Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process

for the ANZICS Centre for Outcome and Resource Evaluation (CORE) of the Australian and New Zealand Intensive Care Society (ANZICS)BACKGROUND Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency. METHODS Monthly mean raw mortality (at hospital discharge) time series, 1995–2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) “in-control” status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance. RESULTS The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag ₄₀ and 35% had autocorrelation through to lag ₄₀; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model. CONCLUSIONS The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues.John L Moran, Patricia J Solomo

Underground Phased Arrays and Beamforming Applications

This chapter presents a framework for adaptive beamforming in underground communication. The wireless propagation is thoroughly analyzed to develop a model using the soil moisture as an input parameter to provide feedback mechanism while enhancing the system performance. The working of array element in the soil is analyzed. Moreover, the effect of soil texture and soil moisture on the resonant frequency and return loss is studied in detail. The wave refraction from the soil–air interface highly degrades the performance of the system. Furthermore, to beam steering is done to achieve high gain for lateral component improving the UG communication. The angle enhancing the lateral wave depends upon dielectric properties and usually ranges from 0∘ to 16∘. These dielectric properties change with the change in soil moisture and soil texture. It is shown from the experiments that optimal UG lateral angle is high at lower soil moisture readings and decreases with decrease in soil moisture. A planar structure of antenna array and different techniques for optimization are proposed for enhanced soil moisture adaptive beamforming. UG channel impulse response is studied from the beamforming aspect to identify the components of EM waves propagating through the soil. An optimum steering method for beamforming is presented which adapts to the changing values of soil moisture. Finally, the limitations of UG beamforming are presented along with the motivation to use it

Proximity Relations and Firms’ Innovative Behaviours: Different Proximities in the Optics Cluster of the Greater Paris Region

Partie III, chapitre 12There have been some important developments in the analysis of proximity relations since its origin. First introduced by a group of French economists (Kirat and Lung 1997; Torre and Gilly 1999), during the 1990s this approach was primarily confined to the analysis of industrial production relations and was specifically developed in the context of the study of innovation processes. Industrial relations, innovation, firm mobility, new technology, territorial resources, local productive systems… all have been studied, endlessly explored and brought back under the spotlight again by the confrontation between theoretical analysis and empirical research (Boschma 2005; Carrincazeaux et al. 2008; Rychen and Zimmermann 2008)