shows enhanced paintability for TPO foam improved surface aesthetics - Digital lifecycle (DLC) that predicts both foam microstructure and properties - DLC maps process effects & microstructure onto FEA mesh for precise predictionItem Biomedical relation extraction with reduced manual effort(University of Delaware, 2018) Li, GangBiomedical relation extraction is an critical text-mining task that concerns automatic extraction of related bio-entities in text. Rule-based and machine learning methods are two main approaches for relation extraction. While these two methods can be used to develop high-performance relation extraction system, considerable amount of manual effort is required by both methods in different phases of the system development. This hinders fast application of both methods to extract new types of relations. ☐ This dissertation focuses on developing techniques to assist the development of biomedical relation extraction systems using rule-based and machine learning methods. For rule-based systems, one main component requiring manual effort is pattern design. Domain experts often need to examine considerable amount of documents and exhaustively collect every pattern that can be used to extract relations. We leverage various linguistic knowledge to automatically generate a comprehensive set of patterns. Our first approach is instantiated to develop miRTex, a system that extracts three kinds of miRNA-gene relations that regulate a wide range of biological processes and are involved with diseases. Only a small number of triggers and rules are needed to achieve the state-of-the-art performance. Our second approach is to translate the ideas in Lexicalized Tree Adjoining Grammar to dependency graph for pattern generation, and adopt Extended Dependency Graph as an abstract sentence representation. This approach is applied to extract five type of post-translational modifications, a class of relations that plays an important role in cellular functions. Evaluations on BioNLP 2011 EPI task show that the resulting system achieves state-of-the-art performance. ☐ For machine learning systems, a sizable training corpus is needed to train the extraction model, while the annotation of the corpus is time- and labor-intensive. We adopt distant supervision in two ways. Our first contribution is to develop noise reduction techniques to improve the data quality of the automatically generated large training set, leading to improvement over existing results for distantly supervised biomedical relation extraction. Secondly, we employ distant supervision in conjunction with human-labeled data and deep neural networks to achieve state-of-the-art performance on some benchmark relation extraction tasks.Item Design optimization of a multi-material, fiber-reinforced composite-intensive body-in-white of a mid-size SUV(CAMX 2023 Conference Proceedings, 2023-10-30) Deshpande, Amit M.; Sadiwala, Rushabh; Brown, Nathan; Lavertu, Pierre-Yves; Pradeep, Sai Aditya; Headings, Leon M.; Zhao, Ningxiner; Losey, Brad; Hahnlen, Ryan; Dapino, Marcelo J.; Li, Gang; Pilla, SrikanthTransportation accounts for almost a third of all energy consumption and emissions in the U.S. With an emphasis on improving the energy efficiency of vehicles and transitioning to electrified vehicles, lightweighting has become relevant to compensate for the added complexity of battery packs and hybrid powertrains. Lightweight materials such as aluminum, magnesium, and fiber-reinforced plastic (FRP) composites can reduce the vehicle’s structural mass, the body-in-white (BIW), by up to 50%. However, the higher proportion of large sports utility vehicles (SUVs) and trucks in the North American fleet poses a challenge, as the larger size and high production scale of the structural components for this segment can significantly increase material costs. Thus, a multi-material approach to deploy FRP composites at select locations in an existing metal BIW can help advance composites design, integration, and manufacturing technologies. Furthermore, these designs can be translated for future EV structures. This study utilizes a systems approach to 1) establish design targets through structural analysis of the baseline SUV BIW design under various static and dynamic load cases, 2) conceptualize multi-material designs, and 3) assess the designs to meet lightweighting, cost, and sustainability objectives. Sustainable recycled carbon fiber-reinforced composites and other cost-effective FRP composite materials manufactured using state-of-the-art high-pressure resin transfer molding (HP RTM) technology were assessed for use in structural elements. An ultrasonic additive manufacturing (UAM) technique was implemented to produce mechanically interlocked metal-fiber transition joints to serve as a joining mechanism between fibers and metals in the multi-material design. To incorporate the transition joint design into the topology optimization scheme, a high-fidelity model of the fiber-metal transition joints that describes the fiber-oriented interactions between the fibers, cured-epoxy matrix, and metal components was developed. This model's results accurately represented the behavior from experimental testing. They can be transferred to the FEA solver as a computationally efficient material card specifically for use at the metal-composite transition regions in the proposed designs. The results from this system-level multi-material composites integration study have been presented.Item miRTex: A Text Mining System for miRNAGene Relation Extraction(PLOS (Public Library of Science), 2015-09-25) Li, Gang; Ross, Karen E.; Arighi, Cecilia N.; Peng, Yifan; Wu, Cathy H.; Vijay-Shanker, K.; Gang Li, Karen E. Ross, Cecilia N. Arighi, Yifan Peng, Cathy H. Wu, K. Vijay-Shanker; Li, Gang; Ross, Karen E.; Arighi, Cecilia N.; Peng, Yifan; Wu, Cathy H.; Vijay-Shanker, K.MicroRNAs (miRNAs) regulate a wide range of cellular and developmental processes through gene expression suppression or mRNA degradation. Experimentally validated miRNA gene targets are often reported in the literature. In this paper, we describe miRTex, a text mining system that extracts miRNA-target relations, as well as miRNA-gene and gene-miRNA regulation relations. The system achieves good precision and recall when evaluated on a literature corpus of 150 abstracts with F-scores close to 0.90 on the three different types of relations. We conducted full-scale text mining using miRTex to process all the Medline abstracts and all the full-length articles in the PubMed Central Open Access Subset. The results for all the Medline abstracts are stored in a database for interactive query and file download via the website at http://proteininformationresource.org/mirtex. Using miRTex, we identified genes potentially regulated by miRNAs in Triple Negative Breast Cancer, as well as miRNA-gene relations that, in conjunction with kinase-substrate relations, regulate the response to abiotic stress in Arabidopsis thaliana. These two use cases demonstrate the usefulness of miRTex text mining in the analysis of miRNA-regulated biological processes.