Search Results (879)

Aluminum nitride (AlN), diamond, and β-phase gallium oxide (β-Ga₂O₃) belong to the family of ultra-wide bandgap (UWBG) semiconductors and exhibit remarkable properties for future power and optoelectronic applications. Compared to conventional wide bandgap (WBG) materials such as silicon carbide (SiC) and gallium nitride (GaN), they demonstrate clear advantages in terms of high-voltage, high-temperature, and high-frequency operation, as well as extremely high breakdown fields. In this work, numerical simulations are performed to evaluate and compare the radiative responses of AlN, diamond, and β-Ga₂O₃ when exposed to neutron irradiation covering the full atmospheric spectrum at sea level, from 1 meV to 10 GeV. The Geant4 simulation framework is used to model neutron interactions with the three materials, focusing on single-particle events that may be triggered. A detailed comparison is conducted, particularly concerning the generation of secondary charged particles and their distributions in energy, linear energy transfer (LET), and range given by SRIM. The contribution of the ¹⁴N(n,p)¹⁴C reaction in AlN is also specifically investigated. In addition, the study examines the consequences of these interactions in terms of electron-hole pair generation and charge deposition, and discusses the implications for the radiation sensitivity of these materials when exposed to atmospheric neutrons. Full article

Generalized frosts have a significant impact on the Pampa Húmeda of Argentina, particularly those without persistence (0DP), defined as events that do not last more than one day, and are the most frequent generalized frosts. This study investigates the dynamical and physical mechanisms that sustain these events, emphasizing the nonlinear interactions represented by the Rossby Wave Source (RWS) equation. Composite analysis of pressure, temperature, wind and geopotential height fields were performed, showing that 0DP events are related to abrupt cold air intrusion linked to the enhancement of upper levels troughs over the eastern Pacific Ocean and transient surface anticyclones over South America. This linear analysis only showed a lack of persistent upper-level maintenance and did not explain the dynamics of the rapid weakening of the circulation. For this reason, a nonlinear analysis based on the decomposition of the RWS equation into its advective and divergent terms is performed. The advective term only acts as an initial trigger, deepening troughs and favoring meridional cold air advection, while the divergent term dominates the events, representing 63–67% of the affected area. This term reinforces ridges, promotes subsidence and favors clear sky conditions that enhance nocturnal radiative cooling and frost formation. Positive anomalies of the divergent RWS term strengthen the ridge and advect cold air over the Pampa Húmeda, whereas subsequent negative anomalies over the southwestern Atlantic act as sinks of wave activity, leading to the rapid dissipation of the synoptic configuration. Consequently, the same mechanism that generates favorable conditions for frost development also determines their lack of persistence. These findings demonstrate that the short-lived nature of 0DP frosts is not due to the absence of dynamical forcing, but rather to nonlinear processes that both enable and constrain frost occurrence. This highlights the importance of incorporating nonlinear diagnostics, such as the RWS, to improve the understanding of short-lived atmospheric extremes. Full article

Recent deep learning-based remote sensing analysis models often struggle with performance degradation due to domain shifts caused by illumination variations (clear to overcast), changing atmospheric conditions (clear to foggy, dusty), and physical scene changes (clear to snowy). Addressing domain shift in aerial image segmentation is challenging due to limited training data availability, including costly data collection and annotation. We propose Multi-Weather DomainShifter, a comprehensive multi-weather domain transfer system that augments single-domain images into various weather conditions without additional laborious annotation, coordinated by a large language model (LLM) agent. Specifically, we utilize Unreal Engine to construct a synthetic dataset featuring images captured under diverse conditions such as overcast, foggy, and dusty settings. We then propose a latent space style transfer model that generates alternate domain versions based on real aerial datasets. Additionally, we present a multi-modal snowy scene diffusion model with LLM-assisted scene descriptors to add snowy elements into scenes. Multi-weather DomainShifter integrates these two approaches into a tool library and leverages the agent for tool selection and execution. Extensive experiments on the ISPRS Vaihingen and Potsdam dataset demonstrate that domain shift caused by weather change in aerial image-leads to significant performance drops, then verify our proposal’s capacity to adapt models to perform well in shifted domains while maintaining their effectiveness in the original domain. Full article

The paper addresses the problem of attitude determination using Global Navigation Satellite System (GNSS) measurements from multiple antennas mounted on a navigation platform. To achieve attitude determination by GNSS with typical accuracy down to tenths of a degree for one-meter baselines, GNSS phase measurements are employed. A key challenge with phase measurements is the presence of unknown integer ambiguities. Consequently, the attitude determination problem traditionally reduces to a nonlinear, non-convex optimization problem with integer constraints. No closed-form solution to this problem is known, and its real-time calculation is computationally intensive. Given an a priori initial attitude approximation, we propose a new algorithm for attitude tracking based on the reduction of the nonlinear orthogonality-constrained attitude estimation problem to a linear integer least squares problem, for which numerical methods are well known and computationally much less demanding. Additionally, a simple a priori model for GNSS measurement error variance is introduced, grounded on the geometry of satellite signal propagation through vacuum and the Earth’s atmosphere, providing a clear physical interpretation. Applying the algorithm to a real dataset collected from a quasi-static multi-antenna, multi-GNSS system with sub-meter baselines, we obtain promising results. Full article

In this study, the long-term (1961–2020) values of the summertime station-based surface air temperature (SAT) data at 151 qualified stations, alongside the corresponding ERA5 gridded data, were analyzed to investigate climate change over the Middle East and the west Indian subcontinent. Significant positive (negative) trends were observed at 134 (2) stations, while trends were insignificant at 15 stations. The positive (negative and insignificant) trends were mainly concentrated in the interior highlands (monsoon-dominated lowlands), where ERA5 exhibited from 10% to 70% overestimations (5% to 26% underestimations). These ERA5-related biases exhibited strong correlations with elevation. To assess the trends’ disparity reasons, we first showed that the outputs of SAT_+AMO − SAT_−AMO are highly positive (negative or near zero) over the overestimated (underestimated) regions. The study then demonstrated that cloudiness, atmospheric circulation, specific humidity, and convective activities above the monsoon-dominated areas differ between +AMO and −AMO. For these areas, the enhanced +AMO-related cloudiness suppresses positive SAT anomalies, while the increased −AMO-associated sunshine offsets negative SAT anomalies. Contrarily, for some areas such as northern Iran, the +AMO (−AMO)-associated cloudiness or clear sky can affect climate change by amplifying the warmness or coldness. In addition, +AMO (−AMO) has caused further convective activities over the Arabian Sea (Bengal Bay). Full article

The solar terminator, due to its unique characteristics, is a remarkable source of atmospheric disturbances. Due to its regularity and constancy, dependent solely on geometric factors, it can serve as a test source of disturbances, which can be used to test the response of the medium through which it passes and determine its state. However, our knowledge of the atmospheric phenomena generated by the terminator is far from complete. One clear indication of the terminator’s influence is geomagnetic disturbances manifested in the vertical and eastward components of the magnetic field measured at magnetic observatories. To determine the sources of geomagnetic disturbances from the solar terminator, which can be identified by the strict phase correlation of these disturbances with the moments of terminator passage, ionospheric irregularities arising during terminator passage were studied. Ionospheric irregularities extending along the boundary of the morning solar terminator were detected in total electron content data, based on measurements by GNSS receivers. Assumptions are made about the possible parameters of the ionospheric current structure that creates variations in the magnetic field associated with the passage of the solar terminator. Full article

Accurate bathymetric information is crucial for coastal management, navigation, and ecosystem monitoring, yet conventional hydrographic surveys are costly and logistically demanding. This study introduces a two-stage semiempirical model for satellite-derived bathymetry (SDB) based on log-ratio reflectance indices from atmospherically corrected Landsat 8 imagery. The approach combines the optical sensitivity of the green/blue band ratio and the attenuation properties of the red/blue ratio within a parametric regression framework, enhancing both stability and interpretability. The methodology was evaluated in two contrasting coastal environments: the turbid Magdalena-Almejas Lagoon System (Mexico) and the clear-water coral reef setting of Buck Island (U.S. Virgin Islands). Results demonstrated that the proposed model outperformed traditional semiempirical approaches (Lyzenga, Stumpf, Hashim), achieving $R^2=0.8155$ (RMSE = 1.16 m) in Magdalena-Almejas and $R^2=0.9157$ (RMSE = 1.38 m) in Buck Island. Performance was statistically superior to benchmark methods according to cross-validated confidence intervals and was comparable to an artificial neural network, while avoiding overfitting in data-scarce environments. These findings highlight the model’s suitability as a transparent, cost-efficient, and scalable alternative for SDB, particularly valuable in regions where in situ data are limited. Full article

In this study, we propose a novel energetic structural material, HfZrTi(TaNbV)_x (x = 0.1, 0.3, 0.5, 0.7, 0.9, Ta:Nb: V = 1:1:1), to improve the ductility and toughness of the HfZrTi high-entropy alloy (HEAs). The transformation of the single-phase Hexagonal Close-Packed (HCP) HfZrTi-based alloy into a Body-Centered Cubic (BCC) phase HfZrTiTaNbV alloy can be achieved by tuning the concentration of Group VB β-stabilizing elements. The proposed alloy combines the insensitivity and excellent mechanical strength of conventional inert alloys with the ability to react with air under high-velocity impact for energy release. The mechanical properties and energy release characteristics of HZTX_x (H = Hf, Z = Zr, T = Ti, X = TaNbV) at various strain rates are systematically investigated, and comprehensive microstructural characterization is performed, establishing a clear structure–property relationship. Under high-rate loading, the rapid oxidation of reactive elements, such as Hf and Zr, with atmospheric oxygen releases substantial chemical energy, which can be further enhanced by an adiabatic temperature rise, inducing local thermal softening through adiabatic shear bands. This study elucidates the connection between the deformation response mechanism of HZTX_x under dynamic loading and the microstructure, providing crucial insights for advancing the application of high-entropy alloys in energetic systems. Full article

We present an experimentally validated, engineering-oriented framework for the design and operation of pin-to-water (PTW) atmospheric discharges to produce hydrogen peroxide (H₂O₂) on demand. Motivated by industrial needs for safe, point-of-use oxidant supply, we combine time-resolved diagnostics (FTIR, OES), liquid-phase analysis (ion chromatography, pH, conductivity), and coupled plasma-chemistry/fluid simulations to link plasma state to aqueous H₂O₂ yield. Under the tested conditions (14.3 kHz, 0.2 kW; electrode to quartz wall distance 12–14 mm; coolant setpoints 0–40 °C), H₂O₂ concentration follows a reproducible non-monotonic trajectory: rapid accumulation during the early treatment (typical peak at ~15–25 min), followed by decline with continued operation. The decline coincides with a robust vibrational-temperature (T_vib) threshold near ~4900 K measured from N₂ emission, and with concurrent NO_X accumulation and bulk acidification. Global chemistry modeling and Fluent flow fields reproduce the observed trend and show that both vibrational excitation (kinetics) and convective transport (mass/heat transfer) determine the productive time window. Based on these results, we formulate practical design rules—electrode gap (power density), discharge current control, thermal/flow management, water quality, and OES-based T_vib monitoring with an automated stop rule—that maximize H₂O₂ yield while avoiding NO_X-dominated suppression. The study provides a clear path for transforming mechanistic plasma insights into deployable, industrial H₂O₂ generator designs. Full article

Driving wheel slippage in agricultural tractors is influenced by soil moisture, density, and penetration resistance. These surface variations reflect post-tillage composition, enabling dynamic mapping via Remotely Piloted Aircraft (RPAs). This study evaluated ballast recommendations based on soil surface data and slippage percentages, correlating added wheel weights at different speeds for a tractor-reversible plow system. Six 94.5 m² quadrants were analyzed for slippage monitored by RPA (Mavic3M-RTK) pre- and post-agricultural operation overflights and soil sampling (moisture, density, penetration resistance). A 2 × 2 factorial scheme (F-test) assessed soil-surface attribute correlations and slippage under varying ballasts (52.5–57.5 kg/hp) and speeds. Results showed slippage ranged from 4.06% (52.5 kg/hp, fourth reduced gear) to 11.32% (57.5 kg/hp, same gear), with liquid ballast and gear selection significantly impacting performance in friable clayey soil. Digital Elevation Model (DEM) and spectral indices derived from RPA imagery, including Normalized Difference Red Edge (NDRE), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), Green–Red Vegetation Index (GRVI), Visible Atmospherically Resistant Index (VARI), and Slope, proved effective. The approach reduced tractor slippage from 11.32% (heavy ballast, 4th gear) to 4.06% (moderate ballast, 4th gear), showing clear improvement in traction performance. The integration of indices and slope metrics supported ballast adjustment strategies, particularly for secondary plowing operations, contributing to improved traction performance and overall operational efficiency. Full article

Radiometric consistency across satellite platforms is fundamental to producing high-quality Climate Data Records (CDRs). Because different cross-calibration methods have distinct advantages and limitations, comparative evaluation is necessary to ensure record accuracy. This study presents a comparative assessment of two widely applied calibration approaches—Simultaneous Nadir Overpass (SNO) and Double Difference (DD)—for the thermal infrared (TIR) bands of FY-3D MERSI. MODIS/Aqua serves as the reference sensor, while radiative transfer simulations driven by ERA5 inputs are generated with the Advanced Radiative Transfer Modeling System (ARMS) to support the analysis. The results show that SNO performs effectively when matchup samples are sufficiently large and globally representative but is less applicable under sparse temporal sampling or orbital drift. In contrast, the DD method consistently achieves higher calibration accuracy for MERSI Bands 24 and 25 under clear-sky conditions. It reduces mean biases from ~−0.5 K to within ±0.1 K and lowers RMSE from ~0.6 K to 0.3–0.4 K during 2021–2022. Under cloudy conditions, DD tends to overcorrect because coefficients derived from clear-sky simulations are not directly transferable to cloud-covered scenes, whereas SNO remains more stable though less precise. Overall, the results suggest that the two methods exhibit complementary strengths, with DD being preferable for high-accuracy calibration in clear-sky scenarios and SNO offering greater stability across variable atmospheric conditions. Future work will validate both methods under varied surface and atmospheric conditions and extend their use to additional sensors and spectral bands. Full article

Catalytic upgrading of vacuum residue (VR) is critical for enhancing fuel yield and reducing waste in petroleum refining. This study explores VR cracking over a novel cerium-loaded acidified metakaolinite catalyst (MKA800–20%Ce) prepared via calcination at 800 °C, acid leaching, and wet impregnation with 20 wt.% Ce. The catalyst was characterized using FTIR, BET, XRD, TGA, and GC–MS to assess structural, textural, and thermal properties. Catalytic cracking was carried out in a fixed-bed batch reactor at 350 °C, 400 °C, and 450 °C. The MKA800@Ce20% catalyst showed excellent thermal stability and surface activity, especially at higher temperatures. At 450 °C, the catalyst yielded approximately 11.72 g of total liquid product per 20 g of VR (representing a ~61% yield), with ~3.81 g of coke (~19.1%) and the rest as gaseous products (~19.2%). GC-MS analysis revealed enhanced production of light naphtha (LN), heavy naphtha (HN), and kerosene in the 400–450 °C range, with a clear temperature-dependent shift in product distribution. Structural analysis confirmed that cerium incorporation enhanced surface acidity, redox activity, and thermal stability, promoting deeper cracking and better product selectivity. Kinetics were investigated using an eight-lump first-order model comprising 28 reactions, with kinetic parameters optimized through a genetic algorithm implemented in MATLAB. The model demonstrated strong predictive accuracy taking into account the mean relative error (MRE = 9.64%) and the mean absolute error (MAE = 0.015) [MAE: It is the absolute difference between experimental and predicted values; MAE is dimensionless (reported simply as a number, not %). MRE is relative to the experimental value; it is usually expressed as a percentage (%)] across multiple operating conditions. The above findings highlight the potential of Ce-modified kaolinite-based catalysts for efficient atmospheric pressure VR upgrading and provide validated kinetic parameters for process optimization. Full article

The Chuankou deposit is a super-large W deposit formed during the Indosinian collision event in South China, and its mineralization is suggested to be related to the Indosinian muscovite granite. However, two types of W mineralizations were discovered in the Caledonian granite porphyry in the Chuankou W deposit: disseminated scheelite and quartz-wolframite-scheelite vein mineralizations. The genesis of W mineralization in the Caledonian granite porphyry is not yet clear. This paper focuses on fluid microthermometry and stable isotopes (C, H, O, S) analysis of the quartz and scheelite in the ores in the Caledonian granite porphyry in the Chuankou W deposit. The aims are to determine the nature and evolution of the ore-forming fluids, the origin of the ore-forming materials involved in the two types of W mineralization in the Caledonian granite porphyry, and to provide a detailed discussion of the deposit’s genesis. Microthermometry results of fluid inclusions with scheelite and quartz from two stages show that the average homogenization temperature in the quartz-veins within the Caledonian granite porphyry is 248 °C, and the average salinity is 6.31 wt.% NaCl eq (n = 85), the average homogenization temperature in the quartz-veins within the slate is 219 °C, and the average salinity is 5.57 wt.% NaCl eq (n = 49). The ore-forming fluids experienced an evolution from high temperature and high salinity to low temperature and low salinity. Sulfur isotope compositions show that the δ³⁴S values of pyrite and arsenopyrite in the quartz-veins within the Caledonian granite porphyry are 2.06 to 3.28‰ and −0.38 to 0.21‰, respectively, and the δ³⁴S value of pyrite in the quartz-veins within the slate is −1.72 to 0.47‰. The δ³⁴S values of each stage are close to 0‰, indicating that the origin of sulfur mainly from magma. The H-O isotope compositions of the quartz indicate that the ore-forming fluid was primarily magmatic water. The low δ¹⁸O_H2O values (1.74 to 1.58‰) are influenced by fluid–rock interactions or the incorporation of atmospheric precipitation. The carbon isotopes (δ¹³C = −9.5 to 8.3‰) indicate a magmatic origin, but the C isotopes of quartz in the quartz-veins within the slate shift toward sedimentary rocks, reflecting the incorporation of rock components in the late mineralization period. These isotopic differences indicate that the fluid–rock interaction gradually strengthened during fluid evolution. Full article

A variety of factors, such as absorption, reflection, and attenuation by atmospheric elements, influence the quantity of solar energy that reaches the surface of the Earth. This, in turn, impacts photovoltaic (PV) power generation. In light of this, a digital assessment of solar energy variability through short-term measurements was conducted to enhance PV power output. The clear-sky index $\left(K_t^{*}\right)$ methodology was employed, effectively eliminating any indications of solar energy obstruction and comparing the measured radiation to the theoretical clear-sky radiation. The solar energy data were gathered in Mozambique, specifically in the southern region at Maputo–1, Massangena, Ndindiza, and Pembe, in the mid-region at Chipera, Nhamadzi, Barue–1, and Barue–2, as well as in the northern region at Nipepe-1, Nipepe-2, Nanhupo-1, Nanhupo-2, and Chomba, over the period from 2005 to 2024, with measurement intervals ranging from 1 to 10 min and 1 h during the measurement campaigns conducted by FUNAE and INAM, with additional data sourced from the PVGIS, Meteonorm, NOAA, and NASA solar databases. The analysis indicates a $K_t^{*}$ value with a density approaching 1 for clear days, while intermediate-sky days exhibit characteristics that lie between those of clear and cloudy days. It can be inferred that there exists a robust correlation among sky types, with values ranging from 0.95 to 0.89 per station, alongside correlated energies, which experience a regression with coefficients between 0.79 and 0.95. Based on the analysis of the sample, the region demonstrates significant potential for solar energy utilization, and similar sampling methodologies can be applied in other locations to optimize PV output and other solar energy projects. Full article

Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine learning (ML) has emerged as a powerful tool to calibrate sensors, detect anomalies, and mitigate drift in large-scale deployment. This survey reviews advances in three methodological categories: traditional ML models, deep learning architectures, and hybrid or unsupervised methods. We also examine spatiotemporal QC frameworks that exploit redundancies across time and space, as well as real-time implementations based on edge–cloud architectures. Applications include personal exposure monitoring, integration with atmospheric simulations, and support for policy decision making. Despite these achievements, several challenges remain. Traditional models are lightweight but often fail to generalize across contexts, while deep learning models achieve higher accuracy but demand large datasets and remain difficult to interpret. Spatiotemporal approaches improve robustness but face scalability constraints, and real-time systems must balance computational efficiency with accuracy. Broader adoption will also require clear standards, reliable uncertainty quantification, and sustained trust in corrected data. In summary, ML-based QC shows strong potential but is still constrained by data quality, transferability, and governance gaps. Future work should integrate physical knowledge with ML, leverage federated learning for scalability, and establish regulatory benchmarks. Addressing these challenges will enable ML-driven QC to deliver reliable, high-resolution data that directly support science-based policy and public health. Full article