Is your article EV-TRACKed?

The EV-TRACK knowledgebase is developed to cope with the need for transparency and rigour to increase reproducibility and facilitate standardization of extracellular vesicle (EV) research. The knowledgebase includes a checklist for authors and editors intended to improve the transparency of methodological aspects of EV experiments, allows queries and meta-analysis of EV experiments and keeps track of the current state of the art. Widespread implementation by the EV research community is key to its success

Click Here for Change: Your Guide to the E-Advocacy Revolution

Describes how organizations are using state-of-the-art technology to engage supporters and improve their advocacy efforts. Includes case studies and lessons on how to incorporate electronic approaches in campaign strategies

Co-integration of Ge detectors and Si modulators in an advanced Si photonics platform

A Si photonics platform is described, co-integrating advanced passive components with Si modulators and Ge detectors. This platform is developed on a 200mm CMOS toolset, compatible with a 130nm CMOS baseline. The paper describes the process flow, and describes the performance of selected electro-optical devices to demonstrate the viability of the flow

Collaboration in the Semantic Grid: a Basis for e-Learning

The CoAKTinG project aims to advance the state of the art in collaborative mediated spaces for the Semantic Grid. This paper presents an overview of the hypertext and knowledge based tools which have been deployed to augment existing collaborative environments, and the ontology which is used to exchange structure, promote enhanced process tracking, and aid navigation of resources before, after, and while a collaboration occurs. While the primary focus of the project has been supporting e-Science, this paper also explores the similarities and application of CoAKTinG technologies as part of a human-centred design approach to e-Learning

Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance

Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner. Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''. The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few. This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage. The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling