Search Results (9,203)

Understanding mechanisms of asphalt in the process of natural aging is crucial for predicting its long-term durability and optimizing performance under diverse environmental conditions. Despite its importance, the microstructural and micromechanical changes induced by natural aging remain poorly understood, particularly under varying climatic influences. This study addresses this gap by analyzing the effects of natural aging on asphalt’s microscopic properties and identifying key indicators that govern its degradation. Asphalt samples were subjected to natural aging across five climatically distinct regions over 6, 12, and 18 months. Atomic force microscopy (AFM) was employed to characterize surface roughness, adhesion forces, and DMT modulus, while correlation analysis and principal component analysis (PCA) were used to identify relationships among micromechanical indicators and streamline the dataset. The results reveal that natural aging induces irreversible transformations in asphalt’s microstructure, driven by the combined effects of temperature, UV radiation, humidity, and oxygen. These processes promote the evolution of “Bee structures,” increase surface roughness, and accelerate phase separation, alongside chemical modifications such as oxidation and polymerization, leading to progressive material hardening and stiffness. Significant regional and temporal variations in adhesion forces and DMT modulus were observed, reflecting the cumulative impact of environmental factors on asphalt’s aging dynamics. Correlation analysis demonstrated strong associations between surface roughness and “Bee structure” area, while mechanical properties such as stiffness and adhesion were largely decoupled from morphological features. Environmental factors interact in complex ways to drive asphalt aging. Humidity enhances adhesion and stiffness via water-induced capillary forces, while temperature reduces surface roughness and adhesion through molecular reorganization. UV radiation accelerates oxidative degradation, promoting surface erosion and stiffness loss, while altitude modulates these dynamics by influencing temperature and UV exposure. Full article

Cloud-based Earth observation platforms, such as data cubes, enable reproducible analyses of long-term satellite time series for climate and urban studies. In parallel, Essential Climate Variables (ECVs) provide a standardised framework for monitoring climate dynamics, with urban land cover and temperature being particularly relevant in historic urban contexts. This study analyses long-term trends and statistical associations between satellite-based ECVs and urbanisation indicators within the Historic Urban Landscape (HUL) of Sfax (Tunisia) from 1985 to 2021. Using the Digital Earth Africa (DEA) data cube, we derived six urban spectral indices (USIs), land surface temperature, air temperature at 2 m, wind characteristics, and precipitation from Landsat and ERA5 reanalysis data. An automated and reproducible Python-based workflow was implemented to assess USI behaviour, evaluate their performance against the Global Human Settlement Layer (GHSL), and explore spatio-temporal co-variations between urbanisation and climate variables. Results reveal a consistent increase in air and surface temperatures alongside a decreasing precipitation trend over the study period. The USIs demonstrate comparable accuracy levels (≈88–90%) in delineating urban areas, with indices based on SWIR and NIR bands (NDBI, BUI, NBI) showing the strongest statistical associations with temperature variables. Correlation and multivariate regression analyses indicate that temporal variations in USIs are more strongly associated with air temperature than with land surface temperature; however, these relationships reflect statistical co-variation rather than causality. By integrating satellite-based ECVs within a data cube framework, this study provides an operational methodology for long-term monitoring of urban-climate interactions in historic Mediterranean cities, supporting both climate adaptation strategies and the objectives of the UNESCO HUL approach. Full article

Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion anchor integrating a pan–tilt–zoom (PTZ) camera and a near-ultrasonic signal transmitter to explicitly perceive indoor geometry, surface materials, and occlusion patterns. First, vision-derived priors are constructed on the anchor side based on line-of-sight reachability, orientation consistency, and directional risk, and are converted into soft anchor weights to suppress the impact of occlusion and pointing mismatch. Second, planar geometry and material cues reconstructed from camera images are used to generate probabilistic room impulse response (RIR) priors that cover the direct path and first-order reflections, where environmental uncertainty is mapped into path-dependent arrival-time variances and prior probabilities. Finally, under the RIR prior constraints, a path-wise posterior distribution is built from matched-filter outputs, and an adaptive fusion strategy is applied to switch between maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators, yielding debiased TOA measurements with calibratable variances for downstream localization filters. Experiments in representative complex indoor scenarios demonstrate mean localization errors of 0.096 m and 0.115 m in static and dynamic tests, respectively, indicating improved accuracy and robustness over conventional TOA estimation. Full article

Sustaining agricultural productivity and soil health under intensive cultivation requires a comprehensive understanding of fertilization effects, particularly on deeper soil layers, which has received limited attention compared to surface soils. This study investigated how different fertilization regimes (inorganic, organic, and combined organic–inorganic fertilizers) influence soil physicochemical properties, microbial diversity, community structure, and functional gene abundances at three soil depths (0–20 cm, 20–40 cm, and 40–60 cm) in a 40-year fertilization experiment. Organic fertilization significantly improved topsoil fertility indicators such as soil organic matter (56.6–109.2%), total nitrogen (66.7–122.0%), total phosphorus (198.6–413.2%), and available phosphorus (984.8–1622.1%) and potassium (35.3–438.1%). Compared with the unfertilized control and nitrogen-only treatment, rice yield increased by 97.1–130.5% under NPK and sole organic fertilization, and further increased by 184.1–255.9% under combined organic–inorganic fertilization. However, fertilization effects diminished with soil depth due to limited nutrient mobility. Microbial diversity significantly decreased with depth and was minimally influenced by fertilization treatments. Microbial community structure varied notably among fertilization treatments at the surface layer, mainly driven by soil nutrients, whereas soil depth had a dominant effect on microbial community structure and compositions. Co-occurrence networks showed the highest complexity in surface soil microbial communities, which declined with soil depth, reflecting potential synergistic and mutualistic relationships in topsoil and the adaptation of microbial communities to nutrient-limited conditions in subsoil. Microbial functional gene analyses highlighted clear depth-dependent distributions, with surface layers enriched in decomposition-related genes, while deeper layers favored anaerobic processes. Overall, long-term fertilization exerted strong depth-dependent effects on soil fertility, microbial community structure, and functional potential in paddy soils. Full article

This study proposes a neural-augmented Libor Market Model (LMM) for swaption surface calibration that enhances expressive power while maintaining the interpretability, arbitrage-free structure, and numerical stability of the classical framework. Classical LMM parametrizations, based on exponential decay volatility functions and static correlation kernels, are known to perform poorly in sparsely quoted and long-tenor regions of swaption volatility cubes. Machine learning–based diffusion models offer flexibility but often lack transparency, stability, and measure-consistent dynamics. To reconcile these requirements, the present approach embeds a compact neural network within the volatility and correlation layers of the LMM, constrained by structural diagnostics, low-rank correlation construction, and HJM-consistent drift. Empirical tests across major currencies (EUR, GBP, USD) and multiple quarterly datasets from 2024 to 2025 show that the neural-augmented LMM consistently outperforms the classical model. Improvements of approximately 7–10% in implied volatility RMSE and 10–15% in PV RMSE are observed across all datasets, with no deterioration in any region of the surface. These results reflect the model’s ability to represent cross-tenor dependencies and surface curvature beyond the reach of classical parametrizations, while remaining economically interpretable and numerically tractable. The findings support hybrid model designs in quantitative finance, where small neural components complement robust analytical structures. The approach aligns with ongoing industry efforts to integrate machine learning into regulatory-compliant pricing models and provides a pathway for future generative LMM variants that retain an arbitrage-free diffusion structure while learning data-driven volatility geometry. Full article

Titanium (Ti) and its alloys are widely used in biomedical applications due to their biocompatibility and corrosion resistance; however, surface modifications are required to enhance biological functionality. Surface functionalization using natural biomolecules has emerged as a promising strategy to improve early cell–surface interactions and biocompatibility of implant materials. In this study, Ti6Al4V alloy surfaces were biofunctionalized using Spirulina platensis (S. platensis) biomass and protein extract to evaluate morphological, chemical, and biological effects. The functionalization process involved activation with piranha solution, silanization with 3-aminopropyltriethoxysilane (APTES), and subsequent biomolecule immobilization. Surface characterization by scanning electron microscopy (SEM), inductively coupled plasma mass spectrometry (ICP-MS), energy-dispersive X-ray spectroscopy (EDS), and Fourier transform infrared spectroscopy (FTIR) confirmed the successful incorporation of microalgal components, including nitrogen-, phosphorus-, and oxygen-rich organic groups. Biomass-functionalized surfaces exhibited higher phosphorus and oxygen content, while protein-coated surfaces showed nitrogen-enrich chemical signatures, reflecting the distinct molecular compositions of the immobilized biomolecules. Cell adhesion assays demonstrated enhanced early cell attachment on biofunctionalized surfaces, particularly in samples functionalized with 5 g/L biomass for three hours, which showed significantly greater cell attachment than both the control and protein-treated samples. These findings highlight the complementary yet distinct roles of S. platensis biomass and protein extract in modulating surface chemistry and cell–material interactions, emphasizing the importance of tailoring biofunctionalization strategies to optimize early biological responses on titanium-based implants. Full article

Accurate estimation of soil moisture (SM) and vegetation optical depth (VOD) is essential for understanding land–atmosphere interactions, climate dynamics, and ecosystem processes. While passive microwave missions such as SMAP and SMOS provide reliable global SM and VOD products, they are limited by coarse spatial resolution and infrequent revisit times. Global Navigation Satellite System Reflectometry (GNSS-R) observations, particularly from the Cyclone GNSS (CYGNSS) mission, offer an improved spatiotemporal sampling rate. This study presents a deep learning framework based on an artificial neural network (ANN) for the simultaneous retrieval of SM and VOD from CYGNSS observations across the contiguous United States (CONUS). Ancillary input features, including specular point latitude and longitude (for spatial context), CYGNSS reflectivity and incidence angle (for surface signal characterization), total precipitation and soil temperature (for hydrological context), and soil clay content and surface roughness (for soil properties), are used to improve the estimates. Results demonstrate strong agreement between the predicted and reference values (SMAP SM and SMOS VOD), achieving correlation coefficients of R = 0.83 and 0.89 and RMSE values of 0.063 m³/m³ and 0.088 for SM and VOD, respectively. Temporal analyses show that the ANN accurately reproduces both seasonal and daily variations in SMAP SM and SMOS VOD (R ≈ 0.89). Moreover, the predicted SM and VOD maps show strong agreement with the reference SM and VOD maps (R ≈ 0.93). Additionally, ANN-derived VOD demonstrates strong consistency with above-ground biomass (R ≈ 0.77), canopy height (R ≈ 0.95), leaf area index (R = 96), and vegetation water content (R ≈ 0.90). These results demonstrate the generalizability of the approach and its applicability to broader environmental sensing tasks. Full article

This study investigates 49 gold solidi issued between the 4th and 5th century AD to determine their chemical composition. The coins were first catalogued by recording mass, diameter, and thickness. All specimens underwent non-destructive µ-EDXRF analysis to identify main elements, followed by semi-quantitative fineness evaluation. To validate these results, six coins were randomly micro-sampled: material was dissolved in aqua regia and analysed by ICP-AES for gold quantification and ICP-MS for high precision trace element determination. The non-destructive analyses showed consistently high gold percentages, confirming authenticity and the extensive use of this noble metal during the studied period. Two distinct groups were identified based on the XRF Pt/Pd ratio, suggesting the use of gold from different sources. Comparison of μ-EDXRF and ICP-AES gold contents shows no statistically significant differences; however, this apparent agreement should be interpreted cautiously, as it mainly reflects the limited resolving power of ICP-AES at very high gold concentrations rather than definitive evidence for the absence of surface-related effects. Trace elements analysis detected low concentrations of Cu, Sn, and Pb suggesting the use of alluvial gold for minting. The presence and correlation of terrigenous elements (Al, Ca, Ti, Cr, Mn, Fe, Ni, Zn, Sr) indicate soil as the burial site. Full article

The scale and magnitude of urban heating are often assessed using Satellite-Derived Land Surface Temperature (SD-LST). Yet, discrepancies in spatial resolution limit SD-LST’s ability to reflect pedestrian thermal experience, potentially leading to ineffective mitigation strategies. Hyper-local measurements of urban heat, defined as surface temperatures (T_S) at the scale of pedestrian activity (e.g., bus stops or street segments), may provide more accurate insights into thermal comfort. This study compares hyper-local ~0.01 m resolution T_S collected via consumer-grade Forward-Looking Infrared (FLIR) thermography with resampled 30 m resolution SD-LST from Landsat 8 and 9 images to evaluate their utility in predicting thermal comfort indices across 60 bus stops in Denver, Colorado. During the summer of 2023, 270 FLIR measurements were collected over 19 dates, with a four-day subset (n = 33) coinciding with Landsat imagery. FLIR T_S averaged 25.12 ± 5.39 °C, while SD-LST averaged 35.90 ± 12.56 °C, a significant 10.77 °C difference (95% CI: 6.81–14.73; p < 0.001). FLIR T_S strongly correlated with biometeorological metrics such as air temperature and mean radiant temperature (r > 0.8; p < 0.001), while SD-LST correlations were weak (r < 0.3). Linear mixed-effects models using FLIR T_S explained 50–66% of the variance in thermal comfort indices and met ISO 7726 standards. Each 1 °C increase in FLIR TS predicted a 0.75 °C rise in mean radiant temperature. These results highlight hyper-local thermography as a reliable, low-cost tool for urban heat resilience planning. Full article

Thermal radiation from high-yield airbursts constitutes a major damage mechanism. To address thermal radiation transmission in complex environments, a ray-tracing-based computational model is developed. This model incorporates atmospheric attenuation, fireball dynamic evolution, building shadowing, and ground/building reflections. Numerical results demonstrate that building shadowing and ground/building reflections significantly alter the thermal radiation distribution in such environments. The impact of ground and building reflections is directly related to surface reflectivity. At a reflectivity of 0.3, reflected radiation can reach 43% of the direct component. While multi-reflection effects are negligible at low reflectivity, they become significant at higher reflectivity values and must be considered in calculations. Full article

Background: This study aimed to explore the effects of neuromuscular electrical stimulation (NMES) combined with water-based resistance training on muscle activation and coordination during freestyle kicking. Methods: Thirty National Level male freestyle swimmers were randomly assigned to an experimental group (NMES + water-based training) or a control group (water-based training only) for a 12-week intervention. The experimental group received NMES pretreatment before each session. Underwater surface electromyography (sEMG) synchronized with high-speed video was used to collect muscle activation data and corresponding kinematic information during the freestyle kick. The sEMG signals were then processed using time-domain analysis, including integrated electromyography (iEMG), which reflects the cumulative electrical activity of muscles, and root mean square amplitude (RMS), which indicates the intensity of muscle activation. Non-negative matrix factorization (NMF) was further applied to extract and characterize muscle synergy patterns. Results: The experimental group showed significantly higher iEMG and RMS values in key muscles during both kicking phases. Within the core propulsion synergy, muscle weighting of vastus medialis and biceps femoris increased significantly, while activation duration of the postural adjustment synergy was shortened. The number of synergies showed no significant difference. Conclusions: NMES combined with water-based resistance training enhances muscle activation and optimizes neuromuscular coordination strategies, offering a novel approach to improving sport-specific performance. Full article

The interaction between moisture and textile materials plays a critical role in transient thermal comfort, particularly through the exothermic heat released during wetting. While the heat of wetting has been extensively characterized at the fiber level, its behavior in finished fabrics, where structure, porosity, and air gaps influence moisture uptake, remains poorly understood. This study quantifies the heat of wetting of clothing fabrics using a TAM Air isothermal microcalorimeter under controlled isothermal conditions (23 °C). Five fabric types representing different fiber chemistries (Merino wool, cotton, viscose, and polyester) were evaluated in both folded and dissected forms to assess the influence of sampling methods. Wool fabrics exhibited the highest heat release, followed by viscose and cotton, whereas polyester showed negligible exothermic response due to its non-hygroscopic nature. Overall, fabric-level heat of wetting values were lower and more variable than the corresponding fiber-level values reported in the literature, reflecting the combined effects of fabric structure, air permeability, surface hydrophilicity, and sampling uniformity. These findings demonstrate the feasibility and limitations of isothermal microcalorimetry for characterizing moisture–fabric interactions and highlight the need for improved sampling and measurement protocols to more accurately capture fabric-level sorption heat relevant to clothing comfort. Full article

We determined the in vivo absorption coefficient (μ_a) for 82 test subjects, all classified as Fitzpatrick skin phototypes II, III, IV, and V. Measurements were conducted using the integrating-sphere technique on the dorsal and palmar surfaces of the hand and the forearm. The reflectance data obtained were processed using the Inverse Adding Doubling algorithm to calculate the absorption coefficient. The mean values for this parameter ranged from 0.0132 mm⁻¹ to 0.1021 mm⁻¹ at a central wavelength of 624 nm. It was found that these parameters may be grouped into a distinct cohort, paving the way for studies and the design of light-based diagnostics and treatments better suited to the population in Mexico and Latin America. Full article

Vertical cavity surface emitting lasers (VCSELs) have shown great potential in high-speed communication, quantum information processing, and 3D sensing due to their excellent beam quality and low power consumption. However, generating high-purity and controllable circularly polarized light usually requires external optical components such as quarter-wave plates, which undoubtedly increases system complexity and volume, hindering chip-level integration. To address this issue, we propose a monolithic integration scheme that directly integrates a custom-designed double-layer asymmetric metasurface onto the upper distributed Bragg reflector of a chiral VCSEL. This metasurface consists of a rotated GaAs elliptical nanocolumn array and an anisotropic grating above it. By precisely controlling the relative orientation between the two, the in-plane symmetry of the structure is effectively broken, introducing a significant optical chirality response at a wavelength of 1550 nm. Numerical simulations show that this structure can achieve a near 100% high reflectivity for the left circularly polarized light (LCP), while suppressing the reflectivity of the right circularly polarized light (RCP) to approximately 33%, thereby obtaining an efficient in-cavity circular polarization selection function. Based on this, the proposed VCSEL can directly emit high-purity RCP without any external polarization control components. This compact circularly polarized laser source provides a key solution for achieving the next generation of highly integrated photonic chips and will have a profound impact on frontier fields such as spin optics, secure communication, and chip-level quantum light sources. Full article

In building measurement using terrestrial laser scanners (TLSs), acquired 3D point clouds (3DPCs) often contain significant reflection artifacts caused by reflective glass surfaces. Such reflection artifacts significantly degrade the performance of downstream applications. This study proposes a novel strategy, called GRASS, to remove these reflection artifacts. Specifically, candidate glass points are identified based on multi-echo returns caused by glass components. These potential glass regions are then refined through planar segmentation using geometric constraints. Then, we trace laser beam trajectories to identify the reflection affected zones based on the estimated glass planes and scanner positions. Finally, reflection artifacts are identified using dual criteria: (1) Reflection symmetry between artifacts and their source entities across glass components. (2) Geometric similarity through a 3D deep neural network. We evaluate the effectiveness of the proposed solution across a variety of 3DPC datasets and demonstrate that the method can reliably estimate multiple glass regions and accurately identify virtual points. Furthermore, both qualitative and quantitative evaluations confirm that GRASS outperforms existing methods in removing reflection artifacts by a significant margin. Full article