IntroductionDespite ongoing maternal health interventions, maternal deaths in Tanzania remain high. One of the main causes of maternal mortality includes postoperative infections. Surgical site infection (SSI) rates are higher in low/middle-income countries (LMICs), such as Tanzania, compared with high-income countries. We evaluated the impact of a multicomponent safe surgery intervention in Tanzania, hypothesising it would (1) increase adherence to safety practices, such as the WHO Surgical Safety Checklist (SSC), (2) reduce SSI rates following caesarean section (CS) and (3) reduce CS-related perioperative mortality rates (POMRs).MethodsWe conducted a pre-cross-sectional/post-cross-sectional study design to evaluate WHO SSC utilisation, SSI rates and CS-related POMR before and 18 months after implementation. Our interventions included training of inter-professional surgical teams, promoting use of the WHO SSC and introducing an infection prevention (IP) bundle for all CS patients. We assessed use of WHO SSC and SSI rates through random sampling of 279 individual CS patient files. We reviewed registers and ward round reports to obtain the number of CS performed and CS-related deaths. We compared proportions of individuals with a characteristic of interest during pre-implementation and post implementation using the two-proportion z-test at p≤0.05 using STATA V.15.ResultsThe SSC utilisation rate for CS increased from 3.7% (5 out of 136) to 95.1% (136 out of 143) with p<0.001. Likewise, the proportion of women with SSI after CS reduced from 14% during baseline to 1% (p=0.002). The change in SSI rate after the implementation of the safe surgery interventions is statistically significant (p<0.001). The CS-related POMR decreased by 38.5% (p=0.6) after the implementation of safe surgery interventions.ConclusionOur findings show that our intervention led to improved utilisation of the WHO SSC, reduced SSIs and a drop in CS-related POMR. We recommend replication of the interventions in other LMICs.

The purpose of this paper is to estimate the quantity and quality of the useful energy that could be converted to work, this analysis was carried out based on the energy and exergy analysis by using the first and second laws of thermodynamics. This paper study the effect of varying the ambient temperature on the performance of the turbine. Results showed increasing in exergy destruction in the turbine solely and in each of its three components(Air compressor, Combustion chambers and the gas turbine) with increasing in ambient temperature. Also results showed by keeping the load unchanged, the exergy destruction are bigger in higher ambient temperatures than in lower ones. The exergy destruction concentrated in the combustion chambers, where the percent of exergy destruction in the combustion chambers to the total exergy destruction in the plant was(87%) followed by the air compressor(9%) and the lower exergy destruction was in the gas turbine(4%).

The human ear has unique and attractive details; therefore, human ear recognition is one of the most important fields in the biometric domains. In this work, we proposed an efficient and intelligent ear recognition technique based on particle swarm optimization, discrete wavelet transform, and fuzzy neural network. Discrete wavelet transform is used to provide comprise and effective features about the ear image, while the particle swarm optimization utilized to select more effective and attractive features. Furthermore, using particle swarm optimization leads to reduce the complexity of the classification stage since it reduces the number of the features. Fuzzy neural network used in the classification stage in order to provide strong distinguishing between the testing and training ear images. many experiments performed using two ear databases to examine the accuracy of the proposed technique. The analysis of the results refers that the presented technique gained high recognition accuracy using various data sets with less complexity. Keywords: Ear recognition; bio-metric; discrete wavelet transform, particle swarm optimization, fuzzy neural network.