Lake Nainital in the heart of Nainital Town in the State of Uttaranchal (India) receives toxic substances through various open drains through the catchment of the lake. The toxic substances of particular interest are heavy metals derived from urban runoff as well as municipal sewage and industrial effluents. Heavy metals entering the lake get adsorbed onto the suspended sediments, which in turn settle down in the bottom of the lake. In this study fractionation of metal ions has been studied on the bed sediments of lake Nainital with the objective to determine the eco-toxic potential of metal ions. Comparison of sediments with average shae values indicated anthropogenic enrichment with nickel, lead, cadmium and zinc. The risk assessment code as applied to the present study reveals that 4-13% of manganese, 4-8% of copper, 17-24% of nickel, 3-5% of chromium, 13-26% of lead, 14-23% of cadmium and 2-3% of zinc exist in exchangeable fraction and therefore comes under low to medium risk category and may enter into food chain. The association of these metals with exchangeable fraction may cause deleterious effects to aquatic life. The present database will help in formulating guidelines for carrying out dredging operations and/or restoration programmes in the Nainital lake.

Maternal complications are common during and following childbirth. However, little information is available on the psychological, social and economic consequences of maternal complications on women's lives, especially in a rural setting. A prospective cohort study was conducted in southern Rajasthan, India, among rural women who had a severe or less-severe, or no complication at the time of delivery or in the immediate postpartum period. In total, 1,542 women, representing 93% of all women who delivered in the field area over a 15-month period and were examined in the first week postpartum by nurse-midwives, were followed up to 12 months to record maternal and child survival. Of them, a subset of 430 women was followed up at 6-8 weeks and 12 months to capture data on the physical, psychological, social, or economic consequences. Women with severe maternal complications around the time of delivery and in the immediate postpartum period experienced an increased risk of mortality and morbidity in the first postpartum year: 2.8% of the women with severe complications died within one year compared to none with uncomplicated delivery. Women with severe complications also had higher rates of perinatal mortality [adjusted odds ratio (AOR)=3.98, confidence interval (CI) 1.96-8.1, p=0.000] and mortality of babies aged eight days to 12 months (AOR=3.14, CI 1.4-7.06, p=0.004). Compared to women in the uncomplicated group, women with severe complications were at a higher risk of depression at eight weeks and 12 months with perceived physical symptoms, had a greater difficulty in completing daily household work, and had important financial repercussions. The results suggest that women with severe complications at the time of delivery need to be provided regular follow-up services for their physical and psychological problems till about 12 months after childbirth. They also might benefit from financial support during several months in the postpartum period to prevent severe economic consequences. Further research is needed to identify an effective package of services for women in the first year after delivery.

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The alternative use of electrical discharge grinding and abrasive grinding, which is applied with the application of slotted wheel named as slotted electrodischarge abrasive grinding, is much suitable for machining of metal matrix composites. But the selection of process parameters is a difficult task due to the complexity of the process. The aim of this study is to optimize the process parameters of slotted electrodischarge abrasive grinding process using a combined approach of artificial neural network and nondominated sorting genetic algorithm II. The artificial neural network architecture has been trained and tested with experimental data, and then the developed model is coupled with nondominated sorting genetic algorithm II to develop a hybrid approach of artificial neural network–nondominated sorting genetic algorithm II, which is used for optimization of process parameters. During experimentation, the effect of current, pulse on-time, pulse off-time, wheel speed and grit number has been studied on material removal rate and average surface roughness (Ra). The results have shown that prediction capability of artificial neural network model is within the range of acceptable limits. The developed hybrid approach of artificial neural network–nondominated sorting genetic algorithm II gives optimal solution with correlation coefficient of material removal rate and Ra as 0.9979 and 0.9982, respectively.