Este trabalho apresenta um modelo de previsão de acidentes com a participação de motocicletas que foi desenvolvido a partir do método da análise de regressão estatística adaptado às particularidades técnicas das rodovias do Estado do Rio Grande do Sul. O objetivo do trabalho é gerar uma ferramenta que possibilite prever o número de acidentes a partir da combinação do nível de exposição veicular associada com os prováveis fatores de risco deste peculiar ambiente. Com esta finalidade, é confrontado o número de acidentes observados com relação às características técnicas das rodovias investigadas com o intuito de avaliar os fatores de risco. Nove variáveis de controle representando atributos físicos, funcionais, econômicos e legais das rodovias foram analisadas sob diversos parâmetros tais como: largura da plataforma, sinuosidade; inclinação, intersecções, condição do pavimento, tráfego de veículos, tráfego de caminhões, urbanização e dispositivos de controle de tráfego. A aplicação do método estatístico permite classificar as rodovias mais importantes quanto ao nível de acidentes; identificar, mensurar e avaliar os fatores de risco; estimar a probabilidade média para a realização do evento sinistro e simular, em nível de projeto, a ocorrência futura de acidentes. Uma vez processado, o modelo obteve um fator de explicação (R2) para os dados em torno de 96%. As variáveis de controle que apresentaram maior efeito na variável de resposta foram obtidas através do tráfego de veículos seguido da largura da plataforma da rodovia. Após a análise do modelo, as rodovias com maior fator de propensão para acidentes foram a ERS734 sendo seguida pela ERS118 e ERS130. Os resultados que foram obtidos indicaram que a frota de motocicletas do Estado do Rio Grande do Sul - Brasil apresenta um risco de envolvimento em acidentes duas vezes maior que aquela incorrida pela frota dos Estados Unido e três vezes maior que aquela apresentada pela frota do Reino Unido. / This article presents an accident prediction model with the participation of motorcycles, developed by statistical regression analysis adapted to the technical peculiarities of the roads of the state of Rio Grande do Sul, Brazil. The aim of the model is to generate a tool to allow predicting the number of accidents based on the combination of vehicle exposure level with possible risk factors. The number of accidents observed is compared with road technical characteristics, aiming at evaluating risk factors. Nine control variables, representing physical, functional, economical and legal road attributes, were analyzed as to different parameters, such as platform width; sinuosity; inclination; junctions ; pavement condition; vehicle traffic; truck traffic; urbanization; and traffic control devices. The application of the statistical method allows the classification of the most important roads in terms of accident level; to identify, measure, and evaluate risk factors; to estimate mean accident probability; and to simulate, at project level, the future occurrence of accidents. Once processed, the model obtained an explanation factor (R2) for the data around 96%. Vehicle traffic, followed by highway platform width had the highest effect on the response variable. After being analyzed by the model, ERS734, followed by ERS118, and ERS130 presented the highest accident probability factor. The results obtained indicated that the risk of motorcycles being involved in accidents in the state of Rio Grande do Sul is twice as high as in the USA, and three times higher than in the United Kingdom.

Orientador: Varese Salvador Timóteo / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Tecnologia / Made available in DSpace on 2018-08-19T10:25:36Z (GMT). No. of bitstreams: 1 Oliveira_TitoRicardoBianchin_M.pdf: 4698545 bytes, checksum: a02de15000b11124637f63ed02fe50ee (MD5) Previous issue date: 2011 / Resumo: O crescimento das redes de terceira geração, aliado a sua alta velocidade para transmissão de dados e banda disponível fazem com que novos terminais lançados no mercado utilizem o comportamento Always On, no qual o dispositivo fica 100% do tempo conectado a rede para transmissão e recepção de dados. Esse comportamento, no entanto, faz com que o consumo de bateria do dispositivo seja maior devido ao uso de aplicativos que recebem informações periodicamente, e principalmente pelo recebimento de pacotes não solicitados provenientes de ataques a rede. Este trabalho tem como objetivo analisar os elementos de rede responsáveis pela transmissão de pacotes de dados, identificando os fatores responsáveis pelo aumento de consumo. Ao final, e proposto um método para melhor aproveitamento dos recursos de radio para transmissão de dados e consequentemente, a diminuição do consumo de energia, utilizando um modelo de previsão / Abstract: The expansion of third generation network and its high speed of data transmission and available bandwidth, made that new designed mobile devices use the "Always On" concept, in which the device is 100% connected in packet switch network, and able for data transmission. This behavior makes the device's energy consumption to be higher due to usage of applications that receives periodically information, and mainly due to the unsolicited packages from hacker attack. This work has as main purpose analyze the network elements responsible for data package transmission, identifying the main factors related to the energy consumption increasing. Finally, it is proposed a method to enhance the radio resources for data transmission and energy consumption decreasing, using a prevision model / Mestrado / Tecnologia e Inovação / Mestre em Tecnologia

Please read the abstract in the section 00front of this document / Thesis (PhD)--University of Pretoria, 2009. / Civil Engineering / unrestricted

Le football européen en général et le football français en particulier font état de difficultés financières et de faillites récurrentes de clubs professionnels. Sur la seule période de 1975 à 2018, 81 clubs français de football engagés dans les championnats des quatre premières divisions ont connu une faillite. Le sujet de la faillite d’entreprises a été largement traité pour les secteurs courants de l’économie. De nombreuses recherches se sont attachées à construire des modèles de prédiction, puis progressivement d’autres travaux se sont concentrés sur le processus et les différentes trajectoires d’entrée dans la faillite.Les seuls travaux menés sur le football français ont appliqué le modèle de prédiction d’Altman (2000) sur les clubs de Ligue 1 et de Ligue 2 et ont cherché à identifier les facteurs de la défaillance. Un accès privilégié aux données financières et aux parties prenantes du football français nous a permis de construire un nouveau modèle de prédiction de faillite adapté aux spécificités du football que nous avons ensuite complété par uneanalyse qualitative proposant une hiérarchisation des facteurs explicatifs et leur enchaînement au sein d’un processus dynamique. Notre thèse conclue à l’impossibilité de généraliser un modèle de prédiction des faillites à l’ensemble des clubs des quatre premières divisions françaises. Néanmoins, les améliorations apportées par notre propre modèle permettent de meilleurs taux de classement entre les clubs défaillants et les clubs sains des trois premières divisions. Nous montrons également qu’au-delà d’un score ponctuel obtenu dans le modèle, son évolution dans le temps est un signal important pour identifier et anticiper la dégradation de la situation financière de chaque club. Les clubs ne passent pas d’un état de bonne santé à leur faillite de manière soudaine. Des entretiens menés avec des dirigeants, des actionnaires, des directeurs financiers et des membres de la Direction Nationale du Contrôle de Gestion nous ont permis de modéliser la dynamique globale d’entrée dans la faillite des clubs. Sur ces bases, nous proposons une nouvelle approche de la régulation financière pour mieux prévenir la faillite des clubs de football. / French football and European football in general regularly report of financial difficulties and even bankruptcies of professional clubs. Between 1975 and 2018, 81 clubs of the four premier French divisions went bankrupt. The issue of bankruptcy in business has been widely studied in the main sectors of the economy. Various studies have endeavoured to build prediction models and subsequently, other work has investigated the process and different ways of going bankrupt.The only work which investigated French football applied Altman’s prediction model (2000) to Ligue 1 and Ligue 2 clubs and aimed to identify the factors which lead to bankruptcy. Privileged access to financial information concerning these clubs and to people who have important roles in this domain allowed us tocreate a new model to predict bankruptcy which is adapted to the particularities of professional football. We then completed our study with qualitative analysis of the data and a proposal of a hierarchy of the explicative factors and their sequencing in what is a dynamic process. Our thesis concludes by stating that it is impossible to generalise a bankruptcy prediction model for all theclubs in each of the top four French divisions. Nevertheless, the improvements brought forward by our model allows for a more accurate division of the financially healthy and unhealthy clubs in the first three divisions. Equally, we show that beyond the initial score a club achieves with our model, the evolution of this score over time in an important indicator to help clubs anticipate a worsening financial situation; clubs do not suddenly go from a state of financial solvency to one of bankruptcy. Interviews undertaken with the executives, stakeholders and financial directors of clubs as well as those carried out with members of the Direction Nationale du Contrôle de Gestion (DNCG) allowed us to model the global dynamic for clubs who go bankrupt. From there, we propose a new approach to financial regulation to avoid more football clubs going bankrupt.

In August, 2014, the Financial Accounting Standards Board issued an update concerning the disclosure of uncertainties about an entity’s ability to continue as a going concern. The standard requires an entities management to evaluate whether there is substantial doubt about the entity’s ability to continue as a going concern and to provide related footnote disclosures in certain circumstances. One consequence of this regulation is the need for guidance for audit testing of management’s assessments in each phase of the audit. This research evaluates the usefulness of bankruptcy prediction models as analytical tools in the planning stage of an audit for going concern assertions and questions the use of precision as the only measure of a model’s effectiveness. I use simulation to manipulate the fundamental accounting data within five bankruptcy prediction models, explore failure rates in an environment with materiality concerns, and consider the total change in market value due to simulated errors. Given the inherent limitations of the information environment and/or current prediction models, my results indicate auditors’ current failure rates are not an indication of audit failure. The results suggest that bright-line testing using bankruptcy prediction models are sensitive to materiality and that the cost trade-off between Type I and Type II errors is an important indicator of model choice.

In this study, the prediction model based on the Stacking principle is called the Stacking fusion model. Little evidence demonstrates that the Stacking fusion model possesses better prediction performance in the field of heart disease diagnosis than other classification models. Since this model belongs to the family of ensemble learning models, which has a bad interpretability, it should be used with caution in medical diagnoses. The purpose of this study is to verify whether the Stacking fusion model has better prediction performance than stand-alone machine learning models and other ensemble classifiers in the field of heart disease diagnosis, and to find ways to explain this model. This study uses experiment and quantitative analysis to evaluate the prediction performance of eight models in terms of prediction ability, algorithmic stability, false negative rate and run-time. It is proved that the Stacking fusion model with Naive Bayes classifier, XGBoost and Random forest as the first-level learners is superior to other classifiers in prediction ability. The false negative rate of this model is also outstanding. Furthermore, the Stacking fusion model is explained from the working principle of the model and the SHAP framework. The SHAP framework explains this model’s judgement of the important factors that influence heart disease and the relationship between the value of these factors and the probability of disease. Overall, two research problems in this study help reveal the prediction performance and reliability of the cardiac disease prediction model based on the Stacking principle. This study provides practical and theoretical support for hospitals to use the Stacking principle in the diagnosis of heart disease.

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.

Vibratory driving is today the most common installation method of sheet piles. The knowledge of the induced ground vibrations is however still deficient. This makes predictions of the vibration magnitudes difficult to carry out with good reliability. To avoid exceeding the limit values, resulting in stops of production, or that vibratory driven sheet piles are discarded for more costly solutions, a need for increased knowledge of the vibration process is imminent. With increased knowledge, a more reliable and practical prediction model can be developed.  The aim of this thesis is to analyze measured data from a field study to increase the understanding of the induced vibrations and their propagation through the soil. The field study was performed in Karlstad in May 2010, where a trial sheet piling prior to an extension of Karlstad Theatre was carried out. During the trial sheet piling, two triaxial geophones were mounted at the ground surface at two different distances from the sheet piles, to measure the vibration amplitude. The field test is associated with some limitations. Only four sheet piles were driven, with one measurement per sheet pile. Some measurements were less successful and some parameters had to be assumed. This limits the accuracy but still provides some interesting results. Another aim is to compare the measured values to existing models for predicting vibrations from piling and sheet piling operations. There are today several prediction models available, which however often provide too crude estimations or alternatively are too advanced to be incorporated in practical use. Two basic empirical prediction models are compared to the measured values in Karlstad, where the first is one of the earliest and most well known models and the other is a later development of the first model. The purpose of this comparison is to evaluate these models to contribute to the development of a new prediction model. The results show that the earlier model greatly overestimates the vibration magnitude while the later developed model provides a better estimation.  A literature study is performed to gain a theoretical background to the problem of ground vibrations and how they are related to the method of vibratory driving of sheet piles. The analysis considering the field study and prediction models is mainly performed by using MATLAB to obtain different graphical presentations of the vibration signals. The conclusions that can be drawn from the results are that the focus of vibration analysis should not always be the vertical vibration components. Horizontal movements of the sheet pile might be introduced, e.g. by the configuration of the clamping device, which generates additional vibrations in horizontal directions. The soil characteristics influence the magnitude of the vibrations. As the sheet pile reaches a stiffer soil layer, the vibration magnitude increases. A realistic and reliable prediction model should take the characteristics of the soil into account.

Identifying the factors associated with medical students who fail Step 1 of the United States Medical Licensing Examination (USMLE) has been a focus of investigation for many years. Some researchers believe lower scores on the Medical Colleges Admissions Test (MCAT) are the sole factor used to identify failure. Other researchers believe lower course outcomes during the first two years of medical training are better indicators of failure. Yet, there are medical students who fail Step 1 of the USMLE who enter medical school with high MCAT scores, and conversely medical students with lower academic credentials who are expected to have difficulty passing Step 1 but pass on the first attempt. Researchers have attempted to find the factors associated with Step 1 outcomes; however, there are two problems associated with their methods used. First is the small sample size due to the high national pass rate of Step 1. And second, research using multivariate regression models indicate correlates of Step 1 but does not predict individual student performance. This study used data mining methods to create models which predict medical students at risk of failing Step 1 of the USMLE. Predictor variables include those available to admissions committees at application time, and final grades in courses taken during the preclinical years of medical education. Models were trained, tested, and validated using a stepwise approach, adding predictor variables in the order of courses taken to identify the point during the medical education continuum which best predicts students who will fail Step 1. Oversampling techniques were employed to resolve the problem of small sample sizes. Results of this study suggest at risk medical students can be identified as early as the end of the first term during the first year. The approach used in this study can serve as a framework which if implemented at other U.S. allopathic medical schools can identify students in time for appropriate interventions to impact Step 1 outcomes

Background The noise-exposed population in the mining industry in South Africa poses unique problems to the occupational audiologist working in this environment, due to the broad linguistic and cultural diversity in the audiology and mining environment. Unfortunately, the problems are also exacerbated by a high incidence of pseudohypacusis within this population who are incentivised by compensation for NIHL. A solution to these specific problems would be the reliable and valid use of an objective test of function such as the DPOAE. The rationale for the study therefore was to extend the body of knowledge about the use of DPOAEs in the noise-exposed mining population. Methodology The current study was divided into two phases: phase one’s objectives entailed the investigation of the characteristics of DPOAEs in a noise-exposed mining population; phase two aimed to develop a multivariate regression model that would facilitate the prediction of the hearing threshold levels from the DPOAE levels in this population. Objectives The objectives in phase one of the study were to investigate the bivariate correlations between DPOAE levels and air-conduction hearing threshold levels in noise-exposed gold miners, for the three stimulus procedures. The study also aimed to investigate the bivariate correlations between various pure-tone averages (PTA) and the DPOAE averages of f2 frequencies closest to those pure-tone frequencies. Similarly, the Speech Recognition Thresholds (SRT) were correlated with DPOAE averages of f2 frequencies closest to the PTA. xx The study further aimed to investigate the characteristics of DPOAEs in noiseexposed gold miners by comparing the average DPOAE levels for different age category groups, different ethnic groups and for different occupation types. Finally, phase one aimed to describe the characteristics of emission level and noise floor differences (DP-NF) in a DPOAE database of a noise-exposed gold mining population. Phase two of the study had the objective of developing a multivariate prediction model using stepwise regression analysis to identify which of the DPOAE frequencies produced the best prediction of the audiogram frequencies when multivariate inputs were used for each stimulus procedure. The objective was also to evaluate the use of the predicted audiograms’ calculated percentage loss of hearing (PLH) with that of the actual PLH. This retrospective record review used an audiological database from a mine in the North West province of South Africa that contained 4800 records. The required sample size to be representative of the population was statistically determined. The records were randomly selected resulting a sample size for the FB2-S group of 161, for the FB1-S group of 177 and the FB1-S group of 155 respectively. The hearing loss characteristics in the samples ranged from normal to profound losses with the majority being mild to moderate hearing losses. Results The findings of phase one showed negative correlations ranging from -0.327 to -0.573 for Frequency Band 1- Replicated (FB1-R) between DPOAE levels and air conduction hearing threshold levels. Similarly, Frequency Band 1-Single (FB1-S) and Frequency Band 2-Single (FB2-S) also showed negative correlations (ranging from -0.203 to -0.609 and -0.274 to -0.738 respectively). These correlation strengths have been confirmed previously by other published studies. xxi Correlations between groups of frequencies on an audiogram and averaged match groups of DPOAE frequencies by intensity levels, both for PTA and SRT, ranged between -0.323 and -0.661. No statistically significant differences were found between the DPOAE measurements and ethnic groups of African and Caucasian (Sample size of 175 for FB1-S, 137 for FB1-R and 161 for FB2-S). No differences were found between the DPOAE levels and the occupation types of mining team members, stopers and drillers. There was, however, a relational finding of a progressive decrement of DPOAE intensity levels by decade of age increase (Sample size of 37 for FB1-S, 45 for FB1-R and 155 for FB2-S). Mean DP levels in this population ranged from 1.5 to -14 dB SPL, and mean NF levels in the sample ranged from 0.1 to -16.8 dB SPL with the mean DP-NF difference ranges form 0.4 to 9.3 dBSPL. More than 60% of the data collected resulted in a DP-NF of less than 10 dB SPL. The simple correlation relationship between hearing threshold levels and DPOAEs did not sufficiently explain the variance within the sample and due to the fact that a number of the independent variables in the sample were highly correlated, there was a call to use a method that allows for multicolinearity (i.e. stepwise regression analysis) in order to develop a prediction model. Consequently, phase two of the study was able to compare actual air-conduction hearing threshold levels with those calculated with the prediction model, and then calculate predicted percentage loss of hearing (PLH) with actual PLH found in the noise-exposed gold miners. In phase two, with the use of the predictive models, the predicted hearing threshold levels were found to differ from the actual thresholds by no more than 7dB HL across all frequencies (average of 5 dB HL for FB1-R, 2 dB HL for FB1-S and 3 dB HL for FB2-S). The differences for each audiogram frequency between the actual and the predicted thresholds are represented on scatter plots in phase two of the thesis. The PLH of the predicted audiograms was calculated using the weighted xxii tables prescribed by the Compensation for Occupational Diseases and Injuries Act (COIDA). A comparison of the predicted PLH with the actual PLH indicated that the predicted PLH ranged between minus 1.3% PLH and plus 6.7% PLH of the actual PLH. Results of the study are discussed with regards to the clinical implications, and the implications for training occupational audiologists in South Africa. The results of this study will improve and inform practice in the mining environment and in the field of compensation for NIHL. By developing a reliable prediction tool which is implemented on an objective test proven to document the extent of damage incurred from noise-exposure, a clinician will gain greater confidence in an accurate diagnosis, thereby further safeguarding a vulnerable population. The results from this study are highly relevant to the mining industry and will add value to the industrial development of South Africa by informing the policy on hearing conservation and compensation, thereby increasing the awareness of the need for improved occupational health and safety conditions and sustainable development in the mining industry.