Northumbria University

About: Northumbria University is a education organization based out in Newcastle upon Tyne, United Kingdom. It is known for research contribution in the topics: Context (language use) & Population. The organization has 5624 authors who have published 17423 publications receiving 381949 citations. The organization is also known as: University of Northumbria at Newcastle.

Glass fabric reinforced cementitious matrix: Tensile properties and bond performance on masonry substrate

Abstract: Fibre-reinforced composite materials have gained an increasing success, mostly for strengthening, retrofitting, and repairing existing structures. However some problems may arise with the use of traditional FRP (Fiber Reinforced Polymer), particularly when the compatibility with the substrate and the reversibility of the intervention are required, as in case of cultural heritage buildings, or specific exposition conditions may compromise the long term effectiveness of the reinforcement, as in presence of high temperature and humidity. Starting from these considerations new composite materials are emerging as a more effective solution in certain fields of application and under specific service conditions; in this context, mortar-based composite systems, consisting of one or more layers of uni- or bi-directional fibre nets embedded in cement/lime-based matrix layers, can be used as reinforcement of both concrete and masonry structures. However, the research work dealing with these emerging materials and their performances when used as a strengthening system for existing structures is still limited. Both experimental and theoretical investigations are needed in order to deliver reliable design methodologies. In this work, a Round Robin Test aimed to the characterization of both bond with the existing substrate and tensile performance of glass fabric (in the form of grids) coupled with inorganic mortar matrices is presented. The investigation was conducted at fifteen laboratories involved in the RILEM Technical Committee 250-CSM (Composites for the Sustainable Strengthening of Masonry). With the aim of studying the bond behaviour between Fabric Reinforced Cementitious Matrix (FRCM) composites and masonry substrate, single and double lap shear tests were carried out on brick-masonry prisms. Results provide useful informations about the mechanical properties, the bond capacity and the failure mechanisms of different commercially available glass FRCM systems. Finally, critical aspects are underlined to address the progress of the research work.

The relationship between visuospatial sketchpad capacity and children's mathematical skills

Abstract: This study examined the association between visuospatial sketchpad (VSSP) capacity and children's mathematics attainment. The aim of the study was to explore age-related differences in the relationship between the visual and spatial memory subcomponents of the VSSP (Logie, 1995) and a range of mathematical skills. Fifty-one 7- to 8-year-old and fifty-six 9- to 10-year-old primary schoolchildren participated in the study. The Visual Patterns Test and the Block Recall task were employed as VSSP measures. The results revealed a differential pattern of associations between children's visual and spatial working memory abilities and their mathematical skills. In younger children, the Block Recall task predicted mathematics performance, whereas, in the older children, the Visual Patterns Test was a significant predictor of mathematics performance.

Reach-scale sediment transfers: an evaluation of two morphological budgeting approaches

Abstract: This paper compares two approaches used to derive measures of annual sediment transfers within a 1 km long piedmont reach of the gravel-bed River Coquet in Northumberland, northern England. The techniques utilize: (i) channel planform and cross-section surveys based on a theodolite/electronic distance measurement (EDM) survey of 21 monumented channel cross-sections and channel and gravel bar margins; and (ii) theodolite-EDM survey generating a series of x,y,z coordinates, from which digital elevation models (DEMs) of the reach were constructed. Calculating the difference between DEM surfaces provided a measure of volumetric change between surveys carried out during the spring of 1999 and 2000. The use of kriging in DEM generation and differencing permits computation of estimate variances and confidence intervals for sediment transfer. Error analysis, validating the DEMs using surveyed cross-sections, indicated a mean error between surveyed and DEM-generated cross-sections of around twice the value of the D50 of the surface sediment in the reach. Comparison of sediment volumes derived from the two approaches suggests that, compared with the DEM method, monumented cross-sections underestimate the magnitude of volumetric changes that occur within the reach. The cross-section approach relies on a simplistic integration of the volumes, whereas DEM differencing provides an estimate at a resolution under the control of the analyst. Furthermore, the cross-section approach does not permit a reliable estimate of the uncertainty of the volumes calculated. In addition, the DEM methodology based on the morphological unit scale provides an explicit identification of spatial patterns of erosion and deposition, a feature that cross-section-based approaches may fail to include. Copyright © 2003 John Wiley & Sons, Ltd.

Supervised Matrix Factorization Hashing for Cross-Modal Retrieval.

Abstract: The target of cross-modal hashing is to embed heterogeneous multimedia data into a common low-dimensional Hamming space, which plays a pivotal part in multimedia retrieval due to the emergence of big multimodal data. Recently, matrix factorization has achieved great success in cross-modal hashing. However, how to effectively use label information and local geometric structure is still a challenging problem for these approaches. To address this issue, we propose a cross-modal hashing method based on collective matrix factorization, which considers both the label consistency across different modalities and the local geometric consistency in each modality. These two elements are formulated as a graph Laplacian term in the objective function, leading to a substantial improvement on the discriminative power of latent semantic features obtained by collective matrix factorization. Moreover, the proposed method learns unified hash codes for different modalities of an instance to facilitate cross-modal search, and the objective function is solved using an iterative strategy. The experimental results on two benchmark data sets show the effectiveness of the proposed method and its superiority over state-of-the-art cross-modal hashing methods.