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  1. Free, publicly-accessible full text available August 4, 2024
  2. Abstract

    Adhesive tissue engineering scaffolds (ATESs) have emerged as an innovative alternative means, replacing sutures and bioglues, to secure the implants onto target tissues. Relying on their intrinsic tissue adhesion characteristics, ATES systems enable minimally invasive delivery of various scaffolds. This study investigates development of the first class of 3D bioprinted ATES constructs using functionalized hydrogel bioinks. Two ATES delivery strategies, in situ printing onto the adherend versus printing and then transferring to the target surface, are tested using two bioprinting methods, embedded versus air printing. Dopamine‐modified methacrylated hyaluronic acid (HAMA‐Dopa) and gelatin methacrylate (GelMA) are used as the main bioink components, enabling fabrication of scaffolds with enhanced adhesion and crosslinking properties. Results demonstrate that dopamine modification improved adhesive properties of the HAMA‐Dopa/GelMA constructs under various loading conditions, while maintaining their structural fidelity, stability, mechanical properties, and biocompatibility. While directly printing onto the adherend yields superior adhesive strength, embedded printing followed by transfer to the target tissue demonstrates greater potential for translational applications. Together, these results demonstrate the potential of bioprinted ATESs as off‐the‐shelf medical devices for diverse biomedical applications.

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    Free, publicly-accessible full text available July 1, 2024
  3. Free, publicly-accessible full text available June 4, 2024
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    Web personalization, e.g., recommendation or relevance search, tailoring a service/product to accommodate specific online users, is becoming increasingly important. Inductive personalization aims to infer the relations between existing entities and unseen new ones, e.g., searching relevant authors for new papers or recommending new items to users. This problem, however, is challenging since most of recent studies focus on transductive problem for existing entities. In addition, despite some inductive learning approaches have been introduced recently, their performance is sub-optimal due to relatively simple and inflexible architectures for aggregating entity’s content. To this end, we propose the inductive contextual personalization (ICP) framework through contextual relation learning. Specifically, we first formulate the pairwise relations between entities with a ranking optimization scheme that employs neural aggregator to fuse entity’s heterogeneous contents. Next, we introduce a node embedding term to capture entity’s contextual relations, as a smoothness constraint over the prior ranking objective. Finally, the gradient descent procedure with adaptive negative sampling is employed to learn the model parameters. The learned model is capable of inferring the relations between existing entities and inductive ones. Thorough experiments demonstrate that ICP outperforms numerous baseline methods for two different applications, i.e., relevant author search and new item recommendation. 
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    Modern physical systems deploy large numbers of sensors to record at different time-stamps the status of different systems components via measurements such as temperature, pressure, speed, but also the component's categorical state. Depending on the measurement values, there are two kinds of sequences: continuous and discrete. For continuous sequences, there is a host of state-of-the-art algorithms for anomaly detection based on time-series analysis, but there is a lack of effective methodologies that are tailored specifically to discrete event sequences. This paper proposes an analytics framework for discrete event sequences for knowledge discovery and anomaly detection. During the training phase, the framework extracts pairwise relationships among discrete event sequences using a neural machine translation model by viewing each discrete event sequence as a "natural language". The relationship between sequences is quantified by how well one discrete event sequence is "translated" into another sequence. These pairwise relationships among sequences are aggregated into a multivariate relationship graph that clusters the structural knowledge of the underlying system and essentially discovers the hidden relationships among discrete sequences. This graph quantifies system behavior during normal operation. During testing, if one or more pairwise relationships are violated, an anomaly is detected. The proposed framework is evaluated on two real-world datasets: a proprietary dataset collected from a physical plant where it is shown to be effective in extracting sensor pairwise relationships for knowledge discovery and anomaly detection, and a public hard disk drive dataset where its ability to effectively predict upcoming disk failures is illustrated. 
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