Search Results (7,706)

Under the combined effects of climate change, overexploitation, and intense grazing, temperate steppe in northern China is experiencing increasing deterioration, which is typified by a shift from structural degradation to functional disruption. Accurately tracking steppe degradation using remote sensing technology has emerged as a crucial scientific concern. Prior research failed to integrate ecosystem structure and function and lacked reference baselines, relying only on individual indicators to quantify degradation. To resolve these gaps, this study established a novel degradation evaluation index system integrating ecosystem structure and function, incorporating vegetation community distribution and proportions of degradation-indicator species to define reference states and quantify degradation severity. Analyzed spatiotemporal evolution and drivers across the temperate typical steppe (2013–2022). Key findings reveal (1) non-degraded and slightly degraded areas dominated (75.57% mean coverage), showing an overall fluctuating improvement trend; (2) minimal transitions between degradation levels, with stable conditions prevailing (59.52% unchanged area), indicating progressive degradation reversal; and (3) natural factors predominated as degradation drivers. The integrated structural–functional framework enables more sensitive detection of early degradation signals, thereby informing more effective steppe restoration management. Full article

To address the technical challenges in bisphenol A (BPA) pollution control, this research introduced a novel synthetic approach combining co-precipitation with subsequent thermal treatment to engineer layered double hydroxides (LDHs) with a spinel-structured CoMn-LDO catalyst. Systematic material characterizations such as a scanning electron microscope (SEM), an X-ray diffractometer (XRD), a transmission electron microscope (TEM), and X-ray photoelectron spectroscopy (XPS) were employed to analyze the structural and chemical properties of the synthesized CoMn-LDO calcined at 500 °C. The catalytic performance was evaluated under optimized conditions (35 °C, pH 7.0, 2.0 mM PMS, 0.3 g/L catalyst), and mechanistic studies were conducted to identify the dominant reactive oxygen species. The CoMn-LDO exhibited exceptional peroxymonosulfate (PMS) activation performance, achieving 96.75% BPA degradation within 90 min and 58.22% TOC removal. The synergistic redox cycling between Co²⁺/Co³⁺ and Mn³⁺/Mn⁴⁺ promoted the generation of ·OH (72.3% contribution) and SO₄·⁻. The catalyst demonstrated superior stability, maintaining 89% degradation efficiency after five cycles. These results provide theoretical and practical insights for developing high-efficiency persulfate-activating catalysts. Full article

Streamflow forecasting in snowmelt-dominated basins is essential for water resource planning, flood mitigation, and ecological sustainability. This study presents a comparative evaluation of statistical, machine learning (Random Forest), and deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Spatio-Temporal Graph Neural Network (STGNN)) using 30 years of data from 20 monitoring stations across the Upper Colorado River Basin (UCRB). We assess the impact of integrating meteorological variables—particularly, the Snow Water Equivalent (SWE)—and spatial dependencies on predictive performance. Among all models, the Spatio-Temporal Graph Neural Network (STGNN) achieved the highest accuracy, with a Nash–Sutcliffe Efficiency (NSE) of 0.84 and Kling–Gupta Efficiency (KGE) of 0.84 in the multivariate setting at the critical downstream node, Lees Ferry. Compared to the univariate setup, SWE-enhanced predictions reduced Root Mean Square Error (RMSE) by 12.8%. Seasonal and spatial analyses showed the greatest improvements at high-elevation and mid-network stations, where snowmelt dynamics dominate runoff. These findings demonstrate that spatio-temporal learning frameworks, especially STGNNs, provide a scalable and physically consistent approach to streamflow forecasting under variable climatic conditions. Full article

This paper investigated the tunnel slags generated from a specific tunnel project to systematically assess their environmental risk through phase composition, chemical composition, acidification potential, and heavy metal speciation. Leaching experiments were conducted under various influencing factors, including particle size, time, liquid-to-solid ratio, pH, temperature. The release concentration of heavy metals from the tunnel slag particles follows the following order: Zn > Cu > Cr. This is primarily attributed to the preferential release of Zn under acidic conditions due to its high acid-soluble state, while Cr, which is predominantly present in the residual state, exhibits very low mobility. Furthermore, decreased particle sizes, increased liquid-to-solid ratios, elevated leaching temperatures, extended leaching times, and lower pH values can effectively promote the dissolution of heavy metals from the tunnel slag. The cumulative leaching curves of Cr, Cu, and Zn from the three types of tunnel slags conform to the Elovich equation (R² > 0.88), indicating that the release process of heavy metals is primarily controlled by diffusion mechanisms. The S- and Fe/Mg-rich characteristics of D3 confers a high acidification risk, accompanied by a rapid and persistent heavy metal release rate. In contrast, D2, which is influenced by the neutralizing effect of carbonate dissolution, releases heavy metals at a steady rate, while D1, which is dominated by inert minerals like quartz and muscovite, exhibits the slowest release rate. It is recommended that waste management engineering prioritize controlling S- and Fe/Mg-rich tunnel slags (D3) and mitigating risks of elements like Zn and Cu under acidic conditions. This study provides a scientific basis and technical support for the environmentally safe disposal and resource utilization of tunnel slag. Full article

Background: EHEC O157:H7 causes severe gastrointestinal illness by first colonizing the large intestine. It intimately attaches to the epithelial lining, orchestrating distinctive “attaching and effacing” lesions that disrupt the host’s cellular landscape. While much is known about the well-established virulence factors, there are much to learn about the surface proteins’ roles in a living host. Methods: This study presents the first in vivo characterisation of the surface proteome, i.e., proteosurfaceome, of Escherichia coli O157:H7 EDL933 during intestinal infection, revealing spatial and temporal adaptations critical for colonisation and survival. Using a murine ileal loop model, surface proteomic profiles were analysed at early (3 h) and late (10 h) infection stages across the ileum and colon. Results: In total, 272 proteins were identified, with only 13 shared across all conditions, reflecting substantial niche-specific adaptations. Gene ontology enrichment analyses highlighted dominant roles in metabolic, cellular, and binding functions, while subcellular localisation prediction uncovered cytoplasmic moonlighting proteins with surface activity. Comparative analyses revealed dynamic changes in protein abundance. Conclusions: These findings indicate a coordinated shift from stress adaptation and virulence to nutrient acquisition and persistence and provide a comprehensive view of EHEC O157:H7 surface proteome dynamics during infection, highlighting key adaptive proteins that may serve as targets for future therapeutic and vaccine strategies. Full article

Background: Measurement uncertainty (MU) is a crucial parameter for ensuring the reliability of analytical methods and the validity of results, as required by ISO 17025:2017. Its estimation is particularly critical for quality control laboratories, where compliance decisions are based on a rigorous interpretation of uncertainties. Methods: In this study, we evaluated the uncertainty associated with an HPLC-UV method for the determination of Metopimazine (MPZ) in a pharmaceutical form, applying two complementary approaches: The ISO-GUM (Guide to the Expression of Uncertainty in Measurement) top-down approach and the Monte Carlo Simulation (MCS). Results: The results of both approaches showed excellent agreement, thus validating the robustness of the evaluation. The analysis of uncertainty sources revealed that the accuracy of the sample volume (V_Sample) and the calibration standard (C_x) were the dominant contributors, representing 39.9% and 36.2% of the total uncertainty, respectively. Combined, these two factors accounted for 76.1% of the variability, underscoring their critical impact on the assay’s precision. The expanded uncertainty (k = 2, 95% confidence level) was determined to be (99.41 ± 0.69)%, reflecting the method’s reproducibility. Conclusions: These results highlight the importance of rigorously controlling calibration standard preparation, sample volume, and repeatability conditions to optimize the reliability of the assay. Full article

River ice is a common feature in most Canadian rivers and streams during the cold season. River channel hydraulics under ice conditions may cause higher water levels at a relatively lower discharge compared to the open-water flood events. Elevated water levels resulting from river ice processes throughout fall freeze-over, mid-winter, and spring break-up are important hydrologic events with diverse morphological, ecological, and socio-economic impacts. This study analyzes the timing of maximum water levels (occurring during freeze-over, spring break-up, and open-water periods) and the typology of maximum ice-related events (at freeze-over, mid-winter, and spring break-up) using data from the Canadian River Ice Database. The study also compares annual maximum water levels during the river ice and open-water periods at selected hydrometric stations from 1966 to 2015, divided into two 25-year windows: 1966–1990 and 1991–2015. A return period classification method was applied to define ice-influenced, open-water, and mixed-regime conditions. The results indicate that the majority of ice-influenced maximum water levels occurred during spring break-up (~79% in 1966–1990 and ~69% in 1991–2015), followed by fall freeze-up (~13% and ~23%) and mid-winter break-up (~8% and ~7%) for the two periods, respectively. Among 15 stations analyzed for 1966–1990 and 42 stations for 1991–2015, the proportion of annual maximum water levels dominated by open-water conditions increased from 47% to 55%, while ice-dominated events decreased from 13% to 12%, and mixed-regime events dropped from 40% to 33%. However, a focused comparison of eight common stations revealed minimal change in the distribution of water level-generating events between the two periods. The findings offer valuable insights into the spatial distribution of maximum water level-generating mechanisms across Canada. Full article

Periodontitis is a chronic oral inflammatory disease whose treatment is often hindered by poor drug retention, prolonged therapeutic regimens, and the rise of antibiotic resistance. In this study, we developed a Hydrogel@ZIF-8@metronidazole (Hydrogel@ZIF-8@MNZ) nanocomposite dressing for targeted, sustained, and in situ antimicrobial therapy. This system integrates ZIF-8, a pH-responsive metal–organic framework, with the antimicrobial agent metronidazole (MNZ), encapsulated within a crosslinked hydrogel matrix to enhance stability and retention in the oral environment. Drug release studies demonstrated that MNZ release was significantly accelerated under acidic conditions (pH 5.0), mimicking the periodontal microenvironment. The Hydrogel@ZIF-8 composite achieved a maximum MNZ adsorption capacity of 132.45 mg·g⁻¹, with a spontaneous and exothermic uptake process best described by a pseudo-second-order kinetic model, suggesting chemisorption as the dominant mechanism. The nanoplatform exhibited strong pH-responsive behavior, with enhanced drug release under acidic conditions and potent dose-dependent bactericidal activity against Fusobacterium nucleatum (Fn). At the highest tested concentration, bacterial survival was reduced to approximately 30%, with extensive membrane disruption observed through live/dead fluorescence microscopy. In summary, the stimuli-responsive Hydrogel@ZIF-8@MNZ nanocomposite offers an intelligent and effective therapeutic strategy for periodontitis. By tailoring its action to the disease microenvironment, this platform enables sustained and localized antibacterial therapy, addressing major challenges in the treatment of chronic oral infections. Full article

Optimal airfoil design remains a critical challenge in aerodynamic engineering, with traditional methods requiring extensive computational resources and iterative processes. This paper presents GEO-DSGA, a novel framework integrating hybrid geometric neural networks with deep symbiotic genetic algorithms for enhanced airfoil optimization. The methodology employs graph-based representations of airfoil geometries through a hybrid architecture combining graph convolutional networks with traditional deep learning, enabling precise capture of spatial geometric relationships. The parametric modeling stage utilizes CST, Bézier curves, and PARSEC methods to generate mathematically robust airfoil representations, subsequently transformed into graph structures preserving local and global shape characteristics. The optimization framework incorporates a deep symbiotic genetic algorithm enhanced with dominant feature phenotyping, applying biological symbiotic principles where design parameters achieve superior performance through mutual enhancement rather than independent optimization. This systematic exploration maintains geometric feasibility and aerodynamic validity throughout the design space. Experimental results demonstrate an 88.6% reduction in computational time while maintaining prediction accuracy within 1.5% error margin for aerodynamic coefficients across diverse operating conditions. The methodology successfully identifies airfoil geometries outperforming baseline NACA profiles by up to 12% in lift-to-drag ratio while satisfying manufacturing and structural constraints, establishing GEO-DSGA as a significant advancement in computational aerodynamic design optimization. Full article

Groundwater chemical composition often exhibits complex characteristics under the combined influence of anthropogenic activities and natural geological conditions. Accurately distinguishing between human-derived and naturally occurring constituents is crucial for formulating effective pollution control strategies and ensuring sustainable groundwater resource management. However, conventional hydrogeochemical analytical methods often face challenges in quantitatively differentiating these overlapping influences. In this study, 66 groundwater samples were collected from the midstream section of the Dawen River Basin, an area subject to significant anthropogenic pressure. An integrated approach combining hydrogeochemical analysis, Self-Organizing Map (SOM) clustering, and Positive Matrix Factorization (PMF) receptor modeling was employed to identify sources of chemical constituents and quantify the proportional contributions of various factors. The results indicate that: (1) The predominant groundwater types in the study area were Cl·SO₄·Ca. (2) SOM clustering classified the groundwater samples into five distinct groups, each reflecting a dominant influence: (i) natural geological processes—samples distributed within the central geological mining area; (ii) agricultural activities—samples located in intensively cultivated zones along both banks of the Dawen River; (iii) hydrogeochemical evolution—samples concentrated in areas with impermeable surfaces on the eastern and western sides of the study region; (iv) mining operations—samples predominantly found in industrial zones at the periphery; (v) domestic wastewater discharge—samples scattered relatively uniformly throughout the area. (3) PMF results demonstrated that natural geological conditions constituted the largest contribution (29.0%), followed by agricultural activities (26.8%), consistent with the region’s extensive farming practices. Additional contributions arose from water–rock interactions (23.9%), mining operations (13.6%), and domestic wastewater (6.7%). This study establishes a methodological framework for quantitatively assessing natural and anthropogenic impacts on groundwater quality, thereby providing a scientific basis for the development of protection measures and sustainable management strategies for regional groundwater resources. Full article

Lava caves commonly occur in basaltic ground and can compromise roadway stability when present beneath pavements; however, their long-term effects remain insufficiently characterized. This study quantitatively evaluates how lava caves influence pavement behavior using numerical analyses in ABAQUS/CAE. The parameters examined include the presence/absence of a cave, cave width, cover depth, pavement thickness, and load range. Load–settlement curves under a uniformly distributed surface load show that narrower load ranges concentrate stresses and produce larger settlements, whereas wider load ranges disperse stresses and reduce deformation. Classification of deformation behavior using a rutting criterion indicates that plastic soil response dominates under most conditions. A Peak Load Reduction (PLR) index further demonstrates that structural resistance decreases markedly with shallow cover, larger cave width, and narrower load range. Overall, pavement stability above lava caves is governed primarily by cover depth, load range, and cave width, while the effect of pavement thickness is negligible. These findings suggest that, in basaltic terrains, design and maintenance should prioritize subsurface conditions and loading characteristics over pavement thickness. Full article

Background/Objectives: Understanding how muscle fatigue contributes to musculoskeletal injuries is critical in sports science. Although joint biomechanics during landing under fatigue has been studied before, limited research has focused on the layup phase under fatigue. This study examined the effects of fatigue on ankle, knee, and hip-joint biomechanics during layup and landing. We hypothesized that fatigue would increase peak vertical ground reaction force (GRF), peak knee extension angle, and peak joint moments. Methods: Fourteen healthy male participants performed 3-step layups and drop landings using their dominant leg on force plates. The fatigue protocol consisted of squat jumps, step-ups, and repeated countermovement jumps (CMJs), with fatigue defined as three consecutive CMJs below 80% of the participant’s pre-established maximum jump height. After a fatigue protocol, they repeated the tasks. Kinematic data were collected using an eight-camera Vicon system (100 Hz), and GRF data were recorded with two AMTI force plates (1000 Hz). Thirty-six reflective markers were placed on lower-limb anatomical landmarks, and data were processed using Visual 3D. Paired t-tests ($\alpha$ = 0.05) were conducted using SPSS (V26.0) to compare pre- and post-fatigue outcomes. Results: Significant increases were found in peak GRF during landing (pre: 3.41 $\pm$ 0.81 BW [Body Weight], post: 3.95 $\pm$ 1.05 BW, p = 0.036), and peak negative hip joint work during landing (pre: $-$0.34 $\pm$ 0.18 J/kg, post: $-$0.66 $\pm$ 0.43 J/kg, p = 0.025). Conclusions: These findings indicate that fatigue may alter landing mechanics, reflected in increased ground reaction forces and negative hip joint work. These preliminary findings should be interpreted cautiously, and future studies with larger samples and additional neuromuscular measures under sport-specific conditions are needed to improve ecological validity. Full article

The dynamic modulus of asphalt mixtures (|E*|) is a key mechanical parameter in the design of road pavements, yet direct laboratory testing is time- and resource-intensive. This study evaluates two predictive models for estimating |E*| using data from 62 asphalt mixtures containing reclaimed asphalt: a grey relational analysis–multiple linear regression (GRA-MLR) hybrid model and a mechanistic sigmoidal model. The results showed that the GRA-MLR model effectively identifies influential variables but achieved moderate predictive accuracy (R² values varying from 0.4743 to 0.6547). In contrast, the sigmoidal model outperformed across all temperature conditions (R² > 0.96) and produced predictions deviating by less than ±20% from measured values. Temperature-dependent shifts in factor influence were observed, with stiffness and gradation dominating at low temperatures and reclaimed asphalt (RA) content becoming more significant at higher temperatures. While the GRA-MLR model is advantageous, offering rapid assessments and early-stage evaluations, the sigmoidal model offers the precision suited for detailed design. Integrating both models can balance computational efficiency and provide a balanced strategy, with strong predictive reliability to advance mechanistic–empirical pavement design. Full article

Macroplastic pollution is a growing environmental concern, threatening the marine environment. Despite growing awareness of marine plastic pollution, few studies have assessed the effectiveness of in situ technologies such as safety nets for macroplastic interception. This study aims to evaluate the effectiveness of safety net (SN) systems in intercepting macroplastic debris in the different zones of recreational Yugang Park Beach (YPB), Zhanjiang Bay, China. Safety nets were installed at stations representing different hydrodynamic conditions, and macroplastic debris (2.5–80 cm) was collected and analyzed for size, color, and shape characteristics. Two survey comparisons revealed a higher debris density in the winter survey (1.8 ± 0.3 items m²) than in the summer survey (1.5 ± 0.3 items m²). Most debris fell within the 10–40 cm range, with transparent low-density polyethylene plastic bags being the dominant type, particularly in the winter survey (80.7%). Statistical analysis indicated that plastic size was likely related to net retention characteristics, while tidal influences accounted for a major portion of spatial variability in debris accumulation. These findings suggest that SN systems are effective tools for macroplastic interception and could inform evidence-based coastal management strategies to reduce plastic pollution in similar coastal environments. Full article

Nitrous oxide (N₂O), a potent greenhouse gas (GHG) and major contributor to climate change, is primarily released through agricultural activities. To better understand and quantify how land management practices, local climate conditions, and soil physicochemical properties affect these agricultural N₂O emissions, we conducted a review of the peer-reviewed literature on N₂O emission from corn [Zea mays L.] and soybean [Glycine max (L.) Merr.] fields. We evaluated the seasonal, cumulative effects of three nitrogen fertilizer rates—no fertilizer (0), low (<188 kg N ha⁻¹), and high (188–400 kg N ha⁻¹)—tillage practices, local climate (precipitation and temperature), soil texture, and soil pH on soil N₂O emissions. This meta-analysis included 77 articles for corn and 22 articles for soybean fields. Average N₂O emissions during the corn rotation were 2.34 and 2.45 kg N₂O-N ha⁻¹ season⁻¹ under low and high N fertilizer rates, respectively, and were both substantially (p < 0.0001) greater than those of non-fertilized corn fields (0.91 kg N₂O-N ha⁻¹ season⁻¹). Non-fertilized soybean fields showed seasonal N₂O emissions of 0.74 kg N₂O-N ha⁻¹, while low fertilizer application triggered a sharp increase (1.87 kg N₂O-N ha⁻¹) in N₂O emissions by roughly 2.5 times (p < 0.028). Increased temperature did not significantly (p > 0.05) affect the emission of N₂O from fertilized or non-fertilized corn fields. Regardless of fertilization and tillage practices, our analysis, including Principal Component Analysis, revealed that in corn fields, precipitation and soil pH are the dominant factors influencing soil N₂O emissions. This study uniquely quantifies the influence of climate–soil factors, such as precipitation and soil pH, alongside agronomic practices, on N₂O emissions, offering new insights beyond previous reviews focused primarily on fertilizer rates or tillage effects. Full article