Abstract
Purpose - In a context of international economic crisis the improvement in the efficiency and productivity of public services is seen as a way to maintain high-quality levels at lower costs. Increased productivity can be promoted through benchmarking exercises, where key performance indicators (KPIs), individually or aggregated, are used to compare health units. The purpose of this paper is to describe a benchmarking platform, called Hospital Benchmarking (HOBE), where hospital's services are used as the unit of analysis. Design/methodology/approach - HOBE platform includes a set of managerial indicators through which hospital services' are compared. The platform also benchmarks services through aggregate service indicators, and provides an aggregate measure of hospital's performance based on a composite indicator of the service's performances. These aggregate indicators were obtained through data envelopment analysis (DEA). Findings - Some results are presented for Portuguese hospitals for the trial years of 2008 and 2009, for which data is publicly available. Details for the service-level analysis are provided for a sample hospital, as well as details on the aggregate performance resulting from services performances. Practical implications - HOBE's features and outcomes show that the platform can be used to guide management actions and to support the design of health policies by administrative authorities, provided that good quality and timely data are available, and that hospitals are involved in the design of the KPIs. Originality/value - The platform is innovative in the sense that it bases its analysis on hospital's services, which are in general more comparable among hospitals than indicators of hospital overall performance. In addition, it makes use of DEA to aggregate performance indicators, allowing for user choice in the inputs and outputs to be aggregated, and it proposes a novel model to aggregate service's efficiencies into a single measure of hospital performance.

Abstract

Abstract
Monte Carlo (MC) techniques are widely applied to check a design on its robustness and for estimating the production yield of integrated circuits. Using standard random MC and the sample yield for estimation, a very large number of samples is required for accurate verification, especially if a high yield is desired. This can make MC extremely time consuming, but if the data follows a normal Gaussian distribution a much faster yield prediction is possible by using the well-known C-PK method. We extended this specification-distance-based scheme for the far more difficult general non-normal case by three different means, ending up in a new generalized process capability index named C-GPK. First, we apply parametric modeling only to the specification-sided distribution part. This way any difficulties in distribution parts that actually have little yield impact do not degrade the model fit anymore. Second, to improve the parametric model we introduce a new tail parameter t. Third, to allow modeling of difficult asymmetrical, multimodal or flat distributions we also introduce a new reference location parameter instead of using the mean. An advantage of improving MC this way is that-in opposite to many other MC enhancements (like importance sampling)-the performance of the C-GPK is not negatively impacted by design complexity. We described the formulation of the C-GPK and derived confidence intervals using an advanced bootstrap scheme. We verified the performance against the sample yield and C-PK for a representative set of distributions, including real production data and MC data from the design of a CMOS operational amplifier and other circuits.

Abstract
To enable the use of smart metering historical information of energy measurements in real time network operation, in this paper is proposed the generation of pseudo-measurements, which can be combined with real-time SCADA measurements and feed an online state estimation procedure. Hence increasing the network operator's situational awareness. The goal is to obtain a better representation of the network operation points, voltage values, than the one that is possible to obtain with the direct use of smart metering data, which is based on average values, by increasing the amount of available real time data points.

Abstract
This last chapter succinctly reviews the main contributions of the procedural modelling methodology presented in this book, briefly compares some of its aspects with other works and presents a set of notes to be considered as future research opportunities. © The Author(s) 2016.

Abstract
This work presents a new topology for the matching networks of an underwater wireless power transfer system. A class-D driver is used in resonance at fundamental and third harmonic frequencies. The double resonance helps reducing the reverse voltage stress at the diode rectifier. We present the analytical derivation of the proposed network and demonstrate the design procedure with an example. We also show that additional degrees of freedom can be acquired with the proposed topology, which improves the design space for time-varying operation conditions of our application, such as the load changing when a battery is being recharged. The performance of our topology is compared to most conventional approaches, such as the series-series and series-parallel networks, revealing a good compromise between power delivery and efficiency across a wide load range.