Pregled utrobnjača (Basidiomycota, Fungi) Hrvatske

A survey of the gasteral Basidiomycota of Croatia is given. 68 species belonging to 26 genera are presented. Five genera and 18 species are reported as new to Croatia. For each species, the published and unpublished sources of data are given, as well as the collections in which the material is deposited.Dat je pregled gljiva utrobnjača Hrvatske. Sadrži 68 vrsta iz 26 rodova. Pet rodova i 18 vrsta prvi je put publicirano za područje Hrvatske. Uz svaku vrstu navedeni su publicirani i nepublicirani izvori podataka, kao i zbirke u kojima je pohranjen sakupljeni materijal

Pregled utrobnjača (Basidiomycota, Fungi) Hrvatske

A survey of the gasteral Basidiomycota of Croatia is given. 68 species belonging to 26 genera are presented. Five genera and 18 species are reported as new to Croatia. For each species, the published and unpublished sources of data are given, as well as the collections in which the material is deposited.Dat je pregled gljiva utrobnjača Hrvatske. Sadrži 68 vrsta iz 26 rodova. Pet rodova i 18 vrsta prvi je put publicirano za područje Hrvatske. Uz svaku vrstu navedeni su publicirani i nepublicirani izvori podataka, kao i zbirke u kojima je pohranjen sakupljeni materijal

Artificial neural network data analysis for classification of soils based on their radionuclide content

The artificial neural network (ANN) data analysis method was used to recognize and classify soils of an unknown geographic origin. A total of 103 soil samples were differentiated into classes according to the regions in Serbia and Montenegro from which they were collected. Their radionuclide (Ra-226, U-238, U-235, K-40, Cs-134, Cs-137, Th-232, and Be-7) activities detected by gamma-ray spectrometry were then used as inputs to ANN. Five different training algorithms with different numbers of samples in training sets were tested and compared in order to find the one with the minimum root mean square error (RMSE). The best predictive power for the classification of soils from the fifteen regions was achieved using a network with seven hidden layer nodes and 2500 training epochs using the online back-propagation randomized training algorithm. With the optimized ANN, most soil samples not included in the ANN training data set were correctly classified at an average rate of 92%

Modelo matemático para estimativa da temperatura média diária do ar no Estado de Goiás Mathematical model for estimating daily average air temperature in Goiás, Brazil

O objetivo deste trabalho foi desenvolver um modelo matemático de estimativa da temperatura média diária do ar no Estado de Goiás, que considera simultaneamente as variações espacial e temporal. O modelo foi desenvolvido por meio de uma combinação linear da altitude, latitude, longitude e da série trigonométrica de Fourier incompleta usando os três primeiros coeficientes harmônicos. Os parâmetros do modelo foram ajustados aos dados de 21 estações meteorológicas, por meio de regressão linear múltipla. O coeficiente de correlação resultante do ajuste do modelo foi de 0,91, e o índice de concordância de Willmott foi igual a 1. O modelo foi testado com os dados de três estações de altitudes diferentes: elevada (1.100 m), média (554 m) e baixa (431 m). O desempenho foi considerado mediano para altitudes baixas e elevadas, e muito bom para altitudes médias.<br>The objective of this work was to develop a mathematical model to predict the daily average of air temperature in Goiás, Brazil. The model was developed through a linear combination of altitude, latitude, longitude, and the incomplete trigonometric Fourier series using the first three harmonic coefficients. The parameters of the model were adjusted with data from 21 weather stations, using multiple linear regression. The resulting correlation coefficient of the model was 0.91, and the Willmott's index of agreement was close to 1. The model was tested with data from three additional weather stations at different altitudes: high (1,100 m), medium (554 m), and low (431 m). The performance of the model was reasonable for both high and low altitude stations, and very good for the medium altitude station