7,086 research outputs found

    Investigating Human Embryo Implantation – Developing Clinical Applications from in vitro Models

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    Introduction: While assisted conception success rates have increased, factors limiting IVF success include inadequacies in identifying viable embryos, and transfer of embryos into uteri with an unknown state of receptivity. Aims and experimental approaches: The aims of this project are to determine the possibility of using non-invasive techniques to reveal differences between preimplantation human embryos which successfully form a pregnancy and those that fail to implant. The experimental approaches are: 1 Sampling of conditioned media and co-culture with a 3D in vitro model of mid-secretory phase normal human endometrium, followed by transcriptomic analysis of these endometrial cells; 2 Development of a time lapse annotation system to improve selection of PN stage frozen embryos cultured to blastocyst and replaced in FET cycles. Methods: Endometrial epithelial and stromal cells in an in vitro model of mid-secretory phase human endometrium were exposed to conditioned media samples from 10 human embryos cultured singly to the blastocyst stage, with known pregnancy outcomes. These cells were subjected to RNA sequencing and transcriptomic analysis. Time lapse recordings of these embryos were taken through an experimental AI model (eM-Life). Retrospective analysis and annotation of time lapse videos of embryo development of 193 PN stage frozen embryos thawed and cultured to the blastocyst stage for replacement in an FET cycle was performed. Results: Endometrial epithelial cells showed changes in gene expression in response to media from successful embryos, while stromal cells responded to a lesser extent to media from unsuccessful embryos. The deep learning model ranked embryos on morphology but did not correlate with endometrial response in this project. From the analysis of 193 PN stage frozen embryos, statistically significant differences in several morphokinetic parameters between implanting and non-implanting embryos were found and morphological differences not previously studied in frozen thawed embryos relating to embryo viability were identified. Conclusions: Both experimental approaches revealed differences between embryos which implant successfully and those which fail, not detected by standard morphological grading. Further work is needed to identify upstream factors in conditioned media which cause gene expression changes in the in vitro endometrial model, and to test the morphokinetic model developed for frozen embryos in culture

    A Survey on Forensics and Compliance Auditing for Critical Infrastructure Protection

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    The broadening dependency and reliance that modern societies have on essential services provided by Critical Infrastructures is increasing the relevance of their trustworthiness. However, Critical Infrastructures are attractive targets for cyberattacks, due to the potential for considerable impact, not just at the economic level but also in terms of physical damage and even loss of human life. Complementing traditional security mechanisms, forensics and compliance audit processes play an important role in ensuring Critical Infrastructure trustworthiness. Compliance auditing contributes to checking if security measures are in place and compliant with standards and internal policies. Forensics assist the investigation of past security incidents. Since these two areas significantly overlap, in terms of data sources, tools and techniques, they can be merged into unified Forensics and Compliance Auditing (FCA) frameworks. In this paper, we survey the latest developments, methodologies, challenges, and solutions addressing forensics and compliance auditing in the scope of Critical Infrastructure Protection. This survey focuses on relevant contributions, capable of tackling the requirements imposed by massively distributed and complex Industrial Automation and Control Systems, in terms of handling large volumes of heterogeneous data (that can be noisy, ambiguous, and redundant) for analytic purposes, with adequate performance and reliability. The achieved results produced a taxonomy in the field of FCA whose key categories denote the relevant topics in the literature. Also, the collected knowledge resulted in the establishment of a reference FCA architecture, proposed as a generic template for a converged platform. These results are intended to guide future research on forensics and compliance auditing for Critical Infrastructure Protection.info:eu-repo/semantics/publishedVersio

    Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach

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    Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, lighting, equipment, photovoltaic, and hot water, to predict the energy performance of buildings at an urban scale. Furthermore, the model's performance is enhanced by employing an ensemble-based machine learning approach, achieving 91% accuracy compared to the traditional approach's 76%. Accurate prediction of building energy performance enables stakeholders, including energy policymakers and urban planners, to make informed decisions when planning large-scale retrofit measures

    Structural Damage Detection in the Wooden Bridge Using the Fourier Decomposition, Time Series Modeling and Machine Learning Methods

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    In this article, a novel approach has been employed to identify structural damage in the wooden bridge structure by utilizing vibration data. This method encompasses the Fourier decomposition method that decompose the response of the bridge into a sequence of Fourier Intrinsic Band Functions (FIBF). These functions comprise the responses of the structure that contain inherent information of structure as well as noise from the vibrations. The time series modeling is utilized to extract damage-sensitive features. The residuals of the time series model of both undamaged and damaged structures are extracted for detecting any damage. To ascertain the presence of damage, supervised classification machine learning algorithms are employed. The algorithms are utilized consist of Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), support vector machines (SVM), ensemble learning, and decision tree. The results indicate that the proposed method of feature extraction is highly effective and reliable in detecting damages. In addition, the capacity of decision tree and ANN algorithms to minimize type 2 error and enhance accuracy is demonstrated when evaluating different machine learning algorithms. The value of the type II error in the ANN model and the decision tree is equal to 13.85% and the accuracy of the model is 93.02%

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Sound Event Detection by Exploring Audio Sequence Modelling

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    Everyday sounds in real-world environments are a powerful source of information by which humans can interact with their environments. Humans can infer what is happening around them by listening to everyday sounds. At the same time, it is a challenging task for a computer algorithm in a smart device to automatically recognise, understand, and interpret everyday sounds. Sound event detection (SED) is the process of transcribing an audio recording into sound event tags with onset and offset time values. This involves classification and segmentation of sound events in the given audio recording. SED has numerous applications in everyday life which include security and surveillance, automation, healthcare monitoring, multimedia information retrieval, and assisted living technologies. SED is to everyday sounds what automatic speech recognition (ASR) is to speech and automatic music transcription (AMT) is to music. The fundamental questions in designing a sound recognition system are, which portion of a sound event should the system analyse, and what proportion of a sound event should the system process in order to claim a confident detection of that particular sound event. While the classification of sound events has improved a lot in recent years, it is considered that the temporal-segmentation of sound events has not improved in the same extent. The aim of this thesis is to propose and develop methods to improve the segmentation and classification of everyday sound events in SED models. In particular, this thesis explores the segmentation of sound events by investigating audio sequence encoding-based and audio sequence modelling-based methods, in an effort to improve the overall sound event detection performance. In the first phase of this thesis, efforts are put towards improving sound event detection by explicitly conditioning the audio sequence representations of an SED model using sound activity detection (SAD) and onset detection. To achieve this, we propose multi-task learning-based SED models in which SAD and onset detection are used as auxiliary tasks for the SED task. The next part of this thesis explores self-attention-based audio sequence modelling, which aggregates audio representations based on temporal relations within and between sound events, scored on the basis of the similarity of sound event portions in audio event sequences. We propose SED models that include memory-controlled, adaptive, dynamic, and source separation-induced self-attention variants, with the aim to improve overall sound recognition

    Health prognosis of bearings based on transferable autoregressive recurrent adaptation with few-shot learning

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    Data-driven prognostic and health management technologies are instrumental in accurately monitoring the health of mechanical systems. However, the availability of few-shot source data under varying operating conditions limits their ability to predict health. Also, the global feature extraction process is susceptible to temporal semantic loss, resulting in reduced generalization of extracted degradation features. To address these challenges, a transferable autoregressive recurrent adaptation method is proposed for bearing health prognosis. In the enhancement of few-shot data, a novel sample generation module with attribute-assisted learning, combined with adversarial generation, is introduced to mine data that better matches the source sample distribution. Additionally, a deep autoregressive recurrent model is designed, incorporating a statistical mode to consider the degradation processes more comprehensively. To complement the semantic loss, a semantic attention module is developed, embedded into the basic model of meta learning. To validate the effectiveness of this approach, extensive bearing prognostics are conducted across six tasks. The results demonstrate the clear advantages of this proposed method in bearing prognosis, especially when dealing with limited bearing data
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