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ItemTesting the Useful Field: Perceptual Learning Is an Important Factor in UFOV Training Improvements(Georgia Institute of Technology, 2023-08-09) Lloyd, MauganComputerized cognitive training on the Useful Field of View (UFOV) is associated with improved driving behavior in older adults, but the underlying reasons remain subject to debate. Some researchers think that UFOV training enhances fundamental cognitive skills such as selective attention or processing speed, while others remain unconvinced of this so-called process-based approach. Typically, UFOV training includes a briefly presented central discrimination task, coupled with a consistently mapped (CM) peripheral localization task. As the peripheral stimuli for both target and distractors remain constant, perceptual learning would be expected with extended practice on the peripheral task. This study compared training on variably mapped targets (VM), in which targets and distractors come from the same set, and consistently mapped versions of a UFOV task to isolate the component of perceptual learning. When comparing the transfer cost for participants trained on an adaptive UFOV paradigm when transferred to unfamiliar stimuli, VM - trained groups do not exhibit the same performance decrements as CM – trained groups due to the difference in target familiarity. Specifically, we observed that transfer to new CM stimuli following extensive practice was associated with a large performance cost for the CM-trained group due to the loss of the familiar stimulus advantage (d = -1.31, t = -7.91, pbonf < 0.001), while smaller changes in performance were noted for VM trained participants transferred to new VM stimuli (d = -0.86, t = -4.93, pbonf < 0.001). Our findings suggest that future research exploring the relationship between cognitive or everyday task performance and training improvements on the UFOV must take the effects of perceptual learning into account. Furthermore, the study challenges previous assertions that UFOV training improves processing speed, which in turn improves older adult driving.
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ItemDecreased Dissolved Oxygen Content of the Pacific Deep Water During the Last Glacial Maximum(Georgia Institute of Technology, 2023-05-05) Kim, GraceThe mechanisms responsible for lowering atmospheric CO2 levels during glaciation have yet to be constrained, but the deep ocean is the most likely reservoir of CO2 drawdown. Deep ocean carbon export and storage are suggested to have increased cyclically during glacial periods due to greater biological pump efficiency and ocean stratification, and poor ventilation. Increased respired carbon in the Eastern Equatorial Pacific Ocean (EEP) would be evident with depleted dissolved oxygen content, but there is insufficient paleo-oxygen data in this region. This study uses a benthic foraminifera Δδ13C proxy to provide quantitative assessments of changes in oxygen concentration between the Holocene and LGM. The proxy relies on the empirical relationship between bottom water oxygen and the carbon isotope gradient between the sediment-water interface and oxic-anoxic interface preserved in benthic foraminifera. Cibicidoides wuellerstorfi and Globobulimina spp. are benthic foraminifera that preferentially reside at these interfaces and record δ13C at equilibrium with bottom water and pore water dissolved inorganic carbon, respectively. The findings of this paper provided oxygen concentrations in the Holocene and LGM that indicate a more depleted bottom water oxygen content and higher mid-depth oxygen concentrations during the last glacial period. This suggests increased carbon storage, poorer ventilation, and greater water mass stratification and supports the respired carbon deepening hypothesis and corresponds to oxygen trends of qualitative paleo-oxygen proxies in the EEP.
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ItemDeep Learning Enhanced Biofilm Topography through Convolutional Neural Network(Georgia Institute of Technology, 2023-05-02) Zhao, LinBiofilms are surface attached communities microbes. One approach to study the formation and growth of biofilms is to observe its surface topography, such as with white light profilometry. However, this technique requires taking images of many fields of view and stitching them together, a time-consuming process. We thus sought to develop a convolutional neural network to that can convert low-resolution images to high-resolution images. Our results show that the technique succeeds with a low mean absolute error (~10^-4). We also found that the model prediction error is highly related to the biofilm's topographic roughness. As a result, highly rough surfaces are crucial resources for training deep learning super-resolution model. Roughness enriches the complexity of the biofilm surface, and a model trained on biofilm formed by strains with high roughness yield a lower error on other strains.
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ItemAssessment of High Resolution Numerical Weather Prediction (NWP) Parameters and their Contribution to Weather Integration Prototype (WIP) Performance to Aid High Energy Laser (HEL) Testing(Georgia Institute of Technology, 2023-05-01) Murdock, Jordan N.Since it was first discussed in 1960, testing and development of high energy lasers (HELs) has only continued to increase in interest for the U.S. military. To yield the most effective engagements with these HEL systems, there are many parameters that must be evaluated including atmospheric variables. The Weather Integration Prototype (WIP) is an instrument and software suite that measures and ingests various atmospheric data to produce a HEL performance assessment. In this work a study was conducted to determine whether mesoscale, high resolution numerical weather Prediction (NWP) model data can more accurately predict observational atmospheric data reported to the WIP from field atmospheric sensors, relative to lower resolution NWP models. Data from the WIP testing conducted during March 2022 are used for this study. The WIP-reported observed atmospheric conditions include temperature, pressure, humidity (expressed in terms of relative humidity or dewpoint), wind speed and direction, aerosol particle counts, and optical turbulence measurements and NWP model data provide similar outputs. Both measured and NWP atmospheric data are then used as inputs to both the Laser Environmental Effects Definition and Reference (LEEDR) and High Energy Laser End-to-End Operational Simulation (HELEEOS) models to determine the accuracy of NWP model data and ultimately the effectiveness in modeling HEL performance. The WIP takes in field sensor data and ingests it to LEEDR and HELEEOS and then the values output from the WIP are compared to the Weather Research Forecast (WRF) high resolution model data to determine mesoscale NWP model data accuracy and both are compared to the HEL diagnostic measurements. The WRF model is the selected high-resolution NWP used in this study. In addition to WRF model data, climatology data that are utilized in both HELEEOS and LEEDR are also examined to determine which atmospheric modeling method would best forecast the observed atmospheric conditions, with an emphasis on optical turbulence. The main goals of this study are to 1) determine whether using mesoscale NWP model data is a more or equally reliable method of forecasting observed atmospheric conditions needed for HEL operations as compared to the WIP; 2) prove that both the WIP and NWP model can accurately predict optical turbulence values; and 3) evaluate if mesoscale NWP forecasts and local measurements can provide the optimal HEL performance assessment as compared to laser diagnostic measurements. This study demonstrates that the WIP can evolve with the addition of higher resolution NWP model data, and become a reliable method to determine HEL performance measurements. The results of this study support the capability of the WIP to provide, with reasonable accuracy, forecasted HEL performance assessments well prior to HEL execution.
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ItemTime-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task(Georgia Institute of Technology, 2023-05-01) Seeburger, DollyThere is ambiguity in the literature about how large-scale brain networks contribute to focused attention. Part of the problem comes from the methods of analyses that treat the correlates of attention as a static and discrete measure when in actuality, attention fluctuates from moment to moment. This continuous change in attention is consistent with the dynamic changes in functional connectivity between brain regions involved in the internal and external allocation of attention (Liu & Dyun, 2013). Namely, the default mode network (DMN) and the task positive network (TPN)(Fox et al., 2005). In this study, I investigated how brain network activity varied across different levels of attentional focus (e.g., “zones”). Participants performed a finger-tapping task and, guided by previous research (Esterman et al., 2013), in-the-zone was marked by low reaction time variability and out-of-the-zone as the inverse. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliably observed spontaneous low-frequency fluctuations), I found that the activity between DMN and TPN was more anti-correlated during in-the-zone states versus out-of-the-zone states. Further investigation showed that it is the fronto-parietal control network (FPCN) of the TPN that drives the differentiation. During in-the-zone periods, FPCN synchronized with the dorsal attention network, while during out-of-the-zone periods, FPCN synchronized with DMN. In contrast, the ventral attention network synchronized more closely with DMN during in-the-zone periods compared to out-of-the-zone periods. These findings suggest that time-varying functional connectivity in the low-frequency can tell us how different networks of the brain work together during periods of sustained attention.