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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Mohammad H. Forouzanfar1, Lily Alexander, H. Ross Anderson, Victoria F Bachman1 +733 more•Institutions (289)
TL;DR: The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) as discussed by the authors provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution.
5,668 citations
University of Udine1, École Polytechnique Fédérale de Lausanne2, University of Lugano3, Leipzig University4, University of Paris5, University of North Texas6, Princeton University7, National Research Council8, International School for Advanced Studies9, Cornell University10, University of Lincoln11, University of Milan12, École Polytechnique13, International Centre for Theoretical Physics14, University of Paderborn15, University of Oxford16, Jožef Stefan Institute17, University of Padua18, Sapienza University of Rome19, Vietnam Academy of Science and Technology20, University of British Columbia21, Centre national de la recherche scientifique22, University of Lorraine23, University of Zurich24, École Normale Supérieure25, Université Paris-Saclay26, Wake Forest University27, Temple University28
TL;DR: Recent extensions and improvements are described, covering new methodologies and property calculators, improved parallelization, code modularization, and extended interoperability both within the distribution and with external software.
Abstract: Quantum ESPRESSO is an integrated suite of open-source computer codes for quantum simulations of materials using state-of-the-art electronic-structure techniques, based on density-functional theory, density-functional perturbation theory, and many-body perturbation theory, within the plane-wave pseudopotential and projector-augmented-wave approaches Quantum ESPRESSO owes its popularity to the wide variety of properties and processes it allows to simulate, to its performance on an increasingly broad array of hardware architectures, and to a community of researchers that rely on its capabilities as a core open-source development platform to implement their ideas In this paper we describe recent extensions and improvements, covering new methodologies and property calculators, improved parallelization, code modularization, and extended interoperability both within the distribution and with external software
3,638 citations
University of Udine1, University of Lugano2, École Polytechnique Fédérale de Lausanne3, Leipzig University4, University of Paris5, University of North Texas6, Princeton University7, National Research Council8, International School for Advanced Studies9, Cornell University10, University of Lincoln11, University of Milan12, École Polytechnique13, International Centre for Theoretical Physics14, University of Paderborn15, University of Oxford16, Jožef Stefan Institute17, University of Padua18, Sapienza University of Rome19, Vietnam Academy of Science and Technology20, University of British Columbia21, University of Lorraine22, Centre national de la recherche scientifique23, University of Zurich24, École Normale Supérieure25, Université Paris-Saclay26, Wake Forest University27, Temple University28
TL;DR: Quantum ESPRESSO as discussed by the authors is an integrated suite of open-source computer codes for quantum simulations of materials using state-of-the-art electronic-structure techniques, based on density functional theory, density functional perturbation theory, and many-body perturbations theory, within the plane-wave pseudo-potential and projector-augmented-wave approaches.
Abstract: Quantum ESPRESSO is an integrated suite of open-source computer codes for quantum simulations of materials using state-of-the art electronic-structure techniques, based on density-functional theory, density-functional perturbation theory, and many-body perturbation theory, within the plane-wave pseudo-potential and projector-augmented-wave approaches. Quantum ESPRESSO owes its popularity to the wide variety of properties and processes it allows to simulate, to its performance on an increasingly broad array of hardware architectures, and to a community of researchers that rely on its capabilities as a core open-source development platform to implement theirs ideas. In this paper we describe recent extensions and improvements, covering new methodologies and property calculators, improved parallelization, code modularization, and extended interoperability both within the distribution and with external software.
2,818 citations
Verneri Anttila1, Verneri Anttila2, Brendan Bulik-Sullivan1, Brendan Bulik-Sullivan2 +717 more•Institutions (270)
TL;DR: It is demonstrated that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine, and it is shown that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures.
Abstract: Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.
1,357 citations
TL;DR: A comprehensive review of research dedicated to applications of machine learning in agricultural production systems is presented, demonstrating how agriculture will benefit from machine learning technologies.
Abstract: Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.
1,262 citations
Authors
Showing all 2452 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| 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 |