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Hi line analysis of Herbig Ae/Be stars using X-Shooter spectra
Herbig Ae/Be stars are intermediate-mass pre-main sequence stars undergoing accretion through their circumstellar disk. The optical and infrared (IR) spectra of HAeBe stars show Hi emission lines belonging to Balmer, Paschen and Brackett series. We used the archival X-Shooter spectra available for 109 HAeBe stars from Vioque et al. (2018) and analysed the various Hi lines present in them. We segregated the stars into different classes based on the presence of higher-order lines in different Hi series. We discussed the dependence of the appearance of higher-order lines on the stellar parameters. We found that most massive and younger stars show all the higher-order lines in emission. The stars showing only lower-order lines have Teff< 12 , 000 K and an age range of 510 Myr. We performed a case B line ratio analysis for a sub-sample of stars showing most of the Hi lines in emission. We noted that all but four stars belonging to the sub-sample show lower Hi line ratios than theoretical values, owing to the emitting medium being optically thick. The Hiline flux ratios do not depend on the stars spectral type. Further, from the line ratios of lower-order lines and Paschen higher-order lines, we note that line ratios of most HAeBe stars match with electron density value in the range of 10 9 10 11 cm - 3 . The electron temperature, however, could not be ascertained with confidence using the line ratios studied in this work. 2023, Indian Academy of Sciences. -
Disentangling the two sub-populations of early Herbig Be stars using VLT/X-shooter spectra
Context. Early Herbig Be (HBe) stars are massive, young stars accreting through the boundary layer mechanism. However, given the rapid (<2 Myr) evolution of early Herbig stars to the main-sequence phase, studying the evolution of the circumstellar medium around these stars can be a cumbersome exercise. Aims. In this work, we study the sample of early (B0-B5) HBe stars using the correlation between H? emission strength and near-infrared excess, complemented by the analysis of various emission features in the X-shooter spectra. Methods. We segregate the sample of 37 early HBe stars based on the median values of H? equivalent width (EW) and near-infrared index (n(J-H)) distributions. The stars with |H? EW| > 50and n(J-H) > -2 are classified as intense HBe stars and stars with |H? EW| < 50and n(J-H) < -2 as weak HBe stars. Using the VLT/X-shooter spectra of five intense and eight weak HBe stars, we visually checked for the differences in intensity and profiles of various HI and metallic emission lines commonly observed in Herbig stars. Results. We propose that the intense HBe stars possess an inner disk close to the star (as apparent from the high near-infrared excess) and an active circumstellar environment (as seen from the high H? EW value and presence of emission lines belonging to FeII, CaII, OI, and [OI]). However, for weak HBe stars, the inner disk has cleared, and the circumstellar environment appears more evolved than for intense HBe stars. Furthermore, we compiled a sample of ~58 000 emission-line stars published in Gaia DR3 to identify more intense HBe candidates. Further spectroscopic studies of these candidates will help us to understand the evolution of the inner (approximately a few au) disk in early HBe stars. The Authors 2023. -
Emission line star catalogues post- Gaia DR3: A validation of Gaia DR3 data using the LAMOST OBA emission catalogue
Aims.Gaia Data Release 3 (DR3) and further releases have the potential to identify and categorise new emission-line stars in the Galaxy. We perform a comprehensive validation of astrophysical parameters from Gaia DR3 with the spectroscopically estimated emission-line star parameters from the LAMOST OBA emission catalogue. Method. We compare different astrophysical parameters provided by Gaia DR3 with those estimated using LAMOST spectra. By using a larger sample of emission-line stars, we performed a global polynomial and piece-wise linear fit to update the empirical relation to convert the Gaia DR3 pseudo-equivalent width to the observed equivalent width, after removing the weak emitters from the analysis. Results. We find that the emission-line source classifications given by DR3 is in reasonable agreement with the classification from the LAMOST OBA emission catalogue. The astrophysical parameters estimated by the esphs module from Gaia DR3 provides a better estimate when compared to gspphot and gspspec. A second degree polynomial relation is provided along with piece-wise linear fit parameters for the equivalent width conversion. We notice that the LAMOST stars with weak H? emission are not identified to be in emission from BP/RP spectra. This suggests that emission-line sources identified by Gaia DR3 are incomplete. In addition, Gaia DR3 provides valuable information about the binary and variable nature of a sample of emission-line stars. 2022 EDP Sciences. All rights reserved. -
Discovery of 2716 hot emission-line stars from LAMOST DR5
We present a catalog of 3339 hot emission-line stars (ELSs) identified from 451 695 O, B and A type spectra, provided by LAMOST Data Release 5 (DR5). We developed an automated Python routine that identified 5437 spectra having a peak between 6561 and 6568 False detections and bad spectra were removed, leaving 4138 good emission-line spectra of 3339 unique ELSs. We re-estimated the spectral types of 3307 spectra as the LAMOST Stellar Parameter Pipeline (LASP) did not provide accurate spectral types for these emission-line spectra. As Herbig Ae/Be stars exhibit higher excess in near-infrared and mid-infrared wavelengths than classical Ae/Be stars, we relied on 2MASS and WISE photometry to distinguish them. Finally, we report 1089 classical Be, 233 classical Ae and 56 Herbig Ae/Be stars identified from LAMOST DR5. In addition, 928 B[em]/A[em] stars and 240 CAe/CBe potential candidates are identified. From our sample of 3339 hot ELSs, 2716 ELSs identified in this work do not have any record in the SIMBAD database and they can be considered as new detections. Identification of such a large homogeneous set of emission-line spectra will help the community study the emission phenomenon in detail without worrying about the inherent biases when compiling from various sources. 2021 National Astronomical Observatories, CAS and IOP Publishing Ltd.. -
Insights into the Publication Trends of Pharmaceutical Reverse Supply Chain Using Data Mining Approach
Due to the non-profitable nature of reverse supply chain of pharmaceutical products, researchers and companies have not shown much interest in this field. Due to stringent regulatory compliances pharmaceutical companies and hospitals are mandated for proper disposal of pharmaceutical wastes. This research aims to highlight the publication trends of pharmaceutical reverse supply chain using data mining approach. The metadata of published literature was extracted from Scopus and analysis was done for the title and abstracts of the articles. It was found that there is limited published literature on this topic. Co-occurrence map of text-based data, time graph of co-occurrence map of text, trigrams word cloud, keywords plus word cloud and unigrams word cloud were formed to get insights into the publication trend. A model had been proposed from the consumers end for pharmaceutical reverse supply chain. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Development of an AI Based Framework for Reverse Supply Chain of Pharmaceutical Products
The pharmaceutical reverse supply chain is an integral part of pharmaceutical industry. Due to the complex nature of the process and strict government regulations, it is important to use different AI technologies to increase the efficiency of the reverse supply chain. This research aims to design an AI driven framework for reverse supply chain of pharmaceutical products which would increase efficiency, speed, automate processes and enhance trust among the stakeholders. The framework consists of five modules namely Collection and Sorting Centre, Return Management, Real-time Inventory Management, Disposal Centre, and Data Analytics. In each module different AI technologies have been embedded to increase the efficiency of the system. The proposed framework offers a holistic approach that not only aligns with stringent pharmaceutical standards but also contributes to a more robust, transparent, and environmentally sustainable reverse supply chain. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Geochemical Data Exploration using Machine Learning Methods
This study introduces a novel ensemble model combining Support Vector Machine (SVM) and Gradient Boosting algorithm (GBC). The model's performance is compared with the two single layered model namely K-Nearest Neighbors (KNN) and Gaussian Naive Bayes (GNB) on a publicly available dataset. Further, Performance is measured using standard metrics such as accuracy, precision, and recall. To have the excellence in detection of types of rocks based on its properties this research explores the stacking approach, contributing in the field of geological studies and also for future exploration making it effective and efficient in identification of mineral deposits. 2025 IEEE. -
Segregation and researcher's positionality: Challenges of conducting policy ethnography in Southern polarized settings
Researchers conducting policy ethnography in conflict environments are faced with a valuable ethical dilemma is there an ethical standard to determine how a dataset should be pursued in the field? What if the method of pursuing data carries the potential of possibly disrupting one's rapport with the community and being perceived as a partisan ideologically driven researcher with ulterior motives? This question becomes more pronounced in socio-legal, conflict and public policy research in spatially polarized settings of the South. In these settings, knowledge is co-produced through one's own positionality and the nuances of grey areas that do not often feature in aggregated datasets. Scholarship on positionality has questioned whether scholars should explicate their position on the field by pointing towards the intentional or unintentional perpetuation of hierarchies. This paper situates itself in the positionality debate with reference to castelessness in socio-legal research through nine months of ethnographic fieldwork in a Southern spatially polarized setting. It grapples with an emerging contrasting view of whether researchers should at all engage in explicating their positionality. The paper argues that data is a socio-spatial product. It is to suggest that the production of data in conflict settings is informed by the spatial dynamics of social relations that emerge in the co-production of knowledge, and the researcher's reflexive positionality that itself impacts the outcome of data that emerges. 2025 The Author(s). Journal of Law and Society 2025 Cardiff University (CU). -
Border Region Railway Development in Sino- Indian Geopolitical Competition
India and China share about 3,488 km long International Boundary, which has three sectors: Western, Middle and Eastern. The Eastern sector comprises two Northeastern states, that is, Arunachal Pradesh measuring 1,124 kms and Sikkim measuring 219 kms, respectively. Due to recent changes in the geopolitical relationship with China, border management and transport infrastructure development have occupied centre stage. In recent years, the Government of India has taken initiatives to develop railway infrastructure in Northeast India. The study will focus on the role of railway transportation in Sino-Indian geopolitical competition. The study is based on secondary data collected from the office of General Manager, Northeast Frontier Railway, the Census of India and reports of Memorandums of Understanding between India and China. The study reveals that railway infrastructure along the border creates geo-psychological pressures on both countries, influencing the divergent geopolitical relationship between India and China. Railway diplomacy is a tool kit of critical geopolitics which reveals the contours of geopolitical competition in borderlands. 2023 Indian Council of World Affairs(ICWA). -
Real-World Breast Cancer Imaging DataLLM Led Analytics for Insights and Evidence Generation
Breast cancer remains one of the most prevalent and deadly forms of cancer worldwide, affecting individuals across all ages and sexes. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in the field of medical diagnostics, offering the potential to enhance the detection, diagnosis, and prediction of breast cancer. Despite these advancements, challenges remain, including the need for large, diverse datasets to train robust models, the integration of AI tools into clinical workflows, and addressing ethical concerns related to AI in healthcare. This paper explores the application of Large Language Models (LLMs) using embeddings in breast cancer management, focusing on its ability to analyze medical data, including imaging, histopathology datasets to identify patterns that may be imperceptible to human experts. Datasets from real-world setting have been secured for analysis across multiple models. Convolutional Neural Network (CNN) model and custom-built large language model are employed to demonstrate the precision and accuracy of Generative AI techniques and observed that custom-built LLM with 98.44% outperforms the traditional AI approaches such as CNN with 61.72%. Future studies can further establish how these models can assist in stratifying patients based on risk, thereby enabling personalized treatment plans that can reduce overtreatment and improve quality of life. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Application of Large Language Models for Data-Driven Analytics in Oncology: Insights and Evidence Generation from Real-World Imaging Data
Breast cancer is one of the most common and serious types of cancer. It can affect people of all ages and genders around the world. The increasing incidence of breast cancer, coupled with its complexity, has placed a significant burden on healthcare systems and patients alike. Traditional diagnostic methods, while effective, often face limitations in early detection and accurate prognosis, which are critical for improving patient outcomes. In recent years, artificial intelligence (AI) and machine learning (ML) are changing the way we solve problems and make decisions in the field of medical diagnostics, enhancing the ability to detect, diagnose and predict breast cancer. However, there are still challenges, such as the need for large and diverse datasets to train these models, making AI tools work smoothly in hospitals, and addressing ethical concerns in healthcare. This paper looks at how AI and ML are used in breast cancer care, especially in analyzing real-world medical data like images, histopathology, and other datasets such as doctor notes & discharge summaries, to identify patterns that may be unnoticeable to medical experts. Large Language Models (LLMs) using embeddings, are highlighted for their capacity to improve the accuracy of image related interpretations, potentially detect early-stage tumours, and predict disease progression and treatment responses. Real-world medical datasets have been collected and analysed using different models. A publicly available Convolutional Neural Network (CNN) and a custom-built Large Language Model (LLM) with embeddings were tested. The Generative AI model achieved 98.44% accuracy, significantly higher than the traditional AI model's 61.72%. Future research can explore how Generative AI can help classify patients based on risk levels. This could lead to personalized treatment plans, reducing unnecessary treatments and improving patients' quality of life. Given the research is primarily focussed on breast cancer, there is an attempt to showcase that by harnessing the power of AI and ML, there is potential to significantly reduce the global burden of breast cancer, offering new avenues for early detection, accurate diagnosis, and tailored therapeutic strategies. Continued research and collaboration among oncologists, data scientists, and policymakers are essential to fully realize the benefits of AI in the fight against breast cancer, ultimately leading to better patient outcomes and a decrease in breast cancer-related mortality. 2025, Modern Education and Computer Science Press. All rights reserved. -
Rayleigh-B nard convection in Casson and hybrid nano uids: An analytical investigation
The thermal Rayleigh-Bard convection (TRBC) in two different fluids namely Casson fluid and hybrid nanofluid is investigated analytically. The stability analysis is performed in both linear and non-linear form. The nanofluid properties like thermal conductivity, viscosity, thermal expansion coefficient and density are considered to be functions of the volume fraction of nanoparticles whereas the same properties of Casson fluid are assumed to be constant. The amount of heat transfer is analyzed in the non-linear analysis using the generalized Lorenz model. The influence of Casson fluid parameter and nanoparticles (single and or binary) which affect the onset of convection is analyzed. It is found that hybrid nanofluid delays the convection and will further enhance the heat transfer rate. Also, the Casson parameter advances the convection while it reduces the heat transfer rate. 2019 by American Scientific Publishers. -
MediCrypt: A Model with Symmetric Encryption for Blockchain Enabled Healthcare Data Protection
In the dynamic field of medicine, combining blockchain technology and data security becomes a vital strategy to solve the problem of protecting sensitive medical data. This study presents a new way to improve the security and privacy of medical data, using MediCrypt as an example of two- way encryption. Doctors initially used algorithms like AES or Blowfish to retrieve medical data. Smart contracts on the Ethereum-based blockchain introduce a layer of protection, combining SHA-256 with symmetric encryption technology. The multi-level transmission model includes encryption time, encryption time, elapsed time, and encryption size. Functionality in this model involves managing patient records (EHR), counterfeit drugs, drug reviews, clinical outcomes, and consent for all care areas. As shown in the methodology, the user ecosystem facilitates the exchange of information by defining the roles and responsibilities of doctors/pharmacists, administrators, and patients. The study shows the deployment of the MediCrypt model in three distinct stages. Distinct comparison of encryption time is done for different encryption algorithms. Also, parameters of MediCrypt model is compared with existing healthcare based blockchain models. 2024 IEEE. -
Facile green synthesis of semiconductive ZnO nanoparticles for photocatalytic degradation of dyes from the textile industry: A kinetic approach
One-pot, facile and green synthesis of zinc oxide nanoparticles are synthesized using cow dung as fuel by combustion procedure. The synthesized material is characterized by using various techniques such as XRD, FTIR, UV, FESEM, and EDX. To assess the photocatalytic efficacy of the as-synthesized material, harmful cationic and anionic dyes such as methylene blue (MB) and alizarin red S (AZ) dyes, respectively, are selected as benchmark dyes. The influence of light source, dye concentration, photocatalyst dosage, and pH value on the efficiency of photocatalyst and kinetics of photodegradation are systematically studied. The photodegradation results revealed that the synthesized ZnO NPs exhibited removal efficiency of MB and AZ dyes upon irradiation with UV light. Concisely, the removal efficacy of the ZnO NPs under UV light irradiation exhibited an MB and AZ degradation of 99.9% and 96.8%, respectively. A reasonable photo-catalytic mechanism for the high photodegradation efficacy of MB and AZ dyes by the prepared photocatalyst is also proposed. The green fabricated photocatalyst is promising material and could be applied for waste-water remediation and other ecological applications. 2022 -
Photocatalytic and eco-emission applications of green synthesized ZnO-CB nanoparticles
Herein, we report the synthesis of ZnO nanoparticles (ZnO-CB NPs) by employing the solution combustion method using an aqueous extract of brinjal calyxes as fuel. Characterization techniques, such as X-ray diffraction (XRD), Fourier transform Infrared spectroscopy (FTIR), UVvisible spectroscopy, and Scanning electron microscopy (SEM), were used to investigate the structural, optical, and morphological properties of synthesized nanoparticles, respectively. Highly porous hexagonal crystalline ZnO-CB NPs with less than 7 nm particle size were obtained. The photocatalytic performance of synthesized material is measured with Malachite green (MG), Basic brown 1 (BB1), and Acid orange 36 (AO36) as benchmark dyes. It showed that the synthesized material worked effectively under pH 10 with UV light irradiation. The synthesized ZnO-CB NP shows good removal effectiveness of the MG, BB1, and AO36 dyes with 99.3 %, 99.6 %, and 99.5 %, respectively, which can be promising photocatalysts for ecological applications such as wastewater remediation. Further, the synthesized ZnO-CB NP was used as blends in the methyl ester of Millettia pinnata oil (MPME), which is blended 20 % with commercial diesel (MPME20). The synthesized ZnO-CB NP was added to the MPME20 in varying amounts to ascertain its effects on the quality of emissions of various greenhouse gases such as hydrocarbons, COx, and NOx. Moreover, brake thermal efficiency (BTHE) and brake-specific fuel consumption (BSFC) were studied for the blends. The blend MPME20 with 25 mg of ZnO-CB NP, i.e., MPME20-25 mg, ZnO-CB, displays the best performance and reduced emissions. 2024 The Author(s) -
Straightforward synthesis of mn3o4/zno/eu2o3-based ternary heterostructure nano-photocatalyst and its application for the photodegradation of methyl orange and methylene blue dyes
Zinc oxide-ternary heterostructure Mn3O4/ZnO/Eu2O3 nanocomposites were successfully prepared via waste curd as fuel by a facile one-pot combustion procedure. The fabricated heterostructures were characterized utilizing XRD, UVVisible, FT-IR, FE-SEM, HRTEM and EDX analysis. The photocatalytic degradation efficacy of the synthesized ternary nanocomposite was evaluated utilizing model organic pollutants of methylene blue (MB) and methyl orange (MO) in water as examples of cationic dyes and anionic dyes, respectively, under natural solar irradiation. The effect of various experimental factors, viz. the effect of a light source, catalyst dosage, irradiation time, pH of dye solution and dye concentration on the photodegradation activity, was systematically studied. The ternary Mn3O4/ZnO/Eu2O3 photocatalyst exhibited excellent MB and MO degradation activity of 98% and 96%, respectively, at 150 min under natural sunlight irradiation. Experiments further conclude that the fabricated nanocomposite exhibits pH-dependent photocatalytic efficacy, and for best results, concentrations of dye and catalysts have to be maintained in a specific range. The prepared photocatalysts are exemplary and could be employed for wastewater handling and several ecological applications. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Challenges and Opportunities: Quantum Computing in Machine Learning
Many computing applications are being developed and applied in almost every aspect of life and in every discipline. With increasing number of problems and complexities, there is requirement for more computational power, faster speed and better results. To overcome these computational barriers, quantum computers, which are based on principles of quantum mechanics were introduced. Faster computation is the main reason behind the evolution of quantum computers which is achieved by using quantum bits instead of bits as quantum bits store both the values 1 and 0 together in superposition. The article focuses on basics of quantum computing in brief and the underlying phenomenon behind quantum computers. Also this article exposes recent trends and the problems that are being faced in this quantum technology. The major impact of quantum machine learning is also discussed. The quantum machine learning is providing better application in this modern field. This article analyses the different research gaps and possible solutions in quantum computing. Recent days quantum computing is implemented in different applications which is also described. 2019 IEEE. -
Rapid Eye Movement (REM) Sleep Behavior Disorder and REM Sleep with Atonia in the Young
Background: Rapid eye movement (REM) sleep behavior disorder (RBD) and REM sleep without atonia (RWA) have assumed much clinical importance with long-term data showing progression into neurodegenerative conditions among older adults. However, much less is known about RBD and RWA in younger populations. This study aims at comparing clinical and polysomnographic (PSG) characteristics of young patients presenting with RBD, young patients with other neurological conditions, and normal age-matched subjects.Methods: A retrospective chart review was carried out for consecutive young patients (<25 years) presenting with clinical features of RBD; and data were compared to data from patients with epilepsy, attention deficit hyperactivity disorder (ADHD), and autism, as well as normal subjects who underwent PSG during a 2-year-period.Results: Twelve patients fulfilling RBD diagnostic criteria, 22 autism patients, 10 with ADHD, 30 with epilepsy, and 14 normal subjects were included. Eight patients with autism (30%), three with ADHD (30%), one with epilepsy (3.3%), and six patients who had presented with RBD like symptoms (50%) had abnormal movements and behaviors during REM sleep. Excessive transient muscle activity and/or sustained muscle activity during REM epochs was found in all patients who had presented with RBD, in 16/22 (72%) autistic patients, 6/10 (60%) ADHD patients compared to only 6/30 (20%) patients with epilepsy and in none of the normal subjects.Conclusion: We observed that a large percentage of young patients with autism and ADHD and some with epilepsy demonstrate loss of REM-associated atonia and some RBD-like behaviors on polysomnography similar to young patients presenting with RBD. 2019 The Canadian Journal of Neurological Sciences Inc. -
Self-esteem, eudemonic well-being and flow at work among managers in banking sector
The present research tries to establish a link among well-being, flow at work and self-esteem among managers working in banking sector. The present study aimed to investigate the gender differences in self-esteem eudemonic well-being and flow at work among managers in banking sector, and ascertain the role of self-esteem and eudemonic well-being in predicting flow at work. The present study employs an ex-post facto research design and uses purposive sampling technique to select the respondents (N=100 male and 100 female managers working in the private banks). The data was first checked for normality and then t- test and stepwise multiple regression analysis was used to analyze it. There are significant gender differences on self-esteem, employee well-being and flow at work. Different set of predictors emerged for flow at work for males and females. Studying self-esteem, eudemonic well-being and flow at work has implications not only for the individual but also for the organizations as well, as employees with better well-being and having high self-esteem will eventually help the organization to achieve its goals and objectives. 2021 Ecological Society of India. All rights reserved. -
Establishing the effectiveness of intervention module on positive youth development among adolescent in India
Purpose: Positive Youth Development (PYD) originated in the west as a pragmatic approach to teaching youth skills and attributes to develop into healthy, productive, and engaged adults. This approach proposes that youth with more developmental resources experience increased academic success, better economic prospects, are more civically engaged, and experience optimal well-being and functioning in the long term. Over time, the need for administering evidence-based interventions was felt by practitioners, researchers, and policymakers. With this background and the absence of research in PYD in India, the present research was carried out to develop and test an intervention module for its effectiveness in bringing about a positive change among youth. Approach: The present research is quantitative in nature with pre-test post-test control group design. The PYD intervention program included activities, non-profit visits, community building exercises, and mentoring programs, creating self-actualizing youth. The paper deliberates on the findings of a six-month interventional program based on the Six Cs model of Learner (2005). Findings: The independent sample t-test was significant, for overall PYD, t (98) = 3.45, p <. 001. and on all the dimensions of PYD, indicating that intervention was effective as there are statistically significant differences among experimental and control groups. Value: The intervention was experientially positive for the students, valued, and commended by the school authorities. The paper recommends enhancing psychological intervention research in school settings, including multiple approaches to address holistic student development, facilitating peer relationships and mentoring, developing resources, and enhancing growth opportunities. 2021 RESTORATIVE JUSTICE FOR ALL.