Relative Importance Of Different Variables Research Articles

Sociality, ecology and developmental constraints predict variation in brain size across birds.

Conflicting theories have been proposed to explain variation in relative brain size across the animal kingdom. Ecological theories argue that the cognitive demands of seasonal or unpredictable environments have selected for increases in relative brain size, whereas the 'social brain hypothesis' argues that social complexity is the primary driver of brain size evolution. Here, we use a comparative approach to test the relative importance of ecology (diet, foraging niche and migration), sociality (social bond, cooperative breeding and territoriality) and developmental mode in shaping brain size across 1886 bird species. Across all birds, we find a highly significant effect of developmental mode and foraging niche on brain size, suggesting that developmental constraints and selection for complex motor skills whilst foraging generally imposes important selection on brain size in birds. We also find effects of social bonding and territoriality on brain size, but the direction of these effects do not support the social brain hypothesis. At the same time, we find extensive heterogeneity among major avian clades in the relative importance of different variables, implying that the significance of particular ecological and social factors for driving brain size evolution is often clade- and context-specific. Overall, our results reveal the important and complex ways in which ecological and social selection pressures and developmental constraints shape brain size evolution across birds.

Application of machine learning algorithms in predicting the photocatalytic degradation of perfluorooctanoic acid

ABSTRACT Perfluorooctanoic acid (PFOA) is used in a variety of industries and is highly persistent in the environment, with potential human health risks. Photocatalysis has been extensively used for the decomposition of various organic pollutants, yet its simulation and modeling are challenging. This research aimed to establish different machine learning (ML) algorithms which can simulate and predict the photocatalytic degradation of PFOA. The published results were used to estimate and predict the photocatalytic degradation of PFOA. Statistical criteria including the coefficient of determination (R2 ), mean absolute error (MAE), and mean squared error (MSE) were considered in assessing the best method of modeling. Among the seven ML algorithms pre-screened, Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) showed the best performance and were chosen for deep modeling and analysis. Grid search was used to optimize the models developed by AdaBoost, GBM, and RF; and permutation variable importance (PVI) was used to analyze the relative importance of different variables. Based on the modeling results, GBM model (R2 = 0.878, MSE = 106.660, MAE = 6.009) and RF model (R2 = 0.867, MSE = 107.500, MAE = 6.796) showed superior performances compared with AdaBoost model (R2 = 0.574, MSE = 388.369, MAE = 16.480). Furthermore, the PVI results suggested that the GBM model provided the best outcome, with the light irradiation time, type of catalyst, dosage of catalyst, solution pH, irradiation intensity, initial PFOA concentration, oxidizing agents (peroxymonosulfate, ammonium persulfate, and sodium persulfate), irradiation wavelength, and solution temperature as the most important process variables in decreasing order.

A simple algorithm for global sensitivity analysis with Shapley effects

Global sensitivity analysis aims at measuring the relative importance of different variables or groups of variables for the variability of a quantity of interest. Among several sensitivity indices, so-called Shapley effects have recently gained popularity mainly because the Shapley effects for all the individual variables are summed up to the overall variance, which gives a better interpretability than the classical sensitivity indices called main effects and total effects. In this paper, assuming that all the input variables are independent, we introduce a quite simple Monte Carlo algorithm to estimate the Shapley effects for all the individual variables simultaneously, which drastically simplifies the existing algorithms proposed in the literature. We present a short Matlab implementation of our algorithm and show some numerical results. A possible extension to the case where the input variables are dependent is also discussed.

A global meta-analysis of grazing effects on plant richness

A major challenge in rangeland ecology is understanding diverse responses of diversity to grazing, what factors determine these responses and what their relative importance is. Numerous site-level studies have explored grazing-diversity relations, but meta-analyses of these empirical results at a global scale are scarce. Our goals were to estimate the mean effect of grazing on plant richness and to assess the relative importance of different variables at a global scale. We conducted a global meta-analysis of 259 comparisons of plant richness in grazed vs. ungrazed sites from 96 published studies. Then, we performed analyses of subgroup, meta-regression and correlation for testing the relative roles of regional and local variables (e.g. evolutionary history of grazing, aridity, stocking rate, etc.).Globally, grazing significantly increased plant richness compared to grazing exclusion, especially in wet grasslands. The effect of evolutionary history of grazing was insignificant, which cannot support the major perception behind Milchunas-Sala-Lauenroth (MSL) model. Aridity, vegetation type, and stocking rate were three important variables that together explained 41 % of the global variation in plant richness. The roles of stocking rate and duration of grazing exclusion were revealed only when aridity and vegetation type were added to the analyses, which showed a stronger effect of stocking rate in wet areas but a stronger effect of the duration of grazing exclusion in dry areas. Changes in plant richness were significantly correlated with changes in soil carbon and nitrogen, both showing hump-shaped patterns.Our findings substantiate that scholars may improve existing theories (e.g. the MSL model, the intermediate disturbance hypothesis) through further expounding the interactions between grazing variables and aridity and vegetation type. Rangeland managers need to devise local-scale grazing strategies for conserving plant diversity according to site-specific conditions, rather than adopting a one-size-fits-all solution.

Thermodynamic modeling of hydrous-melt–olivine equilibrium using exhaustive variable selection

Water in silicate melt influences the phase relations of a hydrous-melt system. Given the importance of water in silicate melts, a quantitative thermodynamic understanding of the non-ideality of hydrous melt is necessary to properly model natural magmatic processes. This paper presents a novel method for quantitative thermodynamic modeling of hydrous-melt–olivine equilibrium. Specifically, a machine learning method, exhaustive variable selection (ES), is used to model the non-ideality of hydrous melts. Using the ES method, we quantitatively validate the predictive capacities of all possible combinations of variables and then adopt the combination with the highest predictive capacity as the optimal model equation. The ES method allows us to obtain the underlying thermodynamic relationship of the hydrous-melt–olivine system, such as the relative importance of different variables to the thermodynamic equilibrium, as well as to construct a robust and generalized model. We show that the combination of a linear term and a squared term of the total water concentration of melt is significant for describing the hydrous-melt–olivine equilibrium. This result is interpreted in terms of the microstructural changes related to the dissociation of water in silicate melt. Calculations using the optimal model reproduce the experimentally determined effects of water on the olivine liquidus and the distribution coefficient for Mg between olivine and hydrous melt. Our study demonstrates that the ES method yields a thermodynamic equilibrium model that captures the essential thermodynamic relationship explaining the high-dimensional and complex experimental data.

Severe fire weather and intensive forest management increase fire severity in a multi-ownership landscape.

Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi-owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon, USA. O&C lands are composed of a checkerboard of private industrial and federal forestland (Bureau of Land Management, BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre-fire forest conditions, and land ownership, we asked (1) what is the relative importance of different variables driving fire severity, and (2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre-fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBR was higher on private industrial forests (RdNBR 521.85±18.67 [mean ± SE]) vs. BLM forests (398.87±18.23) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre-fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi-owner landscapes.

Effects of environmental variables on invasive amphibian activity: using model selection on quantiles for counts

AbstractMany different factors influence animal activity. Often, the value of an environmental variable may influence significantly the upper or lower tails of the activity distribution. For describing relationships with heterogeneous boundaries, quantile regressions predict a quantile of the conditional distribution of the dependent variable. A quantile count model extends linear quantile regression methods to discrete response variables, and is useful if activity is quantified by trapping, where there may be many tied (equal) values in the activity distribution, over a small range of discrete values. Additionally, different environmental variables in combination may have synergistic or antagonistic effects on activity, so examining their effects together, in a modeling framework, is a useful approach. Thus, model selection on quantile counts can be used to determine the relative importance of different variables in determining activity, across the entire distribution of capture results. We conducted model selection on quantile count models to describe the factors affecting activity (numbers of captures) of cane toads (Rhinella marina) in response to several environmental variables (humidity, temperature, rainfall, wind speed, and moon luminosity) over eleven months of trapping. Environmental effects on activity are understudied in this pest animal. In the dry season, model selection on quantile count models suggested that rainfall positively affected activity, especially near the lower tails of the activity distribution. In the wet season, wind speed limited activity near the maximum of the distribution, while minimum activity increased with minimum temperature. This statistical methodology allowed us to explore, in depth, how environmental factors influenced activity across the entire distribution, and is applicable to any survey or trapping regime, in which environmental variables affect activity.

Some Remarks on Standardized Regression Coefficient

대부분의 사회과학분야 연구에서는 다중선형회귀모형에 포함되는 설명변수들의 상대적 중요도에 대해 많은 관심을 가지고 있다. SPSS를 비롯한 여러 상용 통계패키지에서는 표준화 회귀계수의 추정값을 계산해 주고 있으며 표준화 회귀계수의 추정값의 절대값이 큰 설명변수가 중요하다고 해석하기도 하지만 이러한 주장에는 많은 논란이 있다. 여러 회귀분석 교과서에서도 표준화 회귀계수의 맹목적인 사용에 유의할 필요가 있다고 강조하고 있지만 대안은 제시되고 있지않다. 본 연구에서는 설명변수의 상대적 중요도에 대한 구체적인 의미를 생각해보고 몇 가지 잔차산점도에서의 상관계수와 표준화 회귀계수의 관계를 알아보고 설명변수들의 상대적 중요도를 나타내는 적절한 척도를 제안한다. 추가변수그림의 상관계수는 추가적인 설명력을 검정하는 부분 검정통계량의 크기를 나타내는 척도로 사용될 수 있고, 추가결정계수그림의 상관계수는 설명변수의 추가를 통해서 발생하는 결정계수의 증가분을 나타내는 척도로 사용될 수 있다. 또한 보정계수를 이용하여 이러한 척도들과 표준화 회귀계수와의 관계를 구해본다. 제시된 척도들을 실제 자료에 적용시켜본다.In most social science research work there are some interests concerning the rank of relative importance of different variables in the regression model. Statistical packages such as SPSS provide the printouts on the standardized coefficient. But many people make cautionary remarks that the rankings of the standardized coefficients in terms of absolute magnitude does not necessarily reflect the importance of variables in explaining the variability of response variable. Many textbooks give warnings against the misuse of this automatic computer generated output. We consider the relationship between this standardized coefficient and the correlation coefficients of various residual plots. And this suggests that the appropriate correlation coefficients may be used in ranking the relative importance of variables. The correlation coefficient derived from added variable plot is a good measure in determining the ranks of variables according to the size of partial -values for regression coefficient. The correlation coefficient from the additional   plot compares each variable s contribution in terms of additional increase of coefficient of determination over the variability explained by other variables in the model.

Engine Combustion Network (ECN): Global sensitivity analysis of Spray A for different combustion vessels

Global sensitivity analysis was conducted on Spray A of the Engine Combustion Network’s (ECN) experimental conditions across different combustion vessels from different institutions. The main objective was to understand the influence of differences in boundary conditions on specific targets of interest. A list of experimental initial, boundary, and injection conditions with uncertainty ranges were identified for three different ambient conditions for these combustion vessels. Five targets including the liquid length, vapor penetration length, ignition delay, lift-off length, and soot mass in the domain were extracted from hundreds of 3D computational fluid dynamics (CFD) simulations by simultaneously perturbing all the uncertain variables. Each target was analyzed using a global sensitivity analysis (GSA) method and the relative importance of different variables towards specific targets was identified. The uncertainty in the fuel temperature was found to have a profound influence on the liquid length, however, the influence on vapor penetration length, ignition delay, and lift-off length, was rather subtle. Small uncertainties in the initial turbulence level and nozzle diameter was observed to have a significant influence on the vapor penetration. Variables sensitive to ignition delay also showed similar sensitivity to flame lift-off length. The soot mass in the domain was observed to have a closer correlation with the liquid length at least for higher ambient temperature conditions.

Tree spatial structure, host composition and resource availability influence mirid density or black pod prevalence in cacao agroforests in Cameroon.

Combining crop plants with other plant species in agro-ecosystems is one way to enhance ecological pest and disease regulation mechanisms. Resource availability and microclimatic variation mechanisms affect processes related to pest and pathogen life cycles. These mechanisms are supported both by empirical research and by epidemiological models, yet their relative importance in a real complex agro-ecosystem is still not known. Our aim was thus to assess the independent effects and the relative importance of different variables related to resource availability and microclimatic variation that explain pest and disease occurrence at the plot scale in real complex agro-ecosystems. The study was conducted in cacao (Theobroma cacao) agroforests in Cameroon, where cocoa production is mainly impacted by the mirid bug, Sahlbergella singularis, and black pod disease, caused by Phytophthora megakarya. Vegetation composition and spatial structure, resource availability and pest and disease occurrence were characterized in 20 real agroforest plots. Hierarchical partitioning was used to identify the causal variables that explain mirid density and black pod prevalence. The results of this study show that cacao agroforests can be differentiated on the basis of vegetation composition and spatial structure. This original approach revealed that mirid density decreased when a minimum number of randomly distributed forest trees were present compared with the aggregated distribution of forest trees, or when forest tree density was low. Moreover, a decrease in mirid density was also related to decreased availability of sensitive tissue, independently of the effect of forest tree structure. Contrary to expectations, black pod prevalence decreased with increasing cacao tree abundance. By revealing the effects of vegetation composition and spatial structure on mirids and black pod, this study opens new perspectives for the joint agro-ecological management of cacao pests and diseases at the plot scale, through the optimization of the spatial structure and composition of the vegetation.

Does Attribution of Blame Influence Psychological Outcomes in Facial Trauma Victims?

The relative importance of different variables and specific post-traumatic psychological reactions after facial injuries is poorly understood. The aim of the present study was to assess the association between the subjective attribution of blame and anxiety and depression in facial trauma victims. We undertook a comparative cross-sectional study of psychological outcomes in a cohort of adult patients who have sustained a facial injury. The primary predictor variable was the attribution of blame (self-blame vs blame-others). The main outcome variable was the Hospital Anxiety and Depression Scale (HADS) scores. We also considered several demographic and other clinical variables to assess their relationship with the nature of blame attribution. Cochran-Mantel-Haenszel statistics were used to assess the relationship between the primary predictor and outcome variables, adjusted for age, gender, and confounding factors. The sample consisted of 102 facial trauma victims (77 men and 25 women). Of the 77 patients, 63 attributed blame for their injuries to someone else and 39 patients attributed blame to themselves. Psychometric scores suggestive of anxiety and the depressive state were significantly greater in the "blame-others" group than in the "self-blame" group (HADS-Anxiety 22% vs 13%, HADS-Depression 17% vs 8%). The prevalence of psychomorbidity in the blame-others group was approximately twice that found in the self-blame group (odds ratio 2.2). Facial trauma patients who blamed others for their injury were predominantly younger men (P = .01) and typically victims of intentional trauma (P < .001). The results of the present study suggest that external attribution of blame for facial injury is associated with poor postinjury psychiatric outcomes.

Agricultural Explosion in Brazil: Exploring the Impacts of the Brazilian Agricultural Development over the Amazon

Agriculture has always been an important economic activity in Brazil and is now among the most dynamic sectors in the Brazilian economy. However, a lot of controversy has risen over the impact of such phenomenon on the Brazilian Amazon. This paper offers a critical overview of the literature on the expansion of the Brazilian agricultural frontier, demonstrating its impact on the degradation of the Amazon forest based on secondary data and institutional document sources. An overview of the historical development of the Brazilian Amazon basin is conducted by looking in a critical way at some implications of such a phenomenon. Rather then commenting on an empirical study, the objective of this paper is to present a theoretical analysis of the models of development adopted for the Amazon region in Brazil, showing the relative importance of different variables and factors to the depletion of that ecosystem. It is argued that although the figures show net benefit to the Brazilian economy, the prosperity which is advocated to be brought by the agricultural explosion favors mostly established elites and vested international interests, which have contributed to the degradation of the Amazon forest for a long time. It is suggested that the focus on Brazilian agricultural growth as the main driver of deforestation of the Amazon has omitted the historical links to the deforestation problem and the related role of effective international cooperation.

Convective heat transfer in a steeply dipping fault zone and its impact on the interpretation of fission-track data - a modelling study

AbstractThe effects of convective heat transfer by hydrothermal fluid flow on fission-track (FT) thermochronology are studied using numerical modelling techniques. Parameter studies are carried out on two-dimensional crustal segments with a steeply dipping fault zone exposed to constant denudation to evaluate the relative importance of different variables, including denudation rate as well as hydraulic and material properties. Time–temperature histories of particle points are calculated in the vicinity and also a few kilometres away of the fault zone. These time–temperature paths are then used in a forward-modelling approach to determine the expected FT cooling ages and track-length distributions.Modelling results indicate that hydrothermal fluid flow can significantly disturb the background conductive thermal state of the upper crust, and the interpretation of FT data using a steady-state geothermal gradient can result in erroneous denudation rates that overestimate the true erosion rates by more than 80%. A pattern of highly varied FT cooling ages from samples at the same elevation does not necessarily ask for differential tectonic movements, instead it can be generated by deep circulation of groundwater within a few million years (Ma). Denudation rates inferred from FT cooling age–elevation plots are likewise inaccurate in a hydrothermally active area because the important assumption about closure temperature isotherms being horizontal or at a constant depth below the surface is not met.

Comparison procedure of predicting the time to default in behavioural scoring

The paper deals with the problem of predicting the time to default in credit behavioural scoring. This area opens a possibility of including a dynamic component in behavioural scoring modelling which enables making decisions related to limit, collection and recovery strategies, retention and attrition, as well as providing an insight into the profitability, pricing or term structure of the loan. In this paper, we compare survival analysis and neural networks in terms of modelling and results. The neural network architecture is designed such that its output is comparable to the survival analysis output. Six neural network models were created, one for each period of default. A radial basis neural network algorithm was used to test all six models. The survival model used a Cox modelling procedure. Further, different performance measures of all models were discussed since even in highly accurate scoring models, misclassification patterns appear. A systematic comparison ‘3 + 2 + 2’ procedure is suggested to find the most effective model for a bank. Additionally, the survival analysis model is compared to neural network models according to the relative importance of different variables in predicting the time to default. Although different models can have very similar performance measures they may consist of different variables. The dataset used for the research was collected from a Croatian bank and credit customers were observed during a 12-month period. The paper emphasizes the importance of conducting a detailed comparison procedure while selecting the best model that satisfies the users’ interest.

Construction of an Index by Maximization of the Sum of its Absolute Correlation Coefficients with the Constituent Variables

On many occasions we need to construct an index that represents a number of variables. Cost of living index, general price index, human development index, index of level of development, etc are some of the examples that are constructed by a weighted (linear) aggregation of a host of variables. The weights are determined by the importance assigned to the variables to be aggregated. The criterion on which importance of a variable (vis-a-vis other variables) is determined may be varied and usually has its own logic. In many cases the analyst does not have any preferred means or logic to determine the relative importance of different variables. In such cases, weights are assigned mathematically. One of the methods to determine such mathematical weights is the Principal Components analysis. In the Principal Components analysis weights are determined such that the sum of the squared correlation coefficients of the index with the constituent variables is maximized. The method has, however, a tendency to pick up the subset of highly correlated variables to make the first component, assign marginal weights to relatively poorly correlated subset of variables and/or to relegate the latter subset to construction of the subsequent principal components. If one has to construct a single index, such an index undermines the poorly correlated set of variables. The index so constructed is elitist in nature that has a preference to the highly correlated subset over the poorly correlated subset of variables. Further, since there is no dependable method available to obtain a composite index by merging two or more principal components, the deferred set of variables never finds its representation in the further analysis. In this paper we suggest a method to construct an index by maximizing the sum of the absolute correlation coefficients of the index with the constituent variables. We also suggest construction of an alternative index by maximin correlation. Our experiments suggest that the indices so constructed are inclusive or egalitarian. They do not prefer the highly correlated variables much to the poorly correlated variables.

On the Effects of Dimension Reduction Techniques on Some High-Dimensional Problems in Finance

Many problems in finance can be formulated as high-dimensional integrals, which are often attacked by quasi-Monte Carlo (QMC) algorithms. To enhance QMC algorithms, dimension reduction techniques, such as the Brownian bridge (BB) and principal component analysis (PCA), are used to reduce the effective dimension. This paper explores in depth the effects of these techniques on the dimension structure of some typical high-dimensional problems from finance: the pricing of path-dependent options and bond valuation according to term structure models. By deriving explicit expressions for the underlying integrands and the associated weights that control the relative importance of different variables, and by investigating the variance ratios, the effective dimensions, the mean dimension, and their limiting behavior as the nominal dimension tends to infinity, we show theoretically and empirically how and to what extent the BB and PCA algorithms change the dimension structure (including the degree of additivity) of the underlying functions. They change the functions to be strongly weighted and substantially reduce the effective dimensions and the mean dimension; and they enhance the degree of additivity, which is particularly important for QMC. Moreover, the resulting functions are of low effective dimension, not only in the superposition sense, but also in the truncation sense. The variance ratios, the effective dimensions, and the mean dimension associated with these techniques are very insensitive to the nominal dimension (they are essentially constant), which highlights the possibility of removing the curse of dimensionality when dimension reduction techniques are used in combination with QMC. A counterexample is also shown for which the BB and PCA may increase the effective dimension. The investigation provides further insight into the effects of dimension reduction techniques.

Prognostic personal credit risk model considering censored information

Credit scoring is one of the most successful applications of quantitative analysis that helps organizations decide whether or not to grant credit to consumers who apply to them. However, standard credit risk models based on binary classifying approaches appear to have missed several important time-varying factors and censoring information. This paper looks at the extensions of the survival analysis model to analyze personal credit risk. Survival analysis has mainly been used in the clinical domain, which can handle the above issues. This paper investigates the ability of the survival-based approach to predict the probability of personal default. The proposed method can give a prediction of 'time' as well as 'probability' of personal default. We develop a survival-based credit risk model and assess the relative importance of different variables in predicting default. Standard binary classifying models are also developed for assessing a new way in the context of classifying power. These models are applied to personal credit card accounts dataset. According to the experiment results, survival-based credit risk modeling is a more useful approach for classifying risky customers than others. The survival-based approach is a useful alternative and a complement in view of personal credit risk.

Why Are High-Dimensional Finance Problems Often of Low Effective Dimension?

Many problems in mathematical finance can be formulated as high-dimensional integrals, where the large number of dimensions arises from small time steps in time discretization and/or a large number of state variables. Quasi--Monte Carlo (QMC) methods have been successfully used for approximating such integrals. To understand this success, this paper focuses on investigating the special features of some typical high-dimensional finance problems, namely, option pricing and bond valuation. We provide new insight into the connection between the effective dimension and the efficiency of QMC, and we present methods to analyze the dimension structure of a function. We confirm the observation of Caflisch, Morokoff, and Owen that functions from finance are often of low effective dimension, in the sense that they can be well approximated by their low-order ANOVA (analysis of variance) terms, usually just the order-1 and order-2 terms. We explore why the effective dimension is small for many integrals from finance. By deriving explicit forms of the ANOVA terms in simple cases, we find that the importance of each dimension is naturally weighted by certain hidden weights. These weights characterize the relative importance of different variables or groups of variables and limit the importance of the higher-order ANOVA terms. We study thevariance ratios captured by low-order ANOVA terms and their asymptotic properties as the dimension tends to infinity, and we show that with the increase of dimension the lower-order terms continue to play a significant role and the higher-order terms tend to be negligible. This provides some insight into high-dimensional problems from finance and explains why QMC algorithms are efficient for problems of this kind.

Influence of start‐ups with fuel‐oil on the operation of electrostatic precipitators in pulverised coal boilers

AbstractThis article describes the results of a series of tests carried out in a pilot fly ash electrostatic precipitation facility operating with real gases from a 550 MWe pulverized coal‐fired power station. The main goal of these tests was to determine the effect of boiler start‐ups on the performance of the electrostatic precipitator.The tests were carried out during start‐ups of the power station boiler. All tests were carried out with the same fuel. An evaluation was made of the effect of the use of fuel‐oil as auxiliary fuel in start‐ups and shut‐downs of the boiler, and different electrostatic precipitator operation procedures were tested during start‐ups and shut‐downs.The results of the experiments made it possible to assess the relative importance of different variables on the possible deterioration of the efficiency of the precipitator. Also evaluated were operational modes that have demonstrated an improvement in the performance of the precipitators after the transient stage of these operations. As a result of this study, a number of important operational recommendations are made on boiler start‐up and shut‐down procedures. © 2004 American Institute of Chemical Engineers Environ Prog, 23: 29–38, 2004

Value of the Van Nuys Prognostic Index in prediction of recurrence of ductal carcinoma in situ after breast-conserving surgery.

The Van Nuys Prognostic Index (VNPI), an algorithm based on tumour size, tumour grade, presence of necrosis and excision margin width, is claimed to predict local recurrence after breast-conserving surgery for ductal carcinoma in situ (DCIS). The aim of this study was to examine the validity of the VNPI in a UK population. Clinicopathological data, including VNPI subgroups, for 237 patients who had breast-conserving operations for DCIS were examined. Multivariate data analysis was performed using a Cox regression model to examine the independence and relative importance of different variables in predicting recurrence, and to compare the data with those used in derivation of the VNPI. The median follow-up was 47 months. There were 37 ipsilateral local recurrences. Excision margin width (P < 0.001) and tumour grade (by Van Nuys grading (P = 0.014) or simple nuclear grading (P = 0.004)) were the only independent risk factors for local recurrence. Excision margin width had three times more power than grade in predicting local recurrence. Subgrouping data by VNPI score predicted recurrence-free survival (P < 0.001), but stratified 78 per cent of patients into a group with a moderate risk of local recurrence. Excision margin width is the most important predictor of local recurrence after breast-conserving surgery for DCIS. The VNPI lacked discriminatory power for guiding further patient management.