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Hillebrandt, H. F. "Bayesian hierarchical predictive coding of human social behaviour." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1435549/.
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‘Bayesian hierarchical predictive coding of human social behaviour.’ Biological agents are the most complex systems humans encounter in their natural environment and it is critical to model other’s mental states correctly to predict their behaviour. To do this one has to generate a mental representation based on an internal neural model of the other agent (Chapter 1). Here we show, in a series experiments, that people use and update their Bayesian priors in social situations and explain how they create mental representations of others to guide action selection. We investigate the neural mechanisms and the brain connectivity that underlie these social processes and how they develop with age. In chapter 2, we show how experimentally induced prior experience with other people (here social inclusion or exclusion) influences the level of trust towards those people. In chapter 3, we describe an fMRI study using a social perspective-taking task that examines the developmental differences between adolescents and adults in the control of action selection by social information. Using the same task, in chapter 4, we investigate the effective connectivity between the activated regions with Dynamic causal modelling. In Chapter 5, we explore effective connectivity of fMRI data from the Human connectome project (Van Essen et al., 2012). During the task participants viewed animations of triangles moving either randomly or so that they evoke mental state attribution (Castelli et al., 2000). Chapter 6 concludes with a summary of the experiments and integrate them into existing research, as well as provide a critical synthesis of the findings in order to suggest future research directions. We interpret our findings in a hierarchical predictive coding framework, where agents try to create a neural model of the external world to minimize prediction errors, Bayesian surprise and free energy.
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Zoli, Matteo <1983>. "Predictive Factors of Biological Behaviour in Pituitary Adenoma." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amsdottorato.unibo.it/8761/1/Zoli_Matteo_Tesi.pdf.
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Background: Predicting biological behavior of pituitary adenomas is a complex but highly desirable issue for both scientific and practical porpoises. Recently, a clinico-pathological grading based on histopathological and neuroradiological features has been proposed, stratifying the risk of progression/recurrence in 5 classes.
Aim of our study is to perform an independent external validation of this score and identify other potential predictor of aggressive behaviour.
Results: Five hundred sixty-six patients with pituitary adenomas were included in this study (253 FSH/LH, 147 GH, 85 PRL, 72 ACTH and 9 TSH tumors). In 437 cases, pituitary adenomas were non-invasive, with low (grade 1a: 378 cases) or high (grade 1b: 59 cases) proliferative activity. In 129 cases, tumors were invasive, with low (grade 2a: 87 cases) or high (grade 2b: 42 cases) proliferative activity. During the follow-up (mean: 5.8 years), 60 patients developed disease recurrence or progression, with a total of 130 patients with pituitary disease at last follow-up. Univariate analysis demonstrated a significantly higher risk of disease persistence and recurrence/progression in patients with PRL, ACTH and FSH/LH tumors as compared to those with somatotroph tumors, and in those with high proliferative activity (grade 1b and 2b) or >1 cm diameter. Multivariate analysis confirmed that tumor type and grade are independent predictors of disease free-survival and progression free-survival. Tumor grading resulted the first parameter emerging in the decision tree analyses with CHAID growing process to stratify patients according to the risk of recurrence/progression.
Conclusions: Our data confirmed the validity of Trouillas’ score, being tumor type and grade independent predictors of disease evolution. Therefore, we recommend to always consider both features, together with tumor histological subtype, in the clinical setting to early identify patients at higher risk of an aggressive behaviour.
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Wang, Jinhuo. "Predictive modelling and experimental measurement of composite forming behaviour." Thesis, University of Nottingham, 2008. http://eprints.nottingham.ac.uk/10602/.
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Optimised design of textile composite structures based on computer simulation techniques requires an understanding of the deformation behaviour during forming of 3-dimensional double-curvature components. Purely predictive material models are highly desirable to facilitate an optimised design scheme and to significantly reduce time and cost at the design stage, such as experimental characterisation. In-plane shear and out-of-plane bending are usually thought to be the key forming mechanisms. Therefore, this thesis is concerned with studies of the shear and bending behaviour by experimental characterisation and theoretical modelling. Micromechanical interaction between fibre and matrix offers fundamental understanding of deformation mechanisms at the micro-scale level, leading to development of composite viscosity models, as input to shear and bending models. The composite viscosity models were developed based on rheological behaviour during movement of fibres, and validation was performed using experimental results collected from the literature. A novel characterisation method for measuring the bending behaviour, by means of a large-displacement buckling test, was attempted due to some significant advantages over other methods. Development of a bending model was also undertaken for unidirectional composites but experimental validation suggests further study may be required for woven composites. The shear behaviour was characterised using a picture frame test for viscous polymer composites. To obtain reliable experimental data, some efforts of improving the characterisation method were made. The experimental results were then used to validate a shear model, suggesting that further improvement is required, in terms of weave patterns, rate and temperature dependence.
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Keen, Steven Dale. "Modeling driver steering behaviour using multiple-model predictive control." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611428.
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Swan, J. "The role of predictive accuracy in Pavlovian conditioning." Thesis, Bucks New University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378415.
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Stephens, Philip Andrew. "Behaviour based models population dynamics and the conservation of social mammals." Thesis, University of East Anglia, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251708.
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Fisher, Jacinda. "A comparison of predictive models of adherence behaviour in hypertensive patients /." Title page, abstract and contents only, 1998. http://web4.library.adelaide.edu.au/theses/09SPS/09spsf534.pdf.
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Villavicencio, Rojas Maria Daniela. "Predictive modelling of the tribological behaviour of self-lubricating composite materials." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI040.
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Dans les matériaux composites autolubrifiants, la génération de particules d’usure est nécessaire pour assurer la lubrification. Dans les roulements à billes, ce type de lubrification est possible grâce au matériau de la cage, composé d’un matériau composite autolubrifiant, tandis que le reste du roulement est fait en métal (AISI 440C). Pour les applications spatiales, le RT/Duroid 5813 est un composite autolubrifiant reconnu pour les cages de ce type de roulements. Ce matériau a été largement utilisé car il a répondu aux besoins de la lubrification sèche dans l’espace. Cependant, la production de ce matériau a été arrêtée dans les années 90. Cette situation a conduit à la recherche d’un « matériau équivalent » , répondant à la fois aux besoins du marché spatial et aux « besoins tribologiques ». Aujourd’hui, le principal inconvénient lié à ces matériaux est le manque de prévisibilité de leur comportement tribologique. Dans ce travail, une approche couplée expérimentale et numérique a été proposée afin de modéliser le comportement tribologique des matériaux composites autolubrifiants. Le modèle numérique proposé a été nourri par des caractérisations expérimentaux (comme la tomographie à rayons X pour la création de la morphologie du matériau numérique, ou la microscopie à force atomique pour informer la valeur de l’adhésion entre les composants). Le but d’une telle démarche numérique est de palier les limites d’une approche entièrement expérimentale qui ne permet pas d’observer in-situ le contact de par son caractère confiné. L’objectif du présent travail est de donner une réponse au problème de compréhension du comportement tribologique de matériaux composites autolubrifiants dans le mécanisme de double transfert. Ceci en vue de contribuer au développement d’un nouveau matériau tribologique répondant aux besoins des applications spatiales. Parmi tous les matériaux autolubrifiants, le PGM-HT a été sélectionné dans cette étude car sa morphologie grossière a permis de construire une version numérique du matériau (avec la résolution du tomographe à rayons X utilisé dans ce travail). Néanmoins, l’approche proposée ici pour construire le modèle numérique peut être étendue à d’autres matériaux composites autolubrifiants. Le modèle numérique proposé dans ces travaux ouvre de nouvelles perspectives en termes de conception de matériaux, car il permet d’étudier directement les scénarios de dégradation et d’usure des matériaux composites. D’un point de vue général, il est à noter que la tribologie numérique est un outil offrant de multiples possibilités pour la compréhension des matériaux autolubrifiants, et permet d’aider dans le processus de prédiction du comportement tribologique des matériaux autolubrifiants<br>In self-lubricating composite materials, the generation of a stable third body layer is necessary to ensure contact lubrication. This is specially true for the contact in which these materials are directly involved, and also in other contacts implicating its counterface. Such type of lubrication is possible in self-lubricating bearings thanks to its cage material, which is made of the self-lubricating composite, while the rest of the bearing is usually made of AISI 440C. For space applications, RT/Duroid 5813 is a recognized self-lubricating composite cage material for this kind of bearings. This material has been widely used not only because of the space heritage, but also because it has satisfied the needs of space dry lubrication. However, the production of this material has been stopped in the 90’s, and it has placed the latter out of the market. This situation has led to the search for an equivalent material, that meets both the needs of the space market and the "tribological needs." Today, the main inconvenient related to these materials is the lack of predictability of their tribological behaviour. In this work, the "making of" a coupled numerical-experimental approach has been proposed in order to carry out the understanding of these materials. The goal of this numerical approach is to let to "complement" the limitations of a fully experimental or a fully numerical approach (the confined nature of the contact does not allow in situ observation). Such numerical approach has been informed with experimental test (as X-ray tomography for the creation of the numerical morphology, or atomic force microscopy to inform the value of adhesion between the components). Among all the self-lubricating materials, PGM-HT has been selected in this study because its coarse morphology let to build a numerical version of the material (with the resolution of the X-ray tomograph used in this work). Nevertheless, the approach that has been proposed here to build the numerical model, can be extended to other self-lubricating composite materials. The numerical model developed in this work opens new perspectives in terms of material design, as it makes it possible to directly study the scenarios of damage and wear of self-lubricating composite materials. From a general point of view, from this work it can be highlighted that numerical tribology is a tool that offers multiple possibilities in the understanding of self-lubricating materials, and that helps in the predictionof the tribological behaviour of self-lubricating materials. This work has then let to advance in the understanding of these materials
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Meinhardt, HR. "Evaluation of predictive models for pesticide behaviour in South African soils / HR Meinhardt." Thesis, North-West University, 2009. http://hdl.handle.net/10394/5092.
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The re-evaluation of pesticide use patterns is a high priority internationally. The process has led to a reduction in the numbers of pesticides allowed for use in many countries. This withdrawal of pesticides is aimed at consumer and environmental protection. Pesticide mobility and persistence is of major importance especially when considering the protection of water sources. In order to evaluate the suitability of a pesticide for use it is essential that its environmental behaviour is understood and predictable. In this thesis several case studies in which damage to crops may have been caused as a result of herbicides migration are described. Pesticide adsorption and persistence determinations showed that the adsorption coefficients do not differ from those published. Pesticide half lives are however likely to be prolonged in South African soils. Field migration studies show that pesticides are vertically and horizontally mobile in South African soils, seemingly independent of soil type. Both migration as well as upward movement was found, due to the mobility of the pesticides in the test soils. Mobility evaluation was identified as an aspect of importance for registration of pesticides under local conditions. To this end a system is proposed whereby migration can be incorporated into the existing pesticide evaluation framework. The system proposed used the migration model PESTAN as a predictive tool for pesticide migration in the evaluation process. The model is used for the evaluation of pesticides, according to Proposed Pesticide Migration Categories (PPLC). Migration evaluation should be conducted in conjunction with GPS systems to aid in determining potential risk areas where certain pesticide should not be used.<br>Thesis (Ph.D. (Zoology))--North-West University, Potchefstroom Campus, 2009.
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Al, Nasseri Alya Ali Mansoor. "The predictive power of stock micro-blogging sentiment in forecasting stock market behaviour." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13575.
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Online stock forums have become a vital investing platform on which to publish relevant and valuable user-generated content (UGC) data such as investment recommendations and other stock-related information that allow investors to view the opinions of a large number of users and share-trading ideas. This thesis applies methods from computational linguistics and text-mining techniques to analyse and extract, on a daily basis, sentiments from stock-related micro-blogging messages called “StockTwits”. The primary aim of this research is to provide an understanding of the predictive ability of stock micro-blogging sentiments to forecast future stock price behavioural movements by investigating the various roles played by investor sentiments in determining asset pricing on the stock market. The empirical analysis in this thesis consists of four main parts based on the predictive power and the role of investor sentiment in the stock market. The first part discusses the findings of the text-mining procedure for extracting and predicting sentiments from stock-related micro-blogging data. The purpose is to provide a comparative textual analysis of different machine learning algorithms for the purpose of selecting the most accurate text-mining techniques for predicting sentiment analysis on StockTwits through the provision of two different applications of feature selection, namely filter and wrapper approaches. The second part of the analysis focuses on investigating the predictive correlations between StockTwits features and the stock market indicators. It aims to examine the explanatory power of StockTwits variables in explaining the dynamic nature of different financial market indicators. The third part of the analysis investigates the role played by noise traders in determining asset prices. The aim is to show that stock returns, volatility and trading volumes are affected by investor sentiment; it also seeks to investigate whether changes in sentiment (bullish or bearish) will have different effects on stock market prices. The fourth part offers an in-depth analysis of some tweet-market relationships which represent an open problem in the empirical literature (e.g. sentiment-return relations and volume-disagreement relations). The results suggest that StockTwits sentiments exhibit explanatory power in explaining the dynamics of stock prices in the U.S. market. Taking different approaches by combining text-mining techniques with feature selection methods has proved successful in predicting StockTwits sentiments. The applications of the approach presented in this thesis offer real-time investment ideas that may provide investors and their peers with a decision support mechanism. Investor sentiment plays a critical role in determining asset prices in capital markets. Overall, the findings suggest that investor sentiment among noise traders is a priced factor. The findings confirm the existence of asymmetric spillover effects of bullish and bearish sentiments on the stock market. They also suggest that sentiment is a significant factor in explaining stock price behaviour in the capital market and imply the positive role of the stock market in the formation of investor sentiment in stock markets. Furthermore, the research findings demonstrate that disagreement is not only an important factor in determining trading volumes but it is also considered a very significant factor in influencing asset prices and returns in capital markets. Overall, the findings of the thesis provide empirical evidence that failure to consider the role of investor sentiment in traditional finance theory could lead to an imperfect picture when explaining the behaviour of stock prices in stock markets.
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Misbah, Hanim. "Customer switching behaviour : an exploratory study of predictive factors in the UK retail banking context." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/17597.
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The main inquiry for this research is to identify the reasons that contribute to customer switching intention decisions. In addressing this enquiry, two theories - the theory of migration and theory of planned behaviour - were identified as the theoretical framework underpinning the study. Two main objectives of the study were (i) to identify the push-pull and mooring factors and (ii) to measure the relationship between the push-pull and mooring factors towards switching intention. The investigation focused on the impact of with push-pull-mooring factors on switching intention. Early research into switching behaviour studies focused largely on variables that contribute to the switching intention decision, mainly due to the critical incidents encountered by customers that push them from their origin or pull them to another destination or mooring factors that might inhibit or mitigate their switching decision. In view of this, a combination of push-pull and mooring variables were used to measure the switching intention behaviour. A multiple method approach was used to study the issues in two different stages. In the first stage qualitative data collection was used to support and confirm the identification of factors from the literature. For the main quantitative methods, using a hypothetical deductive testing approach, this study (N=2018) used survey data collected via a self-administered, voluntary online survey, to develop switching intention behaviour model. The results indicated that situational factors, positive attachments, perceived switching benefits, positive attitudes towards switching and positive beliefs of others towards switching emerged as consistent push factor while availability of alternatives emerged as the pull factors. Interestingly poor pricing, poor service incidents, positive ability to switch and switching barriers were not supported in this study indicating that there is no relationship between poor pricing, poor service incidents, positive ability to switch and switching barriers towards switching intention.
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Davids, Fawwaaz. "The Theory of Planned Behaviour and the Entrepreneurial Event Model as predictive models of entrepreneurial intention." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27299.
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The Theory of Planned Behaviour and The Entrepreneurial Event Model were used as models to predict entrepreneurial intention amongst final year students. The sufficiency of this paradigm was compared with the aim of determining which model predicts entrepreneurial intention the most within a South African context. A sample of 186 students was used to determine the sufficiency of the Theory of Planned Behaviour. As part of our methodology, a sub-set (n = 123) of the sample was used to determine the sufficiency of the Entrepreneurial Event Model. The sample consisted of final year commerce and engineering students. The results of the regression analysis indicated that the Theory of Planned Behaviour explained 58% of the variance in entrepreneurial intention. The Entrepreneurial Event Model was found to be less sufficient than the Theory of Planned Behaviour and only explained 38% of the variance in entrepreneurial intention. Therefore, when predicting entrepreneurial intention in a South African context, the Theory of Planned Behaviour can be considered the more sufficient model of prediction. Future research should consider using the Theory of Planned Behaviour, rather than Entrepreneurial Event Model, for entrepreneurial intention prediction among students in South Africa.
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Braghieri, Giovanni. "Application of robust nonlinear model predictive control to simulating the control behaviour of a racing driver." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275524.
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The work undertaken in this research aims to develop a mathematical model which can replicate the behaviour of a racing driver controlling a vehicle at its handling limit. Most of the models proposed in the literature assume a perfect driver. A formulation taking human limitations into account would serve as a design and simulation tool for the automotive sector. A nonlinear vehicle model with five degrees of freedom under the action of external disturbances controlled by a Linear Quadratic Regulator (LQR) is first proposed to assess the validity of state variances as stability metrics. Comparison to existing stability and controllability criteria indicates that this novel metric can provide meaningful insights into vehicle performance. The LQR however, fails to stabilise the vehicle as tyres saturate. The formulation is extended to improve its robustness. Full nonlinear optimisation with direct transcription is used to derive a controller that can stabilise a vehicle at the handling limit under the action of disturbances. The careful choice of discretisation method and track description allow for reduced computing times. The performance of the controller is assessed using two vehicle configurations, Understeered and Oversteered, in scenarios characterised by increasing levels of non- linearity and geometrical complexity. All tests confirm that vehicles can be stabilised at the handling limit. Parameter studies are also carried out to reveal key aspects of the driving strategy. The driver model is validated against Driver In The Loop simulations for simple and complex manoeuvres. The analysis of experimental data led to the proposal of a novel driving strategy. Driver randomness is modelled as an external disturbance in the driver Neuromuscular System. The statistics of states and controls are found to be in good agreement. The prediction capabilities of the controller can be considered satisfactory.
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Stamatiou, Ilias. "Characterisation of mass transfer behaviour in continuous gas/liquid/solid catalysed processes including packed bed for predictive scale up/down." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/22305/.
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Hydrogenation is a very frequently occurring example of heterogeneously catalysed reaction widely used in the production routes of the High Value Chemical Manufacturing (HVCM) sector and it is currently based on batch processes, despite the potential benefits from the switching to continuous flow. This mainly occurs due to the luck of an established methodology for transferring quickly such processes from batch to continuous flow. Throughout this research project, the effort to investigate the principles which govern the heterogeneously catalysed hydrogenation led in the development of a new methodology for determining the mass transfer resistances of three-phase reactions in semi-batch Stirred Tank Reactors (STR). The characterisation of the semi-batch STR was found adequate for predicting the concentration profiles of styrene during its hydrogenation over Pd/C in the Continuous Stirred Tank Reactor (CSTR). On the other hand, due to the different behaviour of mass transfer between the STR and the Trickle Bed Reactor (TBR), the transfer of the styrene hydrogenation from the semi-batch STR to TBR was found more demanding; and consequently, a new methodology for characterising the mass transfer behaviour of the TBR was developed. The hydrogenation of styrene over Pd/C in the semi-batch STR, CSTR and TBR was simulated by using the mass transfer coefficients approximated by the new methodologies.
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Åfeldt, Tom. "Adaptive Steering Behaviour for Heavy Duty Vehicles." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215134.
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Today the majority of the driver assistance systems are rule-basedcontrol systems that help the driver control the truck. But driversare looking for something more personal and exible that can controlthe truck in a human way with their own preferences. Machine learningand articial intelligence can help achieve this aim. In this studyArticial Neural Networks are used to model the driver steering behaviourin the Scania Lane Keeping Assist. Based on this, trajectoryplanning and steering wheel torque response are modelled to t thedriver preference. A model predictive controller can be used to maintainstate limitations and to weigh the two modelled driver preferencestogether. Due to the diculties in obtaining an internal plant modelfor the model predictive controller a variant of a PI-controller is addedfor integral action instead. The articial neural network also containsan online learning feature to further customize the t to the driverpreference over time.<br>Idag används till största del regelbaserade reglersystem förförarassistanssystem i lastbilar. Men lastbilschaufförer vill ha någotmer personligt och flexibelt, som kan styra lastbilen på ett mänskligtsätt med förarens egna preferenser. Maskininlärning och artificiell intelligenskan hjälpa till för att uppnå detta mål. I denna studie användsartificiella neurala nätverk för att modellera förarens styrbeteende genomScania Lane Keeping Assist. Med användning av detta modellerasförarens preferenser med avseende på placering på vägbanan och momentpåslag på ratten. En modell prediktiv kontroller kan användas föratt begränsa tillstånd och för att väga de två modellerade preferensernamot varann. Eftersom det var mycket svårt att ta fram den internaprocessmodellen som krävdes för regulatorn används istället en variantav en PI-kontroller för att styra lastbilen. De artificiella neuralanätverken kan också tillåtas att lära sig under körning för att anpassasig till förarens preferenser över tid.
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Curry, Michael. "IT effectiveness efforts as predictors of organizational outcomes : a normative model for assessing IT quality." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/it-effectiveness-efforts-as-predictors-of-organizational-outcomes-a-normative-model-for-assessing-it-quality(1870560e-74dd-451b-9419-428dc208a21d).html.
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Information technology (IT) is a key enabler of modern business practices, yet reliably effective IT systems remain a significant challenge for many organizations. The consequences when systems fail to behave as expected becomes ever-more problematic as IT dependence grows. Therefore, methods for assessing IT effectiveness and generating actionable recommendations for improvement are key drivers of success. For this reason, large organizations often adopt IT best practice frameworks such as COBIT, ITIL or ISO/IEC standards which can offer greater assurances of IT effectiveness. However smaller organizations are rarely able to adopt these frameworks due, in part, to resource constraints, and a preference to eschew authoritative practices in favour of informal guides to action. Consequently, a significant research gap is the lack of IT effectiveness approaches for organizations unable or unwilling to adopt formal IT best practice frameworks. This thesis presents an alternative norms-based approach to IT effectiveness which some organizations might find more suitable. Norms are informal beliefs (e.g. ‘using a complex password helps safeguard data’) which motivate behaviours and can often be expressed using non-technical language. We review the literature to formulate a predictive model connecting norms to IT quality. Employing a scientific methodology defensible on philosophical grounds and accepted research practices, we distil a set of IT effectiveness norms from the COBIT 4.1 IT governance framework and adapt theories of motivation to justify our assertion that IT effectiveness norms can motivate actions. Our work is signficant in its formulation of an alternative approach for assessing IT operations and improving organizational IT outcomes. Our survey instrument –validated in four studies, which include a non-profit and government organization, multiple small businesses, a large pharmaceutical company and a university –is a light-weight and reliable assessment tool. Our predictive model is able to explain 26% of observed variance, and can offer actionable and non-technical insights which can improve organizational outcomes. A norms-based approach may bring many of the same IT effectiveness benefits offered by formal IT best practices into organizations, such as small businesses, which lack the resources for their implementation. This approach may also help bridge important communication gaps between IT professionals and others in the organization by providing a different, less technical perspective for framing, assessing, diagnosing, and communicating about IT processes.
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Lacroix, Daniel. "Behaviour of Light-frame Wood Stud Walls Subjected to Blast Loading." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/24339.
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Deliberate and accidental explosions along with the heightened risk of loss of life and property damage during such events have highlighted the need for research in the behaviour of materials under high strain rates. Where an extensive body of research is available on steel and concrete structures, little to no details on how to address the design or retrofitting of wood structures subjected to a blast threat are available. Studies reported in the literature that focused on full scale light-frame wood structures did not quantify the increase in capacity due to the dynamic loading while the studies that did quantify the increase mostly stems from small clear specimens that are not representative of the behaviour of structural size members with defects. Tests on larger-scale specimens have mostly focused on the material properties and not the structural behaviour of subsystems. Advancements in design and construction techniques have greatly contributed to the emergence of taller and safer wood structures which increase potential for blast threat. This thesis presents results on the flexural behaviour of light-frame wood stud walls subjected to shock wave loading using the University of Ottawa shock tube. The emphasis is on the overall behaviour of the wall subsystem, especially the interaction between the sheathing and the studs through the nailed connection.
The approach employed in this experimental program was holistic, where the specimens were investigated at the component and the subsystem levels. Twenty walls consisting of 38 mm x 140 mm machine stress-rated (MSR) studs spaced 406 mm on center and sheathed with two different types and sheathing thicknesses were tested to failure under static and dynamic loads. The experimental results were used to determine dynamic increase factors (DIFs) and a material predictive model was validated using experimental data. The implications of the code are also discussed and compared to the experimental data.
Once validated, an equivalent single-degree-of-freedom (SDOF) model incorporating partial composite action was used to evaluate current analysis and design assumptions. The results showed that a shock tube can effectively be used to generate high strain-rate flexural response in wood members and that the material predictive model was found suitable to effectively predict the displacement resulting from shock wave loading. Furthermore, it was found that current analysis and design approaches overestimated the wall displacements.
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Lawson, Glyn. "Predicting human behaviour in emergencies." Thesis, University of Nottingham, 2011. http://eprints.nottingham.ac.uk/12027/.
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The outcome of an emergency is largely determined by the behaviour of the people involved. To improve the safety of buildings and to increase the effectiveness of response procedures and training programmes it is often necessary to predict human behaviour in emergency situations. There are several approaches which can be used to make these predictions, but not all had previously been systematically analysed and therefore their appropriateness for any given application was unknown. This thesis describes an analysis of approaches for predicting human behaviour in emergencies. The research focussed on approaches which could be used by human factors professionals to extend the contribution this systems-oriented and user-focussed discipline can make to managing risks and reducing danger. The investigated approaches were evaluated against criteria for judging their quality, including validity, reliability, resources, sensitivity and ethics. In research conducted to test the approaches, fire drills, virtual environments (VEs) and a new talk-through approach, in which participants describe the hypothetical actions they would take in an emergency scenario, demonstrated potential for predicting behaviour in emergency situations. These approaches were subsequently evaluated in a standardised comparison, in which each one was applied to analyse the behaviour demonstrated during an evacuation from a university building. The observed frequencies of behaviour produced by each approach were significantly correlated, as were the sequences of behaviour. All of the approaches demonstrated replicability. The resources required to apply each approach were relatively low, especially for the talk-through approach. Based on the findings from this research, and drawing upon previous work from the scientific literature, guidance was provided for selecting approaches and methods for behavioural prediction in emergency situations. The talk-through approach is suitable for use during the concept phase of a design as it is quick to implement and requires low resources. VEs and simulation tools are more appropriate for design activities when detailed CAD models become available. Fire drills can provide useful measures of human behaviour in evacuation scenarios, but require a physical representation of the building or environment under investigation. Fire drills, VEs and simulation tools can be used to inform emergency response procedures. Predictions from all of the aforementioned approaches can support the development of training programmes. This guidance was previously unavailable to human factors professionals and now serves both to inform design work and support the evaluation of existing evacuation procedures and protocols.
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Puente, Guillen Pablo. "Predicting sleepiness from driving behaviour." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/17938/.
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This research investigates the use of objective EEG analysis to determine multiple levels of sleepiness in drivers. In the literature, current methods propose a binary (awake or sleep) or ternary (awake, drowsy or sleep) classification of sleepiness. Having few classification of sleepiness increases the risk of the driver reaching dangerous levels of sleepiness before a safety system can prevent it. Also, these methods are based on subjective analysis of physiological variables, which leads to lack of reproducibility and loss of data, when a lack of consensus is reached amongst the EEG experts. Therefore, the doctoral challenge was to determine whether multiple levels of sleepiness could be defined with high accuracy, using an objective analysis of EEG, a reliable indicator of sleepiness. The study identified awake, post-awake, pre-sleep and sleep as the multiple levels of sleepiness through the objective analysis of EEG. The research used Neural Networks, a type of Machine Learning algorithm, to determine the accuracy of the proposed multiple levels of sleepiness. The Neural Networks were trained using driving and physiological behaviour. The EEG data and the driving and physiological variables were obtained through a series of experiments aimed to induce sleepiness, conducted in the driving simulator at the University of Leeds. As the Neural Network obtained high accuracy when differentiating between awake and sleep and between post-awake and pre-sleep, it led to the conclusion that the proposed objective classification based on objective EEG analysis was suitable. However, this study did not reach the highest levels of accuracy when the 4 levels of sleepiness are combined, nevertheless the solutions proposed by the researcher to be carried in future work can contribute towards increasing the accuracy of the proposed method.
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Ghafoorzadeh, Nobar Danesh. "Online customer behavior prediction." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=117172.
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Marketing has an important role in any successful business. However, advertising costs can be quite large. The key to a good marketing is to understand the customer behavior toward advertisements, in order to decide through what means one can advertise most efficiently. Traditionally, this process was studied through customer focus groups and surveys, which were then analyzed by statistical means. The increase of on-line purchasing and advertising provides a unique opportunity for gathering large amounts of information from users, which can then be analyzed by modern machine learning methods. In this thesis, by using data from online retailers, we try to predict user reaction to different types of advertisement. Using a machine learning, regression-based approach toward customer behavior prediction, we fit a model of customer behavior which can be used to predict which type of advertising will lead to purchases. We use a large data set provided by an industry partner and show that this approach achieves good prediction accuracy, even though the data is imbalanced.iv<br>Le marketing a un rôle important dans toute entreprise prospère. Toutefois, les frais de publicité peuvent être très importants. La clé d'une bonne stratégique de marketing est de comprendre le comportement des clients devant la publicité qui leurs est présentée, afin de décider par quels moyens son rendement peut être amélioré. Traditionnellement, ce processus a été étudié par le biais de groupes de discussion et de sondages, et dont les résultats sont ensuite analysés par des moyens statistiques. L'augmentation des achats en ligne et de la publicité offrent une occasion unique de rassembler de grandes quantités d'informations auprès des utilisateurs. Celles-ci peuvent ensuite être analysées par des méthodes modernes d'apprentissage automatique. Dans cette thèse, en utilisant les données provenant de sites d'achat en ligne, nous tentons de prédire la réaction des utilisateurs différents types de publicité. Par lutilisation de techniques d'apprentissage automatique, nous avons obtenu un modèle de régression permettant de prédire le comportement d'achat du client face a la publicité auquel il est expose. Nous utilisons un ensemble de données volumineux fourni par un partenaire de l'industrie et démontrons que cette approche permet d'atteindre une bonne précision de prédiction, même en présence de données déséquilibrés.
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郭紹文 and Siu-man Kwok. "Predicting the creep behaviour of plastics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1990. http://hub.hku.hk/bib/B31209762.
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Cheng, Mei-I. "The prediction of employee turnover behaviour." Thesis, University of Nottingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364661.
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Kwok, Siu-man. "Predicting the creep behaviour of plastics /." [Hong Kong] : University of Hong Kong, 1990. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12996312.
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Losik, Len. "Using Oracol® for Predicting Long-Term Telemetry Behavior for Earth and Lunar Orbiting and Interplanetary Spacecraft." International Foundation for Telemetering, 2010. http://hdl.handle.net/10150/604280.
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ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California<br>Providing normal telemetry behavior predictions prior to and post launch will help to stop surprise catastrophic satellite and spacecraft equipment failures. In-orbit spacecraft fail from surprise equipment failures that can result from not having normal telemetry behavior available for comparison with actual behavior catching satellite engineers by surprise. Some surprise equipment failures lead to the total loss of the satellite or spacecraft. Some recovery actions from a surprise equipment failure increase spacecraft risk and involve decisions requiring a level of experience far beyond the responsible engineers.
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Losik, Len. "Using Oracol® for Predicting Long-Term Telemetry Behavior for Earth and Lunar Orbiting and Interplanetary Spacecraft." International Foundation for Telemetering, 2009. http://hdl.handle.net/10150/606127.
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ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada<br>Providing normal telemetry behavior predictions prior to and post launch will help to stop surprise catastrophic satellite and spacecraft equipment failures. In-orbit spacecraft fail from surprise equipment failures that can result from not having normal telemetry behavior available for comparison with actual behavior catching satellite engineers by surprise. Some surprise equipment failures lead to the total loss of the satellite or spacecraft. Some recovery actions as a consequence of a surprise equipment failure are high risk and involve decisions requiring a level of experience far beyond the responsible engineers.
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Boussemart, Yves 1980. "Predictive models of procedural human supervisory control behavior." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/79543.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.<br>Page 150 blank. Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 138-149).<br>Human supervisory control systems are characterized by the computer-mediated nature of the interactions between one or more operators and a given task. Nuclear power plants, air traffic management and unmanned vehicles operations are examples of such systems. In this context, the role of the operators is typically highly proceduralized due to the time and mission-critical nature of the tasks. Therefore, the ability to continuously monitor operator behavior so as to detect and predict anomalous situations is a critical safeguard for proper system operation. In particular, such models can help support the decision making process of a supervisor of a team of operators by providing alerts when likely anomalous behaviors are detected. By exploiting the operator behavioral patterns which are typically reinforced through standard operating procedures, this thesis proposes a methodology that uses statistical learning techniques in order to detect and predict anomalous operator conditions. More specifically, the proposed methodology relies on hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) to generate predictive models of unmanned vehicle systems operators. Through the exploration of the resulting HMMs in two distinct single operator scenarios, the methodology presented in this thesis is validated and shown to provide models capable of reliably predicting operator behavior. In addition, the use of HSMMs on the same data scenarios provides the temporal component of the predictions missing from the HMMs. The final step of this work is to examine how the proposed methodology scales to more complex scenarios involving teams of operators. Adopting a holistic team modeling approach, both HMMs and HSMMs are learned based on two team-based data sets. The results show that the HSMMs can provide valuable timing information in the single operator case, whereas HMMs tend to be more robust to increased team complexity. In addition, this thesis discusses the methodological and practical limitations of the proposed approach notably in terms of input data requirements and model complexity. This thesis thus provides theoretical and practical contributions by exploring the validity of using statistical models of operators as the basis for detecting and predicting anomalous conditions.<br>by Yves Boussemart.<br>Ph.D.
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White, Susan G. "Civic Habits: A Predictive Model of Volunteer Behavior." VCU Scholars Compass, 2008. http://scholarscompass.vcu.edu/etd/862.
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The findings of this research indicate that volunteering is influenced by a number of factors, one of which is gender. The data used in this study reveal a different profile of the volunteer than is presented in much of the research on volunteering, which tends to profile the "most likely" volunteer as female, employed by the public sector, possessing a higher education and having children. The questions addressed in this research are: 1) What are the contextual effects of volunteering and 2) Is there a relationship of one or more of these effects to gender? The findings indicate men in this sample were not only more likely to volunteer, but were more likely to engage in volunteer activities that included political and civic roles. In addition, men were able to volunteer more hours as their family ties increased. The hours women volunteered were found to decrease as family ties increased. Women were less likely to volunteer for political and civic activities and more likely to volunteer for roles that included the care of children, elderly and family-oriented activities. These findings have implications for how volunteer activities contribute to the building of social and political resources for both men and women and bring to light how gendered definitions dominate patterns of civic engagement.
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Turchik, Jessica A. "The Prediction of Sexual Risk Behaviors among College Students Using the Theory of Planned Behavior." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1273864560.
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Walton, Brien C. "Is emotional intelligence predictive or entrepreneurial success?" Thesis, University of Pennsylvania, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10158700.
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<p> There are more self-made, billionaire entrepreneurs than billionaires who simply inherited their fortunes, but the majority of startup ventures fail within five years. A possible factor in business success or failure could be the emotional intelligence (EI) level of the entrepreneur, defined broadly as the ability to perceive, interpret, and manage emotions. Although there is substantial literature on EI applications in established organizations, there are few empirical studies exploring the predictive value of EI in the context of success for startup entrepreneurs. The purpose of this study was to determine whether EI scores can predict how successful an entrepreneur will be using objective success criteria, as defined in this study (Hypothesis 1), and which, if any, EI competencies are particularly relevant for entrepreneurs (Hypothesis 2). Hypotheses were tested using Spearman correlation and Ordinal regression, with sensitivity testing with Pearson correlation and Ordinary Least Squares regression, respectively. Each analysis controlled for the entrepreneur’s demographic profile and subjective success measures. Regression analysis (n=31); ordinal analysis and correlation analysis revealed a statistically significant effect of only one of the 15 EI scores (Empathy) on entrepreneurial success, as defined in this study, but the exponentiated coefficients from the ordinal regression indicate that improving Overall EI scores can increase Overall Success. Specifically, six of the 15 EI scores were more than two times more likely to increase Overall Success scores (Empathy, Interpersonal Relationships, Social Responsibility, Flexibility, Stress Tolerance, and Optimism), which is consistent with Hypothesis 2. This study is one of the first to empirically examine the EI construct in the context of entrepreneurial success with a population of entrepreneurs seeking assistance using the EQ-i 2.0 emotional intelligence assessment. A key implication of the results is that teachers, emergency services personnel, legal and financial services personnel, even sports coaches and single parents, can all develop competencies to make them more successful in their chosen endeavor, have a sense of fulfillment, and increase the success rate of industries that go far beyond entrepreneurs. </p>
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Pierce, Lauren E., and Lauren E. Pierce. "Predicting Performance through Athlete Behavior." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/620962.
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The occurrence of attributions during goal attainment scenarios is important to understand in order to determine how and when individuals tend to process events and make behavioral modifications. In order to study this, a sample of two basketball teams (one male team and one female team) was studied through the course of a season using a standard stimulus, a generalized survey, and game film. According to results of this study, the basketball players in the sample tend to interpret emotions similarly, but process and assign attributions differently based on gender. This process seems to be determined based on field dependence or independence through a difference in relying on the entirety of the proximal context or the abstraction of the event into relevant parts. These findings suggest important interventions for the well-being of players as well as how decision making strategies may be affected by attribution process during play.
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Nilsson, Isak, and Leonard Sandström. "Behavior prediction of concrete dams." Thesis, KTH, Betongbyggnad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289385.
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As many dams were built around 1950, the expected life span of these dams are about tobe reached. With this, the need for monitoring and increased understanding of the damsstructural integrity increases. In order to prevent failures, two warning signals are defined;alert and alarm. The main difference being that the first indicates an unexpected changein behavior that needs to be addressed and evaluated in the near future, while the otherrequires that immediate action must be taken to ensure the safety of the dam.This report aims to evaluate the applicability of different models for designing alert values.In order to achieve this goal, two case studies have been performed. The first being onSchlegeis, an arch dam in Austria, and the second Storfinnforsen, a concrete buttress damin Sweden. The methods used are finite element modelling as well as data-based models.Data-based models work on the presumption that the dam behaviour is governed by variationsin environmental conditions such as temperature and water level. The report hasevaluated two commonly used data-based models, hydrostatic thermal time (HTT) and hydrostaticseasonal time (HST), as well as two machine learning based models artificial neuralnetworks (ANN) and boosted regression trees (BRT).The programs used in this report are BRIGADE plus for finite element method and MATLABfor the multi-linear regression analyses HTT and HST, as well as boosted regressiontrees. The neural networks were constructed in Python using TensorFlow and Keras API.The result from the case studies is that the commonly used data-based models HST andHTT perform well enough for creating predictions and alert levels when given a sufficientamount of historical data, approximately 3-5 years. Machine learning such as artificial neuralnetworks while comparable in prediction quality does not further increase the understandingof the dam behaviour and can due to the complexity of designing an appropriate networkstructure be less suited for this type of analysis. Finite element models can also capturethe behavior of the dam rather well. It is however not as accurate as data-based modelswhen sufficient data is available. An FE-model should be used for definition of alert valueswhen insufficient data exists after the dam conditions have been significantly altered, orwhen newly constructed. The main advantage that machine learning provides is that theyperform better for non-linear behavior than multi-linear regression.
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Murakami, T. "Predicting creep behaviour of silicon nitride ceramics." Thesis, Swansea University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.638280.
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Hamadah, Omar. "Predicting the behaviour of oral precancer lesions." Thesis, University of Newcastle Upon Tyne, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442328.
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Garnham-Lee, Katy P. "Predicting physical activity behaviour across early adolescence." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/35275.
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Physical activity (PA) has been labelled the miracle drug (Pimlott, 2010) and participating in regular PA has ample physical and mental wellbeing benefits. However, physical inactivity remains a critical public health concern, particularly across adolescence. In England the proportion of adolescents aged 13-15 years meeting the recommended guidelines for PA decreased significantly from those at a younger age (Health and Social Care Information Centre, 2009; 2012; 2015). The adolescent years (13 18 years) have been identified as the age of greatest decline in PA, although it is possible that large declines can also be seen at younger ages (Sallis, 2000). Among girls the decline in PA is greater at younger ages (9 12 years old) and among boys it is greater at older ages (13 16 years old) (Dumith, Gigante, Domingues, & Kohl, 2011). Thus, examining behaviour of early adolescents (aged 11-13 years) is a primary focus of this thesis. Researchers have called for a more comprehensive grasp of PA correlates and determinants and their impact on behaviour (Biddle & Mutrie, 2001, 2008). This broader picture needs to incorporate longitudinal study designs to accurately portray developmental changes (Evenson & Mota, 2011). This thesis aims to work towards a better understanding of associations among variables across aspects of the ecological model in relation to PA behaviour during early adolescence. Early adolescents within their first year of secondary school (year 7, aged 11 12 years) were recruited through schools across the East Midlands, United Kingdom (UK). These participants completed various measures across an 18 month period to compile all data required for the thesis. The thesis begins with a focus on active transport as a means of commuting to school which can significantly contribute to overall PA levels (Aibar, Bois, Generelo, Bengoechea, & Paillard, 2015; Slingerland, Borghouts, & Hesselink, 2012). The distance from home to school is an important influence on the decision to use active transport; however, ecological perspectives would suggest this variable may interact with individual, interpersonal and environmental factors. Therefore, the first study of this thesis investigates whether the relationship between distance to school and active transport is moderated by (i) gender, (ii) biological maturation, (iii) perceived family support for PA and (iv) multiple deprivation. Cross-sectional results from the baseline data collected demonstrated that the relationship between distance to school and the likelihood to actively travel to school is moderated by biological maturation, multiple deprivation and family support of PA in adolescents. Further analysis revealed that late-maturing children, those from less socio-economically deprived backgrounds and children with low family support of PA are less likely to actively commute to school as distance to school increases. Due to the interaction between these variables described above, the second study focused on the variables collectively using a person-oriented approach, which aimed to classify distinct profiles of early adolescents based on correlates of PA. The outcome variables were also broadened to include active transport and overall PA across two time points. Findings from this second study illustrate that the highly supported, shortest commuters produced the highest levels of self-reported PA and that affluent, short commuters were the most likely to use active transport to travel to school. The affluent, short commuters lived a relatively short distance to school in areas of the lowest deprivation and had relative moderate family support of PA. The highly supported, shortest commuters were characterised by the highest family support of PA and lived the shortest distance to school in areas of low deprivation. Study 1 evidenced an association between biological maturation and PA behaviour; however, study 2 displayed that biological maturation did not meaningfully contribute to the class characteristics, and were not a predictor of PA. Previous evidence as to whether early, average or late maturing adolescents are more likely to disengage from PA is mixed and tends to focus on one gender only (Sherar, Cumming, Eisenmann, Baxter-Jones, & Malina 2010; Bacil, Mazzardo, Rech, Legnani, & Campos, 2015). Thus for the third study a more focused inspection of biological maturity was undertaken. Biological maturity status was investigated as a predictor of PA behaviour at two subsequent time points (6 9 months after baseline and 12 18 months after baseline) and whether there was variation across genders. Findings displayed that biological maturity status does not predict subsequent PA, with no distinction across genders. To conclude, the final study examined additional forms of PA behaviour. For children to develop and maintain healthy PA behaviours, their PA during the school day, particularly during physical education (P.E) classes is important (Owen, Smith, Lubans, Ng, & Lonsdale, 2014). Self-reported PA was divided into school-time PA (during P.E. lessons, break and lunchtimes) and leisure-time PA (after school, during evenings and weekends). The final study fully utilised the longitudinal data collected and utilised longitudinal growth modelling to describe the changes in PA behaviour across 12-18 months during early adolescence. Results displayed that school-time PA and leisure-time PA are distinct. Males; those from less deprived backgrounds and individuals with higher family support of PA all separately reported more school-time PA than their counterparts (females, those from higher deprived backgrounds and individuals with lower family support of PA) at baseline. Males and those with higher family support of PA also reported more leisure-time PA than their respective counterparts at baseline. On average, both genders decreased in school-time PA across 18 months yet for leisure-time PA, on average, there was no change over time and no significant difference in the rate of change between genders. There were no observed significant differences in the rate of change between multiple deprivation status and biological maturation across the 18 months for both behaviours. For family support, on average school-time PA decreased over time and results showed significant difference in the rate of change between individuals with lower or higher levels of family support of PA across the 18 months. On average, there was no change over time for leisure-time PA yet there was a significant difference in the rate of change between individuals and their family support of PA across 18 months. Further analysis demonstrated if an individual s family support increases, so does their leisure-time PA and vice versa. These overall key findings demonstrate the complexity of PA behaviour throughout early adolescence. This thesis works towards predicting individuals, correlates and determinants that may be susceptible to physical inactivity and/or a decrease in activity over time. Results can be used to target and direct PA intervention work.
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Mangold, Michel. "Predicting Churn Behaviour in the Telecommunications Industry." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-310913.
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Burlutskiy, Nikolay. "Prediction of user behaviour on the Web." Thesis, University of Brighton, 2017. https://research.brighton.ac.uk/en/studentTheses/7ad2ede5-c7e3-4f99-ba68-ef257dc2387a.
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The Web has become an ubiquitous environment for human interaction, communication, and data sharing. As a result, large amounts of data are produced. This data can be utilised by building predictive models of user behaviour in order to support business decisions. However, the fast pace of modern businesses is creating the pressure on industry to provide faster and better decisions. This thesis addresses this challenge by proposing a novel methodology for an effcient prediction of user behaviour. The problems concerned are: (i) modelling user behaviour on the Web, (ii) choosing and extracting features from data generated by user behaviour, and (iii) choosing a Machine Learning (ML) set-up for an effcient prediction. First, a novel Time-Varying Attributed Graph (TVAG) is introduced and then a TVAG-based model for modelling user behaviour on the Web is proposed. TVAGs capture temporal properties of user behaviour by their time varying component of features of the graph nodes and edges. Second, the proposed model allows to extract features for further ML predictions. However, extracting the features and building the model may be unacceptably hard and long process. Thus, a guideline for an effcient feature extraction from the TVAG-based model is proposed. Third, a method for choosing a ML set-up to build an accurate and fast predictive model is proposed and evaluated. Finally, a deep learning architecture for predicting user behaviour on the Web is proposed and evaluated. To sum up, the main contribution to knowledge of this work is in developing the methodology for fast and effcient predictions of user behaviour on the Web. The methodology is evaluated on datasets from a few Web platforms, namely Stack Exchange, Twitter, and Facebook.
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Kadambi, Rupasri. "Analysis of data mining techniques for customer segmentation and predictive modeling a case study /." Diss., Online access via UMI:, 2005.
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Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Dept. of Systems Science and Industrial Engineering, 2005.<br>Includes bibliographical references.
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White, Joanna Kate. "Modelling traffic behaviour on networks." Thesis, Lancaster University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302391.
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Mann, Helen. "Predicting young driver behaviour from pre-driver attitudes, intentions and road behaviour." Thesis, Heriot-Watt University, 2010. http://hdl.handle.net/10399/2396.
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Drivers under 25 years are over-represented in global road accident statistics. The Theory of Planned Behaviour (TPB) has been used to identify individuals who are likely to engage in behaviours, such as speeding, which are associated with increased accident involvement. In an attempt to investigate adolescents‟ attitudes and behaviours from pre- to post-driver training, the studies presented incorporate past behaviour into the TPB. Three questionnaire-based studies were conducted in Scotland and New Zealand. The first study explored adolescent pre-drivers‟ road behaviour, driving attitudes and speeding intentions. Adolescents‟ with the greatest speeding intentions frequently engaged in high-risk road behaviour and had more accepting attitudes towards driving violations. The second study explored the development of attitudes and intentions from pre- to post-driver training. Drivers who frequently violated reported more accepting attitudes towards violations and engagement in frequent high-risk road behaviours as pre-drivers. The third study assessed the stability of pre-drivers‟ driving attitudes and speeding intentions. Adolescents‟ attitudes and intentions fluctuated significantly; however, males reported riskier driving attitudes and greater speeding intentions. This research suggests that the role of road safety education and pre-driver interventions on future driving behaviour has been under-estimated. Interventions that simultaneously reinforce safe road practices and motivate the reduction of dangerous practices will influence the future of adolescents as safe drivers.
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Feng, Qi. "Predictive action in infancy : Evidence of early prospective behavior." Licentiate thesis, Umeå University, Department of Psychology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-20299.
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<p> </p><p>How do young infants understand and act on their constantly changing</p><p>environment? An action perspective on motor development was taken into account.</p><p>The present thesis investigated perception guided predictive action in 6-month-old</p><p>infants, namely, head tracking and reaching for an object moving on a pre-defined</p><p>trajectory, linear or nonlinear, fully visible or partially occluded. The motion trials</p><p>were presented in a randomized order or in a way by using an ABBA block design.</p><p>Study I of this thesis began with the exploration of the principles underlying</p><p>infant predictive action. Infants were presented a fully visible moving object on four</p><p>trajectories: two linear trajectories that intersected at the center of a display and two</p><p>non-linear trajectories that contained a sudden turn at the point of intersection. The</p><p>results supplied evidence that both infants’ head tracking and reaching showed an</p><p>extrapolation of the object’s motion on linear paths, which was described by the</p><p>principle of inertia. No learning effect was found in spite of repeated fully visible</p><p>trials.</p><p>Previous experiments reported that infants of similar age showed a reduction in</p><p>reaching when object motion was occluded briefly. Thus Study II was undertaken, in</p><p>part, to evaluate whether differences in the tasks presented to infants or differences</p><p>in the visibility of the objects account for these findings. This was done by</p><p>investigating infants’ predictive head tracking of an object following the procedure</p><p>presented in Study I with only one exception: object motion was partially occluded</p><p>by a small occluder positioned on the motion trajectory. Study II also raised a second</p><p>question concerning infants’ ability to learn to anticipate upcoming object motions. It</p><p>was found that infants were able to quickly learn to anticipate either linear or</p><p>nonlinear motion but with a superior learning effect from linear motion. This pattern</p><p>suggested a tendency to anticipate the upcoming motion in accord with inertia.</p><p>Although a capacity to anticipate occluded object motion in accord with inertia was</p><p>present, it was weak, as infants’ initial reaction to the occluded object motion</p><p>revealed no such tendency. Learning in all cases was associated with the trajectory of</p><p>the object, not the specific locations at which the object appeared. It was suggested</p><p>that infants might form object representations that are influenced by learning and</p><p>that are just weakly biased toward inertia extrapolation. This finding supported the</p><p>claims that occlusion reduces the presentation of object representation, as suggested</p><p>by single system of object representation theory.</p><p> </p>
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Gallagher, Christopher. "Social Burden and Attributions of Hostility in Predicting Counterproductive Work Behavior." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1541096416412655.
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Khosla, Aditya. "Predicting human behavior using visual media." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/109001.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 161-173).<br>The ability to predict human behavior has applications in many domains ranging from advertising to education to medicine. In this thesis, I focus on the use of visual media such as images and videos to predict human behavior. Can we predict what images people remember or forget? Can we predict the type of images people will like? Can we use a photograph of someone to determine their state of mind? These are some of the questions I tackle in this thesis. Through my work, I demonstrate: (1) It is possible to predict with near human-level correlation, the probability with which people will remember images, (2) it is possible to predictably modify the extent to which a face photograph is remembered, (3) it is possible to predict, with a high correlation, the number of views an image will receive even before it is uploaded, (4) it is possible to accurately identify the gaze of people in images, both from the perspective of a device, and third-person. Further, I develop techniques to visualize and understand machine learning algorithms that could help humans better understand themselves through the analysis of algorithms capable of predicting behavior. Overall, I demonstrate that visual media is a rich resource for the prediction of human behavior.<br>by Aditya Khosla.<br>Ph. D.
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Mestre, María del Rosario. "Bayesian predictive models of user intention." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708641.
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Harris, Jemma. "Psychological need satisfaction and the prediction of behaviour." Thesis, University of Essex, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.486180.
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The main aim of this thesis was to examine the role of generalised basic psychological needs from self-determination theory in predicting motivation, decision-making, anti, ultimately, behaviour. Chapter 1 provides an overview of relevant theories and perspectives, and provides a rationale for the studies reported within subsequent chapters. Chapter 2 reports a cross-lagged examination of constructs at all three levels of the hierarchy of motivation. Results of this study supported the hypothesis that global-level basic psychological need satisfaction exhibited the greatest stability within the hierarchy.However, this study yielded only weak suppOli for the existence of top-down and bottomup effects between global, contextual, and situational level constructs. Therefore Chapters 3 and 4 turned to an in-depth investigation of top-down effects and examined the role of basic psychological needs in a previously proposed integrated motivational sequence from contextual-level motivation to situational-level decision-making as reflected by the theory of planned behaviour.
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Brown, Peter Thomas. "Predicting laterally loaded pile behaviour using the Pressuremeter." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/26216.
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To reliably determine the lateral load-deflection behaviour of a piled foundation, a load test is commonly required. In-situ geotechnical tools, such as the cone penetrometer and the pressuremeter, have been successfully used to predict the lateral capacity of piles without the need for a load test. This research project investigates some of the variables involved in performing a pressuremeter test, and a procedure to predict the load-deflection relationship of laterally loaded piles is developed.
For this thesis a total of eighty pressuremeter tests were conducted at five research sites. The variables pertaining to pressuremeter testing which were studied were: the method of installing the probe (self-boring or full-displacement), the effect of repeating a pressuremeter test at the same depth, the effect of rate of membrane inflation, the result of pore pressure dissipation in fine-grained soils, the effect of stress versus strain controlled membrane inflation, the influence of pre-pushing a small diameter pilot hole, and the effect of performing 10 slow cyclic unload-reload cycles during a test.
Three laterally loaded pile case histories were documented. A total of 10 piles were laterally loaded in six separate tests. Using the pressuremeter and the program LATPILE, a prediction of the load-deflection behaviour was made for each test. In general, the deflected shape of each pile was predicted to within. 20-30% of the actual measured deflected shape.<br>Applied Science, Faculty of<br>Civil Engineering, Department of<br>Graduate
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Williams, Colin Paul. "Predicting the approximate functional behaviour of physical systems." Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/27053.
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Eadie, Edward Norman. "Small resource stock share price behaviour and prediction." Title page, contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09CM/09cme11.pdf.
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Egginton, Robert. "Predicting and Learning the Behaviour of Intelligent Agents." Thesis, University of Bristol, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.521100.
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Hamilton, Robert. "[Credit] scoring : predicting, understanding and explaining consumer behaviour." Thesis, Loughborough University, 2005. https://dspace.lboro.ac.uk/2134/13053.
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This thesis stems from my research into the broad area of (credit) scoring and the predicting, understanding and explaining of consumer behaviour. This research started at the Univers1ty of Edinburgh on an ESRC funded project in 1988. This work, which is being submitted as the partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough Unvers1ty, consists of an introductory chapter and a selection of papers published 1991 - 2001 (inclusive). The papers address some of the key issues and areas of interest and concern arising from the rapidly evolving and expanding credit (card) market and the highly competitive nature of the credit industry. These features were particularly evident during the late 1980's and throughout the 90's Chapter One provides a general background to the research and outlines some of the key (practical) issues involved in building a (credit) scorecard Additionally, it provides a brief summary of each of the research papers appearing in full in Chapters 2- 9 (inclusive) and ends with some general limitations and conclusions. The research papers appearing in Chapters 2-9 inclusive) are all concerned with predicting, understanding and explaining different types of consumer behaviour in relation to the use of credit cards. For example discriminating between 'GOOD' and 'BAD' repayers of credit card debt on the basis of different definitions of good and bad, the identification of 'slow payers' using different statistical methods; examining the characteristics of credit card users and non-users, and identifying the characteristics of credit card holders most likely to return their credit card.
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Wood, Dawn Helaine. "Personality representation : predicting behaviour for personalised learning support." Thesis, University of Hull, 2010. http://hydra.hull.ac.uk/resources/hull:6862.
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The need for personalised support systems comes from the growing number of students that are being supported within institutions with shrinking resources. Over the last decade the use of computers and the Internet within education has become more predominant. This opens up a range of possibilities in regard to spreading that resource further and more effectively. Previous attempts to create automated systems such as intelligent tutoring systems and learning companions have been criticised for being pedagogically ineffective and relying on large knowledge sources which restrict their domain of application. More recent work on adaptive hypermedia has resolved some of these issues but has been criticised for the lack of support scope, focusing on learning paths and alternative content presentation. The student model used within these systems is also of limited scope and often based on learning history or learning styles. This research examines the potential of using a personality theory as the basis for a personalisation mechanism within an educational support system. The automated support system is designed to utilise a personality based profile to predict student behaviour. This prediction is then used to select the most appropriate feedback from a selection of reflective hints for students performing lab based programming activities. The rationale for the use of personality is simply that this is the concept psychologists use for identifying individual differences and similarities which are expressed in everyday behaviour. Therefore the research has investigated how these characteristics can be modelled in order to provide a fundamental understanding of the student user and thus be able to provide tailored support. As personality is used to describe individuals across many situations and behaviours, the use of such at the core of a personalisation mechanism may overcome the issues of scope experienced by previous methods. This research poses the following question: can a representation of personality be used to predict behaviour within a software system, in such a way, as to be able to personalise support? Putting forward the central claim that it is feasible to capture and represent personality within a software system for the purpose of personalising services. The research uses a mixed methods approach including a number and combination of quantitative and qualitative methods for both investigation and determining the feasibility of this approach. The main contribution of the thesis has been the development of a set of profiling models from psychological theories, which account for both individual differences and group similarities, as a means of personalising services. These are then applied to the development of a prototype system which utilises a personality based profile. The evidence from the evaluation of the developed prototype system has demonstrated an ability to predict student behaviour with limited success and personalise support. The limitations of the evaluation study and implementation difficulties suggest that the approach taken in this research is not feasible. Further research and exploration is required –particularly in the application to a subject area outside that of programming.