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The formation of engineering identity was found to play a pivotal role in engineering students’ learning trajectories[1], motivation and persistence[2], sense of belonging[3], and long-term commitment to an engineering career[4]. Focusing on engineering courses with design projects, research on engineering identity produced convincing evidence that links design experiences to engineering identity formation[5]. However, students are not heavily involved in design projects in a typical engineering program until junior and senior years. Freshman and sophomore courses focusing on foundational knowledge rarely provide any design experience and thus have limited contribution to engineering identity development. Introducing Design Thinking (DT) to these foundational courses could address this shortcoming. Evolving from the practice of design, DT has been generalized as a creative, iterative, human-centric process for solving complex problems[6]. Researchers are exploring DT as a pedagogical framework to teach students how to think like a designer[7]. It was found that “intentional implementation, including organization and framing of design thinking pedagogy, was an essential foundation for fostering student interest.”[8] A peer learning and teaching approach is suggested for DT pedagogy[9]. Peer-Led-Team-Learning (PLTL) is an active learning pedagogy that has been shown to improve student performance, retention, and commitment to engineering[10]. PLTL is typically employed as a supplement to regular classroom teaching, in which students voluntarily sign up and participate in PLTL sessions out of class time. As such, only a portion of students in a class offering PLTL can participate[11]. This paper presents a pedagogical framework that integrates in-class PLTL (iPLTL) and DT to foster engineering identity in a two freshman courses for Mechanical and Aerospace Engineering (MAE) students. Preliminary data show a significant improvement in students’ learning outcomes and engagement. 

Abstract BackgroundThe failure of elastomers has begun to be understood through the lens of an intrinsic material lifetime rather than critical energy criteria. ObjectiveThis study gathers and describes phenomenology concerning the effects of speed and temperature on elastomeric puncture in terms of this recently proposed theoretical framework of elastomeric failure. MethodsContinuous indentation experiments until puncture were performed over a range of indentation speeds and temperatures. Delayed puncture tests were conducted for the first time to experimentally measure the incubation time required for rupture under constant load. ResultsContinuous indentation reveals that, like tensile extension, elastomers exhibit higher strength on shorter timescales and under lower temperatures, i.e., the resistant normal force and displacement until puncture increase with indentation speed and decreasing temperature. Furthermore, the measured incubation time $${t}_{del-punct}$$ $t_{del-punct}$ uncovers the elastomeric lifetime as an internal clock, which controls when puncture occurs. Conclusions(a) Stronger resistance to puncture at lower temperatures and higher indentation speed has been demonstrated and explained in terms of network rupture through chain scission, (b) for any pair of speed and temperature, another pair exists that produces puncture at the same strength and degree of indentation, implying the existence of a distinct time–temperature equivalence where the internal clock is network lifetime not polymer relaxation time. 

Abstract BackgroundCharacterizing soft materials at ultra-high strain rates ($$>10^{3}\ \mathrm {s^{-1}}$$ $>{10}^3{s}^{-1}$) remains a significant challenge due to their nonlinear, large-deformation, and rate-dependent mechanical behavior. Despite these challenges, understanding material response in this regime is essential for a wide range of engineering and biomedical applications, including laser eye surgery, lithotripsy, and high-rate energy deposition in soft tissues. Laser-induced cavitation (LIC) has recently emerged as a powerful experimental approach for subjecting soft materials to extreme, localized loading rates on the microscale, regimes that are difficult to access using conventional mechanical testing methods. However, most biological tissues are anisotropic, optically opaque, and prone to damage at high strain rates, and their orientation-dependent ultra-high-rate mechanical behavior remains poorly understood. ObjectiveThe objective of this study is to develop and validate an experimental method that is based on laser-induced inertial cavitation and enables quantitative measurement of anisotropic soft materials and biological tissues at ultra-high strain rates, while explicitly accounting for fiber-directional mechanics and damage evolution. MethodsLIC experiments were performed in synthetic anisotropic polyvinyl alcohol (PVA) hydrogels and fresh chicken breast tissue using nanosecond-duration pulsed lasers to generate ultra-high-rate deformation. Simultaneously, cavitation bubble dynamics were captured using ultra-high-speed videography at 1-2 million frames per second. Two nonlinear hyper-viscoelastic constitutive models (a Poynting-Thomson model and a generalized Maxwell model) were developed and integrated with the measured bubble dynamics to quantify fiber-directional material response and investigate rate-dependent damage mechanisms. In addition, complementary quasistatic and oscillatory shear rheometry tests were conducted to provide low-rate mechanical benchmarks and investigate the rate dependency of soft materials’ mechanical behavior. ResultsLIC experiments revealed pronounced anisotropic bubble dynamics, with preferential elongation along fiber directions in both PVA hydrogels and chicken breast tissue. Model-based fitting of major-axis bubble radius-time histories enabled the extraction of effective ultra-high-rate directional moduli and critical stretch thresholds for damage initiation. ConclusionsIn summary, this study introduces a new experimental methodology for characterizing anisotropic soft materials at ultra-high strain rates. The experimental data and analysis approaches obtained from this introduction of experimental and computational methods will benefit future studies on high-strain-rate material damage and tissue injury, laser and ultrasound-related medical procedures, and anisotropic tissue constitutive modeling. 

Identification and quantification of myocardial scar is important for diagnosis and prognosis of cardiovascular diseases. However, reliable scar segmentation from Late Gadolinium Enhancement Cardiac Magnetic Resonance (LGE-CMR) images remains a challenge due to variations in contrast enhancement across patients, suboptimal imaging conditions such as post-contrast washout, and inconsistencies in ground-truth annotations on diffuse scars caused by interobserver variability. In this work, we propose a curriculum learning-based framework designed to improve segmentation performance under these challenging conditions. The method introduces a progressive training strategy that guides the model from high-confidence, clearly defined scar regions to low-confidence or visually ambiguous samples with limited scar burden. By structuring the learning process in this manner, the network develops robustness to uncertain labels and subtle scar appearances that are often underrepresented in conventional training pipelines. Experimental results show that the proposed approach enhances segmentation accuracy and consistency, particularly for cases with minimal or diffuse scar, outperforming standard training baselines. This strategy provides a principled way to leverage imperfect data for improved myocardial scar quantification in clinical applications. Our code is publicly available on Github. 

Accurate simulation of earthquake scenarios is essential for advancing seismic hazard analysis and risk mitigation strategies. At the San Diego Supercomputer Center (SDSC), our research focuses on optimizing the performance and reliability of large-scale earthquake simulations using the AWP-ODC software. By implementing GPU-aware MPI calls, we enable direct data processing within GPU memory, eliminating the need for explicit data transfers between CPU and GPU. This GPU-aware MPI achieves nearly ideal parallel efficiency at full scale across both Nvidia and AMD GPUs, leveraging the MVAPICH-PLUS support on Frontier at Oak Ridge National Laboratory and Vista at the Texas Advanced Computing Center. We utilized the MVAPICH-Plus 4.0 compiler to enable ZFP compression, which significantly enhances inter-node communication efficiency – a critical improvement given the communication bottleneck inherent in large-scale simulations. Our GPU-aware AWP-ODC versions include linear forward, topography and nonlinear Iwan-type solvers with discontinuous mesh support. On the Frontier system with MVAPICH 4.0, Hip-aware MPI calls on MI250X GPUs deliver nearly ideal weak-scaling speedup up to 8,192 nodes for both linear and topography versions. On TACC’s Vista system, CUDA-aware MPI calls on GH200 GPUs substantially outperform their non-GPU-aware counterparts across all three solver versions. This poster will present a detailed evaluation of GPU-aware AWP-ODC using MVAPICH, including the impact of ZFP message compression compared to the native versions. Our results highlight the importance of Mvapich support for GPU-ware MPI and on-the-fly compression techniques for accelerating and scaling earthquake simulations. 

We have implemented GPU-aware support across all AWP-ODC versions and enhanced message-passing collective communications for this memory-bound finite-difference solver. This provides cutting-edge communication support for production simulations on leadership-class computing facilities, including OLCF Frontier and TACC Vista. We achieved significant performance gains, reaching 37 sustained Petaflop/s and reducing time-to-solution by 17.2% using the GPU-aware feature on 8,192 Frontier nodes, or 65,336 MI250X GCDs. The AWP-ODC code has also been optimized for TACC Vista, an Arm-based NVIDIA GH200 Grace Hopper Superchip, demonstrating excellent application performance. This poster will showcase studies and GPU performance characteristics. We will discuss our verification of GPU-aware development and the use of high-performance MVAPICH libraries, including on-the-fly compression, on modern GPU clusters.