scispace - formally typeset
Search or ask a question
Institution

Brunel University London

EducationLondon, United Kingdom
About: Brunel University London is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Large Hadron Collider & Population. The organization has 10918 authors who have published 29515 publications receiving 893330 citations. The organization is also known as: Brunel & University of Brunel.


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, the authors present the presentation, nomenclature, and methodology associated with the application of physisorption for surface area assessment and pore size analysis.
Abstract: Gas adsorption is an important tool for the characterisation of porous solids and fine powders. Major advances in recent years have made it necessary to update the 1985 IUPAC manual on Reporting Physisorption Data for Gas/Solid Systems. The aims of the present document are to clarify and standardise the presentation, nomenclature and methodology associated with the application of physisorption for surface area assessment and pore size analysis and to draw attention to remaining problems in the interpretation of physisorption data.

11,019 citations

Journal ArticleDOI
TL;DR: In this paper, results from searches for the standard model Higgs boson in proton-proton collisions at 7 and 8 TeV in the CMS experiment at the LHC, using data samples corresponding to integrated luminosities of up to 5.8 standard deviations.

8,857 citations

Journal ArticleDOI
TL;DR: The Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) at CERN as mentioned in this paper was designed to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 10(34)cm(-2)s(-1)
Abstract: The Compact Muon Solenoid (CMS) detector is described. The detector operates at the Large Hadron Collider (LHC) at CERN. It was conceived to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 10(34)cm(-2)s(-1) (10(27)cm(-2)s(-1)). At the core of the CMS detector sits a high-magnetic-field and large-bore superconducting solenoid surrounding an all-silicon pixel and strip tracker, a lead-tungstate scintillating-crystals electromagnetic calorimeter, and a brass-scintillator sampling hadron calorimeter. The iron yoke of the flux-return is instrumented with four stations of muon detectors covering most of the 4 pi solid angle. Forward sampling calorimeters extend the pseudo-rapidity coverage to high values (vertical bar eta vertical bar <= 5) assuring very good hermeticity. The overall dimensions of the CMS detector are a length of 21.6 m, a diameter of 14.6 m and a total weight of 12500 t.

5,193 citations

Book
27 Sep 2011
TL;DR: Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research.
Abstract: There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

3,826 citations

Journal ArticleDOI
TL;DR: The PSIPRED protein structure prediction server allows users to submit a protein sequence, perform a prediction of their choice and receive the results of the prediction both textually via e-mail and graphically via the web.
Abstract: The PSIPRED protein structure prediction server allows users to submit a protein sequence, perform a prediction of their choice and receive the results of the prediction both textually via e-mail and graphically via the web. The user may select one of three prediction methods to apply to their sequence: PSIPRED, a highly accurate secondary structure prediction method; MEMSAT 2, a new version of a widely used transmembrane topology prediction method; or GenTHREADER, a sequence profile based fold recognition method.

3,381 citations


Authors

Showing all 11074 results

NameH-indexPapersCitations
Yang Yang1712644153049
Hongfang Liu1662356156290
Gavin Davies1592036149835
Marjo-Riitta Järvelin156923100939
Matt J. Jarvis144106485559
Alexander Belyaev1421895100796
Louis Lyons138174798864
Silvano Tosi135171297559
John A Coughlan135131296578
Kenichi Hatakeyama1341731102438
Kristian Harder134161396571
Peter R Hobson133159094257
Christopher Seez132125689943
Liliana Teodorescu132147190106
Umesh Joshi131124990323
Network Information
Related Institutions (5)
University of Manchester
168K papers, 6.4M citations

94% related

University of Sheffield
102.9K papers, 3.9M citations

94% related

University of Birmingham
115.3K papers, 4.3M citations

93% related

University of Warwick
77.1K papers, 2.6M citations

93% related

University of Southampton
99.4K papers, 3.4M citations

93% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202380
2022235
20211,532
20201,475
20191,445
20181,345