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a large » _ large (Expand Search)
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1
Integration of temperature and dust effects in siting large PV power plant in hot arid area
Published 2013“…Recently, solar PV technologies witnessed a commercial vigor due to their tremendously decreasing prices. …”
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2
Modification of Pd/CeO2 catalyst by Atomic Layer Deposition of ZrO2
Published 2016“…Modification of a 1 wt% Pd/CeO2 catalyst by Atomic Layer Deposition of thin ZrO2 films was studied. …”
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Fabrication of Selective Sensors for Hg2+ Traces in Water Using Graphene Decorated with Metal Nanoclusters
Published 2016“…Each Hg2+ sensor was fabricated using (1 cm × 1 cm) commercial graphene layer on SiO2/doped-Si substrate (thickness of SiO2 is 285 nm, and Si is p-type with resistivity of 0.001-0.005 ohm.cm). …”
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6
Implementing and Analyzing a Recursive Technique for Building Path Oblivious RAM
Published 2018“…This is typically accomplished by accessing multiple items each access and periodically reshuffling some, or all, of the data on the data-store. One critical limitation of ORAM techniques is the need to have large storage capacity on the client, which is typically a weak device. …”
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7
Processing, characterization and modeling of recycled polypropylene/glass fibre/wood flour composites
Published 2014“…In addition we applied the functionally graded materials concept, the elastic finite element analysis of a layered functionally graded pressurized pipe, which is one of the practical industrial applications, was accomplished in order to have some insight on the performance of such RPMCs. …”
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Vertical zonation and functional diversity of fish assemblages revealed by ROV videos at oil platforms in The Gulf
Published 2018“…These videos were recorded over six years (2007, 2009, 2010, 2011, 2012 and 2014), during day and night, at the 9 platform locations (A-I) within the Al Shaheen Oil Field using a ROV of the model SAAB Seaeye Surveyor Plus 229. …”
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MACHINE LEARNING TECHNIQUES FOR MOTOR GEAR FAULT DETECTION AND DIAGNOSIS
Published 2022“…ML-based techniques for facility condition monitoring are investigated and presented in a comparative analysis. Using convolutional layers, network parameters are greatly decreased in Convolutional Neural Networks (CNNs) by local connectivity and exchanging weights, which comprise a group of kernels with a limited receptive area, unlike a typical neural network with a complete connection through each node. …”
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