Cesarean section, which was introduced into clinical practice as a lifesaving procedure for both the mother and the baby, is one of the most common surgeries performed in modern obstetrics. Formerly it was performed in interest of the mother; currently it is frequently done for fetal indication. As other procedures of some complexity, its use follows the health care inequity pattern of the world; underuse in low income setting, and adequate or even unnecessary use in middle and high income setting (1).
The first modern cesarean section was performed by German gynecologist Ferdinand Adolf Kehrer in 1881. Cesarean section is often performed when a vaginal delivery would put the baby's or mother's life or health at risk. Many are also performed upon request (2).
Pregnancy and delivery are considered a normal physiological state in women; however, of all deliveries approximately 10% are considered high risk, some of which may require Cesarean section (3).
The cesarean delivery rate has increased throughout the world. Cesarean rates have increased over the past 40 years from approximately 5% to more than 30% in many industrialized countries (4). The cesarean rate is approximately 21.1% for the most developed regions of the globe, 14.3% for the less developed regions, and 2% for the least developed regions (5).
Recent studies reaffirm earlier recommendations of the world health organization (WHO), about optimal C-section rates, addressing that the best outcomes of mothers and babies appear to occur with C-section rates of 5% to 10%, while rates above 15% seem to do more harm than good (6).
In US the cesarean rate increased dramatically during the 1970s and early 1980s this may be accredited to the improved technology in detecting pre-birth distress, and began to decline in the late 1980s (based on data from the National Hospital Discharge Survey). Between 1989 and 1996 the total cesarean rate decreased as a result of a decrease in the primary rate and an increase in the rate of vaginal birth after Cesarean (VBAC). Since 1996, these trends have reversed, and increases have been rapid and sustained for primary and repeat Cesareans over the past decade (7).
According to the Iraq Multiple Indicator Cluster Survey 2006 (Iraq MICS 2006) (8), about 20% of births were delivered by C-section (Table 1). Moreover, a brief and rapid survey of C-sections in Baghdad Teaching Hospital, a tertiary referral hospital, indicated that the emergency C-sections account only for 62.2% of the total number of C-sections performed outside the working hours during April 2010 (9).
Electrochemical machining is one of the widely used non-conventional machining processes to machine complex and difficult shapes for electrically conducting materials, such as super alloys, Ti-alloys, alloy steel, tool steel and stainless steel. Use of optimal ECM process conditions can significantly reduce the ECM operating, tooling, and maintenance cost and can produce components with higher accuracy. This paper studies the effect of process parameters on surface roughness (Ra) and material removal rate (MRR), and the optimization of process conditions in ECM. Experiments were conducted based on Taguchi’s L9 orthogonal array (OA) with three process parameters viz. current, electrolyte concentration, and inter-electrode gap. Sig
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Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered
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This research is devoted to study the effect of different in weight percentage of Sio2 particles and glass fibers (5, 10, 15, 20) wt. % on the wear rate epoxy resin. The results show that the value of hardness increase with the increase for the weight percentage of reinforcing particles and fibers, while the wear rate decrease with the increase the load level of the reinforcing particles and fibers . The largest value of the hardness, and the lowest value of the wear rate for epoxy reinforced with 20% of SiO2, the wear rate increase in general with increasing the applied load.
A partial temporary immunity SIR epidemic model involv nonlinear treatment rate is proposed and studied. The basic reproduction number is determined. The local and global stability of all equilibria of the model are analyzed. The conditions for occurrence of local bifurcation in the proposed epidemic model are established. Finally, numerical simulation is used to confirm our obtained analytical results and specify the control set of parameters that affect the dynamics of the model.
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In this research, several estimators concerning the estimation are introduced. These estimators are closely related to the hazard function by using one of the nonparametric methods namely the kernel function for censored data type with varying bandwidth and kernel boundary. Two types of bandwidth are used: local bandwidth and global bandwidth. Moreover, four types of boundary kernel are used namely: Rectangle, Epanechnikov, Biquadratic and Triquadratic and the proposed function was employed with all kernel functions. Two different simulation techniques are also used for two experiments to compare these estimators. In most of the cases, the results have proved that the local bandwidth is the best for all the
... Show MoreBreast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep
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Nimodipine (NMD) is a dihydropyridine calcium channel blocker useful for the prevention and treatment of delayed ischemic effects. It belongs to class ? drugs, which is characterized by low solubility and high permeability. This research aimed to prepare Nimodipine nanoparticles (NMD NPs) for the enhancement of solubility and dissolution rate. The formulation of nanoparticles was done by the solvent anti-solvent technique using either magnetic stirrer or bath sonicator for maintaining the motion of the antisolvent phase. Five different stabilizers were used to prepare NMD NPs( TPGS, Soluplus®, HPMC E5, PVP K90, and poloxamer 407). The selected formula F2, in which Soluplus
has been utilized as a stabilizer, has a par
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Rate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish the well as soon as possible to reduce the expenditures. So, knowing the rate of penetration in the area that is going to be drilled will help in speculation of the cost and that will lead to optimize drilling outgoings. In this research, an intelligent model was built using artificial intelligence to achieve this goal. The model was built using adaptive neuro fuzzy inference system to predict the rate of penetration in
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This paper provides an attempt for modeling rate of penetration (ROP) for an Iraqi oil field with aid of mud logging data. Data of Umm Radhuma formation was selected for this modeling. These data include weight on bit, rotary speed, flow rate and mud density. A statistical approach was applied on these data for improving rate of penetration modeling. As result, an empirical linear ROP model has been developed with good fitness when compared with actual data. Also, a nonlinear regression analysis of different forms was attempted, and the results showed that the power model has good predicting capability with respect to other forms.