Search Results (553)

Ancient timber structures are important carriers of valuable cultural heritage, and their structural safety directly determines whether historic buildings can remain in safe service over time. Cracks represent one of the most widespread and important forms of damage in ancient timber structures. They can directly lead to cross-sectional weakening of structural members, degradation of load-bearing capacity, and the gradual development of overall structural safety risks. To address the limitations of existing crack assessment methods, such as strong subjectivity in weight determination, insufficient accuracy in grade boundary discrimination, and inadequate coupling with mechanical performance, this study proposes a crack safety assessment method for ancient timber structures based on an improved entropy–fuzzy matter-element model. A multi-dimensional evaluation index system is established, incorporating crack geometric characteristics, structural load-bearing capacity, and service time effects. A mechanically driven load-carrying capacity degradation index is introduced to quantitatively characterize the influence mechanism of crack propagation on structural performance deterioration. The entropy weight method is employed to objectively determine the weights of each indicator, and an asymmetric closeness degree is introduced to improve the traditional fuzzy matter-element model, thereby enhancing the stability and accuracy of safety grade classification. A case study of the Bawang Academy, Shenyang Jianzhu University, is conducted. Crack parameters are obtained using image recognition and three-dimensional laser scanning techniques, and a comprehensive structural safety assessment is performed. The results indicate that the proposed method can accurately reflect the actual damage distribution and deterioration level of the structure, providing a reliable theoretical basis and technical support for crack safety evaluation and preventive conservation of ancient timber structures. Full article

Regional water systems in rapidly urbanizing hilly basin cities are affected by hydrological variability, population concentration, industrial water demand, and water-use efficiency. This study evaluated the water resources carrying capacity (WRCC) of Jinhua City, southeastern China, from 2011 to 2023 using an integrated 15-indicator system covering water resources support, water-use and population pressure, economic structure and water-use efficiency, and ecological and environmental support. Indicator definitions, units, directions, and data sources were harmonized using official water resources bulletins and statistical records. A combined weighting method integrating the modified Analytic Hierarchy Process and the entropy weight method was coupled with a fuzzy matter-element model and the Hamming closeness measure. WRCC grades were assigned using standard-derived Hamming closeness thresholds based on pooled-reference membership transformation. Obstacle degree, leave-one-indicator-out sensitivity, and redundancy diagnostics were further used for interpretation and robustness assessment. The combined weights were mainly concentrated in water-use and population pressure (35.85%), water resources support (26.77%), and economic structure and water-use efficiency (26.10%). Industrial water use, per capita comprehensive water use, population density, water consumption per 10,000 yuan industrial value added, and water consumption per 10,000 yuan GDP had the highest indicator weights. Annual Hamming closeness ranged from 0.2621 to 0.6391. Jinhua’s WRCC reached Grade II in 2015, 2019, 2020, and 2021, while the remaining years were classified as Grade III. The highest closeness occurred in 2019, whereas 2022 and 2023 declined to Grade III and were close to the II/III threshold. Obstacle diagnosis showed that water-use and population pressure were the dominant subsystem obstacles. Sensitivity analysis showed that the peak year and the lowest year remained unchanged across all leave-one-indicator-out scenarios, whereas the boundary years showed grade sensitivity. The results provide a transparent annual assessment and diagnostic evidence for WRCC management. Full article

This study investigated the impact of recreational use on trails in the Atlantic Forest (Ubatuba Municipality, São Paulo State, Brazil) using physical, chemical and geochemical indicators. Five trails with different morphological characteristics were selected, and paired samples were collected from the trail surface (TR) and trail-side slope (TA). The statistical approach combined local analyses for each trail with global clustering (n = 19) using Student’s t-test, along with multivariate modeling through Principal Component Analysis (PCA) and Pearson correlation. The analysis included physical attributes (bulk density, particle size and porosity), chemical attributes (pH, organic matter and macronutrients) and geochemical compositions (major oxides and trace elements determined by XRF). The overall results reveal systematic compaction in the trail surface (TR), with bulk density increasing from 1.32 g/cm³ (TA) to 1.37 g/cm³ (TR) (p = 0.038), and total porosity decreasing from 47.26% to 45.34% (p = 0.016). In contrast, the geochemical oxide composition (SiO₂, Al₂O₃, Fe₂O₃) remained stable (p > 0.05), indicating the resilience of the mineral matrix. However, significant local dynamics (p < 0.05) in K₂O and MgO were observed in more preserved trails, associated with surface compaction and fragmentation of the litter layer, and phosphorus showed strong dependence on organic matter (r = 0.85). Multivariate analysis indicates that degradation is predominantly physical and micromorphological at the local scale, with bulk density and porosity being the most sensitive indicators for environmental monitoring. Full article

Introduction: Artificial infrastructure interrupts river longitudinal connectivity, preventing the free flow of water, matter, energy, and organisms through the system, altering the habitat and impacting freshwater biodiversity. Freshwater fishes are especially sensitive to this threat, since they are constrained to the limits of the river network. Transversal obstacles, such as dams and weirs, hinder their movements upstream and downstream and fragment populations. Longitudinal connectivity can be simply measured as the proportion of connected river length in a basin. However, other indices have been suggested more recently, measuring connectivity as the proportion of connected elements of interest (e.g., populations of a species) in a river basin. Objective: The aim of this work was to study (1) the degree of connectivity of native freshwater fish species populations in eleven Spanish river basins and (2) the impact of artificial river fragmentation in these basins on population connectivity. Methodology: Fish populations’ location and size were estimated through sampling presence data, and completed using the predicted occurrence of each species in a river basin, calculated through Species Distribution Models (SDMs). To estimate the degree of connectivity between populations of each species, the Population Connectivity Index (PCI) was calculated under two scenarios: the “current” scenario, considering all the artificial obstacles fragmenting the river network and their specific passabilities, and the “natural” scenario, considering that all the artificial obstacles in the river network were completely passable. Results: Native freshwater fish populations are severely fragmented in Spanish rivers, with a mean current PCI of 9.8% across species and river basins. The impact of artificial fragmentation is high, causing a mean decrease in PCI of 52 percentage points across species and river basins. Moreover, although the impact of artificial river fragmentation is high in all river basins, it is important to point out that there are significant differences between river basins attributed to their size and the specific traits of the ichthyofauna inhabiting them. Conclusions: The degree of connectivity in a river basin varies depending on the elements of interest considered. Therefore, incorporating ichthyofauna into the decision-making process is essential to improve the effectiveness of river restoration actions. Full article

Engineered nanomaterials are widely used in environmental remediation, agriculture, and industrial applications owing to their large specific surface area, high reactivity, and tunable physicochemical properties. However, their release into aquatic environments has raised increasing concerns regarding potential risks to primary producers. Microalgae are highly sensitive to environmental stressors and play essential roles in photosynthesis, nutrient cycling, carbon fixation, and aquatic food-web stability, making them important model organisms for assessing the toxicity of engineered nanomaterials. This review summarizes the toxic effects and mechanisms of representative engineered nanomaterials, including metal and metal oxide nanoparticles, nanoplastics, and carbon-based nanomaterials, on microalgae. Major toxic pathways include nanoparticle attachment and aggregation on algal surfaces, shading effects, membrane damage, altered permeability, cellular internalization, toxic ion release, reactive oxygen species overproduction, photosynthetic inhibition, and metabolic disturbance. The review further discusses how particle size, morphology, surface coating, dissolution, aging, light, pH, and natural organic matter regulate nanomaterial bioavailability and toxicity. Combined toxicity caused by coexisting nanoparticles or emerging pollutants is also considered, with emphasis on synergistic, antagonistic, and concentration-dependent effects. Finally, recent methodological advances, such as near-native imaging, Raman-based spectroscopy, particle-specific elemental analysis, and multi-omics approaches, are highlighted. This review provides an integrated perspective for understanding nanomaterial toxicity to microalgae and supports future ecological risk assessment in aquatic environments. Full article

Soil potentially toxic elements (PTEs) are crucial indicators of soil quality and ecological risk, especially in areas with complex pedogenesis and intensive anthropogenic activities. However, how soil development and land use jointly shape PTEs’ distribution across multiple scales remains unclear. A multi-scale framework encompassing catchment, sub-catchment, and regional scales was employed to examine the impacts of soil development and land use on PTEs’ (Cr, Ni, Cu, Zn, Pb, Cd, As, and Hg) distribution and their dominant drivers in the lower Yangtze River basin’s alluvial soils. Results showed significant scale-dependent variations in PTEs, with concentrations being highest on the regional scale. During pedogenesis, PTEs exhibited distinct evolutionary patterns across scales: Ni, Cu, Zn, and Cd decreased significantly at both the catchment and sub-catchment scales, whereas Cr, Ni, and As showed increasing trends at the regional scale. Land use also demonstrated scale-dependent effects, with drylands exhibiting PTEs’ enrichment at larger scales but significantly lower concentrations compared to woodlands and paddy-dryland rotation (paddies) at the regional scale. The mechanisms through which the Chemical Index of Alteration (CIA) influences PTE concentrations varied across scales, with metal oxide alteration as a key common pathway. Mantel tests showed that PTE distributions are governed by pH and total phosphorus (TP) at larger scales but by organic carbon (OC) and total nitrogen (TN) regionally. These cross-scale insights reveal how pedogenesis and human activity jointly shape HM patterns, highlighting the potential for scale-appropriate sustainable soil management—for instance, regionally tailored adjustments of pH and organic matter can mitigate metal risks while maintaining soil health. Future studies can build on this multi-scale framework by integrating long-term monitoring with predictive models to assess adaptive strategies under land-use change, thereby advancing sustainability in alluvial agroecosystems. Full article

Fine particulate matter (PM_2.5) composition, rather than mass alone, plays a critical role in determining toxicity and health impact. This study examined short-term associations between daily PM_2.5 constituents—black carbon (BC), nitrate (NO₃⁻), ammonium (NH₄⁺), and trace elements—and cardiopulmonary hospital admissions in Makkah, Saudi Arabia. Twelve months of constituent data from the Alharam monitoring site were linked to Herra hospital admissions for cardiovascular (CVD) and pulmonary diseases, stratified by visit type, age, and sex. Negative-binomial generalized linear models estimated adjusted relative risks (aRRs) per interquartile range increase in each constituent, controlling for meteorology, seasonality, and temporal trends. Mean PM_2.5 was 113.6 µg/m³; BC, sulfur, NO₃⁻, and NH₄⁺ dominated the fine fraction. Crustal elements were strongly intercorrelated (r > 0.9), while BC, lead (Pb), and nickel (Ni) showed moderate correlations (r ≈ 0.4–0.6), suggesting shared anthropogenic origins. BC increased CVD emergency/outpatient visits by 18% (aRR = 1.18; 95% CI: 1.08–1.29) and inpatient admissions by 25% (aRR = 1.25; 95% CI: 1.07–1.46). Ni and sulfur were also significant predictors; crustal elements were not. Multi-pollutant models confirmed BC and Pb as independent predictors (aRR = 1.19; 95% CI: 1.02–1.38). Effects were strongest among older adults aged 45–65 at lag 0–2 days. These findings highlight the need for emission controls targeting traffic and industrial combustion sources. Full article

With the increasing penetration of wind power, enhancing the renewable energy accommodation rate and reducing the carbon footprint of the IES, this study proposes a comprehensive evaluation method to assess the impact of a novel dynamic Green Certificate Trading (GCT) and Green Hydrogen Certificate Trading (GHCT) joint mechanism. First, considering the integration of the IES into the carbon trading market, a coupled dynamic GCT-GHCT framework is established. This framework links dynamic green electricity certificate revenues with green hydrogen certificate revenues, leveraging cross-subsidization to incentivize renewable energy consumption. Subsequently, an optimal operation model for the IES is formulated with the objective of minimizing comprehensive costs, which encompass energy procurement, green certificates, carbon trading, and wind curtailment penalties. A piecewise linearization approach is applied to transform the optimization model into a Mixed-Integer Linear Programming problem for efficient solving. Furthermore, based on the dispatch results, a multidimensional evaluation index system is constructed, extracting key indicators from economic, technical, and environmental perspectives. To ensure the rationality of the evaluation, a dynamic reward–penalty asymmetric cloud matter-element (ACME) comprehensive evaluation method based on game theory combinatorial weighting is introduced to calculate the index weights and the final comprehensive evaluation value. Finally, multi-scenario simulations are conducted to verify the superiority of the integrated GCT-GHCT trading framework. The results reveal that the proposed approach not only maximizes renewable energy integration but also provides a robust decision-making tool for the low-carbon transition of multi-energy systems. Full article

Dam constructions alter the river flow, leading to a cascade of physical, chemical, and biological changes in the ecosystem’s structure and function. This study presents a systematic framework for assessing the impact of these built structures on adjacent surface water bodies. The approach integrates mandatory long-term monitoring data with a multivariate statistical approach (Partial Least Squares Discriminant Analysis, PLS-DA) to provide a robust assessment of fourteen of Bulgaria’s major and significant reservoirs’ influence on nearby rivers and streams. Datasets for studied reservoirs include basic physicochemical parameters, and for 8 out of 14 dams—potentially toxic elements (PTEs). To assess the influence of each reservoir on the river, two sampling locations were selected per dam: upstream (U) and downstream (D). Results for the water quality parameters, identified as significant discriminators in each PLS-DA model, are presented. A clear upstream dominance was observed for Pchelina, Saedinenie, and Ticha, a strong downstream pattern was observed for Dospat and Yovkovtsi, and a mixed spatial pattern for the remaining dams. The hierarchical clustering revealed three groups of parameters studied. The first cluster (EC, NO₂⁻, NO₃⁻, TN) likely reflects diffuse inputs. The second cluster (TP, PO₄³⁻) describes the relationship between total and dissolved phosphorus fractions. The third cluster (pH, NH₄⁺, DO, BOD) highlights organic matter decomposition and oxygen dynamics. The results highlight that reservoir impacts are governed by the interplay of hydrological conditions, catchment characteristics, and in-reservoir biogeochemical processes, leading to distinct functional behaviours such as retention, transformation, or release of substances. Full article

This paper introduces COVIA (Code + Video + AI), a constructive Video-Based Learning (VBL) platform aiming to address ongoing challenges in computer science education, particularly those related to student engagement and instructor workload. The system was deployed in a blended learning setting as supplementary instruction in mandatory introductory programming courses at a university in Mexico. Curriculum materials and videos were designed using a human-in-the-loop workflow assisted by Generative AI (GenAI) and validated by subject-matter experts. This study employs a longitudinal, quasi-experimental quantitative design, complemented by a quantitative acceptance assessment based on the Technology Acceptance Model (TAM). The evaluation focused on three distinct phases of the introductory Java curriculum: (1) Language Elements, (2) Selective Structures, and (3) Iterative Structures. Statistical analysis using the Wilcoxon Signed-Rank test confirmed that the Experimental Group achieved highly significant learning gains across all three phases ($p<0.001$). In contrast, the Control Group failed to achieve statistically significant growth in Phase 1 ($p=0.119$) and Phase 3 ($p=0.448$). In conclusion, the results suggest that the COVIA platform supports the development of introductory programming competencies as a supportive tool within a blended instructional framework. Full article

To elucidate the mechanisms of nutrient cycling in rhizosphere soil and microbial metabolism during the prolonged continuous cropping of lavender, this study examined the rhizosphere soil of lavender with different continuous cropping years (1, 4, 7, 10, 15, and 20 years) in the Ili River Valley of Xinjiang, China, measuring physicochemical properties, microbial biomass C/N/P, and eight extracellular enzyme activities. Microbial carbon use efficiency (CUE) and nutrient limitation were quantified using vector analysis, threshold elemental ratios (TERs), and two derived indices (TER_EEA and TER_L). Soil properties exhibited distinct nonlinear patterns: SOC peaked at 4 years (p < 0.05), TN was highest at 20 years, and TP was lowest at 4–7 years. MBC and MBN peaked at 20 years, whereas MBP was significantly lower than in 1-, 4-, and 10-year fields (p < 0.05). EEC and EEN were highest at 20 years, while EEP was lowest at 4 years (p < 0.05). The activity of carbon-related acquisition enzymes increases from 134.81 μmol/g·h in the first year to 393.86 μmol/g·h in the 20th year, an increase of 192%; the activity of nitrogen acquisition enzymes increases from 686.11 μmol/g·h in the first year to 1430.58 μmol/g·h in the 20th year, an increase of 108%. This indicates that the decomposition of organic matter and the nutrient cycling capacity continue to enhance. Vector analysis showed a mean VA of 46° and VL of 0.25, with VA > 45° (P limitation) at 1–4 years shifting to VA < 45° (N limitation) at 20 years. Critically, TEREEA and TERL produced opposite dominant limitations due to differing normalization frameworks—TEREEA scales by microbial biomass stoichiometry—while TERL normalizes against enzyme-derived thresholds. CUET and CUEE ranged from 0.42 to 0.56, with the minimum at 10 years and relatively high values at 15–20 years (p < 0.05). RDA identified CBH (26.2%) and NO₃⁻–N (19.8%) as primary drivers, with extractable phosphorus exhibiting the strongest regulatory effect (pseudo-F = 26.0). These results demonstrate that multi-model stoichiometric assessment is essential, as single indices may yield contradictory diagnoses. These results demonstrate that multi-model stoichiometric assessment is essential, as single indices may yield contradictory diagnoses, and the observed nonlinear shifts in dominant limitation type provide a mechanistic basis for targeted nutrient management in sustainable lavender cultivation. Full article

Photonic and acoustic jets are subwavelength wave localization phenomena formed in the near field of dielectric or elastic scatterers, enabling spatial resolution beyond classical diffraction limits and motivating applications in sensing, imaging, and wave–matter interaction control. This review places photonic and acoustic jets in a unified wave-physics framework and focuses on how composite and layered elements can be used to tune their properties. In photonic systems, refractive index contrast, layer thickness, and optical losses play key roles, while in acoustic systems, acoustic impedance mismatch, dispersion, and viscoelastic damping are critical. Models and numerical approaches, and experimental realizations in both optical and acoustic regimes, are reviewed and summarized to describe jet formation and to analyze the influence of material parameters and geometry. The main findings show that layered and composite scatterers, such as core–shell particles, multilayer spheres and cylinders, and graded-parameter metamaterials, provide additional degrees of freedom for controlling jet intensity, length, focal position, and directionality compared to homogeneous elements. Composite jet-forming elements offer a versatile platform for advanced wave localization and hold promise for metastructures, high-resolution sensing, integration into photonic and acoustic devices, and lab-on-chip technologies. Full article

Train wheel wear is a critical factor affecting train operational safety, making the accurate and objective evaluation of wheel wear condition essential. However, current approaches are still constrained by inadequate measurement accuracy and incomplete evaluation methods. To address this issue, this study proposes an integrated method for the high-precision measurement and wear condition evaluation of train wheels. A multi-sensor data fusion-based measurement method is developed to synchronously acquire key wear-related parameters, including wheel diameter, flange height, and flange thickness. Based on the measured data, a matter-element model combined with game-theoretic weighting is established to evaluate wheel wear condition. Experimental results show that the proposed online measurement method for in-service wheels achieves standard deviations below 0.15 mm, and the measurement errors satisfy the requirements of Chinese railway industry standards. The evaluation results derived from the high-precision measurement data indicate that wheel wear condition gradually deteriorates with increasing service mileage, and that flange height wear is the dominant factor affecting the wear grade. These findings are consistent with actual operating conditions. The proposed method integrates high-precision multi-parameter measurements with wear condition evaluation, providing a reliable technical basis for wheel condition monitoring and predictive maintenance in rail transit. Full article

Reactive oxygen species (ROS) are a class of molecules or free radicals with strong oxidizing properties. They have attracted increasing attention in soil research in recent years because of their perceived importance in many soil biochemical processes. Previous reviews of ROS in soil mainly focused on their impacts on carbon emissions and organic pollutant remediation, with few descriptions of the mechanisms responsible for ROS generation, and a comprehensive understanding of their environmental effects is still lacking. Therefore, the present review provides details on the sources and underlying generation mechanisms of ROS in soil. These mechanisms include inputs via atmospheric deposition, metal–mineral reactions, root exudation, microbial metabolism, enzymatic reactions and various organic matter transformations. In contrast to previous reviews, we also discuss mutual conversion between different types of ROS in soil. The impacts of ROS on the soil environment are further explored, such as element cycling, pollutant degradation, and the growth and reproduction of plants and microorganisms, in order to provide a systematic understanding of the various processes involving ROS in soil, thereby guiding better soil management decisions. Finally, we highlight future research trends, suggesting that the advancement of in situ detection methods is crucial for establishing the precise contribution of abiotic ROS processes to global soil carbon and nutrient models. Full article

Compared with other construction operations, high-altitude operations are more dangerous. Falling from a height is the main type of accident in construction. It is important to study the human risk of falling from height to reduce falling accidents. Based on the Human Factors Analysis and Classification System (HFACS) model, a preliminary evaluation index system for fall risk in building construction was established. Through the Delphi method and sensitivity analysis, the initial indicators were screened, the index factors that did not meet the requirements were removed, and the final human risk index evaluation system was determined. The system includes five first-level indicators and 17 s-level indicators of organizational influence, unsafe supervision, preconditions for unsafe behavior, and unsafe behavior. Subsequently, the analytic network process–entropy weight method (ANP-EWM) is used to subjectively and objectively weight the evaluation indicators, and the combined weight is obtained through game theory. The matter–element extension model is constructed to evaluate the human risk of falling from height in construction. Finally, an empirical analysis is carried out with the Y project as a case study. The novelty of this study lies in integrating human-factor analysis with the matter–element extension model for fall risk assessment in construction, while combining ANP, the entropy weight method, and game theory to balance subjective and objective weighting. The proposed model provides a practical tool for evaluating and controlling human risk in high-altitude construction operations. The results show that the correlation degree calculated according to the matter–element extension model is K₄ = 3.5, and the human risk of falling from height in the construction of Y project has generally reached an excellent level. However, the evaluation level of some evaluation indexes is still low, which is consistent with the actual situation of construction enterprises in Y project. This model provides a direction for the study of human risk assessment of falling from different construction heights. Full article