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Predicting of Land Surface Temperature Distribution in Freetown City, Sierra Leone by Using Polynomial Curve Fitting Model

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Journal of Geographic Information System 2020年第5期12卷 531-544页

作者： elhadi k. mustafa Guoxiang Liu Abubak. Hassan Mohamed A. Damos Musa Tarawally Department of Surveying and Geo-Informatics Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu China School of Resources and Environment University of Electronic Science and Technology of China Chengdu China

Global warming has attracted much concern about the worldwide organization, civil society groups, researchers, and so forth because the worldwide surface temperature has been expanding. This investigation intends to assess and compare the ability of a combination of land cover indices to predict the future distribution of land surface temperatures in Freetown using the Polynomial model analysis. Landsat satellite images of 1988, 1998, 2000, 2010, and 2018 of the Freetown Metropolitan zone were utilized for analysis. The investigation had adopted two land covers indices, Modification of normalized difference water index and Urban Index (UI) (e.g., MNDWI and UI) and applied a multi regression equation for forecasting the future LST. The stimulation results propose that the development will be accompanied by surface temperature increases, especially in Freetown’s western urban area. The temperature prevailing in the west of the metropolitan area may increase in the city somewhere in the range from 1988 to 2018. Additionally, the results of the LST prediction show that the model is perfect. Our discoveries can be represented as a helpful device for policymak.rs and community awareness by giving a scientific basis for sustainable urban planning and management.

关键词： Global Warming Land Surface Temperature Polynomial Curve Fitting Land Cover Indices

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A novel AI approach for modeling land surface temperature of Freetown,Sierra Leone,based on land-cover changes

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International Journal of Digital Earth 2022年第1期15卷 1236-1258页

作者： Mosbeh R.k.loop Mudassir Iqbal Mohamed T.Elnabwy elhadi k.mustafa Jong Wan Hu Department of Civil and Environmental Engineering Incheon National UniversityIncheonKorea Incheon Disaster Prevention Research Center Incheon National UniversityIncheonKorea Public Work. and Civil Engineering Department Mansoura UniversityMansouraEgypt Department of Civil Engineering University of Engineering and Technology PeshawarPeshwarPakistan National Water Research Center Coastal Research InstituteAlexandriaEgypt Department of Surveying and Geo-Informatics Southwest Jiaotong UniversitySichuanPeople’s Republic of China

Land use/land cover(LULC)indices can be considered while developing land surface temperature(LST)*** relationship between LST and LULC indices must be established to accurately estimate the impacts of LST *** study developed novel machine learning models for predicting LST using multispectral Landsat images data of Freetown city in *** neural network.ANN)and gene expression programming(GEP)were employed to develop LST prediction *** of multispectral bands were obtained from Landsat 4-5 and 8 satellites to develop the proposed *** extracted data of LULC indices,such as normal difference vegetation index(NDVI),normal difference built-up index(NDBI),urban index(UI),and normal difference water index(NDWI),were utilized as attributes to model *** results show that the root-mean-square error(RMSE)of the ANN and GEP models were 0.91oC and 1.08 oC,*** GEP model was used to yield a relationship between LULC indices and LST in the form of a mathematical equation,which can be conveniently used to test new data regarding the thematic *** sensitivity analysis revealed that UI is the most influential parameter followed by NDBI,NDVI,and NDWI towards contributing LST.

关键词： Land surface temperature landsat land use/land cover AI modeling

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Diagnostic Performance of glmM Gene and Histological Stains for Detection of Helicobacter pylori in Gastric Biopsy from Patients Admitted to Wad Madani Teaching Hospital, Sudan

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Natural Science 2022年第8期14卷 343-352页

作者： k.rima Ali Hamid Albadawi Abdelbagi Talha Abdalraheem Ali Babik.r Mohammed Ibrahim Malik.Adam Dawoud Abak.r Omer mustafa Elsidig A Saeed Mohmed Bushra Ahmed elhadi Abdalla Ahmed Department of Histopathology and Cytology Faculty of Medical Laboratory Sciences University of Gezira Wad Medani Sudan Department of Clinical Laboratory Sciences College of Applied Medical Sciences Jouf University Al Jouf Kingdom of Saudi Arabia Department of Medicine Faculty of Medicine University of Gezira Wad Medani Sudan Department of Medical Microbiology Faculty of Medical Laboratory Sciences University of Gezira Wad Medani Sudan

Helicobacter pylori is the microbial agent most responsible for gastro-duodenal ulcer and chronic gastritis, which can develop into carcinoma of the stomach. This study was performed in Wad Medani Teaching Hospital, Sudan to detect Helicobacter pylori in stomach samples, and evaluate the performance of the tests used, which were histological stains and PCR. Gastric biopsies were obtained from 105 referred patients during endoscopy, and fixed specimens examined by haematoxylin-eosin and Warthin-Starry silver stains, while DNA was extracted for glmM gene amplification. Epigastric pain was the most common symptom at 78% (82/105) and chronic gastritis recorded with 71% (68/105) of endoscopy results. Warthin-Starry silver stain gave 31% (33/105) as positive for Helicobacter pylori followed by glmM gene 27% (28/105) and haematoxylin-eosin 24% (25/105). The study indicated good performance of histological staining and high specificity of glmM gene in detection of Helicobacter pylori from gastric biopsies.

关键词： Helicobacter pylori Histological Stains glmM Gene Gastric Biopsy Sudan

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