12,185 research outputs found

    Rank-based linkage I: triplet comparisons and oriented simplicial complexes

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    Rank-based linkage is a new tool for summarizing a collection SS of objects according to their relationships. These objects are not mapped to vectors, and ``similarity'' between objects need be neither numerical nor symmetrical. All an object needs to do is rank nearby objects by similarity to itself, using a Comparator which is transitive, but need not be consistent with any metric on the whole set. Call this a ranking system on SS. Rank-based linkage is applied to the KK-nearest neighbor digraph derived from a ranking system. Computations occur on a 2-dimensional abstract oriented simplicial complex whose faces are among the points, edges, and triangles of the line graph of the undirected KK-nearest neighbor graph on SS. In ∣S∣K2|S| K^2 steps it builds an edge-weighted linkage graph (S,L,σ)(S, \mathcal{L}, \sigma) where σ({x,y})\sigma(\{x, y\}) is called the in-sway between objects xx and yy. Take Lt\mathcal{L}_t to be the links whose in-sway is at least tt, and partition SS into components of the graph (S,Lt)(S, \mathcal{L}_t), for varying tt. Rank-based linkage is a functor from a category of out-ordered digraphs to a category of partitioned sets, with the practical consequence that augmenting the set of objects in a rank-respectful way gives a fresh clustering which does not ``rip apart`` the previous one. The same holds for single linkage clustering in the metric space context, but not for typical optimization-based methods. Open combinatorial problems are presented in the last section.Comment: 37 pages, 12 figure

    The Viability and Potential Consequences of IoT-Based Ransomware

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    With the increased threat of ransomware and the substantial growth of the Internet of Things (IoT) market, there is significant motivation for attackers to carry out IoT-based ransomware campaigns. In this thesis, the viability of such malware is tested. As part of this work, various techniques that could be used by ransomware developers to attack commercial IoT devices were explored. First, methods that attackers could use to communicate with the victim were examined, such that a ransom note was able to be reliably sent to a victim. Next, the viability of using "bricking" as a method of ransom was evaluated, such that devices could be remotely disabled unless the victim makes a payment to the attacker. Research was then performed to ascertain whether it was possible to remotely gain persistence on IoT devices, which would improve the efficacy of existing ransomware methods, and provide opportunities for more advanced ransomware to be created. Finally, after successfully identifying a number of persistence techniques, the viability of privacy-invasion based ransomware was analysed. For each assessed technique, proofs of concept were developed. A range of devices -- with various intended purposes, such as routers, cameras and phones -- were used to test the viability of these proofs of concept. To test communication hijacking, devices' "channels of communication" -- such as web services and embedded screens -- were identified, then hijacked to display custom ransom notes. During the analysis of bricking-based ransomware, a working proof of concept was created, which was then able to remotely brick five IoT devices. After analysing the storage design of an assortment of IoT devices, six different persistence techniques were identified, which were then successfully tested on four devices, such that malicious filesystem modifications would be retained after the device was rebooted. When researching privacy-invasion based ransomware, several methods were created to extract information from data sources that can be commonly found on IoT devices, such as nearby WiFi signals, images from cameras, or audio from microphones. These were successfully implemented in a test environment such that ransomable data could be extracted, processed, and stored for later use to blackmail the victim. Overall, IoT-based ransomware has not only been shown to be viable but also highly damaging to both IoT devices and their users. While the use of IoT-ransomware is still very uncommon "in the wild", the techniques demonstrated within this work highlight an urgent need to improve the security of IoT devices to avoid the risk of IoT-based ransomware causing havoc in our society. Finally, during the development of these proofs of concept, a number of potential countermeasures were identified, which can be used to limit the effectiveness of the attacking techniques discovered in this PhD research

    TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH

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    Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)

    Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

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    Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri

    How to Be a God

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    When it comes to questions concerning the nature of Reality, Philosophers and Theologians have the answers. Philosophers have the answers that can’t be proven right. Theologians have the answers that can’t be proven wrong. Today’s designers of Massively-Multiplayer Online Role-Playing Games create realities for a living. They can’t spend centuries mulling over the issues: they have to face them head-on. Their practical experiences can indicate which theoretical proposals actually work in practice. That’s today’s designers. Tomorrow’s will have a whole new set of questions to answer. The designers of virtual worlds are the literal gods of those realities. Suppose Artificial Intelligence comes through and allows us to create non-player characters as smart as us. What are our responsibilities as gods? How should we, as gods, conduct ourselves? How should we be gods

    Machine learning and large scale cancer omic data: decoding the biological mechanisms underpinning cancer

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    Many of the mechanisms underpinning cancer risk and tumorigenesis are still not fully understood. However, the next-generation sequencing revolution and the rapid advances in big data analytics allow us to study cells and complex phenotypes at unprecedented depth and breadth. While experimental and clinical data are still fundamental to validate findings and confirm hypotheses, computational biology is key for the analysis of system- and population-level data for detection of hidden patterns and the generation of testable hypotheses. In this work, I tackle two main questions regarding cancer risk and tumorigenesis that require novel computational methods for the analysis of system-level omic data. First, I focused on how frequent, low-penetrance inherited variants modulate cancer risk in the broader population. Genome-Wide Association Studies (GWAS) have shown that Single Nucleotide Polymorphisms (SNP) contribute to cancer risk with multiple subtle effects, but they are still failing to give further insight into their synergistic effects. I developed a novel hierarchical Bayesian regression model, BAGHERA, to estimate heritability at the gene-level from GWAS summary statistics. I then used BAGHERA to analyse data from 38 malignancies in the UK Biobank. I showed that genes with high heritable risk are involved in key processes associated with cancer and are often localised in genes that are somatically mutated drivers. Heritability, like many other omics analysis methods, study the effects of DNA variants on single genes in isolation. However, we know that most biological processes require the interplay of multiple genes and we often lack a broad perspective on them. For the second part of this thesis, I then worked on the integration of Protein-Protein Interaction (PPI) graphs and omics data, which bridges this gap and recapitulates these interactions at a system level. First, I developed a modular and scalable Python package, PyGNA, that enables robust statistical testing of genesets' topological properties. PyGNA complements the literature with a tool that can be routinely introduced in bioinformatics automated pipelines. With PyGNA I processed multiple genesets obtained from genomics and transcriptomics data. However, topological properties alone have proven to be insufficient to fully characterise complex phenotypes. Therefore, I focused on a model that allows to combine topological and functional data to detect multiple communities associated with a phenotype. Detecting cancer-specific submodules is still an open problem, but it has the potential to elucidate mechanisms detectable only by integrating multi-omics data. Building on the recent advances in Graph Neural Networks (GNN), I present a supervised geometric deep learning model that combines GNNs and Stochastic Block Models (SBM). The model is able to learn multiple graph-aware representations, as multiple joint SBMs, of the attributed network, accounting for nodes participating in multiple processes. The simultaneous estimation of structure and function provides an interpretable picture of how genes interact in specific conditions and it allows to detect novel putative pathways associated with cancer

    A Syntactical Reverse Engineering Approach to Fourth Generation Programming Languages Using Formal Methods

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    Fourth-generation programming languages (4GLs) feature rapid development with minimum configuration required by developers. However, 4GLs can suffer from limitations such as high maintenance cost and legacy software practices. Reverse engineering an existing large legacy 4GL system into a currently maintainable programming language can be a cheaper and more effective solution than rewriting from scratch. Tools do not exist so far, for reverse engineering proprietary XML-like and model-driven 4GLs where the full language specification is not in the public domain. This research has developed a novel method of reverse engineering some of the syntax of such 4GLs (with Uniface as an exemplar) derived from a particular system, with a view to providing a reliable method to translate/transpile that system's code and data structures into a modern object-oriented language (such as C\#). The method was also applied, although only to a limited extent, to some other 4GLs, Informix and Apex, to show that it was in principle more broadly applicable. A novel testing method that the syntax had been successfully translated was provided using 'abstract syntax trees'. The novel method took manually crafted grammar rules, together with Encapsulated Document Object Model based data from the source language and then used parsers to produce syntactically valid and equivalent code in the target/output language. This proof of concept research has provided a methodology plus sample code to automate part of the process. The methodology comprised a set of manual or semi-automated steps. Further automation is left for future research. In principle, the author's method could be extended to allow the reverse engineering recovery of the syntax of systems developed in other proprietary 4GLs. This would reduce time and cost for the ongoing maintenance of such systems by enabling their software engineers to work using modern object-oriented languages, methodologies, tools and techniques

    Breaking Ub with Leishmania mexicana: a ubiquitin activating enzyme as a novel therapeutic target for leishmaniasis

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    Leishmaniasis is a neglected tropical disease, which inflicts a variety of gruesome pathologies on humans. The number of individuals afflicted with leishmaniasis is thought to vary between 0.7 and 1.2 million annually, of whom it is estimated that 20 to 40 thousand die. This problem is exemplary of inequality in healthcare – current leishmaniasis treatments are inadequate due to toxicity, cost, and ineffectiveness, so there is an urgent need for improved chemotherapies. Ubiquitination is a biochemical pathway that has received attention in cancer research. It is the process of adding the ubiquitin protein as a post-translational modification to substrate proteins, using an enzymatic cascade comprised of enzymes termed E1s, E2s, and E3s. Ubiquitination can lead to degradation of substrate proteins, or otherwise modulate their function. As the name suggests, this modification can be found across eukaryotic cell biology. As such, interfering with ubiquitination may interfere with essential biological processes, which means ubiquitination may present a new therapeutic target for leishmaniasis. Before ubiquitination inhibitors can be designed, components of the ubiquitination system must be identified. To this end, a bioinformatic screening campaign employed BLASTs and hidden Markov models, using characterised orthologs from model organisms as bait, to screen publicly-available Leishmania mexicana genome sequence databases, searching for genes encoding putative E1s, E2s, and E3s. To confirm some of these identifications on a protein level, activity-based probes, protein pulldowns, and mass spectrometry were used. Using an activity-based probe that emulates the structure of adenylated ubiquitin, E1s were identified, and their relative abundance quantified. A chemical crosslinker extended the reach of this probe, allowing the identification of an E2 (LmxM.33.0900). It is noted that L. mexicana has two E1s – unusual for a single celled organism. Of these E1s, LmxM.34.3060 was considerably more abundant than LmxM.23.0550 in both major life cycle stages of the in vitro Leishmania cultures. It is important to describe the wider context of these enzymes – what is their interactome, what are their substrates? To study this, CRISPR was used to fuse a proximity-based labelling system, BioID, on genes of interest – LmxM.34.3060 and LmxM.33.0900. The E2 (LmxM.33.0900) was shown to interact with the E1 (LmxM.34.3060), validating the results from the activity-based probe and crosslinker experiments. Due to sequence homology with characterised orthologs, the E2 was hypothesised to function in the endoplasmic reticulum degradation pathway. Immunoprecipitations of a ubiquitin motif, diglycine, were conducted with a view to gathering information on the substrates of ubiquitin. Anti-diglycine peptides included some of those identified by BioID. Experiments examining ubiquitin’s role in the DNA damage response were also initiated, as were improvements to the proximity-based labelling system, however these were not followed to completion due to a lack of time and resources. To examine the possibility of finding novel drug targets in the ubiquitination cascade, recombinant proteins were expressed. LmxM.34.3060 was expressed in a functional form, while a putative SUMO E2 (LmxM.02.0390) was functional after refolding. Expressed LmxM.33.0900 was not functional and could not be refolded into a functional form. Drug assays were conducted on LmxM.34.3060, which found an inhibitor of the human ortholog, TAK-243, to be 20-fold less effective against the Leishmania enzyme. Additional assays found an inhibitor that was 50-fold more effective at inhibiting the Leishmania enzyme as opposed to its human equivalent - 5'O-sulfamoyl adenosine. Furthermore, a new mechanism of action, inhibiting the E1, for was identified for drugs previously characterised to inhibit protein synthesis. LmxM.34.3060 underwent biophysical characterisation, with structural information obtained using SAXS and protein crystallography. A crystal structure was solved to 3.1 Å, with the in-solution SAXS structure complementary to this. TAK-243 was modelled into the LmxM.34.3060 structure and clashes were predicted, concurring with TAK-243’s reduced efficacy against the Leishmania enzyme in the drug assays. This project aimed to characterise the potential of an understudied biochemical system to provide novel therapeutic targets for a neglected tropical pathogen. To achieve this aim it presents the identifications of two E1s, an interactome, a structure, and a potent, selective inhibitor of a Leishmania ubiquitin activating enzyme
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