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Enhancing epoxy polymer composites with MXene nanosheets for improved thermal performance.
Get PDFThermosetting epoxy polymers are widely employed as matrices for fabricating fibre-reinforced composites due to their exceptional strength and stiffness. However, the inherent brittleness of epoxy and its generally low fracture toughness impose limitations on their utilization in high-end applications. To address this challenge, the incorporation of micro-and nanoscale fillers emerges as a promising strategy for enhancing the durability of epoxy. MXene belonging to a versatile family of 2D transition-metal carbides, carbonitrides, and nitrides, offer superior physical and mechanical characteristics, making them ideal candidates for creating multifunctional polymer nanocomposites. In this study, MXene nanosheets (specifically Ti3C2Tx) were introduced at concentrations ranging from 0.1% to 0.5% by weight, and their dispersion in the epoxy-hardener mixture was achieved through ultrasonication. Remarkably, the incorporation of 0.5 wt. % MXene led to an 8°C increase in the glass transition (Tg) temperature and a 5°C elevation in the crystallisation temperature at 0.3 wt. % loadings. However, at higher MXene concentrations, these values exhibited a decrease. Overall, the mechanical characteristics of the nanocomposites demonstrated improvement. This enhancement is attributed to the effective distribution of MXene within the epoxy matrix, contributing to an overall enhancement of the material's properties
Comparison of the self-healing behaviour of 60Sn40Pb and 99.3Sn0.7Cu solder alloy reinforced Al6061 MMCs.
Get PDFThe self-healing characteristics of Al6061 reinforced with CuO have been examined experimentally. The solder alloys 60Pb40Sn and 99.3Sn0.7Cu with low melting points are incorporated to strengthen the Al6061 MMCs'; the self-healing properties have been investigated. Developed self-healing samples have undergone testing for hardness, tensile, and impact characteristics in accordance with ASTM standard test protocols. The findings demonstrate how the solder filling affects the mechanical characteristics of self-healed Al6061 alloy and its MMCs'. The results showed that the composites formed a decent bond between the solder and matrix, confirming successful fabrication. Pb-Sn filled samples demonstrated higher self-healing efficiency for tensile and impact of 90.02% and 90.30% with 6 wt.% of CuO, respectively, and Sn-Cu filled samples witnessed higher self-healing efficiency for tensile and impact of 91.81% and 91.09% with 6 wt.% of CuO respectively. However, the self-healed composite did not split in two when subjected to Charpy impact and tensile strength tests, and the healing efficiency of Sn-Cu-filled composites is higher than that of the Pb-Sn-filled composites
The absence of changes in the relative age effect present an opportunity for lower income soccer clubs to be more efficient than Europe’s elite. [Dataset]
No full textYouth soccer leagues chronologically match children based on their birth year with the aim of matching developmental milestones to ensure fair competition. An overrepresentation of athletes born earlier in the year compared with those born later in the year is known as the relative age effect (RAE). This may be in part due to a physical selection bias for example increased stature, muscle mass, sprint speed etc in older children. The selected children enter talent development systems earlier relative to their age (e.g., two U11 players entering at the same time may contain a December born player who is 12 months younger than a January born player although both are beginning their academy journey during the same section period) and gain higher degrees of exposure to coaching, physical training and competition at a younger age which in turn can lead to an improvement in technical and tactical abilities at older ages. Although this primarily relates to physical advantages as older players are assessed against their younger peers and leads to improvements in physical performance associated with the impact of stature and muscle mass this also impacts sociological and psychological advantages. The RAE expresses itself with the increasing competition level in youth soccer, and impacts coaches' subjective measures of player ability, with these subjective measures of ability being the determining factor of inclusion and exclusion in a soccer academy. The effects of RAE bias persist through to senior/adult level and could influence the judgement of decision makers explaining increased dropout of players who have not yet fulfilled their potential
A life cycle carbon assessment and multi-criteria decision-making framework for building renovation within the circular economy context: a case study.
Get PDFApplying circular economy principles to the renovation of existing buildings is increasingly recognized as essential to achieving Europe's climate and energy goals. However, current decision-making frameworks rarely integrate life cycle carbon assessment with multi-criteria evaluation to support circular renovation strategies. This paper introduces an innovative framework that combines life cycle carbon assessment with multi-criteria decision analysis to identify and sequence circular renovation measures. The framework was applied to a residential case study in the Netherlands, using IES VE for operational carbon assessment and One Click LCA for embodied carbon assessment, with results evaluated using PROMETHEE multi-criteria analysis. Renovation measures were assessed based on operational and embodied carbon (including Module D), energy use intensity, cost, pay-back period, and disruption. The evaluation also introduced the embodied-to-operational carbon ratio (EOCR), a novel metric representing the proportion of embodied carbon, including Module D, relative to operational carbon savings over the building's lifecycle. The homeowner's preferences regarding these criteria were considered in determining the final ranking. The findings show that circular insulation options involving reused materials and designed for disassembly achieved the lowest embodied carbon emissions and lowest EOCR scores, with reused PIR achieving a 94% reduction compared to new PIR boards. The impact of including Module D on the ranking of renovation options varies based on the end-of-life scenario. The framework demonstrates how circular renovation benefits can be made more visible to decision-makers, promoting broader adoption
Classification of artificial intelligence techniques for early architectural design stages.
Get PDFThis paper provides a strategic classification of artificial intelligence (AI) techniques based on a systematic literature review and four levels of potential: the levels of input, output, collaboration and creativity. The classification demonstrates the potential and challenges of the AI techniques when used in early stages of architectural design. We aspire to help architects, researchers and developers to choose which AI techniques might be worth pursuing for specific tasks, optimising the use of today’s computational power in architectural design workflows. The results of the classification strongly indicate that Evolutionary Computing, Transformer Models and Graph Machine Learning hold the greatest potential for impact in early architectural design, and thus merit the attention to achieve that potential. Moreover, the classification assists with building multi-technique applications and helps to identify the most suitable AI technique for different circumstances such as the architect’s programming skills, the availability of training data or the nature of the design problem
Improved lithium battery state of health estimation and enhanced adaptive capacity of innovative kernel extreme learning machine optimized by multi-strategy dung beetle algorithm.
Get PDFAccurate estimation of the state of health (SOH) of lithium batteries is crucial to ensure the reliable and safe operation of lithium batteries. Aiming at the problems of low accuracy of extreme learning machine and poor mapping ability of conventional kernel function, this paper constructs a kernel extreme learning machine model and uses a multi-strategy improved dung beetle algorithm to find the optimal parameters. In this paper, for the poor estimation effect caused by the difficulty of adapting the conventional kernel function to nonlinear batteries, we design a cosine polynomial kernel function, which improves the linear divisibility of the data; in addition, for the global search, local development, and convergence improvement of the dung beetle algorithm, we introduce the optimal Latin hypercubic idea, the Cauchy variation strategy, and the sparrow alert mechanism, which successfully improve the parameter searching capability and sensitivity of the algorithm, respectively. We successfully improve the capability and sensitivity of the algorithm in parameter searching. We experimentally verify the reliability and validity of the proposed model, and the maximum root mean square error and the average absolute percentage error obtained in the test are not higher than 0.00753 and 0.00399, respectively, and the minimum fit is not lower than 0.9921, which reflects the high accuracy and strong adaptive ability of the model
A novel ensemble aggregation method based on deep learning representation.
Get PDFWe propose a novel ensemble aggregation method by using a deep learning-based representation approach. Specifically, we applied the Cross-Validation procedure on training data with a number of learning algorithms to obtain the predictions for training data called meta-data. A neural network model is trained on this meta-data to generate representations associated with class labels. In our method, the neural network model functions as an encoder, learning the relationship between base classifiers' outputs and mapping meta-data to a representation space. The vectors in the mapped space provide a more accurate representation than traditional methods by reducing the distance of vectors in the same class and increasing the distance in different classes. Our method was compared with four well-known ensemble methods: Decision Template, an ensemble with a MultiLayer Perceptron (MLP)-based combiner, gcForest, and XgBoost. Experiments conducted on 20 UCI datasets demonstrate the outstanding performance of our ensemble aggregation method. The results show that our method achieves better delegation of class label representations, enhancing the final results of classification tasks
Evaluation of the impact of conflict management training.
No full textMMF were contracted by CHAS to deliver structured training on conflict management for clinicians working with children with a life-shortening condition and their families. The first cohort commenced June 2024, and the second cohort commenced November 2024. This report presents the evaluation of the impact of that training on clinicians' recognition and understanding of conflict with families and how those conflicts can be managed effectively, de-escalated or resolved. In order to assess the impact of the training on their practice, three surveys were designed. Each cohort were identified by CHAS (by virtue of their registration to attend the training). CHAS sent out an explanatory email and participant information sheets to all those in each cohort who had registered to attend, and gave them an anonymised individual code and a link to the initial survey. Those attendees who consented to participate then completed the survey, identifying themselves only by their anonymised code. This initial survey was completed prior to their attendance at the training session, to give a baseline of their understanding of, and approaches to conflict management. Questions focused on the number of instances of conflict clinicians had experienced, the approaches they used to seek resolution, their confidence in managing those conflicts, and who they would approach for advice or support. On completion of the training, CHAS sent out a second explanatory email, the same anonymised code and link to the post-training evaluation survey. This second survey focussed on whether/how their approaches to resolving conflict had changed as a result of the training, and whether/how they planned to alter their practice in the future. CHAS then sent a final explanatory email, the same anonymised code and link to a 6-month follow-up survey which was designed to capture their understanding and experience of using the skills they had acquired over a period of time since the training took place, and the consequent impact on, or change in their practice. A 6 month interval was selected as participants needed a period of time during which they could reflect on how their understanding and skills had influenced their practice. The research was granted ethical approval by RGU’s School Ethical Review Panel
Image enhancement in turbid water using multiscale weighted features and attention mechanisms.
Get PDFIn this study, we propose a method to improve the image quality in turbid water by combining multiscale weighted features and attention mechanisms, effectively addressing issues such as reduced contrast and color casts. Initially, global color correction was employed using the grayscale space algorithm. Then, a preprocessed dataset was input into an improved Shallow-UWnet network for feature learning. The model included a multiscale weighted feature fusion module to extract global and local features and a parallel attention module to enhance key details and suppress noise in images. To simulate natural turbid water, a dataset of underwater images with different turbidities was constructed. Our method effectively corrected the color of images in different turbid water conditions and significantly enhanced image quality. In the evaluation on the custom dataset, performance improved by 13%–25% across standard image quality metrics compared with the second-best method. Additionally, we conducted generalization performance tests on the EUVP Dark, UIEBD, and UFO-120 datasets, validating the excellent enhancement effect of our method across different types of images. These results highlight the promising potential of our method for applications in underwater detection, rescue, and marine research
Planning the end into the beginning: an integrated multi-criteria approach for well optimization.
Get PDFOptimization in the oil and gas industry is often segmented focusing mainly on project profitability through drilling cost reduction and optimal well positioning for best reservoir production. In the drilling phase, rate of penetration, non-productive time (NPT) and project cost are common parameters for measuring drilling optimization. Well abandonment is often considered a separate activity with its planning often delayed until the late life stages of wells. However, decisions made during the well design, drilling operations and production phases have significant and often underestimated implications on the cost and complexity of well abandonment. Historical data show that factors such as well inclination, casing shoe depths and cement tops which are key factors in drilling optimization have major impacts on well abandonment design. For instance, an inclination greater than 65 degrees above the minimum safe abandonment depth (MSAD), hinders the application of through tubing abandonment, a cost-effective technique, for well abandonment. Other factors such as missing or insufficient drilling and well cementing records, and poor tubing and casing material selection also have cost implications well abandonment. These insights underscore the need for a new approach that integrates optimization for well abandonment into the design phase of oil and gas wells. This study investigates well abandonment challenges, drilling optimization strategies and well management practices to propose an integrated multi-criteria well optimization framework that achieves operational efficiency, reduces environmental footprint and has a potential to reduce well abandonment costs by up to 40 percent
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