The Coast Guard conducts over 20,000 search and rescue cases a year with approximately 5% of them occurring within the coastal waters of the West Florida Shelf (WFS). Each search effort is planned using the Coast Guard’s Search and Rescue Optimal Planning System (SAROPS) which uses model inputs to create composite probability distributions based on the results of Monte Carlo projections of thousands of particle trajectories. However, SAROPS is limited by the quality of model inputs and their associated errors. This study utilizes observations from three surface drifter deployments on the WFS to evaluate the effectiveness of available surface current models, including one model not currently in use by the Coast Guard. Additionally, the performance of high-frequency (HF) Radar observations is evaluated against the models. The HF Radar root-mean-square errors (RMSE) were found to be on the order of 10 cm/s, and a model created with objectively mapped HF Radar data was found to out-perform all available models. Additionally, a comparison of model skills (using a normalized Lagrangian separation method) showed the West Florida Coastal Ocean Model (WFCOM) to have better skill on both the inner and outer shelf regions of the WFS when compared to other models.

No description available.

abstract: This study was designed to examine the associations between food skills, resilience, and coping during the Covid-19 pandemic. Between April and June of 2020, a sample of 154 students, faculty, and staff from Arizona State University were surveyed. Each respondent was administered a survey containing demographic questions, a food skill questionnaire, and the 14-item Resilience Scale (RS). Results indicate that food skill was correlated with resilience (p<0.001) at an r=0.364 and r2=0.1243 and that resilience was correlated with coping during the Covid-19 pandemic (p<0.001) at an r=0.455. Correlations were also run between resilience score and the separate domains of food skill score: all domains remained significantly associated with resilience score (p<0.001) with a r=0.340 and r2=0.1173 for ‘Food Selection and Planning,’ r=0.312 and r2=0.0958 for ‘Food Preparation,’ and r=0.294 and r2=0.0767 for ‘Food Safety.’ Data seems to be consistent with contemporary research suggesting positive associations between diet quality and physiological resilience and positive associations between resilience and coping during the Covid-19 pandemic. / Dissertation/Thesis / Masters Thesis Nutrition 2020

Made available in DSpace on 2015-05-14T12:09:37Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 10447871 bytes, checksum: f75e368c81366ea6407b5bdfc036035e (MD5) Previous issue date: 2014-09-22 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Precipitation is a weather element that serves as the key to most types of analysis input parameter. However, traditional systems of collecting budget record historical, geographical and temporal in obtaining, processing and transfer problems. This fact has mobilized the rise of techniques such as orbital remote sensing (ORS), increasingly applied in studies aimed at monitoring and forecasting through Hydrological Models such as the Distributed Hydrological Models (DHM). However, because they are considered the ground truth science requires intercomparison of results from orbital sensors with them in order to have the analytical scrutiny of verification of responses from performance. However, the methodologies adopted in the comparative analysis of their results - with terrestrial information - usually do not report the nature of the error information, if basing only on larger or smaller link the information. Therefore, this paper aims to contribute to science through the methodological increase with use of statistics related to performance indicators, to assess the particularities involved in the errors associated with each sensor element for measuring rain. Methodologically compared the information of rain sensor 3B42 algorithm s 7 version of Tropical Rainfall Measurement Mission (TRMM) with historical information from rain gauges belonging to Agência Executiva de Gestão de Águas do Estado da Paraíba (AESA), with analysis from 1998 to the year 2011, were methodologically compared followed two analytical biases. At first, there was the spatio-temporal similarity to pixel level (pixel-to-pixel) in what is called approach 1 (A1); and second, there was the similarity of the results in quantitative precipitation bands. Results in analytical approach 1 (A1), the TRMM showed good performance on measures of agreement for negative events correct. According to the second approach (A2) the performances presented to corroborate the depreciation of the estimated sensor. / A precipitação é um elemento meteorológico que serve como parâmetro de entrada fundamental aos mais diversos tipos de análise. No entanto, os sistemas tradicionais de coleta registram históricos problemas orçamentais, geográficos e temporais na sua obtenção, processamento e repasse. Tal fato tem mobilizado a ascensão de técnicas, como o sensoriamento remoto orbital (SRO), que cada vez mais se aplica nos estudos voltados ao monitoramento e previsão, em conjunto com os Modelos Hidrológicos, tal como os Modelos Hidrológicos Distribuídos (MHD). No entanto, por serem considerados a verdade de campo a ciência exige a intercomparação dos resultados dos sensores orbitais com os mesmos afim de se ter o crivo analítico da verificação de respostas de desempenho. Entretanto, as metodologias adotadas na análise comparativa dos seus resultados - com as informações terrestres - normalmente não informam a natureza dos erros de informação, pautando-se apenas na relação maior ou menor entre as informações. Portanto, o trabalho objetiva contribuir com a ciência, por meio de proposta metodológica, com uso de estatísticas ligadas a índices de desempenho, de forma a avaliar as particularidades envolvidas nos erros associados entre sensores de mensuração do elemento chuva no estado da Paraíba. Metodologicamente comparou-se as informações de chuva do algoritmo 3B42 da Versão 7, do sensor orbital Tropical Rainfall Measurement Mission (TRMM) com as informações históricas dos postos pluviométricos pertencentes a Agência Executiva de Gestão de Águas do Estado da Paraíba (AESA), entre os anos de 1998 a 2011. Seguiu-se dois vieses analíticos. No primeiro momento, verificou-se à similaridade espaço-temporal ao nível do pixel (pixel-a-pixel) no que se denomina primeira abordagem (A1); e no segundo, verificou-se à similaridade dos resultados sob faixas quantitativas de chuva, no que se nomeia segunda abordagem (A2). Como resultados na abordagem analítica A1, o TRMM apresentou bons desempenhos às medidas de concordância para eventos de correto negativos. Segundo a abordagem A2 os desempenhos apresentados corroboraram para maiores erros da estimativa do sensor.

Physical tasks such as scrummaging, rucking and mauling are highly specific to rugby and also place unique physiological demands on the different playing positions within the forwards. Traditionally, the recruitment and development of talented rugby union players has focused on the assessment of motor skills and game intelligence aspects of performance, with less emphasis placed on the specific physiological requirements of playing positions in rugby. The purpose of this investigation was to measure the position-specific strength, speed and power characteristics of Premier rugby forwards in order (1) to determine whether any differences existed in the physiological characteristics of the different forward playing positions (prop, lock and loose forwards) and (2) to investigate the relationship between these physiological characteristics and coaches evaluations of football playing ability. Twenty-two male Premier level competition rugby forwards, consisting of eight prop forwards, five lock forwards and nine loose-forwards participated in the study. The Grunt 3000, a rugby specific force testing device was utilised to measure the static and dynamic horizontal strength during simulated scrummaging and rucking/mauling movements. Sprint times relating to acceleration ability (0 -10m, 0-20m) and maximum running speed (20 - 40m) were measured during a 40m sprint running test. In addition, force, power and displacement characteristics of a countermovement vertical jump were calculated from trials performed on a force plate. Also, player performance skill and physical capacity scores were determined independently by experienced coaches who assessed them based on their performances during the season. One-way analysis of variance and effect size statistics evaluated differences in the measured variables between forward playing positions and linear regression analysis evaluated the relationship between the coaches' scores of player performance skill and physical capacity and game specific measures of strength speed and power. Since there were no statistical significant differences between forward groups for horizontal force and countermovement jump variables and these analyses lacked statistical power, an effect size statistic was used to establish trends for differences in force and CMJ variables between the groups. There were moderate effect size differences between groups for horizontal impact force with prop and lock forwards producing 17.7% and 12.8% more force than the loose forwards respectively. No clear differences were apparent between forward positional groups for mean dynamic horizontal force and countermovement jump displacement of the centre of gravity. A significant difference (p =0.049) was shown between forward positional groups over the 0-40m sprint distance. Also, moderate effect size differences between pairs of groups were evident in 0-10m, 0-20m, 20-40m sprint times with both loose forwards and lock forwards on average, 6% faster than the prop forwards. A backward linear regression analysis revealed that the single best predictor of coaches' physical capacity and performance skill scores was the 20 - 40m sprint performance, accounting for 28% of the variance in player's physical capacity scores and 29% of the variance in player's performance skill scores. Whole-body horizontal static strength and impact strength in prop forwards and dynamic horizontal strength (relative to body mass) and sprint acceleration ability in loose forwards represent key factors for consideration when selecting forward players to these positions in the Premier rugby competition. The vertical jumping ability of all forward positional groups needs to be confirmed in a future study utilising a line-out specific countermovement jump test (free use of arm swing and line-out lifters in the jump) on a force plate. Monitoring of performance in rugby forwards should include an acceleration sprint test (0-10m) as this is specific to the sprinting patterns of forward players during a game, and maximum sprinting speed test (20-40m) as this test has the ability to discriminate between skilled and less-skilled rugby union forwards.