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Optical design studies for national large optical-IR telescope
A 1012 m class national large optical-IR telescope (NLOT) is envisaged to meet the growing scientific requirements in astronomy and astrophysics. Telescopes of such dimensions can only be made by segmenting the primary mirror, as it eases a more prominent primary mirrors fabrication, transportation, operation, and maintenance process. This paper presents the various optical designs analyzed for NLOT that can be fabricated using the India TMT Optics Fabrication Facility (ITOFF) at the Centre for Research and Education in Science and Technology (CREST) campus. We present the primary mirror segmentation details, its ideal optical performance, and study each designs advantages and technical complexities. Based on the above analysis, we have narrowed it down to an optimal design, and its performance analysis is also discussed. Indian Academy of Sciences 2024. -
The role of religious and financial factors in eudaimonic well-being among Indian adults
Purpose: The available literature has explored the various psychosocial determinants of well-being to some extent. The earlier works have focused primarily on hedonic well-being with little focus on eudaimonic aspects. Therefore, this study aims to understand the role of parentchild religious attendance during childhood, religious connectedness, and financial-material stability on eudaimonic well-being among adults in India. Design/methodology/approach: The authors used the India data from Global Flourishing Study Wave 1 that addressed flourishing among adults above 18 years. The authors considered 9,076 Indian adults and used descriptive and correlation statistics. In addition, the authors conducted path analysis and t-test. Findings: The likelihood of eudaimonic well-being increased with parentchild religious attendance during childhood (= ?0.044, p < 0.01) along with religious connectedness (= ?0.112, p < 0.01) and financial-material stability (= 0.145, p < 0.01) as an adult. In addition, a significant difference existed in terms of religious connectedness and eudaimonic well-being with income and perceived feelings about income. Originality/value: This study emphasizes financial stabilitys relevance in well-being and suggests the importance of considering religious factors during childhood and adulthood. Emphasizing factors influencing eudaimonic well-being is relevant due to its influence on mental health and quality of life. 2024, Emerald Publishing Limited. -
The role of religious and financial factors in eudaimonic well-being among Indian adults
Purpose: The available literature has explored the various psychosocial determinants of well-being to some extent. The earlier works have focused primarily on hedonic well-being with little focus on eudaimonic aspects. Therefore, this study aims to understand the role of parentchild religious attendance during childhood, religious connectedness, and financial-material stability on eudaimonic well-being among adults in India. Design/methodology/approach: The authors used the India data from Global Flourishing Study Wave 1 that addressed flourishing among adults above 18 years. The authors considered 9,076 Indian adults and used descriptive and correlation statistics. In addition, the authors conducted path analysis and t-test. Findings: The likelihood of eudaimonic well-being increased with parentchild religious attendance during childhood (= ?0.044, p < 0.01) along with religious connectedness (= ?0.112, p < 0.01) and financial-material stability (= 0.145, p < 0.01) as an adult. In addition, a significant difference existed in terms of religious connectedness and eudaimonic well-being with income and perceived feelings about income. Originality/value: This study emphasizes financial stabilitys relevance in well-being and suggests the importance of considering religious factors during childhood and adulthood. Emphasizing factors influencing eudaimonic well-being is relevant due to its influence on mental health and quality of life. 2024, Emerald Publishing Limited. -
A Model for Detecting Type 2 Diabetes Using Mixed Single-Cell RNA Sequencing with Optimized Data
Diabetes is a critical disease and is crucial to personage agility. Type 2 Diabetes (T2D) accounts for 92% of epithetical cases. This paper proposes an optimized type 2 diabetes detection model using mixed single-cell RNA sequencing (scRNA-seq) technology. Diabetes is a chronic metabolic disorder affecting millions of people worldwide. Early detection of the disease can greatly improve treatment outcomes, but current diagnostic methods have limitations. Our proposed model integrates scRNA-seq data from both human pancreatic beta cells to identify gene expression patterns associated with diabetes. Our study shows that the proposed model is highly accurate in identifying diabetes, achieving an area under the curve (AUC) of 0.98. We employed an optimized model to improve the detection of diabetes at an early stage, leading to better treatment outcomes and an improved quality of life for patients. We initially incorporated optimal features from the dataset using the Monte Carlo (MC) feature selection method. This method helped us to estimate the relative importance (RI) score of each gene or feature, which is then used to rank the features. Further, we proposed an optimized deep belief network (ODBN) as a classification model to classify T2D and non-diabetes. To improve the performance of ODBN, an adaptive chimp optimization algorithm (AChOA) is introduced to optimize the weight parameters and achieved a performance accuracy of 96.57%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
An Investigation of Complex Interactions Between Genetically Determined Protein Expression and the Metabolic Phenotype of Human Islet Cells Using Deep Learning
The relationship between gene modules and several genome-scale metrics was examined, including heterozygosity that caused type 2 diabetes due to insulin deuteration, differential expression, genotyping association, methylation, and copy number changes. This work investigates the complex relationships between protein expression, genetic polymorphisms, and metabolic properties of human islet cells using expression quantitative trait loci (eQTL) detection. We looked at the genomic, transcriptomic, and proteomic information from islet cells in persons with type 2 diabetes. From the information from different levels, we noticed novel eQTLs that regulate crucial metabolic and signaling pathways in islet cells. Our study highlights the importance of a systems-level approach in understanding the complicated biological processes by highlighting the complexity of the link between genetic variants, protein expression, and metabolic abnormalities using the PIMA Indian dataset. Our findings provide novel insights into the molecular mechanisms behind islet cell failure in type 2 diabetes, potential targets for emerging treatment strategies, and the genomic implications of variations in gene expression, mutations, and other factors. To accomplish this purpose, we proposed a novel BLB model and obtained 99.89%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
An Human Islet Cell RNA-Seq for Genome-Wide Genotype Deepsec Framework Using Deep Learning Based Diabetes Prediction
Evaluating the tissues responsible for complicated human illnesses is important to rank significance of genetic revision connected to features. In order to make predictions about the regulatory functions of geneticsvariations athwart wide range of epigenetic changes, this article introduces a Convolutional neural network (CNN) model upgraded filters and Deepsec framework incorporated with comprehensive ENCODE and Roadmap consortia have compiled a human epigenetic map that indicates specificity to certain tissues or cell types. Deepsec framework integrates transcription factors, histone modification markers, and RNA accessibility maps to comprehensively evaluate the consequences of non-coding alterations on the most important components, even for uncommon variations or novel mutations. By using trait-associated loci and more than 30 different human pancreatic islets and their subsets of cells sorted using fluorescence-activated cell sorting, annotations of epigenetic profiling were obtained (FACS) on a genome-wide scale. The proposed model, used '1492' publicly available GWAS datasets. My team presented that deepsec framework does epigenetic annotations found important GWAS associations and uncover regulatory loci from background signals when exposed to CNN-based analysis, offering fresh intuition underlying nadir causes of type 2diabetes. The suggested approaches are anticipated to be extensively used in downstream GWAS analysis, making it possible to assess non-coding variations and conduct downstream GWAS analysis 2023 IEEE. -
An Predictive Deep Learning Model is used to Identify Human Tissue-Specific Regulatory Variations For Diabetes
A predictive deep learning model is designed to predict a target variable based on a set of input variables to diagnose the tissue base regulatory variants in the human islets. In this article, the identification on human tissue-specific regulatory variations for Diabetes using the Pima dataset converting data into images, and then the input variables may include genetic data, gene expression data, and the proposed model uses Pima Indian dataset with the attributes such as age, sex, and BMI to predict whether a person has Diabetes or not. And this dataset is incorporated a combination two layered ResNet18 + ResNet50 and SVM classifier. The results obtained are compared with KNN, Naive bayes, SVM Random Forest, Gradient descent and the accuracy achieved is 98%. 2023 IEEE. -
Prognosis of Diabetes Mellitus Paradigm Predictive Techniques
Human life is in the era of data, when almost everything is straped on to data wellspring more- over entire esse are digitises telerecorded. That is data is generated every milli second through several means like Agriculture, Bioinformatics, Web, Cybersecurity, Smart city data, classified in- formation, pda data, flexibility evidence, medical facts, Covid related data from official state too central government portals and a number of other sources are available in todays technological con- text. There are various forms of data like structured, semi-structured, and unstructured data, text, graphics are all feasible. Every day, week, month new genre natural-world features to be resolved, machine learning adroitness have emerged as problem resolver. As a result, data management tools and analytical methodologies capable of extricate penetrated realization related specifics felicitous methodical manner ceaselessly whereby world of nature enactment rely urgently needed. The vast majority of research is focused on machine learning prediction algorithms; thus, we focus on these. Our evaluation aims to provide newbies to the field, as well as more seasoned readers, with a thorough understanding of the primary approaches and algorithms developed over the previous two decades, with an emphasis on the most notable and continuing work. We also present a new taxonomy of state of the art Model, which highlights the many conceptual and technical approaches to training with labeled and unlabeled data. Finally, we show how the fundamental assumptions underlying most machine learning methods are linked to the well-known assumptions. Grenze Scientific Society, 2023. -
Systematic Contemplate Paradigm on Diabetes Mellitus using different Machine Learning Predictive Techniques
As the foodies love fast food, from micro to combined families across the world the ratio of family members 1:4 is affected with silent killer named as diabetes. A very high blood glucose levels, metabolism, improper carbohydrate, damaged hormone insulin alleviating a human body disability leading to the silent killer of the body parts is the diabetes. An estimated 425 million of people around the globe suffering with diabetes up to 108 million to 1.7 trillion will be affected with diabetes. Therefore millennium, the universe ubiquity suffering with diabetes has next to quadrupled, growing from 9 percent and above among the people. As the eating habits of people in this trendy 21st century is dramatically devastating to the risk of overweight or obese. The silent killer diabetes consequences include kidney failure, Diabetic retinopathy, Heart attack, Stiffness of body muscles, Nerves stroke and lower limb amputation leads to type I and type II diabetes. As the researchers across the globe are using the machine learning algorithms as the reliable problem solver, The complications still continue. The purpose of this percu is to help with the apt selection of features garnishing with machine learning paradigm techniques in selecting the accurate attributes for each person to be properly diagnosed. In this archetype survey paper, we have done a systematic review chronologically a decade research which will help the researchers to explore and get the contemplate on various tangible and intangible data sets they can adopt in diagnosing the mellitus diabetes. Grenze Scientific Society, 2023. -
Equity and inclusion in GenAI innovation: Exploring the challenges and strategies for ensuring equitable access to and benefits from GenAI-driven innovation
This chapter examines the critical need for equity and inclusion in the development and deployment of Generative Artificial Intelligence (GenAI) technologies. As GenAI rapidly transforms various sectors, from health care to education, its benefits are not evenly distributed, risking the exacerbation of existing social inequalities leading to a huge digital divide. The chapter explores theoretical frameworks like critical race theory (CRT) and intersectionality to understand how biases embedded in AI systems can perpetuate discrimination. It also highlights the role of open-source platforms and emerging AI initiatives in the Global South in democratizing access to these technologies. Through case studies of companies like Procter & Gamble and Microsoft, the chapter demonstrates both the potential of GenAI to drive innovation and the challenges of integrating AI ethically into global operations. The discussion underscores the importance of deliberate, inclusive strategies to ensure that AI serves as a force for social good, fostering global equity rather than deepening divides. 2025 V. Padmaja, P. Bhanumathi and Bishal Patangia. All rights reserved. -
An ecology intervention in an English studies programme: Contexts, Complexities and Choices
Over the past few decades, there has been a critical mass gained regarding the need to engage purposefully with Ecology. Unfortunately, this has not provoked any stimulating work within the Humanities and Social Sciences academia. In fact, alongside growing realisations about the necessity to address Ecology, there is a glaring absence of any significant engagement. In response to such a vexing reality, the Department of English at Christ University chose to initiate an Ecological venture within its Honours programme. This paper captures - the vigorous debates it lit up, the tough choices that had to be made, and the promise it offers - that complex journey. 2014 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (Dharmaram Vidya Kshetram, Bangalore). -
Stress, immunity, and personality: Mechanisms and interactions
The interaction between personality and immunity is a fascinating area of study that addresses how psychological characteristics can influence mental health. Among the many psychological characteristics, personality patterns play a significant role in shaping how individuals respond to stress and, subsequently, how their immune systems function. This chapter proposes to provide a detailed exploration of the complex interplay between stress and the immune system and its implications on behavior with an emphasis on stress prone and stress resistant personalities. It will delve into the physiological pathways through which stress affects immune function, how different personality types influence stress responses and immune function and discuss the broader implications for health and disease. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Building Character Through Teaching Values: A Positive Youth Development Model for Student Engagement and Development
Character development is a fundamental aspect of whole person education, fostering ethical responsibility, resilience, and social-emotional competence in students. This chapter explores the role of teaching values as a cornerstone of Positive Youth Development (PYD), emphasizing how structured engagement in academic, extracurricular, and community activities shapes students' moral and personal growth. Grounded in PYD theory, this chapter presents a framework for integrating valuesbased education within higher educational institutions, highlighting key strategies such as mentorship, service learning, and student leadership. By cultivating essential virtues like respect, integrity, and empathy, educators can enhance student engagement, promote civic responsibility, and contribute to a thriving society. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Emotional Intelligence and General Well-Being Among Middle Aged People
International Journal of Research in Social Sciences, Vol-2 (4), pp. 454-471. ISSN-2249-2496 -
Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction
Early detection of chronic diseases like diabetes is very important for early treatment and effective management. This chapter describes a machine learning (ML) solution for predicting diabetes risk from clinical structured data and a case study is constructed on the PIMA Indian Diabetes dataset. The solution caters to the entire ML pipeline: problem formulation, preprocessing of data, feature selection (FS), model training, validation, and deployment issues. Different preprocessing techniques including missing value imputation, detection of outliers, and feature normalization were used for improving data quality. FS techniques like correlation analysis, recursive feature elimination, and selection based on domain knowledge were utilized to decrease the dimensionality of the data as well as model interpretability. Extensive comparison was conducted among widely used classification models like logistic regression (LR), random forest, support vector machine, and XGBoost. It was suggested to adopt a stacked ensemble model of LR, RF, SVM, and XGBoost that achieved better performance in terms of accuracy, precision, recall, and F1-score. The findings confirm the tremendous potential of ML to enable early diabetes diagnosis as an unobtrusive, data-driven, and scalable decision-making supporting system for physicians. This is the groundwork for the further development of clinically applicable artificial intelligence-based prediction models within real-world healthcare settings. 2026 Walter de Gruyter GmbH, Berlin/Boston, Genthiner Stra 13, 10785 Berlin. -
A Study on Factors Impeding Online Buying of Households in Bangalore City
Indian Journal of Marketing, Vol. 46, Issue 4, pp. 7-23, ISSN No. 0973-8703 -
Inquiry into reverse logistics and a decision model
A process in which a product is moving in reverse along the supply chain network is called as reverse logistics. The objective of reverse logistics is to recapture the value of the final product. Reverse logistics is gaining ground because of its importance in managing a closed loop supply chain. Companies across the world are showing concern about environmental degradation and are realising the need for sustainable development. Many countries have already passed legal regulations. Good reverse logistics process indicates lot of reuse, recycling and reducing the material consumed, thereby ensuring sustainability. Improving reverse logistics will result in increase in sales up by 10%, a reduction in the supply chain costs by 25% to 40%. In retail sector the profit margins are less and strengthening reverse logistics can increase the profit margins. This paper attempts to inquire into the reverse logistics area and come out with the prioritised variables impacting the different reverse logistics disposition decision. The paper attempts to propose a conceptual model for reverse logistics disposition decision. Copyright 2019 Inderscience Enterprises Ltd. -
A study on factors impeding online buying of household items in Bangalore city
The tremendous growth of the Internet in India has fuelled the growth of e-commerce in the country. The retail business has been affected like all other businesses. This research paper attempted to study the factors responsible for impeding the online buying of household items by customers. The study was limited to Bangalore city only. The study was conducted during June 2013 and December 2014. The data was collected through questionnaires which were distributed among 500 respondents. Among these, 25 questionnaires were partially filled, and were not included as final responses; 280 completely filled questionnaires were utilized for this study. The non online respondents' profile and descriptive statistics were analyzed. Factor analysis was carried out to find the important factors involved in the non online buying decisions of customers. Some recommendations have been made on the strategies which online retailers can adopt to convert non - online buyers to online buyers of household items by addressing some of their concerns. 2016. -
Forensic Investigation Approaches of DNA Analysis and Criminal Investigation
Deoxyribonucleic acid (DNA) has been a significant factor in the criminal justice system since it was first used in forensic investigations. The reference sample's DNA profile is typically compared to the DNA profile from the evidence sample from the crime scene criminal cases. Familial DNA analysis can identify a person and provide significant investigation leads even without a reference sample for comparison in a criminal investigation process. The potential source of a forensic biological sample is determined using several indirect database searching techniques. These DNA-based techniques include Mitochondrial DNA (mtDNA) analysis, investigative genetic genealogy (IGG), familial searching, and Y-STR database searching. This study examined these methods and compares them in terms of searching efficiency, database structures, searching methods, genotyping technologies, data security, data quality, and costs. It also raises several possible legal and privacy problems for scientists to consider further. The significance of familial DNA analysis, the procedures used for finding and identifying relatives using familial DNA, and its benefits in forensics are all covered in this paper. Additionally, future options for the appropriate application of this technology and social, legal, and ethical concerns related to familial DNA analysis have been considered. 2023 WITPress. All rights reserved. -
Secure Image Transmission Scheme based on DNA Sequences
Cryptography and steganography are the most widely utilized and adopted fields of secure data communication. Data transfer can be secured using a combination of these methods. The data is transmitted from sender to receiver using cryptography, the most secure method. At the same time, steganography performs the information hiding in the form of video, image, or text within a cover image. The sensitive data is hidden, so it is not visible to the human eye. The data is stored and transferred more securely by adding DNA technology to the cryptography. It provides additional data security level and is most commonly employed to implement computation. This research developed a new method for combining cryptography with steganography. There are two phases to the proposed method: image encryption and hide phases and image extraction phases. Encryption is done by using the Signcryption algorithm. Four standard images were utilized as test material for the evaluation. Four factors are used to determine the performance of the proposed method as Peak signal-to-noise ratio (PSNR), Mean Square Error, Entropy, and Structural Similarity Index Measure (SSIM) for hiding and extracting the messages. The implementation is done in python. The proposed method achieves better performance when compared to the previously published works. 2022 Seventh Sense Research Group