The accurate measurement of total energy expenditure is a cornerstone of metabolic research. However, there is a lack of measurement methods that are valid, objective, inexpensive, and easy to use. Accelerometry, along with validated prediction equations for resting energy requirements, may provide an opportunity to fill this void. Twenty weight stable adults (12 female, 8 male) who recently participated in a controlled feeding study comprised the study sample. Total energy requirements were assessed from the controlled feeding period in which weight stability was achieved using the intake-balance method. Resting energy expenditure was assessed using the Mifflin-St. Jeor equation. Participants wore accelerometers to objectively assess habitual physical activity. The accelerometer data obtained along with subjects' demographic and biometric data were used to predict non-resting energy expenditure (NREE) using step-wise linear regression in JMP. Bland-Altman plots and Spearman's Rho correlations were used to determine the validity of the total energy requirements obtained from the sum of the predicted non-resting energy expenditure. Estimated resting energy expenditure was compared with the total energy requirements assessed using the intake-balance method from the controlled feeding period. The resulting prediction equation is as follows: 480.93 – 180.69(sex) + 0.21(Accelerometer kcals) + 617.98(BF%) = AEE. The sex was coded as 1 for females and 0 for males. This prediction model has a coefficient of determination of 0.74 (0.70 adjusted). On average, the model overestimates AEE by 76 kcals. This new model could be the key to accurately, inexpensively and objectively measuring total energy requirements. / Master of Science / Accurate measurement of the total amount of energy (i.e. calories) utilized by the body throughout the day, also known as total energy expenditure, is a vital component of metabolic research. However, there is a lack of measurement methods that are valid, objective, inexpensive, and easy to use. Accelerometers combined with equations designed to predict total energy expenditure may be able to fill this gap. Accelerometers are devices worn on the body that measure accelerative forces from physical activity. Twenty weight stable adults (12 female, 8 male), who recently participated in a study in which all dietary intake and exercise were closely monitored (controlled feeding study), comprised the study sample. The amount of energy needed to maintain weight (total energy requirements) was assessed from the controlled feeding period in which weight stability was achieved. Resting energy expenditure, the energy burned while the body is at rest, was assessed using an equation often used to estimate energy expenditure, the Mifflin-St. Jeor equation. Participants wore accelerometers to objectively assess habitual physical activity. The accelerometer data obtained along with subjects’ demographic (age, sex) and biometric (height, weight, BMI, etc.) data were used to predict non-resting energy expenditure (resting energy expenditure subtracted from total energy expenditure). Multiple statistical tests were used to determine the validity of the total energy requirements obtained from the sum of the predicted non-resting energy expenditure (NREE) and resting energy expenditure. Estimated resting energy expenditure was compared with the total energy requirements assessed using the intake-balance method from the controlled feeding period. The resulting prediction equation is as follows: 480.93 – 180.69(sex) + 0.21(Accelerometer kcals) + 617.98(BF%) = NREE. The sex was coded as 1 for females and 0 for males. This prediction model has a coefficient of determination of 0.74 (0.70 adjusted), which means 70% of the variation in non-resting energy expenditure was explained by changes in the variables in the equation. On average, the model overestimates NREE by 76 Calories per day. This new model could be the key to accurately, inexpensively and objectively measuring total energy requirements.

This study evaluates the seismically induced displacements of a slope using the Finite Element Method (FEM) in comparison to the results of twelve empirical predictive approaches. First, the existing methods to analyze the stability of slopes subjected to seismic loads are presented and their capabilities to predict the onset of failure and post-failure behavior are discussed. These methods include the pseudostatic method, the Newmark method, and stress-deformation numerical methods. Whereas the pseudostatic method defines a seismic coefficient for the analysis and provides a safety factor, the Newmark method incorporates a yield coefficient and the actual acceleration time history to estimate permanent displacements. Numerical methods incorporate advanced constitutive models to simulate the coupled stress-strain soil behavior, making the process computationally more costly. In this study, a model slope previously studied at laboratory scale is selected and scaled up to prototype dimensions. Then, the slope is subjected to 88 different input motions, and the seismic displacements obtained from the numerical and empirical approaches are compared statistically. From correlation analyses between seven ground motion parameters and the numerical results, new empirical predictive equations are developed for slope displacements. The results show that overall the FEM displacements are generally in agreement with the numerically developed methods by Fotopoulou and Pitilakis (2015) labelled "Method 2" and "Method 3", and the Newmark-type Makdisi and Seed (1978) and Bray and Travasarou (2007) methods for rigid slopes. Finally, functional forms for seismic slope displacement are proposed as a function of peak ground acceleration (PGA), Arias intensity (Ia), and yield acceleration ratio (Ay/PGA). These functions are expected to be valid for granular slopes such as earth dams, embankments, or landfills built on a rigid base and with low fundamental periods (Ts<0.2). / Master of Science / A landslide is a displacement on a sloped ground that can be triggered by earthquake shaking. Several authors have investigated the failure mechanisms that lead to landslide initiation and subsequent mass displacement and proposed methodologies to assess the stability of slopes subjected to seismic loads. The development of these methodologies has to rely on field data that in most of the cases are difficult to obtain because identifying the location of future earthquakes involves too many uncertainties to justify investments in field instrumentation (Kutter, 1995). Nevertheless, the use of scale models and numerical techniques have helped in the investigation of these geotechnical hazards and has led to development of equations that predict seismic displacements as function of different ground motion parameters. In this study, the capabilities and limitations of the most recognized approaches to assess seismic slope stability are reviewed and explained. In addition, a previous shaking-table model is used for reference and scaled up to realistic proportions to calculate its seismic displacement using different methods, including a Finite Element model in the commercial software Plaxis2D. These displacements are compared statistically and used to develop new predictive equations. This study is relevant to understand the capabilities of newer numerical approaches in comparison to classical empirical methods.

The KiK-net ground motion database is used to develop ground motion prediction equations for Arias Intensity (I_a), 5-95% Significant Duration (Ds_5-95), and 5-75% Significant Duration (Ds_5-75). Relationships are developed both for shallow crustal earthquakes and subduction zone earthquakes (hypocentral depth less than 45 km). The models developed consider site amplification using V_S30 and the depth to a layer with V_S=800 m/s (h₈₀₀). We observe that the site effect for I_α is magnitude dependent. For Ds_5-95 and Ds_5-75, we also observe strong magnitude dependency in distance attenuation. We compare the results with previous GMPEs for Japanese earthquakes and observe that the relationships are similar. The results of this study also allow a comparison between earthquakes in shallow-crustal regions, and subduction regions. This comparison shows that Arias Intensity has similar magnitude and distance scaling between both regions and generally Arias Intensity of shallow crustal motions are higher than subduction motions. On the other hand, the duration of shallow crustal motions are longer than subduction earthquakes except for records with large distance and small magnitude causative earthquakes. Because small shallow crustal events saturate with distance, ground motions with large distances and small magnitudes have shorter duration for shallow crustal events than subduction earthquakes. / This thesis presents the development of new Ground Motion Prediction Equations (GMPEs) for the prediction of the duration and the Arias Intensity of earthquake strong motions. . Arias Intensity is an index for the energy in the ground motion. The GMPEs are based on the Japanese KiK-net database. Based on the causative earthquake source, source to site path, and site properties, GMPEs give estimation of the mean and standard deviation of the parameters. This information is necessary for conducting probabilistic seismic hazard analyses. The characteristics of the ground motions with the same magnitude and source to site distance vary amongst different tectonic regimes. For this reason, we develop different GMPEs for earthquakes from different tectonic regimes (subduction zone and shallow crustal earthquakes). The primary motivation for this research is that no existing GMPEs for duration are directly applicable to subduction-zone earthquakes. In addition, because the same stations recorded both types of events, we can directly compare the effect of tectonic environment on the selected ground motion parameters. The estimation of mean duration and mean Arias intensity made by this study show while magnitude and distance scaling of Arias Intensity is the same for shallow crustal and subduction earthquakes, the tectonic regime has a significant effect on duration of ground motion.

The ocean ambient noise is one of the important parameters in sonar equation. The ocean ambient noise includes diverse and complex sources like waves, marine life, ships, and etc. Using different ways to analyze are needed to understand the complicated properties of ambient noise. Empirical equation obtained from linear regression of wind speed and ambient noise data is a common method to predict noise level. In this article, the ambient noise data were collected from experiments at northeastern sea of Taiwan in 2007, 2008 and 2009. Applying corresponding wind speed data to observed noise level the time series, coefficient of determination is used to estimate how noise fit with wind speed data of regression. The K-S test and Sea States are used to determine the wind speed threshold. Although it is the same sea area in three years, the ocean ambient noise still has variations due to time and variance of sound sources, so it is important to be investigated. This study compares the statistical properties and distribution in ambient noise level and frequencies with corresponding wind speed in same season.

No description available.

Properly accounting for the uncertainties in predicting ground motion parameters is critical for Probabilistic Seismic Hazard Analyses (PSHA). This is particularly important for critical facilities that are designed for long return period motions. Non-ergodic PSHA is a framework that allows for this proper accounting of uncertainties. This, in turn, allows for more informed decisions by designers, owners and regulating agencies. The ergodic assumption implies that the standard deviation applicable to a specific source-path-site combination is equal to the standard deviation estimated using a database with multiple source-path-site combinations. The removal of the ergodic assumption requires dense instrumental networks operating in seismically active zones so that a sufficient number of recordings are made. Only recently, with the advent of networks such as the Japanese KiK-net network has this become possible. This study contributes to the state of the art in earthquake engineering and engineering seismology in general and in non-ergodic seismic hazard analysis in particular. The study is divided in for parts. First, an automated protocol was developed and implemented to process a large database of strong ground motions for GMPE development. A comparison was conducted between the common records in the database processed within this study and other studies. The comparison showed the viability of using the automated algorithm to process strong ground motions. On the other hand, the automated algorithm resulted in narrower usable frequency bandwidths because of the strict criteria adopted for processing the data. Second, an approach to include path-specific attenuation rates in GMPEs was proposed. This approach was applied to a subset of the KiK-net database. The attenuation rates across regions that contains volcanoes was found to be higher than other regions which is in line with the observations of other researchers. Moreover, accounting for the path-specific attenuation rates reduced the aleatoric variability associated with predicting pseudo-spectral accelerations. Third, two GMPEs were developed for active crustal earthquakes in Japan. The two GMPEs followed the ergodic and site-specific formulations, respectively. Finally, a comprehensive residual analysis was conducted to find potential biases in the residuals and propose models to predict some components of variability as a function of some input parameters. / Ph. D.

Ground motions from earthquakes are known to be affected by earth's surface topography. Topographic effects are a result of several physical phenomena such as the focusing or defocusing of seismic waves reflected from a topographic feature and the interference between direct and diffracted seismic waves. This typically causes an amplification of ground motion on convex features such as hills and ridges and a de-amplification on concave features such as valleys and canyons. Topographic effects are known to be frequency dependent and the spectral accelerations can sometimes reach high values causing significant damages to the structures located on the feature. Topographically correlated damage pattern have been observed in several earthquakes and topographic amplifications have also been observed in several recorded ground motions. This phenomenon has also been extensively studied through numerical analyses. Even though different studies agree on the nature of topographic effects, quantifying these effects have been challenging. The current literature has no consensus on how to predict topographic effects at a site. With population centers growing around regions of high seismicity and prominent topographic relief, such as California, and Japan, the quantitative estimation of the effects have become very important. In this dissertation, we address this shortcoming by developing empirical models that predict topographic effects at a site. These models are developed through an extensive empirical study of recorded ground motions from two large strong-motion datasets namely the California small to medium magnitude earthquake dataset and the global NGA-West2 datasets, and propose topographic modification factors that quantify expected amplification or deamplification at a site. To develop these models, we required a parameterization of topography. We developed two types of topographic parameters at each recording stations. The first type of parameter is developed using the elevation data around the stations, and comprise of parameters such as smoothed slope, smoothed curvature, and relative elevation. The second type of parameter is developed using a series of simplistic 2D numerical analysis. These numerical analyses compute an estimate of expected 2D topographic amplification of a simple wave at a site in several different directions. These 2D amplifications are used to develop a family of parameters at each site. We study the trends in the ground motion model residuals with respect to these topographic parameters to determine if the parameters can capture topographic effects in the recorded data. We use statistical tests to determine if the trends are significant, and perform mixed effects regression on the residuals to develop functional forms that can be used to predict topographic effect at a site. Finally, we compare the two types of parameters, and their topographic predictive power. / Ph. D.

Made available in DSpace on 2017-07-10T17:48:33Z (GMT). No. of bitstreams: 1 Ricardo_A_Castilho.pdf: 317352 bytes, checksum: 5d346a654afe0e813bd22d1dea32d956 (MD5) Previous issue date: 2012-09-14 / The aim of this study was to determine the chemical and energetic composition of seven different meat and bone meals (MBM) for swine, adjustment and evaluation of the models to predict of metabolizable energy values (ME). 32 crossbreed swine were used in order to determine the ME castrated males, averaging 26.75 ± 1.45 kg initial weight, allotted in a randomized block design with eight treatments, four replicas and one animal per experimental unit. The treatments consisted of a basal diet and seven meat and bone meals, which replaced 20% of basal diet. The experimental period was done in 12 days, seven days for the animals to adapt to metabolic cages and the diets, and five days period of total, but separated collection of feces and urine, using the ferric oxide (Fe2O3) as fecal marker to define the beginning and end of the collection period. The chemical composition of the different MBM was determined. The dry matter, crude protein, ether extract, crude fiber, ash, calcium and phosphorus ranged from 92.09 to 97.25, 40.73 to 50.28, 8.68 to 12.07, 1.82 to 3.22, 31.90 to 44.66, 10.41 to 15.84 and 5.17 to 7.62%, respectively. The values of the pepsin digestibility of crude protein, NaOH 0.1 N 100 g-1 acidity and average geometric diameter ranged from 48.12 to 80.78%, 0.16 to 2.05 meq 100 g-1 and 809.00 to 1262.00 μm, respectively. The ME values of MBM ranged from 1645 to 2645 kcal kg-1. The prediction equations EM1 = -4233.58 + 0.4134EB +72P +89.62MM -159.06Ca; EM2 = 2087.49 +0.3446EB +31.82MM -189.18Ca; EM3 = 2140.13 + 0.3845EB -112.33Ca; EM4 = -346.58 +0.656EB; EM5 = 3221.27 +178.96EE -76.55MM and EM6 = 5356.45 -84.75MM, generated in this study were effective in predicting the ME from typical Brazilian MBM, calculated from their chemical composition. However, there was no validation of predict models to the values of ME from international researches of MBM / O objetivo proposto neste trabalho foi determinar a composição química e energética de diferentes farinhas de carne e ossos (FCO) para suínos, assim como ajuste e avaliação de modelos para predizer seus valores de energia metabolizável (EM) utilizando conjunto de dados independentes obtidos na literatura brasileira e internacional. Foram utilizados 32 suínos, mestiços, machos castrados, com peso médio inicial de 26,75 ± 1,45 kg, distribuídos em delineamento experimental de blocos ao acaso, com oito tratamentos, quatro repetições e um animal por unidade experimental. Os tratamentos consistiram de uma ração-referência e sete diferentes FCO, que substituíram em 20% a ração-referência. O período experimental teve duração de 12 dias, sendo sete dias de adaptação dos animais às gaiolas de metabolismo e às rações, e cinco dias de coleta de fezes e urina sendo empregada a coleta total de fezes mediante utilização de óxido férrico (Fe2O3) como marcador fecal para definir o início e o final do período de coleta. Foi determinada a composição química das diferentes FCO, e a matéria seca, proteína bruta, extrato etéreo, fibra bruta, matéria mineral, cálcio e fósforo variaram de 92,09 a 97,25; 40,73 a 50,28; 8,68 a 12,07; 1,82 a 3,22; 31,90 a 44,66; 10,41 a 15,84 e 5,17 a 7,62%, respectivamente. Os valores de digestibilidade em pepsina da proteína, acidez em NaOH 0,1 N 100 g-1 e o diâmetro geométrico médio variaram de 48,12 a 80,78%, 0,16 a 2,05 meq 100 g-1 e 809,00 a 1262,00 μm, respectivamente. Os valores de EM das FCO variaram de 1645 a 2645 kcal kg-1. As equações de predição EM1 = -4233,58 + 0,4134EB + 72PB + 89,62MM 159,06Ca; EM2 = 2087,49 + 0,3446EB + 31,82MM 189,18Ca; EM3 = 2140,13 + 0,3845EB 112,33Ca; EM4 = -346,58 + 0,656EB; EM5 = 3221,27 + 178,96EE 76,55MM e EM6 = 5356,45 84,75MM, ajustadas no presente estudo, foram válidas para predizerem os valores de energia metabolizável das farinhas de carne e ossos obtidas em pesquisas da literatura brasileira. Contudo, não houve validação dos modelos para predizer os valores de EM das FCO de pesquisas da literatura internacional

Le méthane (CH4) est un puissant gaz à effet de serre produit lors de la fermentation microbienne anaérobie des aliments dans le rumen. L’un des enjeux majeurs pour le secteur de l’élevage est de trouver des stratégies (alimentaires, génétique) pour réduire les émissions de CH4 tout en maintenant les performances animales. Les techniques de mesure de ces émissions sont coûteuses et difficilement utilisables à grande échelle sur le terrain, d’où la nécessité de trouver des alternatives de mesure ou biomarqueurs pour prédire ces émissions. Les acides gras (AG) du lait ont déjà été identifiés comme indicateurs intéressants de la méthanogenèse chez la vache laitière, mais il convient d’améliorer la précision des équations de prédiction du CH4 existantes ainsi que d'élargir leur domaine d'application à tous types de rations. L'objectif de mon travail de thèse a été de confirmer la pertinence des AG du lait comme indicateurs périphériques de la méthanogénèse chez la vache laitière avec diverses conditions nutritionnelles. Deux bases de données regroupant des données individuelles (issues d’une collaboration scientifique internationale) et moyennes (issues de la littérature) de CH4, de composition en AG du lait et d’autres performances et caractéristiques de l’animal, ainsi que des données de composition chimique des rations, ont été créées. Parallèlement, l’acquisition in vivo de données en conditions expérimentales contrôlées pour des rations mal connues ont permis d’incrémenter la base de données individuelles. Des équations de prédiction des émissions de CH4 [en g/jour, g/kg de matière sèche ingérée (MSI), et g/kg de lait] ont été développées à partir de certains AG du lait, utilisés seuls ou combinés à d’autres variables d’ingestion et de performances laitières, représentant alors des modèles complexes. Des relations entre les émissions de CH4 et la teneur de différents AG du lait (C10:0, iso C17:0 + trans-9 C16:1, iso C16:0, cis-11 C18:1, cis-15 C18:1, cis-9,cis-12 C18:2, et trans-11,cis-15 C18 :2) ont été mises en évidence, confirmant des voies métaboliques communes dans le rumen entre méthanogenèse et métabolisme lipidique. Les équations sont également liées aux types de régimes à partir desquels elles ont été développées. Les équations simples (AG du lait uniquement) sont moins précises que les complexes (erreurs résiduelles de prédiction, respectivement, de 58.6 g/jour, 2.8 g/kg MSI et 3.7 g/kg lait vs. 42.8 g/jour, 2.5 g/kg MSI et 3.3 g/kg lait). Une différence minimum de 16% de CH4 entre stratégies de réduction pourra être mise en évidence par la meilleure équation de prédiction développée. Des équations basées sur des AG bien déterminés par les méthodes infrarouges devront être testées pour évaluer, en routine et à grande échelle, de nouvelles stratégies de réduction des émissions de CH4 entérique chez la vache laitière. / Methane (CH4) is a potent greenhouse gas coming from the anaerobic microbial fermentation of the diet in the rumen. One of the main current challenge for the dairy sector is to find CH4 mitigation strategies (diets or genetics) without altering animal performance. Enteric methane measurement methods are costly and very difficult to apply on a large scale on field. Thus, there is a need to develop alternative measurement methods, such as equations based on proxies to predict CH4 emissions. Milk fatty acids (FA) have been identified as potential predictors of the methanogenesis in dairy cattle, but the prediction ability of extant published CH4 equations must be improved, and their domain of applicability must be enlarged to a wide range of diets. The objective of this PhD thesis was to confirm the potential of milk FA as proxies to predict enteric CH4 emissions in dairy cows fed a wide range of diets. Two databases (based on individual and mean data, respectively) were built thanks to an international collaboration, and gathered data on CH4, milk FA composition, dairy performances, diet and animal characteristics. Two in vivo experiments were conducted with the aim to study the effect of dietary strategies poorly documented, on methanogenesis and milk FA. The data from these experiments were included in the created database. Firstly, simple CH4 prediction equations were developed [g/d, g/kg of DMI (DMI), and g/kg of milk] based only on milk FA, and secondly other variables related to cow intake or characteristics, and dairy performance were added and constituted complex equations. Relationships between CH4 and several milk FA (C10:0, iso C17:0 + trans-9 C16:1, iso C16:0, cis-11 C18:1, cis-15 C18:1, cis-9,cis-12 C18:2, and trans-11,cis-15 C18 :2) were found, confirming common rumen metabolic pathways between methanogenesis and lipid metabolism. Equations were also closely related to the diets included in the database used for their development. Simple equations were less accurate than complex ones (prediction error of 58.6 g/d, 2.8 g/kg DMI and 3.7 g/kg milk vs 42.8 g/d, 2.5 g/kg DMI and 3.3 g/kg milk, respectively). A minimum difference of 16% in CH4 emissions between mitigating strategies can be evidenced with the best prediction equation developed in this PhD. Methane prediction equations based on milk FA well determined by infrared spectrometry methods need to be developed in order to be used on a routine basis and on a large scale. These prediction equations would allow studying the effect of novel mitigation strategies of enteric CH4 emissions in dairy cows.

Avaliar acuradamente o gasto energético de repouso (GER) é de extrema importância no suporte nutricional e a análise de composição corporal influencia seu valor. O estudo teve como objetivos desenvolver equações preditivas de GER por meio de dados de composição corporal obtidos por exame de bioimpedância eléctrica multifrequencial por espectroscopia (BIS); avaliar a adequação das fórmulas mais usuais de predição do GER; medir a correlação dos parâmetros gerados por BIS com o GER, analisar a concordância e a correlação dos dados gerados pelos aparelhos de bioimpedância de frequência simples (BIA) e BIS, além da concordância entre os métodos de classificação do estado nutricional por Índice de Massa Corporal (IMC) e por %MG (Porcentual de Massa Gorda) avaliada por BIA e BIS. Caracterizou-se como um estudo transversal observacional desenvolvido com brasileiros saudáveis, ambos os sexos, entre 20 e 40 anos de idade, estratificados em subgrupos pelo IMC (subnutrido, n=40; eutrófico, n=120; com sobrepeso, n=118 e com obesidade, n=114) e pelo %MG (baixa gordura, n=17; gordura adequada, n=101; excesso de gordura, n=91 e obesidade, n=183). O GER foi medido por calorimetria indireta (CI). Houve emprego do teste de correlação de Spearman e de Pearson e do gráfico de dispersão para avaliar as associações entre as variáveis e de modelos de regressão linear múltipla no desenvolvimento das equações, por método Stepwise. Aplicou-se o teste de BlandAltman, o coeficiente de correlação intraclasse, o teste de Wilcoxon e o coeficiente de correlação kappa para análise de concordância entre medidas e classificações e o teste de Mann-Whitney e Kruskal-Wallis para comparação entre os subgrupos (p<0,05). O GER predito foi considerado adequado quando se encontrou entre 90 e 110% do GER medido por CI. Desenvolveu-se uma equação para a amostra total por sexo e uma para cada categoria do IMC e do %MG e as mesmas apresentaram baixos valores de coeficientes de determinação (R2). As maiores correlações entre as variáveis independentes com o GER ocorreram para o peso, IMC, Massa Gorda7 (MG) e Massa de Tecido Adiposo. Todas as equações usuais avaliadas não foram capazes de predizer corretamente o GER em metade da amostra. As classificações do estado nutricional realizadas por meio do IMC e %MGBIA obtiveram concordância fraca com aquela por %MGBIS. A concordância entre BIA e BIS foi baixa: tecidos corporais de maior hidratação foram superestimados e os menos hidratados subestimados, por BIA frente a BIS, e os vieses entre os dois equipamentos foram maiores com o aumento do IMC. Assim, as equações desenvolvidas apresentaram baixo R2, impossibilitando sua aplicação no cenário clínico. Já as equações de predição do GER avaliadas exibiram baixa adequação, não se recomendando seu uso. A classificação do estado nutricional por meio do IMC subestima as quantidades de MG, sendo mais adequada a utilização de composição corporal para caracterização nutricional. BIA e BIS geram resultados distintos: o tamanho corporal aparece como um fator de confusão na distinção das massas corporais analisadas, mas, a distribuição e a quantidade de água corporal total apresentam-se como fatores limitantes de maior força / The accurate assessment of resting energy expenditure (REE) is extremely important in nutritional support for energy supply adjustment and body composition analysis plays a significant role in determining its value. The objectives of this thesis was to develop prediction equations of REE using body composition assessment data by bioelectrical impedance; assess the adequacy of the more usual prediction equations of REE against the measured value; measuring the correlation of the parameters generated by multifrequency spectroscopy bioelectrical impedance (BIS) in GER and analyze the agreement and correlation of data generated by bioelectrical impedance devices of simple frequency (BIA) and (BIS), in total sample and between subgroups stratified by body mass index (BMI) and body fat percentage (%BF), in addition to assess the agreement between the classification of nutritional state by BMI and %BF generated by BIA and BIS . This was an observational cross-sectional study with healthy Brazilians, both sexes, between 20 and 40 years old, stratified into subgroups by BMI (malnourished, n=40; eutrophic, n=120; overweight, n=118 and obese, n=114) and by %BF (low fat, n=17; suitable fat, n=101; excess fat, n=91 and obesity, n=183). There was the use of anthropometric and epidemiological parameters and those generated by BIS analysis in the equations\'s development. REE was measured by indirect calorimetry (IC). Employment Spearman correlation test and scatterplot to assess the associations between the variables and multiple linear regression models in the development of the equations. Application of Bland-Altman analysis, intraclass correlation coefficient, Wilcoxon test and kappa correlation coefficient for agreement analysis between measurements and classifications and the Mann-Whitney and Kruskal-Wallis test to compare the subgroups (p <0,05). Thus, an equation was developed for the total female sample and one for each of the last three categories of BMI and% BF, the same for males. The equations obtain low values of determination coefficient (R2). The highest correlations between the independent variables with REE occurred, for both females and males, with weight, BMI, BF and9 Adipose Tissue Mass. All the usual equations evaluated had low accuracy since none was able to correctly predict GER in 50% or more of the sample, either for the whole sample or stratified by BMI and %BF. The equations with the highest percentages of the sample within the adequacy limits were Owens, Henry-Rees and Livingston-Kohlstadt 2. The worst percentages of the sample within the adequacy limits were those of Ireton-Jones, FAO/WHO/UN 2 and Frankenfield 1. The nutritional status rankings performed through BMI and %BFBIA obtained weak agreement with that by %BFBIS, tending to classify the individual one or two levels below, underestimating the presence of %BF. The agreement between BIA and BIS was low since the equipment presented different results for all the variables, either in the total sample or in the stratified subgroups. The BIA against BIS underestimated the amounts related to the BF and total body water variables and overestimated those concerning the FFM and BCM. The biases between the two equipments were greater with the increase of BMI. Thus, the developed equations have low R2, which makes it impossible to apply them in the clinical setting. The most common and predictive GER prediction equations presented low accuracy, not proving to be adequate for use in clinical practice. The classification of nutritional status through BMI results in errors that compromise the approach of nutritional therapy, underestimating the amounts of BF and its deleterious potencies, so it is more appropriate to evaluate it through body composition. BIA and BIS generate different results, and body size appears as a confounding factor in the body mass distinction analyzed by BIA, but the distribution and amount of total body water is a limiting factor of greater strength for the BIA