Institution
Edinburgh Napier University
Education•Edinburgh, United Kingdom•
About: Edinburgh Napier University is a education organization based out in Edinburgh, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2665 authors who have published 6859 publications receiving 175272 citations.
Papers published on a yearly basis
Papers
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TL;DR: Intersex specimens were significantly more likely to be infected with microsporidian parasites at sites receiving discharges than reference sites, however relatively few specimens (normal or intersex) were infected at reference sites suggesting parasitism is not the only cause of intersex.
53 citations
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TL;DR: In this paper, a Machiavellian analysis of the determinants of organisational change is presented, where power, leaders and teams, rewards and discipline, and roles, norms and values, serve as drivers, enablers or inhibitors of organizational change.
Abstract: Purpose – The purpose of this paper is to undertake a Machiavellian analysis of the determinants of organisational change. It aims to present a model of how power, leaders and teams, rewards and discipline, and roles, norms and values, serve as drivers, enablers or inhibitors of organisational change.Design/methodology/approach – The paper adopts the sixteenth century Machiavellian text The Prince as a lens through which to examine organisational change.Findings – The paper concludes that Machiavellian thinking provides a valuable guide to the challenges and obstacles in negotiating organisational change and identifies the individual as occupying the central role in determining whether the change intervention will be accepted or rejected.Originality/value – The longevity of Machiavellian thinking underlines the constancy of human behaviour and the relevance of age‐old thinking in understanding and negotiating change in a complex fast‐paced business environment.
52 citations
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TL;DR: A novel automatic classification system for homogenous ( anomaly-free ) and non-homogenous ( with defects ) nanofibers is proposed, and is shown to outperform other standard machine learning models and other recent state-of-the-art techniques.
Abstract: The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope ( SEM ) images of the electrospun nanofiber, to ensure that no structural defects are produced. The presence of anomalies prevents practical application of the electrospun nanofibrous material in nanotechnology. Hence, the automatic monitoring and quality control of nanomaterials is a relevant challenge in the context of Industry 4.0. In this paper, a novel automatic classification system for homogenous ( anomaly-free ) and non-homogenous ( with defects ) nanofibers is proposed. The inspection procedure aims at avoiding direct processing of the redundant full SEM image. Specifically, the image to be analyzed is first partitioned into sub-images ( nanopatches ) that are then used as input to a hybrid unsupervised and supervised machine learning system. In the first step, an autoencoder ( AE ) is trained with unsupervised learning to generate a code representing the input image with a vector of relevant features. Next, a multilayer perceptron ( MLP ) , trained with supervised learning, uses the extracted features to classify non-homogenous nanofiber ( NH-NF ) and homogenous nanofiber ( H-NF ) patches. The resulting novel AE-MLP system is shown to outperform other standard machine learning models and other recent state-of-the-art techniques, reporting accuracy rate up to 92.5% . In addition, the proposed approach leads to model complexity reduction with respect to other deep learning strategies such as convolutional neural networks ( CNN ) . The encouraging performance achieved in this benchmark study can stimulate the application of the proposed scheme in other challenging industrial manufacturing tasks.
52 citations
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TL;DR: It was evident that harbour seals minimise the energetic cost of the moult by hauling out so that they can maintain optimal high skin surface temperature for hair growth.
52 citations
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TL;DR: An anchor-free convolutional network with dense attention feature aggregation, which is a center-point-based ship predictor (CSP), and a novel feature aggregation scheme called DAFA is proposed to obtain a high-resolution feature map with multiscale information.
Abstract: In recent years, with the improvement of synthetic aperture radar (SAR) imaging resolution, it is urgent to develop methods with higher accuracy and faster speed for ship detection in high-resolution SAR images. Among all kinds of methods, deep-learning-based algorithms bring promising performance due to end-to-end detection and automated feature extraction. However, several challenges still exist: (1) standard deep learning detectors based on anchors have certain unsolved problems, such as tuning of anchor-related parameters, scale-variation and high computational costs. (2) SAR data is huge but the labeled data is relatively small, which may lead to overfitting in training. (3) To improve detection speed, deep learning detectors generally detect targets based on low-resolution features, which may cause missed detections for small targets. In order to address the above problems, an anchor-free convolutional network with dense attention feature aggregation is proposed in this paper. Firstly, we use a lightweight feature extractor to extract multiscale ship features. The inverted residual blocks with depth-wise separable convolution reduce the network parameters and improve the detection speed. Secondly, a novel feature aggregation scheme called dense attention feature aggregation (DAFA) is proposed to obtain a high-resolution feature map with multiscale information. By combining the multiscale features through dense connections and iterative fusions, DAFA improves the generalization performance of the network. In addition, an attention block, namely spatial and channel squeeze and excitation (SCSE) block is embedded in the upsampling process of DAFA to enhance the salient features of the target and suppress the background clutters. Third, an anchor-free detector, which is a center-point-based ship predictor (CSP), is adopted in this paper. CSP regresses the ship centers and ship sizes simultaneously on the high-resolution feature map to implement anchor-free and nonmaximum suppression (NMS)-free ship detection. The experiments on the AirSARShip-1.0 dataset demonstrate the effectiveness of our method. The results show that the proposed method outperforms several mainstream detection algorithms in both accuracy and speed.
52 citations
Authors
Showing all 2727 results
Name | H-index | Papers | Citations |
---|---|---|---|
William MacNee | 123 | 472 | 58989 |
Richard J. Simpson | 113 | 850 | 59378 |
Ken Donaldson | 109 | 385 | 47072 |
John Campbell | 107 | 1150 | 56067 |
Muhammad Imran | 94 | 3053 | 51728 |
Barbara Rothen-Rutishauser | 70 | 339 | 17348 |
Vicki Stone | 69 | 204 | 25002 |
Sharon K. Parker | 68 | 238 | 21089 |
Matt Nicholl | 66 | 224 | 15208 |
John H. Adams | 66 | 354 | 16169 |
Darren J. Kelly | 65 | 252 | 13007 |
Neil B. McKeown | 65 | 281 | 19371 |
Jane K. Hill | 62 | 147 | 20733 |
Min Du | 61 | 326 | 11328 |
Xiaodong Liu | 60 | 474 | 14980 |