Context: In the context of exploring the art, science and engineering of programming, the question of which programming languages should be taught first has been fiercely debated since computer science teaching started in universities. Failure to grasp programming readily almost certainly implies failure to progress in computer science. Inquiry: What first programming languages are being taught? There have been regular national-scale surveys in Australia and New Zealand, with the only US survey reporting on a small subset of universities. This the first such national survey of universities in the UK. Approach: We report the results of the first survey of introductory programming courses (N = 80) taught at UK universities as part of their first year computer science (or related) degree programmes, conducted in the first half of . We report on student numbers, programming paradigm, programming languages and environment/tools used, as well as the underpinning rationale for these choices. Knowledge: The results in this first UK survey indicate a dominance of Java at a time when universities are still generally teaching students who are new to programming (and computer science), despite the fact that Python is perceived, by the same respondents, to be both easier to teach as well as to learn. Grounding: We compare the results of this survey with a related survey conducted since (as well as earlier surveys from and ) in Australia and New Zealand. Importance: This survey provides a starting point for valuable pedagogic baseline data for the analysis of the art, science and engineering of programming, in the context of substantial computer science curriculum reform in UK schools, as well as increasing scrutiny of teaching excellence and graduate employability for UK universities. ACM CCS

Parallel surveys of introductory programming courses were conducted in Australasia and the UK, with a view to examining the programming languages being used, the preferred integrated development environments (if any), and the reasons for these choices, alongside a number of other key aspects of these courses. This paper summarises some of the similarities and differences between the findings of the two surveys. In the UK, Java is clearly the dominant programming language in introductory programming courses, with Eclipse as the dominant environment. Java was also the dominant language in Australasia six years ago, but now shares the lead with Python; we speculate on the reasons for this. Other differences between the two surveys are equally interesting. Overall, however, there appears to be a reasonable similarity in the way these undergraduate courses are conducted in the UK and in Australasia. While the degree structures differ markedly between and within these regions-a possible explanation for some of the differences-some of the similarities are noteworthy and have the potential to provide insight into approaches in other regions and countries.

Electrochemical studies have been conducted at copper microelectrodes (125, 50, and 25 μm in diameter) immersed in aqueous 0.5 M NaCl. Cyclic and linear sweep voltammetry were used to explore the corrosion of copper in chloride media. Cyclic voltammetry revealed the reversible Cu(I)/Cu(0) potential at approximately −0.11 V vs. SCE associated with the formation of a dense CuCl blocking layer (confirmed by in situ Raman and fluorescence measurements). Although continuous dissolution of Cu(I) occurs, only an increase in the driving potential into the region of the Cu(II)/Cu(I) potential at approximately +0.14 V vs. SCE started more rapid and stochastic dissolution/corrosion processes. The corrosion process is demonstrated to be linked to two distinct mechanisms based on (A) slow molecular dissolution and (B) fast colloidal dissolution. A polymer of intrinsic microporosity (PIM-EA-TB) is employed to suppress colloidal processes to reveal the underlying molecular processes.

Hip fractures are a major cause of morbidity and mortality in the elderly, and incur high health and social care costs. Given projected population ageing, the number of incident hip fractures is predicted to increase globally. As fracture classification strongly determines the chosen surgical treatment, differences in fracture classification influence patient outcomes and treatment costs. We aimed to create a machine learning method for identifying and classifying hip fractures, and to compare its performance to experienced human observers. We used 3659 hip radiographs, classified by at least two expert clinicians. The machine learning method was able to classify hip fractures with 19% greater accuracy than humans, achieving overall accuracy of 92%.

Data on the progress of about a hundred construction projects were provided by Heathrow Airport. Most projects have many adjustments in their scope which impact the cost and schedule. Can an optimised scheduling of the project lead to decreased costs and more rapid completion? To answer this, both a data-centric approach and a complementary mathematical model were developed to better understand the effect of resource constraints on cost and price extension due to resource competition of concurrent projects. The model takes the form of a discrete time stochastic simulation, whose parameters are t to the existing data. While more data is needed to validate the model, the results suggested that gains can be made if more thoughtful scheduling of projects is implemented, and also if the prioritisation of projects is monitored and adjusted intelligently, in particular by prioritising completion of smaller projects.