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The field of tissue engineering has made significant advancements with extrusion-based bioprinting, which uses shear forces to create intricate tissue structures. However, the success of this method heavily relies on the rheological properties of bioinks. Most bioinks use shear-thinning. While a few component-based efforts have been reported to predict the viscosity of bioinks, the impact of shear rate has been vastly ignored. To address this gap, our research presents predictive models using machine learning (ML) algorithms, including polynomial fit (PF), decision tree (DT), and random forest (RF), to estimate bioink viscosity based on component weights and shear rate. We utilized novel bioinks composed of varying percentages of alginate (2–5.25%), gelatin (2–5.25%), and TEMPO-Nano fibrillated cellulose (0.5–1%) at shear rates from 0.1 to 100 s−1. Our study analyzed 169 rheological measurements using 80% training and 20% validation data. The results, based on the coefficient of determination (R2) and mean absolute error (MAE), showed that the RF algorithm-based model performed best: [(R2, MAE) RF = (0.99, 0.09), (R2, MAE) PF = (0.95, 0.28), (R2, MAE) DT = (0.98, 0.13)]. These predictive models serve as valuable tools for bioink formulation optimization, allowing researchers to determine effective viscosities without extensive experimental trials to accelerate tissue engineering. 

In current near-field holographic imaging, a circular region is scanned using an array of transmitting and receiving antennas over a narrow frequency band. This makes the data acquisition system slow, complex, bulky, and costly. Reducing the number of receiver antennas and using a narrower frequency band can significantly reduce the cost and complexity of the data acquisition system. To do so, we propose a method that uses prior knowledge about the object position, obtained by applying a neural network algorithm, called convolutional neural network (CNN), to the scattered field responses. This prior knowledge is then used to add a new regularization term to the cost function that is minimized in near-field holography. 

This study applied Transmodal Analysis (TMA), a newly developed quantitative ethnographic approach, to examine whether and how virtual patient simulations can aid in educating undergraduate nursing students with competencies that exemplify practice-ready nurses. Multimodal transcripts capturing patient interactions, exam actions, and documentation were obtained from two students who used Elsevier’s Shadow Health® Digital Clinical Experiences (DCE) in Fall 2022 and Spring 2023. Patient scenarios were situated in three content areas (Gerontology, Mental Health, and Community Health) and two assignment types (focused exam and contact tracing). In each scenario, similar patterns of engagement were observed for both students as they completed learning activities such as collecting patient data and establishing a caring relationship. These activities—guided by the instructional design of DCE—indicated how students practiced recognizing and analyzing cues, subjective assessment, diagnosing and prioritizing hypotheses, generating solutions, evaluating outcomes, therapeutic communication, and care coordination and management in relation to each patient’s needs and conditions. A statistical difference was observed between competencies practiced while completing focused exam and contact tracing assignments. This study provides evidence for using simulations to facilitate competency-based education in nursing. Additionally, it provides motivation for using Transmodal Analysis combined with Ordered Network Analysis (T/ONA) to advance quantitative ethnography research in health care and health professions education.