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Augmented reality (AR) is a unique, hands-on tool to deliver information. However, its educational value has been mainly demonstrated empirically so far. In this paper, we present a modeling approach to provide users with mastery of a skill, using AR learning content to implement an educational curriculum. We illustrate the potential of this approach by applying this to an important but pervasively misunderstood area of STEM learning, electrical circuitry. Unlike previous cognitive assessment models, we break down the area into microskills--the smallest segmentation of this knowledge--and concrete learning outcomes for each. This model empowers the user to perform a variety of tasks that are conducive to the acquisition of the skill. We also provide a classification of microskills and how to design them in an AR environment. Our results demonstrated that aligning the AR technology to specific learning objectives paves the way for high quality assessment, teaching, and learning.

The Ruddlesden–Popper (R–P) bilayer nickelate, La3Ni2O7, was recently found to show signatures of high-temperature superconductivity (HTSC) at pressures above 14 GPa (ref. 1). Subsequent investigations achieved zero resistance in single-crystalline and polycrystalline samples under hydrostatic pressure conditions2–4. Yet, obvious diamagnetic signals, the other hallmark of superconductors, are still lacking owing to the filamentary nature with low superconducting volume fraction2,4,5. The presence of a new 1313 polymorph and competing R–P phases obscured proper identification of the phase for HTSC6–9. Thus, achieving bulk HTSC and identifying the phase at play are the most prominent tasks. Here we address these issues in the praseodymium (Pr)-doped La2PrNi2O7 polycrystalline samples. We find that substitutions of Pr for La effectively inhibit the intergrowth of different R–P phases, resulting in a nearly pure bilayer structure. For La2PrNi2O7, pressure-induced orthorhombic to tetragonal structural transition takes place at Pc ≈ 11 GPa, above which HTSC emerges gradually on further compression. The superconducting transition temperatures at 18–20 GPa reach T c onset = 82.5 K and T c zero = 60 K , which are the highest values, to our knowledge, among known nickelate superconductors. Importantly, bulk HTSC was testified by detecting clear diamagnetic signals below about 75 K with appreciable superconducting shielding volume fractions at a pressure of above 15 GPa. Our results not only resolve the existing controversies but also provide directions for exploring bulk HTSC in the bilayer nickelates.Bulk high-temperature superconductivity observed in pressurized tetragonal La2PrNi2O7 was testified by detecting clear diamagnetic signals below about 75 K with appreciable superconducting shielding volume fractions at a pressure of above 15 GPa. [ABSTRACT FROM AUTHOR]

Aiming at the problems of small stems and irregular contours of bitter gourd, which lead to difficult and inaccurate location of picking points in the picking process of mechanical arms, this paper proposes an improved YOLOv5-seg instance segmentation algorithm with a coordinate attention (CA) mechanism module, and combines it with a refinement algorithm to identify and locate the picking points of bitter gourd. Firstly, the improved algorithm model was used to identify and segment bitter gourd and melon stems. Secondly, the melon stem mask was extracted, and the thinning algorithm was used to refine the skeleton of the extracted melon stem mask image. Finally, a skeleton refinement graph of bitter gourd stem was traversed, and the midpoint of the largest connected region was selected as the picking point of bitter gourd. The experimental results show that the prediction precision (P), precision (R) and mean average precision (mAP) of the improved YOLOv5-seg model in object recognition were 98.04%, 97.79% and 98.15%, respectively. Compared with YOLOv5-seg, the P, R and mA values were increased by 2.91%, 4.30% and 1.39%, respectively. In terms of object segmentation, mask precision (P(M)) was 99.91%, mask recall (R(M)) 99.89%, and mask mean average precision (mAP(M)) 99.29%. Compared with YOLOv5-seg, the P(M), R(M), and mAP(M) values were increased by 6.22%, 7.81%, and 5.12%, respectively. After testing, the positioning error of the three-dimensional coordinate recognition of bitter gourd picking points was X-axis = 7.025 mm, Y-axis =5.6135 mm, and Z-axis = 11.535 mm, and the maximum allowable error of the cutting mechanism at the end of the picking manipulator was X-axis = 30 mm, Y-axis = 24.3 mm, and Z-axis = 50 mm. Therefore, this results of study meet the positioning accuracy requirements of the cutting mechanism at the end of the manipulator. The experimental data show that the research method in this paper has certain reference significance for the accurate identification and location of bitter gourd picking points. [ABSTRACT FROM AUTHOR]

The diagnosis of drug‐resistant tuberculosis (TB) by molecular testing of Mycobacterium tuberculosis drug resistance genes is becoming increasingly common clinically. However, M. bovis, as an uncommon pathogen of human TB, may interfere with the test results. A comprehensive understanding of phylogenetically informative mutations in the drug resistance genes of M. bovis is required to distinguish true resistance‐conferring mutations. We analyzed 53 drug resistance genes in 165 M. bovis isolated from humans using whole‐genome sequencing data and found that 98.2% (162/165) of isolates have pyrazinamide intrinsic genotypic resistance, owing to the H57D mutation in the pncA gene. 12.1% (20/165) of M. bovis isolates were resistant to drugs other than pyrazinamide. Furthermore, we discovered 18 phylogenetically informative mutations that differed between M. bovis and the major lineages 1–4 of M. tuberculosis. Additionally, we reported false‐positive ethambutol resistance caused by M. bovis infection due to the phylogenetically informative mutation embB E378A. This study is crucial for gaining insights into the genetic characterization and drug resistance of M. bovis prevalent in humans, as well as contributing to the development of more accurate molecular diagnostic methods and detection tools for drug resistance. [ABSTRACT FROM AUTHOR]

The activation of lattice oxygen oxidation mechanism (LOM) will endow iridium‐based electrocatalysts with desired acid‐available water oxidation activity, compared to the conventional adsorbate evolution mechanism (AEM). However, the inherent symmetric [IrO6] octahedra of commercial Ir‐based catalysts generally thermodynamically favor the AEM pathway contributing to the moderate water oxidation performance. Here, based on typical layered Ca2IrO4 (CIO) modeled materials, the d‐orbitals electron repulsion strategy is demonstrated, via constructing asymmetrically polarized Ir‒O‒Ru configuration in Ru‐CIO, to effectively activate the lattice oxygen participating in water oxidation process for decent oxygen‐related electrocatalytic activity. Specifically, a great increase of ≈700‐fold and ≈170‐fold in mass activity and turnover frequency, respectively, has been realized for the optimal Ru‐CIO electrocatalyst in an acid medium relative to the commercial IrO2 electrocatalysts, where a small overpotential of only 175 mV is required for achieving 10 mA cmgeo‒2. In situ X‐ray fine structure spectroscopies combined with in situ 18O‐ isotope‐labeled differential electrochemical mass spectrometry analyses reveal that desirable LOM has been boosted by the activated lattice oxygen and the flexible Ir(3+δ)+ active sites of asymmetric [IrO6] octahedra, which results in superior OER kinetics for Ir‐based oxide catalysts. [ABSTRACT FROM AUTHOR]