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| 1 | Using AFM to Determine the Porosity in Porous Silicon显示文摘 | Faten Alfeel Fowzi Awad Ibrahim Alghoraibi Fadi Qamar | 2012 | 材料科学与工程(中英文A版)2012，2，9： | 1 |
| 2 | A national survey of lower urinary tract symptoms in Jordan显示文摘Objective To determine the prevalence of lower urinary tract symptoms(LUTS)and their severity population in Jordan.Methods This cross-sectional survey was conducted using a paper-based survey between August and September in 2019.The study was carried out in the health care centers or hospitals in three different regions of Jordan:North(Irbid and Jarash),Middle(Amman,Madaba,Salt,and Zarqa),and South(Karak and Aqaba).Results To estimate the prevalence of LUTS,two definitions were used,including the first definition(presence of any LUTS regardless of the degree of severity)and the second definition(presence of any LUTS that occurs half the time or more).According to the first definition,1038(89.9%)reported LUTS(male:47.3%,female:52.7%),while 763(66.1%)reported LUTS according to the second definition(male:45.6%,female:54.4%).According to the International Prostate Symptom Score characterization,73.9%had nocturia and 62.9%reported daytime increased frequency.Conclusion LUTS are highly prevalent among the Jordanian population,and more than half of them have nocturia or daytime increased frequency as most frequently reported symptoms. | Fadi Sawaqed Ibrahim Kharboush Mohammed Suoub Ismail Albadawi Mohmmad Alhawatmeh Abdallah Murad | 2023 | Asian Journal of Urology2023，10，4： | 0 |
| 3 | Reinforcement Learning in Process Industries:Review and Perspective显示文摘This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries. | Oguzhan Dogru Junyao Xie Om Prakash Ranjith Chiplunkar Jansen Soesanto Hongtian Chen Kirubakaran Velswamy Fadi Ibrahim Biao Huang | 2024 | IEEE/CAA Journal of Automatica Sinica2024，11，2： | 0 |
| 4 | Computer-aided Detection of Tuberculosis from Microbiological and Radiographic Images显示文摘Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools.Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis,this method can be tedious for both Microbiologists and Radiologists and can lead to miss-diagnosis.These challenges can be solved by employing Computer-Aided Detection(CAD)via Al-driven models which learn features based on convolution and result in an output with high accuracy.In this paper,we described automated discrimination of X-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained AlexNet Models.The study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle repository.For classification of tuberculosis using microscopic slide images,the model achieved 90.56%accuracy,97.78%sensitivity and 83.33%specificity for 70:30 splits.For classification of tuberculosis using X-ray images,the model achieved 93.89%accuracy,96.67%sensitivity and 91.11%specificity for 70:30 splits.Our result is in line with the notion that CNN models can be used for classifying medical images with higher accuracy and precision. | Abdullahi Umar Ibrahim Ayse Gunnay Kibarer Fadi Al-Turjman | 2023 | Data Intelligence2023，5，4： | 0 |
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