Background: Diagnosis and treatment planning can be difficult with conventional radiographic methods as the orthodontic-surgical management of impacted canines requires accurate diagnosis and precise localization of the impacted canine and the surrounding structures. This study was aimed to localize and evaluate weather there is any differences in the diagnostic information provided by multi-slice computed tomography three dimensional volumetric CT images and two dimensional reconstructed panorama images (derived from CT) in subjects with impacted maxillary canines. Materials and Methods: Thirty patients including 24 female and 6 male with mean age of 18 years with suspected unilaterally or bilaterally impacted maxillary canines were evalu

Local food samples investigated for the presence of pathogenic bacteria. Hash meat sample was used to isolate Escherichia coli and chicken meat (poultry) was used to isolate Salmonella typhi. Biochemical tests and API20E system used in order to identify these isolates. Two natural vinegar samples (dates & apple cider) were used in order to study its antibacterial activity against the two tested bacteria. Disc diffusion method was used, the results showed that two vinegar samples have antibacterial activity against the two tested bacteria. Date vinegar showed inhibition zone 19mm against E. coli & 9mm against S. typhi, while Apple cider vinegar showed diameter of inhibi

KE Sharquie, AA Noaimi, AG Al-Ghazzi, 2010 - Cited by 2

Assessment of the in service behavior of asphalt stabilized subgrade soil under environmental impact has got little attention by the research workers. However, the sustainability of the roadway depends mainly on the welfare of its subgrade soil condition. In this work, Gypseous soil was stabilized with asphalt emulsion for subgrade usage, the durability of the mixture has been assessed in term of its ability to maintain the compressive strength when practicing the environmental impacts. Specimens of 38 mm in diameter , and 76 mm in height have been prepared with various water-asphalt percentages, and subjected to 30 cycles of (freezing-thawing), (heating-cooling) and (wetting-drying) processes. Specimens have been tested for unconfined comp

     The earth's surface comprises different kinds of land cover, water resources, and soil, which create environmental factors for varied animals, plants, and humans. Knowing the significant effects of land cover is crucial for long-term development, climate change modeling, and preserving ecosystems. In this research, the Google Earth Engine platform and freely available Landsat imagery were used to investigate the impact of the expansion and degradation in urbanized areas, watersheds, and vegetative cover on the land surface temperature in Baghdad from 2004 to 2021. Land cover indices such as the Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Built-up Index (NDVI, NDWI, an

With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has