Shift-and-scale model reduction:an alternative stability-preserving approach

A new stability-preserving model order-reduction method is presented for continuous-time systems. It makes use of the relatively new idea of transformed whole-system parameter matching for calculating the poles of the reduced-order transfer function. This has the advantage of using more of the system information than traditional methods in the approximation of the poles. The method is seen to be flexible and computationally attractive, relying only on readily available algorithms. It is based on a shift-and-scale transformation of the transfer function before applying the order-reduction process. Further, it is shown to be a viable alternative to existing stability-preserving techniques. Some examples illustrate the method

The bilinear method:a new stability-preserving order reduction approach

A new way of reducing the order of linear system transfer functions is presented. It guarantees stability in the approximation of stable systems and differs from existing stability-preserving methods by taking into account whole system parameter information when obtaining the approximate poles, not just that of the system poles. It uses a bilinear transformation in the process, which renders the method more flexible than traditional techniques. Examples are given to highlight the advantages of the new approach

Ethnic Microaggressions, Traumatic Stress Symptoms, And Latino Depression: A Moderated Mediational Model

Although ethnic microaggressions have received increased empirical attention in recent years, there remains a paucity of research regarding how these subtle covert forms of discrimination contribute to Latino mental health. The present study examined the role of traumatic stress symptoms underlying the relationship between ethnic microaggressions and depression. Further, ethnic identity and general self-efficacy were tested as moderators between the ethnic microaggressions and traumatic stress link. Among a sample of 113 Latino adults, moderated mediational analyses revealed statistically significant conditional indirect effects in which traumatic stress symptoms mediated the relationship between ethnic microaggressions and depression while ethnic identity and self-efficacy functioned as moderators. The major findings suggested that the indirect effects were the most robust within low ethnic identity and low self-efficacy. The findings are discussed within a stress and coping framework that highlight the internal resources and stress responses associated with experiencing ethnic microaggressions

Prediction of Recovery From Severe Hemorrhagic Shock Using Logistic Regression.

This paper implements logistic regression models (LRMs) and feature selection for creating a predictive model for recovery form hemorrhagic shock (HS) with resuscitation using blood in the multiple experimental rat animal protocols. A total of 61 animals were studied across multiple HS experiments, which encompassed two different HS protocols and two resuscitation protocols using blood stored for short periods using five different techniques. Twenty-seven different systemic hemodynamics, cardiac function, and blood gas parameters were measured in each experiment, of which feature selection deemed only 25% of the them as relevant. The reduced feature set was used to train a final logistic regression model. A final test set accuracy is 84% compared to 74% for a baseline classifier using only MAP and HR measurements. Receiver operating characteristics (ROC) curve analysis and Cohens kappa statistics were also used as measures of performance, with the final reduced model outperforming the model, including all parameters. Our results suggest that LRMs trained with a combination of systemic hemodynamics, cardiac function, and blood gas parameters measured at multiple timepoints during HS can successfully classify HS recovery groups. Our results show the predictive ability of traditional and novel hemodynamic and cardiac function features and their combinations, many of which had not previously been taken into consideration, for monitoring HS. Furthermore, we have devised an effective methodology for feature selection and shown ways in which the performance of such predictive models should be assessed in future studies

Kingmakers or Cheerleaders? Party Power and the Causal Effects of Endorsements

When parties make endorsements in primary elections, does the favored candidate receive a real boost in his or her vote share, or do parties simply pick the favorites who are already destined to win? To answer this question, we draw on two research designs aimed at isolating the causal effect of Democratic Party endorsements in California’s 2012 primary election. First, we conduct a survey experiment in which we randomly assign a party endorsement, holding all other aspects of a candidate’s background and policy positions constant. Second, we use a unique dataset to implement a regression discontinuity analysis of electoral trends by comparing the vote shares captured by candidates who barely won or barely lost the internal party endorsement contest. We find a constellation of evidence suggesting that endorsements do indeed matter, although this effect appears to be contingent upon the type of candidate and voter: endorsements matter most for candidates in their party’s mainstream, and for voters who identify with that party and for independents. The magnitude of their impact is dramatically smaller than might be estimated from research designs less attuned to recent advances in causal inference

Estimating the Indirect Gaming Contribution of Bingo Rooms

Using data from two repeater market hotel casinos, the relationship between bingo and slot business volumes is explored. Contrary to conjecture supplied by industry executives, the results fail to demonstrate a statistically significant relationship between daily bingo headcount and coin-in. This result was found in three different analyses, including one· attempt to estimate the impact of bingo headcount on low-denomination coin-in. This study advances the literature by challenging the assumption that bingo rooms produce substantial indirect slot profits. Given the minimal direct contribution to property cash flows, if any, the results suggest that bingo rooms are not always the highest and best use of valuable casino floor space

A review of portfolio planning: Models and systems

In this chapter, we first provide an overview of a number of portfolio planning models which have been proposed and investigated over the last forty years. We revisit the mean-variance (M-V) model of Markowitz and the construction of the risk-return efficient frontier. A piecewise linear approximation of the problem through a reformulation involving diagonalisation of the quadratic form into a variable separable function is also considered. A few other models, such as, the Mean Absolute Deviation (MAD), the Weighted Goal Programming (WGP) and the Minimax (MM) model which use alternative metrics for risk are also introduced, compared and contrasted. Recently asymmetric measures of risk have gained in importance; we consider a generic representation and a number of alternative symmetric and asymmetric measures of risk which find use in the evaluation of portfolios. There are a number of modelling and computational considerations which have been introduced into practical portfolio planning problems. These include: (a) buy-in thresholds for assets, (b) restriction on the number of assets (cardinality constraints), (c) transaction roundlot restrictions. Practical portfolio models may also include (d) dedication of cashflow streams, and, (e) immunization which involves duration matching and convexity constraints. The modelling issues in respect of these features are discussed. Many of these features lead to discrete restrictions involving zero-one and general integer variables which make the resulting model a quadratic mixed-integer programming model (QMIP). The QMIP is a NP-hard problem; the algorithms and solution methods for this class of problems are also discussed. The issues of preparing the analytic data (financial datamarts) for this family of portfolio planning problems are examined. We finally present computational results which provide some indication of the state-of-the-art in the solution of portfolio optimisation problems