3,451 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    A knowledge graph-supported information fusion approach for multi-faceted conceptual modelling

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    It has become progressively more evident that a single data source is unable to comprehensively capture the variability of a multi-faceted concept, such as product design, driving behaviour or human trust, which has diverse semantic orientations. Therefore, multi-faceted conceptual modelling is often conducted based on multi-sourced data covering indispensable aspects, and information fusion is frequently applied to cope with the high dimensionality and data heterogeneity. The consideration of intra-facets relationships is also indispensable. In this context, a knowledge graph (KG), which can aggregate the relationships of multiple aspects by semantic associations, was exploited to facilitate the multi-faceted conceptual modelling based on heterogeneous and semantic-rich data. Firstly, rules of fault mechanism are extracted from the existing domain knowledge repository, and node attributes are extracted from multi-sourced data. Through abstraction and tokenisation of existing knowledge repository and concept-centric data, rules of fault mechanism were symbolised and integrated with the node attributes, which served as the entities for the concept-centric knowledge graph (CKG). Subsequently, the transformation of process data to a stack of temporal graphs was conducted under the CKG backbone. Lastly, the graph convolutional network (GCN) model was applied to extract temporal and attribute correlation features from the graphs, and a temporal convolution network (TCN) was built for conceptual modelling using these features. The effectiveness of the proposed approach and the close synergy between the KG-supported approach and multi-faceted conceptual modelling is demonstrated and substantiated in a case study using real-world data

    Undergraduate Catalog of Studies, 2023-2024

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    Digital Innovations for Occupational Safety: Empowering Workers in Hazardous Environments

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    Background: The quest to increase safety awareness, make job sites safer, and promote decent work for all has led to the utilization of digital technologies in hazardous occupations. This study investigated the use of digital innovations for safety and health management in hazardous industries. The key challenges and recommendations associated with such use were also explored. Method: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a total of 48 studies were reviewed to provide a framework for future pathways for the effective implementation of these innovations. Findings: The results revealed four main categories of digital safety systems: wearable-based systems, augmented/virtual reality-based systems, artificial intelligence-based systems, and navigation-based systems. A wide range of technological, behavioral, and organizational challenges were identified in relation to the key themes. Conclusion: Outcomes from this review can inform policymakers and industrial decision-makers about the application of digital innovations for best safety practices in various hazardous work conditions

    ERTNet: an interpretable transformer-based framework for EEG emotion recognition

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    BackgroundEmotion recognition using EEG signals enables clinicians to assess patients’ emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy.MethodsWe developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between channels. Subsequently, the transformer module processes the feature maps to integrate high-level spatiotemporal features, enabling the identification of the prevailing emotional state.ResultsExperiments’ results demonstrated that our model excels in diverse emotion classification, achieving an accuracy of 74.23% ± 2.59% on the dimensional model (DEAP) and 67.17% ± 1.70% on the discrete model (SEED-V). These results surpass the performances of both CNN and LSTM-based counterparts. Through interpretive analysis, we ascertained that the beta and gamma bands in the EEG signals exert the most significant impact on emotion recognition performance. Notably, our model can independently tailor a Gaussian-like convolution kernel, effectively filtering high-frequency noise from the input EEG data.DiscussionGiven its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface

    Graduate Catalog of Studies, 2023-2024

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    PtoMYB031, the R2R3 MYB transcription factor involved in secondary cell wall biosynthesis in poplar

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    IntroductionThe biosynthesis of the secondary cell wall (SCW) is orchestrated by an intricate hierarchical transcriptional regulatory network. This network is initiated by first-layer master switches, SCW-NAC transcription factors, which in turn activate the second-layer master switches MYBs. These switches play a crucial role in regulating xylem specification and differentiation during SCW formation. However, the roles of most MYBs in woody plants are yet to be fully understood.MethodsIn this study, we identified and isolated the R2R3-MYB transcription factor, PtoMYB031, from Populus tomentosa. We explored its expression, mainly in xylem tissues, and its role as a transcriptional repressor in the nucleus. We used overexpression and RNA interference techniques in poplar, along with Yeast two-hybrid (Y2H) and bimolecular fluorescence complementation (BiFC) assays, to analyze the regulatory effects of PtoMYB031.ResultsOverexpression of PtoMYB031 in poplar significantly reduced lignin, cellulose, and hemicellulose content, and inhibited vascular development in stems, resulting in decreased SCW thickness in xylem tissues. Gene expression analysis showed that structural genes involved in SCW biosynthesis were downregulated in PtoMYB031-OE lines. Conversely, RNA interference of PtoMYB031 increased these compounds. Additionally, PtoMYB031 was found to recruit the repressor PtoZAT11, forming a transcriptional inhibition complex.DiscussionOur findings provide new insights into how PtoMYB031, through its interaction with PtoZAT11, forms a complex that can suppress the expression of key regulatory genes, PtoWND1A and PtoWND2B, in SCW biosynthesis. This study enhances our understanding of the transcriptional regulation involved in SCW formation in poplar, highlighting the significant role of PtoMYB031

    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

    Functional iridoid synthases from iridoid producing and non-producing Nepeta species (subfam. Nepetoidae, fam. Lamiaceae)

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    Iridoids, a class of atypical monoterpenes, exhibit exceptional diversity within the Nepeta genus (subfam. Nepetoidae, fam. Lamiaceae).The majority of these plants produce iridoids of the unique stereochemistry, with nepetalactones (NLs) predominating; however, a few Nepeta species lack these compounds. By comparatively analyzing metabolomics, transcriptomics, gene co-expression, and phylogenetic data of the iridoid-producing N. rtanjensis Diklić & Milojević and iridoid-lacking N. nervosa Royle & Bentham, we presumed that one of the factors responsible for the absence of these compounds in N. nervosa is iridoid synthase (ISY). Two orthologues of ISY were mined from leaves transcriptome of N. rtanjensis (NrPRISE1 and NrPRISE2), while in N. nervosa only one (NnPRISE) was identified, and it was phylogenetically closer to the representatives of the Family 1 isoforms, designated as P5βRs. Organ-specific and MeJA-elicited profiling of iridoid content and co-expression analysis of IBG candidates, highlighted NrPRISE2 and NnPRISE as promising candidates for ISY orthologues, and their function was confirmed using in vitro assays with recombinant proteins, after heterologous expression of recombinant proteins in E. coli and their His-tag affinity purification. NrPRISE2 demonstrated ISY activity both in vitro and likely in planta, which was supported by the 3D modeling and molecular docking analysis, thus reclassification of NrPRISE2 to NrISY is accordingly recommended. NnPRISE also displays in vitro ISY-like activity, while its role under in vivo conditions was not here unambiguously confirmed. Most probably under in vivo conditions the NnPRISE lacks substrates to act upon, as a result of the loss of function of some of the upstream enzymes of the iridoid pathway. Our ongoing work is conducted towards re-establishing the biosynthesis of iridoids in N. nervosa
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