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Wearable devices have made transformative advancements driven by the integration of nanomaterials, enhancing their versatility, sensitivity, and overall performance. The emerging 3D printing techniques revolutionize traditional fabrication, enabling the high-efficiency fabrication for sophisticated and miniaturized healthcare monitoring systems. This review summarizes the essential properties of nanomaterials and their roles in 3D printing and examines the pros and cons of various 3D printing methods. Key applications of 3D-printed wearable devices, showcasing the synergistic contributions of nanomaterials, are introduced to provide a comprehensive overview of the state-of-the-art progress and the promising prospects for next-generation healthcare monitoring. 

Thermal ablation of materials is a complex phenomenon that involves physical and chemical processes for the thermal protection of systems. However, due to the extreme thermal conditions and moving boundaries, predicting temperature and heat flux at the ablative material is quite challenging. A physics-informed neural network is a promising technique for many such inverse problems, including the prediction of unsteady heat flux. However, traditional physics-informed machine learning algorithms struggle with heat flux predictions in thermal ablation problems due to moving boundary conditions and lack of temperature data in the inaccessible domain. This study presents a hybrid approach, where an artificial neural network (ANN) is used for the accessible domain of the material and a physics-based numerical solution (PNS) technique is used in the inaccessible domain of the material, to find heat flux at the ablative surface. Temperature data at the accessible sensor points are used to train the ANN model. The heat flux at the ablative boundary was iteratively obtained from the numerical solution of the energy equation in the inaccessible domain by matching the ANN-predicted temperature at the last accessible sensor point. Our results indicate that this hybrid methodology significantly outperforms traditional physics-informed machine learning techniques, achieving excellent accuracy in predicting the temperature profiles and heat fluxes under complex conditions for both constant and variable heat flux and properties. By addressing the limitations of conventional physics-informed machine learning methods, our approach provides a robust and reliable solution for modeling the intricate dynamics of ablative processes. 

Induction heating is one of the cleanest and most efficient methods for heating materials, utilizing electromagnetic fields induced through AC electric current. This article reports an analytical solution for transient heat transfer in a three‐dimensional (3D) cylindrical object under induction heating. A simplified form of Maxwell's equations is solved to determine the heat generation inside the cylinder by calculating the current density distribution within the body. The temperature within the solid is found from the solution of the unsteady heat equation based on Green's function. Owing to multiple spatial dimensions and time, a separation of variables technique is used to find Green's function. In addition, an innovative algorithm is proposed to take care of the variable material properties in analytical treatment. The analytical solution for temperature is verified with the data obtained from experiments for identical operating conditions. The analytical solution is used to study the impact of heat transfer coefficient and input AC current frequency and amplitude during transient heat diffusion. Our analytical solution suggests that the temperature‐dependent material properties significantly affect the thermal response within the solid. Unlike many other conventional heating methods, the thermal boundary condition changes with time in induction heating, which makes our solution much more challenging. 

Not AActive, hands-on learning is essential for engineering education, fostering deep engagement and enhancing knowledge retention. This multi-institutional study investigates how different instructional methods—Hands-On, Virtual, and Lecture-only—combined with four distinct Low-Cost Desktop Learning Modules (LCDLMs: Hydraulic Loss, Double Pipe, Shell & Tube, and Venturi Meter) affect student engagement and learning outcomes. Anchored in the ICAP framework (Interactive, Constructive, Active, Passive), the study measured cognitive engagement through direct observations, virtual screen recordings, and self-reported surveys. It assessed learning gains using normalized pre- and post-tests among 2,316 undergraduate engineering students from eight universities. Results indicate that virtual instruction yields significantly higher learning gains, while the Shell & Tube module enhances active engagement through tangible, hands-on experiences. In contrast, the Hydraulic Loss module demonstrates the greatest impact on quantitative knowledge growth. These findings underscore the potential of integrating virtual simulations with physical learning tools to optimize instructional design in engineering education. Implications for future research include refining measurement instruments and exploring the long-term effects of hybrid instructional models. 

Over the past seven years, our team has disseminated low-cost hands-on learning hardware and associated worksheets in fluid mechanics and heat transfer to provide engineering students with an interactive learning experience. Previous studies have shown (1-5) the efficacy of teaching students with an active learning approach versus a more traditional lecture setup, with a number of approaches already available, such as simple active discussion, think-pair-share, flipped classrooms, etc. Our approach is differentiated by the inclusion of hardware to add both a visual aid and an opportunity for hands-on experimentation while keep the costs low enough for a classroom setting. Learning with a hands-on, interactive approach is supported by social cognitive theory (SCT) (6-7) and information processing theory (8). Unlike earlier views of learning theory, which simply posit that the key to learning is repetition, social cognitive theory considers the agency of the student and the social aspects of learning. The primary assumption of SCT is that students are active participants in the learning process, acquiring and displaying knowledge, skills, and behaviors that align with their goals through a process called triadic reciprocal causation, illustrated in figure 1. 

While the demand for interdisciplinary knowledge is undeniable, there are formidable challenges when offering graduate education to Engineering students. To address that, we designed an educational research project that delves into the effectiveness of an interdisciplinary National Science Foundation (NSF) Research Trainee (NRT) program for engineering students studying robotics and autonomous systems. This newly funded NRT program aims to train next-generation scientists and engineers with professional skills through interdisciplinary courses such as leadership, business, and psychology in addition to cutting-edge technical knowledge in the field. We are using retrospective surveys and content analysis to identify student experience with interdisciplinary training and education programs. Both quantitative and qualitative analysis evidenced an increased level of confidence in soft skills such as interdisciplinary understanding, communication, and collaboration skills throughout participating in the interdisciplinary NRT program.