Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

The purpose of this study was to find out the connection between the water parameters that were examined in the laboratory and the water index acquired from the examination of the satellite image of the study area. This was accomplished by analysing the Landsat-8 satellite picture results as well as the geographic information system (GIS). The primary goal of this study is to develop a model for the chemical and physical characteristics of the Al-Abbasia River in Al-Najaf Al-Ashraf Governorate. The water parameters employed in this investigation are as follows: (PH, EC, TDS, TSS, Na, Mg, K, SO₄, Cl, and NO₃). To collect the samples, ten sampling locations were identified, and the satellite image was obtained on the

Compound 4-(((6-amino-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazin-3-yl)methoxy)methyl)- 2,6-dimethoxyphenol (6) was synthesized by multi steps. The corresponding acetonitrile thioalkyl (7) was cyclized by refluxing with acetic acid to afford 4-(((6-amino-7H-[1,2,4]triazolo[3,4- b][1,3,4]thiadiazin-3-yl)methoxy)methyl)-2,6-dimethoxyphenol (8). Two new series of 4-(((6-(3- (4-aryl)thioureido)-7H-[1,2,4]triazolo[3,4-b][1,3,4] thiadiazin-3-yl)methoxy)methyl)-2,6- dimethoxyphenol (9a-c) and of 4-(((6-(substitutedbenzamido)7H-[1,2,4]triazolo[3,4- b][1,3,4]thiadiazin-3-yl)methoxy)methyl)-2,6-dimethoxyphenol (10a-c) were synthesized as new derivatives for fused 1,2,4-trizaole-thiadiazine(8). The antioxidants of newly compounds were evaluated by DPPH

Compound 4-(((6-amino-7H-[1, 2, 4] triazolo [3, 4-b][1, 3, 4] thiadiazin-3-yl) methoxy) methyl)-2, 6-dimethoxyphenol (6) was synthesized by multi steps. The corresponding acetonitrile thioalkyl (7) was cyclized by refluxing with acetic acid to afford 4-(((6-amino-7H-[1, 2, 4] triazolo [3, 4-b][1, 3, 4] thiadiazin-3-yl) methoxy) methyl)-2, 6-dimethoxyphenol (8). Two new series of 4-(((6-(3-(4-aryl) thioureido)-7H-[1, 2, 4] triazolo [3, 4-b][1, 3, 4] thiadiazin-3-yl) methoxy) methyl)-2, 6-dimethoxyphenol (9a-c) and of 4-(((6-(substitutedbenzamido) 7H-[1, 2, 4] triazolo [3, 4-b][1, 3, 4] thiadiazin-3-yl) methoxy) methyl)-2, 6-dimethoxyphenol (10a-c) were synthesized as new derivatives for fused 1, 2, 4-trizaole-thiadiazine (8). The antioxidant

The study aimed to identify the treatment of the press image of the Great Return Marches in the French international news agency AFP by knowing the most important issues, their direction and the degree of interest in them. The study belongs to the descriptive research, and used the survey method, within the context of the content analysis method, and the researcher relied on the content analysis form tool and the interview tool to collect data. The study population is represented in the photos published by the French News Agency about the Great Return Marches during the period (end of March / 2018 until the end of November / 2019. The researcher chose an intentional sample using the Complete Census method. The study material represented

This study included the isolation and identification of Aspergillus flavus isolates associated with imported American rice grains and local corn grains which collected from local markets, using UV light with 365 nm wave length and different media (PDA, YEA, COA, and CDA ). One hundred and seven fungal isolates were identified in rice and 147 isolates in corn.4 genera and 7 species were associated with grains, the genera were Aspergillus ,Fusarium ,Neurospora ,Penicillium . Aspergillus was dominant with occurrence of 0.47% and frequency of 11.75% in rice grains whereas in corn grains the genus Neurospora was dominant with occurrence of 1.09% and frequency 27.25% ,results revealed that 20 isolates out of 50 A. flavus isolates were able

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve