Search Results (1,842)

Elucidating amyloid formation inside biomolecular condensates requires models that resolve (i) local, chemistry specific contacts controlling β registry and (ii) mesoscale phase behavior and cluster coalescence on microsecond timescales—capabilities beyond single resolution models. We present a hybrid united atom/coarse grained (UA–CG) force field coupling a PACE UA peptide model with the MARTINI CG framework. Cross resolution nonbonded parameters are first optimized against all atom side chain potentials of mean force to balance the relative strength between different types of interactions and then refined through universal parameter scaling by matching radius of gyration distributions for specific systems using. We applied this approach to simulate a recently reported model system comprising the LVFFAR₉ peptide that can co-assemble into amyloid fibrils via liquid–liquid phase separation. Our ten-microsecond simulations reveal rapid droplet formation populated by micelle like nanostructures with its inner core composed of LVFF clusters. The nanostructures can further fuse but the fusion is reaction-limited due to an electrostatic coalescence barrier. β structures emerge once clusters exceed ~10 peptides, and the LVFFAR₉ fraction modulates amyloid polymorphism, reversing parallel versus antiparallel registry at lower LVFFAR₉. These detailed insights generated from long simulations highlight the promise of our hybrid UA–CG strategy in investigating the molecular mechanism of condensate aging. Full article

Woven composite structures are inherently influenced by uncertainties across multiple scales, ranging from constituent material properties to mesoscale geometric variations. These uncertainties give rise to both spatial autocorrelation and cross-correlation among material parameters, resulting in stochastic strength performance and damage morphology at the macroscopic structural level. This study established a comprehensive multiscale uncertainty quantification framework to systematically propagate uncertainties from the microscale to the macroscale. A novel dual-correlation sampling approach, based on multivariate random field (MRF) theory, was proposed to simultaneously capture spatial autocorrelation and cross-correlation with clear physical interpretability. This method enabled a realistic representation of both inter-specimen variability and intra-specimen heterogeneity of material properties. Experimental validation via in-plane tensile tests demonstrated that the proposed approach accurately predicts not only probabilistic mechanical responses but also discrete damage morphology in woven composite structures. In contrast, traditional independent sampling methods exhibited inherent limitations in representing spatially distributed correlations of material properties, leading to inaccurate predictions of stochastic structural behavior. The findings offered valuable insights into structural reliability assessment and risk management in engineering applications. Full article

Cold-air pooling (CAP) and frost risk represent significant climate-related hazards in karstic and agricultural environments, where local topography and surface cover strongly modulate microclimatic conditions. This study focuses on the Mohos sinkhole, Hungary’s cold pole, situated on the Bükk Plateau, to investigate the formation, structure, and persistence of CAPs in a Central European karst depression. High-resolution terrain-based modeling was conducted using UAV-derived digital surface models combined with multiple GIS tools (Sky-View Factor, Wind Exposition Index, Cold Air Flow, and Diurnal Anisotropic Heat). These models were validated and enriched by multi-level temperature measurements and thermal imaging under various synoptic conditions. Results reveal that temperature inversions frequently form during clear, calm nights, leading to extreme near-surface cold accumulation within the sinkhole. Inversions may persist into the day due to topographic shading and density stratification. Vegetation and basin geometry influence radiative and turbulent fluxes, shaping the spatial extent and intensity of cold-air layers. The CAP is interpreted as part of a broader interconnected multi-sinkhole system. This integrated approach offers a transferable, cost-effective framework for terrain-driven frost hazard assessment, with direct relevance to precision agriculture, mesoscale model refinement, and site-specific climate adaptation in mountainous or frost-sensitive regions. Full article

Under harsh sea states, the dynamic characteristics of ocean mesoscale eddies (OMEs) become significantly more complex, posing substantial challenges to their accurate detection and identification. In this study, we propose an artificial intelligence detection method for OMEs based on the domain adaptation technique to accurately perform pixel-level segmentation and ensure its effectiveness under harsh sea states. The proposed model (LCNN) utilizes large kernel convolution to increase the model’s receptive field and deeply extract eddy features. To deal with the pronounced cross-domain distribution shifts induced by harsh sea states, an adversarial learning framework (ADF) is introduced into LCNN to enforce feature alignment between the source (normal sea states) and target (harsh sea states) domains, which can also significantly improve the segmentation performance in our constructed dataset. The proposed model achieves an accuracy, precision, and Mean Intersection over Union of 1.5%, 6.0%, and 7.2%, respectively, outperforming the existing state-of-the-art technologies. Full article

Low-level wind shear (LLWS) is a critical issue in aviation meteorology, posing serious risks to flight safety—especially at plateau airports with high elevation and complex terrain. This study investigates a convective wind shear event at Xining Airport on 29 May 2021. Multi-source observations—including the Doppler Wind Lidar (DWL), the Doppler weather radar (DWR), reanalysis datasets, and automated weather observation systems (AWOS)—were integrated to examine the event’s fine-scale structure and temporal evolution. High-resolution simulations were conducted using the Large Eddy Simulation (LES) framework within the Weather Research and Forecasting (WRF) model. Results indicate that the formation of this wind shear was jointly triggered by convective downdrafts and the gust front. A northwesterly flow with peak wind speeds of 18 m/s intruded eastward across the runway, generating multiple radial velocity couplets on the eastern side, closely associated with mesoscale convergence and divergence. A vertical shear layer developed around 700 m above ground level, and the critical wind shear during aircraft go-around was linked to two convergence zones east of the runway. The event lasted about 30 min, producing abrupt changes in wind direction and vertical velocity, potentially causing flight path deviation and landing offset. Analysis of horizontal, vertical, and glide-path wind fields reveals the spatiotemporal evolution of the wind shear and its impact on aviation safety. The WRF-LES accurately captured key features such as wind shifts, speed surges, and vertical disturbances, with strong agreement to observations. The integration of multi-source observations with WRF-LES improves the accuracy and timeliness of wind shear detection and warning, providing valuable scientific support for enhancing safety at plateau airports. Full article

This study evaluates the forecast performance of four radiation parameterization schemes—the Rapid Radiative Transfer Model for General Circulation Models (RRTMG), its improved version RRTMG-K, the infrequently applied variant, RRTMG-K_60x, and the neural network emulator, RRTMG-K_NN, within a high-resolution numerical weather prediction (NWP) model. The evaluation uses satellite-derived observations of Outgoing Longwave Radiation (OLR) and Outgoing Shortwave Radiation (OSR) from the Clouds and the Earth’s Radiant Energy System (CERES) over the Korean Peninsula during 2020, including an extreme case study of Typhoon Haishen. Results show that RRTMG-K reduces RMSEs by 4.8% for OLR and 17.5% for OSR relative to RRTMG, primarily due to substantial bias reduction (42.3% for OLR, 60.4% for OSR). The RRTMG-K_NN scheme achieves approximately 60-fold computational speedup while maintaining similar or slightly better accuracy than RRTMG-K; specifically, it reduces OLR errors by 1.2% and OSR errors by 1.6% compared to the infrequently applied RRTMG-K_60x. In contrast, the infrequent application of RRTMG-K (RRTMG-K_60x) slightly increases errors, underscoring the trade-off between computational efficiency and accuracy. These findings demonstrate the value of integrating advanced satellite flux observations and machine learning techniques into the evaluation and optimization of radiation schemes, providing a robust framework for improving cloud–radiation interaction representation in NWP models. Full article

This study examines the global mobility of researchers in the smart city domain from 2000 to 2020, using inter-country and intercity affiliation data from the Web of Science. Employing network analysis and spatial econometric models, the paper maps the structural reconfiguration of scientific labor circulation. The results show that the international mobility network is dense yet asymmetric, dominated by a small set of high-frequency corridors such as China–United States, which intensified markedly over the two decades. While early networks were fragmented and polycentric, the later period reveals a multipolar configuration with significant growth in South–South and intra-European exchanges. At the city level, Beijing, Shanghai, Wuhan, and Nanjing emerged as central nodes, reflecting the consolidation of East Asian hubs within the global knowledge system. Mesoscale community detection highlights the coexistence of territorially embedded ecosystems and transregional corridors sustained by thematic and reputational affinities. Growth decomposition indicates that high-income countries benefit from both talent retention and international inflows, while upper-middle-income countries rely heavily on inbound mobility. Spatial regression and quantile models confirm that economic growth and baseline scientific visibility remain robust drivers of urban smart city performance. In contrast, mobility effects are context-dependent and heterogeneous across city positions. Together, these findings demonstrate that researcher mobility is not only a vector of knowledge exchange but also a mechanism that reinforces spatial hierarchies and reshapes the geography of global smart city innovation. Full article

With the deepening implementation of the coordinated development strategy for energy exploitation and ecological conservation, green coal mining technology has become a critical pathway to achieve balanced resource development and environmental protection. This study investigates the stress field evolution and dynamic fracture propagation mechanisms in overlying strata during shallow coal seam mining in the Shenfu mining area. By employing a multidisciplinary approach combining triaxial compression tests (0–15 MPa confining pressure), scanning electron microscopy (SEM) microstructural characterization, elastoplastic theoretical modeling, and FLAC3D numerical simulations, the synergistic failure mechanisms of overlying strata were systematically revealed. Gradient-controlled triaxial tests demonstrated significant variations in stress-strain responses across lithological types. Notably, Class IV sandstone exhibited exceptional uniaxial compressive strength of 106.7 MPa under zero confining pressure, surpassing the average strength of Class I–III sandstones (86.2 MPa) by 23.6%, attributable to its highly compacted grain structure. A nonlinear regression-derived linear strengthening model quantified that each 1 MPa increase in confining pressure enhanced axial peak stress by 4.2%. SEM microstructural analysis established critical linkages between microcrack networks/grain-boundary slippage at the mesoscale and macroscopic brittle failure patterns. Numerical simulations demonstrated that strata failure manifests as tensile-shear composite fractures, with lateral crack propagation inducing bed separation spaces. The stress field exhibited spatiotemporal heterogeneity, with maximum principal stress concentrating near the initial mining cut during early excavation. Fractures propagated obliquely at angles of 55–65° to the horizontal plane in an ‘inverted V’ pattern from the goaf boundaries, extending vertically 12–18 m before transitioning to the bent zone, ultimately forming a characteristic three-zone structure. Experimental and simulated vertical stress distributions showed minimal deviation (≤2.8%), confirming constitutive model reliability. This research quantitatively characterizes the spatiotemporal synergy of strata failure mechanisms in ecologically vulnerable northwestern China, proposing a confining pressure-effect quantification model for support parameter optimization. The revealed fracture dynamics provide critical insights for determining ecological restoration timelines, while establishing a novel theoretical framework for optimizing green mining systems and mitigating ecological damage in the Shenfu mining area. Full article

Summer sea breezes provide cooling in coastal cities; however, their temporal cooling distribution and inland penetration distance remain inadequately studied. This study employed the mesoscale Weather Research and Forecasting (WRF) model to analyze the sea breeze cooling capacity (SBCC) in detail. The results identified the distance from the coast, cooling timing, and proximity to inland rivers as key factors influencing the SBCC. The cooling range and intensity of sea breezes exhibited a temporal pattern, initially increasing and then decreasing, with the rate of increase significantly exceeding the decline. The maximum cooling range (277.44 km²) and strongest cooling intensity (37,989.61 °C.h) occurred at 10:00. Between 11:00 and 14:00, the cooling effect remained stable over its longest inland distance (16.2 km). The SBCC intensified notably closer to the coastline. Furthermore, inland rivers significantly enhanced the cooling effect, with the sea breeze penetration distance correlating positively with the proximity to these rivers. A detailed analysis of the SBCC’s spatial extent and cooling distance provides a crucial basis for effectively mitigating urban heat in coastal cities. Full article

Shale reservoirs offer significant potential for CO₂ geological sequestration due to their extensive nanopore networks and heterogeneous pore systems. This study comparatively assessed the CO₂ storage potential of the Lower Silurian Longmaxi and Lower Cambrian Qiongzhusi shales through an integrated approach involving organic geochemical analysis, mineralogical characterization through X-ray diffraction (XRD), mercury intrusion capillary pressure (MICP), low-pressure nitrogen and carbon dioxide physisorption, field-emission scanning electron microscopy (FE-SEM), stochastic 3D microstructure reconstruction, multifractal analysis, and three-dimensional succolarity computation. The results demonstrate that mineral assemblages and diagenetic history govern pore preservation: Longmaxi shales, with moderate maturity and shallower burial, retain abundant organic-hosted mesopores, whereas overmature and deeply buried Qiongzhusi shales are strongly compacted and mineralized, reducing pore availability. Multifractal spectra and 3D reconstructions reveal that Longmaxi develops broader singularity spectra and higher succolarity values, reflecting more isotropic meso-/macropore connectivity at the SEM scale, while Qiongzhusi exhibits narrower spectra and lower succolarity, indicating micropore-dominated and anisotropic networks. Longmaxi has nanometer-scale throats (D₅₀ ≈ 10–25 nm) with high CO₂ breakthrough pressures (P₁₀ ≈ 0.57 MPa) and ultra-low RGPZ permeability (mean ≈ 1.5 × 10⁻² nD); Qiongzhusi has micrometer-scale throats (D₅₀ ≈ 1–3 μm), very low breakthrough pressures (P₁₀ ≈ 0.018 MPa), and much higher permeability (mean ≈ 4.63 × 10³ nD). Storage partitioning further differs: Longmaxi’s median total capacity is ≈15.6 kg m⁻³ with adsorption ≈ 93%, whereas Qiongzhusi’s median is ≈12.8 kg m⁻³ with adsorption ≈ 70%. We infer Longmaxi favors secure adsorption-dominated retention but suffers from injectivity limits; Qiongzhusi favors injectivity but requires reliable seals. Full article

Conventional altimeter satellites, such as TOPEX/Poseidon and Jason series, can identify ocean mesoscale eddies (MEs) but cannot effectively distinguish submesoscale eddies (SMEs) due to horizontal resolution limitations. The emergence of the Surface Water and Ocean Topography (SWOT) satellite has enabled the resolution (or detection) of SMEs. At present, Data Unification and Altimeter Combination System (DUACS) (MEs-resolving) and SWOT (SMEs-resolving) satellites operate concurrently in orbit, however a systematic comparison and analysis of their observational outputs has yet to be conducted. Using a closed-contour scalar analysis method, this study identifies SMEs in the tropical western Pacific Ocean and compares the results with those from the dataset. The latitude-dependent Rossby deformation radius is employed to differentiate MEs from SMEs. For MEs, SWOT detects 176 per 10.5-day sub-cycle, while DUACS detects 162, which are roughly equivalent. For SMEs, SWOT identifies 273 per sub-cycle, far exceeding the 13 detected by DUACS. For amplitudes, DUACS measures 5.22 cm and 3.67 cm for MEs and SMEs, respectively, while the values reported by the SWOT satellite are 6.13 cm and 4.49 cm. In both datasets, cyclonic eddies are more prevalent in all cases except for the SMEs detected by SWOT, where anticyclonic eddies slightly outnumber cyclonic eddies. Additionally, during the trial operation and scientific orbit phases, SWOT is able to resolve 29 SMEs per orbit. The results indicate that high-resolution data can distinguish phenomena that conventional satellite altimeters cannot capture, providing valuable references for the analysis and application of SME characteristics. Full article

Severe Typhoon Danas over the northern part of the South China Sea and seas near Taiwan in early July 2025 had an erratic path that had not been observed before, according to historical data in the region. Its formation, movement, and intensification posed significant challenges to the timely tropical cyclone (TC) warning services. This paper documents the observational aspect and forecasting aspect of this cyclone. There are key findings: (a) when Danas interacted with the Central Mountain Range of Taiwan, a “secondary cyclone” appeared over the northeastern part of Taiwan, which was observed by both weather radars and meteorological satellite winds, and was simulated to a certain extent by a mesoscale numerical weather prediction (NWP) model; (b) data-driven AI global models performed better than physics-based global NWP models in capturing the formation and the rather erratic track of Danas a couple of days earlier, although AI models generally underestimate the intensity forecasts; and (c) an atmosphere–ocean–wave coupled model was found to perform the best in capturing both the track changes of Danas (because of being driven by an AI global model) and its intensity changes (because of better physical representation of the oceanic impact on the intensity of this TC), whereas AI global models, though with various recent enhancements, still tended to underestimate the strength of Danas. This paper serves as a reference of this rather unusual TC for the weather forecasting services in the region. Full article

This study presents a high-fidelity image acquisition method for asphalt film structure to address the challenge of capturing mesoscale structures, especially fine mineral filler and asphalt mastic. The method is particularly applied to the analysis of the mortar structure in reclaimed asphalt pavement (RAP) mixtures. A digital camera combined with image stacking and texture suppression techniques was used to develop a reproducible imaging protocol. The resulting sub-pixel images significantly improved clarity and structural integrity, particularly for particles smaller than 0.075 mm. U-Net-based segmentation identified 588,513 aggregate particles—34 times more than in standard images (17,428). Among them, 95% were smaller than 0.075 mm compared to just 45% in standard images. Furthermore, segmentation accuracy reached 99.3% in high-resolution images, surpassing the 98.1% in standard images. These results confirm the method’s strong capability to preserve microscale features and enhance fine particle recognition, making it more effective than conventional imaging approaches. This study bridges physical and digital workflows in asphalt material analysis, offering a scalable, reproducible pipeline for fine-structure identification. The methodology provides foundational support for data-driven pavement modeling, material optimization, and future integration into digital twin frameworks for intelligent infrastructure systems. Full article

The melting behavior of alkanes plays a critical role in a wide field of applications. While experimental studies have established the occurrence of premelting phenomena in both short- and long-chain alkanes, molecular-level insights remain limited. In this work, we employ coarse-grained molecular dynamics simulations to investigate the melting behavior of high-molecular-weight alkanes, with a particular focus on continuous premelting dynamics in the transition regime toward polymer-like systems. By simulating alkane chains of varying lengths and analyzing temperature-dependent structural changes, we identify a crossover from discrete phase transitions to a gradual premelting process beyond chain lengths of $N\approx 40$ coarse-grained beads. The extrapolation of melting temperatures to zero heating rate yields values that agree well with the experimental data for the longest simulated chains. Compared to previous simulation studies, the slower heating rates used here offer enhanced quantitative agreement. Overall, the results provide new molecular-level insights into the melting of long-chain alkanes and highlight the utility of coarse-grained models in capturing complex phase behavior. Full article

This study examines the influence of friction at the specimen–bar interface on the macroscopic response of materials during dynamic compression tests using the split Hopkinson Pressure Bar (SHPB) under high-deformation-rate conditions. A mesoscale model is employed to simulate and compare results with experimental data, and a finite element model of cylindrical specimens with varying slenderness ratios is developed in Abaqus/Explicit. Numerical analyzes show that both specimen geometry and boundary conditions, particularly friction, have a decisive impact on the accuracy and reliability of SHPB measurements. A friction correction method based on barreling factor and plastic deformation demonstrates closer agreement with experimental observations than conventional approaches, revealing that the widely used Avitzur model may overestimate friction by 34–39%. The results highlight the importance of accurate friction correction and the selection of optimal specimen dimensions to minimize testing errors. These findings improve the precision of dynamic material characterization and support the development of more reliable constitutive models to predict material behavior across a broad range of strain rates. Full article