112 research outputs found
Advancing the Theory and Practice of Public Sector Reform through the Analysis of Social Mechanisms
No full textNotions of policy effectiveness and implications for policy design: insights from public-private partnerships in India
No full textHow neglecting policy mechanisms can lead to policy failure: insights from public–private partnerships in Indias health sector
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Programmatic strategies for tackling maternal anaemia : lessons from research and experience
No full textMaternal anaemia is associated with a higher risk of maternal mortality and poor perinatal outcomes. Even mild and moderate iron deficiency anaemia can undermine cardiovascular functioning and thereby put at risk both wellbeing and survival. This brief provides: background to the increasing rates of anaemia in Karnataka/Bangalore districts; outlines the barriers to effective prevention and treatment; suggests effective solutions including improving outreach and effectiveness; and makes nine recommendations. The “knowledge-implementation” project brief is intended to clearly spell out the implications of research and put forth specific recommendations for programming and policy
PRIVATE SECTOR SOLUTIONS TO PUBLIC SECTOR PROBLEMS? GOVERNANCE DESIGN CHALLENGES OF NEW PERFORMANCE REGIMES IN INDIA'S PUBLIC HEALTHCARE SYSTEM
No full textPh.DDOCTOR OF PHILOSOPHY (SPP
A Study of Reduced Order 4D-VAR with a Finite Element Shallow Water Model
No full textForecast models often depend on unknown parameters, such as model initial and boundary conditions, or other tunable parameters not necessarily having any physical meaning. Calibration of these parameters to minimize errors between forecasted and observed states is called data assimilation. A common approach in this context are variational methods, of which four dimensional data variation (4D-VAR) is studied in this thesis. In 4D-VAR, a cost function is defined that penalizes misfits between observations and the corresponding numerical model results, obtained by running the model with the chosen configuration. Performing optimization with regard to this cost function yields an improved initial parameter set. Associated with this type of methods, however, are difficulties in connection with programming the adjoint model, which is needed to compute the exact gradient of the cost function. Additionally, having to integrate the adjoint model backwards in time adds significantly to the computational cost of the data assimilation process. To avoid manual implementation of adjoint code and to reduce computational complexity, approximation of the gradient calculation is considered through the use of proper orthogonal decomposition (POD), a flexible data-driven order reduction method. To facilitate this, a finite element model of the shallow water equations is tested with both the full adjoint 4D-VAR method and two different POD-reduced approaches. Twin experiments are performed and comparisons are made in terms of accuracy, computational complexity and sensitivity to perturbation and number of observation points.Applied mathematicsElectrical Engineering, Mathematics and Computer Scienc
Model reduced variational data assimilation for shallow water flow models
No full textIdentifying uncertain parameters in large-scale numerical flow models can be done using the variational method. However, for implementing the variational method the adjoint model have to be available, which requires highly complex computer code and maintenance and thus hampers its applications. To ease this problem, this thesis has explored several methods for efficiently identifying uncertain parameters in a large-scale tidal model of the entire European continental shelf which does not require the implementation of these complex adjoint code. In this study, as a first step an estimation method based on model reduction is developed and investigated for the estimation of diffusion coefficient in a simple 2D-advection diffusion model. Two projection based model reduction methods were considered, namely proper orthogonal decomposition (POD) and Balanced proper orthogonal decomposition (BPOD). In the POD based estimation method an ensemble of forward model simulations is used to determine an approximation of the covariance matrix of the model variability and a small number of the leading eigenvectors of this matrix is used to define a model subspace. By projecting the original model onto this subspace an approximate linear reduced model is obtained. Once the reduced model is available its adjoint can be implemented easily and the minimization problem is solved completely in reduced space with very low computational cost. BPOD is also a model reduction method which considers both inputs and outputs of the system while determining the reduce subspace. The estimation method has been extended by including BPOD procedure into the estimation procedure. Numerical results from a simple pollution model demonstrate that the POD based estimation approach successfully estimate the diffusion coefficient for both advection dominated problems as for diffusion dominated problems. Another important message in this study, although lots of effort had been made in constructing a reduced order model by the BPOD method, the minimization results demonstrated that both the POD and the BPOD methods performed similarly. Preliminary results showed the validity of the POD based model reduction methods for parameter estimation. As a next step, the POD based estimation method is used to calibrate numerical tidal models. Results from (twin) numerical experiments showed that the POD based calibration method performed very efficiently to estimate depth values in the selected regions of the model domain. The computational costs of the POD based calibration method are dominated by the generation of an ensemble of forward model simulations where the simulation period of the ensemble is equivalent to the timescale of the original model. It has also been found in the study that it is not needed to use a full simulations of the original model for the generation of the ensemble. The POD based calibration method has also been implemented for the estimation of the water depth and space varying bottom friction coefficient values in a very large-scale DCSM model. The recently designed large-scale spherical grid based water level model for the northwest European continental shelf (around 1000000 computational grid points) has been used for this purpose. This has been the first application of the POD based calibration method to a very large-scale model and with real data. Results from numerical experiments showed that the calibration method performs very efficiently. An overall improvement of more than 50\% was observed after the calibration in comparison with the initial model. The results also demonstrated that the POD based calibration method offered a very efficient minimization technique compared to the classical adjoint method without the burden of implementation of the adjoint. As a concluding step, to estimate depth values in the model DCSM, a Simultaneous perturbation stochastic approximation (SPSA) method has been used. The method uses stochastic simultaneous perturbation of all model parameters to generate a search at each iteration. SPSA is based on a highly efficient and easily implemented simultaneous perturbation approximation to the gradient. This gradient approximation for the central difference method uses only two objective function evaluations independent of the number of parameters being optimized. The results from experiments showed that SPSA has a lower convergence rate than POD based calibration method, however the computational cost in each iteration of the SPSA method is usually far less then the POD based calibration method. The results also demonstrated that the SPSA algorithm proved to be a promising optimization algorithm for model calibration for cases where adjoint code is not available for computing the gradient of the objective function.Applied mathematicsElectrical Engineering, Mathematics and Computer Scienc
Where is the policy? A bibliometric review of the state of policy research on medical tourism
No full textThe role of medical tourism in bridging health system deficiencies, improving healthcare standards and stimulating local economies has long been recognized, but its use as a welfare and developmental strategy is being increasingly contested. It is imperative that researchers who study medical tourism connect their work with policy, so that its real-world effects can be better understood, and more effectively addressed. This article seeks to gauge the extent of policy thinking in medical tourism research and its overlap with contextual policy concerns. It examines the current state of medical tourism research through a bibliometric review of academic literature on medical tourism, to establish the extent to which researchers apply public policy theories and frameworks in their investigation of medical tourism, or consider the policy imperatives of their work. Further, it compares the content of policy research on medical tour-ism in select source (Canada, United States and United Kingdom) and destination countries (Mexico, India, Thailand, Malaysia and Singapore) to contextual policy challenges, to examine the degree of convergence between research and policy needs, and identify gaps. Findings suggest that policy in medical tourism research is relatively limited, and that what policy there is, is fragmented and not entirely in sync with the policy questions and concerns that exist. This is a potential issue, because failure to make the policy connection is a lost opportunity to frame the public debate on medical tourism, and influence policy thinking. This article is a call to action for greater engagement by policy scholars on the subject, more coherence between policy needs and research, as well as effective feedback mechanisms to translate research results to policy prescriptions, and to channel them into policy action
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