Phylogenetic relationships were inferred for representative Bulbophyllum species of 13 sections from subtribe Bulbophyllinae (Epidendroideae, Orchidaceae) in Peninsular Malaysia. The combined data matrix consists of sequences from ITS nuclear gene region and trnL-F, matK, and rbcL plastid gene regions with 3114 characters. Molecular data were analysed using parsimony and Bayesian inference. The results show that several recognized sections are monophyletic. Section Hirtula with paraphyletic status must split up and section Desmosanthes contain misplaced elements. Furthermore, generic status of Cirrhopetalum and Epicrianthes cannot be supported, because they are deeply embedded within the genus Bulbophyllum. Section Desmosanthes is recognized as the closest group to section Cirrhopetalum; therefore, they can be merged in some aspects.

-In soccer, an attack begins with ball recovery. Therefore, the consistency of this performance indicator during a match and its balanced distribution in the field zones can be one of the distinct characteristics of successful soccer teams. This study aims to investigate the performance consistency of ball recovery during a match within several time periods (6 periods of 15 min) and zones (four zones). To this end, observational methodology and software Focus X2 were adopted to evaluate 28 matches of semi-final teams at FIFA 2014 including Germany, Argentina, Netherlands, and Brazil in terms of ball recovery frequency. In total, 3222 performances were recorded. All teams in each match and in whole competition had homogeneity of distribution of ball recovery during the time periods (χ =37.53, p=0.001, respectively). Most ball recoveries were made in the defensive and middle-defensive zones in accordance with modern soccer. It was found that for a soccer team to be successful, it requires a space distribution of experienced players in the field, which leads to power balance for redesigning a team to be offensive in all zones.Key words: Soccer; Sports; Time. Resumo -No futebol, um ataque começa com recuperação de bola. Por isso, a consistência desse indicador de desempenho pode ser uma das características distintas para o sucesso das equipes de futebol. Este estudo tem como objetivo investigar a consistência da recuperação da bola no desempenho durante um jogo dentro de vários períodos de tempo (6 períodos de 15 min) e zonas (quatro zonas

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Soil depth is a major soil characteristic, which is commonly used in distributed hydrological modelling in order to present watershed subsurface attributes. This study aims at developing a statistical model for predicting the spatial pattern of soil depth over the mountainous watershed from environmental variables derived from a digital elevation model (DEM) and remote sensing data. Among the explanatory variables used in the models, seven are derived from a 10 m resolution DEM, namely specific catchment area, wetness index, aspect, slope, plan curvature, elevation and sediment transport index. Three variables landuse, NDVI and pca1 are derived from Landsat8 imagery, and are used for predicting soil depth by the models. Soil attributes, soil moisture, topographic curvature, training samples for each landuse and major vegetation types are considered at 429 profiles within four subwatersheds. Random forests (RF), support vector machine (SVM) and artificial neural network (ANN) are used to predict soil depth using the explanatory variables. The models are run using 336 data points in the calibration dataset with all 31 explanatory variables, and soil depth as the response of the models. Mean decrease permutation accuracy is performed on Variable selection. Testing dataset is done with the model soil depth values at testing locations (93 points) using different efficiency criteria. Prediction error is computed for both the calibration and testing datasets. Results show that the variables landuse, specific surface area, slope, pca1, NDVI and aspect are the most important explanatory variables in predicting soil depth. RF and SVM models are appropriate for the mountainous watershed areas that have been limited in the depth of the soil and ANN model is more suitable for watershed with the fields of agricultural and deep soil depth.

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