Search

Purpose: This paper aims to apply regression-tree analysis to capture the nonlinear effects of corruption on economic growth. Using data of 103 countries for the period 1996–2017, the authors endogenously detect two distinct areas in corruption quality in which the members share the same model of economic growth. Design/methodology/approach: The authors apply regression tree analysis to capture the nonlinearity of the influences. This methodology allows us to split endogenously the whole sample of countries and characterize the different ways through which corruption impacts economic growth in each group of countries. Findings: The traditional determinants of economic growth have different impacts on countries depending on their level of corruption, which, in turn, confirms the parameter heterogeneity of the Solow model found in other strands of the literature. Originality/value: The authors apply a new approach to a worldwide sample obtaining novel results. [ABSTRACT FROM AUTHOR]

The existence of time delay in complex industrial processes or dynamical systems is a common phenomenon and is a difficult problem to deal with in industrial control systems, as well as in the textile field. Accurate identification of the time delay can greatly improve the efficiency of the design of industrial process control systems. The time delay identification methods based on mathematical modeling require prior knowledge of the structural information of the model, especially for nonlinear systems. The neural network-based identification method can predict the time delay of the system, but cannot accurately obtain the specific parameters of the time delay. Benefit from the interpretability of machine learning, a novel method for delay identification based on an interpretable regression decision tree is proposed. Utilizing the self-explanatory analysis of the decision tree model, the parameters with the highest feature importance are obtained to identify the time delay of the system. Excellent results are gained by the simulation data of linear and nonlinear control systems, and the time delay of the systems can be accurately identified. [ABSTRACT FROM AUTHOR]

There are growing debates and discussions on limitations of inequality of outcome to explain the widening income inequalities within the country across the world. In this context, the scholars and public policy advocates are taking keen interest in measurement of inequality of opportunity (IOp), which is based on the philosophical concept of distributive justice. In this article, we have discussed evolution of the IOp concept, its measurement, and provided empirical results on IOp in India based on data from labour forces surveys conducted by National Statistical Office (NSO). The analysis shows that around one-fifth of the consumption inequality, and one-fourth of income inequality is accounted by unequal circumstances in the country. The findings based on shapely decomposition reveals that parental backgrounds i.e., education and occupation contribute the most in unequal opportunities for regular salaried employment, while gender play a key role in explaining unequal earnings opportunity for casual wage employment, and self-employment. The regression inference tree-based results also indicate that parent's education is the most important variable that determine income or consumption inequality followed by locations (rural-urban), and place of birth (regions) and parents' occupation in India. [ABSTRACT FROM AUTHOR]

Random forest models have recently gained popularity for economic forecasting. Earlier studies demonstrated their potential to provide early warnings of recession and serve as a competitive method to older prediction models. This study offers the first evaluation of the random forest forecast for the Czech economy. The one-step-ahead forecasting results show high accuracy on the Czech data and are proven to outperform forecasts from the Czech Ministry of Finance and the Czech National Bank. The following multistep random forest forecast, estimated for the next four quarters, shows results similar to those from the central institutions. The main difference stems from the household and industrial confidence variables, which significantly impact on the random forest forecast. The variable-importance analysis further emphasizes the soft variables as valuable determinants for Czech forecasting. Overall, the findings motivate other forecasters to exercise this method. [ABSTRACT FROM AUTHOR]

Data Mining plays a decisive role especially in medical domain. Decision trees are predominant model in machine learning. Decision trees are simple and very effective classification approach. The decision tree identifies the utmost prime features of a given problem. One of the most common disease in India is Liver Cirrhosis. It is distinctly difficult to uncover Liver Cirrhosis in its initial stage. However early diagnosis of Liver Cirrhosis is highly important. The liver disease data set has a collection of distinguishing features that affect the healthy state of a patient. Machine Learning methods enable knowledge acquisition in early stages and use of this acquired knowledge plays an important role in solving problems like suppose if we want to predict whether the patient with the Liver Cirrhosis has also been suffering from Hepatitis C or not. In order to easily arrive at this knowledge certainly there is a need for fully integrated system. In this paper the collected Liver disease data set is analyzed and prognosticated whether the patient is suffering from liver cirrhosis or not. [ABSTRACT FROM AUTHOR]

Dilated cardiomyopathy (DCM) is a heart muscle disease characterized by left ventricular (LV) or biventricular dilatation and systolic dysfunction in the absence of either pressure or volume overload or coronary artery disease sufficient to explain the dysfunction. The use of heart rate variability (HRV) analysis as well as of some machine learning algorithms, proved to be a valuable support in the diagnosis of cardiovascular disease. However, till now, only single beats or electrocardiogram segments of subjects affected by DCM were identified using machine learning techniques applied to HRV parameters. In this study, we used linear and non-linear HRV parameters and some clinical parameters (age, sex and left ventricular ejection fraction) evaluated on a large cohort of 972 subjects to early identify subjects suffered from DCM and to find which features could be selected as predictors for a correct diagnosis. By using principal component analysis and stepwise regression, we reduced the original parameters used as inputs for a series of classification and regression trees (CART). The highest accuracy of 97% and Area Under the Curve (AUC) of 95% were achieved using the ratio between low frequency and high frequency (LF/HF), sex and left ventricular ejection fraction (LVEF) parameters as inputs of the classifier. [ABSTRACT FROM AUTHOR]

This article represents an attempt at spatiotemporal modelling of transport demand in a metropolitan area over the period of six years. We employed classical forms of machine learning and created regression models, which predict bike-sharing demand based on daily cumulative sums of bike trips for each considered docking station in the area. We next derived hourly estimates of demand by considering the statistical distribution of demand across individual hours of an average day. Longer-term distribution characteristics of bike trips, such as their average number falling on each day of the week, month of the year, etc., were also fed to these models as input attributes. We validated the models by Hold-Out as well as 10-Fold Cross-Validation, with promising results. [ABSTRACT FROM AUTHOR]

In the article, we study application of tree based regression methods for reconstruction of gaps in marker trajectories in optical motion capture systems. Having perfect ground truth sequences, we artificially introduced gaps of lengths between 0.1 and 2 s, then reconstructed them using various tree based regression methods – CART, LS boost, bagging, random forests, M5P (model tree). We also tested several methods known from the literature – neural networks (NN), interpolation, and matrix completion. We found out that in general, tree based methods perform poorly, but M5P model tree performs very well, especially for longer gaps, with results on average on par or better than neural networks. Finally, we compared the outcomes of M5P and NN, they appear to be uncorrelated, so they offer the possibility to create an ensemble predictor based on both methods. [Display omitted] • A review of tree based regression methods for reconstruction of gaps in mocap sequences. • We identified M5P algorithm as an interesting alternative to neural networks. • Prediction errors between M5P and NN are not correlated, what offers opportunity for ensembling the results. [ABSTRACT FROM AUTHOR]

Highlights • Ten machine learning methods were tested for retrieval of evapotranspiration from remote sensing. • The best results were obtained with bootstrap aggregation tree, multi-layer neural network, and random kernel methods. • Training with same surface type data did not improve algorithm performance. Abstract Remote sensing retrieval of evapotranspiration (ET), or surface latent heat exchange (LE), is of great utility for many applications. Machine learning (ML) methods have been extensively used in many disciplines, but so far little work has been performed systematically comparing ML methods for ET retrieval. This paper provides an evaluation of ten ML methods for estimating daily ET based on daily Global LAnd Surface Satellite (GLASS) radiation data and high-level Moderate-Resolution Imaging Spectroradiometer (MODIS) data products and ground measured ET data from 184 flux tower sites. Measurements of accuracy (RMSE, R2, and bias) and run time were made for each of ten ML methods with a smaller training data set (n = 7910 data points) and a larger training data set (n= 69,752 data points). Inclusion of more input variables improved algorithm performance but had little effect on run time. The best results were obtained with the larger training data set using the bootstrap aggregation (bagging) regression tree (validation RMSE = 19.91 W/m2) and three hidden layer neural network (validation RMSE = 20.94 W/m2), although the less computationally demanding random kernel (RKS) algorithm also produced good results (validation RMSE = 22.22 W/m2). Comparison of results from sites with different ecosystem types showed the best results for evergreen, shrub, and grassland sites, and the weakest results for wetland sites. Generally, performance was not improved by training with data from only the same ecosystem type. [ABSTRACT FROM AUTHOR]

We propose a modeling framework that uses only cross-sectional data to disentangle labor supply and demand choices simultaneously. This modeling framework extends the labor-market analytical toolkits to adapt to environments where data are limited, flexibility in working hours is lacking, or structural changes are present, as is the case in most emerging and low-income countries. We showcase our model by using the 2011 China Household Finance Survey to decipher labor market choices in urban China. We find that the main discrepancies in labor supply between males and females are driven by the number and age of children, the lower utility of working rather than fewer job opportunities for females, and larger impacts of education and work experience on females' job opportunities. Household wealth in the form of 'cash inflow' incentivizes individuals not to work, while wealth in the form of 'stock' induces higher utility to work for both males and females. The interpretation of empirical findings hinges on particular assumptions that might be disputed. • A modeling framework that uses only cross-sectional data to disentangle labor supply and demand choices simultaneously. • This modeling framework extends the labor-market analytical toolkits to adapt to emerging and low-income countries' environments. • We showcase our model by using the 2011 China Household Finance Survey to decipher labor market choices in urban China. [ABSTRACT FROM AUTHOR]

Abstract Emotions mean accepting, understanding, and recognizing something with one's senses. The physiological signals generated from the internal organs of the body can objectively and realistically reflect changes in real-time human emotions and monitor the state of the body. In this study, the two-dimensional space-based emotion model was introduced on the basis of Poincare's two-dimensional plot of the signal of heart rate variability. Four main colors of psychology, blue, red, green, and yellow were used as a stimulant of emotion, and the ECG signals from 70 female students were recorded. Using extracted features of Poincare plot and heart rate asymmetry, two tree based models estimated the levels of arousal and valence with 0.05 mean square errors, determined an appropriate estimation of these two parameters of emotion. In the next stage of the study, four different emotions mean pleasure, anger, joy, and sadness, were classified using IF-THEN rules with the accuracy of 95.71%. The results show the color red is associated with more excitement and anger, while green has small anxiety. So, this system provides a measure for numerical comparison of mental states and makes it possible to model emotions for interacting with the computer and control mental states independently of the pharmaceutical methods. [ABSTRACT FROM AUTHOR]

The objective of this study was to compare the predictive performance of some data mining algorithms implemented in the estimation of egg weight (EW), some egg quality traits measurements in quail. These quality traits are albumen weight (AW), yolk weight (YW), specific weight (SW), albumen index (AI), albumen height (AH), yolk index (YI), shell weight (SHW), shell thickness (SHT), Haugh unit (HU) and shape index (SI). For comparing the predictive performance of these algorithms in Model, goodness of fit criteria such as coefficient of determination (R²%), adjusted coefficient of determination (Adj. R²%), coefficient of variation (CV%), SD ratio, root mean square error (RMSE), relative approximation error (RAE), and Pearson correlation coefficient, between observed and predicted values were calculated. The heaviest average EW of 13.516 g was obtained from the subgroup of those having AW > 8 cm. The results showed that the analysis based on exhaustive CHAID might be useful for further researches linked with characterization of quail egg better than those provided by CART and CHAID algorithms. [ABSTRACT FROM AUTHOR]

Quantifying urban tree cover is important to ensure sustainable urban ecosystem. This study calculates urban percent tree cover (PTC) for Bursa city, Turkey from Sentinel-2 data and evaluates the driving factors of PTC using an Artificial Neural Network-Multi Layer Perception (ANN-MLP) approach. For the PTC calculation, a Regression Tree (RT) analysis was performed using several vegetation indices (NDVI, LAI, fCOVER, MSAVI2, and MCARI) to improve accuracy. Socio-economic, topographic, and biophysical variables were incorporated into the ANN-MLP approach to evaluate the factors that drive urban PTC. A PTC prediction map was generated with an accuracy of 0.95 and a coefficient of determination of 0.87. The ANN-MLP training process yielded a correlation coefficient value of 0.71 and an R-square of 0.82 was achieved between the predicted ANN-MLP and observed tree cover maps. A priority tree cover map was generated considering statistical relationships between the factors and the ANN-MLP prediction map in addition to visual interpretations at the urban scale. Results demonstrate that, unlike other urban forms, PTC has a statistically negative relationship with the gross dwelling density (R2 =0.31). Topographic variables including slope and DEM were positively correlated with PTC with the R2 value of 0.80 and 0.72 respectively. The integration of remote sensing data with vegetation indices and driving factors yielded accurate prediction for identifying and evaluating the variability in the urban PTC. • Relevant components for accurate urban percent tree cover mapping. • Evaluation of different spatial variables on the urban tree cover. • Machine learning modelling to handle the urban vegetation complexity. • Urban tree priority map by considering quantitative modelling results. [ABSTRACT FROM AUTHOR]

Existing rock mass classification systems, such as rock mass rating (RMR) are often used in many empirical design practices in rock engineering contrasting with their original application, i.e. estimation of TBM performance in various ground conditions. However, the use of RMR or similar classification systems in providing an accurate estimation of TBM penetration rate has had limited success due to the nature of the weights assigned to the input parameters. The results of many investigations on this topic have shown a weak correlation between TBM penetration rate and RMR. This limitation can be addressed by performing regression tree analysis which revises the weights assigned to input parameters to better represent influence of rock mass properties on TBM performance. This paper offers an overview of the impact of rock mass classification on TBM performance and introduces a new model based on regression tree using the input parameters of RMR system to predict the performance of hard rock TBMs. The results of the preliminary analysis show that the use of the proposed model can improve the accuracy of TBM performance estimates in various rock masses. This is based on the comparison between the estimated and actual rate of penetration of TBMs in two tunneling projects in igneous and sedimentary rocks. This study shows the potential of regression tree approach to offer more suitable rating of input parameters for this application, if sufficiently diverse database of machine performance is used in the analysis. [ABSTRACT FROM AUTHOR]

In the field of manufacturing engineering, process designers conduct numerical simulation experiments to observe the impact of varying input parameters on certain outputs of a production process. The disadvantage of these simulations is that they are very time consuming and their results do not help to fully understand the underlying process. For instance, a common problem in planning processes is the choice of an appropriate machine parameter set that results in desirable process outputs. One way to overcome this problem is to use data mining techniques that extract previously unknown but valuable knowledge from simulation results. Our research examines the use of such techniques within the field of Virtual Production Intelligence (VPI). This paper proposes a novel approach for applying machine learning models, namely classification and regression trees, to design a laser cutting process. The evaluation shows that the models accurately identify regions in the multidimensional parameter space that increase the quality of the process (i.e. high cut quality). We implemented the models in the web-based VPI-platform, where the user is able to gain valuable insights into the laser cutting process with the aim of optimizing it. [ABSTRACT FROM AUTHOR]

• Statistical analysis and Regression Trees were applied to analyze water consumption. • Construction characteristics and resident-related variables were considered. • Water consumption was influenced by socioeconomic aspects. • Building age and construction characteristics significantly impact water consumption. Understanding the drivers of water consumption is essential for effective water resources management. This paper evaluates how socioeconomic and demographic variables, construction characteristics and behavioral aspects of residents influence domestic water consumption in the city of Joinville, Southern Brazil, analyzing data from 394 households. Statistical analysis and Univariate Regression Trees were applied to the data set. The results show that the average per capita consumption is 143.67 L/person/day, while the average household monthly water consumption is 14.53 m³/household/month. Amidst the findings in the statistical analysis, socioeconomic aspects such as number of residents, income and presence of children in the household showed an effect on water consumption, as well as building age and typology, number of bathrooms and presence of a bathtub and a swimming pool. The regression trees allowed identifying that the number of residents and area per capita are the variables with greater importance to estimate household and per capita water consumption, respectively. As a key implication of this study, the analysis presented herein provides additional empirical support for the body of knowledge on the factors that influence residential water consumption and can help decision-makers to direct actions and public policies aimed at water conservation in the built environment. [ABSTRACT FROM AUTHOR]

Satellite remote sensing has been used successfully to map leaf area index (LAI) across landscapes, but advances are still needed to exploit multi-scale data streams for producing LAI at both high spatial and temporal resolution. A multi-scale Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI) has been developed to generate 4-day time-series of Landsat-scale LAI from existing medium resolution LAI products. STEM-LAI has been designed to meet the demands of applications requiring frequent and spatially explicit information, such as effectively resolving rapidly evolving vegetation dynamics at sub-field (30 m) scales. In this study, STEM-LAI is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) based LAI data and utilizes a reference-based regression tree approach for producing MODIS-consistent, but Landsat-based, LAI. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is used to interpolate the downscaled LAI between Landsat acquisition dates, providing a high spatial and temporal resolution improvement over existing LAI products. STARFM predicts high resolution LAI by blending MODIS and Landsat based information from a common acquisition date, with MODIS data from a prediction date. To demonstrate its capacity to reproduce fine-scale spatial features observed in actual Landsat LAI, the STEM-LAI approach is tested over an agricultural region in Nebraska. The implementation of a 250 m resolution LAI product, derived from MODIS 1 km data and using a scale consistent approach based on the Normalized Difference Vegetation Index (NDVI), is found to significantly improve accuracies of spatial pattern prediction, with the coefficient of efficiency ( E ) ranging from 0.77–0.94 compared to 0.01–0.85 when using 1 km LAI inputs alone. Comparisons against an 11-year record of in-situ measured LAI over maize and soybean highlight the utility of STEM-LAI in reproducing observed LAI dynamics (both characterized by r 2 = 0.86) over a range of plant development stages. Overall, STEM-LAI represents an effective downscaling and temporal enhancement mechanism that predicts in-situ measured LAI better than estimates derived through linear interpolation between Landsat acquisitions. This is particularly true when the in-situ measurement date is greater than 10 days from the nearest Landsat acquisition, with prediction errors reduced by up to 50%. With a streamlined and completely automated processing interface, STEM-LAI represents a flexible tool for LAI disaggregation in space and time that is adaptable to different land cover types, landscape heterogeneities, and cloud cover conditions. [ABSTRACT FROM AUTHOR]

Grass strips are known as one of the most effective management practices in controlling sediment loss to rivers and other surface water bodies. Some physically-based models have been previously developed to predict the amount of sediment retention in grass strips. Although physically-based models can explain the effects and interactions of various factors, they tend to be sophisticated as they require a large amount of input data. A nonparametric supervised learning statistical model was developed to predict the efficiency of grass strips in trapping sediments. Grass type and density, inflow sediment particle size distribution, slope steepness, length of strip, and the antecedent soil moisture were the five major factors on which the statistical model was built. The model was assessed by comparing with an independent dataset. Estimated bias, coefficient of model efficiency, mean absolute percentage error, Pearson product-moment correlation coefficient of the model were 1.01, 0.54, 18.1and 76% respectively. Testing the model predictions, permuting the input data, showed that inflow sediment particle size distribution, length of the buffer strip, and the antecedent soil moisture are the most important factors upon the performance of grass strips in trapping sediments. From the model outputs for a range of likely scenarios it was concluded that very long strips are needed in extreme conditions such as steep slopes, wet soil and sparse grass strips in order to trap sediments effectively. [ABSTRACT FROM AUTHOR]

Accumulations of heavy metals in urban soils are highly spatial heterogeneity and affected by multiple factors including soil properties, land use and pattern, population and climatic conditions. We studied accumulation risks of Cd, Cu, Pb and Zn in unban soils of Beijing and their influencing based on the regression tree analysis and a GIS-based overlay model. Result shows that Zinc causes the most extensive soil pollution and Cu result in the most acute soil pollution. The soil's organic carbon content and CEC and population growth are the most significant factors affecting heavy metal accumulation. Other influence factors in land use pattern, urban landscape, and wind speed also contributed, but less pronounced. The soils in areas with higher degree of urbanization and surrounded by intense vehicular traffics have higher accumulation risk of Cd, Cu, Pb, and Zn. [ABSTRACT FROM AUTHOR]

Self-organizing maps (SOMs) are increasingly popular methods for processing high-dimensional ecological data; however, their potentials are not yet fully utilized. It was our objective to prove evidence on an unknown advantage of SOMs, which we aimed to test using data on the spatial distributional patterns of gammarids. Quantitative samples and a wide spectrum of environmental data were obtained from the catchment area of two of the largest side tributaries of the Tisza River. Distributional patterns and habitat preference of three Gammarus species were described by self-organizing map methods and regression tree analysis (CART) on spatial and temporal scale. Using SOMs helped us bring out distinctions in our data and enhance the differences, thus making them easier to recognize and also, with their help, we were able to model the relations of the species to habitat types that were non-existent among our samples. According to the analysis, Gammarus roeselii preferred low altitudes, high conductivity, fine substrate, deep actual mean depth and dense plant coverage; Gammarus fossarum preferred rocky stream beds, high altitudes, lower temperatures and little actual mean depth; while Gammarus balcanicus preferred coarse substrate, little or no plant coverage and low temperatures. SOMs improved the correlations that proved to be highly useful: besides their use in displaying complex data in a perspicuous way, they have other advantages in bringing out existing relationships in data that would otherwise be difficult to detect. [ABSTRACT FROM AUTHOR]

Background Experimental and ecological studies have shown the role of climatic factors in driving the epidemiology of influenza. In particular, low absolute humidity (AH) has been shown to increase influenza virus transmissibility and has been identified to explain the onset of epidemics in temperate regions. Here, we aim to study the potential climatic drivers of influenza-like illness (ILI) epidemiology in Vietnam, a tropical country characterized by a high diversity of climates. We specifically focus on quantifying and explaining the seasonality of ILI. Methods We used 18 years (1993–2010) of monthly ILI notifications aggregated by province (52) and monthly climatic variables (minimum, mean, maximum temperatures, absolute and relative humidities, rainfall and hours of sunshine) from 67 weather stations across Vietnam. Seasonalities were quantified from global wavelet spectra, using the value of the power at the period of 1 year as a measure of the intensity of seasonality. The 7 climatic time series were characterized by 534 summary statistics which were entered into a regression tree to identify factors associated with the seasonality of AH. Results were extrapolated to the global scale using simulated climatic times series from the NCEP/NCAR project. Results The intensity of ILI seasonality in Vietnam is best explained by the intensity of AH seasonality. We find that ILI seasonality is weak in provinces experiencing weak seasonal fluctuations in AH (annual power <17.6), whereas ILI seasonality is strongest in provinces with pronounced AH seasonality (power >17.6). In Vietnam, AH and ILI are positively correlated. Conclusions Our results identify a role for AH in driving the epidemiology of ILI in a tropical setting. However, in contrast to temperate regions, high rather than low AH is associated with increased ILI activity. Fluctuation in AH may be the climate factor that underlies and unifies the seasonality of ILI in both temperate and tropical regions. Alternatively, the mechanism of action of AH on disease transmission may be different in cold-dry versus hot-humid settings. [ABSTRACT FROM AUTHOR]

During the last decades, ecologists have paid a great deal of attention to the proper techniques to track fish movement in the wild. In this context, otoliths have proved useful and were increasingly used in a wide range of species worldwide. Otolith material incorporates elements from the surrounding water in a layered manner that preserves the timing of deposition. Elemental composition along otolith centre-edge transects thus represent a permanent record of the growing habitats experienced by the fish throughout its entire lifetime. While the analysis of these elements is of primary importance in fishery management and population ecology, the traditional approaches mostly fail to capture the multivariate nature of structural change in time series data. In this paper, I propose a simple approach aiming at coping with the multi-elemental compositional otolith transect in a multi-scale quantitative manner. More specifically, the proposed recursive partitioning method tries to bias the chronological clustering process by accommodating some form of user-specified constraints based on both intra- and inter-groups characteristics. This allows researchers to focus on biologically relevant information (i.e. the one researcher look for and can interpret). The finding of this study may be considered from the more general perspective of the utility of chronological clustering study of multivariate time series. Chronological clustering based on recursive partition is easily interpretable and directly applicable to detection of hidden discontinuities in any multivariate time series. In a multi-proxy context, it also provides a powerful exploratory tool for assessing the relative importance of cross-correlated variables in structuring the environmental histories, as well as the congruency between variables. Ultimately, this approach demonstrates the actual interest in using advanced processing techniques to fully exploit the rich potential of individual biological archives, such as fish otoliths, to characterize individual life history. This appears as a promising new tool for ecologists wishing to extract environmental histories from dendro- and sclerochronological structures and would be a great help for practitioners working with chemical fingerprints. New functions for recursive partitioning using the R software environment are provided in a package and in the online Supplementary material together with a complete tutorial using full worked examples. [ABSTRACT FROM AUTHOR]

With immense and growing pressure on stakeholders in international airport terminals to process passengers faster than previously, there is a great benefit to understanding which factors affect passenger processing times and in which situations. In addition, storing and analysing the collected data in batch is itself a difficult and time consuming task that could be made much simpler with sequential analysis. We aim to present a method for airport managers to discover which variables are important to understanding passenger processing times and identifying problematic passenger profiles without the need for high computational capacity and full historical datasets. In this paper we introduce Bayesian hierarchical models as a method of sequentially processing data, reducing computation time and obviating storage of large amounts of raw data. We use a range of exploratory models to identify which variables are important to predicting passenger processing time using a dataset from a day of operations at an international airport terminal, then compare a range of regression models. A Bayesian hierarchical regression model based on the model of best fit discovered through exploration is then applied to two subsets of data. We demonstrate that sequential updating based on daily data achieves similar results to batch processing based on full historical datasets and can therefore be used as an alternative in appropriate circumstances. Using the presented models, we find that the airline operating a flight is the most important variable to determining passenger processing time, followed by each passenger's age, sex and nationality. We demonstrate that in our dataset, the passenger profiles correlated with higher mean processing times overall were not the same as those passengers most problematic for meeting processing time targets. [ABSTRACT FROM AUTHOR]

The on-going growth of urban areas results in increased sealed surface cover, affecting the environment through fragmentation of the landscape, increased surface runoff and obstruction of water infiltration. Within Flanders (Belgium), which is characterized by a highly sprawled urban network and a sealed surface density much higher than in many other European metropolitan regions, there is a strong need for more accurate high-resolution forecasting of soil sealing densities. This study attempts to meet this need by developing a spatial explanatory model for soil sealing density that is applicable at the regional scale and that can be applied in combination with the Flanders cellular automata land-use change model for evaluating the impact of future land-use scenarios on soil sealing. In the modelling, neighbourhood characteristics such as land-use composition and positioning with respect to the urban network and its major transport axes are considered as attraction/repulsion factors for soil sealing. Global multiple regression and regression tree models are compared with a simple average model. The regression models yield clear improvements in overall estimation accuracy and a favourable distribution of the remaining error. Applying a two-rule partitioning model on a future land-use map, produced by the Flanders land-use change model, shows an increase of 23.6% and 44.6% of sealed surface cover within residential/mixed areas in the study area by 2030 and 2050, respectively. [ABSTRACT FROM AUTHOR]

Aeolian dust can influence nutrient availability, soil fertility, plant interactions, and water-holding capacity in both source and downwind environments. A network of 85 passive collectors for aeolian sediment spanning numerous plant communities, soil types, and land-use histories covering approximately 4000 square kilometers across southeastern Utah was used to sample horizontal emissions of aeolian sediment. The sample archive dates to 2004 and is currently the largest known record of field-scale dust emissions for the southwestern United States. Sediment flux peaked during the spring months in all plant communities (mean: 38.1 g m −2 d −1 ), related to higher, sustained wind speeds that begin in the early spring. Dust flux was lowest during the winter period (mean: 5 g m −2 d −1 ) when surface wind speeds are typically low. Sites dominated by blackbrush and sagebrush shrubs had higher sediment flux (mean: 19.4 g m −2 d −1 ) compared to grasslands (mean: 11.2 g m −2 d −1 ), saltbush shrublands (mean: 10.3 g m −2 d −1 ), and woodlands (mean: 8.1 g m −2 d −1 ). Contrary to other studies on dust emissions, antecedent precipitation during one, two, and three seasons prior to sample collection did not significantly influence emission rates. Physical site-scale factors controlling dust emissions were complex and varied from one vegetation type to another. [ABSTRACT FROM AUTHOR]

Copyright of Rudarsko-Geolosko-Naftni Zbornik is the property of Faculty of Mining, Geology & Petroleum Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Dry anaerobic digestion usually results in inhibitors accumulation, which can be solved by adapting operation. Multiple strategies targeting increased stability are implemented in full-scale, but impacts are poorly documented. Two full-scale dry AD plants treating organic fraction of municipal solid waste (OFMSW) were investigated: a semi-continuous plant with compost addition and a batch plant testing different percolate recirculation strategies and inoculum to substrate ratios. Regression tree analysis was used to evaluate the effect of these strategies on methane yields and inhibitors accumulation. Compost addition in the semi-continuous plant reduced volatile fatty acids content but dropped methane flow up to 10 % when compost constituted over 10.1 % in weight of the incoming feedstock. This reduction was linked to the limited availability of easily degradable material in the compost. In batch dry AD, methane yields increased as percolate recirculated raised up to a range of 182–197 m3 (0.342–0.363 m3/t of biomass mix). Recirculation of higher percolate volumes reduced methane production, probably linked to pile compaction and inhibitors accumulation. The ratio of OFMSW, digestate and woodchip (bulking agent) fed was determinant, and methane production was higher when digestate was over 43.1 %, waste between 45 % and 47.5 % and woodchip over 8.2 % in weight in as received basis. Woodchip influenced percolation through the pile and supported reduced total ammonia levels of 3.2 g/l when kept over 8.2 %, which raised to 5.2 g/l for lower values. [Display omitted] • Compost addition reduces VFA accumulation in semi-continuous dry AD. • Percolate from 0.342 m to 0.363 m3/t mix improved methane yields in batch dry AD. • Bulking agent over 8.2 % is key to reduce inhibitors accumulation in batch dry AD. • Hydrogenotrophic archaea are predominant in dry AD of the OFMSW. • Regression tree is a valid tool to analyse operational parameters effect on dry AD. [ABSTRACT FROM AUTHOR]

The symbolic methods have recently regained popularity due to their reasonable interpretability compared to neural network-based artificial intelligence techniques. The regression tree is such a symbolic method that divides the feature space into several subregions and builds a simple response surface model, such as a constant value or a linear model, for each subregion. However, this strategy may fail when nonlinear structures exist in the subregions. To overcome this problem, this paper proposes a new regression model, named piecewise symbolic regression tree (PS-Tree). Instead of using constant values or linear models as the leaf nodes, PS-Tree builds symbolic regressors for the leaf nodes or subregions. In addition to that, we also propose an adaptive space partition strategy by dynamically adjusting the partition of the space to alleviate the problem caused by incorrect partitioning. PS-Tree is applied to 122 synthetic and real-world datasets, and the results show that it outperforms several state-of-the-art regression methods. [ABSTRACT FROM AUTHOR]

Summary: Background: Minimising time to treatment onset for acute myocardial infarction (AMI) in the emergency department (ED) is essential, yet little is understood about the interactions between variables affecting it. The aim of this study was to develop a regression tree model explicating the influence of patient and non-patient factors on the time taken to commence treatment for patients with AMI in Victorian EDs. Methods: A regression tree model for variables impacting time to treatment was developed on retrospective data for patients aged 18–85 years with AMI treated in Victorian EDs from 2005 to 2010 (n =21,080). Data were partitioned into three subsets, with a complexity parameter set at 0.0005. Results: Four variables emerged in the final regression tree model: triage score; mode of arrival; area of residence; and patient sex. The variable most influencing time to treatment onset for AMI was triage category. For undertriaged patients, treatment time patterns were affected by arrival mode, residential location and their sex, significantly extending delays to treatment onset. Conclusions: Interactions between specific variables influenced whether patients with AMI were treated with equity in Victorian EDs, resulting in previously unidentified evidence–practice gaps and an improved understanding of which patient groups were vulnerable to delayed treatment for AMI. [Copyright &y& Elsevier]

Highlights: [•] Biological traits of bryophytes may indicate species’ responses to partial harvesting. [•] We used fourth-corner analysis to relate species’ traits to the forest environment. [•] Species’ traits were closely related to retention level and pre-harvest forest canopy. [•] Moisture loss after harvest was a strong environmental filter of species’ responses. [•] Sensitivity of species to harvesting may be informed by the traits they exhibit. [ABSTRACT FROM AUTHOR]

Summary: Objective: To examine the factors correlating with repetitive use of complementary and alternative medicine (CAM). Research design: In 2006, a community-based epidemiological survey was conducted in Taiwan. A total of 2457 participants (1237 men and 1220 women) aged 18 and over participated in the study. Results: From the application of regression tree analysis and the Health Belief Model, we have noticed the following findings. First, demand motive was the most dominant discriminator factor for the repetitive use of CAM. Second-layer discriminators were evaluated on the basis of action benefits and barriers, as well as sources of action information. Another predominant factor is the heterogeneity of individual background. When participants used CAM for treatment of illness, their household income was ≥1000 US dollars per month, they had past experience of western medicine seeking without effects and aged between 30 and 60 years, the predicted mean values for all types of CAM use is 5.62 (the highest). By contrast, when participants used CAM for maintenance of health, their household income was <2000 US dollars per month, they are male, with moderate or good self-rated health who had less than 9 education years, the predicted mean values for all types of CAM use is 1.36 (the lowest). Conclusion: Upon the above components, it assists us to understand the multiple interactive reasons on people''s repetitive use of CAM. It also provides essential information for specific CAM issues. Furthermore, from the different perspectives; it stimulates the thoughts for the future medical care projects. [Copyright &y& Elsevier]

Copyright of Brazilian Journal of Oceanography is the property of Instituto Oceanografico da Universidade de Sao Paulo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on the 10-day SPOT VEGETATION NDVI data and the daily meteorological data from 1998 to 2007 in Yan'an City, the main meteorological variables affecting the annual and inter?annual variations of NDVI were determined by using regression tree. It was found that the effects of test meteorological variables on the variability of NDVI differed with seasons and time lags. Temperature and precipitation were the most important meteorological variables affecting the annual variation of NDVI, and the average highest temperature was the most important meteorological variable affecting the inter-annual variation of NDVI. Regression tree was very powerful in determining the key meteorological variables affecting NDVI variation, but could not build quantitative relations between NDVI and meteorological variables, which limited its further and wider application. [ABSTRACT FROM AUTHOR]

Fuel treatments for reducing fire risk are necessarily tied to the landscape structure including forest composition and configuration. Thus understanding the relationships between landscape structure and burn severity is important for developing guidelines and management strategies for fire-resilient forests. The goal of this study was to investigate the relationship between landscape structure as described by spatial pattern metrics and burn severity at the landscape and class levels. In 2000, a mostly severe fire burned 16,210ha of dense forest located in Samchuck on the east coast of the Korean peninsula. Spatial pattern metrics including patch density, largest patch index, mean shape index, area-weighted mean shape index, Euclidean nearest neighborhood distance, and Shannon''s diversity index, as well as topographic characteristics of slope and elevation, were correlated with burn severity based on delta Normalized Burn Ratio (dNBR) assessments. Regression tree analysis was also carried out with the same variables to avoid spatial autocorrelation and to reveal the relative importance of variables to burn severity. The results of this study strongly suggest that both composition and configuration of the forest cover patches are closely tied to burn severity. In particular, both the correlation analysis and regression tree analysis indicated that the area of red pine tree forest cover was the most significant factor in explaining the variance of burn severity. Topography and spatial configuration of forest cover patches were also significantly related to burn severity. The heterogeneity of forests also had a significant influence on burn severity. To reduce fire risk and increase the fire resilience of forests, forest managers and agencies need to consider enhancing the heterogeneity of forests when implementing fuel treatment schemes. However, such fuel treatments for landscape structure may only be effective under moderate weather conditions. [Copyright &y& Elsevier]

This paper discusses the post-war trends in life expectancy worldwide. Even though applying the specification test to a sample of 125 countries suggests that some life expectancy clubs exist, their number and borderlines are not properly distinguished by mechanical splits of the sample. Hence, the clubs are discovered by regression tree analysis. The potential threshold variables are initial per capita income, literacy, fertility change, and the HIV prevalence rate in 2005. Four clubs appear, characterized as High Literacy, Low Literacy, Medium Literacy, and High AIDS, between which considerable life expectancy differentials appear. Excluding the HIV prevalence rate from the threshold candidates re-allocates a considerable number of the members of the High AIDS club, indicating that incomes, literacy, and fertility are unable to predict AIDS completely. The similarity of economic and demographic conditions in the Low Literacy and High AIDS clubs, however, raises concerns about life expectancy convergence in the future. [ABSTRACT FROM AUTHOR]

This paper discusses the post-war trends in life expectancy worldwide. Even though applying the specification test to a sample of 125 countries suggests that some life expectancy clubs exist, their number and borderlines are not properly distinguished by mechanical splits of the sample. Hence, the clubs are discovered by regression tree analysis. The potential threshold variables are initial per capita income, literacy, fertility change, and the HIV-prevalence rate in 2005. Four clubs appear, characterized as High Literacy, Low Literacy, Medium Literacy, and High AIDS, between which considerable life expectancy differentials appear. Excluding the HIV prevalence rate from the threshold candidates re-allocates a considerable number of the members of the High AIDS club, indicating that incomes, literacy, and fertility are unable to predict AIDS completely. The similarity of economic and demographic conditions in the Low Literacy and High AIDS clubs, however, raises concerns about life expectancy convergence in the future. [ABSTRACT FROM AUTHOR]

The article presents a research study which focuses on the habitat requirements of threatened desert tortoises, also known as Gopherus agassizii. It states that in the research study, data derived from various spatial databases, including satellite imagery, was also used. It disusses the field surveys, which collected vegetation and tortoise relative density data.

Background: Computerized adaptive testing (CAT) has been shown to deliver short, accurate, and personalized versions of the CLEFT-Q patient-reported outcome measure for children and young adults born with a cleft lip and/or palate. Decision trees may integrate clinician-reported data (eg, age, gender, cleft type, and planned treatments) to make these assessments even shorter and more accurate.Objective: We aimed to create decision tree models incorporating clinician-reported data into adaptive CLEFT-Q assessments and compare their accuracy to traditional CAT models.Methods: We used relevant clinician-reported data and patient-reported item responses from the CLEFT-Q field test to train and test decision tree models using recursive partitioning. We compared the prediction accuracy of decision trees to CAT assessments of similar length. Participant scores from the full-length questionnaire were used as ground truth. Accuracy was assessed through Pearson's correlation coefficient of predicted and ground truth scores, mean absolute error, root mean squared error, and a two-tailed Wilcoxon signed-rank test comparing squared error.Results: Decision trees demonstrated poorer accuracy than CAT comparators and generally made data splits based on item responses rather than clinician-reported data.Conclusions: When predicting CLEFT-Q scores, individual item responses are generally more informative than clinician-reported data. Decision trees that make binary splits are at risk of underfitting polytomous patient-reported outcome measure data and demonstrated poorer performance than CATs in this study. [ABSTRACT FROM AUTHOR]

Objective: The objective of the study was to evaluate whether surgical removal of regional lymph nodes influences the overall survival of women with endometrial carcinoma. Study Design: All patients with endometrioid-type endometrial cancer treated at our institution between January 1993 and December 2004 were reviewed. The Classification and Regression Tree (CART) method, a form of recursive partitioning, was used. Results: In all, 1035 patients were evaluated. International Federal of Gynecology and Obstetrics stage included the following: stage I, 824; stage II, 65; stage III, 109; and stage IV, 37. Lymph nodes were removed in 524 patients (51%). The median number of nodes removed was 16. Using the CART hierarchically, stage, age, adjuvant therapy, and the removal of 10 lymph nodes or more emerged as predictors of overall survival. Conclusion: This study emphasizes the importance of lymph node dissection in endometrial cancer. Lymph node dissection is essential for accurate surgical staging, which remains the most important prognostic factor. In addition to well-known clinicopathologic risk factors for survival, the removal of 10 or more regional lymph nodes was associated with improved overall survival in lower-stage, older patients who received no adjuvant therapy or brachytherapy only. [Copyright &y& Elsevier]