Faculty of Science

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About Faculty of Science

Faculty of Science

The Faculty of Science is the core at the University of Tripoli, as it was the first that established in this prestigious university. It is also the first faculty of science in Libya. At the present, it includes ten scientific departments: Departments of Zoology, Mathematics, Physics, Chemistry, Botany, Geology, Computer Science and Statistics, Atmospheric science and geophysics. It currently works to create a new department of Archaeology in order to study the scientific and research side of the historical heritage of the Libyan people. Graduates of this college have worked in various governmental sectors, such as oil exploration, extraction and refining, chemical industries complexes in Abu Kamash and Ras Al-Anuf, as well as plastics companies in production and manufacturing, and in factories for soap, cleaning materials and others. They were also recruited by the education sector in different research and pedagogical areas.

 

The graduates of this faculty have led the scientific process for many years and still represent the first building block in all colleges of science, and some other colleges in all Libyan universities for the past five decades. The scope of work for graduates included Faculties of Medicine (in the field of basic sciences, biochemistry, anatomy, histology and microbiology), many departments in the Faculty of Agriculture, general engineering, chemical and geological engineering; in particular, medical technology and pharmacy, and the Faculty of Economics and Arts. The Faculty of Science provides teaching assistants to other faculties and universities in the Libyan state.

 

The Faculty of Science is the first to create graduate studies programs in Libya, despite the nature of graduate studies in basic sciences, which need capabilities other than competent professors. Teaching staff in this institution graduated from international universities in the West and the East (USA, UK, Australia, and other European countries). They graduated from universities that are well-known for their high academic standard.

 

Having obtained their first university degree or higher degrees of specialization from Libya or abroad, graduates of Faculty of Science worked for industrial and nuclear research centers, petroleum sector, marine life, biotechnology, plastics, and other specialized research centers.

 

The Faculty has also enriched the scientific research movement in the fields of basic sciences in the Libyan state through the issuance of refereed basic science journal.

Facts about Faculty of Science

We are proud of what we offer to the world and the community

154

Publications

237

Academic Staff

1830

Students

686

Graduates

Programs

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Who works at the Faculty of Science

Faculty of Science has more than 237 academic staff members

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Dr. Esmaile Abdaslam Moftah Shakman

إسماعيل الشقمان هو احد اعضاء هيئة التدريس بقسم علم الحيوان بكلية العلوم. يعمل السيد إسماعيل الشقمان بجامعة طرابلس كـأستاذ مشارك منذ 2016-03-06 وله العديد من المنشورات العلمية في مجال تخصصه

Publications

Some of publications in Faculty of Science

Density functional theory study of substitutional oxygen in diamond

A few studies have been recently presented for the existence of oxygen in diamond, for example, the N3 EPR centres have been theoretically and experimentally assigned the model made up from complex of substitutional nitrogen and substitutional oxygen as nearest neighbours. We present ab initio calculations of substitutional oxygen in diamond in terms of stability, electronic structures, geometry and hyperfine interaction and show that substitutional oxygen with C$_{2v}\,$, $S=1$ is the ground state configuration. We find that oxygen produces either a donor or acceptor level depending on the position of the Fermi level. arabic 8 English 59
Khaled Mohamed Ramadan Etmimi(12-2015)
Publisher's website

دراسة أولية لإستخلاص مادة الآجار من بعض طحالب الساحل الغربي الليبي (منطقة طرابلس)

جمعت ثلاثة أنواع من الطحالب البحرية الحمراء من منطقة المد والجزر على طول منطقة الدراسة من ساحل مدينة طرابلس وهي تاجوراء وذات العماد وقرقارش. ثم صنفت ونظفت بعناية لإجراء تجارب إستخلاص مادة الآجار. بينت النتائج أن كمية الآجارالمستخلص كانت عالية في فصل الصيف لكل من طحلب Hypnea musciformis وGracilaria verrucosa بينما كانت كمية الآجار المستخلص من طحلب Gelidium latifolium عالية في فصلي الصيف والشتاء وقليلة في فصلي الربيع والخريف. من نتائج هذه الدراسة تبين أن تواجد طحلب Gelidium latifolium كان على مدار السنة ماعدا شهر سبتمبرمقارنة بالطحلبين الآخرين. كما تبين أيضا أن أفضل فصل لتجميع هذه الأنواع لغرض إستخلاص مادة الآجار منها هو فصل الصيف. Abstract Three seaweed species (Rhodophyta) were collected from the inter-tidal zone of Study are, Dat-alemad, Gargaresh, and Tajura along the Tripoli coast. The speciements were sorted and cleaned carefully, then the processes of the agar extraction were done. The results showed that the highest value was in the summer for each of the Hypnea musciformis and Gracilaria verrucosa, whereas the highest value of the Gelidium latifolium was in summer and winter seasons and the value was less in spring and fall.The results show that Gelidium latifolium is able to live throughout the year more than the other two species. The results also show that the best season to collect these species for the purpose of agar extraction is the summer.
انتصار علي رجب أبو ميس (2009)
Publisher's website

Comparison between the Neural Networks Forecasting With Arima Models

لهذه الدراسة هدفان مهمان وهما: أولاً: توضيح فكرة بناء الشبكات؛ العصبية المقترحة ثانياً: مقارنة هذه الطرق بالإدراك الجيد لنماذج السلاسل الزمنية (ARIMA) باستعمال المعيار MSE، وهو المعيار الأول لتدريب الشبكة العصبية والثاني لحساب آلية توقعات نماذج الشبكات العصبية. باستخدام بعض الأمثلة الخاصة اتضح أن الإجراءات حول نموذج الشبكات العصبية وجدت بأنها تقدم توقعات أفضل من نماذج السلاسل الزمنية، وأن نماذج الشبكات العصبية قد تستعمل في التنبؤ ببيانات السلاسل الزمنية بتعديل بعض الأوزان التى تعتبر معالم نماذج الشبكات العصبية والتى يمكن أن تقدر خلال عملية تدريب الشبكة، ودقة التوقعات مقدرة بالدالة المناسبة التى تستعمل في عملية تدريب الشبكة. إن مشكلة تنبؤ النماذج شائعة في التحليلات الإحصائية، وفى الغالب الطرق مستعملة للتعامل مع تنبؤ نموذج الانحدار والسلاسل الزمنية بالرغم من أن هذه الطرق قد لاتكون دقيقة في العينات الصغيرة و النتائج المتحصل عليها في هذا البحث حسبت بفصل مجموعة البيانات إلى مجموعتين جزئيتين أو أكثر، استعملنا الجزء الأول لملائمة النموذج والجزء الأخير لبناء التوقع باستخدام المعيار MSE كأداة للمقارنة بين النماذج, وكلما كانت قيمة هذا المعيار صغيرة كان النموذج أفضل. Abstract This study has two objectives. First, presenting artificial neural networks (ANN) second, comparing the proposed method with the well known ARIMA model, the accuracy of the neural network forecasts is compared with the corresponding ARIMA models by using the mean square error (MSE). By using the proposed (MSE) measures the artificial neural networks (ANN) were found deliver a better forecasts than the ARIMA model. A class of artificial neural networks (ANN) may be used in forecasting time series data. It may be used to approximate unknown expectation function of future observation given past values , thus the weights of these ANN can be viewed as parameters, which can be estimated through the network training. Then the model is used for forecasting. The accuracy of the forecasts is evaluated by suitable function. The problem of forecasting model is common in statistical analysis. One of the mostly used approach to deal with forecasting model is regression and time series. Although, approaches may not accurate in small sample. In an effort to forecast daily flow waters to the three important dams such as Ejdabia, Sirt, Benghazi, we will training to a take new tool if forecasting model which known as neural network model. This tool deal with testing data after made as partition of the original series into two sets first is called training set, were used to fit the model, while the second is called testing sets, were used to make forecasting. In this work the MSE is well known as tool for comparing between the models, further more when the MSE is less, the value of this model is a better than other models.
ساميه محمد ميره (2010)
Publisher's website

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