Search Results (305)

Old-growth forests are recognized as key reference systems for biodiversity conservation and sustainable forest management, yet they remain rare and highly fragmented ecosystems across Europe. In this study, we developed and applied a quantitative, measurement-based framework to assess candidate old-growth beech stands by comparing their structural attributes with reference old-growth forests and with a broader set of beech forests from the National Forest Inventory (NFI). Specifically, we compared the structural attributes of two candidate old-growth (COG) beech stands to assess their degree of naturalness and their similarity to three reference old-growth (ROG) beech forests in the central Apennines. The approach integrated field-based structural measurements, deadwood quantification, dendrochronological assessment of dominant trees and diachronic aerial imagery evaluation, comparing COG stands with both ROG and other beech forests from the NFI database. The two COG stands differ clearly from common beech forests, exhibiting lower stem density, larger size variability, higher occurrence of large trees and of standing wood biomass. Stem diameter distributions followed a reverse J-shaped pattern, typical of uneven-aged and scarcely managed forests. Some very old trees were also found, especially at one of the two COG sites. These characteristics are largely comparable to the ROG beech forests, except for deadwood volumes, lower at the two COG forests. In addition, historical aerial imagery and tree-ring analysis suggested that these forests are intermediate stages along an old-growth gradient. Our findings demonstrate that a prolonged forest management abandonment of over 70 years can induce the development of old-growth attributes in Mediterranean mountain beech forests, even in stands previously coppiced or pastured for a long time. This study provides a replicable measurement-based approach for identifying and validating candidate old-growth forests, under current Italian regulations and contributes to conservation targets consistent with the EU Biodiversity Strategy for 2030. Full article

Urban residential outdoor spaces are increasingly affected by high temperatures, strong solar radiation, and uneven wind conditions, which influence residents’ outdoor activities and thermal comfort. This study proposes an evidence-based approach for the fine-grained design of residential outdoor activity spaces based on age-specific activity patterns and microclimatic conditions. Using 1096 valid questionnaires, field observations, and in situ microclimate measurements across four seasons, we quantified the activity patterns, temporal distributions, and spatial preferences of children, adolescents, adults, and older adults. Results reveal statistically significant age- and season-dependent differences in environmental preferences (Kruskal–Wallis H = 56.78, p < 0.001 for light priority; H = 26.81, p < 0.001 for thermal priority across age groups). Children and older adults exhibited sustained and widely distributed activities, whereas adolescents and adults showed more concentrated temporal patterns. In summer, activities shifted toward mornings and evenings to avoid heat, while in winter, activities peaked around midday and afternoon. Wind and light conditions were prioritized over thermal conditions across all seasons: in summer, 47.2% of respondents ranked wind first; in winter, 50.9% ranked light first. Tree shading consistently reduced air temperature, black globe temperature, and WBGT relative to open areas in every season, with the largest mean differences observed in spring (ΔT_g = 1.19 °C) and the largest instantaneous difference during summer early afternoon (ΔT_g = 1.51 °C at 13:00). Integrating these findings, this study proposes evidence-based design strategies, including optimized functional layouts, shading and ventilation features, sun-exposure management, and nighttime lighting, providing quantitative support for improving comfort, safety, and usability in residential outdoor spaces. Full article

Forest aboveground live biomass carbon storage (hereafter referred to as “forest carbon storage”) is an important indicator of forest vegetation carbon sequestration, and its spatial patterns and associated factors are highly heterogeneous. Identifying these variations can improve the understanding of carbon accumulation in complex mountain forests and support fine-scale carbon assessment in similar ecosystems. The results showed the following. (1) The total forest carbon storage in the study area was 3.75 × 10⁶ t C, with a carbon density of 44.83 t C·hm⁻². Pinus massoniana and hard broad-leaved tree species were the main contributors, and carbon storage peaked at the mature forest stage. (2) Carbon storage exhibited significant spatial clustering (Moran’s I = 0.312), with high-value areas concentrated within the national nature reserve and low-value areas distributed in regions with frequent human activities. (3) The GWR model outperformed the ordinary least squares model, with R² increasing to 0.88 and residual spatial autocorrelation reduced by 42.22%. (4) The positive effect of stand volume increased from northeast to southwest, the effect of stand age differed between eastern and western areas, and shrub layer height, soil depth, and slope exhibited region-specific positive and negative effects, with significant interactions among factors. Full article

This study evaluates the economic efficiency of the timber-production function across 24 forest stands in Czech Republic, representing monocultures, low-diversity mixed stands, mixed stands, and structurally differentiated stands, in the context of the profound changes that have affected forestry in the Czech Republic in recent years. Bark beetle outbreaks, climatic extremes, and the degradation of Norway spruce monocultures have increased concerns about their long-term production reliability and economic stability, highlighting the need to identify more resilient and sustainable management approaches. Mixed and structurally diversified stands, owing to their species diversity and higher ecological stability, represent a potential alternative; however, their management and economic assessment require more complex planning and interpretation. The study analyses the volume production of selected stands, timber market prices by assortments and tree species recalculated on a per-hectare basis and compares silvicultural and harvesting costs. Economic efficiency is expressed using the cost coefficient (Kn) and the efficiency coefficient (Ke), which quantify both direct production costs and the economic return of individual stand types. Results show that monoculture stands, especially those with a high share of valuable assortments, achieved the highest economic efficiency under the applied static cost–revenue assessment. This finding reflects the observed assortment structure, realized timber prices, and selected management costs. In the broader Central European forestry context, however, previous studies indicate that even-aged conifer monocultures may be more exposed to biotic and abiotic disturbance risks, which can affect their long-term production reliability and economic stability. Stands with higher species and structural diversity exhibit an economic profile that differs substantially from that of monocultures. Based on aggregated price and cost inputs for the reference period 2020–2024, low-diversity mixed and mixed stands reach intermediate values of cost intensity and efficiency, whereas structurally differentiated stands display the highest cost intensity and the lowest efficiency. Monocultures, by contrast, achieve the highest economic efficiency, primarily due to a greater share of high-quality timber assortments (classes I–III). Diversified stand structures (mixed and structurally differentiated stands) broaden the assortment composition and produce a more even distribution of monetization across quality classes. Diversification, therefore, did not maximize immediate economic efficiency in the static assessment; rather, it was associated with broader assortment composition and a less concentrated revenue structure across quality classes. Full article

Background/Objectives: Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Early and accurate characterization of suspicious mammographic microcalcifications is essential for improving diagnostic decision-making and reducing unnecessary invasive procedures. Microcalcifications classified as BI-RADS 4 and BI-RADS 5 are clinically important radiological findings; however, differentiating benign from malignant lesions remains challenging because of overlapping morphological and distribution patterns. This study aimed to develop a structured feature-based machine learning model for predicting the pathological diagnosis of breast microcalcifications by integrating mammographic descriptors, patient age, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contrast enhancement findings. Methods: The dataset included 53 biopsy-confirmed cases and consisted of clinical and radiological variables, including patient age, calcification morphology, calcification size, distribution pattern, DCE-MRI contrast enhancement status, and histopathological outcome. Several conventional machine learning algorithms were evaluated, including Logistic Regression, Support Vector Machine with radial basis function kernel, K-Nearest Neighbors, Decision Tree, Random Forest, Extra Trees, Gradient Boosting, AdaBoost, and CatBoost. Hyperparameter optimization was performed using grid search with five-fold cross-validation. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, and log loss. Results: Logistic Regression achieved the highest overall performance, with an accuracy of 0.909 and an F1-score of 0.889, while AdaBoost achieved a recall of 1.000 in the internal evaluation. However, given the limited sample size and lack of external validation, these findings should be interpreted as preliminary. Conclusions: The results suggest that structured radiological descriptors combined with DCE-MRI enhancement information may support malignancy risk stratification of BI-RADS 4–5 microcalcifications, although larger multicenter studies are required before clinical implementation. Full article

Stand-level canopy base height (Cbh) is a key variable controlling crown fire initiation, yet it is commonly computed as the mean of tree height to the base of the crown (hbc), which does not reflect the lower portion of the hbc distribution governing the transition from surface to crown fire. This study investigates the relationship between physically based Cbh definitions and the hbc distribution. We develop a general multi-percentile modeling framework to estimate hbc percentiles at the stand level. Using a dataset of Pinus pinaster Aiton trials in Portugal, percentile-specific models (5th to 50th) were fitted and synthesized into a nonlinear multi-percentile formulation. Results show that the height at which canopy bulk density exceeds the critical threshold does not match mean hbc, but instead corresponds to lower percentiles, typically around the 10th percentile, varying with stand structure and age. Mean-based Cbh tends to overestimate the lower canopy boundary, reflecting its inability to capture structural variability. The final model predicts hbc at any percentile and incorporates effects of stand height, basal area, tree density, and age, ensuring positive predictions and high predictive accuracy (adjusted R² = 0.9770; RSE = 0.4073 m; PRESS R² = 0.9769). The framework provides a consistent representation of canopy base height for fire behavior modelling. Full article

The foot arch functions as a dynamic biomechanical system, maintained by the integrated actions of bones, ligaments, and muscles. A large body of clinical evidence indicates that, in addition to congenital foot deformities, acquired variations in the foot arch caused by factors such as poor gait, aging, weight, or injury can significantly affect quality of life. Early intervention upon detection of foot arch changes can help mitigate progression and prevent further deterioration. Despite the availability of multimodal sensor-integrated running platforms for gait analysis, such systems are inherently bulky and not conducive to routine walking measurement. To overcome the above limitations, this study employed a flexible plantar pressure insole with an integrated accelerometer and a dedicated acquisition circuit to capture plantar pressure and acceleration data. This smart insole system acquires plantar data, performs feature extraction via time–domain and wavelet analysis, and then employs machine learning to classify the foot arch type as a normal foot, flatfoot, or high-arched. A Random Forest classifier was then established to categorize foot arch types based on the collected data, which integrates numerous decision trees through bootstrap aggregation and random feature selection, with final classification determined by majority voting. A total of 30 volunteers participated, including 11 with normal arches, 11 with flat feet, and 8 with high arches. Compared with support vector machine, K nearest neighbors, and decision tree, the Random Forest achieved the highest recognition accuracy of 92%. This system reveals the patterns of plantar pressure distribution and acceleration fluctuations during walking across three foot arches and demonstrates that wavelet entropy can effectively quantify the changes in signal complexity included in foot arch differences. Compared with laboratory force plates, this system features lower cost and a smaller form factor, making it suitable for real-time monitoring. This system can lay the technical foundation for personalized foot orthopedics and health monitoring. Full article

Litchi chinensis var. fulvosus is an important wild litchi resource in Yunnan, China, valued for favorable agronomic traits such as early flowering, early ripening, multiple flowering cycles, and high fruit-setting ability. However, its genetic diversity and population structure remain poorly understood. In this study, 192 accessions were collected from ten counties in Yunnan Province to evaluate their geographic distribution, leaf phenotypic variation, molecular diversity, population structure, and core collection composition. Eight descriptive leaf traits, nine quantitative leaf traits, and ISSR genotyping data from seven primers were analyzed. The accessions were distributed across an altitudinal range of 169–1470 m, with clear habitat differentiation among trees of different ages. Morphological analysis revealed substantial leaf variation, with mean diversity indices of 1.19 for descriptive traits and 2.76 for quantitative traits. ISSR analysis generated 49 scorable bands, of which 34 were polymorphic, corresponding to a polymorphism rate of 68.45%. The mean Shannon–Wiener diversity index was 0.3101, indicating detectable but relatively limited molecular diversity. Integrated phenotypic and molecular analyses divided the germplasm into two subpopulations. A core collection comprising 30 accessions (about 15% of the initial population) showed the best balance between sampling efficiency and diversity retention. These results provide a practical basis for the conservation, evaluation, and efficient utilization of L. chinensis var. fulvosus genetic resources and will support breeding and genetic improvement of litchi. Full article

Drawing on 964 field observations from typical community parks in Changsha, this study utilizes the System for Observing Play and Recreation in Communities (SOPARC) alongside Kernel Density Estimation (KDE) to quantitatively dissect the intrinsic associations between older adults’ outdoor activity patterns and micro-spatial characteristics. The findings reveal a pronounced demographic differentiation in spatial utilization. Specifically, female-dominated collective activities, such as square dancing, exhibit a profound reliance on central plazas with unobstructed sightlines to cultivate a sense of social security. Conversely, male users demonstrate a distinct preference for static social interactions, including playing chess or cards, within semi-enclosed and shaded spaces beneath a tree canopy. Beyond gender distinctions, age-related spatial anchoring is also evident. Intergenerational caregiving activities among the younger old cohort (aged 55 to 74) are exclusively concentrated around children’s playgrounds, whereas older old individuals and those with impaired mobility rely heavily on densely distributed micro-scale resting amenities. Driven by these empirical insights, this research contends that age-friendly park design must transcend generic accessibility standards, advocating instead for a “categorized spatial provision” strategy. By meticulously balancing open exhibition plazas, semi-private shaded units, and composite caregiving zones within a single park ecosystem, this approach can effectively accommodate the multifaceted health and social requirements of various older adult subgroups. Full article

To advance the Sustainable Development Goals, it is essential to correct imbalances in how the benefits of urban trees are distributed across different demographic and socioeconomic groups. Environmental justice studies have frequently overlooked assumptions regarding the data-generating process and have not considered spatial confounding. This oversight potentially misestimates patterns of inequity. This study evaluates the sensitivity of inequity to model assumptions using urban tree inventories from Málaga and Sevilla and Bayesian hierarchical models. City-level differences dominated the inequity patterns, and model specification influenced the magnitude, precision, and credibility of estimated effects, though directionality remained consistent. Patterns were highly consistent across the four ecosystem services, indicating that model assumptions affected all services equivalently. Málaga and Seville exhibited divergent inequity patterns, indicating that local urban context mediates these relationships. In Seville, inequity patterns were inconsistent with the luxury hypothesis and occurred primarily across age-based demographic strata, whereas in Málaga they manifested predominantly along ethnicity, with weaker evidence of income inequities. We advocate for explicitly modeling spatial data-generating processes and comparing conventional versus confounding-mitigated approaches. This city-specific rigor is essential for urban planners to prevent resource misallocation, ensuring that tree-planting strategies address genuine inequities rather than methodological biases. Full article

This manuscript is a continuation of the research published in Fire 2025, 8(8), 302, i.e., it deals with the examination of the cause-and-effect relationships of fires in the Republic of Serbia from the aspect of human safety. Among others, variables related to gender, age, and severity of injuries caused by fires are introduced, on which various methods of statistical analysis and stochastic modeling are first applied. Continuous age variables are modelled using the flexible Generalized Additive Models for Location, Scale, and Shape (GAMLSS) framework, where the Generalized Normal Distribution (GND) is identified as the optimal generative model for injuries, while a Reflected Log-Normal Distribution with positive support (RefLOGND+) provides the best fit for fatalities. The quality of such modeling is formally verified, and the probabilities of injury and death of individuals in certain age categories are predicted, revealing a pronounced concentration of injuries in the working-age population and a markedly higher relative risk of fatal outcomes among elderly individuals. Thereafter, by applying certain Data Mining (DM) techniques, primarily the Apriori algorithm, the most frequently occurring association rules are found, which indicate typical patterns and demographic structure of injuries and deaths in fires in Serbia. Finally, using the CART (Classification and Regression Trees) algorithm, several decision trees are formed that describe the impact and relationship of different causes of fires on injury and death in fires. In this way, some important and frequent patterns are observed that indicate key fire risk factors that significantly affect the demographic structure of human casualties. The results thus obtained provide a basis for developing targeted strategies for fire prevention and improving emergency response planning. Full article

Forest structure is fundamental for linking ecological processes with management outcomes, and it influences key ecosystem services. However, the high cost and complexity of field data collection often limit the application of structural indices to small-scale studies, constraining operational assessments of forest multifunctionality. This study develops and tests an operational indicator of forest multifunctionality based on the structural heterogeneity index derived from forest management plans (FMPs). We analyzed the dendrometric data from 134 management units across 15 FMPs in the Lombardy region (Italy). Horizontal diversity was quantified using a Gini-based index, calculated from tree diameter-class distributions and combined with stand age, timber stock, and tree density using principal component analysis. Two orthogonal gradients emerged: a productivity gradient and a maturity–structural heterogeneity gradient. Generalized linear mixed models were used to assess their effects on carbon sequestration, timber yield, and touristic–recreational value. Structural heterogeneity was positively associated with all three functions, while productivity showed contrasting effects, particularly a negative relationship with recreational value. These results demonstrate that structural complexity and productivity are not necessarily in conflict and highlight the potential of FMPs as cost-effective data sources for operational, landscape-scale assessments of forest multifunctionality. Full article

The radial growth of the tree stem reflects tree responses to climate change. This study examines the response of Fagus orientalis to more than half a century of climate dynamics in Armenia using a dendrochronological approach. Two forest stands were analyzed: one geographically isolated stand in the arid southern part of the country and one stand in the mesic northern mountainous region, where the main beech forests are distributed. The study period was divided into two phases (1965–1993 and 1994–2023). Climate dynamics were assessed by the months of the biological year, from October of the previous year to the end of September of the current growing season. Substantial warming trends were detected at both stands, except in November, December, and April, and in July in the northern part of Armenia. Between periods, the mean ring width increased from 1.67 mm to 2.14 mm at the northern stand, while decreasing from 1.95 mm to 0.89 mm at the southern stand. Despite climate warming and declining precipitation, some study trees exhibited increased (northern) or stable (both stands) radial growth. Comparison of the two periods revealed pronounced ecological and tree-specific variability in climate–growth relationships, including shifts in correlation strength and sign reversals. These patterns were primarily driven by climate sensitivity rather than age-related effects. The results provide valuable insights for conserving the southern stand and may support assisted migration strategies for F. orientalis toward the southern margin of the F. sylvatica distribution range. Full article

In China’s stock-based renewal agenda, many old residential communities display pronounced intergenerational overlap, in which grandparental childcare becomes a dominant pattern of outdoor-space use. Against the backdrop of age-structure shifts, population ageing, and persistently low fertility, community-level outdoor-space supply, distributive equity, and environmental adaptability have become key concerns in renewal practice. Yet, practitioners still lack a rankable, low-cost, and implementable evaluation-to-decision workflow. Using Xingshe Community in Dalian, China as an empirical case, this study establishes and tests an integrated “NLP–AHP–GBDT” assessment framework. Guided by policy discourse and planning theory, over 50 semi-structured interviews were processed via NLP-based semantic analysis and keyword mining to derive a two-tier indicator set (criterion and indicator layers). Seven specialists then applied the analytic hierarchy process to elicit indicator weights, and a resident survey was administered to generate weighted performance scores for diagnosing deficiencies. In the feedback-validation stage, we adopted both a qualitative Framework Method and a quantitative GBDT approach, first using the Framework Method to conduct feedback validation based on community residents’ open-ended evaluations. Subsequently, gradient boosting decision trees were used for supervised verification with renewal-scenario data, providing empirical backing for the weighting scheme and the resulting priority order for interventions. The findings suggest that outdoor spaces are broadly serviceable but fall short in intergenerational friendliness, reflecting a structural misalignment between intergenerational activity patterns and spatial provision. Based on the validated priorities, the study proposes modular, incremental micro-renewal measures focusing on safety and emergency accessibility, environmental comfort and caregiving–recreation coupling, and place identity with community organizational mobilization. Full article

Background: Food allergy (FA) is an increasing public health concern with significant implications for child health and quality of life. Early introduction of allergenic foods has been shown to reduce the risk of food allergy development; however, maternal awareness and adherence to these recommendations remain inconsistent. This study aimed to assess maternal awareness and practices regarding the timing of allergenic food introduction among mothers residing in Makkah, Saudi Arabia. Methods: A descriptive cross-sectional study was conducted between November 2023 and March 2024 involving parents of children aged younger than 48 monthsin the Makkah region. Data were collected via a self-administered electronic questionnaire distributed through social media platforms. Results: A total of 391 parents participated. Parent-reported food allergy was identified in 11.3% of children, while 14.6% had eczema. Early introduction (<12 months) was more common for egg (43.3%) and wheat (71.1%) compared to peanut (28.9%), tree nuts (30.9%), sesame (30.9%), and seafood (28.9%). A considerable proportion of children had not been introduced to key allergenic foods even after 36 months, particularly peanuts (45.3%) and sesame (42.2%). Children with eczema were significantly more likely to have early introduction of egg (p = 0.035), tree nuts (p = 0.046), and seafood (p = 0.031). Similarly, children with a family history of food allergy had higher early introduction rates of tree nuts (55.3% vs. 44.0%, p = 0.043) and seafood (62.3% vs. 49.1%, p = 0.019). Only 25.8% of mothers were aware that early introduction might prevent food allergies, and just 22% reported receiving professional advice to introduce allergenic foods early. Conclusions: Maternal awareness regarding the timely introduction of allergenic foods in Makkah remains limited, with delayed introduction persisting beyond 36 months for several high-risk allergens. These findings underscore the need for targeted educational interventions and improved counseling by healthcare providers. Full article