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Integrated petrophysics linked with structural geology and mineralogy are powerful tools for mineral system characterisation. In this study, open-pit samples and drill core from the Osborne IOCG deposit were measured for anisotropy of magnetic susceptibility (AMS) and remanence. This study focused on the relationships between petrophysical properties and the high amplitude magnetic anomaly of the deposit. The magnetite-rich 'iron formation' typically has high density and magnetic susceptibility, which show a near linear relationship related to magnetite content (R2=0.77). Samples are magnetically anisotropic, with P-factors ranging from 1.1 to 1.4 and define a NNW-SSE-oriented magnetic foliation. However, the K1 (i.e. the maximum susceptibility) principal component of the AMS ellipsoid is close to horizontal, and therefore does not add to the vertical component of the magnetic field or contribute significantly to the TMI anomaly over the deposit. Koenigsberger ratios (Q) are commonly, Open-Access Online Publication: March 03, 2023

Rodent electroencephalography (EEG) in preclinical research is frequently conducted in behaving animals. However, the difficulty inherent in identifying EEG epochs associated with a particular behavior or cue is a significant obstacle to more efficient analysis. In this paper we highlight a new solution, using infrared event stamping to accurately synchronize EEG, recorded from superficial sites above the hippocampus and prefrontal cortex, with video motion tracking data in a transgenic Alzheimer’s disease (AD) mouse model. Epochs capturing specific behaviors were automatically identified and extracted prior to further analysis. This was achieved by the novel design of a ultra- miniature wearable EEG recorder, the NAT-1 device, and its in- situ IR recording module. The device is described in detail, and its contribution to enabling new neuroscience is demonstrated.

An improved miniature biosignal data sensor and recorder device is described, (NAT-1-4G) with 3-axis accelerometer, and a 500 Sa/sec all-channel recording capacity of 36 hours or more with a single zinc-air battery cell, and up to 6 days at 100 Sa/Sec for accelerometer only. Like the previous NAT-1 prototype device, this measures less than 18´22´10 mm and weighs less than 2.3 grams, including the battery. In this paper we describe the device in detail, and introduce the presentation of tremor data measurement captured in the context of Parkinson’s disease fore-arm monitoring. The NAT-1-4G device itself has already achieved translation to commercialization and is currently available.

This paper introduces a binary neural network-based prediction algorithm incorporating both spatial and temporal characteristics into the prediction process. The algorithm is used to predict short-term traffic flow by combining information from multiple traffic sensors (spatial lag) and time series prediction (temporal lag). It extends previously developed Advanced Uncertain Reasoning Architecture (AURA) k-nearest neighbour (k-NN) techniques. Our task was to produce a fast and accurate traffic flow predictor. The AURA k-NN predictor is comparable to other machine learning techniques with respect to recall accuracy but is able to train and predict rapidly. We incorporated consistency evaluations to determine whether the AURA k-NN has an ideal algorithmic configuration or an ideal data configuration or whether the settings needed to be varied for each data set. The results agree with previous research in that settings must be bespoke for each data set. This configuration process requires rapid and scalable learning to allow the predictor to be set-up for new data. The fast processing abilities of the AURA k-NN ensure this combinatorial optimisation will be computationally feasible for real-world applications. We intend to use the predictor to proactively manage traffic by predicting traffic volumes to anticipate traffic network problems.

The development of sustainable software has been identified as one of the key challenges in the field of computational science and engineering. However, there is currently no agreed definition of the concept. Current definitions range from a composite, non-functional requirement to simply an emergent property. This lack of clarity leads to confusion, and potentially to ineffective and inefficient efforts to develop sustainable software systems. The aim of this paper is to explore the emerging definitions of software sustainability from the field of software engineering in order to contribute to the question, what is software sustainability? The preliminary analysis suggests that the concept of software sustainability is complex and multifaceted with any consensus towards a shared definition within the field of software engineering yet to be achieved.

The 2011 e-Science All Hands Meeting (AHM) marked the tenth annual gathering of technologists and scientists first brought together by the UK e-Science Programme—the £250 million research venture funded by the UK Research Councils. In the decade since the founding of this programme in 2001, e-science research has evolved from fundamental research focused on computational grids into work involving research from a wide variety of domains, such as bioinformatics, computing, astronomy, physics and medicine. The AHM community is now multinational, interdisciplinary and dynamic, and has recently moved towards addressing the issues and challenges raised by the increasing emergence of cloud computing in daily life. This continuing popularity and success has been highlighted by AHM 2011, hosted at the University of York by the EPSRC White Rose Grid e-Science Centre and attended by over 200 delegates. This Theme Issue provides a snapshot of the community's vibrant diversity, featuring the best papers presented at the conference, peer-reviewed for this issue, written by researchers from many different countries and backgrounds. The first paper in this issue, ‘The application of cloud computing to scientific workflows: a study of cost and performance’ by Berriman et al. [1], discusses a key factor in the use of cloud computing—is it economically beneficial for scientists to use a third-party cloud provider? Following this, Turilli et al. [2], writing in ‘Flexible services for the support of research’, focus on the adoption of the cloud by higher education institutions and address both flexible on-demand access to storage resources and scalability across a heterogeneous set of cloud infrastructures. Turner et al. [3] write of their experiences in developing secure virtual research environments to integrate multidisciplinary research in ‘Secure data sharing across portals: experiences from OneVRE’. Next, Tablan et al. [4] present ‘GATECloud.net: a platform for large-scale, open-source text processing on the cloud’, a paper discussing the authors' cloud-based platform's ability to perform large-scale data-intensive natural language processing experiments by harnessing the computing power of the Amazon Cloud. Additionally, the paper includes a cost–benefit analysis and usage evaluation. Moving away from the cloud, Yang et al. [5] discuss philosophical issues concerning data quality, identify actual user needs on data quality, review current standards and specifications, and propose an integrated model for data quality in the field of Earth observation in ‘An integrated view of data quality in Earth observation’. Service orientation is another interest of the AHM community, and Weeks et al. [6] present a developmental overview of the CARMEN virtual laboratory platform for integrating scientific code through a service-oriented infrastructure in ‘The CARMEN software as a service infrastructure’. In ‘Enhancing research publications using Rich Interactive Narratives’, Takeda et al. [7] describe a mechanism that provides an interactive framework for research publications in the fields of archaeology and chemistry. Following this, Colling et al. [8] discuss the highly important work of the UK's gridPP project in providing the grid infrastructure needed by experiments at the Large Hadron Collider at CERN in the paper ‘Processing LHC data in the UK’. Returning to the cloud, the work undertaken within the RAPPORT project to investigate the running of a group of multi-domain scientific applications on cloud resources is presented by Cohen et al. [9] in ‘RAPPORT: running scientific high-performance computing applications on the cloud’. Djemame et al. [10] then present their approach to risk management as applied to cloud computing, with a particular emphasis on legal issues, in the paper ‘Legal issues in clouds: towards a risk inventory’. In ‘Developing cloud applications using e-Science Central’, Hiden et al. [11] present an overview of the e-Science Central (e-SC) cloud processing system and its application to a number of e-science projects. Finally, Suresh et al. [12] discuss how cloud computing has the potential for enabling a new technique for using queuing theory to meet specified response-time targets against fluctuating event arrival rates in their paper ‘Scalable and responsive event processing in the cloud’. As can be seen from the above descriptions, the variety, scope and standard of submissions to AHM 2011 was very high; in order to help focus the conference, we are indebted to the Conference Programme Committee, featuring 36 international experts and leaders in their respective fields. Owing to space constraints, many high-quality pieces of research presented at the conference cannot be published in this Theme Issue, but we are confident that the whole body of work presented at the conference will help to continue to drive e-science and digital research both in the UK and abroad.

Studies of neural networks and the processes they controlfrequently employ recording techniques to determinetemporal patterns of activity within individual neuronsand their interactions. Neuroinformatics is the rapidlygrowing science that addresses the manipulation andanalysis of the vast volumes of data generated from suchtechniques. However, although these data are often diffi-cult and expensive to produce, they are rarely shared andcollaboratively exploited, and dissemination of new anal-ysis methods may be restricted by issues of software andfile compatibility. CARMEN (Code Analysis, Repositoryand Modeling for e-Neuroscience) aims to address theseissues by creating an environment for handling time seriesdata and for deploying analysis algorithms using distrib-uted computing technology.The CARMEN infrastructure builds heavily on softwaredeveloped in previous e-science projects. The "Cloud"architecture allows the co-location of data and computa-tion (avoiding the need to repeatedly transfer large quan-tities of data) and enabling users to conduct their sciencethrough a web browser. The data handling capabilities ofthe CARMEN portal have recently been deployed, ena-bling registration of users and upload of data files. Theprimary data consists mainly of files of electrophysiologi-cal data, for which we use Storage Resource Broker tomanage the distributed store. To provide a description ofexperimental protocols, an extensible metadata schemahas been developed [1] and implemented using templatesto avoid the necessity of re-entering values for commonprotocols. A security layer enables the contributor to con-trol access rights to both the data and metadata, so thatthe originator and collaborators can share and analyze thedata in a private environment until publication when thedata may be made public. This repository satisfies therequirements of funding agencies to make research outputpublicly available and provides a resource for computa-tional neuroscientists.The project consortium is developing new analysis meth-ods including spike detection services that use waveletand morphology techniques [2], a spike sorting method-ology that extends WaveClus [3], information theoreticanalysis and Bayesian network analysis to determinecausal relations, and algorithms for resolving spike syn-chrony. An associated thick client tool, Signal DataExplorer, provides data visualization, signal processingand pattern matching capabilities. Because analysis appli-cations need to be executed on a wide range of data for-mats, we have specified a uniform file and formatstructure for data sharing and communication betweenapplications [4]. We are implementing an enactmentengine to enable linking of applications into more com-plex and user-defined workflows.

We describe a high performance Grid based signal search tool for distributed diagnostic applications developed in conjunction with Rolls-Royce plc for civil aero engine condition monitoring applications. With the introduction of advanced monitoring technology into engineering systems, healthcare, etc., the associated diagnostic processes are increasingly required to handle and consider vast amounts of data. An exemplar of such a diagnosis process was developed during the DAME project, which built a proof of concept demonstrator to assist in the enhanced diagnosis and prognosis of aero-engine conditions. In particular it has shown the utility of an interactive viewing and high performance distributed search tool (the Signal Data Explorer) in the aero-engine diagnostic process. The viewing and search techniques are equally applicable to other domains. The Signal Data Explorer and search services have been demonstrated on the Worldwide Universities Network to search distributed databases of electrocardiograph data.

Efficiently mining large volumes of time series data is amongst the most challenging problems that are fundamental in many fields such as industrial process monitoring, medical data analysis and business forecasting. This paper discusses a high-performance neural network for mining large time series data set and some practical issues on time series data mining. Examples of how this technology is used to search the engine data within a major UK eScience Grid project (DAME) for supporting the maintenance of Rolls-Royce aero-engine are presented.

This paper describes the C-NNAP machine, a MIMD implementation of an array of ADAM (advanced distributed associative memory) binary neural networks, primarily designed for image processing. C-NNAP comprises an array of VME cards each containing a DSP, SCSI controller and a new design of the sum and threshold (SAT) peripheral processor. The SAT processor is a dedicated hardware implementation that performs binary neural network computations. The SAT processor yields a potential speed-up of between 108 times to 182 times that of the current DSP with its dedicated coprocessor. C-NNAP in association with the SAT provide a fast, parallel environment for performing binary neural network operations.

Neutron inelastic scattering (NIS) and ultraviolet resonance Raman scattering (RRS) experimental data have been obtained from purine nucleic base samples, i.e., adenine, C8-deuterated adenine, and a (N9, C6-amino) - deuterated species. The two types of spectra have made it possible to carry out complete theoretical assignments of the vibrational modes of adenine and its derivatives. Normal mode wavenumbers as well as atomic displacements have been calculated by the Wilson GF method, using a valence force field and a non-redundant set of internal coordinates

The CARMEN (Code, Analysis, Repository and Modelling for e-Neuroscience) system [1] provides a web based portal platform through which users can share and collaboratively exploit data, analysis code and expertise in neuroscience. The system has been beendeveloped in the UK and currently supports 200 hundred neuroscientists working in a Virtual Environment with an initial focus on electrophysiology data. The proposal here is that the CARMEN system provides an excellent base from which to develop an ‘executable paper’ system. CARMEN has been built by York and Newcastle Universities and is based on over 10 years experience in the construction of eScience based distributed technology. CARMEN started four years ago involving 20 scientific investigators (neuroscientists and computer scientists) at 11 UK Universities (www.CARMEN.org.uk). The project is supported for another 4 years at York and Newcastle, along with a sister project to take the underlying technology and pilot it as a UK platform for supporting the sharing of research outputs in a generic way. An entirely natural extension to the CARMEN system would be its alignment with a publications repository. The CARMEN system is operational on the domain https://portal.CARMEN.org.uk, where it is possible to request a login to try out the system.