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Ibrahim Abdelmageid Hassan,Sara Said,Zeinab Salah,Mohamad Magdy Abdel Wahab 한국대기환경학회 2022 Asian Journal of Atmospheric Environment (AJAE) Vol.16 No.2
The changes in air quality were investigated in six megacities during the shutdown phases in 2020 and were compared to the same time periods in the previous 10 years (2010-2019) using the data of Modern-Era Retrospective Analysis and Research and Application, version 2 (MERRA-2). The concentrations of PM10 and PM2.5 were greatly reduced in all megacities during the lockdown in 2020 when compared to the same period in 2019 and in the previous ten years. The highest reduction in PM10 was recorded in Delhi, and São Paulo (21%, and 15% and by 27%, and 9%), when compared with the concentrations in 2019 and in the period 2010-2019, respectively. Similarly, levels of PM2.5 in Delhi, São Paulo, Beijing, and Mumbai decreased by 20%, 14%, 12%, and 10%, respectively in 2020 when compared to the last ten years. Results indicated that the lockdown is an effective mitigation measure to improve air quality. The MERRA-2 reanalysis dataset could be a vital tool in air quality studies in places with a lack of In-situ observations.
Traditional Software Development for WLAN Propagation Model
Ibrahim Anwar Hassan,Ismail Mahamod,Jumari Kasmiran,Kiong Tiong Sieh The Korean Institute of Electrical Engineers 2007 Journal of Electrical Engineering & Technology Vol.2 No.1
SPWPM traditional software development is surveyed and essential problems are investigated on the basis of system wireless link considerations. This paper presents the current state software planning tools for wireless LAN link optimization. The software directory is based on combination of MatLab and MapInfo software and measurement which gives the best grouping parameters to build up the software development. Among the requirements assumed, the WLAN site selections must be Line-of-sight (LOS) or near line of sight (NLOS) field strength prediction for either point to point or point to multi points. The results obtainable the out put of the program include two-dimensional (2D) and three dimensional (3D) plots for creating the link; design parameters through GUI representing the height and location for each antenna is depending on K-factor of the area and transmit antenna location.
Machine learning and RSM models for prediction of compressive strength of smart bio-concrete
Hassan Amer Algaifi,Suhaimi Abu Bakar,Rayed Alyousef,Abdul Rahman Mohd. Sam,Ali S. Alqarni,M.H. Wan Ibrahim,Shahiron Shahidan,Mohammed Ibrahim,Babatunde Abiodun Salami 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.28 No.4
In recent years, bacteria-based self-healing concrete has been widely exploited to improve the compressive strength of concrete using different bacterial species. However, both the identification of the optimal involved reaction parameters and theoretical framework information are still limited. In the present study, both experimentally and numerical modelling using machine learning (ANN and ANFIS) and response surface methodology (RSM) were implemented to evaluate and optimse the evolution of bacterial concrete strength. Therefore, a total of 58 compressive strength tests of the concrete incorporating new bacterial species were designed using different concentrations of urea, cells concentration, calcium, nutrient and time. Based on the results, the compressive strength of the bacterial concrete improved by 16% due to the decrement of the pore percentage in the concrete skin; specifically, 5 mm from the concrete surface, compared to that of the control concrete. In the same context, both machine the learning and RSM models indicated that the optimal range of urea, calcium, nutrient and bacterial cells were (18-23 g/L), (150-350 mM), (1-3 g/L) and 2×107 cells/mL, respectively. Based on the statistical analysis, RMSE, <i>R</i><sup>2</sup>, MPE, RAE and RRSE were (0.793, 0.785), (0.985, 0.986), (1.508, 1.1), (0.11, 0.09) and (0.121, 0.12) from both the ANN and ANFIS models, respectively, while; the following values (0.839, 0.972, 1.678, 0.131 and 0.165) was obtained from RSM model, respectively. As such, it can be concluded that a high correlation and minimum error were obtained, however, machine learning models provided more accurate results compared to that of the RSM model.
Circulating DNA in Egyptian Women with Breast Cancer
Ibrahim, Iman Hassan,Kamel, Mahmoud M,Ghareeb, Mohamed Asian Pacific Journal of Cancer Prevention 2016 Asian Pacific journal of cancer prevention Vol.17 No.6
The commonest cancer in Egyptian females occurs in the breast cfDNA is a non-invasive marker for tumor detetion and prognostic assessment in many types of cancer including breast cancer. This study aimed to assess the role of cfDNA and its fragmentation pattern in breast cancer prognosis and treatment response. Forty female patients with malignant breast tumors and a comparable group of healthy blood donors were enrolled prospectively. cfDNA levels and fragmentation patterns were investigated after cfDNA extraction, gel electrophoresis and gel analysis. The percentage of breast cancer patients positive for cfDNA (92.5%) was significantly higher than that of controls (55%). Also, mean concentration of cfDNA was significantly higher than in the control group (P<0.05). Most Her-2 positive patients had long cfDNA fragments, this being significant as compared to Her-2 negative patients (P<0.05). Metastasis was also positively linked to significantly higher cfDNA (P<0.05) and the mean cfDNA integrity index was significantly higher in non-responders compared to treatment responders (P<0.05). In conclusion, both qualitative and quantitative aspects of cfDNA and its different fragments in breast cancer patients could be related to prognosis, metastasis and treatment response. Long cfDNA fragments could be particularly useful for prediction purposes.
Ibrahim, Suzan Seif,Al-Attas, Safia Ali,Darwish, Zeinab Elsayed,Amer, Hala Abbas,Hassan, Mona Hassan Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.15
Background: To evaluate the effectiveness of Microlux/DL with and without toluidine blue in screening of potentially malignant and malignant oral lesions. Materials and Methods: In this diagnostic clinical trial clinical examination was carried out by two teams: 1) two oral medicine consultants, and 2) two general dentists. Participants were randomly and blindly allocated for each examining team. A total of 599 tobacco users were assessed through conventional oral examination (COE); the examination was then repeated using Microlux/DL device and toluidine blue. Biopsy of suspicious lesions was performed. Also clinicians opinions regarding the two tools were obtained. Results: The sensitivity and, specificity and positive predictive value (PVP) of Microlux/DL for visualization of suspicious premalignant lesions considering COE as a gold standard (i.e screening device) were 94.3%, 99.6% and 96.2% respectively, while they were 100%, 32.4% and 17.9% when considering biopsy as a gold standard. Moreover, Microlux/DL enhanced detection of the lesion and uncovered new lesions compared to COE, whereas it did not alter the provisional clinical diagnosis, or alter the biopsy site. On the other hand, adding toluidine blue dye did not improve the effectiveness of the Microlux/DL system. Conclusions: The Microlux/DL seems to be a promising adjunctive screening device.
Traditional Software Development for WLAN Propagation Model
Anwar Hassan Ibrahim,Mahamod Ismail,Kasmiran Jumari,Tiong Sieh Kiong 대한전기학회 2007 Journal of Electrical Engineering & Technology Vol.2 No.1
SPWPM traditional software development is surveyed and essential problems are investigated on the basis of system wireless link considerations. This paper presents the current state software planning tools for wireless LAN link optimization. The software directory is based on combination of MatLab and MapInfo software and measurement which gives the best grouping parameters to build up the software development. Among the requirements assumed, the WLAN site selections must be Line-of-sight (LOS) or near line of sight (NLOS) field strength prediction for either point to point or point to multi points. The results obtainable the out put of the program include two-dimensional (2D) and three dimensional (3D) plots for creating the link; design parameters through GUI representing the height and location for each antenna is depending on K-factor of the area and transmit antenna location.
CNN을 이용한 딥러닝 기반 하수관 손상 탐지 분류 시스템
Syed Ibrahim Hassan,Dang Lien Minh,임수현(Su-hyeon Im),민경복(Kyung-bok Min),남준영(Jun-young Nam),문현준(Hyeon-joon Moon) 한국정보통신학회 2018 한국정보통신학회논문지 Vol.22 No.3
연구는 인공지능 분야의 딥러닝 기술을 기반으로 한 하수관 손상의 자동 탐지 분류 시스템을 제안한다. 성능의 최적화를 위하여 DB 획득 시 발생된 조도 및 그림자 변화와 같은 다양한 환경변화에 강인한 시스템을 구현하였다. 제안된 시스템에서는 Convolutional Neural Network(CNN) 기반의 균열 탐지 및 손상 분류 기법을 구현하였다. 최적의 결과를 위하여 256 x 256 픽셀 해상도의 CCTV 영상 9,941개를 이용하여 CNN모델을 적용하여 손상부위에 대한 딥러닝을 수행하였고 그 결과 98.76 %의 인식률을 획득하였다. 기계학습을 통한 딥러닝 모델을 기반으로 다양한 환경의 하수도 DB에서 720 x 480 픽셀 해상도의 646개의 이미지를 추출하여 성능 평가를 수행 하였다. 본 시스템은 다양한 환경에서 구축된 하수관 데이터베이스 에서 손상 유형의 자동 탐지 및 분류에 최적화된 인식률을 제시한다. We propose an automatic detection and classification system of sewer damage database based on artificial intelligence and deep learning. In order to optimize the performance, we implemented a robust system against various environmental variations such as illumination and shadow changes. In our proposed system, a crack detection and damage classification method using a deep learning based Convolutional Neural Network (CNN) is implemented. For optimal results, 9,941 CCTV images with 256 x 256 pixel resolution were used for machine learning on the damaged area based on the CNN model. As a result, the recognition rate of 98.76% was obtained. Total of 646 images of 720 x 480 pixel resolution were extracted from various sewage DB for performance evaluation. Proposed system presents the optimal recognition rate for the automatic detection and classification of damage in the sewer DB constructed in various environments.