Understanding the kink formation in GaAs/InAs heterostructural nanowires

The kinks formation in heterostructural nanowires was observed to be dominant when InAs nanowires were grown on GaAs nanowires. Nanowires were grown through vapor-liquid-solid (VLS) mechanism in an MOCVD (metalorganic chemical vapor deposition) reactor. GaAs nanowires were grown in [1 1 1 ]B direction on a GaAs (1 1 1 )B substrate. When InAs nanowires grown on the GaAs nanowires, most of the InAs nanowires changed their growth directions from [1 1 1 ]B to other 〈111〉B directions. The kinks formation is ascribed to the large compressive misfit strain at the GaAs/InAs interface (7.2% lattice mismatch between GaAs and InAs) and the high mobility of indium species during MOCVD growth. The in-depth analysis of the kinks formation was done by growing InAs for short times on the GaAs nanowires and characterizing the samples. The hindrance to compressively strain InAs to form coherent layers with GaAs pushed the InAs/Au interfaces to the sides of the GaAs nanowires growth ends. New InAs/Au interfaces have generated at the sides of GaAs nanowires, due to lateral growth of InAs on GaAs nanowires. These new interfaces led the InAs nanowires growth in other 〈111〉B directions. The morphological and structural features of these heterostructural kinked nanowires were characterized using scanning electron microscopy (SEM) and transmission electron microscopy (TEM) techniques. © 2006 IEEE.

Independent component analysis of microarray data in the study of endometrial cancer.

Gene microarray technology is highly effective in screening for differential gene expression and has hence become a popular tool in the molecular investigation of cancer. When applied to tumours, molecular characteristics may be correlated with clinical features such as response to chemotherapy. Exploitation of the huge amount of data generated by microarrays is difficult, however, and constitutes a major challenge in the advancement of this methodology. Independent component analysis (ICA), a modern statistical method, allows us to better understand data in such complex and noisy measurement environments. The technique has the potential to significantly increase the quality of the resulting data and improve the biological validity of subsequent analysis. We performed microarray experiments on 31 postmenopausal endometrial biopsies, comprising 11 benign and 20 malignant samples. We compared ICA to the established methods of principal component analysis (PCA), Cyber-T, and SAM. We show that ICA generated patterns that clearly characterized the malignant samples studied, in contrast to PCA. Moreover, ICA improved the biological validity of the genes identified as differentially expressed in endometrial carcinoma, compared to those found by Cyber-T and SAM. In particular, several genes involved in lipid metabolism that are differentially expressed in endometrial carcinoma were only found using this method. This report highlights the potential of ICA in the analysis of microarray data.

Corporate document mining for technology intelligence: An analysis of needs, utilization and possibilities

This paper focuses on document data, one of the most significant sources for technology intelligence. To help organisations use their knowledge in documents effectively, this research aims to identify what organizations really want from documents and what might be possible to obtain from them. The research involves a literature review, a series of in-depth/on-site interviews and a descriptive analysis of document mining applications. The output of the research includes: a document mining framework; an analysis of the current condition of document mining in technology-based organisations together with their future requirements; and guidelines for introducing document mining into an organisation along with a discussion on the practical issues that are faced by users. Copyright © 2011 Inderscience Enterprises Ltd.

Technology confidence in early stage development of medical devices

Innovation is a critical factor in ensuring commercial success within the area of medical technology. Biotechnology and Healthcare developments require huge financial and resource investment, in-depth research and clinical trials. Consequently, these developments involve a complex multidisciplinary structure, which is inherently full of risks and uncertainty. In this context, early technology assessment and 'proof of concept' is often sporadic and unstructured. Existing methodologies for managing the feasibility stage of medical device development are predominantly suited to the later phases of development and favour detail in optimisation, validation and regulatory approval. During these early phases, feasibility studies are normally conducted to establish whether technology is potentially viable. However, it is not clear how this technology viability is currently measured. This paper aims to redress this gap through the development of a technology confidence scale, as appropriate explicitly to the feasibility phase of medical device design. These guidelines were developed from analysis of three recent innovation studies within the medical device industry.

Building performance simulation for the management of thermal performance risks in buildings subject to climate change

This paper reports on research that uses building performance simulation and uncertainty analysis to assess the risks that projected climate change poses to the thermal performance of buildings, and to their critical functions. The work takes meteorological climate change predictions as a starting point, but also takes into account developments and uncertainties in technology, occupancy, intervention and renovation, and others. Four cases are studied in depth to explore the prospects of the quantification of said climate change risks. The research concludes that quantification of the risks posed by climate change is possible, but only with many restrictive assumptions on the input side.

Occlusive Components Analysis

We study unsupervised learning in a probabilistic generative model for occlusion. The model uses two types of latent variables: one indicates which objects are present in the image, and the other how they are ordered in depth. This depth order then determines how the positions and appearances of the objects present, specified in the model parameters, combine to form the image. We show that the object parameters can be learnt from an unlabelled set of images in which objects occlude one another. Exact maximum-likelihood learning is intractable. However, we show that tractable approximations to Expectation Maximization (EM) can be found if the training images each contain only a small number of objects on average. In numerical experiments it is shown that these approximations recover the correct set of object parameters. Experiments on a novel version of the bars test using colored bars, and experiments on more realistic data, show that the algorithm performs well in extracting the generating causes. Experiments based on the standard bars benchmark test for object learning show that the algorithm performs well in comparison to other recent component extraction approaches. The model and the learning algorithm thus connect research on occlusion with the research field of multiple-causes component extraction methods.

Evaluation of the p-y method in the design of monopiles for offshore wind turbines

Monopile foundations, currently designed using the p-y method, are technically viable in supporting larger offshore wind turbines in waters to a depth of 30 m. The p-y method was developed to better understand the behavior of laterally loaded long slender piles required for the offshore oil and gas installations. The lateral load-deformation behavior of two monopiles, 5 and 7.5 m dia, installed in soft clays of varying undrained shear strength and stiffness, was studied. A combination of axial and lateral loads expected at an offshore wind farm location with a water depth of 30 m was used in the analysis. It was established that the Matlock (1970) p-y curves are too soft and under-estimate the ultimate soil reaction at all depths except at the monopile tip. At the pile tip, the base shear was not accounted for in the p-y curves, hence resulting in the over-estimation of the soil reaction. Consequently, the Matlock (1970) p-y formulation significantly underestimates the monopile ultimate lateral capacity. The use of the Matlock (1970) p-y method would result in over-conservative designs of monopiles for offshore wind turbines. This is an abstract of a paper presented at the Offshore Technology Conference (Houston, TX 5/6-9/2013).

Articulating current service development practices: a qualitative analysis of eleven mental health projects.

BACKGROUND: The utilisation of good design practices in the development of complex health services is essential to improving quality. Healthcare organisations, however, are often seriously out of step with modern design thinking and practice. As a starting point to encourage the uptake of good design practices, it is important to understand the context of their intended use. This study aims to do that by articulating current health service development practices. METHODS: Eleven service development projects carried out in a large mental health service were investigated through in-depth interviews with six operation managers. The critical decision method in conjunction with diagrammatic elicitation was used to capture descriptions of these projects. Stage-gate design models were then formed to visually articulate, classify and characterise different service development practices. RESULTS: Projects were grouped into three categories according to design process patterns: new service introduction and service integration; service improvement; service closure. Three common design stages: problem exploration, idea generation and solution evaluation - were then compared across the design process patterns. Consistent across projects were a top-down, policy-driven approach to exploration, underexploited idea generation and implementation-based evaluation. CONCLUSIONS: This study provides insight into where and how good design practices can contribute to the improvement of current service development practices. Specifically, the following suggestions for future service development practices are made: genuine user needs analysis for exploration; divergent thinking and innovative culture for idea generation; and fail-safe evaluation prior to implementation. Better training for managers through partnership working with design experts and researchers could be beneficial.