This paper study two stratified quantile regression models of the marginal and the conditional varieties. We estimate the quantile functions of these models by using two nonparametric methods of smoothing spline (B-spline) and kernel regression (Nadaraya-Watson). The estimates can be obtained by solve nonparametric quantile regression problem which means minimizing the quantile regression objective functions and using the approach of varying coefficient models. The main goal is discussing the comparison between the estimators of the two nonparametric methods and adopting the best one between them

we studied the effect of low level laser therapy (LLLT) using diode laser with wavelength of (790-805) nm in promotion and enhancement of wound healing of episiotomy and to evaluate the analgesic effect of LLLT in reducing the pain sensation caused by the episiotomy wounds. Nineteen women with episiotomy wound were selected and divided into three groups; 1st group (group No.1: control group) given antibiotics without laser therapy, in the 2nd group (group No.2) the wounds were exposed to laser therapy (4 sessions, each session with energy density of 19.90 J /cm2 every other day ) and systemic antibiotics were prescribed for 1 week. In the 3rd group (group No.3) the wounds were exposed to laser therapy (4 sessions, the same as in the 2nd

A total of 96 stool samples were collected from children with bloody diarrhea from two hospitals in Baghdad. All samples were surveyed and examined for the presence of the Escherichia coli O157:H7 and differentiate it from other Non -Sorbitol Fermenting Escherichia coli (NSF E. coli). The Bacterial isolates were identifed by using morphological diagnostic methods, Samples were cultured on liquid enrichment medium, incubated at 37C? for 24 hrs, and then cultured on Cefixime Tellurite -Sorbitol MacConkey Agar (CT- SMAC). 32 non-sorbitol fermenting bacterial isolates were obtained of which 11 were identified as Escherichia coli by using traditional biochemical tests and API20E diagnostic system without differentiation between

DBN Rashid, INTERNATIONAL JOURNAL OF DEVELOPMENT IN SOCIAL SCIENCE AND HUMANITIES, 2021

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d