Search Results (11,443)

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1,030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

To remain competitive in the decarbonization process of the economy worldwide, energy companies must preserve their market value to attract new investors and remain resilient throughout the transition to net zero. This article examines the market value of US energy companies during the period 2012–2024 in relation to their power generation mix. Panel regression analyses reveal that Tobin’s q and price-to-book ratios increase significantly for solar and wind power, while they experience moderate increases for natural gas power. In contrast, Tobin’s q and price-to-book ratios decline for nuclear and coal power. Furthermore, accounting-based profitability, measured by the return on assets (ROA), does not show significant variation with any type of power generation. The findings suggest that market investors prefer solar, wind, and natural gas power generation, thereby attributing greater value (that is, demanding lower risk compensation) to green companies compared to traditional ones. These insights provide guidance to executives, investors, and policy makers on how the power generation mix can influence strategic decisions in the energy sector. Full article

As urbanization processes are no longer characterized by simple linear expansion but exhibit leaping, edge-sparse, and discontinuous features, spatiotemporally continuous impervious surface coverage data are needed to better characterize urbanization processes. This study utilized GAIA impervious surface binary data and employed spatiotemporal aggregation methods to convert thirty years of 30 m resolution data into 1 km resolution spatiotemporal impervious surface coverage data, constructing a long-term time series annual impervious surface coverage dataset for the Beijing–Tianjin–Hebei region. Based on this dataset, we analyzed urban expansion processes and landscape pattern indices in the Beijing–Tianjin–Hebei region, exploring the spatiotemporal response relationships of ecological environment changes. Results revealed that the impervious surface area increased dramatically from 7579.3 km² in 1985 to 37,484.0 km² in 2020, representing a year-on-year growth of 88.5%. Urban expansion rates showed two distinct peaks: 800 km²/year around 1990 and approximately 1700 km²/year during 2010–2015. In high-density urbanized areas with impervious surfaces, the average forest area significantly increased from approximately 2500 km² to 7000 km² during 1985–2005 before rapidly declining, grassland patch fragmentation intensified, while in low-density areas, grassland area showed fluctuating decline with poor ecosystem stability. Furthermore, by incorporating natural and social factors such as Fractional Vegetation Coverage (FVC), Habitat Quality Index (HQI), Land Surface Temperature (LST), slope, and population density, we assessed the vulnerability of urbanization development in the Beijing–Tianjin–Hebei region. Results showed that high vulnerability areas (EVI > 0.5) in the Beijing–Tianjin core region continue to expand, while the proportion of low vulnerability areas (EVI < 0.25) in the northern mountainous regions decreased by 4.2% in 2020 compared to 2005. This study provides scientific support for the sustainable development of the Beijing–Tianjin–Hebei urban agglomeration, suggesting location-specific and differentiated regulation of urbanization processes to reduce ecological risks. Full article

Due to small population size, Population Viability Analysis (PVA) of endangered species often pools all individuals into a single matrix to decrease variation in estimation of transition rates. These pooled populations may mask significant environmental variation among populations, affecting estimates. Using 10 years of population data (2000–2010) on the endangered plant Jacquemontia reclinata in Southeastern Florida, USA, I parameterized a stage-structured matrix model and calculated annual growth rates (lambdas)and elasticity for each year using stochastic matrix models. The metapopulation model incorporating actual dynamics of the two largest populations showed a lower occupancy rate and higher risk of extinction at an earlier time compared to a model that used the average of all natural populations. Analyses were consistent that incorporating population variation versus average dynamics in modeling J. reclinata demography results in more variation and greater extinction risk. Local variation may be due to both weather (including minimum winter temperature and total annual precipitation) and local disturbance dynamics in these urban preserves. Full article

Road networks are essential for access, resource distribution, and population evacuation during natural events. These challenges are pronounced in rural areas, where network redundancy is limited and communities may have social disparities. While traditional risk management systems often focus on the physical consequences of hazard events alone, specialized literature increasingly suggests the development of a more comprehensive approach for risk assessment, where not only physical aspects associated with infrastructure, such as damage level or disruptions, but also the social and economic attributes of the affected population are considered. Consequently, this paper proposes a Vulnerability Access Index (VAI) to support road network decision-making that integrates the social vulnerability of rural communities exposed to natural events, their accessibility to nearby critical infrastructure, and physical risk. The research methodology considers (i) the Social Vulnerability Index (SVI) calculation based on socioeconomic variables, (ii) Importance Index estimation (I_imp) to evaluate access to critical infrastructure, (iii) VAI calculation combining SVI and I_imp, and (iv) application to a case study in the influence area of the Villarrica volcano in southern Chile. The results show that when incorporating social variables and accessibility, infrastructure criticality varies significantly compared to the infrastructure criticality assessment based solely on physical risk, modifying the decision-making regarding road infrastructure robustness and resilience improvements. Full article

The Mapleton Falls National Park transmission line corridor in Queensland, Australia, has received a number of vegetation management treatments over the last decade to maintain and protect the infrastructure and to ensure continuous electricity supply. Recent treatments have included ‘mega-mulching’ (mechanical mastication of vegetation to a mulch layer) in 2020 and targeted herbicide treatment of woody vegetation, with the aim of reducing vegetation height by encouraging a native herbaceous groundcover beneath the transmission lines. We measured vegetation structure (cover and height) and composition (species presence in 15 × 2 m plots), at 12 transects, 90 m in length on the transmission line corridor, to determine if management goals were being achieved and to determine how the vegetation and fire hazard (based on the overall fuel hazard assessment method) varied among the treated corridor, the forest edge environment, and the natural forest. The results showed that vegetation structure and composition in the treated zones had been modified to a state where herbaceous plant species were dominant; there was a significantly (p < 0.05) higher native grass cover and cover of herbs, sedges, and ferns in the treated zones, and a lower cover of trees and tall woody plants (>1 m in height) in these areas. For example, mean native grass cover and the cover of herbs and sedges in the treated areas was 10.2 and 2.8 times higher, respectively, than in the natural forest. The changes in the vegetation structure (particularly removal of tall woody vegetation) resulted in a lower overall fuel hazard in the treated zones, relative to the edge zones and natural forest. The overall fuel hazard was classified as ‘high’ in 83% of the transects in the treated areas, but it was classified as ‘extreme’ in 75% of the transects in the adjacent forest zone. Importantly, there were few introduced species recorded. The results suggest that fuel management has been successful in reducing wildfire risk in the transmission corridor. Temporal monitoring is recommended to determine the frequency of ongoing fuel management. Full article

South Dongting Lake is an essential aquatic ecosystem that receives substantial water inflows from the Xiangjiang and Zishui Rivers. However, it is significantly impacted by human activities, including mining, smelting, and farming. These activities have led to serious contamination of the lake’s sediments with heavy metals (HMs). This study investigated the distribution, mobility, and influencing factors of HMs at the sediment–water interface. To this end, sediment samples were analyzed from three key regions (Xiangjiang River estuary, Zishui River estuary, and northeastern South Dongting Lake) using traditional sampling methods and Diffusive Gradients in Thin Films (DGT) technology. Analysis of fifteen HMs (Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, V, Cr, Cu, Tl, Co, and Fe) revealed significant spatial heterogeneity. The results showed that Cr, Cu, Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, and Fe exhibited high variability (CV > 0.20), whereas V, Tl, and Co demonstrated stable concentrations (CV < 0.20). Concentrations were found to exceed background values of the upper continental crust of eastern China (UCC), Yangtze River sediments (YZ), and Dongting Lake sediments (DT), particularly at the Xiangjiang estuary (XE) and in the northeastern regions. Speciation analysis revealed that V, Cr, Cu, Ni, and As were predominantly found in the residual fraction (F4), while Pb and Co were concentrated in the oxidizable fraction (F3), Mn and Zn appeared primarily in the exchangeable fractions (F1 and F2), and Cd was notably dominant in the exchangeable fraction (F1), suggesting a high potential for mobility. Additionally, DGT results confirmed a significant potential for the release of Pb, Zn, and Cd. Contamination assessment using the Pollution Load Index (PLI) and Geoaccumulation Index (Igeo) identified Pb, Bi, Ni, As, Se, Cd, and Sb as major pollutants. Among these, Bi and Cd were found to pose the highest risks. Furthermore, the Risk Assessment Code (RAC) and the Potential Ecological Risk Index (PERI) highlighted Cd as the primary ecological risk contributor, especially in the XE. The study identified sediment grain size, pH, electrical conductivity, and nutrient levels as the primary influencing factors. The PMF modeling revealed HM sources as mixed smelting/natural inputs, agricultural activities, natural weathering, and mining/smelting operations, suggesting that remediation should prioritize Cd control in the XE with emphasis on external inputs. Full article

Earthquakes, as one of the most destructive natural disasters, often cause significant casualties and severe economic losses. Accurate prediction of earthquake fatalities is of great importance for pre-disaster prevention and mitigation planning, as well as post-disaster emergency response deployment. To address the challenges of small sample sizes, high dimensionality, and strong nonlinearity in earthquake fatality prediction, this paper proposes an integrated modeling approach (PCA-IWOA-XGBoost) combining Principal Component Analysis (PCA), the Improved Whale Optimization Algorithm (IWOA), and Extreme Gradient Boosting (XGBoost). The method first employs PCA to reduce the dimensionality of the influencing factor data, eliminating redundant information and improving modeling efficiency. Subsequently, the IWOA is used to intelligently optimize key hyperparameters of the XGBoost model, enhancing the prediction accuracy and stability. Using 42 major earthquake events in China from 1970 to 2025 as a case study, covering regions including the west (e.g., Tonghai in Yunnan, Wenchuan, Jiuzhaigou), central (e.g., Lushan in Sichuan, Ya’an), east (e.g., Tangshan, Yingkou), north (e.g., Baotou in Inner Mongolia, Helinger), northwest (e.g., Jiashi in Xinjiang, Wushi, Yongdeng in Gansu), and southwest (e.g., Lancang in Yunnan, Lijiang, Ludian), the empirical results showed that the PCA-IWOA-XGBoost model achieved an average test set accuracy of 97.0%, a coefficient of determination (R²) of 0.996, a root mean square error (RMSE) and mean absolute error (MAE) reduced to 4.410 and 3.430, respectively, and a residual prediction deviation (RPD) of 21.090. These results significantly outperformed the baseline XGBoost, PCA-XGBoost, and IWOA-XGBoost models, providing improved technical support for earthquake disaster risk assessment and emergency response. Full article

The increasing use of pharmaceutically active compounds (PhACs), endocrine-disrupting compounds (EDCs), and personal care products (PCPs) has led to the widespread presence of organic micropollutants (OMPs) in aquatic environments, posing a significant global challenge for environmental conservation. In recent years, advanced materials-based nanofiltration (NF) technologies have emerged as a promising solution for water and wastewater treatment. This review begins by examining the sources of OMPs, as well as the risk of OMPs. Subsequently, the key criteria of NF membranes for OMPs are discussed, with a focus on the roles of pore size, charge property, molecular interaction, and hydrophilicity in the separation performance. Against that background, this review summarizes and analyzes recent advancements in materials such as metal organic frameworks (MOFs), covalent organic frameworks (COFs), graphene oxide (GO), MXenes, hybrid materials, and environmentally friendly materials. It highlights the porous nature and structural diversity of organic framework materials, the advantage of inorganic layered materials in forming controllable nanochannels through stacking, the synergistic effects of hybrid materials, and the importance of green materials. Finally, the challenges related to the performance optimization, scalable fabrication, environmental sustainability, and complex separation of advanced materials-based membranes for OMP removal are discussed, along with future research directions and potential breakthroughs. Full article

Vortex-induced vibrations (VIVs) pose significant risks to the structural integrity of subsea cables and pipelines under free-span conditions. It is extremely helpful to be able to screen for VIV and understand for a particular cable or pipeline what the minimum free-span threshold lengths are beyond which in-line and/or cross-flow VIV can be excited, causing fatigue problems. To date screening is a more complex and detailed task. This paper introduces a universal dimensionless velocity, V*, and one graph that can be used across all types of VIV free spans to quickly assess minimum free-span threshold lengths. Natural frequencies are not required to be calculated for screening each time, as they are implicit in the curve. The universal criteria are developed via non-dimensional analysis to establish the significant physical mechanisms, after which the relationships are populated, forming a single curve for in-line and for cross-flow VIV with a typical mass ratio and a conservative zero as-laid tension case. Full article

The naturally occurring radioactive gas radon presents a major public health danger mainly affecting people who spend time in poorly ventilated buildings. The periodic table includes radon as a noble gas which forms through uranium decay processes in soil, rock, and water. The accumulation of radon indoors in sealed or poorly ventilated areas leads to dangerous concentrations that elevate human health risks of lung cancer. The research examines environmental variables affecting radon concentration indoors by studying geothermal installations and their drilling activities, which potentially increase radon emissions. The study was conducted in the basement of the plumbing educational building at the Aristotle University of Thessaloniki to assess the potential impact of geothermal activity on indoor radon levels, as the building is equipped with a geothermal heating system. The key findings based on 150 days of continuous data showed that radon levels peak during the cold days, where the concentration had a mean value of 41.5 Bq/m³ and reached a maximum at about 95 Bq/m³. The reason was first and foremost poor ventilation and pressure difference. The lowest concentrations were on days with increased human activity with measures that had a mean value of 14.8 Bq/m³, which is reduced by about 65%. The results that are presented confirm the hypotheses and the study is making clear that ventilation and human activity are crucial in radon mitigation, especially on geothermal and energy efficient structures. Full article

Soil salinization in oasis areas of arid regions is recognized as a dynamic and multifaceted environmental threat influenced by both natural processes and human activities. In this study, 13 spatiotemporal predictors derived from field surveys and remote sensing are utilized to construct a spatial probabilistic model of salinization. A Bayesian Belief Network is integrated with spline interpolation in ArcGIS to map the likelihood of salinization, while Partial Least Squares Structural Equation Modeling (PLS-SEM) is applied to analyze the interactions among multiple drivers. The test results of this model indicate that its average sensitivity exceeds 80%, confirming its robustness. Salinization risk is categorized into degradation (35–79% probability), stability (0–58%), and improvement (0–48%) classes. Notably, 58.27% of the 1836.28 km² Keriya Oasis is found to have a 50–79% chance of degradation, whereas only 1.41% (25.91 km²) exceeds a 50% probability of remaining stable, and improvement probabilities are never observed to surpass 50%. Slope gradient and soil organic matter are identified by PLS-SEM as the strongest positive drivers of degradation, while higher population density and coarser soil textures are found to counteract this process. Spatially explicit probability maps are generated to provide critical spatiotemporal insights for sustainable oasis management, revealing the complex controls and limited recovery potential of soil salinization. Full article

Implementing nature-based solutions (NbSs) for wildfire risk management and other hazards has been challenging in emerging economies due to the high costs, the lack of immediate returns on investment, and stringent inclusion criteria set by organizations like the IUCN and domain experts. To address these challenges, this exploratory study proposes a new concept: green/blue initiatives. These initiatives represent intermediate steps, encompassing small-scale, community-driven activities that can evolve into recognized NbSs over time. To explore this concept, experiences related to wildfire prevention in the Biobío region of Chile were analyzed through primary and secondary source reviews. The analysis identified three initiatives qualifying as green/blue initiatives: (1) goat grazing in Santa Juana to reduce fuel loads, (2) a restoration prevention farm model in Florida called Faro de Restauración Mahuidanche and (3) the Conservation Landscape Strategy in Nonguén. They were examined in detail using data collected from site visits and interviews. In contrast to Chile’s prevailing wildfire policies, which focus on costly, large-scale fire suppression efforts, these initiatives emphasize the importance of reframing wildfire as a manageable ecological process. Lastly, the challenges and enabling factors for adopting green/blue initiatives are discussed, highlighting their potential to pave the way for future NbS implementation in central Chile. Full article

This article presents a comprehensive assessment of mudflow risk in the Talgar River basin through the application of Multi-Criteria Decision Analysis (MCDA) methods and numerical modeling using the Rapid Mass Movement Simulation (RAMMS) environment. The first part of the study involves a spatial assessment of mudflow hazard and susceptibility using GIS technologies and MCDA. The key condition for evaluating mudflow hazard is the identification of factors influencing the formation of mudflows. The susceptibility assessment was based on viewing the area as an object of spatial and functional analysis, enabling determination of its susceptibility to mudflow impacts across geomorphological zones: initiation, transformation, and accumulation. Relevant criteria were selected for analysis, each assigned weights based on expert judgment and the Analytic Hierarchy Process (AHP). The results include maps of potential mudflow hazard and susceptibility, showing areas of hazard occurrence and risk impact zones within the Talgar River basin. According to the mudflow hazard map, more than 50% of the basin area is classified as having a moderate hazard level, while 28.4% is subject to high hazard, and only 1.8% falls under the very high hazard category. The remaining areas are categorized as very low (4.1%) and low (14.7%) hazard zones. In terms of susceptibility to mudflows, 40.1% of the territory is exposed to a high level of susceptibility, 35.6% to a moderate level, and 5.5% to a very high level. The remaining areas are classified as very low (1.8%) and low (15.6%) susceptibility zones. The predictive performance was evaluated through Receiver Operating Characteristic (ROC) curves, and the Area Under the Curve (AUC) value of the mudflow hazard assessment is 0.86, which indicates good adaptability and relatively high accuracy, while the AUC value for assessing the susceptibility of the territory is 0.71, which means that the accuracy of assessing the susceptibility of territories to mudflows is within the acceptable level of model accuracy. To refine the spatial risk assessment, mudflow modeling was conducted under three scenarios of glacial-moraine lake outburst using the RAMMS model. For each scenario, key flow parameters—height and velocity—were identified, forming the basis for classification of zones by impact intensity. The integration of MCDA and RAMMS results produced a final mudflow risk map reflecting both the likelihood of occurrence and the extent of potential damage. The presented approach demonstrates the effectiveness of combining GIS analysis, MCDA, and physically-based modeling for comprehensive natural hazard assessment and can be applied to other mountainous regions with high mudflow activity. Full article

Background: The aim of this review is to summarize and evaluate the properties of antibacterial polysaccharides for application in dental implantology to identify knowledge gaps and provide new research ideas. Methods: The electronic databases PubMed, Medline, ProQuest, and Google Scholar were used to search for peer-reviewed scientific publications published between 2018 and 2025 that provide insights to answer research questions on the role of antibacterial polysaccharides in combating pathogens in dental implantology without triggering immune reactions and inflammation. Further research questions relate to the efficacy against various dental pathogens and the understanding of the antibacterial mechanism, which may enable the development of functionalized polysaccharides with long-term antibacterial activity. Results: Biomedical implants have revolutionized medicine but also increased the risk of infections. Implant infections are a major problem in implantology and lead to implant failure and replacement. An antibacterial coating could be an excellent strategy to extend the lifespan of implants and improve the quality of the patient’s life. Bacterial resistance to antibiotics poses significant challenges for researchers, forcing them to search for new ways to prevent bacterial infections in implantology. Antibacterial natural polymers have recently received considerable research attention due to their long-term antibacterial activity. Polysaccharides from marine sources, such as chitosan and alginate, or pectin, xanthan, etc., from various plants, appear to be promising biopolymers for such applications in implantology due to their antibacterial activity, biocompatibility, and osteogenic properties. The antibacterial activity of these natural biopolymers depends on their chemical and physical properties. Nanopolysaccharides exhibit higher antibacterial activity than conventional polysaccharides, but their toxicity to human cells must be considered. Their antibacterial activity is based on the disruption of bacterial DNA or RNA synthesis, increased cell wall permeability, membrane disruption, and cytoplasmic leakage. Conclusions: Polysaccharides are a class of natural polymers with a broad spectrum of biological activities. They exhibit antioxidant, immunomodulatory, anticoagulant, anticancer, anti-inflammatory, antibacterial, and antiviral activity. Furthermore, polysaccharides are non-cytotoxic and exhibit good biocompatibility with osteogenic cells. Bactericidal polysaccharides are attractive new antibacterial materials against implant infections and open up new perspectives in implantology. Full article