Towards a Framework for Understanding Fairtrade Purchase Intention in the Mainstream Environment of Supermarkets

© 2014, Springer Science+Business Media Dordrecht. Despite growing interest in ethical consumer behaviour research, ambiguity remains regarding what motivates consumers to purchase ethical products. While researchers largely attribute the growth of ethical consumerism to an increase in ethical consumer concerns and motivations, widened distribution (mainstreaming) of ethical products, such as fairtrade, questions these assumptions. A model that integrates both individual and societal values into the theory of planned behaviour is presented and empirically tested to challenge the assumption that ethical consumption is driven by ethical considerations alone. Using data sourced from fairtrade shoppers across the UK, structural equation modelling suggests that fairtrade purchase intention is driven by both societal and self-interest values. This dual value pathway helps address conceptual limitations inherent in the underlying assumptions of existing ethical purchasing behaviour m odels and helps advance understanding of consumers’ motivation to purchase ethical products

Global warming and recurrent mass bleaching of corals

During 2015–2016, record temperatures triggered a pan-tropical episode of coral bleaching, the third global-scale event since mass bleaching was first documented in the 1980s. Here we examine how and why the severity of recurrent major bleaching events has varied at multiple scales, using aerial and underwater surveys of Australian reefs combined with satellite-derived sea surface temperatures. The distinctive geographic footprints of recurrent bleaching on the Great Barrier Reef in 1998, 2002 and 2016 were determined by the spatial pattern of sea temperatures in each year. Water quality and fishing pressure had minimal effect on the unprecedented bleaching in 2016, suggesting that local protection of reefs affords little or no resistance to extreme heat. Similarly, past exposure to bleaching in 1998 and 2002 did not lessen the severity of bleaching in 2016. Consequently, immediate global action to curb future warming is essential to secure a future for coral reefs

A new method to derive rail roughness from axle-box vibration accounting for track stiffness variations and wheel-to-wheel coupling

Railways require frequent inspection to maintain acceptable levels of rail roughness. Accelerometers mounted on in-service railway vehicles potentially offer a cost-effective method of near-continuous roughness monitoring by using a transfer function in the frequency domain to derive rail roughness spectra from measurements of axle-box vibration. This paper addresses two phenomena that currently limit the effectiveness of such a method: the variation in track support stiffness along the track, which is the least known property of the vehicle-track system, resulting in a variable transfer function; and the vibration coupling between wheels, which limits the effectiveness of a transfer function based on a single wheel. A new method is presented that estimates the track stiffness by curve-fitting a transfer function to the peak in the axle-box acceleration spectrum associated with the so-called ‘P2’ resonance of the wheel-rail system. Wheel-to-wheel coupling is addressed by refining the transfer function to account for multiple wheels running on the same rail. Axle-box acceleration is also affected by wheel roughness, and the use of a comb filter is briefly demonstrated to mitigate this effect. The developments are evaluated by both numerical simulation and measurement trials conducted on London Underground’s Victoria line

Extracting Information from Axle-Box Acceleration: On the Derivation of Rail Roughness Spectra in the Presence of Wheel Roughness

Railhead roughness increases over time, leading to increased environmental noise and vibration. The use of axle-box acceleration (ABA) measurements on in-service railway vehicles to monitor rail roughness is potentially more cost-effective than other techniques. The measured acceleration requires signal processing to derive suitable metrics of railhead condition. A transfer function may be calibrated with direct roughness and ABA measurements made on a reference track, which may then be used to derive roughness spectra from subsequent ABA measurements. However, this approach is affected by variations in track dynamic behaviour, as well as variations in wheel roughness, which is inherently combined with rail roughness in the ABA measurement. This paper proposes an improved approach that (i) extracts the track’s dynamic stiffness parameters from the ABA measurements, enabling the derivation of the roughness-ABA transfer function for each section of track, and (ii) separates the wheel and rail roughness by synchronous averaging over several wheel revolutions. By accounting for variations in track properties and removing the influence of wheel roughness, initial modelling indicates that reliable measurements of rail roughness spectra can be obtained in practice

Extracting Information from Axle-Box Acceleration: On the Derivation of Rail Roughness Spectra in the Presence of Wheel Roughness

Railhead roughness increases over time, leading to increased environmental noise and vibration. The use of axle-box acceleration (ABA) measurements on in-service railway vehicles to monitor rail roughness is potentially more cost-effective than other techniques. The measured acceleration requires signal processing to derive suitable metrics of railhead condition. A transfer function may be calibrated with direct roughness and ABA measurements made on a reference track, which may then be used to derive roughness spectra from subsequent ABA measurements. However, this approach is affected by variations in track dynamic behaviour, as well as variations in wheel roughness, which is inherently combined with rail roughness in the ABA measurement. This paper proposes an improved approach that (i) extracts the track’s dynamic stiffness parameters from the ABA measurements, enabling the derivation of the roughness-ABA transfer function for each section of track, and (ii) separates the wheel and rail roughness by synchronous averaging over several wheel revolutions. By accounting for variations in track properties and removing the influence of wheel roughness, initial modelling indicates that reliable measurements of rail roughness spectra can be obtained in practice

On the derivation of rail roughness spectra from axle-box vibration: Development of a new technique

Railhead roughness on railways is a cause of noise and vibration. Corrugation (a periodic form of roughness) can grow rapidly and unpredictably, generating high levels of noise and vibration. An emerging technique for monitoring rail roughness is by use of axle-box accelerometers on in-service trains, which can be more cost-effective than conventional inspection methods. Axle-box accelerometers measure the vibration induced by roughness, rather than the roughness itself, and hence require signal processing techniques to translate this vibration into suitable metrics of the railhead condition, such as a wavelength spectrum of roughness. This paper presents progress towards a new stochastic frequency-domain inverse method that derives wavelength-spectra of rail roughness from axle-box acceleration measurements. This method compensates for the effects of vehicle speed and track dynamic behaviour on axle-box acceleration, which have adversely affected previous methods that, for example, rely on calibration on a reference section of track or simply take the RMS of the axle-box acceleration. The practical implications of processing and presenting measurements in the frequency domain are discussed, including the effect of varying vehicle speed and the trade-off between resolution and statistical accuracy. An initial algorithm is proposed and demonstrated through timedomain simulations of a theoretical vehicle-track model. Accurate derivation of roughness from axle-box acceleration will facilitate future development of autonomous monitoring systems fitted to in-service trains that continuously 'map' the condition of a rail network in real time, enabling more efficient and proactive scheduling of rail maintenance

On the derivation of rail roughness spectra from axle-box vibration: Development of a new technique

Railhead roughness on railways is a cause of noise and vibration. Corrugation (a periodic form of roughness) can grow rapidly and unpredictably, generating high levels of noise and vibration. An emerging technique for monitoring rail roughness is by use of axle-box accelerometers on in-service trains, which can be more cost-effective than conventional inspection methods. Axle-box accelerometers measure the vibration induced by roughness, rather than the roughness itself, and hence require signal processing techniques to translate this vibration into suitable metrics of the railhead condition, such as a wavelength spectrum of roughness. This paper presents progress towards a new stochastic frequency-domain inverse method that derives wavelength-spectra of rail roughness from axle-box acceleration measurements. This method compensates for the effects of vehicle speed and track dynamic behaviour on axle-box acceleration, which have adversely affected previous methods that, for example, rely on calibration on a reference section of track or simply take the RMS of the axle-box acceleration. The practical implications of processing and presenting measurements in the frequency domain are discussed, including the effect of varying vehicle speed and the trade-off between resolution and statistical accuracy. An initial algorithm is proposed and demonstrated through timedomain simulations of a theoretical vehicle-track model. Accurate derivation of roughness from axle-box acceleration will facilitate future development of autonomous monitoring systems fitted to in-service trains that continuously 'map' the condition of a rail network in real time, enabling more efficient and proactive scheduling of rail maintenance

Nondestructive Testing of Nonmetallic Pipelines Using Microwave Reflectometry on an In-Line Inspection Robot

Microwave and millimeter-wave reflectometry, a form of continuous-wave surface penetrating radar, is an emerging nondestructive inspection technique that is suitable for nonmetallic pipelines. This paper shows a K -band microwave reflectometry instrument implemented onto an in-line pipe-crawling robot, which raster-scanned cracks and external wall loss on a high-density polyethylene (HDPE) pipe of diameter 150 mm and wall thickness 9.8 mm. The pipe was scanned with three environments surrounding the pipe that approximated the use cases of overground HDPE pipelines, plastic-lined metal pipes, and undersea HDPE pipelines. The instrument was most sensitive when cracks were oriented parallel to its magnetic (H) plane. Any small variation in the standoff distance between the instrument's probe antenna and the pipe wall, which was not easy to avoid, was found to obscure the image. To mitigate this problem, a sensitivity analysis showed that an optimal frequency can be chosen at which standoff distance can vary by up to ±0.75 mm within a certain range without distorting the indications of defects on the image