Search Results (644)

Accurate stem-volume estimation supports inventory, valuation and carbon accounting, but Pressler’s single-section formula has never been tested in the highly productive European-beech forests of the Central Rhodope Mountains, Greece. We quantified the bias of Pressler estimates and developed size-specific correction factors. Sixty Fagus sylvatica L. trees felled in 2023–2024 were measured destructively at 1-m intervals. Pressler standing volumes were compared with Smalian-plus-cone reference volumes (hereafter referred to as true volumes) and analysed with generalized additive models. Pressler underestimated true volume (mean bias = −0.088 m³; RMSE = 0.204 m³; MAPE = 21%). Under-estimation increased with diameter. A GAM with DBH and height explained 96.7% of the variance in true volume. We also fit a Random Forest as a complementary check. Multipliers of 1.30 (<25 cm DBH), 1.20 (25–45 cm), 1.30 (45–55 cm) and ≥1.35 (≥55 cm) cut residual error to ≤20% overall and <10% inside the well-sampled 35–45 cm class. A simple DBH-class correction table restores Pressler’s speed while meeting modern accuracy standards for inventory and carbon reporting. Full article

Water supply networks in both developed and developing major cities worldwide were constructed many years ago. Currently, these systems face numerous challenges, including population growth, climate change, emerging technologies, and the policies implemented by local governments. Such factors can impact the design life of water infrastructure, leading to service pressure deficiencies. Consequently, water infrastructure must be reinforced to ensure an adequate and reliable service. This research presents the development of an analytical formulation for hydraulic installations with a pumping station, enabling the calculation of requirements for a new parallel pipeline within an existing water system without altering the current pipe resistance class. To implement the proposed solution, it is essential to maintain the initial pump head by adjusting the impeller size. A construction cost assessment is also undertaken to identify the most cost-effective reinforcement strategy, acknowledging that pipe costs vary significantly with diameter and material, and are proportional to the square of the diameter. The proposed methodology is applied to a 30 km pipeline with a 10% increase in demand, showing that a new parallel pipe of the same diameter as the existing hydraulic installation must be installed to minimise construction costs. A multi-parametric analysis was conducted employing machine learning presets with 309 dataset points. Full article

Seasonally dry tropical forests (SDTFs) are shaped by strong climatic and edaphic constraints, including pronounced rainfall seasonality, extended dry periods, and shallow karst soils with limited water retention. Understanding how tree species respond to these pressures is crucial for predicting ecosystem resilience under climate change. In the Yucatán Peninsula, we characterized sixteen tree species along a spatial and seasonal precipitation gradient, quantifying wood density, predawn and midday water potential, saturated and relative water content, and specific leaf area. Across sites, diameter classes, and seasons, we measured ≈4 individuals per species (n = 319), ensuring replication despite natural heterogeneity. Using a principal component analysis (PCA) based on individual-level data collected during the dry season, we identified five functional groups spanning a continuum from conservative hard-wood species, with high hydraulic safety and access to deep water sources, to acquisitive light-wood species that rely on stem water storage and drought avoidance. Intermediate-density species diverged into subgroups that employed contrasting strategies such as anisohydric tolerance, high leaf area efficiency, or strict stomatal regulation to maintain performance during the dry season. Functional traits were strongly associated with precipitation regimes, with wood density emerging as a key predictor of water storage capacity and specific leaf area responding plastically to spatial and seasonal variability. These findings refine functional group classifications in heterogeneous karst landscapes and highlight the value of trait-based approaches for predicting drought resilience and informing restoration strategies under climate change. Full article

The wide use of antibiotics in multiple fields leads to their entry into the environment, challenging agriculture and ecology and potentially affecting maize seedling growth. In this study, maize variety Longken 10 was chosen as the experimental material. Subsequently, two antibiotics commonly utilized in production, namely oxytetracycline (OTC) belonging to the tetracycline class and ciprofloxacin (CIP) from the quinolone class, were selected. To comprehensively examine the impacts of these antibiotics on the phenotype, photosynthetic enzymes, nitrogen metabolism, and endogenous hormone contents of maize seedlings, a series of different concentration gradients (0, 3, 5, 30, 60, and 120 mg·L⁻¹) were established, and the nutrient solution hydroponic method was employed. The results showed that, compared with the control group (CK), the activities of all indicators of maize seedlings were the strongest and the seedling growth was the most vigorous when the concentration of CIP was 5 mg·L⁻¹ and that of OTC was 3 mg·L⁻¹. The inhibitory effect of OTC on various indicators of maize seedlings was stronger than that of CIP. The underground parts of maize seedlings were more sensitive to OTC and CIP than the aboveground parts. Overall, maize seedlings exhibited a trend where high concentrations (30–120 mg·L⁻¹) of antibiotics inhibited growth, while low concentrations (3–5 mg·L⁻¹) promoted growth. The treatment groups with 3–5 mg·L⁻¹ of OTC and CIP increased maize seedling growth phenotypes, the robust growth of seedlings with enhanced vitality, and the relative water content of maize leaves; decreased the relative electrical conductivity of maize leaves, indicating reduced cell permeability; increased the activities of leaf photosynthetic enzymes (PEPCase, RUBPCase, PPDK, NADP-ME, and NADP-MDH); increased the levels of hormones (IAA, GA, and ZR) in maize leaves and roots; decreased the levels of ABA and MeJA; increased the levels of nitrogen metabolism-related enzymes (GS, GOGAT, and GAD) in roots and leaves; decreased the GDH level; enhanced root activity and increased various root parameters (including average diameter, number of root tips, total volume, total root length, and root surface area), indicating vigorous root growth. Compared with CK, the treatment groups with 30–120 mg·L⁻¹ of OTC and CIP reduced the phenotypes of maize seedlings, decreased the relative water content of maize leaves and increased the relative electrical conductivity of maize leaves, indicating enhanced cell permeability; reduced the activity of leaf photosynthetic enzymes, leading to weakened photosynthesis and decreased photosynthetic productivity; lowered the levels of IAA, GA, and ZR in leaves and roots of maize seedlings, and increased the levels of ABA and MeJA; decreased the levels of GS, GOGAT, and GAD in leaves and roots of maize seedlings, and increased the GDH level; reduced root activity, with the corresponding decrease in various root parameters. Full article

Lighting systems in sports venues have a significant impact on both the user experience and quality of events. However, owing to the large number of luminaires, high individual lamp power, and strict lighting standards, the lighting energy consumption of sports venues is high, accounting for approximately 30% of the total energy use. Therefore, introducing natural light through appropriate means during non-event periods and ensuring adequate lighting via collaborative control between natural light and artificial-lighting systems are crucial for reducing the lighting energy consumption of sports venues. Light pipe systems are a novel form of natural lighting and can effectively supplement artificial lighting. However, no clear methodology for selecting light pipes or designing light pipe systems in high spaces such as sports venues currently exists. Furthermore, developing a method for collaborative control between artificial-lighting systems and light pipe systems under various natural light conditions is an urgent issue in the optimization of the design of sports venue lighting. Therefore, we considered a conventional sports venue as a case study. By conducting HOLIGILM simulation experiments, we first investigated the factors affecting the transmission efficiency of light pipe systems and proposed optimization parameters for system design in terms of the pipe diameter, length, and configuration. Subsequently, using the Chinese Standard for Daylighting Design of Buildings (GB50033-2013) and the construction cost as optimization objectives, we optimized the pipe diameter, length, and placement of the light pipe system by applying non-dominated sorting genetic algorithm II. The simulation results showed that the optimized design of the light pipe system in the sports venue achieved a daylight factor of 1%, which met the standard requirements while reducing the construction cost by approximately 27%. Finally, to meet the indoor Class I (non-tournament) lighting standards stipulated in the Standard for Lighting Design and Test of Sports Venues (JGJ153-2016) and taking energy conservation as the optimization goal, we proposed a strategy for achieving collaborative control between the light pipe system and artificial-lighting system based on a greedy algorithm. The results indicated that under various weather conditions, the collaborative control strategy enabled the lighting of the field of play to meet Class I illuminance standards while reducing the annual lighting energy consumption by 35%. Thus, this study provides a methodological reference for optimizing the design of light pipe systems and achieving collaborative control with artificial-lighting systems in large-scale venues. Although these results were obtained based on meteorological data from Xi’an, China, the research method presented in this study can also be applied to other regions. The study provides a methodological reference for the design and optimization of light pipe systems and associated control systems to operate light pipes alongside artificial lighting systems in sports venues and other large multistory buildings. Full article

The keratin-associated proteins (KAPs) are a class of wool proteins. They form a matrix that cross-links the wool intermediate filament keratins. The KAPs are thought to affect wool fibre structure and properties and have been associated with variation in wool fibre traits. There are many KAP genes in sheep, but not all have been identified. Recently a second member of the KAP22 gene family, KRTAP22-2, was identified in goats, and variation in this caprine gene was associated with cashmere fibre traits. In this study, we identified ovine KRTAP22-2. To ascertain the extent of variation in KRTAP22-2, sheep from eight breeds were investigated using polymerase chain reaction (PCR) followed by single-strand conformational polymorphism (SSCP) analysis. This revealed two unique banding patterns, which upon sequencing gave two novel DNA sequences. These differed by two single nucleotide polymorphisms in the coding region. Three genotypes of the novel KRTAP22-2 sequences were observed in the eight sheep breeds studied. The ovine KRTAP22-2 variant sequences were similar to a goat KRTAP22-2 variant, but a search of ovine expressed sequence tags revealed no matching mRNA sequences in the ovine databases. In a second part of the study, no association was found between the KRTAP22-2 genotypes and mean fibre diameter, fibre diameter standard deviation, coefficient of variation in fibre diameter, and mean fibre curvature, for either the fine wool or heterotypic hair fibres of 255 Chinese Tan lambs. These results suggests that sheep have a KRTAP22-2 gene, but that there may be species-specific differences in the gene’s expression or function. The gene may not affect wool traits in the way that it appears to in goats. Full article

Lubricating oil plays a critical role in protecting mechanical systems. Driven by sustainable development strategies, the development of high-performance, biocompatible green lubricants has become an urgent industry need. Biomass resources, characterized by wide distribution, renewability, and environmental friendliness, represent ideal raw materials for replacing petrochemical-based lubricants. In this study, renewable Xanthoceras sorbifolia oil was utilized as the feedstock. Branched modification was achieved via ring-opening esterification using 2-ethylhexanol (2-EH) as the modifier and tetrafluoroboric acid (HBF₄) as the catalyst. This epoxidation-branching modification process was synergistically combined with Nano-C₁₄MA/MMT treatment. This approach significantly reduced high-temperature kinematic viscosity loss while maintaining excellent low-temperature flow properties, resulting in an Xanthoceras sorbifolia oil-based lubricant with outstanding viscosity–temperature performance and low-temperature fluidity. At a Nano-C₁₄MA/MMT mass ratio of 0.3 wt% of the base oil, the lubricant demonstrated superior wide-temperature performance: KV₄₀ = 424.1 mm²/s, KV₁₀₀ = 50.8 mm²/s, VI = 180.8. The SP was reduced to −43 °C, exceeding the performance requirements of V-class environmentally friendly lubricants (e.g., synthetic ester oils). Furthermore, the coefficient of friction (COF) was 0.011 and the anti-wear scar diameter (AWSD) was 0.44 mm, indicating lubrication performance significantly superior to SN-class lubricants (specifications: COF < 0.12, AWSD < 0.50 mm). Full article

Pediatric dilated cardiomyopathy (DCM) is a rare but important cause of heart failure (HF) and a major indication for cardiac transplantation. Early detection of subclinical myocardial dysfunction is essential for risk stratification and management. This study aimed to evaluate left ventricular (LV) systolic function in children with DCM using conventional echocardiographic parameters and speckle-tracking echocardiography (STE) and to explore the relationship between deformation indices, clinical severity and biomarkers. Methods: We conducted a case–control study including 29 children diagnosed with DCM and 27 healthy controls matched by age and sex. All participants underwent clinical evaluation, NT-proBNP measurement, and transthoracic echocardiography. LV systolic function was assessed using conventional echocardiographic parameters, while STE was used to measure LV global longitudinal strain (GLS) and strain rate (SR) from all apical views. Results: GLS and SR were significantly reduced in the DCM group across all apical views (Global GLS: −11.13 ± 6.79% vs. −19.98 ± 3.25%, Global SR: −0.74 ± 0.39 s⁻¹ vs. −1.12 ± 0.16 s⁻¹; p < 0.01). GLS strongly correlated with functional indices (LV ejection fraction, shortening fraction, S′ lateral wave), LV end-diastolic diameter Z-score and NT-proBNP (p < 0.05), but not with MAPSE. In the primary model, GLS was associated with NYHA/Ross III–IV (OR 1.54 per 1% increase; 95% CI 1.14–2.07; p = 0.005); adding systolic blood pressure (p = 0.798) or heart rate (p = 0.973) did not materially change the GLS estimate (Δ ≤ 2%). In separate collinearity-avoiding models, LVEF (OR 1.12 per 1% decrease; 95% CI 1.03–1.22; p = 0.009), LVSF (OR 1.19 per 1% decrease; 95% CI 1.04–1.36; p = 0.011), and NT-proBNP (≈OR 1.11 per 100 units; p = 0.013) were also associated with advanced class. ROC analysis showed excellent discrimination for NT-proBNP (AUC 0.948) and GLS (AUC 0.906), and good–excellent performance for LVEF (AUC 0.869) and LVSF (AUC 0.875). Conclusions: Speckle-tracking derived parameters such as GLS and SR are sensitive and clinically relevant markers of LV dysfunction in pediatric DCM. Global longitudinal strain demonstrated a strong association with both clinical and biochemical markers of disease severity, after accounting for heart rate and blood pressure, supporting its integration into routine evaluation and risk stratification in pediatric DCM. Full article

This study examined the structural and ecological drivers of burn severity during the March 2025 wildfire in Uiseong County, Republic of Korea, with a focus on developing a predictive framework using the differenced Normalized Burn Ratio (dNBR). Seventeen candidate variables were evaluated, among which the forest type, stand age, tree height, diameter at breast height (DBH), and Normalized Difference Vegetation Index (NDVI) were consistently identified as the most influential predictors. Burn severity increased across all forest types up to the 4th–5th age classes before declining in older stands. Coniferous forests exhibited the highest severity at the 5th age class (mean dNBR = 0.3069), followed by mixed forests (0.2771) and broadleaf forests (0.2194). Structural factors reinforced this pattern, as coniferous and mixed forests recorded maximum severity within the 5–11 m height range, while broadleaf forests showed relatively stable severity across 3–21 m but declined thereafter. In the final prediction model, NDVI emerged as the dominant variable, integrating canopy density, vegetation vigor, and moisture conditions. Notably, NDVI exhibited a positive correlation with burn severity in coniferous stands during this early-spring event, diverging from the generally negative relationship reported in previous studies. This seasonal anomaly underscores the need to interpret NDVI flexibly in relation to the forest type, stand age, and phenological stage. Overall, the model results demonstrate that mid-aged stands with moderate heights and dense canopy cover are the most fire-prone, whereas older, taller stands show reduced susceptibility. By integrating NDVI with structural attributes, this modeling approach provides a scalable tool for the spatial prediction of wildfire severity and supports resilience-based forest management under climate change. Full article

Juglans neotropica Diels, classified as endangered on the IUCN Red List, plays a crucial role in the resilience of Andean montane forests in southern Ecuador—a megadiverse region encompassing coastal, Andean, and Amazonian ecosystems. This study examines how climatic, edaphic, and topographic gradients influence the species’ phenotypic traits across six source localities—Tibio, Merced, Tundo, Victoria, Zañe, and Argelia—all of which are localities situated in the provinces of Loja and Zamora Chinchipe. By integrating long-term climate records, slope mapping, and soil characterization, we assessed the effects of temperature, precipitation, humidity, soil moisture, and terrain steepness on leaf presence, fruit maturation, and tree architecture. Over the past 20 years, temperature increased by 1.5 °C (p < 0.01), while precipitation decreased by 22%, disrupting local edaphoclimatic balances. More than 2000 individuals were measured in forest stands, with estimated ages ranging from 11 to 355 years. ANOVA results revealed that Tundo and Victoria exhibited significantly greater DBH, height, and volume (p ≤ 0.05), with Victoria showing a 30% larger DBH than Argelia, the lowest-performing provenance. Soils ranged from loam to sandy loam, with slopes exceeding 45% and pH levels from slightly acidic to neutral. These findings confirm the species’ pronounced phenotypic plasticity and ecological adaptability, directly informing site-specific conservation strategies and long-term forest management under shifting climatic conditions. Full article

Background. Severe left ventricular hypertrophy (SLVH) is a high-risk structural cardiac abnormality associated with increased risk of heart failure. It is typically assessed using echocardiography or cardiac magnetic resonance imaging, but these modalities are limited by cost, accessibility, and workflow burden. We introduce a deep learning framework that classifies SLVH directly from chest radiographs, without intermediate anatomical estimation models or demographic inputs. A key contribution of this work lies in interpretability. We quantify how clinically relevant attributes are encoded within internal representations, enabling transparent model evaluation and integration into AI-assisted workflows. Methods. We construct class-balanced subsets from the CheXchoNet dataset with equal numbers of SLVH-positive and negative cases while preserving the original train, validation, and test proportions. ResNet-18 is fine-tuned from ImageNet weights, and a Vision Transformer (ViT) encoder is pretrained via masked autoencoding with a trainable classification head. No anatomical or demographic inputs are used during training. We apply Mutual Information Neural Estimation (MINE) to quantify dependence between learned features and five attributes: age, sex, interventricular septal diameter (IVSDd), posterior wall diameter (LVPWDd), and internal diameter (LVIDd). Results. ViT achieves an AUROC of 0.82 [95% CI: 0.78–0.85] and an AUPRC of 0.80 [95% CI: 0.76–0.85], indicating strong performance in SLVH detection from chest radiographs. MINE reveals clinically coherent attribute encoding in learned features: age > sex > IVSDd > LVPWDd > LVIDd. Conclusions. This study shows that SLVH can be accurately classified from chest radiographs alone. The framework combines diagnostic performance with quantitative interpretability, supporting reliable deployment in triage and decision support. Full article

The accurate segmentation of pigmented skin lesions is a critical prerequisite for reliable melanoma detection, yet approximately 30% of lesions exhibit fuzzy or poorly defined borders. This ambiguity makes the definition of a single contour unreliable and limits the effectiveness of computer-assisted diagnosis (CAD) systems. While clinical assessment based on the ABCDE criteria (asymmetry, border, color, diameter, and evolution), dermoscopic imaging, and scoring systems remains the standard, these methods are inherently subjective and vary with clinician experience. We address this challenge by reframing segmentation into three distinct regions: background, border, and lesion core. These regions are delineated using superpixels generated via the Simple Linear Iterative Clustering (SLIC) algorithm, which provides meaningful structural units for analysis. Our contributions are fourfold: (1) redefining lesion borders as regions, rather than sharp lines; (2) generating superpixel-level embeddings with a transformer-based autoencoder; (3) incorporating these embeddings as features for superpixel classification; and (4) integrating neighborhood information to construct enhanced feature vectors. Unlike pixel-level algorithms that often overlook boundary context, our pipeline fuses global class information with local spatial relationships, significantly improving precision and recall in challenging border regions. An evaluation on the HAM10000 melanoma dataset demonstrates that our superpixel–RAG–transformer (region adjacency graph) pipeline achieves exceptional performance (100% F1 score, accuracy, and precision) in classifying background, border, and lesion core superpixels. By transforming raw dermoscopic images into region-based structured representations, the proposed method generates more informative inputs for downstream deep learning models. This strategy not only advances melanoma analysis but also provides a generalizable framework for other medical image segmentation and classification tasks. Full article

In this article, a correspondence between some important classes of numerical semigroups and well-known families of bipartite graphs has been established. Also, it has been demonstrated that, if $\mathrm{m}(\mathrm{S})$ is the multiplicity of a numerical semigroup $\mathrm{S}$, then the diameter and the girth of bipartite gap poset graphs are bounded by the numbers $2\mathrm{m}(\mathrm{S})-3$ and $\mathrm{m}(\mathrm{S})-1$, respectively. Moreover, the Python code to compute the diameter and girth of gap poset graphs has been implemented. Full article

Forests, both natural and managed, provide a critical habitat for a significant part of global biodiversity. Among many different groups of forest biota, tree species occupy a special position as they create conditions upon which the existence of virtually all other forest organisms depends, either directly or indirectly. To permanently play this role, particular tree species must be demographically stable; i.e., their populations should be distinguished by the balanced, size-dependent proportions of individuals representing different developmental stages (from seedlings and saplings to mature and old trees). In this study, we examined the extent to which this condition is met in the managed part of Białowieża Forest in northeastern Poland, an important biodiversity hotspot in Central Europe. Comparison of species-specific equilibrium vs. actual size distributions revealed that almost half of all trees growing in Białowieża Forest represented “inappropriate” (i.e., occurring in excess compared to the balanced models) species and/or diameter ranges. The amount of deficits was also large (around 30% of the current tree number), concerning primarily the smallest trees. Considering this, we recommend targeted, active management strategies to restore the demographic balance of key tree species and, thus, to enhance the conservation of local biodiversity. We also indicate that the key elements of such strategies should be the gradual removal of trees from surplus diameter ranges and assisted regeneration of species with the greatest deficiencies in small diameter classes. Full article

Intra-specific variation in functional traits and their inter-relationships reflect how plants allocate resources, adapt, and evolve in response to environmental changes. This study investigated eight functional traits—leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), chlorophyll content (CHL), leaf nitrogen content (LNC), leaf phosphorus content (LPC), twig tissue density (TTD), and wood density (WD)—in Cunninghamia lanceolata plantations of three stand ages (15, 30, and 50 years), using a space-for-time substitution approach. We examined differences in trait values, intra-specific variation, and trait correlations across forest ages and diameter classes. The results showed that (1) Functional traits exhibited varying degrees of intra-specific variation, with LA having the highest coefficient of variation (21.66%) and LPC is lowest (9.31%). (2) Forest age had a stronger influence on trait variation than diameter class, with all traits differing significantly across ages, while only WD varied significantly among diameter classes. (3) PC1 (25.5%) and PC2 (19.4%) together explained approximately 44.9% of the total variation, with PC1 primarily reflecting functional trait changes driven by forest age. PCA results showed that LA and CHL tended to exhibit higher values in young forests, whereas SLA, LDMC, LPC, and LNC had relatively higher values in mature forests. This pattern suggests a shift in functional trait expression from resource acquisition to resource conservation strategies with increasing forest age. (4) Significant positive correlations between LNC and LPC, and negative correlations between SLA and LDMC, were observed in most groups, except in large-diameter trees at the over-mature stage. C. lanceolata adjusts trait combinations to enhance fitness across developmental stages. Juvenile trees adopt traits favoring efficient light and nutrient use to support rapid growth and competition. Middle-aged trees prioritize balanced water and nutrient use to maintain productivity and resist disturbances. Mature trees focus on sustained resource use and offspring protection to support ecosystem stability and regeneration. These findings reveal age-specific adaptive strategies and provide insights into the coordination and trade-offs among traits in response to environmental conditions. Full article