4,919 research outputs found

    Continuous flow synthesis of hypercrosslinked polymers (HCPs) and its environmental impact evaluation

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    Hypercrosslinked polymers(HCPs) are a class of microporous adsorbents with a wide range of applications, including dye adsorption, and gas storage. Traditionally, HCPs are synthesised through Friedel-Crafts alkylation, which involves a time-consuming synthesis process in batch reactors, posing challenges for scaling up production to meet global demand. The prolong reaction duration issue could be eliminated by means of a new synthetic method to substitute batch reactors. The ultimate aim of this study is to intensify the HCP synthesis process by transitioning from batch reactors to continuous reactors. This shift intents to enhance productivity while maintaining a high specific surface area, crucial for superior adsorption capacity. Additionally, this study aspired to reduce the environmental impact associated with this new method for HCP synthesis. To achieve these objectives, a continuous flow system had been adopted as a replacement for the conventional batch method in HCP synthesis. Three types of HCPs were successfully synthesised using well-established strategies (internally crosslinked, post-crosslinked, and externally crosslinked) in the continuous flow system, showcasing its versatility. The productivity, measured as space-time-yield (STY), of continuous flow synthesis showed an enhancement ranging from 32 – 117-fold when compared to batch synthesis. These improvements were attributed to reducing reaction duration during flow synthesis, from 1440 minutes (24 hours) to 5 – 15 minutes. The specific surface areas of flow-synthesised HCPs were, on average, lower than the batch-synthesised HCPs by 1.5 – 10 %. This meant that when compared to batch-synthesised HCPs, more quantities of flow-synthesised HCPs were needed for dye adsorption and CO2 capture. However, despite this requirement for larger quantities, the environmental assessment of continuous flow synthesis indicated a reduction in negative environmental impacts across most environmental impact indicators. This suggest an improvement in the environmental sustainability of continuous flow HCP synthesis compared to batch synthesis. Furthermore, this study also explored an alternative synthesis method using twin screw extraction (TSE) with deep eutectic solvents (DES), a benign solvent replacement for halogenated solvents, during HCP synthesis. Although this approach offers promising potential as the replacement of continuous flow synthesis using conventional halogenated solvents, further investigations are warranted for its optimisation. In conclusion, this thesis advocates for the adoption of continuous flow synthesis of HCPs, underlining its potential for productivity enhancement and reduced environmental impacts. This study lays the foundation for the potential industrial-scale implementation of continuous flow synthesis, bridging the gap between HCP supply and demand while contributing to lower environmental impacts in the production process

    Effective Drag Coefficient Prediction on single-view 2D Images of Snowflakes

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    The drag coefficient of snowflakes is an crucial particle descriptor that can quantify the relationships with the mass, shape, size, and fall speed of snowflake particles. Previous studies has relied on estimating and improving empirical correlations for the drag coefficient of particles, utilizing 3D images from the Multi-Angled Snowflake Camera Database (MASCDB) to estimate snowflake properties such as mass, geometry, shape classification, and rimming degree. However, predictions of the drag coefficient with single-view 2D images of snowflakes has proven to be a challenging problem, primarily due to the lack of data and time-consuming, expensive methods used to estimate snowflake shape factors such as sphericity and convex hull. In this paper, we propose a cost-effective and time-efficient approach to address the challenges in predicting the drag coefficients from single-view 2D images of falling snowflakes. Our method combines EfficientNetB7 for image preprocessing to remove the background and border from snowflake images, Kernel Principal Component Analysis (KPCA) to extract meaningful features from the snowflake images, and Machine Learning methods, namely Random Forests, XGBoost models, Multilayer Perceptron (MLP) models, and MLP models trained on distinct Reynolds number flow regimes, to predict drag coefficients using the Locatelli and Hobbs dataset. Through comprehensive evaluation, our model achieved a mean squared error of 0.195, outperforming most existing empirical correlations. Moreover, an evaluation of the feature importance using mean decrease impurity (MDI) showed that the KPCA feature extraction added influential and meaningful data points to our machine learning models

    From abuse to trust and back again

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    oai:westminsterresearch.westminster.ac.uk:w7qv

    Designing a professional development programme to support the enhancement of the emotion-cognition partnership in teaching and learning in Higher Education.

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    The relationship between emotions and the quality of thinking has been identified as a pivotal, but frequently avoided, aspect of learning and teaching within higher education. This project has developed a practical toolkit and professional development programme to support academic staff to explicitly consider the role of emotions within their teaching in a way that enhances the quality of productive thought. To achieve this, a three-stage educational design research approach has been adopted. Firstly, the existing conceptions of academics around the role of emotions within teaching, and their roles more widely, were explored (22 interviews and 312 surveyed). Secondly, a three-stage iterative design process was undertaken to develop a professional training programme and finally, the output of this design process was tested and evaluated with a group of 45 academic staff from a range of discipline areas. The professional development approach presented is designed to actively support teachers in higher education to autonomously embed approaches to support the development of the emotion-cognition partnership within multiple aspects of their practice. The need to nuance strategies to the context of the subject, organisation and the priorities of the individual was considered within the design as was the emotional labour inherent within academic roles. Evaluation of the professional training demonstrated that it was successful in its goals with high levels of satisfaction reported in relation to the training content (97%) and application to practice (87%) noted by participants. A movement from an unconscious, reactive and intuitive approach to emotions within teaching, to one that is more conscious and planned in nature, was also revealed. It is suggested that an explicit focus on the interactions between emotions and thinking within higher education may provide an effective frame for academics and students working within increasingly complex institutional structures; this frame may help to integrate, define, link, and delineate aspects of practice within multifaceted HE environments, making the journeys and experiences of students and of staff smoother and more rewarding in practice

    Analysis and Modelling of TTL ice crystals based on in-situ light scattering patterns

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    Even though there are numerous studies on cirrus clouds and its influence on climate, lack of detailed information on its microphysical properties like ice crystal geometry, still exists. Challenges like instrumental limitations and scarcity of observational data could be the reasons behind it. But this knowledge gap has only heightened the error in climate model predictions. Therefore, this study is focused on the Tropical Tropopause Layer (TTL), where cirrus clouds can be seen, and the temperature bias is higher. Since the shape and surface geometry of ice crystals greatly influence the temperature, a detailed understanding of these ice crystals is necessary. So, this paper will look in-depth on finding the morphology of different types of ice crystals in the TTL. The primary objective of this research is to analyse the scattering patterns of ice crystals in the TTL cirrus and find their characteristics like shape and size distributions. As cirrus is a high cloud, it plays a crucial role in the Earth-atmosphere radiation balance and by knowing the scattering properties of ice crystals, their impact on the radiative balance can be estimated. This research further helps to broaden the understanding of the general scattering properties of TTL ice crystals, to support climate modelling and contribute towards more accurate climate prediction. An investigation into the light scattering data is presented. The data consist of 2D scattering patterns of ice crystals of size 1-100μm captured by the Aerosol Ice Interface Transition Spectrometer (AIITS) between the scattering angles 6° and 25° at the wavelength of 532nm. The images were taken during the NERC and NASA Co-ordinated Airborne Studies in the Tropics and Airborne Tropical Tropopause Experiment (known as the CAST-ATTREX campaign) on 5th March 2015 at an altitude between 15-16km over the Eastern Pacific. The features in the scattering patterns are analysed to identify the crystal habit, as they vary with the geometry of the crystal. After the analysis phase, the model crystals of specific types and sizes are generated using an appropriate computer program. The scattering data of the model crystals are then simulated using a Beam Tracing Model (BTM) based on physical optics, as geometric optics doesn’t produce the required information and exact methods (like T-matrix or Discrete Dipole Approximation) are either unsuitable for large size parameters or time-consuming. The simulated scattering pattern of a model crystal is then compared against that of the AIITS to find the characteristics like shape, surface texture and size of the ice crystals. By successive testing and further analysis, the crystal sizes are estimated. Since the manual analysis of scattering patterns is time-consuming, a pilot study on Deep Learning Network has been undertaken to classify the scattering patterns. Previous studies have shown that there are high concentrations of small ice crystals in TTL cirrus. However, these crystals, especially <30μm, are often misclassified due to the limited resolution of the imaging instruments, or even considered as shattered ice. Through this research it was possible to explore both the crystal habit and its surface texture with greater accuracy as the scattering patterns captured by the AIITS are analysed instead of crystal images. It was found that most of the crystals are quasi-spheroidal in shape and that there is indeed an abundance of smaller crystals <30μm. It was also found that over a quarter of the crystal population has rough surfaces

    The development, feasibility, and acceptability of a breakfast group intervention for stroke rehabilitation

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    Background: There are 1.2 million stroke survivors in the UK and the number is projected to increase significantly over the next decade. Research suggests that between 50% and 80% of hospitalised stroke survivors experience difficulties with eating and drinking. Presently, rehabilitation approaches to address these difficulties involve individual rehabilitation sessions led by uni-professionals. Recent national stroke guidance recommends that stroke survivors receive three hours of daily rehabilitation and emphasises the importance of addressing the psychosocial aspects of recovery. Implementing these recommendations presents a challenge to healthcare professionals, who must explore innovative methods to provide the necessary rehabilitation intensity. This study aimed to address these challenges by codesigning a multi-disciplinary breakfast group intervention and implementation toolkit to improve psychosocial outcomes. Methods: The Hawkins 3-step framework for intervention design was used to develop a multidisciplinary breakfast group intervention and to understand if it was acceptable and feasible for patients and healthcare professionals in an acute stroke ward. The Hawkins 3- steps were 1) evidence review and consultations 2) coproduction 3) prototyping. In collaboration with fifteen stakeholders, a prototype breakfast group intervention and implementation toolkit were codesigned over four months. Experience-based Codesign was used to engage stakeholders. Results: The literature review is the first to investigate the psychosocial impact of eating and drinking difficulties post stroke. The key finding was the presence of psychological and social impacts which included, the experience of loss, fear, embarrassment shame and humiliation as well as social isolation. Stroke survivors were striving to get back to normality and this included the desire to socially dine with others. Two prototype iterations of the intervention were tested with 16 stroke survivors across three hospital sites. The multidisciplinary breakfast group intervention was designed to offer intensive rehabilitation in a social group context. The codesigned implementation toolkit guided a personalised and tailored approach. A perceived benefit of the intervention was the opportunity to address the psychosocial aspects of eating and drinking rehabilitation as well as providing physical rehabilitation. Stroke survivors highly value the opportunity to socialise and receive support from their peers. The intervention was acceptable to both patients and healthcare professionals, and the workforce model proved practical and feasible to deliver using a collaborative approach in the context of resource-limited healthcare. Conclusions: The breakfast group interventions, developed through codesign, were positively received by patients and staff and feasible to deliver. They introduce an innovative and novel approach to stroke rehabilitation, personalised to each individual's needs, and offer a comprehensive intervention which addresses both physical and psychosocial aspects which target challenges related to eating and drinking. Unique contributions of this study include a theoretical model for breakfast group interventions, a programme theory and practical tool kit for clinicians to support the translation of research findings and implement breakfast groups in clinical practice

    Die unsicheren Kanäle

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    Zeitgenössische IT-Sicherheit operiert in einer Überbietungslogik zwischen Sicherheitsvorkehrungen und Angriffsszenarien. Diese paranoid strukturierte Form negativer Sicherheit lässt sich vom Ursprung der IT-Sicherheit in der modernen Kryptografie über Computerviren und -würmer, Ransomware und Backdoors bis hin zum AIDS-Diskurs der 1980er Jahre nachzeichnen. Doch Sicherheit in und mit digital vernetzten Medien lässt sich auch anders denken: Marie-Luise Shnayien schlägt die Verwendung eines reparativen, queeren Sicherheitsbegriffs vor, dessen Praktiken zwar nicht auf der Ebene des Technischen angesiedelt sind, aber dennoch nicht ohne ein genaues Wissen desselben auskommen

    Conversations on Empathy

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    In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Machine learning applications in search algorithms for gravitational waves from compact binary mergers

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    Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe. However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing. In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software. Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals
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