Performance evaluation of innovative self-healing corrosion protection coatings for prestressing strands.

This study presents the design and experimental evaluation of advanced corrosion protection coatings for application on prestressing strands which are the core constituents of prestressed concrete structures such as bridges. Variety of self-heal coatings embodying corrective and protective phenomena in response to the degrading effects of corrosion have been designed and tested in simulated aggressive weathering conditions. Standard 7-wire prestressing strands coated with self-heal epoxy, self-heal toughened epoxy and hybrid epoxy coating systems were subjected to salt fog spray up to a duration of 2500 h, and 3M CalCl, 3M NaOH, saturated Ca(OH) solutions and distilled water up to 45 days duration.

Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals.

Game theory-inspired deep learning using a generative adversarial network provides an environment to competitively interact and accomplish a goal. In the context of medical imaging, most work has focused on achieving single tasks such as improving image resolution, segmenting images, and correcting motion artifacts. We developed a dual-objective adversarial learning framework that simultaneously 1) reconstructs higher quality brain magnetic resonance images (MRIs) that 2) retain disease-specific imaging features critical for predicting progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD).

Deep learning for risk-based stratification of cognitively impaired individuals.

Quantifying the risk of progression to Alzheimer's disease (AD) could help identify persons who could benefit from early interventions. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 544, discovery cohort) and the National Alzheimer's Coordinating Center (NACC, n = 508, validation cohort), subdividing individuals with mild cognitive impairment (MCI) into risk groups based on cerebrospinal fluid amyloid-β levels and identifying differential gray matter patterns. We then created models that fused neural networks with survival analysis, trained using non-parcellated T1-weighted brain MRIs from ADNI data, to predict the trajectories of MCI to AD conversion within the NACC cohort (integrated Brier score: 0.

Automated optimization of spatial resolution for single-sided NMR.

Single-sided NMR instruments utilize inhomogeneous magnetic fields with strong gradients to nondestructively probe physical properties of materials. The sensitive region of this type of magnet is often a thin slice above the magnet's surface; measuring planar samples with high spatial resolution requires coplanarity between the sensitive region of the magnet and the sample region of interest. We developed an algorithmic approach to position flat samples coplanar with the magnet's sensitive region.