Assessment of the benefits of Discrete Conditional Survival Models in modelling ambulance response times

Many of the challenges faced in health care delivery can be informed through building models. In particular, Discrete Conditional Survival (DCS) models, recently under development, can provide policymakers with a flexible tool to assess time-to-event data. The DCS model is capable of modelling the survival curve based on various underlying distribution types and is capable of clustering or grouping observations (based on other covariate information) external to the distribution fits. The flexibility of the model comes through the choice of data mining techniques that are available in ascertaining the different subsets and also in the choice of distribution types available in modelling these informed subsets. This paper presents an illustrated example of the Discrete Conditional Survival model being deployed to represent ambulance response-times by a fully parameterised model. This model is contrasted against use of a parametric accelerated failure-time model, illustrating the strength and usefulness of Discrete Conditional Survival models.

Dynamical instability in surface permeability characteristics of building sandstones in response to salt accumulation over time

This paper explores how the surface permeability of sandstone blocks changes over time in response to repeated salt weathering cycles. Surface permeability controls the amount of moisture and dissolved salt that can penetrate in and facilitate decay. Connected pores permit the movement of moisture (and hence soluble salts) into the stone interior, and where areas are more or less permeable soluble salts may migrate along preferred pathways at differential rates. Previous research has shown that salts can accumulate in the near-surface zone and lead to partial pore blocking which influences subsequent moisture ingress and causes rapid salt accumulation in the near-surface zone.

Two parallel salt weathering simulations were carried out on blocks of Peakmoor Sandstone of different volumes. Blocks were removed from simulations after 2, 5, 10, 20 and 60 cycles. Permeability measurements were taken for these blocks at a resolution of 20 mm, providing a grid of 100 permeability values for each surface. The geostatistical technique of ordinary kriging was applied to the data to produce a smoothed interpolation of permeability for these surfaces, and hence improve understanding of the evolution of permeability over time in response to repeated salt weathering cycles.

Results illustrate the different responses of the sandstone blocks of different volumes to repeated salt weathering cycles. In both cases, after an initial subtle decline in the permeability (reflecting pore blocking), the permeability starts to increase — reflected in a rise in mean, maximum and minimum values. However, between 10 and 20 cycles, there is a jump in the mean and range permeability of the group A block surfaces coinciding with the onset of meaningful debris release. After 60 cycles, the range of permeability in the group A block surface had increased markedly, suggesting the development of a secondary permeability. The concept of dynamic instability and divergent behaviour is applied at the scale of a single block surface, with initial small-scale differences across a surface having larger scale consequences as weathering progresses.

After cycle 10, group B blocks show a much smaller increase in mean permeability, and the range stays relatively steady — this may be explained by the capillary conditions set up by the smaller volume of the stone, allowing salts to migrate to the ‘back’ of the blocks and effectively relieving stress at the ‘front’ face.

Implementation of Discrete and Integrated IT: The Role of Organisational Structure and Culture

ntegrated organisational IT systems, such as enterprise resource planning (ERP), supply chain management (SCM) and digital manufacturing (DM), have promised and delivered substantial performance benefits to many adopting firms. However, implementations of such systems have tended to be problematic. ERP projects, in particular, are prone to cost and time overruns, not delivering anticipated benefits and often being abandoned before completion. While research has developed around IT implementation, this has focused mainly on standalone (or discrete), as opposed to integrated, IT systems. Within this literature, organisational (i.e., structural and cultural) characteristics have been found to influence implementation success. The key aims of this research are (a) to investigate the role of organisational characteristics in determining IT implementation success; (b) to determine whether their influence differs for integrated IT and discrete IT projects; and (c) to develop specific guidelines for managers of integrated IT implementations. An in-depth comparative case study of two IT projects was conducted within a major aerospace manufacturing company.

Maximum likelihood estimation of higher-order integer-valued autoregressive processes

In this article, we extend the earlier work of Freeland and McCabe [Journal of time Series Analysis (2004) Vol. 25, pp. 701–722] and develop a general framework for maximum likelihood (ML) analysis of higher-order integer-valued autoregressive processes. Our exposition includes the case where the innovation sequence has a Poisson distribution and the thinning is binomial. A recursive representation of the transition probability of the model is proposed. Based on this transition probability, we derive expressions for the score function and the Fisher information matrix, which form the basis for ML estimation and inference. Similar to the results in Freeland and McCabe (2004), we show that the score function and the Fisher information matrix can be neatly represented as conditional expectations. Using the INAR(2) speci?cation with binomial thinning and Poisson innovations, we examine both the asymptotic e?ciency and ?nite sample properties of the ML estimator in relation to the widely used conditional least
squares (CLS) and Yule–Walker (YW) estimators. We conclude that, if the Poisson assumption can be justi?ed, there are substantial gains to be had from using ML especially when the thinning parameters are large.

Fronts, jumps and secondary introductions suggested as different invasion patterns in marine species, with an increase in spread rates over time

Not all introduced (invasive) species in a region will spread from a single point of introduction. Long-distance dispersal or further introductions can obscure the pattern of spread, but the regional importance of such processes is difficult to gauge. These difficulties are further compounded when information on the multiple scale process of invasive species range expansion is reduced to one-dimensional estimates of spread (e. g. km yr(-1)). We therefore compared the results of two different metrics of range expansion: maximum linear rate of spread and accumulation of occupied grid squares (50 x 50 km) over time. An analysis of records for 54 species of introduced marine macrophytes in the Mediterranean and northeast Atlantic revealed cases where the invasion process was probably missed (e. g. Atlantic Bonnemaisonia hamifera) and suggested cases of secondary introductions or erratic jump dispersal (Dasysiphonia sp. and Womersleyella setacea). A majority of species analysed showed evidence for an accumulation of invaded sites without a clear invasion front. Estimates of spread rate are increasing for more recent introductions. The increase is greater than can be accounted for by temporally varying search effort and implies a historical increase in vector efficiency and/or a decreased resistance of native communities to invasion.

An exploratory study of Principal Investigator roles in UK University Proof-of-Concept processes: an Absorptive capacity perspective

The increasing emphasis on academic entrepreneurship, technology transfer and research commercialisation within UK universities is predicated on basic research being developed by academics into commercial entities such as university spin-off companies or licensing arrangements. However, this process is fraught with challenges and risks, given the degree of uncertainty regarding future returns. In an attempt to minimise such risks, the Proof-of-Concept (PoC) process has been developed within University Science Park Incubators (USIs) to test the technological, business and market potential of embryonic technology. The key or the pivotal stakeholder within the PoC is the Principal Investigator (PI), who is usually the lead academic responsible for the embryonic technology. Within the current literature, there appears to be a lack of research pertaining to the role of the PI in the PoC process. Moreover, Absorptive Capacity (ACAP) has emerged within the literature as a theoretical framework or lens for exploring the development and application of new knowledge and technology, where the USI is the organisation considered in the current study. Therefore, the aim of this paper is to explore the role and influence of the PI in the PoC process within a USI setting using an ACAP perspective. The research involved a multiple case analysis of PoC applications within a UK university USI. The results demonstrate the role of the PI in developing practices and routines within the PoC process. These practices and processes were initially tacit and informal in nature but became more explicit and formal over time so that knowledge was retained within the USI after the PIs had completed the PoC process. © 2010 The Authors. R&D Management © 2010 Blackwell Publishing Ltd.

Modeling Multivariate Interest Rates Using Time-Varying Copulas and Reducible Nonlinear Stochastic Differential Equations

We propose a new approach for modeling nonlinear multivariate interest rate processes based on time-varying copulas and reducible stochastic differential equations (SDEs). In the modeling of the marginal processes, we consider a class of nonlinear SDEs that are reducible to Ornstein--Uhlenbeck (OU) process or Cox, Ingersoll, and Ross (1985) (CIR) process. The reducibility is achieved via a nonlinear transformation function. The main advantage of this approach is that these SDEs can account for nonlinear features, observed in short-term interest rate series, while at the same time leading to exact discretization and closed-form likelihood functions. Although a rich set of specifications may be entertained, our exposition focuses on a couple of nonlinear constant elasticity volatility (CEV) processes, denoted as OU-CEV and CIR-CEV, respectively. These two processes encompass a number of existing models that have closed-form likelihood functions. The transition density, the conditional distribution function, and the steady-state density function are derived in closed form as well as the conditional and unconditional moments for both processes. In order to obtain a more flexible functional form over time, we allow the transformation function to be time varying. Results from our study of U.S. and UK short-term interest rates suggest that the new models outperform existing parametric models with closed-form likelihood functions. We also find the time-varying effects in the transformation functions statistically significant. To examine the joint behavior of interest rate series, we propose flexible nonlinear multivariate models by joining univariate nonlinear processes via appropriate copulas. We study the conditional dependence structure of the two rates using Patton (2006a) time-varying symmetrized Joe--Clayton copula. We find evidence of asymmetric dependence between the two rates, and that the level of dependence is positively related to the level of the two rates. (JEL: C13, C32, G12) Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org, Oxford University Press.

Maximum likelihood estimation of higher-order integer-valued autoregressive processes

Corrigendum Vol. 30, Issue 2, 259, Article first published online: 15 MAR 2009 to correct the order of authors names: Bu R., K. Hadri, and B. McCabe.

Discrete Conditional Phase-type models utilising classification trees: Application to modelling health service capacities

Discrete Conditional Phase-type (DC-Ph) models consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian Phase-type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case-study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three-phase Coxian distribution. The resulting DC-Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.

Exploring the adoption and implementation of discrete and integrated IT systems: A Framework for analysis

Despite the substantial organisational benefits of integrated IT, the implementation of such systems – and particularly Enterprise Resource Planning (ERP) systems – has tended to be problematic, stimulating an extensive body of research into ERP implementation. This research has remained largely separate from the main IT implementation literature. At the same time, studies of IT implementation have generally adopted either a factor or process approach; both have major limitations. To address these imitations, factor and process perspectives are combined here in a unique model of IT implementation. We argue that • the organisational factors which determine successful implementation differ for integrated and traditional, discrete IT • failure to manage these differences is a major source of integrated IT failure. The factor/process model is used as a framework for proposing differences between discrete and integrated IT.

Use of microtechnologies for intensifying industrial processes

The use of new technologies based on microstructured reactors in industrial processes, including the obtainment of hydrogen peroxide, the catalytic oxidation of ammonia, the utilization of rocket fuels, fine organic synthesis, polymerization, and phase transfer catalysis, were considered. The transition to microtechnologies considerably increases the performance of the process; at the same time, the product yield increases as compared with periodically operating reactors, which allows for a reduction of costs at the separation stage of the reaction mixture and the extraction of the reaction products.

The Many Directions of Time

Event duration perception is fundamental to cognitive functioning. Recent research has shown that localized sensory adaptation compresses perceived duration of brief visual events in the adapted location; however, there is disagreement on whether the source of these temporal distortions is cortical or pre-cortical. The current study reveals that spatially localized duration compression can also be direction contingent, in that duration compression is induced when adapting and test stimuli move in the same direction but not when they move in opposite directions. Because of its direction-contingent nature, the induced duration compression reported here is likely to be cortical in origin. A second experiment shows that the adaptation processes driving duration compression can occur at or beyond human cortical area MT+, a specialised motion centre located upstream from primary visual cortex. The direction-specificity of these temporal mechanisms, in conjunction with earlier reports of pre-cortical temporal mechanisms driving duration perception, suggests that our encoding of subsecond event duration is driven by activity at multiple levels of processing.

Timing movements to interval durations specified by discrete or continuous sounds

Understanding how the timing of motor output is coupled to sensory temporal information is largely based on synchronisation of movements through small motion gaps (finger taps) to mostly empty sensory intervals (discrete beats). This study investigated synchronisation of movements between target barriers over larger motion gaps when closing time gaps of intervals were presented as either continuous, dynamic sounds, or discrete beats. Results showed that although synchronisation errors were smaller for discrete sounds, the variability of errors was lower for continuous sounds. Furthermore, finger movement between targets was found to be more sinusoidal when continuous sensory information was presented during intervals compared to discrete. When movements were made over larger amplitudes, synchronisation errors tended to be more positive and movements between barriers more sinusoidal, than for movements over shorter amplitudes. These results show that the temporal control of movement is not independent from the form of the sensory information that specifies time gaps or the magnitude of the movement required for synchronisation.

CRITICAL PROCESSES, LANGEVIN EQUATION AND UNIVERSALITY

In nature there are ubiquitous systems that can naturally approach critical states, The Langevin equation in the discrete version can be used to describe a class of critical processes, which are characterized by power-law behaviors and scaling relations. As an example, we present a simple model for a clinical thermometer, whose reading cannot fall even when its temperature decreases. The fibers bundle model and the spring-block model are also shown to belong to such a class.