scholarly journals Scots Pine and Norway Spruce Wood Properties at Sites with Different Stand Densities

Forests ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 587 ◽  
Author(s):  
Benas Šilinskas ◽  
Iveta Varnagirytė-Kabašinskienė ◽  
Marius Aleinikovas ◽  
Lina Beniušienė ◽  
Jūratė Aleinikovienė ◽  
...  

Background and Objectives: The aim of this study was to determine the effects of different stand densities on wood density (WD), global modulus of elasticity (MOE), and bending strength (MOR) in 35-year-old Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) H. Karst) stands, representing the hemiboreal forest zone. Materials and Methods: Scots pine and Norway spruce sites, representing different stand densities of 3000–3100; 2000–2100 and 1000–1100 trees per hectare, were chosen. Visually healthy model pine and spruce trees were selected, and diameter at breast height (DBH) was measured for model trees; the competition index was calculated; the MOE and MOR were evaluated by the Standards EN 408:2006 and EN 384:2016, at 12% moisture content; WD and the knot diameter were measured; and the strength class of wood was determined by the Standard EN 338:2009. To predict wood quality characteristics based on stand and tree characteristics, linear regression models were developed. Results and Conclusions: Higher stand density led to a significant change in the main wood properties of both conifer species. The highest mean WD, MOE, and MOR were obtained at the sites with the highest stand density. The MOE and MOR were highly correlated, but relatively weak correlations were found between MOE and MOR with tree DBH and WD. Despite the lower quality of Scots pine wood, the Norway spruce wood from more dense sites corresponded to the strength class of C16, according the strength grading of softwoods. The linear regression models did not perform well in describing the relationship of wood properties with stand and tree characteristics. The models for MOR accounted for the highest variation of 62–65% for both Scots pine and Norway spruce. These relationships can be expected to change with increased stand age or with the inclusion of specific crown parameters.

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.


Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.


Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 299
Author(s):  
Jaime Pinilla ◽  
Miguel Negrín

The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.


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