6,877 research outputs found

    Computational and experimental studies on the reaction mechanism of bio-oil components with additives for increased stability and fuel quality

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    As one of the world’s largest palm oil producers, Malaysia encountered a major disposal problem as vast amount of oil palm biomass wastes are produced. To overcome this problem, these biomass wastes can be liquefied into biofuel with fast pyrolysis technology. However, further upgradation of fast pyrolysis bio-oil via direct solvent addition was required to overcome it’s undesirable attributes. In addition, the high production cost of biofuels often hinders its commercialisation. Thus, the designed solvent-oil blend needs to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel. In this thesis, a multi-stage computer-aided molecular design (CAMD) framework was employed for bio-oil solvent design. In the design problem, molecular signature descriptors were applied to accommodate different classes of property prediction models. However, the complexity of the CAMD problem increases as the height of signature increases due to the combinatorial nature of higher order signature. Thus, a consistency rule was developed reduce the size of the CAMD problem. The CAMD problem was then further extended to address the economic aspects via fuzzy multi-objective optimisation approach. Next, a rough-set based machine learning (RSML) model has been proposed to correlate the feedstock characterisation and pyrolysis condition with the pyrolysis bio-oil properties by generating decision rules. The generated decision rules were analysed from a scientific standpoint to identify the underlying patterns, while ensuring the rules were logical. The decision rules generated can be used to select optimal feedstock composition and pyrolysis condition to produce pyrolysis bio-oil of targeted fuel properties. Next, the results obtained from the computational approaches were verified through experimental study. The generated pyrolysis bio-oils were blended with the identified solvents at various mixing ratio. In addition, emulsification of the solvent-oil blend in diesel was also conducted with the help of surfactants. Lastly, potential extensions and prospective work for this study have been discuss in the later part of this thesis. To conclude, this thesis presented the combination of computational and experimental approaches in upgrading the fuel properties of pyrolysis bio-oil. As a result, high quality biofuel can be generated as a cleaner burning replacement for conventional diesel fuel

    Conceptualising adolescents’ pro-environmental behaviour: an exploration in Cyprus with reference to Scotland

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    There is now a global ecological and environmental crisis of unprecedented magnitude, severity and scale, which threatens current and future generations’ well-being. Given the influence of human behaviour on the natural environment addressing current and future environmental issues requires changes in our behaviour. In order to enhance and promote pro-environmental behaviours, one must understand what determines and influences behaviour. This is especially important to consider in adolescents (12-19 years old) given that young people will become those responsible for future environmental protection and will be facing future environmental issues. Whilst previous studies, behavioural models and theoretical frameworks have identified a number of potential behaviour determinants and potential influences, there is still confusion in the literature regarding their definitions and their exact role in determining behaviour. Additionally, despite the successful application of behavioural models, complexities and interrelations between behaviour, determinants and influences have been identified complicating understanding. Also, differences in operationalisation and interpretation of models further complicate conclusions. To conceptualise adolescents’ pro-environmental behaviours, this research explored and evaluated adolescents’ pro-environmental behaviour and behaviour determinants (i.e., environmental attitudes and knowledge), their potential influences (i.e., nature and biodiversity perceptions, nature experiences, connections with nature and current and preferred environmental education practices) and identified potential relationships within and between them. This allowed for further insights regarding how these are understood, the incorporation and simultaneous exploration of a number of determinants and influences from different behavioural models and frameworks and also the exploration of relationships within and between models and frameworks and with pro-environmental behaviours enhancing our current understanding. This research adopted a multiple case study design approach in two different socio-cultural settings, Cyprus and Scotland, with a focus on Cyprus and employed mixed research methods in two data collection phases. Phase one explored and described phenomena in both Cyprus and Scotland. It involved group discussions with 24 groups of 4-8 adolescents, and used A2 posters as a discussion schedule on which participants recorded their ideas. Based on findings from phase one, phase two evaluated and explored phenomena in further depth and identified potential relationships between them. It involved questionnaires with adolescents (Scotland: N=40, Cyprus: N=475) and semi-structured interviews with 5 teachers in Cyprus. Phase two also involved the actualisation of adolescents’ environmental education preferences. This consisted of outdoor environmental education activities with adolescents (Scotland=1 group, Cyprus=7 groups) which were evaluated using questionnaires before and after they took place. Results provide support for previous scholars’ claims regarding the multidimensionality and complexity of pro-environmental behaviours and behaviour determinants whilst also indicating differences in variable aspects. Particularly, whilst some adolescents were able to identify pro-environmental behaviours, were concerned about issues, perceived issues as important and had some environmental knowledge, this was not true for everyone. Results also indicated differences in how often individuals undertake different behaviours, differences in perceptions regarding the importance of different issues, differences in attitudes towards different behaviours, differences in reasoning for being concerned and for undertaking the different behaviours. Moreover, results indicated a number of statistically significant relationships within and between some but not all behaviours and determinants. Additionally, results also provide support for previous scholars’ claims regarding the multidimensionality and complexity of potential influences and indicated the existence of relationships between aspects of the different influences and pro-environmental behaviour and behaviour determinants. With regards to nature perceptions the results indicated a focus on the absence of humans and differences in how different areas are perceived. For contact with nature, the results indicated differences in the levels of engagement with different outdoor areas and differing perceptions of whether nature can be experienced indoors. Results regarding nature connections, indicated strong personal nature connections and a negative relationship between humans and nature. The results also indicated a lack of environmental education courses undertaken by adolescents and as part of the Cyprus school curriculum, the consideration of courses such as Geography and Biology as environmental education courses and preferences by adolescents and educators regarding what environmental education practices should consist of. Additionally, results indicated a number of statistically significant relationships between pro-environmental behaviour, determinants and potential influences. Moreover, this research also indicated that designing environmental education activities based on adolescents’ preferences is achievable and the research’s approach can act as a methodological starting point for developing and evaluating future initiatives based on participatory approaches. The examination of adolescents’ and educators’ current and preferred environmental education practices allowed for the identification of several practice recommendations. Particularly, this research advocates (a) the incorporation of courses focused on environmental education in school curricula, reducing bureaucracy issues and dependence on teachers’ initiatives for course development; (b) the consideration of adolescents’ preferences and adolescents involvement in course design; (c) undertaking courses in both indoor and outdoor locales; (d) consideration of educators’ inputs when planning, and with regards to topic, locale and speaker selection; (e) the incorporation of hands-on, fun, interesting and researching activities and (f) the involvement of educators/speakers who are passionate, relatable and kind-hearted. This research advocates the unravelling and evaluation of the multidimensionality of behaviours, determinants and influences; the combination of behavioural models and frameworks and the evaluation of variables potential effects; and the use of socio-ecological frameworks to conceptualise pro-environmental behaviours in different contexts

    TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data

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    Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95\% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.Comment: 24 pag

    La Forma del cáncer: Socialización y representación visual de la enfermedad en Instagram

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    Social media platforms like Instagram are a source of information and support for cancer patients. On this platform, millions of images shared by patients, organisations and the general public give shape to the social imagination of one of the most feared illnesses around the world. This thesis proposes a method to identify and obtain images of cancer from Instagram, a social media that in 2022 remains nearly inaccessible to research. Through a transdisciplinary lens, it combines the sociology of everyday life, visual sociology and methodologies from social media analysis to discover visual patterns in the images and find alternative discourses. The results show the variety of visual resources that patients use to communicate their illness and support the construction of their identity. They also show how Instagram’s economy of affection favours the publication of positive images, aligned with the discourse of survivorship, while they hamper the expression of other experiences. It concludes with the proposal for a new regime in the communication of cancer, based on the concept of socialisation.Las redes sociales visuales como Instagram son una fuente de información y apoyo para pacientes de cáncer. En esta red, millones de imágenes compartidas por pacientes, organizaciones y por el público general configuran la imaginación social de una de las enfermedades más temidas en todo el mundo. Esta tesis plantea una metodología para extraer y estudiar imágenes de esta red, prácticamente inaccesible para la investigación en 2022, y para su codificación. A través de un enfoque transdisciplinar, combina la sociología de la vida cotidiana, la sociología visual y las metodologías del análisis de redes sociales para descubrir patrones visuales en las imágenes de cáncer e identificar discursos alternativos. Los resultados muestran la variedad de recursos visuales que utilizan los pacientes de cáncer para comunicar su enfermedad y apoyar un proceso de construcción de la identidad. Muestran también cómo la economía afectiva de esta plataforma favorece la publicación de imágenes positivas y alineadas con el discurso de la supervivencia, mientras que supone un reto para visibilizar otras experiencias. Concluye con la propuesta de un nuevo modelo de comunicación sobre cáncer, basado en el concepto de la socialización.Departamento de Sociología y Trabajo SocialDoctorado en Investigación Transdisciplinar en Educació

    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

    Low- and high-resource opinion summarization

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    Customer reviews play a vital role in the online purchasing decisions we make. The reviews express user opinions that are useful for setting realistic expectations and uncovering important details about products. However, some products receive hundreds or even thousands of reviews, making them time-consuming to read. Moreover, many reviews contain uninformative content, such as irrelevant personal experiences. Automatic summarization offers an alternative – short text summaries capturing the essential information expressed in reviews. Automatically produced summaries can reflect overall or particular opinions and be tailored to user preferences. Besides being presented on major e-commerce platforms, home assistants can also vocalize them. This approach can improve user satisfaction by assisting in making faster and better decisions. Modern summarization approaches are based on neural networks, often requiring thousands of annotated samples for training. However, human-written summaries for products are expensive to produce because annotators need to read many reviews. This has led to annotated data scarcity where only a few datasets are available. Data scarcity is the central theme of our works, and we propose a number of approaches to alleviate the problem. The thesis consists of two parts where we discuss low- and high-resource data settings. In the first part, we propose self-supervised learning methods applied to customer reviews and few-shot methods for learning from small annotated datasets. Customer reviews without summaries are available in large quantities, contain a breadth of in-domain specifics, and provide a powerful training signal. We show that reviews can be used for learning summarizers via a self-supervised objective. Further, we address two main challenges associated with learning from small annotated datasets. First, large models rapidly overfit on small datasets leading to poor generalization. Second, it is not possible to learn a wide range of in-domain specifics (e.g., product aspects and usage) from a handful of gold samples. This leads to subtle semantic mistakes in generated summaries, such as ‘great dead on arrival battery.’ We address the first challenge by explicitly modeling summary properties (e.g., content coverage and sentiment alignment). Furthermore, we leverage small modules – adapters – that are more robust to overfitting. As we show, despite their size, these modules can be used to store in-domain knowledge to reduce semantic mistakes. Lastly, we propose a simple method for learning personalized summarizers based on aspects, such as ‘price,’ ‘battery life,’ and ‘resolution.’ This task is harder to learn, and we present a few-shot method for training a query-based summarizer on small annotated datasets. In the second part, we focus on the high-resource setting and present a large dataset with summaries collected from various online resources. The dataset has more than 33,000 humanwritten summaries, where each is linked up to thousands of reviews. This, however, makes it challenging to apply an ‘expensive’ deep encoder due to memory and computational costs. To address this problem, we propose selecting small subsets of informative reviews. Only these subsets are encoded by the deep encoder and subsequently summarized. We show that the selector and summarizer can be trained end-to-end via amortized inference and policy gradient methods

    Data-assisted modeling of complex chemical and biological systems

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    Complex systems are abundant in chemistry and biology; they can be multiscale, possibly high-dimensional or stochastic, with nonlinear dynamics and interacting components. It is often nontrivial (and sometimes impossible), to determine and study the macroscopic quantities of interest and the equations they obey. One can only (judiciously or randomly) probe the system, gather observations and study trends. In this thesis, Machine Learning is used as a complement to traditional modeling and numerical methods to enable data-assisted (or data-driven) dynamical systems. As case studies, three complex systems are sourced from diverse fields: The first one is a high-dimensional computational neuroscience model of the Suprachiasmatic Nucleus of the human brain, where bifurcation analysis is performed by simply probing the system. Then, manifold learning is employed to discover a latent space of neuronal heterogeneity. Second, Machine Learning surrogate models are used to optimize dynamically operated catalytic reactors. An algorithmic pipeline is presented through which it is possible to program catalysts with active learning. Third, Machine Learning is employed to extract laws of Partial Differential Equations describing bacterial Chemotaxis. It is demonstrated how Machine Learning manages to capture the rules of bacterial motility in the macroscopic level, starting from diverse data sources (including real-world experimental data). More importantly, a framework is constructed though which already existing, partial knowledge of the system can be exploited. These applications showcase how Machine Learning can be used synergistically with traditional simulations in different scenarios: (i) Equations are available but the overall system is so high-dimensional that efficiency and explainability suffer, (ii) Equations are available but lead to highly nonlinear black-box responses, (iii) Only data are available (of varying source and quality) and equations need to be discovered. For such data-assisted dynamical systems, we can perform fundamental tasks, such as integration, steady-state location, continuation and optimization. This work aims to unify traditional scientific computing and Machine Learning, in an efficient, data-economical, generalizable way, where both the physical system and the algorithm matter

    The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

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    In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis

    adSformers: Personalization from Short-Term Sequences and Diversity of Representations in Etsy Ads

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    In this article, we present a general approach to personalizing ads through encoding and learning from variable-length sequences of recent user actions and diverse representations. To this end we introduce a three-component module called the adSformer diversifiable personalization module (ADPM) that learns a dynamic user representation. We illustrate the module's effectiveness and flexibility by personalizing the Click-Through Rate (CTR) and Post-Click Conversion Rate (PCCVR) models used in sponsored search. The first component of the ADPM, the adSformer encoder, includes a novel adSformer block which learns the most salient sequence signals. ADPM's second component enriches the learned signal through visual, multimodal, and other pretrained representations. Lastly, the third ADPM "learned on the fly" component further diversifies the signal encoded in the dynamic user representation. The ADPM-personalized CTR and PCCVR models, henceforth referred to as adSformer CTR and adSformer PCCVR, outperform the CTR and PCCVR production baselines by +2.66%+2.66\% and +2.42%+2.42\%, respectively, in offline Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Following the robust online gains in A/B tests, Etsy Ads deployed the ADPM-personalized sponsored search system to 100%100\% of traffic as of February 2023

    A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks

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    Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. Unlike conventional neural networks or updated versions of Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in handling long dependencies between input sequence elements and enable parallel processing. As a result, transformer-based models have attracted substantial interest among researchers in the field of artificial intelligence. This can be attributed to their immense potential and remarkable achievements, not only in Natural Language Processing (NLP) tasks but also in a wide range of domains, including computer vision, audio and speech processing, healthcare, and the Internet of Things (IoT). Although several survey papers have been published highlighting the transformer's contributions in specific fields, architectural differences, or performance evaluations, there is still a significant absence of a comprehensive survey paper encompassing its major applications across various domains. Therefore, we undertook the task of filling this gap by conducting an extensive survey of proposed transformer models from 2017 to 2022. Our survey encompasses the identification of the top five application domains for transformer-based models, namely: NLP, Computer Vision, Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze the impact of highly influential transformer-based models in these domains and subsequently classify them based on their respective tasks using a proposed taxonomy. Our aim is to shed light on the existing potential and future possibilities of transformers for enthusiastic researchers, thus contributing to the broader understanding of this groundbreaking technology
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