Managing technological uncertainty in science incubation:A prospective sensemaking perspective

This paper focuses on the adaption challenge that confronts the top management team (TMT) of science incubators in situations of substantial technological uncertainty. To do that, we draw on the three-year longitudinal analysis of a major bioscience catalyst in the UK. Through the lens of ‘prospective sensemaking’, we follow the TMT as they work with stakeholders in their ecosystem to make sense of a significant technological shift: the convergence of life sciences, IT and other sciences in the health care environment. Our analysis reveals how prospective sensemaking resulted in the launch of a new strategy to exploit these emerging opportunities. However, stakeholders’ increasingly fragmented interpretation of the term convergence and the anticipation of legitimacy challenges in the wider ecosystem resulted in the repositioning of the incubator. Our findings contribute to extant research on science incubation. In particular, the paper sheds light on the complex interactions of incubator TMT’s with stakeholders in situations of technological change and uncertainty. Moreover, responding to technological change does not only affect the structural conditions of an incubator. Rather, it may also require changes to the positioning of the incubator in order to maintain legitimacy in the wider ecosystem. The paper also suggests managerial as well as policy level implications

Robust Padé Approximation via SVD.

Padé approximation is considered from the point of view of robust methods of numerical linear algebra, in particular, the singular value decomposition. This leads to an algorithm for practical computation that bypasses most problems of solution of nearly-singular systems and spurious pole-zero pairs caused by rounding errors, for which a MATLAB code is provided. The success of this algorithm suggests that there might be variants of Padé approximation that are pointwise convergent as the degrees of the numerator and denominator increase to ∞, unlike traditional Padé approximants, which converge only in measure or capacity. © 2013 Society for Industrial and Applied Mathematics

On the steepest descent method for matrix functions

We consider the special case of the restarted Arnoldi method for approximating the product of a function of a Hermitian matrix with a vector which results when the restart length is set to one. When applied to the solution of a linear system of equations, this approach coincides with the method of steepest descent. We show that the method is equivalent with an interpolation process in which the node sequence has at most two points of accumulation. This knowledge is used to quantify the asymptotic convergence rate

Statistical cluster analysis and visualisation for alarm management configuration

The effective performance of an alarm system is a key aspect of asset management for any industrial installation. However, it is not uncommon for alarm systems to be poorly configured, leading to large amounts of alarm noise and a potentially dangerous load on the operators. Here we present a novel method for the identification of redundant or bad actors in alarm systems through the application of statistical cluster analysis. This allows the system to be optimised to reduce the load on the operators through existing systems change management processes