This theoretical research explores the fundamental differences between human literary writing and artificial intelligence–generated texts by examining how language, style, and narrative structure function in each form of authorship. Using Toni Morrison’s Beloved (1987) as the primary literary example, the study analyzes how human writing draws on lived experience, cultural memory, emotional depth, and intentional creativity. In contrast, AI-generated texts rely on statistical patterns rather than consciousness or authentic meaning-making, resulting in writing that may be linguistically coherent but lacks symbolic richness and emotional resonance. Through a descriptive and analytical methodology, supported by insights from Narrative Theory and stylistic analysis, the research clarifies how human writers construct meaning through metaphor, psychological depth, and cultural context. Meanwhile, AI models reproduce patterns derived from training data, limiting their ability to convey moral complexity, emotional authenticity, or cultural nuance. The study concludes that although artificial intelligence can simulate certain linguistic features, it cannot replicate the human capacity for symbolic creativity or experiential meaning-making. These findings contribute to ongoing academic discussions on authorship, creativity, and the future of literary production in the digital age.
This study aims to conduct an exhaustive comparison between the performance of human translators and artificial intelligence-powered machine translation systems, specifically examining the top three systems: Spider-AI, Metacate, and DeepL. A variety of texts from distinct categories were evaluated to gain a profound understanding of the qualitative differences, as well as the strengths and weaknesses, between human and machine translations. The results demonstrated that human translation significantly outperforms machine translation, with larger gaps in literary texts and texts characterized by high linguistic complexity. However, the performance of machine translation systems, particularly DeepL, has improved and in some contexts
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Artificial intelligence has quickly invaded the realms of both creative and information-based writing, raising new questions about human originality, authorship and style. Despite its ability to produce writings that are coherent and stylistically varied, there are still concerns over the uniqueness and cultural neutrality of AI programs such as ChatGPT. This review covers significant recent advancements with artificial intelligence applications in both the literary and non-literary fields. It analyzes 35 recent studies contrasting authorship and creativity, or stylistic considerations and impressions, between human and AI texts. These studies range from poetic and fictional writing through essay, news article and academic publicati
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Orthodontic wires facilitate the required dental adjustments in the context of orthodontic therapy. The archwire has played a crucial role in orthodontic treatment, and the increasing emphasis on aesthetic preferences from patients, as well as the development of composite and ceramic brackets, have prompted investigations into aesthetic archwires that complement these brackets. Orthodontic wires are produced using a diverse range of materials. The utilisation of all available wire types can improve patient comfort, decrease chairside time, and shorten the overall duration of treatment. The individual clinician must possess comprehensive knowledge and comprehension of the various requirements and alternatives throughout the therapeut
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Average per capita GDP income is an important economic indicator. Economists use this term to determine the amount of progress or decline in the country's economy. It is also used to determine the order of countries and compare them with each other. Average per capita GDP income was first studied using the Time Series (Box Jenkins method), and the second is linear and non-linear regression; these methods are the most important and most commonly used statistical methods for forecasting because they are flexible and accurate in practice. The comparison is made to determine the best method between the two methods mentioned above using specific statistical criteria. The research found that the best approach is to build a model for predi
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This article discusses some of the metaphorical use of language units. Here we will define the basic concepts and underline the causes of this phenomenon. Through research it is proven that through the application of names of some body parts achieves the variety of metaphorical meanings.
Water represents as a basic intellectual material in the myths of creation and the start of formation, Thus, water has turned into an intellectual material in literary mythological texts in addition to its function in sculptural Mesopotamian sculpture. The research is in three sections: the first section deals with Myth, its concept, peculiarities and types, the second section is about mythological literature, the third section is about the idea of water and mythical literature. The question research question here is that does the idea of water have any impact on mythological literature? And Does it link to sculptural products? The importance of the research is that it shows the human imagination and its relationship to functioni
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Background: Urolithiasis and hypertension are prevalent and clinically significant conditions in the Middle East, both influenced by shared metabolic and environmental risk factors. Understanding the potential association between them is important for guiding prevention strategies. Objective: To explore the relationship between urolithiasis and hypertension in a sample of Iraqi adult patients. Methods: A cross-sectional observational study was conducted at Alkindy Teaching Hospital, Baghdad, from September 2024 to March 2025. Participants included 237 patients with confirmed urinary tract stones and 244 controls confirmed to be stone-free, matched for age and sex. Exclusion criteria included secondary hypertension, chronic kidney di
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Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise
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