Objective: The study aimed to screen the prepubertal children for idiopathic scoliosis at earlier stages, and find
out the relationship between idiopathic scoliosis and demographic data such as age, sex, body mass index,
heavy backpacks, and heart & lung diseases.
Methodology: A descriptive study was conducted on screening program for prepubertal children in primary
schools at Baghdad city, starting from 24th of February to the end of October 2010. Non- probability
(purposive) sample of 510 prepubertal children were chosen from primary schools of both sides of Al-Karkh
and Al-Russafa sectors. Data was collected through a specially constructed questionnaire format include (24)
items multiple choice questions, and researcher observation. The validity of the questionnaire was determined
through a panel of experts related to the field of the study, and the reliability through a pilot study. The data
were analyzed through the application of descriptive statistical analysis frequency, & percentages, and
inferential statistical analysis, chi-square, are used.
Results: The study results revealed that most of the prepubertal children have idiopathic scoliosis, two third of
the sample (88.4%) were at age 10-12 years and mostly boys. There is highly significant association with (low
Body Mass Index & carry of the school backpack) but no significant association with the age, gender, and lung
& heart diseases. There is highly significant association between prepubertal children's idiopathic scoliosis signs
& the researcher observation for the prepubertal body feature, and Adam's Bending Forward Test which
revealed highly significant association with their idiopathic scoliosis. The results of the study reflect that the
majority of prepubertal children's idiopathic scoliosis deformities have significant association at early detection
than the other spinal deformities (kyphosis & kyphoscoliosis).
Recommendation: The researchers recommended that Ministry Of Health should activate the screening program
of scoliosis within school health service programs, and Ministry of Education should be involved their teachers in
the screening & training program.
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The current research has determined the students of the fifth stage of the preparatory stage and all the branches in the departments of education in Baghdad province the morning study for the academic
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Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c
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