The phenomena of Dust storm take place in barren and dry regions all over the world. It may cause by intense ground winds which excite the dust and sand from soft, arid land surfaces resulting it to rise up in the air. These phenomena may cause harmful influences upon health, climate, infrastructure, and transportation. GIS and remote sensing have played a key role in studying dust detection. This study was conducted in Iraq with the objective of validating dust detection. These techniques have been used to derive dust indices using Normalized Difference Dust Index (NDDI) and Middle East Dust Index (MEDI), which are based on images from MODIS and in-situ observation based on hourly wind speed and visibility during May 4-5 2022 and 25-26 June 2022. In this study, the appropriateness of two various MODIS-based techniques to discover dust in 13 stations in Iraq was examined. The results suggest NDDI index is the most appropriate index to identifying dust storms across Iraq. Also, the MEDI index has impairment to discover dust through multiple land-cover forms. Beside that MEDI consider an ineffective index to detect and discover dust storms throughout whole kinds of land cover over Iraq.
English
This study deals with the seismic reflection interpretation of lower Cretaceous Formations in Dhufria area, including structural and stratigraphic techniques. In the interpretation process, the 3-D seismic data volume and well logs have been used. Based on well logs and synthetic traces two horizons were identified and picked which are the top and bottom of Zubair Formation. These horizons were followed over all the area in order to obtain structural setting as well as studying Kirkuk group Formation of Tertiary age which represents highstand progradational seismic facies.
The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic
... Show MoreBoth the double-differenced and zero-differenced GNSS positioning strategies have been widely used by the geodesists for different geodetic applications which are demanded for reliable and precise positions. A closer inspection of the requirements of these two GNSS positioning techniques, the zero-differenced positioning, which is known as Precise Point Positioning (PPP), has gained a special importance due to three main reasons. Firstly, the effective applications of PPP for geodetic purposes and precise applications depend entirely on the availability of the precise satellite products which consist of precise satellite orbital elements, precise satellite clock corrections, and Earth orientation parameters. Secondly, th
... Show MoreThis work highlights the estimation of the Al-Khoser River water case that disposes of its waste directly into the Tigris River within Mosul city. Furthermore, the work studies the effects of environmental and climate change and the impact of pollution resulting from waste thrown into the Al-Khoser River over the years. Al-Khoser River is located in the Northern Mesopotamia of Mosul city. This study aims to detect the polluted water area and the polluted surrounding area. Temporal remote sensing data of different Landsat generations were considered in this work, specifically Enhanced Thematic Mapper Plus of 2000 and Operational Land Imager of 2015. The study aims to measure the amount of pollution in the study area over 15 years
... Show MoreA program in Visual Basic language was designed to calculate the time interval of radio storm by predict their type at specific Local Time (LT) from Baghdad location, such storms result from the Central Meridian Longitude (CML) of system ΙΙΙ for Jupiter and phase of Io’s satellite (ФIo). These storms are related to position of Io (Io- A,B,C,D). The input parameters for this program were the observer’s location (longitude), year, month and day. The output program results in form of tables provide the observer information about the date and the LT of beginning and end of each type of emitted storm. The year 2011 was taken to apply the results within twelve month; the results of the time interval of radio storm were between (0.08h-5
... Show MoreMachine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a
... Show MoreA total of 243 serum samples were tested for the presence of
Chlamydia antibodies by ind irect immunofluorescent antibody test.Ninety
nine females were suffering from abortions, 64 were infertile and other 80 were none aborted women. The incidence of Ch lamydia were (15%,
9.4%) and (3.8%) in abortion, infertile and non aborted group,
respecti vely. The results also showed a difference in prevalence rate between the age groups. The highest incidence was found in the age group 20-39 &
... Show More