Institution
Celal Bayar University
Education•Magnesia ad Sipylum, Turkey•
About: Celal Bayar University is a education organization based out in Magnesia ad Sipylum, Turkey. It is known for research contribution in the topics: Population & Heat transfer. The organization has 2960 authors who have published 6024 publications receiving 100646 citations.
Topics: Population, Heat transfer, Nanofluid, Nonlinear system, Medicine
Papers published on a yearly basis
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Masaryk University1, Wageningen University and Research Centre2, University of Bayreuth3, University of Greifswald4, University of Belgrade5, Düzce University6, Bulgarian Academy of Sciences7, University of Graz8, University of Göttingen9, University of the Basque Country10, Slovenian Academy of Sciences and Arts11, University of Pécs12, Research Institute for Nature and Forest13, University of Patras14, Aarhus University15, Russian Academy of Sciences16, Carlos III Health Institute17, University of Barcelona18, Complutense University of Madrid19, University of Palermo20, Ministry of Interior (Bahrain)21, Transilvania University of Brașov22, Celal Bayar University23, Martin Luther University of Halle-Wittenberg24, University of Wrocław25, Forest Research Institute26, Taras Shevchenko National University of Kyiv27, University of Novi Sad28, University of Zagreb29, University of Picardie Jules Verne30, National Research Council31, Kazan Federal University32, Babeș-Bolyai University33, University of Latvia34, Slovak Academy of Sciences35, Aristotle University of Thessaloniki36, University of Perugia37, University of Oulu38
TL;DR: The European Vegetation Archive (EVA) as mentioned in this paper is a database of European vegetation plots developed by the IAVS Working Group Europe Vegetation Survey (WGSVSS) since 2012 and made available for use in research projects in 2014.
Abstract: The European Vegetation Archive (EVA) is a centralized database of European vegetation plots developed by the IAVS Working Group European Vegetation Survey. It has been in development since 2012 and first made available for use in research projects in 2014. It stores copies of national and regional vegetation- plot databases on a single software platform. Data storage in EVA does not affect on-going independent development of the contributing databases, which remain the property of the data contributors. EVA uses a prototype of the database management software TURBOVEG 3 developed for joint management of multiple databases that use different species lists. This is facilitated by the SynBioSys Taxon Database, a system of taxon names and concepts used in the individual European databases and their corresponding names on a unified list of European flora. TURBOVEG 3 also includes procedures for handling data requests, selections and provisions according to the approved EVA Data Property and Governance Rules. By 30 June 2015, 61 databases from all European regions have joined EVA, contributing in total 1 027 376 vegetation plots, 82% of them with geographic coordinates, from 57 countries. EVA provides a unique data source for large-scale analyses of European vegetation diversity both for fundamental research and nature conservation applications. Updated information on EVA is available online at http://euroveg.org/eva-database.
250 citations
TL;DR: BBB leakage after ischemia-reperfusion injury in the rat is continuous and long-lasting, without any closure up to several weeks, and the findings are of major clinical and experimental interest.
240 citations
Humboldt University of Berlin1, McMaster University2, National Institutes of Health3, Ghent University Hospital4, University of Amsterdam5, University of Marburg6, Nova Southeastern University7, Transylvania University8, Charité9, Woolcock Institute of Medical Research10, Laval University11, Humanitas University12, University of Cartagena13, University of South Florida14, University of Porto15, Federal University of Bahia16, University of Naples Federico II17, Université Paris-Saclay18, Saint Louis University19, Istanbul University20, Erasmus University Rotterdam21, University of Helsinki22, Odense University Hospital23, University of Crete24, Chiba University25, Wrocław Medical University26, Ukrainian Medical Stomatological Academy27, Hacettepe University28, Medical University of Łódź29, Vilnius University30, National Research Council31, University of Tennessee32, Oslo University Hospital33, University of Beira Interior34, Karolinska Institutet35, University of Cologne36, University of Barcelona37, Russian National Research Medical University38, Monash University39, Ajou University40, Charles University in Prague41, University of Genoa42, Pasteur Institute43, University of Southampton44, University of Edinburgh45, Medical University of Warsaw46, University College London47, Imperial College London48, University of Coimbra49, University of Turku50, University of Bari51, Celal Bayar University52
TL;DR: Next-generation guidelines for the pharmacologic treatment of allergic rhinitis were developed by using existing GRADE-based guidelines forThe disease, real-world evidence provided by mobile technology, and additive studies (allergen chamber studies) to refine the MACVIA algorithm.
Abstract: The selection of pharmacotherapy for patients with allergic rhinitis aims to control the disease and depends on many factors. Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidelines have considerably improved the treatment of allergic rhinitis. However, there is an increasing trend toward use of real-world evidence to inform clinical practice, especially because randomized controlled trials are often limited with regard to the applicability of results. The Contre les Maladies Chroniques pour un Vieillissement Actif (MACVIA) algorithm has proposed an allergic rhinitis treatment by a consensus group. This simple algorithm can be used to step up or step down allergic rhinitis treatment. Next-generation guidelines for the pharmacologic treatment of allergic rhinitis were developed by using existing GRADE-based guidelines for the disease, real-world evidence provided by mobile technology, and additive studies (allergen chamber studies) to refine the MACVIA algorithm.
237 citations
TL;DR: This work identified, categorized and evaluated various SIP authentication and key agreement protocols according to their performance and security features, and observed that there are successful schemes from both the performance andSecurity viewpoint.
Abstract: We present a survey of authentication and key agreement schemes that are proposed for the SIP protocol. SIP has become the center piece for most VoIP architectures. Performance and security of the authentication and key agreement schemes are two critical factors that affect the VoIP applications with large number of users. Therefore, we have identified, categorized and evaluated various SIP authentication and key agreement protocols according to their performance and security features. Although the performance is inversely proportional to the security features provided in general, we observed that there are successful schemes from both the performance and security viewpoint.
235 citations
TL;DR: The methanol extract showed more potent antimicrobial activity than dichloromethane, petroleum ether, ethyl acetate extracts and volatile components.
Abstract: The methanol, dichloromethane, petroleum ether, ethyl acetate extracts and volatile components of Spirulina platensis were tested in vitro for their antimicrobial activity (four Gram-positive, six Gram-negative bacteria and Candida albicans ATCC 10239). GC-MS analysis of the volatile components of S. platensis resulted in the identification of 15 compounds which constituted 96.45% of the total compounds. The volatile components of S. platensis consisted of heptadecane (39.70%) and tetradecane (34.61%) as major components. The methanol extract showed more potent antimicrobial activity than dichloromethane, petroleum ether, ethyl acetate extracts and volatile components.
234 citations
Authors
Showing all 3053 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Michael Berk | 116 | 1284 | 57743 |
| G. Raven | 114 | 1879 | 71839 |
| Tjeerd Ketel | 99 | 1067 | 46335 |
| Francesco Dettori | 95 | 1026 | 41313 |
| Manuel Schiller | 95 | 1004 | 41734 |
| John A. McGrath | 75 | 631 | 24078 |
| E. Pesen | 50 | 206 | 10958 |
| Devendra Singh | 49 | 314 | 10386 |
| Fatih Selimefendigil | 43 | 178 | 4522 |
| Mehmet Karabacak | 40 | 111 | 3515 |
| Nurullah Akkoc | 38 | 193 | 7626 |
| Daiana Stolz | 38 | 239 | 7708 |
| Menemşe Gümüşderelioğlu | 34 | 136 | 3328 |
| Mehmet Sezer | 34 | 184 | 3543 |
| Mehmet Pakdemirli | 33 | 137 | 3581 |