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Optical spectroscopy of Galactic field classical Be stars
In this study, we analyse the emission lines of different species present in 118 Galactic field classical Be stars in the wavelength range of 3800-9000 We re-estimated the extinction parameter (AV) for our sample stars using the newly available data from Gaia DR2 and suggest that it is important to consider AV while measuring the Balmer decrement (i.e. D34 and D54) values in classical Be stars. Subsequently, we estimated the Balmer decrement values for 105 program stars and found that ?20 per cent of them show D34 ? 2.7, implying that their circumstellar disc are generally optically thick in nature. One program star, HD 60855 shows H? in absorption - indicative of disc-less phase. From our analysis, we found that in classical Be stars, H? emission equivalent width values are mostly lower than 40 which agrees with that present in literature. Moreover, we noticed that a threshold value of ?10of H? emission equivalent width is necessary for FeII emission to become visible. We also observed that emission line equivalent widths of H?, P14, FeII 5169, and OI 8446for our program stars tend to be more intense in earlier spectral types, peaking mostly near B1-B2. Furthermore, we explored various formation regions of Ca II emission lines around the circumstellar disc of classical Be stars. We suggest the possibility that Ca II triplet emission can originate either in the circumbinary disc or from the cooler outer regions of the disc, which might not be isothermal in nature. 2021 Oxford University Press. All rights reserved. -
Affiliate Marketing and the Symbiotic Relationship in the Pharma Industry
The objective of the study is to understand the dynamic relationship between customers and the healthcare industry giants in the Indian context. The purpose revolves around how the consumer is benefitting and at the same time, how the indirect third-party affiliates also earn marginal profits along with serving the customers. The study is backed by both primary and secondary data, which were collected from 173 individuals from various fields through a questionnaire. The convenience sampling method was used to select the respondents, and the Technology Acceptance Model (TAM) was used to propose the model for the study. There exists a parallel symbiotic relationship between consumers, pharmaceutical companies, and affiliates. The application of this research can be put to use for the startups, which want to explore and excel in this industry along with the future researchers who want to forecast and study the progress of the pharma companies in the long run. The empirical evidence of this paper highlights a unique relationship between affiliates, the pharma sector, and customers, which drives customer buying behavior and a combination that has not been explored yet. The study provides a unique understanding of how feedback from customers in third-party applications can benefit and produce huge profit margins down the line. 2025 Apple Academic Press, Inc. -
Visual encoding of nudge influencers and exploring their effect on sustainable consumption among children
With the growing number of nuclear families that have a higher disposable income, and a willingness to spend for disparate reasons possibly on the only child in the family, children are unquestionably emerging as a critical market segment that marketers would do well to target. However, while marketing to children is necessary, given the current focus on sustainability, encouraging responsible consumption seems to be a prerequisite. Making children environmentally literate would thereby, significantly help in the ongoing efforts to save our planet from environmental degradation. Based on this backdrop, this study investigates the significance of encouraging children to consume 'sustainably'. Drawing upon Richard H Thaler and Cass R Sunstein's Nudge theory, along with the United Nation's Sustainable Development Goals (UNSDG -12), we employ a novel methodology to visually encode information gleaned from the extant literature. Specifically, we discuss the significance of developing sustainable habits in children and analyze the 'nudges' that motivate children to adopt sustainable habits. Additionally, we specify different nudge elements derived from the extant literature and plot them in a RADAR chart. We observe that 'simplified process' and 'ease of access' nudging have the greatest effect when delivered in school. This study has academic, managerial, and societal implications. The findings of the study would help managers to focus on the nudges in their campaigns. Research scholars and academicians could understand the significance of using the 'RADAR' chart methodology and can expand their studies in various other domains. The present study also helps to understand the extant literature and plan for future research in the domain of sustainable consumption. The findings of the study would help schools and parents understand the effective nudges that result in creating responsible consumers that would largely benefit society. 2023 The Authors -
Fine-tuning Language Models for Predicting the Impact of Events Associated to Financial News Articles
Investors and other stakeholders like consumers and employees, increasingly consider ESG factors when making decisions about investments or engaging with companies. Taking into account the importance of ESG today, FinNLP-KDF introduced the ML-ESG-3 shared task, which seeks to determine the duration of the impact of financial news articles in four languages - English, French, Korean, and Japanese. This paper describes our team, LIPIs approach towards solving the above-mentioned task. Our final systems consist of translation, paraphrasing and fine-tuning language models like BERT, Fin-BERT and RoBERTa for classification. We ranked first in the impact duration prediction subtask for French language. 2024 ELRA Language Resource Association. -
Bacha Posh: Gender Construct in Afghan Culture Examined through the Lens of Children in Literature
With the fall of the Taliban in 2001 and their return in 2021, Afghanistan has undergone drastic socio-political changes. In many families, children are introduced to the practice of Bacha Posh (dressing up like a boy), an Afghan cultural custom where girls are dressed up as boys until they are married off. Despite children being central to this practice, it has not been studied through their eyes. This article examines the custom of Bacha Posh through the childrens perspective and situates it within the current socio-political scenario of the country. A textual and cultural analysis of three literary works is carried out through a study of their child characters to examine how Afghan culture creates its own gender construct. Two are significant works of childrens literature that revolve around real-life stories of Bacha Posh Nadia Hashimis One Half from the East (2016) and Deborah Ellis The Breadwinner (2000). The third work is The Underground Girls of Kabul (2014) by Jenny Nordberg, a seminal work in the study of Bacha Posh in which Nordberg focuses on the practice of Bacha Posh and presents the voice of children. This article then goes on to study the impact of the restrictive nature of the Taliban regime on girls and its influence on the cultural custom of Bacha Posh. It demonstrates how this practice creates an unstable gender construct among children, as evidenced by the gender dysphoria that some girls experience. It thus demonstrates the impact of culture on gender through filling in the gaps between culture, literature and politics. 2023, The International Academic Forum (IAFOR). All rights reserved. -
Computationally Efficient Machine Learning Methodology for Indian Nobel Laureate Classification
A computationally efficient methodology for Indian Nobel Laureate classification is proposed in this study, emphasizing the optimization of image categorization through supervised learning techniques. Leveraging advancements in Convolutional Neural Networks (CNNs), the research aims to enhance the efficiency and precision of image classification tasks. The study utilizes Logistic Regression for dataset analysis, initially employing browser extensions for mass downloading categorized image data. Haar cascade classifiers are then used for data wrangling, focusing on facial, nose, and mouth recognition. Following this, feature engineering through wavelet transformation reduces image dimensionality, preparing the dataset for the chosen ML model, Logistic Regression. The primary focus is to simplify technology for improved image categorization. Support Vector Machines (SVM), Random Forest, and Logistic Regression are examined, with Logistic Regression emerging as the most effective model, achieving an accuracy rate of 87.5%. A thorough evaluation using Confusion Matrices reveals Logistic Regression's superior performance in classifying images of Indian Nobel laureates. A strategic up-sampling approach is implemented to address dataset inconsistencies, ensuring balanced representation across classes. The Haar wavelet transform is then applied for feature extraction, optimizing the dataset for ML models. The dataset is split into training and testing sets (80-20), and the three models are trained and evaluated for accuracy. Logistic Regression proves to be the best performer, offering insights into prominent leaders' identification. The research offers a detailed pipeline for data preprocessing, feature engineering, and model assessment, culminating in a robust image categorization system. Logistic Regression emerges as a reliable method for biographical picture identification, demonstrating superior accuracy over SVM and Random Forest. This research underscores the importance of efficient and accurate image classification methodologies for practical applications in real-world scenarios, particularly in recognizing influential leaders. 2024 IEEE. -
Optimal ordering and discounting policy for a segmented market with price and freshness dependent demand for mixed quality product
Owing to various factors, fresh produce purchased by the retailer is initially of mixed quality. A random proportion of the lot would generally have lost some freshness before being received in stock, while the remaining items would still be fresh. This calls for some discount initially for the former, and later, when the latter product is not so fresh. For demand declining with increase in selling price and decrease in freshness, this paper deals with optimal ordering and discounting policy when the lot received is of mixed quality and the market has two segments differentiated by the initial product quality sold simultaneously at widely different prices. Sufficient conditions for existence and uniqueness of optimal cycle length and the optimal discount are obtained. Sensitivity analysis reveals that increase in freshness time and proportion of initially fresh items in the lot result in increased profit rate. Copyright 2024 Inderscience Enterprises Ltd. -
Is carbon neutrality a reality for India?
India, the third-largest carbon dioxide emitter in the world, aims to achieve zero emissions by 2070. India is committed to its Panchamrit and has launched various initiatives such as green bonds, carbon credits, carbon market, investing in green hydrogen, etc. However, given the present scenario with respect to the dependency on coal-based power generation and lack of green financing, the present article assesses the different solutions and their practicality in achieving carbon neutrality. (2024), (Indian Academy of Sciences). All rights reserved. -
The Shame of Ageing During Fourth Industrial Revolution: A Thematic Analysis of Indian Adults
The Fourth Industrial Revolution (4IR), a term popularised by Klaus Schwab in 2016, connected the physical-biological and the digital world. This is an era of artificial intelligence and computational technologies suited to satiate the needs of the human race. The emphasis is also on a digital identity we have developed alongside our physical and psychological entities. Millennials and Gen Z have a cognizant grip on their digital identity and are known to use the fruits of 4IR in their everyday livelihood. However, with the advent of Industry 4.0, the generation of Baby Boomers and Gen X have had to undergo much re-learning and accommodate the newer ways of integrating digitalization in their lives. The process has brought about occupational threats and shaming related to failure to upgradation and flexibility. This article explores the influences of these social experiences on the identity and self-concept of the quinquagenarians and the sexagenarians. The article follows a qualitative method where using a thematic approach, the emerging themes from the in-depth interviews will be analyzed in detail to form a theoretical framework for shaming among the Indian Baby Boomers and Gen X. The variables in focus are adjustment, coping styles, resilience, the purpose of life, and Self-Image. The study explores the themes of Indian adults, which emerge from interviewing 46 participants, who have been associated with full-time employment and are between 77 and 59 years of age, representing the Baby Boomers, and those between 43 and 58 years of age, representing Gen X. The analysis adopts a psychoanalytic approach, where the data is interpreted using an Eriksonian lens. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Suicide and Youth: Positive Psychology Perspective
Suicidality and self-harm among adolescents and young adults need immediate attention and support. This is often seen as a cry for help where a feeling of entrapment experienced by individuals pushes them to see self-harm as a coping mechanism. Lack of social support and co-morbid emotional disorders influences suicidal ideation into planning and action. A general feeling of helplessness that gets triggered often leads to anguish. These emotions get internalized and directed inwards leading to self-directed anger which facilitates a suicidal act. Early detection and identification can prevent the loss of lives and help individuals to learn effective ways of coping. Gatekeeper Training for caregivers, teachers, and their peers will help in detecting the early signs of suicide risk. Intervention based on positive psychology will help not only in crisis management but also as preventive and maintenance therapy in holistic mental health. The concepts of hope, forgiveness, self-compassion, gratitude, and resilience can be incorporated into the intervention programs to build a better therapeutic system for youths dealing with suicidality. Practicing effective coping mechanisms every day and making it a ritual of ones daily life is the need of the hour. Integrating adaptive ways of emotional expression and learning matured means to deal with painful emotions and trauma is what needs to be incorporated into the intervention plan. The chapter focuses on these aspects and aims to make a connection between assessing and easy identification of youths in distress related to suicidality and providing a holistic intervention to help them cope with the situation. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Embedding behavioral biases into robo-advisory platforms-case of UAE investors
Purpose: This study aims to identify individuals' biases while making investment decisions and explore how these biases can be incorporated into a robo-advisory platform to help mitigate these biases. This paper identifies eight investment-related behavioral biases: mental accounting, gamblers fallacy, hindsight, regret aversion, disposition, trend-chasing, loss aversion and herding. Design/methodology/approach: This study uses primary data from 263 respondents across various age groups, of which approximately 50 were wealth management professionals in the UAE. A random sampling method from probability sampling is employed to gather the primary data. The identified biases serve as dependent variables; the age and income of individuals serve as the independent variables. Findings: Age and income are significantly related to mental accounting, herding, gambler fallacy and loss aversion. Existing studies on behavioral finance demonstrate that individuals who make investment decisions are susceptible to cognitive fallacies, leading to nonrational investment decisions. Practical implications: By studying these biases affecting individuals of varying ages and income levels, wealth management professionals can tailor their financial robo-advisory services to address these biases and help clients build wealth with consistent investment. Originality/value: This study uses survey-based sampling in the context of the UAE; hence, the data and analysis represent originality. 2024, Emerald Publishing Limited. -
Fine-Tuning Large Language Models for Personality Development
Large Language Models (LLMs) are state-of-the-art in Natural Language Processing (NLP) tasks but are extremely challenging to fine-tune with their size and computational needs. The current research targets the fine-tuning of the OpenLLaMA-3B model for personality development tasks with parameter-efficient fine-tuning (PEFT) via LoRA and QLoRA. To deal with hardware limitations, 4-bit quantization through BitsAndBytes was used, lowering memory without affecting accuracy. A FAISS-based Retrieval-Augmented Generation (RAG) pipeline was also used to improve contextual reliability. Semantic similarity (cosine similarity), BLEURT, ROUGE, and human evaluation were used to evaluate the model. The results of this experiment show that semantic similarity greatly increasing from 0% before fine-tuning to 80% after fine-tuning, demonstrating the benefit of PEFT and quantization in domain adaptation. The work illustrates that by leveraging effective fine-tuning, quantization, and retrieval augmentation, LLMs can be deployed at scale under limited resources while providing high contextual accuracy for personality development purposes. This approach not only enhances knowledge of the job, but it also has practical scalability for educational and self-improvement purposes. The findings highlight a viable path forward for applying small, yet strong, LLMs to personalized learning and adaptive human-AI interaction. 2025 IEEE. -
Data-Driven Drug Discovery Optimization for Breast Cancer Using Interpretable Machine Learning Models
Breast cancer remains one of the most prevalent malignancies worldwide, posing significant therapeutic challenges due to tumor heterogeneity and drug resistance. This study presents a reproducible, data-driven machine learning protocol for predicting drug sensitivity in breast cancer cell lines, with the dual objective of identifying potent single agents and synergistic drug combinations. Using curated datasets from the Genomics of Drug Sensitivity in Cancer (GDSC), two predictive approaches were implemented: a standalone XGBoost regressor and a hybrid Autoencoder-XGBoost pipeline. Preprocessing included label encoding, one-hot encoding, Z-score standardization, missing value imputation, and dimensionality reduction via PCA. Model evaluation demonstrated that XGBoost achieved superior performance (MSE = 1.3789, R2 = 0.8145) compared to the hybrid model (MSE = 4.0322, R2 = 0.4577). Interpretability was addressed using SHapley Additive exPlanations (SHAP), which identified TARGET_PATHWAY, DRUG_ID, TARGET, and CELL_LINE_NAME as key predictive features, aligning with established pharmacological mechanisms. Predicted synergy scores, derived from combining model outputs with DrugComb and SynergyDB data, highlighted promising drug pairs such as Bortezomib + Romidepsin and Paclitaxel + Bortezomib. These findings were further supported by PCA-based pharmacological clustering, revealing biologically relevant groupings of drugs with similar mechanisms of action. The proposed protocol provides a transparent and adaptable framework for precision oncology research, enabling both predictive accuracy and biological interpretability. By integrating rigorous preprocessing, model validation, explainability, and drug synergy analysis, this workflow offers a scalable foundation for translational drug discovery and repurposing in breast cancer treatment. 2025 JoVE Journal of Visualized Experiments. -
Privacy over instant messaging platforms: are users making the right decisions?
This article explores the impact of perceived vulnerability, self-efficacy, resistance to change, and habit on users perception of privacy over users intention to use messaging platforms. The conceptual model includes perceived vulnerability, self-efficacy, resistance to change, habit, and its impact on users perception of privacy over users intention to use messaging platforms. A structural equation and hierarchical regression model were used for data analysis. The results show that age and profession affect peoples decision of shifting to a different platform significantly. The study is based on a few specific instant messaging platforms at one particular point in time and is undertaken in India; hence, the findings cannot be extended/applicable to other countries. The paper discusses the factors impacting the users sensitivity to data privacy while using a communication application through an electronic device, especially a mobile phone. Copyright 2025 Inderscience Enterprises Ltd. -
Nudging children towards a sustainable toy story
In a world which is under a huge environmental strain, choosing sustainable products can be a significant way to correct the delicate balance. Population explosion and rapid industrialization with low concern about sustainability are affecting our environment faster than anticipated. The present study explores if children can be nudged to choose a sustainable product. A pre-test, post-test experiment design was used to observe the preference of children towards wooden toys and their packaging materials eco-friendliness. An experimental research approach is chosen in the present study, as the main motive for this study is to examine the cause-effect relationships between communications nudge and an increased preference towards wooden toys. The experiment reveals that after gaining knowledge about the benefits of sustainable toys, children preferred wooden toys over the plastic ones. The experiment was conducted on 36 children after taking their parents consent. It was concluded that persuasive communication used as nudge can help children make better choice. 2026 selection and editorial matter, Dipak Saha, Mrinal Kanti Das, Sunil Sahadev, Rabin Mazumder and Soumya Mukherjee; individual chapters, the contributors. -
DNA Data Storage: A Novel Approach to High Density, Long Term Digital Storage
With global data volume expected to reach 175 zettabytes by 2025, existing data storage systems face growing issues due to limited durability, high costs, and susceptibility to data loss. DNA, a naturally occurring biomolecule with extremely high storage density and extraordinary stability, has emerged as a promising alternative for long-term digital data preservation. Major challenges remain, including the high cost of DNA synthesis and the slow rates of encoding and data retrieval. Recent advances in DNA nanotechnology, molecular computation, nanopore sequencing, and hybrid silicon-DNA systems are helping to overcome these difficulties. Sequence-based techniques provide exceptional density, while structural DNA approaches allow for dynamic rewriting and reusability. Ongoing research and innovation indicate that DNA-based storage is becoming more feasible in terms of efficiency, scalability, and cost-effectiveness, making it a plausible contender for meeting future data preservation demands. 2026, TUBITAK. All rights reserved. -
Sentiment analysis in customer relationship management
Modern networking conversations generate annotated metadata, necessitating a method for synthesizing insights from statistics. Emotion detection is crucial for practical conversations, distinguishing joy, grief, and wrath. Corpora are becoming the standard for human-machine interaction, aiming to make interactions feel natural and real. A paradigm that identifies debates and customer views can provide a human touch to these interactions. Researchers developed a machine learning framework for assessing emotions in English phrases, utilizing LSTM (Long Short Term Memory) perspective and real-time emotion recognition in idiomatic speech. Emotion recognition rule (ERR) is created using ontologies like Word Net and Concept Net, Naive Bayes, and Random Forest. Real-time analysis of written words and facial expressions significantly outperforms current algorithms and commandment classifiers in identifying emotional states. 2025, IGI Global Scientific Publishing. All rights reserved. -
The paradox of sex: A thematic analysis of identity among indian cis-gendered female asexuals
Asexuality is the absence of sexual attraction to others or a low desire for sexual activity. In the collectivistic culture of India, the lack of sexual relations after marriage is judged from a lens of "normalcy." Asexuality is often an invisible spectrum among the queer population and usually does not get the desired attention. Individuals who identify as asexuals may not always be opposed to romantic relationships. However, the heteronomative pressures of adhering to approved social roles for romantic partners may affect them. The study aims to understand the different sources of coercion and constraints in romantic relationships experienced by individuals who identify as asexuals through in-depth interviews. The interviews tap into the mental health of asexual women in a heteronormative society. The variables in focus are coping styles, beliefs, self-image, social attitude, and relationship dynamics. The study follows a thematic approach where the emerging themes from the in-depth interviews will be analyzed in detail to form a theoretical framework to explain the heteronormative pressures on asexual women. The findings will be examined from a feminist perspective, considering the equality of all genders and sexual expressions. The findings are also analyzed using Kristeva's concept of abjection, to explore asexuality as an "abject" in a collectivistic society. Asexuality is thus seen as deviant and worthy of separation from "normal sexual expression" in a society that has conflicting opinions about sex and sexuality. Springer Nature Singapore Pte Ltd. 2025. All rights reserved. -
Promoting Emotional Well-being and Mental Health through Student Mentorship During Human Emergencies
The aim of this chapter is to elucidate the factors that are important in maintaining emotional well-being and promoting mental health through student mentorship in higher education in times of a pandemic. The COVID-19 pandemic has prompted academic institutions to go online prompting a profound change in the pedagogical experience of students and their mentors. It has been a challenge to adapt to this new normal for many, and the socially distant lifestyle has procured novel shortcomings. The lack of focus on awareness of mental health and well-being among academic mentors has been proven to be detrimental to the students. The mental health and well-being of mentors are also a matter of concern in the present situation. Spreading awareness about emotional well-being, imparting the knowledge of positive psychology, and psychoeducation of mental health issues among students will facilitate better coping. Motivating mentors to enhance communication and arrange for outreach programmes can be beneficial to their students. The chapter focuses on these pressing needs in the path of pedagogical experience and aims to help mentors, in turn, help themselves and their students by promoting better mental health. 2025 selection and editorial matter, Kennedy Andrew Thomas and Joseph Varghese Kureethara; individuals, the contributors. -
ENHANCING EXECUTIVE FUNCTIONS THROUGH COGNITIVE-BASED INTERVENTION IN INDIVIDUALS WITH SUICIDAL IDEATION AND ATTEMPTS: A Mixed-Method Pilot Study
One of the primary causes of death around the world can be attributed to suicidality. Almost 1 million people across the globe commit suicide annually. Neurocognition has an impact on suicidal ideation, and deficits in cognitive markers influence the progression of suicide-related thoughts to behaviours. The present study aims to determine the efficacy of cognitive-based intervention on executive functions implicated in suicidal ideation and suicide attempters. A mixed-method approach was followed, which involved intervention and a quantitative and qualitative analysis. A group of 22 participants aged between 18 and 25 years with suicidal ideation and behaviour was chosen. Ten participants reported having suicidal ideation and no history of suicide attempt or self-harm, whereas 12 participants reported having suicidal ideation and at least one attempt at self-harm or suicidal behaviour. All the participants were assessed on planning, verbal fluency, and response inhibition tests. The participants then receive eight sessions of cognitive-behavioural intervention focusing on suicidal behaviour and thoughts. Post-therapy, the participants underwent a reassessment of their executive functions. The results suggested that cognitive behaviour-based therapy significantly improved planning, verbal fluency, and response inhibition. The feeling of entrapment and the level of depression were qualitatively found to be influencing suicidal ideation and suicide attempts. The study paves the way for further exploration of factors that predict suicide and determines the cause-and-effect relationship between the factors. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved.