Beyond GDP? Welfare Across Countries and Time

We propose a simple summary statistic for a nation’s flow of welfare, measured as a consumption equivalent, and compute its level and growth rate for a broad set of countries. This welfare metric combines data on consumption, leisure, inequality, and mortality. Although it is highly correlated with per capita GDP, deviations are often economically significant: Western Europe looks considerably closer to U.S. living standards, emerging Asia has not caught up as much, and many African and Latin American countries are farther behind due to lower levels of life expectancy and higher levels of inequality. In recent decades, rising life expectancy boosts annual growth in welfare by more than a full percentage point throughout much of the world. The notable exception is sub- Saharan Africa, where life expectancy actually declines.Welfare, life expectancy, living standards

The use of open education resources in higher education programmes of academic practice

Digital technologies provide a wide range of tools which research shows can enhance and support teaching and learning. However, this knowledge is not uniformly available across disciplines. This paper reports on a project which aims to provide materials that are cross-disciplinary, applied in an online context and which are used to support the development of understanding of the power of collaboration and the re-purposing of open educational resources (OERs)

Wikis supporting PLM and Technical Documentation

Over the last years, Wikis have arisen as powerful tools for collaborative documentation on the Internet. The Encyclopaedia Wikipedia has become a reference, and the power of community editing in a Wiki allows for capture of knowledge from contributors all over the world. Use of a Wiki for Technical Documentation, along with hyper-links to other data sources such as a Product Lifecycle Management (PLM) system, provides a very effective collaboration tool as information can be easily feed into the system throughout the project life-cycle. In particular for software- and hardware projects with rapidly evolving documentation, the Wiki approach has proved to be successful. Certain Wiki implementations, such as TWiki, are project-oriented and include functionality such as automatic page revisioning. This paper addresses the use of TWiki to document hardware and software projects at CERN, from the requirements and brain-storming phase to end-product documentation. 2 examples are covered: large scale engineering for the ATLAS Experiment, and a network management software project

Structural Characteristics of Load Bearing Straw Bale Walls

Straw bales offer a renewable and affordable construction material suitable for a range of uses as both thermal insulation in walls and roofs, and for low rise loadbearing structural walls. As a co-product of food production, it places no further pressure on land use, and in common with other crop-based materials, straw captures and stores carbon dioxide through photosynthesis, offering the means to construct buildings with a net negative carbon emissions footprint. Straw also further reduces operational carbon emissions by virtue of its excellent thermal resistance. However, despite these benefits, and a successful construction history extending over 100 years in many countries worldwide, straw bale construction has still to make a major commercial impact in the wider construction market. Limited technical understanding of some fundamental performance characteristics (including structural capacity, hygrothermal behaviour, and durability), absence of technical standards, and a lack of certification and product warranty for straw bale, still remain barriers to wider acceptance. In this paper results are presented from a study on full-scale straw bale walls to evaluate the structural performance under vertical loading and lateral loading. The performance of identical straw bale walls, with and without plaster coats, is presented. The study is also unique in presenting on out-of-plane lateral loading and wall performance under eccentric vertical load cases. The research will support structural designers and enable wider uptake of this sustainable form of construction

Malware classification using self organising feature maps and machine activity data

In this article we use machine activity metrics to automatically distinguish between malicious and trusted portable executable software samples. The motivation stems from the growth of cyber attacks using techniques that have been employed to surreptitiously deploy Advanced Persistent Threats (APTs). APTs are becoming more sophisticated and able to obfuscate much of their identifiable features through encryption, custom code bases and in-memory execution. Our hypothesis is that we can produce a high degree of accuracy in distinguishing malicious from trusted samples using Machine Learning with features derived from the inescapable footprint left behind on a computer system during execution. This includes CPU, RAM, Swap use and network traffic at a count level of bytes and packets. These features are continuous and allow us to be more flexible with the classification of samples than discrete features such as API calls (which can also be obfuscated) that form the main feature of the extant literature. We use these continuous data and develop a novel classification method using Self Organizing Feature Maps to reduce over fitting during training through the ability to create unsupervised clusters of similar ‘behaviour’ that are subsequently used as features for classification, rather than using the raw data. We compare our method to a set of machine classification methods that have been applied in previous research and demonstrate an increase of between 7.24% and 25.68% in classification accuracy using our method and an unseen dataset over the range of other machine classification methods that have been applied in previous research

Detecting statistical outliers in psychophysical data

This paper considers how best to identify statistical outliers in psychophysical datasets, where the underlying sampling distributions are unknown. Eight methods are described, and each is evaluated using Monte Carlo simulations of a typical psychophysical experiment. The best method is shown to be one based on a measure of absolute-deviation known as Sn. This method is shown to be more accurate than popular heuristics based on standard deviations from the mean, and more robust than non-parametric methods based on interquartile range. Matlab code for computing Sn is included