Search Results (2,635)

This study investigates the dynamic response characteristics of the tunnel bottom structure, focusing on a heavy-haul railway tunnel. To assess the condition of the tunnel bottom, geological radar and drilling core techniques were employed, along with on-site dynamic testing. The dynamic stress and acceleration response characteristics of the tunnel bottom structure, situated in grade V surrounding rock, were analyzed under axle loads of 25 t, 27 t, and 30 t. Both time-domain and frequency-domain analyses were conducted to explore the impact of structural defects on the dynamic response of the tunnel bottom. The results indicate that the dynamic response of the tunnel bottom structure increases linearly with increasing train axle load. In the presence of void-related defects at the tunnel bottom, the dynamic response of the structure is amplified, with an observed growth rate of up to 26.3%. Furthermore, the load exerted by heavy-duty trains on the tunnel bottom structure is predominantly a low-frequency effect, concentrated within the range of 0–20 Hz. Analysis of the 1/3 octave band reveals that the maximum difference in acceleration levels occurs at a center frequency of 31.5 Hz. Additionally, as the distance between the measurement point and the vibration source increases, the dynamic response induced by the void defect on the tunnel bottom structure weakens. Full article

The real estate industry, known for its complexity and exposure to systemic and idiosyncratic risks, requires increasingly sophisticated investment risk assessment tools. In this study, we present the Real Estate Construction Investment Risk (RECIR) model, a machine learning-based framework designed to quantify and manage multi-dimensional investment risks in construction projects. The model integrates diverse data sources, including macroeconomic indicators, property characteristics, market dynamics, and regulatory variables, to generate a composite risk metric called the total risk score. Unlike previous artificial intelligence (AI)-based approaches that primarily focus on forecasting prices, we incorporate regulatory compliance, forensic risk assessment, and explainable AI to provide a transparent and accountable decision support system. We train and validate the RECIR model using structured datasets such as the American Housing Survey and World Development Indicators, along with survey data from domain experts. The empirical results show the relatively high predictive accuracy of the RECIR model, particularly in highly volatile environments. Location score, legal context, and economic indicators are the dominant contributors to investment risk, which affirms the interpretability and strategic relevance of the model. By integrating AI with ethical oversight, we provide a scalable, governance-aware methodology for analyzing risks in the real estate sector. Full article

The study investigates the impact of motions of floating offshore wind turbine platforms on wake evolution and overall wind farm performance, employing large-eddy simulation (LES) and dynamic wake modeling method. First, the differences between wakes of floating and bottom-fixed wind turbines under forced motion are examined. Subsequently, a systematic comparative analysis is performed for four representative floating platform configurations—Spar, Semi-submersible, Tension-Leg Platform (TLP), and Monopile (Mnpl)—to assess wake dynamics and downstream turbine responses within tandem-arranged arrays. Results indicate that platform pitch motion, by inducing periodic variations in the rotor’s relative inflow angle, significantly enhances wake unsteadiness, accelerates kinetic energy recovery, and promotes vortex breakdown. Tandem-arrange turbines simulations further reveal that platform-dependent motion characteristics substantially influence wake center displacement, velocity deficit, downstream turbine thrust, and overall power fluctuations at the wind farm scale. Among the examined configurations, the Spar platform exhibits the most pronounced wake disturbance and the largest downstream load and power oscillations, with rotor torque and thrust increasing by 10.2% and 10.6%, respectively, compared to other designs. This study elucidates the coupled mechanisms among 6-DOFs (Six Degrees Of Freedom) motions, wake evolution, and power performance, providing critical insights for optimizing floating wind farm platform design and developing advanced cooperative control strategies. Full article

Per- and polyfluoroalkyl substances (PFASs) have attracted global attention due to their persistence and biological toxicity, becoming critical emerging contaminants in river and lake environments worldwide. Building upon existing studies, this work aims to comprehensively understand the pollution patterns, environmental behaviors, and potential risks of PFASs in freshwater systems, thereby providing scientific evidence and technical support for precise pollution control, risk prevention, and the protection of aquatic ecosystems and human health. Based on publications from 2002 to 2025 indexed in the Web of Science (WoS), bibliometric analysis was used to explore the temporal evolution and research hotspots of PFASs, and to systematically review their input pathways, pollution characteristics, environmental behaviors, influencing factors, and ecological and health risks in river and lake environments. Results show that PFAS inputs originate from both direct and indirect pathways. Direct emissions mainly stem from industrial production, consumer product use, and waste disposal, while indirect emissions arise from precursor transformation, secondary releases from wastewater treatment plants (WWTPs), and long-range atmospheric transport (LRAT). Affected by source distribution, physicochemical properties, and environmental conditions, PFASs display pronounced spatial variability among environmental media. Their partitioning, degradation, and migration are jointly controlled by molecular properties, aquatic physicochemical conditions, and interactions with dissolved organic matter (DOM). Current risk assessments indicate that PFASs generally pose low risks in non-industrial areas, yet elevated ecological and health risks persist in industrial clusters and regions with intensive aqueous film-forming foam (AFFF) use. Quantitative evaluation of mixture toxicity and chronic low-dose exposure risks remains insufficient and warrants further investigation. This study reveals the complex, dynamic environmental behaviors of PFASs in river and lake systems. Considering the interactions between PFASs and coexisting components, future research should emphasize mechanisms, key influencing factors, and synergistic control strategies under multi-media co-pollution. Developing quantitative risk assessment frameworks capable of characterizing integrated mixture toxicity will provide a scientific basis for the precise identification and effective management of PFAS pollution in aquatic environments. Full article

The expanding use of rare earth elements (REEs) in high-tech industrials has increased their environmental release, raising concerns about their ecological risks. This study employed the Diffusive Gradients in Thin Films (DGT) technique to assess REE bioavailability, spatial distribution, and ecological risks of REEs in sediments of the Yitong River, a historically polluted urban river in Changchun, China. Sediment characteristics (organic matter, pH, salinity), nutrient dynamics (N, P), and metal concentrations (Fe, Mn, As, etc.) were analyzed alongside REEs to evaluate their interactions and environmental drivers. Results revealed that REE concentrations (0.453–1.687 μg L⁻¹) were dominated by light REEs (50.1%), with levels an order of magnitude lower than heavily industrialized regions. Ecological risk quotients (RQ) for individual REEs were below thresholds (RQ < 1), indicating negligible immediate risks, though spatial trends suggested urban runoff influences. Probabilistic risk assessment integrating DGT data and species sensitivity distributions (SSD) estimated a low combined toxic probability (2.26%) for REEs and nutrients. Microbial community analysis revealed correlations between specific bacterial (e.g., Clostridium, Dechloromonas) and fungal genera (e.g., Pseudeurotium) with metals and REEs, highlighting microbial sensitivity to pollutant shifts. This study provides a multidimensional framework linking REE bioavailability, sediment geochemistry, and microbial ecology, offering insights for managing REE contamination in urban riverine systems. Full article

Background: Epithelial ovarian cancer is an aggressive malignancy with poor prognosis despite advances in multimodal treatment. The systemic immune-inflammation index (SII) has emerged as a prognostic biomarker in various cancers; however, the impact of surgery-induced inflammatory changes remains unclear. Methods: This study evaluated the prognostic significance of postoperative changes in SII among patients with epithelial ovarian cancer undergoing primary surgery. Data from 374 patients treated at Samsung Medical Center and Kangbuk Samsung Hospital between 2016 and 2021 were retrospectively reviewed. SII was calculated from complete blood counts obtained within one month before surgery and on postoperative day 1. The percentage change in SII was analyzed, and the optimal cutoff was determined using receiver operating characteristic curve analysis. Survival outcomes were assessed using Kaplan–Meier and multivariable Cox regression models. Results: Patients with a postoperative SII increase > 98.4% (Group 2) had significantly poorer overall (HR = 1.86, p = 0.009) and progression-free survival (HR = 1.30, p = 0.112) compared with those with smaller changes (Group 1). Discussion: High-grade histology, serous subtype, and greater intraoperative blood loss were associated with higher postoperative SII. A marked postoperative increase in SII independently predicted poor survival, suggesting that dynamic inflammatory responses rather than static baseline levels provide additional prognostic information. Conclusions: Perioperative SII monitoring, easily obtainable from routine blood tests, may help identify high-risk patients who could benefit from intensified surveillance or adjuvant treatment. Prospective multicenter studies are warranted to validate these findings and explore whether perioperative modulation of systemic inflammation can improve outcomes. Full article

Accurately mapping urban residents’ exposure to green space at high spatiotemporal resolutions is essential for assessing disparities and equality across blocks and enhancing urban environment planning. In this study, we developed a framework to generate hourly green space exposure maps at 0.5 m resolution using multiple sources of remote sensing data and an Object-Based Image Classification with Graph Convolutional Network (OBIC-GCN) model. Taking the main urban area in Nanjing city of China as the study area, we proposed a Dynamic Residential Green Space Exposure (DRGE) metric to reveal disparities in green space access across four housing price blocks. The Palma ratio was employed to explain the inequity characteristics of DRGE, while XGBoost (eXtreme Gradient Boosting) and SHAP (SHapley Additive explanation) methods were utilized to explore the impacts of built environment factors on DRGE. We found that the difference in daytime and nighttime DRGE values was significant, with the DRGE value being higher after 6:00 compared to the night. Mean DRGE on weekends was about 1.5 times higher than on workdays, and the DRGE in high-priced blocks was about twice that in low-priced blocks. More than 68% of residents in high-priced blocks experienced over 8 h of green space exposure during weekend nighttime (especially around 19:00), which was much higher than low-price blocks. Moreover, spatial inequality in residents’ green space exposure was more pronounced on weekends than on workdays, with lower-priced blocks exhibiting greater inequality (Palma ratio: 0.445 vs. 0.385). Furthermore, green space morphology, quantity, and population density were identified as the critical factors affecting DRGE. The optimal threshold for Percent of Landscape (PLAND) was 25–70%, while building density, height, and Sky View Factor (SVF) were negatively correlated with DRGE. These findings address current research gaps by considering population mobility, capturing green space supply and demand inequities, and providing scientific decision-making support for future urban green space equality and planning. Full article

Background and Objectives: Dynamic nutrient profiling represents a paradigm shift in personalized nutrition, integrating real-time nutritional assessment with individualized dietary recommendations through advanced algorithmic approaches, biomarker integration, and artificial intelligence. This comprehensive systematic review and meta-analysis examines the current state of dynamic nutrient profiling methodologies for personalized diet planning, evaluating their effectiveness, methodological quality, and clinical outcomes. Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive search of electronic databases (PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and Google Scholar) from inception to December 2024. The protocol was prospectively registered in PROSPERO (Registration: CRD42024512893). Studies were systematically screened using predefined inclusion criteria, quality was assessed using validated tools (RoB 2, ROBINS-I, Newcastle–Ottawa Scale), and data were extracted using standardized forms. Random-effects meta-analyses were performed where appropriate, with heterogeneity assessed using I² statistics. Publication bias was evaluated using funnel plots and Egger’s test. Results: From 2847 initially identified records plus 156 from additional sources, 117 studies met the inclusion criteria after removing 391 duplicates and systematic screening, representing 45,672 participants across 28 countries. Studies employed various methodological approaches: algorithmic-based profiling systems (76 studies), biomarker-integrated approaches (45 studies), and AI-enhanced personalized nutrition platforms (23 studies), with some studies utilizing multiple methodologies. Meta-analysis revealed significant improvements in dietary quality measures (standardized mean difference: 1.24, 95% CI: 0.89–1.59, p < 0.001), dietary adherence (risk ratio: 1.34, 95% CI: 1.18–1.52, p < 0.001), and clinical outcomes including weight reduction (mean difference: −2.8 kg, 95% CI: −4.2 to −1.4, p < 0.001) and improved cardiovascular risk markers. Substantial heterogeneity was observed across studies (I² = 78–92%), attributed to methodological diversity and population characteristics. AI-enhanced systems demonstrated superior effectiveness (SMD = 1.67) compared to traditional algorithmic approaches (SMD = 1.08). However, current evidence is constrained by practical limitations, including the technological accessibility of dynamic profiling systems and equity concerns in vulnerable populations. Additionally, the evidence base shows geographical concentration, with most studies conducted in high-income countries, underscoring the need for research in diverse global settings. These findings have significant implications for shaping public health policies and clinical guidelines aimed at integrating personalized nutrition into healthcare systems and addressing dietary disparities at the population level. Conclusions: Dynamic nutrient profiling demonstrates significant promise for advancing personalized nutrition interventions, with robust evidence supporting improved nutritional and clinical outcomes. However, methodological standardization, long-term validation studies exceeding six months, and comprehensive cost-effectiveness analyses remain critical research priorities. The integration of artificial intelligence and multi-omics data represents the future direction of this rapidly evolving field. Full article

Immersion deep ultraviolet (DUV) lithography remains an indispensable core technology in advanced integrated circuit manufacturing, particularly when combined with multiple patterning techniques to achieve sub-10 nm feature patterning. However, at advanced technology nodes, dynamic instabilities of DUV light sources—including spectral characteristics (bandwidth fluctuations, and center wavelength drift), coherence variations, and pulse-to-pulse energy instability—can adversely affect imaging contrast, normalized image log-slope (NILS), and critical dimension (CD) uniformity. To quantitatively assess the impact of laser parameter fluctuations on NILS and CD, this work establishes systematic physical models for imaging perturbations caused by multi-parameter laser output instabilities under immersion DUV lithography. Through simulations, we evaluate the influence of laser parameter variations on the imaging fidelity of representative line/space (L/S) and tip-to-line (T2L) structures, thereby validating the proposed perturbation model. Research demonstrates that the spectral attributes (bandwidth fluctuation and center wavelength drift), coherence variations, and pulse energy instability collectively induce non-uniform electric field intensity distribution within photoresist, degrading NILS, and amplifying CD variation, which ultimately compromise pattern fidelity and chip yield. Notably, at advanced nodes, pulse energy fluctuation exerts a significantly greater influence on imaging errors compared to bandwidth and wavelength variations. To satisfy the 10% process window requirement for 45 nm linewidths, pulse energy fluctuations should be rigorously confined within 1%. This research provides theoretical foundations and practical insights for the design of dynamic stability control of light source and process optimization of next-generation DUV light sources. Full article

Single-atom catalysts (SACs) have attracted extensive attention owing to their outstanding catalytic performance and nearly complete atom utilization efficiency. However, the environmental sustainability of SACs across their full life cycle has not yet been systematically investigated. This review emphasizes the necessity of integrating life cycle assessment (LCA) into SACs to support their sustainable development. By analyzing the structural characteristics, synthesis strategies, and representative application fields, this study examines how LCA principles can be employed to reveal the hidden environmental burdens associated with raw material extraction, synthesis processes, usage stages, and end-of-life management. Based on existing LCA case studies of catalytic materials, this review identifies the key challenges in the SACs field and proposes a preliminary framework for sustainable SAC design with LCA as a guiding approach. Finally, the review summarizes the current challenges and future perspectives, emphasizing that developing more specific evaluation standards, improving database construction, and adopting dynamic assessment methods are essential to shift LCA from a passive evaluation tool to an active design strategy that drives the green development of next-generation SACs. Full article

Background/Objectives: Naegleria fowleri is a free-living protozoan that causes primary amoebic meningoencephalitis, a rapidly progressing central nervous system infection with high mortality rates and limited treatment options. Targeting virulence-associated proteins is essential for effective drug development. Fowlerpain-1 (FWP1), a papain-like cysteine protease (CP) implicated in extracellular matrix degradation and host–cell cytotoxicity, has been investigated as a therapeutic target. This study aimed to evaluate the FWP1 pocket geometry and stefin binding using an integrated in silico structural biology approach. Methods: A computational pipeline was used, including AlphaFold2-Multimer modeling of FWP1–stefin complexes, 20-ns molecular dynamics simulations under NPT conditions for conformational sampling, and molecular mechanics Poisson–Boltzmann surface area free energy calculations. Three natural CP inhibitors (stefins) were investigated. Structural stability was assessed using root mean square deviations, and binding profiles were characterized using protein–protein interaction analysis. Results: Stable FWP1–stefin interaction interfaces were predicted, with human stefin A showing favorable binding free energy. Two conserved motifs (PG and QVVAG) were identified as critical mediators of active-site recognition. Druggability analysis revealed a concave pocket with both hydrophobic and polar characteristics, consistent with a high-affinity ligand-binding site. Conclusions: This computational study supports a structural hypothesis for selective FWP1 inhibition and identifies stefins as promising scaffolds for developing structure-guided protease-targeted therapeutics against N. fowleri. Full article

Vulnerability to hazards is a critical global issue, as it not only depends on the magnitude of natural hazards but also on the underlying social and economic conditions of communities. Understanding these factors is essential for designing effective risk reduction strategies and informed policy decisions. The objective of this research is to define a social vulnerability index (SoVI) and to analyse its distribution at the provincial and urban levels by applying different aggregation methods. This study provides a novel approach by examining the sensitivity of the index to different weighting methodologies, addressing a gap in the literature regarding the robustness of social vulnerability measures. An alternative approach is provided to determine the sensitivity of the SoVI in regions, in addition to understanding the dynamics of the socioeconomic characteristics considered in the territory and contributing to the theoretical and normative discussion of the construction of the index. To meet the objective, a sensitivity analysis is provided through different methods of weighting the vulnerability dimensions. The results indicate that the distribution of the SoVI in the provinces of Ecuador is heterogeneous, highlighting the importance of considering local socioeconomic contexts in vulnerability assessments. Additionally, the study shows that the values of the constructed index are sensitive to the weighting methods of the dimensions, which underscores the need for a careful selection of aggregation techniques to ensure reliable policy implications. It was also possible to identify that when social vulnerability is analysed at the city level, these show higher values than the corresponding provinces, challenging the common assumption that urban areas inherently provide better living conditions. This finding contributes to the ongoing debate on the impacts of rapid urbanization on social vulnerability. Full article

The Mangalia–Limanu coastal lake system, located in southeastern Romania along the Black Sea, represents a transitional aquatic environment shaped by the interplay between freshwater and marine influences. This study provides an integrated assessment of its physicochemical water parameters, sedimentological and geochemical properties, and benthic macroinvertebrate communities, aiming to evaluate its current ecological status and environmental dynamics. Field measurements using a multiparameter sonde revealed a predominantly freshwater to oligohaline system with moderate spatial heterogeneity. DO levels frequently reached supersaturation (>180%), coupled with high pH (~9.1), indicating intense daytime photosynthetic activity. Conductivity, TDS, and salinity increased longitudinally toward the port water area, while nitrate concentrations showed stronger signals upstream. Sediments were dominated by organic matter (18–88%), with lower carbonate (3–53%) and siliciclastic (8–49%) contents. Organic-rich deposits prevailed in the western-central sector, where reduced hydrodynamics and submerged vegetation favor autochthonous organic accumulation, whereas the eastern sector, exposed to marine action, showed more siliciclastic-rich substrates. Geochemical analyses revealed localized exceedances of Cr, Ni, Cu, Zn, and Pb regulatory thresholds (Order 161/2006), suggesting potential contamination hotspots. Benthic communities included 26 taxa, dominated by polychaetas, gammarids, and gastropods, with moderate diversity (H′ < 2). The results highlight a system under moderate anthropogenic pressure but retaining transitional lagoon characteristics, emphasizing the need for continued ecological monitoring and integrated management measures. Full article

Understanding the seasonal dynamics of the fisheries and crustacean communities are of crucial ecological significance. To investigate the structural characteristics of these communities and their seasonal dynamics in the offshore of the Zhoushan Archipelago Seas, China, this study conducted a four seasons’ trawl survey to collect fisheries data in spring, summer, autumn, and winter of 2022. A normalized abundance size spectrum approach was applied to investigate the seasonal variation in regressed parameters (slope and intercept) for fish-only and fish-plus-crustacean communities. Our study found that average values of the slope of the size spectrum for fish and fish-plus-crustacean were −1.36 and −1.53, respectively; the overall adding effect with crustaceans in all seasons was more negative (a steeper slope). The results also showed that the adding effect of crustaceans in the fisheries communities were season-specific and region-specific. Temporally, adding crustaceans into fisheries communities contributed to more/less negative slopes in temperate/warm seasons, respectively. Regionally, the inclusion of crustaceans induced a reverse distribution pattern (nearshore–offshore) for fish abundance, as well as the re-scaled intercept, which could indicate the abundance in all seasons except in summer. It was assumed that although fish dominated the overall community structure, crustaceans contributed a compensatory effect by regulating the size distribution across trophic levels. This study provides valuable insights for the dynamic assessment and scientific management of fisheries and crustacean resources in the whole ecosystem. Full article

Background: Non-smokers and individuals with minimal smoking history represent a significant proportion of lung cancer cases but are often overlooked in current risk assessment models. Pulmonary nodules are commonly detected incidentally—appearing in approximately 24–31% of all chest CT scans regardless of smoking status. However, most established risk models, such as the Brock model, were developed using cohorts heavily enriched with individuals who have substantial smoking histories. This limits their generalizability to non-smoking and light-smoking populations, highlighting the need for more inclusive and tailored risk prediction strategies. Purpose: We aimed to develop a longitudinal radiomics-based approach for lung cancer risk prediction, integrating time-varying radiomic modeling to enhance early detection in USPSTF-ineligible patients. Methods: Unlike conventional models that rely on a single scan, we conducted a longitudinal analysis of 122 patients who were later diagnosed with lung cancer, with a total of 622 CT scans analyzed. Of these patients, 69% were former smokers, while 30% had never smoked. Quantitative radiomic features were extracted from serial chest CT scans to capture temporal changes in nodule evolution. A time-varying survival model was implemented to dynamically assess lung cancer risk. Additionally, we evaluated the integration of handcrafted radiomic features and the deep learning-based Sybil model to determine the added value of combining local nodule characteristics with global lung assessments. Results: Our radiomic analysis identified specific CT patterns associated with malignant transformation, including increased nodule size, voxel intensity, textural entropy, as indicators of tumor heterogeneity and progression. Integrating radiomics, delta-radiomics, and longitudinal imaging features resulted in the optimal predictive performance during cross-validation (concordance index [C-index]: 0.69), surpassing that of models using demographics alone (C-index: 0.50) and Sybil alone (C-index: 0.54). Compared to the Brock model (67% accuracy, 100% sensitivity, 33% specificity), our composite risk model achieved 78% accuracy, 89% sensitivity, and 67% specificity, demonstrating improved early cancer risk stratification. Kaplan–Meier curves and individualized cancer development probability functions further validated the model’s ability to track dynamic risk progression for individual patients. Visual analysis of longitudinal CT scans confirmed alignment between predicted risk and evolving nodule characteristics. Conclusions: Our study demonstrates that integrating radiomics, sybil, and clinical factors enhances future lung cancer risk prediction in USPSTF-ineligible patients, outperforming existing models and supporting personalized screening and early intervention strategies. Full article