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Register a new userتصميم خلطة إعادة الاستخدام باستخدام المواد المكشوطة
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تعد مشكلة صيانة شبكات الطرق الضخمة الحالية بأقل كلفة ممكنة مع الحفاظ على الثروات الطبيعية و التخلص من الكميات الكبيرة للمواد المكشوطة سنويا"من أهم المشاكل التي تواجـه المهندسـين و المهتمين بهذا القطاع الحيوي و الهام . في هذا البحث تم إلقاء الضوء على أحد أهم خيارات الحل و هي تكنولوجية إعادة الاستخدام , و قـد تم التركيز على إعادة الاستخدام على الساخن باعتبارها الأكثر ملاءمة لشبكة الطـرق السـورية , و يهدف البحث إلى تصميم خلطة إعادة الاستخدام باستخدام المواد المكشوطة في سورية , لـذلك تـم شرح وبشكل مفصل طريقة معهد الإسفلت في تصميم خلطة إعادة الاستخدام , و في الجانب العملـي من البحث تم تصميم خلطة إعادة الاستخدام مخبريا" و إجراء التجارب اللازمة عليهـا للتحقـق مـن مطابقتها للمواصفات الفنية المطلوبة.
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References used
EPPS, J ,GUIDELINES FOR RECYCLING PAVEME MATERIALS ,WASHINGTON:NATIONAL COOPERATIVE HIGHWAY RESEARCH PROGRAM REPORT 224 , 1980
KALLAS, B , FLEXIBLE PAVEMENT MIXTURE DESIGN USING RECLAIMED ASPHALT CONCRETE, U.S.A:ASPHALT INSTITUTE REPORT 84-2,OCTOBER 1984
WOOD, L , E ,RECYCLING OF BITUMINOUS PAVEMENTS ,BALTIMORE: AMERICAN SOCIETY FOR TESTING AND MATERIALS ,1987
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Framing a news article means to portray the reported event from a specific perspective, e.g., from an economic or a health perspective. Reframing means to change this perspective. Depending on the audience or the submessage, reframing can become nece
ssary to achieve the desired effect on the readers. Reframing is related to adapting style and sentiment, which can be tackled with neural text generation techniques. However, it is more challenging since changing a frame requires rewriting entire sentences rather than single phrases. In this paper, we study how to computationally reframe sentences in news articles while maintaining their coherence to the context. We treat reframing as a sentence-level fill-in-the-blank task for which we train neural models on an existing media frame corpus. To guide the training, we propose three strategies: framed-language pretraining, named-entity preservation, and adversarial learning. We evaluate respective models automatically and manually for topic consistency, coherence, and successful reframing. Our results indicate that generating properly-framed text works well but with tradeoffs.
In last decade, many of researches have proposed to study materials with periodic
structures,These structures can be engineered to realize novel electromagnetic properties
and to achieve behaviors that are not found in naturally materials. These ma
terials have
negative permittivity and permeability .Those are called meta-material or Left Hand
Material (LHM), They are fabricated, studied and analyzed within different applications of
microwave and optical communication. Recently, the microwave engineering applications
of artificial metamaterials have been widely discussed and various novel microwave
components were achevid for example, filters, antennas and fiber optic. This paper studys
and analyzes the reflection from fractal distribution of MMs layers in RHM medium, using
the recursive method, and applies it in the design of microwave narrow band filters.
This paper presents an experimental study to determine the physical and
some other important properties of certain waste materials, in order to
identify the appropriate field of their recycling. The experiments have
been carried out using five proposed materials, namely: corn cob, peanut
peel, straw, pine cone, and sticks of the stem Thistle Syrian plants.
Word Sense Disambiguation (WSD) is a historical NLP task aimed at linking words in contexts to discrete sense inventories and it is usually cast as a multi-label classification task. Recently, several neural approaches have employed sense definitions
to better represent word meanings. Yet, these approaches do not observe the input sentence and the sense definition candidates all at once, thus potentially reducing the model performance and generalization power. We cope with this issue by reframing WSD as a span extraction problem --- which we called Extractive Sense Comprehension (ESC) --- and propose ESCHER, a transformer-based neural architecture for this new formulation. By means of an extensive array of experiments, we show that ESC unleashes the full potential of our model, leading it to outdo all of its competitors and to set a new state of the art on the English WSD task. In the few-shot scenario, ESCHER proves to exploit training data efficiently, attaining the same performance as its closest competitor while relying on almost three times fewer annotations. Furthermore, ESCHER can nimbly combine data annotated with senses from different lexical resources, achieving performances that were previously out of everyone's reach. The model along with data is available at https://github.com/SapienzaNLP/esc.
This research focuses on the use of new materials have the ability
to act as an anti-corrosion for the resolution of problems relating to
electrodes analyzers the production of hydrogen. Especially that
most analyzers based on the use of electrodes are expensive.
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