After baking the flour, azodicarbonamide, an approved food additive, can be converted into carcinogenic semicarbazide hydrochloride (SEM) and biurea in flour products. Thus, determine SEM in commercial bread products is become mandatory and need to be performed. Therefore, two accurate, precision, simple and economics colorimetric methods have been developed for the visual detection and quantitative determination of SEM in commercial flour products. The 1st method is based on the formation of a blue-coloured product with λmax at 690 nm as a result of a reaction between the SEM and potassium ferrocyanide in an acidic medium (pH 6.0). In the 2nd method, a brownish-green colored product is formed due to the reaction between the SEM and phosphomolybdic acid (PMA) in a basic medium (pH 9.0). The resulting product absorbs light at λmax 750 nm. The colorimetric methods can be used either as sensors to detect the SEM by bare eye observation as little as 10 ppm and 2.0 ppm within 4−2 min or by spectrophotometry as the determination methods with linearity ranges 8.0−180 ppm and 0.5−30 ppm for the 1st and 2nd methods respectively. The developed methods were successfully applied to determine the SEM in the commercial bread products with a relative standard deviation (RSD) <3 %, <2 % and recovery of 94–103 %, 96–101 % for methods (1st and 2nd) respectively. The visual detection limits of the sensors can be used as a platform for SEM field-portable detection due to their lower limitations than the reported SEM in flour products, which opens the doors for on-site detection of SEM with instrument free.
In this paper, the propose is to use the xtreme value distribution as the rate of occurrence of the non-homogenous Poisson process, in order to improve the rate of occurrence of the non-homogenous process, which has been called the Extreme value Process. To estimate the parameters of this process, it is proposed to use the Maximum Likelihood method, Method of Moment and a smart method represented by the Artificial Bee Colony:(ABC) algorithm to reach an estimator for this process which represents the best data representation. The results of the three methods are compared through a simulation of the model, and it is concluded that the estimator of (ABC) is better than the estimator of the maximum likelihood method and method of mo
... Show MoreThe use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow
... Show MoreThis study aims to assess the formation evaluation of the Jeribe Formation in Hamrin oilfield. The present study involved four selected wells of (Early- Mid Miocene) Jeribe Formation in Hamrin structure-Allas field; HR-2, HR-8, HR-9, and HR-16 located North of Iraq. The work deals with the available data that includes the most required information to improve such studies. Techlog Software V.2015 was used to carry out a reliable calculation of petrophysical properties utilizing conventional logs to determine the reservoir characteristics (lithology, porosity, and saturation). The computed CPI (software resulted) based on log information divided the Jeribe reservoir into two reservoir units (Jr-1 and Jr
... Show MorePredicting the maximum temperature is of great importance because it is related to various aspects of life, starting from people’s lives and their comfort, passing through the medical, industrial, agricultural and commercial fields, as well as concerning global warming and what can result from it. Thus, the historical observations of maximum and minimum air temperature, wind speed and relative humidity were analyzed in this work. In Baghdad, the climatic variables were recorded on clear sky days dawn at 0300 GMT for the period between (2005-2020). Using weather station's variables multiple linear regression equation, their correlation coefficients were calculated to predict the daily maximum air temperature for any day during
... Show MoreIn this study, different methods were used for estimating location parameter and scale parameter for extreme value distribution, such as maximum likelihood estimation (MLE) , method of moment estimation (ME),and approximation estimators based on percentiles which is called white method in estimation, as the extreme value distribution is one of exponential distributions. Least squares estimation (OLS) was used, weighted least squares estimation (WLS), ridge regression estimation (Rig), and adjusted ridge regression estimation (ARig) were used. Two parameters for expected value to the percentile as estimation for distribution f
... Show MoreThe paper aims at investigating the content of particles and minerals of soft clay soil in the Mekong Delta coastal provinces, southern Vietnam, as well as improving soil stability using inorganic adhesives, i.e.lime and cement-based stabilization. To study the composition of soft clay soil, a series of different laboratory methods were carried out and revealed various characteristics related to mineral composition, pH value, soluble salt content, and grain particle composition of soft clay soils. The results demonstrated five soil subtypes, namely high – saline soil (S2), low-saline soil (S1), acid sulfate soil (A), acid sulfate - saline soil (S-A), soil without salt and
acid sulfate content (S0- A0). The soft clay soil (C) in
Deepfake is a type of artificial intelligence used to create convincing images, audio, and video hoaxes and it concerns celebrities and everyone because they are easy to manufacture. Deepfake are hard to recognize by people and current approaches, especially high-quality ones. As a defense against Deepfake techniques, various methods to detect Deepfake in images have been suggested. Most of them had limitations, like only working with one face in an image. The face has to be facing forward, with both eyes and the mouth open, depending on what part of the face they worked on. Other than that, a few focus on the impact of pre-processing steps on the detection accuracy of the models. This paper introduces a framework design focused on this asp
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