Institution
University of Lincoln
Education•Lincoln, Lincolnshire, United Kingdom•
About: University of Lincoln is a education organization based out in Lincoln, Lincolnshire, United Kingdom. It is known for research contribution in the topics: Population & Higher education. The organization has 2341 authors who have published 7025 publications receiving 124797 citations.
Topics: Population, Higher education, Mental health, Health care, Robot
Papers published on a yearly basis
Papers
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TL;DR: Cessation of the habit perioperatively is routinely advised to improve outcomes for patients, and some of the consequences of tobacco smoking in fracture healing are described.
Abstract: Tobacco smoking is the single most avoidable cause of premature death worldwide. In fracture healing, it has been found to be a contributory factor to delayed union, and smokers are significantly disadvantaged, as healing times are often prolonged. The orthopaedic surgeon is likely to be knowledgeable about the detrimental effects of smoking on healing bones, as the problem has been known for some time. Smoking adversely affects bone mineral density, lumbar disc degeneration, the incidences of hip fractures and the dynamics of bone and wound healing. Clinical trials and demographic studies have been more widespread than biochemical analyses, and have reported poor prognosis for fracture patients who smoke. Scientific research has elucidated some of the negative impacts of tobacco use and investigations involving several animal models in cellular and humoral analyses have shown damage caused by various toxicological processes. Cessation of the habit perioperatively, therefore, is routinely advised to improve outcomes for patients. The current review describes some of the consequences of tobacco smoking in fracture healing.
170 citations
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TL;DR: Experimental results show that the proposed IGO-methods significantly outperform popular methods such as Gabor features and Local Binary Patterns and achieve state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition.
Abstract: We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As image data are typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population. We show that replacing pixel intensities with gradient orientations and the l2 norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Within this framework, which we coin Image Gradient Orientations (IGO) subspace learning, we first formulate and study the properties of Principal Component Analysis of image gradient orientations (IGO-PCA). We then show its connection to previously proposed robust PCA techniques both theoretically and experimentally. Finally, we derive a number of other popular subspace learning techniques, namely, Linear Discriminant Analysis (LDA), Locally Linear Embedding (LLE), and Laplacian Eigenmaps (LE). Experimental results show that our algorithms significantly outperform popular methods such as Gabor features and Local Binary Patterns and achieve state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition. In addition to this, the proposed IGO-methods require the eigendecomposition of simple covariance matrices and are as computationally efficient as their corresponding l2 norm intensity-based counterparts. Matlab code for the methods presented in this paper can be found at http://ibug.doc.ic.ac.uk/resources.
168 citations
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TL;DR: In this paper, an overview of the development of academic literature published between 2010 and 2019 with regards to the relationship between digitalization and business models in 198 peer-reviewed articles is presented.
168 citations
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University of Dundee1, University of Padua2, Oak Ridge National Laboratory3, Istituto Italiano di Tecnologia4, University of Tennessee Health Science Center5, Jules Stein Eye Institute6, University of Lincoln7, Institute for Infocomm Research Singapore8, University of Iowa9, National University of Singapore10, RMIT University11, Johns Hopkins University Applied Physics Laboratory12, Johns Hopkins University13, Charles Sturt University14, University of Burgundy15, French Institute of Health and Medical Research16, University of Edinburgh17, Princess Alexandra Eye Pavilion18
TL;DR: In this paper, the authors present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks.
Abstract: This paper concerns the validation of automatic retinal image analysis (ARIA) algorithms. For reasons of space and consistency, we concentrate on the validation of algorithms processing color fundus camera images, currently the largest section of the ARIA literature. We sketch the context (imaging instruments and target tasks) of ARIA validation, summarizing the main image analysis and validation techniques. We then present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks, and capable of running submitted software automatically on the data stored, with clear and widely agreed-on performance criteria, to provide a fair comparison.
166 citations
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TL;DR: It is indicated that folivores and frugivores face similar ecological pressures and the costs of living in larger groups balance or outweigh the benefits, and the effect of group size on behaviour and fitness is analyzed.
166 citations
Authors
Showing all 2452 results
Name | H-index | Papers | Citations |
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David R. Williams | 178 | 2034 | 138789 |
David Scott | 124 | 1561 | 82554 |
Hugh S. Markus | 118 | 606 | 55614 |
Timothy E. Hewett | 116 | 531 | 49310 |
Wei Zhang | 96 | 1404 | 43392 |
Matthew Hall | 75 | 827 | 24352 |
Matthew C. Walker | 73 | 443 | 16373 |
James F. Meschia | 71 | 401 | 28037 |
Mark G. Macklin | 69 | 268 | 13066 |
John N. Lester | 66 | 349 | 19014 |
Christine J Nicol | 61 | 268 | 10689 |
Lei Shu | 59 | 598 | 13601 |
Frank Tanser | 54 | 231 | 17555 |
Simon Parsons | 54 | 462 | 15069 |
Christopher D. Anderson | 54 | 393 | 10523 |