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The Gandhian-Vedic approach to development is synonymous with the advancement of spiritual agency. It emancipates society by trying to raise people's interconnectedness with nature, mitigating capitalism's hegemony of consumerism on people's psyche and hence reducing the chances of individuals perpetuating the cycle of exploitation by adhering to capitalist norms. That is, the Gandhian-Vedic approach to discursive accountability minimises the risk of circularity in the dialectics of contradictions, which occurs when consenting behaviour replaces existing contradictions with another set of contradictions. It enables the actor to step off capitalism's treadmill of materialism and exploitation by centralising spiritual development. Its spiritual revolution involves caring for the whole whilst engaging with social structures. Here the Gandhian-Vedic logic is extended to emancipatory accounting by developing accounting as a discursive risk assessment tool that minimises the fragmentation of time and space aspects of performance. Its holistic representation of performance could change perceptions about interconnectedness between an individual's behaviour, nature and society. It is the antithesis of conventional accounting's prioritisation of private interest over responsibility for the whole.

THE considerable interest in aircraft safety, or more precisely in the safety of people who travel in civil aircraft, which was aroused during the past year can be attributed to speculation in the popular press and other news media on the possibility of a 747 disaster, or on the exploits of the criminal organisations who have chosen to practise anarchism against civil aircraft and their innocent cargoes.

The aim of this paper is to analyse how technological innovations in performance measurement systems make it possible to overcome some of the challenges that public healthcare organizations face where management and control are concerned. The changes that could be applied to the performance measurement system of healthcare organisations were analysed together with an evaluation of the responses developed in order to achieve these changes.

The paper contains an in-depth case-study of a public university hospital which utilises an innovative information system.

The case-study highlights how technological innovations in performance measurement systems impact the management and monitoring information system in a public university hospital, through the implementation of a multidimensional management dashboard.

The limitation of this paper is that only one case-study is analysed, albeit in depth, while it would be interesting to consider more public university hospitals.

The paper highlights the fundamental role of middle management in change processes in the healthcare sector.

The case-study highlights how critical the active involvement of middle management is in performance measurement and management, and how this is achieved thanks to the adoption of a simple, clear method which ensures comprehensible communication of the objectives, as well as the measurement of performance by means of radar plots.

The purpose of this paper is to develop the balance score card (BSC) approach based Lean Six Sigma (LSS) performance measurement system and investigate the critical measures currently practiced by Indian manufacturing organizations.

This study offers insights of LSS performance measurement from manufacturing industry. Initially, the BSC-based framework is developed to recognize the adoption of LSS performance measures. Then, the framework is applied to nine Indian manufacturing organizations to assess the LSS performance measure practice.

The BSC-based framework of LSS performance evaluation for manufacturing industry is formulated. Then, adoption of these LSS performance measures is investigated with nine Indian manufacturing organizations. The result indicates significant variability in terms of practicing level of LSS measures. However, the majority of organizations are more sensitive to the customer perspectives.

This study reveals a background as to why the performance measurement is required for the success of LSS and for providing practical guidelines for designing performance metrics. The framework interrelates and captures various LSS perspectives and indicator measures, and furnishes a comprehensive outlook of the organization for strategic analysis. This study provides BSC-based template for performing the benchmarking study. This analysis may serve as a reference point for manufacturing organization to determine their system weaknesses, and assist them to concentrate on their most vital and suitable criteria and objectives. However, the analysis contributes to the knowledge on LSS performance measurement system and catches differences in theory and practice, paving the approach to newer research.

This study renders an industry-oriented LSS performance measurement practical approach and suggests the easily adopted vital performance measures for different manufacturing organizations.

This paper aims to investigate the application of unsupervised machine learning in the international location decision (ILD). This paper addresses the need for a fast, quantitative and dynamic location decision framework.

Unsupervised machine learning technique, i.e. k-means clustering, is used to carry out the analysis. In total, 24 different indicators of 94 countries, categorized into five groups, have been used in the analysis. After the clustering, the clusters have been compared and scored to select the feasible countries.

A new framework is developed based on k-means clustering that can be used in ILD. This method provides a quantitative output without personal subjectivity. The indicators can be easily added or extracted based on the preferences of the decision-makers. Hence, it was found out that the unsupervised machine learning, i.e. k-means clustering, is a fast and flexible decision support framework that can be used in ILD.

Limitations include the generality of selected indicators and clustering algorithm used. The use of other methods and parameters may lead to alternate results.

The framework developed through the research intends to assist the decision-makers in deciding on the facility locations. The framework can be used in international and national domains. It provides a quantitative, fast and flexible way to shortlist the potential locations. Other methods can also be used to further decide on the specific location.

This paper aims to present an approach of utilizing multiple-input multiple-output (MIMO) radar concept to enhance pedestrian classification in automotive sensors. In a practical environment, radar signals reflected from pedestrians and slow-moving vehicles are similar in terms of reflecting angle and Doppler returns, inducing difficulty for target discrimination. An efficient discrimination between the two targets depends on the ability of the sensor to extract unique characteristics from each target, for example, by exploiting Doppler signatures. This study describes the utilization of MIMO radar for Doppler measurement and demonstrates its application to improve pedestrian classification through actual laboratory measurements.

Multiple non-modulated sinusoidal signals are transmitted orthogonally over a MIMO array using time division scheme, illuminating human and non-human targets. The reflected signal entering each of the receiving antenna are combined at the radar receiver prior to Doppler processing. Doppler histogram was formulated based on a series of measurements, and the Doppler spread of the targets was determined from the histograms. Results were compared between MIMO and conventional single antenna systems.

Measurement results indicated that the MIMO configuration provides able to capture more Doppler information compared to conventional single antenna systems, enabling a more precise discrimination between pedestrian and other slow-moving objects on the road.

The study demonstrated the effectiveness of using MIMO configuration in radar-based automotive sensor to enhance the accuracy of Doppler estimation, which is seldom highlighted in literature of MIMO radars. The result also indicated its usefulness in improving target discrimination capability of the radar, through actual measurement.

An interactive processing scheme is proposed to improve the target detection probability as well as the tracking performance of the radar system.

Firstly, with the spatial-correlated features extracted from the foreground and background statistical models, the thresholds were adapted to distinguish the dim small targets from clutters in the complex incoherent radar images. Then, the target trajectories were constructed with the target tracking algorithm. According to the temporal correlation with the target life cycle, the thresholding values were modified in the neighbourhood of the predicted positions to improve the detection sensitivity in these areas during the tracking process. Finally, the temporal-correlated features of the remained clutters were used to further reduce the false alarm rate.

The proposed algorithm was applied on the simulated data, as well as the image sequences obtained with the incoherent marine radars. The detection results demonstrated that the interactive algorithm could detect and track the dim small targets with relatively low false alarm rate.

The interactive processing scheme could be applied for low-altitude airspace surveillance with incoherent marine radar.

The proposed scheme outperforms the classical radar target detection algorithms and the state-of-the-art image processing algorithms for video-based surveillance.

Scientists are constructing knowledge about global warming by adapting evidence-based disciplines to reflect the Precautionary Principle. It is equally important to communicate the complexities and uncertainties underpinning global warming because inappropriate vehicles for giving accounts could result in defensive decisions that perpetuate the business-as-usual mindset: the method of communication affects how the risk associated with global warming is socialised. Appropriately constructed accounts should facilitate reflective communicative action. Here Beck's theorisation of risk society, Luhmann's sociological theory of risk and Gandhi's vehicle of communicative action (or satyagraha) are used to construct a risk-based accountability mechanism, whilst providing insight into Schumacher's concept of total accountability. These accountability constructs will be illustrated through the lived experiences of South Australian citrus horticulturists in the context of a richly layered narrative of competing discourses about global warming. The reiterative process of theory informing practice is used to construct a couple of dialogical vehicles of accountability.

It was a good Farnborough Show … for those who went to see the flying displays and the weather was a great consolation. But it was an unusual Farnborough in some respects, first because of the good weather and this does bear repetition, and it does make such a difference to setting up the Show to its progress through the week and to the mood of the people there.

Dysarthria is a neuromotor speech disorder caused by neuromuscular disturbances that affect one or more articulators resulting in unintelligible speech. Though inter-phoneme articulatory variations are well captured by formant frequency-based acoustic features, these variations are expected to be much higher for dysarthric speakers than normal. These substantial variations can be well captured by placing sensors in appropriate articulatory position. This study focuses to determine a set of articulatory sensors and parameters in order to assess articulatory dysfunctions in dysarthric speech.

The current work aims to determine significant sensors and parameters associated using motion path and correlation analyzes on the TORGO database of dysarthric speech. Among eight informative sensor channels and six parameters per channel in positional data, the sensors such as tongue middle, back and tip, lower and upper lips and parameters (y, z, φ) are found to contribute significantly toward capturing the articulatory information. Acoustic and positional data analyzes are performed to validate these identified significant sensors. Furthermore, a convolutional neural network-based classifier is developed for both phone-and word-level classification of dysarthric speech using acoustic and positional data.

The average phone error rate is observed to be lower, up to 15.54% for positional data when compared with acoustic-only data. Further, word-level classification using a combination of both acoustic and positional information is performed to study that the positional data acquired using significant sensors will boost the performance of classification even for severe dysarthric speakers.

The proposed work shows that the significant sensors and parameters can be used to assess dysfunctions in dysarthric speech effectively. The articulatory sensor data helps in better assessment than the acoustic data even for severe dysarthric speakers.