Earlier, better prediction of severe AKI has the potential to improve AKI associated patient outcomes. Compared to isolated, context-free changes in SCr, renal angina risk assessment improved accuracy for prediction of severe AKI in critically ill children and young adults.

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A new approach to the design of digital algorithm for voltage phasor and local system frequency estimation is presented. The estimation problem is considered as an unconstrained optimization problem. The algorithm is derived using Newton's iterative method, very commonly used in Load-Flow studies. The algorithm showed a very high level of robustness as well as high measurement accuracy over a wide range of frequency changes. The algorithm convergence of order two provided fast response and adaptability. To demonstrate the performance of the algorithm developed, computer simulated, experimentally obtained and reallife data records are processed. The presented work is a part of a project concerning the application of microprocessors in frequency relaying.

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The problem of making the Kalman filter robust is considered in the paper. Proceeding from the equivalence between the Kalman filter and the least squares regression problem, a statistical approach named M -estimation is suggested to resolve the regression problem robustly. Since the derived robust M -filters do not have an attractive recursive form, the possibility is proposed of designing real-time estimators based on the general formulation of the robust stochastic approximation algorithm and step-by-step optimization with respect to the weighting matrix combined with suitable approximations. Results of simulation demonstrating the robustness of the proposed estimators are also included.