The main objective of this paper is to develop and validate flow injection method, a precise, accurate, simple, economic, low cost and specific turbidimetric method for the quantitative determination of mebeverine hydrochloride (MbH) in pharmaceutical preparations. A homemade NAG Dual & Solo (0-180º) analyser which contains two identical detections units (cell 1 and 2) was applied for turbidity measurements. The developed method was optimized for different chemical and physical parameters such as perception reagent concentrations, aqueous salts solutions, flow rate, the intensity of the sources light, sample volume, mixing coil and purge time. The correlation coefficients (r) of the developed method were 0.9980 and 0.9986 for cell 1 and 2 respectively and showed the linearity of response against concentration over the range of 1.0 to 6.5 and 0.7-6.5mmol/L for cell 1 & 2 respectively. The limit of detections (LOD) for cell 1 and cell 2 were 0.28 and 0.21 mmol/L respectively. The intra-day and inter-day precision for two serial estimations of 3.5 and 5.5 mmol/L of MBH exhibited a relative standard deviation of 0.46%, 0.28%, 0.23%, 0.26% and 0.39%, 0.79%, 0.14%, 0.05% for cell 1 & 2 respectively. The accuracy of the developed method has expressed a recovery percentage (Rec %) and error % which was between 99.22 to 101.13 and 99.39 to 101.17 for cell 1 and cell 2 respectively. The ICH guidelines were followed for method validation. The developed method was successfully applied for the determination of MbH in pure and pharmaceutical preparations and the method can be conveniently used for routine analysis in laboratory as a quality control method since the method permits quantitively determination of 60 samples/h.
The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of
... Show MoreObjectives: The purpose of the study is to ascertain the relationship between the training program and the socio-demographic features of patients with peptic ulcers in order to assess the efficiency of the program on patients' nutritional habits.
Methodology: Between January 17 and October 30 of 2022, The Center of Gastrointestinal Medicine and Surgery at Al-Diwanyiah Teaching Hospital conducted "a quasi-experimental study". A non-probability sample of 30 patients for the case group and 30 patients for the control group was selected based on the study's criteria. The study instrument was divided into 4 sections: the first portion contained 7 questions about demographic information, the second sect
... Show MoreProviding Iraqi students with proficiency in English is the ultimate goal of the educational system which is a way of getting knowledge in the fields of arts, sciences, transferring knowledge and sciences to other communities. Therefore, conducting such a type of study is very important because the contents of English textbooks have a huge influence on learning of the students. Once the content of English textbooks contain errors as the correct one, this will effect on his/her learning. The present study is an attempt to evaluate the new course entitled “English for Iraq” for fifth grade students for secondary schools, by Olivia Johnston and Mark Farell. It aims to answer eleven questions relating to the following domains: strength, obj
... Show MoreThe main objective of this paper is to designed algorithms and implemented in the construction of the main program designated for the determination the tenser product of representation for the special linear group.