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Relative contributions by the coarser and finer grains in a silty sand to its stress–strain response are affected by the intergranular matrix structure. The nature of this contribution is illustrated using an intergranular matrix phase diagram in terms of void ratio (e), fines content (FC), and intergranular and interfine void ratios (es and ef). New intergranular state parameters (ψs, ψf) and (es, ef) are introduced as state variables to characterize silty sands; es and ef dictate the steady-state characteristics of silty sand at low and high fines contents, respectively; ψs and ψf reflect the plastic compressibility characteristics at low and high fines contents, respectively. Using these state variables, the anticipated stress–strain–strength behaviour of silty sand in comparison to that of the host sand is presented. Similar stress-strain behaviour is expected at the same es and initial confining stress σ′c, with a few exceptions. At a constant void ratio e, es increases while ef decreases with addition of fines; a silty sand passes through different states. First, at low fines content, es (< emax,HS, the maximum void ratio of the host sand, case 1), and high ef the stress–strain behaviour is primarily governed by intergranular friction between the coarser grains. The steady-state line (SSL) is primarily dependent on es and is fairly independent of σ′c; When compared at the same es and at the same σ′c(or at the same ψ or ψs), the silty sand and the host sand show similar (not identical) stress–strain behaviour; with addition of fines, as es increases, the collapse potential increases and the stress–strain response becomes weaker. Second, with further addition of fines, when es approaches or exceeds emax,HS, the SSL is influenced by es and σ′c. When es is near emax,HS (case 2), the stress–strain curves for silty sands are similar at the same es and the same σ′c (or at the same ψ or ψs), but different from and stronger than that of the host sand. At very loose states (es > emax,HS; case 3), the stress–strain curves, normalized with respect to σ′c, are similar at the same es (or at the same ψ or ψs). Third, with further addition of fines beyond a threshold value, ef becomes sufficiently low (case 4); the fines impart a significant influence, while the role of intergranular (coarser-grain) friction diminishes; the silty sand is expected to behave similarly (not identical) to the host fines at the same ef and ψf ; at this stage, with further addition of fines, the collapse potential decreases.

Hospitals serving higher concentrations of Medicaid patients and more racial/ethnic diverse patients experienced a less satisfactory patient experience than patients utilizing other payers or patients who were White. Our research magnifies the challenge for addressing the disparities that exist in healthcare. Further research is called for clarifying the underlying reasons for these disparities and the optimal strategies for addressing these problems.

Recent advances in additive manufacturing (AM) reveal an exciting opportunity to build materials with novel internal structures combined with intricate part geometries that cannot be achieved by traditional manufacturing approaches. The large space of potential material chemistries combined with non-equilibrium microstructures obtained in AM presents a significant challenge for a systematic exploration and optimization of the final properties exhibited by AM parts when using the existing knowledge databases established for conventionally processed materials. In this paper, we demonstrate novel high throughput assays that can be used to prototype a large library of material chemistries (and possibly different process histories) in small quantities, and subsequently apply spherical indentation stress-strain protocols to screen them for their mechanical performance. The potential of these new assays is demonstrated on a class of Ti-Ni alloys, whose Ni composition ranges between 0 and 11%wt.

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Machine learning algorithms are used in language processing, automated driving, and for prediction. Though the theory of machine learning has existed since the 1950s, it was not until the advent of advanced computing that their potential has begun to be realized. Gifted education is a field where machine learning has yet to be utilized, even though one of the underlying problems of gifted education is classification, which is an area where learning algorithms have become exceptionally accurate. We provide a brief overview of machine learning with a focus on neural networks and supervised learning, followed by a demonstration using simulated data and neural networks for classification issues with a practical explanation of the mechanics of the neural network and associated R code. Implications for gifted education are then discussed. Finally, the limitations of supervised learning are discussed. Code used in this article can be found at https://osf.io/4pa3b/

Mesoscale interfaces and interphases play a central role in controlling the many macroscale mechanical properties and performance characteristics of structural materials. Modern instrumented indenters present an unprecedented opportunity to measure, reliably and consistently, the local mechanical responses at a multitude of length scales ranging from tens of nanometers to hundreds of microns. When these high-fidelity measurements are combined with rigorous data analyses protocols, it is possible to systematically study the mechanical role of individual mesoscale interfaces and quantify their contributions to the overall mechanical response of the material system. The advantages of these new measurement and analyses protocols as well as the potential for development and implementation of novel high-throughput assays is discussed.