Estimation of productivity in Korean electric power plants:a semiparametric smooth coefficient model

This paper analyzes the impact of load factor, facility and generator types on the productivity of Korean electric power plants. In order to capture important differences in the effect of load policy on power output, we use a semiparametric smooth coefficient (SPSC) model that allows us to model heterogeneous performances across power plants and over time by allowing underlying technologies to be heterogeneous. The SPSC model accommodates both continuous and discrete covariates. Various specification tests are conducted to compare performance of the SPSC model. Using a unique generator level panel dataset spanning the period 1995-2006, we find that the impact of load factor, generator and facility types on power generation varies substantially in terms of magnitude and significance across different plant characteristics. The results have strong implication for generation policy in Korea as outlined in this study.

Estimation of productivity in Korean electric power plants:a semiparametric smooth coefficient model

This paper analyzes the impact of load factor, facility and generator types on the productivity of Korean electric power plants. In order to capture important differences in the effect of load policy on power output, we use a semiparametric smooth coefficient (SPSC) model that allows us to model heterogeneous performances across power plants and over time by allowing underlying technologies to be heterogeneous. The SPSC model accommodates both continuous and discrete covariates. Various specification tests are conducted to assess the performance of the SPSC model. Using a unique generator level panel dataset spanning the period 1995-2006, we find that the impact of load factor, generator and facility types on power generation varies substantially in terms of magnitude and significance across different plant characteristics. The results have strong implications for generation policy in Korea as outlined in this study.

On the basic reproduction number and the topological properties of the contact network: An epidemiological study in mainly locally connected cellular automata

We study the spreading of contagious diseases in a population of constant size using susceptible-infective-recovered (SIR) models described in terms of ordinary differential equations (ODEs) and probabilistic cellular automata (PCA). In the PCA model, each individual (represented by a cell in the lattice) is mainly locally connected to others. We investigate how the topological properties of the random network representing contacts among individuals influence the transient behavior and the permanent regime of the epidemiological system described by ODE and PCA. Our main conclusions are: (1) the basic reproduction number (commonly called R(0)) related to a disease propagation in a population cannot be uniquely determined from some features of transient behavior of the infective group; (2) R(0) cannot be associated to a unique combination of clustering coefficient and average shortest path length characterizing the contact network. We discuss how these results can embarrass the specification of control strategies for combating disease propagations. (C) 2009 Elsevier B.V. All rights reserved.

Residuals for log-Burr XII regression models in survival analysis

In this paper, we compare three residuals to assess departures from the error assumptions as well as to detect outlying observations in log-Burr XII regression models with censored observations. These residuals can also be used for the log-logistic regression model, which is a special case of the log-Burr XII regression model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to the modified martingale-type residual in log-Burr XII regression models with censored data.

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model.

Log-modified Weibull regression models with censored data: Sensitivity and residual analysis

This paper proposes a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider maximum likelihood and Jackknife estimators for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and we also present some ways to perform global influence. Besides, for different parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the modified deviance residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for a martingale-type residual in log-modified Weibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the modified deviance residual are performed to select appropriate models. (c) 2008 Elsevier B.V. All rights reserved.

Regression models for grouped survival data: Estimation and sensitivity analysis

In this study, regression models are evaluated for grouped survival data when the effect of censoring time is considered in the model and the regression structure is modeled through four link functions. The methodology for grouped survival data is based on life tables, and the times are grouped in k intervals so that ties are eliminated. Thus, the data modeling is performed by considering the discrete models of lifetime regression. The model parameters are estimated by using the maximum likelihood and jackknife methods. To detect influential observations in the proposed models, diagnostic measures based on case deletion, which are denominated global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to those measures, the local influence and the total influential estimate are also employed. Various simulation studies are performed and compared to the performance of the four link functions of the regression models for grouped survival data for different parameter settings, sample sizes and numbers of intervals. Finally, a data set is analyzed by using the proposed regression models. (C) 2010 Elsevier B.V. All rights reserved.

A Model for comparing reduced impact logging with conventional logging for an Eastern Amazonian Forest

Using data from a logging experiment in the eastern Brazilian Amazon region, we develop a matrix growth and yield model that captures the dynamic effects of harvest system choice on forest structure and composition. Multinomial logistic regression is used to estimate the growth transition parameters for a 10-year time step, while a Poisson regression model is used to estimate recruitment parameters. The model is designed to be easily integrated with an economic model of decisionmaking to perform tropical forest policy analysis. The model is used to compare the long-run structure and composition of a stand arising from the choice of implementing either conventional logging techniques or more carefully planned and executed reduced-impact logging (RIL) techniques, contrasted against a baseline projection of an unlogged forest. Results from log and leave scenarios show that a stand logged according to Brazilian management requirements will require well over 120 years to recover its initial commercial volume, regardless of logging technique employed. Implementing RIL, however, accelerates this recovery. Scenarios imposing a 40-year cutting cycle raise the possibility of sustainable harvest volumes, although at significantly lower levels than is implied by current regulations. Meeting current Brazilian forest policy goals may require an increase in the planned total area of permanent production forest or the widespread adoption of silvicultural practices that increase stand recovery and volume accumulation rates after RIL harvests. Published by Elsevier B.V.

Stochastic structure and individual-tree growth models

The majority of past and current individual-tree growth modelling methodologies have failed to characterise and incorporate structured stochastic components. Rather, they have relied on deterministic predictions or have added an unstructured random component to predictions. In particular, spatial stochastic structure has been neglected, despite being present in most applications of individual-tree growth models. Spatial stochastic structure (also called spatial dependence or spatial autocorrelation) eventuates when spatial influences such as competition and micro-site effects are not fully captured in models. Temporal stochastic structure (also called temporal dependence or temporal autocorrelation) eventuates when a sequence of measurements is taken on an individual-tree over time, and variables explaining temporal variation in these measurements are not included in the model. Nested stochastic structure eventuates when measurements are combined across sampling units and differences among the sampling units are not fully captured in the model. This review examines spatial, temporal, and nested stochastic structure and instances where each has been characterised in the forest biometry and statistical literature. Methodologies for incorporating stochastic structure in growth model estimation and prediction are described. Benefits from incorporation of stochastic structure include valid statistical inference, improved estimation efficiency, and more realistic and theoretically sound predictions. It is proposed in this review that individual-tree modelling methodologies need to characterise and include structured stochasticity. Possibilities for future research are discussed. (C) 2001 Elsevier Science B.V. All rights reserved.

Worse renal outcome of lupus nephritis in male patients: a case-control study

Background: Progression and long-term renal outcome of lupus nephritis (LN) in male patients is a controversial subject in the literature. The aim of this study was to evaluate the influence of male gender on the renal outcome of LN. Methods: All male (M) LN patients who fulfilled American College of Rheumatology lupus criteria and who were referred for a kidney biopsy from 1999 to 2009 were enrolled in the study. Subjects with end-stage renal disease at baseline, or follow-up time below 6 months, were excluded. Cases were randomly matched to female (F) patients according to the class of LN, baseline estimated glomerular filtration rate (eGFR, Modification of Diet in Renal Disease simplified formula) and follow-up time. Treatment was decided by the clinical staff based on usual literature protocols. The primary endpoint was doubling of serum creatinine and/or end-stage renal disease. The secondary endpoint was defined as a variation of glomerular filtration rate (GFR) per year (Delta GFR/y index), calculated as the difference between final and initial eGFR adjusted by follow-up time for each patient. Results: We included 93 patients (31 M : 62 F). At baseline, M and F patients were not statistically different regarding WHO LN class (II 9.7%, IV 71%, V 19.3%), eGFR (M 62.4 +/- 36.4 ml/min/1.73 m(2) versus F 59.9 +/- 32.7 ml/min/1.73 m(2)), follow-up time (M 44.2 +/- 27.3 months versus F 39.9 +/- 27.9 months), and 24-hour proteinuria (M 5.3 +/- 4.6 g/day versus F 5.2 +/- 3.0 g/day), as well as age, albumin, C3, antinuclear antibody, anti-DNA antibody and haematuria. There was no difference in the primary outcome (M 19% versus F 13%, log-rank p = 0.62). However, male gender was significantly associated with a worse renal function progression, as measured by Delta GFR/y index (beta coefficient for male gender -12.4, 95% confidence interval -22.8 to -2.1, p = 0.02). The multivariate linear regression model showed that male gender remained statistically associated with a worse renal outcome even after adjustment for eGFR, proteinuria, albumin and C3 complement at baseline. Conclusion: In our study, male gender presented a worse evolution of LN (measured by an under GFR recovering) when compared with female patients with similar baseline features and treatment. Factors that influence the progression of LN in men and sex-specific treatment protocols should be further addressed in new studies. Lupus (2011) 20, 561-567.

The relationship between body mass index and the variation in plasma levels of triglycerides after short-term red wine consumption

BACKGROUND: Alcoholic beverages may have protective cardiovascular effects but are known to increase the plasma levels of triglycerides (TG). Both TG and the ratio of TO to high-density lipoprotein cholesterol (TG/HDL-cholesterol) are associated with increased cardiovascular risk. OBJECTIVES: To determine the predictive factors for variations in plasma levels of TO and the TG/HDL-cholesterol ratio in patients after they had consumed red wine for 14 days. METHODS: Forty-two subjects (64% men, 46 +/- 9 years, baseline body mass index [BMI] 25.13 +/- 2.76 kg/m(2)) were given red wine (12% or 12.2% alc/vol, 250 mL/day with meals). Plasma concentration of lipids and glucose were measured before and after red wine consumption. Blood was collected after 12 hours of fast and alcohol abstention. RESULTS: Red wine increased plasma levels of TO from 105 +/- 42 mg/dL to 120 +/- 56 mg/dL (P = .001) and the TG/HDL-cholesterol ratio from 2.16 +/- 1.10 to 2.50 +/- 1.66 (P = .014). In a multivariate linear regression model that included age, baseline BMI, blood pressure, lipids, and glucose, only BMI was independently predictive of the variation in plasma TO after red wine (beta coefficient 0.592, P < .001). BMI also predicted the variation in TG/HDL-cholesterol ratio (beta coefficient 0.505, P = .001, adjusted model). When individuals were divided into three categories, according to their BMI, the average percentage variation in TG after red wine was -4%, 17%, and 33% in the lower (19.60-24.45 kg/m(2)), intermediate, and greater (26.30-30.44 kg/m(2)) tertiles, respectively (P = .001). CONCLUSIONS: Individuals with higher BMI, although nonobese, might be at greater risk for elevation in plasma TO levels and the TG/HDL-cholesterol ratio after short-term red wine consumption. (C) 2011 National Lipid Association. All rights reserved.

A Manipulação de resultados em Portugal através do Goodwill

Dissertação para obtenção do Grau de Mestre em Contabilidade e Finanças Orientador: Doutor José Manuel da Veiga Pereira

Social contextual factors contributing to child and adolescent labor: an ecological analysis

OBJECTIVE: To examine the relationship between social contextual factors and child and adolescent labor. METHODS: Population-based cohort study carried out with 2,512 families living in 23 subareas of a large urban city in Brazil from 2000 to 2002. A random one-stage cluster sampling was used to select families. Data were obtained through individual household interviews using questionnaires. The annual cumulative incidence of child and adolescent labor was estimated for each district. New child and adolescent labor cases were those who had their first job over the two-year follow-up. The annual cumulative incidence of child and adolescent labor was the response variable and predictors were contextual factors such as lack of social support, social deprivation, unstructured family, perceived violence, poor school quality, poor environment conditions, and poor public services. Pearson's correlation and multiple linear regression were used to assess the associations. RESULTS: There were selected 943 families corresponding to 1,326 non-working children and adolescents aged 8 to 17 years. Lack of social support, social deprivation, perceived violence were all positively and individually associated with the annual cumulative incidence of child and adolescent labor. In the multiple linear regression model, however, only lack of social support and perceived violence in the neighborhood were positively associated to child and adolescent labor. No effect was found for poor school quality, poor environment conditions, poor public services or unstructured family. CONCLUSIONS: Poverty reduction programs can reduce the contextual factors associated with child and adolescent labor. Violence reduction programs and strengthening social support at the community level may contribute to reduce CAL.

Acute Kidney Injury after Pediatric Cardiac Surgery: Risk Factors and Outcomes. Proposal for a Predictive Model

Objectives: To characterize the epidemiology and risk factors for acute kidney injury (AKI) after pediatric cardiac surgery in our center, to determine its association with poor short-term outcomes, and to develop a logistic regression model that will predict the risk of AKI for the study population. Methods: This single-center, retrospective study included consecutive pediatric patients with congenital heart disease who underwent cardiac surgery between January 2010 and December 2012. Exclusion criteria were a history of renal disease, dialysis or renal transplantation. Results: Of the 325 patients included, median age three years (1 day---18 years), AKI occurred in 40 (12.3%) on the first postoperative day. Overall mortality was 13 (4%), nine of whom were in the AKI group. AKI was significantly associated with length of intensive care unit stay, length of mechanical ventilation and in-hospital death (p<0.01). Patients’ age and postoperative serum creatinine, blood urea nitrogen and lactate levels were included in the logistic regression model as predictor variables. The model accurately predicted AKI in this population, with a maximum combined sensitivity of 82.1% and specificity of 75.4%. Conclusions: AKI is common and is associated with poor short-term outcomes in this setting. Younger age and higher postoperative serum creatinine, blood urea nitrogen and lactate levels were powerful predictors of renal injury in this population. The proposed model could be a useful tool for risk stratification of these patients.

Prevalence and factors associated with HCV infection among elderly individuals in a southern Brazilian city

Introduction Few Latin American studies have assessed the prevalence of hepatitis C virus (HCV) infection in elderly individuals, in whom the highest rates are expected. We aimed to investigate the prevalence of and factors associated with HCV infection in elderly residents in the municipality of Tubarão, Santa Catarina. Methods This cross-sectional study included 820 individuals (aged ≥ 60 years) who were selected by simple random sampling. The presence of anti-HCV antibodies was tested by chemiluminescence, and HCV RNA detection was performed for the anti-HCV-reactive subjects. Those individuals who were anti-HCV reactive but had undetectable HCV RNA levels were tested using a third-generation recombinant immunoblot assay. The variables were compared using the chi-squared test or Fisher's exact test, and those variables with p < 0.05 were included in the logistic regression model. Results The mean patient age was 68.6 years (SD 7.0 years); 39% were men, and 92% were Caucasian. Eighteen subjects were anti-HCV positive. Among these individuals, 4 were characterized as false-positives, leaving 14 (1.7%) individuals with confirmed infections for analysis. HCV infection was associated with an age older than 65 years, households with 3 or more residents and the previous transfusion of blood products. In the logistic regression analysis, the following variables were independently associated with HCV infection: households with 3 or more residents (OR 7.9, 95% CI 1.7–35.9, p = 0.008) and previous blood transfusion (OR 6.2, 95% CI 2.1–18.6, p = 0.001). Conclusions The HCV prevalence in the elderly population in the municipality of Tubarão was higher than that found in previous studies of blood donors in the same region. Although exposure to contaminated blood products remained important, other transmission routes, such as household transmission, could play a role in HCV infection.