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Hinkle, Janice L.,Becker, Kyra J.,Kim, Jong S.,Choi-Kwon, Smi,Saban, Karen L.,McNair, Norma,Mead, Gillian E. American Heart Association, Inc. 2017 Stroke Vol.48 No.7
<P>At least half of all stroke survivors experience fatigue; thus, it is a common cause of concern for patients, caregivers, and clinicians after stroke. This scientific statement provides an international perspective on the emerging evidence surrounding the incidence, prevalence, quality of life, and complex pathogenesis of poststroke fatigue. Evidence for pharmacological and nonpharmacological interventions for management are reviewed, as well as the effects of poststroke fatigue on both stroke survivors and caregivers.</P>
Silicon Interfacial Passivation Layer Chemistry for High-<i>k</i>/InP Interfaces
Dong, Hong,Cabrera, Wilfredo,Qin, Xiaoye,Brennan, Barry,Zhernokletov, Dmitry,Hinkle, Christopher L.,Kim, Jiyoung,Chabal, Yves J.,Wallace, Robert M. American Chemical Society 2014 ACS APPLIED MATERIALS & INTERFACES Vol.6 No.10
<P>The interfacial chemistry of thin (1 nm) silicon (Si) interfacial passivation layers (IPLs) deposited on acid-etched and native oxide InP(100) samples prior to atomic layer deposition (ALD) is investigated. The phosphorus oxides are scavenged completely from the acid-etched samples but not completely from the native oxide samples. Aluminum silicate and hafnium silicate are possibly generated upon ALD and following annealing. The thermal stability of a high-<I>k</I>/Si/InP (acid-etched) stack are also studied by in situ annealing to 400 and 500 °C under ultrahigh vacuum, and the aluminum oxide/Si/InP stack is the most thermally stable. An indium out-diffusion to the sample surface is observed through the Si IPL and the high-<I>k</I> dielectric, which may form volatile species and evaporate from the sample surface.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/aamick/2014/aamick.2014.6.issue-10/am500752u/production/images/medium/am-2014-00752u_0010.gif'></P>
Papamarkou, Theodore,Guy, Hayley,Kroencke, Bryce,Miller, Jordan,Robinette, Preston,Schultz, Daniel,Hinkle, Jacob,Pullum, Laura,Schuman, Catherine,Renshaw, Jeremy,Chatzidakis, Stylianos Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.2
Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.