Search Results (245)

Because of the increasing negative effects of drought on sectors such as water resources, the economy and agriculture, there is a strong need to study drought and its projections. Given the importance of studying different scenarios regarding drought and climate change, this study presents an integrated modelling framework to generate future drought projections for the Western Black Sea Basin, Türkiye, under four Shared Socioeconomic Pathway (SSP) scenarios derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Monthly precipitation data from seven General Circulation Models—ACCESS-CM2, CanESM5, CNRM-CM6-1, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-LR and MRI-ESM2-0—were statistically downscaled to 31 meteorological observation stations using a Multivariate Adaptive Regression Splines (MARS) approach, trained over the 1979–2014 period. Precipitation records from all 32 available meteorological stations were quality-controlled, and the 31 stations with continuous temperature records were retained for the full analysis, so that both indices could be computed at the same locations. Systematic biases in the downscaled outputs were subsequently corrected using the Quantile Delta Mapping (QDM) method, an essential step that simultaneously reduces distributional errors and preserves the future climate change signal. Both the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were computed at 3-, 6- and 12-month scales, with distribution parameters estimated once over the 1979–2014 window of the bias-corrected model series and held fixed for 2015–2100 so that baseline and future statistics remain comparable. The two indices yield markedly different projections. Although the number of drought events falls, the share of months below the moderate-drought threshold at the 12-month scale rises from 16.5% in the baseline to 47.0% by 2061–2100 under SSP5-8.5 for the SPI and to 88.4% for the SPEI. All 217 station–GCM combinations show the SPEI as the drier index in every scenario and period. The reason is the temperature term: under SSP5-8.5, basin-mean precipitation changes by −20 mm yr⁻¹ by the late century while potential evapotranspiration rises by +192 mm yr⁻¹, so that about 91% of the change in the climatic water balance is attributable to evaporative demand rather than to rainfall. What changes is therefore not how often drought occurs but how long it lasts and how much deficit it accumulates. Projections under SSP1-2.6 indicate substantially less severe drought conditions than those under the higher-emission pathways at every station, although drought exposure still increases relative to the baseline. Full article

The objective was to compare the performance of various data mining algorithms in estimating the body weight of fish. The effects of age, farm type and body morphological characteristics on the body weight of the hybrid silver carp (Hypophthalmichthys spp.) were analyzed using some data mining methods. The body weight of the fish was predicted using different morphological measurements taken during its lifetime and after slaughter. Prediction performances of the Chi-Squared Automatic Interaction Detector (CHAID), Classification and Regression Trees (CART), Artificial Neural Networks (ANN), Random Forest (RF), and Multivariate Adaptive Regression Splines (MARS) algorithms were compared. The prediction capabilities of the fitted models were assessed using model fit statistics. The MARS algorithm was demonstrated to be the most effective model for characterizing body weight. Body length showed the greatest relative importance among all traits measured during the fish’s lifetime. When the body length of fish exceeds 145 mm, the body weight is expected to reach 173 g. The MARS algorithm is the most effective model for predicting the body weight of fish, based on the experimental data. It provides an excellent alternative to existing data mining techniques. Full article

Background and Objectives: Spiritual well-being is consistently associated with better quality of life in oncology, but the pathways involved are disputed. Two competing accounts exist: spiritual well-being may buffer patients against the impact of physical symptoms, or it may act mainly through emotional distress. We examined whether cross-sectional data from a Romanian cancer cohort are compatible with either account. Materials and Methods: In this single-centre cross-sectional study, 175 adults with cancer completed the FACIT-Sp-12, the Religious Commitment Inventory-10, the EORTC QLQ-C30 and the Hospital Anxiety and Depression Scale; the FACIT-Sp-12, QLQ-C30 and HADS were administered in locally adapted formats that deviate from the validated instruments, so all findings pertain to these adapted versions. Patients were grouped by tertile of FACIT-Sp-12 score. The primary outcome was global health status/quality of life. Multivariable linear regression with heteroscedasticity-consistent standard errors, prespecified bootstrapped mediation analyses (interpreted as cross-sectional statistical decompositions), a multiplicative moderation test, restricted cubic splines and nine prespecified sensitivity analyses were performed. Results: Patients were 62.2 ± 13.1 years old, 65.1% had advanced disease and 51.4% screened positive for clinically significant distress (HADS-Anxiety or HADS-Depression ≥ 8). Global quality of life rose monotonically across spiritual well-being tertiles (53.8 ± 28.0, 65.9 ± 22.3 and 74.5 ± 23.1 points; p < 0.001), with an adjusted high-versus-low difference of 14.9 (4.3 to 25.5) points. In the prespecified primary model, each standard deviation of spiritual well-being was associated with 7.15 (3.45 to 10.86) points of global quality of life. In separate prespecified single-mediator models, the indirect associations through HADS-anxiety (4.21 points, 95% CI 1.89 to 6.86) and HADS-depression (3.03 points, 95% CI 1.17 to 5.43) were both statistically significant (bootstrapped p < 0.001); these cross-sectional decompositions are equally compatible with reverse, bidirectional or common-cause structures. No statistically detectable spiritual well-being × symptom burden interaction was found (β = −1.198, 95% CI −6.355 to 3.959; p = 0.649), but the confidence interval included clinically relevant effect modification in either direction, and there was no evidence of departure from linearity in the dose–response relationship (p for non-linearity = 0.510). Coefficients for the peace subscale were consistently larger than those for faith, although the subscales were modelled separately. Conclusions: Higher scores on the adapted FACIT-Sp-12 were cross-sectionally associated with better global quality of life and lower emotional distress. Anxiety and depression statistically accounted for part of this association, but temporal ordering and causality cannot be determined from these data, and the interaction analysis was too imprecise to support or exclude buffering. Routine assessment of spiritual and emotional concerns alongside symptom control is supported; whether interventions that increase spiritual well-being improve quality of life requires longitudinal and interventional study using the validated instrument formats. Full article

Background/Objectives: Vitamin D deficiency may increase the risk of IBD and be associated with disease activity. The aim of the study was to assess vitamin D levels, their association with disease activity in patients with IBD. Methods: A total of 231 subjects took part in the cross-sectional comparative study, including 129 patients with IBD and 102 healthy individuals Disease Activity Index and the Montreal classification were used to assess disease activity in patients with CD. For patients with UC, the Partial Mayo Score and the Montreal classification were applied. To determine total 25(OH)D chemiluminescent immunoassay (CLIA) technology was used. Results: The concentration of 25(OH)D was significantly lower in the group of patients with IBD compared to the control group (25.5 ng/mL vs. 28.8 ng/mL, p = 0.0027). Differences in 25(OH)D concentrations depended on IBD activity, with significantly higher vitamin D concentrations found in patients in remission compared to those with active IBD (28.9 ± 7.2 ng/mL vs. 22.3 ± 6.1 ng/mL, p = 0.0022). This association was confirmed for both UC and CD patients. A multivariate adaptive regression model using spline curves revealed a relationship between serum 25(OH)D concentrations in patients with IBD and total dietary vitamin D intake, including vitamin D supplementation, consumption of one serving of fish per week, and disease remission. Conclusions: Serum 25(OH)D levels in IBD patients may serve as an additional, useful, and non-invasive marker of disease activity. Full article

Blast-induced ground vibration is a critical environmental hazard in open-pit mining operations, capable of causing severe damage to adjacent structures and infrastructure. Accurately predicting vibration intensity is universally quantified by the peak particle velocity (PPV) index. Among the various factors affecting blast-induced ground vibrations, the distance from the blast face to the monitoring point (D) and the charge weight per delay (W) are the most influential and controllable parameters in a specific mine site. Therefore, these variables were selected as inputs for PPV estimation. The present study develops advanced white-box machine learning (ML) models, including Multi-Expression Programming (MEP), Gene Expression Programming (GEP), Multivariate Adaptive Regression Splines (MARS), and Stronger Variable Creator Machines (SVCMs), for predicting PPV. The general explicit equation was derived from the developed ML models implemented in a spreadsheet program, which can be easily used to estimate PPV. The MEP model achieves the highest accuracy, with a correlation coefficient (CC) of 0.993177 and a root mean square error (RMSE) of 0.836337, followed by the MARS, GEP, and SVCM models. The results of the present study, supported by k-fold cross-validation and parametric analysis, confirmed the potential of the proposed models for PPV estimation. Full article

Structural static testing is paramount for validating the structural integrity of critical aerospace components. However, conventional test rigs are often constrained to fixed loading axes and frequently induce parasitic torques. Accurate reproduction of aero-engine pylon flight loads therefore requires a mechanism that combines omnidirectional vector loading, high stiffness, and efficient force transmission. Achieving these coupled requirements is primarily a geometric synthesis problem, yet the associated workspace, stiffness, and load–capacity indices are nonlinear, mutually coupled, and expensive to evaluate over dense pose samples. To address this optimization bottleneck, this work develops a task-specific 6-UPS loading mechanism and a bio-inspired sensitivity-weighted NSGA-II algorithm for its geometric synthesis. Inspired by gene/locus-specific heterogeneity in biological evolution, the algorithm assigns variable-wise search intensities according to design-variable sensitivities, which are estimated using Multivariate Adaptive Regression Splines (MARS). In this way, influential design genes receive stronger local exploitation, whereas less sensitive ones retain broader exploration. Numerical simulations demonstrate that the proposed approach reduces computation time from about 30 h to 3 h relative to direct optimization with the baseline NSGA-II, while simultaneously improving workspace, stiffness, and load-carrying capacity. A hybrid physical prototype was further tested under 240 loaded pose conditions; the system maintained force magnitude errors below 0.64% (63.42 N) and directional deviations below 1.15°. These results support the efficacy of the proposed bio-inspired optimization-based design methodology for high-fidelity static testing of aero-engine pylons under the adopted hybrid setup. Full article

The spatial distribution and productivity of subsistence resources are central to food security, nutrition, and cultural vitality in circumpolar Indigenous communities. Yet few studies incorporate Indigenous Knowledge in methodology to monitor subsistence plant species. Here, we apply participatory action research to develop a monitoring system for the culturally and nutritionally important Rubus chamaemorus (atsalugpiaq, salmonberry) near the Yup’ik village of Quinhagak in southwest Alaska. With support from community members, two ground-truth surveys assessed berry productivity at nine sites within Quinhagak’s Traditional Land Use Area. Seventeen interviews identified key themes related to subsistence harvest and highlighted winter meteorological factors important for analysis. We compiled a multi-year dataset including PlanetScope eight-band SuperDove imagery (3 m GSD); airborne LiDAR and satellite-derived DEMs; and four meteorological parameters. Linear regression and multiple adaptive regression splines were tested to evaluate relationships among vegetation health, climate, landscape features, and berry productivity. Model outputs identified chlorophyll-related vegetation indices, particularly MTCI, as strong predictors of harvest outcomes, with higher flowering-season MTCI values associated with greater berry abundance. This work establishes a foundational, scalable approach for the long-term monitoring of Arctic subsistence plants in conjunction with Arctic communities and demonstrates the value of multi-layer data integration in regions historically challenging for remote sensing and ground surveys improving outcomes for regional harvest predictions and increased understanding of possible mechanisms controlling berry productivity in Arctic regions. Full article

The polished stone value (PSV) is a key parameter for assessing the resistance of aggregates to polishing in the laboratory. It is included in technical specifications and serves as both a regulatory and contractual criterion for selecting aggregates for wearing courses. Its determination requires non-negligible amounts of material, long testing durations, and skilled operators. This study aims to develop a predictive modeling approach to estimate the polished stone value (PSV) from the mineralogical and chemical composition of aggregates. A curated database was compiled from the peer-reviewed literature, and compositional data were transformed using Isometric Log-Ratio (ILR) to generate physically interpretable balances and avoid constant-sum artifacts. Machine learning algorithms, including Gradient Boosting, CatBoost, and Multivariate Adaptive Regression Splines (MARS), were trained and evaluated using repeated 10 × 2 K-Fold cross-validation with preprocessing embedded within the loop. CatBoost achieved the highest accuracy, with 90.4% of predictions within ±20% of the measured PSV. Model interpretability using permutation feature importance and SHAP analysis identified meaningful drivers, highlighting the roles of CO₂/SO₃ versus the major-oxide framework, and silica-rich oxides versus CaO/MgO, consistent with petrographic expectations. The proposed workflow provides a practical and interpretable approach for predicting PSV from compositional data. It offers a time- and resource-efficient alternative to conventional laboratory tests, while also providing insight into the material factors that control aggregate polishing resistance. Limitations related to dataset size and inter-source variability are discussed. Full article

Background: Non-alcoholic fatty liver disease (NAFLD) represents the most widespread chronic liver disorder globally, impacting roughly 30% of the general population. Numerous factors have been linked to NAFLD, including obesity, type 2 diabetes, diet, physical inactivity, age, sex, genetic factors, and metabolic syndrome. Previous research predominantly treated NAFLD as a categorical outcome, providing less granular data compared to the continuous fatty liver index (FLI). This investigation enrolled healthy young Taiwanese men and applied multivariate adaptive regression spline (MARS) modeling to develop a predictive equation. Our aims were twofold: 1. To assess the predictive accuracy of traditional multiple linear regression (MLR) versus MARS. 2. To construct a MARS-derived equation for estimating FLI in this demographic. Methods: Data originated from the Taiwan MJ Cohort, comprising 5496 men aged 20–50 years not using medications for metabolic syndrome. MARS was used to formulate the FLI estimation equation. Model performance was compared using symmetric mean absolute percentage error (SMAPE), relative absolute error (RAE), root relative squared error (RRSE), and root mean squared error (RMSE). Results: Evaluation indicated that MARS yielded lower estimation errors than MLR, demonstrating its superior performance. The derived equation is: FLI = 65.224 − 0.436 × B1 − 0.490 × B2 + 0.252 × B3 − 2.962 × B4 + 2.231 × B5 − 0.292 × B6 + 0.189 × B7 − 0.361 × B8 − 0.699 × B9 + 0.160 × B10 − 2.715 × B11 + 0.799 × B12 − 0.153 × B13 + 0.084 × B14 − 35.274 × B15 − 4.424 × B16. Conclusions: Using MLR as a benchmark, our analysis revealed that MARS delivered better predictive performance. The presented equation explains 62.7% of the variance in FLI (r² = 0.627). Based on standardized variable importance scores (nsubsets metric), CRP emerged as the most influential predictor, followed by WBC, UA, HDL-C, AST, age, ALT, FPG, SBP, and LDL in this cohort of healthy young Taiwanese men. Full article

In acidic tea soil, boron (B) adsorption and desorption processes are dominated by the complex relationship between soil acidity, mineralogy, and organic matter. This study investigated B adsorption–desorption behavior in five acidic tea soils (pH 3.8–5.6) collected from the Eastern Black Sea region of Türkiye and evaluated the potential of machine learning (ML) algorithms to predict B desorption. Laboratory batch experiments were conducted using five initial B concentrations, and adsorption data were interpreted using the Langmuir isotherm model. Adsorption experiments indicated that B interacted with Fe/Al-oxide-containing clay minerals, which had low but favorable binding affinity, as indicated by Langmuir maximum adsorption capacities (Qmax) ranging from 46.5 to 181.8 mg kg⁻¹. Desorption experiments revealed a high degree of reversibility, particularly in soils with lower adsorption capacities, ensuring potential B leaching. To capture the governing B desorption, six machine learning (ML) algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Regression (SVR), Gaussian Process Regression (GP), Elastic Net Regression (EN), and Multivariate Adaptive Regression Splines (MARS)—were trained on 75 data points. Among the tested models, Elastic Net showed the highest predictive accuracy (R² = 0.735). This model does not replace adsorption experiments. It offers a within-assay determination of desorption given measured adsorption, which may reduce the requirement for separate desorption equilibration and analyses. Permutation importance analysis identified B_ads as the dominant predictor of B desorption, with smaller contributions from pH_ads and EC_ads. The results demonstrate that integrating laboratory experiments with machine learning provides an effective framework for predicting B mobility in acidic tea soils, offering a parameterized experimental framework for describing boron desorption behavior in acidic tea soils. Full article

Climate variability threatens smallholder maize production in semi-arid Southern Africa, necessitating accurate irrigation management. We developed an Earth Observation–machine learning framework integrating Sentinel-1 SAR, TU Wien retrievals, and meteorological data to generate daily 10 m resolution root-zone soil moisture estimates (0–100 cm) for South Africa’s Vhembe District (2017–2022). Five algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Multivariate Adaptive Regression Splines (MARS)—were calibrated using ~50,000 observations from two monitoring stations across six depths and five growing seasons. RF and XGBoost achieved highest accuracy (R² = 0.96–0.97, RMSE < 0.025 cm³/cm³), detecting critical irrigation thresholds (management allowable depletion = 0.23 cm³/cm³, field capacity = 0.35 cm³/cm³) with operational precision (nRMSE < 0.05). Depth-stratified validation revealed strong SAR surface correlations (r = 0.84–0.85 at 10 cm) declining systematically with depth (r < 0.2 below 40 cm), confirming ML models integrate satellite observations at shallow layers with meteorological gap-filling at depth. District mapping showed 79–94% of maize areas required irrigation during dry years (2017–2019, 2021–2022) versus 32% in wet 2020–2021. The framework provides a transferable pathway for precision irrigation in smallholder systems, pending vegetation-corrected retrievals and expanded validation. Full article

Background/Objectives: Traumatic experiences may lead to both negative and positive outcomes. Positive psychological changes following trauma are commonly referred to as posttraumatic growth (PTG). The present study aims to examine factors associated with posttraumatic growth among Syrian refugees who have been living in Turkey for an extended period. Methods: This cross-sectional study included a sample of 240 Syrian refugees. Participants completed the Posttraumatic Stress Disorder Checklist (PCL-5), the Posttraumatic Growth Inventory (PTGI), and the Mindful Attention Awareness Scale (MAAS). Path analysis was conducted to examine the effects of PTSD symptoms and mindfulness levels on posttraumatic growth. In addition, Multivariate Adaptive Regression Spline (MARS) analysis was used to identify threshold values for the contributions of these variables to posttraumatic growth. Results: The mean age of the participants was 36.9 ± 10.4 years, and 47% were female. The direct effect of PTSD symptoms on posttraumatic growth was negative and statistically significant (β = −0.291, p < 0.001). PTSD symptoms also had an indirect effect on posttraumatic growth through mindfulness (β = −0.254), resulting in a total effect of −0.545. According to the MARS model, when MAAS scores exceeded 78, mindfulness demonstrated a positive effect on posttraumatic growth. Conclusions: The findings indicate that PTSD symptoms among refugees are associated with posttraumatic growth through both direct and indirect pathways. Furthermore, mindfulness emerges as a key factor in understanding the development of posttraumatic growth in this population. Full article

Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F₂ population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (>99%) and Precision (>90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation. Full article

Compressed air systems are widely used in industry, and air leaks that occur over time lead to significant and unnecessary energy losses. This study aims to quantify the energy-saving potential of compressed air leaks in a manufacturing plant and to develop machine learning (ML) regression models for sustainable leak management. A total of 230 leak points were identified by measuring three periods using an ultrasonic device. Using the measured acoustic emission level (dB) and probe distance (x) as inputs, the leak flow rate, annual energy-saving potential, cost loss, and carbon footprint were calculated. As a result of the repairs, energy consumption improved by 8% compared to the initial state. Three regression models were compared to predict leak flow: Linear Regression, Bagging Regression Trees, and Multivariate Adaptive Regression Splines. Among the models evaluated, the Bagging Regression Trees model demonstrated the best prediction performance, achieving an R² value of 0.846, a mean squared error (MSE) of 389.85 (L/min²), and a mean absolute error (MAE) of 12.13 L/min in the independent test set. Compared to previous regression-based approaches, the proposed ML method contributes to sustainable production strategies by linking leakage prediction to energy performance indicators. Full article

This study aims to forecast the combined impacts of drought and flood in the future using an integrated framework. This framework integrates U-Net++, quantile mapping (QM), Copula models, and ISIMIP3b gridded large-scale discharge data (1985–2014, 2021–2050, and 2071–2100). Copula models analyze compound effects in four dimensions to determine return periods for droughts and floods. The standalone U-Net++ and its integration with multiple linear regression, multiple nonlinear regression, M5 model tree, multivariate adaptive regression splines, and QM downscaled ISIMIP3b model river flows. U-Net++QM outperformed other models, with a 58% lower RRMSE. Ensemble GCMs showed less uncertainty than other models in river flow downscaling. For the Ensemble model, the highest drought severity was −300, the maximum duration was 300 months, flood peak flow reached 12,000 m³/s, and intervals lasted up to 22 months. Moreover, the return periods of compound events for this model ranged from 50 to 3000 years. Future river flow projections, using the Ensemble model and emission scenarios (SSP126, SSP370, and SSP585), showed increased vulnerability in 2071 and 2025 versus the observed period. Introducing an integrated framework serves as a management tool for addressing extreme combined phenomena under climate change. Full article