Search

Understanding the dynamics of tree growth is extremely important to develop effective forest conservation and management strategies. Generally, tree growth is well fitted by non-linear regression models. However, it can commonly present problems caused by heteroscedasticity or possible asymmetry in the distribution of residues. An alternative to overcome this problem is quantile regression, which allows estimates at different quantiles, thus generating a more complete mapping of the development of the forest under study. The objective of this study was to compare the adjustment of the non-linear models Logístico, Gompertz, von Bertalanffy, Brody, Chapman-Richards and Weibull using the least squares method and quantile regression, for data on diameter at breast height (DBH) accumulated over the overtime for 56 trees sampled in native forest using a non-destructive technique. The coefficient of determination, the mean absolute deviation and the Akaike information criterion were used to evaluate the quality of the adjustments and the suitability of the models was verified through residual analysis, with the Brody model being the one that best adhered to the data. All computational analysis was carried out using the free software R. [ABSTRACT FROM AUTHOR]

This work presents a computational tool for Integrated Risk Management in a transmission enterprise, through data integration and analysis using Artificial Intelligence (AI), statistical and Business Intelligence (BI) methods. The life cycle phases of transmission projects defined for this work are: (1) pre-auction, preparation for concession dispute; (2) feasibility, engineering and environmental licensing studies; (3) implementation, of construction of the transmission line and substations, with execution of environmental programs; 4) operation and maintenance, for the entry of the enterprise into commercial operation. The areas of interest defined for Risk Management are: (a) Environment; (b) Land ownership; (c) Implementation Engineering (Construction); (d) O&M Engineering (Operation and Maintenance); (e) Regulatory; (f) Relationship with Investors/New Businesses. The objectives of this work are: to identify possible data sources that can be associated with transmission project risks; and analyze, through the application of several methods, how these data are correlated with each other. For this, an optimized analysis of the main causes of delays in the delivery of projects, cost increases and interruption of energy transmission is carried out and, through surveys carried out by professionals from each of the defined areas of interest, previous causes that may be mapped occur throughout the life cycle of projects. Analyzes are presented in the form of dashboards, with the aim of helping managers to make more assertive decisions. Another objective is to identify unresolved issues during the implementation phase, preventing them from having any impact on the operation phase. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciência Agronômica is the property of Revista Ciencia Agronomica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This cross-sectional study estimated associations of eating contexts (including regularity of meals, places where they occur, and if they take place with attention and in company) with food consumption by degree of industrial processing and overweight indicators in a sample of Brazilian adolescents (14-19 years old) enrolled in 29 public schools in Juiz de Fora, MG (n = 805). We used an exploratory questionnaire, which was submitted to cluster analysis. Three clusters were identified: cluster 1 (n = 572), "appropriate eating contexts at breakfast, lunch, and dinner"; cluster 2 (n = 139), "inappropriate eating context at breakfast"; and cluster 3 (n = 94) "inappropriate eating context at dinner". The evaluation of food consumption involved two 24-h dietary recalls, whose items were analyzed according to the NOVA classification system. Linear regression models were performed, using cluster 1 as reference. Clusters 2 and 3 were associated with an increase in the energy fraction from ultra-processed foods [respectively, β = 2.55% (IC 95%: 0.50; 5.05) and β = 4.18% (IC 95%: 1.21; 7.14)]; and cluster 2 was associated with a reduction in the energy fraction from unprocessed or minimally processed foods and processed culinary ingredients [β = -3.61% (IC 95%: -6.40; -0.82)]. Additionally, clusters 2 and 3 were associated with an increase of body mass index for age [respectively, β = 0.23 z-score (IC 95%: 0.01; 0.46) and β = 0.27 z-score (IC 95%: 0.02; 0.54)]; and cluster 2 was associated with an increase of body fat [β = 1.21% (IC 95%: 0.23; 2.64)]. In conclusion, inappropriate eating contexts at breakfast and dinner were associated with higher ultra-processed food intake, higher body mass index and higher body fat percentage. [ABSTRACT FROM AUTHOR]