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Shedding Light on Thermodynamics and Physical Properties of Deep Eutectic Solvents for Separating Toluene-Heptane Mixtures Using COSMO-RS
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A total of 150 deep eutectic solvents (DESs) with varying salt, hydrogen bond donor, and molar ratios were studied to develop a screening tool for separating toluene-heptane mixtures. The activity coefficient at infinite dilution (γ∞) of each DES was predicted using COSMO-RS, and selectivity (S∞), capacity (C∞), and performance index (PI) were calculated. Key DES properties, including density, viscosity, melting/freezing point, surface tension, and conductivity, were compiled from the literature to create a DES property library. A comprehensive screening tool with four evaluation criteria was developed, which identified ethyl triphenylphosphonium bromide:ZnCl2 (1:4) as the optimal solvent for toluene-heptane separation. Tetrabutyl- based DESs exhibited higher S∞, while phenyl phosphonium-based DESs showed higher C∞. DESs with Cl- anions provided higher selectivity, whereas those with Br- anions had higher capacity. Generally, DESs with high S∞ also showed high PI, indicating superior separation performance.

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Publication Date
Fri Jan 01 2021
Journal Name
Artificial Intelligence For Covid-19
An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings
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Publication Date
Tue Jul 09 2024
Journal Name
Diagnostics
A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification
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Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

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Publication Date
Sun Oct 01 2017
Journal Name
Journal Of Engineering
Comparison the Physical and Mechanical Properties of Composite Materials (Al /SiC and Al/ B4C) Produced by Powder Technology
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In this investigation, metal matrix composites (MMCs) were manufactured by using powder technology. Aluminum 6061 is reinforced with two different ceramics particles (SiC and B4C) with different volume fractions as (3, 6, 9 and 12 wt. %). The most important applications of particulate reinforcement of aluminum matrix are: Pistons, Connecting rods etc. The specimens were prepared by using aluminum powder with 150 µm in particle size and SiC, B4C powder with 200 µm in particle size. The chosen powders were mixed by using planetary mixing setup at 250 rpm for 4hr.with zinc stearate as an activator material in steel ball milling. After mixing process the powders were compacted by hydraulic

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Publication Date
Wed Jul 01 2020
Journal Name
Journal Of Engineering
Securing Physical Layer for FHSS Communication System Using Code andPhase Hopping Techniques in CDMA, System Design and Implementation
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The Frequency-hopping Spread Spectrum (FHSS) systems and techniques are using in military and civilianradar recently and in the communication system for securing the information on wireless communications link channels, for example in the Wi-Fi 8.02.X IEEE using multiple number bandwidth and frequencies in the wireless channel in order to hopping on them for increasing the security level during the broadcast, but nowadays FHSS problem, which is, any Smart Software Defined Radio (S-SDR) can easily detect a wireless signal at the transmitter and the receiver for the hopping sequence in both of these, then duplicate this sequence in order to hack the signal on both transmitter and receiver messages using the order of the se

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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Face Recognition and Emotion Recognition from Facial Expression Using Deep Learning Neural Network
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Abstract<p>Face recognition, emotion recognition represent the important bases for the human machine interaction. To recognize the person’s emotion and face, different algorithms are developed and tested. In this paper, an enhancement face and emotion recognition algorithm is implemented based on deep learning neural networks. Universal database and personal image had been used to test the proposed algorithm. Python language programming had been used to implement the proposed algorithm.</p>
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Publication Date
Sun Jun 05 2016
Journal Name
Baghdad Science Journal
Synthesis and Characterization of Some New Monemer and Polymers Containing Hetero Cyclic Rings With Study of Their Physical Properties.
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Number of new polyester and polyamide are prepared as derivatives from 5,5`-(1,4-phenylene)-bis-(1,3,4-thiadiazole-2-amine) [C1], three series of heterocyclic compounds were synthesized.The first series includes the Schiff base [C2] prepared from the reaction between compound [C1] with p-hydroxy benzaldehyde in presence of acetic acid and absolute ethanol , then these derivatives have reaction with maleic anhydride , phthalic anhydride and sodium azide, respectively to obtain the compounds [C3-5] contaning (oxazepine and tetrazole) rings.The third series of compounds [C1-5] has transformed to their polymers [C6-15] by reaction with adipoyl chloride and glutroyl chloride , respectively. The reaction was followed by T.L.C and ident

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Publication Date
Sat Feb 26 2022
Journal Name
Sport Tk-revista Euroamericana De Ciencias Del Deporte
Effect of physical exertion training on bio-kinetic abilities in weightlifting among students of Physical Education and Sports Sciences
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The objective of this research was to study the effect of physical exertion training on bio-kinetic abilities in weightlifting among students of Physical Education and Sports Sciences. The present study had a pre-posttest experimental design in which participants were recruited in two groups: experimental group and control group. A total of 30 students of Physical Education and Sports Sciences constituted the sample of the study. The sample selection was done by a comprehensive inventory method. Participants were equally allocated into two groups with n=15 in each group. Participants of experimental group were made to perform special physical exercises with three training units per week during eight weeks. The statisti

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Publication Date
Wed Aug 29 2018
Journal Name
Biomedical And Pharmacology Journal
Comparison of Some Mechanical and Physical Properties of three Types of Impression Materials with Different Dental Implant Angulations
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Choosing an appropriate impression material is a challenge for many dentists, yet an essential component to provide an excellent clinical outcome and improve productivity and profit. The purpose of present study was to compare wettability, tear strength and dimensional accuracy of three elastomeric impression materials, with the same consistencies (light-body). Three commercially available light body consistency and regular set 3M ESPE Express polyvinylsiloxane (PVS), 3M ESPE Permadyne polyether (PE), and Identium (ID), impression materials were comparedTear strength test, contact angle test and linear dimensional accuracy were evaluated for three elastic impression material. Among the three experimental groups PE impression materia

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Publication Date
Mon Dec 02 2024
Journal Name
Iranian Society Of Parasitology
Single Nucleotide Polymorphism of IL-18 (Rs 1946519) in Recurrent Aborted Iraqi Women and Its Association with Toxoplasmosis
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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Machine Learning And Computing
Facial Emotion Recognition from Videos Using Deep Convolutional Neural Networks
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Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

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