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Structural performance of a novel sustainable and demountable composite floor system - recycled aggregate concrete-steel composite beam utilising demountable shear connectors
Get PDFThis study examines sustainable construction methods, particularly the reusing of steel beams and the recycling of concrete materials. This study specifically looks at how recycled aggregate concrete (RAC) and demountable shear connectors could be used in composite floor systems, an area that has yet to be looked into enough in the past. The demand for sustainable solutions in the construction sector is rising, and this research responds to that necessity by offering an innovative flooring system that integrates RAC with demountable shear connectors, specifically for application in temporary and short-term leasing structures.
A literature study on concrete microstructure indicated that residual mortar on recycled aggregate alters the interfacial transition zone (ITZ), influencing the mechanical properties of RAC. A review was conducted on the structural uses of RAC, encompassing RAC-filled steel tubes and composite slabs. For the development of the proposed innovative floor system, two types of demountable shear connectors were evaluated, with the bolted type chosen for its practicality. Bondek II was chosen for profiled steel decking because of its benefits and prevalent application in the local market. A study gap was identified: the majority of studies on RAC employ a fixed mix design, altering the percentage of recycled aggregate instead of sustaining a consistent goal concrete strength. This complicates the assessment of whether diminished structural resistance results from the utilisation of recycled aggregates or from a reduction in design strength relative to normal aggregate concrete (NAC). A fixed design strength approach was suggested to provide target strength by augmenting recycled aggregate replacement and diminishing the water-to-cement ratio. The primary scientific challenge is ascertaining the ideal water-to-cement ratio to facilitate recycled aggregates substitution and attain the requisite concrete strength.
Two main tests are proposed. The first assessment is the push-off test, frequently employed to assess the shear behaviour of shear connections. Two categories of push-off specimens have been developed, each comprising four identical geometries, distinguished by shear connector dimensions: M20 and M24. The M20 bolts are positioned in closer proximity to establish a complete connection, whilst the larger M24 bolts are arranged at greater intervals for apartial connection. The recycled aggregate substitution ratio ranges from 0% to 30%, 70%, and 100%,
delineating the four specimen categories. Test results demonstrate that the shear resistance of bolted connectors in RAC increases by up to 40% with a higher proportion of recycled aggregate, corresponding to a lower water-to-cement ratio. Full bending tests were performed to evaluate the overall structural performance of the proposed composite floor system. Five test specimens were produced and classified into two categories according to the size of the shear connectors. The first type employed M20 connectors at 200 mm intervals, aligning with the push-off test, and utilised three concrete mixtures: 0% recycled aggregate (baseline), 30%, and 100% replacement. The second variant employed M24 connectors at 400 mm intervals, adhering to the identical push-off test design, utilising two mixtures: 30% and 100% replacement. Test results indicate that composite beams with bolted connectors provide up to a 10% enhancement in flexural resistance in RAC relative to NAC.
Subsequent to testing, the pertinent codes were employed to compute resistances and juxtapose them with experimental outcomes. EC3 and EC4 appropriately forecasts shear resistance for bolted connectors in NAC but underestimates it in RAC by 16–51%. AISC and ACI provide more precise average projections; nonetheless, they yield unsafe estimates for NAC, underestimating by as much as 13%, and do not reliably identify the failure mode. Both EC4 and AISC accurately forecast the bending resistance of composite sections, with a mere 2% overestimation of NAC resistance. Nonetheless, they underestimate the bending resistance of RAC by as much as 9%, with AISC exhibiting better precision compared to EC4. Furthermore, the findings demonstrate that bending resistance predictions derived from push-off test data closely correspond with experimental bending test outcomes, indicating that push-off tests function as a reliable indirect approach for evaluating bending resistance with enhanced precision
Understanding virus-host interactions using artificial intelligence
No full textViruses are associated with a wide range of hosts, encompassing all cellular life: humans, animals, plants and bacteria. Virus-host interactions are complex and diverse across species, mostly mediated by protein-protein interactions (PPIs). PPIs play important roles in essential biological activities, including forming protein complexes or more transient interactions in the context signalling pathways, regulatory networks etc., some of which are important for virus infection such as viral entry and replication. Understanding PPI mechanisms is helpful for revealing virus-host interactions, identifying PPIs associated with diseases and discovering potential therapeutic targets. However, the host specificity of most viruses remains unknown, and PPI networks remain sparse except for a few well-studied host species such as human. In this thesis, we developed computational approaches to predict host species of viruses and PPIs from genomes and corresponding protein sequences alone. Firstly, we introduce EvoMIL, a deep learning framework that leverages the protein language model (PLM) for viral protein representations and trains a multiple instance learning (MIL) model to predict prokaryotic and eukaryotic host species. We show that EvoMIL improves the accuracy of host species prediction and can identify key viral proteins that contribute to host specificity. Next, we introduce a deep-learning model, PLM-interact, jointly encoding protein pairs to learn protein interactions, analogous to the nextsentence prediction task in natural language processing (NLP). We show that PLM-interact improves PPI prediction in the intra-species benchmarking task and can identify mutational impacts of human PPIs. We show that PLM-interact can be implemented to predict virus-host PPIs. To enhance training datasets, we construct a dataset by integrating seven public virus-human PPI databases. We introduce three data-splitting strategies to create training, validation and test datasets where training and test sets have varying protein similarities, enabling comprehensive model evaluation. We discover that fine-tuning the human model on virus-human PPIs improves virus-human PPI prediction, offering the potential for developing a generalizable PPI model. In summary, this thesis aims to use deep learning techniques to predict the host specificity for viruses and identify PPIs within and between species using protein sequences. This broadens our view of virus-host interactions and provides insights into developing vaccines, drugs and therapies for human diseases
Neuromorphic signal processing for wearable devices
Get PDFWearable health devices have a strong demand in real-time biomedical signal processing. Over the past few decades, advances in Artificial Intelligence (AI), and particularly the development of neural networks, have significantly impacted the field of signal processing, enabling more efficient and accurate analysis of complex biomedical signals. However, continuous monitoring could generate massive amounts of data, resulting in an information bottleneck that challenges data transfer and subsequent post-processing. Neuromorphic computing, an emerging brain-inspired computational architecture, has garnered significant research attention in recent years. In contrast to the conventional von Neumann architecture, which relies on a separation between memory and processing units, neuromorphic systems integrate computation and data storage within a unified physical framework. This design emulates the synaptic dynamics observed in biological neural networks. Such methods can process data proximate to the sensor with reduced power consumption, latency and bandwidth, providing new solutions to signal processing for wearable devices. Among neuromorphic systems, Physical Reservoir Computing (PRC) has emerged as a compelling solution. PRC harnesses the intrinsic dynamics of physical systems to accelerate and reduce the energy consumption of machine learning computations.
This thesis focuses on modelling and implementing PRC frameworks in signal processing applications. In the initial stage, the potential of PRC as a predictor for biomedical applications was explored. The model successfully maps the Magnetomyography (MMG) signal to Electromyography (EMG) with an acceptable normalized root mean square error (NRMSE) of 0.3894. In addition, an average NRMSE of 0.3690 was obtained for predicting Electrocardiography (ECG) to Phonocardiography (PCG).
However, practical applications of signal processing present more complex challenges. While the neuromorphic signal processing underperforms compared to state-of-the-art Deep Learning (DL) algorithms, and relatively few studies have investigated its application in classification tasks, the research is extended to examine the use of PRC in a complex biometric identification task. An overall classification accuracy of 89.03% in identifying twelve testing subjects was achieved during the intermediate stage.
Nevertheless, a significant concern emerged with respect to maintaining precision when implementing high-resolution signals via electronic circuits in PRC-based systems, particularly due to limitations in electronic components. This challenge motivated the research to the next stage that explores a hybrid approach of combining PRC and Spiking Neural Network (SNN), which allows for the conversion of signals from the high-precision analogue domain into a low-precision discrete spiking domain, thereby reducing hardware and storage costs. Consequently, an event-driven PRC framework was proposed and validated in the final stages to address this concern. An average classification accuracy of 80.3% was obtained in classifying 50 gestures which outperforms current SNN-based methods. The results pipeline a new insight into processing real-time signals at the edge for wearable devices, promising compact and ultra-low power electronic systems for temporal signal processing in wearable devices
Diagnosis and phenotypes of coronary microvascular dysfunction
No full textAbstract not currently available
Reality anchors: investigating the use of reality cues for socially acceptable immersive technologies in transit
Get PDFImmersive technologies, such as virtual reality (VR) headsets, offer opportunities to transform time spent in transit by customising the user's reality with virtual content rendered anywhere around them. However, their widespread use remains limited due to the disconnect they create from the surrounding environment, reducing user’s awareness and ability to respond to social cues. To address this challenge, this thesis proposes the concept of Reality Anchors, which integrate cues from the real world into virtual environments to retain immersion and alleviate concerns about using immersive technology in transit. Through a series of studies, this research investigates how Reality Anchors can address awareness needs and support the adoption of immersive technologies in transit.
Studies I and II identified barriers to adoption through surveys, confirming that immersive headset use in transit raises concerns about safety, awareness, and social acceptance. Rooted in users' lack of awareness of surroundings, other passengers, personal belongings, and journey progress, these concerns varied with journey length. Longer journeys, such as on flights, showed higher acceptance due to lower awareness needs and greater interest in entertainment, while shorter journeys, like those on buses, posed greater challenges requiring heightened awareness. These findings informed the design of initial Reality Anchors focused on addressing safety, awareness and social concerns. Building on this, Study III evaluated Reality Anchors using VR simulations of short transit journeys, identifying people and personal belongings as the most useful anchors. Study IV extended this exploration, investigating anchor usage in journey types categorised as self-managed and externally managed. Findings revealed that Reality Anchors must be flexible to accommodate changing user needs, with self-managed journeys requiring more anchor support. Finally, Studies V and VI bridge the gap between lab and real-world contexts. Study V explored asymmetric co-located passenger experiences, where passengers using different devices navigated real unexpected interactions. Study VI examined how passengers maintained awareness under changing real-world conditions. Together, these studies demonstrate the potential of Reality Anchors to reduce key safety, awareness, and social concerns. This thesis represents a first step toward enhancing immersive technology acceptance in transit environments and provides actionable recommendations for the future design of Reality Anchors
Practical and theoretical considerations for working with emotions in autonomous systems: applying the appraisal theory of emotion to guide methodological design
No full textAbstract not currently available
Disinvestment initiatives in healthcare
Get PDFBackground:
Health systems worldwide are increasingly adopting disinvestment initiatives to enhance care quality and maximize value by eliminating ineffective and obsolete interventions. However, removing low-value care (LVC) is challenging and complex, as many were adopted without rigorous evidence of clinical or cost-effectiveness, as well as resistance from healthcare stakeholders due to uncertainty in the outcomes of disinvestment. Inconsistent systems for identifying such technologies exacerbate this issue, along with unclear methodologies to assess these LVC. Existing disinvestment efforts have achieved mixed success, highlighting a critical need for structured, inclusive, and evidence-based approaches to address these gaps and enhance the implementation of the initiatives.
Methods:
This multi-method study included a scoping review of healthcare disinvestment initiatives, a mixed-method study integrating an online survey and key informant interviews with Malaysian healthcare stakeholders, and triangulation of findings to inform the development of a decision-making framework. Two case studies were conducted to assess LVC candidates, and the proposed framework was pilot-tested in a stakeholder workshop.
Results:
Stakeholder engagement emerged as pivotal for the success of disinvestment initiatives. Through stakeholder engagement, scoping review, and consultation with experts, this thesis introduces a novel decision-making framework based on the value of de-implementation concept, which incorporates five key domains: health impact, equity considerations, enablers for disinvestment, system readiness, and economic impact. Pilot-testing with Malaysian stakeholders demonstrated the framework’s feasibility and acceptance, supporting its role in promoting a systematic, transparent, and inclusive approach to disinvestment.
Implications:
Implementing healthcare disinvestment as a policy-driven initiative requires a complex approach of stakeholder engagement and empowerment, as well as systematic and comprehensive methodology. This process is heavily dependent on reliable data and evidence to support the assessment and decision-making process, availability of guidance or framework for decision-making, as well as leadership commitment and resources to ensure the sustainability of the effort. Future work should aim to integrate public and patient perspectives into the decision-making process, with opportunities to refine, evaluate, and validate the proposed framework. Further research is needed in assessing the impact of disinvestment decisions and recommendations, especially in optimising value in health service delivery and transition to higher-value strategies as a result of de-implementing LVC
Conceptualising the Fintech ecosystem: a case study of Kazakhstan
No full textAbstract not currently available
The entrepreneurial orientation of females in Saudi Arabia: the associations with informal and formal institutions
Get PDFWhile entrepreneurship is considered a critical factor in developing economies, it is essential to note that the focus on entrepreneurship has broadened from investigating key individual characteristics influencing entrepreneurship to exploring the influence of institutional factors such as policies, education, financial support and other informal factors. Therefore, this study examined the impact of national culture (NC) represented by Hofstede’s dimensions, namely power distance tolerance (PD), uncertainty avoidance (UA), individualism (IND), masculinity (MAS) and long-term orientation (LTO), on entrepreneurial orientation (EO) among female Saudi entrepreneurs in Saudi Arabia. The study also examined the moderating effect of access to finance (ATF) on the relationship between cultural dimensions and EO. The study revealed valuable results, including the fact that female entrepreneurs reported a quite different set of cultural values from those of Saudi Arabia as a whole, and that their IND and MAS positively influenced their EO respectively. In contrast, their LTO, UA and PD showed no significant association with EO. Furthermore, the hypothesised moderating effect of ATF on the relationships between NC and EO was not supported. The findings emphasise various important conclusions. For example, as ATF was not significant here, there may be a need to provide comprehensive support systems to enhance EO, such as mentorship programmes, customised entrepreneurial training, and other market access initiatives. Saudi policymakers may focus on combining financial and non-financial support, including capacity-building and support, which can be done by developing multifaceted strategies that enhance entrepreneurial ecosystems and manage entrepreneurs' challenges. Furthermore, multi-faceted strategies developed by policymakers may wish to address gender-specific challenges by implementing tailored entrepreneurial training programmes, increasing female representation in leadership roles, and creating inclusive networking opportunities
The politics of autotheory: forms, strategies, feminisms
No full textAbstract not currently available
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