Multicomponent metallic glasses (MGs) are a fascinating class of advanced alloys known for their exceptional properties such as limit-approaching strength, high hardness and corrosion resistance, and near-net-shape castability. One important question regarding these materials that remains unanswered is how the different elements and atomic bonds within them control their strength and deformability. Here, we present a detailed visual and statistical analysis of the behaviors of various elements and atomic bonds in the Zr₄₇Cu₄₆Al₇ (at%) MG during a uniaxial tensile test (in the z-direction) simulated using molecular dynamics. Specifically, we investigate the identities of atoms undergoing significant shear strain, and the averaged bond lengths, projected z-lengths, and z-angles (angles with respect to the z-direction) of all the atomic bonds as functions of increasing strain. We show that, prior to yielding, the Zr element and the intermediate (Zr-Zr, Cu-Al) and stronger (Zr-Al, Zr-Cu) bonds dominate the elastic deformation and strength, while the Cu and Al elements and the weaker Al-Al and Cu-Cu bonds contribute more to the highly localized shear transformation. The significant reconstruction, as signified by the cessation of bond-length increment and bond-angle decrement, of the intermediate and the stronger bonds triggers yielding of the material. After yielding, all the elements and bonds participate in the plastic deformation while the stronger bonds contribute more to the residual strength and the ultimate (fracture) strain. The results provide new insights into the atomic mechanisms underlying the mechanical behavior of multicomponent MGs, and may assist in the future design of MG compositions towards better combination of strength and deformability.

The mechanical properties of materials are fundamentally determined by the behavior of atomic bonds under stress. Probing bond behavior during deformation, however, is highly challenging, particularly for materials with complex chemical compositions and/or atomic structures, such as metallic glasses (MGs). As a result, a significant gap exists in the current understanding of the mechanical properties of MGs in relation to the atomic bond behavior and how this relationship is influenced by metallurgical factors (e.g., alloy composition, processing conditions). Here, we present our study of the compositional effects on the tensile behavior of atomic bonds in Cu_93−xZr_xAl₇ (x = 40, 50, 60 at.%) MGs using large-scale molecular dynamics (MD) simulations and statistical analysis. Specifically, we examine the populations (fractions), mean bond lengths, mean bond z-lengths, and mean bond z-strains of the different bond types before and during tensile loading (in the z-direction), and we compare these quantities across the different alloy compositions. Among our key findings, we show that increasing the Zr content in the alloy composition leads to shortened Zr-Zr, Al-Cu, Al-Zr, and Cu-Zr bonds and elongated Cu-Cu bonds, as evidenced by their mean bond lengths. During deformation, the shorter Zr-Zr bonds and longer Cu-Cu bonds in the higher-Zr-content alloys, compared with those in the x = 40 alloy, appear stronger (more elastic stretching in the z-direction) and weaker (less z-stretching), respectively, consistent with general expectations. In contrast, the Al-Cu, Al-Zr, and Cu-Zr bonds in the higher-Zr-content alloys appear weaker in the elastic regime, despite their shortened mean bond lengths. This apparent paradox can be reconciled by considering the fractions of these bonds associated with icosahedral clusters, which are known to be more resistant to deformation than the rest of the glassy structure. We also discuss how the compositional effects on the bond behavior relate to variations in the overall stress–strain behavior of the different alloys.

The Max-Flow/Min-Cut problem is a fundamental tool in graph theory, with applications in many domains, including data mining, image segmentation, transportation planning, and many types of assignment problems, in addition to being an essential building block for many other algorithms. The Ford-Fulkerson Algorithm for Max-Flow/Min-Cut and its variants are therefore commonly taught in undergraduate and beginning graduate algorithms classes. However, these algorithms - and in particular the so-called residual graphs they utilize - often pose significant challenges for students. To help students achieve a deeper understanding, we developed iFlow, an interactive visualization tool for the Ford-Fulkerson Algorithm and its variants. iFlow lets users design or import flow networks, and execute the algorithm by hand. In particular, the user can select an augmentation path and amount, and then update the residual graph. The user is given detailed feedback on mistakes, and can also have iFlow auto-complete each step, to use it as a demonstration tool while still in the initial learning stages. iFlow has been made publicly available and open-sourced. We deployed iFlow in an undergraduate algorithms class, and collected students' self-reported learning benefits via an optional survey. All respondents considered the tool at least somewhat useful and engaging, with most rating it either as useful/engaging or very useful/engaging. Students also generally reported a significant increase in understanding of the algorithm.

Volumetric video allows users 6 degrees of freedom (6-DoF) in viewing continuously evolving scenes in 3D. Given broadband speeds today, volumetric video conferencing will soon be feasible. Even so, these scenes will need to be compressed, and compression will need to adapt to variations in bandwidth availability. Existing 3D compression techniques cannot adapt to bandwidth availability, are slow, and utilize bandwidth inefficiently, so they don't scale well to large scene descriptions. LiVo achieves low-latency and large-scene two-way conferencing by maximally leveraging existing 2D video infrastructure, including compression standards, rate-adaptive codecs, and real-time transport protocols. To achieve high quality, LiVo must carefully compose scenes from multiple cameras into multiple streams, encode scene geometry in a novel way, adapt to and apportion available bandwidth dynamically between streams to ensure high reconstruction quality, and cull content outside the receiver's field of view to reduce information sent into the network. These novel contributions enable LiVo to outperform the state-of-the-art by over 20% in objective quality. In a user study, LiVo achieves a mean opinion score of 4.1, while other approaches achieve significantly lower values.