Search Results (2,920)

Tourism destinations increasingly require data-driven tools to interpret competitiveness, capacity use, housing-related pressure, and post-shock change. This study develops a machine-learning-based destination marketing intelligence framework for a non-probability analytical sample of 29 European destinations observed annually between 2015 and 2024. Destinations were retained when sufficiently comparable information was available across the common study window for the six raw indicators required to construct the performance-pressure framework. Tourism demand, accommodation capacity, labor, investment intensity, and housing-cost pressure are transformed into normalized indicators and analyzed using principal component analysis, k-means clustering, classification trees, random forests, and robustness checks. The first three principal components explain 84.2% of total variance. Although silhouette favors three clusters, the four-cluster solution provides stronger Calinski–Harabasz separation and leave-one-destination-out stability. The retained solution identifies four relative destination-state configurations: lower performance with near-average pressure; high rotation, moderate performance, and lower pressure; high performance with lower pressure; and extreme housing pressure. Under leave-one-destination-out validation, random forests achieve 86.6% accuracy and a Cohen’s kappa of 76.9%. The configurations are pressure-sensitive marketing-intelligence categories rather than comprehensive sustainability classifications or permanent country typologies. Full article

We investigate the potential of technology-based attention training (AT), particularly virtual reality (VR), as a stress-management tool. Mental stress is rising globally, and researchers increasingly use immersive technologies, wearable sensors, and machine learning (ML) for its detection and control. This feasibility study examines the impact of fully immersive VR-based AT on mental stress using electroencephalogram (EEG) signals and automated classification. We designed virtual exercises targeting different attention types and analyzed EEG responses with an ML framework; the resulting dataset, collected at the Arab Academy for Science and Technology (Alexandria, Egypt), is publicly available. For an unbiased estimate, we adopt a leakage-free evaluation in which the train/test split is performed by time, before segmentation into overlapping windows, so neighboring windows cannot appear in both sets. Subject-specific tree-based classifiers detected stress with a mean accuracy of about 97%, whereas leave-one-subject-out (LOSO) validation yielded about 67%, indicating strongly individual stress signatures and motivating a subject-specific strategy. Using these models, we compared the number of classifier-predicted stress segments before and after AT and visualized the feature space with T-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). Under subject-specific models the number of stress-predicted segments decreased after AT (Wilcoxon signed-rank $p<0.05$); however, because this reduction was corroborated neither by a non-circular (LOSO) detector nor by a centroid-separation measure, we interpret it as an exploratory, classifier-predicted effect rather than independently validated stress reduction. The results highlight the promise—and the current limits—of integrating immersive VR with EEG-guided analytics for mental-health support. Full article

Deciduous dipterocarp forests (DDFs), widespread in mainland Southeast Asia, are potentially important yet poorly quantified sources of isoprene, a dominant biogenic VOC and ozone/aerosol precursor, and observation-based estimates are essentially absent for DDFs in northern Thailand. We present a pilot study estimating canopy isoprene over a DDF at the University of Phayao using a zero-dimensional (0-D) Guenther (G93) box model, benchmarked against tree-based machine learning (ML). Modeled concentrations were compared with 30 wet-season tower samples (parts per trillion; thermal-desorption GC–MS) at three heights (12, 27, and 42 m) over four days. An effective mixing height was calibrated per level and evaluated by strict leave-one-day-out cross-validation. The model reproduced the mean daytime level and diurnal accumulation shape (cross-validated R² = 0.64–0.68; MAPE ≈ 11%); because the clear-sky forcing is near-invariant, this reflects the within-day signal rather than day-to-day skill. A sensitivity analysis showed that the emission factor, loss rate, and mixing height are confounded through the ratio E_s/(kH). In this exploratory comparison, supplying the box-model output as an ML feature recovered skill otherwise lost (ΔR² up to +1.16), but an hour-of-day feature did the same, so the box model matches a diurnal-climatology baseline and its advantage is interpretability. This exploratory workflow offers a simple, low-data approach for tropical BVOC estimation. Full article

Climate change drives shifts in forest vegetation zones, and the major tree species of warm–temperate evergreen broad-leaved forests in South Korea are also expected to undergo changes in their potential distributions under future climate conditions. This study applied a Committee Averaging (CA) ensemble species distribution model to Quercus acuta, Machilus thunbergii, Quercus glauca, and Castanopsis sieboldii to project changes in their potential distributions under four Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) across two future periods (2050s and 2090s). To compare interspecific differences in response more clearly, we applied a two-tier threshold scheme that distinguished potential habitat (agreement ≥0.6) from highly suitable habitat (>0.8), and we concurrently conducted a Multivariate Environmental Similarity Surface (MESS) analysis to assess predictive uncertainty arising from extrapolation into future climates. The CA ensemble models showed excellent predictive performance (AUC 0.947–0.989; TSS 0.837–0.943). For Q. acuta, M. thunbergii, and Q. glauca, potential habitat expanded consistently across all SSP scenarios, and highly suitable habitat shifted northward into parts of the central and Gangwon regions. In contrast, both the potential habitat and the highly suitable habitat of C. sieboldii contracted under most future scenarios. These results demonstrate that even species belonging to the same warm–temperate evergreen broad-leaved forest community can respond differently to future climate. The two-tier threshold scheme applied in this study was effective not only for assessing whether distributions expand but also for delineating and evaluating climatically stable core habitats. Although our findings need to be interpreted in light of the uncertainty associated with extrapolation into future climates, they can serve as useful baseline data for establishing climate-change-adaptive forest conservation and species-specific management strategies. Full article

Activated carbon is a highly porous adsorbent material that is often used to treat wastewater using physical and chemical adsorption. Agricultural and urban biomass waste valorization to activated carbon is a low-cost, renewable solution to commercial adsorbents, and can help prevent waste from tree pruning from being dumped in landfills or openly burned. In this study, the ability of the ground and unground Ficus nitida leaves to efficiently adsorb Rhodamine B dye and total chromium from model aqueous solutions was investigated. Chemical activation was performed using phosphoric acid (H₃PO₄) at different impregnation ratios (1:1, 2:1, and 4:1). Samples obtained as a result of the above activation were labeled G1–G3 (ground) and UG1–UG3 (unground). The adsorption test showed that the samples with the highest activation ratio (G3 and UG3) gave the best results, removing 92% and 94% RhB, respectively, in 20 minutes. After 24 h, sample G3 showed the best efficiency of 73.31% (13.35 ppm remaining) in chromium removal, where the adsorption kinetics were well described by the pseudo-second-order model (R² > 0.98), indicating that there may be some chemical interactions occurring during the adsorption process along with physisorption, and the RhB adsorption isotherms for sample UG3 were well described by the Langmuir isotherm (R² > 0.95). The higher activation ratio and grinding increased the carbon content (up to 90% C for G3), surface functional groups, and textural properties (BET surface area of 699 m²/g and total pore volume of 3.06 cm³/g). Furthermore, reusability tests over five consecutive cycles demonstrated the excellent recyclability of sample G3, retaining removal efficiencies of 80.5% for RhB and 50.2% for total chromium. The results revealed that Ficus nitida leaf-based AC can be used as an efficient, economical, and reusable adsorbent material for sustainable environmental cleanup and water purification systems and will create a circular economy for waste management. Full article

Nonlinear mixed-effects models (NLMMs) provide a flexible framework for modeling repeated-measures trajectories. However, how best to forecast future observations, especially at ages well beyond those represented in the data, remains relatively underexamined. In this study, we develop a Chapman–Richards NLMM with a spatial-power autocorrelation structure for irregularly spaced repeated measures and compare three forecasting strategies: (i) population-averaged forecasts based on the fixed-effects component only; (ii) subject-specific forecasts in which empirical best linear unbiased predictors (EBLUPs) of the random effects are obtained via a first-order Taylor series expansion with an iterative Newton–Raphson update, including the case of new progenies not used in model fitting; and (iii) forecasts that combine the population-averaged prediction with prior repeated measures through the fitted autocorrelation matrix. Forecast accuracy was assessed with progeny-level validation under fully held-out and partially observed scenarios, using root mean square prediction error (RMSPE) and mean absolute error (MAE), and was examined as a function of: (i) the number of available prior measures and (ii) the accuracy of the fixed-effects component of the NLMM. The methods were illustrated with repeated-measures data from hybrid spruce (Picea engelmannii Parry ex Engelmann × Picea glauca (Moench) Voss) progeny trials at three planting sites in British Columbia, Canada, with measurement ages from 2 to 42 years. Subject-specific forecasts had the lowest prediction errors when sufficient prior measures were available and were also the least affected by misspecification of the fixed-effects component. With only two prior measurements, autocorrelation-based forecasts had the lowest or tied-lowest observed errors, although differences among the three approaches were small. Using all measurements taken before age 42, subject-specific forecasts of height at age 42 achieved an RMSPE of 0.50 m. With only two prior measurements, the corresponding RMSPEs were approximately 1.31–1.33 m across the forecasting approaches. Although demonstrated with a single hybrid spruce dataset from three planting sites, the comparison is, in principle, applicable to other repeated-measures settings in which long-horizon predictions are required from short observation histories; broader applicability remains to be confirmed. Full article

Sapindus delavayi (Franch.) Radlk. is a non-wood tree species of considerable ornamental, ecological, and medicinal value, and its fruits are rich in saponins, with notable cleansing and skin-care properties. However, this tree species is currently facing the dilemma of low fruit yield and unstable fruiting. Fertilization is an effective measure to improve this cultivation situation. Considering the harm of nitrogen fertilizer in soil, we designed four schemes to replace nitrogen fertilizer with organic fertilizers in this study, aiming to obtain the optimal fertilization combination for the growth of Sapindus delavayi (Franch.) Radlk. Three-year-old seedlings were subjected to applications of nitrogen fertilizer, organic fertilizer, and their combinations to evaluate their effects on plant physiological responses and soil physicochemical properties. Results revealed that the sole application of organic fertilizer significantly promoted the elongation of new shoots. Compared with the unfertilized control, the combined treatment of 30% organic fertilizer and 70% nitrogen fertilizer significantly increased soil nitrate nitrogen content by 161.54%, while ammonium nitrogen content decreased by 24.30%. Principal component analysis indicated that glutamine synthase (GS), glutamate synthase (GOGAT), and nitrate reductase (NR) were the major enzymes influencing leaf physiological responses. Therefore, we concluded that fertilization has affected the rate of nitrogen assimilation and altered the process of amino acid synthesis, thereby influencing the metabolism and maintenance of cellular function in Sapindus delavayi (Franch.) Radlk. Structural equation modeling further indicated that fertilization primarily influenced total carbon content in plant leaves by affecting soil organic matter and alkali-hydrolyzable nitrogen. These findings elucidate the regulatory relationship between plant physiological processes and soil properties under co-application of nitrogen and organic fertilizer, providing a scientific reference for field fertilization management of this tree species. Full article

Accurately estimating latent heat flux (LE) across water-limited grassland ecosystems is critically hampered by strong land-surface heterogeneity and pronounced intra-annual variability. Here, we proposed a ConvTransformer framework by integrating MODIS remote sensing products, China Meteorological Forcing Dataset (CMFD) data, and eddy covariance observations from six grassland sites to estimate daily LE across the Inner Mongolia grasslands. The model was evaluated using a leave-one-site-out cross-validation strategy and compared with three widely used machine learning models, including random forest (RF), gradient boosting regression trees (GBRT), and support vector regression (SVR). Across the six validation sites, the ConvTransformer achieved an average R² of 0.69, an RMSE of 12.56 W m⁻², a Bias of 0.67 W m⁻², and an average KGE of 0.82. Although RF produced slightly higher R² values at several individual sites, the ConvTransformer exhibited the highest overall KGE and the most stable performance, indicating superior cross-site generalization. Based on the trained model, a 1 km daily LE dataset for the Inner Mongolia grasslands during 2003–2018 was generated. The estimated LE revealed a distinct decreasing gradient from southeast to northwest and marked seasonality, with summer dominating the annual latent heat exchange. These results suggest that the ConvTransformer constitutes an effective framework for regional LE estimation, while also offering a valuable alternative for ecohydrological studies and regional water-resource assessment in water-limited grassland ecosystems. Full article

Heavy metal deposition along high-traffic roadsides poses a persistent threat to agricultural safety, yet the partition barrier efficiency across rhizosphere–root–shoot interfaces in perennial nut crops remains poorly understood, representing a significant research gap. This study determined the concentrations of potentially toxic elements in the rhizosphere soils and distinct organs (leaves, pericarp, and edible seeds) of Siirt pistachio trees along a distance gradient (0, 50, and 100 m, plus a control site) in the Siirt and Tillo districts. To filter analytical baseline noise, all raw datasets were subjected to strict solid-matrix limit of detection (LOD) screening using a standardized dilution factor of 30 mL/g (DF = 15 mL final volume/0.5 g sample mass). Soil analysis revealed that the alkaline pH (6.90–7.27) and highly calcareous nature (21.97–65.75%) of the rhizosphere acted as a powerful edaphic barrier, immobilizing metals in the soil and limiting their translocation to aboveground tissues. Plant accumulation followed a leaf > pericarp > seed hierarchy, proving the canopy’s role as an effective vegetative filter. Crucially for food safety, highly toxic Cd (<1.74 µg/kg) and Bi remained entirely below detection limits in edible seeds. Cr peaked in leaves (730.42–795.00 µg/kg) but was highly restricted in seeds. Detected kernel concentrations of As, Co, Ni, Pb, and Sb were strictly below international toxic thresholds, while essential Cu physiologically concentrated in seeds and leaves. Consequently, the cumulative Hazard Index (HI) remained exceptionally below the 1.0 critical safety limit for both adults (<0.18) and children (<0.32). This confirms that roadside pistachios pose zero non-carcinogenic health hazards and are completely safe for human consumption. Full article

Urban heat islands (UHI) intensify as cities expand, exposing residents to elevated thermal stress and complicating urban climate adaptation planning. Existing satellite-based approaches to detecting surface urban heat islands (SUHI) typically rely on a single class of data and narrow temporal windows, limiting their ability to capture the full range of processes driving surface overheating. This study develops and evaluates a random forest model for SUHI detection in Warsaw, Poland, integrating two classical spectral indices (NDVI, NDBI) derived from Landsat 8/9 Collection 2 imagery with three land-cover probability layers (built-up, tree, water) from the Dynamic World deep-learning product, processed in Google Earth Engine. Both a multi-year summer median composite (2020–2025) and individual annual summer composites were used, the latter enabling a leave-one-year-out temporal validation. Heat island pixels were defined as those whose land surface temperature anomaly exceeded +3 °C relative to the study area mean, a local criterion rather than a city-versus-rural contrast. The model achieved high and stable performance (accuracy = 0.831, AUC = 0.910 on the test set; AUC = 0.907 ± 0.004 in five-fold cross-validation and 0.905 ± 0.018 in leave-one-year-out validation). An ablation analysis showed that combining the probability layers with the spectral indices clearly outperformed the indices alone (AUC = 0.852 vs. 0.905), whereas the additional gain over the Dynamic World layers alone remained within uncertainty. Vegetation-related predictors (NDVI and tree probability) contributed more to classification than built-up indicators. These results indicate that vegetation deficit, rather than built-up presence alone, is the primary driver of surface overheating in Warsaw and that the proposed open-data workflow offers municipalities a low-cost screening tool for identifying priority areas for climate adaptation and, thanks to its reliance solely on open data, can be adapted to other cities, subject to further validation. Full article

Nuclear Factor Y, subunit A (NFYA) constitutes a family of transcription factors that play critical roles in plant growth, development and abiotic stress responses. Taxodium hybrid ‘Zhongshanshan’ (T. mucronatum × T. distichum) is a fast-growing tree species with high industrial value and remarkable flooding tolerance. However, the systematic characteristics and abiotic stress response patterns of the ThNFYA gene family remain unclear. In this study, a total of 11 ThNFYA genes were identified. The encoded proteins ranged from 67 to 372 amino acids in length, with predicted molecular weights between 16.84 and 40.12 kDa. Phylogenetic analysis classified plant NFYAs into four clades, with all ThNFYAs falling into clades I and IV. Expression profiling revealed tissue-specific patterns, with six members showing the highest transcript levels in the cambium. Multiple cis-acting elements associated with stress and hormone responses were detected in the promoter regions of ThNFYAs. Most ThNFYAs were differentially regulated under salt, drought, and flooding stresses. Notably, most clade IV members (ThNFYA3, ThNFYA4, and ThNFYA6-ThNFYA8) were downregulated in the wood under partial submergence. This indicates their potential role in modifying wood properties in response to flooding. Co-expression network analysis identified ThNFYA1 and ThNFYA8 as central hub genes in leaves under partial submergence. Overall, these results suggest that the ThNFYA family may serve as candidate regulators of development and stress adaptation in T. hybrid ‘Zhongshanshan’. This study provides valuable insights for further functional verification of ThNFYAs and lays a foundation for marker-assisted breeding of stress-tolerant varieties. Full article

Compost maturity screening supports safe land application and organic waste recycling, but germination index (GI) assays are not always available for rapid process assessment. This study performed a secondary GI-based maturity classification reconstruction using a published Nature Food composting dataset. From this source, 184 observations from 24 manure-based composting batch trajectories across five feedstock domains were retained when GI and routine physicochemical variables were available. GI values were converted into three maturity stages and a binary mature/non-mature endpoint, while the GI itself was excluded from model inputs. Logistic regression, random forest, and extra trees models were evaluated under random split, batch-aware group split, and leave-one-feedstock-domain-out validation. Random and group splits showed stronger apparent performance than cross-feedstock validation, indicating sensitivity to feedstock-domain transfer. In binary classification, the area under the receiver operating characteristic curve (ROC-AUC) remained relatively high in several model–domain combinations, whereas mature-class F1 declined, revealing a discrimination decision gap under default thresholds. Training-domain threshold calibration partially improved mature-class detection without using the held-out feedstock domain for threshold selection. These results support feedstock-aware validation and calibrated decision thresholds for sustainable compost maturity screening. Full article

Potassium (K) is an essential nutrient for humans, but impaired kidney function requires dietary K restriction, increasing the demand for low-K crops. K is also essential for plants, playing important roles in osmotic regulation and enzyme activation. Most studies on low-K crop production have focused on vegetables, whereas research on fruit trees remains limited. In southern highbush blueberry (SHB), the greatest reported reduction in K content in the fruits is 53%, and the maximum K restriction period is five months. Therefore, this study investigated the effects of long-term K restriction on SHB after 17 and 29 months of treatment. Long-term K restriction reduced K content in the fruits by 72–83%, but also significantly decreased yield and induced severe K deficiency symptoms throughout the canopy. Decreases in K content in both fruits and leaves were accompanied by increases in sodium, calcium, and/or magnesium contents, depending on the organ. K content in the fruits reached approximately 20–30 mg·100 g⁻¹ FW after 17 and 29 months of K restriction. These findings demonstrate that long-term K restriction effectively reduces K content in SHB fruits. However, further studies are required to optimize K management to maintain plant vigor and yield while enabling the production of low-K SHB fruits. Full article

Flooding and farmyard manure (FYM) application trigger complex, non-linear redox reactions that govern micronutrient availability in calcareous soils, yet predictive modelling of these dynamics using machine learning (ML) remains largely unexplored, and the present study was designed to address this gap. To this end, seven supervised ML algorithms—Ridge Regression, Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Artificial Neural Network (ANN), and Cubist—were compared under a unified nested cross-validation scheme to predict DTPA-extractable Fe, Mn, Cu, and Zn concentrations in a flooding incubation experiment comprising 10 contrasting calcareous soils (Entisol, Mollisol, Inceptisol, Vertisol) from the Atabey Plain (Isparta, Türkiye), two FYM doses, and five flooding durations (n = 100). Under Leave-One-Out Cross-Validation (LOO-CV), rule- and tree-based ensemble methods consistently outperformed linear and neural network models, with Cubist achieving the best performance for Fe (R² = 0.812) and Mn (R² = 0.915), XGBoost for Cu (R² = 0.929), and GBM for Zn (R² = 0.919). However, a stricter leave-one-soil-out (LOSO) validation with grouped inner cross-validation revealed that this accuracy is element-specific in its transferability: Mn predictions remained robust on previously unseen soils (R²cv = 0.739) and Fe moderate (R²cv = 0.412), whereas Cu and Zn did not generalise beyond the soils used for training, indicating that their high within-soil accuracy reflects soil-specific rather than transferable structure. SHAP analysis revealed that flooding duration was the dominant predictor of Fe and Mn availability, amorphous Fe oxide content was the primary driver for Cu, and plant-available phosphorus (Olsen-P) was the principal feature for Zn. These findings demonstrate that combining ensemble ML with SHAP interpretability enables element-specific, cross-soil-validated and mechanistically interpretable prediction of micronutrient dynamics under varying redox and organic amendment conditions, while highlighting cross-soil transferability as a critical consideration for deploying such models in calcareous agroecosystems. Full article

Exploring plant nutrient allocation and stoichiometry is critical to understanding the adaptive strategies of endangered trees in heterogeneous habitats. This study determined the concentrations of carbon (C), nitrogen (N), phosphorus (P), and potassium (K) in seven organs (leaves, current-year twigs, perennial branches, phloem, xylem, transport roots, and absorptive roots) of Parrotia subaequalis from eight wild populations in the Dabie Mountains and then analyzed the stoichiometric characteristics, chemical plasticity, and allometric relationships of these elements among organs. The concentrations of C, N, P, and K ranged from 416.31–513.61, 4.74–12.99, 0.74–12.89, and 2.62–10.34 mg g⁻¹, respectively. Belowground organs had significantly higher C:P, N:P, and N:K ratios than aboveground ones (by 72.83%–740.30%). Perennial organs (xylem, phloem, perennial branches) showed higher C concentrations and C:N, C:P, C:K, and P:K ratios but lower N, P, and K concentrations than current-year ones (leaves, current-year twigs). Xylem, phloem, and perennial branches exhibited the lowest coefficient of variation and plasticity index. N, P, and K exhibited isometric scaling (α = 0.96–1.04) between absorptive and transport roots. Leaf N and P were positively correlated with K (R² ≥ 0.53). Organ age is a critical determinant influencing the variation in stoichiometric characteristics of the organs. Overall, P. subaequalis adapts to nitrogen-limited wild habitats by adjusting N, P, and K nutrient up-take rates and allocation ratios across current-year organs. Full article