Cohesion is well known as the study of the relationships, whether grammatical and/or lexical, between the different elements of a particular text by the use of what are commonly called 'cohesive devices'. These devices bring connectivity and bind a text together. Besides, the nature and the amount of such cohesive devices usually affect the understanding of that text in the sense of making it easier to comprehend. The present study is intendedto examine the use of grammatical cohesive devicesin relation to narrative techniques. The story of Joseph from the Holy Quran has been selected to be examined by using Halliday and Hasan's Model of Cohesion (1976, 1989). The aim of the study is to comparatively examine to what extent the types of grammatical cohesive devices and their frequencies and densities are affected by thetechniques of narration, namely, internal and external or as commonly known as conversational and narrative. The researcher has come into the conclusion that the grammatical cohesive devices form one third of the story and accordingly, they affect the structure and interpretation of the text. Moreover, thegrammatical cohesive devices are more frequent in the conversational part when compared to the narrative part. It is also concluded that the endophoric reference is the dominant category in the conversational and narrative parts of the story, unlike the exophoric that can be identified only in relation to the background knowledge of the outside situation.
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To evaluate and improve the efficiency of photovoltaic solar modules connected with linear pipes for water supply, a three-dimensional numerical simulation is created and simulated via commercial software (Ansys-Fluent). The optimization utilizes the principles of the 1st and 2nd laws of thermodynamics by employing the Response Surface Method (RSM). Various design parameters, including the coolant inlet velocity, tube diameter, panel dimensions, and solar radiation intensity, are systematically varied to investigate their impacts on energetic and exergitic efficiencies and destroyed exergy. The relationship between the design parameters and the system responses is validated through the development of a predictive model. Both single and mult
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Alpha-tocopherol acetate is one of the most important vitamin E derivatives,that were used as antioxidants. Adsorbents like kaolin, magnesium carbonate, and microcrystalline cellulose were used successfully to incorporate oily alpha-tocopherol acetate into an acceptable powder dosage form. The results revealed that microcrystalline cellulose as an adsorbents gave the best results with 50% loading capacity at time, 8 minutes before and after incubation period (3 months at 30C°), while kaolin and magnesium carbonate have been shown a significant difference before and after incubation. Addition of 1% w/w magnesium carbonate to the kaolin enhanced the loading capacity by decreasing the time of adsorption from 20 to 6 minutes and 47
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Coronavirus: (COVID-19) is a recently discovered viral disease caused by a new strain of coronavirus.
The majority of patients with corona-virus infections will have a mild-moderate respiratory disease that recovers without special care. Most often, the elderly, and others with chronic medical conditions such as asthma, coronary disease, respiratory illness, and malignancy are seriously ill.
COVID-19 is spread mostly by salivary droplets or nasal secretions when an infected person coughs or sneezes.
COVID-19 causes severe acute respiratory illness (SARS-COV-2). The first incidence was recorded in Wuhan, China, in 2019. Since then it spreads leading to a pandemic.
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Introduction: Methadone hydrochloride (MDN) is an effective pharmacological substitution treatment for opioids dependence, adopted in different countries as methadone maintenance treatment (MMT) programmes. However, MDN can exacerbate the addiction problem if it is abused and injected intravenously, and the frequent visits to the MMT centres can reduce patient compliance. The overall aim of this study is to develop a novel extended-release capsule of MDN using the sol-gel silica (SGS) technique that has the potential to counteract medication-tampering techniques and associated health risks and reduce the frequent visits to MMT centres. Methods: For MDN recrystallisation, a closed container method (CCM) and hot-stage method (HSM) were conduc
... Show MoreLymphoma is a cancer arising from B or T lymphocytes that are central immune system components. It is one of the three most common cancers encountered in the canine; lymphoma affects middle-aged to older dogs and usually stems from lymphatic tissues, such as lymph nodes, lymphoid tissue, or spleen. Despite the advance in the management of canine lymphoma, a better understanding of the subtype and tumor aggressiveness is still crucial for improved clinical diagnosis to differentiate malignancy from hyperplastic conditions and to improve decision-making around treating and what treatment type to use. This study aimed to evaluate a potential novel biomarker related to iron metabolism,
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Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with
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Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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