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The aim of the present study was to compare the predictive performance of the Bagging algorithm with other decision tree-based methods, including Regression Tree (RT), Random Forest (RF) and Boosting in genomic selection. A genome including ten chromosomes for 1,000 individuals on which 10,000 single nucleotide polymorphisms (SNP) were evenly distributed was simulated. QTL effects were assigned to 10% of the polymorphic SNPs, with effects sampled from a gamma distribution. Predictive performance measures including accuracy of prediction, reliability and bias were used to compare the methods. Computing time and memory requirements of the studied methods were also measured. In all methods studied, the accuracy of genomic evaluation increased following increase in the heritability level from 0.10 to 0.50. While RT was the most efficient user of time and memory, it was not recommended for genomic selection due to its poor predictive performance. The obtained results showed that the predictive performance of Bagging was equal to RF and higher than RT and Boosting. However, it required significantly higher computational time and memory requirements. Considering the overall performance, Bagging was recommended for genomic selection, especially when due to the size and structure of the genomic data, the use of RF is limited. [ABSTRACT FROM AUTHOR]

Network-based Semi-Supervised Clustering (NeSSC) is a semi-supervised approach for clustering in the presence of an outcome variable. It uses a classification or regression model on resampled versions of the original data to produce a proximity matrix that indicates the magnitude of the similarity between pairs of observations measured with respect to the outcome. This matrix is transformed into a complex network on which a community detection algorithm is applied to search for underlying community structures which is a partition of the instances into highly homogeneous clusters to be evaluated in terms of the outcome. In this paper, we focus on the case the outcome variable to be used in NeSSC is numeric and propose an alternative selection criterion of the optimal partition based on a measure of overlapping between density curves as well as a penalization criterion which takes accounts for the number of clusters in a candidate partition. Next, we consider the performance of the proposed method for some artificial datasets and for 20 different real datasets and compare NeSSC with the other three popular methods of semi-supervised clustering with a numeric outcome. Results show that NeSSC with the overlapping criterion works particularly well when a reduced number of clusters are scattered localized. [ABSTRACT FROM AUTHOR]

Background: Prior studies on longevity often examine predictors in isolation and rely solely on baseline information, limiting our understanding of the most important predictors and their dynamic nature. In this study, we used an innovative regression tree model to explore the common characteristics of those who lived longer than their age and sex peers in 35-years follow-up. We identified different pathways leading to a long life, and examined to how changes in characteristics over 10 years (from 1979 to 1989) affect the findings on longevity predictors. Methods: Data was obtained from the "Tampere Longitudinal Study on Ageing" (TamELSA) in Finland. Survey data was collected in 1979 from 1056 participants aged 60–89 years (49.8% men). In 1989, a second survey was conducted among 432 survivors from the 1979 cohort (40.2% men). Dates of death were provided by the Finnish Population Register until 2015. We employed an individual measure of longevity known as the realized probability of dying (RPD), which was calculated based on each participant's age and sex, utilizing population life tables. RPD is based on a comparison of the survival time of each individual of a specific age and sex with the survival time of his/her peers in the total population. A regression tree analysis was used to examine individual-based longevity with RPD as an outcome. Results: This relative measure of longevity (RPD) provided a complex regression tree where the most important characteristics were self-rated health, years of education, history of smoking, and functional ability. We identified several pathways leading to a long life such as individuals with (1) good self-rated health (SRH), short smoking history, and higher education, (2) good SRH, short smoking history, lower education, and excellent mobility, and (3) poor SRH but able to perform less demanding functions, aged 75 or older, willing to do things, and sleeping difficulties. Changes in the characteristics over time did not change the main results. Conclusion: The simultaneous examination of a broad range of potential predictors revealed that longevity can be achieved under very different conditions and is achieved by heterogeneous groups of people. [ABSTRACT FROM AUTHOR]

This study aims to execute machine learning methods to predict the mechanical properties containing TS and CS of HPC. They are essential parameters for the durability, workability, and efficiency of concrete structures in civil engineering. In this regard, obtaining the estimation of the mechanical properties of HPC is complex energy and time‐consuming. Due to this, an observed database was compiled, including 168 datasets for CS and 120 for TS. This database trained and validated two machine learning models: SVR and RT. The models combine the prediction outputs from the meta‐heuristic algorithms to build hybrid and ensemble‐hybrid models, which include dwarf mongoose optimization, PPSO, and moth flame optimization. According to the observed outputs, the ensemble models have great potential to be a recourse to deal with the overfitting problem of civil engineering, thus leading to the development of more supportable and less polluting concrete structures. This research significantly improves the efficiency and accuracy of predicting vital mechanical properties in high‐performance concrete by integrating machine learning and metaheuristic algorithms, offering promising avenues for enhanced concrete structure design and development. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs. A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow aquifers. The stray gas can dissolve in groundwater leading to chemical and biological reactions, which could negatively affect groundwater quality and contribute to atmospheric emissions. The knowledge of light hydrocarbon solubility in the aqueous environment is essential for the numerical modelling of flow and transport in the subsurface. Herein, we compiled a database containing 2129 experimental data of methane, ethane, and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and pressure. Two machine learning algorithms, namely regression tree (RT) and boosted regression tree (BRT) tuned with a Bayesian optimization algorithm (BO) were employed to determine the solubility of gases. The predictions were compared with the experimental data as well as four well-established thermodynamic models. Our analysis shows that the BRT-BO is sufficiently accurate, and the predicted values agree well with those obtained from the thermodynamic models. The coefficient of determination (R2) between experimental and predicted values is 0.99 and the mean squared error (MSE) is 9.97 × 10−8. The leverage statistical approach further confirmed the validity of the model developed. [ABSTRACT FROM AUTHOR]

The time of concentration is an important concept in hydrology. It provides a characteristic hydrological response time (CHRT) useful in many applications. Estimation of the time of concentration is challenging because small watersheds (<100 km2) with sub-daily flow and precipitation records are uncommon. Many practitioners therefore use empirical equations developed from watersheds exposed to different climates and with different attributes. The main objective of this study is to develop an approach to estimate the CHRT from physiographic characteristics for small watersheds located in Ontario, Québec and the northeastern USA. Regression trees are used to identify the physiographic characteristics associated with CHRT. The fraction of lakes and wetlands was identified as the most significant attribute related to CHRT, followed by the ratio between the main watercourse length and the square root of the main watercourse slope. Uncertainties on estimated CHRT values based on regression tree are also provided. [ABSTRACT FROM AUTHOR]

Antimicrobial-resistance genes (ARGs) are spread among bacteria by horizontal gene transfer, however, the effect of environmental factors on the dynamics of the ARG in water environments has not been very well understood. In this systematic review, we employed the regression tree algorithm to identify the environmental factors that facilitate/inhibit the transfer of ARGs via conjugation in planktonic/biofilm-formed bacterial cells based on the results of past relevant research. Escherichia coli strains were the most studied genus for conjugation experiments as donor/recipient in the intra-genera category. Conversely, Pseudomonas spp. Acinetobacter spp. and Salmonella spp. were studied primarily as recipients across inter-genera bacteria. The conjugation efficiency (ce) was found to be highly dependent on the incubation period. Some antibiotics, such as nitrofurantoin (at ≥0.2 µg ml−1) and kanamycin (at ≥9.5 mg l−1) as well as metallic compounds like mercury (II) chloride (HgCl2, ≥3 µmol l−1), and vanadium (III) chloride (VCl3, ≥50 µmol l−1) had enhancing effect on conjugation. The highest ce value (−0.90 log10) was achieved at 15°C–19°C, with linoleic acid concentrations <8 mg l−1, a recognized conjugation inhibitor. Identifying critical environmental factors affecting ARG dissemination in aquatic environments will accelerate strategies to control their proliferation and combat antibiotic resistance. [ABSTRACT FROM AUTHOR]

This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models—an artificial neural network (ANN), a multiple linear regression, a support vector machine, and a regression tree—are employed and compared for performance, using evaluation metrics such as mean absolute deviation, root mean square error, coefficient of correlation, and mean absolute percentage error. After preprocessing 1030 samples, the dataset is split into two subsets: 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations. [ABSTRACT FROM AUTHOR]

The traditional CAPM explains the return on each security, while the current study suggests a new methodology for "picking" a broad index, based on regression trees. Analysts' recommendations on market prices are analyzed, and a one to three‐stage method is employed. The analysis suggests a few rules of thumb (only one to three stages) for buying or selling the CRSP US market index. The findings are robust, and the rules are reliable. Researchers and practitioners may benefit greatly from the new rules. [ABSTRACT FROM AUTHOR]

Airports are vital for global connectivity. However, the increasing volume of air travel has presented significant challenges in airport managing. Accurate predictions of taxi-out times (TXOT) offer potential to enhance airport performance, minimize delays, optimize airline schedules, and enhance customer satisfaction. This paper focuses on developing a machine learning model to forecast taxi-out times at Mohammed V Airport. Historical taxiing data from various airports will be analyzed to predict taxi-out times based on diverse runway-stand combinations and congestion levels. we used neural network (NN), support vector machines (SVM), and regression tree (RT) in order to create a real-time model that forecasts TXOT and congestion levels for different runway-stand combinations. The result showed that the NN model outperformed other forecasting models when their performances are compared using the mean absolute percentage error, root mean square error as accuracy measures. [ABSTRACT FROM AUTHOR]

In this paper, we propose a tree-based method called Multivariate RE-EM tree, which combines the regression tree and the linear mixed effects model for modeling multivariate response longitudinal or clustered data. The Multivariate RE-EM tree method estimates a population-level single tree structure that is driven by the multiple responses simultaneously and object-level random effects for each response variable, where correlation between the response variables and between the associated random effects are each allowed. Through simulation studies, we verify the advantage of the Multivariate RE-EM tree over the use of multiple univariate RE-EM trees and the Multivariate Regression Tree. We apply the Multivariate RE-EM tree to analyze a real data set that contains multidimensional nonfinancial characteristics of poverty of different countries as responses, and various potential causes of poverty as predictors. [ABSTRACT FROM AUTHOR]

The Bradley-Terry Regression Trunk (BTRT) model combines the log-linear Bradley-Terry model, including subject-specific covariates, with a particular tree-based model, the so-called regression trunk. It aims to consider simultaneously the main effects and the interaction effects of covariates on data expressed as paired comparisons. We apply this model to financial data expressed as rankings and then transformed into paired comparisons. Tax revenues differentiated by category represent the statistical units of the analysis (i.e., taxes on income, social security contributions, taxes on property, and taxes on goods and services). We combine data from OECD, World Bank, and IMF databases for the year 2018 to investigate the effect size of socio-economic covariates and their interaction on the composition of tax revenues for a set of 100 countries worldwide. We also present a comparison with a more established method proposed in tax determinants literature and with two alternative models used for matched pairs. Finally, we discuss the implications of reported results for stakeholders and policymakers. [ABSTRACT FROM AUTHOR]

Neural networks and decision tree algorithms are essential tools in machine learning and data science. They deal with patterns among inputs and provide predictions for targets. In this article, we use a hybrid approach in regression trees by incorporating possible dependencies among inputs and apply neural networks in terminal nodes. The proposed approach implements neural networks on the basis of dependency structures among inputs. We allow that the weights in training neural networks differ in various terminal nodes. In both regression and classification problems, the performance of the new approach is assessed by analyzing various real datasets and by conducting a Monte–Carlo simulation study. We show that the proposed approach provides more flexibility for neural networks when associations among inputs are observed. [ABSTRACT FROM AUTHOR]

Indian Regional Navigation Satellite System (IRNSS) or Navigation with Indian Constellation (NavIC) provides positioning, navigation and timing information services to various users in Indian region. Standalone NavIC may not meet the position accuracies for certain application such as civil aviation. Differential NavIC is used for improving the position accuracy of rover receiver, which make use of differential corrections (transmitted from reference station). However, if the satellite signals are temporarily lost due to abruptly changing atmosphere, satellite health issues or if the satellite signals are attenuated due to city infrastructures in urban areas, tree canopies, the accuracy of NavIC will be degraded. This article compares regression tree and bagging tree based differential corrections prediction algorithm with the actual differential corrections, by considering the NavIC satellite signal strength (C/No) and elevation angle (El), to improve the NavIC positioning accuracy. The improvement in the position accuracy is obtained by utilizing predicted differential corrections. The position accuracy of rover using actual differential corrections (2DRMS – 3.09 m), regression tree predicted differential corrections (2DRMS – 5.96 m) and bagged tree predicted differential corrections (2DRMS – 3.06 m) are compared. Here, the rover accuracy using actual differential corrections and bagged tree predicted differential corrections are approximately equal. So, the position accuracy using bagged tree predicted differential corrections are accurate when compared to regression tree predicted differential corrections. [ABSTRACT FROM AUTHOR]

With the emergence of digitilization, numeric prediction has become a prominent problem in various fields including finance, engineering, industry, and medicine. Among several machine learning methods, regression tree is a widely preferred method due to its simplicity, interpretability and robustness. Motivated by this, we introduce a novel ensemble regression tree based methodology, namely LOF-BRT+OR. The proposed methodology is an integrated solution approach with outlier removal, regression tree and ensemble learning. First, irregular data points are removed using local outlier factor (LOF), which measures the degree of being an outlier for each point. Next, a novel regression tree with LOF weighted node model is introduced. In the proposed node model, the weights of the points in the nodes are determined according to their surrounding neighborhood, as a function of LOF values and neighbor ranks. Finally, in order to increase the prediction performance, ensemble learning is adopted. In particular, bootstrap aggregation is used to generate multiple regression trees with LOF weighted node model. The experimental study shows that the proposed methodology yields the best root mean squared error (RMSE) values in five out of nine data sets. Also, the non-parametric tests demonstrate the statistical significance of the proposed approach over the benchmark methods. The proposed methodology can be applicable to various prediction problems. [ABSTRACT FROM AUTHOR]

Regression Tree (RT) and K-Nearest Neighbor (KNN) models play significant roles in machine learning. RT facilitates interpretable decision-making, aiding in the comprehension of complex data relationships, while KNN is valued for its simplicity, adaptability to non-linear data, and robustness to noise, making it a versatile tool across various applications. The primary drawback of Regression Tree is its tendency to assign the same predicted value (average value) to all tuples satisfying the same corresponding splitting criterion. K-Nearest Neighbors (KNN) is sensitive to irrelevant or redundant features since all features contribute to similarity. This paper proposes a hybrid regression model based on Regression Tree (RT) and KNN, addressing the aforementioned issues. The model's performance is compared with KNN using 10 types of distance measures and further assessed against RT, KNearest Neighbor regression (KNN), and Support Vector Regression (SVR) through a Monte Carlo simulation study. Simulation results indicate that the hybrid model outperforms all other regression models, regardless of sample size, when observations follow normal distributions or t-distributions. The proposed model's effectiveness is demonstrated through a real-life application using data on global warming in Delhi. [ABSTRACT FROM AUTHOR]

Copyright of Sigmae is the property of Universidade Federal de Alfenas (UNIFAL-MG) 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.)

Copyright of Sigmae is the property of Universidade Federal de Alfenas (UNIFAL-MG) 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.)

Aerobiological predictive model development is of increasing interest, despite the distribution and variability of data and the limitations of statistical methods making it highly challenging. The use of concentration thresholds and models, where a binary response allows one to establish the occurrence or non-occurrence of the threshold, have been proposed to reduce difficulties. In this paper, we use logistic regression (logit) and regression trees to predict the daily concentration thresholds (low, medium, high, and very high) of six airborne fungal spore taxa (Alternaria, Cladosporium, Agaricus, Ganoderma, Leptosphaeria, and Pleospora) in eight localities in Catalonia (NE Spain) using data from 1995 to 2014. The predictive potential of these models was analyzed through sensitivity and specificity. The models showed similar results regarding the relationship and influence of the meteorological parameters and fungal spores. Ascospores showed a strong relationship with precipitation and basidiospores with minimum temperature, while conidiospores did not indicate any preferences. Sensitivity (true-positive) and specificity (false-positive) presented highly satisfactory validation results for both models in all thresholds, with an average of 73%. However, seeing as logit offers greater precision when attempting to establish the exceedance of a concentration threshold and is easier to apply, it is proposed as the best predictive model. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the factors of affecting watermelon yield in Diyarbakır province. The data was obtained from surveying of 80 watermelon farmers in Diyarbakır province, Turkey by Simple Random Sampling Method using the Chi-square automatic interaction detector (EXHAUSTIVE CHAID) algorithm of the Data Mining Regression Tree methods. In the model created, the dependent variable was WY (watermelon yield), and the independent variables were determined as R (region), AF (age of farmer), EL (education level), CA (cultivation are), FD (fertilization date), FA (amount of fertilization), DS (date of spraying), AS (amount of spraying), NI (number of irrigation), IT (irrigation time), AN (anchor number), HT (harvest time). As a result of the study, the factors that significantly affect the yield of watermelon; AN, NI, HT, CA, R has been determined. An average of 4488.9 kg watermelon yield per decare was obtained and the number of hoes was the variable that most affected the watermelon yield. As a result in order to get a higher yield per unit area, watermelon producers should anchor number more than 4 times, irrigate 5 to 6 times at less than 2 hours, and apply fertilizer in May. In addition, Çermik, Eğil, Yenişehir and Bismil were determined as more suitable regions for watermelon production. [ABSTRACT FROM AUTHOR]

Water stored in reservoirs has a lot of crucial function, including generating hydropower, supporting water supply, and relieving lasting droughts. During floods, water deliveries from reservoirs must be acceptable, so as to guarantee that the gross volume of water is at a safe level and any release from reservoirs will not trigger flooding downstream. This study aims to develop a well-versed assessment method for managing reservoirs and pre-releasing water outflows by using the machine learning technology. As a new and exciting AI area, this technology is regarded as the most valuable, time-saving, supervised and cost-effective approach. In this study, two data-driven forecasting models, i.e., Regression Tree (RT) and Support Vector Machine (SVM), were employed for approximately 30 years' hydrological records, so as to simulate reservoir outflows. The SVM and RT models were applied to the data, accurately predicting the fluctuations in the water outflows of a Bhakra reservoir. Different input combinations were used to determine the most effective release. For cross-validation, the number of folds varied. It is found that quadratic SVM for 10 folds with seven different parameters would give the minimum RMSE, maximum R2, and minimum MAE; therefore, it can be considered as the best model for the dataset used in this study. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the differences and the importance of the differences in terms of some socio-economic factors and agricultural practices of the producers who had soil analysis in Edirne and Tekirdağ provinces and to evaluate the opinions of the producers about soil analysis and soil analysis subsidies. Three laboratories were selected among the laboratories with the highest number of sample acceptances for soil analysis in the provinces determined in the study. For each province, total of 60 producers who applied to the laboratories in 2015 and who utilized from soil analysis subsidies, and consequently, total of 120 producers were interviewed. The analysis of the differences of the producers who had soil analysis in Edirne and Tekirdağ provinces was determined by the regression tree model (CHAID analysis). According to the CHAID analysis, in which the provinces were taken as the dependent variable, it was seen that the first most distinctive feature was the state-supported agricultural insurance. Of the producers who stated that they did not have agricultural insurance, 31% were located in Edirne and 69% were located in Tekirdağ. It was seen that 56% of the producers in Edirne and 44% of the producers in Tekirdağ had agricultural insurance. The most important feature of the second node was the education level of the producers, the third sub-node decision point was whether the producers had training on fertilization, and the fourth decision point was whether the producers faced risks in agriculture in the last three years. The four most important reasons for the producers to have soil analysis in Edirne province were to increase the product yield, to reduce the cost, to increase product quality and protect the environment, respectively. In the province of Tekirdağ, the criteria of increasing the product yield was on the first rank, increasing the product quality in the second rank, reducing the cost in the third rank and protecting the environment in the fourth rank. It is expected that it will be beneficial to introduce soil analysis conditions to the producers at the stage of purchasing fertilizers, and thus to ensure that the producer purchases fertilizer by determining the type and amount of fertilizer to be disposed of according to the analysis results. [ABSTRACT FROM AUTHOR]

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]

Climate change models for southern Africa predict less frequent, but more intense, rainfall events, and an increased frequency of tropical cyclones. With their steep topography and small catchments, headwater streams generate large floods following intense rainfall events. Large flooding events in headwater streams are under studied in southern Africa. In this paper, we explore flooding in the upper Klaserie River, Limpopo River System, South Africa, to determine the flow distribution and flood frequency for the catchment. In addition, we determine the return level for a large, economically damaging, flood generated following the landfall of a sub-tropical depression in January 2012 and, attempt to identify rainfall patterns that resulted in similar floods. An annual hydrological cycle with summer maxima and winter minima for both rainfall and flow was identified. The flood frequency analysis demonstrated that the January 2012 flood had an estimated return level of 225 years. This flood had a peak flowrate exceeding 1200 m3s-1 in a system with an average daily flowrate of 1 m3s-1. Regression tree analysis showed that a two-day rainfall in excess of 240 was a predictor for four of the five largest floods. A two-day rainfall in excess of 400 mm distinguished the January 2012 flood from other floods. Non-stationarity analyses for the flow and rainfall data and a SWAT hydrological model are recommend for the upper Klaserie River to evaluate climate and land cover changes, and their relationship to the magnitude of the 2012 flood. Our study demonstrates that South African river monitoring data can be used to detect and characterize major floods, despite deficiencies in these data. Continuation of these monitoring programs is vital for river health monitoring and the detection of trends in floods resulting from human activities and climate change. [ABSTRACT FROM AUTHOR]

Coastal areas are one of the most threatened natural systems in the world. Environmental beach indicators, such as erosion and deposition rates of exposed beaches in Andalusia (640 km), were calculated using the upper limit of the active beach profile and detailed orthophotos (1:2500) for the periods 1956–1977, 1977–2001 and 2001–2011. A hybrid classification method, both supervised and unsupervised, based on machine-learning (ML) techniques was then applied to model beach response and dynamics for this 55-year period. The use of a K-means technique allowed stratification into four beach groups that have responded similarly in terms of coastline mobility and erosion/deposition patterns. Furthermore, the application of a classification and regression tree (CART) based on the K-means results helped to identify the threshold values for erosional and depositional rates and the period that characterises each cluster or stratum, enabling correct classification of 1415 out of 1509 beaches (93.77%). [ABSTRACT FROM AUTHOR]

Road friction has long been recognized as one of the most effective winter road maintenance (WRM) performance measures. It allows WRM personnel to make more informed decisions to improve their services and helps road users make trip-related decisions. In this paper, a machine-learning-based methodological framework was developed to model road friction using inputs from mobile road weather information systems (RWIS) that collect spatially continuous road weather data and road grip. This study also attempts to estimate friction using data from stationary RWIS that are installed far from each other, thereby leaving large areas unmonitored. To fill in the spatial gaps, a kriging interpolator was developed to create a continuous friction map. Slippery road risk levels were classified to provide an overview of road conditions via a risk warning map. The proposed method was evaluated with a selected highway segment in Alberta, Canada. Results show that the models developed herein are highly accurate (93.3%) in estimating friction and identifying dangerous road segments via a color-coded risk map. Given its high performance, the developed model has the potential for large-scale implementation to facilitate more efficient WRM services while also improving the safety and mobility of the traveling public. [ABSTRACT FROM AUTHOR]

Problem definition: Airports and airlines have been challenged to improve decision making by producing accurate forecasts in real time. We develop a two-phased predictive system that produces forecasts of transfer passenger flows at an airport. In the first phase, the system predicts the distribution of individual transfer passengers' connection times. In the second phase, the system samples from the distribution of individual connection times and produces distributional forecasts for the number of passengers arriving at the immigration and security areas. Academic/practical relevance: To our knowledge, this work is the first to apply machine learning for predicting real-time distributional forecasts of journeys in an airport using passenger level data. Better forecasts of these journeys can help optimize passenger experience and improve airport resource deployment. Methodology: The predictive system developed is based on a regression tree combined with copula-based simulations. We generalize the tree method to predict distributions, moving beyond point forecasts. We also formulate a newsvendor-based resourcing problem to evaluate decisions made by applying the new predictive system. Results: We show that, when compared with benchmarks, our two-phased approach is more accurate in predicting both connection times and passenger flows. Our approach also has the potential to reduce resourcing costs at the immigration and transfer security areas. Managerial implications: Our predictive system can produce accurate forecasts frequently and in real time. With these forecasts, an airport's operating team can make data-driven decisions, identify late passengers, and assist them to make their connections. The airport can also update its resourcing plans based on the prediction of passenger flows. Our predictive system can be generalized to other operations management domains, such as hospitals or theme parks, in which customer flows need to be accurately predicted. Funding: This work was funded by Eurocontrol (APOC Business Process Reengineering Big Data Study) [Grant 15-220643-AWP6.3.1]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0975. [ABSTRACT FROM AUTHOR]

Random forest (RF) is one of the most popular machine learning (ML) models used for both classification and regression problems. As an ensemble model, it demonstrates high predictive accuracy and low variance, while being easy to learn and optimize. In this study, we use RF for short-term load forecasting (STLF), focusing on data representation and training modes. We consider seven methods of defining input patterns and three training modes: local, global and extended global. We also investigate key RF hyperparameters to learn about their optimal settings. The experimental part of the work demonstrates on four STLF problems that our model, in its optimal variant, can outperform both statistical and ML models, providing the most accurate forecasts. [ABSTRACT FROM AUTHOR]

• CNTs, graphene and graphene oxide were distributed at 25, 50, and 100 mg/liter in B20. • CNT, graphene and graphene oxide of 100 ppm demonstrated 7.5, 14 and 19 % improvements in BTE. • CO was reduced by 6.5%, 13%, and 20% but HC was declined by 15%, 25%, and 36%, respectively. • Emissions and performance were predicted using Gaussian regression and Least-squares Boost. • It is recommended to use B20 with CNT, graphene, and graphene oxide at 100 ppm dose. High viscosity and density of biodiesel lead to the issues in fuel atomization and vaporization in cold climate. Nano additives were employed to enhance the physical, chemical and thermal properties. Methyl ester was produced from WCO and blended with diesel at 20 %. Carbon nanotubes, graphene and graphene oxide were distributed in B20 at 25, 50, and 100 ppm. Effects of methyl ester mixture with nano materials on emissions and engine performance were studied. CNT, graphene and graphene oxide of 100 ppm demonstrated 7.5, 14 and 19 % improvements in thermal efficiency but maximum reductions in specific fuel consumption were 7, 15 and 20 % compared to B20. When 100 ppm of CNT, graphene, and graphene oxide were introduced, CO emissions were reduced by 6.5 %, 13 %, and 20 % but HC were declined by 15 %, 25 %, and 36 %, respectively. Greatest decreases in NOx emission were 11 %, 24 %, and 35 % for B20 + CNT100, B20 + G100, and B20 + GO100, respectively, whereas the largest decreases in smoke were 4 %, 15 %, and 21 %, respectively about B20. Emissions and performance were predicted using regression tree, Gaussian process regression and Least-squares Boost. Gaussian process regression outperformed regression tree and LSBoost in terms of R2 above 0.97 for all variables. It is recommended to use B20 with CNT, graphene, and graphene oxide at 100 ppm to achieve more environmental, sustainable and efficient engine operation. [ABSTRACT FROM AUTHOR]

Using data from the nationally representative National Immunization Survey (NIS), we applied conditional linear regression tree methodology to examine relationships between demographic and geographic factors and propensity of receiving various doses of COVID-19 vaccine over time; these analyses identified temporal changes in these relationships that heretofore had not been identified using conventional logistical regression methodologies. Three regression tree models were built using an R package, Recursive Partitioning for Modeling Survey (rpms), to examine propensities over time of receiving a (1) first dose of a two-dose COVID-19 mRNA primary vaccination series or single dose of the Janssen vaccine (vaccine initiation), (2) primary series completion, and (3) monovalent booster dose, using a conditional linear effect model. Persons ≥50 years were more likely to complete a primary series and receive a first booster dose; persons reporting having received non-COVID-19 vaccines recently were more likely to initiate vaccination, complete the primary series, and get a first booster dose; persons reporting having work or school requirements were more likely to complete the primary series. Persons not reporting having received non-COVID-19 vaccines in 2 years but reporting having work or school vaccination requirements were more likely to initiate vaccination than those without work/school requirements. Among persons not reporting having received non-COVID-19 vaccines in 2 years and not reporting having work or school vaccination requirements, those aged ≥50 years were more likely to initiate vaccination than were younger adults. Propensity of receiving various doses was correlated with age, having recently received non-COVID 19 vaccines, and having vaccination requirements at work or school. Regression tree methodology enabled modeling of different COVID-19 vaccination dose propensities as a linear effect of time, revealed changes in relationships over time between demographic factors and propensity of receipt of different doses, and identified populations that may benefit from vaccination outreach efforts. [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]

Short-term electric power load forecasting is a critical and essential task for utilities in the electric power industry for proper energy trading, which enables the independent system operator to operate the network without any technical and economical issues. From an electric power distribution system point of view, accurate load forecasting is essential for proper planning and operation. In order to build most robust machine learning model to forecast the load with a good accuracy irrespective of weather condition and type of day, features such as the season, temperature, humidity and day-status are incorporated into the data. In this paper, a machine learning model, namely a regression tree, is used to forecast the active power load an hour and one day ahead. Real-time active power load data to train and test the machine learning models are collected from a 33/11 kV substation located in Telangana State, India. Based on the simulation results, it is observed that the regression tree model is able to forecast the load with less error. [ABSTRACT FROM AUTHOR]

Background: Identification of suitable factors that influence significantly to the response is crucial for the traits based breeding program to make a better decision about improvement in productivity. Multiple linear regression (MLR) is the benchmark method commonly using to identify suitable factors for crop improvement. It doesn't work always due to stringent assumption (Multicollinearity, Linearity) behind the MLR model. Here we tried to develop an efficient model for the selection of major traits that contribute to seed yield in soybean by comparing different models. Methods: Field experiment was conducted using 98 soybean core population through augmented design. 18 morphometric traits obtain from soybean core population were considered under the study as regressors. Multiple linear regression (MLR), principle component regression (PCR), regression tree and Random Forest models were compared to select traits based on prediction accuracy. Result: All the models identified the number of pods per plant (NPP) has the most influencing variable to the soybean yield. However random forest has a much higher prediction power (RMSE=4.59, MAPE=0.18) compared to other models under study. The results of random forest revealed that the number of pods per plant, number of branches per plant and other associated characters like plant height at harvest as highly influencing traits for seed yield in soybean. Finally, tried to identify genotypesthat possess superiority about most influencing morphological characters on seed yield using cluster analysis. [ABSTRACT FROM AUTHOR]

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Increasing nonresponse rates is a pressing issue for many longitudinal panel studies. Respondents frequently either refuse participation in single survey waves (temporary dropout) or discontinue participation altogether (permanent dropout). Contemporary statistical methods that are used to elucidate predictors of survey nonresponse are typically limited to small variable sets and ignore complex interaction patterns. The innovative approach of Bayesian additive regression trees (BART) is an elegant way to overcome these limitations because it does not specify a parametric form for the relationship between the outcome and its predictors. We present a BART event history analysis that allows identifying predictors for different types of nonresponse to anticipate response rates for upcoming survey waves. We apply our novel method to data from the German National Educational Panel Study including N = 4,559 students in Grade 5 that observed nonresponse rates of up to 36% across five waves. A cross-validation and comparison with logistic regression models with least absolute shrinkage and selection operator penalization underline the advantages of the approach. Our results highlight the potential of Bayesian discrete-time event modeling for the long-term projection of panel stability across multiple survey waves. Finally, potential applications of this approach for operational use in survey management are outlined. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is one of the key soil components for cultivated soils. SOC is regularly monitored and mapped to improve the quality, health, and productivity of the soil. However, traditional SOC-level monitoring is expensive for land managers and farmers. Estimating SOC using satellite imagery provides an easy, efficient, and cost-effective way to monitor surface SOC levels. The objective of this study was to estimate the surface SOC distribution in selected soils of Major Land Resource Areas (MLRA), 102A (Rolling Till Plain, Brookings County, SD), and 103 (Central Iowa and Minnesota Till Prairies, Lac qui Parle County, MN), using satellite imagery with different resolutions (Landsat 8 and PlanetScope). The dominant soils in the study area are Haplustolls, Calciustolls, and Endoaquolls, which are formed in silty sediments, local silty alluvium, and till. Landsat 8 and PlanetScope spectral bands were used to develop SOC prediction models. Parametric and data-driven methods were employed to predict the SOC. Multiple linear regression and Linear Spatial Mixed Model (LSMM) were used on the Landsat 8 and PlanetScope data. In addition to the parametric models, Regression Trees and Random Forest were also employed on both satellite data. The results showed that reduced LSMM provided the lowest RMSE, which are 0.401 and 0.367 for Landsat 8 and PlanetScope, respectively. Furthermore, the random forest has the highest RPD and RPIQ for Landsat 8 (RPD of 2.67 and RPIQ of 2.49) and PlanetScope (RPD of 2.85 and RPIQ of 3.7). In all cases, models obtained from PlanetScope are better than those obtained from Landsat 8. [ABSTRACT FROM AUTHOR]

The measurement of efficiency of water utilities has been traditionally carried out using econometric methods or linear programming techniques. Alternatively, in this study a data mining non-parametric method is used, such as an artificial neural network (ANN) approach, to predict the efficiency of several water companies in England and Wales. The further use of a regression tree model allowed us to visualize and quantify the impact of operating characteristics on efficiency. The average efficiency score for the water industry was 0.411. Average scores for water only companies and waterand sewerage companies were 0.210 and 0.626, respectively. Only one water company was identified as being fully efficient. This indicates that most of the English and Welsh water companies need to make substantial improvements in their managerial practices to catch-up with the most efficient ones in the industry. Several operating characteristics such as water leakage, water taken from different sources and population density were found to influence efficiency. The percentage of water leakage was identified as the most relevant operational variable influencing the efficiency of water companies. The findings of our study aim to support benchmarking analysis in regulated industries and to get a better insight on what drives efficiency. [ABSTRACT FROM AUTHOR]

Quantifying forest systems is of importance for ecological services and economic benefits in ecosystem models. This study aims to map the percent tree cover (PTC) of various forest stands in the Buyuk Menderes Basin, located in the western part of Turkey with different characteristics in the Mediterranean and Terrestrial transition regions Sentinel-2 data with 10-m spatial resolution. In recent years, some researches have been carried out in different fields to show the capabilities and potential of Sentinel-2 satellite sensors. However, the limited number of PTC researches conducted with Sentinel-2 images reveals the importance of this study. This study aimed to demonstrate reliable PTC data in landscape planning or ecosystem modeling by introducing an advanced approach with high spatial, spectral, and temporal resolution and more cost-effective. In this study, a regression tree algorithm, one of the popular machine learning techniques for ecological modeling, was used to estimate the tree cover's dependent variable based on high-resolution monthly metrics' spectral signatures. Six frames of TripleSat images were used as training data in the regression tree. Monthly Sentinel-2 bands and produced metrics including NDVI, LAI, fCOVER, MSAVI2, and MCARI were almost the first time used as predictor variables. Stepwise linear regression (SLR) was applied to select these predictor bands in the regression tree and a correlation coefficient of 0.83 was obtained. Result PTC maps were produced and the results were evaluated based on coniferous and broadleaf. The results were tested using high spatial resolution TripleSat images and higher model accuracy was determined in both forest types. The high correlation is due to the Sentinel 2 satellite's band characteristics and the metrics are directly related to the tree cover. As a result, the high-accuracy availability of the Sentinel2 satellite is seen to map the PTC on a regional scale, including complex forest types between the Mediterranean and terrestrial transition climates. [ABSTRACT FROM AUTHOR]

As one of the important flood control engineering measures, reservoirs play an important role in ensuring the sustainable growth of national economy, maintaining social stability and alleviating the contradiction between supply and demand of water resources. Meanwhile, scientific and reasonable reservoir flood control operation can effectively promote its role. In order to solve the problem of insufficient utilization of historical flood operation data, this paper proposes a new method of using random forest (RF) regression algorithm to formulate reservoir flood control operation scheme. First of all, the optimal flood operation data set is generated by optimizing the historical flood data, and the main factors affecting the reservoir discharge are determined by the RF regression algorithm. Then, the main influencing factors and discharges of the training sample flood processes are input into the RF regression algorithm to construct the discharge regression trees. Finally, the discharges of the verification sample flood processes are determined by regression trees. The example of Tianzhuang Reservoir in Shandong Province proves the operability and rationality of this method. [ABSTRACT FROM AUTHOR]

Background: Every year, volunteers of the Belgian Red Cross provide onsite medical care at more than 8000 mass gathering events and other manifestations. Today standardized planning tools for optimal preventive medical resource use during these events are lacking. This study aimed to develop and validate a prediction model of patient presentation rate (PPR) and transfer to hospital rate (TTHR) at mass gatherings in Belgium.Methods: More than 200,000 medical interventions from 2006 to 2018 were pooled in a database. We used a subset of 28 different mass gatherings (194 unique events) to develop a nonlinear prediction model. Using regression trees, we identified potential predictors for PPR and TTHR at these mass gatherings. The additional effect of ambient temperature was studied by linear regression analysis. Finally, we validated the prediction models using two other subsets of the database.Results: The regression tree for PPR consisted of 7 splits, with mass gathering category as the most important predictor variable. Other predictor variables were attendance, number of days, and age class. Ambient temperature was positively associated with PPR at outdoor events in summer. Calibration of the model revealed an R2 of 0.68 (95% confidence interval 0.60-0.75). For TTHR, the most determining predictor variables were mass gathering category and predicted PPR (R2 = 0.48). External validation indicated limited predictive value for other events (R2 = 0.02 for PPR; R2 = 0.03 for TTHR).Conclusions: Our nonlinear model performed well in predicting PPR at the events used to build the model on, but had poor predictive value for other mass gatherings. The mass gathering categories "outdoor music" and "sports event" warrant further splitting in subcategories, and variables such as attendance, temperature and resource deployment need to be better recorded in the future to optimize prediction of medical usage rates, and hence, of resources needed for onsite emergency medical care. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is a crucial element for deriving irrigation scheduling of major crops. Thus, precise projection of ET0 is essential for better management of scarce water resources in many parts of the globe. This study evaluates the potential of a Hierarchical Fuzzy System (HFS) optimized by Particle Swarm Optimization (PSO) algorithm (PSO-HFS) to predict daily ET0. The meteorological variables and estimated ET0 (using FAO-56 Penman–Monteith equation) were employed as inputs and outputs, respectively, for the PSO-HFS model. The prediction accuracy of PSO-HFS was compared with that of a Fuzzy Inference System (FIS), M5 Model Tree (M5Tree), and a Regression Tree (RT) model. Ranking of the models was performed using the concept of Shannon's Entropy that accounts for a set of performance evaluation indices. Results revealed that the PSO-HFS model performed better (with Entropy weight = 0.93) than the benchmark models (Entropy weights of 0.77, 0.74, and 0.90 for the FIS, RT, and M5Tree, respectively). Furthermore, the generalization capabilities of the proposed models were evaluated using the dataset from a test station. Generalization performances revealed that the models performed equally well with the unseen test dataset and that the PSO-HFS model provided superior performance (with R = 0.93, RMSE = 0.59 mm d−1 and IOA = 0.94) while the RT model (with R = 0.82, RMSE = 0.90 mm d−1, and IOA = 0.83) exhibited the worst performance for the test dataset. The overall results imply that the PSO-HFS model could effectively be utilized to model ET0 quite efficiently and accurately. [ABSTRACT FROM AUTHOR]

In this paper, a new tree-structured regression model—the projection pursuit regression tree—is proposed. It combines the projection pursuit classification tree with the projection pursuit regression. The main advantage of the projection pursuit regression tree is exploring the independent variable space in each range of the dependent variable. Additionally, it retains the main properties of the projection pursuit classification tree. The projection pursuit regression tree provides several methods of assigning values to the final node, which enhances predictability. It shows better performance than CART in most cases and sometimes beats random forest with a single tree. This development makes it possible to find a better explainable model with reasonable predictability. [ABSTRACT FROM AUTHOR]

Identifying areas prone to flooding is a key step in flood risk management. The purpose of this study is to develop and present a novel flood susceptibility model based on Bayesian Additive Regression Tree (BART) methodology. The predictive performance of the new model is assessed via comparison with the Naïve Bayes (NB) and Random Forest (RF) based methods that were previously published in the literature. All models were tested on a real case study based in the Kan watershed in Iran. The following fifteen climatic and geo-environmental variables were used as inputs into all flood susceptibility models: altitude, aspect, slope, plan curvature, profile curvature, drainage density, distance from river distance from road, stream power index (SPI), topographic wetness index (TPI), topographic position index (TPI), curve number (CN), land use, lithology and rainfall. Based on the existing flood field survey and other information available for the analyzed area, a total of 118 flood locations were identified as potentially prone to flooding. The data available were divided into two groups with 70% used for training and 30% for validation of all models. The receiver operating characteristic (ROC) curve parameters were used to evaluate the predictive accuracy of the new and existing models. Based on the area under curve (AUC) the new BART (86%) model outperformed the NB (80%) and RF (85%) models. Regarding the importance of input variables, the results obtained showed that the location's altitude and distance from the river are the most important variables for assessing flooding susceptibility. [ABSTRACT FROM AUTHOR]

Anthropogenic influences significantly modify the hydrochemical properties and material flow in riverine ecosystems across Asia, potentially accounting for 40–50% of global emissions. Despite the pervasive impact on Asian rivers, there is a paucity of studies investigating their correlation with carbon dioxide (CO 2) emissions. In this study, we computed the partial pressure of CO 2 (pCO 2) using the carbonate equilibria-based model (pCO 2 SYS) and examined its correlation with hydrochemical parameters from historical records at 91 stations spanning 2013–2021 in the Ganga River. The investigation unveiled substantial spatial heterogeneity in the pCO 2 across the Ganga River. The pCO 2 concentration varied from 1321.76 μatm, 1130.98 μatm, and 1174.33 μatm in the upper, middle, and lower stretch, respectively, with a mean of 1185.29 μatm. Interestingly, the upper stretch exhibited elevated mean pCO 2 and FCO 2 levels (fugacity of CO 2 : 3.63 gm2d−1) compared to the middle and lower stretch, underscoring the intricate interplay between hydrochemistry and CO 2 dynamics. In the context of pCO 2 fluctuations, nitrate concentrations in the upper segment and levels of biological oxygen demand (BOD) and dissolved oxygen (DO) in the middle and lower segments are emerging as crucial explanatory factors. Furthermore, regression tree (RT) and importance analyses pinpointed biochemical oxygen demand (BOD) as the paramount factor influencing pCO 2 variations across the Ganga River (n = 91). A robust negative correlation between BOD and FCO 2 was also observed. The distinct longitudinal patterns of both parameters may induce a negative correlation between BOD and pCO 2. Therefore, comprehensive studies are necessitated to decipher the underlying mechanisms governing this relationship. The present insights are instrumental in comprehending the potential of CO 2 emissions in the Ganga River and facilitating riverine restoration and management. Our findings underscore the significance of incorporating South Asian rivers in the evaluation of the global carbon budget. [Display omitted] • PCO 2 concentration higher in upstream region than downstream region of Ganges. • Nitrate predict pCO 2 well in upstream region. • BOD and DO are best predictors of pCO 2 in downstream regions. • BOD emerged as a crucial influencing factor for pCO 2 across the Ganga River. [ABSTRACT FROM AUTHOR]

Empirical studies have demonstrated associations between ten original adverse childhood experiences (ACEs) and multiple health outcomes. Identifying expanded ACEs can capture the burden of other childhood adversities that may have important health implications. We sought to identify childhood adversities that warrant consideration as expanded ACEs. We hypothesized that experiencing expanded and original ACEs would be associated with poorer adult health outcomes compared to experiencing original ACEs alone. The 11,545 respondents of the National Longitudinal Surveys (NLS) and Child and Young Adult Survey were 48.9 % female, 22.7 % Black, 15.8 % Hispanic, 36.1 % White, 1.7 % Asian/Native Hawaiian/Pacific Islander/Native American/Native Alaskan, and 7.5 % Other. This study used regression trees and generalized linear models to identify if/which expanded ACEs interacted with original ACEs in association with six health outcomes. Four expanded ACEs—basic needs instability, lack of parental love and affection, community stressors, and mother's experience with physical abuse during childhood —significantly interacted with general health, depressive symptom severity, anxiety symptom severity, and violent crime victimization in adulthood (all p -values <0.005). Basic needs instability and/or lack of parental love and affection emerged as correlates across multiple outcomes. Experiencing lack of parental love and affection and original ACEs was associated with greater anxiety symptoms (p = 0.022). This is the first study to use supervised machine learning to investigate interaction effects among original ACEs and expanded ACEs. Two expanded ACEs emerged as predictors for three adult health outcomes and warrant further consideration in ACEs assessments. [ABSTRACT FROM AUTHOR]

Neighborhood externalities influence residential land prices and housing prices simultaneously, contributing to a nexus relationship between them. Most existing studies have focused on the causal relationship between the two, with limited attention to their nexus. This research matched land parcel transactions from 2003 to 2015 and housing transaction records with purchasers' housing purchase records before 2019 to construct a spatial-temporal land-housing dataset in Shanghai. Both spatial statistical tools and regression tree models were employed to explore the underlying mechanism, focusing on proximity to industries. According to the modeling results, residential land prices and housing prices are highly sensitive to polluting industries and producer service industries. The impacts of polluting industries on residential land prices have intensified, but the impacts on housing prices have not. The regression tree models further showed that living service industries were the primary positive contributor to residential land and housing prices. Regarding the social environment, non-local people are taking over communities with high-priced housing on high-value residential land parcels. These research outcomes encourage more efforts to explore the determinants of the nexus between residential land prices and housing prices, rather than discussion of the causality relationship. • Analyzing the nexus between land and housing price using matched land and housing transaction records. • Construct a joint model to estimate the determinants of the nexus between land and housing prices. • Living service related industries could increase both land and housing price. • Non-local's communities are usually located on the high-value land parcels with higher housing price. [ABSTRACT FROM AUTHOR]

Every taxable arrangement is subject to an anti-abuse test. Abusive arrangements are treated as not valid for tax purposes, which is similar to the treatment of artifi cial arrangements in civil law. The European Union has introduced in its Anti-Tax Avoidance Directive a general anti-abuse test which must be transposed into the domestic laws of Member States. Such a test has its inner structure, consisting of an elimination and requalifi cation stage, while the elimination stage entails genuineness and a tax benefi t test. The general anti-abuse test has a great potential (or scalability when speaking in the language of start-ups) of being automated and integrated into diff erent legal application processes (such as taxpayer self-assessment systems, transactions certifi ed by public notary or merger and acquisition deals) to discover debt push down abuses or other arrangement structures which may have abusive content. While the best method for create a reliable algorithm is a decision tree type model, the inner ambiguity of the general anti-abuse test prevents using the full benefi ts of automation of tax laws. The purpose of this article is to design a decision tree type model for the test and address the main challenges of such a model, both from the perspective of the clarity of concepts and the quality of input information such an engine would use. [ABSTRACT FROM AUTHOR]

The present study was conducted to evaluate the comparative predictive performances of Classification and Regression trees (CART), Chi Squared Automatic Interaction Detector (CHAID) and Exhausted CHAID algorithms used to predict body weights of Awassi lambs at 60-d (W60) and 90-d (W90) of age. For this purpose, 730 Awassi lamb records were collected from 3 base flocks in Turkey in 2014-2016. The potential predictors included in this study were dam age, sex, birth type, flock, lambing season and birth year. In order to determine the best one among these decision tree algorithms, model evaluation criteria i.e RMSE, MAPE, RAE, SDratio, MAD, Pearson coefficient, Coefficient of determination (푅 2) and adjusted coefficient of variation (푅 2Adj) values were calculated. For the prediction of W60 and W90, the best decision tree algorithm was found to be the CART algorithm. 푅 2 for W60 and W90 were 0.614 and 0.978 and RMSE estimates for W60 and W90 were 0.94 and 0.321, respectively. The influential predictors affecting W90 were flock and W60. However, flock, birth weight (BW), birth type and birth year were found as significant factors for W60. In conclusion, CART algorithm may be a useful tool in describing breed standards of the Awassi for selection purposes in animal breeding. Also, it outperformed Exhausted CHAID and CHAID decision tree algorithms in predictive performance to predict W60 and W90 of Awassi lambs. [ABSTRACT FROM AUTHOR]