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Implementing AI Turnitin detection.
Get PDFThe launch of ChatGPT 3 in November 2022 was a pivotal moment that pushed Generative AI to the top of many institutional agendas. Like other universities in the sector, Robert Gordon University (RGU) was faced with the challenge of how to support staff and students to embrace Generative AI while also ensuring the maintenance of academic standards. When Turnitin launched its AI writing detection tool in April 2023, the institution had to decide whether to switch on this functionality as part of its approach to promoting academic integrity in the context of AI. Despite some concerns, RGU took the decision to switch on Turnitin AI Detection; the functionality was switched on in October 2023. In this case study, the author reflects on the context of this decision, actions taken and what further action still needs to occur
Data scarcity: a challenge for enhanced drilling optimisation.
Get PDFData-driven strategies and machine learning algorithms are being increasingly employed in the oil and gas industry to optimise drilling operations, reduce nonproductive time (NPT), and lower operational costs. However, data scarcity is a significant obstacle that affects many aspects of drilling operations and limits the efficacy of these sophisticated optimisation techniques. This presentation examines the ways in which restricted data sharing practices, inconsistent data quality, incomplete sensor coverage, and small datasets hinder the development and application of reliable drilling optimisation models. Critical applications such as real-time drilling parameter optimisation, drilling equipment predictive maintenance, formation evaluation, and wellbore stability prediction are especially impacted by data scarcity. Drilling environments can be heterogeneous, making it difficult for models trained on data from one geological setting or rig configuration to generalise to other contexts. Moreover, industry competition and proprietary concerns result in fragmented data repositories, which hinder the sharing of datasets necessary for training advanced machine learning models. In addition to evaluating existing mitigation strategies, such as synthetic data generation, transfer learning, and physics-informed machine learning approaches, this study examines the technical, organisational, and financial barriers that contribute to data scarcity in drilling operations. It also suggests a framework for resolving data limitations while improving drilling optimisation capabilities. For drilling operations to fully benefit from digital transformation, it is imperative to comprehend and overcome data scarcity
Enhanced quantile regression long short-term memory hybrid neural network for the state of charge point and interval estimation of lithium-ion batteries.
No full textThe state of charge (SOC) estimation accuracy of lithium-ion batteries directly affects the reliability and management efficiency of clean energy storage systems. However, due to the nonlinear characteristics of batteries and complex working conditions, there are still significant challenges in high-precision SOC estimation. Therefore, this paper proposes a hybrid neural network model based on long short-term memory (LSTM). Specifically, the model extracts multidimensional features through two-dimensional convolution and LSTM neural network with attention mechanism is performed for estimation. In addition, the quantile regression loss function is used in the training of the hybrid neural network to give it confidence interval estimation capability. Finally, the experimental data of different working conditions at multiple temperatures were utilized to validate and analyze the proposed method. The results show that the proposed estimation method has an MAE less than 0.58%, an MSE less than 0.008%, an RMSE less than 0.81%, an R2greater than 99.91%, and a stable confidence interval estimation capability. In summary, this paper innovatively proposes an effective SOC estimation solution, which provides new ideas for future SOC estimation of energy storage battery management systems, and has important theoretical and practical application significance
Comparative evaluation of the performance properties of resin-infused plain-woven jute reinforced thermoset composites for energy infrastructure: experimental analysis, finite element modelling and statistical validation.
Get PDFThis study presents a comprehensive experimental and numerical evaluation of resin-infused plain-woven jute fiber composites reinforced with thermoset epoxy and polyester matrices in 2-, 4-, and 6-ply configurations, produced via vacuum-assisted resin infusion. The aim is to assess the influence of matrix type and ply architecture on the mechanical, thermal, and microstructural behavior of sustainable composites for renewable energy infrastructure. Mechanical characterization involved tensile, flexural, impact, and hardness tests, while thermal and microstructural properties were evaluated using thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). Finite Element Analysis (FEA) was used to simulate stress distribution, and Analysis of Variance (ANOVA) determined the statistical significance of ply count and matrix effects. The 6-ply epoxy composite exhibited the highest structural performance, achieving tensile and flexural strengths of 76.85 MPa and 90.48 MPa with improvements of 19.3 % and 31.8 % over polyester counterparts. Although polyester-based composites exhibited lower strength, they showed higher impact resistance (1.15 J, +33.9 %). Peak hardness (114.4 HRB) was recorded in 4-ply epoxy laminates, and density increased with ply count, with polyester showing slightly higher values. TGA confirmed enhanced thermal stability in epoxy systems, with onset degradation at 341.5 °C versus 304.3 °C in polyester. SEM revealed superior fiber–matrix bonding and fewer voids in epoxy composites. FEA predictions were within 5 % of experimental results, and ANOVA confirmed statistically significant effects (p ≤ 0.05) of matrix and ply count. These findings position 6-ply epoxy laminates as promising candidates for structural applications in renewable energy systems
An experimental test of cyanotoxins as a potential driver of microbial community structure.
Get PDFCyanobacterial harmful algal blooms (CyanoHABs) are common biological disturbances in freshwater ecosystems, impacting microbial community diversity and composition. While extensive research has focused on these blooms, the direct effects of cyanotoxins on microbial communities remain less understood. In this study, we investigated the impact of various cyanotoxins on the microbial community of an oligotrophic lake in Quebec, Canada (45.99°N, 74.00°W). Water samples were exposed to different concentrations of MC-LR, MC-RR, MC-LF, and CYN, both individually and in combination. These toxins were selected based on their prevalence, toxicity, and distinct chemical properties. Toxin concentrations were chosen in relation to the World Health Organization (WHO) regulatory thresholds, 1 μg/L as indicative of low toxin exposure (drinking water limit) and 1000 μg/L as indicative of high exposure (lake threshold). We performed a longitudinal analysis of 16S rRNA to assess changes in microbial community diversity and composition at 24-h, 48-h, and 72-h intervals. Our findings showed a significant change in alpha and beta diversity, highlighting shifts in community structure in response to high cyanotoxin doses. Conversely, no significant changes were detected across diverse cyanotoxin compositions. We then performed a differential analysis and identified several amplicon sequence variants (ASVs) with significant changes in relative abundance across cyanotoxin doses. This analysis highlighted potential cyanotoxins degrading bacteria, such as Paucibacter and Ideonella. Overall, our results showed that the changes were more associated with cyanotoxin doses than with composition. Understanding how cyanotoxins could impact oligotrophic lakes is essential for better predicting their ecological impacts, especially as these lakes are increasingly affected by cyanobacterial blooms
Embedding circularity assessments in building projects' frontend decision-making: presenting a compelling behaviour change case.
Get PDFThe circular economy (CE) paradigm has helped the building sector reduce its environmental impacts. However, existing circularity assessment (CA) frameworks have not guided practitioners in making circular decisions at the project frontend. This paper develops and validates a CA framework to inform circular decision-making (DM) at an early stage in building projects. The proposed CA framework, comprising 12 circularity indicators (CIs), was calibrated using the analytic hierarchy process. A state-of-the-art case study, involving a cutting-edge engineering building at the forefront of the sustainability design stage, was undertaken to validate the proposed model and identify potential challenges through the lens of the theory of planned behaviour (TPB). (1) Current building sustainability assessments lack clear conceptual contours between different pathways to sustainability due to a focus on carbon/energy instead of materials flows; (2) the fragmentation of project roles resulted in a lack of collaborative effort in CA, with designers primarily driven by clients' requirements and often emphasising traditional sustainability metrics over circularity benefits; and (3) the transition to CE in the built environment has been partial and completing the transition involves a behaviour change case including all stakeholders. This study contributes to the current body of knowledge by revealing behavioural challenges related to CA within the field of circular building design. It supports building designers to embed CA in building projects’ front-end DM. It also refocuses policymakers' attention to embodied carbon, circular public procurement and economic incentives as levers for driving CA implementation. The validation of a novel set of CIs using a cutting-edge building project case study offers unique insights, underpinned by TPB, into behavioural challenges, relevant to incorporating circularity into frontend DM processes
Review of Falk Hübner: method, methodology and research design in artistic research.
Get PDFThe language of artistic research can sometimes feel slippery to grasp, given the ongoing generation of epistemologies, methods, metaphors, forms and methodologies operating within and across art practice and academic work. Readers of JAR may identify a kind of consolidation emerging, especially in terms of methodological and ethical issues, yet at the same time understand how artistic research is marked more by multiplicity and friction than consensus and clarity. Falk Hübner’s book offers an interesting map and model of this slippery terrain, especially if you agree with a key premise of the book: that the field of artistic research sits uneasily between methodological rigour and intuitive 'emergence'. For Hübner, emergence and unpredictability are key elements of artistic inquiry and, in short, the aim of this book is to facilitate this
The optoelectric tunability effect of structurally patterned Fe 3 O 4 -Au assembly on Rhodamine 6G signals under Magneto-SERS measurements.
Get PDFThe magneto-plasmonic tunability property of magnetite-gold complex (Fe3O4-Au) colloids has garnered significant interest in bio-sensory applications like surface-enhanced Raman spectroscopy (SERS). In many studies, this tunability does not only depend on the external magnetic field contribution but also on the concentration ratio between Fe3O4 and Au. This would require multiple preparation of Fe3O4-Au colloidal badges. In this study, a magnetically stimulated Fe3O4-Au colloidal suspension in polyvinyl alcohol was spin-coated, forming a micro-patterned thin film on a silicon wafer substrate for assessing the SERS vibrational signal response of Rhodamine 6G (R6G). The varying concentration ratio between Fe3O4 and Au across three regions of interest within the single cast resulted in differing optoelectronic behaviour. Such was observed from diffuse reflectance UV-Vis-NIR spectroscopy measurements, and its impact on different Raman signals of R6G. The introduction of an external magnetic field also led to approximately 133% higher peak intensity and improved spectral resolution of R6G under SERS measurements. This implies that the magnetic field polarization of Fe3O4 domain electrons influences plasmon electrons of Au nanoparticles leading to tuning of electronic-influenced vibrations of nearby analyte molecules. The variability of nanoparticle concentration ratios, configurations and the magneto-optoelectronic effect of this design template, provide flexibility and tunability in diagnosing biomolecule signals under SERS
Retrofitting homes for gender equity and wellbeing: a salutogenic perspective.
Get PDFThe relationship between the built environment and human health is increasingly recognised; however, retrofit practice continues to address health primarily through a pathogenic lens, focusing on risks associated with substandard housing conditions. Current retrofit standards prioritise technical performance, often overlooking the unintended social consequences – particularly, the potential to exacerbate existing health inequalities. Furthermore, retrofit policy and delivery remain predominantly carbon-driven, with insufficient attention to how interventions might foster resilience, social cohesion, and wellbeing at the neighbourhood scale. Psycho-social determinants of health, which shape individual's ability to interpret and cope with their environments, remain underexplored in retrofit assessment and practice. A salutogenic approach to environmental design, grounded in Antonovsky's concept of health promotion, offers a promising alternative – focusing on long-term health and wellbeing by supporting Sense of Coherence (SOC): meaningfulness, manageability, and comprehensibility in people’s everyday settings. This paper draws attention to gender-specific vulnerabilities, particularly those experienced by women due to socio-economic inequalities as highlighted by UN Women. It argues that these inequalities influence women's priorities in the home, especially regarding safety, autonomy, and control. By employing a phenomenological evaluation of lived experience within a small sample group, the study explores how motivations for retrofit are shaped by these gendered needs. It advocates for neighbourhood scale, place-based retrofit strategies that incorporate a human centred, salutogenic framework, recognising wellbeing, inclusion, and life course health as essential to sustainable and equitable retrofit outcomes
Threat detection in smart homes: a sociotechnical multimodal conversational approach for improved cyber situational awareness.
Get PDFSmart homes are becoming increasingly more complex and difficult to defend. Expanding Internet of Things (IoT) devices has reshaped socio-technical interactions within smart homes, yet security remains a secondary concern. In addition, users have been shown to lack awareness of potential vulnerabilities, leaving smart homes susceptible to attacks. This paper explores how humans can interact with conversational agents (CAs) to improve their awareness and detection of threats in smart homes. Utilising Endsley's situational awareness model, this research examines how users perceive, comprehend, and project knowledge of their surroundings to identify security threats when interacting with CAs through a multimodal framework. A mixed-methods study combining quantitative pre-test/post-test analysis with qualitative evaluations revealed that CAs significantly enhanced threat detection accuracy, efficiency, and user confidence across all dimensions of situational awareness when using a multi-modal approach
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