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| 1 | The transformation of the inherited historical urban and architectural characteristics of Al-Najaf's Old City and possible preservation insights显示文摘This paper focuses on the dramatic transformation of the inherited historical urban and architectural characteristics of the city of Al-Najafin Iraq,seeking new ways of seeing the possibilities of preserving and revitalizing the historical center of the city.In addition to being one of the most sacred centers for Shia Muslims,Al-Najaf has a rich cultural and architectural heritage.Its architectural characteristics and urban structure not only reflect the history of the city but also stand as evidence of the most important junctures in Islamic history:the martyrdom of Imam AU ibn Abi Talib,who is a central Islamic spiritual personality and the second most important figure after the Prophet Muhammad.Since its estab-Ushment in 750 AD,the formation of Al-Najaf has never separated from its sociopolitical context and Islamic principles,particularly Shia Muslims'beliefs.Today,the city is a destination for more than million Shia Muslim pilgrims each year,and the number is rapidly increasing.However,as the city continues to develop,its architecture and unique urban structure transform dramatically.The cultural and architectural heritage of the city is seriously threatened.Several factors played a crucial role in the formation and the transformation of the city's specific architectural and urban structures.The most influential factors were mainly related to religion,environment,and politics.This paper introduces the main forces behind the transformation of the deep organizational structures of the architecture and urban fabric in Al-Najaf.The question of whether it is still possible to protect and preserve the remaining parts and revitalize the Old City center is also examined.The goal is to identify the major preservation issues and provide possible insights based on successful preservation experiences that address similar issues indifferent contexts. | Sabeeh Farhan Venus Akef Zuhair Nasar | 2020 | Frontiers of Architectural Research2020，9，4： | 2 |
| 2 | Vibrational characteristics of cracked shafts 显示文摘 | Rajab M D Sabeeh A A | 1991 | Journal of Sound and Vibration1991，147，3： | 1 |
| 3 | Pseudo-Ch6diak-Higashi inclusions together with Auer rods inacute myeloid leukemia 显示文摘 | Abdulsalam AH Sabeeh N Bain BJ | 2011 | Am J Hematol2011，86，7： | 1 |
| 4 | Vibrational characteristics of cracked shafts显示文摘 | RAJABM D SABEEH A A | | 0，，03： | 1 |
| 5 | Bone marrow aspirate in Chédiak-Higashi syndrome显示文摘 | Abdulsalam AH Sabeeh N Bain BJ | | 0，，01： | 1 |
| 6 | Multi-phase particle swarm optimization显示文摘 | Buthainah Sabeeh No'man Al-kazemi | 2002 | Syracuse University2002，，5： | 1 |
| 7 | Quantum Transport of Dirac Electrons in Graphene in the Presence of a Spatially Modulated Magnet- ic Field 显示文摘 | TAHIR M SABEEH K | 2008 | Phys Rev B2008，77，19： | 1 |
| 8 | Malware Detection in Android IoT Systems Using Deep Learning显示文摘The Android Operating System(AOS)has been evolving since its inception and it has become one of the most widely used operating system for the Internet of Things(IoT).Due to the high popularity and reliability ofAOS for IoT,it is a target of many cyber-attacks which can cause compromise of privacy,financial loss,data integrity,unauthorized access,denial of services and so on.The Android-based IoT(AIoT)devices are extremely vulnerable to various malwares due to the open nature and high acceptance of Android in the market.Recently,several detection preventive malwares are developed to conceal their malicious activities from analysis tools.Hence,conventional malware detection techniques could not be applied and innovative countermeasures against such anti-detection malwares are indispensable to secure the AIoT.In this paper,we proposed the novel deep learning-based real-time multiclass malware detection techniques for the AIoT using dynamic analysis.The results show that the proposed technique outperforms existing malware detection techniques and achieves detection accuracy up to 99.87%. | Muhammad Waqar Sabeeh Fareed Ajung Kim Saif Ur Rehman Malik Muhammad Imran Muhammad Usman Yaseen | 2023 | Computers, Materials & Continua2023，，2： | 0 |
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