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Abstract A major intent of scientific research is the replication of the behaviour observed in natural spaces. In robotics, these can be through biomimetic movements in devices and inspiration from diverse actions in nature, also known as bioinspired features. An interesting pathway enabling both features is the fabrication of soft actuators. Specifically, 3D‐printing has been explored as a potential approach for the development of biomimetic and bioinspired soft actuators. The extent of this method is highlighted through the large array of applications and techniques used to create these devices, as applications from the movement of fern trees to contraction in organs are explored. In this review, different 3D‐printing fabrication methods, materials, and types of soft actuators, and their respective applications are discussed in depth. Finally, the extent of their use for present operations and future technological advances are discussed. 

Abstract The development of a general‐purpose machine learning algorithm capable of quickly identifying optimal 3D‐printing settings can save manufacturing time and cost, reduce labor intensity, and improve the quality of 3D‐printed objects. Existing methods have limitations which focus on overall performance or one specific aspect of 3D‐printing quality. Here, for addressing the limitations, a multi‐objective Bayesian Optimization (BO) approach which uses a general‐purpose algorithm to optimize the black‐box functions is demonstrated and identifies the optimal input parameters of direct ink writing for 3D‐printing different presurgical organ models with intricate geometry. The BO approach enhances the 3D‐printing efficiency to achieve the best possible printed object quality while simultaneously addressing the inherent trade‐offs from the process of pursuing ideal outcomes relevant to requirements from practitioners. The BO approach also enables us to effectively explore 3D‐printing inputs inclusive of layer height, nozzle travel speed, and dispensing pressure, as well as visualize the trade‐offs between each set of 3D‐printing inputs in terms of the output objectives which consist of time, porosity, and geometry precisions through the Pareto front. 

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. 

Acoustic oscillations in cryogenic systems can either be imposed intentionally, as in pulse-tube cryocoolers, or occur spontaneously due to Taconis-type thermoacoustic instabilities. To predict the propagation of sound waves in ducts with sudden changes in cross-sectional areas, minor losses associated with such transitions in oscillatory flows must be known. However, the current modeling approaches usually rely on correlations for minor loss coefficients obtained in steady flows, which may not accurately represent minor losses in sound waves. In this study, high-fidelity computational fluid dynamics simulations are undertaken for acoustic oscillations at transitions between tubes of different diameters filled with cryogenic hydrogen. The variable parameters include the tube diameter ratios, temperatures (80 K and 30 K), and acoustic impedances corresponding to standing and traveling waves. Computational simulation results are compared with reduced-order acoustic models to develop corrections for minor loss coefficients that describe transition losses in sound waves more precisely. The present findings can improve the accuracy of design calculations for acoustic cryocoolers and predictions of Taconis instabilities. 

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. 

Model Predictive Control (MPC) is widely used to achieve performance objectives, while enforcing operational and safety constraints. Despite its high performance, MPC often demands significant computational resources, making it challenging to implement in systems with limited computing capacity. A recent approach to address this challenge is to use the Robust-to-Early Termination (REAP) strategy. At any time instant, REAP converts the MPC problem into the evolution of a virtual dynamical system whose trajectory converges to the optimal solution, and provides guaranteed sub-optimal and feasible solution whenever its evolution is terminated due to limited computational power. REAP has been introduced as a continuous-time scheme and its theoretical properties have been derived under the assumption that it performs all the computations in continuous time. However, REAP should be practically implemented in discrete-time. This paper focuses on the discrete-time implementation of REAP, exploring conditions under which anytime feasibility and convergence properties are maintained when the computations are performed in discrete time. The proposed methodology is validated and evaluated through extensive simulation and experimental studies. 

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. 

Wearable sweat biosensors have shown great progress in noninvasive, in situ, and continuous health monitoring to demonstrate individuals’ physiological states. Advances in novel nanomaterials and fabrication methods promise to usher in a new era of wearable biosensors. Here, we introduce a threedimensional (3D)-printed flexible wearable health monitor fabricated through a unique one-step continuous manufacturing process with self-supporting microfluidic channels and novel single-atom catalyst-based bioassays for measuring the sweat rate and concentration of three biomarkers. Direct ink writing is adapted to print the microfluidic device with self-supporting structures to harvest human sweat, which eliminates the need for removing sacrificial supporting materials and addresses the contamination and sweat evaporation issues associated with traditional sampling methods. Additionally, the pick-and-place strategy is employed during the printing process to accurately integrate the bioassays, improving manufacturing efficiency. A single-atom catalyst is developed and utilized in colorimetric bioassays to improve sensitivity and accuracy. A feasibility study on human skin successfully demonstrates the functionality and reliability of our health monitor, generating reliable and quantitative in situ results of sweat rate, glucose, lactate, and uric acid concentrations during physical exercise. 

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.