Search Results (1,866)

Understanding patterns and processes affecting genetic diversity and structure of fragmented tree populations is essential for defining effective conservation strategies. Here, we investigate the genetic diversity of the Macedonian oak (Quercus trojana Webb 1839) across its entire Italian range by sampling 546 georeferenced trees from 30 forest stands. Using ten polymorphic nuclear microsatellite markers, we assessed within-population genetic variability and population structure, estimated effective population size (Ne), bottleneck signatures, and fine-scale relatedness among individual trees. Our results showed overall moderate genetic diversity, but marked variation emerged among populations and between two subregions. Populations in the North-Western Murge subregion exhibited lower allelic richness (Ar), critically small Ne (<50 in 12 populations), evidence of past bottlenecks, and higher pairwise differentiation, with three populations standing out as genetically distinct. Isolation by distance was significant only within the South-Eastern Murge subregion, suggesting contrasting demographic histories. Fine-scale relatedness analysis revealed higher within-site relatedness in North-Western Murge and a substantial proportion (30%) of related dyads across subregions, indicating large-scale connectivity due to natural or anthropogenic factors. Overall, our results reveal a complex scenario of historical and ongoing genetic erosion in some populations, particularly in the northern part of the range, alongside relatively high gene flow across the study area, especially in the south. We recommend prioritizing conservation actions for populations with critically low Ne and distinct gene pools, while maintaining genetic diversity hotspots and landscape connectivity. Full article

Diameter at breast height (DBH) is a crucial indicator for obtaining tree phenotypes in orchard management, plantation monitoring, and agroforestry systems. LiDAR technology has high measurement accuracy, but it is costly and difficult to deploy flexibly in outdoor scenarios, while smartphones have emerged as a viable alternative due to their portability and low cost. In this paper, we propose a DBH estimation method based on monocular depth estimation, supported by a mobile application for algorithm deployment and result visualization. To address the limited computing resources on mobile devices, we design HR-DiffusionDepth, a lightweight diffusion-based monocular depth estimation network for smartphones, which generates pixel-wise 3D coordinates from a single image using camera intrinsics, thereby replacing LiDAR for DBH calculation. Experiments on the KITTI and SPREAD datasets show that HR-DiffusionDepth achieves the best depth estimation accuracy among similar lightweight models, reducing Abs Rel by up to 25.3% relative to the state-of-the-art (SoTA) lightweight baseline, with only 6.26 M parameters. The validation results show that the root mean square error (RMSE) of DBH estimation is 3.10 cm and the mean absolute error (MAE) is 2.25 cm, demonstrating the potential of this approach for agricultural scenarios such as orchards and plantations. Full article

Climate change is expected to profoundly affect forest biodiversity, yet its impacts on saproxylic and non-saproxylic insects remain largely mediated by forest structure and management. Saproxylic beetles strongly depend on tree-related microhabitats (TreMs), which reflect stand development, deadwood dynamics, and habitat continuity. In this study, we assessed how future climate change may influence saproxylic beetle richness and TreM diversity across the Italian Apennines by integrating long-term field data from 336 forest plots with machine-learning models and the process-based forest model 3D-CMCC-FEM. Beetle and TreM richness were projected under a baseline current climate scenario (CCS) and two future climate scenarios (RCP4.5 and RCP8.5) until 2100. Gaussian process regression models with strong extrapolation performance were used to forecast richness trajectories based on simulated forest structural and climatic variables. Results show that host tree species and stand structural development are the primary drivers of both beetle and TreM richness, while differences among climate scenarios are comparatively small. Richness trajectories differed markedly among forest types: beech stands showed temporary mid-century increases under climate change scenarios, chestnut forests remained relatively stable, and silver fir and Turkey oak stands exhibited long-term declines or convergence toward lower richness levels. Patterns of TreM richness closely mirrored beetle richness, highlighting the central role of structural complexity and microhabitat availability. Overall, the effects of climate change on beetle diversity appear to be largely indirect, acting through forest growth, mortality, and microhabitat dynamics rather than through direct climatic constraints. These findings emphasize that forest management practices maintaining structural heterogeneity and microhabitat continuity can substantially mitigate climate-driven biodiversity changes, supporting the integration of TreMs into climate-adaptive forest management strategies. Full article

Quantum cyber-physical systems can involve computation-based strategies like quantum algorithms, physical parts, and communication-based infrastructures such as physical quantum bits (qubits), measurement units, and communication links. A photonics-driven quantum cyber-physical system can rely on physical parameters of the environment or communication links such as the refractive index. The injection of false data into the actual value of the refractive index can cause the use of an incorrect value of the refractive index in the system if needed, and as a result, an incorrect interpretation of information or even access to non-real information instead of the actual information. The first attempt to avoid this issue can be the detection of the existence of the cyber-attacks in the system. This paper will address this challenge using artificial intelligence for binary classification to detect the cyber-attacks in the system. For the proof of concept, the proposed strategy is examined deploying the real and imaginary parts of the refractive index value corresponding to a semiconductor, i.e., GaAs. Different scenarios are performed, including a single evaluation, an analysis of several training runs, a comparison considering two types of normalization techniques, variations in the size of artificial intelligence, and a comparison among different machine learning techniques, including artificial neural networks, decision tree models, a logistic regression model, and support vector machine classifiers. Based on the obtained results, for the single evaluation, the class of 95.24 % of the testing samples could be classified successfully. In addition, for the case of the analysis of several training runs, a total of 9000 runs were run for 60 shallow artificial neural networks with different sizes. For 58 neural networks, the maximum achieved accuracy was 100 %. Besides, for the case of the comparison between the normalization techniques, two methods were evaluated, i.e., min-max and z-score normalization. The results were very close to each other, but, more accurately, z-score normalization indicated a better performance and a higher accuracy. Finally, among the mentioned machine learning models, artificial neural networks mostly showed higher accuracies. Full article

The ICARIA Horizon Europe risk-assessment framework and web-based Decision Support System (DSS) were applied in the Southern Aegean Region (Greece) to quantify single- and multi-hazard climate risks (wildfire, heatwave, extreme wind) and to assess adaptation under historical and future climates (SSP1-2.6, SSP5-8.5). The study integrates high-resolution hazard layers (5 km Fire Weather Index, downscaled temperature extremes), satellite-derived land surface temperature (30 m LST for building exposure), asset-level exposure and vulnerability, and probabilistic event-tree-based multi-hazard modelling, and was tested with stakeholders in Rhodes and Syros. Results show increases in wildfire and ecological multi-hazard risk: ecological high-risk (categories 4–5) areas expand by ~10–15%, and economic high-risk zones by ~5–8%. On Rhodes, very high fire-weather days (FWI > 70) rise under SSP5–8.5, and the central mountains shift into the highest ecological risk (category 5). A multi-hazard indicator (FWI > 80, Tmax > 30 °C, wind > 10.8 m/s) identifies persistent future hotspots. The 2023 Rhodes wildfire (~17,600 acres burned; ~€1.29 M electricity, ~€400 k water damage, ~2500 livestock and ~60,000 olive trees lost; ~19,000 evacuees) supports model projections. Adaptation substantially reduces risk. Vegetation substitution (e.g., Ceratonia siliqua, low-flammability stands) cuts category-5 ecological areas by ~30–32% and high-risk economic areas by ~20% (single hazard) and ~28–34% (multi-hazard). On Syros, retrofitting 1980–2010 buildings reduces top-risk classes (4–5) by ~25–35% and lowers thermal-stress exposure. Stakeholder trials found the DSS user-friendly and useful for comparing business-as-usual versus adaptation scenarios and for strategic planning, while highlighting needs for broader hazard coverage and continued support. Full article

Sales forecasting plays a critical role in business decision-making, including financial planning, resource allocation, and inventory management. Despite the growing adoption of machine learning in forecasting applications, controlled evaluations of how preprocessing decisions and contextual feature availability affect model performance remain limited. This study presents a controlled benchmarking evaluation that systematically examines preprocessing strategies and contextual feature availability across seven representative tree-based machine learning models, using two publicly available retail datasets: BigMart and Rossmann. Multiple preprocessing configurations are evaluated on both datasets, and ablation studies are conducted on the Rossmann dataset to quantify the independent contribution of seasonal and promotional features and the Customers variable to forecasting accuracy. Results show that GradientBoosting achieved the best performance on BigMart (R² = 0.611), while RandomForest and ExtraTrees led on Rossmann ${\mathrm{R}}^2=0.884$ under realistic conditions excluding the Customers variable, and ${\mathrm{R}}^2=0.965$ in an ablation scenario where Customers, typically unavailable at forecast time, is included. Ablation analysis confirmed that seasonal and promotional features are critical drivers of forecasting accuracy, and that the Customers variable is the single most influential predictor in the Rossmann dataset. These findings highlight the importance of preprocessing design and contextual feature availability in retail sales forecasting and provide practical insights for building effective forecasting pipelines. Full article

To elucidate the response characteristics of radial growth and water use strategies in coniferous forests of cold-arid regions to drought stress, this study focused on Larix sibirica in different forestry areas of the Altai Mountains. Using dendrochronology and stable isotope techniques, we calculated the basal area increment (BAI) and intrinsic water-use efficiency (iWUE), and combined these with the standardized precipitation–evapotranspiration index (SPEI) to identify drought events, to investigate tree growth and water-use efficiency responses to climate variability. The results showed that drought years were characterized by reduced radial growth and increased iWUE in Larix sibirica across both forest regions, and tree-ring-derived intercellular CO₂ concentration (Ci) increased with rising atmospheric CO₂ concentrations, whereas the Ci/Ca ratio remained relatively stable throughout the study period. Scenario analysis revealed that, prior to 1980, the long-term trend in iWUE was more consistent with the constant Ci scenario, suggesting relatively strong stomatal regulation. After 1980, iWUE trends became more closely aligned with the constant Ci/Ca scenario, indicating that trees maintained a relatively stable Ci/Ca ratio to balance carbon assimilation and water loss. With increasing drought severity, drought resistance declined in both forest regions; however, substantial spatial differences were observed in drought responses. Under moderate drought conditions, trees in the Haba-River forest area exhibited higher resistance, whereas trees in the Hanaslin forest area showed greater recovery capacity and ecological resilience. Winter temperature, growing-season temperature, late-season temperature, and water availability were identified as key climatic factors influencing variations in the radial growth and iWUE of Larix sibirica. Overall, under the combined influences of rising atmospheric CO₂ concentrations and increasing water limitations, Larix sibirica exhibited adaptive adjustments in carbon–water regulation; however, enhanced iWUE did not fully compensate for the negative effects of drought on radial growth. These findings provide valuable insights into the responses and adaptive strategies of cold-arid forest ecosystems under ongoing climate change and offer scientific support for the conservation and sustainable management of Larix sibiric forests. Full article

Urban green areas contain numerous non-native tree species that may escape from cultivation and potentially become invasive. Climate change is expected to exacerbate this risk by creating favourable conditions for species that are currently climatically restricted. Here, we provide a comprehensive risk screening of 34 non-native urban tree species in continental Europe under current and projected future climate scenarios using the Terrestrial Plant Species Invasiveness Screening Kit (TPS-ISK). Under current conditions, 10 species (29.4%) were categorized as high risk, 23 (67.6%) as medium risk, and one (2.9%) as low risk. Under the projected climate change, 11 species were classified as high risk, including seven categorized as very high risk. Ailanthus altissima, Diospyros virginiana, and Quercus rubra consistently ranked among the highest-risk species, while Acer tataricum subsp. ginnala, Koelreuteria paniculata, Magnolia kobus, Phellodendron amurense, Pseudotsuga menziesii, and Robinia pseudoacacia showed an increased invasion potential under future climate conditions. These findings indicate that climate change may facilitate the establishment and spread of several currently cultivated urban tree species. Our study provides the first comprehensive TPS-ISK screening of non-native urban trees for mainland Europe and identifies priority species for early detection, monitoring, and management. Full article

Model predictive control (MPC) has emerged as one of the most effective control strategies for autonomous vehicle path tracking owing to its capability to explicitly handle dynamic constraints and optimize future control actions. However, its tracking performance strongly depends on the appropriate tuning of prediction, control, and weighting parameters, which remains a challenging and computationally demanding task under varying driving conditions. This paper proposes an Adaptive Curvature-Aware MPC (CAMPC) framework optimized through a hybrid Bayesian Optimization–Tree-structured Parzen Estimator (BO–TPE) approach to automatically identify optimal MPC parameters while accounting for upcoming road curvature. The proposed controller incorporates future curvature information to adapt the vehicle speed profile and steering behavior, thereby improving tracking accuracy and control smoothness in complex road geometries. The framework is evaluated in the CARLA autonomous driving simulator under three challenging driving scenarios, including a roundabout, an urban environment, and a highly curved road. Experimental results demonstrate that the proposed CAMPC consistently outperforms a conventional PID controller, achieving improvements of 47%, 58%, and 85% in trajectory-tracking performance across average and maximum lateral and angular errors while reducing steering, braking, and acceleration efforts by more than 80%. Furthermore, the controller satisfies real-time execution requirements with an average computation time of 31 ms and a 95th-percentile latency of 35 ms, confirming its suitability for practical autonomous driving applications. These results demonstrate that integrating curvature-aware prediction with adaptive BO–TPE parameter optimization significantly enhances the robustness, accuracy, computational efficiency, and real-time capability of MPC for autonomous vehicle path tracking in challenging driving environments. Full article

Precise step length estimation (SLE) is a key necessity for not only navigation systems design but also gait health monitoring in neurological conditions. Among existing solutions, non-invasive inertial sensor-based approaches operating without dedicated infrastructure are more cost-effective. Many such methods, however, rely on bodily affixed inertial sensors rather than freely held smartphone sensors. Traditional signal processing approaches, on the other hand, offer varying accuracy across diverse gait patterns due to user parameter calibration. This study, thus, proposes a regression-based SLE framework employing eight regression algorithms: linear regression (LR), k-nearest neighbors, support vector machine, decision tree, elastic network, random forest, histogram-based gradient boosting (HGB) regressor, and artificial neural network (ANN). Their extensive and rigorous evaluations across varied window sizes, using a dataset collected in normal and fast walking modes with two device positions (hand-held and trouser-pocket) during three evaluation scenarios, demonstrate the HGB regressor’s outstanding performance, achieving the lowest mean absolute error (MAE) below 1 cm across four different contexts under leave-one-out cross-validation-based evaluation and three in the seen test evaluations. Moreover, the findings report the ANN’s exceptional generalization capacity over other models and the previous method IRT-SD-SLE in unseen test evaluations, with an MAE not exceeding $6.3$ cm. The extensive evaluations of training and testing times reveal the highest computational efficiency for LR, moderate efficiency for the HGB regressor, and the highest training cost for the ANN, indicating a clear trade-off between MAE and computational expense. Additionally, this study includes an insightful discussion on the performance results, including the trade-offs between accuracy and efficiency. Full article

Drought stress is a major environmental factor limiting plant growth and distribution, with severe drought leading to plant mortality. This study investigates the physiological mechanisms underlying drought-induced mortality in one-year-old seedlings of the valuable timber tree species Phoebe bournei (Hemsl.) Yang, aiming to clarify the relative roles of hydraulic failure and carbon starvation. A 51-day controlled pot experiment was conducted to simulate progressive drought using 10 experimental groups (n = 6): a well-watered control and four drought treatment groups harvested at key physiological stages. Stage I (baseline) corresponded to a relative soil water content of approximately 89%. Stage II (photosynthetic cessation) was reached after approximately 15 days of water withholding, at a relative soil water content of approximately 60% and a predawn leaf water potential of approximately −3.4 MPa. Stage III (complete leaf wilting) was reached after approximately 36 days of water withholding. Stage IV (stem browning) occurred at a relative soil water content of approximately 18%, after approximately 45–51 days of water withholding. We systematically measured key physiological parameters, including leaf water potential, gas exchange parameters, the percentage loss of xylem conductivity in stems, and the concentrations of non-structural carbohydrates (including soluble sugars and starch) in different tissues. Results showed that stomatal conductance and net photosynthetic rate approached zero when leaf water potential fell to approximately −3.4 MPa. Stem percentage loss of xylem conductivity increased significantly with advancing drought, exceeding 75% at complete leaf wilting and reaching over 98% at stem browning, reflecting a near-complete loss of xylem hydraulic conductance. Concurrently, non-structural carbohydrate concentrations underwent transient accumulation during early drought, reflecting sink-limited carbon dynamics, followed by progressive depletion. Notably, partial non-structural carbohydrate reserves persisted even at the stem browning stage, suggesting that these reserves may have become physically inaccessible or metabolically unavailable rather than entirely exhausted. The findings point to a tightly coupled, sequential interaction between hydraulic failure and carbon starvation across the drought progression. The findings will provide a scientific basis for evaluating drought tolerance, informing adaptive management practices, and ensuring the sustainable cultivation of P. bournei under future climate scenarios. Full article

Climate change is increasingly reshaping the distribution and long-term persistence of urban greening tree species, yet national-scale assessments of widely planted species in China remain limited. Salix babylonica is one of the most representative and extensively planted urban greening trees in China, but its future climate vulnerability and redistribution patterns are still poorly understood. In this study, we integrated 425 occurrence records and 16 environmental variables to project the potential distribution of S. babylonica under current and future (2050s, 2070s, and 2090s) climate scenarios across three shared socioeconomic pathways (SSP126, SSP370, and SSP585) using an optimized MaxEnt model. The optimized model demonstrated strong predictive performance and identified annual precipitation (≥309.64 mm), precipitation of the wettest month (≥82.01 mm), elevation (≤2251.24 m), mean temperature of the coldest quarter (≥−10.77 °C), annual mean temperature (≥3.94 °C), and minimum temperature of the coldest month (≥−19.11 °C) as the dominant environmental factors (threshold) constraining species distribution. Under the current climate, suitable habitats of S. babylonica are primarily distributed in East, Central, Southwest, and South China, with highly suitable habitats concentrated in the eastern and central plains. Under future climate scenarios, high suitability areas are projected to contract significantly and become progressively fragmented, with reductions ranging from 2.81% (2070s-SSP126) to 40.01% (2090s-SSP585), whereas medium and low suitability areas are projected to expand substantially, with increases ranging from 6.76% to 48.10%. The overall centroid of suitable habitats exhibits a consistent northward shift across scenarios. These results indicate that climate change will not simply expand the potential distribution of S. babylonica, but will reorganize habitat quality and increase long-term risks for its continued use in urban greening. Our findings provide a spatially explicit basis for climate-adaptive urban forestry planning and support risk-differentiated management, including the conservation of stable core areas, monitoring of declining risk zones, and cautious introduction trials in newly emerging suitable areas. Full article

The full-scope simulator plays a critical role in nuclear power plant emergency drills, personnel training, and accident analysis. Traditional system programs lack sufficient computational performance to meet real-time requirements when simulating complex accident scenarios in reactor systems. This study focuses on the small-break loss-of-coolant accident (SBLOCA) in nuclear power plants, generating large-scale datasets through digital simulations. After data preprocessing and normalization, a light gradient boosting decision tree (LightGBM) regression model was developed using machine learning algorithms. SHAP (SHapley Additive exPlanations) analysis identified the contributing factors, enabling the model to predict key parameters such as peak fuel cladding temperature, primary reactor coolant pressure, and pressurizer water level. The model achieved a mean square error (MSE) below 0.002 and a coefficient of determination (R²) exceeding 0.98, with a prediction speed approximately 32,500 times faster than traditional system programs, requiring less than $4\times {10}^{-4}$ seconds per data point. This study provides a novel solution for complex condition simulations in nuclear power plant full-scope simulators. Full article

Hybrid renewable energy systems (HRESs) represent a promising strategy for reducing carbon emissions across multiple sectors by integrating complementary resources such as solar, wind, hydropower, and energy storage technologies. Identifying the most suitable location for a pilot installation requires a comprehensive evaluation that balances technical performance, environmental benefits, social considerations, and economic feasibility. This study employs an enhanced multi-criteria decision analysis (MCDA) framework, supported by machine learning (ML) techniques, to assess four pilot sites developed within the HY4RES project: a rural community, an aquaculture facility, a port installation, and an agriculture network. A comprehensive set of key performance indicators (KPIs) was established to capture technical, environmental, social, and economic dimensions. These include the degree of hybridization, carbon intensity, community benefit scores, net present value, levelized cost of energy, and payback period. After collecting and normalizing the site-specific data, ML EL-SVM, decision tree, and logistic regression models as computational surrogates designed to bypass the multi-step, matrix inversion mathematical requirements of the AHP when screening massive numbers of future scenario outputs supporting consistency checks and sensitivity exploration were used, along with criterion adjustments, to refine the relative importance of each KPI. The Analytical Hierarchy Process (AHP) was employed to assess potential factors and rank the sites, with the rural site achieving the highest overall score in the system, driven by its complex four-source hybrid configuration and strong community-level benefits. The agriculture scheme ranked second, demonstrating significant potential for carbon emission reductions. The port pilot placed third, distinguished by high technical innovation but more limited social impact. The aquaculture site ranked fourth, primarily due to environmental scores, despite its economic self-sufficiency. Full article

Modern power transmission grids are increasingly operated under volatile load, variable generation, and near-limit line-flow conditions. In such environments, deterministic line-flow forecasting is insufficient for operational decision support because operators require calibrated risk probabilities, uncertainty intervals, and reliable early warning signals. This article proposes B-TGPRF, a Bayesian Temporal Graph Probabilistic Risk Forecaster for calibrated overload-risk forecasting in power transmission grids. The proposed framework is positioned as a hybrid probabilistic graph-temporal forecasting system rather than as a new end-to-end graph neural network; its novelty lies in the leakage-controlled sequential combination of temporal forecasting, electrical graph descriptors, calibrated Bayesian risk estimation, residual correction, and compact interval uncertainty assessment. The model integrates temporal line-flow, load, and generation features with graph-topological descriptors, operating-regime indicators, residual correction, conformal interval estimation, probability calibration, and a Bayesian risk layer. A leakage-controlled data preparation pipeline was built using an open large-scale benchmark for machine learning applications in transmission grids. The final modeling dataset contains more than 3.48 million observations, 100 selected critical lines, train-only risk thresholds, and chronological train, validation, test, and external-like scenario splits. B-TGPRF was compared with persistence baselines, linear models, Bayesian baselines, tree ensembles, boosting models, neural temporal models, and compact state-of-the-art-style temporal and graph-temporal architectures. On the strict external-like test, B-TGPRF achieved MAE = 0.3650, RMSE = 0.5478, R² = 0.9962, Brier Score = 0.0147, and ECE = 0.0064. The results show that the proposed model provides a strong overall balance between line-flow forecasting accuracy, calibrated risk estimation, compact interval prediction, and low false-positive risk-signaling, while boosting models remain highly competitive for pure risk-class detection. Full article