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Institution

University of Johannesburg

EducationJohannesburg, South Africa
About: University of Johannesburg is a education organization based out in Johannesburg, South Africa. It is known for research contribution in the topics: Population & Tourism. The organization has 8070 authors who have published 22749 publications receiving 329408 citations. The organization is also known as: UJ.


Papers
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Journal ArticleDOI
TL;DR: An integrated theoretical account of how the unique demands of acquiring instrumental skills and cultural conventions provide insight into when children imitate, when they innovate, and to what degree is proposed.

290 citations

Book ChapterDOI
01 Jan 2020
TL;DR: It is concluded that with AI, there will be new frontiers that include the certainty of sharing of intelligent information that would, in the context of the agency theory, be available to both the agent and the principal.
Abstract: In this chapter, we discuss the agency theory. The agency theory is a principle utilized in an attempt to explain the complicated relationship that exists between the owners (principal) and managers (agents) of the business. Based on this, we propose that the agency theory is an attempt to explain the complexity of human behaviour in the principal-agent relationship. We are of the view that when the desires or goals of the principal and agent conflict, it is difficult or expensive for the principal to verify what the agent is doing. Since the theory is an attempt to explain the complexity of human behaviour in the principal-agent relationship, we pose the question: what happens to the theory in the era dominated by intelligent machines? We conclude that with AI, there will be new frontiers. These new frontiers include, among other things, the certainty of sharing of intelligent information that would, in the context of the agency theory, be available to both the agent and the principal. Further, we observe that the advantages of intelligent systems are updateability and connectivity. Using these strengths, we think intelligent agents will be swift in picking up the information discrepancies. Intelligent systems have the capability of harvesting information from different sources. Once gathered, this information will be updated in the principal’s system, given that the systems will be integrated. Finally, we think it conceivable that perhaps the agent, knowing that intelligent agents are deployed widely and that these intelligent systems have the capability of harvesting data from different repositories, will moderate his or her behaviour to be closely aligned to that of the principal.

289 citations

Journal ArticleDOI
TL;DR: The voluminous 2.5-Ga banded iron formations (BIFs) from the Hamersley Basin (Australia) and Transvaal Craton (South Africa) record an extensive period of Fe redox cycling.

289 citations

Journal ArticleDOI
Georges Aad1, Georges Aad2, Brad Abbott2, Brad Abbott3  +5559 moreInstitutions (188)
TL;DR: In this paper, the performance of the missing transverse momentum reconstruction was evaluated using data collected in pp collisions at a centre-of-mass energy of 7 TeV in 2010.
Abstract: The measurement of missing transverse momentum in the ATLAS detector, described in this paper, makes use of the full event reconstruction and a calibration based on reconstructed physics objects. The performance of the missing transverse momentum reconstruction is evaluated using data collected in pp collisions at a centre-of-mass energy of 7 TeV in 2010. Minimum bias events and events with jets of hadrons are used from data samples corresponding to an integrated luminosity of about 0.3 nb(-1) and 600 nb(-1) respectively, together with events containing a Z boson decaying to two leptons (electrons or muons) or a W boson decaying to a lepton (electron or muon) and a neutrino, from a data sample corresponding to an integrated luminosity of about 36 pb(-1). An estimate of the systematic uncertainty on the missing transverse momentum scale is presented.

288 citations

Journal ArticleDOI
B. P. Abbott1, Richard J. Abbott1, T. D. Abbott2, M. R. Abernathy1  +1619 moreInstitutions (220)
TL;DR: In this article, the sky localization of the first observed compact binary merger is presented, where the authors describe the low-latency analysis of the LIGO data and present a sky localization map.
Abstract: A gravitational-wave (GW) transient was identified in data recorded by the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) detectors on 2015 September 14. The event, initially designated G184098 and later given the name GW150914, is described in detail elsewhere. By prior arrangement, preliminary estimates of the time, significance, and sky location of the event were shared with 63 teams of observers covering radio, optical, near-infrared, X-ray, and gamma-ray wavelengths with ground- and space-based facilities. In this Letter we describe the low-latency analysis of the GW data and present the sky localization of the first observed compact binary merger. We summarize the follow-up observations reported by 25 teams via private Gamma-ray Coordinates Network circulars, giving an overview of the participating facilities, the GW sky localization coverage, the timeline, and depth of the observations. As this event turned out to be a binary black hole merger, there is little expectation of a detectable electromagnetic (EM) signature. Nevertheless, this first broadband campaign to search for a counterpart of an Advanced LIGO source represents a milestone and highlights the broad capabilities of the transient astronomy community and the observing strategies that have been developed to pursue neutron star binary merger events. Detailed investigations of the EM data and results of the EM follow-up campaign are being disseminated in papers by the individual teams.

288 citations


Authors

Showing all 8414 results

NameH-indexPapersCitations
Vinod Kumar Gupta16571383484
Arnold B. Bakker135506103778
Trevor Vickey12887376664
Ketevi Assamagan12893477061
Diego Casadei12373369665
Michael R. Hamblin11789959533
E. Castaneda-Miranda11754556349
Xiaoming Li113193272445
Katharine Leney10845952547
M. Aurousseau10340344230
Mika Sillanpää96101944260
Sahal Yacoob8940825338
Evangelia Demerouti8523649228
Lehana Thabane8599436620
Sahal Yacoob8439935059
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023196
2022526
20213,152
20202,933
20192,706
20182,150