12,805 research outputs found

    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

    Increased lifetime of Organic Photovoltaics (OPVs) and the impact of degradation, efficiency and costs in the LCOE of Emerging PVs

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    Emerging photovoltaic (PV) technologies such as organic photovoltaics (OPVs) and perovskites (PVKs) have the potential to disrupt the PV market due to their ease of fabrication (compatible with cheap roll-to-roll processing) and installation, as well as their significant efficiency improvements in recent years. However, rapid degradation is still an issue present in many emerging PVs, which must be addressed to enable their commercialisation. This thesis shows an OPV lifetime enhancing technique by adding the insulating polymer PMMA to the active layer, and a novel model for quantifying the impact of degradation (alongside efficiency and cost) upon levelized cost of energy (LCOE) in real world emerging PV installations. The effect of PMMA morphology on the success of a ternary strategy was investigated, leading to device design guidelines. It was found that either increasing the weight percent (wt%) or molecular weight (MW) of PMMA resulted in an increase in the volume of PMMA-rich islands, which provided the OPV protection against water and oxygen ingress. It was also found that adding PMMA can be effective in enhancing the lifetime of different active material combinations, although not to the same extent, and that processing additives can have a negative impact in the devices lifetime. A novel model was developed taking into account realistic degradation profile sourced from a literature review of state-of-the-art OPV and PVK devices. It was found that optimal strategies to improve LCOE depend on the present characteristics of a device, and that panels with a good balance of efficiency and degradation were better than panels with higher efficiency but higher degradation as well. Further, it was found that low-cost locations were more favoured from reductions in the degradation rate and module cost, whilst high-cost locations were more benefited from improvements in initial efficiency, lower discount rates and reductions in install costs

    The applied psychology of addictive orientations : studies in a 12-step treatment context.

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    The clinical data for the studies was collected at The PROMIS Recovery Centre, a Minnesota Model treatmentc entre for addictions,w hich encouragesth e membership and use of the 12 step Anonymous Fellowships, and is abstinence based. The area of addiction is contextualised in a review chapter which focuses on research relating to the phenomenon of cross addiction. A study examining the concept of "addictive orientations" in male and female addicts is described, which develops a study conductedb y StephensonM, aggi, Lefever, & Morojele (1995). This presents study found a four factor solution which appeared to be subdivisions of the previously found Hedonism and Nurturance factors. Self orientated nurturance (both food dimensions, shopping and caffeine), Other orientated nurturance (both compulsive helping dimensions and work), Sensation seeking hedonism (Drugs, prescription drugs, nicotine and marginally alcohol), and Power related hedonism (Both relationship dimensions, sex and gambling. This concept of "addictive orientations" is further explored in a non-clinical population, where again a four factor solution was found, very similar to that in the clinical population. This was thought to indicate that in terms of addictive orientation a pattern already exists in this non-clinical population and that consideration should be given to why this is the case. These orientations are examined in terms of gender differences. It is suggested that the differences between genders reflect power-related role relationships between the sexes. In order to further elaborate the significance and meaning behind these orientations, the next two chapters look at the contribution of personality variables and how addictive orientations relate to psychiatric symptomatology. Personality variables were differentially, and to a considerable extent predictably involved with the four factors for both males and females.Conscientiousness as positively associated with "Other orientated Nurturance" and negatively associated with "Sensation seeking hedonism" (particularly for men). Neuroticism had a particularly strong association with the "Self orientated Nurturance" factor in the female population. More than twice the symptomatology variance was explained by the factor scores for females than it was for males. The most important factorial predictors for psychiatric symptomatology were the "Power related hedonism" factor for males, and "Self oriented nurturance" for females. The results are discussed from theoretical and treatment perspectives

    Anytime algorithms for ROBDD symmetry detection and approximation

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    Reduced Ordered Binary Decision Diagrams (ROBDDs) provide a dense and memory efficient representation of Boolean functions. When ROBDDs are applied in logic synthesis, the problem arises of detecting both classical and generalised symmetries. State-of-the-art in symmetry detection is represented by Mishchenko's algorithm. Mishchenko showed how to detect symmetries in ROBDDs without the need for checking equivalence of all co-factor pairs. This work resulted in a practical algorithm for detecting all classical symmetries in an ROBDD in O(|G|³) set operations where |G| is the number of nodes in the ROBDD. Mishchenko and his colleagues subsequently extended the algorithm to find generalised symmetries. The extended algorithm retains the same asymptotic complexity for each type of generalised symmetry. Both the classical and generalised symmetry detection algorithms are monolithic in the sense that they only return a meaningful answer when they are left to run to completion. In this thesis we present efficient anytime algorithms for detecting both classical and generalised symmetries, that output pairs of symmetric variables until a prescribed time bound is exceeded. These anytime algorithms are complete in that given sufficient time they are guaranteed to find all symmetric pairs. Theoretically these algorithms reside in O(n³+n|G|+|G|³) and O(n³+n²|G|+|G|³) respectively, where n is the number of variables, so that in practice the advantage of anytime generality is not gained at the expense of efficiency. In fact, the anytime approach requires only very modest data structure support and offers unique opportunities for optimisation so the resulting algorithms are very efficient. The thesis continues by considering another class of anytime algorithms for ROBDDs that is motivated by the dearth of work on approximating ROBDDs. The need for approximation arises because many ROBDD operations result in an ROBDD whose size is quadratic in the size of the inputs. Furthermore, if ROBDDs are used in abstract interpretation, the running time of the analysis is related not only to the complexity of the individual ROBDD operations but also the number of operations applied. The number of operations is, in turn, constrained by the number of times a Boolean function can be weakened before stability is achieved. This thesis proposes a widening that can be used to both constrain the size of an ROBDD and also ensure that the number of times that it is weakened is bounded by some given constant. The widening can be used to either systematically approximate an ROBDD from above (i.e. derive a weaker function) or below (i.e. infer a stronger function). The thesis also considers how randomised techniques may be deployed to improve the speed of computing an approximation by avoiding potentially expensive ROBDD manipulation

    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

    Understanding the Relationship among Durable Goods, Academic Achievement, and School Attendance in Colombia

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    A joint report from the United Nations Development Program and the Oxford Poverty and Human Development Initiative indicates that while the number of people living with less than 1.90adaydeclinedglobally,droppingfrom2billionin1990to736millionin2015,thenumberofpeoplewhoexperiencednon−incomepovertyreached1.3billionin2020.Non−incomepoverty,referredtoasmultidimensionalpoverty,assessestheextenttowhichpeoplearedeprivedfromaccessingbasicservicessuchashealth,education,orattainingdecentlivingstandards,despitehavingincomelevelswellabove1.90 a day declined globally, dropping from 2 billion in 1990 to 736 million in 2015, the number of people who experienced non-income poverty reached 1.3 billion in 2020. Non-income poverty, referred to as multidimensional poverty, assesses the extent to which people are deprived from accessing basic services such as health, education, or attaining decent living standards, despite having income levels well above 1.90. Research on development and welfare economics points to assets as the missing piece in the poverty puzzle because they can build capacity. In general, assets can be used to generate income or to enhance quality of life. Income-generating assets such as bonds, credit, or home ownership help people gain economic stability, acquire other assets, and prepare for economic shocks. Quality-of-life-enhancing assets help people improve their living standards, develop agency, and participate in political as well as in social life. Examples of quality-of-life-enhancing assets include education, social capital, and durable goods such as TVs or computers. Most research on assets examines the relationship either between financial assets and poverty or between financial assets and education. An exploration of durable goods and education was the focus of this dissertation. Although not a nascent field, most studies in this area have focused on analyzing how durable goods relate to academic achievement and school attendance mainly in African and Asian countries. From a methodological standpoint, these studies have modeled durable goods utilizing a binary approach, where ownership of durable goods is measured as possession of any durable good, or as an index, using principal component analysis (PCA), which research suggests is not the most robust method for index creation. Such methodological decisions have provided only a partial understanding of the relationship between durable goods and education. For example, findings indicate that possession of durable goods improves achievement in reading, but not in math. However, further research is needed to assess whether different types of durable goods have differential effects on educational outcomes. Hence, this study explored the relationship among durable goods, academic achievement, and school attendance in Colombia through three methodological approaches to operationalize durable goods: inventory, attributional, and index approaches. Data come from the 2017 SABER test, a nation-wide examination that assesses reading and math skills, for fifth and ninth grade students, (N = 621,218). Students with complete durable goods information (N = 364,436) were included. This research added to the existing literature on this field by using different methodological approaches to model durable goods, including the construction of a durable goods index employing exploratory factor analysis (EFA), and by expanding the geographic scope to Latin America. By using hierarchical linear and nonlinear modeling, this study found that, overall, durable goods were positively associated with reading and math outcomes, particularly for fifth graders. Similarly, results indicated that students whose families owned washing machines, computers, or who had Internet access were more likely to go to school

    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

    Internationalisation dynamics in contemporary South American life sciences: the case of zebrafish

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    We tend to assume that science is inherently international. Geographical boundaries are not a matter of concern in science, and when they do – e.g. due to the rise of nationalist or populist movements – they are thought to constitute a threat to the essence of the scientific enterprise; namely, the global mobility of ideas, knowledge and researchers. Quite recently, we also started to consider that research could become ‘more international’ under the assumption that in doing so it becomes better, i.e. more collaborative, innovative, dynamic, and of greater quality. Such a positive conceptualisation of internationalisation, however, rests on interpretations coming almost exclusively from the Global North that systematically ignore power dynamics in scientific practice and that regard scientific internationalisation as an unproblematic transformative process and as a desired outcome. In Science and Technology Studies (STS), social research on model organisms is perhaps the clearest example of the influence of the dominant vision of internationalisation. This body of literature tends to describe model organism science and their research communities as uniform and harmonious international ecosystems governed by a strong collaborative ethos of sharing specimens, knowledge and resources. But beyond these unproblematic descriptions, how does internationalisation actually transform research on life? To what extent do the power dynamics of internationalisation intervene in contemporary practices of knowledge production and diffusion in this field of research? This thesis revisits the dynamics and practices of scientific internationalisation in contemporary science from the perspective of South American life sciences. It takes the zebrafish (Danio rerio), a small tropic freshwater fish, originally from the Ganges region in India and quite popular in pet shops, as a case study of how complex dynamics of internationalisation intervene in science. While zebrafish research has experienced a remarkable growth in recent years at the global scale, in South America its growth has been unprecedented, allowing average laboratories, which often operate with small budgets and with less well-developed science infrastructures, to conduct world-class research. My approach is based on a consideration of internationalisation as a conceptual model of change. I consider internationalisation to be a process essentially marked by tensions in the spatial, cognitive and evaluative dimensions of scientific practice. These tensions, I claim, are not just a key feature of internationalisation, but also aspects of a conceptual opposition that is geared towards explaining how change comes about in science. By studying the dynamics of internationalisation, I seek to understand various transformations of zebrafish research: from its construction as a research artefact to its diffusion across geographical boundaries. My focus on South America, on the other hand, helps me to understand the complexity of such dynamics beyond the lenses of the dominant discourse of internationalisation that prevails in the STS literature on model organisms. I use mixed-methods (i.e. semi-structured interviews, document analysis, bibliometrics and social network analysis) to observe and interpret transformations of internationalisation at different scales and levels. My analysis suggests first, that internationalisation played an important role in the construction of the zebrafish as a model organism and that, in the infrastructures and practices of resource exchange that sustain the scientific value of the organism internationally, dynamics of asymmetry and empowerment problematise the collaborative ethos of this community. Second, I found that collaborative networks – measured through co-authorships – also played an important role in the diffusion of zebrafish as a model organism in South America. However, I did not find a clear indication of international dependency in the diffusion of zebrafish, explained by a geographical concentration of scientific expertise in the zebrafish collaboration network. Rather than exposing peripheral researchers to novel ideas, networks of international collaboration seem to be more related to access to privileged material infrastructures resulting from the social organisation of scientific labour worldwide. Lastly, by examining practices of biological data curation and researchers’ international mobility trajectories, I describe how dynamics of internationalisation shape the notion of research excellence in model organism science. In this case, I found mobility trajectories to play a key role in boosting researchers’ contributions to the community’s database, especially among researchers from peripheral communities like South America. Overall, while these findings show the value of considering internationalisation as a conceptual model of change in science, more research is needed on the intervention of complex dynamics of internationalisation in other cases and fields of research

    A Heuristically Generated Metric Approach to the Solution of Chase Problem

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    In this work, heuristic, hyper-heuristic, and metaheuristic approaches are reviewed. Distance metrics are also examined to solve the “puzzle problems by searching” in AI. A viewpoint is brought by introducing the so-called Heuristically Generated Angular Metric Approach (HAMA) through the explanation of the metrics world. Distance metrics are applied to “cat and mouse” problem where cat and mouse makes smart moves relative to each other and therefore makes more appropriate decisions. The design is built around Fuzzy logic control to determine route finding between the pursuer and prey. As the puzzle size increases, the effect of HAMA can be distinguished more clearly in terms of computation time towards a solution. Hence, mouse will gain more time in perceiving the incoming danger, thus increasing the percentage of evading the danger. ‘Caught and escape percentages vs. number of cats’ for three distance metrics have been created and the results evaluated comparatively. Given three termination criteria, it is never inconsistent to define two different objective functions: either the cat travels the distance to catch the mouse, or the mouse increases the percentage of escape from the cat
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