Taking pluralism seriously:Embedded moralities in management accounting and control systems

Professor Norman Macintosh has long been a leading, and at times a dissonant, voice in critical accounting studies, exhibiting an intellectual dexterity seldom encountered in the accounting academy. His work ranges from the application of traditional organizational theories within work organizations to poststructural renderings of capital market exigencies. Here, we consider and extend Professor Macintosh's work contemplating the morality embedded within, and propagated by, management accounting and control systems (macs). We begin with Macintosh (1995) employing structuration theory in investigating the ethics of profit manipulation within large, decentralized corporations. The work highlights the fundamental dialectical contradictions within these work organizations, demonstrates the indeterminacy of traditional ethical reasoning, and shows the extent to which macs provide legitimating underpinnings for management action. We propose to extend the conversation using the tools provided in Macintosh's subsequent work: a Levinasian ethic (Macintosh et al., 2009), and heteroglossic accounting (Macintosh, 2002)—both emerging from his poststructuralist predilections. A Levinasian perspective provides an ontologically grounded ethic, and heteroglossic accounting calls for multiple accountings representing alternative moral voices. A critical dialogic framework is proposed as a theoretic for imagining heteroglossic accounting that takes pluralism seriously by recognizing the reality of irresolvable differences and asymmetric power relationships associated with assorted moral perspectives.

The MNC as an externally embedded organization: An investigation of embeddedness overlap in local subsidiary networks

MNCs have been conceptualized as differentiated networks that, in turn, are embedded in external networks. Previous research has predominantly focused on the embeddedness of established subsidiaries into their local environment, omitting to shed light on the phenomenon of headquarters linkages to the local context which creates embeddedness overlap. We develop a model of why MNCs develop overlapping linkages to local subsidiary networks even if the subsidiaries have grown out of the initial start-up phase. Using detailed information on 168 European subsidiaries, we find that MNCs build and maintain more overlapping network ties when subsidiaries are high performers, hold important resources, operate in turbulent environments, and are closely connected to multinational actors as opposed to purely domestic firms.

Assessing and measuring impact of a digital collection in the humanities: An analysis of the SPHERE (Stormont Parliamentary Hansards: Embedded in Research and Education) Project

Although a substantial corpus of digital materials is now available to scholarship across the disciplines, objective evidence of their use, impact, and value, based on a robust assessment, is sparse. Traditional methods of assessment of impact in the humanities, notably citation in scholarly publications, are not an effective way of assessing impact of digital content. These issues are problematic in the field of Digital Humanities where there is a need to effectively assess impact to justify its continued funding and existence. A number of qualitative and quantitative methods exist that can be used to monitor the use of digital resources in various contexts although they have yet to be applied widely. These have been made available to the creators, managers, and funders of digital content in an accessible form through the TIDSR (Toolkit for the Impact of Digital Scholarly Resources) developed by the Oxford Internet Institute. In 2011, the authors of this article developed the SPHERE project (Stormont Parliamentary Hansards: Embedded in Research and Education) specifically to use TIDSR to evaluate the use and impact of The Stormont Papers, a digital collection of the Hansards of the Stormont Northern Irish Parliament from 1921 to 1972. This article presents the methodology, findings, and analysis of the project. The authors argue that TIDSR is a useful and, critically, transferrable method to understand and increase the impact of digital resources. The findings of the project are modified into a series of wider recommendations on protecting the investment in digital resources by increasing their use, value, and impact. It is reasonable to suggest that effectively showing the impact of Digital Humanities is critical to its survival.

Embedded C programming: a practical course introducing programmable microprocessors

This paper presents a new laboratory-based module for embedded systems teaching, which addresses the current lack of consideration for the link between hardware development, software implementation, course content and student evaluation in a laboratory environment. The course introduces second year undergraduate students to the interface between hardware and software and the programming of embedded devices; in this case, the PIC (originally peripheral interface controller, later rebranded programmable intelligent computer) microcontroller. A hardware development board designed for use in the laboratories of this module is presented. Through hands on laboratory experience, students are encouraged to engage with practical problem-solving exercises and develop programming skills across a broad range of scenarios.

Developing mRNA-based biomarkers from formalin-fixed paraffin-embedded tissue

Cancer is a complex and heterogeneous disease which is one of the leading causes of death in Western civilisations. Thus, oncology is viewed as a primary focus for personalized medicine. It is recognised that cancer treatment needs to be better tailored in order to improve patient outcome. Patient tumor samples will be required to characterize cancer at a molecular level and identify where there may be disease subgroups that should be treated differently. The use of formalin-fixed paraffin-embedded tissue is important for enabling such studies. In this report, we focus on the challenges that have been faced to date along with the technological developments that have now made this possible. We also highlight the impact this may have on drug and diagnostic development.

SPHERE: Stormont Parliamentary Hansards: Embedded in Research and Education

SPHERE (Stormont Parliamentary Hansards: Embedded in Research and Education) was a JISC-funded project based at King’s College, London and Queen’s University, Belfast, working in Partnership with the Northern Ireland Assembly Library, and the NIA Official Report (Hansard). Its purpose was to assess the use, value and impact of The Stormont Papers digital resource, and to use the results of this assessment to make recommendations for a series of practical approaches to embed the resource within teaching, learning and research among the wider user community. The project began in November 2010 and was concluded in April 2010.

A series of formal reports on the project are published by JISC online at http://www.jisc.ac.uk/whatwedo/programmes/digitisation/impactembedding/sphere.aspx

SPHERE Impact analysis summary
Portable Document Format
SPHERE interviews report
SPHERE Outreach use case
SPHERE research use case
SPHERE teaching use_case
SPHERE web survey report
SPHERE web analysis

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Calculation of Critical Clearing Time of Embedded Generation for Relay Setting Based on Optimisation Method

his paper proposes an optimisation-based method to calculate the critical slip (speed) of dynamic stability and critical clearing time (CCT) of a self-excited induction generator (SEIG). A simple case study using the Matlab/Simulink environment has been included to exemplify the optimisation method. Relationships between terminal voltage, critical slip and reactance of transmission line, CCT and inertial constant have been determined, based on which analysis of impact on relaying setting has been further conducted for another simulation case.

Pull-out behaviour of steel grid soil reinforcement embedded in silty sand

This paper investigates the pull-out behaviour (particularly the bearing resistance) of a steel grid reinforcement embedded in silty sand using laboratory tests and numerical analyses. It is demonstrated that the various common analytical equations for calculating the bearing component of pull-out resistance give a wide range of calculated values, up to about 200% disparity. The disparity will increase further if the issue of whether to use the peak or critical state friction angle is brought in. Furthermore, these equations suggest that the bearing resistance factor, N, is only a function of soil friction angle which is not consistent with some design guidelines. In this investigation, a series of large scale laboratory pull-out tests under different test pressures were conducted. The test results unambiguously confirmed that the N factor is a function of test pressure. A modified equation for calculating N is also proposed. To have more in-depth understanding of the pull-out behaviour, the tests were modelled numerically. The input parameters for the numerical analysis were obtained from laboratory triaxial tests. The analysis results were compared with the experimental results. Good agreement between experimental and numerical results was achieved if the strain-softening behaviour from peak strength to critical state condition was captured by the soil model used. © 2013 Elsevier Ltd.

Microstrcutural evolution of SiC fibre embedded AA6061 matrix induced by ultrasonic consolidation

Ultrasonic consolidation (UC) uses high frequency (20-40KHz) mechanical vibrations to produce a solid-state metallurgical bond (weld) between metal foils. UC as a novel layered manufacturing technique is used in this research to embed reinforcing members such as silicon carbide fibers into the aluminium alloy 6061's matrices. It is known that UC induce volume and surface effect in the material it is acting on. Both effects are employed in embedding active/passive elements in the metal matrix. Whilst the process and the two effects are used and identified at macro level, what is happening at micro level is unknown and hardly studied. In this research we are investigating the phenomena occurring in the microstructure of the parts during UC process to obtain better understanding about how and why the process works. In this research, high-resolution electron backscatter diffraction is used to study the effects of the UC process on the evolution of microstructure in AA6061 with and without fibre elements. The inverse pole figures (IPF), pole figures (PF) and the correlated misorientation angle distribution of the mentioned samples are obtained. The characteristics of the crystallographic orientation, the grain structure and the grain boundary are analysed to find the effect of ultrasonic vibration and embedding fibre on the microstructure and texture of the bond. The ultrasonic vibration will lead to exceptional refinement of grains to a micron level along the bond area and affect the crystallographic orientation. Additional plastic flow occurs around the fibre which leads to the fibre embedding. © 2008 Materials Research Society.

Weaving behavior into feature models for embedded system families

Product Line software Engineering depends on capturing the commonality and variability within a family of products, typically using feature modeling, and using this information to evolve a generic reference architecture for the family. For embedded systems, possible variability in hardware and operating system platforms is an added complication. The design process can be facilitated by first exploring the behavior associated with features. In this paper we outline a bidirectional feature modeling scheme that supports the capture of commonality and variability in the platform environment as well as within the required software. Additionally, 'behavior' associated with features can be included in the overall model. This is achieved by integrating the UCM path notation in a way that exploits UCM's static and dynamic stubs to capture behavioral variability and link it to the feature model structure. The resulting model is a richer source of information to support the architecture development process.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.