Search Results (32)

Microwave sounding observations obtained from the National Oceanic and Atmospheric Administration (NOAA) and the European Meteorological Operational Satellite Program (METOP) satellites have been used for retrieving the cloud ice water path (IWP). However, the IWP algorithms developed in the past cannot be applied to the Fengyun-3F (FY-3F) microwave radiometers due to the differences in frequency of the primary channels and the fields of view. In this study, the IWP algorithm was tailored for the FY-3F satellite, and the retrieved IWP was compared with the fifth generation of reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5) and the Meteorological Operational Satellite-C (METOP-C) products. The results indicate that the IWP distribution retrieved from FY-3F observations demonstrates strong consistency with the cloud ice distributions in ERA5 data and METOP-C products in low-latitude regions. However, discrepancies are observed among the three datasets in mid- to high-latitude regions. ERA5 data underestimate the frequency of high IWP values and overestimate the frequency of low IWP values. The IWP retrieval results from satellite datasets demonstrate a high level of consistency. Furthermore, an analysis of the IWP time series reveals that the retrieval algorithm used in this study better captures variability and seasonal characteristics of IWP compared to ERA5 data. Additionally, a comparison of FY-3F retrieval results with METOP-C products shows a high correlation and generally consistent distribution characteristics across latitude bands. These findings confirm the high accuracy of IWP retrieval from FY-3F data, which holds significant value for advancing IWP research in China. Full article

In nature, orchid seed germination is extremely low, making in vitro asymbiotic seed germination essential for the propagation and conservation of endangered Vanda coerulea. This study optimized a micropropagation protocol and evaluated the genetic homogeneity of regenerated orchids. The synergistic effect of kinetin (KN) with auxins in the Mitra (M) medium best supported protocorm formation and seedling development. The highest shoot multiplication (5.62 ± 0.09) was achieved with 1.2 mg L⁻¹ KN and 0.6 mg L⁻¹ IBA (indole-3-butyric acid) in the medium. Enhanced leaf production (4.81 ± 0.37) was observed when 3.2 mg L⁻¹ KN was combined with 1.8 mg L⁻¹ IAA (indole-3-acetic acid), while root development was superior when 3.2 mg L⁻¹ KN together with 2.4 mg L⁻¹ IAA was incorporated in the medium. Anatomical sections confirmed well-developed leaf and root structures. Genetic fidelity assessment using random amplified polymorphic DNA (RAPD), inter-simple sequence repeat (ISSR), inter-primer binding site (iPBS), and start codon targeted (SCoT) markers revealed 97.17% monomorphism (240/247 bands) and low Nei’s genetic distances (0.000–0.039), indicating high similarity among the regenerants. Dendrogram clustering was supported by a high cophenetic correlation coefficient (CCC = 0.806) and strong resolution in Principal Coordinate Analysis (PCoA) (44.03% and 67.36% variation on the first two axes). The Mantel test revealed a significant correlation between both ISSR and SCoT markers with the pooled marker data. Flow cytometry confirmed the genome stability among the in vitro-propagated orchids, with consistently low CV (FL2-A) values (4.37–4.94%). This study demonstrated the establishment of a reliable in vitro protocol for rapidly propagating genetically identical V. coerulea via asymbiotic seed germination. Full article

Pseudomonas aeruginosa (PA), as a common pathogen of nosocomial infections, has been experiencing an increasing rate of drug resistance with the widespread use and abuse of antimicrobial drugs. High-drug-resistance and high-virulence phenotypes are two distinctive features of the strong pathogenicity of multi-drug-resistant PA. Exploring the characterization of virulence factor expression and its relationship with the multi-drug resistance phenotype is essential to reduce the further development of resistance as well as a high standard of infection prevention and control. A total of 50 PA isolated from clinical practice were collected. The Kirby-Bauer  test was used for drug-sensitive screening, and the results showed that 16 strains were resistant and 16 strains were sensitive. The drug resistance rate of multi-drug-resistant PA against cefepime, cefazolin, ampicillin, and imipenem was up to 100%. The multi-drug-resistant groups were superior in producing pyocyanin and forming biofilm to the sensitive groups. The distribution of isolates with different swarming motility capacities and elastase levels did not show pronounced differences among the multi-drug-resistant and sensitive groups. In addition, biofilm formation was moderately associated with imipenem resistance. Among the strains with strong virulence factor expression, the gene bands showed little difference, suggesting that the gene is highly homologous. The virulence factor matrix analysis showed that there were different degrees of correlation among the 4 virulence factors. The correlation between multidrug-resistant PA and virulence factor expression is complex. PA, which were good at producing pyocyain and forming biofilm, were highly resistant to cephalosporins, beta-lactams and carbepenems; hence, such drugs are not proper for anti-infective treatment in clinics. Full article

The bandgap is a critical factor influencing the energy density of batteries and a key physical quantity that determines the semiconducting behavior of materials. To further improve the prediction accuracy of the bandgap in silicon oxide lithium-ion battery materials, a boosting machine learning model was established to predict the material’s bandgap. The optimal model, AdaBoost, was selected, and the SHapley Additive exPlanations (SHAP) method was used to quantitatively analyze the importance of different input features in relation to the model’s prediction accuracy. It was found that AdaBoost performed exceptionally well in terms of prediction accuracy, ranking as the best among five predictive models. Using the SHAP method to interpret the AdaBoost model, it was discovered that there is a significant positive correlation between the energy of the conduction band minimum (cbm) of silicon oxides and the bandgap, with the bandgap size showing an increasing trend as the cbm rises. Additionally, the study revealed a strong negative correlation between the Fermi level of silicon oxides and the bandgap, with the bandgap expanding as the Fermi level decreases. This research demonstrates that boosting-type machine learning models perform superiorly in predicting the bandgap of silicon oxide materials. Full article

Cyanobacterial harmful algal blooms release toxins and form thick blanket layers on the water surface causing widespread problems, including serious threats to human health, water ecosystem, economics, and recreation. To identify the potential drivers for the bloom, there is a need for extensive observations of the water sources with bloom occurrences. However, the traditional methods for monitoring water sources, such as collection of point ground samples, have proven limited due to spatial and temporal variability of water resources, and the cost associated with collecting samples that accurately represent this variability. These limitations can be addressed through the use of high-frequency satellite data. In this study, we explored the use of Random Forest (RF), which is one of the widely used machine learning architectures, to evaluate the performance of Sentinel-3 OLCI (Ocean and Land Color Imager) images in predicting bloom proxies in the western region of Lake Erie. The sixteen available bands of Sentinel-3 images were used as the predictor variables, while four proxies of the cyanobacterial masses, including Chlorophyll-a, Microcystin, Phycocyanin, and Secchi-depth, were considered as response variables in the RF models, with one RF model per proxy. Each of the proxies comes with a unique set of traits that can help with bloom detection. Among four RF models, the model for Chlorophyll-a performed the best with R² = 0.55 and RMSE = 20.84 µg/L, while R² performance for the rest of the other proxies was less than 0.5. This is because Chlorophyll-a is the most dominant and optically active pigment in water, while Phycocyanin, which is a strong indicator of harmful bloom, is present in low concentrations. Additionally, Microcystin, responsible for bloom toxicity, has limited spectral sensitivity, and Secchi-depth could be influenced by various factors besides blooms, such as colored dissolved organic and inorganic matter. On further examining the relationship between the proxies, Microcystin and Secchi-depth were significantly correlated with Chlorophyll-a, which enhances the usefulness of Chlorophyll-a in accurately identifying the presence of algal blooms. Full article

Winter wheat is an important grain that plays a crucial role in agricultural production and ensuring food security. Its yield directly impacts the stability and security of the global food supply. The accurate monitoring of grain yield is imperative for precise agricultural management. This study aimed to enhance winter wheat yield predictions with UAV remote sensing and investigate its predictive capability across diverse environments. In this study, RGB and multispectral (MS) data were collected on 6 May 2020 and 10 May 2022 during the grain filling stage of winter wheat. Using the Pearson correlation coefficient method, we identified 34 MS features strongly correlated with yield. Additionally, we identified 24 texture features constructed from three bands of RGB images and a plant height feature, making a total of 59 features. We used seven machine learning algorithms (Cubist, Gaussian process (GP), Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM), Random Forest (RF)) and applied recursive feature elimination (RFE) to nine feature types. These included single-sensor features, fused sensor features, single-year data, and fused year data. This process yielded diverse feature combinations, leading to the creation of seven distinct yield prediction models. These individual machine learning models were then amalgamated to formulate a Bayesian Model Averaging (BMA) model. The findings revealed that the Cubist model, based on the 2020 and 2022 dataset, achieved the highest R² at 0.715. Notably, models incorporating both RGB and MS features outperformed those relying solely on either RGB or MS features. The BMA model surpassed individual machine learning models, exhibiting the highest accuracy (R² = 0.725, RMSE = 0.814 t·ha⁻¹, MSE = 0.663 t·ha⁻¹). Additionally, models were developed using one year’s data for training and another year’s data for validation. Cubist and GLM stood out among the seven individual models, delivering strong predictive performance. The BMA model, combining these models, achieved the highest R² of 0.673. This highlights the BMA model’s ability to generalize for multi-year data prediction. Full article

The Xiong’an New Area was officially established in 2018 to construct a new, intelligent, and efficient urban area to alleviate Beijing’s non-capital functions. Using Landsat satellite images, we employed the dimidiate pixel model, band operation, and transition matrix to analyze the temporal and spatial variations in FVC (Fractional Vegetation Coverage) within the Xiong’an New Area in 2005, 2013, and 2019, respectively. Urbanization rate, precipitation, temperature, and population were considered potential driving forces, which we analyzed using grey relational analysis and linear regression to explore the correlation between FVC and these factors. The findings are as follows: from 2005 to 2019, overall improvement and significant degradation have been observed. In Baiyangdian, a part of the national key ecological area, water bodies and FVC have increased. Grey relational analysis revealed that precipitation had the highest grey relational value of 0.76. The average correlation among natural factors was 0.67, while that among human factors was 0.60. Generally, the Xiong’an New Area vegetation exhibited instability, while Baiyangdian demonstrated relatively stable FVC. Grey relational analysis indicates a strong potential for social and economic development in the Xiong’an New Area. Full article

Soil salinization seriously threatens agricultural production and ecological environments in arid areas. The accurate and rapid monitoring of soil salinity and its spatial variability is of great significance for the amelioration of saline soils. In this study, 191 soil samples were collected from cotton fields in southern Xinjiang, China, to obtain spectral reflectance and electrical conductivity (EC) indoors. Then, multi-granularity spectral segmentation (MGSS) and seven conventional spectral preprocessing methods were employed to preprocess the spectral data, followed by the construction of partial least squares regression (PLSR) models for soil EC estimation. Finally, the performance of the models was compared. The results showed that compared with conventional spectral preprocessing methods, MGSS could greatly improve the correlation between spectrum and soil EC, extract the weak spectral information of soil EC, and expand the spectral utilization range. The model validation results showed that the PLSR model based on the second-order derivative (2nd-der-PLSR) had the highest estimation accuracy among the models constructed by conventional methods. However, the PLSR model based on MGSS (MGSS-PLSR) had the highest estimation accuracy among all models, with R_p² (0.901) and RPD (3.080) being 0.151 and 1.302 higher than those of the 2nd-der-PLSR model, respectively, and nRMSEP (5.857%) being 4.29% lower than that of the 2nd-der-PLSR model. The reason for the high accuracy of the MGSS-PLSR model is as follows: In the continuous segmentation of the raw spectrum by MGSS, the bands with strong and weak correlations with respect to soil EC were concentrated during low granularity segmentation. With the increase in granularity level, the spectral features decreased and were distributed discretely. In addition, the locations of spectral features were also different at different granularity levels. Therefore, the spectral features of soil EC can be effectively extracted by the MGSS, which significantly improves the spectral estimation accuracy of soil salinity. This study provides a new technical means for soil salinity estimation in arid areas. Full article

Erbium-doped cadmium selenide thin films grown on 7059 Corning glass by means of a chemical bath at 80 °C were prepared. Doping was performed by adding an aqueous Er(NO3)₃3·H₂O dilution to the CdSe growth solution. The volume of Er doping solution was varied to obtain different Er concentration (x at%). Thus, in the Cd_1−xEr_xSe samples, the x values obtained were in the 0.0–7.8 at% interval. The set of the CdSe:Er thin films synthesized in the hexagonal wurtzite (WZ) crystalline phase are characterized by lattice parameters (a and c) that increase until x = 2.4% and that subsequently decrease as the concentration of x increases. Therefore, in the primitive unit cell volume (UC), the same effect was observed. Physical parameters such as nanocrystal size, direct band gap (E_g), and optical longitudinal vibrational phonon on the other hand, shift in an opposite way to that of UC as a function of x. All the samples exhibit photoluminescence (PL) emission which consists of a single broad band in the 1.3 ≤ hν ≤ 2.5 eV range (954 ≥ λ ≥ 496 nm), where the maximum of the PL-band shift depends on x in the same way as the former parameters. The PL band intensity shows a singular behavior since it increases as x augments but exhibits a strong decreasing trend in the intermediate region of the x range. Dark d.c. conductivity experiences a high increase with the lower x value, however, it gradually decreases as x increases, which suggests that the Er³⁺ ions are not only located in Cd²⁺ sites, but also in interstitial sites and at the surface. Different physical properties are correlated among them and discussed considering information from similar reports in the literature. Full article

The original EEG data collected are the 1D sequence, which ignores spatial topology information; Feature Pyramid Networks (FPN) is better at small dimension target detection and insufficient feature extraction in the scale transformation than CNN. We propose a method of FPN and Long Short-Term Memory (FPN-LSTM) for EEG feature map-based emotion recognition. According to the spatial arrangement of brain electrodes, the Azimuth Equidistant Projection (AEP) is employed to generate the 2D EEG map, which preserves the spatial topology information; then, the average power, variance power, and standard deviation power of three frequency bands ($\alpha$, $\beta$, and $\gamma$) are extracted as the feature data for the EEG feature map. BiCubic interpolation is employed to interpolate the blank pixel among the electrodes; the three frequency bands EEG feature maps are used as the G, R, and B channels to generate EEG feature maps. Then, we put forward the idea of distributing the weight proportion for channels, assign large weight to strong emotion correlation channels (AF3, F3, F7, FC5, and T7), and assign small weight to the others; the proposed FPN-LSTM is used on EEG feature maps for emotion recognition. The experiment results show that the proposed method can achieve Value and Arousal recognition rates of 90.05% and 90.84%, respectively. Full article

Anisotropic materials possess direction dependent properties as a result of symmetry within their structure. Bismuth sulfide (Bi₂S₃) is an important semiconductor exhibiting anisotropy due to its crystalline and stratified structure. In this manuscript we present a new and straightforward procedure to deposit Bi₂S₃ thin films on soda lime glass substrates by the chemical bath deposition (CBD) technique. We studied two fundamental parameters, the time to deposit a single layer and the total number of layers deposited. The single layer deposition time was varied between 70 and 100 min and samples were coated with a total of 1, 2, or 3 layers. It is important to note that a fresh aqueous solution was used for every layer. Visible and near infra-red spectroscopy, scanning electron microscopy, X-ray photoelectrons spectroscopy, and X-ray diffraction were the characterization techniques used to study the resulting films. The calculated band gap values were found to be between 1.56 and 2.1 eV. The resulting Bi₂S₃ deposited films with the new formulation showed uniform morphology and orthorhombic crystalline structure with an average crystallite size of 19 nm. The thickness of the films varied from 190 to 600 nm in direct correlation to the deposition time and in agreement with the number of layers. The XPS results showed the characteristic bismuth doublet centered around 164.11 and 158.8 eV corresponding with the presence of Bi₂S₃. The symmetry within the Bi₂S₃ structure makes it a strong anisotropic crystal with potential applications in optoelectronic and photovoltaic devices, catalysis, and photoconductors among others. Full article

Fluvial biological habitat types are classified using the diversity in physical characteristics of a water channel. Recent ecological management studies have highlighted the potential of underwater sound as a quantitative indicator of habitat characteristics. We investigate the relationship between underwater acoustic characteristics and hydraulic factors of 12 habitat types in the Namdae Stream in Yangyang, Korea, namely riffles, pools, and step riffle habitats. In the riffles and pools, the underwater sound levels were measured as sound pressure levels (SPLs). SPL(RMS) and 1/3 octave band have been measured in the frequency range between 8 Hz and 20 kHz. Among riffles, high SPL corresponded to the descending level of flow velocity. Pools generally had a low SPL. Low-frequency sound waves in the upper regions are better transmitted in the deeper water. To quantitatively analyze the water depth and flow velocity, we used a regression between the observed water depth, flow velocity, and acoustic SPL. The application of this study was certificated. The correlation coefficients between SPL and flow velocity/water depth revealed specific frequency bands with very strong positive correlations between SPL and flow rate in riffles and very strong negative correlations between SPL and pool water depth. Consequently, underwater sound can be used as an alternative for evaluating biological habitats. Full article

In this study, using the Box–Behnken model, we optimized the ethanolic extraction of phytochemicals from Enantia chloranta bark for the first time, assessed the composition with HPLC-MS/MS, performed the green synthesis of silver nanoparticles (AgNPs) and characterized them with UV-Vis spectrophotometry, photon cross-correlation spectroscopy, energy-dispersive X-ray fluorescence spectrometry, and Fourier transform infrared spectroscopy. The antibacterial and antibiotic-resistance reversal properties of optimized extract (O-ECB) and AgNPs were assessed on various microorganisms (15 Gram−, 7 Gram+, and 2 fungi) using the well diffusion method and microbroth dilution assay. The mechanism of action was investigated on growth kinetic and proton pumps of Escherichia coli. The in vivo antimicrobial activity and toxicity were assessed on Galleria mellonella larvae. The optimal mass yield (14.3%) related to the highest antibacterial activity (31 mm vs. S. aureus ATCC 6538) was obtained with the following operating conditions: % EtOH—100%; ratio m/v—20 g/mL; and extraction time—6 h. All the compounds identified in O-ECB were alkaloids and the major constituents were palmatine (51.63%), columbamine +7,8-dihydro-8-hydroxypalmatine (19.21%), jatrorrhizine (11.02%), and pseudocolumbamine (6.33%). Among the minerals found in O-ECB (S, Si, Cl, K, Ca, Mn, Fe, Zn, and Br), Br, Fe, and Cl were the most abundant with mean fluorescence intensities of 4.6529, 3.485,4, and 2.5942 cps/uA, respectively. The synthesized AgNPs revealed a strong absorption plasmon band between 430 and 450 nm and an average hydrodynamic diameter ×50 of 59.74 nm, and the presence of Ag was confirmed by a characteristic peak in the spectrum at the silver Kα line of 22.105 keV. Both O-ECB and AgNPs displayed noteworthy and broad-spectrum antimicrobial activities against 20/24 and 24/24 studied microorganisms, respectively, with recorded minimal inhibitory concentrations (MICs) ranging from 8 to ≥1024 µg/mL and 2 to 64 µg/mL. O-ECB and AgNPs showed antibiofilm properties and significantly enhanced the efficacy of conventional antibiotics against selected multidrug-resistant bacteria, and the mechanistic investigations revealed their interference with bacterial growth kinetic and the inhibition of H+-ATPase proton pumps. LD50s were 40 mg/mL and 0.6 mg/mL for O-ECB and AgNPs, respectively. In conclusion, the current study provides a strong experimental baseline to consider Enantia chlorantha bark and their green synthetized AgNPs as potent antimicrobial compounds in this era of antimicrobial resistance. Full article

Unsupervised band selection has gained increasing attention recently since massive unlabeled high-dimensional data often need to be processed in the domains of machine learning and data mining. This paper presents a novel unsupervised HSI band selection method via band grouping and adaptive multi-graph constraint. A band grouping strategy that assigns each group different weights to construct a global similarity matrix is applied to address the problem of overlooking strong correlations among adjacent bands. Different from previous studies that are limited to fixed graph constraints, we adjust the weight of the local similarity matrix dynamically to construct a global similarity matrix. By partitioning the HSI cube into several groups, the model is built with a combination of significance ranking and band selection. After establishing the model, we addressed the optimization problem by an iterative algorithm, which updates the global similarity matrix, its corresponding reconstruction weights matrix, the projection, and the pseudo-label matrix to ameliorate each of them synergistically. Extensive experimental results indicate our method outperforms the other five state-of-the-art band selection methods in the publicly available datasets. Full article

Understanding the co-movement and lag–lead relations among indices is integral to financial decision making. These parameters show the reactiveness of the market towards new information. Understanding them helps to minimize risk and facilitates optimal portfolio diversification. By employing the wavelet coherence econometric model, the authors of this study analyzed the intricate relations among the Bond and Ṣukūk indices using global data belonging to the United States (US), the United Kingdom (UK), Middle East and North Africa (MENA), and Gulf Cooperation Council (GCC) countries. The findings indicated the presence of strong but similar implications of the initial shock of COVID-19 deaths on both Islamic and conventional markets’ volatilities, especially in long-term investment bands (64–128 days). The results oppose the general belief that Islamic finance is more sustainable and less volatile to crises than its traditional counterparts. Moreover, the authors of this study report diverse relationships among bond and Ṣukūk indices throughout the sample periods. We consistently found low correlations in short-term investment bands (4–16), leading to optimal diversification opportunities. However, high correlations were reported due to COVID-19 in the long-term investment bands (128–256), leading to low diversification opportunities for long-term investors. Full article