Academic writing is a key skill for success in academic life, particularly for graduate students of a foreign language. The importance of writing to academic culture, practice, and knowledge building has led to a great deal of research in many fields, including rhetoric and composition, linguistics, applied linguistics, and English for Academic Purposes (EAP). Often, studies and research investigating academic writing are motivated by the need to inform the learning of writing to native and non-native English-speaking students, through both descriptions of professional academic writing as well as through comparisons of novice writer (native and non-native Englishspeaking) and expert production. However, while learning about academic writing to better inform teaching content and practices is an important aim, Bazerman (1994, P. 10) points out that understanding language use in the disciplines also helps us to use language more effectively, can guide writers and editors as they work with contributor texts, and helps provide non-specialist readers with access to the discourse of the disciplines. Thus, describing and understanding patterns and pragmatic of argumentation of language use in academic writing allows us to understand the disciplinary cultures and practices that they embody. This is why many linguists and scholars have long been fascinated with the language of academia, particularly in the form of written texts. This interest has developed and expanded over the past few decades, in part due to the premise that much can be learned about disciplinary practices and cultures by examining academic writing: the primary means of the transmission of knowledge in academic fields.
يهدف هذا البحث الى تحليل إثر التوجهـات الأيديولوجيـة في السياسيات التحريرية للصحف العراقية، ومدى تأثيرها على التزام القائمين بالاتصال بالمعايير المهنية في أدائهم الصحفي، وذلك عن طريق دراسة ميدانية تضمنت عينة قصدية قوامها (54) صحفياً يعملون في سبع صحف ذات أنماط ملكية مختلفة (شبه رسمية، وحزبية، وخاصة). واعتمدَ البحث على المنهج الوصفي التحليلي، باستخدام الاستبانة كأداة لجمع البيانات، بهدف رصد العلاقة بين
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This paper presents a cognition path planning with control algorithm design for a nonholonomic wheeled mobile robot based on Particle Swarm Optimization (PSO) algorithm. The aim of this work is to propose the circular roadmap (CRM) method to plan and generate optimal path with free navigation as well as to propose a nonlinear MIMO-PID-MENN controller in order to track the wheeled mobile robot on the reference path. The PSO is used to find an online tune the control parameters of the proposed controller to get the best torques actions for the wheeled mobile robot. The numerical simulation results based on the Matlab package show that the proposed structure has a precise and highly accurate distance of the generated refere
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Abstract
The term public budget defects became nowadays a chronic, economical phenomenon, almost all the countries weather advanced or development country suffered from it, despite the different visions to economic schools of a thought to accept or reject the deficit in public budget but the prevailed opinion that is needed to rule the role of the state by reducing the public spending which led to continuous deficits in public budget and the consequent upon increase in government borrowing, increase taxes on income and wealth, thus weakening the in contrive for private investment which contributed to the increase of in flationary stagnation, it became a duty to state covered by the lack of financial sources
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Products’ quality inspection is an important stage in every production route, in which the quality of the produced goods is estimated and compared with the desired specifications. With traditional inspection, the process rely on manual methods that generates various costs and large time consumption. On the contrary, today’s inspection systems that use modern techniques like computer vision, are more accurate and efficient. However, the amount of work needed to build a computer vision system based on classic techniques is relatively large, due to the issue of manually selecting and extracting features from digital images, which also produces labor costs for the system engineers.
 
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Homomorphic encryption became popular and powerful cryptographic primitive for various cloud computing applications. In the recent decades several developments has been made. Few schemes based on coding theory have been proposed but none of them support unlimited operations with security. We propose a modified Reed-Muller Code based symmetric key fully homomorphic encryption to improve its security by using message expansion technique. Message expansion with prepended random fixed length string provides one-to-many mapping between message and codeword, thus one-to many mapping between plaintext and ciphertext. The proposed scheme supports both (MOD 2) additive and multiplication operations unlimitedly. We make an effort to prove
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Cancer is in general not a result of an abnormality of a single gene but a consequence of changes in many genes, it is therefore of great importance to understand the roles of different oncogenic and tumor suppressor pathways in tumorigenesis. In recent years, there have been many computational models developed to study the genetic alterations of different pathways in the evolutionary process of cancer. However, most of the methods are knowledge-based enrichment analyses and inflexible to analyze user-defined pathways or gene sets. In this paper, we develop a nonparametric and data-driven approach to testing for the dynamic changes of pathways over the cancer progression. Our method is based on an expansion and refinement of the pathway bei
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med
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