14,275 research outputs found

    Openness in Education as a Praxis: From Individual Testimonials to Collective Voices

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    Why is Openness in Education important, and why is it critically needed at this moment? As manifested in our guiding question, the significance of Openness in Education and its immediate necessity form the heart of this collaborative editorial piece. This rather straightforward, yet nuanced query has sparked this collective endeavour by using individual testimonies, which may also be taken as living narratives, to reveal the value of Openness in Education as a praxis. Such testimonies serve as rich, personal narratives, critical introspections, and experience-based accounts that function as sources of data. The data gleaned from these narratives points to the understanding of Openness in Education as a complex, multilayered concept intricately woven into an array of values. These range from aspects such as sharing, access, flexibility, affordability, enlightenment, barrier-removal, empowerment, care, individual agency, trust, innovation, sustainability, collaboration, co-creation, social justice, equity, transparency, inclusivity, decolonization, democratisation, participation, liberty, and respect for diversity. This editorial, as a product of collective endeavour, invites its readers to independently engage with individual narratives, fostering the creation of unique interpretations. This call stems from the distinctive character of each narrative as they voice individual researchers’ perspectives from around the globe, articulating their insights within their unique situational contexts

    Machine Learning Approaches for the Prioritisation of Cardiovascular Disease Genes Following Genome- wide Association Study

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    Genome-wide association studies (GWAS) have revealed thousands of genetic loci, establishing itself as a valuable method for unravelling the complex biology of many diseases. As GWAS has grown in size and improved in study design to detect effects, identifying real causal signals, disentangling from other highly correlated markers associated by linkage disequilibrium (LD) remains challenging. This has severely limited GWAS findings and brought the method’s value into question. Although thousands of disease susceptibility loci have been reported, causal variants and genes at these loci remain elusive. Post-GWAS analysis aims to dissect the heterogeneity of variant and gene signals. In recent years, machine learning (ML) models have been developed for post-GWAS prioritisation. ML models have ranged from using logistic regression to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models (i.e., neural networks). When combined with functional validation, these methods have shown important translational insights, providing a strong evidence-based approach to direct post-GWAS research. However, ML approaches are in their infancy across biological applications, and as they continue to evolve an evaluation of their robustness for GWAS prioritisation is needed. Here, I investigate the landscape of ML across: selected models, input features, bias risk, and output model performance, with a focus on building a prioritisation framework that is applied to blood pressure GWAS results and tested on re-application to blood lipid traits

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: a critical review

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    With the predicted depletion of natural resources and alarming environmental issues, sustainable development has become a popular as well as a much-needed concept in modern process industries. Hence, manufacturers are quite keen on adopting novel process monitoring techniques to enhance product quality and process efficiency while minimizing possible adverse environmental impacts. Hardware sensors are employed in process industries to aid process monitoring and control, but they are associated with many limitations such as disturbances to the process flow, measurement delays, frequent need for maintenance, and high capital costs. As a result, soft sensors have become an attractive alternative for predicting quality-related parameters that are ‘hard-to-measure’ using hardware sensors. Due to their promising features over hardware counterparts, they have been employed across different process industries. This article attempts to explore the state-of-the-art artificial intelligence (Al)-driven soft sensors designed for process industries and their role in achieving the goal of sustainable development. First, a general introduction is given to soft sensors, their applications in different process industries, and their significance in achieving sustainable development goals. AI-based soft sensing algorithms are then introduced. Next, a discussion on how AI-driven soft sensors contribute toward different sustainable manufacturing strategies of process industries is provided. This is followed by a critical review of the most recent state-of-the-art AI-based soft sensors reported in the literature. Here, the use of powerful AI-based algorithms for addressing the limitations of traditional algorithms, that restrict the soft sensor performance is discussed. Finally, the challenges and limitations associated with the current soft sensor design, application, and maintenance aspects are discussed with possible future directions for designing more intelligent and smart soft sensing technologies to cater the future industrial needs

    Explainable fault prediction using learning fuzzy cognitive maps

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    IoT sensors capture different aspects of the environment and generate high throughput data streams. Besides capturing these data streams and reporting the monitoring information, there is significant potential for adopting deep learning to identify valuable insights for predictive preventive maintenance. One specific class of applications involves using Long Short-Term Memory Networks (LSTMs) to predict faults happening in the near future. However, despite their remarkable performance, LSTMs can be very opaque. This paper deals with this issue by applying Learning Fuzzy Cognitive Maps (LFCMs) for developing simplified auxiliary models that can provide greater transparency. An LSTM model for predicting faults of industrial bearings based on readings from vibration sensors is developed to evaluate the idea. An LFCM is then used to imitate the performance of the baseline LSTM model. Through static and dynamic analyses, we demonstrate that LFCM can highlight (i) which members in a sequence of readings contribute to the prediction result and (ii) which values could be controlled to prevent possible faults. Moreover, we compare LFCM with state-of-the-art methods reported in the literature, including decision trees and SHAP values. The experiments show that LFCM offers some advantages over these methods. Moreover, LFCM, by conducting a what-if analysis, could provide more information about the black-box model. To the best of our knowledge, this is the first time LFCMs have been used to simplify a deep learning model to offer greater explainability

    Integrating materials supply in strategic mine planning of underground coal mines

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    In July 2005 the Australian Coal Industry’s Research Program (ACARP) commissioned Gary Gibson to identify constraints that would prevent development production rates from achieving full capacity. A “TOP 5” constraint was “The logistics of supply transport distribution and handling of roof support consumables is an issue at older extensive mines immediately while the achievement of higher development rates will compound this issue at most mines.” Then in 2020, Walker, Harvey, Baafi, Kiridena, and Porter were commissioned by ACARP to investigate Australian best practice and progress made since Gibson’s 2005 report. This report was titled: - “Benchmarking study in underground coal mining logistics.” It found that even though logistics continue to be recognised as a critical constraint across many operations particularly at a tactical / day to day level, no strategic thought had been given to logistics in underground coal mines, rather it was always assumed that logistics could keep up with any future planned design and productivity. This subsequently meant that without estimating the impact of any logistical constraint in a life of mine plan, the risk of overvaluing a mining operation is high. This thesis attempts to rectify this shortfall and has developed a system to strategically identify logistics bottlenecks and the impacts that mine planning parameters might have on these at any point in time throughout a life of mine plan. By identifying any logistics constraints as early as possible, the best opportunity to rectify the problem at the least expense is realised. At the very worst if a logistics constraint was unsolvable then it could be understood, planned for, and reflected in the mine’s ongoing financial valuations. The system developed in this thesis, using a suite of unique algorithms, is designed to “bolt onto” existing mine plans in the XPAC mine scheduling software package, and identify at a strategic level the number of material delivery loads required to maintain planned productivity for a mining operation. Once an event was identified the system then drills down using FlexSim discrete event simulation to a tactical level to confirm the predicted impact and understand if a solution can be transferred back as a long-term solution. Most importantly the system developed in this thesis was designed to communicate to multiple non-technical stakeholders through simple graphical outputs if there is a risk to planned production levels due to a logistics constraint

    Architecture and Advanced Electronics Pathways Toward Highly Adaptive Energy- Efficient Computing

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    With the explosion of the number of compute nodes, the bottleneck of future computing systems lies in the network architecture connecting the nodes. Addressing the bottleneck requires replacing current backplane-based network topologies. We propose to revolutionize computing electronics by realizing embedded optical waveguides for onboard networking and wireless chip-to-chip links at 200-GHz carrier frequency connecting neighboring boards in a rack. The control of novel rate-adaptive optical and mm-wave transceivers needs tight interlinking with the system software for runtime resource management

    Combining Metabolic Engineering and Synthetic Biology Approaches for the Production of Abscisic Acid in Yeast

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    Nature presents us with a myriad of complex and diverse molecules. Many of these molecules prove to be useful to humans and find applications as pharmaceuticals, biofuels, agrochemicals, cosmetic ingredients or food additives. One highly promising natural product with a broad range of potential applications is the terpenoid abscisic acid (ABA). ABA fulfils a pivotal role in higher plants by regulating various developmental processes as well as abiotic stress responses. However, ABA is also produced in many other organisms, including humans. It appears to be a ubiquitous and evolutionary conserved signalling molecule throughout nature. Genetically engineered microorganisms, referred to as microbial cell factories, can be a sustainable source of natural products. In this thesis, a cell factory for the heterologous production of ABA was established and optimized employing the yeast Saccharomyces cerevisiae. Cell factory development is an inherently time-consuming process. As an enabling technology for subsequent work on the ABA cell factory, we expanded the modular cloning toolkit for yeast and made it more applicable for common genetic engineering tasks (Paper I). The ABA biosynthetic pathway of Botrytis cinerea was used to construct an ABA-producing S. cerevisiae strain (Paper II). The activity of two B. cinerea proteins, BcABA1 and BcABA2, was found to limit ABA titers. Two optimization approaches were devised for the following studies. Firstly, various rational engineering targets were explored, of which the native yeast gene PAH1 was identified as the most promising candidate (Paper III). Knockdown of PAH1 benefited ABA production without affecting growth. Secondly, platform strains for screening BcABA1 and BcABA2 enzyme libraries were developed, which utilize an ABA biosensor and enable a high throughput screening approach (Paper IV). In this work, we combined metabolic engineering and synthetic biology approaches for the heterologous production of ABA, and furthermore provided tools and insights that will be useful beyond the scope of this project

    Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review

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    Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables)

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena
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