Search Results (86,229)

This paper addresses the critical challenge of selecting suitable pile foundations in port engineering by systematically investigating the axial bearing behavior of large-diameter steel pipe piles and prestressed high-strength concrete (PHC) piles. The study integrates both numerical simulations and field tests within the context of the Yancheng Dafeng Port Security Facilities Project. A self-balanced static load numerical model for PHC piles was developed using Plaxis 3D, enabling the simulation of load-displacement responses, axial force transfer, and side resistance distribution. The accuracy of the model was verified through a comparison with field static load test data. With the verified model parameters, the internal force distribution of steel pipe piles was analysed by modifying material properties and adjusting boundary conditions. A comparative analysis of the two pile types was conducted under identical working conditions. The results reveal that the ultimate bearing capacities of the 1# steel pipe pile and the 2# PHC pile are 6734 kN and 6788 kN, respectively. Despite the PHC pile having a 20% larger diameter, its ultimate bearing capacity is comparable to that of the steel pipe pile, suggesting a more efficient utilisation of material strength in the latter. Further numerical simulations indicate that, under the same working conditions, the ultimate bearing capacity of the steel pipe pile exceeds that of the PHC pile by 18.43%. Additionally, the axial force distribution along the steel pipe pile shaft is more uniform, and side resistance is mobilised more effectively. The reduction in side resistance caused by construction disturbances, combined with the slenderness ratio (L/D = 41.7) of the PHC pile, results in 33.87% of the pile’s total bearing capacity being attributed to tip resistance. The findings of this study provide crucial insights into the selection of optimal pile types for terminal foundations, considering factors such as bearing capacity, environmental conditions, and economic viability. Full article

Bladder cancer pathogenesis is closely linked to epigenetic alterations, particularly DNA methylation and demethylation processes. Environmental carcinogens and persistent inflammatory stimuli—such as recurrent urinary tract infections—can induce aberrant DNA methylation, altering gene expression profiles and contributing to malignant transformation. This review synthesizes current evidence on the role of DNA methyltransferases (DNMT1, DNMT3a, DNMT3b) and the hypermethylation of key tumour suppressor genes, including A2BP1, NPTX2, SOX11, PENK, NKX6-2, DBC1, MYO3A, and CA10, in bladder cancer. It also evaluates the therapeutic application of DNA-demethylating agents such as 5-azacytidine and highlights the impact of chronic inflammation on epigenetic regulation. Promoter hypermethylation of tumour suppressor genes leads to transcriptional silencing and unchecked cell proliferation. Urine-based DNA methylation assays provide a sensitive and specific method for non-invasive early detection, with single-target approaches offering high diagnostic precision. Animal models are increasingly employed to validate these findings, allowing the study of methylation dynamics and gene–environment interactions in vivo. DNA methylation represents a key epigenetic mechanism in bladder cancer, with significant diagnostic, prognostic, and therapeutic implications. Integration of human and experimental data supports the use of methylation-based biomarkers for early detection and targeted treatment, paving the way for personalized approaches in bladder cancer management. Full article

Recurrent pregnancy loss (RPL) is defined as the occurrence of two or more pregnancy losses before 20 weeks of gestation. RPL is a common medical condition among reproductive-age women, with approximately 23 million cases reported annually worldwide. Up to 5% of pregnant women may experience two or more consecutive pregnancy losses. Previous studies have investigated risk factors for RPL, including maternal age, uterine pathology, genetic anomalies, infectious agents, endocrine disorders, thrombophilia, and immune dysfunction. However, RPL is a disease caused by a complex interaction of genetic factors, environmental factors (e.g., diet, lifestyle, and stress), epigenetic factors, and the immune system. In addition, due to the lack of research on genetics research related to RPL, the etiology remains unclear in up to 50% of cases. Platelets play a critical role in pregnancy maintenance. This study examined the associations of platelet receptor and ligand gene variants, including integrin subunit beta 3 (ITGB3) rs2317676 A > G, rs3809865 A > T; fibrinogen gamma chain (FGG) rs1049636 T > C, rs2066865 T > C; glycoprotein 1b subunit alpha (GP1BA) rs2243093 T > C, rs6065 C > T; platelet endothelial cell adhesion molecule 1 (PECAM1) rs2812 C > T; and platelet endothelial aggregation receptor 1 (PEAR1) rs822442 C > A, rs12137505 G > A, with RPL prevalence. In total, 389 RPL patients and 375 healthy controls (all Korean women) were enrolled. Genotyping of each single nucleotide polymorphism was performed using polymerase chain reaction–restriction fragment length polymorphism and the TaqMan genotyping assay. All samples were collected with approval from the Institutional Review Board at Bundang CHA Medical Center. The ITGB3 rs3809865 A > T genotype was strongly associated with RPL prevalence (pregnancy loss [PL] ≥ 2: adjusted odds ratio [AOR] = 2.505, 95% confidence interval [CI] = 1.262–4.969, p = 0.009; PL ≥ 3: AOR = 3.255, 95% CI = 1.551–6.830, p = 0.002; PL ≥ 4: AOR = 3.613, 95% CI = 1.403–9.307, p = 0.008). The FGG rs1049636 T > C polymorphism was associated with a decreased risk in women who had three or more pregnancy losses (PL ≥ 3: AOR = 0.673, 95% CI = 0.460–0.987, p = 0.043; PL ≥ 4: AOR = 0.556, 95% CI = 0.310–0.997, p = 0.049). These findings indicate significant associations of the ITGB3 rs3809865 A > T and FGG rs1049636 T > C polymorphisms with RPL, suggesting that platelet function influences RPL in Korean women. Full article

Soil sealing and land take are increasingly recognised as critical environmental and land use planning challenges across Europe. Although these issues have received limited attention in Baltic policymaking and the academic literature to date, available data indicate ongoing land consumption despite population decline. This study aims to analyse soil sealing patterns in Estonia, Latvia, and Lithuania between 2018 and 2021 using CLC+ Backbone data, linking them to demographic shifts and local planning frameworks. Results reveal that soil sealing increased in nearly all municipalities across the Baltic states, regardless of population trends. The analysis highlights that shrinking municipalities, constrained by limited resources and declining populations, are structurally disadvantaged in terms of land use efficiency, particularly when measured by sealed area per capita. Moreover, this study discusses emerging policy tensions, including the narrowing conceptual gap between land take and soil sealing in the proposed EU Soil Monitoring and Resilience Directive, as well as the risk of overlooking broader land artificialisation. The findings underscore the need for context-sensitive, multi-scalar approaches to land use monitoring and governance, particularly in sparsely populated and demographically imbalanced regions, such as the Baltic states. Full article

Second-language (L2) written processing has often been linked to cognitive disfluency, resembling fluency disruptions caused by perceptual challenges, such as visual degradation. This study used Virtual Reality to investigate whether cognitive disfluency in L2 mirrors perceptual disfluency by simulating adverse weather conditions (sunny vs. rainy) and applying visual masking. Spanish–English bilinguals completed a language decision task, identifying orthotactically unmarked words as either Spanish (L1) or English (L2) while experiencing these perceptual manipulations. Results showed that visual masking significantly increased reaction times, particularly for L1 words, suggesting that masking can diminish the native language advantage. Spanish words under masking elicited slower responses than unmasked ones, whereas L2 word recognition remained comparatively stable. Additionally, rainy weather conditions consistently slowed responses across both languages, indicating a general effect of environmental disfluency. A significant interaction between language and masking emerged, highlighting distinct cognitive effects for different disfluency types. These findings suggest that cognitive disfluency in L2 does not equate to perceptual disfluency; each affects processing differently. The use of Virtual Reality enabled the controlled manipulation of realistic environmental variables, offering valuable insights into how perceptual and linguistic challenges jointly influence bilingual language processing. Full article

The development of gas sensors with high sensitivity and low operating temperatures is essential for practical applications in environmental monitoring and industrial safety. SnO₂-based gas sensors, despite their widespread use, often suffer from high working temperatures and limited sensitivity to H₂ gas, which presents significant challenges for their performance and application. This study addresses these issues by introducing a novel SnO₂-based sensor featuring a three-dimensional (3D) nanostructure, designed to enhance sensitivity and allow for room-temperature operation. This work lies in the use of a 3D anodic aluminum oxide (AAO) template to deposit SnO₂ nanoparticles through ultrasonic spray pyrolysis, followed by modification with platinum (Pt) nanoparticles to further enhance the sensor’s response. The as-prepared sensors were extensively characterized, and their H₂ sensing performance was evaluated. The results show that the 3D nanostructure provides a uniform and dense distribution of SnO₂ nanoparticles, which significantly improves the sensor’s sensitivity and repeatability, especially in H₂ detection at room temperature. This work demonstrates the potential of utilizing 3D nanostructures to overcome the traditional limitations of SnO₂-based sensors. Full article

Urban forests, as a form of green infrastructure, play a vital role in enhancing urban resilience, environmental health, and quality of life. However, users perceive and utilize these spaces in diverse ways. This study aims to identify latent perception types among urban forest visitors and analyze their behavioral, demographic, and policy-related characteristics in Incheon Metropolitan City (Republic of Korea). Using latent class analysis, four distinct visitor types were identified: multipurpose recreationists, balanced relaxation seekers, casual forest users, and passive forest visitors. Multipurpose recreationists preferred active physical use and sports facilities, while balanced relaxation seekers emphasized emotional well-being and cultural experiences. Casual users engaged lightly with forest settings, and passive forest visitors exhibited minimal recreational interest. Satisfaction with forest elements such as vegetation, facilities, and management conditions varied across visitor types and age groups, especially among older adults. These findings highlight the need for perception-based green infrastructure planning. Policy recommendations include expanding accessible neighborhood green spaces for aging populations, promoting community-oriented events, and offering participatory forest programs for youth engagement. By integrating user segmentation into urban forest planning and governance, this study contributes to more inclusive, adaptive, and sustainable management of urban green infrastructure. Full article

This study investigates the spatial heterogeneity of carbon pools in young Betula sp. stands on former arable lands in the southern Moscow region, Russia. The findings could be useful for the current estimates and predictions of the carbon balance in such forest ecosystems. The research focuses on understanding the interactions between plant cover and the environment, i.e., how environmental factors such as stand density, tree diameter and height, light conditions, and soil properties affect ecosystem carbon pools. We also studied how heterogeneity in edaphic conditions affects the formation of plant cover, particularly tree regeneration and the development of ground layer vegetation. Field measurements were conducted on a permanent 50 × 50 m sampling plot divided into 5 × 5 m subplots, in order to capture variability in vegetation and soil characteristics. Key findings reveal significant differences in carbon stocks across subplots with varying stand densities and light conditions. This highlights the role of the spatial heterogeneity of soil properties and vegetation cover in carbon sequestration. The study demonstrates the feasibility of indirect estimation of carbon stocks using stand parameters (density, height, and diameter), with results that closely match direct measurements. The total ecosystem carbon stock was estimated at 80.47 t ha⁻¹, with the soil contribution exceeding that of living biomass and dead organic matter. This research emphasizes the importance of accounting for spatial heterogeneity in carbon assessments of post-agricultural ecosystems, providing a methodological framework for future studies. Full article

The accumulation of end-of-life tires and the rapid increase in demolition activities pose significant environmental and waste-management challenges. The redevelopment of construction materials incorporating this waste is a potentially promising strategy for minimizing environmental impact while promoting the principles of a circular economy. This study investigates the performance of self-compacting concrete (SCC) incorporating up to 20% rubber aggregates (sand and gravel) and 40% recycled concrete aggregate (RCA) for non-structural applications. A series of tests was conducted to assess fresh and hardened properties, including flowability, compressive strength, tensile strength, flexural strength, water absorption, and density. The results indicated that increasing RCA content reduced density and compressive strength, while tensile and flexural strengths were only moderately affected. Response surface methodology (RSM), utilizing a Box–Behnken design, was employed to optimize compressive, tensile, and flexural strength responses. Statistical analysis was used to identify the optimal mix proportions, which balance the mechanical performance and sustainability of SCC with recycled components. Mixtures incorporating moderate rubber content—specifically, 5–5.5% sand rubber and 0–6% coarse rubber—and 40% recycled-concrete aggregate (RCA) achieved the highest predicted performance, with compressive strength ranging from 20.00 to 28.26 MPa, tensile strength from 2.16 to 2.85 MPa, and flexural strength reaching 5.81 MPa, making them suitable for sidewalks and walkways. Conversely, mixtures containing higher rubber proportions (5.5–20% sand rubber and 20% coarse rubber) combined with the same RCA level (40%) showed the lowest mechanical performance, with compressive strength between 5.2 and 10.08 MPa, tensile strength of 1.05–1.41 MPa, and flexural strength from 2.18 to 3.54 MPa. These findings underscore the broad performance range achievable through targeted optimization. They confirm the viability of recycled materials for producing environmentally friendly SCC in non-structural applications. Full article

Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R² (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals. Full article

Powdery mildew, caused by Erysiphe quercicola, is one of the primary diseases responsible for the reduction in natural rubber production in China. This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into an epidemic under favorable environmental conditions. Accurate prediction and determination of the prevention and control period represent both a critical challenge and key focus area in managing rubber-tree powdery mildew. This study investigates the effects of spore concentration, environmental factors, and infection time on the progression of powdery mildew in rubber trees. By employing six distinct machine learning model construction methods, with the disease index of powdery mildew in rubber trees as the response variable and spore concentration, temperature, humidity, and infection time as predictive variables, a preliminary predictive model for the disease index of rubber-tree powdery mildew was developed. Results from indoor inoculation experiments indicate that spore concentration directly influences disease progression and severity. Higher spore concentrations lead to faster disease development and increased severity. The optimal relative humidity for powdery mildew development in rubber trees is 80% RH. At varying temperatures, the influence of humidity on the disease index differs across spore concentration, exhibiting distinct trends. Each model effectively simulates the progression of powdery mildew in rubber trees, with predicted values closely aligning with observed data. Among the models, the Kernel Ridge Regression (KRR) model demonstrates the highest accuracy, the R² values for the training set and test set were 0.978 and 0.964, respectively, while the RMSE values were 4.037 and 4.926, respectively. This research provides a robust technical foundation for reducing the labor intensity of traditional prediction methods and offers valuable insights for forecasting airborne forest diseases. Full article

Forests are considered one of the most valuable natural areas in metropolitan region landscapes. Considering the sensitivity and ecosystem services provided by trees, the definition of urban forest ecosystems is nowadays based on a comprehensive understanding of the entire urban ecosystem. The effective capturing of particulate matter is one of the ecosystem services provided by urban forests. These ecosystems function as efficient biological filters. Plants accumulate pollutants passively via their leaves. Therefore, another ecosystem service provided by city forests could be the use of tree organs as bioindicators of pollution. This paper aims to estimate differences in trace metal pollution between the wooded urban areas of Vienna and Krakow using leaves of evergreen and deciduous trees as biomonitors. An additional objective of the research was to assess the ability of the applied tree species to act as biomonitors. Plant samples of five species—Norway spruce, Scots pine, European larch, common white birch, and common beech—were collected within both areas, in seven locations: four in the “Wienerwald” Vienna forest (Austria) and three in the “Las Wolski” forest in Krakow (Poland). Concentrations of Cr, Cu, Cd, Pb, and Zn in plant material were determined. Biomonitoring studies with deciduous and coniferous tree leaves showed statistically higher heavy metal contamination in the “Las Wolski” forest compared to the “Wienerwald” forest. Based on the conducted analyses and the literature study, it can be concluded that among the analyzed tree species, only two: European beech and common white birch can be considered potential indicators in environmental studies. These species appear to be suitable bioindicators, as both are widespread in urban woodlands of Central Europe and have shown the highest accumulation levels of trace metals. Full article

Development of forming methods for surface-coated metals is a current concern due to their economic and environmental advantages. For a successful forming operation, it is necessary that both components, the substrate and the coating, are able to withstand stress without damage until the final shape and dimensions are reached. This goal can be achieved through good knowledge of the elastic and plastic properties of the substrate and the coating, the compatibility between them, the appropriate surface treatment, and the rigorous control of technological forming parameters. Our study was carried out with flat specimens of pre-painted Al 99.0 sheet that were electromagnetically formed by bulging. Forming behavior was investigated as depending on the initial thickness of the substrate, on the aluminum sheet pretreatment, as well as on the plastic deformation path of the metal–paint structure. To verify the damage to the paint layer, tests with increasing strains were performed, and the interface between the metal and the coating layer was investigated by scanning electron microscopy. The obtained results indicate that electromagnetic forming of pre-painted sheets can be a feasible method for specific applications if the forming degree of the substrate is tightly correlated with the type of desired coating and with the pretreatment method used for the metal surface. Full article

The countries and regions along the Belt and Road are rich in wetland carbon sink resources, crucial for mitigating greenhouse gas emissions and achieving global emission reduction. This paper uses policy analysis and desk research to analyze the overview of wetland carbon sinks in these countries. It explores the necessity of legal system construction for their carbon sink trading. This study finds that smooth trading requires clear property rights definition rules, efficient market trading entities, definite carbon sink trading price rules, financial support aligned with the Equator Principles, and support from biodiversity-compatible environmental regulatory principles. Currently, there are still obstacles in wetland carbon sink trading in the Belt and Road, such as property rights confirmation, an accounting system, an imperfect market trading mechanism, and the coexistence of multiple trading risks. Therefore, this paper first proposes to clarify the goal of the legal guarantee mechanism. Efforts should focus on promoting a consensus on wetland carbon sink ownership and establishing a unified accounting standard system; simultaneously, the relevant departments should conduct field investigations and monitoring, standardize the market order, and strengthen government financial support and funding guarantees. Full article

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article