Keele University

About: Keele University is a education organization based out in Newcastle-under-Lyme, United Kingdom. It is known for research contribution in the topics: Population & Stars. The organization has 11318 authors who have published 26323 publications receiving 894671 citations. The organization is also known as: Keele University.

Optical tweezers for single cells

Abstract: Optical tweezers (OT) have emerged as an essential tool for manipulating single biological cells and performing sophisticated biophysical/biomechanical characterizations. Distinct advantages of using tweezers for these characterizations include non-contact force for cell manipulation, force resolution as accurate as 100 aN and amiability to liquid medium environments. Their wide range of applications, such as transporting foreign materials into single cells, delivering cells to specific locations and sorting cells in microfluidic systems, are reviewed in this article. Recent developments of OT for nanomechanical characterization of various biological cells are discussed in terms of both their theoretical and experimental advancements. The future trends of employing OT in single cells, especially in stem cell delivery, tissue engineering and regenerative medicine, are prospected. More importantly, current limitations and future challenges of OT for these new paradigms are also highlighted in this review.

The Appeal to ‘Professionalism’ as a Disciplinary Mechanism

Abstract: The paper examines the deployment of ‘professional’ discourses in occupational domains not traditionally associated with the professions (eg management, clerical or sales staff are turned into ‘pro...

Choices of Degree or Degrees of Choice? Class, `Race' and the Higher Education Choice Process

Abstract: This paper draws on data from an on-going ESRC project on choice of higher education. It focuses primarily on the experiences of non-traditional applicants to higher education. Although these students are not typical of the entire university entry cohort, their narratives raise important issues in relation to race, class and higher education choice processes. These `success stories' reveal important causes for concern as well as reasons for celebration. In particular, their experiences of the choice process are qualitatively different from those of their more privileged middle-class counterparts, highlighting key class and racial differences and inequalities.

Does the technology acceptance model predict actual use? A systematic literature review

Abstract: Context: The technology acceptance model (TAM) was proposed in 1989 as a means of predicting technology usage. However, it is usually validated by using a measure of behavioural intention to use (BI) rather than actual usage. Objective: This review examines the evidence that the TAM predicts actual usage using both subjective and objective measures of actual usage. Method: We performed a systematic literature review based on a search of six digital libraries, along with vote-counting meta-analysis to analyse the overall results. Results: The search identified 79 relevant empirical studies in 73 articles. The results show that BI is likely to be correlated with actual usage. However, the TAM variables perceived ease of use (PEU) and perceived usefulness (PU) are less likely to be correlated with actual usage. Conclusion: Care should be taken using the TAM outside the context in which it has been validated.

A Theory of Network Localization

Abstract: In this paper, we provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring the distances to their neighbors. We construct grounded graphs to model network localization and apply graph rigidity theory to test the conditions for unique localizability and to construct uniquely localizable networks. We further study the computational complexity of network localization and investigate a subclass of grounded graphs where localization can be computed efficiently. We conclude with a discussion of localization in sensor networks where the sensors are placed randomly