A microwave dielectric biosensor based on suspended distributed MEMS transmission lines

Design and characterization of a miniature microwave dielectric biosensor based on distributed microelectromechanical systems (MEMS) transmission lines (DMTL) is reported in this paper. The biosensor has been realized by bonding the DMTL device with an acrylic fluidic channel. In order to demonstrate the sensing mechanism, the sensor is used to detect the small variation of the concentration of aqueous glucose solutions by measuring the electromagnetic resonant frequency shift of the device. It is observed from the results that the second notch of the reflection coefficient (S-11) varies from 7.66 to 7.93 GHz and the third notch of the reflection coefficient varies from 15.81 to 15.24 GHz when the concentration of the glucose solution ranges from 0 to 347 mg/ml, which indicates that higher order notches have higher sensitivities if looking at the absolute change in frequency

Strategy process in manufacturing SMEs (Small Medium Enterprises)

Strategy process has been widely publicised during the last three decades, but what has been accomplished by strategy management literature in manufacturing small to medium sized enterprises (SMEs)? The application of strategy management in manufacturing SME practices can be seen as posing particular challenges. It is argued in literature that there is a need to understand whether and how managers in manufacturing SMEs have taken up the language and practice of planning, strategic analysis and execution. This research suggests a process and activity based approach to look at the practice of strategy management in SMEs in order to tackle this challenge. This exploratory study based on four comprehensive case studies investigates the strategy stories via exploring key strategic initiatives and activities, how they link together and which strategy tools, methods and techniques are used. This research concludes that a process based approach is useful and valid to understand strategy in SMEs because this view decomposes the process phases into activities which managers are more comfortable to talk through. However, we need to understand SME managers' language around strategising. There is an indication that if we change the language of SME managers, the findings of this study would map onto main stream strategy management theory clearly. It is found that the dynamics of the manufacturing SME strategy process have both emergent and planned dimensions. SME managers execute the strategy process mainly from an informal fashion by holding multiple functions and with limited application of strategy management methods and techniques. At an activity level, SMEs seem to be putting more emphasis on external environmental scanning (customers, suppliers, competitors, universities and lenders) and defining grand strategy and goals. This implies that SME strategy process is characterised by market based orientation, opportunity seeking and strategic awareness rather than resources or core competencies/ capabilities. Although this study's findings may be criticised because they are grounded on four companies, robust dimensions and insights into dynamics of the strategy process in manufacturing SMEs are achieved through saturation among emergent themes in data

Systems Engineering Leading Indicators Guide, Version 1.0

The Systems Engineering Leading Indicators guide set reflects the initial subset of possible indicators that were considered to be the highest priority for evaluating effectiveness before the fact. A leading indicator is a measure for evaluating the effectiveness of a how a specific activity is applied on a program in a manner that provides information about impacts that are likely to affect the system performance objectives. A leading indicator may be an individual measure, or collection of measures, that are predictive of future system performance before the performance is realized. Leading indicators aid leadership in delivering value to customers and end users, while assisting in taking interventions and actions to avoid rework and wasted effort. The Systems Engineering Leading Indicators Guide was initiated as a result of the June 2004 Air Force/LAI Workshop on Systems Engineering for Robustness, this guide supports systems engineering revitalization. Over several years, a group of industry, government, and academic stakeholders worked to define and validate a set of thirteen indicators for evaluating the effectiveness of systems engineering on a program. Released as version 1.0 in June 2007 the leading indicators provide predictive information to make informed decisions and where necessary, take preventative or corrective action during the program in a proactive manner. While the leading indicators appear similar to existing measures and often use the same base information, the difference lies in how the information is gathered, evaluated, interpreted and used to provide a forward looking perspective

Systems Engineering Leading Indicators Guide, Version 2.0

The Systems Engineering Leading Indicators Guide editorial team is pleased to announce the release of Version 2.0. Version 2.0 supersedes Version 1.0, which was released in July 2007 and was the result of a project initiated by the Lean Advancement Initiative (LAI) at MIT in cooperation with: the International Council on Systems Engineering (INCOSE), Practical Software and Systems Measurement (PSM), and the Systems Engineering Advancement Research Initiative (SEAri) at MIT. A leading indicator is a measure for evaluating the effectiveness of how a specific project activity is likely to affect system performance objectives. A leading indicator may be an individual measure or a collection of measures and associated analysis that is predictive of future systems engineering performance. Systems engineering performance itself could be an indicator of future project execution and system performance. Leading indicators aid leadership in delivering value to customers and end users and help identify interventions and actions to avoid rework and wasted effort. Conventional measures provide status and historical information. Leading indicators use an approach that draws on trend information to allow for predictive analysis. By analyzing trends, predictions can be forecast on the outcomes of certain activities. Trends are analyzed for insight into both the entity being measured and potential impacts to other entities. This provides leaders with the data they need to make informed decisions and where necessary, take preventative or corrective action during the program in a proactive manner. Version 2.0 guide adds five new leading indicators to the previous 13 for a new total of 18 indicators. The guide addresses feedback from users of the previous version of the guide, as well as lessons learned from implementation and industry workshops. The document format has been improved for usability, and several new appendices provide application information and techniques for determining correlations of indicators. Tailoring of the guide for effective use is encouraged. Additional collaborating organizations involved in Version 2.0 include the Naval Air Systems Command (NAVAIR), US Department of Defense Systems Engineering Research Center (SERC), and National Defense Industrial Association (NDIA) Systems Engineering Division (SED). Many leading measurement and systems engineering experts from government, industry, and academia volunteered their time to work on this initiative

Introduction of CAA into a mathematics course for technology students to address a change in curriculum requirements

The mathematical requirements for engineering, science and technology students has been debated for many years and concern has been expressed about the mathematical preparedness of students entering higher education. This paper considers a mathematics course that has been specifically designed to address some of these issues for technology education students. It briefly chronicles the changes that have taken place over its lifetime and evaluates the introduction of Computer Assisted Assessment (CAA) into a course already being delivered using Computer Aided Learning (CAL). Benefits of CAA can be categorised into four main areas. 1. Educational – achieved by setting short, topic related, assessments, each of which has to be passed, thereby increasing curriculum coverage. 2. Students – by allowing them to complete assessments at their own pace removing the stress of the final examination. 3. Financial – increased income to the institution, by broadening access to the course. Improved retention rate due to self-paced learning. 4. Time – staff no longer required to set and mark exams. Most students preferred this method of assessment to traditional exams, because it increased confidence and reduced stress levels. Self-paced working, however, resulted in a minority of students not completing the tests by the deadline

Exact Bayesian curve fitting and signal segmentation.

We consider regression models where the underlying functional relationship between the response and the explanatory variable is modeled as independent linear regressions on disjoint segments. We present an algorithm for perfect simulation from the posterior distribution of such a model, even allowing for an unknown number of segments and an unknown model order for the linear regressions within each segment. The algorithm is simple, can scale well to large data sets, and avoids the problem of diagnosing convergence that is present with Monte Carlo Markov Chain (MCMC) approaches to this problem. We demonstrate our algorithm on standard denoising problems, on a piecewise constant AR model, and on a speech segmentation problem

An evaluation of an online student portfolio for the development of engineering graduate attributes

An online student portfolio was evaluated as a means for engaging students with the concept of graduate attributes, and for documenting student attainment of graduate attributes. Students rated the portfolio system as easy to use, and indicated that it helped them to appreciate the skills and knowledge they had developedNo Full Tex

Can we (control) Engineer the degree learning process?

This paper investigates how control theory could be applied to learning processes in engineering education. The initial point for the analysis is White’s Double Loop learning model of human automation control modified for the education process where a set of governing principals is chosen, probably by the course designer. After initial training the student decides unknowingly on a mental map or model. After observing how the real world is behaving, a strategy to achieve the governing variables is chosen and a set of actions chosen. This may not be a conscious operation, it maybe completely instinctive. These actions will cause some consequences but not until a certain time delay. The current model is compared with the work of Hollenbeck on goal setting, Nelson’s model of self-regulation and that of Abdulwahed, Nagy and Blanchard at Loughborough who investigated control methods applied to the learning process

Digitally Enhanced Advanced Services: Manufacturing Theme Research Agenda

This report presents the research priorities for the EPSRC funded Digitally Enhanced Advanced Services (DEAS) NetworkPlus in the manufacturing sector. Recent developments in digital technologies make it easy for manufacturers to monitor the use of their products in the field and then offer services based on the capability of their product. These outcome-based services are known as Digitally Enhanced Advanced Services and these have the potential to deliver sustained value for the UK economy