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The study aimed to identify the difficulties of using the Moodle platform from the point of view of members of the teaching staff at the Faculty of Education at Tishreen University, where the study sample consisted of (50) members of the teaching sta ff at the Faculty of Education at Tishreen University. A questionnaire consisting of three axes (difficulties related to members of the educational staff, difficulties related to students, difficulties related to infrastructure), each axis includes a number of items, the study used the descriptive approach, and the results showed that the most difficult difficulties experienced by members of the educational staff from their point of view The lack of conviction in the effectiveness of the Moodle platform for the member of the educational staff, and the difficulty of the student's inability to understand the study material through the platform, which came with a high degree. (Academic degree, number of years of experience, gender ) .
Brain Computer Interface (BCI), especially systems for recognizing brain signals using deep learning after characterizing these signals as EEG (Electroencephalography), is one of the important research topics that arouse the interest of many research ers currently. Convolutional Neural Nets (CNN) is one of the most important deep learning classifiers used in this recognition process, but the parameters of this classifier have not yet been precisely defined so that it gives the highest recognition rate and the lowest possible training and recognition time. This research proposes a system for recognizing EEG signals using the CNN network, while studying the effect of changing the parameters of this network on the recognition rate, training time, and recognition time of brain signals, as a result the proposed recognition system was achieved 76.38 % recognition rate, And the reduction of classifier training time (3 seconds) by using Common Spatial Pattern (CSP) in the preprocessing of IV2b dataset, and a recognition rate of 76.533% was reached by adding a layer to the proposed classifier.
This research was carried out at the Agricultural Scientific Research Center in Hama, in seasons 2020, 2021 to study the effect of foliar spray of seaweed extract on the growth and productivity of the olive tree cv. Kaisi, Where the foliar spray was applied with seaweed extract called alga 600 in concentrate of (0.5 g/l) According to treatments: A0 control without spraying seaweed extract, A1 one spray one week before flowering, A2 one spray after fruit set, A3 one spray one month before harvest, A4 two sprays (before flowering and after fruit set), A5 two sprays (after fruit set and one month before harvest) A6, three sprays (before flowering, after fruit set, and one month before harvest) with the addition of ground fertilization according to the Fertilizer recommendation. The results of foliar fertilization showed a significant effect on the vegetative growth traits, as The treatment one month before harvest was superior to the rest of the treatments with a primary shoots length average 6.94 cm, while the control was 4.75 cm, while seaweed extract had a positive effect on the total number of flowers. Where the spraying treatment before flowering and after the fruit set contract was superior to the rest of the treatments, as the average number of flowers was 203.11 compared to the control with an average number of flowers 164.19, as well as in the percentage of fruit set, as the highest percentage of the contract when spraying treatment before flowering was 3.20% and in control 2.19% The results also showed a clear superiority in the productivity of the tree, especially the treatment before flowering and after the contract, Where the average tree productivity was 37.07 kg, and in the control it was 14.07 kg It was observed that there was a significant increase in the percentage of oil for the fruits, as a treatment a month before harvest was significantly superior to the rest of the treatments in both seasons, as the highest value of the oil percentage reached 20.28%, followed by a treatment before flowering, after fruit set and one month before harvest, with an oil percentage of 20.27 compared to the control 17.17%.
Indebtedness is considered one of the most important problems facing Arab countries, due to its negative dimensions on the process of economic development in these countries and its threat to the stability of their financial system. which prompted th ese countries to borrow, which constituted a huge burden on the borrowing Arab countries in paying the installments agreed upon by the creditor and the interest arising from these loans, and of course, these burdens that are paid are at the expense of the basic services provided by these countries to their citizens, which It leaves negative effects on social and political conditions, in addition to stifling economic growth rates. This research aims to shed light on the factors that played an important role in exacerbating the external indebtedness crisis of Arab countries, and to identify ways that can mitigate the impact of external indebtedness at the local level. Arab and international
The current research aims to know the prevalence of marital compatibility among married female students at Tishreen University, and know the differences in marital compatibility according to some variables (method of marriage, place of residence, pre sence of children). The research was applied to a sample of married students at Tishreen University, whose number was (100) students. To achieve this goal, the marital compatibility scale prepared by (Ammar, 2015) was used, which includes dimensions (intellectual, affectionalemotional, sexual, and social compatibility) distributed within (54) items. The researcher conducted the psychometric study of the scale to ensure its validity and reliability in relation to the current research sample is high, and there are no statistically significant differences in the marital compatibility of the research sample according to the variable of the place of residence and the presence of children. As for the variable of the method of marriage it was found that there were statistically significant differences in favor of marriage after a love story.
يهدف البحث إلى تحديد استجابة محصول الذرة الصفراء للري بالمياه المالحة خلال مراحل النمو المختلفة , ودراسة التراكم الملحي في التربة ودرجة تفاعلها وكفاءة استخدام المياه , وتسليط الضوء على امكانية الاستفادة من النظام الثنائي للري بالمياه العذبة والمالحة , وتقنين الماء المستخدم للري . استخدمت ثلاثة انواع من مياه الري تراكيز الملح فيها (1.5 , 4.5 , 6.5 ) ملموز | سم على التوالي بثلاث مكررات لكل منهما , استخدم تصميم القطاعات العشوائية الكاملةBRCD في التجربة وحللت احصائيا باستخدام SPSS واوجدت الفروقات المعنوية عند مستوى 5% وفق طريقة Duncan . اظهر التحليل الاحصائي وجود فروقات معنوية بتاثير لملوحة ماء الري عند 4.5 ملموز | سم عينة (T2) ادى الى الاضرار بنسبة تقترب من 50% بالنمو الخضري والجذري ونمو العرنوس ومساحة الاوراق ووزن 500 حبة وحاصل الحبوب وملوحة التربة مقارنة لو تم ريها بماء نسبة ملوحته 1.5 ملموز | سم عينة (T1) ماء النهر وبينت التجربة ايضا ان استخدام ماء نسبة ملوحته 6.5 ملموز | سم عينة (T3) ماء بزل ادى الى خفض جميع الصفات الظاهرية للنبات بنسبة 75% , كما حصل ارتفاع لملوحة التربة في نهاية التجربة مقارنة مع ملوحتها في بداية التجربة عند استخدام الري الثنائي , وهذا الضرر يزداد بزيادة التوصيل الكهربائي للمياه المستخدمة في عمليات الري , كما تمت ملاحظة ان PHالتربة ينخفض كلما زادت ملوحة التربة .
The cities of the Canaanite civilization flourished on the eastern coast of the Mediterranean, and its centers extended from the Iskenderun region in northern Syria to Palestine, and it was famous for its cities: Ugarit, Arwad, Jbeil, Beirut, Tyre, J ericho, Acre and Gaza as major cities, with the presence of other smaller civilized centers. Part of this civilization has been spread in Syria and Lebanon. For example, the archaeological discovery of the centers of the Canaanite civilization, but the archaeological data provided by the discovered cities on the level of human civilization were very important, and it is an issue that raises the need for archaeological excavations, and the beginning of the historical Canaanite presence in its Syrian cultural and civilizational milieu. And if the Canaanite civilization left in its mother land, Syria, many achievements and cultural and cultural data, which is a rich field for historical and archaeological studies, but its cultural and civilizational radiation was not limited to the Syrian land, as it spread throughout the known ancient world, specifically in the West, where The Greeks knew it at the beginning of their civilization, and they called the Canaanites the name of the Phoenicians and their civilization, the Phoenician civilization, where their oldest historical and written sources spoke about the Canaanites, and provided a lot of important information about the Canaanite civilization. Hence, looking at the information related to the information contained in the original (Greek) sources, shedding light on it, and dealing with it with a scientific methodology to clarify it and present it to researchers and postgraduate students to benefit from it in its history and archaeology.
"Strategic Planning in the Construction Sector: A Proposed Model for the Strategic Plan to Adopt BIM in Syria." Research Summary: Comprehensive strategic planning for the Syrian state is an inevitable necessity to address the catastrophic effects of the war that the country has suffered from, and its effects are still ongoing in light of government incapacity and gross failure that has affected all economic, industrial, and scientific aspects. This is reflected in the global development and knowledge indicators. Therefore, the Syrian state must adopt comprehensive planning concepts and strive to make knowledge its primary destination to create a strong economy and a strong industry ready for the upcoming reconstruction phase. It should adopt modern administrative concepts, cognitive and engineering sciences, and seek to incorporate them into its plans. Building Information Modeling (BIM) modeling may occupy the forefront of these sciences due to its great importance in transferring Syrian engineering work in the construction sector to advanced countries. Therefore, the study presented in its chapters the concepts of comprehensive planning and strategic planning on the one hand, and BIM modeling on the other hand, through analyzing and comparing current strategies to adopt BIM technology and the most important global trends and experiences, and identifying the most important obstacles and challenges that faced it. Then, the current situation of BIM technology was studied and analyzed, exploring the extent of its spread in the Syrian construction industry, with the aim of formulating a framework for integrating BIM modeling technology effectively within the life cycle of engineering projects in Syria, and proposing a strategic plan to adopt BIM in Syria. As a result, the research produced a set of findings throughout its chapters as follows: In chapter two, a model was constructed to integrate government plans that contribute to achieving the BIM plan. This was done through a comprehensive study of planning and strategic planning, as well as an examination of the reality of planning and the various government plans in Syria, which revealed weaknesses in both the planning mechanism and plan implementation mechanisms. In chapter three, a comprehensive study was conducted on the current situation of BIM adoption in Syria, and the problems and difficulties that hinder its implementation. The BIM maturity matrix was applied to companies in both the public and private sectors, revealing weaknesses in both sectors in terms of BIM adoption, despite the private sector's superiority in most areas. Based on this, a SWOT analysis was conducted on the current situation in Syria regarding BIM adoption, which indicated strengths, weaknesses, opportunities, and threats. In chapter four, a proposed framework was developed for implementing the strategic plan for BIM adoption in Syria. This resulted in a roadmap for BIM adoption in Syria from the beginning of 2023 until the end of 2030. In chapter five, the plan was practically applied to a performance management program called BSC DESIGNER, resulting in a strong and robust performance management system for implementing the strategic plan according to a timeline from the beginning of 2023 until the end of 2030. This research is a bold attempt by the researcher to complement various sciences within a comprehensive strategic planning framework. This research aims to reach decision-makers and help put Syria on the global BIM map by translating the plan's vocabulary and goals into practical reality that contributes to shaping the future of the construction industry in Syria. This study recommends coordination and cooperation between decision-makers and stakeholders in the construction sector to implement the proposed BIM adoption strategy through its four axes (policies, technologies, processes, knowledge, and skills) and secure financial support. Keywords: strategic planning, comprehensive planning, building information modeling, performance management, engineering projects, BIM adoption plan, Syria.
The purpose of this paper is to extract roads from satellite images, based on developing the performance of the deep convolutional neural network model (Deeplabv3+) for roads segmentation, and to evaluate and test the performance of this mode l after training on our data.This experimental study was applied at Google Colab cloud platform, by software instructions and advanced libraries in the Python.We conducted data pre -processing to prepare ground truth masks,then we trained the model.The training and validation process required (Epochs=4), by(Patch Size=4images).The Loss function decreased to its minimum value (0.025). Training time was three hours and ten minutes, aided by the advanced Graphics Processing Unit (GPU) and additional RAM.We achieved good results in evaluating the accuracy of the predictions of the trained model (IoU = 0.953). It was tested on two different areas, one of which is residential and the other agricultural in Lattakia city. The results showed that the trained model (DeepLabv3+) in our research can extract the road network accurately and effectively.But its performance is poor in some areas which includes tree shadows on the edges of the road, and where the spectral characteristics are similar to the road, such as the roofs of some buildings, and it is invalid for extracting side and unpaved roads. The research presented several recommendations to improve the performance of the (Deeplabv3+) in extracting roads from high-resolution satellite images, which is useful for updating road maps and urban planning works.
The electric power service in the Syrian Arab Republic suffers from many difficulties resulting from the lack of resources (fuel), in addition to the sabotage of many generation centers by terrorist groups, which led to the implementation of rationin g programs in the governorates according to the consumption of those governorates and the production centers located in them. (factories, pumping centers, hospitals and the population). Forecasting electric energy consumption also requires knowledge of daily consumption quantities, consumption times and other influencing factors that constitute large amounts of data. Predicting the exact electrical load is still a challenging task due to many problems such as the non-linear nature of the time series or the seasonal patterns it displays, which are very time consuming and affect the accuracy of the prediction performance. The process can be improved by using RNNs.[2] Initially, the optimal and appropriate consumption for the region was determined, compared with production and the possibility of passing the surplus to other backup operations or providing production centers with the surplus that could be obtained through the previous forecasting process. Also, Recurrent Neural Networks (RNN) were used, which are time series based on data sequences according to time indices and their ability to predict future values ​​based on past data. Then the performance of those networks was compared with DNN networks (Dense Neural Network) to obtain an optimal future prediction that can be served by the Ministry of Electricity in the Syrian Arab Republic and to solve the problem of predicting the electrical load compared to previous studies. The time-based successive division method has also been adopted, which has the ability to work more accurately for randomly sampled data. For cases of low regulation of the hourly data for wattage consumption, we can sample a set of data over time and take 20 percent of the data for example as training and test samples. Based on the prediction values ​​resulting from this study, work is being done to distribute electrical energy in the most appropriate manner and in accordance with the importance of higher usage.

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د.فيصل البكار

has been joined in   from Syria specialize Education


Vero Postive

has been joined in   from Yemen specialize Philosphy


احمد طيباوي

has been joined in   from Algeria specialize Literature

Last Question

تقسيم التعليم ما قبل الجامعي في سوريا

15  - - أحمد أحمد العبد الله asked   - publish in Researchers Society  

ما هي المراحل المعتمدة من قبل الوزارة للتعليم ما قبل الجامعي كوثيقة رسمية توثق ذلك؟

مرحلة التعليم الأساسي، الثانوية ، – حلقة ثانية

كيف يتم تقييم أداء نظام الترجمة الآلية بشكل آلي؟

93  - - Shadi Shadi Saleh asked   - publish in Artificial Intelligence  

يمكن القيام به باستخدام مقاييس ومعايير مختلفة. إليك بعض الطرق الشائعة لتقييم أداء نظام الترجمة الآلية بشكل آلي:

  1. BLEU (Bilingual Evaluation Understudy): BLEU هي إحدى القياسات الأكثر شيوعًا لتقييم أداء نظام الترجمة الآلية. يقوم BLEU بمقارنة الترجمة المولدة آليًا بالترجمة الإنسانية المرجعية ويقيم مدى تشابههما من خلال قياس الأتفاق بين الكلمات.

BLEU (Bilingual Evaluation Understudy) هو مقياس شائع يُستخدم لتقييم جودة الترجمة الآلية عن طريق مقارنتها بالترجمة الإنسانية المرجعية. يستخدم BLEU معلومات على مستوى الكلمات لقياس التشابه بين الترجمتين. يمكنك استخدام مكتبة Python لحساب مقياس BLEU بسهولة. فيما يلي شرح مفصل لمقياس BLEU مع مثال في Python:

أولاً، تحتاج إلى تثبيت مكتبة nltk (Natural Language Toolkit) إذا لم تكن مثبتة بالفعل. يمكنك فعل ذلك باستخدام الأمر التالي:

pip install nltk 

استيراد المكتبات الضرورية:

import nltk
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction

تحديد النصوص المرجعية والترجمة المستهدفة, النصوص المرجعية reference هيي النصوص التي تعبر عن الترجمة الصحيحة, اي دائما تحتاج إلى هذه النصوص لكي تقوم باختبار النظام, بالاضافة إلى النصوص المترجمة من قبل النظام الآلي candidate:

reference = [['the', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog']]
candidate = ['the', 'fast', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog']

بالنهاية يمكن حساب مقياس BLEU كمايلي:

bleu_score = sentence_bleu(reference, candidate)

NIST (The National Institute of Standards and Technology): يستخدم NIST مقاييس مشابهة لـ BLEU لتحسين تقييم أداء الترجمة الآلية من خلال مقارنة الترجمة بالترجمة الإنسانية المرجعية.

METEOR (Metric for Evaluation of Translation with Explicit ORdering): يقيم METEOR الأداء باستخدام عدة معايير مثل الأتفاق على مستوى الكلمات والترتيب والأمانة. يمكن أن يكون أكثر دقة في بعض الحالات من BLEU.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation): يستخدم ROUGE بشكل رئيسي في تقييم جودة الخلاصات والملخصات النصية. يمكن أن يكون مفيدًا في تقييم الترجمة الآلية للملخصات النصية.

تقييم يدوي بشري: بالإضافة إلى القياسات الآلية، يمكن أيضًا اللجوء إلى تقييم بشري حيث يتم طلب آراء وتقييمات من الناس لفهم مدى جودة الترجمة. يمكن استخدام هذا التقييم لتحسين أداء نظام الترجمة.

يعتمد اختيار الطريقة على نوع النصوص والغرض من الترجمة. تذكر أنه يمكن تحسين أداء نظام الترجمة الآلية باستمرار من خلال تجربة وتعديل الموديلات والمعايير المستخدمة.


الترجمة الآلية

ماهي الشبكات العصبونية المتكررة؟

334  - - Shamra Shamra Editor asked   - publish in Artificial Intelligence  

الشبكات العصبونية المتكررة Recurrent Neural Network هي نوع خاص من الشبكات العصبية الاصطناعية التي تتكيف مع بيانات السلاسل الزمنية أو البيانات التي تتضمن تسلسلات حيث يتم تغذية الاخراج من الخطوة السابقة كمدخل إلى الخطوة الحالية.

في الشبكات العصبية التقليدية تكون جميع المدخلات والمخرجات مستقلة عن بعضها البعض ولكن في حالات مثل عندما يكون مطلوباً التنبؤ بالكلمة التالية من الجملة تكون الكلمات السابقة مهمة وبالتالي هناك حاجة لتذكر الكلمات السابقة.

وهكذا ظهرت شبكات RNN والتي حلت هذه المشكلة بمساعدة الطبقات المخفية. تمتلك RNNs مفهوم الذاكرة الذي يساعد على تخزين حالات أو معلومات المدخلات السابقة لتوليد المخرجات التالية من التسلسل وهذا يجعلها قابلة للتطبيق على مهام مثل التعرف على خط اليد غير المقسم و المتصل أو التعرف على الكلام.

مشاكل نواجهها الـ RNNs:

  1. تلاشي التدرجات
  • في تسلسل طويل، يتم ضرب التدرجات في (transpose أو منقول)مصفوفة الأوزان في كل خطوة زمنية. إذا كانت هناك قيم صغيرة في مصفوفة الوزن، فإن معيار (norm) التدرج يتقلص بمقدار أسي.
  1. انفجار التدرجات
  • إذا كانت لدينا مصفوفة ذات أوزان كبيرة و اللاخطية في الطبقة التكرارية غير مشبعة، فسوف تنفجر التدرجات. سوف تتباعد الأوزان في كل خطوة. و قد نُضطر إلى استخدام معدل تعلم صغير حتى يعمل الانحدار التدريجي بشكل جيد.

أحد أسباب استخدام الـ RNNs هو ميزة تذكر المعلومات السابقة. ومع ذلك، قد تفشل RNN بسيطة في حفظ المعلومات لفترة طويلة دون بعض الحيل.

مثال لمشكلة التدرجات المتلاشية:

تمثل المدخلات رموزًا من برنامج بلغة C. سيحدد النظام ما إذا كان برنامجًا صحيحًا نحويًا أم لا. يجب أن يحتوي البرنامج الصحيح نحويًا على عدد صالح من الأقواس. و بالتالي، يجب أن تتذكر الشبكة عدد الأقواس والأقواس المفتوحة التي يجب التحقق منها، و ما إذا كنا قد أغلقناها جميعًها. يجب أيضا على الشبكة تخزين هذه المعلومات في حالات مخفية مثل العداد. ومع ذلك، و بسبب التدرجات المتلاشية، فإنها ستفشل في الحفاظ على هذه المعلومات في برنامج لمدة طويلة.


الشبكات العصبونية الشبكات العصبونية المتكررة الشبكات العصبونية الالتفافية الذكاء الاصطناعي

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