The centers of cities and historical quarter are exposed to a severe threat to the values of the physical and legal urban environment as a result of the value deterioration and the emergence, emergence and spread of new values on the intellectual and urban context, which generates the loss of the urban environment for its spatio-temporal continuity, flexibility, adaptation and continuity, and thus urban obsolescence, Hence the problem of the research in “the lack of comprehensiveness of studies on the phenomenon of urban obsolescence and its impact on the decline in the values of the quality of the built environment in historical quarters”, and accordingly the goal of the research was determined in “building a theoretical framework for the phenomenon of urban obsolescence and exploring the impact of this phenomenon on the quality of the built environment of historical quarters”, The research assumes the multiplicity and varying levels of urban obsolescence in the historical quarters, which affects the quality of the assets of these quarters. The research aims to provide this knowledge by adopting the descriptive analytical approach for a number of urban studies to reach the identification of the main vocabulary related to the phenomenon of urban obsolescence and to extract the most important definitions and causes. This is reflected in the quality levels of urban assets in them.
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Background: Obesity represents a clear and
present danger to the health of children and
adolescents. Its prevalence among American
youth has doubled in the past 3 decades, and
there are now more overweight and obese
adults in the United States than adults of
normal weight.
Objectives of the study:
1- Finding whether screen watching among
adolescents has an effect on increasing
prevalence overweight and obesity.
2- The effect of other variables like physical
activity, eating in front of screen, eating under
stress on obesity and overweight among the
subjects sample.
Patients &Methods: During 3 months period a
cross sectional survey was conducted on 4
high schools at Baghdad with total
Objectives: The study aims to assess some of the contributing factors to abdominal postoperative wound infection and
to find out the relationship between postoperative wound infections and some socio-demographic characteristics such as
age, gender, level of education, occupation and residential area.
Methodology: A descriptive study was carried out in Sulaimani Teaching Hospital (Kurdistan Region). The study has
started from January 15th up to October 31th, 2009. To achieve the study objectives, a purposive "non probability"
sample of (100) patients who have abdominal postoperative wound infection, Adult female and male patients who were
admitted to Sulaimani Teaching Hospital was selected.
The data were collected th
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Mishrif Formation was deposited during The Cenomanian-Early Turonian, which has been studied in selected Tuba and Zubair OilFields, these wells (TU-5, TU-24, TU-40, ZB-41, ZB-42, and ZB-46) are located within Mesopotamian basin at southern Iraq and considered as a major carbonate reservoir in Iraq and the Arabian Gulf. The palaeontological investigations mainly depending on benthonic foraminifera of the studied wells of Tuba and Zubair Oilfields in Mishrif Formation, twenty-four species belonging to fourteen genera are recognized of benthonic foraminifera, which has been recognized through this study, especially benthonic foraminiferal, indicating four zones as follows:
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The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi
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