Les transferts monétaires et commerciaux des Marocaines et le développement local au Maroc

L'évocation de l'émigration marocaine, décrite dans la littérature spécialisée, fait généralement référence à l'émigration masculine et à ses implications économiques sur le pays d'origine. Les épouses, en revanche, ont été longtemps perçues comme inactives, dépendantes et responsables de la dépense des sommes expédiées par les maris, aussi bien avant que pendant l'immigration. Cependant, elles sont de plus en plus nombreuses à ne plus vouloir se contenter d'observer leur conjoint agir. Elles décident de prendre des initiatives qui leur permettent de s'affirmer et de montrer qu'elles sont capables de jouer un rôle tout aussi important que leur époux dans le projet migratoire familial et en même temps de participer au développement économique local dans le pays d'origine

Eucalyptus

In Egypt, the River Red Gum (Eucalyptus camaldulensis) is a well-known tree and is highly appreciated by the rural and urban dwellers. The role of Eucalyptus trees in the ecology of Cryptococcus neoformans is documented worldwide. The aim of this survey was to show the prevalence of C. neoformans during the flowering season of E. camaldulensis at the Delta region in Egypt. Three hundred and eleven samples out of two hundred Eucalyptus trees, including leaves, flowers, and woody trunks, were collected from four governorates in the Delta region. Thirteen isolates of C. neoformans were recovered from Eucalyptus tree samples (4.2%). Molecular identification of C. neoformans was done by capsular gene specific primer CAP64 and serotype identification was done depending on LAC1 gene. This study represents an update on the ecology of C. neoformans associated with Eucalyptus tree in Egyptian environment

An Assistive Object Recognition System for Enhancing Seniors Quality of Life

AbstractThis paper presents an indoor object recognition system based on the histogram of oriented gradient and Machine Learning (ML) algorithms; such as Support Vector Machines (SVMs), Random Forests (RF) and Linear Discriminant Analysis (LDA) algorithms, for classifying different indoor objects to improve quality of elderly people's life. The proposed approach consists of three phases; namely segmentation, feature extraction, and classification phases. Datasets used for these experiments, are totally consisted of 347 images with different eight indoor objects used for both training and testing datasets. Training dataset is divided into eight classes representing the different eight indoor objects. Experimental results showed that RF classification algorithm outperformed both SVMs and LDA algorithms, where RF achieved 80.12%, SVMs and LDA achieved 77.81% and 78.76% respectively

Tree: A Potential Source of Cryptococcus neoformans in Egyptian Environment

In Egypt, the River Red Gum (Eucalyptus camaldulensis) is a well-known tree and is highly appreciated by the rural and urban dwellers. The role of Eucalyptus trees in the ecology of Cryptococcus neoformans is documented worldwide. The aim of this survey was to show the prevalence of C. neoformans during the flowering season of E. camaldulensis at the Delta region in Egypt. Three hundred and eleven samples out of two hundred Eucalyptus trees, including leaves, flowers, and woody trunks, were collected from four governorates in the Delta region. Thirteen isolates of C. neoformans were recovered from Eucalyptus tree samples (4.2%). Molecular identification of C. neoformans was done by capsular gene specific primer CAP64 and serotype identification was done depending on LAC1 gene. This study represents an update on the ecology of C. neoformans associated with Eucalyptus tree in Egyptian environment

Presidential candidates in Egypt: who is more worthy of the presidential title?

With the upcoming presidential elections on the doors, everyone is confused about the right candidate to support and/or vote for, when the time comes. AUC\u27s Nora ElHariri reports

Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models

Crack detection on historical surfaces is of significant importance for credible and reliable inspection in heritage structural health monitoring. Thus, several object detection deep learning models are utilized for crack detection. However, the majority of these models are powerful at most in achieving the task of classification, with primitive detection of the crack location. On the other hand, several state-of-the-art studies have proven that pixel-level crack segmentation can powerfully locate objects in images for more accurate and reasonable classification. In order to realize pixel-level deep crack segmentation in images of historical buildings, this paper proposes an automated deep crack segmentation approach designed based on an exhaustive investigation of several U-Net deep learning network architectures. The utilization of pixel-level crack segmentation with U-Net deep learning ensures the identification of pixels that are important for the decision of image classification. Moreover, the proposed approach employs the deep learned features extracted by the U-Net deep learning model to precisely describe crack characteristics for better pixel-level crack segmentation. A primary image dataset of various crack types and severity is collected from historical building surfaces and used for training and evaluating the performance of the proposed approach. Three variants of the U-Net convolutional network architecture are considered for the deep pixel-level segmentation of different types of cracks on historical surfaces. Promising results of the proposed approach using the U2−Net deep learning model are obtained, with a Dice score and mean Intersection over Union (mIoU) of 71.09% and 78.38% achieved, respectively, at the pixel level. Conclusively, the significance of this work is the investigation of the impact of utilizing pixel-level deep crack segmentation, supported by deep learned features, through adopting variants of the U-Net deep learning model for crack detection on historical surfaces

Strawberry-DS

- An annotated benchmark image dataset for training and validation of strawberry ripeness detection systems based on Machine learning (ML) and, Deep Learning (DL). - 247 Raw RGB digital images (.jpg) of strawberry fruits were taken in an orchard of the Central Laboratory for Agricultural Climate (CLAC), Agricultural Research Center, Cairo - Egypt.-The images have been captured from the fruit top view considering different view angles using Sony Xperia Z2 LTE-A D6503 smartphone 20.7 MP camera with a CMOS sensor system and resolution of 3840 x 2160 pixels (Mpix). The dataset images, which contain both fully-visible strawberry fruits and partially-visible strawberry fruits concealed by leaves or by other fruits, were manually annotated, using Roboflow Annotate annotation tool. The data formats of files in Strawberry-DS dataset are RGB digital images (.jpg) and their corresponding YOLO format (.txt) annotation files.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Historical_Building_Crack_2019

- An annotated benchmark image dataset for training and validation of crack detection systems based on Machine learning (ML) and, Deep Learning (DL) for a historical building. - Real images of historical building cracks were taken at an ancient building suffering from cracking problem (the Mosque (Masjed) of Amir al-Maridani, located in Sekat Al Werdani, El-Darb El-Ahmar, in Cairo Governorate). It was built during the era of the Mamluk Sultanate of Cairo, Egypt in 1339- 40 CE. It is distinguished by its octagonal minaret and its large dome and considered as one of the most distinctively decorated historical buildings. - Raw RGB digital images (.jpg) were captured using Canon camera (Canon EOS REBEL T3i) with 5184 × 3456 resolution over two years (2018 and 2019). - The dataset contains most of the challenges facing historical buildings crack defect detection in real-world environments, such as dust, illumination, separators, crack-like, blurring, deep texture, wood patterns, etc. - All images are divided into sub-images 256 X 256 to enlarge the dataset. - The final Crack Dataset consisted of 3886 images [ 757 for crack and 3139 for non-crack] To enlarge dataset size for training deep learning models, data augmentation process can be applied to increase training dataset size via generating new samples that are similar to the training samples. The following steps can be used: 1- Flipping image (vertically, horizontally and, vertically + horizontally), 2- Rotating image by 90 and -90 individually, 3- Flipping rotated images vertically, 4- Combining the output images of (1, 2 and 3) with the original images to create new dataset, 5- Adding noise to images of the new dataset such as Gaussian and salt and pepper noise, 6- Combining the output images of steps (4 and 5) to create the final augmented dataset

Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models

Crack detection on historical surfaces is of significant importance for credible and reliable inspection in heritage structural health monitoring. Thus, several object detection deep learning models are utilized for crack detection. However, the majority of these models are powerful at most in achieving the task of classification, with primitive detection of the crack location. On the other hand, several state-of-the-art studies have proven that pixel-level crack segmentation can powerfully locate objects in images for more accurate and reasonable classification. In order to realize pixel-level deep crack segmentation in images of historical buildings, this paper proposes an automated deep crack segmentation approach designed based on an exhaustive investigation of several U-Net deep learning network architectures. The utilization of pixel-level crack segmentation with U-Net deep learning ensures the identification of pixels that are important for the decision of image classification. Moreover, the proposed approach employs the deep learned features extracted by the U-Net deep learning model to precisely describe crack characteristics for better pixel-level crack segmentation. A primary image dataset of various crack types and severity is collected from historical building surfaces and used for training and evaluating the performance of the proposed approach. Three variants of the U-Net convolutional network architecture are considered for the deep pixel-level segmentation of different types of cracks on historical surfaces. Promising results of the proposed approach using the U2−Net deep learning model are obtained, with a Dice score and mean Intersection over Union (mIoU) of 71.09% and 78.38% achieved, respectively, at the pixel level. Conclusively, the significance of this work is the investigation of the impact of utilizing pixel-level deep crack segmentation, supported by deep learned features, through adopting variants of the U-Net deep learning model for crack detection on historical surfaces