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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Power-electronic converters are essential elements for the effective interconnection of renewable energy sources to the power grid, as well as to include energy storage units, vehicle charging stations, microgrids, etc. Converter models that provide an accurate representation of their wideband operation and interconnection with other active and passive grid components and systems are necessary for reliable steady state and transient analyses during normal or abnormal grid operating conditions. This paper introduces two Laplace domain-based approaches to model buck and boost DC-DC converters for electromagnetic transient studies. The first approach is an analytical one, where the converter is represented by a two-port admittance model via mo
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Metoclopramide (MCP) ion selective electrodes based on metoclopramide-phosphotungstic acid (MCP-PT) ion pair complex in PVC matrix membrane were constructed. The plasticizers used were tri-butyl phosphate (TBP), di-octyl phenyl phosphonate (DOPP), di-butyl phthalate (DBPH), di-octyl phthalate (DOP), di-butyl phosphate (DBP), bis 2-ethyl hexyl phosphate (BEHP). The sensors based on TBP, DOPP, DBPH and DOP display a fast, stable and linear response with slopes 59.9, 57.7, 57.4, 55.3 mV/decade respectively at pH ranged 2-6. The linear concentration range between 1.0×10-5 – 1.0×10-2 M with detection limit 3.0×10-6 and 4.0×10-6 M for electrodes using TBP, DOPP and DBPH while e
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Upper limb amputation is a condition that severely limits the amputee’s movement. Patients who have lost the use of one or more of their upper extremities have difficulty performing activities of daily living. To help improve the control of upper limb prosthesis with pattern recognition, non-invasive approaches (EEG and EMG signals) is proposed in this paper and are integrated with machine learning techniques to recognize the upper-limb motions of subjects. EMG and EEG signals are combined, and five features are utilized to classify seven hand movements such as (wrist flexion (WF), outward part of the wrist (WE), hand open (HO), hand close (HC), pronation (PRO), supination (SUP), and rest (RST)). Experiments demonstrate that usin
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