The purpose of this study was to develop a personalized step test and a valid regression model that used non-exercise data and data collected during the step test to estimate VO2max in males and females 18 to 30 years of age. All participants (N= 80) successfully completed a step test with the starting step rate and step height being determined by the self-reported perceived functional ability (PFA) score and participant's height, respectively. All participants completed a maximal graded exercise test (GXT) to measure VO2max. Multiple linear regression analysis yielded the following equation (R = 0.90, SEE = 3.43 mL/kg/min): 45.938 + 9.253(G) - 0.140(KG) + 0.670(PFA) + 0.429(FSR) - 0.149(45sRHR) to predict VO2max (mL/kg/min) where: G is gender (0=female;1=male), KG is body mass in kg, PFA is the sum of the two PFA questions, FSR is the final step rate (step-ups/min), and 45sRHR is the recovery heart rate 45 seconds following the conclusion of the step test. Each independent variable was significant (p < 0.05) in predicting VO2max and the resulting regression equation accounted for roughly 83% (R2=0.8281) of the shared variance of measured VO2max. Based on the standardized B-weights, gender (0.606) explained the largest proportion of variance in VO2max values followed by PFA (0.315), body mass (-0.256), FSR (-0.248), and the 45sRHR (-0.238). The cross validation statistics (RPRESS = 0.88, SEEPRESS = 3.57 (mL/kg/min-1) show minimal shrinkage in the accuracy of the regression model. This study presents a relatively accurate model to predict VO2max from a submaximal step test that is convenient, easy to administer, and individualized.

No description available.

No description available.

No description available.

The rigorous quality criterion and intricate shapes of plastic injection molded parts require molders to improve process control systems in order to keep their competitive status in the market. In recent research, various advanced control algorithms are employed to develop in-line process controllers. In modem controllers design, in-mold process variables play a very important role in connecting machine variables and quality variables. Model-based predictive control (MPC) is used to investigate the controllability of cavity pressure and cavity temperature within a cycle or cycle-to-cycle. The objective of the present work is to demonstrate a procedure to develop MPC controllers based on simulation results. Moldflow® was used to simulate the injection molding process for a thin-wall cell phone cover. Cavity pressure profiles and part surface temperature profiles were extracted to develop the dynamic model for controller design. Thermal analysis for the cooling stage was investigated by ANSYS® FEM software. Mold surface temperature profiles were used for controller design. Dynamic matrix control, a type of MPC control, was developed by using Matlab® MPC Toolbox. A single-input/single-output MPC controller was developed to control cavity pressure in filling stage by manipulating injection flow rate. Simulation studies were then used to develop a MPC controller to implement a closed-loop control. The controller performed very well to control the pressure profile to trace the set-point, even with melt temperature or mold temperature change. Two MPC controllers were developed to control cavity surface cycle average temperature by manipulating coolant flow rate and coolant temperature. Both controllers show good controllability for cycle average temperature control. A two-input/two-output DMC controller was implemented to control cavity pressure and part surface temperature in the packing stage. Packing pressure and mold temperature were manipulated to trace the controlled profile set-points in each sampling time. Results shows that the controller was able to meet the set-point very well, for an unmeasured disturbance, based on a closed-loop test. All the controllers were developed based on simulation results, which will have some differences with real production data. Therefore, the model parameter and controller tuning parameter should be validated and modified if needed before real-time application. / Thesis / Master of Applied Science (MASc)

本篇論文的主要研究目的是希望探討Didier所發展出的金融危機預測模型是否也能夠適用於預測復甦現象?如同先前許多研究所指出的，美國股市指數波動在崩盤以及復甦下呈現截然不同的現象。當在復甦時，指數成長緩慢，波動程度小。但是當蕭條時，指數波動程度大，並且快速。這些差異增加了使用同一種方法來預測金融復甦與危機的困難度。 / Purpose of this study was to investigate Can the crisis prediction model proposed by Didier Sornette still work on blooming. As previous studies pointed out that the U.S. stock market index fluctuated different when under the blooming stage and the recession stage. When Economic recovery, a change into the positive cycle, the stock market index rose slowly, the index change in the short term rate is small. When recession came, changes in stock market index fiercely. These differences make it hard to using the same way predict the economic recovery and collapse.

<p>The procedure of determining if a site is suitable for wind power production requiresconvincing statistical data describing the long-term behavior of the average wind speed.This can be achieved by measuring the wind speed for a short time period, e.g. a year,and after that a Measure-Correlate-Predict (MCP) method can be performed. The shorttermmeasured wind data must be used in combination with a long-term referenceseries. This long-term reference series can be global reanalysis data reaching 20 to 30years back in time. In a MCP method different regression methods can be used. Aftercreating a long-term corrected wind data series, it is possible to analyze the conditionsat the investigated site. To be able to study the behavior of different reference series andregression methods, a model was created in MATLAB. As short-term wind speed dataVattenfall Wind Power supplied data from two measuring masts, Ringhals andOskarshamn, with maximum heights of 96 and 100 meters, respectively. From UppsalaUniversity data were supplied from a measuring mast near Marsta with maximummeasurement height of 29 meters.When creating these long-term corrected wind data series there were many methodsavailable. In this Master thesis methods such as Ordinary-Least-Square, Least-Absolute-Deviation and Reduced-Major-Axis regression methods have been used. With eachmethod three reference series were used in combination with the short-termmeasurement data. These were data from NCAR 850 hPa, NCAR 42-meter sigma leveland a confidential source.Regression methods in combination with reference series were studied and the deviationfrom mean wind speed was obtained for each of these cases. Studies were performed onhow the length of the short-term measurement series affected the deviation from themeasured mean wind speed. It was also investigated if the time of the year had anyinfluence on the measurements.The general conclusion drawn after performing the above-mentioned studies was thatthe NCAR 850 hPa wind speed data and the Reduced-Major-Axis regression methodgave the smallest deviation from the measured mean wind speed in most cases. It wasalso concluded that when a short-term measurement series reached 10 to 14 monthsthere was a significant decrease in deviation from the mean wind speed, regardless ofreference series or method used. Calculations from the model regarding seasonaldependence stated that there was a slight dependency on which period of the year ameasurement was performed.</p> / <p>I processen att bedöma om en plats är lämplig för utbyggnad av vindkraft måste detfinnas övertygande statistiska data som beskriver den genomsnittliga vindhastighetenöver en längre tid. Genom att utföra vindhastighetsmätningar på den tänkta platsenunder en kortare tid, exempelvis ett år, och därefter tillämpas en Measure-Correlate-Predict (MCP) metod i kombination med en långtidsreferens, exempelvis en globalmodell som sträcker sig 20 till 30 år bakåt i tiden kan detta göras. I en MCP-metod kanolika typer av regressionsmetoder användas. När en långtidskorrigerad vinddataseriefinns tillgänglig kan dess beteende på den tänkta platsen analyseras. För att kunna göradetta för flera olika typer av referensserier och regressionsmetoder skapades en modell iMATLAB. Två vinddataserier erhölls från Vattenfall Vindkraft. Dessa var Ringhals ochOskarshamn med högsta mäthöjd på 96 respektive 100 meter. En ytterligarevinddataserie erhölls av Uppsala Universitet från en mätmast nära Marsta med högstamäthöjd på 29 meter.Det fanns flera metoder tillgängliga för att skapa de långtidskorrigeradevinddataserierna. I det här examensarbetet har metoderna Ordinary-Least-Square-,Least-Absolute-Deviation- och Reduced-Major-Axis regressioner använts. För varjemetod testades tre referensserier i kombination med de kortare vinddataserierna. Dessavar NCAR 850 hPa vindhastigheter, NCAR 42 meters sigmanivå vindhastigheter ochannan meteorologisk data.Regressionsmetoderna utvärderades genom att avvikelsen från de kortare mätseriernasmedelvindhastigheter beräknades. Det undersöktes också hur längden på användvinddata från de kortare mätserierna påverkade avvikelsen i medelvindhastighet och omdet fanns något säsongsberoende på när under året som mätningen av vinddata vargjord.Slutsatserna från undersökningarna var att NCAR 850 hPa vindhastigheter ochregressionsmetoden Reduced-Major-Axis generellt gav de lägsta avvikelserna frånuppmätt medelvindhastighet. Slutsatser kunde också dras om längden av användmätdata. Det var tydligt att oavsett referensserie och regressionsmetod uppstod enminskningen i avvikelse från medelvindhastigheten mellan 10 till 14 månaders längd påmätserien. Resultat angående säsongsberoende kunde påvisas i form av avvikelsermellan mätningar gjorda under olika tidpunkter på året. Storlek och tecken påavvikelsen berodde på vilken referensserien i kombination med regressionsmetod somanvändes.</p>

The procedure of determining if a site is suitable for wind power production requiresconvincing statistical data describing the long-term behavior of the average wind speed.This can be achieved by measuring the wind speed for a short time period, e.g. a year,and after that a Measure-Correlate-Predict (MCP) method can be performed. The shorttermmeasured wind data must be used in combination with a long-term referenceseries. This long-term reference series can be global reanalysis data reaching 20 to 30years back in time. In a MCP method different regression methods can be used. Aftercreating a long-term corrected wind data series, it is possible to analyze the conditionsat the investigated site. To be able to study the behavior of different reference series andregression methods, a model was created in MATLAB. As short-term wind speed dataVattenfall Wind Power supplied data from two measuring masts, Ringhals andOskarshamn, with maximum heights of 96 and 100 meters, respectively. From UppsalaUniversity data were supplied from a measuring mast near Marsta with maximummeasurement height of 29 meters.When creating these long-term corrected wind data series there were many methodsavailable. In this Master thesis methods such as Ordinary-Least-Square, Least-Absolute-Deviation and Reduced-Major-Axis regression methods have been used. With eachmethod three reference series were used in combination with the short-termmeasurement data. These were data from NCAR 850 hPa, NCAR 42-meter sigma leveland a confidential source.Regression methods in combination with reference series were studied and the deviationfrom mean wind speed was obtained for each of these cases. Studies were performed onhow the length of the short-term measurement series affected the deviation from themeasured mean wind speed. It was also investigated if the time of the year had anyinfluence on the measurements.The general conclusion drawn after performing the above-mentioned studies was thatthe NCAR 850 hPa wind speed data and the Reduced-Major-Axis regression methodgave the smallest deviation from the measured mean wind speed in most cases. It wasalso concluded that when a short-term measurement series reached 10 to 14 monthsthere was a significant decrease in deviation from the mean wind speed, regardless ofreference series or method used. Calculations from the model regarding seasonaldependence stated that there was a slight dependency on which period of the year ameasurement was performed. / I processen att bedöma om en plats är lämplig för utbyggnad av vindkraft måste detfinnas övertygande statistiska data som beskriver den genomsnittliga vindhastighetenöver en längre tid. Genom att utföra vindhastighetsmätningar på den tänkta platsenunder en kortare tid, exempelvis ett år, och därefter tillämpas en Measure-Correlate-Predict (MCP) metod i kombination med en långtidsreferens, exempelvis en globalmodell som sträcker sig 20 till 30 år bakåt i tiden kan detta göras. I en MCP-metod kanolika typer av regressionsmetoder användas. När en långtidskorrigerad vinddataseriefinns tillgänglig kan dess beteende på den tänkta platsen analyseras. För att kunna göradetta för flera olika typer av referensserier och regressionsmetoder skapades en modell iMATLAB. Två vinddataserier erhölls från Vattenfall Vindkraft. Dessa var Ringhals ochOskarshamn med högsta mäthöjd på 96 respektive 100 meter. En ytterligarevinddataserie erhölls av Uppsala Universitet från en mätmast nära Marsta med högstamäthöjd på 29 meter.Det fanns flera metoder tillgängliga för att skapa de långtidskorrigeradevinddataserierna. I det här examensarbetet har metoderna Ordinary-Least-Square-,Least-Absolute-Deviation- och Reduced-Major-Axis regressioner använts. För varjemetod testades tre referensserier i kombination med de kortare vinddataserierna. Dessavar NCAR 850 hPa vindhastigheter, NCAR 42 meters sigmanivå vindhastigheter ochannan meteorologisk data.Regressionsmetoderna utvärderades genom att avvikelsen från de kortare mätseriernasmedelvindhastigheter beräknades. Det undersöktes också hur längden på användvinddata från de kortare mätserierna påverkade avvikelsen i medelvindhastighet och omdet fanns något säsongsberoende på när under året som mätningen av vinddata vargjord.Slutsatserna från undersökningarna var att NCAR 850 hPa vindhastigheter ochregressionsmetoden Reduced-Major-Axis generellt gav de lägsta avvikelserna frånuppmätt medelvindhastighet. Slutsatser kunde också dras om längden av användmätdata. Det var tydligt att oavsett referensserie och regressionsmetod uppstod enminskningen i avvikelse från medelvindhastigheten mellan 10 till 14 månaders längd påmätserien. Resultat angående säsongsberoende kunde påvisas i form av avvikelsermellan mätningar gjorda under olika tidpunkter på året. Storlek och tecken påavvikelsen berodde på vilken referensserien i kombination med regressionsmetod somanvändes.

Being able to predict a merger or acquisition before it takes place could lead to an investor earning a premium, if they owned shares of the targeted firm before the merger or acquisition attempt is announced. On average acquiring firms pay a premium when acquiring or merging with a targeted firm. This study uses publicly available financial information for 7,267 attempted takeover targets and 52,343 non-targeted firms for the period January 3, 2000 through December 31, 2007 to estimate (using logit) predictive models. Financial ratios are constructed based on six hypotheses found in the literature. Although statistical evidence supports a few of the hypotheses, the low predictive power of the models does not indicate the ability to accurately predict targeted firms ahead of time, let alone with any economic significance.

Os escorregamentos translacionais rasos são freqüentes na região da Serra do Mar, principalmente quando associados a eventos pluviométricos extremos, como em Janeiro de 1985 (380 mm, em 2 dias). Quando deflagrados de forma generalizada, podem ser catastróficos causando danos para a sociedade. Inúmeros métodos vêm sendo propostos para compreender a ocorrência destes processos na paisagem. O objetivo deste trabalho é avaliar a suscetibilidade a escorregamentos translacionais rasos na Serra do Mar por meio da aplicação de um modelo matemático em bases físicas, tendo como resultado um índice de estabilidade, que aponta, em forma de perigo relativo, áreas passíveis de instabilizações. As etapas de trabalho dividiramse na construção do Modelo Digital de Terreno e em produtos derivados (ângulo da encosta, curvatura, aspecto e área de contribuição), no mapeamento das cicatrizes de 1985 e na simulação dos cenários de suscetibilidade. Os mapas dos parâmetros topográficos, assim como, os mapas de suscetibilidade foram correlacionados com o mapa de cicatrizes e avaliados utilizando-se dos índices de Concentração de Cicatrizes (CC) e Potencial de Escorregamento (PE). Foram mapeadas 216 cicatrizes para uma área de 2,5 km² com uma produção de sedimentos estimado em 135,525m³. Os resultados apontam que encostas com ângulos entre 30° e 40° e com formato retilíneo foram as mais suscetíveis. A área foi considerada instável, segundo o modelo, em todos os cenários, tendo a melhor calibração para o cenário C2. O emprego de diferentes métodos demonstrou-se bastante satisfatório e concordante na análise do resultado final. Além disso, podem auxiliar como ferramentas de apoio de decisão no planejamento do uso do solo, principalmente em regiões onde é freqüente a ocorrência de movimentos de massa. Portanto, o resultado da avaliação a susceptibilidade a escorregamentos rasos na Serra do Mar pode direcionar ações mitigadoras político-administrativas e ambientais, tendo em vista minimizar o impacto sócio-ambiental de eventos futuros. / The shallow landslides are frequent in the Serra do Mar, especially when associated with intense rainfall events, as in January 1985 (380 mm in 2 days). When triggered generalized, causing damage to society. Several methods have been proposed to understand the occurrence of these processes in the landscape. The aim of this study is to evaluate the susceptibility to shallow landslides in the Serra do Mar by applying a physically based models, resulting in a stability index, which points in the form of relatively hazard and susceptible areas. The stages of his research were divided in building the Digital Terrain Model in their products derived (angle of slope, curvature, aspect and area of contribution), mapping the scars of 1985 and simulation of susceptibility scenarios. The maps of the parameters topographic, as well as the susceptibility maps were correlated with the scars map and evaluated using the indices of Scars Concentration (SC) and Landslide Potential (LP). 216 scars were mapped into here area of 2.5 km² with an estimated production of 135.525 m³ sediments. The results show that slopes with angles between 30° and 40° with rectilinear curvature were the most susceptible. The area was considered unstable, according to the model in all scenarios, with the best calibration for scenario C2. The use of different methods showed to be satisfactory and consistent when analyzing with these results. Moreover they can assist as tools for decision support in planning the soil use and, especially in regions where much frequent the occurrence of mass movement. Therefore, the result of susceptibility to shallow landslides in the Serra do Mar can help in the mitigation actions and politicaladministrative environment, aiming minimizes the environmental and social impact of future events.