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Particle migration dynamics in viscoelastic fluids in spiral channels have attracted interest in recent years due to potential applications in the 3D focusing and label-free sorting of particles and cells. Despite a number of recent studies, the underlying mechanism of Dean-coupled elasto-inertial migration in spiral microchannels is not fully understood. In this work, for the first time, we experimentally demonstrate the evolution of particle focusing behavior along a channel downstream length at a high blockage ratio. We found that flow rate, device curvature, and medium viscosity play important roles in particle lateral migration. Our results illustrate the full focusing pattern along the downstream channel length, with side-view imaging yielding observations on the vertical migration of focused streams. Ultimately, we anticipate that these results will offer a useful guide for elasto-inertial microfluidics device design to improve the efficiency of 3D focusing in cell sorting and cytometry applications. 

Antarctica is almost completely covered by the world’s largest ice sheet, and its hidden geologic structure partially controls the behavior of the ice layer. Recent advances in geophysical and remote sensing tools have allowed us to observe various transient phenomena, such as tectonic earthquakes, glacial bed slip events, and iceberg calving signals, all of which can be used to investigate solid Earth – cryosphere interactions. We analyzed seismic data collected by the TAMNNET temporary deployment as well as other stations in East Antarctica to identify and locate local icequakes, earthquakes, and other seismic events that occurred between 2012-2015. We employ two event detection approaches. The first is based on phase match filtering and waveform cross-correlation, which uses known events as templates to search through continuous data and to identify similar seismic signals. The second uses EQtransformer, a deep-learning-based event signal detector and phase picker. Event detections identified with both approaches will be compared to assess the effectiveness of these methods in East Antarctica. We also plan to use the combined constraints from our initial approaches to train a new machine-learning model and to assess its performance. Ultimately, our results will be used to evaluate automated event detection approaches for polar environments and to address fundamental questions related to tectonic-cryospheric interactions. 

Inertial migration of spherical particles has been investigated extensively using experiments, theory, and computational modeling. Yet, a systematic investigation of the effect of particle shape on inertial migration is still lacking. Herein, we numerically mapped the migration dynamics of a prolate particle in a straight rectangular microchannel using smoothed particles hydrodynamics (SPH). For the first time, we identified a new logrolling behavior of a prolate ellipsoidal particle in the confined channel. Our findings are especially relevant to the applications where particle shape and alignment are used for sorting and analysis, such as shape-based enrichment of microalgae, bacteria, and chromosomes. 

In this work, full 3-D numerical simulations are performed to study the combined effects of elastic and inertial forces along the Y and Z-midline of the channel. Ultimately, simulation results are compared and matched with experimental fluorescent streak images of the focusing of particles under the same parametric conditions. We reported that shear-gradient (FSG), N2-induced secondary flow transversal drag (FSF), and elastic (FEL) lift are the main forces responsible for the focusing of particles in the elasto-inertial regime. 

Abstract We select 48 multiflare gamma-ray bursts (GRBs) (including 137 flares) from the Swift/XRT database and estimate the spectral lag with the discrete correlation function. It is found that 89.8% of the flares have positive lags and only 9.5% of the flares show negative lags when fluctuations are taken into account. The median lag of the multiflares (2.75 s) is much greater than that of GRB pulses (0.18 s), which can be explained by the fact that we confirm that multiflare GRBs and multipulse GRBs have similar positive lag–duration correlations. We investigate the origin of the lags by checking the E peak evolution with the two brightest bursts and find the leading models cannot explain all of the multiflare lags and there may be other physical mechanisms. All of the results above reveal that X-ray flares have the same properties as GRB pulses, which further supports the observation that X-ray flares and GRB prompt-emission pulses have the same physical origin. 

Online education technologies, such as intelligent tutoring systems, have garnered popularity for their automation. Whether it be automated support systems for teachers (grading, feedback, summary statistics, etc.) or support systems for students (hints, common wrong answer messages, scaffolding), these systems have built a well rounded support system for both students and teachers alike. The automation of these online educational technologies, such as intelligent tutoring systems, have often been limited to questions with well structured answers such as multiple choice or fill in the blank. Recently, these systems have begun adopting support for a more diverse set of question types. More specifically, open response questions. A common tool for developing automated open response tools, such as automated grading or automated feedback, are pre-trained word embeddings. Recent studies have shown that there is an underlying bias within the text these were trained on. This research aims to identify what level of unfairness may lie within machine learned algorithms which utilize pre-trained word embeddings. We attempt to identify if our ability to predict scores for open response questions vary for different groups of student answers. For instance, whether a student who uses fractions as opposed to decimals. By performing a simulated study, we are able to identify the potential unfairness within our machine learned models with pre-trained word embeddings.