Spectral Bands Research Articles

A novel cocrystal approach celecoxib with piperine: Simultaneously enhance dissolution rate and compressibility

Celecoxib, a selective COX-2 inhibitor non-steroidal anti-inflammatory drug (NSAID), exhibits analgesic and anti-inflammatory properties similar to piperine, the secondary metabolite of Piper nigrum L. Unfortunately, celecoxib has a low compressibility and low dissolution rate in aqueous medium. This study aimed to prepare a cocrystal of celecoxib and piperine to enhance the dissolution rate and compressibility properties of celecoxib. The cocrystal was synthesized using the seeding method and thoroughly characterized using Powder X-ray diffraction (XRD), differential scanning calorimetry (DSC), infrared spectrophotometry, and single-crystal X-ray diffraction techniques. The complete change in PXRD, decrease in melting point in DSC measurements, and shift in the NH stretching band in the FT-IR spectrum suggested the formation of cocrystals phase. Single-crystal XRD confirmed the formation of an equimolar ratio of cocrystals of celecoxib and piperine. The intrinsic dissolution test was conducted to confirm the impact on the cocrystal to dissolution, and it showed a slight increase compared to intact celecoxib. To assess the physico-mechanical properties, the cocrystal powders were compressed into tablets with varying forces. The results demonstrated a significant improvement in compressibility compared with intact celecoxib owing to the slip plane in the crystal lattice of the cocrystal. In conclusion, our novel celecoxib-piperine cocrystal exhibited distinct physicochemical characteristics compared to intact celecoxib, showing enhanced dissolution rate and compressibility.

Preparation of Jackfruit Artocarpus Heterophyllus Seed Starch-Grafted Polyvinyl Acetate by Emulsion Polymerization

Abstract Petrochemical-based materials are depleting for the past decades and alternative methods to replace them are being studied over the years. This includes a wide range of probable applications such as adhesive industry. Starch, in its native form, possesses inadequate bond strength and viscosity. The said properties limit the possible applications of starch thus improvement should be employed. Jackfruit seed (JFS) starch is a viable substitute for other starches that is commonly used in the industry. This research developed a starch grafted polyvinyl acetate formula that can be used as an alternative to non-renewable and food consumable resources. Parameters such as monomer/starch ratio, temperature, and amount of initiator were observed and how it affected the graft percent, shear strength, and viscosity of the samples. This process was conducted via emulsion polymerization using PPS as initiator and SDS as emulsifier, Ethanol was also utilized to wash away homopolymers. Each run was done under ambient conditions. The samples were triplicated in the software to ensure the data credibility. From the statistical analysis, it was concluded that all three parameters which are temperature, monomer/starch ratio, and initiator amount were all significant. The appearance of 1735 cm−1 band in FTIR spectrum of the grafted starch and the visible roughness in the spherical-shaped granules in SEM photographs indicated that the VAc has been successfully grafted onto jackfruit starch. DSC analysis showed that Tg of starch-g PVAc was shifted to a little higher value as compared to Tg of native jackfruit starch which suggests that material can be used in a wider temperature range.

Spectral, Thermal and Upconversion Properties of Dy3+ Doped Borotellurite Glasses with Large Stability Parameter

Glass sample of yttrium zinc lithium lead sodium alumino borotellurite (25-x) TeO2:10ZnO:10Li2O:10PbO:10Na2O:10Al2O3:10Y2O3:15B2O3:xDy2O3. (wherex=1,1.5 and 2 mol%) have been prepared by melt-quenching technique. The amorphous nature of the prepared glass samples was confirmed by X-ray diffraction. Optical absorption, excitation spectrum and fluorescence spectra were recorded at room temperature for all glass samples. Judd-Ofelt intensity parameters Ωλ (λ=2, 4 and 6) are evaluated from the intensities of various absorption bands of optical absorption spectra. Using these intensity parameters various radiative properties like spontaneous emission probability, branching ratio, radiative life time and stimulated emission cross–section of various emission lines have been evaluated

High resolution laser diode spectroscopy of the hot bands of C2HD in the first overtone region of C-H stretching

The frequencies of C2HD hot bands ro-vibrational lines of the first overtone of C-H stretching were measured using tunable diode lasers. To expand the measuring range to the region of the large rotational numbers, a heated gas cell was used. Measurements were made in the range R1–R36 and P2–P14 for the transition (2,0,0,1,0) ← (0,0,0,1,0), and in the range R1–R18 for the transition (2,0,0,0,1) ← (0,0,0,0,1). The rotational parameters of the upper and lower states for the band (2,0,0,1,0) ← (0,0,0,1,0) were determined. The doublet structure of ro-vibrational lines was studied. For the transition (2,0,0,1,0) ← (0,0,0,1,0), a sharp increase in the splitting value was detected at J > 25.

High sensitivity distinguishing detection of fluoroquinolones with a cage-based lanthanide metal-organic framework in food

Sensitive and selective detection of fluoroquinolones, especially from different sources, is challenging. This study reported an uncommon three-cage lanthanide metal-organic framework (1-Eu) with a Eu3+ cluster as its structural primitive using a C2-symmetric 5,5-(Pyrazine-2,6-diyl) diisophthalic acid ligand. The 1-Eu probe effectively detected four fluoroquinolones through distinct color changes and spectral emission bands, demonstrating excellent performance with low detection limits: moxifloxacin (LOD: 9.3 nM), danofloxacin (LOD: 33.7 nM), gatifloxacin (LOD: 67.9 nM), and ofloxacin (LOD: 238.6 nM). Mechanistic studies revealed that internal filtration and photoinduced electron transfer (a-PET) effects were key factors. Furthermore, 1-Eu was successfully used to detect fluoroquinolones in food samples. Additionally, portable paper-based sensors were developed to quickly semi-quantify analyte concentrations using a smartphone color recognition app, underscoring the practical potential of this probe. This study introduces a novel methodology for the identification and detection of fluoroquinolones to enhance food safety.

Calibration of MAJIS (Moons and Jupiter Imaging Spectrometer): V. Validation with mineral samples and reference materials.

MAJIS (Moons and Jupiter Imaging Spectrometer) is the imaging spectrometer onboard ESA's JUICE (JUpiter and ICy Moons Explorer) spacecraft that operates in the visible and near/mid-infrared between 0.5 and 5.54μm. Before the launch of JUICE in April 2023, MAJIS underwent a comprehensive on-ground calibration campaign in between August and September 2021 in the IAS (Institut d'Astrophysique Spatiale, Université Paris-Saclay) calibration facilities. Among all the operations, calibration sequences using a set of natural mineral samples and synthetic reference materials were acquired in order to characterize MAJIS performances under conditions assumed to be close to certain future observation configurations. Here, we analyze these calibration measurements using comparison with laboratory reference spectra to quantify MAJIS spectral and spatial performances while observing these solid surfaces. We first assess the MAJIS absolute spectral calibration of the visible and near-infrared channel covering half of the wavelength range. We then quantify spectral performances in terms of global spectral slopes, band detection, band shape, and depth retrievals, over most of the spectral range using six mineral samples. We conclude that for most configurations, the MAJIS instrument demonstrates excellent spectral performances compliant with the requirements. MAJIS can, however, be affected by stray light contributions, notably for wavelengths lower than about 1.2μm, and some performances of the instrument may then be significantly impacted depending on viewing conditions. In particular, we have identified cases of spectral contrast reduction up to 40%, absolute spectral shifts up to 2-3nm, and spectral smile variability by +/1nm. Finally, we used the MAJIS internal scanning mirror to test its ability to construct hyperspectral images of a few samples: we present the first band depth maps derived with MAJIS while observing a serpentine/carbonate sample, as well as an evaluation of MAJIS spatial point spread function. Overall, the analysis of MAJIS behavior while observing samples confirms most MAJIS expected performance requirements, while revealing subtle spectral perturbations that may be related to stray light and viewing conditions. These differences will be further investigated in-flight during the cruise, with a solar reflected target such as the Moon, as well as Jupiter before the JUICE orbital insertion.

Feasibility and potential of terahertz spectral and imaging technology for Apple Valsa canker detection: A preliminary investigation

Apple Valsa canker (AVC) caused by the Ascomycete Valsa mali, seriously constrains the production and quality of apple fruits. The symptomless incubation characteristics of Valsa mali make it highly challenging to detect AVC at an early infection stage. After infecting the wound of apple bark, the pathogenic hyphae of AVC will expand and colonize the phloem tissue. Meanwhile, various enzymes and toxic substances released by hyphae cause the decomposition of cellulose and lignin, and the generation of poisonous secondary metabolites in bark tissue. However, these early symptoms of AVC are invisible from the bark’s appearance. Fortunately, Terahertz Spectral Imaging (ThzSI) technology with the advantage of penetrating, and fingerprinting is promising for detecting hidden or slight symptoms of the fungal infection. This study is a preliminary investigation of terahertz frequency-domain spectra for AVC in the early stage of infection. Healthy and two-week-infected apple tree branches were prepared for capturing ThzS images, and the spectral data were preprocessed by Multivariate scattering correction (MSC), Savitzky-Golay convolution smoothing (SG), and standard normal variate (SNV) respectively to remove data noise and improve data quality. Principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and random frog (RFROG) were employed to extract the spectral feature bands to eliminate redundant data and improve computational efficiency. Machine learning models were established based on the spectral features to detect AVC at an early infection stage, where 11 of them exhibited the best performance with F1-score of 99.72%. To further explore disease information in spatial spectra, imaging data were acquired using terahertz imaging technology. Based on imaging data, pseudo-color imaging, histogram equalization, and Otsu segmentation were employed to visualize early infection areas in apple barks. Furthermore, histogram feature (HF), shape feature (SF), and local binary pattern (LBP) extracted from terahertz spectral images were utilized to establish the SVM, RF, and KNN models. HF-SF-KNN and HF-SF-LBP-KNN with the best performance achieved F1-score of 98.82%. This study presents a preliminary application of terahertz spectral and imaging technology for early-stage AVC detection and demonstrates its feasibility. Additionally, it provides a new way to detect AVC, which expands the application of ThzSI technology in tree disease detection in orchards and lays the foundation for further research.

Explainability of deep convolutional neural networks when it comes to NIR spectral data: A case study of starch content estimation in potato tubers

Explainable AI is gaining popularity as a way to understand the decision-making processes of neural networks and gain insight into their predictions. In this paper, Integrated Gradients (IG) was applied to assess the relevance of spectral features used by deep convolutional neural networks in predicting the starch content of potatoes. For this purpose, spectral information of 7651 tubers of 12 potato varieties was acquired using a NIR spectrometer in the spectral range of 940–1650 nm. This was followed by a reference measurement of starch content. Three one-dimensional deep convolutional neural networks i.e. VGG-19, InceptionV3, and SpectraNet-32 were developed using the Keras API. The deep networks outperformed traditional models in the starch content prediction, with SpectraNet-32 achieving the highest prediction accuracy (R2 = 0.84, RMSE = 1.41%, RPD = 2.46, and rRMSE = 9.88%). Further analysis of the neural networks by IG indicated that the predictions were generated based on starch relevant spectral bands. The results of this study demonstrated that the deep convolutional neural networks not only could accurately predict starch content in potatoes, but also provided certainty in the predictions.

Identification of varieties of wheat seeds based on multispectral imaging combined with improved YOLOv5

The identification of seed variety is important in wheat production because the growth and yield are highly related with its variety. Traditional identification methods for wheat seed varieties were suffered with time consuming and contamination. This study proposed a method for convenient identification of wheat seed varieties by using improved YOLOv5 combined with multispectral images. Three optimal spectral bands images including 405nm, 570nm and 890nm were selected to fuse new image from all 19 bands using Genetic algorithm and confusion matrix. The YOLOv5s model for wheat seed variety identification was improved by adding a convolutional block attention module (CBAM) and the identification model was developed with the fusion images. The identification performance of proposed method achieved an accuracy of 99.38% in testing set, which was better than traditional VOLOv5 with RGB images or with the multispectral images. Meanwhile, the evaluation indexes of the model such as P/%, R/%, F1/% and mAP/% were all higher than 90%, which showed that the method was suitable for identification of wheat seeds variety rapidly and non-destructively.

Where Have All the Sulfur Atoms Gone? Polycyclic Aromatic Hydrocarbon as a Possible Sink for the Missing Sulfur in the Interstellar Medium. I. The C–S Band Strengths

Despite its biogenic and astrochemical importance, sulfur (S), the 10th most abundant element in the interstellar medium (ISM) with a total abundance of S/H ≈ 2.2 × 10−5, largely remains undetected in molecular clouds. Even in the diffuse ISM where S was previously often believed to be fully in the gas phase, in recent years, observational evidence has suggested that S may also be appreciably depleted from the gas. What might be the dominant S reservoir in the ISM remains unknown. Solid sulfides like MgS, FeS, and SiS2 are excluded as major S reservoirs due to the nondetection of their expected infrared spectral bands in the ISM. In this work, we explore the potential role of sulfurated polycyclic aromatic hydrocarbon (PAH) molecules—PAHs with sulfur heterocycles (PASHs)—as a sink for the missing S. Utilizing density function theory, we compute the vibrational spectra of 18 representative PASH molecules. It is found that these molecules exhibit a prominent C–S stretching band at ∼10 μm and two relatively weak C–S deformation bands at 15 and 25 μm that are not mixed with the nominal PAH bands at 6.2, 7.7, 8.6, 11.3, and 12.7 μm. If several parts per million of S (relative to H) are locked up in PAHs, the 10 μm C–S band would be detectable by Spitzer and the James Webb Space Telescope (JWST). To quantitatively explore the amount of S/H depleted in PASHs, a detailed comparison of the infrared emission spectra of PASHs with the Spitzer and JWST observations is needed.

Remote sensing-based green and blue agricultural water footprint estimation at the river basin scale

Since the development of Water Footprint environmental indicator, significant research on blue and green crop water use and the respective water footprint estimations has been published. Such research is commonly approached using different methodologies that leverage tabulated values for crop development characterisation, while studies based on remote sensing data are less abundant, despite crop monitoring using remote sensing-based vegetation indices having demonstrated great capabilities and operability. To help fill this gap, we present a methodology that uses a remote sensing vegetation index time series from Sentinel-2 satellite near infra-red and red spectral bands data to derive basal crop coefficient time series to subsequently be used under the Remote Sensing-based Soil Water Balance approach that follows the globally operative FAO56 procedure. It provides pixel-based temporal and spatially distributed estimations of net irrigation requirements and adjusted crop evapotranspiration, with the aim being to divide up the latter and estimate the remote sensing-based green and blue crop water use and the subsequent green and blue water footprint. This is all done under the Agricultural Water Footprint Assessment framework for a growing crop or tree. This methodology was applied over a large, crop-diverse Spanish river basin district (Júcar) and across two different climatological years (humid vs. dry). Its feasibility was demonstrated by the acceptable behaviour of the remote sensing-based blue crop water use estimation for different herbaceous and woody crops, against the official dataset for irrigation water accounting at two water management scales (of a relative mean absolute error of 15.4 % in the case of the largest water user association and of 17.1 % in the case of the river basin water authorities’ own estimations). The proposed approach, which we call Remote Sensing-based Agricultural Water Accounting and Footprint, aims to provide reliable and accurate spatially and temporally distributed thematic cartography about the remote sensing-based blue and green crop water use and water footprint. This information is essential for water managers with the goal of generating transparent and complementary information to incorporate into their own working scales.

Biological and Photocatalytic Activities of Silver Nanoparticles Synthesized from the Leaf Extract of Euphorbia royleana Boiss.

Silver nanoparticles (AgNPs) have gained significance due to their practical use in the medicinal field, especially in the treatment of tumors and cancer. The current article explores a green synthetic method for the preparation of AgNPs using leaf extract of Euphorbia royleanas. The synthesis was conducted at different parameters like concentration of AgNO3, pH, salt concentration, temperature and time to optimize best results for their biochemical applications. It was validated through UV-V spectroscopy (400-450 nm) with 1:3 (concentration ratio of leaf ethanolic extract and 1 mM AgNO3 solution) at a pH value of 8 at 35oC, which were the best optimization conditions. The FTIR spectral bands showed the presence of C-N and -OH functional groups, indicating that -OH stretching and the aliphatic -C-H stretching were involved in the reduction of Ag ions. The XRD pattern showed the face-centered cubic structure of silver nanoparticles. The results of SEM revealed that AgNPs were predominantly spherical in shape, mono-dispersed, and arranged in scattered form. EDX analysis testified the presence of metallic silver along with other elements like Cl, C, and O. The investigation of biochemical parameters showed that AgNPs were influential in the discoloration of dye wastewater (methylene blue ), where 80% of dye color was removed in 20 min, followed by the significant (p < 0.05) analgesic activity with an inhibition percentage of 86.45% at a dose of 500 mg/kg. Similarly, the antioxidant activity with the highest percent inhibition was 55.4% (p < 0.0001), shown by the AgNPs at 500 μg/mL. AgNPs showed a 30 mm zone of inhibition at 100 μl/mL against Aspergillus niger. It was concluded that AgNPs provide a baseline in medical technology for the treatment of simple to chronic diseases.

VenSpec-H spectrometer on the ESA EnVision mission: Design, modeling, analysis

VenSpec is a spectrometer suite on board ESA’s EnVision mission to planet Venus, due for launch in November 2031. VenSpec consists of three spectrometers, VenSpec-M , VenSpec-U and VenSpec-H. VenSpec-H stands for Venus Spectrometer with High resolution. It operates in the near-infrared wavelength range between 1.15 and 2.5 μm and it aims at mapping the near surface atmosphere during the night and the atmosphere above the cloud deck during the day. More specific, VenSpec-H will measure gases related to volcanism and surface changes on Venus. It will perform its measurements by means of nadir observations.In this paper an overview is given of the main design requirements, followed by a description of the design activities performed during the feasibility study (phase A) and the preliminary definition (phase B1) of the instrument, including mathematical modeling and analysis, and prototyping. Focus is put on the optical working principle of the instrument, where an echelle grating, used as diffractive element, is combined with an inventive combination of filters for spectral band selection.The design and development of VenSpec-H is done in a consortium under Belgian management and with important contributions from Belgian, Swiss, Spanish, and Dutch research institutes, universities, and industrial partners.

The Northern Cross Fast Radio Burst project

Context. Fast radio bursts (FRBs) are energetic, millisecond-duration radio pulses observed at extragalactic distances and whose origins are still a subject of heated debate. A fraction of the FRB population have shown repeating bursts, however it’s still unclear whether these represent a distinct class of sources. Aims. We investigated the bursting behaviour of FRB 20220912A, one of the most active repeating FRBs known thus far. In particular, we focused on its burst energy distribution, linked to the source energetics, and its emission spectrum, with the latter directly related to the underlying emission mechanism. Methods. We monitored FRB 20220912A at 408 MHz with the Northern Cross radio telescope and at 1.4 GHz using the 32-m Medicina Grueff radio telescope. Additionally, we conducted 1.2 GHz observations taken with the upgraded Giant Meter Wave Radio Telescope (uGMRT) searching for a persistent radio source coincident with FRB 20220912A, and included high energy observations in the 0.3–10 keV, 0.4–100 MeV and 0.03–30 GeV energy range. Results. We report 16 new bursts from FRB 20220912A at 408 MHz during the period between October 16th 2022 and December 31st 2023. Their cumulative spectral energy distribution follows a power law with slope αE = −1.3 ± 0.2 and we measured a repetition rate of 0.19 ± 0.03 hr−1 for bursts having a fluence of ℱ ≥ 17 Jy ms. Furthermore, we report no detections at 1.4 GHz for ℱ ≥ 20 Jy ms. These non-detections imply an upper limit of β &lt; −2.3, with β being the 408 MHz – 1.4 GHz spectral index of FRB 20220912A. This is inconsistent with positive β values found for the only two known cases in which an FRB has been detected in separate spectral bands. We find that FRB 20220912A shows a decline of four orders of magnitude in its bursting activity at 1.4 GHz over a timescale of one year, while remaining active at 408 MHz. The cumulative spectral energy distribution (SED) shows a flattening for spectral energy Eν ≥ 1031 erg Hz−1, a feature seen thus far in only two hyperactive repeaters. In particular, we highlight a strong similarity between FRB 20220912A and FRB 20201124A, with respect to both the energy and repetition rate ranges. We also find a radio continuum source with 240 ± 36 μJy flux density at 1.2 GHz, centered on the FRB 20220912A coordinates. Finally, we place an upper limit on the γ to radio burst efficiency η to be η &lt; 1.5 × 109 at 99.7% confidence level, in the 0.4–30 MeV energy range. Conclusions. The strong similarity between the cumulative energy distributions of FRB 20220912A and FRB 20201124A indicate that bursts from these sources are generated via similar emission mechanisms. Our upper limit on β suggests that the spectrum of FRB 20220912A is intrinsically narrow-band. The radio continuum source detected at 1.2 GHz is likely due to a star formation environment surrounding the FRB, given the absence of a source compact on millisecond scales brighter than 48 μJy beam−1. Finally, the upper limit on the ratio between the γ and radio burst fluence disfavours a giant flare origin for the radio bursts unlike observed for the Galactic magnetar SGR 1806-20.

Remote sensing image fusion method based on depth feature extraction

Abstract Remote sensing image fusion involves combining data from various remote sensing sensors or merging multiple images acquired by the same sensor but in different spectral bands to create a superior quality composite image, high resolution, multi-band, and multi-temporal remote sensing image through certain algorithms and technologies. By utilizing fusion techniques, remote sensing images of varying bands, resolutions, and phases can be combined to enhance the accuracy of ground object information. This paper introduces a remote sensing image fusion method that leverages deep learning for depth feature extraction. Swin Transformer makes full use of the characteristics of the relationship between adjacent pixels, and adaptively extracts panchromatic image (PAN) and multispectral image (MS) features through two adaptive feature extraction channels. High-quality satellite data is employed to confirm the efficacy of the suggested approach, with the findings demonstrating strong performance in ERGAS, SCC, SSIM, and PSNR, and other evaluation indexes and has good spectral information fidelity and spatial information integration degree.

Continuing Innovations in the CBRS Shared Spectrum Band

Continuing Innovations in the CBRS Shared Spectrum Band

Integrated Sensing and Communication Signal Processing Based on Compressed Sensing Over Unlicensed Spectrum Bands

Integrated Sensing and Communication Signal Processing Based on Compressed Sensing Over Unlicensed Spectrum Bands

Hyperspectral Prediction Models of Chlorophyll Content in Paulownia Leaves under Drought Stress.

This study explored the quantitative inversion of the chlorophyll content in Paulownia seedling leaves under drought stress and analyzed the factors influencing the chlorophyll content from multiple perspectives to obtain the optimal model. Paulownia seedlings were selected as the experimental materials for the potted water control experiments. Four drought stress treatments were set up to obtain four types of Paulownia seedlings: one pair of top leaves (T1), two pairs of leaves (T2), three pairs of leaves (T3), and four pairs of leaves (T4). In total, 23 spectral transformations were selected, and the following four methods were adopted to construct the prediction model, select the best spectral preprocessing method, and explore the influence of water bands: partial least squares modeling with all spectral bands (all-band partial least squares, AB-PLS), principal component analysis partial least squares (PCA-PLS), correlation analysis partial least squares (CA-PLS), correlation analysis (water band) partial least squares, ([CA(W)-PLS]), and vegetation index modeling. Based on the prediction accuracy and the uniformity of different leaf positions, the optimal model was systematically explored. The results of the analysis of spectral reflectance showed significant differences at different leaf positions. The sensitive bands of chlorophyll were located near 550 nm, whereas the sensitive bands of water were located near 1440 and 1920 nm. The results of the vegetation index models indicate that the multiple-index models outperformed the single-index models. Accuracy decreased as the number of indicators decreased. We found that different model construction methods matched different optimal spectral preprocessing methods. First derivative spectra (R') was the best preprocessing method for the AB-PLS, PCA-PLS, and CA-PLS models, whereas the inverse log spectra (log(1/R)) was the best preprocessing method for the CA(W)-PLS model. Among the 14 indices, the green normalized difference vegetation index (GNDVI) was most correlated with the chlorophyll content sensitivity indices, and the water index (WI) was most correlated with the water sensitive indices. At the same time, the water band affected the cross validation accuracy. When characteristic bands were used for modeling, the cross validation accuracy was significantly increased. In contrast, when vegetation indices were used for modeling, the accuracy of the cross validation increased slightly but its predictive ability was reduced; thus, these changes could be ignored. We found that leaf position also affected the prediction accuracy, with the first pair of top leaves exhibiting the worst predictive ability. This was a bottleneck that limited predictive capability. Finally, we found that the CA(W)-PLS model was optimal. The model was based on 23 spectral transformations, four PLS construction methods, water bands, and different leaf positions to ensure systematicity, stability, and applicability.

Low-lying [formula omitted] electronic states of BiF: Perturbative versus variational approaches of spin–orbit coupling

The low-lying Ω states of bismuth monofluoride (BiF) below 50000 cm−1 have been theoretically studied using the multi-reference configuration interaction and the equation-of-motion coupled-cluster methods, where the spin–orbit coupling (SOC) effects are respectively considered perturbatively or variationally. Since the perturbative treatment of SOC is not a good approximation for the heavy atom Bi, the latter method exhibits higher accuracy and better agreements with the experimental spectroscopic constants. Our results show that the second 3Π state of BiF is due to the occupation on the Rydberg 7s-shell of Bi, which gives rise to the so-called “triplet C” state and its sub-states reported in the early literatures. With the help of our theoretical results, some experimentally assigned spectral bands with considerable confusion have also been clarified and reassigned.

Modelling Eucalyptus globulus spatial distribution in the upper Blue Nile basin using multi spectral Sentinel-2 and environmental data

Eucalyptus plantations are widespread in the highlands of northern Ethiopia. The species has been used for centuries for various purposes. However, there are controversies surrounding the species with excessive soil nutrient and water consumption. Modelling the spatial distribution of the species is fundamental to understand its ecological and hydrological effects in the region for policy inputs. Therefore, the purpose of this study is to develop a model for mapping the spatial distribution of Eucalyptus globulus. We used the spectral bands of Sentinel-2 data, vegetation indices, and environmental data as predictor variables and three machine learning algorithms (Random Forest, Support Vector Machine, and Boosted Regression Trees) to model the current distribution of Eucalyptus globulus. Eleven of the twenty-five predictor variables were filtered using a variance inflation factor (VIF). 419 in situ georeferenced data points were used for training, and validating the models. The area under the curve (AUC), kappa statistic (K), true skill statistic (TSS), Root Mean Squared Error and coefficient of determination (R2) were used to validate the models’ performance. The model validation metrics confirmed the highest performance of Random Forest. The prediction map of Random Forest revealed that Eucalyptus globulus was fairly detected in non-Eucalyptus globulus woody vegetation (R2 = 0.86, P < 0.001; RMSE = 0.31). We found that the Green Normalized Difference Vegetation Index and environmental variables, such as elevation and distance from the road, were the most important predictor variables in explaining the distribution of Eucalyptus globulus. Our findings demonstrate that machine learning algorithms with Sentinel-2 spectral bands and vegetation indices compounded with environmental data can effectively model the spatial distribution of Eucalyptus globulus.