Standardisation is fundamental to ensuring that new technologies develop and grow unhindered by manufacturer-led standards. Dismissing this vital issue can have a detrimental effect on society regarding adopting new technologies, particularly when government targets and regulations are crucial for their success. We have witnessed competing global industries struggle for dominance, such as Betamax versus VHS, where each had a similar user outcome, but the confusion of differing formats slowed growth. We analyse emerging standards for electric vehicle rapid charging and investigate how standardisation challenges affect stakeholders by reviewing the existing literature on single-mode and polymodal harmonisation. By assimilating existing evidence, we then develop a new understanding of the science behind multi-model standardisation (MMS) approaches. Our literature review reveals three primary standardisation issues: (1) charge connections, (2) car to charger communication protocols, and (3) charge payment methods. We then analyse each mode type’s benefit, observing how each example contributes to the overall outcome, and suggest that their impact depends on car to charger handshake timing and intuitive user interaction. Using a structured survey of 282 respondents, we analyse end-user satisfaction for factors affecting growth in the EV sector and compare these findings with the factors identified during our literature review. We consequently articulate a programme for future research to understand EV rapid charger standardisation better, proposing recommendations for vested stakeholders that embrace sponsors in societal, technological and scientific transformation.

Home based Telehealth is a combination of communications, imaging, sensing and human computer interaction technologies targeted at diagnosis, treatment and monitoring patients without disturbing the quality of lifestyle. This paper proposes development of a low cost medical sensing, communication and analytics device that is real-time monitoring internet enabled patients physiological conditions. Internet of Things (IoT) network will provide active and real-time engagement of patient, hospitals, caretaker and doctors. Massaging and synchronising the system has been the based focus in this paper, where it applies the suggested algorithm to predict the minimum time period that separates two consecutive bursts of messages and measures the minimum queue sizes for the health care personals nods, to manage the traffic and avoid the dropping of messages. NS2 simulator was employed to simulate the Telehealth environment algorithm

No abstract

The traditional methods cannot be used to meet the requirements of rapid and objective detection of meat freshness. Electronic nose (E-Nose), computer vision (CV), and artificial tactile (AT) sensory technologies can be used to mimic humans’ compressive sensory functions of smell, look, and touch when making judgement of meat quality (freshness). Though individual E-Nose, CV, and AT sensory technologies have been used to detect the meat freshness, the detection results vary and are not reliable. In this paper, a new method has been proposed through the integration of E-Nose, CV, and AT sensory technologies for capturing comprehensive meat freshness parameters and the data fusion method for analysing the complicated data with different dimensions and units of six odour parameters of E-Nose, 9 colour parameters of CV, and 4 rubbery parameters of AT for effective meat freshness detection. The pork and chicken meats have been selected for a validation test. The total volatile base nitrogen (TVB-N) assays are used to define meat freshness as the standard criteria for validating the effectiveness of the proposed method. The principal component analysis (PCA) and support vector machine (SVM) are used as unsupervised and supervised pattern recognition methods to analyse the source data and the fusion data of the three instruments, respectively. The experimental and data analysis results show that compared to a single technology, the fusion of E-Nose, CV, and AT technologies significantly improves the detection performance of various freshness meat products. In addition, partial least squares (PLS) is used to construct TVB-N value prediction models, in which the fusion data is input. The root mean square error predictions (RMSEP) for the sample pork and chicken meats are 1.21 and 0.98, respectively, in which the coefficient of determination (R2) is 0.91 and 0.94. This means that the proposed method can be used to effectively detect meat freshness and the storage time (days).

The University of Gloucestershire accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement.