Form follows function:model-driven engineering for clinical trials

We argue that, for certain constrained domains, elaborate model transformation technologies-implemented from scratch in general-purpose programming languages-are unnecessary for model-driven engineering; instead, lightweight configuration of commercial off-the-shelf productivity tools suffices. In particular, in the CancerGrid project, we have been developing model-driven techniques for the generation of software tools to support clinical trials. A domain metamodel captures the community's best practice in trial design. A scientist authors a trial protocol, modelling their trial by instantiating the metamodel; customized software artifacts to support trial execution are generated automatically from the scientist's model. The metamodel is expressed as an XML Schema, in such a way that it can be instantiated by completing a form to generate a conformant XML document. The same process works at a second level for trial execution: among the artifacts generated from the protocol are models of the data to be collected, and the clinician conducting the trial instantiates such models in reporting observations-again by completing a form to create a conformant XML document, representing the data gathered during that observation. Simple standard form management tools are all that is needed. Our approach is applicable to a wide variety of information-modelling domains: not just clinical trials, but also electronic public sector computing, customer relationship management, document workflow, and so on. © 2012 Springer-Verlag.

Stochastically driven instability in rotating shear flows

Origin of hydrodynamic turbulence in rotating shear flows is investigated. The particular emphasis is on flows whose angular velocities decrease but specific angular momenta increase with increasing radial coordinate. Such flows are Rayleigh stable, but must be turbulent in order to explain observed data. Such a mismatch between the linear theory and observations/experiments is more severe when any hydromagnetic/magnetohydrodynamic instability and the corresponding turbulence therein is ruled out. The present work explores the effect of stochastic noise on such hydrodynamic flows. We focus on a small section of such a flow which is essentially a plane shear flow supplemented by the Coriolis effect. This also mimics a small section of an astrophysical accretion disk. It is found that such stochastically driven flows exhibit large temporal and spatial correlations of perturbation velocities, and hence large energy dissipations, that presumably generate instability. A range of angular velocity profiles (for the steady flow), starting with the constant angular momentum to that of the constant circular velocity are explored. It is shown that the growth and roughness exponents calculated from the contour (envelope) of the perturbed flows are all identical, revealing a unique universality class for the stochastically forced hydrodynamics of rotating shear flows. This work, to the best of our knowledge, is the first attempt to understand origin of instability and turbulence in the three-dimensional Rayleigh stable rotating shear flows by introducing additive stochastic noise to the underlying linearized governing equations. This has important implications in resolving the turbulence problem in astrophysical hydrodynamic flows such as accretion disks.

Magnetohydrodynamic stability of stochastically driven accretion flows

We investigate the evolution of magnetohydrodynamic (or hydromagnetic as coined by Chandrasekhar) perturbations in the presence of stochastic noise in rotating shear flows. The particular emphasis is the flows whose angular velocity decreases but specific angular momentum increases with increasing radial coordinate. Such flows, however, are Rayleigh stable but must be turbulent in order to explain astrophysical observed data and, hence, reveal a mismatch between the linear theory and observations and experiments. The mismatch seems to have been resolved, at least in certain regimes, in the presence of a weak magnetic field, revealing magnetorotational instability. The present work explores the effects of stochastic noise on such magnetohydrodynamic flows, in order to resolve the above mismatch generically for the hot flows. We essentially concentrate on a small section of such a flow which is nothing but a plane shear flow supplemented by the Coriolis effect, mimicking a small section of an astrophysical accretion disk around a compact object. It is found that such stochastically driven flows exhibit large temporal and spatial autocorrelations and cross-correlations of perturbation and, hence, large energy dissipations of perturbation, which generate instability. Interestingly, autocorrelations and cross-correlations appear independent of background angular velocity profiles, which are Rayleigh stable, indicating their universality. This work initiates our attempt to understand the evolution of three-dimensional hydromagnetic perturbations in rotating shear flows in the presence of stochastic noise. © 2013 American Physical Society.

Interactive data in financial reporting:the construction of the non-professional investor

The Securities and Exchange Commission (SEC) in the United States and in particular its immediately past chairman, Christopher Cox, has been actively promoting an upgrade of the EDGAR system of disseminating filings. The new generation of information provision has been dubbed by Chairman Cox, "Interactive Data" (SEC, 2006). In October this year the Office of Interactive Disclosure was created(http://www.sec.gov/news/press/2007/2007-213.htm). The focus of this paper is to examine the way in which the non-professional investor has been constructed by various actors. We examine the manner in which Interactive Data has been sold as the panacea for financial market 'irregularities' by the SEC and others. The academic literature shows almost no evidence of researching non-professional investors in any real sense (Young, 2006). Both this literature and the behaviour of representatives of institutions such as the SEC and FSA appears to find it convenient to construct this class of investor in a particular form and to speak for them. We theorise the activities of the SEC and its chairman in particular over a period of about three years, both following and prior to the 'credit crunch'. Our approach is to examine a selection of the policy documents released by the SEC and other interested parties and the statements made by some of the policy makers and regulators central to the programme to advance the socio-technical project that is constituted by Interactive Data. We adopt insights from ANT and more particularly the sociology of translation (Callon, 1986; Latour, 1987, 2005; Law, 1996, 2002; Law & Singleton, 2005) to show how individuals and regulators have acted as spokespersons for this malleable class of investor. We theorise the processes of accountability to investors and others and in so doing reveal the regulatory bodies taking the regulated for granted. The possible implications of technological developments in digital reporting have been identified also by the CEO's of the six biggest audit firms in a discussion document on the role of accounting information and audit in the future of global capital markets (DiPiazza et al., 2006). The potential for digital reporting enabled through XBRL to "revolutionize the entire company reporting model" (p.16) is discussed and they conclude that the new model "should be driven by the wants of investors and other users of company information,..." (p.17; emphasis in the original). Here rather than examine the somewhat illusive and vexing question of whether adding interactive functionality to 'traditional' reports can achieve the benefits claimed for nonprofessional investors we wish to consider the rhetorical and discursive moves in which the SEC and others have engaged to present such developments as providing clearer reporting and accountability standards and serving the interests of this constructed and largely unknown group - the non-professional investor.

Understanding data collection behaviour of mental health practitioners

Failure to detect patients at risk of attempting suicide can result in tragic consequences. Identifying risks earlier and more accurately helps prevent serious incidents occurring and is the objective of the GRiST clinical decision support system (CDSS). One of the problems it faces is high variability in the type and quantity of data submitted for patients, who are assessed in multiple contexts along the care pathway. Although GRiST identifies up to 138 patient cues to collect, only about half of them are relevant for any one patient and their roles may not be for risk evaluation but more for risk management. This paper explores the data collection behaviour of clinicians using GRiST to see whether it can elucidate which variables are important for risk evaluations and when. The GRiST CDSS is based on a cognitive model of human expertise manifested by a sophisticated hierarchical knowledge structure or tree. This structure is used by the GRiST interface to provide top-down controlled access to the patient data. Our research explores relationships between the answers given to these higher-level 'branch' questions to see whether they can help direct assessors to the most important data, depending on the patient profile and assessment context. The outcome is a model for dynamic data collection driven by the knowledge hierarchy. It has potential for improving other clinical decision support systems operating in domains with high dimensional data that are only partially collected and in a variety of combinations.

A quality driven framework for adaptive video streaming in mobile wireless networks

The concern over the quality of delivering video streaming services in mobile wireless networks is addressed in this work. A framework that enhances the Quality of Experience (QoE) of end users through a quality driven resource allocation scheme is proposed. To play a key role, an objective no-reference quality metric, Pause Intensity (PI), is adopted to derive a resource allocation algorithm for video streaming. The framework is examined in the context of 3GPP Long Term Evolution (LTE) systems. The requirements and structure of the proposed PI-based framework are discussed, and results are compared with existing scheduling methods on fairness, efficiency and correlation (between the required and allocated data rates). Furthermore, it is shown that the proposed framework can produce a trade-off between the three parameters through the QoE-aware resource allocation process.

Quality-driven resource utilization methods for video streaming in wireless communication networks

This research is focused on the optimisation of resource utilisation in wireless mobile networks with the consideration of the users’ experienced quality of video streaming services. The study specifically considers the new generation of mobile communication networks, i.e. 4G-LTE, as the main research context. The background study provides an overview of the main properties of the relevant technologies investigated. These include video streaming protocols and networks, video service quality assessment methods, the infrastructure and related functionalities of LTE, and resource allocation algorithms in mobile communication systems. A mathematical model based on an objective and no-reference quality assessment metric for video streaming, namely Pause Intensity, is developed in this work for the evaluation of the continuity of streaming services. The analytical model is verified by extensive simulation and subjective testing on the joint impairment effects of the pause duration and pause frequency. Various types of the video contents and different levels of the impairments have been used in the process of validation tests. It has been shown that Pause Intensity is closely correlated with the subjective quality measurement in terms of the Mean Opinion Score and this correlation property is content independent. Based on the Pause Intensity metric, an optimised resource allocation approach is proposed for the given user requirements, communication system specifications and network performances. This approach concerns both system efficiency and fairness when establishing appropriate resource allocation algorithms, together with the consideration of the correlation between the required and allocated data rates per user. Pause Intensity plays a key role here, representing the required level of Quality of Experience (QoE) to ensure the best balance between system efficiency and fairness. The 3GPP Long Term Evolution (LTE) system is used as the main application environment where the proposed research framework is examined and the results are compared with existing scheduling methods on the achievable fairness, efficiency and correlation. Adaptive video streaming technologies are also investigated and combined with our initiatives on determining the distribution of QoE performance across the network. The resulting scheduling process is controlled through the prioritization of users by considering their perceived quality for the services received. Meanwhile, a trade-off between fairness and efficiency is maintained through an online adjustment of the scheduler’s parameters. Furthermore, Pause Intensity is applied to act as a regulator to realise the rate adaptation function during the end user’s playback of the adaptive streaming service. The adaptive rates under various channel conditions and the shape of the QoE distribution amongst the users for different scheduling policies have been demonstrated in the context of LTE. Finally, the work for interworking between mobile communication system at the macro-cell level and the different deployments of WiFi technologies throughout the macro-cell is presented. A QoEdriven approach is proposed to analyse the offloading mechanism of the user’s data (e.g. video traffic) while the new rate distribution algorithm reshapes the network capacity across the macrocell. The scheduling policy derived is used to regulate the performance of the resource allocation across the fair-efficient spectrum. The associated offloading mechanism can properly control the number of the users within the coverages of the macro-cell base station and each of the WiFi access points involved. The performance of the non-seamless and user-controlled mobile traffic offloading (through the mobile WiFi devices) has been evaluated and compared with that of the standard operator-controlled WiFi hotspots.

A knowledge driven approach towards the validation of externally acquired traceability datasets in supply chain business processes

The sharing of near real-time traceability knowledge in supply chains plays a central role in coordinating business operations and is a key driver for their success. However before traceability datasets received from external partners can be integrated with datasets generated internally within an organisation, they need to be validated against information recorded for the physical goods received as well as against bespoke rules defined to ensure uniformity, consistency and completeness within the supply chain. In this paper, we present a knowledge driven framework for the runtime validation of critical constraints on incoming traceability datasets encapuslated as EPCIS event-based linked pedigrees. Our constraints are defined using SPARQL queries and SPIN rules. We present a novel validation architecture based on the integration of Apache Storm framework for real time, distributed computation with popular Semantic Web/Linked data libraries and exemplify our methodology on an abstraction of the pharmaceutical supply chain.

Topographic visual analytics of multibeam dynamic SONAR data

This paper considers the problem of low-dimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point relative similarity measures) appropriate to the below-water maritime domain, we are investigating a preliminary prototype topographic visualisation model. The focus of the current paper is on the mathematical problem of exploiting a relative dissimilarity representation of signals in a visual informatics mapping model, driven by real-world sonar systems. A realistic noise model is explored and incorporated into non-linear and topographic visualisation algorithms building on the approach of [9]. Concepts are illustrated using a real world dataset of 32 hydrophones monitoring a shallow-water environment in which targets are present and dynamic.

Precompetitive data sharing as a catalyst to address unmet needs in Parkinson's disease

Parkinson's disease is a complex heterogeneous disorder with urgent need for disease-modifying therapies. Progress in successful therapeutic approaches for PD will require an unprecedented level of collaboration. At a workshop hosted by Parkinson's UK and co-organized by Critical Path Institute's (C-Path) Coalition Against Major Diseases (CAMD) Consortiums, investigators from industry, academia, government and regulatory agencies agreed on the need for sharing of data to enable future success. Government agencies included EMA, FDA, NINDS/NIH and IMI (Innovative Medicines Initiative). Emerging discoveries in new biomarkers and genetic endophenotypes are contributing to our understanding of the underlying pathophysiology of PD. In parallel there is growing recognition that early intervention will be key for successful treatments aimed at disease modification. At present, there is a lack of a comprehensive understanding of disease progression and the many factors that contribute to disease progression heterogeneity. Novel therapeutic targets and trial designs that incorporate existing and new biomarkers to evaluate drug effects independently and in combination are required. The integration of robust clinical data sets is viewed as a powerful approach to hasten medical discovery and therapies, as is being realized across diverse disease conditions employing big data analytics for healthcare. The application of lessons learned from parallel efforts is critical to identify barriers and enable a viable path forward. A roadmap is presented for a regulatory, academic, industry and advocacy driven integrated initiative that aims to facilitate and streamline new drug trials and registrations in Parkinson's disease.

A quality driven adaptation scheme for DASH streaming

In this work we present a quality driven approach to DASH (Dynamic Adaptive Streaming over HTTP) for segment selection in varying network conditions. Current adaption algorithms focus largely on regulating data rates using network layer parameters by selecting the level of quality on offer that can eliminate buffer underrun without considering picture fidelity. In reality, viewers may accept a level of buffer underrun in order to achieve an improved level of picture fidelity. In this case, the conventional DASH algorithms can cause extreme degradation of the picture fidelity when attempting to eliminate buffer underrun with scarce bandwidth availability. Our work is concerned with a quality-aware rate adaption scheme that maximizes the client's quality of experience in terms of both continuity and fidelity (picture quality). Results show that the scheme proposed can maintain a high level of quality for streaming services, especially at low packet loss rates. It is also shown that by eliminating buffer underrun completely, the PSNR that reflects the picture quality of the video is greatly reduced. Our scheme offers the offset between continuity-based quality and resolution-based quality, which can be used to set threshold values for the level of quality desired by clients with different quality requirements. © 2013 IEEE.

Radio frequency identification and time-driven activity based costing:RFID-TDABC application in warehousing

Purpose: This paper extends the use of Radio Frequency Identification (RFID) data for accounting of warehouse costs and services. Time Driven Activity Based Costing (TDABC) methodology is enhanced with the real-time collected RFID data about duration of warehouse activities. This allows warehouse managers to have accurate and instant calculations of costs. The RFID enhanced TDABC (RFID-TDABC) is proposed as a novel application of the RFID technology. Research Approach: Application of RFID-TDABC in a warehouse is implemented on warehouse processes of a case study company. Implementation covers receiving, put-away, order picking, and despatching. Findings and Originality: RFID technology is commonly used for the identification and tracking items. The use of the RFID generated information with the TDABC can be successfully extended to the area of costing. This RFID-TDABC costing model will benefit warehouse managers with accurate and instant calculations of costs. Research Impact: There are still unexplored benefits to RFID technology in its applications in warehousing and the wider supply chain. A multi-disciplinary research approach led to combining RFID technology and TDABC accounting method in order to propose RFID-TDABC. Combining methods and theories from different fields with RFID, may lead researchers to develop new techniques such as RFID-TDABC presented in this paper. Practical Impact: RFID-TDABC concept will be of value to practitioners by showing how warehouse costs can be accurately measured by using this approach. Providing better understanding of incurred costs may result in a further optimisation of warehousing operations, lowering costs of activities, and thus provide competitive pricing to customers. RFID-TDABC can be applied in a wider supply chain.