Environmental impact assessment of wheat straw based alkyl polyglucosides produced using novel chemical approaches

This paper evaluates and quantifies the environmental performance of alkyl polyglucosides sourced from wheat straw (WS-APG), a low-cost and low-ecological impact agricultural residue, compared to that of their commercial counterpart, which is sourced from palm kernel oil and wheat grain (PW-APG). Escalating pressure to consider the environmental sustainability of fossil derived surfactant consumption has driven biosurfactants to become the product of choice within the surfactant market, and a class of ‘plant’ based non-ionic surfactants called alkyl polyglucosides (APG) are particularly prevalent. However, the existing food based feedstock of APG such as coconut oil, palm oil, wheat and corn (in addition to being expensive) will potentially undermine the claimed ‘sustainability’ of the APG products (i.e. the ‘food vs. chemical’ issue). Here, we present the “cradle-to-gate” life cycle impact assessment of a suggested alternative, hybridised APG synthesis technique where the Fisher glycosidation method is supplemented by novel, green chemistry based techniques. This evaluation provides a quantitative insight into direct GHG intensity and other ecological impact indicators, including land use, waste generation and energy consumption. Upon evaluation, the wheat straw-derived pathway delivered GHG-emission savings in the range of 84–98%, compared to that of the palm kernel–wheat grain pathway. Waste generated from the production of unit mass of the product amounted to 0.43 kg and 10.73 kg per kg of WS-APG and PW-APG, respectively. In addition to the above mentioned facts, the ‘cradle–gate’ stages of WS-APG production were also found to consume relatively lower amounts of water and fossil-derived energy. In conclusion, of the two APG production pathways, the suggested ‘hybrid’ pathway using an agricultural residue, wheat straw, was found to be sustainable and to demonstrate better environmental performance

Practicing stewardship: EU biofuels policy and certification in the UK and Guatemala

Biofuels have transitioned from a technology expected to deliver numerous benefits to a highly contested socio-technical solution. Initial hopes about their potential to mitigate climate change and to deliver energy security benefits and rural development, particularly in the Global South, have unravelled in the face of numerous controversies. In recognition of the negative externalities associated with biofuels, the European Union developed sustainability criteria which are enforced by certification schemes. This paper draws on the literature on stewardship to analyse the outcomes of these schemes in two countries: the UK and Guatemala. It explores two key issues: first, how has European Union biofuels policy shaped biofuel industries in the UK and Guatemala? And second, what are the implications for sustainable land stewardship? By drawing attention to the outcomes of European demand for biofuels, we raise questions about the ability of European policy to drive sustainable land practices in these two cases. The paper concludes that, rather than promoting stewardship, the current governance framework effectively rubberstamps existing agricultural systems and serves to further embed existing inequalities

A Deep Learning Based ECG Segmentation Tool for Detection of ECG Beat Parameters

The role of ECG segmentation tool has been pivotal in automated analysis of real-time ECG signals for detection of non-invasive cardiovascular and physiological conditions. Most of the existing approaches focus on traditional signal processing and/or traditional machine learning based approaches which are highly dependent on signal noise, inter/intra subject variability, etc. With the advent of deep learning based networks, it is possible to design and develop the classification model based on local features along with spatial and temporal context of the physiological signals. In this paper, we developed the attention based Convolutional Bidirectional Long Short Term Memory (Conv-BiLSTM) architecture network based on local beat features and temporal sequencing while correlating ECG beat across different positions. The performance of our ECG segmentation tool has been evaluated against the state-of-the art approaches in terms of ECG segmentation and fiducial point detection accuracy. The ECG segmentation accuracy was 95% whereas fiducial point detection accuracy was 99.4%