Heart rate variability analysis can be used for a diagnosis of the cardiac diseases. The HRV analysis methods are divided into linear and nonlinear methods. Time-domain method is one of the simplest method and belongs to linear methods. Detrended fluctuation analysis DFA is nonlinear method made relatively recently. In this paper, it has been done the comparison of these two methods based on the changes detection in HRV caused by an ischemia.

Accelerometer data can be analyzed using a variety of methods which are effective in the clinical setting. Time-series analysis is used to analyze spatiotemporal variables in various populations. More recently, investigators have focused on gait complexity and the structure of spatiotemporal variations during walking and running. This study evaluated the use of time-series analyses to determine gait parameters during running. Subjects were college-age female soccer players. Accelerometer data were collected using GPS-embedded trunk-mounted accelerometers. Customized Matlab® programs were developed that included Gaussian continuous wavelet transform (CWT) to determine spatiotemporal characteristics, detrended fluctuation analysis (DFA) to examine gait complexity and autocorrelation analyses (ACF) to assess gait regularity. Reliability was examined using repeated running efforts and intraclass correlation. Proof of concept was determined by examining differences in each variable between various running speeds. Applicability was established by examining gait before and after fatiguing activity. The results showed most variables had excellent reliability. Test-retest R2 values for these variables ranged from 0.8 to 1.0. Low reliability was seen in bilateral comparisons of gait symmetry. Increases in running speed resulted in expected changes in spatiotemporal and acceleration variables. Fatiguing exercise had minimal effects on spatiotemporal variables but resulted in noticeable declines in complexity. This investigation shows that GPS-embedded trunk-mounted accelerometers can be effectively used to assess running gait. CWT and DFA yield reliable measures of spatiotemporal characteristics of gait and gait complexity. The effects of running speed and fatigue on these variables provides proof of concepts and applicability for this analytical approach. / Master of Science / Fitness trackers have become widely accessible and easy to use. So much so that athletic teams have been using them to track activity throughout the season. Researchers are able to manipulate data generated from the fitness monitors to assess many different variables including gait. Monitoring gait may generate important information about the condition of the individual. As a person fatigues, running form is theorized to breakdown, which increases injury risk. Therefore the ability to monitor gait may be advantageous in preventing injury. The purpose of this study is to show that the methods in this study are reproducible, respond reasonably to changes in speed, and to observe the changes of gait in the presence of fatigue or on tired legs. Three analyses are used in this study. The first method called autocorrelation, overlays acceleration signals of consecutive foot strikes, and determines the similarity between them. The second method utilizes a wave transformation technique that is able to determine foot contact times. The final method attempts to determine any pattern in the running stride. This method looks for changes in the structure of the pattern. Less structure would indicate a stride that is fatigued. The results showed that the methods of gait analysis used in this study were reproducible and responded appropriately with changes in speed. Small changes in gait were observed due to the presence of fatigue. Further investigation into the use of these methods to determine changes in gait due to the presence of fatigue are warranted.

Recently there have been reports that certain physiological data seem to have the properties of long-range correlation and self-similarity. These two properties can be characterized by a long-range dependent parameter d, as well as a self-similar parameter H. In Peng et al (1995), the alteration of long-range correlations with life-threatening pathologies are studied by analyzing the heart rate data of different groups of subjects. The self-similarity properties of two well-known processes, namely the Fractional Brownian Motion (FBM) and the Fractional ARIMA (FARIMA), are of interest to see if it is suitable to be used to model the heart rate data in order to examine the health conditions of some patients. The Embedded Branching Process (EBP) method for estimating parameter $H$ and a goodness of fit test for examining the self-similarity of a process based on the EBP method are proposed in Jones and Shen (2004). In this work, the performance of the goodness of fit test are examined using simulated data from the FBM and FARIMA processes. A modification of the distribution of the test statistics under null hypothesis is proposed and has been modified to be more appropriate. Some simulation comparisons of different estimation methods of the parameter $H$ for some FARIMA processes are also presented and applied to heart rate data obtained from Kaohsiung Veterans General Hospital.

Submitted by (ana.araujo@ufrpe.br) on 2016-05-25T14:39:16Z No. of bitstreams: 1 Barbara Camboim Lopes de Figueiredo.pdf: 2032958 bytes, checksum: d463c6ab534a96f1ce5aac33c2dde210 (MD5) / Made available in DSpace on 2016-05-25T14:39:16Z (GMT). No. of bitstreams: 1 Barbara Camboim Lopes de Figueiredo.pdf: 2032958 bytes, checksum: d463c6ab534a96f1ce5aac33c2dde210 (MD5) Previous issue date: 2014-02-24 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The study of climate has great importance, given that a variation of climatic elements affect the economy of a certain region and life of the inhabitants. Climate variables temperature, humidity, atmospheric pressure, solar radiation, precipitation and wind can be affected by geophysical and environmental factors such as latitude, altitude, air mass, proximity to sea, sea currents and vegetation. Wind is the most complex climate element representing the natural phenomenon of turbulence, it is characterized by high temporal and spatial variability. Wind is generated by atmospheric air mass movement, and has influence on various environmental phenomena such as soil erosion, pollutant dispersal and transport of pollen and seeds. Knowing wind speed temporal and spatial distribution is crucial to evaluate the potential for generation of eolic energy. In this work we study long-term correlations in wind speed temporal series registered at twelve meteorological stations in the state of Pernambuco, Brazil. To this end we apply Multifractal Detrended Fluctuation Analysis (MF-DFA) on hourly wind speed data for the period 2008-2011. All the analyzed series exhibit multifractal properties with generalized Hurst exponents above 0.5 indicating persistent temporal dynamics for both, small and large fluctuations. We also calculate other multifractal measures Rényi exponent and singularity spectrum, and complexity parameters, position of maximum, width and asymmetry of multifractral spectrum. No correlation was detected between complexity parameters and the geographic parameters longitude, latitude and altitude of the station, except for asymmetry of multifractal spectrum: negative correlation with longitude for maximum wind speed and negative correlation with latitude for average wind speed. However for all stations the strength of multifractality (indicated by width of multifractal spectrum) is greater for maximum wind speed then for average wind speed. These results contribute to a better understanding of the nature of stochastic processes governing wind dynamics which is necessary for development of more accurate predictive models for wind speed temporal variability and diverse phenomena influenced by wind. / O estudo do clima tem grande importância visto que a variação em elementos climáticos afeta a economia de uma região e a vida das pessoas que ali habitam. As variáveis climáticas temperatura, umidade, pressão atmosférica, radiação solar, precipitação e vento podem ser influenciadas por diversos fatores, geofísicos e ambientais, tais como latitude, altitude, massas de ar, continentalidade e maritmidade, relevo e vegetação. Um dos mais complexos elementos do clima é o vento, pelo fato de representar um fenômeno natural de turbulência, caracterizado por uma grande variabilidade temporal e espacial. O vento é gerado pelo movimento das massas de ar e pode influenciar vários fenômenos ambientais como erosão do solo, dispersão de poluentes e transporte de pólen e sementes. O conhecimento da distribuição temporal e espacial da velocidade do vento é crucial para avaliação do potencial eólico de uma região. Neste trabalho estudaram-se correlações de longo alcance das séries temporais de velocidade do vento registradas em 12 estações meteorológicas durante o período de 2008 a 2011 no estado de Pernambuco aplicando-se o método Multifractal Detrended Fluctuation Analysis (MF-DFA) nas séries temporais horárias. Todas as séries analisadas mostram as propriedades multifractais com valores de expoente generalizado de Hurst acima de 0,5 indicando uma dinâmica persistente para pequenas e grande flutuações. Foram calculadas também as outras medidas multifractais, o expoente Rényi e o espectro multifractal bem como os parâmetros de complexidade: posição do máximo, largura e assimetria do espectro multifractal. Não foram encontradas correlação entre os parâmetros de complexidade e as coordenadas geográficas: longitude, latitude e altitude, exceto a medida de assimetria do espectro multifractal: correlação negativa entre a rajada e longitude e entre velocidade e latitude. Para todas estações as larguras do espectro multifractal foram maiores para a rajada que para a velocidade, indicando uma multifractalidade mais forte. Estes resultados contribuem para uma melhor compreensão da natureza dos processos estocásticos geradores da dinâmica do vento, necessária para o desenvolvimento de modelos confiáveis para predição da variabilidade temporal do vento e dos diversos fenômenos influenciados pelo mesmo.

Submitted by (ana.araujo@ufrpe.br) on 2016-08-02T15:32:53Z No. of bitstreams: 1 Luciano Rodrigues da Silva.pdf: 1477739 bytes, checksum: e1ea61981eacbff2c9319865f5504f91 (MD5) / Made available in DSpace on 2016-08-02T15:32:53Z (GMT). No. of bitstreams: 1 Luciano Rodrigues da Silva.pdf: 1477739 bytes, checksum: e1ea61981eacbff2c9319865f5504f91 (MD5) Previous issue date: 2009-10-20 / Vegetation fires represent a natural hazard with severe ecological, social, health and economic consequences. Every year fires burn millions of hectares of forest worldwide and their number have been increasing, principally because of the increase in population and combustion material. The preservation of the environment depends on global and regional policies and methods of prevention and suppression of fires. To establish these methods it is necessarily to know the profile of fires: spatial location, time of occurrence, burned area, why they occur, and how they initiate and propagate. Recently, various methods of Statistical Physics (including data analysis and computational models) have been applied to provide additional information about spatial and temporal distribution of fire sequences, which is crucial for assessing various consequences of burning, such as emissions of gasses and particulates to the atmosphere, loss of biodiversity, loss of wildlife habitat, soil erosion etc. Several satellite systems (with different capabilities in terms of spatial resolution, sensitivity, spectral bands, and times and frequency of overpasses) are currently available for monitoring different fire characteristics: dry areas that are susceptible to wild fire outbreak, actively flaming fires, burned area and smoke, and trace gas emissions. Hotspots are satellite image pixels with infrared intensity corresponding to burning vegetation. A hotspot may represent one fire, or be one of several hotspots representing a larger fire. Together with other satellite data, thenumber of hot-spots can be used to estimate the burned area. In this work we study the dynamics of hotspots using the Detrended Fluctuation Analysis (DFA) method, which serves to quantify correlations in non stationary time series. We analyze daily hotspot temporal series detected in Brazil by various satellites during the period 1998-2008. The results show the existence of power-law long-range correlations that represent an important property of the underlying stochastic process. This property, also found in climatic phenomena, should be incorporated in theoretical models and computer simulations of the fire dynamics. / Incêndios em vegetação é um tipo de desastre natural com conseqüências ambientais, sociais econômicas, etc. Todos os anos incêndios destroem milhões de hectares das florestas e aumentam em número como conseqüência de vários fatores, principalmente de crescimento populacional e acúmulo de material combustível. A preservação de meio ambiente depende das políticas protecionistas globais e regionais adequadas às características de cada região. Para estabelecer essas políticas de controle e prevenção é necessário conhecer o perfil dos incêndios florestais: onde, quando e porque ocorrem. Além das estatísticas de ocorrências de incêndios os métodos emergentes da Física Estatística incluindo análise de dados e modelos computacionais, providenciam as informações adicionais sobre a distribuição e agrupamento espaço-temporal dos incêndios, que são cruciais para o estudo de várias conseqüências de fogo, como emissão de gases e partículas em atmosfera, perda de biodiversidade, erosão de solo, etc. Vários satélites (com características diferentes em termos de resolução espacial, bandas espectrais, tempo e freqüência de escaneamento) são disponíveis para monitoramento das varias características de fogos: áreas de risco, incêndios atualmente ativos, área queimada, fumaça, emissão de poluentes etc. Focos de calor são pixels na imagem de satélite com intensidade infravermelha correspondente a vegetação queimada. Um foco pode representar uma queimada, parte de um incêndio maior ou outras fontes de calor como, por exemplo, a reflexão de luz da superfície de um lago. O número de focos junto com outras informações providenciadas pelos satélites podem ser usados para estimar a área queimada, para detecção e monitoramento dos incêndios florestais, estimação de risco de fogo, e para avaliação da influencia de outros fatores ambientais. Neste trabalho estudamos a dinâmica de focos de calor no Brasil usando o método Detrended Fluctuation Analysis (DFA), desenvolvido para quantificar as correlações em séries temporais não estacionárias. Analisamos séries temporais diárias de focos de calor detectados no Brasil pelo vários satélites, durante o período 1998-2008. Os resultados mostram a existência de correlações de longo alcance persistentes, que representa uma propriedade importante dos processos estocásticos geradores desse fenômeno. Esta propriedade, também presente em fenômenos climáticos deveria ser incorporada em modelos teóricos e simulações computacionais de dinâmica de incêndios.

Submitted by (ana.araujo@ufrpe.br) on 2016-08-12T14:04:00Z No. of bitstreams: 1 Rosangela Silveira do Nascimento.pdf: 1012353 bytes, checksum: b5f0d139481ab0bfdd6a1f689175c1f6 (MD5) / Made available in DSpace on 2016-08-12T14:04:00Z (GMT). No. of bitstreams: 1 Rosangela Silveira do Nascimento.pdf: 1012353 bytes, checksum: b5f0d139481ab0bfdd6a1f689175c1f6 (MD5) Previous issue date: 2008-02-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / In the present work we analyze the dynamics of electrical cortical activity during the phenomenon of spreading depression (DA) and during the periods before and after this phenomenon. The characteristic of DA is reduced amplitude of spontaneous electrical activity that occurs in neural tissue after the application of stimulus that can be electrical, chemical, mechanical, luminous etc. In order to study properties of time series of electrical cortical activity recorded by ECoG (electrocortiogram) before,during and after DA, we apply Detrended Fluctuation Analysis(DFA). This method is designed to quantify long term correlations (memory) in temporal series such ECoG register. The method was successfully applied in studies of DNA sequences and non-stationary time series as heart rate variability, stride intervals, financial time series etc. The application of DFA results in scaling exponentα that quantifies correlation properties of nonlinear dynamical systems. This experiment indicates if temporal series posses long term correlations. In this work we calculate exponent α for different intervals: control (before the stimulus), after the stimulus, during the avalanche, during DA and after DA for two experimental groups of rats, nourished and malnourished. For both experimental groups the values of exponent α indicates persistent behavior for all intervals except during the avalanche in which correlations degrade. The presence of long term correlations in physiological time series observed in healthy organisms represents complexity that guaranties the organism’s adaptability to stress and disease. The absence of correlations during the avalanche indicates the loss of this complexity. Non-parametric Wilcoxon test was used to compare mean values of exponents α for all intervals of analyzed time series. In cases of nourished rats, the mean values ofα are significantly different for control, stimulus, avalanche, DA and after-DA intervals. Wilcoxon test was also used to compare mean values of α for corresponding intervals for the two experimental groups. The result is significant difference in mean values of α for control, stimulus avalanche, DA and after DA intervals between two experimental groups. The hypothesis that α =0.5 for avalanche intervals was not rejected by test, confirming the loss of correlations in this phase. Comparison of mean values of α for different intervals (control, stimulus, DA and after DA) with avalanche using the Wilcoxon test results in significant difference between two groups. / O presente estudo se propõe a analisar a dinâmica da atividade elétrica cortical durante o fenômeno da depressão alastrante (DA) e nos períodos que antecede e sucede o fenomêno. A DA é caracterizada pela redução da amplitude da atividade elétrica espontânea que ocorre no tecido neural, após a aplicação de um estímulo de natureza elétrica, química, mecânica, luminosa e outros. Visando estudar o comportamento da série temporal da atividade elétrica cortical, registrada no ECoG (eletrocorticograma), durante a DA e nos períodos que precede e sucede o fenômeno, foi aplicado o método do DFA (Detrended Fluctuation Analysis). Este método permite quantificar a existência de correlação de longo alcance (memória) numa série temporal, como é o caso do registro do ECoG. Anteriormente, o método foi aplicado em seqüências de DNA e no estudo de séries temporais não estacionárias, tais como, dinâmica da variabilidade cardíaca, flutuações de eletroencefalograma de humanos, intervalos entre passos sucessivos de humanos, séries econômicas e outros. A aplicação do DFA numa série temporal permite a determinação de um expoente de escalonamento α, que pode contribuir para a compreensão das propriedades dos sistemas dinâmicos não lineares. Este expoenteα revela se a série temporal apresenta correlação de longo alcance ou não. Neste trabalho os expoentes α foram calculados nas fases de controle, estímulo, avalanche, DA e após a DA para o ECoG, em dois grupos experimentais, ratos nutridos e ratos desnutridos. Em ambos os grupos experimentais, os valores obtidos para o expoente de escalonamento α denotam que a série temporal do ECoG apresenta correlação persistente (comportamento da série no presente se mantém no futuro) em todas as fases do processo com exceção da avalanche, período no qual ocorre perda de correlação. A presença de correlação de longo alcance numa série temporal biológica é uma resposta sempre observada em organismos saudáveis cuja complexidade do sinal registrado garante a adaptabilidade do organismo a situações de estresse e/ou distúrbios. Enquanto a ausência de correlação, observada na avalanche, indica a perda de propriedades fractais nos sistemas fisiológicos. O uso do método não-paramétrico de Wilcoxon, para comparar os valores médios dos expoentes α obtidos para o grupo de animais nutrido, durante as fases de controle, estimulação, DA, após DA, revelou que essas diferentes fasesdiferem significativamente. Os valores médios dos expoentes α obtidos para o grupo de animais desnutrido, durante as fases de controle, estimulação, DA, após DA, também não foram significativamente diferentes, quando comparados pelo método de Wilcoxon. Na comparação dos valores médios de α nas fases de controle, estimulação, DA, após DA entre os dois grupos de animais (nutrido e desnutrido) o teste de Wilcoxon revelou que as médias dos expoentes α em cada fase para os animais nutridos diferem significativamente daquelas obtidas para os animais desnutridos. Na avalanche a hipótese de que o expoente α é igual a 0,5, não foi rejeitada pelo teste de Wilcoxon, ou seja, o teste confirmou a perda de correlação nessa fase. Na comparação entre as médias dos expoentes α nos diferentes intervalos (controle, estimulação, DA, após DA) com o valor do expoente α na avalanche, o teste de Wilcoxon acusou diferença significativa tanto no grupo dos nutridos como no grupo dos desnutridos.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2015-10-21T12:02:13Z No. of bitstreams: 2 Dissertação - Renata da Costa Barbosa - 2014.pdf: 2041466 bytes, checksum: b05943f80e79bca8b9f3398d1706e187 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-10-21T12:10:02Z (GMT) No. of bitstreams: 2 Dissertação - Renata da Costa Barbosa - 2014.pdf: 2041466 bytes, checksum: b05943f80e79bca8b9f3398d1706e187 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2015-10-21T12:10:02Z (GMT). No. of bitstreams: 2 Dissertação - Renata da Costa Barbosa - 2014.pdf: 2041466 bytes, checksum: b05943f80e79bca8b9f3398d1706e187 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-11-28 / Understanding how the postural control system is impaired with aging can help identify elderly at risk of falling. In order to study the postural control, center of pressure (CP) behavior can be analyzed. Classical descriptors are commonly used for the CP analysis, however, modern descriptors have been developed aiming to provide more information about the underlying processes involved in the postural control. Aims: Analyze and compare classical and modern descriptors used to analyze the postural control in elderly subjects in quiet standing posture, using data acquired from a force platform in horizontal and inclined surfaces. Methods: The study sample consisted of 17 elderly subjects who remained on a force platform in the upright posture for 70 seconds. The data acquisition was performed with the platform on a horizontal surface and again on a surface inclined at 14 degrees with dorsiflexion and later with plantar flexion of the ankle. For each slope, the procedure was repeated three times with eyes open (EO ) and three times with eyes closed (EC). The initial 10 seconds were discarded and then, CP times series were analyzed in the anteroposterior (AP) and mediolateral (ML) directions. The classical descriptors used were in the time in the frequency domain and the modern descriptors were: Detrended Fluctuations Analysis (DFA), Stabilogram Diffusion Analysis (SDA) and the Sway Density Curve (SDC). Results: In the classical analysis, the results showed significant differences in all comparisons made and, in the modern analysis, the variables provided by the SDA and SDC also showed significant differences between comparisons, however, the DFA did not provide any difference between the conditions. Conclusion: Results provided by the classical variables and by the SDA and the SDC suggest a lower stability of elderly subjects in the inclined surface with dorsiflexion followed by plantar flexion and the eyes closed condition. More studies with the modern descriptors are necessary to better understand their results. / Entender como o sistema de controle postural é comprometido com o processo de envelhecimento pode contribuir na identificação de idosos com risco de quedas. Para estudar o controle postural pode-se analisar o comportamento do Centro de Pressão (CP). Descritores clássicos comumente são utilizados para a análise do CP, no entanto, descritores modernos têm sido desenvolvidos, com o intuito de fornecer mais informações sobre os processos subjacentes ao controle postural. Objetivos: Analisar e comparar descritores clássicos e modernos para análise do controle postural em sujeitos idosos na postura ereta quieta, utilizando dados adquiridos na plataforma de força no plano e com inclinação. Métodos: A amostra do estudo foi composta por 17 indivíduos idosos que permaneceram sobre uma plataforma de força na postura ortostática por 70 segundos. A aquisição dos dados foi realizada com uma plataforma sobre uma superfície horizontal e depois sobre uma superfície inclinada a 14 graus nas posições de flexão dorsal e flexão plantar do tornozelo. Para cada inclinação, o procedimento foi repetido três vezes com os olhos abertos (OA) e três vezes com os olhos fechados (OF). Depois de descartados os 10 s iniciais, foram analisadas as séries temporais do CP na direção anteroposterior (AP) e mediolateral (ML). Foram utilizados alguns descritores clássicos no domínio do tempo e da frequência e os descritores modernos: Detrended Fluctuation Analysis (DFA), Stabilogram Diffusion Analysis (SDA) e pela Sway Density Curve (SDC). Resultados: Na análise clássica os resultados indicaram diferenças significativas em todas as comparações realizadas, na análise moderna, as variáveis fornecidas pela SDA e pela SDC também apresentaram diferenças significativas entre as comparações, porém, a DFA não conseguiu apontar tais diferenças. Conclusão: Os resultados fornecidos pelas variáveis clássicas, SDA e a SDC sugerem uma menor estabilidade de sujeitos idosos na superfície inclinada com flexão dorsal seguida da flexão plantar e na condição de olho fechado. Ainda são necessários a realização de mais estudos utilizando tais descritores para uma melhor compreensão de seus resultados.

Human movement is inherently complex, requiring the control and coordination of many neurophysiological and biomechanical degrees-of-freedom, and the extent to which individuals exhibit variation in their movement patterns is captured by the construct of motor variability (MV). MV is being used increasingly to describe movement quality and function among clinical populations and elderly individuals. However, current evidence presents conflicting views on whether increased MV offers benefits or is a hindrance to performance. To better understand the utility of MV for performance prediction, we focused on current research needs in the military domain. Dismounted soldiers, in particular, are expected to perform at a high level in complex environments and under demanding physical conditions. Hence, it is critical to understand what strategies allow soldiers to better adapt to fatigue and diverse environmental factors, and to develop predictive tools for estimating changes in soldier performance. Different aspects of performance such as motor learning, experience, and adaptability to fatigue were investigated when soldiers performed various gait tasks, and gait variability (GV) was quantified using four different types of measures (spatiotemporal, joint kinematics, detrended fluctuation analysis, and Lyapunov exponents). During a novel obstacle course task, we found that frontal plane coordination variability of the hip-knee and knee-ankle joint couples exhibited strong association with rate of learning the novel task, explaining 62% of the variance, and higher joint kinematic variability during the swing phase of baseline gait was associated with faster learning rate. In a load carriage task, GV measures were more sensitive than average gait measures in discriminating between experience and load condition: experienced cadets exhibited reduced GV (in spatiotemporal measures and joint kinematics) and lower long-term local dynamic stability at the ankle, compared to the novice group. In the final study investigating multiple measures of obstacle performance, and variables predictive of changes in performance following intense whole-body fatigue, joint kinematic variability of baseline gait explained 28-59% of the variance in individual performances changes. In summary, these results support the feasibility of anticipating and augmenting task performance based on individual motor variability. This work also provides guidelines for future research and the development of training programs specifically for improving military training, performance prediction, and performance enhancement. / Doctor of Philosophy / All people move with some level of inherent variability, even when doing the same activity, and the extent to which individuals exhibit variation in their movement patterns is captured by the construct of motor variability (MV). MV is being increasingly used to describe movement quality and function among clinical populations and elderly individuals. However, it is still unclear whether increased MV offers benefits or is a hindrance to performance. To better understand the utility of MV for performance prediction, we focused on current research needs in the military domain. Dismounted soldiers, in particular, are expected to perform at a high level in complex environments and under demanding physical conditions. Hence, it is critical to understand what strategies allow soldiers to better adapt to fatigue and diverse environmental factors, and to develop tools that might predict changes in soldier performance. Different aspects of performance were investigated, including learning a new activity, experience, and adaptability to fatigue, and gait variability was quantified through different approaches. When examining how individual learn a novel obstacle course task, we found that certain aspects of gait variability had strong associations with learning rate. In a load carriage task, variability measures were determined to be more sensitive to difference in experience level and load condition compared to typical average measures of gait. Specifically, variability increased with load, and the experienced group was less variable overall and more stable in the long term. Lastly, a subset of gait variability measures were associated with individual differences in fatigue-related changes in performance during an obstacle course. In summary, the results presented here support that it may be possible to both anticipate and enhance task performance based on individual variability. This work also provides guidelines for future research and the development of training programs specifically for improving military training, performance prediction, and performance enhancement.

The vegetation responses to seven silvicultural treatments one growing season after harvesting were examined on seven sites in the southern Appalachian mountains of Virginia and West Virginia. Treatments included: 1) control, 2) understory control by herbicide, 3) group selection, 4) high-leave shelterwood, 5) low-leave shelterwood, 6) leave tree, and 7) clearcut. The effects of harvesting were compared between treatments and between pre-harvest and post-harvest samplings. Species richness, percent cover, and local species extinctions were calculated for sample plots ranging in size from 1m2 to 2 ha. Vegetation richness and cover increased with increasing harvest intensity. Local species extinctions were similar in the control and disturbed treatments. Additional analyses were performed using the control, high-leave shelterwood, and clearcut on five of the seven sites to determine the relationships between soil, litter, and other environmental characteristics and vegetation in the herbaceous layer (<1 m in height). Multivariate analysis techniques were used to analyze average differences in species abundance between pre-harvest and post-harvest and to relate post-harvest vegetation to microsite characteristics. Regional-scale differences in site location were more important in explaining the presence of a species than were environmental characteristics. Within a region, species primarily were distributed along a light/litter weight gradient and secondarily along a soil properties and nutrient gradient. / Master of Science

Séries temporais financeiras, como índices de mercado e preços de ativos, são produzidas por interações complexas dos agentes que participam do mercado. As propriedades fractais e multifractais destas séries fornecem evidências para detectar com antecedência a ocorrência de movimentos bruscos de mercado (crashes). Tais evidências são obtidas ao aplicar o conceito de Calor Específico Análogo C(q), proveniente da equivalência entre a Multifractalidade e Termodinâmica. Na proximidade de um crash, C(q) apresenta um ombro anômalo à direita de sua curva, enquanto que na ausência de um crash, possui o formato parecido com uma distribuição gaussiana. Com base neste comportamento, o presente trabalho propõe um novo indicador temporal IA(i), definido como a taxa de variação da área sob a curva de C(q). O indicador foi construído por intermédio de uma janela temporal de tamanho s que se movimenta ao longo da série, simulando a entrada de dados na série ao longo do tempo. A análise de IA(i) permite detectar com antecedência a ocorrência de grandes movimentos, como os famosos crashes de 1929 e 1987 para os índices Dow Jones, S&P500 e Nasdaq. Além disso, a análise simultânea de medidas como a Energia Livre, a Dimensão Multifractal e o Espectro Multifractal, sugerem que um crash de mercado se assemelha a uma transição de fase. A robustez do método para diferentes ativos e diferentes períodos de tempo, demonstra a importância dos resultados. Além disso, modelos estatísticos não lineares para a volatilidade foram empregados no trabalho para estudar grandes flutuações causadas por crashes e crises financeiras ao longo do tempo. / Financial time series such as market index and asset prices, are produced by complex interactions of agents that trade in the market. The fractal and multifractal properties of these series provides evidence for early detection of the occurrence of sudden market movements (crashes). This evidence is obtained by applying the concept of Analog Specific Heat C(q), from the equivalence between the Multifractal Analysis and Thermodynamics. In the vicinity of a crash, C(q) exhibits a shoulder at the right side of its curve, while in the absence of a crash, C(q) presents a form similar to a Gaussian distribution curve. Based on this behavior, it is proposed in this work a new temporal indicator IA(i) defined here as the area variation rate over the Specific Heat function. We have constructed the mentioned indicator from a window of data with the first points (size s), that moves throughout the series, simulating the actual input of data over time. The indicator IA(i) allows one detecting in advance the occurrence of large financial market movements, such as those occurred in 1929 and 1987 for the marked indexes Dow Jones, Nasdaq and S&P500. Moreover, the simultaneous analysis of measures such as the Free Energy, Multifractal Dimension and Multifractal Spectrum suggest that a market crash resembles a phase transition. The robustness of the method for others assets and different periods of time demonstrates the importance of the results. Moreover, nonlinear statistical models for volatility have been employed in the work to study large fluctuations caused by crashes and financial crises over time.