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Hydrogen at RGU: expertise, projects and facilities.
Get PDFIn this presentation Professor Nadimul Faisal highlights some of the hydrogen research projects being undertaken at Robert Gordon University to support the development of renewable hydrogen technologies needed to support the Scottish Government's ambition of 5GW installed hydrogen production capacity by 2030. The testing facility, Hy-One, which will be established at the NSC, is a comprehensive one-stop hydrogen storage testing facility, providing plug-and-play testing and demonstrations for hydrogen storage systems and prototypes. Scalable metamaterial thermally sprayed catalyst coatings for nuclear reactor based high temperature solid oxide water electrolysis (METASIS), aims to design, fabricate, and test thermally sprayed novel large-scale and large-length scale meta-surface area coatings for anode supported solid oxide water electrolysis (SOWE) cell in the steam electrolysis mode for hydrogen production over a temperature range of 800 °C to 900 °C. Thermally sprayed coatings for thermochemical electrolysis at nuclear reactors (THERMOSIS) project aims to develop solution for zero emission hydrogen production by designing, fabricating, and testing thermally sprayed novel large metasurface area coatings for anode supported solid oxide steam electrolysis (SOSE). This will be an innovative electrolyser catalyst and cell design that will warrant efficient hydrogen production with stable structure for high temperature operation at nuclear reactor. H2Gen Hydrogen Fuel Cell UPS (EETF) is to develop a novel, scalable Hydrogen-cell based modular Energy Storage System (H2GEN) to eliminate the usage of Lead-Acid battery in UPS in public buildings, mitigated power supply interruptions, and provide storage for excess renewable generated energy to be used or sold to grid during peak demand
Data systems education: curriculum recommendations, course syllabi, and industry needs.
Get PDFData systems have been an important part of computing curricula for decades, and an integral part of data-focused industry roles such as software developers, data engineers, and data scientists. However, the field of data systems encompasses a large number of topics ranging from data manipulation and database distribution to creating data pipelines and data analytics solutions. Due to the slow nature of curriculum development, it remains unclear (i) which data systems topics are recommended across diverse higher education curriculum guidelines, (ii) which topics are taught in higher education data systems courses, and (iii) which data systems topics are actually valued in data-focused industry roles. In this study, we analyzed computing curriculum guidelines, course contents, and industry needs regarding data systems to uncover discrepancies between them. Our results show, for example, that topics such as data visualization, data warehousing, and semi-structured data models are valued in industry, yet seldom taught in courses. This work allows professionals to further align curriculum guidelines, higher education, and data systems industry to better prepare students for their working life by focusing on relevant skills in data systems education
Plastic waste over the ocean: an approach to surface recognition.
Get PDFTo address the challenges of low detection accuracy and slow recognition of floating objects on water surfaces, an enhanced YOLOv5 algorithm has been developed. This improved algorithm incorporates a coordinate attention mechanism to enhance spatial recognition and employs the GIOU loss function with updated anchor matching for quicker decision-making. The new method achieved a 97.39% mAP, marking a 15.28% improvement over the original YOLOv5, demonstrating its effectiveness
Microplastics in agricultural soils following sewage sludge applications: evidence from a 25-year study.
Get PDFSewage sludges applied to agricultural soils are sources of microplastic pollution, however, little is known about the accumulation, persistence, or degradation of these microplastics over time. This is the first study to provide long-term, high temporal resolution quantitative evidence of microplastics in agricultural soils following sewage sludge application. The abundance and degradation of microplastics was assessed in soils sampled biennially from an experimental field over a 25-year period managed under an improved grassland regime following the application of five different sewage sludges. The sludges contained different microplastic compositions reflecting the different sources of the sludges. Microplastic abundance increased by 723–1445% following sewage sludge applications (p 0.05). All sludges predominantly added white/transparent microfibres to soil. Microfilms, microfibres, and fragments were most susceptible to degradation, potentially creating micro(nano)plastics. Of note was the discoloration of coloured microfibres, which may be environmentally hazardous due to the toxicity of textile dyes in soil ecosystems. We also found that plastic composition could be used to trace its source. This evidence is useful in informing regulation on sewage sludge use and management, and in assessing the fate and impact of microplastics in soil
Development and pilot study of a university research culture questionnaire.
Get PDFA positive working environment and culture are essential for researchers as these enable them to conduct valuable, high-quality research. Yet, university staff frequently report their research culture as less than ideal. To understand researchers' experiences of research culture to inform tangible change, several surveys have been conducted by research-related organisations and individual universities. However, despite a plethora of studies, there does not appear to be a widely adopted research culture questionnaire, with variation in content and length in those used to date. A 37-item research culture questionnaire was developed based on the extant literatures. It was piloted in one small-medium sized university with 177 academic staff across a range of disciplines engaged in research. Qualitative questions were included to provide a richer insight into current research perceptions. Exploratory factor analysis identified eight factors, providing an initial framework of research culture. This consisted of: School Research Value, University Research Value, Research Support, Research Knowledge, Collaboration, Wellbeing & Inclusivity, Open Research and Research Integrity. Whilst it will require further testing and refinement, a preliminary psychometric analysis provides initial indications of internal structure and internal reliability. The factor set provides insight into research culture drivers which can be used to target effective interventions. This type of research culture questionnaire would allow universities to not only assess their own culture but also benchmark their results against other universities. A standardised research culture measurement process (e.g., questionnaires, narratives), feeding into research evaluation activities, may have wider implications for those looking to facilitate research culture changes
Artificial intelligence-based conversational agents used for sustainable fashion: systematic literature review.
Get PDFIn the past five years, the textile industry has undergone significant transformations in response to evolving fashion trends and increased consumer garment turnover. To address the environmental impacts of fast fashion, the industry is embracing artificial intelligence (AI) and immersive technologies, particularly leveraging conversational agents as personalised guides for sustainable fashion practices. In this research paper, we conduct a systematic literature review to categorise techniques, platforms, and applications of conversational agents in promoting sustainability within the fashion industry. Additionally, the review aims to scrutinise the solutions offered, identify gaps in the existing literature, and provide insights into the effectiveness and limitations of these conversational agents. Utilising a predefined search strategy on IEEE Xplore, Google Scholar, SCOPUS, and Web of Science, 15 relevant articles were selected through a step-by-step procedure based on the guidelines of the PRISMA framework. The findings reveal a notable global interest in AI-powered conversational agents, with Italy emerging as a significant centre for research in this domain. The studies predominantly focus on consumer perceptions and intentions regarding the adoption of AI technologies, indicating a broader curiosity about how individuals incorporate such innovations into their daily lives. Moreover, a substantial proportion of the studies employs diverse methods, reflecting a comprehensive approach to understanding the functionality and performance of conversational agents in various contexts. While acknowledging the historical precedence of text-based agents, the review highlights a research gap related to embodied agents. The conclusion emphasises the need for continued exploration, particularly in understanding the broader impact of these technologies on creating sustainable and environmentally-friendly business models in the e-retail sector
Deep learning framework designed for high-performance lithium-ion batteries state monitoring.
No full textAccurate state of charge (SOC) estimation is crucial for ensuring the safety of batteries, especially in real-time battery management system (BMS) applications. Deep learning methods have become increasingly popular, driving significant advancements in battery research across various fields. However, their accuracy is limited due to the nonlinear adverse driving conditions batteries experience during operation and an over-reliance on raw battery information. In this work, a deep-stacked denoising autoencoder is established for a long short-term memory model that incorporates a transfer learning mechanism to estimate and study the SOC from an electrochemical perspective. More importantly, this proposed model is designed to extract and optimize the electrochemical features from the training data on a secondary scale, improving noise reduction and the precision of initial weights. This adaptation allows for accurate SOC estimation of batteries while minimizing interference and divergence. For large-scale applicability, the proposed model is tested with high-performance lithium-ion batteries featuring different morphologies under a range of complex loads and driving conditions. The experimental results highlight the distinct behaviors of the tested batteries. Moreover, the performance of the proposed model demonstrates its effectiveness and outperforms existing models, achieving a mean absolute error of 0.04721% and a coefficient of determination of 98.99%, facilitating more precise state monitoring of batteries through secondary feature extraction
Improved adaptive fusion parameter identification and chaotic gravitational search-Kalman particle filtering method for state-of-energy accurate estimation of lithium-ion batteries.
No full textState-of-energy (SOE) is an important parameter in the battery management system, which determines the current maximum possible range of electric vehicles. In this study, an improved chaotic gravitational search-Kalman particle filtering method for SOE estimation of lithium-ion batteries based on adaptive fusion dual-factor parameter identification is proposed. Firstly, the adaptive forgetting factor-limited memory recursive extended least squares algorithm is designed by integrating the forgetting factor and the memory length factor to improve the accuracy and generalization ability of online parameter identification. Secondly, to address the problem of particle degradation and loss of diversity, this study introduces the square root cubature Kalman filtering and the chaotic gravitational search algorithm to improve the accuracy and stability of particle filtering. Finally, a chaotic gravitational search-square root cubature Kalman particle filtering model is constructed to effectively improve the estimation performance of SOE. The experimental results under complex working conditions show that the mean absolute error of the parameter identification method proposed in this study is between 0.56 % and 0.68 %, and the root mean square error of the proposed estimation method for SOE remains between 1.04 % and 1.17 %, indicating that the method proposed in this study has high robustness and accuracy
The translucence of transparency: extractive industry beneficial ownership disclosure as an emerging transparency regime.
Get PDFThis study explores the nature and limits of transparency in the context of the Extractive Industry Transparency Initiative's beneficial ownership regime. To do this, we draw on Ball's (2009) three transparency metaphors – public value or norm of behaviour, openness, and complexity – to frame our study and conceptualise transparency as an ambiguous and ambivalent concept connoting light and darkness, clarity and opacity. Empirically, we draw on diverse country-level data (supplemented by company-level data to highlight exemplars) from the period between 2013 and 2021. Our findings show how the beneficial ownership regime's intersection with the wider political culture provides a space wherein the nature of transparency and the resultant visibilities and invisibilities are negotiated and contested and eventually compromised. We conceptualise this space as a zone of in-betweenness, or translucence, and represent it as an opacity–transparency continuum. As such, what is revealed is the social construction of translucence – a state in which there is neither full transparency nor complete opaqueness but, rather, something in-between. Our findings also highlight how resistance – in both subtle and confrontational forms –influences placement within this zone of in-betweenness (translucence)
GLVMamba: a global-local visual state space model for remote sensing image segmentation.
Get PDFSemantic segmentation of remote sensing images has significant advances with the adoption of deep neural networks, taking the advantages of Convolutional Neural Networks (CNNs) in local feature extraction with Transformers in global information modeling. However, due to the limitations of CNNs in long-range modeling capabilities and the computational complexity constraints of Transformers, remote sensing semantic segmentation still faces issues such as serious holes, rough edge segmentation, false and even missed detections caused by the light, shadow and other factors. To address these issues, we propose a visual state space model called GLVMamba, which employs CNNs as the encoder and the proposed Global-Local Visual State Space (GLVSS) block as the core decoder. Specifically, the GLVSS block introduces locality forward feedback and shift window mechanism to addresses the deficiency of insufficient modeling of neighboring pixel dependencies of Mamba, which enhances the integration of global and local context during feature reconstruction, boosts object perception capabilities of the model, and effectively refines edge contours. Additionally, the scale-aware pyramid pooling (SCPP) module is proposed to fully merge the features from various scales and adaptively fuse and extract the distinguishing features to mitigate the holes and false detections. The GLVMamba effectively captures global-local semantic information and multi-scale feature through the GLVSS block and the SCPP module, achieving efficient and accurate remote sensing semantic segmentation. Extensive experiments on two widely used datasets have effectively demonstrated the superiority of our proposed method over the other state-of-the-art methods. The code will be available at https://github.com/Tokisakiwlp/GLVMamba
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