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Perceptions of Senior Pharmacy Students Towards the Impact of Artificial Intelligence on University Education and Scientific Writing: A Qualitative Study
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Background: The roles of AI in the academic community continue to grow, especially in the enhancement of learning outcomes and the improvement of writing quality and efficiency. Objectives: To explore in depth the experience of senior pharmacy students in using artificial intelligence for academic purposes. Methods: This qualitative study included face-to-face individual interviews with senior pharmacy students from March to May 2023 using a pre-planned interview guide of open-ended questions. All interviews were audio-recorded. Thematic analysis was used to analyze the data. Results: The results were obtained from 15 in-depth face-to-face interviews with senior pharmacy students (5th and 4th years). Eight participants were male, and seven were female (21–25 years old). All participants used ChatGPT for a few months for academic purposes, especially when writing take-home assignments. The perceptions were positive about the students’ gains from using ChatGPT. Still, at the same time, they admitted that AI might negatively impact the student’s motivation to learn new academic skills. Conclusions: The students believed that AI was very helpful, with concerns that it did not enhance their critical thinking or writing skills. Thus, educators need to change their strategies for teaching and testing students to improve student skills and identify students’ own work.

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Publication Date
Tue Sep 06 2022
Journal Name
Methods And Objects Of Chemical Analysis
Spectrophotometric Analysis of Quaternary Drug Mixtures using Artificial Neural network model
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A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twenty four samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

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Publication Date
Mon Mar 30 2009
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of bubble size in Bubble columns using Artificial Neural Network
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In the literature, several correlations have been proposed for bubble size prediction in bubble columns. However these correlations fail to predict bubble diameter over a wide range of conditions. Based on a data bank of around 230 measurements collected from the open literature, a correlation for bubble sizes in the homogenous region in bubble columns was derived using Artificial Neural Network (ANN) modeling. The bubble diameter was found to be a function of six parameters: gas velocity, column diameter, diameter of orifice, liquid density, liquid viscosity and liquid surface tension. Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 7.3 % and correlation coefficient of 92.2%. A

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Publication Date
Sat Jan 01 2022
Journal Name
Methods And Objects Of Chemical Analysis
Spectrophotometric Analysis of Quaternary Drug Mixtures using Artificial Neural network model
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A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

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Publication Date
Mon Dec 02 2024
Journal Name
Engineering, Technology & Applied Science Research
An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength
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This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

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Publication Date
Wed Sep 20 2023
Journal Name
Journal Of Aeronautical Materials
The Effect of A Dry Training Program Using Aids On The Results Of Error Measures And The Effect Size On The Air Pistol Accuracy Of Correction
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The world is keeping pace with evolution in all its fields as a result of scientists' pursuit of continuous scientific and technological development. This evolution included the sports field, which had a large space in the aspect of development and for all disciplines, Therefore, it's reflected today in what we see of records and advanced achievements in sporting events and activities. The development in the field of sports was the result of scientific research (Hussein and Jawad., 2022), where the interest in the training process has become one of the most important pillars of the development of achievement (Neamah and Altay., 2020). The shooting sport has also witnessed a remarkable development due to the diversity and development of its

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Publication Date
Thu Mar 31 2022
Journal Name
Iraqi Geological Journal
Development of New Models to Determine the Rheological Parameters of Water-Based Drilling Fluid using Artificial Neural Networks
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It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works represent valid and reliable results, they are expensive and time consuming. In contrast, continuous and regular determination of the rheological fluid properties can perform its essential functions during good construction. The aim of this study is to develop empirical models to estimate the drilling mud rheological properties of water-based fluids with less need for lab measurements. This study provides two predictive techniques, multiple regression analysis and artificial neural networks, to determine the rheological

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Publication Date
Sat Oct 01 2016
Journal Name
Al–bahith Al–a'alami
Semiotics of Joseph Curtis… A Study in Perspective and Concept
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Joseph Curtis wrote in his book (Introduction to Semiotics narrative and rhetoric) on the development of a semiotic project in exploring the meaning of communion. The book is rich in theoretical and analytical benefits both in the approach of texts and in the pursuit of letters in search of mechanisms of scriptural and scriptural generation and mechanics in narrative production, especially as the book offers exercises that help to assess the distance separating the narrative spread from the sin of the text, for the semiotics, Refers to the theoretical developments of the science of relations, through the combination of conventional definitions and the representative and comparative limitations, to provide an approach to the application o

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Publication Date
Sun Jun 01 2014
Journal Name
Baghdad Science Journal
Study the effect of acid immersion on the hardness of (Epoxy – Granite) composite
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This work has been done with using of epoxy resin mixed with Granite powder were weighted by percent volume (5,10,15, and 20)%and then mixed with epoxy polymer to compose polymer composite. Hand lay-up technique is used in fabrication of the composite samples. Hardness test was carried out for the proper samples in both normal condition and after immersion in HCL (1 M and 2 M) solutions for periods ranging up to 10 weeks. After comparing the results between the polymer and their composite, the hardness increased with increasing Granite weight percent, it was found that Hardness were greater for the composites before immersion compared with their values after immersion.

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Publication Date
Sun Jan 01 2023
Journal Name
Aip Conference Proceedings
Study the effect of curved edges on the aerodynamics performance of suction slot
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Publication Date
Tue Oct 30 2018
Journal Name
Journal Of Engineering
Study the Effect of Various Parameters on the Synthesis of ZSM-5 Zeolite
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ZSM-5 zeolite was synthesis under hydrothermal conditions at 175oC. The synthesis parameters have been investigated to find optimum synthesis method. Firstly, the crystallization time has been investigated to find the optimum crystallization time. Also, the ageing time was studied. The morphology, structure, and composition of the synthesized ZSM-5 zeolite were characterized using scanning electron microscopy (SEM), pH meter, viscometer, and X-ray powder diffraction (XRD). The bulk Si/Al ratio of ZSM-5 zeolite was in the range of 9.5—11.7. The synthesized ZSM-5 zeolite with appropriate ageing time could adjust crystal size and degree of the crystallinity. The crystal size of ZSM-5 zeolite obtained at an agei

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