Notch effect on the fracture of several rocks: Application of the Theory of Critical Distances

The paper analyses the fracture behaviour of several rocks, namely a sandstone, a limestone and two marbles, one of them being a Carrara marble. The experimental program comprises in total 216 fracture specimens, tested in 4-point bending conditions and including specimens with notch radii varying from 0.15 mm up to 15 mm. The notch effect is analysed through the evolution of the apparent fracture toughness and the application of the Theory of Critical Distances. The present study aims to generalize a previous study on a granite and a limestone to a broader range of rocks. The point and line methods of the Theory of the Critical Distances successfully explain the notch effect on the fracture specimens. The value of the critical distance of these rocks is of the order of mm. Finally, the results show a correlation between the microstructural features of the rocks, specifically the grain size, and their critical distances.The authors of this work would like to express their gratitude to the Spanish Ministry of Economy and Competitiveness for financing the National Plan Project (Ref.: BIA2015-67479-R) under the name of ‘La Distancia Crítica en la Fractura de Rocas’ (The Critical Distance in Rock Fracture)

Analysis of notch effect on the fracture behaviour of granite and limestone: An approach from the Theory of Critical Distances

This paper presents the analysis of the notch effect on granite and limestone fracture specimens. The research is based on the results obtained in an experimental programme composed of 84 fracture specimens, combining the two materials and 7 different notch radii varying from 0.15 mm up to 10 mm. The notch effect is analysed through the evolution of the apparent fracture toughness and the application of the Theory of the Critical Distances. The results reveal a significant notch effect in the limestone, whereas the notch effect in the granite is negligible for the range of notch radii analysed. Both observations are justified by the corresponding critical distance of the material

Genetics of photoreceptor degeneration and regeneration in zebrafish

Zebrafish are unique in that they provide a useful model system for studying two critically important problems in retinal neurobiology, the mechanisms responsible for triggering photoreceptor cell death and the innate stem cell–mediated regenerative response elicited by this death. In this review we highlight recent seminal findings in these two fields. We first focus on zebrafish as a model for studying photoreceptor degeneration. We summarize the genes currently known to cause photoreceptor degeneration, and we describe the phenotype of a few zebrafish mutants in detail, highlighting the usefulness of this model for studying this process. In the second section, we discuss the several different experimental paradigms that are available to study regeneration in the teleost retina. A model outlining the sequence of gene expression starting from the dedifferentiation of Müller glia to the formation of rod and cone precursors is presented

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Corrosion of glass contact refractories for the vitrification of radioactive wastes: a review

A key consideration for all radioactive waste vitrification technologies is the physical and chemical integrity of the melting vessel. Most melting vessels require refractory liners that can safely withstand the high-temperature, highly corrosive environment, contain molten waste mixtures during melting and provide robust and reproducible service lifetimes. In this review article the key glass contact refractory materials used in radioactive waste vitrification melters, their properties,their applications and the mechanisms by which they become corroded during service are reviewed