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University of Birmingham
Education•Birmingham, United Kingdom•
About: University of Birmingham is a education organization based out in Birmingham, United Kingdom. It is known for research contribution in the topics: Population & Poison control. The organization has 51794 authors who have published 115304 publications receiving 4335316 citations. The organization is also known as: Birmingham University & Uni of Birmingham.
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University of Genoa1, University of Manchester2, KEK3, CERN4, Imperial College London5, Stanford University6, Tata Institute of Fundamental Research7, Istituto Nazionale di Fisica Nucleare8, University of Pittsburgh9, Lyon College10, TRIUMF11, Northeastern University12, Thomas Jefferson National Accelerator Facility13, University of Córdoba (Spain)14, Goethe University Frankfurt15, University of Southampton16, University of Udine17, University of Alberta18, Tokyo Metropolitan University19, Helsinki Institute of Physics20, National Research Nuclear University MEPhI21, University of Bath22, Niigata University23, Naruto University of Education24, Kobe University25, University of Calabria26, University of Trieste27, European Space Agency28, University of Birmingham29, Ritsumeikan University30, Qinetiq31, École Polytechnique Fédérale de Lausanne32, Massachusetts Institute of Technology33, Brookhaven National Laboratory34
01 Jul 2003-Nuclear Instruments & Methods in Physics Research Section A-accelerators Spectrometers Detectors and Associated Equipment
TL;DR: The Gelfant 4 toolkit as discussed by the authors is a toolkit for simulating the passage of particles through matter, including a complete range of functionality including tracking, geometry, physics models and hits.
Abstract: G eant 4 is a toolkit for simulating the passage of particles through matter. It includes a complete range of functionality including tracking, geometry, physics models and hits. The physics processes offered cover a comprehensive range, including electromagnetic, hadronic and optical processes, a large set of long-lived particles, materials and elements, over a wide energy range starting, in some cases, from 250 eV and extending in others to the TeV energy range. It has been designed and constructed to expose the physics models utilised, to handle complex geometries, and to enable its easy adaptation for optimal use in different sets of applications. The toolkit is the result of a worldwide collaboration of physicists and software engineers. It has been created exploiting software engineering and object-oriented technology and implemented in the C++ programming language. It has been used in applications in particle physics, nuclear physics, accelerator design, space engineering and medical physics.
18,904 citations
University of Jyväskylä1, University of California, Los Angeles2, California Polytechnic State University3, Los Alamos National Laboratory4, National Research University – Higher School of Economics5, University of California, Berkeley6, University of Birmingham7, Australian Nuclear Science and Technology Organisation8, University of Washington9, University of Massachusetts Amherst10, University of West Bohemia11, Brigham Young University12, University of Texas at Austin13, Universidade Federal de Minas Gerais14, Google15
TL;DR: SciPy as discussed by the authors is an open source scientific computing library for the Python programming language, which includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics.
Abstract: SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.
12,774 citations
TL;DR: In this paper, it is shown that to answer several questions of physical or engineering interest, it is necessary to know only the relatively simple elastic field inside the ellipsoid.
Abstract: It is supposed that a region within an isotropic elastic solid undergoes a spontaneous change of form which, if the surrounding material were absent, would be some prescribed homogeneous deformation. Because of the presence of the surrounding material stresses will be present both inside and outside the region. The resulting elastic field may be found very simply with the help of a sequence of imaginary cutting, straining and welding operations. In particular, if the region is an ellipsoid the strain inside it is uniform and may be expressed in terms of tabulated elliptic integrals. In this case a further problem may be solved. An ellipsoidal region in an infinite medium has elastic constants different from those of the rest of the material; how does the presence of this inhomogeneity disturb an applied stress-field uniform at large distances? It is shown that to answer several questions of physical or engineering interest it is necessary to know only the relatively simple elastic field inside the ellipsoid.
11,784 citations
TL;DR: The best understood form of long-term potentiation is induced by the activation of the N-methyl-d-aspartate receptor complex, which allows electrical events at the postsynaptic membrane to be transduced into chemical signals which, in turn, are thought to activate both pre- and post Synaptic mechanisms to generate a persistent increase in synaptic strength.
Abstract: Long-term potentiation of synaptic transmission in the hippocampus is the primary experimental model for investigating the synaptic basis of learning and memory in vertebrates. The best understood form of long-term potentiation is induced by the activation of the N-methyl-D-aspartate receptor complex. This subtype of glutamate receptor endows long-term potentiation with Hebbian characteristics, and allows electrical events at the postsynaptic membrane to be transduced into chemical signals which, in turn, are thought to activate both pre- and postsynaptic mechanisms to generate a persistent increase in synaptic strength.
11,123 citations
Theo Vos1, Amanuel Alemu Abajobir, Kalkidan Hassen Abate2, Cristiana Abbafati3 +775 more•Institutions (305)
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016.
10,401 citations
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| Name | H-index | Papers | Citations |
|---|---|---|---|
| F. C. T. Moore | 7 | 17 | 973 |
| Ramón González-Méndez | 7 | 15 | 145 |
| Shivani S. Singh | 7 | 8 | 270 |
| Dai Liu | 7 | 11 | 79 |
| Jinglei Yu | 7 | 9 | 452 |
| Sarah Niukyun Lim Choi Keung | 7 | 34 | 189 |
| Sergiu Bursuc | 7 | 23 | 259 |
| Anne M. Evans | 7 | 8 | 851 |
| Michael Dobson | 7 | 17 | 416 |
| Sang Hoon Yeo | 7 | 16 | 350 |
| Kirsty Wilson | 7 | 14 | 127 |
| Gurdip Heer | 7 | 10 | 103 |
| Harish Tayyar Madabushi | 7 | 33 | 178 |
| Tim Noblet | 7 | 18 | 163 |
| Orna Rosenthal | 7 | 11 | 235 |