End-of-range defects in germanium and their role in boron deactivation

We investigated the thermal evolution of end-of-range (EOR) defects in germanium and their impact on junction thermal stability. After solid-phase epitaxial regrowth of a preamorphized germanium layer, EOR defects exhibiting dislocation loop-like contrast behavior are present. These defects disappear during thermal annealing at 400 °C, while boron electrical deactivation occurs. After the whole defect population vanishes, boron reactivation is observed. These results indicate that germanium self-interstitials, released by EOR defects, are the cause of B deactivation. Unlike in Si, the whole deactivation/reactivation cycle in Ge is found to take place while the maximum active B concentration exceeds its solubility limit. © 2010 American Institute of Physics.

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/.

The front-end of eco-innovation for eco-innovative small and medium sized companies

This paper studies the Front End of Eco-Innovation (FEEI), the initial phase of the eco-innovation process. Incorporating environmental concerns at the front-end of innovation is important, as product parameters are still flexible. This paper investigates the FEEI for 42 small and medium sized eco-innovators in the Netherlands by using a survey. The results show that SMEs embrace informal, systematic, and open innovation approaches at the FEEI. Teams appear to be multidisciplinary, and creativity and environmental knowledge are essential. Experimentation played a significant role at the FEEI. The paper concludes with recommendations for future research and implications for managers. © 2013 Elsevier B.V.

Decision making at the front end of innovation: The hidden influence of knowledge and decision criteria

Decision making at the front end of innovation is critical for the success of companies. This paper presents a method, called decision making based on knowledge (DeBK), which was created to analyze the decision-making process at the front end. The method evaluates the knowledge of project information and the importance of decision criteria, compiling a measure that indicates whether decisions are founded on available knowledge and what criteria are in fact being considered to delineate them. The potential contribution of DeBK is corroborated through two projects that faced decision-making issues at the front end of innovation. © 2014 RADMA and John Wiley & Sons Ltd.