رنا كامل محمد البو نواص

Abstract. The regional distribution of hepatitis D is evaluated by the utilization of the disease surveillance tool SaTScan and its spatial scan statistic. The statistic for the spatial scan was used in detecting the area that has a significantly higher prevalence of hepatitis. The spatial scan statistic determined Najaf, Babil, Wasit, Maysan, Dha Qar, Basrah, and Muthanna to be most likely to constitute areas of cluster presence for hepatitis D. The relative risk (RR) for men was 1.49 (P < 0.001) and that for females was 1.46 (P < 0.001). After considering all the parameters under this model, we found that the most probable location for clustering hepatitis in the country is the central areaNajaf, Babil, Wasit, Maysan Dha Qar Muthanna Diyala Baghdad Karbala Salah Ad Din and Tam'mim with a relative risk of 6.65(P < 0.001).

This study employed spatial analysis methods using Geographic Information System (GIS) and scanning statistical analysis to examine the predominant occurrences of breast cancer in Al- Najaf city between the years 2019 and 2023. The Moran’s I index locally, reveal a significant pattern concentrated in the eastern, southern, western, and central areas of the province. The hot spot analysis using the Getis-Ord Gi* statistic indicates. Five hot spots of Breast Cancer (BC) cases, with one hot spot being strongly significant at a 99 % confidence interval level (p<0.01) located in the Al-Hawraa-Zeinab region, and the other three hot spots at a 95 % confidence level (p<0.05) being significant in the Al Furat, Al Karama, and AL Rahmaa regions. The spatial scan statistic identifies four locations with significantly low incidence rates (p>0.05) and two locations where

Abstract Land surface temperature (LST) has a significant impact on local climate and ecosystem. Landsat data provided many potentials for understanding the land processes through remote sensing. In this study, LST was identified using the thermal bands of satellite imageries of Landsat 5 TM of 2000 and 2010, and Landsat 8 OLI of 2021 for comparing the data. The image was also employed to create the normalized difference index (NDVI) and bare soil index ( BSI). The correlations between LST, NDVI, and BSI were expressed using linear regression analysis. The research found that the vegetation area (NDVI) has a strong negative relationship with LST, whereas Bare soil Land (LST) has a positive strong relationship with LST. The study adopted NDVI and BSI indicators to apply the multiple regression equation for LST prediction. The results of multiple regression for the three years showed a significant match between actual and predicted temperature.

Abstract Temperatures are greatly influenced by climate and environmental changes. Landsat data offer many possibilities for understanding land-based processes through remote sensing. In this study, thermal bands from Landsat 5 satellite images in February 2010, and Landsat 8 OLI in March 2021 were used to identify the Land Surface Temperature LST in Al Najaf province. These images were also used to create the Normalized Difference Water Index NDWI, the Normalized Difference Vegetation Index NDVI, and the Bare Soil Index BSI. The aim of this research is to find the relationship between all of the above indicators with the LST. The correlation coefficient was found by using simple linear regression. The research results showed that there is a strong inverse relationship between NDWI and NDVI with LST, while BSI has a strong positive relationship with LST.