An Opportunity for Diagonal Development in Global Surgery: Cleft Lip and Palate Care in Resource-Limited Settings

Global cleft surgery missions have provided much-needed care to millions of poor patients worldwide. Still, surgical capacity in low- and middle-income countries is generally inadequate. Through surgical missions, global cleft care has largely ascribed to a vertical model of healthcare delivery, which is disease specific, and tends to deliver services parallel to, but not necessarily within, the local healthcare system. The vertical model has been used to address infectious diseases as well as humanitarian emergencies. By contrast, a horizontal model for healthcare delivery tends to focus on long-term investments in public health infrastructure and human capital and has less often been implemented by humanitarian groups for a variety of reasons. As surgical care is an integral component of basic healthcare, the plastic surgery community must challenge itself to address the burden of specific disease entities, such as cleft lip and palate, in a way that sustainably expands and enriches global surgical care as a whole. In this paper, we describe a diagonal care delivery model, whereby cleft missions can enrich surgical capacity through integration into sustainable, local care delivery systems. Furthermore, we examine the applications of diagonal development to cleft care specifically and global surgical care more broadly

State Space Realization of Model Predictive Controllers Without Active Constraints

To enable the use of traditional tools for analysis of multivariable controllers such as model predictive control (MPC), we develop a state space formulation for the resulting controller for MPC without constraints or assuming that the constraints are not active. Such a derivation was not found in the literature. The formulation includes a state estimator. The MPC algorithm used is a receding horizon controller with infinite horizon based on a state space process model. When no constraints are active, we obtain a state feedback controller, which is modified to achieve either output tracking, or a combination of input and output tracking. When the states are not available, they need to be estimated from the measurements. It is often recommended to achieve integral action in a MPC by estimating input disturbances and include their effect in the model. We show that to obtain offset free steady state the number of estimated disturbances must equal the number of measurements. The estimator is included in the controller equation, and we obtain a formulation of the overall controller with the set-points and measurements as inputs, and the manipulated variables as outputs. One application of the state space formulation is in combination with the process model to obtain a closed loop model. This can for example be used to check the steady-state solution and see whether integral action is obtained or not

Operator Factorization and Other Aspects of the Analysis of Linear Systems

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Linear Control and Estimation Using Operator Factorization

The filtering, prediction and smoothing problems as well as the linear quadratic control problems can very generally be formulated as operator equations using basic linear algebra. The equations are of Fredholm type II and difficult to solve directly. It is shown how the operator can be factorized into two Volterra operators using a matrix Riccati equation. Recursive solution of these triangular operator equations is then obtained by two initialvalue differential equations