Characterization of flours from the aroeira leaf (Schinus terebinthifolius Raddi), obtained by different drying methods.

The present work aimed at the development and characterization of aroeira leaf flour (Schinus terebinthifolius Raddi), obtained by lyophilization and drying in an air circulation oven. The technological, physical, physico-chemical, morphological, functional, and microbiological aspects were analyzed. Physico-chemical analysis identified the following properties with values provided respectively for fresh leaves (FOin) and flours (FES and FLIO): low water activity (0.984, 0.370, 0.387 g/100 g), moisture (64.52, 5.37, 7.97 g /100 g), ash (2.69, 6.51, and 6.89 g/100 g), pH (0.89, 4.45, 4.48 g/100 g), lipids (0.84, 1.67, 5.23 g/100 g), protein (3.29, 8.23, 14.12 g/100 g), carbohydrates (17.02, 53.12, 33.02 g/100 g), ascorbic acid (19.70, 34.20, 36.90 mg/100 g). Sources of fiber from plant leaves and flours (11.64, 25.1, 32.89 g/100 g) showed increased levels of luminosity. For NMR, the presence of aliphatic and aromatic compounds with olefinic hydrogens and a derivative of gallic acid were detected. The most abundant minerals detected were potassium and calcium. Micrographs identified the presence of irregular, non-uniform, and sponge-like particles. The main sugars detected were: fructose, glucose, and maltose. Malic, succinic, citric, lactic, and formic acids were found. Fifteen phenolic compounds were identified in the samples, highlighting: kaempferol, catechin, and caffeic acid. The values ​​found for phenolics were (447, 716.66, 493.31 mg EAG/100 g), flavonoids (267.60, 267.60, 286.26 EC/100 g). Antioxidant activity was higher using the ABTS method rather than FRAP for analysis of FOin, FES, and FLIO. Since the flours of the aroeira leaf have an abundant matrix of nutrients with bioactive properties and antioxidant activity, they have a potential for technological and functional use when added to food.

Optimizing forest road planning in a sustainable forest management area in the Brazilian Amazon.

The construction of forest roads in Brazilian Amazon is costly and has a significant environmental impact. Several practices and principles must be observed to comply with legislation, to preserve the remaining forest, and to ensure sustainable exploitation. Road planning is complex in this context, based on the number of aspects and variables that must be considered. This research aimed to evaluate computational methods' effectiveness in planning forest roads, optimizing resources to reduce damage to the remaining forest, compared to traditional planning methods. The study area was a native forest under a sustainable forest management regime located in municipalities of Terra Santa and Oriximiná, in Pará, in Brazilian Amazon. Data obtained from area made it possible formulate six instances of different sizes. A binary integer linear programming model was used, solved using CPLEX software, and Dijkstra, Bellman-Ford, Dial, and D'Esopo-Pape shortest path algorithm, implemented in C programming language. During processing of instances, the time taken to obtain the solution increased according to size of instance, however, time difference was not significant. Among the evaluated algorithms, the D'Esopo-Pape algorithm showed the best performance. The evaluated methods were effective in obtaining an optimal solution for proposed forest road planning. The solutions obtained using computational methods more effectively considered the restrictions associated with sustainable forest management, in contrast to those derived from the traditional planning by forestry company.

Metaheuristics applied for storage yards allocation in an Amazonian sustainable forest management area.

In the sustainable management of Amazonian forests, it is essential to carry out the optimal planning of logging infrastructures to reduce costs and environmental impacts. However, there is a high degree of complexity due to the number of variables involved. Among these infrastructures, wood storage yards are of utmost importance as they directly influence the opening of forest roads and trails. The objective of this research was to evaluate the allocation of wood storage yards through exact solution and metaheuristics in a forest management area. The study area was a native forest under sustainable forest management regime located in the Brazilian Amazon. Three instances were formulated involving 5947 trees and 3172 wood storage yards facilities. We used a binary integer programming model solved by CPLEX and the metaheuristics Greedy Randomized Adaptive Search Procedure (GRASP), Tabu Search (TS), Variable Neighborhood Search (VNS) and Simulated Annealing (SA). GAP values increased as a function of instances. Although all metaheuristics obtained significant solutions with shorter processing times, only SA obtained feasible solutions in all executions for all three instances. In general, the metaheuristics were efficient in obtaining feasible solutions faster than CPLEX, which represents the feasibility of the planning of allocation storage large areas, and without significant losses of best-known solution. The SA presented the best performance in the three evaluated instances. Contribution of this study can be highlighted: evaluation of alternative computational methods for planning the allocation of wooden storage yards; evidence was obtained of effectiveness and efficiency of assessed metaheuristics and, the applicability of approximate methods in this problem was evaluated.

Computational techniques applied to volume and biomass estimation of trees in Brazilian savanna.

The Brazilian Savannah, known as Cerrado, has the richest flora in the world among the savannas, with a high degree of endemic species. Despite the global ecological importance of the Cerrado, there are few studies focused on the modeling of the volume and biomass of this forest formation. Volume and biomass estimation can be performed using allometric models, artificial intelligence (AI) techniques and mixed regression models. Thus, the aim of this work was to evaluate the use of AI techniques and mixed models to estimate the volume and biomass of individual trees in vegetation of Brazilian central savanna. Numerical variables (diameter at height of 1.30 m of ground, total height, volume and biomass) and categorical variables (species) were used for the training and fitting of AI techniques and mixed models, respectively. The statistical indicators used to evaluate the training and the adjustment were the correlation coefficient, bias and Root mean square error relative. In addition, graphs were elaborated as complementary analysis. The results obtained by the statistical indicators and the graphical analysis show the great potential of AI techniques and mixed models in the estimation of volume and biomass of individual trees in Brazilian savanna vegetation. In addition, the proposed methodologies can be adapted to other biomes, forest typologies and variables of interest.