Procter & Gamble (United Kingdom)
Open Access Institutional Repository at Robert Gordon UniversityNot a member yet
9563 research outputs found
Sort by
An exploration into student pharmacists' experiences of practice-based interprofessional education during experiential learning placements.
Get PDFThis study aimed to explore student pharmacists' experiences of interprofessional education (IPE) during experiential learning (EL) placements. A paper questionnaire was used to collect data; distributed to all penultimate/final year student pharmacists enrolled on the Master of Pharmacy (MPharm) programs at Robert Gordon University (RGU) or the University of Strathclyde (UoS) (n=485). Data collection took place between January-March 2023, shortly after student pharmacists attended EL placements in various practice settings. Participation in the research was voluntary; questionnaires were completed anonymously. Thematic analysis was used to identify themes from responses to open-ended questions; aligning to the research aim. Ethical approval was granted by the RGU School Ethics Research Committee. The questionnaire was completed by 328 (67.6%) student pharmacists. Themes identified included (1) Nature of IPE experiences: mostly unplanned/informally planned, with few examples of formally planned IPE; opportunities varied across areas of practice; professional groups varied with medicine and nursing most represented.(2) Factors influencing interprofessional learning: related to EL facilitator (preceptor), student pharmacist, placement and cultural factors. Facilitators included prioritization of IPE/positive role modeling by mentors; barriers included student pharmacists' perceived lack of preparedness for IPE, lack of specific IPE learning outcomes and sector specific limitations. (3) Student pharmacists' perceived value of IPE: experiences supported the development of collaborative competencies, personal, professional and interprofessional identity development. More focus on the relevance of IPE in the EL curriculum and wider MPharm curriculum could maximize learning from opportunistic IPE. The lack of formally planned IPE opportunities demands further attention
Improved dynamic discount function identification strategy for adaptive current transients and capturing complex carrier behavior inside lithium-ion batteries.
No full textIn the application of lithium-ion batteries, instantaneous changes in the magnitude and direction of the current cause distortion during online parameter identification, resulting in increased instability and errors. To address this problem, this paper proposes an automatic identification strategy for the parameter identification of discount functions. The specific idea is to adaptively adjust the dynamic discount factor based on the coupling relationship between the previous identification results and the error domain, enabling real-time adjustment of the forgetting factor for the next time step. Meanwhile, by introducing a parameter entropy function, the impact of dynamic behaviors like carrier migration on model parameter changes is quantitatively analyzed, revealing the relationship between model parameters and actual physical processes, thereby enhancing the physical significance of the model parameters. The experimental results show that the mean absolute error and the root mean square error of voltage tracking under three complex working conditions can be limited to 11.96 mV and 15.34 mV, respectively. Compared with the recursive least squares (RLS) and forgetting factor recursive least squares (FFRLS) algorithms, the proposed strategy reduces the entropy and entropy of the model parameters by up to 53.69% and 25.90%, respectively
Advanced modelling and analysis for quality assessment and enhancement of underwater multimodal imageries.
Get PDFUnderwater sensing plays a crucial role in environmental protection and sustainable energy transitions, supporting marine ecosystem monitoring, resource management, and infrastructure development. However, visual perception in underwater environments is significantly restricted by light absorption and scattering, leading to colour distortion, contrast loss, and reduced visibility. Low-light conditions and turbidity further degrade image quality, limiting the acquisition of high-quality data. To address these challenges, multimodal sensing, which integrates optical and acoustic imaging, has been widely adopted. Despite its advantages, modality-specific degradations persist, resulting in low-quality data. This thesis introduces novel image quality assessment and enhancement techniques tailored for multimodal underwater imagery, improving perception in optical, Sound Navigation and Ranging (SONAR), and stereo vision sensors to support decision-making for both remotely operated and autonomous systems. A novel method is presented for evaluating underwater optical images by analysing edge structures, perceptual features, and colour dispersion. A directional Kirsch kernel-based approach captures scattering-induced degradation, while contour and saliency maps enhance object boundary emphasis. Channel-wise dispersion rates quantify colour distortion, and saturation and hue metrics measure colour purity and distinguishability, improving correlation with human subjective scores by 10% over state-of-the-art techniques. Additionally, a SONAR image quality assessment method is introduced that employs wavelet domain analysis to separate low-frequency noise from high-frequency object details for quantifying perceptual and utility quality, respectively. By extracting micro and macro-scale texture and contour features from decomposed components, this approach measures object visibility and identifiability, achieving an 11% higher prediction accuracy than existing methods. To mitigate proportional degradation and non-uniform colour casts in optical images, a deep neural network is designed that integrates inception modules and channel-wise attention mechanisms. Multi-scale feature extraction and channel-specific attention allow for improved assessment of colour loss and distribution, enhancing colour restoration by 2% in terms of structural similarity quality compared to prior methods. An integrated framework is further developed for automatic underwater object identification and distance measurement by refining stereo vision-based depth images and extracting object information from SONAR data. The fusion of stereo vision and SONAR data enables precise depth and range estimation, facilitating robotic manipulation and achieving an error rate of less than 1 centimetre. Extensive validation on benchmark datasets and in-lab experiments demonstrates significant improvements over existing techniques. These contributions enhance under-water inspection, condition monitoring, and maintenance by improving real-time positioning and sensing reliability. By advancing multimodal sensing technologies, this thesis strengthens underwater image analysis, enabling more effective robotic applications that support environmental sustainability and energy infrastructure management. To facilitate further research, source codes and collected data are publicly available at https://github.com/hfarhaditolie
Semirenewable polyamides containing disulfide bonds: synthesis, degradation, self-healing, and triboelectric properties.
Get PDFWe report here the synthesis, degradation, and properties of polyamides containing disulfide bonds. The polyamides have been prepared using a two-step melt polycondensation process from 4,4'-dithiodibutyric acid and bioderived Priamine. The degradation of these polymers has been investigated using a combination of tools, such as visible light photocatalysis, and UV-mediated degradation. The chemical, physical, and mechanical properties of these polymers were also studied. The disulfide-containing polymers exhibit elastomeric and self-healing properties while showing high thermal stability. Furthermore, the novel application of these unique tribopositive polymers as self-repairable triboelectric nanogenerators for energy harvesting has also been demonstrated
Health and wellbeing lens of the national existing building database in Scotland.
Get PDFReliability of data is one of the most important factors determining cost and successful project planning in the social and public sector. Understanding condition, construction methods, trigger points and contextual environmental, climatic and deprivation data can also profoundly impact on the risk mitigation and evaluation of whole building approaches to retrofit. This paper explores the value of contextual information and outputs beyond carbon reduction driven fabric improvements alone. It evaluates what a National Existing Building Database (NEBD) could offer for the social sustainability outcomes, supporting holistic approaches to neighbourhood scale retrofit and promoting cross-sectoral collaboration. The implications of novel approach to data collection and management could include evidence-based policy development, informed mitigation of risks, scalability of approaches stimulating local networks, skill and supply chains for a socially sensitive delivery of retrofit at scale. The study focused on the social housing sector in Scotland, which has clear guidelines on implementation of energy efficiency measures aligned with forthcoming net-zero legislative changes. The data was collected through a series of 3 interactive stakeholder workshops, with participants directly and indirectly involved in retrofit, upgrading and management of existing social housing assets in Scotland. Questions in discussions varied from current practice and reliability of data, to what the database capabilities could mean in a cross-sectoral approach, identifying health and wellbeing as one of the key outcomes. The quantitative questionnaire (n59) was the result of the discussions, recommendations and recognised limitations of a database. The questionnaire was distributed after two workshops, where the last interactive workshop offered an opportunity to review the results and discuss further recommendations for next phases of the project beyond feasibility
Liquid crystal trimers containing tertiary benzanilide groups.
Get PDFThe rational design of new liquid crystal materials relies on an understanding of the relationship between molecular structure and the formation of liquid crystalline phases. The development of new materials can benefit from the use of a wide range of functional groups, but some groups prove challenging to combine with liquid crystallinity. Tertiary benzanilide groups are a clear example of this, as their strong conformational preferences disrupt liquid crystallinity when included in typical liquid crystalline structures. This means that it has not been possible to harness the molecular design possibilities offered by amide N-substitution. However, designing flexible structures to accommodate the conformation of tertiary benzanilides has allowed us to synthesise a variety of liquid crystal trimers forming nematic and smectic phases, and investigate the effect of lateral and N-substitution on their phase behaviour. Trimers with large (benzyl and decyl) N-substituents favour the formation of an orthogonal smectic (SmA) phase, and, unusually, exhibit pronounced negative thermal expansion
Neuroinclusive assessment: mental health and wellbeing perspective.
No full textThis panel presentation is based on the author's PhD research, looking at the mental health and wellbeing of engineering students. The talk focuses on this from a stress and anxiety perspective, with particular reference to neurodivergent students
Resolving enzyme–substrate–activator ambiguity: a minimalist enzyme kinetic framework for PROTAC design and optimization.
No full textPROteolysis TArgeting Chimera (PROTAC) are heterobifunctional small molecules that represent a new modality of therapy by degrading endogenous proteins. PROTACs possess two warheads: one for an E3 ligase and another one for the protein of interest, tethered by a linker. These are large molecules that break the Lipinski’s rule of five and need substantial optimization for uptake. Once the PROTAC localizes to the site of action, it binds to the specific E3 ligase (CRBN or VHL have been exploited extensively) and protein of interest (POI) and brings them into closer proximity to facilitate the ubiquitination of the POI. The binding of the PROTAC with the respective proteins E3 and POI to form the ternary complex can either follow an ordered mechanism (with preference for binding to the E3 ligase followed by the POI or vice versa depending on the extent of positive cooperativity) or it could be random (in the absence of any cooperativity). The ternary complex recruits a ubiquitin charged E2 ubiquitin conjugating enzyme that transfers the ubiquitin onto the POI. This initial ubiquitination leads to subsequent chain elongation and priming the protein for degradation by the proteasomal system (Figure 1). PROTACs are extensively implemented against target proteins in cancer but investigations are underway to explore treatment for other conditions such as autoimmune diseases, neurological disorders, and infections
Análisis de proyectos eléctricos a partir de diagramas unifilares mediante IA generativa.
Get PDFThis communication presents a methodology for the automatic generation of electrical projects using generative artificial intelligence, based on single-line diagrams. The proposed approach aims to optimize the process of developing technical documentation in electrical projects, significantly reducing the time and resources required, while ensuring compliance with current regulations. The developed system employs advanced image processing and deep learning techniques to interpret single-line diagrams, extracting crucial information about the topology and components of the electrical installation. In this first stage, the topology is extracted and interpreted, associating characteristics such as power, etc., to each element or line. This allows for calculations to be performed and, ultimately, through natural language models specifically trained in electrical regulations and project standards, to automatically generate the justifying calculations. Results from the system's implementation in real case studies are presented, demonstrating its effectiveness in terms of accuracy, coherence, and compliance with legislation. Furthermore, the implications of this technology for engineering firms are discussed, highlighting its potential to improve efficiency and quality in project development
Gibb's knitwear in the 1970s: collaboration, innovation and "slow" fashion.
No full textGibb's knitwear continues to be recognized as groundbreaking where his collaborations with Kaffe Fassett, Mildred Boulton and Gould's of Leicester produced innovations in techniques, patterns, and garment shapes that helped to pave the way for future generations of designers specializing in knit. Today's interest in knitting undoubtedly stems from pioneering designers in the 1970s who repositioned knitwear as contemporary design, making it fashionable for a new generation of consumers. The chapter considers the values that Gibb's knitwear demonstrated which resonated during the 1970s around environmental and sustainable production methods celebrating craft heritage and traditional artisan skills that are now often referred today as 'slow' design in opposition to mass-production. Reflecting on Gibb's knitwear and how it led to innovations the chapter discusses the ways in which his work can be viewed now, towards a more relevant model for fashion knitwear design in the 21st Century
Data sources:
Access Repository Dashboard
Do you manage Open Access Institutional Repository at Robert Gordon University? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!