This paper aims to evaluate large-scale water treatment plants’ performance and demonstrate that it can produce high-level effluent water. Raw water and treated water parameters of a large monitoring databank from 2016 to 2019, from eight water treatment plants located at different parts in Baghdad city, were analyzed using nonparametric and multivariate statistical tools such as principal component analysis (PCA) and hierarchical cluster analysis (HCA). The plants are Al-Karkh, Sharq-Dijlah, Al-Wathba, Al-Qadisiya Al-Karama, Al-Dora, Al-Rasheed, Al-Wehda. PCA extracted six factors as the most significant water quality parameters that can be used to evaluate the variation in drinking water quality and responsible for 73.389% of the variance in the data set. Based on this selection criterion, the most significant water quality parameters that can be used to evaluate the variation in drinking water quality parameters are the mineral-related parameters (e.g., Ca+2, Mg+2, salinity, hardness), the nutrient parameters (i.e., dissolved nitrate and nitrite and orthophosphate), and a physical parameter. HCA analysis was able to group water treatment plants with similar raw water and treated water quality based on the water quality data from eight WTPs into three clusters.
The present study deals with the effect of self -observation on EFL University students` achievement in conversation classes. The process of self-observation helps the teacher to understand one’s own actions and the reactions in the process of teaching. The sample of this study is EFL students in the third stage at the Department of English Language, morning studies, College of Education /Ibn-Rushd .The sample of the study consists of (84) students distributed on experimental group(A) includes (42) students, and (42) students as control group(B). In order to achieve the aim of the study ,and to gain a closer idea about the impact of reflective teaching technique(self-observation) on the students achievement in conversation classes, a chec
... Show MoreProblem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a
... Show MoreThe result revealed that the peak of population density of cabbage aphid Brevicoryne brassicae was 523.20 individuals/plant on 21 March in edges of rapeseed field and was 1141.67 individuals/plant in center of the field. Results revealed that population density of cabbage aphid in rapeseed fields surrounded by cover crops significantly were low compared with that of monoculture rapeseed. The location of rapeseed plants (in edges or in center) significantly affected (p<0.05) the tested pest density, e.g. optimum density was 146.69 individuals/plant in the center of the field. Whereas was 93.32 in the edges. Effect of the interaction between location and surrounding vegetation was significant on aphid density, which their population densit
... Show More
Abstract
The human mind knew the philosophy and logic in the ancient times, and the history afterwards, while the semiotics concept appeared in the modern time, and became a new knowledge field like the other knowledge fields. It deals, in its different concepts and references, with the processes that lead to and reveals the meaning through what is hidden in addition to what is disclosed. It is the result of human activity in its pragmatic and cognitive dimensions together. The semiotic token concept became a knowledge key to access all the study, research, and investigation fields, due to its ability of description, explanation, and dismantling. The paper is divided into two sections preceded by a the
... Show MoreDeep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d
... Show MoreFirstly, in this study, a brief updated description and applications of different solar collectors used in renewable energy systems for supplying electric and thermal energy was presented. Secondly, an attempt was made to utilize tilting orientation of solar collector for maximizing collector energy with time in respect to horizontal orientation. For energy calculation, global solar radiation was used since they are directly related. For that purpose, field measurements of half-hourly radiation on two flat panels of tilting and horizontal orientations were carried out throughout 8-month period under local climate of Baghdad. Then, energy gain and radiation level averages were calculated based on the field radiation
... Show MoreAA Noaimi, BM Fadheel, Saudi medical journal, 2008 - Cited by 25