Characterization of delamination and transverse cracking in graphite/epoxy laminates by acoustic emission

Efforts to characterize and differentiate between two major failure processes in graphite/epoxy composites - transverse cracking and Mode I delamination are described. Representative laminates were tested in uniaxial tension and flexure. The failure processes were monitored and identified by acoustic emission (AE). The effect of moisture on AE was also investigated. Each damage process was found to have a distinctive AE output that is significantly affected by moisture conditions. It is concluded that AE can serve as a useful tool for detecting and identifying failure modes in composite structures in laboratory and in service environments

Hygrothermal effects on mechanical behavior of graphite/epoxy laminates beyond initial failure

An investigation was conducted to determine the critical load levels and associated cracking beyond which a multidirectional laminate can be considered as structurally failed. Graphite/epoxy laminates were loaded to different strain levels up to ultimate failure. Transverse matrix cracking was monitored by acoustic and optical methods. Residual stiffness and strength that were parallel and perpendicular to the cracks were determined and related to the environmental/loading history. Results indicate that cracking density in the transverse layers has no major effect on laminate residual properties as long as the angle ply layers retain their structural integrity. Exposure to hot water revealed that cracking had only a small effect on absorption and reduced swelling when these specimens were compared with uncracked specimens. Cracked, moist specimens showed a moderate reduction in strength when compared with their uncracked counterparts. Within the range of environmental/loading conditions of the present study, it is concluded that the transverse cracking process is not crucial in its effect on the structural performance of multidirectional composite laminates

Hygrothermal influence on delamination behavior of graphite/epoxy laminates

The hygrothermal effect on the fracture behavior of graphite-epoxy laminates was investigated to develop a methodology for damage tolerance predictions in advanced composite materials. Several T300/934 laminates were tested using a number of specimen configurations to evaluate the effects of temperature and humidity on delamination fracture toughness under mode 1 and mode 2 loading. It is indicated that moisture has a slightly beneficial influence on fracture toughness or critical strain energy release rate during mode 1 delamination, but has a slightly deleterious effect on mode 2 delamination and mode 1 transverse cracking. The failed specimens are examined by SEM and topographical differences due to fracture modes are identified. It is concluded that the effect of moisture on fracture topography can not be distinguished

Optical fiber interferometer for the study of ultrasonic waves in composite materials

The possibility of acoustic emission detection in composites using embedded optical fibers as sensing elements was investigated. Optical fiber interferometry, fiber acoustic sensitivity, fiber interferometer calibration, and acoustic emission detection are reported. Adhesive bond layer dynamical properties using ultrasonic interface waves, the design and construction of an ultrasonic transducer with a two dimensional Gaussian pressure profile, and the development of an optical differential technique for the measurement of surface acoustic wave particle displacements and propagation direction are also examined

Macroscopic Quantum Tunneling of a Domain Wall in a Ferromagnetic Metal

The macroscopic quantum tunneling of a planar domain wall in a ferromagnetic metal is studied based on the Hubbard model. It is found that the ohmic dissipation is present even at zero temperature due to the gapless Stoner excitation, which is the crucial difference from the case of the insulating magnet. The dissipative effect is calculated as a function of width of the wall and is shown to be effective in a thin wall and in a weak ferromagnet. The results are discussed in the light of recent experiments on ferromagnets with strong anisotropy. PACS numbers:75.60.Ch, 03.65.Sq, 75.10.LpComment: 13page

Geometry meets semantics for semi-supervised monocular depth estimation

Depth estimation from a single image represents a very exciting challenge in computer vision. While other image-based depth sensing techniques leverage on the geometry between different viewpoints (e.g., stereo or structure from motion), the lack of these cues within a single image renders ill-posed the monocular depth estimation task. For inference, state-of-the-art encoder-decoder architectures for monocular depth estimation rely on effective feature representations learned at training time. For unsupervised training of these models, geometry has been effectively exploited by suitable images warping losses computed from views acquired by a stereo rig or a moving camera. In this paper, we make a further step forward showing that learning semantic information from images enables to improve effectively monocular depth estimation as well. In particular, by leveraging on semantically labeled images together with unsupervised signals gained by geometry through an image warping loss, we propose a deep learning approach aimed at joint semantic segmentation and depth estimation. Our overall learning framework is semi-supervised, as we deploy groundtruth data only in the semantic domain. At training time, our network learns a common feature representation for both tasks and a novel cross-task loss function is proposed. The experimental findings show how, jointly tackling depth prediction and semantic segmentation, allows to improve depth estimation accuracy. In particular, on the KITTI dataset our network outperforms state-of-the-art methods for monocular depth estimation.Comment: 16 pages, Accepted to ACCV 201

Macroscopic Quantum Coherence in a Magnetic Nanoparticle Above the Surface of a Superconductor

We study macroscopic quantum tunneling of the magnetic moment in a single-domain particle placed above the surface of a superconductor. Such a setup allows one to manipulate the height of the energy barrier, preserving the degeneracy of the ground state. The tunneling amplitude and the effect of the dissipation in the superconductor are computed.Comment: RevTeX, 4 pages, 1 figure. Submitted to Phys. Rev. Let

Covering problems in edge- and node-weighted graphs

This paper discusses the graph covering problem in which a set of edges in an edge- and node-weighted graph is chosen to satisfy some covering constraints while minimizing the sum of the weights. In this problem, because of the large integrality gap of a natural linear programming (LP) relaxation, LP rounding algorithms based on the relaxation yield poor performance. Here we propose a stronger LP relaxation for the graph covering problem. The proposed relaxation is applied to designing primal-dual algorithms for two fundamental graph covering problems: the prize-collecting edge dominating set problem and the multicut problem in trees. Our algorithms are an exact polynomial-time algorithm for the former problem, and a 2-approximation algorithm for the latter problem, respectively. These results match the currently known best results for purely edge-weighted graphs.Comment: To appear in SWAT 201

Preventing respiratory viral transmission in long-term care: Knowledge, attitudes, and practices of healthcare personnel

OBJECTIVETo examine knowledge and attitudes about influenza vaccination and infection prevention practices among healthcare personnel (HCP) in a long-term-care (LTC) setting.DESIGNKnowledge, attitudes, and practices (KAP) survey.SETTINGAn LTC facility in St Louis, Missouri.PARTICIPANTSAll HCP working at the LTC facility were eligible to participate, regardless of department or position. Of 170 full- and part-time HCP working at the facility, 73 completed the survey, a 42.9% response rate.RESULTSMost HCP agreed that respiratory viral infections were serious and that hand hygiene and face mask use were protective. However, only 46% could describe the correct transmission-based precautions for an influenza patient. Correctly answering infection prevention knowledge questions did not vary by years of experience but did vary for HCP with more direct patient contact versus less patient contact. Furthermore, 42% of respondents reported working while sick, and 56% reported that their coworkers did. In addition, 54% reported that facility policies made staying home while ill difficult. Some respondents expressed concerns about the safety (22%) and effectiveness (27%) of the influenza vaccine, and 28% of respondents stated that they would not get the influenza vaccine if it was not required.CONCLUSIONSThis survey of staff in an LTC facility identified several areas for policy improvement, particularly sick leave, as well as potential targets for interventions to improve infection prevention knowledge and to address HCP concerns about influenza vaccination to improve HCP vaccination rates in LTCs.Infect Control Hosp Epidemiol 2017;38:1449–1456</jats:sec

Stereo Computation for a Single Mixture Image

This paper proposes an original problem of \emph{stereo computation from a single mixture image}-- a challenging problem that had not been researched before. The goal is to separate (\ie, unmix) a single mixture image into two constitute image layers, such that the two layers form a left-right stereo image pair, from which a valid disparity map can be recovered. This is a severely illposed problem, from one input image one effectively aims to recover three (\ie, left image, right image and a disparity map). In this work we give a novel deep-learning based solution, by jointly solving the two subtasks of image layer separation as well as stereo matching. Training our deep net is a simple task, as it does not need to have disparity maps. Extensive experiments demonstrate the efficacy of our method.Comment: Accepted by European Conference on Computer Vision (ECCV) 201