Turbidity is a visual property of water that expresses the amount of suspended substances in the water. Its presence in quantities more significant than the permissible limit makes the water undrinkable and reduces the effectiveness of disinfectants in treating pathogens. On this basis, turbidity is used as a basic indicator for measuring water quality. This study aims to evaluate the removal efficiency of AL- Muthanna WTP. Water turbidity was used as a basic parameter in the evaluation, using performance improvement evaluation and data from previous years (2016 to 2020). The average raw water turbidity was 26.7 NTU, with a minimum of 14 NTU, with a maximum of 48 NTU. Water turbidity value for 95% of settling daily readi

Turbidity is a visual property of water that expresses the amount of suspended substances in the water. Its presence in quantities more significant than the permissible limit makes the water undrinkable and reduces the effectiveness of disinfectants in treating pathogens. On this basis, turbidity is used as a basic indicator for measuring water quality. This study aims to evaluate the removal efficiency of AL- Muthanna WTP. Water turbidity was used as a basic parameter in the evaluation, using performance improvement evaluation and data from previous years (2016 to 2020). The average raw water turbidity was 26.7 NTU, with a minimum of 14 NTU, with a maximum of 48 NTU. Water turbidity value for 95% of settling daily reading data was

Today, problems of spatial data integration have been further complicated by the rapid development in communication technologies and the increasing amount of available data sources on the World Wide Web. Thus, web-based geospatial data sources can be managed by different communities and the data themselves can vary in respect to quality, coverage, and purpose. Integrating such multiple geospatial datasets remains a challenge for geospatial data consumers. This paper concentrates on the integration of geometric and classification schemes for official data, such as Ordnance Survey (OS) national mapping data, with volunteered geographic information (VGI) data, such as the data derived from the OpenStreetMap (OSM) project. Useful descriptions o

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.

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Background: This study was conducted among diabetic persons to assess the sweet and salty taste sensitivity with its effect on gingival health in relation to salivary serotonin levels. Materials and methods: A cross-sectional comparative study design was used. All patients with diabetes aged 12-14 years that attend the Paediatric hospital at Baghdad medical city with specific inclusion criteria were involved in the sample of the present study (patients group 50 patients) compared with non-diabetic persons matched in age and gender of the study sample (control group 70 patients) who were attending dental unit in the college of dentistry/university of Baghdad. A two-alternative forced choice question including each component presented at f