MODELING OF FORWARDER PRODUCTIVITY AND COSTS IN THINNED PINE STANDS

The aim of this study was to assess the influence of some operational variables on the forwarder productivity and production cost in thinned Pinus taeda L. stands by means of mathematical modeling. This study was carried out in a forest company located at Quedas do Iguaçu, state of Paraná, Brazil. Two stands at 9 and 10 years old from sites with high productivity and similar soil and relief features were studied. A time-motion study was applied to determine the operational cycle time, operational efficiency, productivity, and production costs. By means of mathematical modeling, we verified the influence of the variables: age of stand; cycle time; load volume; and extraction distance on the forwarder productivity and production costs. Models were fitted for estimating the forwarder productivity using cycle time, load volume, and extraction distance. Thus, we obtained the determination adjusted coefficients of 0.88 and 0.94, with an estimate standard error between 6.9% and 13.5%. Models for estimating production cost through the load volume variable presented a determination coefficient of 0.64 and 0.86, with an estimate standard error of 23.1% and 26.7%. Such results have shown the possibility of using mathematical models to estimate the performance of forest machines as a tool for planning the timber harvesting operations.

Biomass and carbon stock from Pinus caribaea var. hondurensis under homogenous stands in southwest Bahia, Brazil

ABSTRACT: There is a large number of studies evaluating methods to quantify biomass for the genus Pinus in different regions of Brazil. However, knowledge about this subject in the Northeast region of Brazil is still incipient. The objective of the present study was to assess the biomass and carbon stocks and select mathematical models to estimate these variables in Pinus caribaea var. hondurensis , which is established in homogenous stands in the Southwest region of the state of Bahia (Brazil). The biomass was quantified using the destructive method. Samples of needles, bole, bark, and branches were analyzed to determine their carbon contents. Ten models were tested, and the best of them were chosen based on the following statistical indicators: adjusted determination coefficient, estimate standard error, maximum likelihood logarithm, and graphical analysis of waste. Values for total biomass and carbon stocks were 69 and 42Mg ha-1, respectively. Curtis and Schumacher-Hall Log models showed to be the most indicated to estimate the total dry biomass and carbon of the species under the conditions studied.

Frequency, Health Care Resource Use (HCRU), and Costs Associated With Skeletal Related Events (SREs) In US Patients With Multiple Myeloma (MM)

Background Existing data demonstrate that SREs can impose a significant economic burden. Much of the cost data for MM patients are combined with other tumor types and do not make comparisons between MM patients with SRE and MM patients without SRE. This research provides updated, comprehensive cost data in MM patients to address these gaps. Methods Patients 18 years of age or older were required to have ≥2 claims in any position with a diagnosis of MM (ICD-9-CM codes 203.00, 203.01, 203.02), at least 30 days apart, between 01 January 2005 and 31 December 2010; the date of the first MM diagnosis was the index date. Marketscan(r) data were used to select patients continuously enrolled in a non-capitated commercial health plan with a medical and pharmacy benefit for 12 months before the index date (i.e., baseline period) and at least 3 months after the index date (post-index period). Patients were followed until the earliest of death or disenrollment from the health plan or end of the study period on 31 December 2011. MM patients with ≥ 1 SRE were compared to those without SRE to characterize the associated incremental cost of SRE. Frequency of SREs, HCRU (events/person-mth), and costs (USD/person-mth) were determined. Cost is defined as total gross payment to providers for a specific service, which includes amount paid by the patient and payer. Due to skewed distributions, bootstrapping (replication=1000) methodology was used to estimate standard error of rates of HCRU and costs and to compare cohorts. P-values were generated utilizing 2-sample t-tests. Results MM patients with SRE (n=596, mean age=67.8, 45.6 % male, mean Charlson/Deyo [CD] =1.1) were compared to MM patients without SRE (n=432, mean age=65.7, 47.5 % male, mean CD=0.8). Patients are categorized by number of SREs experienced during the study period in Table 1. MM patients with SRE experienced significantly greater HCRU during both the baseline and post-index periods (Tables 2&3). The HCRU increases translated into mean total costs for patients with SRE that were significantly greater during both the baseline (w/o SRE: USD 953; w/SRE: USD 1328; p=0.005) and post-index (w/o SRE: USD 3307; w/SRE: USD 4763; p<0.0001) periods. Increasing frequency of SRE was significantly associated with increasing trend of HCRU and cost throughout the study (all p values <0.0001). Conclusion In addition to the clinical burden, SREs significantly add to the economic burden associated with MM in US patients by increasing the rate of HCRU and cost. Disclosures: Smyth: Eli Lilly and Company: Employment, Equity Ownership. Off Label Use: The frequency of bisphosphonate use (both oral and IV) will be described in the poster. Conti:Eli Lilly and Company: Employment, Equity Ownership. Wooldridge:Eli Lilly and Company: Employment, Equity Ownership. Bowman:Eli Lilly and Company: Employment, Equity Ownership. Li:Eli Lilly and Company: Employment, Equity Ownership. Nelson:Eli Lilly and Company: Employment. Xie:Eli Lilly and Company: Employment, Equity Ownership. Ball:Eli Lilly and Company: Employment, Equity Ownership.

Drought-driven change in wildlife distribution and numbers: a case study of koalas in south west Queensland

Context Global climate change will lead to increased climate variability, including more frequent drought and heatwaves, in many areas of the world. This will affect the distribution and numbers of wildlife populations. In south-west Queensland, anecdotal reports indicated that a low density but significant koala population had been impacted by drought from 2001–2009, in accord with the predicted effects of climate change. Aims The study aimed to compare koala distribution and numbers in south-west Queensland in 2009 with pre-drought estimates from 1995–1997. Methods Community surveys and faecal pellet surveys were used to assess koala distribution. Population densities were estimated using the Faecal Standing Crop Method. From these densities, koala abundance in 10 habitat units was interpolated across the study region. Bootstrapping was used to estimate standard error. Climate data and land clearing were examined as possible explanations for changes in koala distribution and numbers between the two time periods. Key results Although there was only a minor change in distribution, there was an 80% decline in koala numbers across the study region, from a mean population of 59 000 in 1995 to 11 600 in 2009. Most summers between 2002 and 2007 were hotter and drier than average. Vegetation clearance was greatest in the eastern third of the study region, with the majority of clearing being in mixed eucalypt/acacia ecosystems and vegetation on elevated residuals. Conclusions Changes in the area of occupancy and numbers of koalas allowed us to conclude that drought significantly reduced koala populations and that they contracted to critical riparian habitats. Land clearing in the eastern part of the region may reduce the ability of koalas to move between habitats. Implications The increase in hotter and drier conditions expected with climate change will adversely affect koala populations in south-west Queensland and may be similar in other wildlife species in arid and semiarid regions. The effect of climate change on trailing edge populations may interact with habitat loss and fragmentation to increase extinction risks. Monitoring wildlife population dynamics at the margins of their geographic ranges will help to manage the impacts of climate change.

Inferential Methods for the Tetrachoric Correlation Coefficient

The tetrachoric correlation describes the linear relation between two continuous variables that have each been measured on a dichotomous scale. The treatment of the point estimate, standard error, interval estimate, and sample size requirement for the tetrachoric correlation is cursory and incomplete in modern psychometric and behavioral statistics texts. A new and simple method of accurately approximating the tetrachoric correlation is introduced. The tetrachoric approximation is then used to derive a simple standard error, confidence interval, and sample size planning formula. The new confidence interval is shown to perform far better than the confidence interval computed by SAS. A method to improve the SAS confidence interval is proposed. All of the new results are computationally simple and are ideally suited for textbook and classroom presentations.

Application of a New Processing Method to Post-LDL-apheresis Data

Our aim was to elaborate a method to optimise treatment intervals for the individual low-density lipoprotein (LDL)-apheresis treated patients. After each treatment, plasma LDL concentrations show a time-related increase with a decreasing speed until a maximum level.We searched to interpret the post-LDL-apheresis experimental data trend as the physical process that produces the observed curve, so that the fitting presupposed theoretical function is a direct consequence of the physic process, because to establish the better time. Applying the proposed fitting method to a succession of 15 samples obtained from the mean of six plasmapheresis executed on five different subjects, small estimate standard error (5 mg/dl) and relative error (1.7%) with a dispersion evidently related to the experimental error were observed. Obviously, applying the same method to a single case, the dispersion is more marked (relative error <5%), with a SE of 10-13 mg/dl, even though the aspect of a casual phenomenon is conserved. Our physical interpretation appears to be a practical model to predict the LDL-rebound kinetic of the single patient.

Who Is at Risk of What?

If you have calculated the sample size required for an employee survey or an observational study of departmental practices but found that the number of observations required is larger than the number of employees, chances are the error is due to use of approximation formulae. Many of us unknowingly were taught to use approximations that fail to include the finite population correction factor. Depending on the objective of a study and the proportion of a population sampled, it may be necessary to consider this correction factor in order to estimate standard error and sample size accurately.