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Performance Enhancement of Metasurface Grating Polarizer Using Deep Learning for Quantum Key Distribution Systems
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Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction ration of »60000 at visible spectral wavelength of 632 nm, could be achieved.

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Publication Date
Thu Mar 22 2018
Journal Name
Environmental Toxicology And Chemistry
Fate, uptake, and distribution of nanoencapsulated pesticides in soil–earthworm systems and implications for environmental risk assessment
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Abstract<sec><label></label><p>Nanopesticides are novel plant protection products offering numerous benefits. Because nanoparticles behave differently from dissolved chemicals, the environmental risks of these materials could differ from conventional pesticides. We used soil–earthworm systems to compare the fate and uptake of analytical‐grade bifenthrin to that of bifenthrin in traditional and nanoencapsulated formulations. Apparent sorption coefficients for bifenthrin were up to 3.8 times lower in the nano treatments than in the non‐nano treatments, whereas dissipation half‐lives of the nano treatments were up to 2 times longer. Earthworms in the nano treatments accumulated approximately 50% more b</p></sec> ... Show More
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Publication Date
Sat Apr 01 2023
Journal Name
The Ocular Surface
Detecting dry eye from ocular surface videos based on deep learning
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Publication Date
Mon Jun 01 2009
Journal Name
Journal Of Economics And Administrative Sciences
High performance work systems and general industrial enterprise performance
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High performance work systems and general industrial enterprise performance

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Publication Date
Mon Jan 01 2024
Journal Name
Ieee Access
Transfer Learning and Hybrid Deep Convolutional Neural Networks Models for Autism Spectrum Disorder Classification From EEG Signals
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Publication Date
Fri Jun 01 2012
Journal Name
The Journal Of Engineering Research [tjer]
Thermal Performance of Solar Hot Water Systems Using a Flat Plate Collector of Accelerated Risers
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 This study focuses on a comparison of the performance of two similar locally-fabricated solar water heaters. One of the collectors features a new design for accelerated absorber; its risers are made of converging ducts whose exit area is half that of the entrance. The other collector is a conventional absorber, with risers of the same cross sectional area along its length. Each collector is the primary part of an indirect thermosyphon circulation solar hot water system. Both collectors face south with a fixed tilt angle of 33.3

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
linear equivalence determination of key-stream sequence using Z-Transform
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the research ptesents a proposed method to compare or determine the linear equivalence of the key-stream from linear or nonlinear key-stream

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Publication Date
Mon Sep 04 2023
Journal Name
2023 International Conference On Advanced Mechatronic Systems (icamechs)
Performance Analysis of Finite-Time Generalized Proportional Integral Observer for Uncertain Brunovsky Systems
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This paper proposes a novel finite-time generalized proportional integral observer (FTGPIO) based a sliding mode control (SMC) scheme for the tracking control problem of high order uncertain systems subject to fast time-varying disturbances. For this purpose, the construction of the controller consists of two consecutive steps. First, the novel FTGPIO is designed to observe unmeasurable plant dynamics states and disturbance with its higher time derivatives in finite time rather than infinite time as in the standard GPIO. In the FTGPO estimator, the finite time convergence rate of estimations is well achieved, whereas the convergence rate of estimations by classical GPIO is asymptotic and slow. Secondly, on the basis of the finite and fast e

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Publication Date
Wed Aug 30 2023
Journal Name
Baghdad Science Journal
Deep Learning-based Predictive Model of mRNA Vaccine Deterioration: An Analysis of the Stanford COVID-19 mRNA Vaccine Dataset
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The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA

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Publication Date
Mon Feb 18 2019
Journal Name
Iraqi Journal Of Physics
Relative intensity distribution in the rotational structure for B1Σ+- A1Π and B1Σ+- X1Σ+ electronic systems of BeO molecule
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Theoretical spectroscopic study of  Beryllium Oxide has been carried out, Boltzmann distribution of P, Q and R branches in the range of (0<J<13) at temperature 4200K for (0-0) band for electronic transitions B1Σ+-A1Π and B1Σ-X1Σ. The Boltzmann distribution of these branches has a maximum values at equal J approximately while the values of relative population are different. For the B1Σ+- X1Σ+ transition the branch's lines extend towards lower wavenumber. This is because (Bv'-Bv") value is negative, i.e  Bv'< Bv" For B1Σ+-A1Π

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Publication Date
Wed Mar 08 2023
Journal Name
Sensors
A Critical Review of Remote Sensing Approaches and Deep Learning Techniques in Archaeology
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To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip

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