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Spillway hydrodynamics can be obtained through physical modeling or numerical modeling. Physical modeling of spillway is expensive, cumbersome and time consuming. Numerical simulation of rapidly varied, supercritical, free surface turbulent flow over spillway with all flow regimes is a challenging task. This paper explains the formulation and development of a numerical model based on weakly compressible flow equations for flow over spillway. The developed model is used to investigate the hydraulic characteristics of flow over spillway crest profile by simulating the velocity distribution, pressure distribution and discharge characteristics. The developed numerical model was applied to the two different types of spillways. First spillway studied is a surface spillway with broad crested parabolic downstream crest profile. The second spillway investigated is sluice spillway, which is a submerged type of spillway. The numerical results were compared with the respective physical model results. Good agreement is observed between the experimental and numerical results showing the applicability of the weakly compressible flow equations for the hydrodynamics simulation of flow over spillways

Information on the depth of the scour hole formed downstream of a ski-jump bucket type of energy dissipator is necessary in determining the safety of dams and adjoining structures. Traditional formulae available to predict scour depth suffer from many limitations including one arising out of the technique of data analysis commonly employed, namely, statistical regression. This paper presents an alternative to regression in the form of neural networks. A feed forward network is developed to predict the depth of the scour hole below ski-jump spillways from the specified values of head and discharge intensity. Field measurements collected from a variety of published literature are used to train the network. The validation of the developed network using observations that were not involved in the training indicated the usefulness of the neural network approach for the prediction problem under consideration. Network-yielded values are found to be more accurate than those given by the traditional equations.A matrix of weights and bias values for general use in any location is specified

Child malnutrition is pervasive in Bangladesh, with nearly one-half of all children below the age of five years being either underweight or stunted. The country has, however, made significant progress in reducing the incidence of child malnutrition during the last 15 years. There are large disparities in child malnutrition across gender, geographical regions, and economic groups. The empirical evidence reviewed in this article suggests that public transfer and relief programs that provide food to the poor, such as the Vulnerable Group Feeding program and the Food-for-Work program, as well as interventions by nongovernmental organizations have had significant impacts on reducing child malnutrition, especially among the poor. To attain the millennium development goal of halving child undernutrition by 2015, Bangladesh will have to scale up targeted income-poverty interventions, public food transfer programs targeted to the poor and to the regions with the largest number of malnourished children, interventions that reduce the risk and vulnerability of poor households to natural disasters, nutrition education to mothers and caregivers, and rapid pro-poor economic growth.

A common dilemma facing educational planners engaged in educational reform in developing countries is the allocation of sharply-reduced resources to the expansion of school facilities versus improvement of existing school facilities (say, by raising the teacher/pupil ratio). The empirical results presented in this paper for Kenya suggest that the two interventions have diametrically opposite effects on poor and nonpoor children. An expansion of school facilities increases the enrollment of children in the poorest expenditure quintiles but has no impact on the enrollment of children in the top quintiles. On the other hand, an improvement in the teacher-pupil ratio increases the enrollment rate of children in the top quintiles, and actually reduces the enrollment of children in the poor quintiles. These findings suggest that in situations where there is less than universal primary enrollment (UPE) and the government has set a time-bound goal of UPE, such as in Kenya, policies that serve to expand the number of school facilities may make more sense than interventions that increase the teacher-pupil ratio.