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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]

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]

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 stretch-shortening cycle (SSC) has been classified into fast (<250 ms) and slow (>250 ms) groups based on ground contact time (GCT) threshold values. However, there are gaps in the literature on how the 250 ms threshold value was found and which variables affect it. The purpose of this study is to validate the 250 ms threshold by investigating the factors affecting this threshold. For this purpose, force–time variables during a drop jump (DJ) with a force plate and achilles tendon (AT) muscle-tendon unit mechanical properties using shear-wave elastography in 46 recreationally active men were analysed. A regression tree analysis was conducted using R studio to classify GCT with correlated variables (p < 0.05). The new GCT threshold values (GCT < 188 ms, 188 ≤ GCT < 222 ms and GCT ≥ 222 ms) were found according to the lowest root mean square error of approximation value (0.1985) at reactive strength index. Comparisons of GCT groups showed significant differences in force, time, power variables and AT length (p < 0.05). AT length is the main variable differentiating GCT groups: Short AT results in a short GCT and long AT results in a long GCT. This study reveals that SSC can be classified into three groups using new GCT threshold values, offering a new perspective for SSC assessment. [ABSTRACT FROM AUTHOR]

Abstract 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. The study was conducted to predict the body weight of mixed breeds of pigs in Nigeria through morpho-biometric traits (body length, chest girth, height at withers, ear length, head length, foreleg length, and hind leg length) using classification and regression tree models. The data were produced using 500 randomly selected mixed breeds of pigs from various farms in five Local Government Areas of Kaduna State, North West Nigeria. The collected data were analysed using the Statistical Package for Social Sciences (SPSS, 2016). Body weight correlated well with morphometric characteristics except with foreleg length, which had a low correlation and no significant (P>0.05) difference. Two body dimensions were shown to be more effective in predicting the body weight of the mixed-breeds based on the significance of the independent variables: chest girth and body length. The largest dividing variable was determined to be chest girth, which explained roughly 88.60 % of the difference in body weight. The decision tree model revealed that pigs with chest girth or chest circumference greater than 76.00 cm are expected to have a higher body weight, which livestock producers and researchers could use to determine the feed amount, drug dose, and market price of an animal, as well as the management, selection, and genetic improvement of mixed breeds of pigs in Nigeria.

This paper aimed to characterize and verify whether the linear relationships between agronomic traits of white oat are different between crops with and without chemical fertilization; and identify the agronomic ideotype that enhances the agronomic performance of white oats. Two uniformity tests were carried out with and without chemical fertilization in the 2020 harvest. In each trial, on 285 plants, agronomic traits were measured. Pearson's linear correlation coefficients and the direct and indirect effects of the trial analysis were calculated. The regression tree algorithm and Kohonen map neural network were used to identify the agronomic ideotype. The linear relationships between agronomic characters of white oat are similar between crops with and without chemical fertilization. White oat genotypes with greater panicle grain weight can be selected indirectly by panicle weight, regardless of cultivation with or without fertilization. White oat genotypes measuring 114.57 cm in height, 97.41 cm in panicle insertion, 18.11 cm in panicle length, 1.31 g in panicle weight and 27.92 grains in the panicle characterize the agronomic ideotype that maximizes panicle grain weight.

AbstractAnalyzing soft interval data for uncertainty quantification has attracted much attention recently. Within this context, regression methods for interval data have been extensively studied. As most existing works focus on linear models, it is important to note that many problems in practice are nonlinear in nature and the development of nonlinear regression tools for interval data is crucial. This paper proposes an interval-valued random forests model that defines the splitting criterion of variance reduction based on an L2 type metric in the space of compact intervals. The model simultaneously considers the centers and ranges of the interval data as well as their possible interactions. Unlike most linear models that require additional constraints to ensure mathematical coherences, the proposed random forests model estimates the regression function in a nonparametric way, and so the predicted interval length is naturally nonnegative without any constraints. Simulation studies show that the new method outperforms typical existing regression methods for various linear, semi-linear, and nonlinear data archetypes and under different error measures. To demonstrate the applicability, a real data example is presented where the price range data of the Dow Jones Industrial Average index and its component stocks are analyzed. [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]

Regression trees (RT) involve sorting samples based on a particular feature and identifying the splitting point that yields the highest drop in variance from a parent node to its children. The optimal factor for reducing mean squared error (MSE) is the target variable itself. Consequently, employing the target variable as the basis for splitting sets an upper limit on the reduction of MSE and, equivalently, a lower limit on the residual MSE. Building upon this observation, we define lepto-regression as the process of constructing an RT of a target feature on itself. Lepto-variance pertains to the portion of variance that cannot be mitigated by any regression tree, providing a measure of inherent variance at a specific tree depth. This concept is valuable as it offers insights into the intrinsic structure of the dataset by establishing an upper boundary on the "resolving power" of RTs for a sample. The maximal variance that can be accounted for by RTs with depths up to k is termed the sample k-bit macro-variance. At each depth, the overall variance within a dataset is thus broken into lepto- and macro-variance. We perform 1- and 2-bit lepto-variance analysis for the entire US stock universe for a large historical period since 1926. We find that the optimal 1-bit split is a 30–70 balance. The two children subsets are centered roughly at −1% and 0.5%. The 1-bit macro-variance is almost 42% of the total US stock variability. The other 58% is structure beyond the resolving power of a 1-bit RT. The 2-bit lepto-variance equals 26.3% of the total, with 42% and 47% of the 1-bit lepto-variance of the left and right subtree, respectively.

AbstractThe 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]

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]

The aim of the current study was to predict the body weight from linear body measurements of Astrolope, Boschveld and indigenous Sacco genotype using Classification and regression tree (CART) and Multivariate Adaptive Regression Spline (MARS) algorithm. A total of 389 body weight (BW) records, including five continuous predictors such as Neck length (NL), body circumference (BC), shank length (SL), body length (BL) and shank circumference (SC) were used. The best model was selected based on goodness of fit, such as, standard deviation ratio (SDR), root mean square error (RMSE), coefficient of variation (CV), adjusted coefficient of determination (ARsq), coefficient of determination (Rsq) and Pearson's correlation coefficients (PC). The Rsq (%) values ranged from 59 (MARS) to 69 (CART). The lowest SDR was recorded by CART (0.56) and the highest by MARS (0.70). The CART was selected to be the best algorithm with sex, genotype, SC, SL, BL, NL, and BC as influential predictor of BW. The heaviest body weight on females of genotype (Boschveld, Sacco) was recorded when BL was less than 43 cm and BL higher than 47 cm. The goodness of fit criteria suggest that CART model outperformed the MARS model on predicting the body weight of the three genotypes. The findings will assist farmers in the prediction of body wight and selection of heavier chickens. [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]

This study focuses on the multiscale calculation of different plan curvature forms to enhance the modeling of soil penetration resistance and gravimetric soil water content utilizing the classification and regression trees algorithm in a low-relief watershed. To that end, three forms of plan curvature were derived using the Wood method from a two-meter digital elevation model on six neighborhood sizes. The results showed that the neighborhood size influenced the plan curvature values and there was little difference between the utilization of three forms of plan curvature in the landform determination. The modeling results indicated that the three forms of plan curvature on most neighborhood scales have different contributions to each other in modeling the spatial variability of each soil property. The neighborhood scale was a critical factor in soil modeling because it controls the smoothing rate of plan curvature. The overall results suggest that soil models with poor performance could be constructed if the plan curvature forms and the neighborhood size are not considered in the geomorphometric analysis. Therefore, it is recommended to use the procedure implemented in this study for digital soil mapping in various regions. [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]

DC microgrids have gained significant attention in recent years due to their potential to enhance energy efficiency, integrate renewable energy sources, and improve the resilience of power distribution systems. However, the reliable operation of DC microgrids relies on the early detection and location of faults to ensure an uninterrupted power supply. This paper aims to develop fast and reliable fault detection and location mechanisms for DC microgrids, thereby enhancing operational efficiency, minimizing environmental impact, and contributing to resource conservation and sustainability goals. The fault detection method is based on compressed sensing (CS) and Regression Tree (RT) techniques. Besides, an accurate fault location method using the feature matrix and long short-term memory (LSTM) model combination has been provided. To implement the proposed fault detection and location method, a DC microgrid equipped with photovoltaic (PV) panels, the vehicle-to-grid (V2G) charging station, and a hybrid energy storage system (ESS) are used. The simulation results represent the proposed methods' superiority over the recent studies. The fault occurrence in the studied DC microgrid is detected in 1 ms, and the proposed fault location method locates the fault with an accuracy of more than 93%. The presented techniques enhance DC microgrid reliability while conserving renewable resources, vital to promoting a greener and more sustainable power grid. [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]

AbstractPurposeMethodsResultsConclusion\nIMPLICATIONS FOR REHABILITATIONIdentify patient subgroups with different functional outcomes after SCI and study the association between functional status and initial ISNCSCI components.Using CART, we performed an observational cohort study on data from 675 patients enrolled in the Rick-Hansen Registry(RHSCIR) between 2014 and 2019. The outcome was the Spinal Cord Independence Measure (SCIM) and predictors included AIS, NLI, UEMS, LEMS, pinprick(PPSS), and light touch(LTSS) scores. A temporal validation was performed on data from 62 patients treated between 2020 and 2021 in one of the RHSCIR participating centers.The final CART resulted in four subgroups with increasing totSCIM according to PPSS, LEMS, and UEMS: 1)PPSS < 27(totSCIM = 28.4 ± 16.3); 2)PPSS ≥ 27, LEMS < 1.5, UEMS < 45(totSCIM = 39.5 ± 19.0); 3)PPSS ≥ 27, LEMS < 1.5, UEMS ≥ 45(totSCIM = 57.4 ± 13.8); 4)PPSS ≥ 27, LEMS ≥ 1.5(totSCIM = 66.3 ± 21.7). The validation model performed similarly to the original model. The adjusted R-squared and F-test were respectively 0.556 and 62.2(P-value <0.001) in the development cohort and, 0.520 and 31.9(P-value <0.001) in the validation cohort.Acknowledging the presence of four characteristic subgroups of patients with distinct phenotypes of functional recovery based on PPSS, LEMS, and UEMS could be used by clinicians early after tSCI to plan rehabilitation and establish realistic goals. An improved sensory function could be key for potentiating motor gains, as a PPSS ≥ 27 was a predictor of a good function.After a traumatic Spinal Cord Injury (SCI), early neurological examination using the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) is recommended to determine initial injury severity and prognosis.This study identified three initial ISNCSCI components defining four subgroups of SCI patients with different expectations in functional outcomes, namely the initial pinprick sensory score, the Lower Extremity Motor Score, and the Upper Extremity Motor Score.Clinicians could use these subgroups early after tSCI to plan rehabilitation and set realistic therapeutic goals regarding functional outcomes.In clinical practice, careful and accurate assessment of pinprick sensation early after the SCI is crucial when predicting function or stratifying patients based on the expected function. [ABSTRACT FROM AUTHOR]

The mortality of wildlife on roadways is a major conservation concern worldwide. Amphibians and reptiles are especially vulnerable to vehicular collisions, and this is of particular concern in the Frontenac Arch Biosphere Reserve (Ontario, Canada) where several species are near their geographic limits of distribution and designated as species-at-risk. We completed regular surveys (n = 270) of two major highways in the Reserve, each slightly less than 40 km in length. All observations of wildlife-vehicle collisions were documented for two years on each road, including 18,278 frogs, turtles, and snakes. We used kernel density estimation to map relative magnitude of this mortality and built a suite of regression tree models to assess the influence of landcover and other habitat factors on roadkill at two scales (1 ha and 20 ha). Sample size was large enough to conduct species-level analyses for Chrysemys picta marginata (midland painted turtle) and Nerodia sipedon (northern watersnake). Spatial clustering of roadkill was evident on both roads and for all taxa. However, the extent of clustering varied between the two roadways due to differences in landcover pattern and clustering was more discrete for frogs and turtles than for snakes. For frogs, turtles, and northern watersnakes we found that elevated levels of mortality were positively associated with the amount of wetland and open water in adjacent areas as well as the proximity of water features. However, mortality locations for other species of snakes were more closely associated with upland habitat types. While some generalities emerge from our study, the variation also suggests that caution be exercised when attempting to extend results to different taxa and roadways, especially since these results may vary with scale. Nonetheless, scale-related differences can be informative for identifying the location of roadkill mitigation efforts and we illustrate how such an approach could be implemented for snakes that exhibit less discrete clustering of mortality.

Despite a decrease in the prevalence of low birth weight (LBW) over time, its ongoing significance as a public health concern in Bangladesh remains evident. Low birth weight is believed to be a contributing factor to infant mortality, prolonged health complications, and vulnerability to non-communicable diseases. This study utilizes nationally representative data from the Multiple Indicator Cluster Surveys (MICS) conducted in 2012-2013 and 2019 to explore factors associated with birth weight. Modeling birth weight data considers interactions among factors, clustering in data, and spatial correlation. District-level maps are generated to identify high-risk areas for LBW. The average birth weight has shown a modest increase, rising from 2.93 kg in 2012-2013 to 2.96 kg in 2019. The study employs a regression tree, a popular machine learning algorithm, to discern essential interactions among potential determinants of birth weight. Findings from various models, including fixed effect, mixed effect, and spatial dependence models, highlight the significance of factors such as maternal age, household head's education, antenatal care, and few data-driven interactions influencing birth weight. District-specific maps reveal lower average birth weights in the southwestern region and selected northern districts, persisting across the two survey periods. Accounting for hierarchical structure and spatial autocorrelation improves model performance, particularly when fitting the most recent round of survey data. The study aims to inform policy formulation and targeted interventions at the district level by utilizing a machine learning technique and regression models to identify vulnerable groups of children requiring heightened attention.

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.

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.

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

Background: Intracortical inhibitory/faciliatory measures are affected after stroke; however, the evidence is conflicting., Objective: This meta-analysis aimed to investigate the changes in motor threshold (MT), motor evoked potential (MEP), short-interval intracortical inhibition (SICI), and intracortical facilitation (ICF), and identify sources of study variability using a machine learning approach., Methods: We identified studies that objectively evaluated corticospinal excitability and intracortical inhibition/facilitation after stroke using transcranial magnetic stimulation. Pooled within- (ie, affected hemisphere [ AH ] vs unaffected hemisphere [ UH ]) and between-subjects (ie, AH and UH vs Control ) standardized mean differences were computed. Decision trees determined which factors accurately predicted studies that showed alterations in corticospinal excitability and intracortical inhibition/facilitation., Results: A total of 35 studies (625 stroke patients and 328 healthy controls) were included. MT was significantly increased and MEP was significantly decreased (ie, reduced excitability) in the AH when compared with the UH and Control ( P  < .01). SICI was increased (ie, reduced inhibition) for the AH when compared with the UH , and for the AH and UH when compared with Control ( P  < .001). ICF was significantly increased (ie, increased facilitation) in the AH when compared with UH ( P  = .016) and decreased in UH when compared with Control ( P  < 0.001). Decision trees indicated that demographic and methodological factors accurately predicted (73%-86%) studies that showed alterations in corticospinal and intracortical excitability measures., Conclusions: The findings indicate that stroke alters corticospinal and intracortical excitability measures. Alterations in SICI and ICF may reflect disinhibition of the motor cortex after stroke, which is contrary to the notion that stroke increases inhibition of the affected side., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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]

Background: Reducing health disparities is a public health issue. Identification of low-health-interest populations is important, but a definition of people with low health interest has not yet been established. We aimed to quantitatively define low-health-interest populations., Methods: A nationwide cross-sectional internet survey was conducted in 2022. We compiled regression tree (RT) analyses with/without adjustment for age, sex, and socioeconomic status with the 12-item Interest in Health Scale (IHS, score range 12-48) as an explanatory variable and the 10 composite health behaviors as a dependent variable. We defined the first IHS branching condition from the root node as a lower-health-interest group and the terminal node with the lowest health behaviors as the lowest-health-interest group., Results: The mean IHS value of 22,263 analyzed participants was 32.1 ± 5.6; it was higher in females and in those who were aged over 45 years, had a high education, a high income, or a spouse. The first branching condition was IHS 31.5, and the terminal node branched at 24.5, before/after adjustment for covariates., Conclusions: We determined the cutoff values of the IHS as <32 for a lower-health-interest group and <25 for the lowest-health-interest group. Using these cutoffs might enable us to reveal the characteristics of low-health-interest populations.

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]

In this paper, a predictive model using regression trees is represented, which can predict the depleted isotopic composition (IC) of 21 isotopes of the VVER-1000 reactor based on effective days, initial enrichments, percentage of gadolinium absorber, and zones of fuel elements within the fuel assembly (FA). First, multiple regression trees (RT) are generated for a particular isotopic, and predictions of the isotopic composition of that isotope have been made by averaging the results from all trees. Multiple regression trees (RT) models have been generated by applying optimal cross-validation (CV) fold number, which is identified by investigating four parameters of the model. The model's performance has been evaluated by the root mean squared error (RMSE), R2 value, and p-value for the heteroscedasticity test. It has been found that the percentage of RMSE for the training set and test set is less than 0.15%, the R2 value is close to unity, and the p-value for the heteroscedasticity test is higher than 5% with a 95% confidence interval. After completing the training process takes a fraction of a second to compute isotopic composition. [ABSTRACT FROM AUTHOR]