We studied the ontogenetic growth of goat wethers (castrated male goats) of the Saanen and Swiss Alpine breeds based on a large range of intraspecific body mass (BM). The body parts and the chemical constituents of the empty body were described by the allometric function by using BM and the empty body mass (EBM) as the predictors for morphological traits and chemical composition, respectively. We fitted the allometric scaling function by applying the SAS NLMIXED procedure, but to evaluate assumptions regarding variances in morphological and compositional traits, we combined the scaling function with homoscedastic (MOD1), and the heteroscedastic exponential (MOD2) and power-of-the-mean (MOD3) variance functions. We also predicted the ontogenetic growth by using the traditional log-log transformation and back-transformed results into the arithmetic scale (MOD4). We obtained predictions from MOD4 in the arithmetic scale by a two-step process, and evaluated MOD1, MOD2 and MOD3 by a model selection framework, and compared MOD4 with MOD1, MOD2 and MOD3 based on goodness-of-fit measures. Based on information criteria for model selection, heterogeneous variance functions were more likely to describe 10 over 36 traits with a low level of model selection uncertainty. One trait was predicted by averaging the MOD1 and MOD2 variance functions; and nine traits were better described by averaging the MOD2 and MOD3 variance functions. The predictions for other 16 traits were averaged from MOD1, MOD2 and MOD3. However, MOD4 better described 11 traits according to the goodness-of-fit measures. Depending on the variable being analyzed, the body parts and the chemical amounts exhibited the three types of allometric behavior with respect to BM and EBM, that is, positive, negative and isometric ontogenetic growth. Reference BMs, that is, 20, 27, 35 and 45 kg, were used to compute the net protein and energy requirements based on the first derivative of the scaling function, and the results were presented in reference to the EBM and EBM0.75. Both the net protein and energy requirements scaled to EBM0.75 increased from 20 to 45 kg of BM.

A estimativa da composição quimíco-bromatológica dos alimentos envolve uma série de estudos que avaliam principalmente a fração fibrosa, uma vez que apresenta grande variabilidade quando comparada aos demais componentes. O crescente número de publicações sobre as técnicas analíticas disponiveís para a determinação do valor nutritivo dos alimentos acabam generalizando o uso de termos mal definidos. Existem diversas técnicas de avaliação dos componentes da forragem, sendo o sistema de detergente o mais utilizado, embora existam métodos mais modernos. Contudo, a acessibilidade e os custos destes métodos modermos são fatores que limitam o uso dos mesmos em diversos laboratórios. Ademais, alguns destes métodos não são reconhecidos como método oficial de análise. Nexte contexto, os objetivos desta revisão foram: evidenciar os conceitos mais importantes na determinação do valor nutrivo dos alimentos; melhorar o uso e as dificuldades de interpretação dos resultados analíticos. Concluiu-se que o uso dos métodos analíticos permite estimar a composição e a disponibilidade das diferentes frações da parede celular. Mas variabilidade dos constituintes da parede celular exige conhecimento das diferentes metodologias analíticas disponíveis. Os métodos analíticos, tradicionais ou alternativos, ainda são empíricos visto que apresentam resultados distintos para uma mesma análise. Essas variações são geradas na maioria das vezes pelas diferenças entre as etapas dos procedimentos analíticos. Fica claro que o aperfeiçoamento dos métodos analíticos é de suma importante para estimativa do valor nutritivo dos alimentos.

Well logging records the physical properties of geological formations and the fluids traversed by the wells. This operation is interested in parameters such as lithology, hydrocarbon presence, permeability, porosity, and fluid saturation. Generally, oil reservoirs are sandstone or carbonate rocks, and the latter’s characterization is a critical question in the petrophysical properties distribution, mainly permeability. Estimating permeability is a complex task due to the heterogeneity of these reservoirs. Therefore, this work used conventional logs to estimate the permeability of wells A03 and A10, both belonging to the oilfield A, Campos Basin, Southeastern Brazil. Alongside the logs, permeability measured in the laboratory in rock samples was used to validate the achieved estimates. Thus, the estimates used basic logs as input and approaches such as Timur empirical equation, multilinear regression, and machine learning techniques, like fuzzy logic, neural network, and decision tree. Pearson's coefficient of determination R was used as the comparison metric with the experimental data. The number of samples in training was 70%, with 15% in the validation and testing, and the results show that the first four estimates showed bad fits (R£0.60), while the decision tree showed good fits (R>0.60). This approach also showed that the gamma-ray and resistivity logs are the ones that have the most significant weight in the estimates.