Value roadmapping: A structured approach for early stage technology investment decisions

Deciding to invest in early stage technologies is one of the most important tasks of technology management and arguably also the most uncertain. It assumes a particular significance in the rise of technology companies in emerging economies, which have to make appropriate investment decisions. Technology managers already have a wide range of methods and tools at their disposal, but these are mostly focussed on quantitative measures such as discounted cash flow and real options techniques. However, in the early stages of technology development there seems to be a lot of dissatisfaction with these techniques as there appears to be a lack of accuracy with respect to the underlying assumptions that these models require. In order to complement these models this paper will discuss an alternative approach that we call value road-mapping. By adapting roadmapping techniques the potential value streams of early stages technologies can be plotted and hence a clearer consensus based picture of the future potential of new technologies emerges. Roadmapping is a workshop-based process bringing together multifunctional perspectives, and supporting communication in particular between technical and commercial groups. The study is work in progress and is based on a growing number of cases. (c) 2006 PICMET.

Fabrication and mechanical property of carbon nanotube/metal composites

Carbon nanotubes (CNTs) are known to exhibit extraordinary mechanical properties such as high tensile strength, the highest Young modulus etc. These, combining with their large aspect ratio, make CNTs an excellent additive candidate to complement or substitute traditional carbon black or glass fiber fillers for the development of nano-reinforced composites. CNTs have thus far been used as additives in polymers, ceramics and metals to be pursued on practical applications of their composites. © 2010 IEEE.

MicroRNAs and other small RNAs enriched in the Arabidopsis RNA-dependent RNA polymerase-2 mutant.

The Arabidopsis genome contains a highly complex and abundant population of small RNAs, and many of the endogenous siRNAs are dependent on RNA-Dependent RNA Polymerase 2 (RDR2) for their biogenesis. By analyzing an rdr2 loss-of-function mutant using two different parallel sequencing technologies, MPSS and 454, we characterized the complement of miRNAs expressed in Arabidopsis inflorescence to considerable depth. Nearly all known miRNAs were enriched in this mutant and we identified 13 new miRNAs, all of which were relatively low abundance and constitute new families. Trans-acting siRNAs (ta-siRNAs) were even more highly enriched. Computational and gel blot analyses suggested that the minimal number of miRNAs in Arabidopsis is approximately 155. The size profile of small RNAs in rdr2 reflected enrichment of 21-nt miRNAs and other classes of siRNAs like ta-siRNAs, and a significant reduction in 24-nt heterochromatic siRNAs. Other classes of small RNAs were found to be RDR2-independent, particularly those derived from long inverted repeats and a subset of tandem repeats. The small RNA populations in other Arabidopsis small RNA biogenesis mutants were also examined; a dcl2/3/4 triple mutant showed a similar pattern to rdr2, whereas dcl1-7 and rdr6 showed reductions in miRNAs and ta-siRNAs consistent with their activities in the biogenesis of these types of small RNAs. Deep sequencing of mutants provides a genetic approach for the dissection and characterization of diverse small RNA populations and the identification of low abundance miRNAs.

Investigating the link between well-being and energy use; an explorative case study between passive and active domestic energy management systems

The aim of this study was to explore how the remote control of appliances/lights (active energy management system) affected household well-being, compared to in-home displays (passive energy management system). A six-week exploratory study was conducted with 14 participants divided into the following three groups: active; passive; and no equipment. The effect on well-being was measured through thematic analysis of two semi-structured interviews for each participant, administered at the start and end of the study. The well-being themes were based on existing measures of Satisfaction and Affect. The energy demand for each participant was also measured for two weeks without intervention, and then compared after four weeks with either the passive or active energy management systems. These measurements were used to complement the well-being analysis. Overall, the measure of Affect increased in the passive group but Satisfaction decreased; however, all three measures on average decreased in the active group. The measured energy demand also highlighted a disconnect between well-being and domestic energy consumption. The results point to a need for further investigation in this field; otherwise, there is a risk that nationally implemented energy management solutions may negatively affect our happiness and well-being. © 2013 Elsevier Ltd.

Nonlinear analysis of cardiac rhythm fluctuations using DFA method

An approach by which the detrented fluctuation analysis (DFA) method can be used to help diagnose heart failure was demonstrated. DFA was applied to patients suffering from congestive heart failure (CHF) to check correlations between DFA indices and CHF, and determine a correlation between DFA indices and mortality, with a particular attention to the residue parameter, which is a measure of the departure of the DFA from its power law approximation. DFA parameters proved to be useful as a complement to the physiological parameters weber and FE to sort out the patients into three prognostic group.

Enhanced integrated modelling approach to reconfiguring manufacturing enterprises

Dynamism and uncertainty are real challenges for present day manufacturing enterprises (MEs). Reasons include: an increasing demand for customisation, reduced time to market, shortened product life cycles and globalisation. MEs can reduce competitive pressure by becoming reconfigurable and change-capable. However, modern manufacturing philosophies, including agile and lean, must complement the application of reconfigurable manufacturing paradigms. Choosing and applying the best philosophies and techniques is very difficult as most MEs deploy complex and unique configurations of processes and resource systems, and seek economies of scope and scale in respect of changing and distinctive product flows. It follows that systematic methods of achieving model driven reconfiguration and interoperation of component based manufacturing systems are required to design, engineer and change future MEs. This thesis, titled Enhanced Integrated Modelling Approach to Reconfiguring Manufacturing Enterprises , introduces the development and prototyping a model-driven environment for the design, engineering, optimisation and control of the reconfiguration of MEs with an embedded capability to handle various types of change. The thesis describes a novel systematic approach, namely enhanced integrated modelling approach (EIMA), in which coherent sets of integrated models are created that facilitates the engineering of MEs especially their production planning and control (PPC) systems. The developed environment supports the engineering of common types of strategic, tactical and operational processes found in many MEs. The EIMA is centred on the ISO standardised CIMOSA process modelling approach. Early study led to the development of simulation models during which various CIMOSA shortcomings were observed, especially in its support for aspects of ME dynamism. A need was raised to structure and create semantically enriched models hence forming an enhanced integrated modelling environment. The thesis also presents three industrial case examples: (1) Ford Motor Company; (2) Bradgate Furniture Manufacturing Company; and (3) ACM Bearings Company. In order to understand the system prior to realisation of any PPC strategy, multiple process segments of any target organisation need to be modelled. Coherent multi-perspective case study models are presented that have facilitated process reengineering and associated resource system configuration. Such models have a capability to enable PPC decision making processes in support of the reconfiguration of MEs. During these case studies, capabilities of a number of software tools were exploited such as Arena®, Simul8®, Plant Simulation®, MS Visio®, and MS Excel®. Case study results demonstrated effectiveness of the concepts related to the EIMA. The research has resulted in new contributions to knowledge in terms of new understandings, concepts and methods in following ways: (1) a structured model driven integrated approach to the design, optimisation and control of future reconfiguration of MEs. The EIMA is an enriched and generic process modelling approach with capability to represent both static and dynamic aspects of an ME; and (2) example application cases showing benefits in terms of reduction in lead time, cost and resource load and in terms of improved responsiveness of processes and resource systems with a special focus on PPC; (3) identification and industrial application of a new key performance indicator (KPI) known as P3C the measuring and monitoring of which can aid in enhancing reconfigurability and responsiveness of MEs; and (4) an enriched modelling concept framework (E-MUNE) to capture requirements of static and dynamic aspects of MEs where the conceptual framework has the capability to be extended and modified according to the requirements. The thesis outlines key areas outlining a need for future research into integrated modelling approaches, interoperation and updating mechanisms of partial models in support of the reconfiguration of MEs.

Development of a nanoporous and multilayer drug-delivery platform for medical implants.

Biodegradable polymers can be applied to a variety of implants for controlled and local drug delivery. The aim of this study is to develop a biodegradable and nanoporous polymeric platform for a wide spectrum of drug-eluting implants with special focus on stent-coating applications. It was synthesized by poly(DL-lactide-co-glycolide) (PLGA 65:35, PLGA 75:25) and polycaprolactone (PCL) in a multilayer configuration by means of a spin-coating technique. The antiplatelet drug dipyridamole was loaded into the surface nanopores of the platform. Surface characterization was made by atomic force microscopy (AFM) and spectroscopic ellipsometry (SE). Platelet adhesion and drug-release kinetic studies were then carried out. The study revealed that the multilayer films are highly nanoporous, whereas the single layers of PLGA are atomically smooth and spherulites are formed in PCL. Their nanoporosity (pore diameter, depth, density, surface roughness) can be tailored by tuning the growth parameters (eg, spinning speed, polymer concentration), essential for drug-delivery performance. The origin of pore formation may be attributed to the phase separation of polymer blends via the spinodal decomposition mechanism. SE studies revealed the structural characteristics, film thickness, and optical properties even of the single layers in the triple-layer construct, providing substantial information for drug loading and complement AFM findings. Platelet adhesion studies showed that the dipyridamole-loaded coatings inhibit platelet aggregation that is a prerequisite for clotting. Finally, the films exhibited sustained release profiles of dipyridamole over 70 days. These results indicate that the current multilayer phase therapeutic approach constitutes an effective drug-delivery platform for drug-eluting implants and especially for cardiovascular stent applications.

Cell types, network homeostasis, and pathological compensation from a biologically plausible ion channel expression model.

How do neurons develop, control, and maintain their electrical signaling properties in spite of ongoing protein turnover and perturbations to activity? From generic assumptions about the molecular biology underlying channel expression, we derive a simple model and show how it encodes an "activity set point" in single neurons. The model generates diverse self-regulating cell types and relates correlations in conductance expression observed in vivo to underlying channel expression rates. Synaptic as well as intrinsic conductances can be regulated to make a self-assembling central pattern generator network; thus, network-level homeostasis can emerge from cell-autonomous regulation rules. Finally, we demonstrate that the outcome of homeostatic regulation depends on the complement of ion channels expressed in cells: in some cases, loss of specific ion channels can be compensated; in others, the homeostatic mechanism itself causes pathological loss of function.