Search Results (114)

‘Korla fragrant’ pear has a long history of cultivation in Xinjiang, China, with favorable economic and social benefits. The selection of tree growth has a direct impact on improvements in fruit yield and quality. In order to provide a theoretical basis for the efficient and high-quality cultivation of ‘Korla fragrant’ pear, two ‘Korla fragrant’ pear tree growth forms, namely trunk shape and small-canopy shape, were selected as experimental materials to study the differences in the parameters of different tree growth forms, as well as the effect on photosynthetic activity and fruit quality. The results show that the small-canopy-shape trees exhibited significantly improved photosynthetic activity, with a 60.64% higher net photosynthetic rate (Pn) in the upper canopy compared to the trunk-shape trees. Fruit quality was also superior in the small-canopy-shape trees, with increases in single-fruit weight (29.36–46.91%), soluble solids content (13.51–14.39%), soluble sugar content (25.79–27.56%), and vitamin C content (up to 0.4363 mg·100 g⁻¹ in the upper layer). However, the yield per unit area of the trunk-shape trees was significantly higher than that of the small-canopy-shape trees by 19.32% because of the higher number of short fruit branches and increased prevalence of smaller row spacing. In addition, within the same tree growth forms, photosynthetic activity and fruit quality were improved in the upper layers compared to the lower layers. Full article

Impatiens parviflora DC. occurs in various plant communities. Its occurrence has been confirmed in Poland across 13 natural habitats protected under the Habitats Directive. The aim of our work is to determine the differences between the plots with and without I. parviflora in terms of the species richness and ecological conditions of the 9180* habitat-type forest. Using data from 315 plots on which a phytosociological relevés was carried out, we analyzed the geographical variability, the Shannon-Winner index and the indicator species for old forests. Flora diversity was represented using the DCA, and the IndVal index was calculated to determine the species that best characterize the differentiated groups. The highest percentage of monitoring plots with I. parviflora is located in the Sudetes Mountains (67.7%) and the lowest in the Bieszczady Mountains (7.5%). Plots with I. parviflora were characterized by significantly lower tree cover, a higher number of tree species in the stand, a lower height of both the understory and herb layer and a lower number of old forest species. Impatiens parviflora does not affect the total number of species in the understorey but is associated with a lower proportion of species typical of old forests. The presence of I. parviflora also correlates with a higher proportion of young trees in the understorey, suggesting a link with successional processes and habitat disturbance. The spread of I. parviflora is limited by shade-loving trees such as Abies alba Mill. and Fagus sylvatica L. The diversity of the distribution of I. parviflora depends on local conditions, so conservation efforts should take into account the local ecological context. Full article

Meeting global forest restoration targets by 2030 requires a transition from labor-intensive and opaque practices to scalable, intelligent, and verifiable systems. This paper introduces a cyber–physical digital twin architecture for forest restoration, structured across four layers: (i) a Physical Layer with drones and IoT-enabled sensors for in situ environmental monitoring; (ii) a Data Layer for secure and structured transmission of spatiotemporal data; (iii) an Intelligence Layer applying AI-driven modeling, simulation, and predictive analytics to forecast biomass, biodiversity, and risk; and (iv) an Application Layer providing stakeholder dashboards, milestone-based smart contracts, and automated climate finance flows. Evidence from Dronecoria, Flash Forest, and AirSeed Technologies shows that digital twins can reduce per-tree planting costs from USD 2.00–3.75 to USD 0.11–1.08, while enhancing accuracy, scalability, and community participation. The paper further outlines policy directions for integrating digital MRV systems into the Enhanced Transparency Framework (ETF) and Article 5 of the Paris Agreement. By embedding simulation, automation, and participatory finance into a unified ecosystem, digital twins offer a resilient, interoperable, and climate-aligned pathway for next-generation forest restoration. Full article

Forest trees have emerged as a promising passive solution for mitigating low-frequency ground vibrations generated by railway operations, offering ecological and cost-effective advantages. This study proposes a three-dimensional semi-analytical method developed for evaluating the dynamic responses of the coupled track–ground–tree system. The thin-layer method is employed to derive an explicit Green’s function corresponding to a har-monic point load acting on a layered half-space, which is subsequently applied to couple the foundation with the track system. The forest trees are modeled as surface oscillators coupled on the ground surface to evaluate the characteristics of multiple scattered wavefields. The vibration attenuation capacity of forest trees in mitigating railway-induced ground vibrations is systematically investigated using the proposed method. In the direction perpendicular to the track on the ground surface, a graded array of forest trees with varying heights is capable of forming a broad mitigation frequency band below 80 Hz. Due to the interaction of wave fields excited by harmonic point loads at multiple locations, the attenuation performance of the tree system varies significantly across different positions on the surface. The influence of variability in tree height, radius, and density on system performance is subsequently examined using a Monte Carlo simulation. Despite the inherent randomness in tree characteristics, the forest still demonstrates notable attenuation effectiveness at frequencies below 80 Hz. Among the considered parameters, variations in tree height exert the most pronounced effect on the uncertainty of attenuation performance, followed sequentially by variations in density and radius. Full article

This study investigates the spatial heterogeneity of carbon pools in young Betula sp. stands on former arable lands in the southern Moscow region, Russia. The findings could be useful for the current estimates and predictions of the carbon balance in such forest ecosystems. The research focuses on understanding the interactions between plant cover and the environment, i.e., how environmental factors such as stand density, tree diameter and height, light conditions, and soil properties affect ecosystem carbon pools. We also studied how heterogeneity in edaphic conditions affects the formation of plant cover, particularly tree regeneration and the development of ground layer vegetation. Field measurements were conducted on a permanent 50 × 50 m sampling plot divided into 5 × 5 m subplots, in order to capture variability in vegetation and soil characteristics. Key findings reveal significant differences in carbon stocks across subplots with varying stand densities and light conditions. This highlights the role of the spatial heterogeneity of soil properties and vegetation cover in carbon sequestration. The study demonstrates the feasibility of indirect estimation of carbon stocks using stand parameters (density, height, and diameter), with results that closely match direct measurements. The total ecosystem carbon stock was estimated at 80.47 t ha⁻¹, with the soil contribution exceeding that of living biomass and dead organic matter. This research emphasizes the importance of accounting for spatial heterogeneity in carbon assessments of post-agricultural ecosystems, providing a methodological framework for future studies. Full article

Photovoltaic panels have become a promising solution for generating renewable energy and reducing our reliance on fossil fuels by capturing solar energy and converting it into electricity. The effectiveness of this conversion depends on several factors, such as the quality of the solar panels and the amount of solar radiation received in a specific region. This makes accurate solar irradiance forecasting essential for planning and managing efficient solar power systems. This study examines the application of machine learning (ML) models for accurately predicting global horizontal irradiance (GHI) using a three-year dataset from six distinct photovoltaic stations: NELHA, ULL, HSU, RaZON+, UNLV, and NWTC. The primary aim is to identify optimal shared features for GHI prediction across multiple sites using a 30 min time shift based on autocorrelation analysis. Key features identified for accurate GHI prediction include direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and solar panel temperatures. The predictions were performed using tree-based algorithms and ensemble learners, achieving $R^2$ values exceeding 95% at most stations, with NWTC reaching 99%. Gradient Boosting Regression (GBR) performed best at NELHA, NWTC, and RaZON, while Multi-Layer Perceptron (MLP) excelled at ULL and UNLV. CatBoost was optimal for HSU. The impact of time-shifting values on performance was also examined, revealing that larger shifts led to performance deterioration, though MLP performed well under these conditions. The study further proposes a stacking ensemble approach to enhance model generalizability, integrating the strengths of various models for more robust GHI prediction. Full article

Tree belts are commonly applied over agricultural terrain where seeds of wheat and other vegetation are planted in the ground in order to prevent the seeds from being blown by the wind. The tree belt comprises a long and thin (10–20 m thick) section of trees, which spans in a direction normal to the prevailing wind direction. While serving its agricultural goal, the belt does inevitably modify the boundary layer profile of the wind. This, on its part, is likely to affect the operation of small-scale wind turbines installed in the vicinity of the belt. The goal of this study is to determine the span and range at which this effect manifests itself. It was found that in the near vicinity downstream and slightly above the tree belt, the wind velocity actually increased due to the mass conservation. The flow became independent on the tree belt drag coefficient when its value was higher than 0.2 1/m. The turbulence introduced by the belt was restricted to a height of 1.5–2 tree belts. Full article

This paper describes the E-Sense Artificial Intelligence system. It comprises a memory model with two levels of information and then a more neural layer above that. The lower memory level stores source data in a Markov (n-gram) structure that is unweighted. Then, a middle ontology level is created from a further three aggregating phases that may be deductive. Each phase re-structures from an ensemble to a tree, where the information transposition is from horizontal set-based sequences into more vertical, typed-based clusters. The base memory is essentially neutral, but bias can be added to any of the levels through associative networks. The success of the ontology typing is open to question, but the results suggested related associations more than direct ones. The third level is more functional, where each function can represent a subset of the base data and learn how to transpose across it. The functional structures are shown to be quite orthogonal, or separate, and are made from nodes with a progressive type of capability, including unordered to ordered. Comparisons with the columnar structure of the neural cortex can be made and the idea of ordinal learning, or just learning relative positions, is introduced. While this is still a work in progress, it offers a different architecture to the current frontier models and is probably one of the most biologically inspired designs. Full article

Air pollution, particularly PM₁₀ particulate matter, poses significant health risks related to respiratory and cardiovascular diseases as well as cancer. Accurate identification of PM₁₀ reduction factors is therefore essential for developing effective sustainable development strategies. According to the current state of knowledge, machine learning methods are most frequently employed for this purpose due to their superior performance compared to classical statistical approaches. This study evaluated the performance of three machine learning algorithms—Decision Tree (CART), Random Forest, and Cubist Rule—in predicting PM₁₀ concentrations and estimating long-term trends following meteorological normalisation. The research focused on Tarnów, Poland (2010–2022), with comprehensive consideration of meteorological variability. The results demonstrated superior accuracy for the Random Forest and Cubist models (R² ~0.88–0.89, RMSE ~14 μg/m³) compared to CART (RMSE 19.96 μg/m³). Air temperature and boundary layer height emerged as the most significant predictive variables across all algorithms. The Cubist algorithm proved particularly effective in detecting the impact of policy interventions, making it valuable for air quality trend analysis. While the study confirmed a statistically significant annual decrease in PM₁₀ concentrations (0.83–1.03 μg/m³), pollution levels still exceeded both the updated EU air quality standards from 2024 (Directive (EU) 2024/2881), which will come into force in 2030, and the more stringent WHO guidelines from 2021. Full article

This review paper synthesizes the application of knowledge graphs (KGs) in Alzheimer’s disease (AD) research, based on two basic questions, as follows: what types of input data are available to construct these knowledge graphs, and what purpose the knowledge graph is intended to fulfill. We synthesize results from existing works to illustrate how diverse knowledge graph structures behave in different data availability settings with distinct application targets in AD research. By comparative analysis, we define the best methodology practices by data type (literature, structured databases, neuroimaging, and clinical records) and application of interest (drug repurposing, disease classification, mechanism discovery, and clinical decision support). From this analysis, we recommend AD-KG 2.0, which is a new framework that coalesces best practices into a unifying architecture with well-defined decision pathways for implementation. Our key contributions are as follows: (1) a dynamic adaptation mechanism that adapts methodological elements automatically according to both data availability and application objectives, (2) a specialized semantic alignment layer that harmonizes terminologies across biological scales, and (3) a multi-constraint optimization approach for knowledge graph building. The framework accommodates a variety of applications, including drug repurposing, patient stratification for precision medicine, disease progression modeling, and clinical decision support. Our system, with a decision tree structured and pipeline layered architecture, offers research precise directions on how to use knowledge graphs in AD research by aligning methodological choice decisions with respective data availability and application goals. We provide precise component designs and adaptation processes that deliver optimal performance across varying research and clinical settings. We conclude by addressing implementation challenges and future directions for translating knowledge graph technologies from research tool to clinical use, with a specific focus on interpretability, workflow integration, and regulatory matters. Full article

Forest ecosystems are crucial in mitigating air pollution and improving air quality. Therefore, investigating the relationships between air quality, forest structure, and environmental factors in different forest types is of significant importance. This study conducted three months of continuous monitoring (June–September 2023) of air quality factors (particulate matter (PM_2.5 and PM₁₀), ozone (O₃), and negative air ions (NAI)) and environmental factors (air temperature (TA), relative humidity (RH), light intensity (LI), and wind speed (WS)) in four subtropical forest types, along with vegetation characteristic surveys. The effects of forest structure and environmental factors on air quality were determined by correlation and multiple regression analysis. The results showed that the forest air quality is at its best in July during the summer season. Concentrations of particulate matter (PM) and ozone (O₃) in mixed coniferous and broadleaf forests (MCB), as well as deciduous broadleaf forests (DB), are lower than those in moso bamboo forests (MB) and evergreen broadleaf forests (EB). The troughs of PM concentrations occur in the early morning (4:00–6:00), while the troughs of O₃ concentrations occur in the early morning (4:00–6:00) and in the evening (18:00). NAI concentrations were highest in DB (1287 ions/cm³), followed by MCB (1187 ions/cm³), MB (896 ions/cm³), and EB (584 ions/cm³), with NAI concentrations peaking between 14:00 and 16:00. PM concentrations in forest air were primarily influenced by stand density (SD) and the Shannon–Wiener index of herbaceous layer (SWH) (p < 0.05); ozone concentrations were significantly affected by tree height (TH) and canopy density (CD) (p < 0.05); and NAI concentrations were primarily related to TH and diameter at breast height (DBH). Air particulate matter concentrations were negatively affected by TA and RH (p < 0.01), and ozone concentrations were negatively influenced by RH and WS and were positively influenced by TA. TA has a direct and significant positive effect on the NAI concentration (p < 0.01), and RH indirectly influences the changes in NAI concentration through its interaction with TA. This study provides new insights for vegetation optimization in forest parks and planning forest health-promoting activities for sub-healthy populations. Full article

Soil moisture content has a direct effect on the growth rate and survival rate of trees. However, previous studies on soil moisture have often focused on the topsoil, lacking effective monitoring of long-term dynamic changes in deep soil layers. In this study, 16 time-domain reflectometer (TDR) probes were installed in the Haikou plantation in Kunming to conduct long-term continuous monitoring of soil moisture within a depth range of 0 to 300 cm. The results indicate that the vertical distribution of soil moisture can be classified into three levels: the active layer from 0 to 70 cm ($\theta =0.23\pm 0.08\mathrm{c}{\mathrm{m}}^3{\mathrm{c}\mathrm{m}}^{-3})$, where the moisture content fluctuates significantly due to precipitation events; the transitional accumulation layer from 70 to 170 cm $(\theta =0.26\pm 0.06\mathrm{c}{\mathrm{m}}^3{\mathrm{c}\mathrm{m}}^{-3})$, where moisture content increases with depth and peaks at 170 cm; and the deep dissipative layer from 170 to 300 cm $(\theta =0.24\pm 0.08\mathrm{c}{\mathrm{m}}^3{\mathrm{c}\mathrm{m}}^{-3})$, where moisture content decreases with depth, forming a noticeable steep drop zone at 290 cm. The Hydrus-1D (Version 4.xx) model demonstrated high simulation capabilities (${\mathrm{R}}^2=0.58$) in shallow (10 to 50 cm) and deep (280 to 300 cm) layers, while its performance decreased (${\mathrm{R}}^2=0.39$) in the middle layer (110 to 200 cm). This study systematically reveals the dynamics of soil moisture from the surface active zone to the deep transition zone and evaluates the simulation ability of the Hydrus-1D model in this specific environment, which is also significant for assessing the groundwater resource conservation function of plantation ecosystems. Full article

Vertical structure monitoring of urban vegetation provides data support for urban green space planning and ecological management, playing a significant role in promoting sustainable urban ecological development. Three-dimensional green volume (3DGV) is a comprehensive index used to characterize the ecological benefit of urban vegetation. As a critical component of urban vegetation, street trees play a key role in urban ecological benefits evaluation, and the quantitative estimation of their 3DGV serves as the foundation for this assessment. However, current methods for measuring 3DGV based on point cloud data often suffer from issues of overestimation or underestimation. To improve the accuracy of the 3DGV for urban street trees, this study proposed a novel approach that used convex hull coupling k-means clustering convex hulls. A new method based on terrestrial laser scanning (TLS) data was proposed, referred to as the Convex Hull Coupling Method (CHCM). This method divides the tree crown into two parts in the vertical direction according to the point cloud density, which better adapts to the lower density of the upper layer of TLS data and obtains a more accurate 3DGV of individual trees. To validate the effectiveness of the CHCM method, 30 sycamore (Platanus × acerifolia (Aiton) Willd.) plants were used as research objects. We used the CHCM and five traditional 3DGV calculation methods (frustum method, convex hull method, k-means clustering convex hulls, alpha-shape algorithm, and voxel-based method) to calculate the 3DGV of individual trees. Additionally, the 3DGV was predicted and analyzed using five fitting models. The results show the following: (1) Compared with the traditional methods, the CHCM improves the estimation accuracy of the 3DGV of individual trees and shows a high consistency in the data verification, which indicates that the CHCM method is stable and reliable, and (2) the fitting results R² of the five models were all above 0.75, with the exponential function model showing the best fitting accuracy (R² = 0.89, RMSE = 74.85 m³). These results indicate that for TLS data, the CHCM can achieve more accurate 3DGV estimates for individual trees, outperforming traditional methods in both applicability and accuracy. The research results not only offer a novel technical approach for 3DGV calculation using TLS data but also establish a reliable quantitative foundation for the scientific assessment of the ecological benefits of urban street trees and green space planning. Full article

Soil nutrient and fertility assessments provide a direct measure for evaluating forest management effects. In this study, we examined soil nutrient content in Chinese fir (Cunninghamia lanceolata) plantations under four reconstruction patterns: pure plantation, introduced broadleaf, introduced needleleaf, and introduced mixed broadleaf-needleleaf. The soil fertility index (SFI) evaluation model was constructed based on partial least squares path modeling (PLS-PM), revealing the influence of stand characteristics on SFI in early stand reconstruction. The results showed that, compared to pure plantations, total nutrient content increased in the introduced needleleaf pattern by 13.94% to 21.15% and available nutrient content by 18.21% to 26.91%. In contrast, both introduced broadleaf and mixed broadleaf-needleleaf exhibited a declining trend. Significant differences were observed among the reconstruction patterns (p < 0.05). In the SFI evaluation model, soil chemistry total nutrient (SCT) and soil chemistry available nutrient (SCA) made significant contributions. The weights of SCT and SCA in SFI were 0.52 and 0.48, respectively. The SFI of four patterns ranged from 0.43 to 0.58, indicating relatively low soil fertility. Compared to pure plantations, introduced trees did not enhance soil fertility in early stand reconstruction. The SFI of the introduced needleleaf was significantly higher than that of the other two reconstruction patterns (p < 0.05). Stand construction (including diameter at breast height, tree density, and tree biomass) explained 14.69% of SFI variation, with a contribution of 31.72% in the surface soil layer (0~20 cm). Tree biomass significantly influenced SFI variation, accounting for over 40% of the total stand factors. Retained tree biomass had a substantially greater effect than introduced tree biomass, contributing twice as much to SFI variation. PLS-PM could effectively reflect the soil nutrient status and accurately estimate the weight of soil fertility. In early stand reconstruction, retained tree biomass might be the major influence on soil fertility variation. We suggest determining reasonable thinning intensity to retain enough Chinese fir and promote the growth of introduced trees. This study introduces a novel approach to soil fertility assessment and provides theoretical support for formulating effective forest management strategies in the early reconstruction of Chinese fir plantations. Full article

In response to the demands of climate change and urban sustainability, urban green space construction in China has rapidly expanded, while simultaneously giving rise to allergenic pollen pollution. Focusing on the central urban area of Xi’an, Shaanxi Province, China, this study utilizes urban surveys, field measurements, and pollen particle microscopy to analyze the seasonal variation in allergenic pollen pollution concentrations and the physical dispersion characteristics of allergenic pollen particles in residential areas. The study also examines the impact of urban residential area spatial layout on regulating allergenic pollen pollution. The results show that (1) allergenic pollen pollution in Xi’an’s residential areas exhibits significant seasonal characteristics, with spring, summer, and autumn being the primary seasons. The highest concentrations occur in spring, dominated by tree pollen, followed by summer and autumn with a predominance of herbaceous pollen. (2) Pollution concentrations in residential areas are affected by the diurnal temperature variation, with higher concentrations observed in public green spaces compared to residential green spaces and roadside green spaces. (3) Allergenic pollen pollution shows a layered characteristic in the vertical direction, with concentrations concentrated around 13 m above ground due to the effects of diurnal temperature variation and local microclimate. (4) Urban pollen pollution concentrations are positively correlated with high temperatures and negatively correlated with high humidity, while local circulations influence pollen dispersion concentrations in residential areas. (5) Design indicators such as plot ratio and building stagger affect the dispersion concentrations of allergenic pollen pollution in residential areas. The findings provide a scientific basis for optimizing residential area spatial design to mitigate allergenic pollen pollution and offer strategic guidance for improving the health and livability of urban environments. Full article