Near-infrared Data Research Articles

Far-ultraviolet to Far-infrared Spectral Energy Distribution Modeling of the Star Formation History across M31

Our neighboring galaxy M31 has been recently surveyed at the far- and near-ultraviolet (FUV and NUV) with the UVIT telescope on AstroSat, which provides unprecedented sensitivity to young stellar populations. Here the UVIT data are supplemented with optical data, near-infrared (IR) data (Spitzer), and mid- and far-IR data (Herschel). The observations are processed to obtain the spectral energy distributions (SEDs) for 73 regions covering M31. The SEDs are modeled using the Cigale SED fitting code with old and young stellar populations. The old stellar population has an age of 12 Gyr across M31 but has longer formation times at further distances from the center. Significant dependences on the position of dust extinction, dust emission, and young stellar population properties are found. Across M31, there are regions with a low-age (≲100 Myr) young population and regions with an intermediate-age (∼1 Gyr) young population. The mass in the young population has declined by a factor of ∼10 for ages 800–100 Myr ago but has increased again for ages ≲100 Myr. This indicates that cold gas available for star formation has been changing over the past Gyr, whether it is caused by a changing merger rate, changing gas infall, or changes in the gas reservoir in M31. We find that the dust luminosity, based on far-IR observations, is driven by the youngest stars, which are primarily measured in the FUV and NUV bands.

Visible Near-Infrared Hyperspectral Soil Organic Matter Prediction Based on Combinatorial Modeling

Non-destructive, fast, and accurate prediction of soil organic matter content in farmland is of great significance for soil fertility assessment and rational fertilization. In the process of soil organic matter prediction, it is important to give full play to the advantages of different prediction models and to integrate different prediction models to innovatively construct a combined prediction model of soil organic matter content so as to improve the prediction accuracy and generalization ability of the model. In this study, the soil organic matter content of agricultural soils was taken as the research object, and the visible near-infrared hyperspectral curves of soils were measured by the Starter Kit indoor mobile scanning platform (Headwall Photonics, Bolton, MA, USA), and the original spectral curves were firstly de-noised by Savitzky–Golay (S-G) smoothing. Secondly, the smoothed and denoised spectral data were subjected to a first-order differential transform, and the features were selected based on the first-order differential transformed spectral data using the L1-paradigm algorithm features. Then, secondly, eight algorithms based on the selected feature bands, such as LASSO Regression (LASSO) (Model 1), Multilayer Perceptron (MLP) (Model 2), Random Forest (RF) (Model 3), Gaussian Kernel Regression (GKR) (Model 4), Ridge Regression (Model 5), Long Short-Term Memory (LSTM) (Model 6), Convolutional Neural Networks (CNN) (Model 7), and Support Vector Regression (SVR) (Model 8), were applied to construct a single-prediction model of soil organic matter content. Finally, a superior linear combination-prediction model was proposed by the eight single-prediction models constructed, and the standard deviation-based prediction validity was added to test the model. The results showed the following: (1) the weights of the eight single-prediction models in the combined prediction model were ω1*=0.099, ω2*=0.202, ω3*=0.000, ω4*=0.357, ω5*=0.088, ω6*=0.089, ω7*=0.000, ω8*=0.165, respectively; (2) The average precision E of the predicted values of soil organic matter content constructed based on the eight single-prediction models was 0.856; the average standard deviation σ was 0.181, and the average prediction validity M was 0.702; (3) The accuracy E of the predicted value of soil organic matter content of the combined model was 0.893, which was 4.322% higher than the average accuracy of the single model; the standard deviation of the combined model was 0.129, which was 28.333% lower than the average standard deviation of the single model, and the prediction validity M of the combined model was 0.778, which was 10.826% higher than the average prediction validity of the single model. The combined model can be used for the effective estimation of soil organic matter content in farmland with visible near-infrared spectral data, which can provide a basis and reference for the rapid detection of soil organic matter content in farmland.

Enhancing Building Point Cloud Reconstruction from RGB UAV Data with Machine-Learning-Based Image Translation.

The performance of three-dimensional (3D) point cloud reconstruction is affected by dynamic features such as vegetation. Vegetation can be detected by near-infrared (NIR)-based indices; however, the sensors providing multispectral data are resource intensive. To address this issue, this study proposes a two-stage framework to firstly improve the performance of the 3D point cloud generation of buildings with a two-view SfM algorithm, and secondly, reduce noise caused by vegetation. The proposed framework can also overcome the lack of near-infrared data when identifying vegetation areas for reducing interferences in the SfM process. The first stage includes cross-sensor training, model selection and the evaluation of image-to-image RGB to color infrared (CIR) translation with Generative Adversarial Networks (GANs). The second stage includes feature detection with multiple feature detector operators, feature removal with respect to the NDVI-based vegetation classification, masking, matching, pose estimation and triangulation to generate sparse 3D point clouds. The materials utilized in both stages are a publicly available RGB-NIR dataset, and satellite and UAV imagery. The experimental results indicate that the cross-sensor and category-wise validation achieves an accuracy of 0.9466 and 0.9024, with a kappa coefficient of 0.8932 and 0.9110, respectively. The histogram-based evaluation demonstrates that the predicted NIR band is consistent with the original NIR data of the satellite test dataset. Finally, the test on the UAV RGB and artificially generated NIR with a segmentation-driven two-view SfM proves that the proposed framework can effectively translate RGB to CIR for NDVI calculation. Further, the artificially generated NDVI is able to segment and classify vegetation. As a result, the generated point cloud is less noisy, and the 3D model is enhanced.

Evaluating the Consistency of Cosmological Distances Using Supernova Siblings in the Near-infrared

The study of supernova (SN) siblings, supernovae with the same host galaxy, is an important avenue for understanding and measuring the properties of Type Ia SN Ia light curves (LCs). Thus far, sibling analyses have mainly focused on optical LC data. Considering that LCs in the near-infrared (NIR) are expected to be better standard candles than those in the optical, we carry out the first analysis compiling SN siblings with only NIR data. We perform an extensive literature search of all SN siblings and find six sets of siblings with published NIR photometry. We calibrate each set of siblings ensuring they are on homogeneous photometric systems, fit the LCs with the SALT3-NIR and SNooPy models, and find median absolute differences in μ values between siblings of 0.248 and 0.186 mag, respectively. To evaluate the significance of these differences beyond measurement noise, we run simulations that mimic these LCs and provide an estimate for uncertainty on these median absolute differences of ∼0.052 mag, and we find that, statistically, our analysis rules out the nonexistence of intrinsic scatter in the NIR at the 99% level. When comparing the same sets of SN siblings, we observe a median absolute difference in μ values between siblings of 0.177 mag when using optical data alone as compared to 0.186 mag when using NIR data alone. It is unclear if these results may be due to limited statistics or poor quality NIR data, all of which will be improved with the Nancy Grace Roman Space Telescope.

Unveiling the Cosmic Cradle: Clustering and massive star formation in the enigmatic Galactic bubble N59

Abstract In this paper, we have conducted an investigation focused on a segment of the Spitzer mid-infrared bubble N59, specifically referred to as R1 within our study. Situated in the inner Galactic plane, this region stands out for its hosting of five 6.7 GHz methanol masers, as well as numerous compact $\mathrm{H}\, \small {\mathrm{II}}$ regions, massive clumps, filaments, and prominent bright rims. As 6.7 GHz masers are closely linked to the initial phases of high-mass star formation, exploring regions that exhibit a high abundance of these maser detections provides an opportunity to investigate relatively young massive star-forming sites. To characterize the R1 region comprehensively, we utilize multi-wavelength (archival) data from optical to radio wavelengths, together with 13CO and C18O data. Utilizing the Gaia DR3 data, we estimate the distance towards the bubble to be 4.66 ± 0.70 kpc. By combining near-infrared (NIR) and mid-infrared (MIR) data, we identify 12 Class I and 8 Class II sources within R1. Furthermore, spectral energy distribution (SED) analysis of selected sources reveals the presence of four embedded high-mass sources with masses ranging from 8.70-14.20 M⊙. We also identified several O and B-type stars from radio continuum analysis. Our molecular study uncovers two distinct molecular clouds in the region, which, although spatially close, occupy different regions in velocity space. We also find indications of a potential hub-filament system fostering star formation within the confines of R1. Finally, we propose that the feedback from the $\mathrm{H}\, \small {\mathrm{II}}$ regions has led to the formation of prominent Bright Rimmed Clouds (BRC) within our region of interest.

Spatial Variations of Dust Opacity and Grain Growth in Dark Clouds: L1689, L1709, and L1712

The far-infrared (FIR) opacity of dust in dark clouds within the Ophiuchus molecular cloud is investigated through multiwavelength infrared observations from UKIDSS, Spitzer, and Herschel. Employing the infrared color excess technique with both near-infrared and mid-infrared photometric data, a high-resolution extinction map in the K band (A K ) is constructed for three dark clouds: L1689, L1709, and L1712. The derived extinction map has a resolution of 1′ and reaches a depth of A K ∼ 3 mag. The FIR optical depths τ 250 at a reference wavelength of 250 μm are obtained by fitting the Herschel PACS and SPIRE continuum data at 100, 160, 250, 350, and 500 μm using a modified blackbody model. The average dust opacity per unit gas mass at 250 μm, r κ 250, is determined through a pixel-by-pixel correlation of τ 250 with A K , yielding a value of approximately 0.09 cm2 g−1, which is about 2–3 times higher than the typical value in the diffuse interstellar medium. Additionally, an independent analysis across 16 subregions within the Ophiuchus cloud indicates spatial variations in dust opacity, with values ranging from 0.07 to 0.12 cm2 g−1. Although the observed trend of increasing dust opacity with higher extinction implies grain growth, our findings indicate that rapid grain growth has clearly not yet occurred in the dark clouds studied in this work.

A Formally Motivated Retrieval Framework Applied to the High-resolution Transmission Spectrum of HD 189733 b

Ground-based high-resolution spectra provide a powerful tool for characterizing exoplanet atmospheres. However, they are greatly hampered by the dominating telluric and stellar lines, which need to be removed prior to any analysis. Such removal techniques (“preparing pipelines”) deform the spectrum; hence, a key point is to account for this process in the forward models used in retrievals. We develop a formal derivation on how to prepare froward models for retrievals, in the case where the telluric and instrumental deformations can be represented as a matrix multiplied element-wise with the data. We also introduce the notion of a “bias pipeline metric,” which can be used to compare the bias potential of preparing pipelines. We use the resulting framework to retrieve simulated observations of 1D and 3D exoplanet atmospheres and to reanalyze high-resolution ( R≈80,400 ) near-infrared (0.96–1.71 μm) CARMENES transit data of HD 189733 b. We compare these results with those obtained from a cross-correlation function analysis. With our fiducial retrieval, we find a blueshift of the absorption features of −5.51−0.53+0.66 km s−1. In addition, we retrieve a H2O log10(VMR) of −2.39−0.16+0.12 and a temperature of 660−11+6 K. We are also able to put upper limits for the abundances of CH4, CO, H2S, HCN, and NH3, consistent with a subsolar-metallicity atmosphere enriched in H2O. We retrieve a broadened line shape, consistent with rotation- and wind-induced line broadening. Finally, we find a lower limit for the pressure of an opaque cloud consistent with a clear atmosphere, and we find no evidence for hazes.

Fast Discrimination and Quantification Analysis of Atractylodis rhizoma Using NIR Spectroscopy Coupled with Chemometrics Tools.

In this study, near-infrared (NIR) spectroscopy and high-performance liquid chromatography (HPLC) combined with chemometrics tools were applied for quick discrimination and quantitative analysis of different varieties and origins of Atractylodis rhizoma samples. Based on NIR data, orthogonal partial least squares discriminant analysis (OPLS-DA) and K-nearest neighbor (KNN) models achieved greater than 90% discriminant accuracy of the three species and two origins of Atractylodis rhizoma. Moreover, the contents of three active ingredients (atractyloxin, atractylone, and β-eudesmol) in Atractylodis rhizoma were simultaneously determined by HPLC. There are significant differences in the content of the three components in the samples of Atractylodis rhizoma from different varieties and origins. Then, partial least squares regression (PLSR) models for the prediction of atractyloxin, atractylone, and β-eudesmol content were successfully established. The complete Atractylodis rhizoma spectra gave rise to good predictions of atractyloxin, atractylone, and β-eudesmol content with R2 values of 0.9642, 0.9588, and 0.9812, respectively. Based on the results of this present research, it can be concluded that NIR is a great nondestructive alternative to be applied as a rapid classification system by the drug industry.

Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition

Illumination variation has been a long-term challenge in real-world facial expression recognition (FER). Under uncontrolled or non-visible light conditions, near-infrared (NIR) can provide a simple and alternative solution to obtain high-quality images and supplement the geometric and texture details that are missing in the visible (VIS) domain. Due to the lack of large-scale NIR facial expression datasets, directly extending VIS FER methods to the NIR spectrum may be ineffective. Additionally, previous heterogeneous image synthesis methods are restricted by low controllability without prior task knowledge. To tackle these issues, we present the first approach, called for NIR-FER Stochastic Differential Equations (NFER-SDE), that transforms face expression appearance between heterogeneous modalities to the overfitting problem on small-scale NIR data. NFER-SDE can take the whole VIS source image as input and, together with domain-specific knowledge, guide the preservation of modality-invariant information in the high-frequency content of the image. Extensive experiments and ablation studies show that NFER-SDE significantly improves the performance of NIR FER and achieves state-of-the-art results on the only two available NIR FER datasets, Oulu-CASIA and Large-HFE.

Galaxies in the zone of avoidance: Misclassifications using machine learning tools

Automated methods for classifying extragalactic objects in large surveys offer significant advantages compared to manual approaches in terms of efficiency and consistency. However, the existence of the Galactic disk raises additional concerns. These regions are known for high levels of interstellar extinction, star crowding, and limited data sets and studies. In this study, we explore the identification and classification of galaxies in the zone of avoidance (ZoA). In particular, we compare our results in the near-infrared (NIR) with X-ray data. We analyzed the appearance of objects in the Galactic disk classified as galaxies using a published machine-learning (ML) algorithm and make a comparison with the visually confirmed galaxies from the VVV NIRGC catalog. Our analysis, which includes the visual inspection of all sources cataloged as galaxies throughout the Galactic disk using ML techniques reveals significant differences. Only four galaxies were found in both the NIR and X-ray data sets. Several specific regions of interest within the ZoA exhibit a high probability of being galaxies in X-ray data but closely resemble extended Galactic objects. Our results indicate the difficulty in using ML methods for galaxy classification in the ZoA, which is mainly due to the scarcity of information on galaxies behind the Galactic plane in the training set. They also highlight the importance of considering specific factors that are present to improve the reliability and accuracy of future studies in this challenging region.

Long-term monitoring of large-scale magnetic fields across optical and near-infrared domains with ESPaDOnS, Narval, and SPIRou. The cases of EV Lac, DS Leo, and CN Leo

Dynamo models describing the generation of stellar magnetic fields for partly and fully convective stars are guided by observational constraints. Zeeman-Doppler imaging has revealed a variety of magnetic field geometries and, for fully convective stars in particular, a dichotomy: either strong, mostly axisymmetric, and dipole-dominated or weak, non-axisymmetric, and multipole-dominated. This dichotomy is explained either by dynamo bistability (i.e. two coexisting and stable dynamo branches) or by long-term magnetic cycles with polarity reversals, but there is no definite conclusion on the matter. Our aim is to monitor the evolution of the large-scale field for a sample of nearby M dwarfs with masses between 0.1 and 0.6 $M_ which is of prime interest to inform distinct dynamo theories and explain the variety of magnetic field geometries studied in previous works. This also has the potential to put long-term cyclic variations of the Sun's magnetic field into a broader context. We analysed optical spectropolarimetric data sets collected with ESPaDOnS and Narval between 2005 and 2016, and near-infrared SPIRou data obtained between 2019 and 2022 for three well-studied, active M dwarfs: EV Lac, DS Leo, and CN Leo. We looked for secular changes in time series of longitudinal magnetic field, width of unpolarised mean-line profiles, and large-scale field topology as retrieved with principal component analysis and Zeeman-Doppler imaging. We retrieved pulsating (EV Lac), stable (DS Leo), and sine-like (CN Leo) long-term trends in longitudinal field. The width of near-infrared mean-line profiles exhibits rotational modulation only for DS Leo, whereas in the optical it is evident for both EV Lac and DS Leo. The line width variations are not necessarily correlated to those of the longitudinal field, suggesting complex relations between small- and large-scale field. We also recorded topological changes in the form of a reduced axisymmetry for EV Lac and transition from a toroidal-dominated to poloidal-dominated regime for DS Leo. For CN Leo, the topology remained predominantly poloidal, dipolar, and axisymmetric, with only an oscillation in field strength. Our results show a peculiar evolution of the magnetic field for each M dwarf individually, with DS Leo and EV Lac manifesting more evident variations than CN Leo. These findings confirm that M dwarfs with distinct masses and rotation periods can undergo magnetic long-term variations and suggest an underlying variety of cyclic behaviours of their magnetic fields.

Improve the accuracy of FT-NIR for determination of zearalenone content in wheat by using the characteristic wavelength optimization algorithm

The research contributes a unique method to achieve high-precision quantification of zearalenone (ZEN) in wheat, significantly improving accuracy in the analysis. Fourier transform near infrared spectroscopy (FT-NIR) was employed to capture the spectral information of wheat with different mildew degrees. Three feature selection models, competitive adaptive reweighted sampling (CARS), support vector machine-recursive feature elimination (SVM-RFE), and multiple feature-spaces ensemble-least absolute shrinkage and selection operator (MFE-LASSO) were introduced to processed pre-processed near-infrared spectral data and established partial least squares (PLS) regression according to the selected features. The outcomes indicated that the optimal generalization performance was achieved by the PLS model optimized through the MFE-LASSO model. The root mean square error of prediction (RMSEP) was 18.6442 μg·kg-1, coefficient of predictive determination (RP2) was 0.9545, and relative percent deviation (RPD) was 4.3198. According to the results, it is feasible to construct a stoichiometric model for the quantitative determination of ZEN in wheat by using FT-NIR combined with feature selection algorithm, and this method can also be extended to the detection of various molds in other cereals in the future.

Predictive models for sensory score and physicochemical composition of Yuezhou Longjing tea using near-infrared spectroscopy and data fusion

In this study, NIR quantitative prediction model was established for sensory score and physicochemical components of different varieties and quality grades of Yuezhou Longjing tea. Firstly, L, a, b color factors and diffuse reflection spectral data are collected for each sample. Subsequently, the original spectrum is preprocessed. Three techniques for selecting variables, CARS, BOSS, and SPA, were utilized to extract optimal feature bands. Finally, the spectral data extracted from feature bands were fused with L, a and b color factors to build SVR and PLSR prediction models. enabling the rapid non-destructive discrimination of different varieties and grades of Yuezhou Longjing tea. The outcomes demonstrated that BOSS was the best variable selection technique for sensory score and the distinctive caffeine wavelengths, CARS, however, was the best variable selection technique for catechins distinctive wavelengths. Additionally, the middle-level data fusion-based non-linear prediction models greatly outperformed the linear prediction models. For the prediction models of sensory score, catechins, and caffeine, the relative percent deviation (RPD) values were 2.8, 1.6, and 2.6, respectively, suggesting the good predictive ability of the models. In conclusion, evaluating the quality of the five Yuezhou Longjing tea varieties using near-infrared spectroscopy and data fusion have proved as feasible.

Neural Network-Based Analysis and Its Application to Spectroscopy for Mango

Sugar derived from crops is a crucial organic energy source studied in the Earth sciences, serving as a renewable and clean energy alternative. Biofuels produced from crop sugars are more environmentally friendly than traditional fossil fuel sources and contribute to solar energy storage and conversion within the Earth’s cycle. Using mangoes as a case study, this research employs near-infrared spectral analysis technology to develop an algorithm for a mango brix detection device. The study investigates the relationship between brix and absorbance, as well as changes in brix levels, and their application for on-site mango brix detection. Near-infrared spectral data in the range of 1300 nm to 2300 nm were collected during the mango ripening season in summer and preprocessed using various techniques. A neural network-based least squares modeling approach was utilized to develop a mango sugar content detection model, resulting in a correlation coefficient of 0.9055 and a root-mean-square error of 0.2192. To enhance model accuracy and avoid local optimization issues, this study incorporated the simulated annealing algorithm for model optimization, leading to a correlation coefficient of 0.9854 and a root-mean-square error of 0.0431. The findings demonstrate that the non-destructive testing model of mangoes based on near-infrared spectroscopy effectively detects brix changes and storage potential post-harvest, offering valuable insights for mango quality assessment, optimal picking and selling times, and market selection.

Determination of asbestos cement rooftop surface composition using regression analysis and hyper-spectral reflectance data in the visible and near-infrared ranges

The effects of asbestos on human health have spurred numerous studies examining its risks in urban environments. Recent works have shifted towards less-invasive techniques for remote detection and classification of asbestos-cement. In this context, this study combines visible (VIS) and near-infrared (NIR) reflectance data collected in-situ with reference signals from the USGS spectral library, utilizing optimized regression analysis to determine the surface composition of corrugated asbestos-cement rooftops. An outlier filter was successfully implemented to enhance the accuracy of regression calculations, achieving a high level of agreement with actual field observations. The regression analysis revealed varying proportions of weathered cement, hazardous asbestos fibers (specifically chrysotile and cummingtonite), and biological growth (such as lichens and moss). These results are consistent with previous research on the composition of asbestos-cement rooftops, including a comparable field study and XRD analysis conducted in 2019. This underscores the importance of using regression analysis, preceded by an outlier filtering step, on VIS and NIR reflectance data to ascertain the surface composition of asbestos-cement rooftops. This methodology holds potential for application to larger hyperspectral datasets across more extensive sample surfaces and areas.

Near-infrared spectroscopy combined with fuzzy fast pseudoinverse linear discriminant analysis to discriminate mee tea grades

Mee tea, one of the major types of green tea in China, is often used for export because of its elegant appearance, high fragrance and strong taste. However, the quality of tea differs greatly due to the difference in raw material selection and production technology level. In order to accurately and quickly differentiate different grades of Mee tea, fuzzy fast pseudoinverse linear discriminant analysis (FFPLDA) was proposed based on fast pseudoinverse linear discriminant analysis (FPLDA) for extracting discriminant information from near-infrared (NIR) spectra. Firstly, NIR spectra of Mee tea samples were acquired, and then they were preprocessed by multiplicative scatter correlation (MSC). Secondly, the compression of data was achieved by principal component analysis (PCA). Thirdly, linear discriminant analysis (LDA), FPLDA, FFPLDA and fuzzy Foley-Sammon transformation (FFST) were respectively performed to retrieve discriminant information from NIR data. Finally, the K-nearest neighbor (KNN) was utilized to classify Mee tea grades. In this study, experimental results showed that the accuracy of FFPLDA was higher than that of LDA, FFST and FPLDA. Therefore, NIR spectroscopy coupled with FFPLDA and KNN has a good effect in discrimination of Mee tea grades and also a great application potential.

The transient event in NGC 1566 from 2017 to 2019. I. An eccentric accretion disk and a turbulent, disk-dominated broad-line region unveiled by double-peaked Ca II and O I lines

is a local face-on Seyfert galaxy and is known for exhibiting recurrent outbursts that are accompanied by changes in spectral type. The most recent transient event occurred from 2017 to 2019 and was reported to be accompanied by a change in Seyfert classification from Seyfert 1.8 to Seyfert 1.2. We aim to study the transient event in detail by analyzing the variations in the optical broad-line profiles. In particular, we intend to determine the structure and kinematics of the broad-line region. We analyzed data from an optical spectroscopic variability campaign of taken with the 9.2\,m Southern African Large Telescope (SALT) between July 2018 and October 2019 triggered by the detection of hard X-ray emission in June 2018. We supplemented this data set with optical to near-infrared (NIR) spectroscopic archival data taken by VLT/MUSE in September 2015 and October 2017, and investigated the emission from different line species during the event. exhibits pronounced spectral changes during the transient event. We observe the emergence and fading of a strong power-law-like blue continuum as well as strong variations in the Balmer He i and He ii lines and the coronal lines Fe vii Fe X and Fe XI . Moreover, we detect broad double-peaked emission line profiles of O i and the Ca ii triplet. This is the first time that genuine double-peaked O i and Ca ii emission in AGN is reported in the literature. All broad lines show a clear redward asymmetry with respect to their central wavelength and we find indications for a significant blueward drift of the total line profiles during the transient event. The profiles and the FWHM of the Balmer lines remain largely constant during all observations. We show that the double-peaked emission line profiles are well approximated by emission from a low-inclination, relativistic eccentric accretion disk, and that single-peaked profiles can be obtained by broadening due to scale-height-dependent turbulence. Small-scale features in the O i and Ca ii lines suggest the presence of inhomogeneities in the broad-line region. We conclude that the broad-line region in is dominated by the kinematics of a relativistic eccentric accretion disk. The broad-line region can be modeled to be vertically stratified with respect to scale-height turbulence with O i and Ca ii being emitted close to the disk in a region with high (column) density, while the Balmer and helium lines are emitted at greater scale height above the disk. The observed blueward drift might be attributed to a low-optical-depth wind launched during the transient event. Except for this wind, the observed kinematics of the broad-line region remain largely unchanged during the transient event.

Ejection Patterns in the DG Tau Jet over the Last 40 yr: Insights into Mass Accretion Variability* * Based on observations obtained at the international Gemini Observatory, a program of NSF's NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation on behalf of the Gemini Observatory partnership.

We aim to clarify the link between mass accretion and ejection by analyzing DG Tau’s jet observations from optical and near-infrared data spanning 1984–2019, alongside photometric variations between 1983 and 2015. We classified 12 moving knot groups among 17 total knot groups based on their constant proper motions and comparable radial velocities. A strong correlation emerges between the deprojected flow velocities of the knots and the photometric magnitudes of DG Tau. From 1983 to 1995, as the deprojected ejection velocities surged from ∼273 ± 15 to ∼427 ± 16 km s−1, the photometric magnitudes (V) concurrently brightened from 12.3 to 11.4. Notably, when DG Tau became brighter than 12.2 in the V band, its (B − V) color shifted bluer than its intrinsic color range of K5–M0. During this period, the launching point of the jet in the protoplanetary disk moved closer to 0.06 au from the star in 1995. Following a V magnitude drop from 11.7 to 13.4 in 1998, the star may have experienced significant extinction due to a dust wall created by the disk wind during the ejection of the high-velocity knot in 1999. Since then, the magnitude became fainter than 12.2, the (B − V) and (V − R) colors became redder, and the deprojected velocities consistently remained below 200 km s−1. The launching point of the jet then moved away to ∼0.45 au by 2008. The prevailing factor influencing photometric magnitude appears to be the active mass accretion causing the variable mass ejection velocities.

Near-infrared spectroscopy data for foot skin oxygen saturation in healthy subjects.

Our objective was to evaluate normative data for near-infrared spectroscopy (NIRS) in 110 healthy volunteers by Fitzpatrick skin type (FST) and region of the foot. We obtained measurements of the dorsum and plantar foot using a commercially available device (SnapshotNIR, Kent Imaging, Calgary Canada). On the dorsum of the foot, people with FST6 had significantly lower oxygen saturation compared to FST1-5 (p < 0.001), lower oxyhaemoglobin compared to FST2-5 (p = 0.001), but there was no difference in deoxyhaemoglobin. No differences were found on the plantar foot. When comparing dorsal and plantar foot, there was higher oxyhaemoglobin (0.40 ± 0.09 vs. 0.51 ± 0.12, p < 0.001) and deoxyhaemoglobin (0.16 ± 0.05 vs. 0.21 ± 0.05, p < 0.001) on the plantar foot, but no differences in oxygen saturation (dorsal 70.7 ± 10.8, plantar 70.0 ± 9.5, p = 0.414). In 6.4% of feet, there were black areas, for which no NIRS measurements could be generated. All areas with no data were on the dorsal foot and only found in FST 5-6. People with FST6 had significantly larger areas with no data compared to FST 5 (22.2 cm2  ± 20.4 vs. 1.9 cm2  ± 0.90, p = 0.007). These findings should be considered when using NIRS technology. Skin pigmentation should be evaluated in future NIRS studies.

Detection of bruises on red apples using deep learning models

The detection and sorting of bruised apples after harvest play a crucial role in improving their economic value by eliminating surface defects. This also reduces the risk of contamination of infected apples during transport and storage. It can be done by using manual detection or machine vision techniques in red, green, and blue (RGB) colors to detect bruises on apples of various skin colors; however, in the early stages of bruising, it is challenging. Therefore, the main purpose of this study is determin of the effectiveness of Deep Learning models combined with the Near Infrared (NIR) imaging system for naturally bruised Super Chief red apples immediately after harvest. In total, 1000 images for the healthy class and 500 images for the bruised class were acquired from 500 apples. After the images were acquired with the RGB and NIR cameras, the data sets were divided into training (70 %), validation (15 %), and testing (15 %) sets. The Alexnet, the Inceptipon-V3, and the VGG16 network structures were trained using the training and validation data sets, and the trained network was evaluated using the test dataset. The VGG16 model achieved the highest test accuracy (86 %) when trained on the RGB data set, while the AlexNet model exhibited the lowest test accuracy (74.6 %). When the models were trained and tested with NIR datasets, 99.33 %, 100 % and 100 % accuracy rates were obtained for AlexNet, Inception V3, and VGG16, respectively. During the experiments, the VGG16 model trained with the NIR dataset achieved the lowest loss rate of 0.0002, whereas when trained and tested with the RGB dataset, the same VGG16 model also recorded the lowest loss rate of 0.353.These findings indicate that the deep learning models, particularly when trained with NIR data, demonstrate high accuracy rates in classifying apples as healthy or bruised, making them suitable for industrial classification applications. Therefore, the NIR data set is recommended for precise and reliable apple classification in industrial settings.