12,439 research outputs found
BEYOND THE MYTH: Screenwriting Approaches to Biographical Films
This PhD submission comprises an original screenplay on the relationship between African American activist Paul Robeson and the mining community of south Wales titled Robeson: They Canât Stop Us Singing, and the accompanying exegesis. The aim is to explore, by academic study (gnosis) and creative practice (praxis), the previously overlooked field of writing biographical films, or biopics, and to acknowledge the role of the screenwriter in telling a personâs life story on film. The script is the experiment; the exegesis is the analysis and methodology. The role of the screenwriter is underrepresented across cinema studies, but no more so than in the discussion of biopics. My exegesis begins by exploring what academic and popular writing already exists on English-language biopics, highlighting that amidst auteurist approaches prevalent in cinema studies, little credit has been afforded to screenwriters. I seek to address this by examining how screenwriters have responded to historiographical and socio-political contexts while balancing the needs of the audience with factual integrity (or sometimes not), before using the case studies of Abraham Lincoln and Charles Lindbergh to explore how American hero figures have been represented on screen. How does a script written on Lincoln in 1939, for example, differ in terms of tone and political philosophy to one delivered in the 21st century? Using historical approaches, the exegesis then examines the life of Paul Robeson and the Welsh miners he knew, to observe the meticulous choices required by the screenwriter researching and writing a biopic script. Using primary sources (interviews with living dramatic writers, including the BAFTA-nominated screenwriter of the biopic, Good Vibrations) and secondary sources (screenplays, films, audio, interviews, other academic writing), I question where and when to begin and end a biographical story, which parts of a personâs life to include or jettison, how to make a historical figureâs events pertinent to a contemporary audience, and how to utilise fictionalised elements in a drama while adhering to a central truth. My own screenplay on Robeson and Wales is the embodiment of this research. The script demonstrates the myriad artistic decisions that need to be made to present the qualities and flaws of the historical figure. It shows why fictionalised moments and composite characters contribute to an understanding of a real personâs motives and feelings in a way documentary and historical writing cannot. And it stands as a record of the screenwriterâs previously overlooked contribution to creating biographical films
Coincidental Generation
Generative AI models are emerging as a versatile tool across diverse
industries with applications in synthetic data generation computational art
personalization of products and services and immersive entertainment Here we
introduce a new privacy concern in the adoption and use of generative AI models
that of coincidental generation Coincidental generation occurs when a models
output inadvertently bears a likeness to a realworld entity Consider for
example synthetic portrait generators which are today deployed in commercial
applications such as virtual modeling agencies and synthetic stock photography
We argue that the low intrinsic dimensionality of human face perception implies
that every synthetically generated face will coincidentally resemble an actual
person all but guaranteeing a privacy violation in the form of a
misappropriation of likeness
Associated Random Neural Networks for Collective Classification of Nodes in Botnet Attacks
Botnet attacks are a major threat to networked systems because of their
ability to turn the network nodes that they compromise into additional
attackers, leading to the spread of high volume attacks over long periods. The
detection of such Botnets is complicated by the fact that multiple network IP
addresses will be simultaneously compromised, so that Collective Classification
of compromised nodes, in addition to the already available traditional methods
that focus on individual nodes, can be useful. Thus this work introduces a
collective Botnet attack classification technique that operates on traffic from
an n-node IP network with a novel Associated Random Neural Network (ARNN) that
identifies the nodes which are compromised. The ARNN is a recurrent
architecture that incorporates two mutually associated, interconnected and
architecturally identical n-neuron random neural networks, that act
simultneously as mutual critics to reach the decision regarding which of n
nodes have been compromised. A novel gradient learning descent algorithm is
presented for the ARNN, and is shown to operate effectively both with
conventional off-line training from prior data, and with on-line incremental
training without prior off-line learning. Real data from a 107 node packet
network is used with over 700,000 packets to evaluate the ARNN, showing that it
provides accurate predictions. Comparisons with other well-known state of the
art methods using the same learning and testing datasets, show that the ARNN
offers significantly better performance
Digital Inclusion of the Farming Sector Using Drone Technology
Agriculture continues to be the primary source of income for most rural people in the developing economy. The worldâs economy is also strongly reliant on agricultural products, which accounts for a large number of its exports. Despite its growing importance, agriculture is still lagging behind to meet the demands due to crop failure caused by bad weather conditions and unmanaged insect problems. As a result, the quality and quantity of agricultural products are occasionally affected to reduce the farm income. Crop failure could be predicted ahead of time and preventative measures could be taken through a combination of conventional farming practices with contemporary technologies such as agri-drones to address the difficulties plaguing the agricultural sectors. Drones are actually unmanned aerial vehicles that are used for imaging, soil and crop surveillance, and a variety of other purposes in agricultural sectors. Drone technology is now becoming an emerging technology for large-scale applications in agriculture. Although the technology is still in its infancy in developing nations, numerous research and businesses are working to make it easily accessible to the farming community to boost the agricultural productivity
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Antecedents of business intelligence system use
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Organisational reliance on information has become vital for organisational competitiveness. With increasing data volumes, Business Intelligence (BI) becomes a cornerstone of the decision-support system. However, employee resistance to use Business Intelligence Systems (BIS) is evident. This creates a problem to organisations in realising the benefits of BIS. It is thus important to study the enablers of sustained use of BIS amongst employees.
This thesis identifies existing theories that can be used to study BI system use. It integrates and extends technology use theories through a framework focusing on Business Intelligence System Use (BISU). Empirical research is then conducted in Kuwaitâs telecom and banking industries through a close-ended, self-administered questionnaire using a five-point Likert scale. Responses were received from 211 BI users. The data was analysed using SmartPLS to study the convergent and discriminant validity and reliability. Partial least squares structural equation modelling (PLS-SEM) was used to study the direct and indirect relationships between constructs and answer the hypotheses. In addition to SmartPLS, SPSS was used for descriptive analysis.
The results indicated that UTAUT factors consisting of performance expectancy, effort expectancy and social influence positively impact BI system use. Voluntariness of use was found to positively moderate the relationship between social influence and BI system use. Furthermore, BI system quality positively impacts both performance expectancy and effort expectancy. The BI userâs self-efficacy also positively impacts effort expectancy. In addition, social influence was found to be positively influenced by organisational factors, namely top management support and information culture.
The findings of this research contribute to literature by determining and quantifying the factors that influence BISU through the lens of employee perspectives. This thesis also explains how employeesâ object-based beliefs about BI affect their behavioural beliefs, which in turn impact BISU. Limitations of this research include the omission of UTAUTâs facilitating conditions and the limited variance of respondent demographics
Towards a more just refuge regime: quotas, markets and a fair share
The international refugee regime is beset by two problems: Responsibility for refuge falls
disproportionately on a few states and many owed refuge do not get it. In this work, I explore
remedies to these problems. One is a quota distribution wherein states are distributed
responsibilities via allotment. Another is a marketized quota system wherein states are free to buy
and sell their allotments with others. I explore these in three parts. In Part 1, I develop the prime
principles upon which a just regime is built and with which alternatives can be adjudicated. The
first and most important principle â âJustice for Refugeesâ â stipulates that a just regime provides
refuge for all who have a basic interest in it. The second principle â âJustice for Statesâ â stipulates
that a just distribution of refuge responsibilities among states is one that is capacity considerate. In
Part 2, I take up several vexing questions regarding the distribution of refuge responsibilities
among states in a collective effort. First, what is a stateâs âfair shareâ? The answer requires the
determination of some logic â some metric â with which a distribution is determined. I argue that
one popular method in the political theory literature â a GDP-based distribution â is normatively
unsatisfactory. In its place, I posit several alternative metrics that are more attuned with the
principles of justice but absent in the political theory literature: GDP adjusted for Purchasing
Power Parity and the Human Development Index. I offer an exploration of both these. Second,
are states required to âtake up the slackâ left by defaulting peers? Here, I argue that duties of help
remain intact in cases of partial compliance among states in the refuge regime, but that political
concerns may require that such duties be applied with caution. I submit that a market instrument
offers one practical solution to this problem, as well as other advantages. In Part 3, I take aim at
marketization and grapple with its many pitfalls: That marketization is commodifying, that it is
corrupting, and that it offers little advantage in providing quality protection for refugees. In
addition to these, I apply a framework of moral markets developed by Debra Satz. I argue that a
refuge market may satisfy Justice Among States, but that it is violative of the refugeesâ welfare
interest in remaining free of degrading and discriminatory treatment
Supernatural crossing in Republican Chinese fiction, 1920sâ1940s
This dissertation studies supernatural narratives in Chinese fiction from the mid-1920s to the 1940s. The literary works present phenomena or elements that are or appear to be supernatural, many of which remain marginal or overlooked in Sinophone and Anglophone academia. These sources are situated in the May Fourth/New Culture ideological context, where supernatural narratives had to make way for the progressive intellectualsâ literary realism and their allegorical application of supernatural motifs. In the face of realism, supernatural narratives paled, dismissed as impractical fantasies that distract one from facing and tackling real life.
Nevertheless, I argue that the supernatural narratives do not probe into another mystical dimension that might co-exist alongside the empirical world. Rather, they imagine various cases of the charactersâ crossing to voice their discontent with contemporary society or to reflect on the notion of reality. âCrossingâ relates to charactersâ acts or processes of trespassing the boundary that separates the supernatural from the conventional natural world, thus entailing encounters and interaction between the natural and the supernatural. The dissertation examines how crossing, as a narrative device, disturbs accustomed and mundane situations, releases hidden tensions, and discloses repressed truths in Republican fiction.
There are five types of crossing in the supernatural narratives.
Type 1 is the crossing into âhauntedâ houses. This includes (intangible) human agency crossing into domestic spaces and revealing secrets and truths concealed by the scary, feigned âhauntingâ, thus exposing the hidden evil and the other house occupiersâ silenced, suffocated state.
Type 2 is men crossing into female ghostsâ apparitional residences. The female ghosts allude to heart-breaking, traumatic experiences in socio-historical reality, evoking sympathetic concern for suffering individuals who are caught in social upheavals.
Type 3 is the crossing from reality into the charactersâ delusional/hallucinatory realities. While they physically remain in the empirical world, the charactersâ abnormal perceptions lead them to exclusive, delirious, and quasi-supernatural experiences of reality. Their crossings blur the concrete boundaries between the real and the unreal on the mental level: their abnormal perceptions construct a significant, meaningful reality for them, which may be as real as the commonly regarded objective reality.
Type 4 is the crossing into the netherworld modelled on the real world in the authorsâ observation and bears a spectrum of satirised objects of the Republican society.
The last type is immortal visitors crossing into the human world. This type satirises humanityâs vices and destructive potential.
The primary sources demonstrate their writersâ witty passion to play with super--natural notions and imagery (such as ghosts, demons, and immortals) and stitch them into vivid, engaging scenes using techniques such as the gothic, the grotesque, and the satirical, in order to evoke sentiments such as terror, horror, disgust, dis--orientation, or awe, all in service of their insights into realist issues. The works also creatively tailor traditional Chinese modes and motifs, which exemplifies the revival of Republican interest in traditional cultural heritage. The supernatural narratives may amaze or disturb the reader at first, but what is more shocking, unpleasantly nudging, or thought-provoking is the problematic society and peopleâs lives that the supernatural (misunderstandings) eventually reveals. They present a more compre--hensive treatment of reality than Republican literature with its revolutionary consciousness surrounding class struggle. The critical perspectives of the supernatural narratives include domestic space, unacknowledged history and marginal individuals, abnormal mentality, and pervasive weaknesses in humanity.
The crossing and supernatural narratives function as a means of better understanding the lived reality.
This study gathers diverse primary sources written by Republican writers from various educational and political backgrounds and interprets them from a rare perspective, thus filling a research gap. It promotes a fuller view of supernatural narratives in twentieth-century Chinese literature. In terms of reflecting the social and personal reality of the Republican era, the supernatural narratives supplement the realist fiction of the time
Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process
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 Rowenâs 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 engineâs 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 turbineâs 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 turbineâs 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
Hybridizing cost saving with trust for blockchain technology adoption by financial institutions
Distributed Ledger Technology (DLT) is transforming the financial industry and leading to a rise in the modern banking system. Like in developed nations, disruptive technology is necessary to advance the traditional banking system in emerging economies. The present study aims to investigate the critical factors that influence a userâs intention to accept blockchain technology for financial institutions. The proposed model is based on Technology Acceptance Model (TAM) constructs with trust and cost-saving, tested using structural equation modelling. Findings from an online survey of 188 practitioners working in Malaysia's financial sector confirm that all constructs except trust on perceived usefulness were found to have a significant impact during the blockchain implementation. Moreover, cost-saving matters most during the disruptive technology adoption for financial institutions. Based on the findings, the subsequent theoretical and practical implications are assessed, albeit with notable limitations
Digital asset management via distributed ledgers
Distributed ledgers rose to prominence with the advent of Bitcoin, the first provably secure protocol to solve consensus in an open-participation setting. Following, active research and engineering efforts have proposed a multitude of applications and alternative designs, the most prominent being Proof-of-Stake (PoS). This thesis expands the scope of secure and efficient asset management over a distributed ledger around three axes: i) cryptography; ii) distributed systems; iii) game theory and economics. First, we analyze the security of various wallets. We start with a formal model of hardware wallets, followed by an analytical framework of PoS wallets, each outlining the unique properties of Proof-of-Work (PoW) and PoS respectively. The latter also provides a rigorous design to form collaborative participating entities, called stake pools. We then propose Conclave, a stake pool design which enables a group of parties to participate in a PoS system in a collaborative manner, without a central operator. Second, we focus on efficiency. Decentralized systems are aimed at thousands of users across the globe, so a rigorous design for minimizing memory and storage consumption is a prerequisite for scalability. To that end, we frame ledger maintenance as an optimization problem and design a multi-tier framework for designing wallets which ensure that updates increase the ledgerâs global state only to a minimal extent, while preserving the security guarantees outlined in the security analysis. Third, we explore incentive-compatibility and analyze blockchain systems from a micro and a macroeconomic perspective. We enrich our cryptographic and systems' results by analyzing the incentives of collective pools and designing a state efficient Bitcoin fee function. We then analyze the Nash dynamics of distributed ledgers, introducing a formal model that evaluates whether rational, utility-maximizing participants are disincentivized from exhibiting undesirable infractions, and highlighting the differences between PoW and PoS-based ledgers, both in a standalone setting and under external parameters, like market price fluctuations. We conclude by introducing a macroeconomic principle, cryptocurrency egalitarianism, and then describing two mechanisms for enabling taxation in blockchain-based currency systems
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