This study is an attempt to investigate the semantic and syntactic features of English and Arabic verbs of eating. After surveying the literature on the meaning of verbs in both languages, three chapters address the major issues in this subject. The problem to be investigated in this study can be summarized in the following points: 1. The overlapping of semantic and syntactic features within the category of verbs of eating in English and Arabic. 2. Which semantic classification is more accurate and through which method? 3. Which classification, the semantic or the syntactic, is more important? This study hypothesized the following: 1. The semantic features are more influential in analyzing the category of verbs of eating than the syntactic ones. 2. There is a similarity in terms of semantic and syntactic characteristics of verbs of eating in English as well as in Arabic Chapter two deals with the semantic classification of English and Arabic verbs of eating. It starts with classifying verbs of eating according to the semantic roles of their subjects and the semantic domains of these verbs, such as intentional/unintentional and stative dynamic features. The relationship between English and Arabic semantic roles and the metaphorical usage of this category of verbs has been addressed in two separate sections. Chapter Three studies the syntactic features of verbs of eating in both languages. It is an attempt to show whether these verbs are transitive/intransitive and regular irregular. It also shows that in every semantic class there is a combination of semantic features on one hand and syntactic features on the other hand. (53) English verbs of eating and (53) Arabic verbs of eating have been surveyed in Chapter Four. They have been analyzed in terms of their meanings, their semantic features, the semantic roles of their subjects, their syntactic features, and their ordinary usage and metaphorical usage. In light of the findings of the study, several recommendations are suggested
A compact microstrip six-port reflectometer (SPR) with extended bandwidth is proposed in this paper. The design is based on using 16-dB multi-section coupled line directional couplers and a multi-section 3-dB Wilkinson power divider operating from 1 to 6 GHz. The proposed SPR employs only two calibration standards: a matched load and an open load. As compared to other dielectric substrates, fabricating the proposed SPR involves using a low-cost (FR4) substrate. A novel algorithm is also proposed to estimate the complex reflection coefficient over the frequency ranges at which the standard performance of the circuit components is not fully satisfied. The new algorithm is based on the circles’ intersection points, which have been de
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The objective of this article is to delve into the intricate dynamics of marriage relationships, exploring the impact of emotions such as fear, love, financial considerations and likability. In our investigation, we adopt a perspective that acknowledges the nonlinear nature of interactions among individuals. Diverging from certain prior studies, we propose that the fear element within the context of marriage is not a singular, isolated factor but rather a manifestation resulting from the amalgamation of numerous social issues. This, in turn, contributes to the emergence of strained and unsuccessful relationships. Unlike conventional approaches, we extensively examine the conditions essential for the existence of all socially signifi
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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Because the Coronavirus epidemic spread in Iraq, the COVID-19 epidemic of people quarantined due to infection is our application in this work. The numerical simulation methods used in this research are more suitable than other analytical and numerical methods because they solve random systems. Since the Covid-19 epidemic system has random variables coefficients, these methods are used. Suitable numerical simulation methods have been applied to solve the COVID-19 epidemic model in Iraq. The analytical results of the Variation iteration method (VIM) are executed to compare the results. One numerical method which is the Finite difference method (FD) has been used to solve the Coronavirus model and for comparison purposes. The numerical simulat
... Show MoreRheumatoid arthritis is a chronic, progressive, inflammatory autoimmune disease of unidentified etiology, associated with articular, extra-articular and systemic manifestation that require long-standing treatment. Taking patient’s beliefs about the prescribed medication in consideration had been shown to be an essential factor that affects adherence of the patient in whom having positive beliefs is an essential for better adherence. The purpose of the current study was to measure beliefs about medicines among a sample of Iraqi patients with Rheumatoid arthritis and to determine possible association between this belief and some patient-certain factors. This study is a cross-sectional study carried out on 250 already diagnosed rheumatoid
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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