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Timely and accurate tree species mapping is crucial for forest resource inventory, supporting management, conservation biology, and ecological restoration. This study utilized Sentinel-1 and Sentinel-2 data to classify five dominant tree species in Chengde and Beijing. To effectively capture the influence of multi-temporal data, data were acquired in March, June, September, and December 2020, extracting various features, including bands, spectral indices, texture features, and topographic variables. The optimal input variable combination was explored using 1519 field survey samples for training and testing datasets. Classification employed Random Forest, XGBoost, and deep learning models, with performance evaluated through out-of-bag estimation and cross-validation. The XGBoost model achieved the highest accuracy of 81.25% (kappa = 0.74) when using Sentinel-1 and Sentinel-2 bands, indices, texture features, and DEM data. Results demonstrate the effectiveness of using Sentinel data for tree species classification and emphasize the value of machine learning algorithms. This study underscores the potential of combining synthetic aperture radar (SAR) and optical data for large-scale tree species classification, with significant implications for forest monitoring and management. [ABSTRACT FROM AUTHOR]

The effect of cerium content (0, 0.011, 0.017, 0.075 wt%) on non-metallic inclusions and solidification microstructures of 55SiCr high-strength spring steel was experimentally studied, along with thermodynamic calculations. The results show that Ce addition changes the type and size of inclusions in this steel and influences the characteristics of the solidification microstructure. In the sample without Ce addition, the main inclusions are MnS, SiO2, SiO2–MnS, and CaO–SiO2–MgO, and the equiaxed ratio in the solidification structure is 44.63%. However, when Ce content increases to 0.011 wt%, the inclusions in the steel become mainly Ce–S, Ce–O–S, and a small amount of MnS, and the equiaxed ratio increases to 50.42%. As the Ce content increases to 0.017 wt%, the inclusions are predominantly Ce–S, Ce–O–S, and Ce–O–S–Ca, while some Ce–P and Ce–O–P–C inclusions are also observed. The equiaxed ratio increases to 67.63%, showing the best effect on heterogeneous nucleation during solidification. When Ce content in the steel reaches 0.075 wt%, the Ce-containing inclusions are Ce–S, Ce–O, Ce–P, Ce–P–O, and Ce–O–S–As, and the size becomes larger. The formation mechanism of inclusions is explained by Gibbs free energy calculations and thermodynamic diagrams. [ABSTRACT FROM AUTHOR]

In microfluidic systems, it is important to maintain flow stability to execute various functions, such as chemical reactions, cell transportation, and liquid injection. However, traditional flow sources, often bulky and prone to unpredictable fluctuations, limit the portability and broader application of these systems. Existing fluidic stabilizers, typically designed for specific flow sources, lack reconfigurability and adaptability in terms of the stabilization ratios. To address these limitations, a modular and standardized stabilizer system with tunable stabilization ratios is required. In this work, we present a Lego-like modular microfluidic stabilizer system, which is fabricated using 3D printing and offers multi-level stabilization combinations and customizable stabilization ratios through the control of fluidic RC constants, making it adaptable to various microfluidic systems. A simplified three-element circuit model is used to characterize the system by straightforwardly extracting the RC constant without intricate calculations of the fluidic resistance and capacitance. By utilizing a simplified three-element model, the stabilizer yields two well-fitted operational curves, demonstrating an R-square of 0.95, and provides an optimal stabilization ratio below 1%. To evaluate the system's effectiveness, unstable input flow at different working frequencies is stabilized, and droplet generation experiments are conducted and discussed. The results show that the microfluidic stabilizer system significantly reduces flow fluctuations and enhances droplet uniformity. This system provides a new avenue for microfluidic stabilization with a tunable stabilization ratio, and its plug-and-play design can be effectively applied across diverse applications to finely tune fluid flow behaviors in microfluidic devices. [ABSTRACT FROM AUTHOR]

Long-term exposure to high concentrations of fine particles can cause irreversible damage to people's health. Therefore, it is of extreme significance to conduct large-scale continuous spatial fine particulate matter (PM2.5) concentration prediction for air pollution prevention and control in China. The distribution of PM2.5 ground monitoring stations in China is uneven with a larger number of stations in southeastern China, while the number of ground monitoring sites is also insufficient for air quality control. Remote sensing technology can obtain information quickly and macroscopically. Therefore, it is possible to predict PM2.5 concentration based on multi-source remote sensing data. Our study took China as the research area, using the Pearson correlation coefficient and GeoDetector to select auxiliary variables. In addition, a long short-term memory neural network and random forest regression model were established for PM2.5 concentration estimation. We finally selected the random forest regression model (R2 = 0.93, RMSE = 4.59 μg m−3) as our prediction model by the model evaluation index. The PM2.5 concentration distribution across China in 2021 was estimated, and then the influence factors of high-value regions were explored. It is clear that PM2.5 concentration is not only related to the local geographical and meteorological conditions, but also closely related to economic and social development. [ABSTRACT FROM AUTHOR]

Grassland desertification stands as an ecological concern globally. It is crucial for desertification prevention and control to comprehend the variation in area and severity of desertified grassland (DGL), clarify the intensities of conversion among DGLs of different desertification levels, and explore the spatial and temporal driving factors of desertification. In this study, a Desertification Difference Index (DDI) model was constructed based on albedo-EVI to extract desertification information. Subsequently, intensity analysis, the Geo-detector model, and correlation analysis were applied to analyze the dynamics and driving factors of desertification. The results showed the following: (1) Spatially, the DGL in Xilingol exhibited a zonal distribution. Temporally, the degree of DGL decreased, with the proportion of severely and moderately desertified areas decreasing from 51.77% in 2000 to 37.23% in 2020, while the proportion of nondesertified and healthy areas increased from 17.85% in 2000 to 37.40% in 2020; (2) Transition intensities among different desertification levels were more intense during 2000–2012, stabilizing during 2012–2020; (3) Meteorological factors and soil conditions primarily drive the spatial distribution of DDI, with evapotranspiration exhibiting the most significant influence (q-value of 0.83), while human activities dominate interannual DDI variations. This study provides insights into the conversion patterns among different desertification levels and the divergent driving forces shaping desertification in both spatial and temporal dimensions in Xilingol. [ABSTRACT FROM AUTHOR]

With the development of infrared technology, infrared dim and small target detection plays a vital role in precision guidance applications. To address the problems of insufficient dataset coverage and huge actual shooting costs in infrared dim and small target detection methods, this paper proposes a method for generating infrared dim and small target sequence datasets based on generative adversarial networks (GANs). Specifically, first, the improved deep convolutional generative adversarial network (DCGAN) model is used to generate clear images of the infrared sky background. Then, target–background sequence images are constructed using multi-scale feature extraction and improved conditional generative adversarial networks. This method fully considers the infrared characteristics of the target and the background, which can achieve effective expansion of the image data and provide a test set for the infrared small target detection and recognition algorithm. In addition, the classifier's performance can be improved by expanding the training set, which enhances the accuracy and effect of infrared dim and small target detection based on deep learning. After experimental evaluation, the dataset generated by this method is similar to the real infrared dataset, and the model detection accuracy can be improved after training with the latest deep learning model. [ABSTRACT FROM AUTHOR]

Nighttime light data is a mainstay method in confirming and supporting other traditional economic data indicators, which in turn influence business and policy-making decisions. Accuracy in and clear definition of economic data and its related indicators are thus of great importance not only for analysis of urban development and related policies seeking sustainable development, but also plays a key role in whether or not these policies are successful. Discovering and recognizing the applications and limitations of nighttime light and other peripheral data could prove helpful in future data analysis and sustainable development policy. One possible limitation could exist in GDP decrease, and whether or not nighttime light would decrease accordingly, as most studies show nighttime light increase confirms economic growth, thus affecting the reliability of the data's correlation with economic data. This study utilizes nighttime light data in a cross-reference with GDP data during instances of global GDP shrinkage over the years of 2007–2017, split between 2007–2012 for the DMSP dataset and 2013–2017 for the VIIRs dataset. It seeks to establish through linear regression whether or not yearly average nighttime light data products show a positive correlation even during periods of economic decline, thereby providing a clearer understanding of the strengths and limitations of NTL as an economic validation indicator. Analysis shows that both years of global GDP decrease in turn also displayed global nighttime light decrease, in addition to linear regression giving satisfactory results pointing to a positive correlation over the timespan. The VIIRS data series resulted in higher regression coefficients, which is in line with the results of previous literature. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. Using the Mann–Kendall methods, Pearson correlation, and the Geodetector, this study investigated the spatiotemporal variation and driving factors of GPP from 2000 to 2020. The results showed that (1) in terms of spatial distribution, GPP showed a trend of "low-high-low" regions, with low values for grassland and arable land and a high value for forest land. The growth trend is fast in forest areas, while the growth trend is not obvious in cultivated areas. The regions with significant growth accounted for 68.73% of the whole region. (2) The whole region shows a growth rate of 2.07 g C∙m−2∙yr−1, showing obvious seasonality, with a slow growth trend in spring and autumn and a fast growth trend in summer. (3) The driving factors of GPP spatial differentiation in the Beijing-Tianjin-Hebei region were land surface temperature, land use type, and nighttime light data, while precipitation and downward surface shortwave radiation show no strong explanatory power for the spatial differentiation of GPP, which means that these two factors have less driving force on the spatial differentiation of GPP. The interaction of LUCC with the other factors presents two-factor enhancement, while the LST interaction with the other three factors presents non-linear enhancement. This study could provide a theoretical basis for the sustainable development of the Beijing-Tianjin-Hebei Region. [ABSTRACT FROM AUTHOR]

The combination of natural environment changes and human activities affects the growth of grasslands. In order to quantitatively assess the causes of spatial and temporal variation of grasslands in Xilingol, this study assessed the spatial and temporal evolution patterns of grassland health based on MOD13A1 long time series Normalized Difference Vegetation Index (NDVI) data from 2000–2019 using trend analysis. The geodetector model was used to explore the dominant drivers of spatial variation in grassland NDVI, combined with 34 factors covering natural environmental changes and human disturbances over the same period. The results show that the grasslands of Xilingol showed an overall recovery trend from 2000 to 2019, with an average annual NDVI growth rate of 0.0028/a, a monthly increasing rate of 0.0005/month, and 68.06% of the grassland at an average recovery level. Moisture-dominated natural climate change factors, such as Growing Season Precipitation (Prep2), Annual Mean Water Vapor Pressure (WVP), and Annual Mean Relative Humidity (RH), were the underlying cause of grassland health changes during the study period, with the highest explanatory factor being growing season precipitation (q value of 0.59 on a multi-year average). The influence of primary production value among human activities was greater, and the explanatory factor of tertiary production value showed an increasing trend. The interactions among natural and anthropogenic factors significantly enhances their explanatory credibility for NDVI, with the type of interaction dominated by the two-factor enhancement. Risk detection of the top 10 dominant drivers in terms of q statistic were carried out to obtain the threshold range of each driver in the high zone of grassland NDVI, which can provide a scientific reference for the sustainable restoration of grassland. [ABSTRACT FROM AUTHOR]

Cracking con-rod is an advanced high-precision connecting structure based on brittle expansion, breaking and reconnection of steel, to solve the problem of assembly circle missing. High carbon micro-alloyed steel C70S6, as a dominant material for the production of cracking con-rod, has extremely strict requirements on non-metallic inclusions in steel and microstructure stability. Continuous casting tundish plays an important role in removing large-sized inclusions and stabilizing casting quality. Aiming at the inconsistent casting quality of C70S6 steel produced by a three-strand asymmetric tundish and the frequent occurrence of slag entrapment problems in Xining Special Steel, the tundish structure was optimized by means of physical modelling combined with numerical simulation, and the quality of the bloom castings and subsequent hot-rolled products before and after optimization were compared based on volume production. The results show that a new flow control design to the tundish can effectively improve the consistency of its metallurgical effect for each of the three strands and the following overall product quality, in which the flow field and temperature field in the tundish are more uniform. This is due to the adoption of a vortex inhibitor and an optimized wall structure according to the measured RTD curve, ink trajectory and numerical simulation on the 3-D streamline contours and temperature distribution in the tundish. The peak concentration of outlet 1 is decreased from 6.5 before optimization to less than 2.0 after optimization, which means the elimination or alleviation of the local short-circuit flow. The maximum temperature difference of C70S6 molten steel measured at the outlets of the tundish three strands is decreased from 2–5 °C to 1–3 °C, which is in good agreement with the numerical simulation results. The difference in columnar crystal ratio of the corresponding bloom castings is decreased from 2.27–3.17% to 1.26–1.85%, and the consistency of central carbon segregation index is also significantly improved. In addition, the difference in oxygen content among the three strand blooms is decreased from 1.7–3.5 ppm to 0.8–1.9 ppm. As a result, the overall mechanical properties and microstructure stability of the hot-rolled products are improved statistically, in which the hardness fluctuation is decreased from 84 HBW to 60 HBW, the inclusion grade of types B + C + D + Ds is reduced to 1.105, and the occurrence rate of Ds dropped to 0.118%. Accordingly, the failure rate of the cracking con-rod is controlled stably within 4‰, and the fracture is generally smoother than that before tundish optimization. In summary, the flow field optimization to a multi-strand asymmetric tundish has a clear effect on improving the overall quality of its bloom castings and rolled products, which should be paid more attention industrially. Meanwhile, the present study provides a reliable theoretical and experimental reference for the improvement of metallurgical effects of an asymmetric-typed tundish commonly used in special steel production. [ABSTRACT FROM AUTHOR]

The large area estimation of forest canopy closure (FCC) using remotely sensed data is of high interest in monitoring forest changes and forest health, as well as in assessing forest ecological services. The accurate estimation of FCC over the regional or global scale is challenging due to the difficulty of sample acquisition and the slow processing efficiency of large amounts of remote sensing data. To address this issue, we developed a novel bounding envelope methodology based on vegetation indices (BEVIs) for determining vegetation and bare soil endmembers using the normalized differences vegetation index (NDVI), modified bare soil index (MBSI), and bare soil index (BSI) derived from Landsat 8 OLI and Sentinel-2 image within the Google Earth Engine (GEE) platform, then combined the NDVI with the dimidiate pixel model (DPM), one of the most commonly used spectral-based unmixing methods, to map the FCC distribution over an area of more than 90,000 km2. The key processing was the determination of the threshold parameter in BEVIs that characterizes the spectral boundary of vegetation and soil endmembers. The results demonstrated that when the threshold equals 0.1, the extraction accuracy of vegetation and bare soil endmembers is the highest with the threshold range given as (0, 0.3), and the estimated spatial distribution of FCC using both Landsat 8 and Sentinel-2 images were consistent, that is, the area with high canopy density was mainly distributed in the western mountainous region of Chifeng city. The verification was carried out using independent field plots. The proposed approach yielded reliable results when the Landsat 8 data were used (R2 = 0.6, RMSE = 0.13, and 1-rRMSE = 80%), and the accuracy was further improved using Sentinel-2 images with higher spatial resolution (R2 = 0.81, RMSE = 0.09, and 1-rRMSE = 86%). The findings demonstrate that the proposed method is portable among sensors with similar spectral wavebands, and can assist in mapping FCC at a regional scale while using multispectral satellite imagery. [ABSTRACT FROM AUTHOR]

Tundish with channel-type induction heating is one of new technologies adopted widely in China by the steel industry in the recent years, which can supply a constant liquid steel temperature control for the sequenced continuous casting process. For a five-strand tundish with induction heating in service, a kind of novel bifurcated split channel has been designed to solve the poor consistency of temperature and fluid flow for each strand that occurs with the conventional straight channel-type. The temperature distribution and fluid flow behaviors under the two structure modes were compared numerically by an electromagnetic-heat-flow multi-physics field coupling model. The results show that the maximum temperature difference between each strand outlet of the tundish can drop to less than 4 °C upon using the bifurcated channel, as compared to 10 °C under the original straight channel mode. According to the simulated results, case FK-A0 has been chosen as the optimized structure for industrial application. It has been verified through temperature measurements during the casting operation that the novel bifurcated split channel can improve the consistency of steel temperature for every strand of the tundish. The average temperature difference between the edge strand and the middle strand is 4.25 °C lower than the original straight channel, resulting in an upgraded metallurgical effect for the induction heated tundish. [ABSTRACT FROM AUTHOR]

Accurate information on tree species is in high demand for forestry management and further investigations on biodiversity and environmental monitoring. Over regional or large areas, distinguishing tree species at high resolutions faces the challenges of a lack of representative features and computational power. A novel methodology was proposed to delineate the explicit spatial distribution of six dominant tree species (Pinus tabulaeformis, Quercus mongolia, Betula spp., Populus spp., Larix spp., and Armeniaca sibirica) and one residual class at 10 m resolution. Their spatial patterns were analyzed over an area covering over 90,000 km2 using the analysis-ready large volume of multisensor imagery within the Google Earth engine (GEE) platform afterwards. Random forest algorithm built into GEE was used together with the 20th and 80th percentiles of multitemporal features extracted from Sentinel-1/2, and topographic features. The composition of tree species in natural forests and plantations at the city and county-level were performed in detail afterwards. The classification achieved a reliable accuracy (77.5% overall accuracy, 0.71 kappa), and the spatial distribution revealed that plantations (Pinus tabulaeformis, Populus spp., Larix spp., and Armeniaca sibirica) outnumber natural forests (Quercus mongolia and Betula spp.) by 6% and were mainly concentrated in the northern and southern regions. Arhorchin had the largest forest area of over 4500 km2, while Hexingten and Aohan ranked first in natural forest and plantation area. Additionally, the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. We suggest focusing more on the suitable areas modeling for tree species using species' distribution models and environmental factors based on the classification results rather than field survey plots in further studies. [ABSTRACT FROM AUTHOR]

A novel orange-yellow-emitting polymethyl methacrylate derivative grafted with cationic iridium(III) complex units was synthesized and used as down-conversion luminescent materials in light-emitting diodes (LEDs). The polymer had a thermal decomposition temperature (Td) of 275 °C. With the temperature increasing from 20 to 100 °C, its photoluminescent intensity decreased to 76.8% with thermal quenching activation energy (Ea) of 0.2775 eV. A series of LEDs was fabricated by 460 nm blue GaN chips and the polymer blended in silicone at different concentrations. At 4.0 wt %, a cold white LED was obtained, the correlated color temperature (CCT) was 10,050 K, color rendering index (CRI) was 71.2, luminous efficiency (ηL) was 5.3 lm·w−1, and Commission Internationale de L'Eclairage (CIE) chromaticity coordinates were (0.30, 0.24). At 5.0 wt.%, the LED emitted neutral white light, its CCT was 4938 K, CRI was 75, ηL was 13.8 lm·w−1, and the CIE value was (0.34, 0.27). At 5.5 wt %, 6.0 wt %, 7.0 wt %, and 8.0 wt %, the LEDs all emitted warm white light; their CCTs were 3446, 3093, 2557, and 2337 K, respectively; their CRIs were 73.6, 71.8, 63.8, and 59.0, respectively; their ηL were 18.1, 16.3, 14.8, and 13.7 lm·w−1, respectively; and their CIE values were (0.36, 0.30), (0.40, 0.35), (0.45, 0.38), and (0.50, 0.42), respectively. At 9.0 wt %, the blue light of GaN chip was completely absorbed by the polymer and only the orange-yellow light of the polymer emitted. The results suggested the polymer was a promising orange-yellow-emitting phosphor candidate for white LEDs, especially for warm white LEDs. [ABSTRACT FROM AUTHOR]

A novel coordination polymer, {[Cd4(Dccbp)4]·H2O} (1) (Dccbp = 3,5-dicarboxy-1-(3-carboxybenzyl)pyridin-1-ium) was synthesized under hydrothermal conditions by a zwitterionic organic ligand and characterized by single crystal X-ray diffraction, infrared spectrum (IR), thermogravimetric analysis (TG), powder X-ray diffraction (PXRD) and luminescence. Complex 1 with a pyridine cation basic skeleton has the potential to serve as the first case of a luminescent material based on the zwitterionic type of organic ligand for selective, sensitive, and recyclable sensing of 2,4,6-trinitrophenol in the aqueous phase. [ABSTRACT FROM AUTHOR]

A novel orange cationic iridium(III) complex [(TPTA)₂Ir(dPPOA)]PF₆ (TPTA: 3,4,5-triphenyl-4 H -1,2,4-triazole, dPPOA: N,N-diphenyl-4-(5-(pyridin-2-yl)-1,3,4-oxadiazol-2-yl)aniline) was synthesized and used as a phosphor in light-emitting diodes (LEDs). [(TPTA)₂Ir(dPPOA)]PF₆ has high thermal stability with a decomposition temperature ( T d ) of 375 °C, and its relative emission intensity at 100 °C is 88.8% of that at 25°C. When only [(TPTA)₂Ir(dPPOA)]PF₆ was used as a phosphor at 6.0 wt % in silicone and excited by a blue GaN (GaN: gallium nitride) chip (450 nm), an orange LED was obtained. A white LED fabricated by a blue GaN chip (450 nm) and only yellow phosphor Y₃Al₅O 12 :Ce 3+ (YAG:Ce) (1.0 wt % in silicone) emitted cold white light, its CIE (CIE: Commission International de I'Eclairage ) value was (0.32, 0.33), color rendering index (CRI) was 72.2, correlated color temperature (CCT) was 6877 K, and luminous efficiency ( η L ) was 128.5 lm∙W -1 . Such a cold white LED became a neutral white LED when [(TPTA)₂Ir(dPPOA)]PF₆ was added at 0.5 wt %; its corresponding CIE value was (0.35, 0.33), CRI was 78.4, CCT was 4896 K, and η L was 85.2 lm∙W -1 . It further became a warm white LED when [(TPTA)₂Ir(dPPOA)]PF₆ was added at 1.0 wt %; its corresponding CIE value was (0.39, 0.36), CRI was 80.2, CCT was 3473 K, and η L was 46.1 lm∙W -1 . The results show that [(TPTA)₂Ir(dPPOA)]PF₆ is a promising phosphor candidate for fabricating warm white LEDs., Competing Interests: The authors declare no conflict of interest.