Search Results (367)

The use of artificial oxygenation to counteract the effects of hypoxia and improve living standards in high-altitude, low-oxygen settings is widespread. A recognized consequence of this intervention is that it elevates the risk of fire occurrence. In this study, we simulated a real fire environment with low-pressure oxygen enrichment in a plateau area. A new multi-measuring apparatus was constructed by integrating an electronic control cone heater and a low-pressure oxygen enrichment combustion platform to enable the simultaneous measurement of multiple parameters. The combined effects of varying oxygen concentrations and thermal irradiance on the combustion behavior of wood plastic composites (WPCs) under specific low-pressure conditions were investigated, and alterations in crucial combustion parameters were examined and evaluated. Increasing the oxygen concentration and heat flux significantly reduced the ignition and combustion times. For instance, at 50 kW/m², the ignition time decreased from 75 s to 16 s as the oxygen concentration increased from 21% to 35%. This effect was suppressed by higher heat fluxes. Compared with low oxygen concentrations and low thermal radiation environments, the ignition time of the material under high oxygen concentrations and high thermal radiation conditions was shortened by more than 78%, indicating that its flammability is enhanced under extreme conditions. Higher oxygen concentrations enhanced the heat feedback to the fuel surface, which accelerated pyrolysis and yielded a more compact flame with reduced dimensions and a color transition from blue-yellow to bright yellow. This intensified combustion was further manifested by an increased mass loss rate (MLR), elevated flame temperature, and a decline in residual mass percentage. The combustion of WPCs displayed distinct stage characteristics, exhibiting “double peak” features in both the MLR and flame temperature, which were attributed to the staged pyrolysis of its wood fiber and plastic components. Full article

This study develops an explainable machine learning framework for wildfire prediction across Australia, integrating region-specific models and feature attribution to identify key environmental drivers. Three wildfire indicators, Estimated Fire Area (FA), Mean Fire Brightness Temperature (FBT), and Fire Radiative Power (FRP), were modeled using Lasso, Random Forest, LightGBM, and XGBoost. Performance metrics (RMSEC, RMSECV, RMSEP) confirmed strong calibration and generalization, with Tasmania and Queensland achieving the lowest prediction errors for FA and FRP, respectively. Feature importance and SHAP analyses revealed that soil moisture, solar radiation, precipitation, and humidity variability are dominant predictors. Extremes and variance-based measures proved more influential than mean climatic values, indicating that fire dynamics respond non-linearly to environmental fluctuations. Lasso models captured stable linear dependencies in arid regions, while ensemble models effectively represented complex interactions in tropical climates. The results highlight a hierarchical process where cumulative soil and radiation stress establish fire potential, and short-term meteorological variability drives ignition and spread. Projected climate shifts, declining soil water and increased radiative load, are likely to intensify these drivers. The framework supports interpretable, region-specific mitigation planning and paves the way for incorporating generative AI and multi-source data fusion to enhance real-time wildfire forecasting. Full article

In this study, the properties of Cu(Co) alloy films were investigated to assess their utility as an alternative material for interconnections in hybrid bonding applications. Thin films of Cu(Co) were deposited using electrochemical deposition in a standard sulfate-based electrolyte. X-ray photoelectron spectroscopy (XPS) of the films revealed that an increasing current density during deposition resulted in an increase in cobalt concentration. Bright-field scanning transmission electron microscopy (STEM) coupled with energy-dispersive x-ray spectroscopy (EDS) was used to visualize the fine-grained microstructure and confirmed grain boundary segregation of cobalt in the films. X-ray diffraction with a heated stage determined that the coefficient of thermal expansion (CTE) increased linearly with increasing cobalt content, from 17.5 ppm/K for pure copper to a maximum of 27.5 ppm/K for a film containing 24 at.% Co. Nanoindentation experiments found that the mechanical properties depended non-linearly on composition, with hardness increasing from 3.5 GPa for a 0% cobalt film to a maximum of 4.5 GPa (24 at.% Co) and the Young’s modulus increasing from 118 GPa to 214 GPa, respectively. Four-point probe electrical measurements confirmed the expected linear increase in resistivity as Co content increased. Since electrical and mechanical properties have differing dependences on the film composition, an optimal alloy composition that balances an acceptable increase in resistance with improved mechanical properties could enable more reliable, low-temperature bonding solutions in advanced microelectronic devices. Full article

Extremely high temperatures can severely impact urban livability and public health safety. However, risk assessments for high temperatures in cold-region cities remain inadequate. This study focuses on Dalian, a coastal city in northeastern China. Utilizing multi-source data, we established a population density prediction model based on the random forest algorithm and a heat vulnerability index (HVI) framework following the “Exposure-Sensitivity-Adaptability” paradigm constructed using an indicator system method, thereby building a high-temperature risk assessment system suited for more refined research. The results indicate the following: (1) Strong positive correlations exist between nighttime light brightness (NL), Road Density (RD), the proportion of flat area (SLP), the land surface temperature (LST), and the population distribution density, with correlation coefficients reaching 0.963, 0.963, 0.956, and 0.954, respectively. (2) Significant disparities exist in the spatial distribution of different criterion layers within the study area. Areas characterized by high exposure, high sensitivity, and low adaptability account for 13.04%, 8.05%, and 21.44% of the total area, respectively, with exposure being the primary contributing factor to high-temperature risk. (3) Areas classified as high-risk or extremely high-risk for high temperatures constitute 31.57% of the study area. The spatial distribution exhibits a distinct pattern, decreasing gradually from east to west and from the coast inland. This study provides a valuable tool for decision-makers to propose targeted adaptation strategies and measures based on the assessment results, thereby better addressing the challenges posed by climate change-induced high-temperature risks and promoting sustainable urban development. Full article

The L-band radiative transfer-forward modeling plays a crucial role in data assimilation for meteorological forecasting. By utilizing information from the underlying surface (typically land surface parameters and variables), such as soil moisture, soil temperature, snow cover, freeze–thaw status, and vegetation, the corresponding brightness temperatures can be simulated through the physical processes described by radiative transfer models. Data assimilation becomes meaningful when the errors introduced by the simulated brightness temperatures are smaller than the simulation accuracy of the land surface variables. However, radiative transfer models at the L-band cannot accurately simulate TB operationally. In this study, four machine learning methods, including random forest (RF), long short-term memory (LSTM), support vector machine (SVM), and deep neural networks (DNN), are employed to reconstruct the forward relationship from land surface parameters to brightness temperatures, serving as an alternative to traditional radiative transfer models. The performance of these methods is evaluated using ground-truthed soil moisture data, soil texture static data, and leaf area index (LAI). The results indicate that DNN and RF exhibit superior performance, with DNN achieving the lowest average unbiased root mean square error (ubRMSE) of 6.238 K for vertical polarization brightness temperature (TBv) and 9.033 K for horizontal polarization brightness temperature (TBh). Regarding correlation coefficients between the retrieved brightness temperatures and satellite measurements, RF leads for H-polarized TB with a value of 0.943, while both RF and SVM perform well for V-polarized TB with values of 0.930 and 0.932, respectively. In conclusion, our study shows that DNN is the optimal method for retrieving brightness temperatures, outperforming other machine learning approaches regarding error metrics and correlation with satellite measurements. These findings highlight the potential of DNN in improving data assimilation processes in meteorological forecasting. Full article

Anthraquinone acid dyes are widely used in dyeing polyamide due to their good exhaustion and brightness. While ionic interactions primarily govern dye–fiber bonding, the molecular weight (Mw) of these dyes can significantly influence migration, apparent color strength, and fastness behavior. This study offers comparative insight into how the Mw of structurally similar anthraquinone acid dyes impacts their diffusion, fixation, and functional outcomes (e.g., UV protection) on polyamide 6 fabric, using Acid Blue 260 (Mw~564) and Acid Blue 127:1 (Mw~845) as representative low- and high-Mw dyes. The effects of dye concentration, pH, and temperature on color strength (K/S) were evaluated, migration index and zeta potential were measured, and UV protection factor (UPF) and FTIR analyses were used to assess fabric functionality. Results showed that the lower-Mw dye exhibited higher migration tendency, particularly at increased dye concentrations, while the higher-Mw dye demonstrated greater color strength and superior wash fastness. Additionally, improved UPF ratings were associated with higher-Mw dye due to enhanced light absorption. These findings offer practical insights for optimizing acid dye selection in polyamide coloration to balance color performance and functional attributes. Full article

This study investigates the thermal stability and microstructural evolution of the solid electrolyte medium used in DLyte^® dry electropolishing and dry anodizing processes. Samples were thermally aged between 30 °C and 45 °C to simulate Joule heating during industrial operation. Visual and SEM analyses revealed shape deformation and microcrack formation at temperatures above 40 °C, potentially reducing particle packing efficiency and electrolyte performance. Particle size distribution shifted from bimodal to trimodal upon aging, with an overall size reduction of up to 39.5% due to dehydration effects, impacting ionic transport properties. Weight-loss measurements indicated a diffusion-limited dehydration mechanism, stabilizing at 15–16% mass loss. Fourier transform infrared analysis confirmed water removal while maintaining the essential sulfonic acid groups responsible for ionic conductivity. In dry anodizing tests on titanium, aged electrolytes enhanced process efficiency, producing TiO₂ films with improved optical properties—color and brightness—while preserving thickness and uniformity (~70 nm). The results highlight the need to carefully control thermal exposure to maintain electrolyte integrity and ensure consistent process performance. Full article

Convective updrafts are one of the main characteristics of convective clouds, responsible for the convective mass flux and the redistribution of energy and condensate in the atmosphere. During the early stages of their lifecycle, convective clouds experience rapid cloud-top ascent manifested by a decrease in the geostationary IR brightness temperature ($T{B}_{IR}$). Under the assumption that the convective cloud top behaves like a black body, the ascent rate of the convective cloud top can be estimated as (${\displaystyle \frac{\partial T{B}_{IR}}{\partial t}}$), and it can be used to infer the near cloud-top convective updraft. The temporal resolution of the geostationary IR measurements and non-uniform beam-filling effects can influence the convective updraft estimation. However, the main shortcoming until today was the lack of independent verification of the strength of the convective updraft. Here, Doppler radar observations from the ESCAPE and TRACER field experiments provide independent estimates of the convective updraft velocity at higher spatiotemporal resolution throughout the convective core column and can be used to evaluate the updraft velocity estimates from the IR cooling rate for limited samples. Isolated convective cells were tracked with dedicated radar (RHIs and PPIs) scans throughout their lifecycle. Radial Doppler velocity measurements near the convective cloud top are used to provide estimates of convective updrafts. These data are compared with the geostationary IR and VIS channels (from the GOES satellite) to characterize the convection evolution and lifecycle based on cloud-top cooling rates. Full article

Owing to the inherently lower sensitivity of microwave aperture synthesis radiometers (ASRs), Soil Moisture and Ocean Salinity (SMOS) satellite brightness temperature (TB) measurements exhibit significantly greater system noise than real-aperture microwave radiometers do. This paper introduces a novel noise mitigation method for the SMOS L1C multi-angle TB product. The proposed method develops a multi-angle sea surface TB relationship lookup table, enabling the mapping of SMOS L1C multi-angle TB data to any single-angle TB, thereby averaging to the measurements to reduce noise. Validation experiments demonstrate that the processed SMOS TB data achieve noise levels comparable to those of the Soil Moisture Active Passive (SMAP) satellite. Additionally, the salinity retrieval experiments indicate that the noise mitigation technique has a clear positive effect on SMOS salinity retrieval. Full article

We investigate the relativistic jet of the powerful radio-emitting blazar J1429+5406 at redshift $z=3.015$. Our understanding of jet kinematics in $z\ge 3$ quasars is still rather limited, based on a sample of less than about 50 objects. The blazar J1429+5406 was observed at a high angular resolution using the method of very long baseline interferometry over more than two decades, between 1994 and 2018. These observations were conducted at five radio frequencies, covering a wide range from $1.7$ to 15 GHz. The outer jet components at ∼20–40 milliarcsecond (mas) separations from the core do not show discernible apparent motion. On the other hand, three jet components within the central 10 mas region exhibit significant proper motion in the range of (0.045–0.16) mas year⁻¹, including one that is among the fastest-moving jet components at $z\ge 3$ known to date. Based on the proper motion of the innermost jet component and the measured brightness temperature of the core, we estimated the Doppler factor, the bulk Lorentz factor, and the inclination angle of the jet with respect to the line of sight. The core brightness temperature is at least $3.6\times {10}^{11}$ K, well exceeding the equipartition limit, indicating Doppler-boosted radio emission. The low jet inclination (≲5.4°) firmly places J1429+5406 into the blazar category. Full article

This study presents an objective scheme to construct hurricane bogus vortices based on satellite microwave scatterometer observations of sea surface wind vectors. When specifying a bogus vortex using Fujita’s formula, the required parameters include the center position and the radius of the maximum gradient of sea level pressure (R₀). We first propose determining the tropical cyclone (TC) center position as the cyclonic circulation center obtained from sea surface wind observations and then establishing a regression model between R₀ and the radius of 34-kt sea surface wind of scatterometer observations. The radius of 34-kt sea surface wind (R₃₄) is commonly used as a measure of TC size. The center positions determined from HaiYang-2B/2C/2D Scatterometers, MetOp-B/C Advanced Scatterometers, and FengYun-3E Wind Radar compared favorably with the axisymmetric centers of hurricane rain/cloud bands revealed by Advanced Himawari Imager observations of brightness temperature for the western Pacific landfalling typhoons Doksuri, Khanun, and Haikui in 2023. Furthermore, regression equations between R₀ and the scatterometer-determined radius of 34-kt wind are developed for tropical storms and category-1, -2, -3, and higher hurricanes over the Northwest Pacific (2022–2023). The bogus vortices thus constructed are more realistic than those built without satellite sea surface wind observations. Full article

This paper describes the benefits of using a microwave radiative transfer model (RTM) to improve the inter-satellite radiometric calibration (XCAL) between two independent satellite microwave radiometers. Because this work was sponsored by the NASA Global Precipitation Mission, the emphasis of this paper is on radiometer channels that are used for atmospheric precipitation retrievals; however, this technique is applicable for microwave remote sensing in general, over a wide range of satellite remote-sensing applications. An XCAL example is presented for the NASA Global Precipitation Mission, whereby the GPM Microwave Imager is used to calibrate another microwave radiometer (TROPICS) within the GPM constellation of satellites. This approach involves intercomparing near-simultaneous measured brightness temperatures from these radiometers viewing a common homogeneous ocean scene. The double difference between observed and theoretical brightness temperature, derived using a radiative transfer model, is used to establish a radiometric calibration offset or bias. On-orbit comparisons are presented for two different approaches, namely, with and without the aid of the RTM. The results demonstrate significant improvements in the XCAL biases derived when using the RTM, and this is especially beneficial when one radiometer produces anomalous brightness temperatures. Full article

Achieving a stable color output across wide temperature ranges in automotive LED applications is challenging, especially when using cost-sensitive chips with limited computational resources. This study proposes an improved temperature model that integrates Fourier heat conduction and thermal resistance concepts to more accurately capture self-heating and power dissipation effects. To accommodate the constraints of low-cost automotive-grade microcontrollers (MCUs), the associated optical algorithm is converted from floating-point to a 16.16 fixed-point format, reducing both memory usage and computational overhead. Experimental results conducted from −40 °C to 120 °C show that the improved model predicts LED temperatures within 5 °C of measured values, reducing errors by up to 30% compared to conventional PN-junction-based methods. Furthermore, by comparing the chromaticity points generated under the new and traditional models—and implementing an additional three-duty-cycle offset at 1% brightness—the improved approach reduces chromaticity drift by approximately 0.0052 in the CIE 1931 xy color space. These findings confirm the superior stability and accuracy of the new model for both thermal management and chromaticity compensation, offering a cost-effective solution for automotive LED systems requiring precise color control under constrained MCU resources. Full article

In this study, we investigated the influence of pre-fire tree mortality on fire behavior. Although other studies have focused on the environmental factors affecting wildfire, the influence of pre-fire tree mortality has not been explored in detail. We used high-spatial-resolution (1.6 m) airborne multispectral orthoimages to detect and map pre-fire dead trees in a portion of the San Bernardino Mountains, where the ‘Old Fire’ burned in 2003, and assessed whether spatial patterns of fire intensity and burn severity coincide with patterns of tree mortality. Dead trees were mapped through a hybrid deep learning classification and manual editing approach and facilitated with Google Earth Pro historical images. Apparent thermal infrared (TIR) brightness temperature captured during the Old Fire was derived from maximum digital number values from FireMapper airborne thermal infrared imagery (7 m) as a measure of fire intensity. Burn severity was analyzed using normalized burn ratio maps derived from pre- and post-fire Landsat 5 satellite imagery (30 m). Pre-fire dead trees were prevalent with 192 dead trees and 108 live trees per ha, with most dead trees clustered near the northwestern part of the study area east of Lake Arrowhead. The degree of spatial correspondence among dead tree density, fire intensity, and burn severity was analyzed using graphical and statistical analyses. The results revealed a significant but weak spatial association of dead trees with fire intensity (R² = 0.31) and burn severity (R² = 0.14). The findings revealed that areas impacted by pre-fire tree mortality were subject to higher fire intensity, followed by severe burn effects, though other biophysical factors also influenced these fire behavior variables. These results contradict a previous study that found no effect of tree mortality on the behavior of the Old Fire. Full article

We present the results of photometric and spectroscopic observations of a poorly studied B-type supergiant with infrared excess, the hot post-AGB star IRAS 21546+4721. Based on our photometric observations in the $UBV{R}_C{I}_C$ bands, we detected rapid, night-to-night, non-periodic brightness variations in the star with peak-to-peak amplitudes up to $0{.}^{m}3$ in the V band, as well as color–color and color–brightness correlations. Based on its variability characteristics, IRAS 21546+4721 appears similar to other hot post-AGB stars. Possible causes of the photometric variability are discussed. Additionally, we acquired low-resolution spectra in a wavelength range from 3500 to 7500 Å. The spectrum contains absorption lines typical of an early B-type star, along with a set of emission lines of H I, He I, [O I], [O II], [N II], [S II], and C II originating from an ionized circumstellar envelope. An analysis of the emission spectrum allowed us to estimate the parameters of the gas envelope ($N_e$∼ 10⁴ cm⁻³, $T_e$∼ 10,000 K) and the star’s temperature (∼26,500 K). The radial velocity measured from the emission lines was $V_r=-141\pm 7$ km s⁻¹. Full article