Reducing the size of lasers to microscale dimensions enables new technologies that are specifically tailored for operation in confined spaces ranging from ultra-high-speed microprocessors to live brain tissue. However, reduced cavity sizes increase optical losses and require greater input powers to reach lasing thresholds. Multiphoton-pumped lasers that have been miniaturized using nanomaterials such as lanthanide-doped upconverting nanoparticles (UCNPs) as lasing media require high pump intensities to achieve ultraviolet and visible emission and therefore operate under pulsed excitation schemes. Here, we make use of the recently described energy-looping excitation mechanism in Tm-doped UCNPs to achieve continuous-wave upconverted lasing action in stand-alone microcavities at excitation fluences as low as 14 kW cm. Continuous-wave lasing is uninterrupted, maximizing signal and enabling modulation of optical interactions. By coupling energy-looping nanoparticles to whispering-gallery modes of polystyrene microspheres, we induce stable lasing for more than 5 h at blue and near-infrared wavelengths simultaneously. These microcavities are excited in the biologically transmissive second near-infrared (NIR-II) window and are small enough to be embedded in organisms, tissues or devices. The ability to produce continuous-wave lasing in microcavities immersed in blood serum highlights practical applications of these microscale lasers for sensing and illumination in complex biological environments.
Divergent thinking (DT) tests are useful for the assessment of creative potentials. This article reports the semantics-based algorithmic (SBA) method for assessing DT. This algorithm is fully automated: Examinees receive DT questions on a computer or mobile device and their ideas are immediately compared with norms and semantic networks. This investigation compared the scores generated by the SBA method with the traditional methods of scoring DT (i.e., fluency, originality, and flexibility). Data were collected from 250 examinees using the “Many Uses Test” of DT. The most important finding involved the flexibility scores from both scoring methods. This was critical because semantic networks are based on conceptual structures, and thus a high SBA score should be highly correlated with the traditional flexibility score from DT tests. Results confirmed this correlation (r = .74). This supports the use of algorithmic scoring of DT. The nearly-immediate computation time required by SBA method may make it the method of choice, especially when it comes to moderate- and large-scale DT assessment investigations. Correlations between SBA scores and GPA were insignificant, providing evidence of the discriminant and construct validity of SBA scores. Limitations of the present study and directions for future research are offered.
Merge trees represent the topology of scalar functions. To assess the topological similarity of functions, one can compare their merge trees. To do so, one needs a notion of a distance between merge trees, which we define. We provide examples of using our merge tree distance and compare this new measure to other ways used to characterize topological similarity (bottleneck distance for persistence diagrams) and numerical difference (L ∞ -norm of the difference between functions).
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