Thèses : « Predicting model »

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Andeta, Jemal Ahmed. « Road-traffic accident prediction model : Predicting the Number of Casualties ». Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20146.

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Efficient and effective road traffic prediction and management techniques are crucial in intelligent transportation systems. It can positively influence road advancement, safety enhancement, regulation formulation, and route planning to save living things in advance from road traffic accidents. This thesis considers road safety by predicting the number of casualties if an accident occurs using multiple traffic accident attributes. It helps individuals (drivers) or traffic offices to adjust and control their contributions for the occurrence of an accident before emerging it. Three candidate algorithms from different regression fit patterns are proposed and evaluated to conduct the thesis: the bagging, linear, and non-linear fitting patterns. The gradient boosting machines (GBoost) from the bagging, Linearsupport vector regression (LinearSVR) from the linear, and extreme learning machines (ELM) also from the non-linear side are the selected algorithms. RMSE and MAE performance evaluation metrics are applied to evaluate the models. The GBoost achieved a better performance than the other two with a low error rate and minimum prediction interval value for 95% prediction interval. A SHAP (SHapley Additive exPlanations) interpretation technique is applied to interpret each model at the global interpretation level using SHAP’s beeswarm plots. Finally, suggestions for future improvements are presented via the dataset and hyperparameter tuning.

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li, yiwen. « Predicting Hearing Loss Using Auditory Steady-State Responses ». Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/84.

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Auditory Steady-State Response (ASSR) is a promising tool for detecting hearing loss. In this project, we analyzed hearing threshold data obtained from two ASSR methods and a gold standard, pure tone audiometry, applied to both normal and hearing-impaired subjects. We constructed a repeated measures linear model to identify factors that show significant differences in the mean response. The analysis shows that there are significant differences due to hearing status (normal or impaired) and ASSR method, and that there is a significant interaction between hearing status and test signal frequency. The second task of this project was to predict the PTA threshold (gold standard) from the ASSR-A and ASSR-B thresholds separately at each frequency, in order to measure how accurate the ASSR measurements are and to obtain a ¡°correction function¡± to correct the bias in the ASSR measurements. We used two approaches. In the first, we modeled the relation of the PTA responses to the ASSR values for the two hearing status groups as a mixture model and tried two prediction methods. The mixture modeling was successful, but the predictions gave disappointing results. A second approach, using logistic regression to predict group membership based on ASSR value and then using those predictions to obtain a predictor of the PTA value, gave successful results.

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Kingwell, Stephen. « Predicting Complications After Spinal Surgery : Surgeons’ Aided and Unaided Predictions ». Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41559.

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Despite the emergence of artificial intelligence (AI) and machine learning (ML) in medicine and the resultant interest in predictive analytics in surgery, there remains a paucity of research on the actual impact of prediction models and their effect on surgeons’ risk assessment of post-surgical complications. This research evaluated how spinal surgeons predict post-surgical complications with and without additional information generated by a ML predictive model. The study was conducted in two stages. In the preliminary stage an ML prediction model for post-surgical complications in spine surgery was developed. In the second stage, a survey instrument was developed, using patient vignettes, to determine how providing ML model support affected surgeons’ predictions of post-surgical complications. Results show that support provided by a ML prediction model improved surgeons’ accuracy to correctly predict the presence or absence of a complication in patients undergoing spinal surgery from 49.1% to 54.8% (p=0.024). It is clear that predicting post-surgical complications in patients undergoing spinal surgery is difficult, for models and experienced surgeons, but it is not surprising that additional information provided by the ML model prediction was beneficial overall. This is the first study in the spine surgery literature that has evaluated the impact of a ML prediction model on surgeon prediction accuracy of post-surgical complications.

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Degerman, Engfeldt Johnny. « Predicting Electrochromic Smart Window Performance ». Licentiate thesis, KTH, Tillämpad elektrokemi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-95167.

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The building sector is one of the largest consumers of energy, where the cooling of buildings accounts for a large portion of the total energy consumption. Electrochromic (EC) smart windows have a great potential for increasing indoor comfort and saving large amounts of energy for buildings. An EC device can be viewed as a thin-film electrical battery whose charging state is manifested in optical absorption, i.e. the optical absorption increases with increased state-of-charge (SOC) and decreases with decreased state-of-charge. It is the EC technology's unique ability to control the absorption (transmittance) of solar energy and visible light in windows with small energy effort that can reduce buildings' cooling needs. Today, the EC technology is used to produce small windows and car rearview mirrors, and to reach the construction market it is crucial to be able to produce large area EC devices with satisfactory performance. A challenge with up-scaling is to design the EC device system with a rapid and uniform coloration (charging) and bleaching (discharging). In addition, up-scaling the EC technology is a large economic risk due to its expensive production equipment, thus making the choice of EC material and system extremely critical. Although this is a well-known issue, little work has been done to address and solve these problems. This thesis introduces a cost-efficient methodology, validated with experimental data, capable of predicting and optimizing EC device systems' performance in large area applications, such as EC smart windows. This methodology consists of an experimental set-up, experimental procedures and a twodimensional current distribution model. The experimental set-up, based on camera vision, is used in performing experimental procedures to develop and validate the model and methodology. The two-dimensional current distribution model takes secondary current distribution with charge transfer resistance, ohmic and time-dependent effects into account. Model simulations are done by numerically solving the model's differential equations using a finite element method. The methodology is validated with large area experiments. To show the advantage of using a well-functioning current distribution model as a design tool, some EC window size coloration and bleaching predictions are also included. These predictions show that the transparent conductor resistance greatly affects the performance of EC smart windows.
Byggnadssektorn är en av de största energiförbrukarna, där kylningen av byggnader står för en stor del av den totala energikonsumtionen. Elektrokroma (EC) smarta fönster har en stor potential för att öka inomhuskomforten och spara stora mängder energi för byggnader. Ett elektrokromt fönster kan ses som ett tunnfilmsbatteri vars laddningsnivå yttrar sig i dess optiska absorption, d.v.s. den optiska absorptionen ökar med ökad laddningsnivå och vice versa. Det är EC-teknologins unika egenskaper att kunna kontrollera absorptionen (transmittansen) av solenergi och synligt ljus i fönster med liten energiinsats som kan minska byggnaders kylningsbehov. EC-teknologin används idag till att producera små fönster och bilbackspeglar, men för att nå byggnadsmarknaden är det nödvändigt att kunna producera stora EC-anordningar med fullgod prestanda. En välkänd utmaning med uppskalning är att utforma EC-systemet med snabb och jämn infärgning (laddning) och urblekning (urladdning), vilket även innebär att uppskalning är en stor ekonomisk risk på grund av den dyra produktionsutrustningen. Trots att detta är välkända problem har lite arbete gjorts för att lösa dessa. Denna avhandling introducerar ett kostnadseffektivt tillvägagångssätt, validerat med experimentella data, kapabelt till att förutsäga och optimera ECsystems prestanda för anordningar med stor area, såsom elektrokroma smarta fönster. Detta tillvägagångssätt består av en experimentell uppställning, experiment och en tvådimensionell strömfördelningsmodell. Den experimentella uppställningen, baserad på kamerateknik, används i de experimentella tillvägagångssätten så att modellen kan utvecklas och valideras. Den tvådimensionella strömfördelningsmodellen inkluderar sekundär strömfördelning med laddningsöverföringsmotstånd, ohmska och tidsberoende effekter. Modellsimuleringarna görs genom att numeriskt lösa en modells differentialekvationer med hjälp av en finita-element-metod. Tillvägagångssättet är validerat med experiment gjorda på stora EC anordningar. För att visa fördelarna med att använda en väl fungerande strömfördelningsmodell som ett designverktyg, har några prediktioner av infärgning och urblekning av EC-fönster inkluderats. Dessa prediktioner visar att den transparenta strömtilledarresistansen har stor påverkan på EC-fönsters prestanda.

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Barnhart, Gregory J. « Predicting hail size using model vertical velocities ». Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://bosun.nps.edu/uhtbin/hyperion-image.exe/08Mar%5FBarnhart.pdf.

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Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 2008.
Thesis Advisor(s): Nuss, Wendell. "March 2008." Description based on title screen as viewed on April 25, 2008. Includes bibliographical references (p. 47-49). Also available in print.

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Sofi, Backman. « A model for predicting robot dresspack damage ». Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149369.

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McClain, Michael Patrick. « A micromechanical model for predicting tensile strength ». Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-10052007-143117/.

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Gao, Zhiyuan, et Likai Qi. « Predicting Stock Price Index ». Thesis, Halmstad University, Applied Mathematics and Physics (CAMP), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-3784.

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This study is based on three models, Markov model, Hidden Markov model and the Radial basis function neural network. A number of work has been done before about application of these three models to the stock market. Though, individual researchers have developed their own techniques to design and test the Radial basis function neural network. This paper aims to show the different ways and precision of applying these three models to predict price processes of the stock market. By comparing the same group of data, authors get different results. Based on Markov model, authors find a tendency of stock market in future and, the Hidden Markov model behaves better in the financial market. When the fluctuation of the stock price index is not drastic, the Radial basis function neural network has a nice prediction.

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Seidu, Mohammed Nazib. « Predicting Bankruptcy Risk : A Gaussian Process Classifciation Model ». Thesis, Linköpings universitet, Institutionen för datavetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119120.

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This thesis develops a Gaussian processes model for bankruptcy risk classification and prediction in a Bayesian framework. Gaussian processes and linear logistic models are discriminative methods used for classification and prediction purposes. The Gaussian processes model is a much more flexible model than the linear logistic model with smoothness encoded in the kernel with the potential to improve the modeling of the highly nonlinear relationships between accounting ratios and bankruptcy risk. We compare the linear logistic regression with the Gaussian process classification model in the context of bankruptcy prediction. The posterior distributions of the GPs are non-Gaussian, and we investigate the effectiveness of the Laplace approximation and the expectation propagation approximation across several different kernels for the Gaussian process. The approximate methods are compared to the gold standard of Markov Chain Monte Carlo (MCMC) sampling from the posterior. The dataset is an unbalanced panel consisting of 21846 yearly observations for about 2000 corporate firms in Sweden recorded between 1991−2008. We used 5000 observations to train the models and the rest for evaluating the predictions. We find that the choice of covariance kernel affects the GP model’s performance and we find support for the squared exponential covariance function (SEXP) as an optimal kernel. The empirical evidence suggests that a multivariate Gaussian processes classifier with squared exponential kernel can effectively improve bankruptcy risk prediction with high accuracy (90.19 percent) compared to the linear logistic model (83.25 percent).

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Chen, Dong. « Neural network model for predicting performance of projects ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0021/MQ48059.pdf.

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Allen, Susan Elise. « A Model for Predicting Bereavement Outcome in Widowhood ». Thesis, University of North Texas, 1990. https://digital.library.unt.edu/ark:/67531/metadc331737/.

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The present longitudinal study examined the effects and interactions of several variables thought to affect adjustment to conjugal bereavement. Questionnaires were administered to 147 conjugally bereaved subjects and to 46 persons bereaved of close relatives other than spouse. Independent variables included experienced competence (self-esteem, locus of control, coping self-efficacy, and prior coping strategies), impact of loss (anticipation and preventabillty of loss, centrality of relationship and life change), and perceived resources. All of the independent variables were found to be important predictors of adjustment in conjugal bereavement. However, hypothesized interactions among variables were generally not found. Experimental variables were better predictors of adjustment in conjugal bereavement than were demographic variables. Contrary to prediction, widowed subjects tended to become more lonely and showed increased bereavement adjustment difficulties over a six month period of time. However, post hoc analysis suggested that levels of adjustment do not decline over the long term in widowhood. The present study supports a view of widowhood as a multidimensional event, characterized by seemingly contradictory feelings, experiences, and behaviors.

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Sandie, Reatha. « Predicting the Evolution of Influenza A ». Thesis, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/22679.

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Vaccination against the Influenza A virus (IAV) is often an important and critical task for much of the population, as IAV causes yearly epidemics, and can cause even deadlier pandemics. Designing the vaccine requires an understanding of the current major circulating strains of Influenza, as well as an understanding of how those strains could change over time to become either less harmful or more deadly, or simply die out completely. An error in the prediction process can lead to a non-immunized population at risk of epidemics, or even a pandemic. Presented here is a posterior predictive approach to generate emerging influenza strains based on a realistic genomic model that incorporates natural features of viral evolution such as selection and recombination. Also introduced is a sequence sampling scheme to relieve the computational burden of the posterior predictive analysis by clustering sequences based on their pairwise similarity. Finally, the impact of “evolutionary accidents” that take the form of bursts of evolution and or of recombination on the predictive power of our procedure is tested. An analysis of the impact of these bursts is carried out in a retrospective study that focuses on the unexpected emergence of a new H3N2 strain in the 2007-08 influenza season. Measuring the R2 values of both pairwise and patristic distances, the model reaches a predictive power of ∼40%, but is not able to simulate the emergence of the target Brisbane/10/2007 sequence with a high probability. The inclusion of “evolutionary accidents” improved the algorithm’s ability to predict HA sequences, but the prediction power of the NA gene remained low.

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Bowring, James Frederick. « Modeling and Predicting Software Behaviors ». Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/19754.

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Software systems will eventually contribute to their own maintenance using implementations of self-awareness. Understanding how to specify, model, and implement software with a sense of self is a daunting problem. This research draws inspiration from the automatic functioning of a gimbal---a self-righting mechanical device that supports an object and maintains the orientation of this object with respect to gravity independently of its immediate operating environment. A software gimbal exhibits a self-righting feature that provisions software with two auxiliary mechanisms: a historical mechanism and a reflective mechanism. The historical mechanism consists of behavior classifiers trained on statistical models of data that are collected from executions of the program that exhibit known behaviors of the program. The reflective mechanism uses the historical mechanism to assess an ongoing or selected execution. This dissertation presents techniques for the identification and modeling of program execution features as statistical models. It further demonstrates how statistical machine-learning techniques can be used to manipulate these models and to construct behavior classifiers that can automatically detect and label known program behaviors and detect new unknown behaviors. The thesis is that statistical summaries of data collected from a software program's executions can model and predict external behaviors of the program. This dissertation presents three control-flow features and one value-flow feature of program executions that can be modeled as stochastic processes exhibiting the Markov property. A technique for building automated behavior classifiers from these models is detailed. Empirical studies demonstrating the efficacy of this approach are presented. The use of these techniques in example software engineering applications in the categories of software testing and failure detection are described.

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Bojcic, Predrag. « Development of a model for predicting thickener rake torque / ». St. Lucia, Qld, 2000. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17870.pdf.

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Ducey, Adam J. « Predicting Tablet Computer Use : An Extended Technology Acceptance Model ». Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4471.

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While information technology has rapidly changed work in the United States in the past 50 years, some businesses and industries have been slow to adopt new technologies. Healthcare is one industry that has lagged behind in information technology investment for a variety of reasons. Recent federal initiatives to encourage IT adoption in the healthcare industry provide an ideal context to study factors that influence technology acceptance. Data from 261 practicing pediatricians were collected to evaluate an extended Technology Acceptance Model. Results indicated that individual (i.e., perceived usefulness, perceived ease of use), organizational (i.e., subjective norm), and device (i.e., compatibility, reliability) characteristics collectively influence pediatricians' intention to adopt tablet computers in their medical practice. Theoretical and practical implications are discussed. Future research should examine additional variables that influence information technology adoption in organizations.

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Mued, Licha. « A model for predicting the performance of IP videoconferencing ». Thesis, University of Plymouth, 2004. http://hdl.handle.net/10026.1/1952.

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With the incorporation of free desktop videoconferencing (DVC) software on the majority of the world's PCs, over the recent years, there has, inevitably, been considerable interest in using DVC over the Internet. The growing popularity of DVC increases the need for multimedia quality assessment. However, the task of predicting the perceived multimedia quality over the Internet Protocol (IP) networks is complicated by the fact that the audio and video streams are susceptible to unique impairments due to the unpredictable nature of IP networks, different types of task scenarios, different levels of complexity, and other related factors. To date, a standard consensus to define the IP media Quality of Service (QoS) has yet to be implemented. The thesis addresses this problem by investigating a new approach to assess the quality of audio, video, and audiovisual overall as perceived in low cost DVC systems. The main aim of the thesis is to investigate current methods used to assess the perceived IP media quality, and then propose a model which will predict the quality of audiovisual experience from prevailing network parameters. This thesis investigates the effects of various traffic conditions, such as, packet loss, jitter, and delay and other factors that may influence end user acceptance, when low cost DVC is used over the Internet. It also investigates the interaction effects between the audio and video media, and the issues involving the lip sychronisation error. The thesis provides the empirical evidence that the subjective mean opinion score (MOS) of the perceived multimedia quality is unaffected by lip synchronisation error in low cost DVC systems. The data-gathering approach that is advocated in this thesis involves both field and laboratory trials to enable the comparisons of results between classroom-based experiments and real-world environments to be made, and to provide actual real-world confirmation of the bench tests. The subjective test method was employed since it has been proven to be more robust and suitable for the research studies, as compared to objective testing techniques. The MOS results, and the number of observations obtained, have enabled a set of criteria to be established that can be used to determine the acceptable QoS for given network conditions and task scenarios. Based upon these comprehensive findings, the final contribution of the thesis is the proposal of a new adaptive architecture method that is intended to enable the performance of IP based DVC of a particular session to be predicted for a given network condition.

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MONTEIRO, CAMILA MARIA DO NASCIMENTO. « MODEL FOR PREDICTING SHORT-TERM SPEED USING HOLT-WINTERS ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2013. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=23270@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Após o choque de racionamento de energia elétrica, decorrente do desequilíbrio entre oferta e demanda, os vários setores da sociedade brasileira constataram a real e iminente necessidade de diversificação das fontes de geração de energia elétrica e de seu uso racional. Busca-se hoje novas fontes, entre as quais a energia eólica, uma alternativa nova e promissora. A energia eólica está aumentando no mundo todo e o Brasil tem um enorme potencial devido a sua localização geográfica e o governo tem investido neste tipo de energia. O principal objetivo desta dissertação é estudar e desenvolver modelos de previsão de velocidade de vento, de curto prazo da velocidade do vento. Os métodos de amortecimento exponencial, em particular o método de Holt-Winters e suas variações, são apropriados para este contexto devido à sua alta adaptabilidade e robustez. Para aplicação da metodologia considerou-se o município de São João do Cariri (Estado de Paraíba), onde está localizada uma das estações de referência do projeto SONDA (Sistema Nacional de Dados Ambientais para o setor de energia). Será utilizado o método de Holt-Winters, que será comparado com os modelos: de persistência, neuro-fuzzy (ANFIS) e estatísticos.
After the shock of electricity rationing, due to the imbalance between supply and demand, the various sectors of the Brazilian society found a real and imminent need to diversify sources of electricity generation and its rational use. New sources are searched today, including wind power, a promising new alternative. Wind energy has been increasing worldwide and Brazil has huge potential due to its geographical location and the government has invested in this type of energy. The main objective of this thesis is to study and develop forecasting models, of short-term wind speed. The methods of exponential smoothing, in particular the method Holt-Winters and its variations, are suitable in this context because of its high adaptability and robustness. The city of São João do Cariri (State of Paraíba), where it is located one of the reference stations of project SONDA (National Environmental Data for the energy sector) was chosen in order to apply the methodology. The method that will be used is Holt-Winters, who will be compared with the models: persistence, neuro-fuzzy (ANFIS) and statistics.

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Scheckler, Rebecca Klein. « Predicting drug interactions with a three level causal model ». Thesis, Virginia Tech, 1990. http://hdl.handle.net/10919/42156.

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A medical expert system for predicting qualitative pharmacodynamic interactions of the cardiovascular system is described. TLCM traces causal paths of drug action through up to three levels of drug action. The three levels which are molecular/receptor level, physiological level and clinical level provide both deep and shallow reasoning in order to overcome the problem of unknowns in medical expert systems. Sparsity of information in pharmacology results from necessity of using non-invasive techniques for monitoring drug effects in the human subject and difficulty in isolating effect from feedback. The qualitative nature of TLCM is another attempt to deal with incomplete information in pharmacology.
Master of Science

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Wright, James Frederick Carleton University Dissertation Geography. « A hybrid model for predicting permafrost occurrence and thickness ». Ottawa, 1995.

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Bayer, Lucy. « The investment model and organizational commitment predicting workplace behaviors / ». Diss., Connect to the thesis, 2009. http://hdl.handle.net/10066/3733.

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McGinnis, Daniel Frank. « Predicting Oxygen Transfer in Hypolimnetic Oxygenation Devices ». Thesis, Virginia Tech, 2000. http://hdl.handle.net/10919/32286.

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The purpose of this research was to apply a discrete-bubble model to predict the performance of several hypolimnetic oxygenators. The model is used to predict the oxygen transfer rate in a hypolimnetic oxygenator based on the initial bubble size formed at the diffuser. The discrete-bubble model is based on fundamental principles, and therefore could also be applied to other mass transfer applications involving the injection of bubbles into a fluid. The discrete-bubble model has been applied to a linear bubble-plume diffuser, a full-lift hypolimnetic aerator and the Speece Cone with promising results.

The first step in this research was to investigate the principals of bubble formation at a submerged orifice, bubble rise velocity and bubble mass transfer. The discrete-bubble model is then presented. The model traces a single bubble rising through a fluid, accounting for changes in bubble size due to mass transfer, temperature and hydrostatic pressure. The bubble rise velocity and mass transfer coefficients are given by empirical correlations that depend on the bubble size. Bubble size is therefore recalculated at every increment and the values for the bubble rise velocity and mass transfer coefficients are continually updated. The discrete-bubble model is verified by comparison to experimental data collected in large-scale oxygen transfer tests.

Finally, the discrete-bubble model is applied to the three most common hypolimnetic oxygenation systems: the Speece Cone, the bubble-plume diffuser, and the full-lift hypolimnetic oxygenation systems. The latter being presented by Vickie Burris in her thesis, Hypolimnetic Aerators: Predicting Oxygen Transfer and Water Flow Rate.
Master of Science

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au, Knebworth@iinet net, et Iain Cameron. « Understanding, modelling and predicting transport mobility in urban environments ». Murdoch University, 2004. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20050513.141512.

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In the last three decades the global population has been growing at an essentially constant rate, at around 1.5 per cent per year, to about 6.026 billion in 2000 when it was estimated that 47% of that population live in an urban environment. Further, a United Nations projection indicates that 60% of the total global population may be living in an urban settlement by the year 2025. This increasing urbanisation brings with it increased employment, that delivers affluence, which then continues the cycle of migration and movement to these growing metropolitan areas in both developed and developing countries. As cities increase in population and expand their urban area, there is a consequential expansion of urban transportation and accompanying service infrastructure. People travel daily, irrespective of their vast differences in culture, economic conditions and means of transportation. This daily mobility is sought for its own sake as well as to bridge the spatial distance that separates their homes from the work place, to accomplish their households domestic needs and to undertake social journeys, such as visiting friends and taking holidays. As the worlds urban population undertakes its daily mobility by a variety of transportation modes, an individuals mobility behaviour and mode-choice is governed by a complex matrix of physical and human, social and management indicators, measures and/or drivers. A literature review describes the current understanding of this complex matrix and concludes by identifying and defining a set of fundamental underlying measures that drive private motorised, public transport and non-motorised (walking and bicycling) mobility at national, city and household levels. As practical instruments, transportation models play an important role in providing decision-makers with analytical tools to help them understand their citys transportation and the different future scenarios it may face. While not necessarily producing foolproof information or predictions, models are still the best methods available to test the likely implications of alternative transportation policy decisions in a rapidly changing urban environment. Urban transport models are generally based on the notion that traffic can be modelled in aggregate measures through statistical data and predictive modelling techniques. In this research, dimensional analysis is used to derive sketch-plan models for private motorised, public transport and non-motorised mobility for any urban environment based on four-decades of detailed land-use and travel pattern data from a large international sample of cities. These models are developed on the basis of a set of fundamental underlying measures that are deemed to drive private motorised, public transport and non-motorised (walking and bicycling) mobility at the city level. Importantly, the models also embody three key attributes. They are: easy to use, minimising user requirements and data inputs policy-sensitive, capable of assessing a sufficient range of policy options reliable and robust over time, so that the results can be consistently believed. The capacity of the sketch-plan models to predict personal mobility in an urban environment is statistically validated against an independent land-use and travel pattern data set for 83 cities located on five continents. Despite their simplicity and maintaining a consistent functional form over a time-series of four-decades and across all geographic and cultural regions, the private motorised mobility model can consistently explain up to 92% of the variance in private motorised urban mobility. The results for the public transport mobility model are less reliable and consistent, in particular when developing cities are part of the model. Results for developed or wealthier cities are much better. Reasons for these results and their inadequacies are discussed. The non-motorised modes mobility model is the least successful part of the modelling work. This can be attributed to a combination of inadequate data and, very likely, the more micro-level determinants of usage of these modes. The private motorised urban mobility sketch-plan model equation developed in this thesis is able to predict present and future trends of automobile use in individual cities to a high degree of statistical reliability. The model equation offers urban transport planners a focused direction on the fundamental measures that have the potential to control and deliver automobile restraint policies and strategies. A series of case studies shows that this model has wide applications in understanding past trends in private motorised mobility and in developing urban environmental strategy and policy through its ability to calculate and assess current and future motor vehicle emissions inventories in cities. The thesis makes suggestions for future work in this area of metropolitan level transport modelling, in particular, how to improve the public and non-motorised transport models so that total urban transport mobility can be better understood and modelled.

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Wei, Jiang. « Modeling and predicting of different stock markets with GARCH model ». Thesis, Uppsala universitet, Statistiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175808.

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Sato, Toru. « Sociotropy-autonomy and interpersonal schemas, an interpersonal model predicting affect ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0003/NQ43450.pdf.

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Smith, Melanie Anne. « Predicting northern goshawk dynamics using an individual-based spatial model ». CONNECT TO THIS TITLE ONLINE, 2007. http://etd.lib.umt.edu/theses/available/etd-05312007-161744/.

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Liu, Zhong. « Combining measurements with deterministic model outputs : predicting ground-level ozone ». Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/362.

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The main topic of this thesis is how to combine model outputs from deterministic models with measurements from monitoring stations for air pollutants or other meteorological variables. We consider two different approaches to address this particular problem. The first approach is by using the Bayesian Melding (BM) model proposed by Fuentes and Raftery (2005). We successfully implement this model and conduct several simulation studies to examine the performance of this model in different scenarios. We also apply the melding model to the ozone data to show the importance of using the Bayesian melding model to calibrate the model outputs. That is, to adjust the model outputs for the prediction of measurements. Due to the Bayesian framework of the melding model, we can extend it to incorporate other components such as ensemble models, reversible jump MCMC for variable selection. However, the BM model is purely a spatial model and we generally have to deal with space-time dataset in practice. The deficiency of the BM approach leads us to a second approach, an alternative to the BM model, which is a linear mixed model (different from most linear mixed models, the random effects being spatially correlated) with temporally and spatially correlated residuals. We assume the spatial and temporal correlation are separable and use an AR process to model the temporal correlation. We also develop a multivariate version of this model. Both the melding model and linear mixed model are Bayesian hierarchical models, which can better estimate the uncertainties of the estimates and predictions.

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Tam, C. M. « Discriminant analysis model for predicting contractor performance in Hong Kong ». Thesis, Loughborough University, 1992. https://dspace.lboro.ac.uk/2134/6842.

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This thesis describes the development of an operational research model for the identification of determinating variables and prediction of contractor performance in Hong Kong. The mathematical technique used is the Discriminant Analysis approach. The model is also verified with two other analyses Multiple Regression Analysis and Unidimensional Scaling Analysis. One of the aims of the research is to betray the underlying factors that influence contractor performance which are measured in the clients' point of view. The second aim is to develop an accurate model for predicting contractor performance used by clients in vetting contractors. All aspects of the model's development are described, including the quantification of the variables, data collection, analysis of the model results, verification of the model results with other models and testing the model using independent data. Further, the variables adopted in the model are compared with the actual practices in Hong Kong. The predictive model produced by the study is made up of six variables measuring the three dimensions namely the inherent characteristics of the project, the contractor's internal attributes and the external influence of the project team, including the complexity of the project, the working experience of the project leaders, the percentage of professionally qualified staff in the company, the past performance of the contractor, the origin of the company and the architect's or client's supervision and control on the quality of work and work progress. However, the developed ndels should only be used as part of an assessment process and with caution as there are other unpredictable factors which are not able to quantify and include in the model such as the changing of the ccmpany structure and straty, change in management quality, profitability and the happening of overtrading. Nevertheless, the use of the model to exclude cximpanies fran tender lists could accelerate the contractor selection process and spare niore time for clients to concentrate on more important issues.

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Sun, Wei. « Mathematical Model for Predicting Trace Organic Compounds in Anaerobic Digesters ». University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378197057.

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Backer-Meurke, Henrik, et Marcus Polland. « Predicting Road Rut with a Multi-time-series LSTM Model ». Thesis, Högskolan Dalarna, Institutionen för information och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-37599.

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Road ruts are depressions or grooves worn into a road. Increases in rut depth are highly undesirable due to the heightened risk of hydroplaning. Accurately predicting increases in road rut depth is important for maintenance planning within the Swedish Transport Administration. At the time of writing this paper, the agency utilizes a linear regression model and is developing a feed-forward neural network for road rut predictions. The aim of the study was to evaluate the possibility of using a Recurrent Neural Network to predict road rut. Through design science research, an artefact in the form of a LSTM model was designed, developed, and evaluated.The dataset consisted of multiple-multivariate short time series where research was limited. Case studies were conducted which inspired the conceptual design of the model. The baseline LSTM model proposed in this paper utilizes the full dataset in combination with time-series individualization through an added index feature. Additional features thought to correlate with rut depth was also studied through multiple training set variations. The model was evaluated by calculating the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE) for each training set variation. The baseline model predicted rut depth with a MAE of 0.8110 (mm) and a RMSE of 1.124 (mm) outperforming a control set without the added index. The feature with the highest correlation to rut depth was curvature with a MAEof 0.8031 and a RMSE of 1.1093. Initial finding shows that there is a possibility of utilizing an LSTM model trained on multiple-multivariate time series to predict rut depth. Time series individualization through an added index feature yielded better results than control, indicating that it had the desired effect on model performance.

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Zimmer, Zachary. « Predicting NFL Games Using a Seasonal Dynamic Logistic Regression Model ». VCU Scholars Compass, 2006. http://scholarscompass.vcu.edu/etd_retro/97.

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The article offers a dynamic approach for predicting the outcomes of NFL games using the NFL games from 2002-2005. A logistic regression model is used to predict the probability that one team defeats another. The parameters of this model are the strengths of the teams and a home field advantage factor. Since it assumed that a team's strength is time dependent, the strength parameters were assigned a seasonal time series process. The best model was selected using all the data from 2002 through the first seven weeks of 2005. The last weeks of 2005 were used for prediction estimates.

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Ryan, Jeanne A. M. « Predicting positive youth development outcomes using the social development model / ». Thesis, Connect to this title online ; UW restricted, 2000. http://hdl.handle.net/1773/8141.

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Saiyasombati, Penpan. « Mathematical model for predicting percutaneous absorption of fragrance raw materials ». Cincinnati, Ohio : University of Cincinnati, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=ucin1061561348.

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Yao, Jie. « Model observers for predicting human performance on signal detection tasks ». Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186612.

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Various model observers have been applied to the objective assessment of medical image quality. However, the relevance of this application to clinical efficacy depends largely on how well model observers predict human performance. Attempting to answer this question, this dissertation focuses on the investigation of a linear observer known as the Hotelling observer and a modified version of the Hotelling observer, known as the channelized Hotelling observer. Performances of these observers for a signal-known-exactly detection task are calculated and compared to the performance of the human observer. Several psychophysical studies suggest that the Hotelling observer, formulated on the first- and second-order statistical properties of the images, could predict the human performance very well. To investigate the effect of certain higher-order statistical information on human performance, an experiment was designed in which the mean, variance, and covariance of three groups of images were kept the same, while the shapes of the image grey-level histogram were varied. The results showed little practical difference in the human performance among the three groups; thus the higher-order statistical information represented by the shape of the grey-level histogram did not influence the human observer's signal-detection performance for the task considered in this experiment. Another linear model observer, the nonprewhitening observer has been found in previous work to predict human performance better than the Hotelling observer for images with uniform backgrounds and correlated noise. When the images contain nonuniform background and uncorrelated noise, however, the Hotelling observer is found to be better in predicting human performance. To unify these results, a channelized Hotelling observer was proposed whose performance resembles that of a nonprewhitening observer for images with correlated noise, and that of a Hotelling observer for images with nonuniform background. Moreover, the channelized Hotelling observer is able to predict human performance when images have both the nonuniform background and correlated noise. A nonlinear version of the channelized Hotelling observer has also been found to predict human performance well.

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bin, Syed Alaudin Al Qadiri Syed Kamalludin. « An empirical model for predicting the feasibility of corporate turnaround ». Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 1998. https://ro.ecu.edu.au/theses/1432.

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The primary objective of this research was to identify the determinants of corporate turnaround feasibility and their effect (in terms of their state of existence and their existence) on the probability of corporate turnaround feasibility in Successful and Non Successful Turnaround Companies. The other objective was the development of an empirical model of the determinants of corporate turnaround feasibility capable of predicting the feasibility of corporate turnarounds. One hundred 'troubled companies ' were identified out of two hundred and eleven publicly listed companies of the Kuala Lumpur Stock Exchange through observations of their share price performance, earnings before interest and tax, earnings after interest and tax and by the Malaysian Z- Score (PNB Score) failure detection model test. They were further demarcated into 57 Successful Turnaround Companies (STC) and 43 Non Successful Turnaround Companies (NSTC). These two groups were then compared on the determinants of turnaround feasibility. The study confirms that the feasibility of corporate turnaround of an organisation is dependent on the existence (exists or non existant) and the state of existence (whether favourable or non favourable) of a set of variables or determinants of corporate turnaround feasibility i.e. Causes of Decline, Severity of Crisis, Company's Historical Strategy, Industry Characteristics, Company's Cost Price Structure, Commitment of Shareholders, Commitment of Bankers, Commitment of Creditors, Commitment of Employees, New Competent Management, Viable Core Business, Bridge Capital and Realistic Turnaround Plan. In identifying the existence and the state of existence of the key determinants of corporate turnaround feasibility in the STC and the NSTC, it was found that the STC had higher occurrences of favourable states of existence for the key determinants than the NSTC. STC's were also found to experience higher occurrences of existence (exists) in the key determinants compared to the NSTC. A 'Corporate Turnaround Feasibility Intensity Model' was developed to test corporate turnaround feasibility intensity level. Subsequently, the empirical model or the multivariate logistic regression model was then applied to finalise and reaffirm the feasibility of the corporate turnaround of the organisation. The qualitative and empirical models complement each other in their application, or used on their own can test the feasibility of corporate turnaround. The availability of both qualitative and empirical models above to test and to predict the feasibility of corporate turnaround from this research can help solve one of the biggest dilemmas facing numerous shareholders, top management, management consultants and bankers, namely, deciding whether to go ahead with the turnaround process or not. The models can help save costly errors in terms of money, labour cost, psychological turmoil, time and wasteful resources due to wrong decision making. They also constitute a new contribution to knowledge.

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Glardon, Christian. « PREDICTING RISKS OF INVASION OF CAULERPA SPECIES IN FLORIDA ». Master's thesis, University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4219.

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Invasions of exotic species are one of the primary causes of biodiversity loss on our planet (National Research Council 1995). In the marine environment, all habitat types including estuaries, coral reefs, mud flats, and rocky intertidal shorelines have been impacted (e.g. Bertness et al. 2001). Recently, the topic of invasive species has caught the public's attention. In particular, there is worldwide concern about the aquarium strain of the green alga Caulerpa taxifolia (Vahl) C. Agardh that was introduced to the Mediterranean Sea in 1984 from the Monaco Oceanographic Museum. Since that time, it has flourished in thousands of hectares of near-shore waters. More recently, C. taxifolia has invaded southern Californian and Australian waters. Since the waters of Florida are similar to the waters of the Mediterranean Sea and other invasive sites my study will focus on determining potential invasion locations in Florida. I will look at the present distribution of C. taxifolia - native strain in Florida as well as the distribution of the whole genus around the state. During this study, I address three questions: 1) What is the current distribution of Caulerpa spp. in Florida? 2) Can I predict the location of potential Caulerpa spp. invasions using a set of environmental parameters and correlate them to the occurrence of the algae with the support of Geographic Information System (GIS) maps? 3) Using the results of part two, is there an ecological preferred environment for one or all Caulerpa spp. in Florida? To answer these questions, I surveyed 24 areas in each of 6 zones chosen in a stratified manner along the Floridian coastline to evaluate the association of potential indicators Caulerpa. Latitude, presence or absence of seagrass beds, human population density, and proximity to marinas were chosen as the 4 parameters expected to correlate to Caulerpa occurrences. A logistic regression model assessing the association of Caulerpa occurrence with measured variables has been developed to predict current and future probabilities of Caulerpa spp. presence throughout the state. Fourteen different species of Caulerpa spp. were found in 26 of the 132 sites visited. There was a positive correlation between Caulerpa spp. and seagrass beds presence and proximity to marinas. There was a negative correlation with latitude and human population density. C. taxifolia – aquarium strain wasn't found. Percent correct for our model was of 61.5% for presence and 98.1% for absence. This prediction model will allow us to focus on particular areas for future surveys.
M.S.
Department of Biology
Arts and Sciences
Biology

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Mahzoon, Mohammad Javad. « Student Sequence Model| A Temporal Model For Exploring and Predicting Risk From Heterogeneous Student Data ». Thesis, The University of North Carolina at Charlotte, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10791643.

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Data models built for analyzing student data often obfuscate temporal relationships for reasons of simplicity, or to aid in generalization. We present a sequence model that is based on temporal relationships in heterogeneous student data as the basis for building predictive models to identify and understand students at risk. The properties of our sequence data model include temporal structure, segmentation, contextualization, and storytelling. To demonstrate the benefits of these properties, we have collected and analyzed 10 years of student data from the College of Computing at UNC Charlotte in a between-semester sequence model, and used data in an introductory course in computer science to build a within-semester sequence model. Our results for the two sequence models show that analytics based on the sequence data model can achieve higher predictive accuracy than non-temporal models with the same data. The sequence model not only outperforms non-temporal models to predict at risk students, but also provides interpretability by contextualizing the analytics with the context features in the data model. This ability to interpret and explore the analytics, enables the development of an interactive exploratory learning analytics framework to involve the domain experts in the process of knowledge discovery. To show this potential of the sequence model, we developed a dashboard prototype and evaluated the prototype during focus group with our college faculty, advisors, and leadership. As a result, the dashboard facilitates generating new hypotheses about student data, and enables the discovery of actionable knowledge for domain experts.

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Lee, Son Matthew Robert. « Predicting returns with the Put-Call Ratio ». Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/30616.

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Over 22 billion derivative contracts were traded on different stock exchanges globally during the year 2010 of which almost 50% were futures while the remaining 50% were options. An overall 25% increase in such contracts was registered as compared to those traded in the year 2009 (International Options Market Association (IOMA) Report, 2011).Investors often use a wide array of trading tools, market indicators and market trading strategies to get the best possible returns for the money that was invested. The main objective of this paper is to focus on the use of market sentiment indicators, specifically the Put-Call Ratio (PCR) as a predictor of returns for an investor.The Put-Call Ratio is defined as a ratio of the trading volume of put options to call options. It is called a sentiment indicator because it measures the “feelings” of option traders. Additionally, it has longed been viewed as an indicator of investors’ sentiment in the market (Put-Call Ratio, 2012) and is possibly the most favoured description of market psychology (James, 2011).
Dissertation (MBA)--University of Pretoria, 2012.
Gordon Institute of Business Science (GIBS)
unrestricted

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Burris, Vickie Lien. « Hypolimnetic Aerators : Predicting Oxygen Transfer and Water Flow Rate ». Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/30936.

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The objective of this research was to characterize the performance of hypolimnetic aerators with respect to oxygen transfer and water flow rate to allow the development of two comprehensive process models. The oxygen transfer model is the first model that applies discrete-bubble principles to a hypolimnetic aerator, and the water flow rate model is the first that applies an energy balance to this particular type of lake aeration device. Both models use fundamental principles to predict hypolimnetic aerator performance, as opposed to empirical correlations. The models were verified with data collected from a full-scale hypolimnetic aerator installed in Lake Prince, which is a water supply reservoir for the City of Norfolk, Virginia. Water flow rate, gas-phase holdup and dissolved oxygen profiles were measured as a function of air flow rate. The initial bubble size was calculated by the oxygen transfer model using field data. The range of bubble diameters obtained using the model was 2.3-3.1 mm. The Sauter mean diameters of bubbles measured in a laboratory system ranged from 2.7-3.9 mm. The riser and downcomer DO profiles and gas holdups predicted by the model are in close agreement with experimental results. The water flow rate model was fitted to the experimental water velocity by varying the frictional loss coefficient for the air-water separator. An empirical correlation that predicts the loss coefficient as a function of superficial water velocity was obtained. The results of the correlation were similar to those predicted by literature equations developed for external airlift bioreactors.
Master of Science

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Ottman, Michael. « Predicting Wheat Growth Using the CSM-Cropsim-CERES - Wheat Crop Model ». College of Agriculture, University of Arizona (Tucson, AZ), 2008. http://hdl.handle.net/10150/203650.

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CSM-Cropsim-CERES -Wheat is a crop growth model that predicts crop development stages, among other things, using genetic coefficients for vernalization and photoperiod. We used this model to predict flowering date for 12 durum varieties seeded in trials at Maricopa and Yuma from 1998 to 2006. The difference between simulated and measured flowering date averaged 4 days without genetic coefficients and improved to 3.5 days if genetic coefficients for flowering and vernalization were included for each variety.

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Weaver, Sallie J. « Characteristics for success predicting intervention effectiveness with the job characteristics model / ». Orlando, Fla. : University of Central Florida, 2008. http://purl.fcla.edu/fcla/etd/CFE0002040.

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Wang, Yin-Han. « Model and software development for predicting fish growth in trout raceways ». Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4751.

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Thesis (M.S.)--West Virginia University, 2006.
Title from document title page. Document formatted into pages; contains xii, 105 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 47).

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Searcy, Chad Randall. « A multiscale model for predicting damage evolution in heterogeneous viscoelastic media ». Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/1251.

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A multiple scale theory is developed for the prediction of damage evolution in heterogeneous viscoelastic media. Asymptotic expansions of the field variables are used to derive a global scale viscoelastic constitutive equation that includes the effects of local scale damage. Damage, in the form discrete cracks, is allowed to grow according to a micromechanically-based viscoelastic traction-displacement law. Finite element formulations have been developed for both the global and local scale problems. These formulations have been implemented into a two-scale computational model Numerical results are given for several example problems in order to demonstrate the effectiveness of the technique.

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Simonsson, Mårten. « Predicting IT Governance Performance : A Method for Model-Based Decision Making ». Doctoral thesis, KTH, Industriella informations- och styrsystem, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-9129.

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Contemporary enterprises are largely dependent on Information Technology (IT), which makes decision making on IT matters important. There are numerous issues that confuse IT decision making, including contradictive business needs, financial constraints, lack of communication between business and IT stakeholders and difficulty in understanding the often heterogeneous and integrated IT systems. The discipline of IT governance aims at providing the decision making structures, processes, and relational mechanisms, needed in order for IT to support and perpetuate the business. The adjacent discipline of enterprise architecture provides a broad range of frameworks and tools for model-based management of IT. Enterprise architecture is a commonly and successfully used approach, but the frameworks need to be adapted with respect to the concerns at stake in order to become truly useful. The IT organization includes all people involved in decision making regarding IT. The quality of the IT organization differs between enterprises and depends on aspects such as: are rights and responsibilities assigned to the appropriate people, are formalized processes implemented, and does proper documentation exist? This internal IT organization efficiency is labeled IT governance maturity. One might argue that internal efficiency metrics of the IT organization are of moderate interest only. What really matters is the external effectiveness of services that the IT organization delivers to the business. This latter effectiveness is labeled IT governance performance. Even though it is reasonable to believe that enterprises with good IT governance maturity also achieve high IT governance performance, the validity of this assumption has never been tested. IT management’s ability to make well-informed decisions regarding internal IT organization matters would increase if it were possible to predict IT governance performance. The contribution of this thesis is a method for model-based IT governance decision making. The method includes a metamodel, i.e. a modeling language, and a framework for the assessment of IT governance maturity and performance. The method also allows prediction of IT governance performance. This thesis is a composite thesis consisting of four papers and an introduction. Paper A presents an overview of the method for model-based IT governance decision making. Paper B presents the mathematical foundation of the prediction apparatus, i.e. a Bayesian network that is based on statistical data. Paper C presents how the method can be used in practice to support IT governance decision making. Finally, Paper D analyzes the correlation of IT governance maturity and performance. The analysis is based on statistical data from case studies in 35 organizations.
QC 20100909

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Roman, Christopher W. « Using a Model of Emotional Self-Efficacy in Predicting Work Outcomes ». Thesis, Alliant International University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10744906.

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Organizations are increasingly examining the potential benefits of integrating insights concerning emotional intelligence (EI) into their employee training and development programs to enhance their mission. Petrides’s EI model of trait emotional self-efficacy (ESE) has been defined as a constellation of emotion-related self-perceptions and dispositions assessed through self-report. This study explored the relationship between the four factors of Petrides’s ESE model (well-being, self-control, emotionality, and sociability) and the well-researched work outcomes of job satisfaction, counter-productive work behavior, and turnover intent. Based on existing research, the study controlled for age, gender, job tenure, and social desirability. The study relied on an archival data set drawn from a similar pilot study, and included a sample population (N = 157) of certified nurse assistants and registered nurses, selected both out of convenience and because this population is known for its affect-laden work. The study employed a 239-item survey. ESE was measured using the TEIQue long form, and psychological instruments were used to measure outcomes. Confirmatory factor analysis found a lack of fit for the four-factor model, and a new, two-factor model was found using an exploratory factor analysis. The first factor, comprised mostly of the facets well-being and self-control, was named emotional constitution. The second factor, comprised mostly of emotionality and sociability, was named emotional awareness. Data analysis included four-step hierarchical regression models to assess unique variance in each of the three outcome variables using emotional awareness and emotional constitution as predictors. Results showed that emotional awareness predicts nurse job satisfaction (β = .21, p < .05), and emotional constitution negatively predicts both nurse CWB (β = –.49,p < .01) and turnover intent (β = –.31,p < .05). No difference between groups (RNs and CNAs) on these factor dynamics was found. Implications of this study are discussed.

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Knight, Karla Grace. « A fuzzy logic model for predicting commercial building design cost overruns ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ60445.pdf.

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Kowitlawakul, Yanika. « Technology acceptance model predicting nurses' acceptance of telemedicine technology (eICU®) / ». Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3058.

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Thesis (Ph.D.)--George Mason University, 2008.
Vita: p. 116. Thesis director: Jean Burley Moore. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Nursing. Title from PDF t.p. (viewed July 3, 2008). Includes bibliographical references (p. 108-115). Also issued in print.

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Huang, Xiaoyan. « Predicting Short-Term Exchange Rates with a Hybrid PPP/UIP Model ». Scholarship @ Claremont, 2013. http://scholarship.claremont.edu/scripps_theses/236.

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This study creates a model to predict short-term exchange rates as a combination of the relative purchasing power parity model (Grossman and Simpson 2011) and the interest power parity model. I then use the statistical techniques ARMA and GARCH to account for the variance of the terms. Previous works considered the effects of these models individually, but mine consider them in unison. I consider both in-sample and out-of-sample tests. I use data on five major exchange rates (JPY/USD, CAD/USD, CHF/USD, GBP/USD, and AUD/USD) sampled at a monthly frequency from 1989-2013. My model statistically significantly predicts these exchange rates over the January 2012 to January 2013 period.

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Weaver, Sallie. « CHARACTERISTICS FOR SUCCESS : PREDICTING INTERVENTION EFFECTIVENESS WITH THE JOB CHARACTERISTICS MODEL ». Master's thesis, University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2697.

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The current study examines the effects of the five core job characteristics (skill variety, task significance, task identity, autonomy, and feedback) proposed by Hackman-Oldham (1974) at the team level by investigating whether the model variables are related to the effectiveness of a motivationally-based team-level productivity enhancement intervention. Previous literature has almost exclusively focused on the effects of these job characteristics at the individual level and their direct relationships with employee attitudes and subjective measures of performance. This thesis aims to further the job characteristics literature by exploring the effects of the characteristics at the team level, as well as the moderating effect of the team construct of value congruence, while simultaneously exploring boundary conditions of the Productivity Measurement and Enhancement System (ProMES) developed by Pritchard (1990). Hypotheses postulated a negative relationship between the characteristics and intervention effectiveness; such that effectiveness is negatively impacted when the characteristics already exist at high levels. Results, though non-significant, are tenatively suggestive of this counter-intuitive negative relationship between four of the characteristics and intervention effectiveness. Value congruence between team leaders and members was not a significant moderator of the relationship between the characteristics and effectiveness. Results suggest that a more powerful study to further parse out these relationships would be valuable. iii
M.S.
Department of Psychology
Sciences
Industrial Org Psychology MS

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TAVARA, EDWIN GERMAN MALDONADO. « MULTIOBJETIVE GENETIC ALGORITHM FOR PREDICTING PROTEIN STRUCTURES IN HYDROPHOBIC – POLAR MODEL ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2012. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=23526@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
O problema da predição das estruturas de proteínas (Protein Structure Prediction (PSP)) é um dos desafios mais importantes na biologia molecular. Pelo fato deste problema ser muito difícil, têm sido propostos diferentes modelos simplificados para resolvê-lo. Um dos mais estudados é o modelo, Hidrofóbico-Polar (HP), o modelo HP fornece uma estimativa da energia da proteína com base na soma de interações entre pares de aminoácidos hidrofóbicos (contatos H-H). Entretanto, apesar das simplificações feitas no modelo HP, o problema permanece complexo, pertencendo à classe NP-Difícil. Muitas técnicas têm sido propostas para resolver este problema entre elas, técnicas baseadas em algoritmos genéticos. Em muitos casos, as técnicas baseadas em AG foram usadas com sucesso, mas, no entanto, abordagens utilizando AG muitas vezes não tratam adequadamente as soluções geradas, prejudicando o desempenho da busca. Além disso, mesmo que eles, em alguns casos, consigam atingir o mínimo de energia conhecido para uma conformação, estes modelos não levam em conta a forma da proteína um fator muito importante na hora de obter proteínas mais compactas. Foi desenvolvido um algoritmo genético multiobjetivo para PSP no modelo HP, de modo de avaliar de forma mais eficiente, as conformações produzidas. O modelo utiliza como avaliação uma combinação baseada no número de colisões, número de contatos hidrofóbicos, compactação dos aminoácidos hidrofóbicos e hidrofílicos, obtendo, desta forma estruturas mais naturais e de mínima energia. Os resultados obtidos demonstram a eficiência desse algoritmo na obtenção de estruturas proteicas compactas providenciando indicadores da compactação dos aminoácidos hidrofóbicos e hidrofílicos da proteína.
The problem of protein structured prediction (PSP) is one of the most important challenges in molecular biology. Because this problem is very difficult, different simplified models have been proposed to solve it. One of the most studied is the Hydrophobic-Polar model HP this model provides an estimate of the protein energy based on the sum of hydrophobic contacts. However, despite the simplifications made in the HP model, the problem remains complex, belonging to the class of NP-Hard problems. Many techniques have been proposed to solve this problem as genetic algorithms. In many cases the GA techniques have been used successfully, but, however, with GA approaches often do not adequately address the generated solutions, impairing the performance of the search. Furthermore, in some cases would attain the minimum energy for a known conformation, these models do not take care the protein shape, a very important factor to obtain more compact proteins. This work developed a multiobjective genetic algorithm to PSP in HP model evaluating more efficiently, the conformations produced. This model is a combination of assessment based on the collisions numbers, hydrophobic contacts, hydrophobic and hydrophilic core compression, obtaining thus more natural structures with minimum energy. The results demonstrate the efficiency of this algorithm to obtain protein structures indicators providing compact compression of the hydrophobic and hydrophilic core protein.

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ARRIETA, CLARENA PATRICIA ARRIETA. « STATISTICAL MODEL FOR PREDICTING THE SUPPLY OF HIGHER EDUCATION : 2015-2035 ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35308@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Segundo o INEP/MEC, nos últimos 20 anos, o número de matrículas da educação superior de graduação no Brasil cresceu mais de duas vezes, com uma taxa de crescimento anual verificada a partir de 2001 em torno de 5,7 por cento ao ano. Ainda segundo esta instituição, em 2008 houve o ingresso de 1.505.819 novos estudantes nos cursos presenciais, ao mesmo tempo em que 1.479.318 vagas não foram ocupadas, sendo que 54,6 por cento do total de vagas ofertadas pelo setor privado. Tendo em conta que São Paulo é o maior estado do Brasil, é muito importante que o Ministério da Educação tome conhecimento de como se dará a dinâmica da oferta de educação superior nos próximos 20 anos para que suas ações (políticas públicas, sobretudo) possam ser realizadas com êxito. O objetivo deste trabalho é aplicar modelagem estatística para estimar a oferta do ensino superior do Estado de São Paulo no período de 2015 a 2035, considerando dados da INEP de educação superior. A motivação para este trabalho é melhorar o planejamento da oferta de curso superior e fazer a replicação do modelo preditivo para outros estados do Brasil. A metodologia usada é modelagem estatística (modelos de regressão linear) e séries temporais (Holt). Como resultado, têm-se as áreas e/os cursos onde o governo federal deve investir no futuro aprimorando seu planejamento.
According to INEP/MEC, in the last 20 years, the number of undergraduate higher education enrollments in Brazil has grown more than twice, with an annual growth rate of 5,7 percent per year since 2001. According to this institution, in 2008 there were 1.505.819 new students enrolled in presential courses, while 1.479.318 vacancies were not filled, with 54.6 percent of the total number of vacancies offered by the private sector. Given that São Paulo is the largest state in Brazil, it is very important that the Ministry of Education becomes aware of the dynamics of the offer of higher education in the next 20 years so that its actions (mainly public policies) can be successfully executed. The objective of this study is to apply statistical modeling to estimate the offer of higher education in the State of São Paulo in the period from 2015 to 2035, considering data from INEP about higher education. The motivation for this work is to improve the planning of the offer of higher education and to replicate the predictive model for other Brazilian states. The methodology used concerns statistical modeling (linear regression models) and time series (Holt). As a result, it is obtained the areas and/or courses where the federal government should invest in the future, improving its planning.
Según el INEP/MEC, en los últimos 20 años, el número de matrículas de educación superior en Brasil creció más de dos veces, con una tasa de crecimiento anual verificada a partir de 2001 en torno al 5,7 por ciento por año. Según esta institución, en 2008 hubo un ingreso de 1.505.819 nuevos estudiantes en los cursos presenciales, al mismo tiempo que 1.479.318 vacantes no fueron ocupadas, siendo el 54,6 por ciento del total de vacantes ofrecidas por el sector privado. Dado que São Paulo es el mayor estado de Brasil, es muy importante que el Ministerio de Educación tome conocimiento de cómo se dará la dinámica de la oferta de educación superior en los próximos 20 años para que sus acciones (políticas públicas, sobre todo) puedan realizarse con éxito. El objetivo de este trabajo es aplicar modelos estadísticos para estimar la oferta de educación superior del Estado de São Paulo en el período de 2015 a 2035, considerando datos de INEP de educación superior. La motivación para este trabajo es mejorar la planificación de la oferta de curso superior y hacer replicación del modelo predictivo para otros estados de Brasil. La metodología utilizada es modelos estadístico (modelos de regresión lineal) y series tiempo (Holt). Como resultado, se tienen las áreas y/o cursos donde el gobierno federal debe invertir en el futuro mejorando su planificación.

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