Background: Ultrasonography has been used to examine the thickness of the lower uterine segment in women with previous cesarean sections in an attempt to predict the risk of scar dehiscence during subsequent pregnancy. The predictive value of such measurement has not been adequately assessed. Objectives: To correlate lower uterine segment thickness measured by trans abdominal ultrasound in pregnant women with previous cesarean section with that measured during cesarean section by caliper and to find out minimum lower uterine segment thickness indicative of integrity of the scar.Methods: A prospective observational study at Elwyia Maternity Teaching Hospital, from January 2011 to January 2012. A total of 143 women were enrolled in the study. Those women who were included were pregnant with gestational age (36-40) weeks, all had history of previous one or more cesarean section. Transabdominal ultrasound measurement of thickness uterine segment thickness done with moderately full bladder before delivery and correlated with these measured directly during operation using a caliper. The sensitivity and specificity of ultrasound calculated with positive and negative predictive value. Results: The sensitivity and specificity of trans abdominal ultrasound in detecting patient at risk of scar dehiscence in patient with previous and cesarean section not starting uterine contractions were very high 90%and 92% respectively with positive and negative predictive value of 90% and 92% respectively with a cut off value of uterine segment thickness of 4.5 mm. It was also has high sensitivity and positive predictive value of 93.4% and 93% respectively with patients that started labor but with low specificity and negative predictive value of 50% and 38% respectively with the same cut off value.Conclusions: Sonographic lower uterine segment thickness is a strong predictor for uterine scar defect in women with prior Caesarean section. However, no ideal cut-off value can yet be recommended, whenever uterine contractions started. But this method carries a high sensitivity and specificity in patients who did not start uterine contractions with a cut-off value of 4.5 mm.
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Most companies use social media data for business. Sentiment analysis automatically gathers analyses and summarizes this type of data. Managing unstructured social media data is difficult. Noisy data is a challenge to sentiment analysis. Since over 50% of the sentiment analysis process is data pre-processing, processing big social media data is challenging too. If pre-processing is carried out correctly, data accuracy may improve. Also, sentiment analysis workflow is highly dependent. Because no pre-processing technique works well in all situations or with all data sources, choosing the most important ones is crucial. Prioritization is an excellent technique for choosing the most important ones. As one of many Multi-Criteria Decision Mak
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In this study, biodiesel was prepared from chicken fat via a transesterification reaction using Mussel shells as a catalyst. Pretreatment of chicken fat was carried out using non‐catalytic esterification to reduce the free fatty acid content from 36.28 to 0.96 mg KOH/g oil using an ethanol/ fat mole ratio equal to 115:1. In the transesterification reaction, the studied variables were methanol: oil mole ratio in the range of (6:1 ‐ 30:1), catalyst loading in the range of (9‐15) wt%, reaction temperature (55‐75 °C), and reaction time (1‐7) h. The heterogeneous alkaline catalyst was greenly synthesized from waste mussel shells throughout a calcin
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In this study, biodiesel was prepared from chicken fat via a transesterification reaction using Mussel shells as a catalyst. Pretreatment of chicken fat was carried out using non‐catalytic esterification to reduce the free fatty acid content from 36.28 to 0.96 mg KOH/g oil using an ethanol/ fat mole ratio equal to 115:1. In the transesterification reaction, the studied variables were methanol: oil mole ratio in the range of (6:1 ‐ 30:1), catalyst loading in the range of (9‐15) wt%, reaction temperature (55‐75 °C), and reaction time (1‐7) h. The heterogeneous alkaline catalyst was greenly synthesized from waste mussel shells throughout a calcin
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This study sought to investigate the impacts of big data, artificial intelligence (AI), and business intelligence (BI) on Firms' e-learning and business performance at Jordanian telecommunications industry. After the samples were checked, a total of 269 were collected. All of the information gathered throughout the investigation was analyzed using the PLS software. The results show a network of interconnections can improve both e-learning and corporate effectiveness. This research concluded that the integration of big data, AI, and BI has a positive impact on e-learning infrastructure development and organizational efficiency. The findings indicate that big data has a positive and direct impact on business performance, including Big
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This study reports on natural convection heat transfer in a square enclosure of length (L=20 cm) with a saturated porous medium (solid glass beads) having same fluid (air) at lower horizontal layer and free air fill in the rest of the cavity's space. The experimental work has been performed under the effects of heating from bottom by constant heat flux q=150,300,450,600 W/m2 for four porous layers thickness Hp (2.5,5,7.5,1) cm and three heaters length δ(20,14,7) cm. The top enclosure wall was good insulated and the two side walls were symmetrically cooled at constant temperature. Four layers of porous media with small porosity, Rayleigh number range (60.354 - 241.41) and (Da) 3.025x10-8 has been investigated. The obtained data of temperatu
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