Maternal health during pregnancy and perinatal mortality in Bangladesh: evidence form a large-scale community-based clinical trial

Perinatal mortality is very high in Bangladesh. In this setting, few community-level studies have assessed the influence of underlying maternal health factors on perinatal outcomes. We used the data from a community-based clinical controlled trial conducted between 1994 and 1997 in the catchment areas of a large MCH/FP hospital located in Mirpur, a suburban area of Dhaka in Bangladesh, to investigate the levels of perinatal mortality and its associated maternal health factors during pregnancy. A total of 2007 women were followed after recruitment up to delivery, maternal death, or until they dropped out of the study. Of these, 1584 who gave birth formed our study subjects. The stillbirth rate was 39.1 per 1000 births [95% confidence interval (CI) 39.0, 39.3] and the perinatal mortality rate (up to 3 days) was 54.3 per 1000 births [95% CI 54.0, 54.6] among the study population. In the fully adjusted logistic regression model, the risk of perinatal mortality was as high as 2.7 times [95% CI 1.5, 4.9] more likely for women with hypertensive disorders, 5.0 times [95% CI 2.3, 10.8] as high for women who had antepartum haemorrhage and 2.6 times [95% CI 1.2, 5.8] as high for women who had higher haemoglobin levels in pregnancy when compared with their counterparts. The inclusion of potential confounding variables such as poor obstetric history, sociodemographic characteristics and preterm delivery influenced only marginally the net effect of important maternal health factors associated with perinatal mortality. Perinatal mortality in the study setting was significantly associated with poor maternal health conditions during pregnancy. The results of this study point towards the urgent need for monitoring complications in high-risk pregnancies, calling for the specific components of the safe motherhood programme interventions that are designed to manage these complications of pregnancy.

Parallel implementation of stochastic simulation for large scale cellular processes

Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes

A combined neural network and DEA for measuring efficiency of large scale datasets

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement of the efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper proposes a neural network back-propagation Data Envelopment Analysis to address this problem for the very large scale datasets now emerging in practice. Neural network requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Finally, the back-propagation DEA algorithm is applied to five large datasets and compared with the results obtained by conventional DEA.

A Franco-British comparison of patterns of working hours in large-scale grocery retailing, with specific reference to part-time work

In this thesis patterns of working hours in large-scale grocery retailing in Britain and France are compared. The research is carried out using cross-national comparative methodology, and the analysis is based on information derived from secondary sources and empirical research in large-scale grocery retailing involving employers and trade unions at industry level and case studies at outlet level. The thesis begins by comparing national patterns of working hours in Britain and France over the post-war period. Subsequently, a detailed comparison of working hours in large-scale grocery retailing in Britain and France is carried out through the analysis of secondary sources and empirical data. Emphasis is placed on analyzing part-time working hours. They are contrasted and compared at national level and explained in terms of supply and demand factors. The relationships between the structuring of, and satisfaction with, working hours and factors determining women's integration in the workforce in Britain and France are investigated. Part-time hours are then compared and contrasted in large-scale grocery retailing in the context of the analysis of working hours. The relationship between the structuring of working hours and satisfaction with them is examined in both countries through research with women part-timers in case study outlets. The cross-national comparative methodology is used to examine whether dissimilar national contexts in Britain and France have led to different patterns of working hours in large-scale grocery retailing. The principal conclusion is that significant differences are found in the length, organization and flexibility of working hours and that these differences can be attributed to dissimilar socio-economic, political, and cultural contexts in the two countries.