Search Results (427)

There are many approaches for training decision trees. This work introduces a novel gradient-based method for constructing decision trees that optimize arbitrary differentiable loss functions, overcoming the limitations of heuristic splitting rules. Unlike traditional approaches that rely on heuristic splitting rules, the proposed method refines predictions using the first and second derivatives of the loss function, enabling the optimization of complex tasks such as classification, regression, and survival analysis. We demonstrate the method’s applicability to classification, regression, and survival analysis tasks, including those with censored data. Numerical experiments on both real and synthetic datasets compare the proposed method with traditional decision tree algorithms such as CART, Extremely Randomized Trees, and SurvTree. The implementation of the method is publicly available, providing a practical tool for researchers and practitioners. This work advances the field of decision tree-based modeling, offering a more flexible and accurate approach for handling structured data and complex tasks. By leveraging gradient-based optimization, the proposed method bridges the gap between traditional decision trees and modern machine learning techniques, paving the way for further innovations in interpretable and high-performing models. Full article

Timely and accurate mapping of tree species is essential for forest resource inventory, biodiversity conservation, and sustainable ecosystem management, particularly in dryland environments where structural heterogeneity, spectral similarity, and data scarcity complicate classification. This study develops a machine learning-based framework implemented in Google Earth Engine to map dominant tree species in the Elnour Natural Forest Reserve (ENFR), Blue Nile, Sudan, using multi-temporal and multi-sensor remote sensing data. Multi-temporal Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 MSI imagery were integrated with vegetation index (NDVI), topographic variables derived from a digital elevation model (DEM), and field observations. The performance of Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and an unweighted ensemble approach was evaluated across four reference years (2008, 2013, 2018, and 2021). Results show that RF and SVM consistently achieved high classification performance, with overall accuracy (OA) ranging from 85.0% to 92.0% and Kappa coefficients (κ) from 0.81 to 0.89, while maintaining stable and ecologically realistic species-area estimates. CART showed greater sensitivity to class imbalance and overestimated minor species (OA = 72.0–80.0%, κ = 0.65–0.74), whereas the ensemble approach amplified misclassification of rare classes (OA = 78.0–84.0%, κ = 0.70–0.78). The integration of Sentinel-2 data improved species discrimination due to enhanced spatial and spectral resolution, particularly in the red-edge region; however, algorithm selection remained the dominant factor controlling performance. Feature importance analysis identified near-infrared (NIR), shortwave infrared (SWIR), and NDVI variables as the most influential predictors. Multi-temporal analysis revealed declining class separability, reflected by decreasing MCC values, and a shift in species composition, including a decline in Acacia seyal (Delile) and an increase in Sterculia setigera Delile. These patterns indicate increasing ecological complexity driven primarily by anthropogenic pressures, with climatic variability acting as an additional stressor. Full article

Traditional concrete faces challenges such as low energy absorption, brittleness and major environmental impacts, attributed to its dependence on natural resources. Integrating natural fibers with recycled coarse aggregates into concrete presents a promising method of enhancing concrete’s sustainability and mechanical performance. Still, accurately predicting the mechanical properties of these innovative concrete mixes remains complex. This research investigates the predictive abilities of two machine learning (ML) models, classification and regression trees (CART) and stepwise polynomial regression (SPR), for estimating the compressive and splitting tensile strengths of NF-reinforced concrete containing recycled coarse aggregates. The CART model showed greater predictive accuracy, reaching R² = 0.91 for compressive strength and R² = 0.89 for splitting tensile strength. Additionally, the model demonstrated consistently lower error metrics (RMSE, MAD, MAPE, MSE) than comparable approaches. For compressive strength, CART achieved R² = 0.91, RMSE = 5.5686, MSE = 31.0098, MAD = 4.1076, and MAPE = 0.1055, while for splitting tensile strength, it achieved R² = 0.89, RMSE = 0.3954, MSE = 0.1563, MAD = 0.2996, and MAPE = 0.0939. These results emphasize the significant potential of ML, particularly CART, to optimize the design of sustainable concrete mixtures, enabling more accurate and effective strength predictions and finally contributing to more resilient and sustainable infrastructure. Full article

This study investigated injury severity in 18,528 police-reported vehicle-to-pedestrian crashes involving elderly pedestrians in legally classified local areas of South Korea during 2012–2021. Injury severity was coded into four ordered categories: fatal, serious, minor, and reported injury. To stabilize scenario extraction from a categorical crash database, an integrated screening workflow was applied, including near-zero-variance filtering, redundancy control among overlapping roadway encodings, representative-variable selection within redundant groups, and chi-square association checks. Classification and regression tree (CART) modeling was then used to identify rule-based combinations of environmental, roadway, driver, pedestrian, and vehicle factors associated with elevated severity, while tree complexity was controlled through cost-complexity pruning and 10-fold cross-validation. A scenario-based sensitivity analysis was further conducted to evaluate counterfactual shifts in severity distributions under targeted control of key conditions within representative high-risk scenarios. The results showed that severe outcomes were concentrated in stacked-risk combinations rather than in single factors alone. A dominant pathway involved nighttime conditions combined with maneuver-related driving contexts and speeding-related violations. High-fatality scenarios persisted even when speed-related predictors were excluded, underscoring the roles of nighttime exposure, visibility limitations, conflict-prone roadway settings, heavy-vehicle involvement, and pedestrian exposure behaviors. The proposed framework translates administrative crash records into concise, operationally interpretable scenarios and intervention-relevant evidence for local-area safety. Full article

This manuscript is a continuation of the research published in Fire 2025, 8(8), 302, i.e., it deals with the examination of the cause-and-effect relationships of fires in the Republic of Serbia from the aspect of human safety. Among others, variables related to gender, age, and severity of injuries caused by fires are introduced, on which various methods of statistical analysis and stochastic modeling are first applied. Continuous age variables are modelled using the flexible Generalized Additive Models for Location, Scale, and Shape (GAMLSS) framework, where the Generalized Normal Distribution (GND) is identified as the optimal generative model for injuries, while a Reflected Log-Normal Distribution with positive support (RefLOGND+) provides the best fit for fatalities. The quality of such modeling is formally verified, and the probabilities of injury and death of individuals in certain age categories are predicted, revealing a pronounced concentration of injuries in the working-age population and a markedly higher relative risk of fatal outcomes among elderly individuals. Thereafter, by applying certain Data Mining (DM) techniques, primarily the Apriori algorithm, the most frequently occurring association rules are found, which indicate typical patterns and demographic structure of injuries and deaths in fires in Serbia. Finally, using the CART (Classification and Regression Trees) algorithm, several decision trees are formed that describe the impact and relationship of different causes of fires on injury and death in fires. In this way, some important and frequent patterns are observed that indicate key fire risk factors that significantly affect the demographic structure of human casualties. The results thus obtained provide a basis for developing targeted strategies for fire prevention and improving emergency response planning. Full article

Background/Objectives: Burnout among academic health professionals affects well-being and performance of critical responsibilities—clinical, research, administrative, and teaching. Despite growing attention, study limitations hinder understanding the mechanisms of burnout among health professionals fully. This study identifies individual and institutional factors associated with faculty burnout at a U.S. academic cancer center. Methods: From 2019 to 2021, all faculty at a large research hospital, regardless of rank, were invited to complete employee surveys, which assessed institutional support, work–life balance, and job demands. Burnout in 2021 served as the primary outcome, measured using a validated single-item scale with five response options: 1–2 were classified as “not burned out” and 3–5 as “burned out.” Using classification and regression tree (CART) analysis, a flexible, non-parametric approach that does not require distributional assumptions of the outcome variable and is well-suited for handling complex, non-linear relationships and interactions among multiple predictors, we explored without a priori hypotheses factors contributing to burnout status in 2021, using prior burnout experience and institutional factors assessed in both years as predictors. Results: This cross-sectional analysis revealed both report of burnout in 2019 and perceptions of low institutional inclusion linked to burnout in 2021, while higher report of job accomplishment and of empowerment was associated with lower burnout in 2021. Past burnout did not doom faculty to future burnout when they felt a strong sense of institutional inclusion and support in adapting to institutional change, indicating that burnout can be mitigated, even after a pandemic. Conclusions: Patterns of burnout were related to faculty engagement with the institution and leadership and their perceptions of work–life quality and control over their work, revealing opportunities for intervention. Strengthening support systems, promoting strategies for managing professional and personal demands better, and optimizing workloads may mitigate risk for faculty in academic health centers. Full article

Soil respiration (Rs) plays a pivotal role in carbon cycling within semi-arid ecosystems. In our millet field experiment, we measured Rs, autotrophic respiration (Ra), heterotrophic respiration (Rh), water consumption (ET), yield (Y), water use efficiency (WUE), and key soil environmental properties to examine the effects of supplemental irrigation and selenium application on Rs dynamics and to clarify the controlling factors. The experiment was conducted from 2023 to 2024 with four treatments and three replicates per treatment each year. These treatments comprised conventional rainfed (CK), supplemental irrigation (SI, 50 mm), rainfed with Se addition (CS, 67.84 g·hm⁻²), and supplemental irrigation with Se addition (SIS). SI increased CO₂ emissions in the millet field, whereas selenium application (CS) suppressed them. Ra was the dominant component of Rs and was 1.03–4.01 times greater than Rh. SI and CS significantly affected cumulative CO₂ emissions through Ra (p < 0.05), whereas their effects on Rh were minor. The CS treatment resulted in the lowest cumulative CO₂ emissions at 4233 and 4009 g·m⁻² in 2023 and 2024, respectively. Diurnal variation patterns of Rs, Ra, and Rh differed across millet growth stages. Both supplemental irrigation and selenium application improved soil water retention, soil enzyme activity, and soil organic matter (SOM), and moderated soil temperature. Classification and Regression Tree (CART) algorithm analysis revealed that Ra was primarily driven by soil temperature, with a feature weight of 86.95% determined by CART based on machine learning, whereas Rh was mainly influenced by soil enzyme activity, with a feature weight of 76.11%. The CS treatment enhanced production while promoting emission mitigation. The combined SIS treatment achieved the highest WUE and maintained a lower Rs than SI. These findings suggest an environmentally sustainable management strategy for millet production in semi-arid regions. However, due to the limited number of parcels in this study, further field-scale validation and additional experimental research involving multiple levels of supplemental irrigation and Se addition are necessary. Full article

Understanding how collaborative network structures and digital transformation jointly shape firm innovation has become a critical issue amid rapid technological change. Drawing on social network theory and a configurational perspective, this study investigates the nonlinear and interactive effects of collaborative network characteristics and digital transformation on firm innovation performance. Using patent data from Chinese listed manufacturing firms for the period between 2012 and 2022, inter-firm technological collaboration networks are constructed based on co-patenting relationships. A Classification and Regression Tree (CART) model is employed to uncover complex configurational patterns, complemented by regression-based robustness tests. The results reveal that innovation outcomes are not driven by single network attributes but by joint configurations of structural hole positions, centrality measures, and digital transformation. Among all factors, structural holes emerge as the most influential determinant. The findings further show that digital transformation interacts with network positions, generating multiple paths leading to high or low innovation performance. Model comparisons demonstrate that the CART approach outperforms traditional linear models in capturing nonlinear effects. This study contributes to the literature by highlighting the configurational logic of collaborative innovation and providing a machine learning-based framework for analyzing network–digital transformation interplay. Full article

Elemental migration and enrichment are important processes influencing tea plant growth and the assembly of rhizosphere bacterial communities within the rock–soil–plant continuum. This study explores how soil parent materials (granite, quartz schist, and sericite schist) are potentially associated with these processes and their observed associations with the elemental composition of tea leaves. Exploratory statistical analyses revealed distinct, lithology-specific biogeochemical patterns that serve as a foundation for hypothesis generation. In granite soils, chlorite correlated with the mobility of Cr, Pb, Cu, Ni, Mg, and Na, coinciding with shifts in the relative abundances of Verrucomicrobia, Armatimonadetes, and Chloroflexi. In quartz schist, kaolinite exhibited notable correlations with the dynamics of Pb, Cr, Ni, Zn, and As, which were statistically linked to Planctomycetes, Proteobacteria, and Acidobacteria. Complex mineral–microbe interactions were observed in sericite schist soils, where clay minerals (e.g., chlorite, illite) were closely associated with the migration of multiple elements (Pb, K, Ca, Cd, As, Al, Fe, Zn), paralleling structural variations in communities of Actinobacteria, Planctomycetes, Chloroflexi, and Proteobacteria. Potassium (K), calcium (Ca), and manganese (Mn) showed bioaccumulation tendencies in tea leaves across all lithologies, with an enrichment capacity order of Ca > K > Mn > Mg > Na > Al. Exploratory Classification and Regression Tree (CART) analysis suggested that the migration of K, Ca, Cu, Zn, and Hg corresponded most closely with their soil concentrations. Manganese (Mn) exhibited a mineral-associated trend, with kaolinite content as a potential correlate, while cadmium (Cd) migration was statistically linked to the relative abundance of Armatimonadetes. These findings highlight potential candidate relationships between mineralogy, microbes, and elemental mobility rather than confirming causal mechanisms, emphasizing the need for further validation in larger or experimental datasets. Full article

High-dimensional mixed data often lack a unified semantic representation for continuous and discrete attributes, which hinders mixed-attribute similarity modeling and can result in unstable reducts and overfitting in existing neighborhood rough set (NRS) methods. To address this issue, we propose IF-EMD-SPA, an attribute reduction method for NRS grounded in Information Flow theory. Unlike conventional NRS methods that rely on discretization or a single reduction criterion, IF-EMD-SPA first establishes a unified representation framework for heterogeneous attributes based on classifications and an Information Channel Core. It then integrates Earth Mover’s Distance (EMD) and Set Pair Analysis (SPA) to define a similarity metric for mixed attributes. In addition, a three-stage greedy reduction strategy is designed under the dual constraints of dependency preservation and structural error, consisting of dependency-driven forward selection, similarity-driven structure completion, and backward redundancy removal. Experiments on five UCI benchmark datasets and two high-dimensional gene expression datasets show that IF-EMD-SPA achieves average accuracies of 93.5% (k-Nearest Neighbors, KNN), 93.9% (Support Vector Machine, SVM), and 90.8% (Classification and Regression Trees, CART), with SVM achieving the best results on all seven datasets. Under CART, it reaches 100% accuracy on Wine and WPBC, improving performance by up to 37.5 percentage points over comparison methods. Full article

This study evaluated the influence of soil health in riparian and ecotone zones on water quality in four high-Andean rivers (Atillo, Ozogoche, Yasepan, and Cebadas) within the Cebadas River sub-basin, Ecuador. Soil and water samples were collected from 20 sites during three field campaigns (2022–2024). Soil properties included organic carbon concentration, soil organic carbon stock (SOC), bulk density, moisture, and potential microbial activity estimated through laboratory CO₂–C efflux. Water quality parameters were integrated into the National Sanitation Foundation Water Quality Index (NSF-WQI), and riparian condition was assessed using the QBR-And index. Multivariate statistical approaches, including Random Forest and Classification and Regression Trees (CART), were used to identify the most influential predictors of ecosystem quality. Results revealed marked spatial contrasts. Riparian SOC stocks ranged from 22.8 to 32.8 Mg C/ha in the more disturbed Cebadas and Yasepan rivers to 91.4–133.6 Mg C/ha in the better-conserved Atillo and Ozogoche systems. Sites with higher SOC and lower bulk density consistently exhibited better water quality, with NSF-WQI values classified as “good”, whereas more degraded sites showed lower riparian quality and “fair” water quality. Riparian forest quality was strongly correlated with water quality (r = 0.81). Random Forest models identified ammoniacal nitrogen, fecal coliforms, and altitude as the most influential predictors of riparian ecosystem condition. These findings demonstrate that soil health and riparian integrity are tightly linked to water quality patterns in high-Andean fluvial systems and support their integration into ecosystem-based watershed management. Full article

Objectives: To improve the differentiation of borderline ovarian-adnexal tumours (BOTs) from malignant ovarian-adnexal masses, most of which fall into the indeterminate O-RADS MRI 4 category, by developing a multimodal predictive model that integrates clinical, analytic, and MRI parameters. Methods: This retrospective, single-centre study included 248 women who underwent standardised MRI for ovarian-adnexal mass characterisation between 2019 and 2024. Of these, 201 had true ovarian-adnexal masses (114 benign, 22 borderline, and 65 malignant), confirmed by histopathology or stability after ≥12-month follow-up. Forty-one clinical, laboratory, and imaging variables were initially assessed, and after a bivariate evaluation, 18 final predictors with clinical relevance were selected for model construction with thresholds learned from the data. A classification and regression tree (CART) model (“Full Model”) was applied as a second-stage tool after O-RADS MRI scoring, using 10-fold cross-validation to prevent overfitting. A pruned “Simplified Model” was also derived to enhance interpretability. Results: O-RADS MRI performed well at the extremes (scores 2–3 and 5) but showed limited discrimination between BOTs and malignancies within category 4 (PPV for borderline = 0.50). The decision-tree models significantly improved diagnostic performance, increasing overall accuracy from 0.856 with O-RADS MRI alone to 0.905 (Simplified Model) and 0.955 (Full Model). The PPV for BOTs within the intermediate O-RADS MRI 4 category increased from 0.49 with O-RADS MRI alone to 0.77 and 0.90 with the simplified and full models, respectively, while maintaining high accuracy for benign and malignant lesions. Conclusions: In this retrospective single-centre cohort, the addition of an interpretable rule-based predictive model as a second-line tool within O-RADS MRI category 4 was associated with improved discrimination between borderline and invasive malignant ovarian-adnexal tumours. These findings suggest that multimodal integration of clinical, laboratory, and MRI features may help refine risk stratification in indeterminate cases; however, external validation in prospective multicentre cohorts is required before clinical implementation. Full article

Background/Objectives: Acute mesenteric ischemia (AMI) is a time-dependent condition associated with exceptionally high in-hospital mortality, particularly among elderly and comorbid patients. Early identification of patients at high risk of death remains challenging and has important implications for clinical decision-making. The objective of this study was to derive and internally validate a prognostic score for in-hospital mortality of patients with AMI. Materials and Methods: We conducted a multicenter, observational, retrospective cohort study including patients with AMI from 10 participating hospitals. A descriptive and analytical approach was performed. A Classification and Regression Tree (CART) model was used to determine cut-off points for continuous variables and assess their association with mortality. Based on these thresholds, a univariate analysis was performed, and variables with statistical significance (p < 0.05) were incorporated into a multivariate logistic regression model. A score—the RADIAL score—was then derived from the beta coefficients. The discriminative ability of the score was evaluated using the receiver operating characteristic (ROC) curve. Results: A total of 693 patients were studied. Thee mean age was 81 years (IQR 73–86) and 54.2% were women. A history of cardiovascular disease was present in 75.3% of participants. Overall mortality was 62.4%. Most patients (74%) were managed conservatively. Significant variables in the bivariate analysis included hypotension, age > 65 years, pH < 7.3, creatinine > 1.7 mg/dL, and absence of rectal bleeding. These variables were incorporated into the multivariate model. The resulting score showed an area under the ROC curve of 0.78 (95% CI: 0.74–0.82). Conclusions: The RADIAL score demonstrated robust predictive performance and allowed the identification of three mortality-risk groups: 30–40% (low), 50–60% (intermediate), and 80% (high). This tool may support clinical decision-making in the management of patients with AMI. Full article

Background: Recent studies have highlighted the significant impact of combined thermal and lighting conditions on human comfort. However, there is limited understanding of how these factors influence cognitive performance in older adults. This study explored the effects of complex thermal and lighting conditions on various cognitive functions and physiological responses in older adults. Additionally, a predictive cognitive model was developed using physiological indicators as well as demographic factors. Methods: Twenty-two older adults participated in a within-subject design experiment under different thermal and lighting combinations. The study focused on two temperature conditions, 18 °C and 28 °C, and two lighting conditions, 480 nm with 5500 K and 644 nm with 3200 K. Conclusions: The finding showed that males significantly performed better at 18 °C under 480 nm lighting, while females excelled at 28 °C under 644 nm lighting. Electrodermal activity (EDA) increased in warmer conditions with warmer lighting, and pupil size expanded similarly but decreased under cooler conditions. Males’ EDA was negatively correlated with cognitive performance, while females’ pupil size and BMI were positively correlated. Using the classification and regression tree (CART) algorithm, predictive model demonstrated 89.7% accuracy. These findings emphasize the potential of optimizing thermal and lighting conditions to enhance cognitive functions and predict performance in older adults Full article

In this study, we describe a non-invasive approach to objectively assess fragrance-induced emotions using multiplex salivary biomarker profiling. Traditional self-reports, physiological monitoring, and neuroimaging remain limited by subjectivity, invasiveness, or poor temporal resolution. Saliva offers an advantageous alternative, reflecting rapid neuroendocrine changes linked to emotional states. We combined four key salivary biomarkers, cortisol, alpha-amylase, dehydroepiandrosterone, and oxytocin, to capture multidimensional emotional responses. Two clinical studies (n = 30, n = 63) and one user study (n = 80) exposed volunteers to six fragrances, with saliva collected before and 5 and 20 min after olfactory stimulation. Subjective emotional ratings were also obtained through questionnaires or an implicit approach. Rigorous analytical validation accounted for circadian variation and sample stability. Biomarker patterns revealed fragrance-induced emotional profiles, highlighting subgroups of participants whose biomarker dynamics correlated with particular emotional states. Increased oxytocin and decreased cortisol levels aligned with happiness and relaxation; in comparison, distinct biomarker combinations were associated with confidence or dynamism. Classification and Regression Trees (CART) analysis results demonstrated high sensitivity for detecting these profiles. Validation in an independent cohort using an implicit association test confirmed concordance between molecular profiles and behavioral measures, underscoring the robustness of this method. Our findings establish salivary biomarker profiling as an objective tool for decoding real-time emotional responses. Beyond advancing affective neuroscience, this approach holds translational potential in personalized fragrance design, sensory marketing, and therapeutic applications for stress-related disorders. Full article