A Red Pine Bark Factor Equation for Michigan

Abstract A multiple linear regression equation was developed to predict bark factor for red pine in Michigan as a function of tree height. The equation was validated on independent data sets. The prediction equation yielded average relative errors less than ± 1% at all tree heights. Procedures are described for using the bark factor equation to estimate diameter inside bark from diameter outside bark and vice versa at any tree height. Specific uses of the prediction equation are also discussed. North. J. Appl. For. 5:28-30, March 1988.

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A Jack Pine Bark Factor Equation for Michigan

Northern Journal of Applied Forestry ◽

10.1093/njaf/10.2.86 ◽

1993 ◽

Vol 10 (2) ◽

pp. 86-89 ◽

Cited By ~ 1

Author(s):

Gary W. Fowler

Keyword(s):

Tree Height ◽

Prediction Equation ◽

Jack Pine ◽

Data Sets ◽

Independent Data ◽

Linear Regression Equation ◽

Breast Height ◽

Relative Errors ◽

New Equation ◽

Factor Equation

Abstract A multiple linear regression equation was developed to predict bark factor for jack pine in Michigan as a function of tree height. The equation was validated on independent data sets. The prediction equation yielded average relative errors from -2.9 to 0.4% for all tree heights above stump height. At stump height the average relative errors varied from -5.3 to -2.3%. The jack pine equation was compared with red pine and aspen bark factor equations. The new equation can be used to more accurately estimate tree and log wood volumes than when using a constant bark factor determined at breast height, which, in general leads to underestimates of wood volume. North. J. Appl. For. 10(2):86-89.

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An Aspen Bark Factor Equation for Michigan

Northern Journal of Applied Forestry ◽

10.1093/njaf/8.1.12 ◽

1991 ◽

Vol 8 (1) ◽

pp. 12-15

Author(s):

Gary W. Fowler

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Tree Height ◽

Prediction Equation ◽

Regression Equation ◽

Linear Regression Equation ◽

Trembling Aspen ◽

Breast Height ◽

Relative Errors ◽

Factor Equation

Abstract A multiple linear regression equation was developed to predict bark factor for aspen in Michigan as a function of tree height. Bark factors for bigtooth aspen were, in general, somewhat larger than bark factors for trembling aspen. Even though equations were developed for both species, the differences between the two equations were small, and not statistically significant, and a pooled equation based on both species is recommended. The pooled prediction equation yielded average relative errors from - 2.3 to 0.87% and - 1.02 to 3.83% at all tree heights for bigtooth and trembling aspen, respectively. For more accurate predictions of bark factor, the separate prediction equations for bigtooth and trembling aspen should be used. The new equations can be used to more accurately estimate tree and log wood volumes than when using a constant bark factor determined at breast height, which, in general, leads to underestimates of wood volumes. North. J. Appl. For 8(1):12-15.

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Individual Tree Volume Equations for Red Pine in Michigan

Northern Journal of Applied Forestry ◽

10.1093/njaf/14.2.53 ◽

1997 ◽

Vol 14 (2) ◽

pp. 53-58 ◽

Cited By ~ 5

Author(s):

Gary W. Fowler

Keyword(s):

Red Pine ◽

Data Sets ◽

Regression Equations ◽

Independent Data ◽

Data Set ◽

Individual Tree ◽

Standing Trees ◽

Pine Stands ◽

Volume Estimates ◽

Volume Equations

Abstract New total, pulpwood, sawtimber, and residual pulpwood cubic foot individual tree volume equations were developed for red pine in Michigan using nonlinear and multiple linear regression. Equations were also developed for Doyle, International 1/4 in., and Scribner bd ft volume, and a procedure for estimating pulpwood and residual pulpwood rough cord volumes from the appropriate cubic foot equations was described. Average ratios of residual pulpwood (i.e., topwood, cubic foot or cords) to mbf were developed for 7.6 and 9.6 in. sawtimber. Data used to develop these equations were collected during May-August 1983-1985 from 3,507 felled and/or standing trees from 27 stands in Michigan. Sixteen and 11 stands were located in the Upper and Lower Peninsulas, respectively. All equations were validated on an independent data set. Rough cord volume estimates based on the new pulpwood equation were compared with contemporary tables for 2 small cruise data sets. The new equations can be used to more accurately estimate total volume and volume per acre when cruising red pine stands. North. J. Appl. For. 14(2):53-58.

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The linear regression equation for pressurised metered-dose inhaler: Using canister weight to predict balance actuation in asthma inhaler

10.21203/rs.3.rs-92960/v1 ◽

2020 ◽

Author(s):

Gobi Hariyanayagam Gunasekaran ◽

Syazwan Faiz b. Kamal Al Arif ◽

Shargunan Selvanthan Gunasekaran ◽

Sera Selvanthan Sundram Gunasekaran

Keyword(s):

Linear Regression ◽

Prediction Equation ◽

Regression Equation ◽

Dose Inhaler ◽

Linear Regression Equation ◽

Metered Dose Inhalers ◽

Active Ingredients ◽

Metered Dose ◽

Data Points ◽

Best Fit

Abstract INTRODUCTION Pressurised metered-dose inhalers (pMDIs) are not equipped with dose counters hence the balance actuation in a canister could not be determined. Each actuation expels a considerable amount of active ingredients and excipients from a canister, thus the balance actuation remaining in a pMDI based on canister weight could be evaluated using a linear regression equation.METHODOLOGY New pMDIs of 5 active ingredients [salbutamol (GSK) 200 actuation, budesonide (Glenmark) 300 actuation, ipratropium/fenoterol (Boehringer) 200 actuation, fluticasone (GSK/innovator) 120 actuation, fluticasone (Cipla/generic) 120 actuation, and beclometasone (Ivax) 200 actuation] was weighted . using a laboratory scale (Sartorius R200D; 0.01g accuracy). Two of each pMDI were weighed after each actuation, with a 30-second inter-puff interval, and the mean weight was recorded. To minimise variability in measurements, weighing was limited to one operator. The canister was considered empty when there were no changes in weight after repeated actuation. The prediction equation (one for each pMDI) was the line of best fit through data points on the scatter plot of the number of actuations versus weight.RESULTS AND DISCUSSION There was low variability between pMDIs weights (SD: 0.03g-0.08g)] of the same active ingredients indicating manufacturing uniformity among canisters. Prediction equations were generated for each type of active ingredients, where the general equation is: Actuation remaining = Constant + β*pMDI weight.CONCLUSION This study produced a prediction equation that can be used to estimate remaining actuation in a pMDI based on its weight. Weighing medication canister could be used to measure actuation remaining in pMDIs, as well as patients’ adherence to pMDIs.

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Pear Scab: Components of Potential Ascospore Dose and Validation of an Ascospore Maturity Model

Plant Disease ◽

10.1094/pdis.2000.84.6.681 ◽

2000 ◽

Vol 84 (6) ◽

pp. 681-683 ◽

Cited By ~ 6

Author(s):

Robert A. Spotts ◽

Louis A. Cervantes ◽

Franz J. A. Niederholzer

Keyword(s):

Linear Regression ◽

Leaf Litter ◽

Original Model ◽

Infected Leaf ◽

Data Sets ◽

Maturity Model ◽

Linear Regression Equation ◽

Degree Days ◽

Pear Scab ◽

Venturia Pirina

Variables necessary to calculate potential ascospore dose (PAD) for Venturia pirina, the causal agent of pear scab, were measured: area of individual leaves averaged 20.0 cm2, number of lesions per infected leaf ranged from 1.8 to 2.2, 13.5 to 41.7% of lesions were fertile (contained pseudothecia), number of pseudothecia per fertile lesion ranged from 15.2 to 24.4, and number of asci per pseudothecium averaged 131.5. The proportions of orchard floor covered with leaves (leaf litter density) were 0.34 and 0.67 for mowed and nonmowed orchards, respectively. Incidence of infected leaves ranged from 0.1 to 32.3%. Calculated PAD ranged from 1,114 to 708,320 ascospores per m2 in a commercial and an unsprayed orchard, respectively. For validation of a previously published model relating ascospore maturity to degree-days with a 0°C base, a linear regression equation was developed using 11 data sets collected between 1981 and 1998. The slope of the line was not different (P = 0.951) from that of the original model.

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The visual arrays task…visual storage capacity or attention control?

10.31234/osf.io/u92cm ◽

2019 ◽

Cited By ~ 1

Author(s):

Jessie Martin ◽

Jason S. Tsukahara ◽

Christopher Draheim ◽

Zach Shipstead ◽

Cody Mashburn ◽

...

Keyword(s):

Working Memory ◽

Storage Capacity ◽

Working Memory Capacity ◽

Memory Capacity ◽

Attention Control ◽

Data Sets ◽

Independent Data ◽

Visual Storage ◽

Design Selection ◽

The Relationship

**The uploaded manuscript is still in preparation** In this study, we tested the relationship between visual arrays tasks and working memory capacity and attention control. Specifically, we tested whether task design (selection or non-selection demands) impacted the relationship between visual arrays measures and constructs of working memory capacity and attention control. Using analyses from 4 independent data sets we showed that the degree to which visual arrays measures rely on selection influences the degree to which they reflect domain-general attention control.

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Risk Scoring System of Mortality and Prediction Model of Hospital Stay for Critically Ill Patients Receiving Parenteral Nutrition

Healthcare ◽

10.3390/healthcare9070853 ◽

2021 ◽

Vol 9 (7) ◽

pp. 853

Author(s):

Jee-Yun Kim ◽

Jeong Yee ◽

Tae-Im Park ◽

So-Youn Shin ◽

Man-Ho Ha ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Linear Regression ◽

Parenteral Nutrition ◽

Prediction Model ◽

Scoring System ◽

Prediction Equation ◽

Icu Patients ◽

Risk Scoring ◽

Risk Scoring System

Predicting the clinical progression of intensive care unit (ICU) patients is crucial for survival and prognosis. Therefore, this retrospective study aimed to develop the risk scoring system of mortality and the prediction model of ICU length of stay (LOS) among patients admitted to the ICU. Data from ICU patients aged at least 18 years who received parenteral nutrition support for ≥50% of the daily calorie requirement from February 2014 to January 2018 were collected. In-hospital mortality and log-transformed LOS were analyzed by logistic regression and linear regression, respectively. For calculating risk scores, each coefficient was obtained based on regression model. Of 445 patients, 97 patients died in the ICU; the observed mortality rate was 21.8%. Using logistic regression analysis, APACHE II score (15–29: 1 point, 30 or higher: 2 points), qSOFA score ≥ 2 (2 points), serum albumin level < 3.4 g/dL (1 point), and infectious or respiratory disease (1 point) were incorporated into risk scoring system for mortality; patients with 0, 1, 2–4, and 5–6 points had approximately 10%, 20%, 40%, and 65% risk of death. For LOS, linear regression analysis showed the following prediction equation: log(LOS) = 0.01 × (APACHE II) + 0.04 × (total bilirubin) − 0.09 × (admission diagnosis of gastrointestinal disease or injury, poisoning, or other external cause) + 0.970. Our study provides the mortality risk score and LOS prediction equation. It could help clinicians to identify those at risk and optimize ICU management.

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Predicting in Vivo Soft Tissue Masses of the Lower Extremity Using Segment Anthropometric Measures and DXA

Journal of Applied Biomechanics ◽

10.1123/jab.21.4.371 ◽

2005 ◽

Vol 21 (4) ◽

pp. 371-382 ◽

Cited By ~ 22

Author(s):

Jeffrey D. Holmes ◽

David M. Andrews ◽

Jennifer L. Durkin ◽

James J. Dowling

Keyword(s):

Soft Tissue ◽

Lower Extremity ◽

Prediction Equation ◽

Regression Equations ◽

Anthropometric Measures ◽

Soft Tissue Masses ◽

Highly Correlated ◽

Relative Errors ◽

Dxa Scans

The purpose of this study was to derive and validate regression equations for the prediction of fat mass (FM), lean mass (LM), wobbling mass (WM), and bone mineral content (BMC) of the thigh, leg, and leg + foot segments of living people from easily measured segmental anthropometric measures. The segment masses of 68 university-age participants (26 M, 42 F) were obtained from full-body dual photon x-ray absorptiometry (DXA) scans, and were used as the criterion values against which predicted masses were compared. Comprehensive anthropometric measures (6 lengths, 6 circumferences, 8 breadths, 4 skinfolds) were taken bilaterally for the thigh and leg for each person. Stepwise multiple linear regression was used to derive a prediction equation for each mass type and segment. Prediction equations exhibited high adjustedR2values in general (0.673 to 0.925), with higher correlations evident for the LM and WM equations than for FM and BMC. Predicted (equations) and measured (DXA) segment LM and WM were also found to be highly correlated (R2= 0.85 to 0.96), and FM and BMC to a lesser extent (R2= 0.49 to 0.78). Relative errors between predicted and measured masses ranged between 0.7% and –11.3% for all those in the validation sample (n= 16). These results on university-age men and women are encouraging and suggest that in vivo estimates of the soft tissue masses of the lower extremity can be made fairly accurately from simple segmental anthropometric measures.

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