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التقدير الشامل للعمليات الديمغرافية و تحديد اتجاهها الأمثل

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 Publication date 2003
and research's language is العربية
 Created by Shamra Editor




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الأشقر ، أحمد، ١٩٧٨ ، الاقتصاد الرياضي ، منشورات جامعة حلب .
الخطيب الكسواني، ممدوح ، ١٩٨٨ ، الاقتصاد الرياضي ، منشورات جامعة دمشق .
رولان، ب.، ترجمة: رزق الله، حلا نوفل، ١٩٩٠ ، معجم المصطلحات الديمغرافية، القاهرة..
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Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training. However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with out-of-knowledge-graph (OOKG) entities added frequently, and we need to effi ciently represent these entities. Most existing Knowledge Graph Embedding (KGE) methods cannot represent OOKG entities without costly retraining on the whole KG. To enhance efficiency, we propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions. Moreover, given pretrained embeddings of the in-knowledge-graph (IKG) entities, our method even needs no additional learning. Experimental results on two KGC tasks with OOKG entities show that our method outperforms the previous methods by a large margin with higher efficiency.
تتمثل أهم مصادر المعلومات لتقدير مستوى الخصوبة أو اتجاهها بشكل أساسي في معدل الخصوبة الكلي و معدل التكاثر ( التوالد ) الصافي و عدد الأطفال النهائي لجيل من الإناث أو لسنوات عقود الزواج، و لكل من هذه المعدلات ميزاتها و مساوئها، و من أهم هذه المساوئ: عدم توافر معلومات عن معدل الخصوبة الكلية و معدل التكاثر الصافي بعد سنة الدراسة، كما يجب أن تكون المعطيات عن عدد الأطفال النهائي لجيل من الإناث أو لسنوات عقود الزواج متوافرة لفترة طويلة تمتد من ( 15-30 ) سنة. و تجدر الإشارة إلى أن المعطيات المتوافرة حاليًا لأي مجتمع سكاني متأثرة بالتركيب العمري له، بالإضافة إلى تأثرها بالتقلبات الموسمية و بطول أيام الشهر و توزيع أيام الأسبوع. نقدم في هذا البحث طريقة جديدة لتقدير معدل الخصوبة الكلية بعد تصحيحه من التقلبات الشهرية، و الأسبوعية و الموسمية انطلاقًا من أعداد المواليد الخام الشهرية، و تستند هذه الطريقة في التقدير إلى أساس العلاقة الموجودة بين أعداد المواليد و معدل الخصوبة الكلية و عدد جيل الإناث في عمر الإنجاب.
A model has been developed for optimal reservoir operation system of Alfatha dam upstream Samerra Barrage which is considered as a strategic node in the Tigris river system in Iraq. This model is based on combining an optimization dynamic programmi ng model with a flood routing simulation model within an optimal control framework. The predictive reservoir operation method provides optimal time operation of flood control system with incorporation of current and predicted flood wave. The model utilizes a dynamic programming with successive algorithm, interacting with hydrologic routing method. From the developed optimization model connected with the hydrologic routing model analyses of a number of predicted flood waves of Tigris river released from Mosul dam, Greater Zab and Dokan dam on the Lesser Zab have been accomplished. It was concluded that the optimal maximum operation water level in the reservoir of proposed Alfatha dam realized that all the hydraulic topographic and design constraints were 164 m.a.s.l. for the two depended predicted operation scenarios of the reservoir. While a maximum water level of 165 m.a.s.l. is the optimal operation level for the worst operation scenario of the reservoir. This operation scenario is recommended in the future hydraulic design of the Alfatha dam.
The drastically increase in energy demand and the problems resulted from environmental pollution have led to a serious trend towards energy utilization efficiency improvement through scientific researches related to renewable energies, particularit y that Syria has a high solar irradiance and prolonged sunshine hours. This research focuses on determining the thermal efficiency for a prototype of a parabolic trough solar concentrator (PTC) to obtain the optimal prototype thermally and economically according to climatic conditions in Damascus. It has been deposited the mathematical model of the studied concentrator depending on the calculated design parameters through calculating the solar angles equations and direct solar irradiance throughout the year. And it has been solved this model by using numerous computer programs (Excel, EES).
Determining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods. Recently, authorship representations learned using neural networks have been found to outperform alternatives, particularly in large-scale settings involving hundreds of thousands of authors. But do such representations learned in a particular domain transfer to other domains? Or are these representations inherently entangled with domain-specific features? To study these questions, we conduct the first large-scale study of cross-domain transfer for authorship verification considering zero-shot transfers involving three disparate domains: Amazon reviews, fanfiction short stories, and Reddit comments. We find that although a surprising degree of transfer is possible between certain domains, it is not so successful between others. We examine properties of these domains that influence generalization and propose simple but effective methods to improve transfer.
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