Background: Nasopharyngeal carcinoma (NPC) is one of the most challenging tumors because of their relative inaccessibility and that their spread can occur without significant symptoms with few signs, but Radiotherapy (RT) has a role in treatment of it.
Objectives: To show that RT is still the modality of choice in the treatment of NPC, to study modes of presentations, commonest histopathological types and their percentages, to show differences in the sensitivities of these types to RT and to find out a 5 year survival rate(5YSR) and its relation with lymph node involvement.
Methods: This is a retrospective study of 44 patients with NPC who were treated with routine RT from 1988-2007 at the institute of radiology and nuclear medicine. All patients were treated with megavoltage x-ray with a total dose to the primary lesion was 60-70 Grays (1 Gray = 100 Rads) so we gave 6000-7000 Rads in 6-8 weeks and 50 Grays were applied to the cervical lymphatic chain bilaterally.
Results: 10 out of 44 patients treated have survived more than 5 years (with a 5YSR of 22.7%). In this series of cases, the 5- year overall survival rate is: 60% with stage I, 33.3% with stage II, 28.5% with stage III and 13.7% with stage IV. But, it should be noted that most of them were advanced with stages III and IV accounting for 36 patients i.e 81.8%.
Conclusion: Radiotherapy (RT) is the modality of choice in the treatment of NPC and we must irradiate areas of probable spread with the primary lesion because spread can occur without significant signs and symptoms .The most common histopathological type is undifferentiated carcinoma which is more sensitive to RT than squamous cell carcinoma (scc) or other types of carcinoma.
Also we see that stages III and IV NPC (advanced) comprises high number of the total and the 5-YSR decreases as the patient advances from stage I to stage IV, therefore, early detection and diagnosis is very important.
The main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media with a realistic appearance. To increase the accuracy of distinguishing a real video from fake one, a new model has been developed based on deep learning and noise residuals. By using Steganalysis Rich Model (SRM) filters, we can gather a low-level noise map that is used as input to a light Convolution neural network (CNN) to classify a real face from fake one. The results of our work show that the training accuracy of the CNN model
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Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the networ
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structural and electrical of CuIn (Sex Te1-x)2
In the absence of environmental regulation, food stays to be contaminated with heavy metals, which is becoming a big worry for human health. The present research focusses on the environmental and health effects of irrigating a number of crops grown in the soils surrounding the Al-Rustamia old plant using treated wastewater generated by the plant. The physicochemical properties, alkalinity, and electrical conductivity of the samples were evaluated, and vegetable samples were tested for Cd, Pb, Ni, and Zn, levels, and even the transfer factor (TF) from soils to crops and crop and multi-targeted risk, daily intake (DIM) of metals, and health risk index (HRI) was calculated. The findings found that the average contents of Zn, Pb, Ni, an
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he assignment model represents a mathematical model that aims at expressing an important problem facing enterprises and companies in the public and private sectors, which are characterized by ensuring their activities, in order to take the appropriate decision to get the best allocation of tasks for machines or jobs or workers on the machines that he owns in order to increase profits or reduce costs and time As this model is called multi-objective assignment because it takes into account the factors of time and cost together and hence we have two goals for the assignment problem, so it is not possible to solve by the usual methods and has been resorted to the use of multiple programming The objectives were to solve the problem of
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The robot arm is the most popular robotic form used in industry. Thus, it is crucial to make a system programming which could controlled the movement of each part in the industrial robot to make it works properly. One of the simplest models of the robot arm is EDARM ED-7100 which has a controller to control the movement of the robot arm manually. In this study, the robot controller has been redesigned in order to improve this robot's function. The new controller system used AT89S52 microcontroller which has wire connected to the robot hand. A function has been added with this controller to improve the system of controlling and becomes better than the previous system (only manually). The functions of the new system include three mo
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