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Encapsulated deep eutectic solvent for esterification of free fatty acid
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A novel encapsulated deep eutectic solvent (DES) was introduced for biodiesel production via a two-step process. The DES was encapsulated in medical capsules and were used to reduce the free fatty acid (FFA) content of acidic crude palm oil (ACPO) to the minimum acceptable level (< 1%). The DES was synthesized from methyltriphenylphosphonium bromide (MTPB) and p-toluenesulfonic acid (PTSA). The effects pertaining to different operating conditions such as capsule dosage, reaction time, molar ratio, and reaction temperature were optimized. The FFA content of ACPO was reduced from existing 9.61% to less than 1% under optimum operating conditions. This indicated that encapsulated MTPB-DES performed high catalytic activity in FFA esterification reaction and showed considerable activity even after four consecutive recycling runs. The produced biodiesel after acid esterification and alkaline transesterification met the EN14214 international biodiesel standard specifications. To our best knowledge, this is the first study to introduce an acidic catalyst in capsule form. This method presents a new route for the safe storage of new materials to be used for biofuel production. Conductor-like screening model for real solvents (COSMO-RS) representation of the DES using σ-profile and σ-potential graphs indicated that MTPB and PTSA is a compatible combination due to the balanced presence and affinity towards hydrogen bond donor and hydrogen bond acceptor in each constituent.

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Publication Date
Wed Aug 05 2020
Journal Name
Advances In Civil Engineering
Strength compensation of deep beams with large web openings using carbon fiber–reinforced polymer sheets
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This article presents the results of an experimental investigation of using carbon fiber–reinforced polymer sheets to enhance the behavior of reinforced concrete deep beams with large web openings in shear spans. A set of 18 specimens were fabricated and tested up to a failure to evaluate the structural performance in terms of cracking, deformation, and load-carrying capacity. All tested specimens were with 1500-mm length, 500-mm cross-sectional deep, and 150-mm wide. Parameters that studied were opening size, opening location, and the strengthening factor. Two deep beams were implemented as control specimens without opening and without strengthening. Eight deep beams were fabricated with openings but without strengthening, while

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Publication Date
Wed Apr 23 2025
Journal Name
Iraqi Journal Of Science
Synthesis and characterization of some 2-sulphanyl benzimidazole derivatives and study of effect as corrosion inhibitors for carbon steel in sulfuric acid solution
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This research involves the synthesis of some sulphanyl benzimidazole derivatives (Ia-c), which were prepared from reaction of 2-mercaptobenzimidazole substituted benzyl halide, and structures were identified by spectral methods[FTIR, 1H-NMR and 13C-NMR].These compounds were investigated as corrosion inhibitors for carbon steel in 1M H2SO4 solution using weight loss, potentiostatic polarization methods; obtained results showed that the sulphanyl benzimidazole derivatives retard both cathodic and anodic reactions in acidic media, by virtue of adsorption on the carbon steel surface. This adsorption obeyed Langmuir’s adsorption isotherm. The inhibition efficiency of (Ia-c) ranging between (65-92) %. By using different Ib derivative conc

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Publication Date
Sun Dec 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Free Vibration Analysis of Laminated Composite Plates with General Boundary Elastic Supports Under Initial Thermal Load
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Free vibration behavior was developed under the ratio of critical buckling temperature of laminated composite thin plates with the general elastic boundary condition. The equations of motion were found based on classical laminated plate theory (CLPT) while the solution functions consists of trigonometric function and a continuous function that is added to guarantee the sufficient smoother of the so-named remaining displacement function at the boundaries, in this research, a modified Fourier series were used, a generalized procedure solution was developed using Ritz method combined with the imaginary spring technique. The influences of many design parameters such as angles of layers, aspect ratio, thickness ratio, and ratio of initial in-

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Publication Date
Mon Mar 13 2017
Journal Name
Journal Of Baghdad College Of Dentistry
Salivary Free Testosterone and Gingival Health Condition among a Group of Women with Polycystic Ovary Syndrome
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ABSTRACT Background: Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders affecting women in their reproductive age.It is characterized by anovulation or oligo-ovulation and hyperandrogensim.Androgen excess is the central defect in polycystic ovary syndrome. It is a complex disorder affects general health in addition to oral health.This study aimed to assess the gingival health status among a group of women with polycystic ovary syndrome as well as to estimate the levels of salivaryfree testosterone in unstimulated saliva in relation to gingival health condition. Materials and methods: Sixty two women with an age range 20-25 years old and with a body mass index range18.5-24.9 (normal weight) were included in this s

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Publication Date
Mon Jun 19 2023
Journal Name
Journal Of Engineering
PARAMETRIC STUDY OF LAMINAR FREE CONVECTION IN INCLINED POROUS ANNULUS WITH FINS ON THE INNER CYLINDER
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An experimental and numerical study has been carried out to investigate the heat transfer by natural convection in a three dimensional annulus enclosure filled with porous media (silica sand) between two inclined concentric cylinders with (and without) annular fins attached to the inner cylinder under steady state condition; The experiments were carried out for a range of modified Rayleigh number (0.2 ≤Ra*≤ 11) and extended to Ra* =500 for numerical study, annulus inclination angle of (δ = 0˚, 30˚, 60˚ and 90˚). The numerical study was to write the governing equation under an assumptions used Darcy law and Boussinesq’s approximation and then solved numerically using finite difference approximation. It was found that the averag

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Publication Date
Fri Dec 30 2011
Journal Name
Al-kindy College Medical Journal
The Role of the Use of Low Molecular Weight Heparin in the Prevention of Deep Venous Thrombosis after Total Knee Arthroplasty
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Background A prospective clinical study was
performed to compare the efficacy of the use of lowmolecular-
weight heparin group (enoxparin group)
with control group in the prevention of deep-vein
thrombosis after total knee arthroplasty.
Aim of the study: to assess the prevalence of DVT
after total knee arthroplasty and evaluate the
importance of the use of low molecular weight
heparin in the prevention of this DVT.
Methods Thirty-three patients undergoing total
knee arthroplasty were randomly divided into two
groups. One group consisted of 12 patients who
received no prophylaxis with an anticoagulant (the
control group), other group consisted of 21 patients
who received the low-molecular-weight h

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Publication Date
Thu Jun 06 2024
Journal Name
Journal Of Applied Engineering And Technological Science (jaets)
Deep Learning and Its Role in Diagnosing Heart Diseases Based on Electrocardiography (ECG)
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Diagnosing heart disease has become a very important topic for researchers specializing in artificial intelligence, because intelligence is involved in most diseases, especially after the Corona pandemic, which forced the world to turn to intelligence. Therefore, the basic idea in this research was to shed light on the diagnosis of heart diseases by relying on deep learning of a pre-trained model (Efficient b3) under the premise of using the electrical signals of the electrocardiogram and resample the signal in order to introduce it to the neural network with only trimming processing operations because it is an electrical signal whose parameters cannot be changed. The data set (China Physiological Signal Challenge -cspsc2018) was ad

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Publication Date
Sat Dec 01 2012
Journal Name
Journal Of Petroleum Research And Studies
Stress Ratio Method to Predict Fracture Pressure Gradient in Southern Iraqi Deep Wells
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This research presents a method for calculating stress ratio to predict fracture pressure gradient. It also, describes a correlation and list ideas about this correlation. Using the data collected from four wells, which are the deepest in southern Iraqi oil fields (3000 to 6000) m and belonged to four oil fields. These wells are passing through the following formations: Y, Su, G, N, Sa, Al, M, Ad, and B. A correlation method was applied to calculate fracture pressure gradient immediately in terms of both overburden and pore pressure gradient with an accurate results. Based on the results of our previous research , the data were used to calculate and plot the effective stresses. Many equations relating horizontal effective stress and vertica

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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
Fri Aug 12 2022
Journal Name
Future Internet
Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder
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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

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