Search Results (6,188)

Green infrastructure (GI) provides essential ecosystem services for urban sustainability in the face of urbanization and climate change, including stormwater management, heat mitigation, and reduction in carbon dioxide (CO₂) concentration levels. Existing studies often focus on single-dimensional ecological effects, lacking a systematic investigation of their synergies and trade-offs. This study developed a coupled framework integrating scenario design, model simulation, and multi-indicator evaluation. Fifty-six scenarios, varying by GI combinations, weather conditions, and total annual runoff control rate (RCR), were applied to a high-density industrial district in Nanjing. The results showed that: (1) GI combinations enhanced comprehensive benefits, with the combination including bioretention (BR), permeable pavement (PP), and green roof (GR) performing most effectively. This was followed by the combination of BR and PP, then by BR and GR, while the use of BR alone provided the lowest effectiveness. (2) PP was a key synergistic component, improving heat mitigation and reducing CO₂ concentration levels through the beneficial effects of rainfall events. (3) Exceeding the optimal RCR threshold for some GI combinations diminished tree space and three-dimensional green volume, shifting synergies into trade-offs. (4) Three-dimensional green volume was positively correlated with reductions in Physiological Equivalent Temperature (PET) and CO₂ concentration, confirming its core role. (5) Rainfall boosted carbon sinks, while a significant cooling enhancement required PP. This study elucidates the water–heat–carbon synergy in small-scale GI, supporting multi-objective optimization in high-density urban renewal. Full article

Accurate estimation of Body Carcass Fat (BCF) is essential for evaluating the physiological condition of ewes. Traditional assessment via Body Condition Score (BCS) through palpation is inaccurate and subjective. BCF can now be predicted more precisely using objective measurements. This study presents a comparative analysis of eight machine learning (ML) models for predicting BCF in Coopworth ewes, using weight and RGB-image-based body measurements. Four non-linear regression methods and four neural network architectures were evaluated using a dataset of 74 ewes with 13 independent variables. The dataset was partitioned into training (52 ewes), validation (11 ewes), and testing (11 ewes) sets. The Gradient Boosting Regression achieved the highest predictive accuracy with an R² value of 0.9434 using body weight and width, followed by Ensemble Neural Network (R² = 0.9371) using body weight. The findings demonstrate the effectiveness of the Gradient Boosting Regression, Ensemble Neural Network and Random Forest tree-based approaches for morphometric prediction tasks in biological applications. BCF values obtained from image analysis were validated against those derived from computerized tomography (CT), considered the gold standard. These findings highlight the potential of image-guided, ML-driven models for objective, non-invasive, cost-effective assessment of ewe body composition in modern livestock systems. Full article

Heartwood proportion (HWP) and specific gravity (SG) are two important properties of Dalbergia retusa and Platymiscium curuense wood, which is considered to be of high value. The objective of this study was to establish which morphological and soil fertility parameters present the greatest influence on HWP and SG. For this, increment cores were extracted, and soil samples were collected. The results showed that D. retusa presented a lower HWP (22.65%) than P. curuense (28.75%), and D. retusa averaged a higher value (0.87) than P. curuense (0.63). The forward stepwise regression analysis for D. retusa showed that the magnesium content was the most important factor for SG, while for the HWP, the potassium content was the most important, followed by diameter at breast height (DBH). SG was most strongly influenced by total height in P. curuense, and HWP was most strongly influenced by DBH. Additional notable results showed that the SG of D. retusa was primarily determined by soil fertility conditions, whereas the SG of P. curuense was more strongly influenced by tree morphology. Meanwhile, the HWP in both species was mainly affected by DBH and total height, and to a lesser extent by soil fertility conditions. These results show that plantation management should be focused on trees with large diameters and HWP, since soil conditions demonstrated little effect on this property. Full article

This study examines the confined compressive strength (Fcc) of circular, square, and rectangular column geometries under varying confinement conditions. Results indicate that circular columns have the highest Fcc values, exceeding those of square and rectangular shapes. Increased confinement through clamps significantly enhances compressive strength. Five machine learning models, Linear Regression, Decision Tree, Random Forest, AdaBoost, and Gradient Boosting, were used to predict Fcc based on geometric and confinement parameters. Linear Regression and Decision Tree models achieved moderate predictive performance, with R² values of 0.84 and 0.83, respectively, and relatively higher error measures (RMSE, MAE, and MAPE), indicating limited ability to capture complex nonlinear relationships in the data. In contrast, ensemble-based methods demonstrated superior performance. The Random Forest model improved the coefficient of determination to 0.90 while substantially reducing all error metrics, reflecting enhanced generalization through bagging. The boosting-based approaches yielded the best results, with AdaBoost achieving the highest R² value of 0.99 and the lowest RMSE, MAE, and MAPE among all models, followed closely by Gradient Boosting with an R² of 0.98. These results confirm that ensemble learning techniques, particularly boosting algorithms, yield more accurate and robust predictions than single learners for the problem studied. Data visualization techniques, including Regression Error Characteristic curves (REC) and SHapley Additive exPlanations (SHAP) value analysis, highlighted model performance and feature importance, emphasizing the roles of confinement and geometry in compressive strength. This research demonstrates the potential of machine learning to optimize structural engineering design and suggests further exploration of alternative shapes and confinement strategies to enhance structural integrity. Full article

Accurate aboveground biomass (AGB) estimation of farmland shelterbelts is critical for evaluating shelterbelt degradation and guiding restoration in arid agricultural landscapes. However, satellite-based retrieval is challenging for narrow linear belts affected by strong edge effects and canopy gaps under degradation. Here we developed a plot-scale Unmanned Aerial Vehicle (UAV) workflow that fuses Light Detection and Ranging (LiDAR) structural metrics and multispectral vegetation indices to estimate individual-tree AGB for Populus euphratica Olivier (Xinjiang poplar) shelterbelts in Tiemenguan, Xinjiang, China. Field measurements were collected in October 2024 from three belts representing healthy, moderately degraded, and severely degraded conditions (n = 135 trees; 45/50/40). Because destructive sampling was infeasible, AGB was derived as allometry-based reference values, with a prior-constrained scale factor (ρ) used to ensure physically plausible ranges. We compared multiple linear regression, random forest, and Support vector regression (SVR) models under LiDAR-only, multispectral-only, and fused inputs. Fusion consistently improved agreement with reference AGB, and the fused SVR achieved the best performance (test R²= 0.846/0.848/0.718 for healthy/moderately/severely degraded belts). The workflow highlights spectral–structural complementarity for degraded shelterbelts, while broader deployment requires local calibration and independent biomass validation. Full article

Climate change influences the agronomic behaviour of fruit trees. It is necessary to determine which cultivars adapt best to conditions in which water supplies are becoming increasingly scarce. This study analyses different phenological, morphological, physiological, agronomic and productive parameters to evaluate water stress tolerance in six late-blooming almond cultivars widely grown in Spain (‘Ferragnès’, ’Francolí’, ‘Masbovera’, ‘Glorieta’, ’Guara’ and ‘Lauranne’). Two different plots were analysed: one under regulated deficit irrigation, at Les Borges Blanques, Lleida, with a water deficit (146.2 mm/year) and the other under rainfed conditions, at Mas Bové, Constantí, Tarragona, with a water deficit (284.5 mm/year). Parameters, including an increase in canopy volume, leaf-to-air thermal gradient, and slope between leaf water potential and level of leaf saturation, have proven to be good indicators of resistance to water stress. Yield variation and leaf temperature variation between rainfed and irrigated conditions also perform quite well. An assessment of leaf chlorophyll content, measured using SPAD-502, suggested the presence of a collateral effect resulting from the opacity of the biomass, as well as to chlorophyll-related cuticular colouring. Finally, under the experimental conditions, ‘Guara’ and ‘Masbovera’ proved the most resistant cultivars; ‘Glorieta’ and ‘Francolí’ exhibited an intermediate level, and ‘Lauranne’ and ‘Ferragnès’ were the least resistant cultivars. Full article

In recent years, with the widespread application of shortwave infrared (SWIR) spectroscopy in mineral identification and hydrothermal alteration studies, an increasing number of studies have attempted to integrate SWIR spectral data with machine learning approaches to fully exploit mineralization-related discriminative information embedded in high-dimensional spectral datasets. In this study, the Zhunuo porphyry copper deposit in Tibet was selected as the research target. SWIR drill core spectral data were systematically acquired, and a random forest (RF) machine learning model was applied to full-band SWIR spectra (1300–2500 nm) to conduct integrated analyses of copper grade regression and mineralization discrimination. A total of 2140 drill core samples were measured, with three replicate measurements per sample, yielding 6420 spectra. After standardized preprocessing and interpolation resampling, a unified spectral feature dataset was constructed for regression and classification analyses. SWIR spectral data are characterized by a large number of bands, strong inter-band correlations, and relatively limited sample sizes; under such conditions, model generalization ability and stability become critical factors in method selection. Based on ensemble learning, the random forest model constructs multiple decision trees and aggregates their predictions through voting or averaging, effectively reducing model variance and mitigating overfitting, and is therefore well suited for high-dimensional, small-sample, and highly correlated geological spectral datasets. In porphyry copper systems, the spectral characteristics of hydrothermal alteration minerals and mineralization intensity commonly exhibit complex nonlinear relationships, which can be effectively captured by random forest models without requiring predefined functional forms. The regression results indicate that accurate quantitative prediction of copper grade based solely on SWIR spectral data remains limited. In contrast, when a threshold-based binary classification was introduced using an industrial cutoff grade of 0.2% Cu, the model achieved an overall accuracy of 75%, an F1 score of 0.69, and an area under the ROC curve (AUC) of 0.80, demonstrating strong mineralization discrimination capability and stability. Overall, the integration of SWIR spectroscopy with machine learning methods provides an efficient, reliable, and geologically interpretable technical approach for early-stage exploration and detailed drill core interpretation in porphyry copper deposits. Full article

We investigated benzene variability in an urban environment using an interpretable, setting-based artificial intelligence framework. A seven-year dataset (2017–2023) of hourly pollutant concentrations (benzene, NO₂, SO₂, CO, O₃) measured in Zagreb (Croatia) was analyzed, as were meteorological variables. Multiple-ensemble decision tree models were developed, with hyperparameters optimized using metaheuristic algorithms. The best-performing model, Extra Trees optimized by the Sine Cosine Algorithm, achieved an R² of 0.87. Model interpretation employed Shapley additive explanations (SHAP), followed by PaCMAP embedding and HDBSCAN clustering to identify coherent environmental settings. Seven settings (C0–C6) and one residual group were identified, representing pollution-enhancing, suppressing, and transitional regimes. Two settings dominated benzene extremes. C6 reflected winter stagnation, characterized by strong combustion influence (CO contribution of 11.9%), shallow boundary layers (~290 m), weak winds, and high humidity. C4 represented a synoptic stability regime with enhanced heat fluxes and diminished after the COVID-19 period, consistent with altered anthropogenic activity. Low-benzene settings (C0, C1, C3) were associated with stronger mixing and higher oxidizing capacity, while transitional settings (C2, C5) reflected moderate conditions. Overall, the results show that a small number of environmental settings governed the benzene extremes, providing a transferable and interpretable framework for air quality assessment and policy support. Full article

Hazelnut (Corylus avellana L.) productivity may be constrained by source–sink imbalances. However, field-based evidence under commercial orchard conditions on the use of branch girdling and cytokinin sprays in hazelnut remains limited. This two-season study conducted in a commercial orchard evaluated the effects of branch girdling (30 mm in October; 3 mm in November) and foliar 6-benzyladenine (6-BA; 30 or 60 mg L⁻¹) applications on the physiology, yield, and nut quality of ‘Tonda di Giffoni’ under Mediterranean conditions. Treatments were evaluated in a randomized complete block design (eight trees per treatment) using linear mixed models. Neither girdling nor 6-BA significantly improved fruit set or estimated yield (p > 0.18) and branch productivity was primarily determined by the initial floral load. However, intense October girdling markedly reduced return bloom (p < 0.001) and impaired gas exchange. In contrast, late-season or split 6-BA applications (T7–T9) consistently increased kernel yield (%), although sometimes at the expense of fruit size and weight. These findings suggest that while the total yield remained unchanged, specific treatments modulated physiological and quality traits, with late 6-BA enhancing kernel fill and early girdling posing risks to subsequent reproductive performance. Full article

Background and Objectives: The use of a standard chest X-ray has become a routine diagnostic method in daily clinical practice for the evaluation of a wide range of lung diseases. During the COVID-19 pandemic, significant challenges occurred in achieving accurate diagnostics and selecting appropriate therapies for patients with different symptoms of diseases. The aim was to cross-correlate radiological findings and clinical data and to develop models to predict hospitalization status, while evaluating the prognostic importance of the different variables. Materials and Methods: A set of variables including Brixia score, and clinical data: gender, age, hypertension, and diabetes was used to explore their association with patient hospitalization. Four different machine learning (ML) methods (Decision Tree—DT, Logistic Regression—LR, Random Forest—RF and Support Vector Machine—SVM) were used for hospitalization outcome prediction. Results: SVM appeared to be with the highest AUC (0.851), with low sensitivity, while DT was the most balanced in the context of AUC, accuracy, sensitivity, and specificity. Brixia score appeared to be the most important predictor for hospitalization within the group of predictors (gender, age, hypertension and diabetes). Conclusions: All four ML models that used in this study provided “good” prediction capabilities (AUC > 0.8), with the exception of SVM that had low sensitivity, emphasizing Brixia score as the strongest predictor of hospitalization. Application of ML methods have considerable potential in various aspects of medical clinical practice and future studies could potentially indicate the importance of applying the ML model in more precise diagnosis, therapy and prognosis of the patient’s clinical condition. Full article

Polymer additive manufacturing (AM) has grown rapidly in the past decade, with material extrusion, vat photopolymerization, powder bed fusion and jetting now widely used for functional polymer parts. The mechanical performance of these parts depends strongly on process parameters such as layer height, build orientation, energy input and post-processing conditions, which motivate the development of predictive models for process–property relationships. Classical approaches based on Taguchi designs, ANOVA and response surface methodology have provided valuable insight, but the potential of modern machine learning (ML) techniques is not yet fully exploited. This review surveys recent work on ML-based prediction of mechanical properties of polymer AM parts using process parameters as inputs. Across the literature, well-tuned artificial neural networks, tree-based ensembles and support vector regression typically achieve prediction errors below about 5–10% for strength and modulus, showing that data-driven surrogates can substantially reduce experimental trial-and-error in process optimization. Ongoing challenges include small datasets, missing standardized error metrics, and limited coverage of non-quasi-static phenomena like fatigue, impact, and environmental degradation. Full article

Nowadays, air quality is a major issue, especially in large cities. Apart from air pollution, particulate matter (PM), especially PM2.5, poses serious health risks to individuals with respiratory conditions. Accurate forecasting of PM levels is crucial to warn vulnerable populations and reduce exposure. Machine learning models can effectively predict PM concentrations based on historical data and barometric conditions such as temperature and humidity. Such predictions can support timely public health interventions and environmental policy decisions. The selection of the optimal machine learning model for time series forecasting requires a careful balance between predictive accuracy and computational efficiency. This study evaluates a number of widely used models, such as Random Forest (RF), Long Short-Term Memory (LSTM), Convolutional Neural Network-LSTM (CNN–LSTM), Extreme Gradient Boosting (XGB/HistGradientBoosting), and hybrid approaches (LSTM embeddings + RF), in the context of time series forecasting for particulate matter (PM) concentrations. Performance is assessed using three key error metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Scaled Error (MASE). Additionally, the computational demands and development complexity of each model are analyzed. The overall results are of great interest for each application model, and in more detail, it is shown that the best compromise between accuracy and efficiency can be achieved, while a corresponding prediction model with satisfactory predictive performance can be implemented. The results show that CNN–LSTM and hybrid approaches provide high accuracy, while tree-based models are computationally efficient, offering practical options for real-time forecasting systems. Full article

Soil health is the sustained ability of soil to function as a vital ecosystem that supports plants, animals, and humans. Conventional agricultural practices, such as intensive tillage and monocropping, degrade soils by depleting organic matter, causing erosion, and reducing biodiversity. Agroforestry systems, by contrast, mimic natural ecosystems and offer a viable solution to restore and protect this crucial resource. The purpose of this review was to explore agroforestry’s effects on soil health in the context of sustainable agriculture. By restoring and building soil health, the review revealed that agroforestry provides a solution to combat soil degradation, enhance biodiversity, and increase agricultural sustainability. Benefits to soil are diverse and include improving its physical, chemical, and biological aspects, which boosts ecosystem services and resilience. Despite its clear advantages, agroforestry has not been widely adopted. Challenges to adoption include time lag for trees to mature, insecure land tenure and lack of expertise and institutional support. Overcoming these barriers through supportive policies, financial incentives and farmer participatory approaches offers clear pathways towards more resilient and profitable farming systems. This will require site-specific studies to optimize species selection and system designs compatible with local conditions. Long-term agroforestry success is determined by aligning site-specific conditions (soil, slope, climate) with appropriate species selection, expert management, and farmer knowledge. In conclusion, intentionally combining trees and crops provides a powerful solution for building resilient soil ecosystems and ensuring agricultural sustainability. Full article

Background/Objectives: Forest bathing (Shinrin-yoku) is a nature-based approach with potential preventive health relevance. This review summarizes evidence on its effects on immune function, stress physiology, and neuroprotective pathways. Methods: A narrative review of peer-reviewed studies was conducted using major scientific databases, including observational and interventional research assessing physiological or neurocognitive outcomes following forest exposure. Results: Forest bathing is associated with enhanced natural killer (NK) cell activity, modulation of inflammatory cytokine profiles, reductions in cortisol levels, and shifts toward parasympathetic autonomic dominance. Evidence also suggests a contributory role of tree-derived biogenic volatile organic compounds and phytoncides in immune and stress-regulatory effects. Emerging findings indicate potential benefits for cognitive restoration, emotional regulation, and neurotrophic signaling; however, substantial heterogeneity in study design, exposure characteristics, and outcome measures limits direct comparability and causal inference. Conclusions: Current evidence supports forest bathing as a promising, low-risk strategy for supporting immune resilience, stress regulation, and neurocognitive well-being within a preventive health framework. Preliminary findings also suggest potential benefits in chronic neurological conditions, supporting its neuroprotective role within multimodal neurorehabilitation strategies. Standardized intervention protocols, mechanistic biomarkers, and longitudinal studies are required to strengthen clinical relevance and guide evidence-based integration into public health and lifestyle medicine. Full article

Old trees function as enduring ecological legacies that preserve historical biodiversity within intensively human-modified landscapes, yet the relative influence of environmental versus anthropogenic drivers on their diversity remains unclear. Here, we aim to disentangle the joint effects of climate, urbanization intensity and cultural preservation on old-tree density and community composition. We analyzed a province-wide census of 21,733 old-tree individuals across 115 counties in Shanxi Province, China, encompassing species origin (native vs. nonnative) and growth form (trees vs. shrubs). Old-tree density was assessed using spatial simultaneous autoregressive error models, while compositional dissimilarity was quantified using generalized dissimilarity modeling. In total, 131 species were recorded, with four dominant species comprising more than 75% of all individuals. Old-tree density increased with mean annual temperature, human population density, and cultural heritage abundance, but declined sharply with cropland coverage. Driver importance varied among groups: native species were primarily governed by climatic conditions, nonnative species by land-use intensity, and tree-form old trees were positively associated with cultural heritage abundance, an effect absent in shrub-form old trees. Compositional dissimilarity was driven mainly by climatic gradients and spatial distance, with additional contributions from human-related variables, particularly for nonnative assemblages. Our findings demonstrate that climate and spatial processes establish the regional framework of old-tree community composition, while cultural and demographic contexts promote local retention of old trees. By explicitly integrating ecological filters with socio-cultural drivers, this study advances old-tree research through a large-scale empirical framework, providing both scientific insight and socially relevant guidance for conservation under land-use intensification and climate warming. Full article