Search Results (683)

This study presents a rigorous experimental and numerical investigation of the synergistic effect of metakaolin (MK) and waste glass (WG) on the structural performance of reinforced concrete (RC) beams without stirrups. A two-phase methodology was adopted: (i) optimization of MK and WG replacement levels through concrete-equivalent mortar mixtures and (ii) evaluation of the fresh and hardened properties of concrete, including compressive and tensile strengths, elastic modulus, sorptivity, and beam shear capacity. Five beam groups incorporating up to 30% MK, 15% WG, and 1% steel fiber were tested under four-point bending. The results demonstrated that MK enhanced compressive strength (up to 22%), WG improved workability but reduced ductility, and the combined system achieved a 13% increase in shear strength relative to the control. Steel fibers further restored ductility, increasing the ductility index from 1.338 for WG-only beams to 2.489. Finite Element Modeling (FEM) using ABAQUS with the Concrete Damage Plasticity (CDP) model reproduced experimental (EXP) load–deflection responses, peak loads, and crack evolution with high fidelity. This confirmed the predictive capability of the numerical framework. By integrating material-level optimization, structural-scale testing, and validated FEM simulations, this study provides robust evidence that MK–WG concrete, especially when fiber-reinforced, delivers mechanical, durability, and structural performance improvements. These findings establish a reliable pathway for incorporating sustainable cementitious blends into design-oriented applications, with direct implications for the advancement of performance-based structural codes. Full article

Offshore wind turbines positioned in deepwater areas are increasingly favored due to them providing superior and stable wind resources. However, the dynamic stability of floating offshore wind turbines (FOWTs) under complex environmental loading remains challenging. This study proposes a novel semi-submersible platform featuring a fractal structure inspired by the venation of Victoria Amazonica and investigates the effects of fractal branching level and biomimetic structural height on platform motions, with the aim of enhancing the overall system stability of FOWTs. Within a high-fidelity computational fluid dynamics (CFD) framework coupled with a dynamic fluid–body interaction (DFBI) model and a volume-of-fluid (VOF) wave model, the dynamic responses of the biomimetic platform are investigated under varying fractal dimensions (D_f) and structural heights. The results indicate that increasing fractal complexity enhances the local wall viscosity effect, significantly improving energy dissipation capabilities within the fractal cavities. Specifically, an eight-level fractal structure shows optimal performance, achieving reductions of approximately 16.94%, 23.26%, and 35.63% in heave, pitch, and rotational energy responses, respectively. Additionally, the increasing fractal height further strengthens energy dissipation, markedly enhancing stability, particularly in pitch motion. These findings underscore the substantial potential of biomimetic fractal designs in enhancing the dynamic stability of FOWTs. Full article

Understanding and modeling the flow of information in human societies is essential for capturing phenomena such as polarization, opinion formation, and misinformation diffusion. Traditional agent-based models often rely on simplified behavioral rules that fail to capture the nuanced and context-sensitive nature of human decision-making. In this study, we explore the potential of Large Language Models (LLMs) as data-driven, high-fidelity agents capable of simulating individual opinions under varying informational conditions. Conditioning LLMs on real survey data from the 2020 American National Election Studies (ANES), we investigate their ability to predict individual-level responses across a spectrum of political and social issues in a zero-shot setting, without any training on the survey outcomes. Using Jensen–Shannon distance to quantify divergence in opinion distributions and F1-score to measure predictive accuracy, we compare LLM-generated simulations to those produced by a supervised Random Forest model. While performance at the individual level is comparable, LLMs consistently produce aggregate opinion distributions closer to the empirical ground truth. These findings suggest that LLMs offer a promising new method for simulating complex opinion dynamics and modeling the probabilistic structure of belief systems in computational social science. Full article

Frequent building fires seriously threaten the safety of steel structures. According to the data, fire accidents account for about 35% of the total number of production safety accidents. The collapse of steel structures accounted for 42% of the total collapse. The early warning problem of steel structure fire collapse is imminent. This study aims to address this challenge by establishing a novel early warning framework, which is used to quantify the critical early warning threshold of steel frames based on elastic modulus degradation and its correlation with ultrasonic wave velocity under different collapse modes. The sequential thermal–mechanical coupling numerical method is used in the study. Firstly, Pyrosim is used to simulate the high-fidelity fire to obtain the real temperature field distribution, and then it is mapped to the Abaqus finite element model as the temperature load for nonlinear static analysis. The critical point of structural instability is identified by monitoring the mutation characteristics of the displacement and the change rate of the key nodes in real time. The results show that when the steel frame collapses inward as a whole, the three-level early warning elastic modulus thresholds of the beam are 153.6 GPa, 78.6 GPa, and 57.5 GPa, respectively. The column is 168.7 GPa, 122.4 GPa, and 72.6 GPa. Then the three-level warning threshold of transverse and longitudinal wave velocity is obtained. The three-stage shear wave velocity warning thresholds of the fire column are 2828~2843 m/s, 2409~2434 m/s, and 1855~1874 m/s, and the three-stage longitudinal wave velocity warning thresholds are 5742~5799 m/s, 4892~4941 m/s, and 3804~3767 m/s. The core innovation of this study is to quantitatively determine a three-level early warning threshold system, which corresponds to the three stages of significant degradation initiation, local failure, and critical collapse. Based on the theoretical relationship, these elastic modulus thresholds are converted into corresponding ultrasonic wave velocity thresholds. The research results provide a direct and reliable scientific basis for the development of new early warning technology based on acoustic emission real-time monitoring and fill the gap between the mechanism research and engineering application of steel structure fire resistance design. Full article

Small-target detection in Unmanned Aerial Vehicle (UAV) aerial images remains a significant and unresolved challenge in aerial image analysis, hampered by low target resolution, dense object clustering, and complex, cluttered backgrounds. In order to cope with these problems, we present AeroLight, a novel and efficient detection architecture that achieves high-fidelity performance in resource-constrained environments. AeroLight is built upon three key innovations. First, we have optimized the feature pyramid at the architectural level by integrating a high-resolution head specifically designed for minute object detection. This design enhances sensitivity to fine-grained spatial details while streamlining redundant and computationally expensive network layers. Second, a Dynamic Feature Fusion (DFF) module is proposed to adaptively recalibrate and merge multi-scale feature maps, mitigating information loss during integration and strengthening object representation across diverse scales. Finally, we enhance the localization precision of irregular-shaped objects by refining bounding box regression using a Shape-IoU loss function. AeroLight is shown to improve mAP50 and mAP50-95 by 7.5% and 3.3%, respectively, on the VisDrone2019 dataset, while reducing the parameter count by 28.8% when compared with the baseline model. Further validation on the RSOD dataset and Huaxing Farm Drone dataset confirms its superior performance and generalization capabilities. AeroLight provides a powerful and efficient solution for real-world UAV applications, setting a new standard for lightweight, high-precision object recognition in aerial imaging scenarios. Full article

Understanding long-term movement ecology is critical for conserving endangered species; however, comprehensive spatiotemporal analyses remain limited. In this study, we leveraged a decade-long GPS tracking dataset (2014–2024) of 31 endangered Crested Ibis (Nipponia nippon) individuals to elucidate their spatiotemporal behavioral patterns. The study focused on three key aspects: (1) fidelity to nesting, foraging, and roosting sites; (2) movement patterns and their ecological drivers; and (3) foraging habitat preferences across regions and activity periods. The results revealed exceptional fidelity to nesting, foraging (mean value = 0.253), and roosting sites (mean value = 0.261), underscoring the species’ pronounced spatial memory. Temporal factors emerged as the primary drivers of movement patterns, demonstrated by a significant annual reduction in home range size (p < 0.01) and a decline in daily flight distance in 2019 (β = −1890 ± 772 m, p < 0.05) and 2022 (p = 0.052). Behavioral factors also significantly influenced daily flight distance, with notable variations across different activity periods. Foraging habitat selection exhibited considerable spatial heterogeneity (14.2% constrained variance, p < 0.01). Cultivated lands, particularly paddy fields (Yangxian population) and drylands (Tongchuan population), served as core foraging zones. In contrast, spatiotemporal variables such as age had limited effects (<5% variance). This study provides the first empirical evidence of long-term site fidelity and habitat partitioning in the Crested Ibis, emphasizing the importance of landscape-level conservation planning. To this end, we propose two targeted strategies: establishing habitat corridors to enhance connectivity and safeguarding stable foraging areas within agricultural landscapes. These findings contribute to movement ecology theory while offering actionable frameworks for endangered species management. Full article

Hyperspectral target detection (HTD) aims to identify pixel-level targets within complex backgrounds, but existing HTD methods often fail to fully exploit multi-scale features and integrate global–local information, leading to suboptimal detection performance. To address these challenges, a novel hybrid Transformer–Mamba network (HTMNet) is proposed to reconstruct the high-fidelity background samples for HTD. HTMNet consists of the following two parallel modules: the multi-scale feature extraction (MSFE) module and the global–local feature extraction (GLFE) module. Specifically, in the MSFE module, we designed a multi-scale Transformer to extract and fuse multi-scale background features. In the GLFE module, a global feature extraction (GFE) module is devised to extract global background features by introducing a spectral–spatial attention module in the Transformer. Meanwhile, a local feature extraction (LFE) module is developed to capture local background features by incorporating the designed circular scanning strategy into the LocalMamba. Additionally, a feature interaction fusion (FIF) module is devised to integrate features from multiple perspectives, enhancing the model’s overall representation capability. Experiments show that our method achieves AUC(P_F, P_D) scores of 99.97%, 99.91%, 99.82%, and 99.64% on four public hyperspectral datasets. These results demonstrate that HTMNet consistently surpasses state-of-the-art HTD methods, delivering superior detection performance in terms of AUC(P_F, P_D). Full article

Star trackers are critical electro-optical devices used for satellite attitude determination, typically tested using Optical Ground Support Equipment (OGSE). Within the POR FESR 2014–2020 program (funded by Regione Toscana), we developed MINISTAR, a compact electro-optical prototype designed to generate synthetic star fields in apparent motion for realistic ground-based testing of star trackers. MINISTAR supports simultaneous testing of up to three units, assessing optical, electronic, and on-board software performance. Its reduced size and weight allow for direct integration on the satellite platform, enabling testing in assembled configurations. The system can simulate bright celestial bodies (Sun, Earth, Moon), user-defined objects, and disturbances such as cosmic rays and stray light. Radiometric and geometric calibrations were successfully validated in laboratory conditions. Under the PR FESR TOSCANA 2021–2027 initiative (also funded by Regione Toscana), the concept was further developed into STARLITE (STAR tracker LIght Test Equipment), a next-generation OGSE with a higher Technology Readiness Level (TRL). Based largely on commercial off-the-shelf (COTS) components, STARLITE targets commercial maturity and enhanced functionality, meeting the increasing demand for compact, high-fidelity OGSE systems for pre-launch verification of attitude sensors. This paper describes the working principles of a generic system, as well as its main characteristics and the early advancements enabling the transition from the initial MINISTAR prototype to the next-generation STARLITE system. Full article

In this study, we propose a novel pixel-level non-local similarity (PNS)-based reconstruction method for millimeter-wave interferometric synthetic aperture radiometer (InSAR) imaging. Unlike traditional compressed sensing (CS) methods, which rely on predefined sparse transforms and often introduce artifacts, our approach leverages structural redundancies in InSAR images through an enhanced sparse representation model with dynamically filtered coefficients. This design simultaneously preserves fine details and suppresses noise interference. Furthermore, an iterative refinement mechanism incorporates raw sampled data fidelity constraints, enhancing reconstruction accuracy. Simulation and physical experiments demonstrate that the proposed InSAR-PNS method significantly outperforms conventional techniques: it achieves a 1.93 dB average peak signal-to-noise ratio (PSNR) improvement over CS-based reconstruction while operating at reduced sampling ratios compared to Nyquist-rate fast fourier transform (FFT) methods. The framework provides a practical and efficient solution for high-fidelity millimeter-wave InSAR imaging under sub-Nyquist sampling conditions. Full article

Internal waves (IWs) play a crucial role in the transport of energy and matter within the ocean while also posing significant risks to marine engineering, navigation, and underwater communication systems. Consequently, effective segmentation methods are essential for mitigating their adverse impacts and minimizing associated hazards. A promising strategy involves applying remote sensing image segmentation techniques to accurately identify IWs, thereby enabling predictions of their propagation velocity and direction. However, current IWs segmentation models struggle to balance computational efficiency and segmentation accuracy, often resulting in either excessive computational costs or inadequate performance. Motivated by recent developments in the Mamba2 architecture, this paper introduces the state-space model meets linear attention (SMLA), a novel segmentation framework specifically designed for IWs. The proposed hybrid architecture effectively integrates three key components: a feature-aware serialization (FAS) block to efficiently convert spatial features into sequences; a state-space model with linear attention (SSM-LA) block that synergizes a state-space model with linear attention for comprehensive feature extraction; and a decoder driven by hierarchical fusion and upsampling, which performs channel alignment and scale unification across multi-level features to ensure high-fidelity spatial detail recovery. Experiments conducted on a dataset of 484 synthetic-aperture radar (SAR) images containing IWs from the South China Sea achieved a mean Intersection over Union (MIoU) of 74.3%, surpassing competing methods evaluated on the same dataset. These results demonstrate the superior effectiveness of SMLA in extracting features of IWs from SAR imagery. Full article

This paper presents a modular framework for character-coherent, emotion-aware role-playing dialogue with large language models (LLMs), centered on a novel Verifiable Emotion Reward (VER) objective. We introduce VER as a reinforcement-style signal derived from frozen emotion classifiers to provide both turn-level and dialogue-level alignment, effectively mitigating emotional drift across long interactions. To amplify VER’s benefits, we construct Character-Coherent Dialogues (CHARCO), a large-scale multi-turn dataset of over 230,000 dialogues, richly annotated with persona profiles, semantic contexts, and ten emotion labels. Our experiments show that fine-tuning LLMs on CHARCO significantly enhances VER’s impact, driving marked improvements in emotional consistency, role fidelity, and dialogue coherence. Through the evaluation that integrates lexical diversity metrics, automatic scoring with GPT-4, and human assessments, we demonstrate that the collaboration between a purpose-built multi-turn dataset and the VER objective leads to significant advancements in the field of persona-aligned conversational agents. Full article

Despite extensive research on sediment provenance in the Bohai Sea (BS), a significant knowledge gap persists concerning long-term provenance evolution, particularly in the western BS since the Middle Pleistocene. This shortcoming limits reconstructions of paleoenvironmental evolution and its interplay with climatic variability and sea-level fluctuations. This study presents integrated Rare Earth Element (REE) geochemical and sedimentological analyses of sediments from core DZQ01 in the western BS. The mean ΣREE concentration of 178.78 μg/g is characterized by pronounced light REE (LREE) enrichment relative to heavy REE (HREE). Chondrite- and upper continental crust (UCC)-normalized patterns exhibit distinct negative Eu anomalies, variable Ce anomalies, marked LREE enrichment, and pronounced LREE/HREE fractionation. Grain size exerts the dominant control on REE distribution, whereas the weak correlation between HREE fractionation parameter indices (e.g., Gd/Yb) and redox-sensitive proxies (e.g., δEu_UCC and δCe_UCC) confirms their fidelity as provenance indicators. When integrated with the δEu_UCC-δCe_UCC diagram, discriminant functions, and paleoenvironmental proxies (Rb/Sr and Mg/Ca ratios), the data indicate that, during interglacial highstands, the Yellow River (YR) was the principal source, delivering fine-grained terrigenous material from the Loess Plateau and thereby elevating REE concentrations. Conversely, glacial lowstands shifted the depositional environment to subaerial conditions, with the YR, Hai River, and Luan River supplying a coarse-fine admixture. Multi-river provenance and dilution by coarse detritus collectively lowered REE concentrations during these intervals. Full article

Background: Globally, socioeconomic disparities persist across the trajectory of chronic kidney disease and are pronounced among people with kidney failure. Unmet health-related social needs contribute to these disparities, but limited guidance exists about how best to address them. To guide implementation, we conducted a rapid scoping review to identify and characterize interventions that address health-related social needs among people with kidney failure. Methods: We adapted established scoping review methods to conduct a rapid review. We searched Embase, PubMed, CINAHL, SCOPUS, and PsychInfo for articles and conference abstracts published since 2013 that described interventions to address health-related social needs as identified in the Centers for Medicare and Medicaid Services’ Accountable Health Communities Health-Related Social Needs Screening Tool. We applied the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, Maintenance) to synthesize findings and characterize intervention components. Results: Our review identified three articles and five conference abstracts that described diverse interventions to address health-related social needs among people with kidney failure. Six targeted social support, one addressed food insecurity, and one addressed transportation needs. Two pilot studies to address social support reported high recruitment and retention rates. One study formally tested an intervention to address social support among adolescents with kidney failure and reported negative findings (no change in social exclusion). The level of detail about intervention implementation varied across studies, but none described excluded participants or intervention fidelity, adaptations, or cost. Conclusions: Despite recent attention, there remains a lack of evidence to guide interventions addressing health-related social needs among people with kidney failure. From limited available data, interventions to address social support may be feasible and acceptable. Full article

Ensuring the structural integrity of wind turbines is crucial for the sustainability of wind energy. A significant challenge remains in transitioning from mere defect detection to objective, scalable criticality assessment for prioritizing maintenance. In this work, we propose a novel comprehensive framework that leverages multispectral unmanned aerial vehicle (UAV) imagery and a novel standards-aligned Fuzzy Inference System to automate this task. Our contribution is validated on two open research-oriented datasets representing small on- and offshore machines: the public AQUADA-GO and Thermal WTB Inspection datasets. An ensemble of YOLOv8n models trained on fused RGB-thermal data achieves a mean Average Precision (mAP@.5) of 92.8% for detecting cracks, erosion, and thermal anomalies. The core novelty, a 27-rule Fuzzy Inference System derived from the IEC 61400-5 standard, translates quantitative defect parameters into a five-level criticality score. The system’s output demonstrates exceptional fidelity to expert assessments, achieving a mean absolute error of 0.14 and a Pearson correlation of 0.97. This work provides a transparent, repeatable, and engineering-grounded proof of concept, demonstrating a promising pathway toward predictive, condition-based maintenance strategies and supporting the economic viability of wind energy. Full article

Leaf area index (LAI) is a crucial parameter reflecting the crown structure of the grassland. Accurately obtaining LAI is of great significance for estimating carbon sinks in grassland ecosystems. However, spectral noise interference and pronounced spatial heterogeneity within vegetation canopies constitute significant impediments to achieving high-precision LAI retrieval. This study used hyperspectral sensor mounted on an unmanned aerial vehicle (UAV) to estimate LAI in a typical grassland, Hulun Lake Basin. Multiple machine learning (ML) models were constructed to reveal a relationship between hyperspectral data and grassland LAI using two input datasets, namely spectral transformations and vegetation indices (VIs), while SHAP (SHapley Additive ExPlanation) interpretability analysis was further employed to identify high-contribution features in the ML models. The analysis revealed that grassland LAI has good correlations with the original spectrum at 550 nm and 750 nm–1000 nm, first and second derivatives at 506 nm–574 nm, 649 nm–784 nm, and vegetation indices including the triangular vegetation index (TVI), enhanced vegetation index 2 (EVI2), and soil-adjusted vegetation index (SAVI). In the models using spectral transformations and VIs, the random forest (RF) models outperformed other models (testing R² = 0.89/0.88, RMSE = 0.20/0.21, and RRMSE = 27.34%/28.98%). The prediction error of the random forest model exhibited a positive correlation with measured LAI magnitude but demonstrated an inverse relationship with quadrat-level species richness, quantified by Margalef’s richness index (MRI). We also found that at the quadrat level, the spectral response curve pattern is influenced by attributes within the quadrat, like dominant species and vegetation cover, and that LAI has positive relationship with quadrat vegetation cover. The LAI inversion results in this study were also compared to main LAI products, showing a good correlation (r = 0.71). This study successfully established a high-fidelity inversion framework for hyperspectral-derived LAI estimation in mid-to-high latitude grasslands of the Hulun Lake Basin, supporting the spatial refinement of continental-scale carbon sink models at a regional scale. Full article