Search Results (650)

A notable decline in the frequency of sea fog inflows and an increase in low-cloud ceiling height were observed following the construction of the Saemangeum Seawall west of the Gunsan Airport, an area traditionally prone to frequent sea fog events. To the mechanisms underlying these changes, a numerical experiment was conducted using the Weather Research and Forecasting model. An 11-m-high seawall was used as a physical barrier, and an elevated sea surface temperature (SST) was established within the enclosed area to simulate realistic post-construction conditions. The model successfully reconstructed sea fog occurrences, and the cloud–water mixing ratio effectively captured the spatial distribution of sea fog. Deviations from the control experiment showed a consistent pattern of reduced cloud–water mixing ratios near the surface and enhanced concentrations at high levels. Decreased buoyancy frequency in the surface layer enhanced atmospheric instability, inducing upward motion and intensified condensation activity. Increases in the turbulence kinetic energy within the planetary boundary layer (TKE within the PBL), vertical wind shear, and temperature further corroborated the reduction in sea fog and enhanced stratus formation. These findings indicate that the increased SST and seawall significantly influence the modification of the sea fog structure and its inflow dynamics. Full article

A continuum model for describing pseudo-turbulent flows of a dispersed phase is developed using a statistical approach based on the kinetic equation for the probability density of particle velocity and temperature. The introduction of the probability density function enables a statistical description of the particle ensemble through equations for the first and second moments, replacing the dynamic description of individual particles derived from Langevin-type equations of motion and heat transfer. The lack of detailed dynamic information on individual particle behavior is compensated by a richer statistical characterization of the motion and heat transfer within the particle continuum. A numerical simulation of the unsteady flow of a gas–particle suspension generated by the interaction of a shock wave with a particle cloud is performed using an interpenetrating continua model and equations for the first and second moments of both gas and particles. Numerical methods for solving the two-phase gas dynamics equations—formulated using a two-velocity and two-temperature model—are discussed. Each phase is governed by conservation equations for mass, momentum, and energy, written in a conservative hyperbolic form. These equations are solved using a high-order Godunov-type numerical method, with time discretization performed by a third-order Runge–Kutta scheme. The study analyzes the influence of two-dimensional effects on the formation of shock-wave flow structures and explores the spatial and temporal evolution of particle concentration and other flow parameters. The results enable an estimation of shock wave attenuation by a granular backfill. The extended pressure relaxation region is observed behind the cloud of particles. Full article

High-speed impact is a critical mechanism for structural damage. The infrared signatures generated during fragment formation provide essential data for damage assessment, protective system design, and target identification. This study investigated an aluminum alloy blunt projectile penetrating a target plate by employing smoothed particle hydrodynamics to simulate the debris ejection thermal and infrared properties. The infrared signatures of the debris clouds were calculated using Mie scattering theory under a spherical particle approximation. The reverse Monte Carlo methodology was applied to solve the radiative transfer equations and quantify the infrared emission characteristics. The infrared radiation characteristics of the debris cloud were investigated for projectile impact velocities of 800, 1000, and 1200 m/s. The results showed that the anterior debris regions reached peak temperatures of approximately 1200 K, with a temperature rise of 150–200 K per 200 m/s velocity increase behind the target. The medium-wave (3–5 μm) infrared intensity of the debris cloud was higher than the long-wave (8–12 μm) infrared intensity. The development of debris clouds enhanced the dispersion effect and slowed the increase in infrared radiation intensity in the same time interval. This study provides theoretical foundations for the dynamic infrared radiation characteristics of fragments generated by high-velocity projectile impacts. The infrared radiation characteristics within typical spectral bands can be utilized to assess hit probability and kill effectiveness. Full article

Comets are chemically rich and thermally extreme, spanning surface temperatures from ~50 K in the Oort Cloud to >1000 K for sungrazing bodies. These conditions may support key steps of prebiotic chemistry, including the synthesis of nucleic acid precursors. This study present a thermodynamic evaluation of seven candidate reactions, producing nitrogenous bases, sugars, nucleosides, and nucleotides, across the cometary temperature spectrum, 50–1000 K. Purine nucleobase synthesis, including adenine formation via aminoacetonitrile polymerization and HCN polymerization, is strongly exergonic at all temperatures. Sugar formation from formaldehyde is also exergonic, while intermediate pathways, e.g., 2-aminooxazole synthesis, become thermodynamically viable only above ~700 K. Nucleoside formation is thermodynamically neutral at low T but becomes favorable at elevated temperatures, whereas phosphorylation to AMP, i.e., adenosine-monophosphate, a nucleotide serving as a critical regulator of cellular energy status, remains highly endergonic under the entire T range studied. My analysis suggests that, under standard-state assumptions, comets can thermodynamically support formation routes of nitrogenous bases and simple sugars but not a complete nucleotide assembly. This supports a dual-phase origin scenario, where comets act as molecular reservoirs, with further polymerization and biological activation occurring post-delivery on planetary surfaces. Importantly, these findings represent purely thermodynamic assessments under standard-state assumptions and do not address kinetic barriers, catalytic influences, or adsorption effects on ice or mineral surfaces. The results should therefore be viewed as a baseline map of feasibility, subject to modifications in more complex chemical environments. Full article

Part of the relics of colonialism on the African continent is the loss of social identity caused by the adoption of colonial languages, leading to the endangered status of indigenous African languages. This qualitative study examines the potential and challenges of podcasting in indigenous African languages, with a focus on Yoruba. We conducted a sentiment analysis of the podcast “I Speak Yoruba Too” and “learn Yoruba online” to assess the range of audience feedback on the podcast. 735 data points were gathered and preprocessed, Hugging face transformers were used to analyse the sentiments on audience feedback. The result of the analysis shows that the negative reviews were 183, the neutral reviews 226, and the positive reviews 326. The visualisation of the word cloud of the labels shows the words frequently used in the reviews, revealing the challenges and the appreciation of the commenter. An in-depth interview was conducted with the host of the “I Speak Yoruba Too” podcast and the “learn Yoruba online Podcast”. The findings reveal that part of the challenges of podcasting include the absence of a standard Yoruba curriculum for foreign learners and time constraints. This paper argues that the deterministic nature of podcast technology offers opportunities to content creators and listeners, based on the medium’s flexibility and ease of access in facilitating language acquisition. Audience reviews and interview results also confirm the potential of the podcast to generate community building and social identity formation among learners. However, the monetisation of such digital products is often underexplored by both emerging and established podcasters. Full article

Ship berthing operations are inherently challenging for maritime vessels, particularly within restricted port areas and under unfavorable weather conditions. Contrary to autonomous open-sea navigation, autonomous ship berthing remains a significant technological challenge for the maritime industry. Lidar and optical camera systems have been deployed as auxiliary tools to support informed berthing decisions; however, these sensing modalities are severely affected by weather and light conditions, respectively, while cameras in particular are inherently incapable of providing direct range measurements. In this paper, we introduce a comprehensive, Radar-Based Berthing-Aid Dataset (R-BAD), aimed to cultivate the development of safe berthing systems onboard ships. The proposed R-BAD dataset includes a large collection of Frequency-Modulated Continuous Wave (FMCW) radar data in point cloud format alongside timestamped and synced video footage. There are more than 69 h of recorded ship operations, and the dataset is freely accessible to the interested reader(s). We also propose an onboard support system for radar-aided vessel docking, which enables obstacle detection, clustering, tracking and classification during ferry berthing maneuvers. The proposed dataset covers all docking/undocking scenarios (arrivals, departures, port idle, and cruising operations) and was used to train various machine/deep learning models of substantial performance, showcasing its validity for further autonomous navigation systems development. The berthing-aid system is tested in real-world conditions onboard an operational Ro-Ro/Passenger Ship and demonstrated superior, weather-resilient, repeatable and robust performance in detection, tracking and classification tasks, demonstrating its technology readiness for integration into future autonomous berthing-aid systems. Full article

Jets and outflows are key components of low-mass star formation, regulating accretion and shaping the surrounding molecular clouds. These flows, traced by molecular species at (sub)millimeter wavelengths (e.g., CO, SiO, SO, H₂CO, and CH₃OH) and by atomic, ionized, and molecular lines in the infrared (e.g., H₂, [Fe II], [S I]), originate from protostellar accretion disks deeply embedded within dusty envelopes. Jets play a crucial role in removing angular momentum from the disk, thereby enabling continued mass accretion, while directly preserving a record of the protostar’s outflow history and potentially providing indirect insights into its accretion history. Recent advances in high-resolution, high-sensitivity observations, particularly with the James Webb Space Telescope (JWST) in the infrared and the Atacama Large Millimeter/submillimeter Array (ALMA) at (sub)millimeter wavelengths, have revolutionized studies of protostellar jets and outflows. These instruments provide complementary views of warm, shock-excited gas and cold molecular component of the jet–outflow system. In this review, we discuss the current status of observational studies that reveal detailed structures, kinematics, and chemical compositions of protostellar jets and outflows. Recent analyses of mass-loss rates, velocities, rotation, molecular abundances, and magnetic fields provide critical insights into jet launching mechanisms, disk evolution, and the potential formation of binary systems and planets. The synergy of JWST’s infrared sensitivity and ALMA’s high-resolution imaging is advancing our understanding of jets and outflows. Future large-scale, high-resolution surveys with these facilities are expected to drive major breakthroughs in outflow research. Full article

This study aims to investigate the spatiotemporal evolution of cavitating flow and the associated acoustic responses around a NACA0015 hydrofoil. A coupled fluid–acoustic interaction model is developed by integrating a nonlinear cavitation model with vortex–sound coupling theory. Numerical simulations are conducted within a computational domain established for the hydrofoil to capture the interactions between cavitation dynamics and acoustic radiation. The results indicate that the temporal variations in cavity evolution and pressure fluctuations agree well with experimental observations. The simulations predict a dominant pressure fluctuation frequency of 30.15 Hz, consistent with the cavitation shedding frequency, revealing that the evolution of leading-edge vortex structures governs the periodic variations in the lift-to-drag ratio. Cavitation significantly modifies the development of vortex structures, with vortex stretching effects mainly concentrated near cavitation regions. The dilation–contraction term is closely associated with cavity formation, while the pressure–torque tilting term predominantly affects cloud cavitation collapse. Dynamic mode decomposition (DMD) shows that the coherent structures of the leading modes exhibit morphological similarity to multiscale cavitation and vortex structures. Furthermore, hydrofoil cavitation noise consists mainly of loading noise and cavitation-induced pulsating radiation noise, with surface acoustic sources concentrated in cloud cavitation shedding regions. The dominant frequency of cavitation-induced radiation noise is highly consistent with experimental measurements. Full article

Maintaining operational efficiency in distributed solar energy systems requires intelligent coordination of inspection tasks and workforce resources to handle diverse fault conditions. This study presents a bi-level multi-objective optimization framework that addresses two tightly coupled problems: dynamic job scheduling and skill-based team formation. The job scheduling component assigns geographically dispersed inspection tasks to mobile teams while minimizing multiple conflicting objectives, including travel distance, tardiness, and workload imbalance. Concurrently, the team formation component ensures that each team satisfies fault-specific skill requirements by optimizing team cohesion and compactness. To solve the bi-objective team formation problem, we propose HMOO-AOS, a hybrid algorithm integrating six metaheuristic operators under an NSGA-II framework with an Upper Confidence Bound-based Adaptive Operator Selection. Experiments on datasets of up to seven instances demonstrate statistically significant improvements ($p<0.05$) in solution quality, skill coverage, and computational efficiency compared to NSGA-II, NSGA-III, and MOEA/D variants, with computational complexity $\mathcal{O}\left(G·N·(M+logN)\right)$ (time complexity), $\mathcal{O}(N·L)$ (space complexity). A cloud-integrated system architecture is also proposed to contextualize the framework within real-world solar inspection operations, supporting real-time data integration, dynamic rescheduling, and mobile workforce coordination. These contributions provide scalable, practical tools for solar operators, maintenance planners, and energy system managers, establishing a robust and adaptive approach to intelligent inspection planning in renewable energy operations. Full article

Mineral dust plays a vital role in the Earth’s climate system, influencing radiation, cloud formation, biogeochemical cycles, and air quality. Accurately simulating dust transport in atmospheric models remains challenging, particularly for coarse and super-coarse particles, which are often underrepresented due to limitations in model physics and numerical treatment. Observations have shown that particles larger than 20 μm can remain airborne longer than expected, suggesting that standard gravitational settling formulations may be insufficient. One potential contributor to this discrepancy is the numerical diffusion introduced by advection schemes used to model sedimentation processes. In this study, we compare the commonly used first-order upwind advection scheme, which is highly diffusive, to a third-order scheme (UNO3) that reduces numerical diffusion while maintaining computational efficiency. Using 2-D sensitivity tests, we show that UNO3 retains up to 50% more dust mass for the coarsest particles compared to the default scheme, although overall dust lifetime shows little change. In 3-D simulations of the ASKOS 2022 dust campaign, both schemes reproduced similar large-scale dust patterns, with UNO3 yielding slightly lower dust. Overall, domain-averaged dust load differences remain small (less than 2%), with minor decreases in fine dust ~3% and slight increases in coarse dust ~2%, indicating that reducing numerical diffusion modestly enhances the presence of larger particles. Near the surface, UNO3 produces a ~4% increase in dust concentration, with local differences up to 50 μg/m³. These results highlight that while numerical diffusion does affect dust transport—especially for super-coarse fractions—its impact is relatively small compared to the larger underestimation of super-coarse dust commonly observed in models compared to measurements. Addressing the fundamental physics of super-coarse dust emission and lofting may therefore be a higher priority for improving dust model fidelity than further refining advection numerics. Future studies may also consider implementing more computationally intensive schemes, such as the Prather scheme, to further minimize numerical diffusion where highly accurate size-resolved transport is critical. Full article

Cirrus clouds cover 25% of the Earth at any given time. However, significant uncertainties remain in our understanding of cirrus cloud formation, in particular, how it is impacted by aerosols. This study investigates the formation and properties of dust-induced cirrus clouds using long-term observational datasets, focusing on Central Asia’s Aral Sea region and the Iberian Peninsula. We identify cirrus events influenced by mineral dust using an algorithm that uses CALIPSO satellite data through spatial and temporal proximity analysis. Results indicate significant seasonal and regional variations in the prevalence of dust-induced cirrus clouds, with spring emerging as the peak season for the Aral Sea and high-altitude Saharan dust transport influencing the Iberian Peninsula. With the help of DARDAR-Nice data, we characterize dust-induced cirrus clouds as being thicker, forming at higher altitudes, and exhibiting distinct microphysical properties, including reduced ice crystal concentrations and smaller frozen water content. Furthermore, a statistical test using a non-parametric Mann–Whitney U test is employed and confirms the robustness of the study. These findings enhance our understanding of the interactions between mineral dust and cloud microphysics, with implications for global climate modeling and weather forecasting. This study provides methodological advancements for dust-induced cloud detection and highlights the need for integrating a dust–cloud feedback mechanism in weather and climate models. Full article

Precise maintenance is vital for ensuring the safety of tunnel structures; however, traditional visual inspections are subjective and hazardous. Digital technologies such as LiDAR and imaging offer promising alternatives, but each has complementary limitations in geometric precision and visual representation. This study addresses these limitations by developing a three-dimensional modeling framework that integrates image and point cloud data and evaluates its effectiveness. Terrestrial LiDAR and UAV images were acquired three times over a freeze–thaw cycle at an aging, abandoned tunnel. Based on the data obtained, three types of 3D models were constructed: TLS-based, image-based, and fusion-based. A comparative evaluation results showed that the TLS-based model had excellent geometric accuracy but low resolution due to low point density. The image-based model had high density and excellent resolution but low geometric accuracy. In contrast, the fusion-based model achieved the lowest root mean squared error (RMSE), the highest geometric accuracy, and the highest resolution. Time-series analysis further demonstrated that only the fusion-based model could identify the complex damage progression mechanism in which leakage and icicle formation (visual changes) increased the damaged area by 55.8% (as measured by geometric changes). This also enabled quantitative distinction between active damage (leakage, structural damage) and stable-state damage (spalling, efflorescence, cracks). In conclusion, this study empirically demonstrates the necessity of data fusion for comprehensive tunnel condition diagnosis. It provides a benchmark for evaluating 3D modeling techniques in real-world environments and lays the foundation for digital twin development in data-driven preventive maintenance. Full article

Understanding and predicting the distribution of mineral dust in the atmosphere remains a major scientific challenge due to the complex nature of dust emission, transport, and deposition processes. Dust aerosols have a profound impact on climate, air quality, and biogeochemical cycles, making their accurate representation in models critical. In this study, we employ the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate dust events over the Mediterranean. To reduce model uncertainties, we assimilate satellite-derived dust optical depth observations from the MIDAS (Mineral Dust Aerosol Satellite) dataset. The assimilation of MIDAS data leads to significant improvements in the spatial and temporal accuracy of dust forecasts. The enhanced model outputs offer continuous in time and space dust fields that are particularly valuable for applications such as air quality management and the optimization of solar energy systems. Full article

This study explores the role of mineral dust in ice nucleation using WRF-L model simulations during the ASKOS-ESA and CPEX-CV campaigns (Cabo Verde, 2022). Numerical experiments are carried out to examine dust impacts and secondary ice production via the Hallett–Mossop process. The results show variability in ice and liquid water paths, with the modeled aerosol optical depth aligning well with AERONET data. A case study of 15 September 2022 reveals notable cloud structure differences in aerosol-aware simulations. These findings can inform future LES simulations with assimilated aerosol fields and radar comparisons, emphasizing the importance of accurately representing aerosol–cloud interactions in atmospheric models. Full article

With the advancement of 3D sensing technologies, point clouds have become a key data format in the construction industry, supporting tasks such as as-built verification and BIM integration. However, robust and accurate edge detection from unstructured point cloud data remains a critical challenge, particularly in architectural environments characterized by structured geometry and variable noise conditions. This study presents a comparative evaluation of two classical edge detection algorithms—Random Sample Consensus (RANSAC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)—applied to terrestrial laser-scanned point cloud data of eight rectangular structural columns. After preprocessing with the Statistical Outlier Removal (SOR) algorithm, the algorithms were evaluated using four performance criteria: edge detection quality, BIM-based geometric accuracy (via Cloud-to-Cloud distance), robustness to noise, and density-based performance. Results show that RANSAC consistently achieved higher geometric fidelity and stable detection across varying conditions, while DBSCAN showed greater resilience to residual noise and flexibility under low-density scenarios. Although DBSCAN occasionally outperformed RANSAC in local accuracy, it tended to over-segment edges in high-density regions. These findings underscore the importance of selecting algorithms based on data characteristics and project goals. This study establishes a reproducible framework for classical edge detection in architectural point cloud processing and supports future integration with BIM-based quality control systems. Full article