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Structural integrity and failure mechanisms of thermoplastic composite pipes for offshore applications: insights from compressive and flexural testing.
Get PDFThis research investigates the structural integrity and mechanical behaviour of a thermoplastic composite pipe (TCP) that is particularly used in the offshore energy industry. The TCP offers enhanced strength and high strength-to-weight ratio ideal for applications subject to varying loading conditions. Despite its structural benefits, the composite pipe is susceptible to delamination and other damage modes that compromise its performance. This study addresses the limited research on curved composite structures, especially in the context of debonding and stress distribution, by focusing on the behaviour of the TCP under compressive and flexural loading conditions. Non-destructive testing (NDT) methods, including X-ray computed tomography (XCT) and ultrasonic inspection, are employed to characterize internal damage mechanisms from these tests such as microcracking, fibre breakage, and matrix deformation at a microstructural level. Flexural testing indicates that failure initiates through tensile cracks in the outer layers, while compression testing reveals progressive damage through delamination, matrix degradation, and fibre buckling. The pipe stiffness and elastic modulus were ascertained to be 2184.2MPa and 13.18GPa respectively. Microstructural analyses of compressive failure further reveal the complex failure pathways. This shows that matrix cracking and delamination are primary failure mechanisms driven by the polymer matrix's limited fracture toughness and the complex stress interactions within the laminate. Delamination and matrix cracking are localized yet progressive, exacerbating the fibre to matrix separation which impact load-bearing capacity of the pipe. These findings underscore the importance of optimizing fibre orientation, matrix-fibre adhesion, and layer configuration to enhance structural toughness. This comprehensive evaluation of mechanical performance and failure mechanisms provides valuable insights for optimizing the manufacturing processes of TCP, aiming to improve durability, reduce material waste, and enhance long-term reliability in demanding service environments
A scoping review of evidence of community pharmacist independent prescribing for common clinical conditions: beyond protocol prescribing
No full textPharmacist independent prescribers (PIPs) enhance patient care but there is variability in their integration in community pharmacy (CP). The objective was to collate and characterise literature on the integration of 'standard of care' model PIPs in CP for acute common clinical conditions (CCC). This review followed the Arksey and O'Malley framework. Eligibility criteria, search databases and terms were defined. Information sources were searched from January 2006 to October 2023. Following screening, full text review and data extraction a narrative synthesis approach was used to address the review objectives. All steps were independently checked by two of the review team. Barriers and facilitators for integration used the Consolidated Framework for Implementation Research as a theoretical lens. Ten papers remained for full text review from 1075 records. Most studies were from Canada and focused on pharmacist views, evaluation of safety, effectiveness and patient satisfaction. A range of CCCs were included with a focus on antimicrobial prescribing. A wide range of barriers and facilitators to implementation were identified; 'regulatory constraints' and 'fiscal challenges' at a macro socio-organisational level and several challenges within organisations; lack of clarity on the pharmacists' scopes of practice and linked consumer confusion, staffing levels and workload with specific mention of paperwork and access to patient records. Most evidence for CCC management by PIPs relates to antimicrobials, originates in Canada and identifies multiple challenges. Given this there is a need to consider this topic further to identify ways to address the challenges and facilitate integration of PIPs in CP
Artificial intelligence in construction project management: a structured literature review of its evolution in application and future trends.
Get PDFThe integration of Artificial Intelligence (AI) in construction project management is revolutionising the industry; offering innovative solutions to enhance efficiency, reduce costs, and improve decision making. This structured literature review explored the current applications, benefits, challenges, and future trends of AI in construction project management. This study synthesised findings from 135 peer-reviewed articles published between 1985 and 2024; representing Industry 3.0 (3IR), Industry 4.0 (4IR), and Industry 4.0 Post COVID-19 (4IR PC). Analysis showed that the Planning and Monitoring and Control phases of the project have the greatest application of AI, while decision making, prediction, optimisation, and performance improvement are the most common purposes of AI use in the construction industry. The drivers of AI adoption within the construction industry include technology availability, project outcome and performance improvement, a competitive advantage, and a focus on sustainability. Despite these advancements, the review revealed several barriers to AI adoption, including data integration issues, the high cost of AI implementation, resistance to change among stakeholders, and ethical concerns surrounding data privacy, amongst others. This review also identified future ongoing applications of AI in the construction industry, such as sustainability and energy efficiency, digital twins, advanced robotics and autonomous construction, and optimisation. By providing a comprehensive analysis of the evolution of practices and the future direction of AI application, this study serves as a resource for researchers, practitioners, and policymakers seeking to understand the evolving landscape of AI in construction project management
Cross-modal gated feature enhancement for multimodal emotion recognition in conversations.
Get PDFEmotion recognition in conversations (ERC), which involves identifying the emotional state of each utterance within a dialogue, plays a vital role in developing empathetic artificial intelligence systems. In practical applications, such as video-based recruitment interviews, customer service, health monitoring, intelligent personal assistants, and online education, ERC can facilitate the analysis of emotional cues, improve decision-making processes, and enhance user interaction and satisfaction. Current multimodal emotion recognition research faces several challenges, such as ineffective emotional information extraction from single modalities, underused complementary features, and inter-modal redundancy. To tackle these issues, this paper introduces a cross-modal gated attention mechanism for emotion recognition. The method extracts and fuses visual, textual, and auditory features to enhance accuracy and stability. A cross-modal guided gating mechanism is designed to strengthen single-modality features and utilize a third modality to improve bimodal feature fusion, boosting multimodal feature representation. Furthermore, a cross-modal distillation loss function is proposed to reduce redundancy and improve feature discrimination. This function employs a dual-supervision mechanism with teacher and student models, ensuring consistency in single-modal, bimodal, and trimodal feature representations. Experimental results on the IEMOCAP and MELD datasets indicate that the proposed method achieves higher accuracy and comparable F1 scores than existing approaches, highlighting its effectiveness in capturing multimodal dependencies and balancing modality contributions
Attention-based framework for automated symbol recognition and wiring design in electrical diagrams. [Dataset]
No full textThe research presents an end-to-end deep learning framework combining YOLOv8 object detection with attention mechanisms to improve symbol recognition in electrical diagrams, followed by a graph-based wiring algorithm that automates wire routing between detected symbols. The system is tested across proprietary and public datasets, including: CGHD (Circuit Graph Hand-drawn Diagrams), DCD (Digital Circuit Diagrams)
Nigerian well abandonment landscape: challenges and lessons from global trends.
Get PDFPermanent plug and abandonment is the last activity in the lifecycle of every oil and gas well. With a rising number of mature assets globally, a wave of well abandonment and infrastructure removal projects faces the industry. As a maturing basin, Nigeria needs to prepare ahead of time for the decommissioning scope. This study analyses open-source datasets to understand the decommissioning trends in five selected countries in addition to Nigeria to provide recommendations for decommissioning excellence in Nigeria. The available information indicates that approximately 8,211 wells have been drilled in Nigeria, with 7,079 wells drilled from inception to 2011 and an additional 1,132 wells drilled in the country between 2011 and 2023. The dataset does not specify the completion dates for 9 percent of the 7,079 wells. However, 77% of wells drilled on or before 2011 in Nigeria have at least one scanned log record. Although these logs are undefined in the dataset, their availability suggests good value for data in the industry. 46 percent of wells with completion dates in Nigeria are over 40 years old, but there is limited data on completed decommissioning operations in the country. However, the recent announcement that about 400 of the 600 wells recently acquired from a multinational by an indigenous operator are inactive suggests that a significant amount of the old well stock in Nigeria is yet to be decommissioned. This study recommends that decommissioning excellence will be achieved in Nigeria through increased data sharing and collaboration driven by the regulator, expansion of the indigenous capacity, openness to alternative technologies and barrier materials and subjecting every decommissioning project in Nigeria to a comprehensive cost and life cycle assessment at the panning phase. The design of future wells should also be optimized for efficient decommissioning
Correction to: Machine learning for improved size estimation of complex marine particles from noisy holographic images.
Get PDFThis correction contains an update version of Table 2, which was originally published at https://doi.org/10.3389/fmars.2025.158793
Deep neural network geothermometer: enhancing temperature prediction accuracy in geothermal reservoirs through large-scale data analysis.
Get PDFThis study presents a novel deep learning chemical geothermometer designed to accurately predict the subsurface temperature of deep geothermal reservoirs. The research aims to overcome the limitations of traditional geothermometry methods while supporting the UN Sustainable Development Goals for clean energy and climate action. The study utilised a comprehensive dataset of 674 water samples from the geothermal regions of Nevada. The research methodology integrated classical geothermometry, multi-component geothermometry, and regional thermal database extrapolation. Four machine learning algorithms were evaluated: Random Forest, Gradient Boosting, Artificial Neural Network and Deep Neural Network. Data pre-processing, exploratory analysis and clustering techniques were used to improve the understanding of geothermal processes. The deep neural network model showed exceptional performance in predicting geothermal reservoir temperatures. The model achieved over 97% explained variance in both training and test datasets. Its robustness and generalizability were validated by successful evaluation on 42 new well samples from different geothermal fields worldwide, showing strong correlation with measured temperature data in different geological environments. This performance significantly outperformed previous models in the field, which used only 83 samples in one study and 155 samples in another study to train machine learning models. The superior performance of the developed model can be attributed to its comprehensive training dataset of 647 water samples, which is significantly larger than previous studies. The deep neural network model represents a significant advance in solute geothermometry by introducing the first large-scale, comprehensive thermo-geochemical dataset from complex geological settings for machine learning model training. Unlike previous studies limited by small datasets or specific geological environments, this research provides a robust, data-driven approach that can accurately predict temperatures in diverse geothermal systems. The integration of multiple machine learning algorithms and traditional geoscience knowledge enables a deeper understanding of geochemical signatures and subsurface thermal regimes, advancing both theoretical understanding and practical applications in geothermal exploration
Resilience and responses to interconnecting crises in informal settlements. [Video recording]
No full textThis film is part of a research project, led by Dr Natascha Mueller-Hirth (Robert Gordon University Aberdeen, Scotland) and Professor Stephen Vertigans, that examines how community-based organisations in a number of informal settlements in Nairobi, Kenya, respond to the huge, interconnecting and chronic crises and everyday risks faced by residents in such areas. The kinds of crises and risks referred to here include economic inequalities, poor environmental conditions, high levels of morbidity and mortality and low levels of education, employment and healthcare, and intersect with the conditions commonly associated with informality, such as no or little access to water, sanitation and essential public services and infrastructure. It is worth noting that, globally, over 1 billion people – including close to 500 million children – live in so-called slums. At the same time, we learned from our previous research that informal settlements (and the work of community groups in them) are characterised by huge resilience, creativity, diversity and entrepreneurism. This is contrary to the more common negative and often stigmatising portrayal of such areas
Predicting fear of childbirth during pregnancy, the positive role of self-efficacy and mental well-being: a cross-sectional study.
Get PDFWhilst the negative impact of fear of childbirth (FOC) is well established, there is limited understanding of the factors that reduce it. This study, aimed to explore the relationship between positive mental well-being, childbirth self-efficacy, and FOC during pregnancy through a Salutogenic lens. Using a cross-sectional design, 88 pregnant women recruited from a public hospital in North East Scotland completed an online survey including the Warwick Edinburgh Mental Well-Being Scale (WEMWBS), Childbirth Self-Efficacy Inventory (CBSEI), and Wijma Delivery Expectancy/Experience Questionnaire (W-DEQ). The sample scored a mean of 50.36 on WEMWBS, 84.63 for efficacy-expectancy, 127.00 for efficacy-outcome on CBSEI, and 62.19 on W-DEQ. Twelve per cent exhibited a severe FOC. FOC was negatively correlated with mental well-being, childbirth self-efficacy expectancy, and self-efficacy outcome. Multiple regression analysis indicated that higher mental well-being (β = −0.39, p < 0.001) was the strongest predictor of lower FOC. The findings highlights the important role of positive mental well-being and childbirth self-efficacy in reducing FOC, and suggest a need for antenatal education targeted at mastering childbirth techniques and enhancing positive emotions, sense of purpose, and meaning. These findings align with global health priorities by emphasizing the importance of antenatal care that supports and promotes both physical and mental well-being
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