ORCID Profile
0000-0002-2683-2695
Current Organisation
Royal Holloway University of London
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Publisher: Elsevier BV
Date: 09-2005
Publisher: Elsevier BV
Date: 12-2014
Publisher: Elsevier BV
Date: 03-2003
Publisher: Proceedings of the National Academy of Sciences
Date: 23-07-2012
Abstract: Marked changes in human dispersal and development during the Middle to Upper Paleolithic transition have been attributed to massive volcanic eruption and/or severe climatic deterioration. We test this concept using records of volcanic ash layers of the C anian Ignimbrite eruption dated to ca. 40,000 y ago (40 ka B.P.). The distribution of the C anian Ignimbrite has been enhanced by the discovery of cryptotephra deposits (volcanic ash layers that are not visible to the naked eye) in archaeological cave sequences. They enable us to synchronize archaeological and paleoclimatic records through the period of transition from Neanderthal to the earliest anatomically modern human populations in Europe. Our results confirm that the combined effects of a major volcanic eruption and severe climatic cooling failed to have lasting impacts on Neanderthals or early modern humans in Europe. We infer that modern humans proved a greater competitive threat to indigenous populations than natural disasters.
Publisher: Elsevier BV
Date: 03-2012
Publisher: Elsevier BV
Date: 02-2008
Publisher: Elsevier BV
Date: 03-2012
Publisher: Elsevier BV
Date: 03-2012
Publisher: Elsevier BV
Date: 06-2015
Publisher: Elsevier BV
Date: 2008
Publisher: Hindawi Limited
Date: 22-08-2022
DOI: 10.1155/2022/7273469
Abstract: The sensor units are considered one of the significant technologies that use solar energy as an assistant power source to the batteries. Despite their advantages over the other forms of renewables, solar energy has an intermittent nature which negatively affects the operation of these units. Reaching an effective operation ensuring sustainable units requires a prior prediction of the harvested solar energy. Artificial neural networks (ANNs) appeared recently as a promising prediction approach with those units. This is attributed to the high accuracy compared to the conventional stochastic and statistical ones. Till now, the optimal neural network that fits with sensor units has not been precisely determined. This paper is aimed at finding the optimal neural network that would be applied with solar-supplied sensor units. This is performed by applying a cascaded input/structure direct optimization. The optimization process handles the aspects of accuracy, computational efforts, and complexity. It mainly identifies the type and number of parameters that would be utilized as inputs in the first stage. Then, it optimizes the structure by addressing the number of hidden layers and hidden neurons. The corresponding analysis has been implemented for premeasured real data over five-year time period. The results showed that the optimal neural network can be achieved by using three input parameters which are the air temperature (AT), the relative humidity (RH), and the zenith angle ( θ z ). For the structure, it has been concluded that the proposed optimal ANN should have two hidden layers with ten neurons in each of them. Lastly, the proposed optimal ANN was verified against the associated prediction error which is minimized to less than 2%.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Hussamaldeen Jaradat.