Background Medical doctors with postgraduate training in Global Health and Tropical Medicine (MDGHTM) from the Netherlands, a high-income country with a relatively low caesarean section rate, assist associate clinicians in low-income countries regarding decision-making during labour. Objective of this study was to assess impact of the presence of MDGHTMs in a rural Malawian hospital on caesarean section rate and indications. Methods This retrospective pre- and post-implementation study was conducted in a rural hospital in Malawi, where MDGHTMs were employed from April 2015. Indications for caesarean section were audited against national protocols and defined as supported or unsupported by these protocols. Caesarean section rates and numbers of unsupported indications for the years 2015 and 2016 per quarter for different staff cadres were assessed by linear regression. Results Six hundred forty-five women gave birth by caesarean section in the study period. The caesarean rate dropped from 20.1 to 12.8% (p < 0.05, R2 = 0.53, y = − 0.0086x + 0.2295). Overall 132 of 501 (26.3%) auditable indications were not supported by documentation in medical records. The proportion of unsupported indications dropped significantly over time from 47.0 to 4.4% (p < 0.01, R2 = 0.71, y = − 0.0481x + 0.4759). Stratified analysis for associate clinicians only (excluding caesarean sections performed by medical doctors) showed a similar decrease from 48.3 to 6.5% (p < 0.05, R2 = 0.55, y = − 0.0442x + 0.4805). Conclusions Our results indicate that presence of MDGHTMs was accompanied by considerable decreases in caesarean section rate and proportion of unsupported indications for caesarean section in this facility. Their presence is likely to have influenced decision-making by associate clinicians.
In this paper, the particle swarm optimization (PSO) algorithm is proposed to solve the lift gas optimization problem in the crude oil production industry. Two evolutionary algorithms, genetic algorithm (GA) and PSO, are applied to optimize the gas distribution for oil lifting problem for a 6-well and a 56-well site. The performance plots of the gas intakes are estimated through the artificial neural network (ANN) method in MATLAB. Comparing the simulation results using the evolutionary optimization algorithms and the classical methods, proved the better performance and faster convergence of the evolutionary methods over the classical approaches. Moreover, the convergence rate of PSO is 13 times faster than GA's for this problem.
Capacitor optimal placement is one of the most important designs and control issues of power systems in order to reduce network losses, improve the voltage profile, reduce the reactive load, and reducing the power factor. The distribution network operator, taking into account two major goals of reducing real power losses and maximizing the return on investment required for installation of capacitive banks for sale to the transmission system, obtains the position, number, and capacity of capacitive banks. In this paper, the optimization problem is formulated for different values of the parameter "reactive energy value". After evaluating the objective function and implementing an optimization algorithm for each value of this parameter, the arrangement and capacitance of the capacitors in the network load nodes are obtained. Meanwhile, using the objective function defined in this paper, you can obtain the threshold for the sale of reactive energy, and by selling it to the transmission network, the investment in installing capacitor banks will be profitable for the distribution network operator.
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