Predicting Biochemical Oxygen Demand at the Inlet of Al-Rustumiya Wastewater Treatment Plant Using Different Mathematical Techniques

Water quality planning relies on Biochemical Oxygen Demand BOD. BOD testing takes five days. The Particle Swarm Optimization (PSO) is increasingly used for water resource forecasting. This work designed a PSO technique for estimating everyday BOD at Al-Rustumiya wastewater treatment facility inlet. Al-Rustumiya wastewater treatment plant provided 702 plant-scale data sets during 2012-2022. The PSO model uses the daily data of the water quality parameters, including chemical oxygen demand (COD), chloride (Cl-), suspended solid (SS), total dissolved solids (TDS), and pH, to determine how each variable affects the daily incoming BOD. PSO and multiple linear regression (MLR) findings are compared, and their performance is evaluated using mean square error, relative absolute mistake, and coefficient of determination. PSO utilised COD, TDS, SS, pH, and Cl- as inputs, generating a mean square error of 1029.10, an average absolute relative error of 9.41%, and a coefficient of determination of 0.89. Comparisons demonstrated that the PSO model could accurately calculate the daily BOD at Al-Rustumiya wastewater treatment plant's inlet.

Artificial Intelligence Approaches in Student Modeling: Half Decade Review (2010-2015)

Intelligent Tutoring Systems (ITSs) are special classes of E-learning systems designed using Artificial Intelligence (AI) approaches to provide adaptive and personalized tutoring based on the individuality of students. The student model is an important component of an ITS that provides the base for this personalization. During the course of interaction between student and the ITS, the system observe student’s actions and other behavioral properties, create a quantitative representation of these student’s attributes called a student model

PROMISING THE DREAM: changing destination image of London through the effect of website place

Drawing on theories of place identity and social identity, this study aims to fill a gap in place identity studies regarding the effect of a place website on the destination image of customers/visitors/tourists. The research addresses three questions: (1) what are the main impacts of tourists’ attitude on place identity and the place website, (2) what are the factors that influence destination image, and (3) what are the main impacts of a favorable destination image? The favorability of a destination image is reflected by the extent to which visitors positively regard that place website. Results reveal the importance of the destination image in enhancing the intention to revisit and recommend. Also, visitors’ satisfaction impacts on their intention to revisit and recommend the place. Significant implications for place managers and researchers are highlighted

PROMISING THE DREAM: changing destination image of London through the effect of website place

Drawing on theories of place identity and social identity, this study aims to fill a gap in place identity studies regarding the effect of a place website on the destination image of customers/visitors/tourists. The research addresses three questions: (1) what are the main impacts of tourists’ attitude on place identity and the place website, (2) what are the factors that influence destination image, and (3) what are the main impacts of a favorable destination image? The favorability of a destination image is reflected by the extent to which visitors positively regard that place website. Results reveal the importance of the destination image in enhancing the intention to revisit and recommend. Also, visitors’ satisfaction impacts on their intention to revisit and recommend the place. Significant implications for place managers and researchers are highlighted