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Showing 1 - 20 results of 151 for search '(((((( deep decrease ) OR ( pet decrease ))) OR ( matters decrease ))) OR ( mean decrease ))', query time: 0.13s Refine Results
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    Reactive Extrusion of Polyethylene Terephthalate Waste and Investigation of Its Thermal and Mechanical Properties after Treatment by Mahmoud A. Mohsin (19691662)

    Published 2017
    “…Nanoindentation results revealed that both the mechanical characteristics, elastic modulus and hardness, decrease with increasing EG concentration. On the other hand, the melt flow index of treated PET exhibited an increase with increasing EG concentration in the PET matrix.…”
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    Influence of temperature on tire-pavement noise in hot climates: Qatar case by Okan, Sirin

    Published 2021
    “…There is an apparent trend of decreasing temperature coefficient with increasing mean texture depth of dense graded asphalt pavements. …”
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    The Effect of Number of Corrugation on Crashworthiness of Aluminum Corrugated Tube under Lateral Loading by Hozhabr Mozafari (18891892)

    Published 2017
    “…The results show that tubes with corrugations have a higher mean crushing force which is directly proportional to the number of corrugations. …”
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    Influence of temperature on tire-pavement noise in hot climates: Qatar case by Okan Sirin (14603304)

    Published 2021
    “…There is an apparent trend of decreasing temperature coefficient with increasing mean texture depth of dense graded asphalt pavements. …”
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    The Trace Element Composition of Size Fractionated Suspended Particulate Matter Samples from the Qatari EEZ of the Arabian Gulf: The Role of Atmospheric Dust by Oguz Yigiterhan (14151006)

    Published 2019
    “…<p dir="ltr">We analyzed net-tow samples of natural assemblages of plankton, and associated particulate matter, from the Exclusive Economic Zone (EEZ) of Qatar in the Arabian Gulf. …”
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    Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA by Muhammad Shehab (16904880)

    Published 2022
    “…Driven by the rising deployment of deep reinforcement learning (DRL) techniques that are capable of coping with solving non-convex optimization problems, we employ DRL to predict and optimally tune the IRS phase shift matrices. …”
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    Trace element composition of size-fractionated suspended particulate matter samples from the Qatari Exclusive Economic Zone of the Arabian Gulf: the role of atmospheric dust by Oguz Yigiterhan (14151006)

    Published 2020
    “…<p dir="ltr">We analyzed net-tow samples of natural assemblages of plankton, and associated particulate matter, from the Exclusive Economic Zone (EEZ) of Qatar in the Arabian Gulf. …”
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    A kinetic evaluation of nutrient and organic matter removal in greywater for green Walls: Assessing the performance of Mineral-Based, Organic, and Waste-Derived plant support media by Shaheeda Marakkar Anangadan (19239607)

    Published 2024
    “…<p dir="ltr">Greywater recycling provides a means to address rising water demand with reduced environmental impact due to its abundance and lightly polluted nature. …”
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    Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects by Majdi Mansouri (16869885)

    Published 2021
    “…Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. …”
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    Wave-packet dynamics in monolayer graphene with periodic scattering potentials by M.M. Suleimanov (22921019)

    Published 2025
    “…For example, depending on the height and size of the incident wave packet, the transmission probability can decrease by more than 30%. The alteration of the polarity of the potential barriers also contributes to the transmission probabilities of the wave packets in graphene. …”
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    Global, regional, and national burden of household air pollution, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021 by Fiona B, Bennitt

    Published 2025
    “…We first estimated the mean fuel type-specific concentrations (in μg/m3) of fine particulate matter (PM2·5) pollution to which individuals using solid fuels for cooking were exposed, categorised by fuel type, location, year, age, and sex. …”
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