Near-infrared Data Research Articles

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.

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

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.

Near-Infrared Spectroscopy for Distinguishing Malignancy in Thyroid Nodules.

Thyroid nodules are common clinical entities, with a significant proportion being malignant. Early, accurate, and non-invasive tools to differentiate benign and malignant nodules can optimize patient management and reduce unnecessary surgery. This study aimed to evaluate the efficacy and accuracy of near-infrared spectroscopy (NIRS) in distinguishing benign from malignant thyroid nodules. A diffuse reflectance spectrum for a total of 20 thyroid nodule samples (10 samples as colloid goiter and 10 samples as thyroid cancer), were acquired in the wavelength range from 1000 to 2500 nm. Spectral data from NIRS were analyzed by means of principal component analysis (PCA), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) to classify and differentiate thyroid nodule samples. The present study found that NIRS effectively distinguished colloid goiter and thyroid cancer using the first two principal components (PCs), explaining 90% and 10% of the variance, respectively. QDA discrimination plot displayed a clear separation between colloid goiter and thyroid cancer with minimal overlap, aligning with reported 95% accuracy. Additionally, applying LDA to seven PCs from PCA achieved a 100% accuracy rate in classifying colloid goiter and thyroid cancer from near-infrared spectral data. In conclusion, NIRS offers a promising, non-invasive complementing diagnostic tool for differentiating benign from malignant thyroid nodules with high accuracy. Future work should integrate these results into predictive model development, emphasizing external validation, alternative performance metrics, and protecting against potential overfitting translation of a machine learning model to a clinical setting.

Near-infrared spectral interval screening based on hierarchical variables clustering and group SCAD in multivariate calibration

Spectral interval screening is a critical step in multivariate calibration, which can improve the model predictive performance and data interpretation. In this study, a novel method for interval selection is proposed based on a hierarchical variables clustering and group smoothly clipped absolute deviation(group SCAD) in combination with partial least squares(VCG-PLS). The proposed method makes use of hierarchical variables clustering to yield a variables partitioning into groups at each level, and these groups of variables from different clustering levels are then used as input for group SCAD. The method is designed to select informative wavelength intervals for near-infrared(NIR) spectroscopic data analysis. The proposed method mainly consists of three steps. Firstly, an effective hierarchical clustering is employed to cluster wavelengths(variables), which generates a partition of variables into groups at each hierarchy level and obtains all possible wavelength intervals. Then, the series of group variables obtained from various hierarchy levels are given as input to group-SCAD, and group-SCAD can generate potential group variables corresponding to each regularization parameter value. Finally, a collection of PLS models is constructed recursively by employing all wavelength intervals except one, until the optimal wavelength intervals are obtained. The optimal intervals correspond to the lowest root mean square error of prediction. The VCG-PLS integrates the advantages of hierarchical variable clustering and group SCAD, which is an efficient technique to enhance the performance of PLS in interval selection. The performance of VCG-PLS was tested on three real NIR datasets. The results demonstrate that VCG-PLS can improve prediction performance with fewer variables and may be a good wavelength interval selection strategy.

Spatially resolved microlensing time-scale distributions across the Galactic bulge with the VVV survey

ABSTRACT We analyse 1602 microlensing events found in the VISTA Variables in the Via Lactea (VVV) near-infrared (NIR) survey data. We obtain spatially resolved, efficiency-corrected time-scale distributions across the Galactic bulge (|ℓ| &amp;lt; 10°, |b| &amp;lt; 5°), using a Bayesian hierarchical model. Spatially resolved peaks and means of the time-scale distributions, along with their marginal distributions in strips of longitude and latitude, are in agreement at a 1σ level with predictions based on the Besançon model of the Galaxy. We find that the event time-scales in the central bulge fields (|ℓ| &amp;lt; 5°) are on average shorter than the non-central (|ℓ| &amp;gt; 5°) fields, with the average peak of the lognormal time-scale distribution at 23.6 ± 1.9 d for the central fields and 29.0 ± 3.0 d for the non-central fields. Our ability to probe the structure of the bulge with this sample of NIR microlensing events is limited by the VVV survey’s sparse cadence and relatively small number of detected microlensing events compared to dedicated optical surveys. Looking forward to future surveys, we investigate the capability of the Roman telescope to detect spatially resolved asymmetries in the time-scale distributions. We propose two pairs of Roman fields, centred on (ℓ = ±9, 5°, b = −0.125°) and (ℓ = −5°, b = ±1.375°) as good targets to measure the asymmetry in longitude and latitude, respectively.

Stellar Atmospheric Parameters for Cool Dwarfs in Gaia Data Release 3

We provide a catalog of atmospheric parameters for 1,806,921 cool dwarfs from Gaia Data Release 3 (DR3) that lie within the range covered by LAMOST cool dwarf spectroscopic parameters: 3200 K < T eff < 4300 K, −0.8 < [M/H] < 0.2 dex, and 4.5 < log g < 5.5 dex. Our values are derived based on machine-learning models trained with multiband photometry corrected for dust. The photometric data comprise optical data from the Sloan Digital Sky Survey r, i, and z bands, near-infrared data from the Two Micron All Sky Survey J, H, and K bands, and mid-infrared data from the ALLWISE W1 and W2 bands. We used both random forest and light gradient boosting machine machine-learning models and found similar results from both, with an error dispersion of 68 K, 0.22 dex, and 0.05 dex for T eff, [M/H], and log g, respectively. Assessment of the relative feature importance of different photometric colors indicated W1 − W2 as most sensitive to both T eff and log g, with J − H being most sensitive to [M/H]. We find that our values show a good agreement with the Apache Point Observatory Galactic Evolution Experiment, but are significantly different to those provided as part of Gaia DR3.

Enlightening the Universe behind the Milky Way bulge

Context. The location of the Solar System complicates the detection extragalactic sources beyond the Milky Way plane. The optical observations are hampered in the so-called zone of avoidance (ZOA), where stellar crowding and Galactic absorption are severe. Observations at longer wavelengths are needed in order to discover new background galaxies and complete the census in the ZOA. Aims. The goal of this work is to identify galaxies behind the Milky Way bulge using near-infrared (NIR) data from the VISTA Variables in Vía Láctea (VVV) survey. Methods. To this end, we made use of different VISTA Science Archive (VSA) tools in order to extract relevant information from more than 32 billion catalogued sources in the VVV bulge region. We find that initial photometric restriction on sources from the VSA vvvSource table combined with restrictions on star–galaxy separation parameters obtained from Source Extractor is a successful strategy for achieving acceptable levels of contamination (60%) and high completeness (75%) in the construction of a galaxy target sample. To remove contaminating Galactic sources from the initial target sample, our methodology also incorporates a visual inspection of false-colour RGB images, a crucial quality control carried out following a specifically defined procedure. Results. Under this methodology, we find 14 480 galaxy candidates in the VVV bulge region, making this sample the largest catalogue to date in the ZOA. Moreover, these new sources provide a fresh picture of the Universe hidden behind the curtain of stars, dust, and gas in the unexplored Milky Way bulge region. Conclusions. The results from this work further demonstrate the potential of the VVV/VVVX survey to find and study a large number of galaxies and extragalactic structures obscured by the Milky Way, expanding our knowledge of the Universe in this challenging and impressive region of the sky.

Shadowing in the Protoplanetary Disk of ZZ Tau IRS with HST

An inner component misaligned from an outer component in a protoplanetary disk can result in the former casting shadows on the latter. We present a new instance of shadowing on the outer disk around a very-low-mass star, ZZ Tau IRS. Through the analysis of near-infrared (NIR) archival data at λ = 1.6 μm acquired with the Wide Field Camera 3 on the Hubble Space Telescope, we identified brightness asymmetries in the top and bottom halves of the highly inclined outer disk, separated by a dark lane. The brighter sides in the top and bottom halves are on the opposite sides, which we attributed to shadows cast by a misaligned inner disk. Radiative transfer modeling of the system with a misaligned angle of 15 deg between the inner and outer disks well reproduced the observations. Additionally, we found an elevated brightness temperature of 12CO (3–2) at r ∼ 30 au on the brighter side in NIR wavelengths in the top half disk, which can be explained by the shadowing effect too. While the origin of the misaligned inner disk remains unclear, future monitoring observations to search for temporal variations in brightness asymmetries will likely provide useful clues.

Star Formation in the H ii Region Sh2-87: Evidence of Global Hierarchical Collapse

We present a detailed study of the Sh2-87 H ii region using a multiwavelength data set in optical to radio bands. A Herschel column density map revealed that the host cloud is filamentary in nature, and together they formed a central dense hub. The extinction map generated using near-infrared photometric data also signifies the nonuniform distribution of the cloud and reveals its filamentary nature. We estimated a sizable variable extinction over the region up to A V = 34.4 mag, with an average value of A V = 3.4 mag. Using the various infrared color–color criteria, we identified 13 Class I and 202 Class II young stellar objects (YSOs) and 22 Hα-emitting sources toward this region. Further analysis showed that the cluster is mainly composed of low-mass YSOs with a typical age of ∼3 Myr having masses in the range of 0.1–6.0 M ⊙. The identified evolved YSOs (i.e., Class II YSOs) are primarily distributed along the filaments and in the outer parts of the cloud, while the recent star formation, inferred by the presence of Class I YSOs, ionized gas, and star-forming clumps, is observed in the hub region. The overall star formation scenario in the Sh2-87 region resembles the global hierarchical collapse model of star formation, where younger massive star formation activity is expected at the central hub along with the distribution of evolved low-mass YSOs in the filaments and the outer parts of the cloud.

Remote sensing through the fog of war: Infrastructure damage and environmental change during the Russian-Ukrainian conflict revealed by open-access data

The Russian-Ukrainian conflict spawned a high-intensity war that shattered decades of peace in Europe. The use of drones and social media elevates open-source intelligence as a critical strategic asset. However, information from these sources is sporadic, difficult to confirm, and prone to manipulation. Here, we use open-access spaceborne remote sensing data to probe the damage to infrastructure on and off the frontline at the city, region, and country-wide scales in Ukraine. Nighttime light data and Synthetic Aperture Radar images reveal widespread blackout and unveil the destruction of battleground cities, offering contrasted perspectives on the impact of the conflict. Optical satellite images capture extensive flooding along the Dnipro River in the aftermath of the breach of the Kakhovka dam. Leveraging visible, near-infrared, and microwave satellite data, we bring to light disruption of human activities, havoc in the environment, and the annihilation of entire cities during the protracted conflict. Open-source remote sensing can offer objective information about the nature and extent of devastation during military conflicts.

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data.

Classification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80-88% TSI, and more than 88% TSI. The 80-88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance.

Establishment of Detection Model of Soybean Quality Traits by Near Infrared Spectroscopy

Background: Rapid prediction with near infrared (NIR) spectroscopy on quality traits is pretty popular recently, for the convenience and simple operation. But to make good use of this technology, precise and suitable calibration equations are very important to get dependable result. In this study, we mostly refer to the building of the equation and how the pretreatment effect them. Methods: In this paper, near infrared (NIR) spectroscopy was used to simultaneously predict the quality traits of soybean, including oil content, protein content, oleic acid content, linoleic acid content, stearic acid content. Near infrared spectral data of a total of 112 samples is collected from given materials in Chongqing. Samples were scanned from 1000 nm to 2500 nm using a monochromator instrument (SuperNIR-2700). Calibration equations were developed from NIR data using partial least squares (PLS) regression with internal cross validation. In addition, in this study, we also cover the affection of different pre-treatments to the different calibration equations predicting different quality traits. And measure the effect with three indicators including R, SECV and RPD. Result: Eventually we find the most suitable combination of pre-treatments for each calibration equation predicting a certain trait soybean. The present study would lay the foundations of rapid detection of quality traits in soybean.

Noninvasive Early Detection of Nutrient Deficiencies in Greenhouse-Grown Industrial Hemp Using Hyperspectral Imaging

Hyperspectral imaging is an emerging non-invasive technology with potential for early nutrient stress detection in plants prior to visible symptoms. This study evaluated hyperspectral imaging for early identification of nitrogen, phosphorus, and potassium (NPK) deficiencies across three greenhouse-grown industrial hemp plant cultivars (Cannabis sativa L.). Visible and near-infrared spectral data (380–1022 nm) were acquired from hemp samples subjected to controlled NPK stresses at multiple developmental timepoints using a benchtop hyperspectral camera. Robust principal component analysis was developed for effective screening of spectral outliers. Partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) were developed and optimized to classify nutrient deficiencies using key wavelengths selected by variable importance in projection (VIP) and interval partial least squares (iPLS). The 16-wavelength iPLS-C-SVM model achieved the highest precision of 0.75 to 1 on the test dataset. Key wavelengths for effective nutrient deficiency detection spanned the visible range, underscoring the hyperspectral imaging sensitivity to early changes in leaf pigment levels prior to any visible symptom development. The emergence of wavelengths related to chlorophyll, carotenoid, and anthocyanin absorption as optimal for classification, highlights the technology’s capacity to detect subtle impending biochemical perturbations linked to emerging deficiencies. Identifying stress at this pre-visual stage could provide hemp producers with timely corrective action to mitigate losses in crop quality and yields.

Open Data Sources for Post-Consumer Plastic Sorting: What We Have and What We Still Need

The global plastic crisis is a significant concern. Addressing it requires a multi-faceted approach involving legislative, social, and technological measures to reduce post-consumer plastic waste and increase the recycling rate and material circularity. The EU Packaging Directive mandates a 50% recycling rate for plastics by the end of 2025 and 55% by 2030. Machine learning techniques are increasingly being explored to improve the efficiency of waste sorting, thereby contributing to achieving the aforementioned recycling rate goals. However, their effectiveness relies on the availability of high-quality and comprehensive datasets. One of the challenges in this field is the need for suitable datasets for training and testing machine learning models. Furthermore, access to detailed and real-world data, such as information from recycling plants, is often restricted due to paywalls, limiting the accessibility of critical data for research and development. This review sheds light on the situation of RGB (red, green, blue) image datasets and NIR (near-infrared) data availability to identify current dataset gaps and advocate for open access to relevant data sources. The target is to promote collaboration and innovation to combat the plastic crisis and promote sustainable practices collaboratively.

Census of Young Stars in Galactic Star Forming Region Sh2-88

We present here the census and characterization of Young Stellar Objects (YSOs) associated with the active star-forming H II region Sh2-88 with multi-wavelength data situated in the Vulpecula OB1 association. To understand the extinction properties of Sh2-88 region, we generate an extinction map over 20×20 arcminutes using the neighbourhood method of Gutermuth et al. (2005) from the deep near-infrared HK band photometric archival data of UKIDSS. We find a sizable variable extinction over the region up to AV ≃11.7 mag, with a median value of AV ≃4.3 mag. The high extinction regions trace the dense filamentary structure, which was found earlier in the far-infrared and sub-mm data. Following the YSO classification scheme of Gutermuth et al. (2009), we have identified 74 YSOs in that region using Spitzer-IRAC data from four mid-infrared bands (3.6, 4.5, 5.8 and 8 μm) and 2MASS near-infrared K-band (2.16 μm) data. Among 74 YSOs, six are class I and 68 class II objects. Using IPHAS Hα, r, and i-band photometry, we have identified 41 additional YSOs. Among these 41, four sources are common with IR excess YSOs. From the spatial distribution of YSOs, it is apparent that YSOs are primarily distributed over the relatively higher extinction regions of the cloud, and star-formation activity might be ongoing in those dense parts of the cloud.

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 $\mathrm{H}\, \rm {{\small II}}$ 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 multiwavelength (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 to 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 $\mathrm{H}\, \rm {{\small II}}$ regions has led to the formation of prominent Bright Rimmed Clouds (BRCs) within our region of interest.

Feature Selection Model Development on Near-Infrared Spectroscopy Data

Feature Selection Model Development on Near-Infrared Spectroscopy Data