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  1. Abstract The warm-to-cold densification of Atlantic Water (AW) around the perimeter of the Nordic Seas is a critical component of the Atlantic Meridional Overturning Circulation (AMOC). However, it remains unclear how ongoing changes in air-sea heat flux impact this transformation. Here we use observational data, and a one-dimensional mixing model following the flow, to investigate the role of air-sea heat flux on the cooling of AW. We focus on the Norwegian Atlantic Slope Current (NwASC) and Front Current (NwAFC), where the primary transformation of AW occurs. We find that air-sea heat flux accounts almost entirely for the net cooling of AW along the NwAFC, while oceanic lateral heat transfer appears to dominate the temperature change along the NwASC. Such differing impacts of air-sea interaction, which explain the contrasting long-term changes in the net cooling along two AW branches since the 1990s, need to be considered when understanding the AMOC variability. 
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    Free, publicly-accessible full text available December 1, 2024
  2. Free, publicly-accessible full text available December 12, 2024
  3. Free, publicly-accessible full text available February 23, 2024
  4. Micro-Raman spectroscopy has become an important tool in probing thermophysical properties in functional materials. Localized heating by the focused Raman excitation laser beam can produce both stress and local nonequilibrium phonons in the material. Here, we investigate the effects of hot optical phonons in the Raman spectra of molybdenum disulfide and distinguish them from those caused by thermally induced compressive stress, which causes a Raman frequency blue shift. We use a thermomechanical analysis to correct for this stress effect in the equivalent lattice temperature extracted from the measured Raman peak shift. When the heating Gaussian laser beam is reduced to 0.71  μm, the corrected peak shift temperature rise is 17% and 8%, respectively, higher than those determined from the measured peak shift and linewidth without the stress correction, and 32% smaller than the optical phonon temperature rise obtained from the anti-Stokes to Stokes intensity ratio. This nonequilibrium between the hot optical phonons and the lattice vanishes as the beam width increases to 1.53 μm. Much less pronounced than those reported in prior micro-Raman measurements of suspended graphene, this observed hot phonon behavior agrees with a first-principles based multitemperature model of overpopulated zone-center optical phonons compared to other optical phonons in the Brillouin zone and acoustic phonons of this prototypical transition metal dichalcogenide. The findings provide detailed insight into the energy relaxation processes in this emerging electronic and optoelectronic material and clarify an important question in micro-Raman measurements of thermal transport in this and other two-dimensional materials. 
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  5. Free, publicly-accessible full text available May 1, 2024
  6. Anthropogenic surface warming dominates and drives a global acceleration of the upper ocean currents in a warmer climate. 
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  7. Online education technologies, such as intelligent tutoring systems, have garnered popularity for their automation. Wh- ether it be automated support systems for teachers (grading, feedback, summary statistics, etc.) or support systems for students (hints, common wrong answer messages, scaffold- ing), these systems have built a well rounded support sys- tem 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 au- tomated open response tools, such as automated grading or automated feedback, are pre-trained word embeddings. Re- cent 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 embed- dings. We attempt to identify if our ability to predict scores for open response questions vary for different groups of stu- dent answers. For instance, whether a student who uses fractions as opposed to decimals. By performing a simu- lated study, we are able to identify the potential unfairness within our machine learned models with pre-trained word embeddings. 
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