The predictability of modulus of elasticity (MOE), modulus of rupture (MOR) and density of 120 samples of Scots pine (Pinus sylvestrisL.) were investigated using various non-destructive variables (such as time of flight of stress wave, natural frequency of longitudinal vibration, penetration depth, pullout resistance, visual grading and concentrated knot diameter ratio), and based on multivariate algorithms, applying WEKA as machine learning software. The algorithms used were: multivariate linear regression (MLR), Gaussian, Lazy, artificial neural network (ANN), Rules and decision Tree. The models were quantified based on the root-mean-square error (RMSE) and the coefficient of determination (R2). To avoid model overfitting, the modeling was built and the results validated via the so-called 10-fold cross-validation. MLR with the “greedy method” for variable selection based on the Akaike information metric (MLRak) significantly reduced the RMSE of MOR and MOE compared to univariate linear regressions (ULR). However, this reduction was not significant for density prediction. The predictability of MLRak was not improved by any other of the tested algorithms. Specifically, non-linear models, such as multilayer perceptron, did not contribute any significant improvements over linear models. Finally, MLRak models were simplified by discarding the variables that produce the lowest RMSE increment. The resulted models could be even further simplified without significant RMSE increment.

The main aim is to test the influence of anatomical structure (grain direction and elements size), wood hardness and machining conditions on wood surface roughness. 180 samples defect-free were obtained from beech, oak and pine and processed with different machining methods (planning, sanding with 60 grit or sanding with 180 grit). Roughness, hardness, and anatomical structure were analysed using international methodologies. An analysis of variance of the data from all the samples with the four factors in the experimental design were performed. Results showed that machining processes and species are the factors that significantly affect surface roughness, as opposed to grain direction (plane of section and stylus-grain angle), which was only shown to be significant in some subgroups. Roughness parameters of samples sanded with 180 grit were lower in contrast to samples planned or sanded with 60 grit. Hardness was found to be the property of the wood that most clearly affects its final roughness, and makes it difficult to achieve better roughness results as the hardness increases.

Measurement errors in the use of smartphones as lowcost forestry hypsometersVillasante A., Fernandez C. (2014). Measurement errors in the use of smartphones as low-cost forestry hypsometers. Silva Fennica vol. 48 no. 5 article id 1114. 11 p. Highlights• We analysed two smartphones (HTC Desire and Samsung Galaxy Note) to determine the errors in the height measurements.• The calibration included with the Android applications is insufficient.• After appropriate calibration, the smartphone errors are similar to other forest hypsometers (Blume Leiss and Vertex). AbstractVarious applications currently available for Android allow the estimation of tree heights by using the 3D accelerometer on smartphones. Some make the estimation using the image on the screen, while in others, by pointing with the edges of the terminal. The present study establishes the measurement errors obtained with HTC Desire and Samsung Galaxy Note compared to those from Blume Leiss and Vertex IV. Six series of 12 measurements each were made with each hypsometer (for heights of 6 m, 8 m, 10 m and 12 m). A Kruskall Wallis test is applied to the relative errors to determine whether there are significant differences between the devices. The results indicate that the errors of the uncalibrated smartphones significantly exceed those of traditional forestry apparatus. However, calibration is a very easy procedure that can be done by means of a linear regression line between real angles (obtained with a Digital Angle Finder or with a series of measurements taken independently of the experiment), and the angles of the accelerometer. With this adjustment, the smartphones achieve adequate quality levels although the bias was not totally eliminated. The relative errors when pointing with the edges of the terminal show no significant differences compared to Blume Leiss. Applications that use the screen image give better results (no significant differences were detected with Vertex). There is currently no application that offers calibration of the linear regression slope, which is an essential requirement for ensuring the accuracy of height measurements obtained with smartphones.

The potential for using smartphones extends to forest inventory. Several applications allow the estimation of basal areas, transforming the device into a low-cost virtual relascope. The accuracy of the Bitterlich relascope application was analysed using four low-performance devices. The results were compared with those of a Spiegel Relaskop. The measurements were taken with artificial targets; this increased the discrimination of the results in comparison with tests made by counting trees. To check the accuracy in real field conditions, the Bitterlich relascope application was compared to a Spiegel Relaskop. No significant differences in accuracy were found between the data obtained by the traditional relascope and by the Android devices, neither for the artificial target nor in field conditions. The obtained biases were also similar between the Spiegel Relaskop and the devices tested. To achieve high-quality measurements with the smartphones a realistic calibration, resembling forest inventory is needed.

Aim of study: To determine the effect on wood from Pinus sylvestris of treatment with preservatives on mechanical properties and to establish the relation between the penetration and compression strength.Area of study: SpainMaterial and Methods: 40 samples of defect-free wood from Pinus sylvestris L. were treated with Light Organic Solvent Preservative (Vacsol Azure WR 2601) and 50 with waterborne Copper Azole (Tanalith E 3492). 40 control samples were not treated (water or preservative). Mechanical resistance to static bending, modulus of elasticity and compression strength parallel to the grain were compared with untreated wood. Regression analysis between the penetration and compression strength parallel was done with the samples treated with waterborne preservative.Main results: The results indicate that the treated wood (with either product) presents a statistically significant increase in mechanical resistance in all three mechanical characteristics. The results obtained differ from earlier studies carried out by other authors.There was no correlation between parallel compression strength and the degree of impregnation of the wood with waterborne Copper Azole . The most probable explanation for these results concerns changes in pressure during treatment.The use of untreated control samples instead of samples treated only with water is more likely to produce significant results in the mechanical resistance studies.Research highlights: Treated wood presents a statistically significant increase in MOE, modulus of rupture to static bending  and parallel compression strength.There was no correlation between parallel compression strength and the degree of impregnation with waterborne preservative.Keywords: Light Organic Solvent Preservative; MOE; parallel compression; static bending; waterborne Copper Azole; wood technology.