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Fiber-reinforced concrete (FRC) can have improved durability and tensile properties, potentially enabling the more efficient use of concrete and lowering greenhouse gas (GHG) emissions. Yet, systematic quantifications of the environmental impacts of FRC, particularly when paired with changes to mechanical properties and the implications for material longevity, are limited. Herein, an assessment following the life-cycle assessment methodology for four common FRCs was performed, namely, those reinforced with polyvinyl alcohol (PVA), steel (ST), polypropylene (PP), and polyethylene terephthalate (PET). The analysis was bound to a cradle-to-gate scope, and solely virgin fiber material production was considered for the environmental impacts. Coupled changes in compressive and tensile strength, environmental impacts, and the role of material longevity and cost relative to unreinforced concrete were examined. Findings from this work show that, similar to unreinforced concrete, cement remains a key source of GHG emissions in FRC production. However, in FRCs fibers can drive additional emissions by up to 55%. Notably, PVA and ST led to the highest impacts and costs, which were minimal for inclusions of PP and PET. Yet ST contributed to the greatest benefits in flexural and compressive strengths. When the effects of longevity were integrated, FRC with PP reinforcement could offer desired emissions reductions with minimal increase in use period and cost, but the other fiber reinforcements considered may need to offer longer service life extension to reduce emissions compared with conventional concrete. These results indicate that FRC can enhance mechanical performance, but fiber type selections should be informed by the design life to achieve actual GHG emissions reductions. 

Abstract Information processing under conditions of uncertainty requires the involvement of cognitive control. Despite behavioral evidence of the supramodal function (i.e., independent of sensory modality) of cognitive control, the underlying neural mechanism needs to be directly tested. This study used functional magnetic imaging together with visual and auditory perceptual decision-making tasks to examine brain activation as a function of uncertainty in the two stimulus modalities. The results revealed a monotonic increase in activation in the cortical regions of the cognitive control network (CCN) as a function of uncertainty in the visual and auditory modalities. The intrinsic connectivity between the CCN and sensory regions was similar for the visual and auditory modalities. Furthermore, multivariate patterns of activation in the CCN predicted the level of uncertainty within and across stimulus modalities. These findings suggest that the CCN implements cognitive control by processing uncertainty as abstract information independent of stimulus modality. 

We study how human brains activate to process input information and execute necessary cognitive tasks. Understanding the process is crucial in improving our diagnostic and treatment of different neurological disorders. Given functional MRI images recorded when human subjects execute tasks with different levels of information uncertainty, we need to identify the similarity and difference between brain activities at different regions of interest (ROIs), and thus gain insights into the underlying mechanism. To achieve this goal, we propose a new ROI- reweight 3D convolutional neural network (CNN). Our CNN not only learns to classify the task-evoked fMRIs with a high accuracy, but also locates crucial ROIs based on a reweight layer. Our findings reveal several brain regions to be crucial in differentiating brain activity patterns facing tasks of different uncertainty levels.