An elasto-plastic damage model for concrete

Constitutive modeling of concrete using continuum damage mechanics and plasticity theory is presented in this work. In order to derive the constitutive equations the strain equivalence hypothesis is adopted. Menetrey-William type yield function (in the effective stress space) with multiple hardening functions is used to define plastic loading of the material. Non-associated plastic flow rule is used to control inelastic dilatancy. DruckerPrager type function is chosen as a plastic potential. Damage is assumed to be isotropic and two damage variables are used to represent tensile and compressive damage independently. Damage parameter is driven based on the plastic strain. Fully implicit integration scheme is employed and the consistent elastic-plastic-damage tangent operator is also derived. The overall performance of the proposed model is verified by comparing the model predictions to various numerical simulations, cyclic uniaxial tensile and compressive tests, monotonic biaxial compression test and reinforced concrete beam test

Lensfree optofluidic plasmonic sensor for real-time and label-free monitoring of molecular binding events over a wide field-of-view

We demonstrate a high-throughput biosensing device that utilizes microfluidics based plasmonic microarrays incorporated with dual-color on-chip imaging toward real-time and label-free monitoring of biomolecular interactions over a wide field-of-view of >20 mm^2. Weighing 40 grams with 8.8 cm in height, this biosensor utilizes an opto-electronic imager chip to record the diffraction patterns of plasmonic nanoapertures embedded within microfluidic channels, enabling real-time analyte exchange. This plasmonic chip is simultaneously illuminated by two different light-emitting-diodes that are spectrally located at the right and left sides of the plasmonic resonance mode, yielding two different diffraction patterns for each nanoaperture array. Refractive index changes of the medium surrounding the near-field of the nanostructures, e.g., due to molecular binding events, induce a frequency shift in the plasmonic modes of the nanoaperture array, causing a signal enhancement in one of the diffraction patterns while suppressing the other. Based on ratiometric analysis of these diffraction images acquired at the detector-array, we demonstrate the proof-of-concept of this biosensor by monitoring in real-time biomolecular interactions of protein A/G with immunoglobulin G (IgG) antibody. For high-throughput on-chip fabrication of these biosensors, we also introduce a deep ultra-violet lithography technique to simultaneously pattern thousands of plasmonic arrays in a cost-effective manner

Field-portable optofluidic plasmonic biosensor for wide-field and label-free monitoring of molecular interactions

We demonstrate a field-portable optofluidic plasmonic sensing device, weighing 40 g and 7.5 cm in height, which merges plasmonic microarrays with dual-wavelength lensfree on-chip imaging for real-time monitoring of protein binding kinetics

Bidding structure, market efficiency and persistence in a multi-time tariff setting

The purpose of this study is to examine the fractal dynamics of day ahead electricity prices by using parametric and semi parametric approaches for each time zone in a multi-time tariff setting in the framework of bidding strategies, market efficiency and persistence of exogenous shocks. We find that that electricity prices have long term correlation structure for the first and third time zones indicating that market participants bid hyperbolically and not at their marginal costs, market is not weak form efficient at these hours and exogenous shocks to change the mean level of prices will have permanent effect and be effective. On the other hand, for the second time zone we find that price series does not exhibit long term memory. This finding suggests the weak form efficiency of the market in these hours and that market participants bid at their marginal costs. Furthermore this indicates that exogenous shocks will have temporary effect on electricity prices in these hours. These findings constitute an important foundation for policy makers and market participants to develop appropriate electricity price forecasting tools, market monitoring indexes and to conduct ex-ante impact assessment. © 2015 Elsevier B.V

Voltage contrast X-ray photoelectron spectroscopy reveals graphene-substrate interaction in graphene devices fabricated on the C- and Si- faces of SiC

We report on an X-ray photoelectron spectroscopy (XPS) study of two graphene based devices that were analyzed by imposing a significant current under +3 V bias. The devices were fabricated as graphene layers(s) on hexagonal SiC substrates, either on the C- or Si-terminated faces. Position dependent potential distributions (IR-drop), as measured by variations in the binding energy of a C1s peak are observed to be sporadic for the C-face graphene sample, but very smooth for the Si-face one, although the latter is less conductive. We attribute these sporadic variations in the C-face device to the incomplete electrical decoupling between the graphene layer(s) with the underlying buffer and/or substrate layers. Variations in the Si2p and O1s peaks of the underlayer(s) shed further light into the electrical interaction between graphene and other layers. Since the potential variations are amplified only under applied bias (voltage-contrast), our methodology gives unique, chemically specific electrical information that is difficult to obtain by other techniques. © 2015 AIP Publishing LLC

Virtual histological staining of unlabeled autopsy tissue

Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time. These challenges can become more pronounced during global health crises when the availability of histopathology services is limited, resulting in further delays in tissue fixation and more severe staining artifacts. Here, we report the first demonstration of virtual staining of autopsy tissue and show that a trained neural network can rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images that match hematoxylin and eosin (H&E) stained versions of the same samples, eliminating autolysis-induced severe staining artifacts inherent in traditional histochemical staining of autopsied tissue. Our virtual H&E model was trained using >0.7 TB of image data and a data-efficient collaboration scheme that integrates the virtual staining network with an image registration network. The trained model effectively accentuated nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining failed to provide consistent staining quality. This virtual autopsy staining technique can also be extended to necrotic tissue, and can rapidly and cost-effectively generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.Comment: 24 Pages, 7 Figure