Gardens’ City is a new city in newly discovered area in the Egyptian western desert, which is rich to be developed. It lies in new Farafra Oasis. The site has different potential aspects for sustainable development; it has agricultural and industrial economic bases. The city center's area is designed to be about 5% of the city's area. The area of the industrial zone is about 22% of city area. This paper refers to the development of the city with a focus on the central and the industrial zones. The city center has the major managerial and commercial services. The industrial zone includes industrial areas as well as the major industrial education, training and managerial services. Renewable energy will be generated with different methods. This city will be the first step of development series opportunities in Egypt. Gardens’ City will have different sustainable options and the estimated yearly net profit for it would be 63-90 Million Egyptian pound (LE) and 394-535 Million LE yearly net profit for the whole new Farafra Oasis from olive, palm and wheat only. This city will be the first step that opens great development opportunities in Egypt

Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy. Our proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner. We show the generality and superiority of our proposed text recognition architecture by achieving state of the art results on seven public benchmark datasets, covering a wide spectrum of text recognition tasks, namely: Handwriting Recognition, CAPTCHA recognition, OCR, License Plate Recognition, and Scene Text Recognition. Our proposed architecture has won the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset.

Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy. Our proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner. We show the generality and superiority of our proposed text recognition architecture by achieving state of the art results on seven public benchmark datasets, covering a wide spectrum of text recognition tasks, namely: Handwriting Recognition, CAPTCHA recognition, OCR, License Plate Recognition, and Scene Text Recognition. Our proposed architecture has won the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset.

Channel-based microfluidics was proven to be a helpful platform for reproducible preparation of nanoparticles (NPs), where controlled mixing of fluids allows homogeneous and tuned process of NPs formation. Nanoprecipitation is a popular method for polymeric NPs formation based on controlled precipitation of a polymer upon mixing of two miscible solvents. Conventionally, flow rate, flow rate ratio and polymer concentration have been utilized to control NPs size and polydispersity. However, minimum attention has been given to the effect of channel geometry on nanoprecipitation process. In our study, we investigated the effect of channel geometry and design on the size and polydispersity index (PDI) of poly (lactic-co-glycolic) acid (PLGA) NPs. Ten different designs with varied channel length, aspect ratio, number of interfaces and channel curvature were fabricated and tested. These variations were introduced to modify the diffusion rate, the interface area or to introduce Dean flow, all of which will change the mixing time . The effects of these variations were compared to that of different flow parameters. Change in channel length did not have a significant effect on particle size. However, increasing the diffusion area and reducing significantly reduced NPs’ size. Moreover, when curvature was introduced into the channel, mixing was enhanced, and particle size was decreased in a manner dependent on the velocity of the generated Dean flow. While different flow parameters continue to be the main approach for adjusting NPs properties, we demonstrate that channel geometry modification enables tuning of NPs’ size using simple designs that can be easily adapted.

Channel-based microfluidics was proven to be a helpful platform for reproducible preparation of nanoparticles (NPs), where controlled mixing of fluids allows homogeneous and tuned process of NPs formation. Nanoprecipitation is a popular method for polymeric NPs formation based on controlled precipitation of a polymer upon mixing of two miscible solvents. Conventionally, flow rate, flow rate ratio and polymer concentration have been utilized to control NPs size and polydispersity. However, minimum attention has been given to the effect of channel geometry on nanoprecipitation process. In our study, we investigated the effect of channel geometry and design on the size and polydispersity index (PDI) of poly (lactic-co-glycolic) acid (PLGA) NPs. Ten different designs with varied channel length, aspect ratio, number of interfaces and channel curvature were fabricated and tested. These variations were introduced to modify the diffusion rate, the interface area or to introduce Dean flow, all of which will change the mixing time . The effects of these variations were compared to that of different flow parameters. Change in channel length did not have a significant effect on particle size. However, increasing the diffusion area and reducing significantly reduced NPs’ size. Moreover, when curvature was introduced into the channel, mixing was enhanced, and particle size was decreased in a manner dependent on the velocity of the generated Dean flow. While different flow parameters continue to be the main approach for adjusting NPs properties, we demonstrate that channel geometry modification enables tuning of NPs’ size using simple designs that can be easily adapted.

Channel-based microfluidics was proven to be a helpful platform for reproducible preparation of nanoparticles (NPs), where controlled mixing of fluids allows homogeneous and tuned process of NPs formation. Nanoprecipitation is a popular method for polymeric NPs formation based on controlled precipitation of a polymer upon mixing of two miscible solvents. Conventionally, flow rate, flow rate ratio and polymer concentration have been utilized to control NPs size and polydispersity. However, minimum attention has been given to the effect of channel geometry on nanoprecipitation process. In our study, we investigated the effect of channel geometry and design on the size and polydispersity index (PDI) of poly (lactic-co-glycolic) acid (PLGA) NPs. Ten different designs with varied channel length, aspect ratio, number of interfaces and channel curvature were fabricated and tested. These variations were introduced to modify the diffusion rate, the interface area or to introduce Dean flow, all of which will change the mixing time . The effects of these variations were compared to that of different flow parameters. Change in channel length did not have a significant effect on particle size. However, increasing the diffusion area and reducing significantly reduced NPs’ size. Moreover, when curvature was introduced into the channel, mixing was enhanced, and particle size was decreased in a manner dependent on the velocity of the generated Dean flow. While different flow parameters continue to be the main approach for adjusting NPs properties, we demonstrate that channel geometry modification enables tuning of NPs’ size using simple designs that can be easily adapted.

Channel-based microfluidics was proven to be a helpful platform for reproducible preparation of nanoparticles (NPs), where controlled mixing of fluids allows homogeneous and tuned process of NPs formation. Nanoprecipitation is a popular method for polymeric NPs formation based on controlled precipitation of a polymer upon mixing of two miscible solvents. Conventionally, flow rate, flow rate ratio and polymer concentration have been utilized to control NPs size and polydispersity. However, minimum attention has been given to the effect of channel geometry on nanoprecipitation process. In our study, we investigated the effect of channel geometry and design on the size and polydispersity index (PDI) of poly (lactic-co-glycolic) acid (PLGA) NPs. Ten different designs with varied channel length, aspect ratio, number of interfaces and channel curvature were fabricated and tested. These variations were introduced to modify the diffusion rate, the interface area or to introduce Dean flow, all of which will change the mixing time . The effects of these variations were compared to that of different flow parameters. Change in channel length did not have a significant effect on particle size. However, increasing the diffusion area and reducing significantly reduced NPs’ size. Moreover, when curvature was introduced into the channel, mixing was enhanced, and particle size was decreased in a manner dependent on the velocity of the generated Dean flow. While different flow parameters continue to be the main approach for adjusting NPs properties, we demonstrate that channel geometry modification enables tuning of NPs’ size using simple designs that can be easily adapted.

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