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Classism Hate Speech in Katherine Mansfield's Short Story ‘The Doll’s House’: A Pragmatic Study: ايمان نوري جاسم , مصلح شويش احمد
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Hate speech (henceforth HS) has recently spread and become an important issue. This type of speech in children's writings has a particular formulation and specific objectives that the authors intend to convey. Thus, the study aims at examining qualitatively and quantitatively the classism HS and its pragmatic functions via identifying the speech acts used to express  classism HS, the implicature instigated as well as impoliteness. Since pragmatics is the study of language in context, which is greatly related to the situations and speaker’s intention, this study depends on pragmatic theoriespeech acts, impoliteness and conversational implicature) to analyze the data which are taken from Katherine Mansfield's short story (The Doll’s House). The data has been analyzed qualitatively and quantitatively. It is qualitative, as it is dedicated to describe HS phenomenon that is found in the selected short story, depending on an eclectic model. Regarding the quantitative analysis, the researchers have used SPSS 23 program to determine the frequencies and percentages of the strategies that are intended to be measured. The study has concluded that HS has multiple dimensions that are difficult to interpret outside the context of speech. It can be conveyed by many strategies, both explicit and covert. Further, the simplest  form of HS involves an insult in addition to other functions, such as disapproval and humiliation. 

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Publication Date
Mon Jan 01 2024
Journal Name
Ieee Access
A Direct Solution Scheme for Wide-Angle Electromagnetic Scattering Problems Using Compressive Sensing-Based Method of Moments
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Publication Date
Fri Jul 27 2012
Journal Name
Journal Of Prosthodontics
A Three-Dimensional Finite Element Analysis for Overdenture Attachments Supported by Teeth and/or Mini Dental Implants
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Publication Date
Tue Dec 01 2020
Journal Name
Baghdad Science Journal
A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset
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Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

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Publication Date
Wed Apr 14 2021
Journal Name
Wireless Personal Communications
A Partial CSI Estimation Approach for Downlink FDD massive-MIMO System with Different Base Transceiver Station Topologies
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Massive multiple-input multiple-output (massive-MIMO) is a promising technology for next generation wireless communications systems due to its capability to increase the data rate and meet the enormous ongoing data traffic explosion. However, in non-reciprocal channels, such as those encountered in frequency division duplex (FDD) systems, channel state information (CSI) estimation using downlink (DL) training sequence is to date very challenging issue, especially when the channel exhibits a shorter coherence time. In particular, the availability of sufficiently accurate CSI at the base transceiver station (BTS) allows an efficient precoding design in the DL transmission to be achieved, and thus, reliable communication systems can be obtaine

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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Selecting Optimum Dimensions for a Three-Phase Horizontal Smart Separator for Khor Mor Gas-Condensate Processing Plant
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      The Khor Mor gas-condensate processing plant in Iraq is currently facing operational challenges due to foaming issues in the sweetening tower caused by high-soluble hydrocarbon liquids entering the tower. The root cause of the problem could be liquid carry-over as the separation vessels within the plant fail to remove liquid droplets from the gas phase. This study employs Aspen HYSYS v.11 software to investigate the performance of the industrial three-phase horizontal separator, Bravo #2, located upstream of the Khor Mor sweetening tower, under both current and future operational conditions. The simulation results, regarding the size distribution of liquid droplets in the gas product and the efficiency gas/liquid separation, r

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Publication Date
Mon Nov 28 2016
Journal Name
Separation Science And Technology
Using sewage sludge as a permeable reactive barrier for remediation of groundwater contaminated with lead and phenol
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Publication Date
Mon May 01 2023
Journal Name
Complementary Therapies In Medicine
Effects of almond intake on oxidative stress parameters: A systematic review and meta-analysis of clinical trials
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Publication Date
Thu Mar 30 2023
Journal Name
Optics Continuum
Ultrafast lithium disilicate veneer debonding time assisted by a CO<sub>2</sub> laser with temperature control
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We report on using a CO2 (10.6 µm) laser to debond the lithium disilicate veneers. Sixty-four sound human premolar teeth and 64 veneer specimens were used in the study. The zigzag movement via CO2 laser handpiece along with an air-cooled jet to prevent temperature elevation above the necrosis temperature limit (5.5 C°) was applied. The optimal deboning irradiation time was super-fast, at about 5 seconds at 3 Watt CO2 laser power. It is 20 times less than any previously published work for veneers debonding. The enamel beneath the debonded veneers has been assessed by atomic force microscopy (AFM) and shear stress technique as criteria for the easiness of debonding. The

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Publication Date
Tue Dec 12 2017
Journal Name
Al-khwarizmi Engineering Journal
Model Reference Adaptive Control based on a Self-Recurrent Wavelet Neural Network Utilizing Micro Artificial Immune Systems
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Abstract 

This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self-recurrent wavelet neural network (SRWNN) to control nonlinear systems. The proposed SRWNN is an improved version of a previously reported wavelet neural network (WNN). In particular, this improvement was achieved by adopting two modifications to the original WNN structure. These modifications include, firstly, the utilization of a specific initialization phase to improve the convergence to the optimal weight values, and secondly, the inclusion of self-feedback weights to the wavelons of the wavelet layer. Furthermore, an on-line training procedure was proposed to enhance the control per

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Publication Date
Wed Jan 01 2020
Journal Name
Aip Conf. Proc.
Removal of chromium ions from a real wastewater of leather industry using electrocoagulation and reverse osmosis processes
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This study focused on treatment of real wastewater rejected from leather industry in Al-Nahrawan city in Iraq by Electrocoagulation (EC) process followed by Reverse Osmosis (RO) process. The successive treatment was applied due to high concentration of Cr3+ ions (about 1600 ppm) rejected in wastewater of this industry and for applying EC with moderate power consumption and better results of produced water. In Electrocoagulation process (EC), the effect of NaCl concentration (1.5, 3 g/l), current density (C.D.) (15-25 mA/cm2), electrolysis time (1-2 h), and distance between electrodes (E.D.) (1-2 cm) were examined in a batch cell by implementing Taguchi experimental design. According to the results obtained from multiple regression and signa

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