scholarly journals EVOLUÇÃO DO SORTIMENTO EM POVOAMENTOS DE Pinus taeda NOS ESTADOS DO PARANÁ E SANTA CATARINA

FLORESTA ◽  
2015 ◽  
Vol 45 (3) ◽  
pp. 545 ◽  
Author(s):  
Sintia Valerio Kohler ◽  
Henrique Soares Koehler ◽  
Afonso Figueiredo Filho ◽  
Julio Eduardo Arce ◽  
Sebastião Do Amaral Machado
Keyword(s):  
Pinus Taeda ◽  
Fractional Power ◽  
Age Classes ◽  
Santa Catarina ◽  
Data Set ◽  
Power Equation ◽  
Fast Change ◽  
Using Data

A dinâmica da evolução do sortimento em povoamentos de Pinus taeda foi estudada utilizando-se dados de análise de tronco em povoamentos estabelecidos nos estados do Paraná e Santa Catarina, amostrando-se 126 e 120 árvores, respectivamente. O polinômio de potências inteiras e fracionárias foi ajustado para os dados estratificados em classes de idade. Com o programa FlorExel®, foram calculados os volumes por sortimento nas diferentes idades dos povoamentos. Os volumes totais médios por árvore aos 18 anos foram de 1,157 m³ para o estado do Paraná e de 0,959 m³ para o estado de Santa Catarina. Volumes para celulose foram mais representativos na classe de 7 a 9 anos, com 50% do volume. Volumes para serraria tipo 1 foram estimados a partir da idade de 7 anos, e para serraria tipo 2, a partir da idade 8 e 9 anos, respectivamente, representando 70% do volume total (serraria tipo 1 + serraria tipo 2) na classe de 10 a 12 anos. Volumes para laminação foram obtidos a partir dos 13 anos em ambos os estados, chegando a apresentar mais de 30% do volume total a partir da idade de 19 anos. Observa-se uma mudança rápida no sortimento “nobre” a partir da idade de 13 anos.AbstractEvolution of assortment of Pinus taeda stands located at Paraná and Santa Catarina States. The dynamic evolution of assortment of Pinus taeda stands was studied using data from complete stem analysis in stands established at Paraná and Santa Catarina states, sampling 126 and 120 trees respectively. The integer and fractional power equation was fitted to the data set stratified by age classes. With FlorExel ® program volumes assortments by the different ages of stands were estimated. The average tree total volume was 1,157 m³, at age of 18 years in Paraná state and 0,959 m³ at Santa Catarina State. Wood for cellulose had higher representation in the class of 7 to 9 years, with 50% of the volume. Volumes for sawmill type 1 were estimated in plantations from 7 years, and for sawmill type 2 from 8 and 9 years old, respectively, representing 70% of the total volume (sawmill type 1 + sawmill type 2) at 10 to 12 years of age. Tree volumes for veneer were obtained from 13 years of age on, in both stands, arriving to present more than 30% of the total volume from the age 19 years. It was observed a fast change in the "noble" assortment after the age of 13 years.Keywords: Assortment dynamic; multproduct; taper models.

Parameter Prediction Based on Type-2 Fuzzy Clustering

2018 ◽  
Vol 26 (06) ◽  
pp. 877-892 ◽  
Author(s):  
Türkan Erbay Dalkiliç ◽  
Seda Sağirkaya
Keyword(s):  
Fuzzy Logic ◽  
Regression Analysis ◽  
Fuzzy Clustering ◽  
Alternative Methods ◽  
Data Set ◽  
Clustering Problem ◽  
Fuzzy Clustering Method ◽  
Parameter Prediction

In regression analysis, the data have different distributions which requires to go beyond the classical analysis during the prediction process. In such cases, the analysis method based on fuzzy logic is preferred as alternative methods. There are couple important steps in the regression analysis based on fuzzy logic. One of them is identification of the clusters that generate the data set, the other is the degree of memberships that are determined the grades of the contributions of the data contained in these clusters. In this study, parameter prediction based on type-2 fuzzy clustering is discussed. Firstly, type-1 fuzzy clustering problem was solved by the fuzzy c-means (FCM) method when the fuzzifier index is equal to two. Then the fuzzifier index m is defined as interval number. The membership degrees to the sets are determined by type-2 fuzzy clustering method. Membership degree obtained as a result of clustering based on type-1 and type-2 fuzzy logic are used as weight and parameter prediction using these membership degrees that determined by the proposed algorithm. Finally, the prediction result of the type-1 and type-2 fuzzy clustering parameter is compared with the error criterion based on the difference between observed values and the predicted values.

A PSO-Based Framework for Designing Fuzzy Systems from Noisy Data Set

2012 ◽  
pp. 210-228
Author(s):  
Satvir Singh ◽  
J. S. Saini ◽  
Arun Khosla
Keyword(s):  
Noisy Data ◽  
Search Space ◽  
Fuzzy Logic System ◽  
Human Reasoning ◽  
Data Set ◽  
Swarm Optimization ◽  
Design Type ◽  
Interval Type

In most of Fuzzy Logic System (FLS) designs, human reasoning is encoded into programs to make decisions and/or control systems. Designing an optimal FLS is equivalent to an optimization problem, in which efforts are made to locate a point in fitness search-space where the performance is better than that of other locations. The number of parameters to be tuned in designing an FLS is quite large. Also, fitness search space is highly non-linear, deceptive, non-differentiable, and multi-modal in nature. Noisy data, from which to construct the FLS, may make the design problem even more difficult. This chapter presents a framework to design Type-1 (T1) and Interval Type-2 (IT2) FLSs (Liang and Mendel, 2000c, Mendel, 2001, 2007, Mendel et al., 2006) using Particle Swarm Optimization (PSO) (Eberhart and Kennedy, 1995, Kennedy and Eberhart, 1995). This framework includes the use of PSO based Nature Inspired (NI) Toolbox discussed in the chapter titled, “Nature-Inspired Toolbox to Design and Optimize Systems.”

scholarly journals Residual Mean Circulation and Temperature Changes during the Evolution of Stratospheric Sudden Warming Revealed in MERRA

2016 ◽  
Author(s):  
Byeong-Gwon Song ◽  
Hye-Yeong Chun
Keyword(s):  
Negative Temperature ◽  
Data Set ◽  
Stratospheric Sudden Warming ◽  
Temperature Changes ◽  
Temperature Advection ◽  
Mean Circulation ◽  
Residual Mean Circulation ◽  
Temperature Tendency

Abstract. Residual mean circulation and temperature changes during the evolution of major stratospheric sudden warming (SSW) are investigated by composite analyses of 22 SSW events from 1979 to 2012 during the Northern Hemisphere winter (November–March) using four reanalysis data sets (MERRA, ERA-Interim, NCEP-NCAR, and JRA-55). The SSW events are classified as Type-1 or Type-2 based on the relative amplitude of planetary waves with zonal wavenumbers 1 and 2. The residual mean circulation induced by each forcing term in the Transformed Eulerian mean (TEM) momentum equation and the temperature advection associated with the circulation are calculated for both types of SSW, based on the generalized downward control principle using the MERRA data set. When ‘Lag = 0’ is defined as the day on which the wind reversal occurred at 60° N and 10 hPa, strong poleward and downward motion exists at Lag = −8 and Lag = −1 for Type-1 and at Lag = −3 for Type-2, which is induced primarily by the Eliassen–Palm flux divergence forcing (EPD). The poleward and downward motion is stronger for Type-2 than for Type-1. Gravity wave drag (GWD) produces a smaller contribution to the residual circulation than EPD. During the warming phase (at Lag = −2), strong temperature advection by the EPD induces primarily polar stratospheric warming. On the other hand, during the temperature recovery phase (at Lag = +2), anomalous negative temperature advection and diabatic cooling produce negative temperature tendency anomalies. Structures in the temperature tendency and temperature advection calculated using the MERRA data set are similar to those calculated using the ERA-Interim data set.

scholarly journals 2000. Utilization of a ‘Never Event’ Framework to Classify Antimicrobial Appropriateness

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S670-S671
Author(s):  
Jiajun Liu ◽  
Nicholas Mercuro ◽  
Susan L Davis ◽  
Paul Yarnold ◽  
Twisha S Patel ◽  
...  
Keyword(s):  
Linear Models ◽  
Antibiotic Consumption ◽  
Patient Demographics ◽  
Highly Sensitive ◽  
Henry Ford ◽  
Using Data ◽  
Susceptible Organism ◽  
Never Event

Abstract Background Contemporary strategies can be leveraged to predict antimicrobial overuse, yet little information is gained on the appropriateness of antibiotics prescribed. Classifying appropriateness is complicated by the lack of a standard definition for appropriateness. Thus, we created and implemented a novel ‘antibiotic never event’ (NE) framework to systematically classify the most inappropriate usages of vancomycin and correlated these NE to abnormal consumption trends (i.e., antibiotic outbreaks). Methods Vancomycin use was categorized by an algorithm using data query from the electronic medical records. Extracted data included vancomycin use, relevant patient demographics, and microbiological data. Electronic classifications placed each vancomycin therapy into type 1 (use for non-susceptible organism after susceptibility finalization) or type 2 (use exceeding 48h after susceptibility report when a safe de-escalation is possible) NE. Patients were categorized as cases or controls (no NE) at Northwestern Memorial Hospital (NM) and Henry Ford Hospital (HF) between January 2014 and October 2017. A manual chart review was performed. Sensitivity (SEN), specificity (SPEC), PPV, and NPV were calculated for NE prediction. Vancomycin use was quantified during the same period. Linear models with prediction intervals (PI) were generated to identify potential outbreaks, which were linked to monthly NE counts defined as a binary factor. Results A total of 220 NE cases were electronically identified for vancomycin at NM (n = 197) and HF (n = 23). Random cases were matched 1:1 (NM = 200) and 1:5 (HF = 115) to controls for manual review. At NM and HF, 35 and 24 true positives were identified, respectively. Thus, overall SEN and SPEC were 93.7% and 75.1% and PPV and NPV were 45.7% and 98.1%, respectively. Linear models revealed 11 potential outbreak periods at HF and 5 at NM. A PI of 80% showed a combined SEN below 10% and SPEC above 90%, respectively. Conclusion The methodology was generalizable across two centers. In the pilot review, our method was highly sensitive and an effective screening tool for NE identification. Antibiotic consumption trends did not correlate with NE. In summary, the NE classification was sensitive in assessment of antibiotic appropriateness, whereas consumption alone does not predict NE. Disclosures All authors: No reported disclosures.

scholarly journals Effect of COVID-19 on the clinical course of diabetic ketoacidosis (DKA) in people with type 1 and type 2 diabetes

2021 ◽  
Vol 10 (4) ◽  
pp. 371-377
Author(s):  
Punith Kempegowda ◽  
Eka Melson ◽  
Agnes Johnson ◽  
Lucy Wallett ◽  
Lucretia Thomas ◽  
...  
Keyword(s):  
Natural History ◽  
Clinical Course ◽  
External Control ◽  
Multi Centre Study ◽  
Significant Difference ◽  
History Of ◽  
Using Data ◽  
Worse Prognosis

Objective COVID-19 in people with diabetes is associated with a disproportionately worse prognosis. DKA is an acute complication of diabetes with a mortality rate of approximately 0.67%. Little is known about the natural history of DKA in the presence of COVID-19. This study aimed to explore the effects of COVID-19 on presentation, clinical course and outcome in patients presenting with DKA. Design Retrospective cohort study. Methods All patients treated for DKA between 1 March 2020 and 30 May 2020 were included. Patients were categorised as COVID-positive or COVID-negative based on the swab test. A pre-COVID group was established using data from 01 March 2019 to 30 May 2019 as external control. Data regarding demographics, diabetes type, pH, bicarbonate, lactate, glucose, DKA duration, complications and outcome were collected. Results A total of 88 DKA episodes were included in this study. There was no significant difference in the severity or duration of DKA between the three groups. COVID-positive T1DM were more hyperglycaemic on admission compared to COVID-negative and pre-COVID patients. There was an over representation of T2DM in COVID-positive patients with DKA than in pre-COVID or COVID-negative groups. Conclusion COVID-19 appears to influence the natural history of DKA differently in T1DM and T2DM. Patients with T1DM and COVID-19 presented with more hyperglycaemia (60 mmol/L (35.9–60.0) vs 31.4 mmol/L (28.0–39.1) vs 24 mmol/L (20.2–33.75), respectively). Patients with T2DM were unusually presenting in DKA when infected with COVID-19 with greater ICU need and higher mortality rates. A collaborative, multi-centre study is needed to provide more definitive results.

scholarly journals Modeling changes in glucose and glycerol rates of appearance when true basal rates of appearance cannot be readily determined

2016 ◽  
Vol 310 (5) ◽  
pp. E323-E331 ◽  
Author(s):  
Laura Pyle ◽  
Bryan C. Bergman ◽  
Kristen J. Nadeau ◽  
Melanie Cree-Green
Keyword(s):  
Population Based ◽  
Data Set ◽  
Glucose Release ◽  
Isotope Tracers ◽  
Hepatic Glucose ◽  
Insulin Dependent ◽  
Predicted Values ◽  
The Individual

Advancing diabetes care requires accurate physiological assessments. Hyperinsulinemic clamps with stable isotope tracers can simultaneously measure insulin's ability to suppress lipolysis and hepatic glucose release. Traditionally, these methods require an assessment of basal glucose and glycerol rate of appearance (Ra). Basal Ra is challenging to measure in insulin-dependent diabetes, where exogenous insulin required to maintain normoglycemia can raise peripheral insulin concentrations sufficiently to suppress basal Ra. Thus we identified two alternative statistical approaches to describe changes in glucose and glycerol Ra that are less reliant on basal assessments. Sixteen youths (4 type 1 diabetic, 4 type 2 diabetic, 4 lean controls, and 4 obese nondiabetic) underwent a four-phase (“basal” and 10, 16, and 80 mU·m2·min−1) hyperinsulinemic euglycemic clamp with glucose and glycerol tracers. Glucose and glycerol Ra were calculated per phase. A statistical method, the standard two-stage (STS) algorithm, was applied to the individual log insulin vs. Ra curves to calculate a single predicted Ra value. A population-based mixed-effects model (MEM) compared the group average Ra with log insulin curves and described individual deviations from group means and was used to calculate individual predicted Ra. Both models were applied to the participant data, and predicted Ras at the mean insulin concentration per phase (10 for glycerol, 16 for glucose) were calculated, with good agreement between observed and predicted values. In our data set, the MEM was better able to detect group differences. Both STS and MEM can model lipolysis and endogenous glucose release in insulin-dependent states when basal Ra cannot be accurately measured.

scholarly journals Evolution of tree stem taper in Pinus taeda stands

2016 ◽  
Vol 46 (7) ◽  
pp. 1185-1191 ◽  
Author(s):  
Sintia Valerio Kohler ◽  
Henrique Soares Koehler ◽  
Afonso Figueiredo Filho ◽  
Julio Eduardo Arce ◽  
Sebastião do Amaral Machado
Keyword(s):  
Pinus Taeda ◽  
Fractional Power ◽  
Form Factors ◽  
Analysis Data ◽  
Age Classes ◽  
Stem Analysis ◽  
Stem Form ◽  
Stem Taper ◽  
Identity Test ◽  
Fitting In

ABSTRACT: This study aimed to evaluate the evolution of stem taper of trees in Pinus taedastands using stem analysis data from 126 and 120 trees sampled from established stands in Paraná and Santa Catarina States, respectively. The integer and fractional power polynomial was fitted to estimate diameters inside bark along the stem and was used to construct taper curves by age class. The model identity test was applied to verify the change in stem form of trees. The evolution of natural and artificial form factors in the trees was also analyzed. The curves constructed from the fitted taper functions and form factors showed that the stems become more cylindrical with increasing age; the identity test indicated that, in general, the taper curves statistically differ between themselves. This showed that stratifying the data into age classes for fitting in taper functions can produce more accurate assortment estimates.

scholarly journals How do Personality and Social Structures Interact with Each other to Predict Important Life Outcomes? The Importance of Accounting for Personality Change

2017 ◽  
Vol 31 (3) ◽  
pp. 279-290 ◽  
Author(s):  
Christopher J. Boyce ◽  
Alex M. Wood ◽  
Liam Delaney ◽  
Eammon Ferguson
Keyword(s):  
Methodological Issue ◽  
Personality Change ◽  
Social Structures ◽  
Life Outcomes ◽  
Personality Psychology ◽  
Personality Changes ◽  
Using Data ◽  
Type 2 Errors

Personality is important for a range of life outcomes. However, despite evidence that personality changes across time, there is a concerning tendency for researchers outside of personality psychology to treat measures of personality as if they are non–changing when establishing whether personality predicts important life outcomes. This is problematic when personality changes in response to outcomes of interest and creates a methodological issue that may result in misleading conclusions. We illustrate this methodological issue and suggest using measures before the outcome takes place to mitigate concerns. We then demonstrate, using data from Germany, that using post–event personality measures, as opposed to pre–outcome measures, to predict both occurrence of, and reactions to, socio–economic events results in inconsistent conclusions in the directions hypothesized and therefore increases the likelihood of Type 1 and Type 2 errors. This has implications for research investigating the importance of personality for psychological, behavioural, and socio–economic outcomes. Copyright © 2017 European Association of Personality Psychology

The complex and covert web of financial protectionism

2014 ◽  
Vol 16 (4) ◽  
pp. 579-613 ◽  
Author(s):  
Kevin Young
Keyword(s):  
Financial Crisis ◽  
Global Financial Crisis ◽  
Government Interventions ◽  
Crisis Type ◽  
The 2008 Financial Crisis ◽  
Using Data ◽  
The Global Financial Crisis ◽  
Type 3

In the wake of the 2008 financial crisis, governments turned to protectionist policies to support their financial sectors. Yet these policy choices have been highly variegated, and like many recent protectionist policies reflect a process of adaptation to changing circumstances. Using data on a variety of government interventions in the financial sector since the crisis, I show that financial protectionism comes in at leastthreetypes, only two of which have witnessed a traceable increase since the global financial crisis. These are protection through market entry restrictions (Type 1), through asymmetrically applied regulation (Type 2) and protection through subsidies (Type 3). While Type 1 has not appeared to change significantly since the crisis, Type 2 and Type 3 have. I present empirical evidence which suggests that while Type 3 financial protectionism proliferated during and shortly after the crisis, it is unlikely to continue. Type 2 financial protectionism, I conclude, is more likely to take off into the future because of the nature of interest group effects associated with asymmetric regulation as a form of government intervention.

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