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Institution

University of Malaya

EducationKuala Lumpur, Malaysia
About: University of Malaya is a education organization based out in Kuala Lumpur, Malaysia. It is known for research contribution in the topics: Population & Fiber laser. The organization has 25087 authors who have published 51491 publications receiving 1036791 citations. The organization is also known as: UM & Universiti Malaya.


Papers
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Journal ArticleDOI
TL;DR: The results showed that heavy metal contaminations at S2 and S3 was more severe than at other sampling sites, especially for Zn, Cu, Ni and Pb, and the element concentrations from top to bottom layers decreased predominantly.

469 citations

Journal ArticleDOI
Vardan Khachatryan1, Albert M. Sirunyan1, Armen Tumasyan1, Wolfgang Adam2  +2802 moreInstitutions (215)
04 Jun 2015-Nature
TL;DR: In this paper, the branching fractions of the B meson (B-s(0)) and the B-0 meson decaying into two oppositely charged muons (mu(+) and mu(-)) were observed.
Abstract: The standard model of particle physics describes the fundamental particles and their interactions via the strong, electromagnetic and weak forces. It provides precise predictions for measurable quantities that can be tested experimentally. The probabilities, or branching fractions, of the strange B meson (B-s(0)) and the B-0 meson decaying into two oppositely charged muons (mu(+) and mu(-)) are especially interesting because of their sensitivity to theories that extend the standard model. The standard model predicts that the B-s(0)->mu(+)mu(-) and B-0 ->mu(+)mu(-) decays are very rare, with about four of the former occurring for every billion B-s(0) mesons produced, and one of the latter occurring for every ten billion B-0 mesons(1). A difference in the observed branching fractions with respect to the predictions of the standard model would provide a direction in which the standard model should be extended. Before the Large Hadron Collider (LHC) at CERN2 started operating, no evidence for either decay mode had been found. Upper limits on the branching fractions were an order of magnitude above the standard model predictions. The CMS (Compact Muon Solenoid) and LHCb(Large Hadron Collider beauty) collaborations have performed a joint analysis of the data from proton-proton collisions that they collected in 2011 at a centre-of-mass energy of seven teraelectronvolts and in 2012 at eight teraelectronvolts. Here we report the first observation of the B-s(0)->mu(+)mu(-) decay, with a statistical significance exceeding six standard deviations, and the best measurement so far of its branching fraction. Furthermore, we obtained evidence for the B-0 ->mu(+)mu(-) decay with a statistical significance of three standard deviations. Both measurements are statistically compatible with standard model predictions and allow stringent constraints to be placed on theories beyond the standard model. The LHC experiments will resume taking data in 2015, recording proton-proton collisions at a centre-of-mass energy of 13 teraelectronvolts, which will approximately double the production rates of B-s(0) and B-0 mesons and lead to further improvements in the precision of these crucial tests of the standard model.

467 citations

Journal ArticleDOI
TL;DR: The findings demonstrate the utility of HBM constructs in understanding COVID-19 vaccination intent and WTP and it is important to improve health promotion and reduce the barriers to CO VID-19 vaccine.
Abstract: Background This study attempts to understand coronavirus disease 2019 (COVID-19) vaccine demand and hesitancy by assessing the public’s vaccination intention and willingness-to-pay (WTP). Confidence in COVID-19 vaccines produced in China and preference for domestically-made or foreign-made vaccines was also investigated. Methods A nationwide cross-sectional, self-administered online survey was conducted on 1–19 May 2020. The health belief model (HBM) was used as a theoretical framework for understanding COVID-19 vaccination intent and WTP. Results A total of 3,541 complete responses were received. The majority reported a probably yes intent (54.6%), followed by a definite yes intent (28.7%). The perception that vaccination decreases the chances of getting COVID-19 under the perceived benefit construct (OR = 3.14, 95% CI 2.05–4.83) and not being concerned about the efficacy of new COVID-19 vaccines under the perceived barriers construct (OR = 1.65, 95% CI 1.31–2.09) were found to have the highest significant odds of a definite intention to take the COVID-19 vaccine. The median (interquartile range [IQR]) of WTP for COVID-19 vaccine was CNY¥200/US$28 (IQR CNY¥100–500/USD$14–72). The highest marginal WTP for the vaccine was influenced by socio-economic factors. The majority were confident (48.7%) and completely confident (46.1%) in domestically-made COVID-19 vaccine. 64.2% reported a preference for a domestically-made over foreign-made COVID-19 vaccine. Conclusions The findings demonstrate the utility of HBM constructs in understanding COVID-19 vaccination intent and WTP. It is important to improve health promotion and reduce the barriers to COVID-19 vaccination.

467 citations

Journal ArticleDOI
TL;DR: In this article, the authors provide a comprehensive survey of state-of-the-art remote sensing deep learning research for remote sensing applications, focusing on theories, tools, and challenges for the remote sensing community.
Abstract: In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools, and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.

467 citations

Journal ArticleDOI
TL;DR: In this article, the antioxidant activity of two selected Malaysian honeys, as well as their ethyl acetate extracts, were evaluated, which indicated that honey has antioxidative and radical scavenging properties, which are mainly due to its phenolic content.

460 citations


Authors

Showing all 25327 results

NameH-indexPapersCitations
Diederick E. Grobbee1551051122748
Intae Yu134137289870
Ovsat Abdinov12986478489
Jyothsna Rani Komaragiri129109782258
Odette Benary12884474238
Paul M. Vanhoutte12786862177
Irene Vichou12676272520
Ian O. Ellis126105175435
Louisa Degenhardt126798139683
Matthew Jones125116196909
Andrius Juodagalvis118106967138
Martin Ravallion11557055380
R. St. Denis11292165326
Xiao-Ming Chen10859642229
A. Yurkewicz10651451537
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202391
2022418
20213,698
20203,646
20193,239
20183,203