Search Results (33,953)

Fine particulate matter (PM_2.5) and ozone (O₃) are the main pollutants affecting the air quality in China, yet their common influencing factors and spatial patterns remain unclear. Focusing on the year 2020, this study adopted the least absolute shrinkage and selection operator algorithm to construct land use regression models with 34 environmental variables for the O₃ concentration at the air quality monitoring stations in the Shenyang Metropolitan Area. For comparison, PM_2.5 models had been developed in our previous work using the same approach. Model performance was satisfactory (cross-validated R² = 0.49–0.81 for O₃; 0.56–0.65 for PM_2.5 in our previous study), confirming the robustness of the approach. The results showed that: (1) Tree cover and grassland exerted synergistic, co-directional mitigation on both pollutants, whereas built-up areas and permanent water bodies were positively associated with their concentrations; (2) Longitude, elevation, and population, as well as atmospheric components such as nitrous dioxide column density and aerosol optical depth, displayed opposite effects on both pollutants, indicating trade-offs; (3) Spatially, PM_2.5 played the dominant role in shaping the pattern of combined pollution, with higher PM_2.5 levels than O₃ in nearly half of the area (46.97%), while O₃-dominant regions were rare (4.27%) and mostly confined to localized zones. This study contributes to a deeper understanding of the synergies and trade-offs driving PM_2.5 and O₃ pollution as well as providing a scientific basis for formulating policies on integrated control measures against combined pollution. Full article

Parks serve as vital components of green infrastructure within urban ecosystems, providing recreational opportunities that not only enhance human well-being but also support bird diversity. However, the shared use of park spaces by both humans and birds inevitably leads to spatial overlap and natural competition between the two groups. Consequently, addressing the diverse needs of both groups and balancing the ecosystem services provided to each has become an urgent and critical issue. In this study, we conducted bird and social surveys in an urban park and employed the SolVES and MaxEnt models to investigate the spatial patterns of cultural ecosystem services (CES), supporting ecosystem services (SES), and bird plumage color CES in the park. We then analyzed the trade-offs and synergies between different ecosystem service relationship pairs, as well as the factors influencing them, using bivariate spatial autocorrelation and geographical detectors analyses. Our results indicated a synergistic relationship between the recreational value of park CES and both park SES and bird plumage color CES. High-coverage vegetation areas along main roads promoted synergy, benefiting visitors’ appreciation of cultural services, bird roosting, and the supply of plumage color CES. Meanwhile, trade-offs were observed between the aesthetic value of park CES, park SES, and bird plumage color CES, primarily in fitness plazas where noise levels exceeded 70 dB. In contrast, visitors reacted more strongly to disturbances than birds. Furthermore, the colonization of colorful insectivorous birds enhanced the visual aesthetic value while simultaneously increasing the number of bird-feeding guilds and strengthening ecosystem stability. Our study suggests that planting tall trees, especially along park boundaries, expanding the perimeter green separation zone, and incorporating micro-water landscapes will help improve both avian CES and provide a more pleasant environment for visitors in parks. Full article

Fruit tree rootstock breeding is prolonged by extended juvenile phases, high heterozygosity, limited germplasm diversity, and hybrid incompatibilities, often requiring four decades to release new cultivars. Direct somatic embryogenesis (DSE) in established peach rootstocks presents a promising avenue for rapid genetic transformation and breeding. However, peach is highly recalcitrant to in vitro regeneration, posing major challenges for organogenesis and somatic embryogenesis (SE). This study evaluated the effects of 2,4-dichlorophenoxyacetic acid (2,4-D) and Kinetin (KIN) on SE %, SE productivity, and callus % rate in the widely used Guardian^® peach rootstock. A 5 × 3 full factorial completely randomized design was used to test 15 different combinations of 2,4-D and KIN on immature cotyledons, classified as upper or lower based on their position on the preculture medium. Media formulation containing a higher concentration (3.2 µM) of 2,4-D and KIN induced SE in ~50% of lower and ~85% of upper cotyledons. Optimal SE productivity occurred with higher KIN (3.2 µM) and reduced 2,4-D (2.6 µM). Callus formation peaked with 1.8 µM 2,4-D and 3.2 µM KIN. This highly reproducible research establishes a robust whole plant regeneration system via DSE in Guardian^® peach rootstock using immature cotyledons, providing a foundation for expedited trait manipulation through biotechnological approaches. 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

To address suboptimal throughput performance in conventional intelligent apple harvesting systems predominantly employing single manipulators, a dual-arm harvesting robot prototype was engineered. Leveraging the AUBO-i5 manipulator framework and kinematic characteristics, a coordinated workspace arrangement was established. Subsequently, the dual-manipulator harvesting platform was fabricated. A dynamic task allocation methodology and intelligent fruit sequencing approach were formulated, grounded in U-tube optimization principles. This framework achieved parallel operation ratios between 82.1% and 99%, with combined trajectory lengths spanning 9.24–11.90 m. Building upon established apple harvesting knowledge, a sequencing strategy incorporating dynamic manipulator zoning was developed. Validation was conducted through V-REP kinematic simulations where end-effector poses were continuously tracked, confirming zero limb interference during coordinated motion. Field assessments yielded parallel operation rates of 85.7–93.3%, total harvest durations of 17.8–22.3 s, and inter-manipulator path differentials of 267–541 mm. Throughout testing, collision-free operation was maintained while successfully harvesting all target fruits according to planned sequences. These outcomes validate the efficacy of U-tube-based dynamic zoning and sequencing methodologies for dual-manipulator fruit harvesting in intelligent orchard applications. Full article

GitHub hosts over 10 million repositories, providing researchers with vast opportunities to study diverse software engineering problems. However, as anyone can create a repository for any purpose at no cost, open-source platforms contain many non-cooperative or non-developmental noise projects (e.g., repositories of dotfiles). When selecting open-source projects for analysis, mixing collaborative coding projects (e.g., machine learning frameworks) with noisy projects may bias research findings. To solve this problem, we optimize the Semi-Automatic Decision Tree Method (SADTM), an existing Collaborative Coding Project (CCP) classification method, to improve its generality and accuracy. We evaluate our method on the GHTorrent dataset (2012–2020) and find that it effectively enhances CCP classification in two key ways: (1) it demonstrates greater stability than existing methods, yielding consistent results across different datasets; (2) it achieves high precision, with an F-measure ranging from 0.780 to 0.893. Our method outperforms existing techniques in filtering noise and selecting CCPs, enabling researchers to extract high-quality open-source projects from candidate samples with reliable accuracy. Full article

The rapid growth of the automobile industry has intensified the demand for accurate price prediction models in the used car market. Buyers often struggle to determine fair market value due to the complexity of factors such as mileage, brand, model, transmission type, accident history, and overall condition. This study presents a comparative analysis of machine learning models for used car price prediction, with a strong emphasis on the impact of feature engineering. We begin by evaluating multiple models, including Linear Regression, Decision Trees, Random Forest, Support Vector Regression (SVR), XGBoost, Stacking Regressor, and Keras-based neural networks, on raw, unprocessed data. We then apply a comprehensive feature engineering pipeline that includes categorical encoding, outlier removal, data standardization, and extraction of hidden features (e.g., vehicle age, horsepower). The results demonstrate that advanced preprocessing significantly improves predictive performance across all models. For instance, the Stacking Regressor’s R² score increased from 0.14 to 0.8899 after feature engineering. Ensemble methods, such as CatBoost and XGBoost, also showed strong gains. This research not only benchmarks models for this task but also serves as a practical tutorial illustrating how engineered features enhance performance in structured ML pipelines for the fellow researchers. The proposed workflow offers a reproducible template for building high-accuracy pricing tools in the automotive domain, fostering transparency and informed decision making. Full article

Background/Objectives: While some dietary indices have been developed to assess diet quality and chronic disease risk, their comparative effectiveness within the same population remains unclear due to methodological limitations in observational studies. This study employs a causal inference framework to compare nine dietary indices for reducing all-cause and cardiovascular mortality, while investigating inflammatory pathways through multiple mediation analysis. Methods: Using dietary data from 33,881 adults (aged ≥ 20 years, median follow-up 92 months), we applied a causal directed acyclic graph to identify the minimum sufficient adjustment set and implemented generalized propensity score matching to address confounding. Robust Cox proportional hazards regression assessed associations between nine dietary indices—Dietary Inflammatory Index (DII), Composite Dietary Antioxidant Index (CDAI), Healthy Eating Index 2015/2020 (HEI-2015/2020), Alternate Healthy Eating Index (AHEI), Alternate Mediterranean Diet (aMED), Mediterranean Diet Index (MEDI), and Dietary Approaches to Stop Hypertension (DASH/DASHI)—and mortality outcomes. Multiple additive regression trees (MART) algorithm was used for multiple mediation analysis to examine inflammatory markers (PAR, SII, NPR, TyG, LMR, PLR, ELR, CRP) as mechanistic mediators. Results: Among 33,881 participants (mean age 47.07 years, 51.34% women), 4,230 deaths occurred, including 827 cardiovascular deaths. Under the causal inference framework, higher DII scores increased both all-cause (HR: 1.07; 95% CI: 1.02–1.12) and cardiovascular mortality risk (HR: 1.07; 95% CI: 1.04–1.10) by 7%. The aMED demonstrated the strongest protective association, reducing all-cause mortality by 12% (HR: 0.88; 95% CI: 0.80–0.97) and cardiovascular mortality by 11% (HR: 0.89; 95% CI: 0.80–0.98), followed by MEDI with similar magnitude effects. Other healthy dietary indices showed modest 1–3% risk reductions. Multiple mediation analysis revealed that inflammatory markers, particularly neutrophil-to-platelet ratio (NPR) and systemic immune-inflammation index (SII), significantly mediated diet–mortality associations across all indices, with C-reactive protein (CRP) serving as the most frequent mediator. Conclusions: Using causal inference methodology, the Mediterranean dietary pattern (aMED) shows the strongest causal association with reduced mortality risk, with inflammatory pathways serving as key mediating mechanisms. These findings provide robust evidence for prioritizing Mediterranean dietary patterns in public health interventions and clinical practice, while highlighting inflammation as a critical therapeutic target for dietary interventions aimed at reducing mortality risk. Full article

The Chinese holly (Ilex chinensis Sims.), an evergreen tree species native to China, is distributed mainly in regions south of the Qinling Mountains and Huai River. This research aimed to characterize the molecular profiles and genetic relationships of 40 Chinese holly genotypes via inter-simple sequence repeat (ISSR) and start codon targeted (SCoT) polymorphism markers. Genetic diversity analysis revealed that the ISSR markers detected 111 polymorphic bands from 13 primers, with a polymorphism rate of 88.10%. The analysis generated parameters such as the observed allele number (Na = 1.876), effective allele number (Ne = 1.461), Shannon’s information index (I = 0.271), and expected heterozygosity (H = 0.411). In comparison, the SCoT markers produced 65 polymorphic bands from the 6 primers, resulting in a 100% polymorphism rate, with Na = 2.000, Ne = 1.695, I = 0.393, and H = 0.575. Cluster analysis classified the 40 genotypes into two main clusters with genetic similarity coefficients of 0.69 (ISSR) and 0.55 (SCoT). The ISSR markers presented the greatest similarity between the ZSS and ZLS genotypes, whereas the ZZDH and ZWW genotypes presented lower similarity. Conversely, the SCoT markers identified ZZP and ZJDS as the most similar, with ZLJ and ZHX showing less similarity. These results provide a theoretical basis for hybrid breeding, germplasm innovation, and conservation strategies of Chinese holly in China. Full article

Urban tree diversity plays a crucial role in enhancing the resilience of cities by contributing to ecosystem services such as mitigating the effects of land degradation, combating urban heat islands, improving air quality, and fostering biodiversity habitats. A diverse tree population enhances resilience to vulnerabilities related to climatic stress, disease, and habitat loss by promoting stability, adaptability, and efficiency within the ecosystem. Little is known about urban tree diversity in Serbia; therefore, this study examines the diversity of tree species in the City of Niš, Serbia, to assess its implications for urban resilience and biodiversity preservation in the context of land-use change. Using the Shannon Diversity Index, we quantify species richness and evenness across both central and suburban zones of the city. The results are benchmarked against similar indices in five other European cities to assess how patterns of urban tree distribution vary under different urbanisation pressures. The study reveals that tree diversity is markedly lower in the city centre than in peripheral areas, highlighting spatial inequalities in green infrastructure that may accelerate biodiversity loss due to compact urban development. These findings demonstrate how urban expansion and infrastructure density contribute to ecological fragmentation, potentially leading to long-term effects on ecosystem services. This study emphasises the strategic importance of integrating greenery diversity into urban and landscape planning, particularly in rapidly growing urban centres in Southeastern Europe. This research contributes to the existing body of literature, providing a deeper understanding of the interdependencies between urban tree diversity, land degradation, and biodiversity loss, offering data-driven insights. This enables urban planners, landscape architects, and policy advisors to make informed decisions about street tree diversity and green city infrastructure, contributing to the development of sustainable cities. Full article

The integration of Artificial Intelligence (AI) technologies in students’ lives necessitates the systematic incorporation of foundational AI literacy into educational curricula. Students are challenged to develop conceptual understanding of computational frameworks such as Machine Learning (ML) algorithms and Decision Trees (DTs). In this context, unplugged (i.e., computer-free) pedagogical approaches have emerged as complementary to traditional coding-based instruction in AI education. This study examines the pedagogical effectiveness of an instructional intervention employing unplugged activities to facilitate conceptual understanding of DT algorithms among 47 9th-grade students within a Computer Science (CS) curriculum in Greece. The study employed a quasi-experimental design, utilizing the Structure of Observed Learning Outcomes (SOLO) taxonomy as the theoretical framework for assessing cognitive development and conceptual mastery of DT principles. Quantitative analysis of pre- and post-intervention assessments demonstrated statistically significant improvements in student performance across all evaluated SOLO taxonomy levels. The findings provide empirical support for the hypothesis that unplugged pedagogical interventions constitute an effective and efficient approach for introducing AI concepts to secondary education students. Based on these outcomes, the authors recommend the systematic implementation of developmentally appropriate unplugged instructional interventions for DTs and broader AI concepts across all educational levels, to optimize AI literacy acquisition. Full article

The large, white paired bract is a unique trait, as well as the most intriguing feature of the dove tree (Davidia involucrata). However, the mechanisms underlying bract development remain unclear. Our previous comparative transcriptome analysis concerning Davidia bracts at different developmental stages has identified a number of bract-specific genes. Among these, the genes encoding PRPs (proline-rich proteins) show dramatic expression variation, indicating the participation of this gene family in bract development. In this study, we screened the whole Davidia genome and identified twelve Davidia PRP (DiPRP) genes, showing obvious expression variation during bract development, with some upregulated up to 100-fold at the fast-developing stage. These PRP genes are evenly distributed on seven Davidia chromosomes. The cis-element composition of the promoter regions of the DiPRPs demonstrates that these genes might be controlled by phytohormones (especially ABA, GA, and MeJA), light, and the circadian clock, which is consistent with the environmental cues during Davidia bract development. Synteny analysis indicated that the PRP genes from the Davidia genome have higher collinearity with naturally bracted plants, such as Antirhonum majus and Bougainvillea glabra, but lower collinearity with non-bracted species. Our results suggest that high expression of certain PRP genes, specifically in bracts, might be critical for leaf metamorphosis. Full article

In this study, we examine the presence of bias in weighted least squares (WLS) estimation within the context of first-order bifurcating autoregressive (BAR(1)) models. These models are widely used in the analysis of binary tree-structured data, particularly in cell lineage research. Our findings suggest that WLS estimators may exhibit significant and problematic biases, especially in finite samples. The magnitude and direction of this bias are influenced by both the autoregressive parameter and the correlation structure of the model errors. To address this issue, we propose two bootstrap-based methods for bias correction of the WLS estimator. The paper further introduces shrinkage-based versions of both single and fast double bootstrap bias correction techniques, designed to mitigate the over-correction and under-correction issues that may arise with traditional bootstrap methods, particularly in larger samples. Comprehensive simulation studies were conducted to evaluate the performance of the proposed bias-corrected estimators. The results show that the proposed corrections substantially reduce bias, with the most notable improvements observed at extreme values of the autoregressive parameter. Moreover, the study provides practical guidance for practitioners on method selection under varying conditions. Full article

The transformation of natural ecosystems into agroecosystems due to changes in land use/land cover (LULC) has been shown to significantly affect soil characterization and classification. The impact of LULC on soil taxonomy was assessed in a primary forest located in central–eastern Honduras, which had been deforested approximately forty years prior to the study. Morphological, physical, and physicochemical analyses were performed by describing 10 representative profiles, applying the Soil Taxonomy (ST) and World Reference Base for Soil Resources (WRB) nomenclatures. LULC resulted in physical degradation in agricultural areas, as evidenced by lighter-colored horizons (P02), reduced granular structure (P01, P02, P05), higher bulk densities (≤1.73 Mg m⁻³), and surface crusting (P02, P05); this phenomenon was also observed in pastures (P06–P09). SOC loss was 62% in croplands, 47–53% in agroforestry systems (P03) and fruit tree plantations (P04), and 25% in pastures. All profiles exhibited pH values between 6.5 and 8.4 and complete base saturation (BS), except for P08 and P09, which had pH values below 5.5, high levels of Al³⁺, and reduced BS (50–60%). Mollic epipedons and variability in the endopedons were also observed. According to the ST of the System of Soil Classification (SSC), the soils were classified as Mollisols, Entisols, Vertisols, and Alfisols; and as Phaeozems, Fluvisols, Gleysols, Anthrosols, Gypsisols, and Plinthosols by the WRB. We advocate for the inclusion of Anthropogenic Soils as a distinct Order within Soil Taxonomy (ST). The implementation of sustainable agricultural practices, in conjunction with the formulation of regulatory frameworks governing land use based on capacity and suitability, is imperative, particularly within the context of fragile tropical systems. Full article

In many hardwood forests, resprouting is a common response to disturbance and basal resprouts may represent a substantial component of the forest understory, especially post-wildfire. Despite this, resprouts are often overlooked in biomass assessments and drivers of resprouting responses in certain species are still unknown. These knowledge gaps are problematic as the contribution of resprouts to understory fuel loads are needed for wildfire risk modeling and effective forest stewardship. Here, we validated the handheld mobile laser scanning (HMLS) of basal resprout volume and field measurements of stem count and clump height as methods to estimate the mass of California Bay Laurel (Umbellularia californica) basal resprouts at Pepperwood and Saddle Mountain Preserves, Sonoma County, California. In addition, we examined the role of tree size and wildfire severity in predicting post-wildfire resprouting response. Both field measurements (clump height and stem count) and remote sensing (HMLS-derived volume) effectively estimated dry mass (total, leaf and wood) of U. californica resprouts, but underestimated dry mass for a large resprout. Tree size was a significant factor determining post-wildfire resprouting response at Pepperwood Preserve, while wildfire severity significantly predicted post-wildfire resprout size at Saddle Mountain. These site differences in post-wildfire basal resprouting predictors may be related to the interactions between fire severity, tree size, tree crown topkill, and carbohydrate mobilization and point to the need for additional demographic and physiological research. Monitoring post-wildfire changes in U. californica will deepen our understanding of resprouting dynamics and help provide insights for effective forest stewardship and wildfire risk assessment in fire-prone northern California forests. Full article