The research aims to show the impact of the new modern malls on urban adjacent land uses; it handled concise study of the old markets, contemporary shopping malls, and the standards and regulations for commercial use. Sample study has been taken from new allocated malls in the city of Baghdad. The results of the sample analysis, and statistical analysis has proven hypothesis; that the locating of new malls impacted physical, social, and economic on urban adjacent, especially the exotic on urban fabric. It also turned out that the shopping demand from these malls is inelastic, which gives an opportunity to the planner and the decision-maker, to locate the modern new malls where spatial appropriate in the city are available. The major reco

Background: Presence of maxillary sinus septa has been known to be a complicating factor for sinus elevation procedure and implant placement in posterior maxilla. The maxillary sinuses septa are thin walls of cortical bone inside the sinus. They vary in number, location, and height. This study aimed to discover the accuracy of Spiral Computed Tomographic Scan in evaluation the maxillary sinus septa (prevalence, location, height) in subjects with dentate, partially edentulous and completely edentulous maxilla. Material and method: This study included (267) subjects ranged from (20-70 years), (132) male and (135) female divided into three groups, (97) fully dentate group, (102) partially edentulous group and (68) completely edentulous group w

This paper  deals  to how to estimate points non measured spatial data when the number of its terms (sample spatial) a few, that are not preferred for the estimation process, because we also know that whenever if the data is large, the estimation results of the points non measured to be better and thus the variance estimate less, so the idea of this paper is how to take advantage of the data other secondary (auxiliary), which have a strong correlation with the primary data (basic) to be estimated single points of non-measured, as well as measuring the variance estimate, has been the use of technique Co-kriging in this field to build predictions spatial estimation process, and then we applied this idea to real data in th

The density-based spatial clustering for applications with noise (DBSCAN) is one of the most popular applications of clustering in data mining, and it is used to identify useful patterns and interesting distributions in the underlying data. Aggregation methods for classifying nonlinear aggregated data. In particular, DNA methylations, gene expression. That show the differentially skewed by distance sites and grouped nonlinearly by cancer daisies and the change Situations for gene excretion on it. Under these conditions, DBSCAN is expected to have a desirable clustering feature i that can be used to show the results of the changes. This research reviews the DBSCAN and compares its performance with other algorithms, such as the tradit

The research Was based to on a real problem and realistically of represented by that  Iraqi Airways company does not have the electronic cost accounting system and therefore  be the process of  the pricing various services provided by a company sample research respecting  air transport and air cargo and aviation fuel and services and catering are not properly especially in the presence of new data from the new companies entering competition in Iraqi aviation industry and therefore does not provide price flexibility in order to compete in getting market share, And then research this problem addressed through design an electronic cost Accounting system covers all the costs incurred by the compan

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

Rationing is a commonly used solution for shortages of resources and goods that are vital for the citizens of a country. This paper identifies some common approaches and policies used in rationing as well asrisks that associated to suggesta system for rationing fuelwhichcan work efficiently. Subsequently, addressing all possible security risks and their solutions. The system should theoretically be applicable in emergency situations, requiring less than three months to implement at a low cost and minimal changes to infrastructure.