The knowledge of the chemical composition of invertebrates as sea cucumber contributes to improving our understanding of these living organisms. This study compared the chemical composition of wild sea cucumber Isosti...The knowledge of the chemical composition of invertebrates as sea cucumber contributes to improving our understanding of these living organisms. This study compared the chemical composition of wild sea cucumber Isostichopus sp., between February 2013 and January 2014. Sea cucumbers were captured by hand by artisanal fishermen and transported alive to the laboratory of Aquaculture of the Universidad del Magdalena (Colombia), where they were subsequently killed and taken to freeze until analysis. For proximate analysis 20 g of muscle were used for each sample. The analysis (in triplicate) was performed according to [1]. Significant differences (p Isostichopus sp. was similar to that reported for fresh sea cucumbers internationally traded, which indicates that it is a species with a competitive commercial value for use in food.展开更多
Solar radiation is one of the most important parameters for applications, development and research related to renewable energy. However, solar radiation measurements are not a simple task for several reasons. In the c...Solar radiation is one of the most important parameters for applications, development and research related to renewable energy. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly on the search for relationships between weather variables, such as temperature, humidity, precipitation, cloudiness, sunshine hours, etc. But, many of these are subjective and difficult to measure, and thus they are not always available. In this paper, we propose a method for estimating daily global solar radiation, combining empirical models and artificial neural networks. The model uses temperature, relative humidity and atmospheric pressure as the only climatic input variables. Also, this method is compared with linear regression to verify that the data have nonlinear components. The models are adjusted and validated using data from five meteorological stations in the province of Tucumán, Argentina. Results show that neural networks have better accuracy than empirical models and linear regression, obtaining on average, an error of 2.83 [MJ/m<sup>2</sup>] in the validation dataset.展开更多
文摘The knowledge of the chemical composition of invertebrates as sea cucumber contributes to improving our understanding of these living organisms. This study compared the chemical composition of wild sea cucumber Isostichopus sp., between February 2013 and January 2014. Sea cucumbers were captured by hand by artisanal fishermen and transported alive to the laboratory of Aquaculture of the Universidad del Magdalena (Colombia), where they were subsequently killed and taken to freeze until analysis. For proximate analysis 20 g of muscle were used for each sample. The analysis (in triplicate) was performed according to [1]. Significant differences (p Isostichopus sp. was similar to that reported for fresh sea cucumbers internationally traded, which indicates that it is a species with a competitive commercial value for use in food.
文摘Solar radiation is one of the most important parameters for applications, development and research related to renewable energy. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly on the search for relationships between weather variables, such as temperature, humidity, precipitation, cloudiness, sunshine hours, etc. But, many of these are subjective and difficult to measure, and thus they are not always available. In this paper, we propose a method for estimating daily global solar radiation, combining empirical models and artificial neural networks. The model uses temperature, relative humidity and atmospheric pressure as the only climatic input variables. Also, this method is compared with linear regression to verify that the data have nonlinear components. The models are adjusted and validated using data from five meteorological stations in the province of Tucumán, Argentina. Results show that neural networks have better accuracy than empirical models and linear regression, obtaining on average, an error of 2.83 [MJ/m<sup>2</sup>] in the validation dataset.
