Combustion of coal, bagasse and blends thereof Part I: Emissions from batch combustion of fixed beds of fuels

Batch combustion of fixed beds of coal, bagasse and blends thereof took place in a pre-heated two-stage electric laboratory furnace, under high-heating rates. The average input fuel/air equivalence ratios were similar for all fuels. The primary and secondary furnace temperatures were varied from 800 degrees C to 1000 degrees C. The effects of fuel blending, combustion staging, and operating furnace temperatures on the emissions from the two fuels were assessed. Furnace effluents were analyzed for carbon dioxide and for products of incomplete combustion (PIC) including CO, volatile and semi-volatile hydrocarbons, as well as particulate matter. Results showed that whereas CO2 was generated during both the observed sequential volatile matter and char combustion phases of the fuels, PICs were only generated during the volatile matter combustion phase. CO2 emissions were the highest from coal, whereas CO and other PIC emissions were the highest from bagasse. Under this particular combustion configuration, combustion of the volatile matter of the blends resulted in lower yields of PIC, than combustion of the volatiles of the neat fuels. Though CO and unburned hydrocarbons from coal as well as from the blends did not exhibit a clear trend with furnace temperature, such emissions from bagasse clearly increased with temperature. The presence of the secondary furnace (afterburner) typically reduced PIC, by promoting further oxidation of the primary furnace effluents. (C) 2012 Elsevier Ltd. All rights reserved.

Removal of Pb2+ from aqueous solutions using two Brazilian rocks containing zeolites

The removal of Pb2+ from aqueous solution by two Brazilian rocks that contain zeolites-amygdaloidal dacite (ZD) and sandstone (ZS)-was examined by batch experiments. ZD contains mordenite and ZS, stilbite. The effects of contact time, concentration of metal in solution and capacity of Na+ to recover the adsorbed metals were evaluated at room temperature (20A degrees C). The sorption equilibrium was reached in the 30 min of agitation time. Both materials removed 100% of Pb2+ from solutions at concentrations up to 50 mg/L, and at concentrations larger than 100 mg/L of Pb2+, the adsorption capacity of sandstone was more efficient than that of amygdaloidal dacite due to the larger quantities and the type of zeolites (stilbite) in the cement of this rock. All adsorbed Pb2+ was easily replaced by Na+ in both samples. The analysis of the adsorption models using nonlinear regression revealed that the Sips and the Freundlich isotherms provided the best fit for the ZS and ZD experimental data, respectively, indicating the heterogeneous adsorption surfaces of these zeolites.

Local power properties of some asymptotic tests in symmetric linear regression models

In this paper we obtain asymptotic expansions, up to order n(-1/2) and under a sequence of Pitman alternatives, for the nonnull distribution functions of the likelihood ratio, Wald, score and gradient test statistics in the class of symmetric linear regression models. This is a wide class of models which encompasses the t model and several other symmetric distributions with longer-than normal tails. The asymptotic distributions of all four statistics are obtained for testing a subset of regression parameters. Furthermore, in order to compare the finite-sample performance of these tests in this class of models, Monte Carlo simulations are presented. An empirical application to a real data set is considered for illustrative purposes. (C) 2011 Elsevier B.V. All rights reserved.

Error Autocorrelation and Linear Regression for Temperature-Based Evapotranspiration Estimates Improvement

Estimates of evapotranspiration on a local scale is important information for agricultural and hydrological practices. However, equations to estimate potential evapotranspiration based only on temperature data, which are simple to use, are usually less trustworthy than the Food and Agriculture Organization (FAO)Penman-Monteith standard method. The present work describes two correction procedures for potential evapotranspiration estimates by temperature, making the results more reliable. Initially, the standard FAO-Penman-Monteith method was evaluated with a complete climatologic data set for the period between 2002 and 2006. Then temperature-based estimates by Camargo and Jensen-Haise methods have been adjusted by error autocorrelation evaluated in biweekly and monthly periods. In a second adjustment, simple linear regression was applied. The adjusted equations have been validated with climatic data available for the Year 2001. Both proposed methodologies showed good agreement with the standard method indicating that the methodology can be used for local potential evapotranspiration estimates.

A NEW METHOD TO QUANTIFY X-RAY SUBSTRUCTURES IN CLUSTERS OF GALAXIES

We present a new method to quantify substructures in clusters of galaxies, based on the analysis of the intensity of structures. This analysis is done in a residual image that is the result of the subtraction of a surface brightness model, obtained by fitting a two-dimensional analytical model (beta-model or Sersic profile) with elliptical symmetry, from the X-ray image. Our method is applied to 34 clusters observed by the Chandra Space Telescope that are in the redshift range z is an element of [0.02, 0.2] and have a signal-to-noise ratio (S/N) greater than 100. We present the calibration of the method and the relations between the substructure level with physical quantities, such as the mass, X-ray luminosity, temperature, and cluster redshift. We use our method to separate the clusters in two sub-samples of high-and low-substructure levels. We conclude, using Monte Carlo simulations, that the method recuperates very well the true amount of substructure for small angular core radii clusters (with respect to the whole image size) and good S/N observations. We find no evidence of correlation between the substructure level and physical properties of the clusters such as gas temperature, X-ray luminosity, and redshift; however, analysis suggest a trend between the substructure level and cluster mass. The scaling relations for the two sub-samples (high-and low-substructure level clusters) are different (they present an offset, i. e., given a fixed mass or temperature, low-substructure clusters tend to be more X-ray luminous), which is an important result for cosmological tests using the mass-luminosity relation to obtain the cluster mass function, since they rely on the assumption that clusters do not present different scaling relations according to their dynamical state.

Improved low-voltage-ride-through capability of fixed-speed wind turbines using decentralised control of STATCOM with energy storage system

The design and implementation of a new control scheme for reactive power compensation, voltage regulation and transient stability enhancement for wind turbines equipped with fixed-speed induction generators (IGs) in large interconnected power systems is presented in this study. The low-voltage-ride-through (LVRT) capability is provided by extending the range of the operation of the controlled system to include typical post-fault conditions. A systematic procedure is proposed to design decentralised multi-variable controllers for large interconnected power systems using the linear quadratic (LQ) output-feedback control design method and the controller design procedure is formulated as an optimisation problem involving rank-constrained linear matrix inequality (LMI). In this study, it is shown that a static synchronous compensator (STATCOM) with energy storage system (ESS), controlled via robust control technique, is an effective device for improving the LVRT capability of fixed-speed wind turbines.

Torque removal evaluation of prosthetic screws after tightening and loosening cycles: an in vitro study

Objectives: The aim of this study was to evaluate the variation in removal torque of implant prosthetic abutment screws after successive tightening and loosening cycles, in addition to evaluating the influence of the hexagon at the abutment base on screw removal torque. Material and methods: Twenty hexagonal abutments were tightened to 20 regular external hex implants with a titanium alloy screw, with an insertion torque of 32 N cm, measured with a digital torque gauge. The implant/abutment/screw assemblies were divided into two groups: ( 1) abutments without hexagon at the base and ( 2) abutments with a hexagon at the base. Each assembly received a provisional restoration and was submitted to mechanical loading cycles. After this, the screws were removed and the removal torque was measured. This sequence was repeated 10 times, then the screw was replaced by a new one, and another cycle was performed. Linear regression analysis was performed. Results: Removal torque values tended to decrease as the number of insertion/removal cycles increased, for both groups. Comparisons of the slopes and the intercepts between groups showed no statistical difference. There was no significant difference between the mean values of last five cycles and the 11th cycle. Within the limitations of this in vitro study, it was concluded that ( 1) repeated insertion/removal cycles promoted gradual reduction in removal torque of screws, ( 2) replacing the screw with a new one after 10 cycles did not increase resistance to loosening, and ( 3) removal of the hexagon from the abutment base had no effect on the removal torque of the screws.

The Poisson-exponential regression model under different latent activation schemes

In this paper, a new family of survival distributions is presented. It is derived by considering that the latent number of failure causes follows a Poisson distribution and the time for these causes to be activated follows an exponential distribution. Three different activation schemes are also considered. Moreover, we propose the inclusion of covariates in the model formulation in order to study their effect on the expected value of the number of causes and on the failure rate function. Inferential procedure based on the maximum likelihood method is discussed and evaluated via simulation. The developed methodology is illustrated on a real data set on ovarian cancer.

Examination of the performance of different pointwise linear regression progression criteria to detect glaucomatous visual field change

Background: We aimed to investigate the performance of five different trend analysis criteria for the detection of glaucomatous progression and to determine the most frequently and rapidly progressing locations of the visual field. Design: Retrospective cohort. Participants or Samples: Treated glaucoma patients with =8 Swedish Interactive Thresholding Algorithm (SITA)-standard 24-2 visual field tests. Methods: Progression was determined using trend analysis. Five different criteria were used: (A) =1 significantly progressing point; (B) =2 significantly progressing points; (C) =2 progressing points located in the same hemifield; (D) at least two adjacent progressing points located in the same hemifield; (E) =2 progressing points in the same Garway-Heath map sector. Main Outcome Measures: Number of progressing eyes and false-positive results. Results: We included 587 patients. The number of eyes reaching a progression endpoint using each criterion was: A = 300 (51%); B = 212 (36%); C = 194 (33%); D = 170 (29%); and E = 186 (31%) (P = 0.03). The numbers of eyes with positive slopes were: A = 13 (4.3%); B = 3 (1.4%); C = 3 (1.5%); D = 2 (1.1%); and E = 3 (1.6%) (P = 0.06). The global slopes for progressing eyes were more negative in Groups B, C and D than in Group A (P = 0.004). The visual field locations that progressed more often were those in the nasal field adjacent to the horizontal midline. Conclusions: Pointwise linear regression criteria that take into account the retinal nerve fibre layer anatomy enhances the specificity of trend analysis for the detection glaucomatous visual field progression.

Mammography-based screening program: preliminary results from a first 2-year round in a Brazilian region using mobile and fixed units

Background: Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer deaths among women worldwide. The use of mobile mammography units to offer screening to women living in remote areas is a rational strategy to increase the number of women examined. This study aimed to evaluate results from the first 2 years of a government-organized mammography screening program implemented with a mobile unit (MU) and a fixed unit (FU) in a rural county in Brazil. The program offered breast cancer screening to women living in Barretos and the surrounding area. Methods: Based on epidemiologic data, 54 238 women, aged 40 to 69 years, were eligible for breast cancer screening. The study included women examined from April 1, 2003 to March 31, 2005. The chi-square test and Bonferroni correction analyses were used to evaluate the frequencies of tumors and the importance of clinical parameters and tumor characteristics. Significance was set at p < 0.05. Results: Overall, 17 964 women underwent mammography. This represented 33.1% of eligible women in the area. A mean of 18.6 and 26.3 women per day were examined in the FU and MU, respectively. Seventy six patients were diagnosed with breast cancer (41 (54%) in the MU). This represented 4.2 cases of breast cancer per 1000 examinations. The number of cancers detected was significantly higher in women aged 60 to 69 years than in those aged 50 to 59 years (p < 0.001) or 40 to 49 years (p < 0.001). No difference was observed between women aged 40 to 49 years and those aged 50 to 59 years (p = 0.164). The proportion of tumors in the early (EC 0 and EC I) and advanced (CS III and CS IV) stages of development were 43.4% and 15.8%, respectively. Conclusions: Preliminary results indicate that this mammography screening program is feasible for implementation in a rural Brazilian territory and favor program continuation.

Bartlett corrections in Birnbaum-Saunders nonlinear regression models

Lemonte and Cordeiro [Birnbaum-Saunders nonlinear regression models, Comput. Stat. Data Anal. 53 (2009), pp. 4441-4452] introduced a class of Birnbaum-Saunders (BS) nonlinear regression models potentially useful in lifetime data analysis. We give a general matrix Bartlett correction formula to improve the likelihood ratio (LR) tests in these models. The formula is simple enough to be used analytically to obtain several closed-form expressions in special cases. Our results generalize those in Lemonte et al. [Improved likelihood inference in Birnbaum-Saunders regressions, Comput. Stat. DataAnal. 54 (2010), pp. 1307-1316], which hold only for the BS linear regression models. We consider Monte Carlo simulations to show that the corrected tests work better than the usual LR tests.

A model of road effect using line integrals and a test of the performance of two new road indices using the distribution of small mammals in an Atlantic Forest landscape

Effects of roads on wildlife and its habitat have been measured using metrics, such as the nearest road distance, road density, and effective mesh size. In this work we introduce two new indices: (1) Integral Road Effect (IRE), which measured the sum effects of points in a road at a fixed point in the forest; and (2) Average Value of the Infinitesimal Road Effect (AVIRE), which measured the average of the effects of roads at this point. IRE is formally defined as the line integral of a special function (the infinitesimal road effect) along the curves that model the roads, whereas AVIRE is the quotient of IRE by the length of the roads. Combining tools of ArcGIS software with a numerical algorithm, we calculated these and other road and habitat cover indices in a sample of points in a human-modified landscape in the Brazilian Atlantic Forest, where data on the abundance of two groups of small mammals (forest specialists and habitat generalists) were collected in the field. We then compared through the Akaike Information Criterion (AIC) a set of candidate regression models to explain the variation in small mammal abundance, including models with our two new road indices (AVIRE and IRE) or models with other road effect indices (nearest road distance, mesh size, and road density), and reference models (containing only habitat indices, or only the intercept without the effect of any variable). Compared to other road effect indices, AVIRE showed the best performance to explain abundance of forest specialist species, whereas the nearest road distance obtained the best performance to generalist species. AVIRE and habitat together were included in the best model for both small mammal groups, that is, higher abundance of specialist and generalist small mammals occurred where there is lower average road effect (less AVIRE) and more habitat. Moreover, AVIRE was not significantly correlated with habitat cover of specialists and generalists differing from the other road effect indices, except mesh size, which allows for separating the effect of roads from the effect of habitat on small mammal communities. We suggest that the proposed indices and GIS procedures could also be useful to describe other spatial ecological phenomena, such as edge effect in habitat fragments. (C) 2012 Elsevier B.V. All rights reserved.

Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients

Background: Tuberculosis (TB) remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission. Methods: Cross sectional study of patients admitted to CFFH from March 2003 to December 2004. A classification and regression tree (CART) model was generated and validated. The area under the ROC curve (AUC), sensitivity, specificity, positive and negative predictive values were used to evaluate the performance of model. Validation of the model was performed with a different sample of patients admitted to the same hospital from January to December 2005. Results: We studied 290 patients admitted with clinical suspicion of TB. Diagnosis was confirmed in 26.5% of them. Pulmonary TB was present in 83.7% of the patients with TB (62.3% with positive sputum smear) and HIV/AIDS was present in 56.9% of patients. The validated CART model showed sensitivity, specificity, positive predictive value and negative predictive value of 60.00%, 76.16%, 33.33%, and 90.55%, respectively. The AUC was 79.70%. Conclusions: The CART model developed for these hospitalized patients with clinical suspicion of TB had fair to good predictive performance for pulmonary TB. The most important variable for prediction of TB diagnosis was chest radiograph results. Prospective validation is still necessary, but our model offer an alternative for decision making in whether to isolate patients with clinical suspicion of TB in tertiary health facilities in countries with limited resources.

Response plateau models fitting via isotonic regression

Within the nutritional context, the supplementation of microminerals in bird food is often made in quantities exceeding those required in the attempt to ensure the proper performance of the animals. The experiments of type dosage x response are very common in the determination of levels of nutrients in optimal food balance and include the use of regression models to achieve this objective. Nevertheless, the regression analysis routine, generally, uses a priori information about a possible relationship between the response variable. The isotonic regression is a method of estimation by least squares that generates estimates which preserves data ordering. In the theory of isotonic regression this information is essential and it is expected to increase fitting efficiency. The objective of this work was to use an isotonic regression methodology, as an alternative way of analyzing data of Zn deposition in tibia of male birds of Hubbard lineage. We considered the models of plateau response of polynomial quadratic and linear exponential forms. In addition to these models, we also proposed the fitting of a logarithmic model to the data and the efficiency of the methodology was evaluated by Monte Carlo simulations, considering different scenarios for the parametric values. The isotonization of the data yielded an improvement in all the fitting quality parameters evaluated. Among the models used, the logarithmic presented estimates of the parameters more consistent with the values reported in literature.

Latent residual analysis in binary regression with skewed link

Model diagnostics is an integral part of model determination and an important part of the model diagnostics is residual analysis. We adapt and implement residuals considered in the literature for the probit, logistic and skew-probit links under binary regression. New latent residuals for the skew-probit link are proposed here. We have detected the presence of outliers using the residuals proposed here for different models in a simulated dataset and a real medical dataset.